0]\n if len(new_df) == 0:\n new_df = df.head(1)\n new_df['reaction'] = np.nan\n new_df['label'] = new_df['gene']\n return new_df","title":""},{"docid":"8f15ae29fe533b404dc846a9c9957e64","score":"0.5465945","text":"def drop_nan(self):\n self.df = self.df.dropna(axis=0, how='any')\n return self.df","title":""},{"docid":"c2d52c882873b9b394ba08aa17a39614","score":"0.54497117","text":"def remove_rows_with_missing_values(self):\n clean_data = []\n for row in self.data:\n hasMissing = False\n for col in row:\n if col == \"NA\" or col == \"\":\n hasMissing = True\n if not hasMissing:\n clean_data.append(row)\n self.data = clean_data","title":""},{"docid":"ae8ead52a7b3c87ce6d0aa2874c477a8","score":"0.54421103","text":"def make_log_no_trips(self):\n self.df['log_trips'] = self.df[(self.df['trips_in_first_30_days'] != 0)].trips_in_first_30_days.apply(np.log)\n self.df['log_trips'] = self.df['log_trips'].apply(lambda x: 0 if np.isnan(x) else x)","title":""},{"docid":"6a2a3655355cff1d9e36930017d6a14d","score":"0.5433729","text":"def remove_empty_values_marks(self, data):\n\n data_new = np.ndarray(shape=data.shape, dtype=data.dtype)\n\n __ACCURACY = 0.00001\n\n sample_number = 0\n for features in data:\n feature_index = 0\n for value in features:\n if abs(self.__EMPTY_VALUE - value) > __ACCURACY:\n data_new[sample_number][feature_index] = value\n else:\n data_new[sample_number][feature_index] = None\n feature_index += 1\n sample_number += 1\n print(\"Remove marks about empty values has done!\")\n return data_new","title":""},{"docid":"2ddec8050279af60f2721fd30cf43fe3","score":"0.54108644","text":"def _remove_nan(values: TypeValList) -> TypeValList:\n values = np.asarray(values, dtype=float)\n return values[~np.isnan(values)]","title":""},{"docid":"85ffd26d43c6c9bd7e9ff00c77464868","score":"0.541016","text":"def check_for_nan(df):\n return df.dropna()","title":""},{"docid":"f070ce783a1e1cc0ab8923b612b26e5e","score":"0.54039186","text":"def filter_all(data):\n #filter individual blocks of continuous data\n datablocks = [list(v) for k,v in groupby(data,np.isfinite) if k]\n filtereddatablocks = [filterfunction(v) for v in datablocks]\n if np.any(np.isnan(data)): #if there are gaps\n #get information about gaps\n gap_indeces = np.where(np.isnan(data))[0]\n gap_locs = []\n gap_lengths = []\n for k, g in groupby(enumerate(gap_indeces), lambda ix:ix[1]-ix[0]):\n temprun = list(map(itemgetter(1), g))\n gap_lengths.append(np.size(temprun))\n gap_locs.append(temprun[0])\n #build filtered data with gaps\n filteredrun = np.array([]);\n if gap_locs[0]==0: #if the first space is a gap, append and remove\n filteredrun = np.append(filteredrun, np.full(gap_lengths[0],np.NaN) )\n gap_locs = np.delete(gap_locs,0)\n gap_lengths = np.delete(gap_lengths,0)\n for i in range(np.size(filtereddatablocks)):\n filteredrun = np.append(filteredrun,filtereddatablocks[i])\n if i 100 mph\n df = df[df['speed'] < 100]\n\n # replace unknowns with mean of column\n df = df.replace(-99999, np.nan)\n df = df.fillna(df.mean())\n return df","title":""},{"docid":"0430e6d2ce3529d2c69f6a112c139784","score":"0.5377258","text":"def _clean_missing(data, params):\n attributes = params['attributes']\n cleaning_mode = params['cleaning_mode']\n frag = params['id_frag']\n\n if cleaning_mode == \"REMOVE_COLUMN\":\n\n thresh = params['thresh']\n if thresh:\n subset = []\n cols = params['columns_drop']\n for c in cols.index:\n if cols.loc[c] > thresh:\n subset.append(c)\n else:\n subset = params['columns_drop']\n data = data.drop(subset, axis=1)\n\n elif cleaning_mode == \"MEAN\":\n\n sizes, sums = params['values']\n values = np.divide(sums, sizes)\n\n for v, a in zip(values, attributes):\n data[a] = data[a].fillna(value=v)\n\n elif cleaning_mode == \"MODE\":\n dict_mode = params['dict_mode']\n for att in dict_mode:\n mode = dict_mode[att].idxmax()\n data[att] = data[att].fillna(value=mode)\n\n elif cleaning_mode == 'MEDIAN':\n medians = params['values']\n for att in medians:\n data[att] = data[att].fillna(value=medians[att])\n\n data.reset_index(drop=True, inplace=True)\n info = generate_info(data, frag)\n return data, info","title":""},{"docid":"bc3286b60c67caabbf08b38675cf0de7","score":"0.53675956","text":"def drop_missing(X):\n nonmissing=X[0].copy()\n nonmissing['Nonmissing']=True\n nonmissing=nonmissing['Nonmissing']\n for x in X:\n nonmissing.where(pd.notnull(x).all(axis=1),False,inplace=True)\n\n for i in range(len(X)):\n X[i] = X[i].loc[nonmissing,:]\n\n return tuple(X)","title":""},{"docid":"ed07a105be7b32edd490f39e6279fde8","score":"0.53658754","text":"def flag_na(df,remove_rows=False):\n df = df.copy()\n n_bad = sum(np.ravel(df.isnull().values))\n print('{} bad values in the dataset.')\n if remove_rows == True:\n df.dropna()\n return(df)","title":""},{"docid":"d3ccf0fca21cb7d020e01721e6d73f4f","score":"0.5357542","text":"def _getmissingsensors(self):\n currentcams=[]\n currenttemp=[]\n currentni=[]\n missingcams=[]\n missingtemp=[]\n missingni=[]\n for par in self._paramlist:\n if par[\"type\"]==\"camera\":\n currentcams.append((par[\"vendor\"],par[\"camid\"]))\n if par[\"type\"]==\"temperature\":\n currenttemp.append(par[\"handle\"])\n if par[\"type\"]==\"nianalog\":\n currentni.append(par[\"devstr\"])\n \n for cam in self._connectedcams:\n if not cam in currentcams:\n missingcams.append(cam)\n if not self._connectedtemp is None:\n for temp in self._connectedtemp:\n if not temp in currenttemp:\n missingtemp.append(temp)\n if not self._connectedni is None:\n for ni in self._connectedni:\n if not ni in currentni:\n missingni.append(ni)\n missing={\"camera\":missingcams, \"temperature\":missingtemp,\"nianalog\":missingni}\n return missing","title":""},{"docid":"1c24a661c654de184a93b6d3fcc684cf","score":"0.53526807","text":"def nan_trim(data):\r\n data_diff = np.diff(data)\r\n\r\n for i, diff in reversed(list(enumerate(data_diff))):\r\n if diff == 1:\r\n data.pop(i)\r\n\r\n return data","title":""},{"docid":"d773f973c4e24a2274159b9e08fef1b8","score":"0.53520674","text":"def nans_remove(self):\n msk = np.isfinite(self.y)\n return self.__class__(self.x[msk], self.y[msk])","title":""},{"docid":"11f98e0c9343893178195cbd3f034352","score":"0.5342439","text":"def remove_na_lines(self, max_na_proportion:float=None):\n\n if max_na_proportion:\n drop_lines = list(self.df.loc[self.df.isna().sum(axis=1)>len(self.df.columns)*max_na_proportion].index) \n else:\n drop_lines = self.na_lines\n #print(f'Lines removed:{drop_lines[0:4]}...')\n self.df = self.df.drop(drop_lines)\n return self.df","title":""},{"docid":"075d66d00dfa6a68a225f3dacc5ab7fe","score":"0.5338691","text":"def cleanData(dataToClean):\n for i in range(len(dataToClean)):\n for j in range(len(dataToClean[0])):\n if np.isnan(dataToClean[i][j]):\n dataToClean[i][j]=0","title":""},{"docid":"a12091d6b20c49b7725690d923460001","score":"0.5331909","text":"def clean_df(df):\n df = df.where(pd.notnull(df), None)\n df = df.fillna(value=0)\n return df","title":""},{"docid":"3d6bb36d5febd036ea6d7c4c4267a6a5","score":"0.533119","text":"def _drop_obs(df):\n drop_subj = []\n for sid, row in df.iterrows():\n try:\n if sum(row.dropna()) == 0:\n drop_subj.append(sid)\n elif row.count() == 0:\n drop_subj.append(sid)\n except TypeError: # compatible to ados\n if sum(row.astype(float).dropna()) == 0:\n drop_subj.append(sid)\n elif row.astype(float).count() == 0:\n drop_subj.append(sid)\n return drop_subj","title":""},{"docid":"260be91c0b8510b71ae7a1d740960b03","score":"0.53306866","text":"def NaN_cleaning(df):\n df = df.replace(np.nan, 'unknown')\n return df.reset_index(drop=True)","title":""},{"docid":"5be06db39233a32605860ac8b3689a5c","score":"0.53283554","text":"def _remove_missing_data(\n data_values: NDArray,\n geometry_array: geopandas.GeoSeries,\n) -> tuple[NDArray, geopandas.GeoSeries]:\n not_missing_data = ~pandas.isnull(data_values)\n geometry_array = geometry_array[not_missing_data]\n data_values = data_values[not_missing_data]\n return data_values, geometry_array","title":""},{"docid":"aeb9b42a6eaa6253ee3b8d523139e299","score":"0.5324406","text":"def quick_clean(data):\n rows_missing_data = (data == 0).sum(1)\n return data[rows_missing_data == 0, :]","title":""},{"docid":"82d9e2a4681978a2e091275a72005b3c","score":"0.5316683","text":"def clean_nan(data, data_column):\r\n data.dropna(subset=[data_column, \\\r\n 'Quality'], inplace=True)\r\n return data","title":""},{"docid":"a7f38a2777c41e92eecb61343ef10b22","score":"0.53023773","text":"def filter_zeroes(self):\n self.U[self.U == 0] = np.nan\n self.V[self.V == 0] = np.nan\n self.W[self.W == 0] = np.nan","title":""},{"docid":"985aa5c0aa5f067935e58f73b2100dbc","score":"0.5297176","text":"def remove_nans(array_like):\n data = array_like.copy()\n \n # Astropy table object\n if type(data) is Table:\n data = data.filled(NAN_VALUE)\n # Pandas DataFrame object\n elif type(data) is pd.DataFrame:\n data = data.fillna(NAN_VALUE)\n else:\n print(\"Unrecognized data format:\", type(data))\n return None\n \n # Return final data structure\n return data","title":""},{"docid":"914a197db781cb12a5fe19ab43161b5d","score":"0.5292508","text":"def test_DataCleaner_big_nan_handler_warning(self):\n dc = DataCleaner(max_na_frac=0.01)\n df = pd.DataFrame(\n np.random.randint(0, 100, size=(100, 4)), columns=list(\"ABCD\")\n )\n dc.fit(df, \"D\")\n self.assertEqual(len(dc.warnings), 0)\n\n df[\"A\"].iloc[10:20] = np.nan\n df[\"B\"].iloc[:99] = np.nan\n dc.fit(df, \"D\")\n self.assertEqual(len(dc.warnings), 1)","title":""},{"docid":"dc851de57a11012e4155217bb31adc96","score":"0.52870226","text":"def delete_rows(df):\r\n\r\n df = df.drop(df[df.img.isna()].index)\r\n df = df.drop(df[df.cost.isna()].index)\r\n df = df.drop(df[df.rarity.isna()].index)\r\n return df","title":""},{"docid":"7fbcab9e0a3ed7e49af6f0c03573e528","score":"0.5284864","text":"def test_nans_replaced_none():\n\n #generate test data\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\n dataframe['A'][5] = numpy.nan\n\n #run function on dataframe\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\"none\", removal_time_frame=\"day\",\n fault_placement=\"calendar\")\n\n assert dataframe.size == result_dataframe.size, \"nan has not been dropped\"\n assert result_dataframe['A'][5] != numpy.nan, \"nan was removed but shouldn't have\"","title":""},{"docid":"08006fa93818a2f08ef6539d0e78d6b8","score":"0.5269665","text":"def remove_duplicates_and_full_nulls(data):\n data = data.copy()\n data = data.drop_duplicates()\n data = data.dropna(how ='all')\n return data","title":""},{"docid":"d9cd6b277461a674b517515b1a4294eb","score":"0.52642363","text":"def remove_null(dataframe):\n\n dataframe = dataframe.dropna()\n return dataframe","title":""},{"docid":"52409db8b7b10f8ebaa0732ca7d73c16","score":"0.52546704","text":"def _rmDataWithoutBrightStar(self, neighborStarMap, starMap,\n wavefrontSensors):\n\n # Collect the sensor list without the bright star\n noStarSensorList = []\n for detector, stars in neighborStarMap.items():\n if (len(stars.getId()) == 0):\n noStarSensorList.append(detector)\n\n # Remove the data in map\n for detector in noStarSensorList:\n neighborStarMap.pop(detector)\n starMap.pop(detector)\n wavefrontSensors.pop(detector)","title":""},{"docid":"3392f154927a0b3ea4e7a68b784f6179","score":"0.5254399","text":"def filter_nan(s,o):\n data = np.array([s.flatten(),o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n\n return data[:,0],data[:,1]","title":""},{"docid":"bbe7cf61ecf67b86675bc79f599d13c2","score":"0.5251705","text":"def test_filter_by_NaN(self):\n test_scope = scope.Scope(-4.9, -4.70, 48, 49, 0, 1500000000)\n test_preprocessing = preprocessing.Preprocessing(test_scope, self.test_ships)\n\n self.assertEqual(len(self.test_ships), 15)\n test_preprocessing.filter_by_NaN()\n self.assertEqual(len(self.test_ships), 7)\n\n number_states = {'245257000': 29, '228037600': 259, '304091000': 1,\n '227705102': 249, '227443000': 92, '228931000': 21,\n '228131600': 350, '228064900': 4, '37100300': 7}\n\n for ship in self.test_ships:\n self.assertEqual(len(ship.ais_states), number_states[ship.sourcemmsi],\n msg=\"sourcemmsi is \" + ship.sourcemmsi)","title":""},{"docid":"9ee4fe5bb4730d0e6d8c78730fa48d55","score":"0.52495563","text":"def remove_rows_with_missing_values(self):\n row_length = len(self.column_names)\n table_copy = []\n \n for i in range(len(self.data)):\n has_NA = False\n for j in range(row_length):\n if self.data[i][j] == \"NA\" or self.data[i][j] == \"\":\n has_NA = True\n if has_NA == False: \n table_copy.append(self.data[i])\n \n self.data.clear()\n self.data = copy.deepcopy(table_copy) \n pass # TODO: fix this","title":""},{"docid":"16ead744219067e1a2c3a93e51f6bfe2","score":"0.5245453","text":"def _clean_useless_rows(self):\r\n self.temp_stock_data = pandas.DataFrame()\r\n dates = []\r\n for i in xrange(0, self.stock_data[self.company_list.Companies.irow(0)].count()):\r\n #print i\r\n row = self.stock_data.irow(i)\r\n if len(row[numpy.isnan(row)]) == len(row):\r\n dates.append(self.date_list[i])\r\n \r\n for single_date in dates:\r\n self.stock_data = self.stock_data.drop(pandas.Timestamp(single_date))\r\n print \"Data cleaned from useless rows.\"","title":""},{"docid":"6fc475ee4cc92954c4f70406962b6150","score":"0.52325326","text":"def data_cleaning(df: DataFrame) -> DataFrame:\n print(\"Cleaning the data to remove problems\\n \")\n\n nulls = df.isnull().sum()\n nulls = nulls[nulls != 0]\n\n if not nulls.empty:\n print(f\"The columns below some 'NaN' values:\\n{nulls}\")\n\n # for index_with_nan in enumerate(nulls.keys()[:]):\n # print(f\"Column: {index_with_nan} \\n \")\n\n if 'Birth Year' in df.columns.values.tolist():\n df = dob_trim(df)\n\n time.sleep(2)\n\n no_user_type: float = format(df['User Type'].isna().sum()/df.shape[0]*100, '.2g')\n print(f\"A few rows don't have any information for 'User Type' but it is only {no_user_type}% so we'll just drop them\")\n df = df.dropna(axis = 0, subset=['User Type'])\n\n if 'Gender' in df.columns.values.tolist():\n gender_neutral: float = format(df['Gender'].isna().sum()/df.shape[0]*100, '.2g')\n print(f\"{gender_neutral}% of trips don't have any information for 'Gender' so we'll forward-fill.\")\n df.fillna(method=\"ffill\")\n # gender_neutral: float = format(nulls['Gender']/df.shape[0]*100, '.2g')\n # print(f\"A few rows don't have any information for 'Gender' but it is only {gender_neutral}% so we'll just drop them\")\n # df = df.dropna(axis = 0, subset=['Gender'], inplace=True)\n \n return df","title":""},{"docid":"658b7ab760716acff016b703ca146fca","score":"0.5225665","text":"def removeNansSingle(data):\n # set an x array\n x = np.arange(data.size)\n # find locations of nans - this is a bool array with True in locations with nan values\n nanLocs = np.isnan(data)\n # if no nans, do nothing\n if not np.any(nanLocs):\n return data # no nans to remove\n # create mask\n mask = np.ones(data.size, np.bool)\n mask[nanLocs] = 0 # using numpy indexing with bool arrays\n # no need to group, want to remove every nan\n data[nanLocs] = np.interp(x[nanLocs], x[mask], data[mask])\n return data","title":""},{"docid":"5cb6066ce4a1f9dc1a890971b21dec1f","score":"0.5221285","text":"def remove_notes(data):\n return data[pd.to_numeric(data.iloc[:, 0], errors='coerce').notnull()]","title":""},{"docid":"6a937fac4a9481e0cde52d043b27035f","score":"0.52128845","text":"def filter_spikes(df:pd.DataFrame):\n \"\"\"first return argument is non-spike signals, the second one for the spikes\"\"\"\n new_df=pd.DataFrame()\n spike_cluster=pd.DataFrame()\n for i in range(len(df.columns)):\n tmp=df.iloc[:, i] \n uniques=tmp.unique()\n uniques = [x for x in uniques if str(x) != 'nan']\n uniques = [x for x in uniques if x not in [0.0,1.0]]\n if(len(uniques)<=1): #spike like\n spike_cluster.insert(len(spike_cluster.columns),tmp.name,tmp)\n else:\n new_df.insert(len(new_df.columns),tmp.name,tmp)\n return new_df,spike_cluster","title":""},{"docid":"4a372da4cfe63a9a92a7f200bb86f20a","score":"0.5209513","text":"def nan_clean(array):\n for i in range(len(array)):\n if np.isnan(array[i]) == True:\n array[i] = 0.0\n return array","title":""},{"docid":"6a41fc2d468a3aa9657429af2f10331b","score":"0.52041674","text":"def remove_nan_observations(x, y, z):\n x_ = x[~np.isnan(z)]\n y_ = y[~np.isnan(z)]\n z_ = z[~np.isnan(z)]\n return x_, y_, z_","title":""},{"docid":"7b824e07c74f481cca378320af481262","score":"0.5198521","text":"def filterNaN(instr, datacol, outfile, logfile, verbose):\n try:\n nanclean = instr[1].header['NANCLEAN']\n except:\n naxis2 = 0\n for i in range(len(instr[1].columns.names)):\n if 'time' in instr[1].columns.names[i].lower():\n timecol = instr[1].columns.names[i]\n try:\n instr[1].data.field(datacol)\n except:\n msg = (\"ERROR -- KEPIO.FILTERNAN: cannot find column {}\"\n \"in the infile\".format(datacol))\n kepmsg.err(logfile, msg, verbose)\n try:\n for i in range(len(instr[1].data.field(0))):\n if (str(instr[1].data.field(timecol)[i]) != '-inf' and\n str(instr[1].data.field(datacol)[i]) != '-inf'):\n instr[1].data[naxis2] = instr[1].data[i]\n naxis2 += 1\n instr[1].data = instr[1].data[:naxis2]\n comment = 'NaN cadences removed from data'\n kepkey.new('NANCLEAN', True, comment, instr[1], outfile, logfile,\n verbose)\n except:\n errmsg = ('ERROR -- KEPIO.FILTERNAN: Failed to filter NaNs from '\n + outfile)\n kepmsg.err(logfile, errmsg, verbose)\n return instr","title":""},{"docid":"e14120618e0caa9c1a1adf8bf742894f","score":"0.51975524","text":"def fix_na_values(df):\n ignored = [\"SUBJECT_ID\", \"HADM_ID\", \"ADMITTIME\", \"DISCHTIME\"]\n df_core = df.drop(ignored, axis=1)\n\n while df_core.isna().sum().sum():\n # get column with least amount of missing values\n cols_with_na = df_core.isna().sum()\n col = cols_with_na[cols_with_na > 0].idxmin()\n # impute that column\n df_core.loc[df_core[col].isna(), col] = impute_column(df_core, col)\n\n return pd.concat([df_core, df[ignored]], axis=1)","title":""},{"docid":"13995298c15ddffef72188fbf4ae9e9e","score":"0.51944923","text":"def test_nans_dropped():\n\n #generate test data\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\n dataframe['A'][5] = numpy.nan\n\n #run function on dataframe\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\"drop\", removal_time_frame=\"day\", fault_placement=\"calendar\")\n assert dataframe.size != result_dataframe.size, \"nan have not been dropped\"","title":""},{"docid":"9105baa116f0859c24acfe0688351a8b","score":"0.51941323","text":"def drop_missing(self):\n df1 = self.df\n df2 = df1.dropna(axis=0)\n\n self.df = df2","title":""},{"docid":"fe78430afcdbf63973f5fb1eef658c15","score":"0.5192824","text":"def remove_missing_values(x):\n x_clean = x.copy()\n x_clean = x_clean[:, [np.count_nonzero(x.T[i] == -999) == 0 for i in range (x.shape[1])]]\n return x_clean","title":""},{"docid":"26e1f1380c66022fe6c1a3408b34b606","score":"0.51923764","text":"def remove_rcna(df):\n df.dropna(how = 'all', axis = 'columns', inplace = True)\n df.dropna(how = 'all', axis = 'rows', inplace = True)\n return df","title":""},{"docid":"97e3227a5225488f9568a8c415801ea9","score":"0.51922977","text":"def generate_query_ratesnull(self):\n pass","title":""},{"docid":"934058862a1706b0deffe110b8780021","score":"0.518677","text":"def _series_cleaner(self, obs_data: Union[dict, list]) -> pd.DataFrame:\n series = pd.json_normalize(obs_data[\"observations\"])\n series.drop(columns=[\"realtime_start\", \"realtime_end\"], inplace=True)\n series[\"value\"] = series[\"value\"].replace(self.nan_char, np.nan)\n return series","title":""},{"docid":"eabc39526af7b6e52689bfe97d78ffba","score":"0.5184404","text":"def drop_missing(self):\n self.miss = np.rint(len(self.data.columns)*self.miss)\n self.data = self.data[self.data.isnull().sum(axis=1)<=self.miss]","title":""},{"docid":"b6e550833aaa3e203af1bd5115ecd7f9","score":"0.5179664","text":"def clean_data(df):\n data_types = get_missing_columns()\n \n for idx in data_types.index:\n column = idx\n if column in df.columns:\n df[column] = df[column].replace(data_types.loc[column]['nan_vals'], np.nan)\n\n \n # remove selected columns\n df.drop(['AGER_TYP', 'ALTER_HH', 'ALTER_KIND1', 'ALTER_KIND2', 'ALTER_KIND3', 'ALTER_KIND4', 'ALTERSKATEGORIE_FEIN',\\\n 'D19_BANKEN_ONLINE_QUOTE_12', 'D19_GESAMT_ONLINE_QUOTE_12', 'D19_KONSUMTYP', 'D19_LETZTER_KAUF_BRANCHE',\\\n 'D19_LOTTO', 'D19_SOZIALES', 'D19_TELKO_ONLINE_QUOTE_12', 'D19_VERSAND_ONLINE_QUOTE_12',\\\n 'D19_VERSI_ONLINE_QUOTE_12', 'EXTSEL992','GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'TITEL_KZ', 'LNR'],\n axis=1, inplace=True)\n \n print(\"Removal of Columns with over 20% null values complete...\")\n \n # remove selected rows\n df = df[df.isnull().sum(axis=1) < 9].reset_index(drop=True)\n \n # remove selected columns again\n features_not_in_feat_info_42 = ['AKT_DAT_KL', 'ANZ_KINDER', 'ANZ_STATISTISCHE_HAUSHALTE', 'CJT_KATALOGNUTZER','CJT_TYP_1',\\\n 'CJT_TYP_2', 'CJT_TYP_3', 'CJT_TYP_4', 'CJT_TYP_5', 'CJT_TYP_6', 'DSL_FLAG',\\\n 'EINGEZOGENAM_HH_JAHR','FIRMENDICHTE', 'GEMEINDETYP', 'HH_DELTA_FLAG', 'KBA13_ANTG1',\\\n 'KBA13_ANTG2', 'KBA13_ANTG3','KBA13_ANTG4', 'KBA13_BAUMAX', 'KBA13_CCM_1401_2500',\\\n 'KBA13_GBZ', 'KBA13_HHZ', 'KBA13_KMH_210','KONSUMZELLE', 'MOBI_RASTER', 'RT_KEIN_ANREIZ',\\\n 'RT_SCHNAEPPCHEN', 'RT_UEBERGROESSE', 'STRUKTURTYP','UMFELD_ALT', 'UMFELD_JUNG',\\\n 'UNGLEICHENN_FLAG', 'VERDICHTUNGSRAUM', 'VHA', 'VHN', 'VK_DHT4A','VK_DISTANZ',\\\n 'VK_ZG11','D19_KONSUMTYP_MAX', 'KOMBIALTER','EINGEFUEGT_AM']\n \n df.drop(features_not_in_feat_info_42,axis=1, inplace=True)\n \n print(\"Removal of undocumented columns complete...\")\n \n \n # select, re-encode, and engineer column values.\n # # feature engineering of categorical features\n\n df['OST_WEST_KZ'].replace(['W','O'], [1, 0], inplace=True)\n \n multi = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CJT_GESAMTTYP', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',\\\n 'D19_BANKEN_DATUM','D19_BANKEN_OFFLINE_DATUM', 'D19_BANKEN_ONLINE_DATUM', 'D19_GESAMT_ANZ_12',\\\n 'D19_GESAMT_ANZ_24', 'D19_GESAMT_DATUM','D19_GESAMT_OFFLINE_DATUM', 'D19_GESAMT_ONLINE_DATUM',\\\n 'D19_TELKO_DATUM', 'D19_TELKO_OFFLINE_DATUM', 'D19_TELKO_ONLINE_DATUM','D19_VERSAND_DATUM',\\\n 'D19_VERSAND_OFFLINE_DATUM', 'D19_VERSAND_ONLINE_DATUM', 'D19_VERSI_DATUM', 'D19_VERSI_OFFLINE_DATUM',\\\n 'D19_VERSI_ONLINE_DATUM', 'FINANZTYP', 'GEBAEUDETYP', 'GFK_URLAUBERTYP','LP_FAMILIE_FEIN','LP_FAMILIE_GROB',\\\n 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP']\n \n df = pd.get_dummies(df, columns=multi, prefix=multi)\n \n \n # feature engineering of mixed features\n df['PRAEGENDE_JUGENDJAHRE_decade'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_decade)\n df['PRAEGENDE_JUGENDJAHRE_movement'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_movement)\n df.drop('PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)\n \n print(\"Feature Engineering PRAEGENDE_JUGENDJAHRE complete...\")\n \n \n df['CAMEO_INTL_2015_wealth'] = df['CAMEO_INTL_2015'].apply(make_wealth) \n df['CAMEO_INTL_2015_life_stage'] = df['CAMEO_INTL_2015'].apply(make_life_stage)\n df.drop('CAMEO_INTL_2015', axis=1, inplace=True)\n \n print(\"Feature Engineering CAMEO_INTL_2015 complete...\")\n \n df['WOHNLAGE_rural'] = df['WOHNLAGE'].map({0:0,1:0,2:0,3:0,4:0,5:0,7:1,8:1})\n df['WOHNLAGE_rating_class'] = df['WOHNLAGE'].map({0:0,1:1,2:2,3:3,4:4,5:5,7:0,8:0})\n \n print(\"Feature Engineering WOHNLAGE complete...\")\n \n df = df.drop(['LP_LEBENSPHASE_FEIN','LP_LEBENSPHASE_GROB','WOHNLAGE','PLZ8_BAUMAX'], axis=1)\n \n print(\"Finished!\")\n \n # Return the cleaned dataframe.\n return df","title":""},{"docid":"50b15f9a5bc9f58c3f1fda20bd23a98b","score":"0.5174193","text":"def clean_data(self):\n # Mask files with lat = 0 or lon = 0\n mask = np.where(np.logical_or(self.data['VEH_LAT']==0, \n self.data['VEH_LONG']==0))\n\n for k in self.data.keys():\n if isinstance(self.data[k][0], float):\n self.data[k][mask] = np.nan","title":""},{"docid":"0fb2a849eb9288c2238968e9235b9752","score":"0.51717937","text":"def drop_null_rows(self, x: bool):\n if x:\n total_null_rows = self.df[self.target].isin(\n [' ', 'NULL', np.nan]).sum()\n if total_null_rows > 0:\n print(\"Dropping rows having [' ', 'NULL', numpy.nan] values\")\n self.df.dropna(inplace=True)\n self.df.reset_index(drop=True, inplace=True)\n self.null_values_present = False\n print_in_red(f\"Total Null rows dropped: {total_null_rows}\\n\")\n self._start_analysis()\n else:\n print(colored(\"There is no null rows present.\\n\", \"green\"))","title":""},{"docid":"52449af6e301d2d873cf47553c2b38b8","score":"0.5167622","text":"def filter_nan(s, o):\n data = np.array([s.flatten(), o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n return data[:, 0], data[:, 1]","title":""},{"docid":"6512e72d0658c2f1c0378e699cc56170","score":"0.51613533","text":"def clean_timeseries(self):\n if 'tb_incidence' in self.timeseries_log.keys():\n self.timeseries_log['tb_incidence'][0] = 0.","title":""},{"docid":"248a5d7d28357321ce39d2c77fe712a7","score":"0.5160788","text":"def filter_values_2(self, counter):\n inf = float(\"inf\")\n last_mea = list(self.scan_values_2[counter])\n for i in range(len(last_mea)):\n if last_mea[i] == inf:\n for k in range(1, MEASUREMENTS):\n if self.scan_values_2[counter-k] != inf:\n last_mea[i] = self.scan_values_2[k][i]\n break\n scan = LaserScan()\n scan.ranges = last_mea\n self.laser_filtered_pub.publish(scan)","title":""},{"docid":"16cc667bcf39fc41ca499a7580cf48d0","score":"0.51605296","text":"def _get_instr_after_remove(self, remove_size_null, remove_adj_cap_nan):\n if any([remove_size_null, remove_adj_cap_nan]): # will update instr_df\n instr_df = self.concat_instr_df.copy()\n else: # nothing will be changed in self.concat_instr_df so no need to copy\n instr_df = self.concat_instr_df\n if instr_df.empty:\n raise Exception('concat_instr_df is empty. Cannot display metrics')\n if remove_size_null:\n no_instrs = len(instr_df)\n idx_size_null = instr_df['stake'] == 0\n if sum(idx_size_null) > 0:\n self.logger.warning('Dropping {}/{} instructions with stake 0'.format(\n sum(idx_size_null), no_instrs))\n instr_df = instr_df.loc[~idx_size_null]\n if remove_adj_cap_nan:\n if 'adj_cap' not in instr_df.columns:\n raise Exception('concat_instr_df do not have column '\n 'adj_cap and so cannot remove_adj_cap_nan')\n no_instrs = len(instr_df)\n idx_nan = instr_df['adj_cap'].isnull()\n if sum(idx_nan) > 0:\n self.logger.warning('Keeping {}/{} instructions with known capital adjustment'\n .format(no_instrs - sum(idx_nan), no_instrs))\n # remove nan in adjst_capital\n instr_df = instr_df.loc[~idx_nan]\n return instr_df","title":""}],"string":"[\n {\n \"docid\": \"8361c7d6bb8408ccdc93d98e06906cb5\",\n \"score\": \"0.63010675\",\n \"text\": \"def _removerawnans(self):\\n for band in self.data[self.root]:\\n for i, f in enumerate(self.data[self.root][band]['frequency']):\\n if math.isnan(f):\\n for param in self.data[self.root][band]:\\n if len(self.data[self.root][band][param]) == \\\\\\n len(self.data[self.root][band]['frequency']):\\n np.delete(self.data[self.root][band][param],i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96cebc2aed0cbec825c4d54bfa1ccdd9\",\n \"score\": \"0.62637925\",\n \"text\": \"def cleanNaN (raw_df):\\n #first step:cleaning the NaN values when the whole row or whole columns is empty:\\n raw_df.dropna(axis=1, how='all' )\\n raw_df.dropna(axis=0, how='all')\\n \\n\\n while True:\\n\\n #removing indicators (columns)\\n NaN_per_col = raw_df.apply(numMissing, axis=0).to_frame().T #axis=0 defines that function is to be applied on each column\\n max_NaN_per_col = np.amax(NaN_per_col.iloc[0,:].values)\\n min_NaN_per_col=np.amin(NaN_per_col.iloc[0,:].values)\\n if max_NaN_per_col == min_NaN_per_col+2 or max_NaN_per_col == min_NaN_per_col+1 : \\n threshold1=len(raw_df.index)-max_NaN_per_col+1\\n else:\\n threshold1=len(raw_df.index)-max_NaN_per_col+3 #if rate of 2 here, check the condition at +1\\n raw_df=raw_df.dropna(axis=1, thresh=threshold1)\\n \\n \\n # check conditions to stop the while loop\\n if raw_df.isnull().sum().sum() == 0:\\n break\\n \\n \\n #removing countries (rows)\\n NaN_per_row = raw_df.apply(numMissing, axis=1).to_frame().T #axis=1 defines that function is to be applied on each row\\n max_NaN_per_row = np.amax(NaN_per_row.iloc[0,:].values)\\n threshold0=len(raw_df.columns)-max_NaN_per_row +1\\n raw_df=raw_df.dropna(axis=0, thresh=threshold0)\\n \\n\\n # check conditions to stop the while loop\\n if raw_df.isnull().sum().sum() == 0:\\n break\\n \\n return raw_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7414a0fef1fec27eac411ce030507b3\",\n \"score\": \"0.6119339\",\n \"text\": \"def _remove_nan_inf(self):\\n # infinite and nan check\\n self.TB10A[np.isfinite(self.TB10A) is False] = self.bad_data\\n self.TB19A[np.isfinite(self.TB19A) is False] = self.bad_data\\n self.TB37A[np.isfinite(self.TB37A) is False] = self.bad_data\\n self.TB85A[np.isfinite(self.TB85A) is False] = self.bad_data\\n if hasattr(self, 'TB10B'): # Assume if one, all are there\\n self.TB10B[np.isfinite(self.TB10B) is False] = self.bad_data\\n self.TB19B[np.isfinite(self.TB19B) is False] = self.bad_data\\n self.TB37B[np.isfinite(self.TB37B) is False] = self.bad_data\\n self.TB85B[np.isfinite(self.TB85B) is False] = self.bad_data\\n else: # Set all the B channels to bad, these are single-channel data\\n for freq in FREQS:\\n setattr(self, 'TB' + freq + 'B',\\n self.bad_data * np.ones((self.nscans, self.ncross),\\n dtype='float'))\\n # Address geolocations if available\\n if hasattr(self, 'Latitude'):\\n self.Latitude[np.isfinite(self.Latitude) is False] = self.bad_data\\n self.Longitude[np.isfinite(self.Longitude) is False] = \\\\\\n self.bad_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756ddf8bd32157a47110f845a19de702\",\n \"score\": \"0.6115779\",\n \"text\": \"def removeNans(data: Dict):\\n # find nan in the dataset and removes the values\\n for chan in data:\\n data[chan] = removeNansSingle(data[chan])\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77edef415c0e04d3ef1580f96c9c6df4\",\n \"score\": \"0.6110685\",\n \"text\": \"def handle_missing_values(df):\\n print(\\\"just drop all the samples with missing values, consider a better approach\\\")\\n newdf= df.dropna()\\n return newdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3266ab1d07f9f838365bd8fbde7a1f7\",\n \"score\": \"0.604752\",\n \"text\": \"def remove_nans(self):\\n s = \\\"::: removing NaNs from %s :::\\\" % self.name\\n print_text(s, self.color)\\n\\n self.x = self.x[self.good_x]\\n self.y = self.y[self.good_y]\\n self.x_min = self.x.min()\\n self.x_max = self.x.max()\\n self.y_min = self.y.min()\\n self.y_max = self.y.max()\\n self.nx = len(self.x)\\n self.ny = len(self.y)\\n\\n for i in self.data.keys():\\n self.data[i] = self.data[i][self.good_y, : ]\\n self.data[i] = self.data[i][:, self.good_x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e963843fe13ec0998f958dfce64dfc\",\n \"score\": \"0.5992643\",\n \"text\": \"def dropna(df):\\n df = df[df < math.exp(709)] # big number\\n df = df[df != 0.0]\\n df = df.dropna()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb517438e75d14790c556d6c26530e4\",\n \"score\": \"0.5991817\",\n \"text\": \"def dropna(self):\\n self.transformed_data = list(\\n filter(lambda line_obj: line_obj.has_null is False, self.orig_data))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52259136ad5716d8e4e3adceb77bace3\",\n \"score\": \"0.5938397\",\n \"text\": \"def remove_nans(sig):\\n\\n sig_nans = np.isnan(sig)\\n sig_removed = sig[np.where(~np.isnan(sig))]\\n\\n return sig_removed, sig_nans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59b786a4ada6804cec21d78e75eb0698\",\n \"score\": \"0.59266084\",\n \"text\": \"def dropna(df):\\n df = df[df < math.exp(709)] # big number\\n df = df[df != 0.0]\\n df = df.dropna()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5c0c67abd61e6060f97e87d779570eb\",\n \"score\": \"0.5856776\",\n \"text\": \"def clean_nans(record):\\n floats = [field for field in record if isinstance(record[field], float)]\\n for field in floats:\\n if np.isnan(record[field]):\\n del record[field]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82fb941e9652d6c2263194543d36ee88\",\n \"score\": \"0.57872057\",\n \"text\": \"def prune_records(self,rec):\\n return {k: v for k, v in json.loads(rec).items() if not (v is None or (isinstance(v, list) and sum(pd.isnull(v) > 0)) or\\n (isinstance(v, float) and math.isnan(v))\\n or isinstance(v, pd.tslib.NaTType))}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ccc1c304ac258099fe94a5f4329c384\",\n \"score\": \"0.5760188\",\n \"text\": \"def _strip(session) -> tuple:\\n eeg, *rest = session\\n ind = -next(i for i, value in enumerate(eeg[0, ::-1]) if not np.isnan(value))\\n if ind == 0:\\n ind = None\\n return tuple((eeg[:, :ind], *rest))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1c465dc06a8c0c07afedbcc6d05af9\",\n \"score\": \"0.57554376\",\n \"text\": \"def remove_null_values(dataframe):\\r\\n dataframe.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968d84511f33f982e53d2dced989779c\",\n \"score\": \"0.57538193\",\n \"text\": \"def filter_values_1(self):\\n inf = float(\\\"inf\\\")\\n last_mea = list(self.scan_values[-1])\\n for i in range(len(last_mea)):\\n if last_mea[i] == inf:\\n for k in reversed(xrange(MEASUREMENTS-1)):\\n if self.scan_values[k][i] != inf:\\n last_mea[i] = self.scan_values[k][i]\\n break\\n scan = LaserScan()\\n scan.ranges = last_mea\\n self.laser_filtered_pub.publish(scan)\\n self.counter = 0\\n self.scan_values = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d79578ca01616242fae7179a5eef34\",\n \"score\": \"0.5741\",\n \"text\": \"def prune (self):\\r\\n #Remove excess zero data points characteristic of a multi-event spectrum acquisition structure that occur inside peaks.\\r\\n #Thermo data sometimes does this depending on acquisition parameters.\\r\\n if self.msevents > 1:\\r\\n zstart = None\\r\\n zend = None\\r\\n for x,intensity in enumerate(self.i):\\r\\n if intensity == 0:\\r\\n if not zstart:\\r\\n zstart = x\\r\\n zend = x\\r\\n else:\\r\\n if zend and zstart:\\r\\n length = zend-zstart+1\\r\\n if length == self.msevents-1:\\r\\n self.i[zstart:zend+1] = list(itertools.repeat(-1,length))\\r\\n zstart = None\\r\\n zend = None\\r\\n while True:\\r\\n try:\\r\\n where = self.i.index(-1)\\r\\n except:\\r\\n break\\r\\n self.i.pop(where)\\r\\n self.t.pop(where)\\r\\n self.time.pop(where)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413b28d3b07ead5cb86d8f3729fcefd2\",\n \"score\": \"0.57369703\",\n \"text\": \"def nan_filter(self):\\r\\n \\r\\n # filter out nan values in numerical attributes\\r\\n for att in self.catalog_atts:\\r\\n if getattr(self, att).shape[0] == 0:\\r\\n pass\\r\\n elif (type(getattr(self, att)[0]) == str) or (type(getattr(self, att)[0]) == bytes):\\r\\n # FIXME: intent here unclear: \\r\\n # note float('nan') is an IEEE NaN, getattr(.) is a str, and != on NaNs is special\\r\\n i = np.where(getattr(self, att) != float('nan'))[0]\\r\\n self.revise_lists(i)\\r\\n # exclude non-numerical types\\r\\n elif type(getattr(self, att)[0]) not in (np.unicode_, np.string_, np.bool_, bytes):\\r\\n if att == 'coords':\\r\\n i1 = np.where(~np.isnan(self.coords.ra.to('deg').value))[0]\\r\\n i2 = np.where(~np.isnan(self.coords.dec.to('deg').value))[0]\\r\\n i = np.intersect1d(i1,i2)\\r\\n else:\\r\\n i = np.where(~np.isnan(getattr(self, att)))[0]\\r\\n self.revise_lists(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1763b3f28d3ff59fd197556ba56b060a\",\n \"score\": \"0.5727697\",\n \"text\": \"def remove_data_for_training_from_dataframe(data_df,Mask_NA,missing_fraction=0.3):\\n\\n def convert_series_to_diag_df(Series):\\n return pd.DataFrame(columns=Series.index, index=Series.index, data=np.diag(Series.values))\\n \\n #removed_vals = (Mask_NA == 1).any(axis=0).astype(int)\\n NAs = (Mask_NA == 1).any(axis=1).astype(int)\\n NA_diag = convert_series_to_diag_df(NAs)\\n\\n NA_map = PandasFunctions.CompressDataFrame(NA_diag,[1])\\n Val_map = PandasFunctions.CompressDataFrame(Mask_NA,[1])\\n #############################################################\\n np.random.seed(0)\\n remover = np.random.rand(data_df.shape[0], NA_map.shape[0])\\n remover = np.where( missing_fraction>remover , 1, 0)\\n ############################################################# \\n NA_1s = np.matmul(remover, NA_map.values)\\n Values_0s = np.matmul(remover, Val_map.values)\\n ############################################################# \\n data_missing_df = data_df.copy()\\n matrix = data_missing_df.values\\n matrix[NA_1s ==1] = 1\\n matrix[Values_0s==1] = 0\\n data_missing_df[:] = matrix\\n #############################################################\\n return data_missing_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed86b9e8b99ac7b505b79ccb5753c9a8\",\n \"score\": \"0.57137537\",\n \"text\": \"def remove_nan_rows(micro_df: pd.DataFrame):\\n data_cols = micro_df.columns.tolist()[1:]\\n micro_df.dropna(how = 'all', subset = data_cols, inplace=True)\\n micro_df.reset_index(inplace=True, drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cf93e8603b92c72a804674e53a47cc\",\n \"score\": \"0.5667109\",\n \"text\": \"def filter_nan(s,o):\\n '''\\n data = np.array([s.flatten(),o.flatten()])\\n data = np.transpose(data)\\n #data = data[~np.isnan(data).any(1)]\\n data = data[~pd.isnull(data)]\\n #data = data[~np.isnan(data)]\\n return data[:,0],data[:,1]\\n #return data\\n '''\\n s = s.flatten()\\n o = o.flatten()\\n s = np.transpose(s)\\n o = np.transpose(o)\\n s[np.invert(~np.isnan(o))] = np.nan\\n o[np.invert(~np.isnan(s))] = np.nan\\n s = s[~pd.isnull(s)]\\n o = o[~pd.isnull(o)]\\n return s,o\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bbdbe56e3df32572a4fa12f73449eb4\",\n \"score\": \"0.56610817\",\n \"text\": \"def wash_out_all_nan(data):\\n # wash out the data with nan\\n return data[~np.isnan(np.sum(data, 1))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689966188b7db502832026a5b59f8021\",\n \"score\": \"0.5639008\",\n \"text\": \"def clean_data(df):\\n df = df[~df.location.isnull()]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88cb616fb2e42ac45b5a8d4bd9062a4f\",\n \"score\": \"0.56378675\",\n \"text\": \"def process_na_data_in_df(df: 'DataFrame') -> 'DataFrame':\\n\\n # Remove Weekends: Sat & Sun\\n df = df[(df.index.dayofweek != 5)&(df.index.dayofweek != 6)]\\n\\n # Removing all columns with NA values > 10% entries\\n df = df.loc[:, df.isna().sum()/df.shape[0] <= 0.1]\\n\\n # Dropping rows with all NA values\\n df = df.dropna(axis=0,how='all')\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208cc3adf0e73a81cd66b4d8257283ab\",\n \"score\": \"0.56377816\",\n \"text\": \"def clean_dataset(df):\\n assert isinstance(df, pd.DataFrame), \\\"df needs to be a pd.DataFrame\\\"\\n df.dropna(inplace=True)\\n indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\\n return df[indices_to_keep].astype(np.float64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d825919fec8aa76095fd7978826676\",\n \"score\": \"0.5636558\",\n \"text\": \"def _filter_na_for_fit(self, X: pd.DataFrame, y):\\n # let's also treat infinite values as NA\\n # scikit-learn's check_estimator might throw those at us\\n with pd.option_context(\\\"mode.use_inf_as_na\\\", True):\\n flt = pd.isna(X).values\\n X_out = X[~flt]\\n if y is not None and len(y) > 0:\\n y_out = y[~flt]\\n else:\\n y_out = y\\n return X_out, y_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d89a4a2ee9109d29df82591e9344b0\",\n \"score\": \"0.5592618\",\n \"text\": \"def clean_null_values(data_frame):\\n half_count = len(data_frame) / 2\\n # axis = 1 indicates remove of cols, and axis = 0 indicates remove of rows\\n data_frame = data_frame.dropna(thresh=half_count, axis=1) # Drop any column with more than 50% missing values\\n # drop any row with null values\\n data_frame = data_frame.dropna(axis=0, how='any')\\n return data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cf87fea3e49c01afa9f06ca623a419\",\n \"score\": \"0.5588972\",\n \"text\": \"def remove_nan_entries(df):\\n\\tdf_initsize = len(df)\\n\\tprint('Initial dataset size: ', df.shape)\\n\\tdisplay(HTML(df.tail().to_html()))\\n\\tprint('\\\\nRemoving the NaN values...')\\n\\t\\n\\tfor col in df.columns:\\n\\t col_type = df[col].dtype\\n\\t #print('col infos: {} dtype={}'.format(col, col_type))\\n\\t if(col_type != np.dtype('int64')):\\tdf.drop(df[~df[col].notna()].index, inplace=True)\\n\\n\\tdisplay(HTML(df.tail().to_html()))\\n\\tprint('\\\\nDataset size after NaN removing: ', df.shape)\\n\\tred_per = 100*(df_initsize - len(df)) / df_initsize\\n\\tprint(\\\"=> %.0f%% reduction of data.\\\" % red_per)\\n\\n\\treturn df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90230baf2be6f9623288c70f2a631e38\",\n \"score\": \"0.5581642\",\n \"text\": \"def _getnonconnectedsensors(self):\\n currentcams=[]\\n currenttemp=[]\\n currentni=[]\\n overcams=[]\\n overtemp=[]\\n overni=[]\\n for par in self._paramlist:\\n if par[\\\"type\\\"]==\\\"camera\\\":\\n currentcams.append((par[\\\"vendor\\\"],par[\\\"camid\\\"]))\\n if par[\\\"type\\\"]==\\\"temperature\\\":\\n currenttemp.append(par[\\\"handle\\\"])\\n if par[\\\"type\\\"]==\\\"nianalog\\\":\\n currentni.append(par[\\\"devstr\\\"])\\n \\n currentcamsset=set(currentcams)\\n currenttempset=set(currenttemp)\\n currentniset=set(currentni)\\n \\n if not self._connectedcams is None:\\n camintersection=currentcamsset.intersection(self._connectedcams)\\n for cam in list(camintersection):\\n currentcamsset.remove(cam)\\n\\n if not self._connectedtemp is None:\\n tempintersection=currenttempset.intersection(self._connectedtemp)\\n for temp in list(tempintersection):\\n currenttempset.remove(temp)\\n \\n if not self._connectedni is None:\\n niintersection=currentniset.intersection(self._connectedni)\\n for ni in list(niintersection):\\n currentniset.remove(ni)\\n \\n missing={\\\"camera\\\":list(currentcamsset),\\\"temperature\\\":list(currenttempset),\\\"nianalog\\\":list(currentniset)}\\n \\n return missing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1dbb732594bd0adf24b12f262abf13\",\n \"score\": \"0.55662936\",\n \"text\": \"def test_filter_missing_default(self, meas):\\n print(meas.data.columns)\\n meas.set_regression_cols(\\n power='meter_power',\\n poa='met1_poa_refcell',\\n t_amb='met2_amb_temp',\\n w_vel='met1_windspeed',\\n )\\n assert all(meas.rview('all', filtered_data=True).isna().sum() == 0)\\n assert meas.data_filtered.shape[0] == 1440\\n meas.data_filtered.loc['10/9/90 12:00', 'meter_power'] = np.NaN\\n meas.data_filtered.loc['10/9/90 12:30', 'met1_poa_refcell'] = np.NaN\\n meas.data_filtered.loc['10/10/90 12:35', 'met2_amb_temp'] = np.NaN\\n meas.data_filtered.loc['10/10/90 12:50', 'met1_windspeed'] = np.NaN\\n meas.filter_missing()\\n assert meas.data_filtered.shape[0] == 1436\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50765df3afe4062e887fd495f7d20620\",\n \"score\": \"0.5561974\",\n \"text\": \"def eliminate_singles(files):\\n # allocate memory for the output\\n filtered_traces = files.copy()\\n # for all the columns\\n for col in np.arange(files.shape[1]):\\n # get the target trace\\n original_trace = files.iloc[:, col]\\n # find the derivative of the nan trace\\n nan_positions = np.diff(np.isnan(original_trace).astype(np.int32), n=2)\\n # find the coordinates of the singles\\n single_positions = np.argwhere(nan_positions == 2) + 1\\n # single_positions = np.argwhere((nan_positions[:-1] == 1) & (nan_positions[1:] == -1))\\n # nan the singles\\n filtered_traces.iloc[single_positions, col] = np.nan\\n\\n return filtered_traces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d28291e59ab3392cb75585f375154b\",\n \"score\": \"0.5544502\",\n \"text\": \"def clean_df(self, df):\\n df = df.dropna(axis='columns', how='all')\\n df = df.dropna(axis='rows', how='any')\\n df = df.astype('float64')\\n df.index = df.index.astype('int64')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7beeecf20699561c694c5c237529de88\",\n \"score\": \"0.5533206\",\n \"text\": \"def filter_nan(s,o):\\n data = np.array([s.flatten(),o.flatten()])\\n data = np.transpose(data)\\n data = data[~np.isnan(data).any(1)]\\n #data = data[~np.isnan(data)]\\n return data[:,0],data[:,1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6deb83b721aaaea9e4f2f2aac5032b3f\",\n \"score\": \"0.5526761\",\n \"text\": \"def drop_na(self):\\n self.df = self.df.dropna(axis=0, how='any')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"535115677c38ae302efaebcf05b89078\",\n \"score\": \"0.55242294\",\n \"text\": \"def clean_non_targets(self, df):\\n # drop contaminants\\n df = df[df[\\\"Potential contaminant\\\"] != \\\"+\\\"]\\n # drop reverse\\n df = df[df[\\\"Reverse\\\"] != \\\"+\\\"]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302ac812d6a4d6da03d1689fbe1ac5e8\",\n \"score\": \"0.55231535\",\n \"text\": \"def clean_nan_inf(ts):\\n ts = to_np_array(ts)\\n search = (np.isinf(ts) | np.isnan(ts))\\n ts[search] = 0\\n\\n return ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d584df708be962da48a56ddd6b8d43d2\",\n \"score\": \"0.5511816\",\n \"text\": \"def remove_nulls(self):\\n \\n print('Removing Null Data...')\\n \\n for column in ['Open', 'Close']:\\n for index in self.df[column].index[self.df[column].apply(np.isnan)].tolist():\\n self.df.drop(labels=index,axis=0,inplace=True)\\n print('NULL ELEMENT REMOVED!')\\n\\n print('All Null Data Removed Successfully!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a224e4e86808e5779ba93a7e2c895b22\",\n \"score\": \"0.5505433\",\n \"text\": \"def remove_rows_with_wrong_values(self):\\n print(\\\" removing rows with wrong values...\\\")\\n self.df.dropna(inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0945fb57da5b13eac3a5116d028644a8\",\n \"score\": \"0.54922837\",\n \"text\": \"def drop_row_with_NA(self):\\n\\n # self.main_df = self.main_df[self.main_df[columns] != 0]\\n self.main_df = self.main_df.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"310836c18424bdaa13136de14364ea51\",\n \"score\": \"0.54829514\",\n \"text\": \"def no_modify(data):\\n # wash out the data with nan\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e827c22aa3c1ef2d5d904975bd256876\",\n \"score\": \"0.5474302\",\n \"text\": \"def remove_empty_features(self, tx):\\n tx = np.where(tx == -999, np.NaN, tx)\\n tx = tx[:, ~np.all(np.isnan(tx), axis=0)]\\n return tx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6d97796efe7051da7f69e82026797c\",\n \"score\": \"0.54705954\",\n \"text\": \"def drop_na_reactions_or_single_row(df):\\n new_df = df.dropna(subset=['reaction'])\\n new_df = new_df[new_df.reaction_count_equiv > 0]\\n if len(new_df) == 0:\\n new_df = df.head(1)\\n new_df['reaction'] = np.nan\\n new_df['label'] = new_df['gene']\\n return new_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f15ae29fe533b404dc846a9c9957e64\",\n \"score\": \"0.5465945\",\n \"text\": \"def drop_nan(self):\\n self.df = self.df.dropna(axis=0, how='any')\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d52c882873b9b394ba08aa17a39614\",\n \"score\": \"0.54497117\",\n \"text\": \"def remove_rows_with_missing_values(self):\\n clean_data = []\\n for row in self.data:\\n hasMissing = False\\n for col in row:\\n if col == \\\"NA\\\" or col == \\\"\\\":\\n hasMissing = True\\n if not hasMissing:\\n clean_data.append(row)\\n self.data = clean_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8ead52a7b3c87ce6d0aa2874c477a8\",\n \"score\": \"0.54421103\",\n \"text\": \"def make_log_no_trips(self):\\n self.df['log_trips'] = self.df[(self.df['trips_in_first_30_days'] != 0)].trips_in_first_30_days.apply(np.log)\\n self.df['log_trips'] = self.df['log_trips'].apply(lambda x: 0 if np.isnan(x) else x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a2a3655355cff1d9e36930017d6a14d\",\n \"score\": \"0.5433729\",\n \"text\": \"def remove_empty_values_marks(self, data):\\n\\n data_new = np.ndarray(shape=data.shape, dtype=data.dtype)\\n\\n __ACCURACY = 0.00001\\n\\n sample_number = 0\\n for features in data:\\n feature_index = 0\\n for value in features:\\n if abs(self.__EMPTY_VALUE - value) > __ACCURACY:\\n data_new[sample_number][feature_index] = value\\n else:\\n data_new[sample_number][feature_index] = None\\n feature_index += 1\\n sample_number += 1\\n print(\\\"Remove marks about empty values has done!\\\")\\n return data_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ddec8050279af60f2721fd30cf43fe3\",\n \"score\": \"0.54108644\",\n \"text\": \"def _remove_nan(values: TypeValList) -> TypeValList:\\n values = np.asarray(values, dtype=float)\\n return values[~np.isnan(values)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85ffd26d43c6c9bd7e9ff00c77464868\",\n \"score\": \"0.541016\",\n \"text\": \"def check_for_nan(df):\\n return df.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f070ce783a1e1cc0ab8923b612b26e5e\",\n \"score\": \"0.54039186\",\n \"text\": \"def filter_all(data):\\n #filter individual blocks of continuous data\\n datablocks = [list(v) for k,v in groupby(data,np.isfinite) if k]\\n filtereddatablocks = [filterfunction(v) for v in datablocks]\\n if np.any(np.isnan(data)): #if there are gaps\\n #get information about gaps\\n gap_indeces = np.where(np.isnan(data))[0]\\n gap_locs = []\\n gap_lengths = []\\n for k, g in groupby(enumerate(gap_indeces), lambda ix:ix[1]-ix[0]):\\n temprun = list(map(itemgetter(1), g))\\n gap_lengths.append(np.size(temprun))\\n gap_locs.append(temprun[0])\\n #build filtered data with gaps\\n filteredrun = np.array([]);\\n if gap_locs[0]==0: #if the first space is a gap, append and remove\\n filteredrun = np.append(filteredrun, np.full(gap_lengths[0],np.NaN) )\\n gap_locs = np.delete(gap_locs,0)\\n gap_lengths = np.delete(gap_lengths,0)\\n for i in range(np.size(filtereddatablocks)):\\n filteredrun = np.append(filteredrun,filtereddatablocks[i])\\n if i 100 mph\\n df = df[df['speed'] < 100]\\n\\n # replace unknowns with mean of column\\n df = df.replace(-99999, np.nan)\\n df = df.fillna(df.mean())\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0430e6d2ce3529d2c69f6a112c139784\",\n \"score\": \"0.5377258\",\n \"text\": \"def _clean_missing(data, params):\\n attributes = params['attributes']\\n cleaning_mode = params['cleaning_mode']\\n frag = params['id_frag']\\n\\n if cleaning_mode == \\\"REMOVE_COLUMN\\\":\\n\\n thresh = params['thresh']\\n if thresh:\\n subset = []\\n cols = params['columns_drop']\\n for c in cols.index:\\n if cols.loc[c] > thresh:\\n subset.append(c)\\n else:\\n subset = params['columns_drop']\\n data = data.drop(subset, axis=1)\\n\\n elif cleaning_mode == \\\"MEAN\\\":\\n\\n sizes, sums = params['values']\\n values = np.divide(sums, sizes)\\n\\n for v, a in zip(values, attributes):\\n data[a] = data[a].fillna(value=v)\\n\\n elif cleaning_mode == \\\"MODE\\\":\\n dict_mode = params['dict_mode']\\n for att in dict_mode:\\n mode = dict_mode[att].idxmax()\\n data[att] = data[att].fillna(value=mode)\\n\\n elif cleaning_mode == 'MEDIAN':\\n medians = params['values']\\n for att in medians:\\n data[att] = data[att].fillna(value=medians[att])\\n\\n data.reset_index(drop=True, inplace=True)\\n info = generate_info(data, frag)\\n return data, info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3286b60c67caabbf08b38675cf0de7\",\n \"score\": \"0.53675956\",\n \"text\": \"def drop_missing(X):\\n nonmissing=X[0].copy()\\n nonmissing['Nonmissing']=True\\n nonmissing=nonmissing['Nonmissing']\\n for x in X:\\n nonmissing.where(pd.notnull(x).all(axis=1),False,inplace=True)\\n\\n for i in range(len(X)):\\n X[i] = X[i].loc[nonmissing,:]\\n\\n return tuple(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed07a105be7b32edd490f39e6279fde8\",\n \"score\": \"0.53658754\",\n \"text\": \"def flag_na(df,remove_rows=False):\\n df = df.copy()\\n n_bad = sum(np.ravel(df.isnull().values))\\n print('{} bad values in the dataset.')\\n if remove_rows == True:\\n df.dropna()\\n return(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ccf0fca21cb7d020e01721e6d73f4f\",\n \"score\": \"0.5357542\",\n \"text\": \"def _getmissingsensors(self):\\n currentcams=[]\\n currenttemp=[]\\n currentni=[]\\n missingcams=[]\\n missingtemp=[]\\n missingni=[]\\n for par in self._paramlist:\\n if par[\\\"type\\\"]==\\\"camera\\\":\\n currentcams.append((par[\\\"vendor\\\"],par[\\\"camid\\\"]))\\n if par[\\\"type\\\"]==\\\"temperature\\\":\\n currenttemp.append(par[\\\"handle\\\"])\\n if par[\\\"type\\\"]==\\\"nianalog\\\":\\n currentni.append(par[\\\"devstr\\\"])\\n \\n for cam in self._connectedcams:\\n if not cam in currentcams:\\n missingcams.append(cam)\\n if not self._connectedtemp is None:\\n for temp in self._connectedtemp:\\n if not temp in currenttemp:\\n missingtemp.append(temp)\\n if not self._connectedni is None:\\n for ni in self._connectedni:\\n if not ni in currentni:\\n missingni.append(ni)\\n missing={\\\"camera\\\":missingcams, \\\"temperature\\\":missingtemp,\\\"nianalog\\\":missingni}\\n return missing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c24a661c654de184a93b6d3fcc684cf\",\n \"score\": \"0.53526807\",\n \"text\": \"def nan_trim(data):\\r\\n data_diff = np.diff(data)\\r\\n\\r\\n for i, diff in reversed(list(enumerate(data_diff))):\\r\\n if diff == 1:\\r\\n data.pop(i)\\r\\n\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d773f973c4e24a2274159b9e08fef1b8\",\n \"score\": \"0.53520674\",\n \"text\": \"def nans_remove(self):\\n msk = np.isfinite(self.y)\\n return self.__class__(self.x[msk], self.y[msk])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f98e0c9343893178195cbd3f034352\",\n \"score\": \"0.5342439\",\n \"text\": \"def remove_na_lines(self, max_na_proportion:float=None):\\n\\n if max_na_proportion:\\n drop_lines = list(self.df.loc[self.df.isna().sum(axis=1)>len(self.df.columns)*max_na_proportion].index) \\n else:\\n drop_lines = self.na_lines\\n #print(f'Lines removed:{drop_lines[0:4]}...')\\n self.df = self.df.drop(drop_lines)\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"075d66d00dfa6a68a225f3dacc5ab7fe\",\n \"score\": \"0.5338691\",\n \"text\": \"def cleanData(dataToClean):\\n for i in range(len(dataToClean)):\\n for j in range(len(dataToClean[0])):\\n if np.isnan(dataToClean[i][j]):\\n dataToClean[i][j]=0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a12091d6b20c49b7725690d923460001\",\n \"score\": \"0.5331909\",\n \"text\": \"def clean_df(df):\\n df = df.where(pd.notnull(df), None)\\n df = df.fillna(value=0)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d6bb36d5febd036ea6d7c4c4267a6a5\",\n \"score\": \"0.533119\",\n \"text\": \"def _drop_obs(df):\\n drop_subj = []\\n for sid, row in df.iterrows():\\n try:\\n if sum(row.dropna()) == 0:\\n drop_subj.append(sid)\\n elif row.count() == 0:\\n drop_subj.append(sid)\\n except TypeError: # compatible to ados\\n if sum(row.astype(float).dropna()) == 0:\\n drop_subj.append(sid)\\n elif row.astype(float).count() == 0:\\n drop_subj.append(sid)\\n return drop_subj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260be91c0b8510b71ae7a1d740960b03\",\n \"score\": \"0.53306866\",\n \"text\": \"def NaN_cleaning(df):\\n df = df.replace(np.nan, 'unknown')\\n return df.reset_index(drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5be06db39233a32605860ac8b3689a5c\",\n \"score\": \"0.53283554\",\n \"text\": \"def _remove_missing_data(\\n data_values: NDArray,\\n geometry_array: geopandas.GeoSeries,\\n) -> tuple[NDArray, geopandas.GeoSeries]:\\n not_missing_data = ~pandas.isnull(data_values)\\n geometry_array = geometry_array[not_missing_data]\\n data_values = data_values[not_missing_data]\\n return data_values, geometry_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb9b42a6eaa6253ee3b8d523139e299\",\n \"score\": \"0.5324406\",\n \"text\": \"def quick_clean(data):\\n rows_missing_data = (data == 0).sum(1)\\n return data[rows_missing_data == 0, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d9e2a4681978a2e091275a72005b3c\",\n \"score\": \"0.5316683\",\n \"text\": \"def clean_nan(data, data_column):\\r\\n data.dropna(subset=[data_column, \\\\\\r\\n 'Quality'], inplace=True)\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f38a2777c41e92eecb61343ef10b22\",\n \"score\": \"0.53023773\",\n \"text\": \"def filter_zeroes(self):\\n self.U[self.U == 0] = np.nan\\n self.V[self.V == 0] = np.nan\\n self.W[self.W == 0] = np.nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985aa5c0aa5f067935e58f73b2100dbc\",\n \"score\": \"0.5297176\",\n \"text\": \"def remove_nans(array_like):\\n data = array_like.copy()\\n \\n # Astropy table object\\n if type(data) is Table:\\n data = data.filled(NAN_VALUE)\\n # Pandas DataFrame object\\n elif type(data) is pd.DataFrame:\\n data = data.fillna(NAN_VALUE)\\n else:\\n print(\\\"Unrecognized data format:\\\", type(data))\\n return None\\n \\n # Return final data structure\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914a197db781cb12a5fe19ab43161b5d\",\n \"score\": \"0.5292508\",\n \"text\": \"def test_DataCleaner_big_nan_handler_warning(self):\\n dc = DataCleaner(max_na_frac=0.01)\\n df = pd.DataFrame(\\n np.random.randint(0, 100, size=(100, 4)), columns=list(\\\"ABCD\\\")\\n )\\n dc.fit(df, \\\"D\\\")\\n self.assertEqual(len(dc.warnings), 0)\\n\\n df[\\\"A\\\"].iloc[10:20] = np.nan\\n df[\\\"B\\\"].iloc[:99] = np.nan\\n dc.fit(df, \\\"D\\\")\\n self.assertEqual(len(dc.warnings), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc851de57a11012e4155217bb31adc96\",\n \"score\": \"0.52870226\",\n \"text\": \"def delete_rows(df):\\r\\n\\r\\n df = df.drop(df[df.img.isna()].index)\\r\\n df = df.drop(df[df.cost.isna()].index)\\r\\n df = df.drop(df[df.rarity.isna()].index)\\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fbcab9e0a3ed7e49af6f0c03573e528\",\n \"score\": \"0.5284864\",\n \"text\": \"def test_nans_replaced_none():\\n\\n #generate test data\\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\\n dataframe['A'][5] = numpy.nan\\n\\n #run function on dataframe\\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\\\"none\\\", removal_time_frame=\\\"day\\\",\\n fault_placement=\\\"calendar\\\")\\n\\n assert dataframe.size == result_dataframe.size, \\\"nan has not been dropped\\\"\\n assert result_dataframe['A'][5] != numpy.nan, \\\"nan was removed but shouldn't have\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08006fa93818a2f08ef6539d0e78d6b8\",\n \"score\": \"0.5269665\",\n \"text\": \"def remove_duplicates_and_full_nulls(data):\\n data = data.copy()\\n data = data.drop_duplicates()\\n data = data.dropna(how ='all')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9cd6b277461a674b517515b1a4294eb\",\n \"score\": \"0.52642363\",\n \"text\": \"def remove_null(dataframe):\\n\\n dataframe = dataframe.dropna()\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52409db8b7b10f8ebaa0732ca7d73c16\",\n \"score\": \"0.52546704\",\n \"text\": \"def _rmDataWithoutBrightStar(self, neighborStarMap, starMap,\\n wavefrontSensors):\\n\\n # Collect the sensor list without the bright star\\n noStarSensorList = []\\n for detector, stars in neighborStarMap.items():\\n if (len(stars.getId()) == 0):\\n noStarSensorList.append(detector)\\n\\n # Remove the data in map\\n for detector in noStarSensorList:\\n neighborStarMap.pop(detector)\\n starMap.pop(detector)\\n wavefrontSensors.pop(detector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3392f154927a0b3ea4e7a68b784f6179\",\n \"score\": \"0.5254399\",\n \"text\": \"def filter_nan(s,o):\\n data = np.array([s.flatten(),o.flatten()])\\n data = np.transpose(data)\\n data = data[~np.isnan(data).any(1)]\\n\\n return data[:,0],data[:,1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe7cf61ecf67b86675bc79f599d13c2\",\n \"score\": \"0.5251705\",\n \"text\": \"def test_filter_by_NaN(self):\\n test_scope = scope.Scope(-4.9, -4.70, 48, 49, 0, 1500000000)\\n test_preprocessing = preprocessing.Preprocessing(test_scope, self.test_ships)\\n\\n self.assertEqual(len(self.test_ships), 15)\\n test_preprocessing.filter_by_NaN()\\n self.assertEqual(len(self.test_ships), 7)\\n\\n number_states = {'245257000': 29, '228037600': 259, '304091000': 1,\\n '227705102': 249, '227443000': 92, '228931000': 21,\\n '228131600': 350, '228064900': 4, '37100300': 7}\\n\\n for ship in self.test_ships:\\n self.assertEqual(len(ship.ais_states), number_states[ship.sourcemmsi],\\n msg=\\\"sourcemmsi is \\\" + ship.sourcemmsi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee4fe5bb4730d0e6d8c78730fa48d55\",\n \"score\": \"0.52495563\",\n \"text\": \"def remove_rows_with_missing_values(self):\\n row_length = len(self.column_names)\\n table_copy = []\\n \\n for i in range(len(self.data)):\\n has_NA = False\\n for j in range(row_length):\\n if self.data[i][j] == \\\"NA\\\" or self.data[i][j] == \\\"\\\":\\n has_NA = True\\n if has_NA == False: \\n table_copy.append(self.data[i])\\n \\n self.data.clear()\\n self.data = copy.deepcopy(table_copy) \\n pass # TODO: fix this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ead744219067e1a2c3a93e51f6bfe2\",\n \"score\": \"0.5245453\",\n \"text\": \"def _clean_useless_rows(self):\\r\\n self.temp_stock_data = pandas.DataFrame()\\r\\n dates = []\\r\\n for i in xrange(0, self.stock_data[self.company_list.Companies.irow(0)].count()):\\r\\n #print i\\r\\n row = self.stock_data.irow(i)\\r\\n if len(row[numpy.isnan(row)]) == len(row):\\r\\n dates.append(self.date_list[i])\\r\\n \\r\\n for single_date in dates:\\r\\n self.stock_data = self.stock_data.drop(pandas.Timestamp(single_date))\\r\\n print \\\"Data cleaned from useless rows.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc475ee4cc92954c4f70406962b6150\",\n \"score\": \"0.52325326\",\n \"text\": \"def data_cleaning(df: DataFrame) -> DataFrame:\\n print(\\\"Cleaning the data to remove problems\\\\n \\\")\\n\\n nulls = df.isnull().sum()\\n nulls = nulls[nulls != 0]\\n\\n if not nulls.empty:\\n print(f\\\"The columns below some 'NaN' values:\\\\n{nulls}\\\")\\n\\n # for index_with_nan in enumerate(nulls.keys()[:]):\\n # print(f\\\"Column: {index_with_nan} \\\\n \\\")\\n\\n if 'Birth Year' in df.columns.values.tolist():\\n df = dob_trim(df)\\n\\n time.sleep(2)\\n\\n no_user_type: float = format(df['User Type'].isna().sum()/df.shape[0]*100, '.2g')\\n print(f\\\"A few rows don't have any information for 'User Type' but it is only {no_user_type}% so we'll just drop them\\\")\\n df = df.dropna(axis = 0, subset=['User Type'])\\n\\n if 'Gender' in df.columns.values.tolist():\\n gender_neutral: float = format(df['Gender'].isna().sum()/df.shape[0]*100, '.2g')\\n print(f\\\"{gender_neutral}% of trips don't have any information for 'Gender' so we'll forward-fill.\\\")\\n df.fillna(method=\\\"ffill\\\")\\n # gender_neutral: float = format(nulls['Gender']/df.shape[0]*100, '.2g')\\n # print(f\\\"A few rows don't have any information for 'Gender' but it is only {gender_neutral}% so we'll just drop them\\\")\\n # df = df.dropna(axis = 0, subset=['Gender'], inplace=True)\\n \\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"658b7ab760716acff016b703ca146fca\",\n \"score\": \"0.5225665\",\n \"text\": \"def removeNansSingle(data):\\n # set an x array\\n x = np.arange(data.size)\\n # find locations of nans - this is a bool array with True in locations with nan values\\n nanLocs = np.isnan(data)\\n # if no nans, do nothing\\n if not np.any(nanLocs):\\n return data # no nans to remove\\n # create mask\\n mask = np.ones(data.size, np.bool)\\n mask[nanLocs] = 0 # using numpy indexing with bool arrays\\n # no need to group, want to remove every nan\\n data[nanLocs] = np.interp(x[nanLocs], x[mask], data[mask])\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb6066ce4a1f9dc1a890971b21dec1f\",\n \"score\": \"0.5221285\",\n \"text\": \"def remove_notes(data):\\n return data[pd.to_numeric(data.iloc[:, 0], errors='coerce').notnull()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a937fac4a9481e0cde52d043b27035f\",\n \"score\": \"0.52128845\",\n \"text\": \"def filter_spikes(df:pd.DataFrame):\\n \\\"\\\"\\\"first return argument is non-spike signals, the second one for the spikes\\\"\\\"\\\"\\n new_df=pd.DataFrame()\\n spike_cluster=pd.DataFrame()\\n for i in range(len(df.columns)):\\n tmp=df.iloc[:, i] \\n uniques=tmp.unique()\\n uniques = [x for x in uniques if str(x) != 'nan']\\n uniques = [x for x in uniques if x not in [0.0,1.0]]\\n if(len(uniques)<=1): #spike like\\n spike_cluster.insert(len(spike_cluster.columns),tmp.name,tmp)\\n else:\\n new_df.insert(len(new_df.columns),tmp.name,tmp)\\n return new_df,spike_cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a372da4cfe63a9a92a7f200bb86f20a\",\n \"score\": \"0.5209513\",\n \"text\": \"def nan_clean(array):\\n for i in range(len(array)):\\n if np.isnan(array[i]) == True:\\n array[i] = 0.0\\n return array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a41fc2d468a3aa9657429af2f10331b\",\n \"score\": \"0.52041674\",\n \"text\": \"def remove_nan_observations(x, y, z):\\n x_ = x[~np.isnan(z)]\\n y_ = y[~np.isnan(z)]\\n z_ = z[~np.isnan(z)]\\n return x_, y_, z_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b824e07c74f481cca378320af481262\",\n \"score\": \"0.5198521\",\n \"text\": \"def filterNaN(instr, datacol, outfile, logfile, verbose):\\n try:\\n nanclean = instr[1].header['NANCLEAN']\\n except:\\n naxis2 = 0\\n for i in range(len(instr[1].columns.names)):\\n if 'time' in instr[1].columns.names[i].lower():\\n timecol = instr[1].columns.names[i]\\n try:\\n instr[1].data.field(datacol)\\n except:\\n msg = (\\\"ERROR -- KEPIO.FILTERNAN: cannot find column {}\\\"\\n \\\"in the infile\\\".format(datacol))\\n kepmsg.err(logfile, msg, verbose)\\n try:\\n for i in range(len(instr[1].data.field(0))):\\n if (str(instr[1].data.field(timecol)[i]) != '-inf' and\\n str(instr[1].data.field(datacol)[i]) != '-inf'):\\n instr[1].data[naxis2] = instr[1].data[i]\\n naxis2 += 1\\n instr[1].data = instr[1].data[:naxis2]\\n comment = 'NaN cadences removed from data'\\n kepkey.new('NANCLEAN', True, comment, instr[1], outfile, logfile,\\n verbose)\\n except:\\n errmsg = ('ERROR -- KEPIO.FILTERNAN: Failed to filter NaNs from '\\n + outfile)\\n kepmsg.err(logfile, errmsg, verbose)\\n return instr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e14120618e0caa9c1a1adf8bf742894f\",\n \"score\": \"0.51975524\",\n \"text\": \"def fix_na_values(df):\\n ignored = [\\\"SUBJECT_ID\\\", \\\"HADM_ID\\\", \\\"ADMITTIME\\\", \\\"DISCHTIME\\\"]\\n df_core = df.drop(ignored, axis=1)\\n\\n while df_core.isna().sum().sum():\\n # get column with least amount of missing values\\n cols_with_na = df_core.isna().sum()\\n col = cols_with_na[cols_with_na > 0].idxmin()\\n # impute that column\\n df_core.loc[df_core[col].isna(), col] = impute_column(df_core, col)\\n\\n return pd.concat([df_core, df[ignored]], axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13995298c15ddffef72188fbf4ae9e9e\",\n \"score\": \"0.51944923\",\n \"text\": \"def test_nans_dropped():\\n\\n #generate test data\\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\\n dataframe['A'][5] = numpy.nan\\n\\n #run function on dataframe\\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\\\"drop\\\", removal_time_frame=\\\"day\\\", fault_placement=\\\"calendar\\\")\\n assert dataframe.size != result_dataframe.size, \\\"nan have not been dropped\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9105baa116f0859c24acfe0688351a8b\",\n \"score\": \"0.51941323\",\n \"text\": \"def drop_missing(self):\\n df1 = self.df\\n df2 = df1.dropna(axis=0)\\n\\n self.df = df2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe78430afcdbf63973f5fb1eef658c15\",\n \"score\": \"0.5192824\",\n \"text\": \"def remove_missing_values(x):\\n x_clean = x.copy()\\n x_clean = x_clean[:, [np.count_nonzero(x.T[i] == -999) == 0 for i in range (x.shape[1])]]\\n return x_clean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e1f1380c66022fe6c1a3408b34b606\",\n \"score\": \"0.51923764\",\n \"text\": \"def remove_rcna(df):\\n df.dropna(how = 'all', axis = 'columns', inplace = True)\\n df.dropna(how = 'all', axis = 'rows', inplace = True)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e3227a5225488f9568a8c415801ea9\",\n \"score\": \"0.51922977\",\n \"text\": \"def generate_query_ratesnull(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934058862a1706b0deffe110b8780021\",\n \"score\": \"0.518677\",\n \"text\": \"def _series_cleaner(self, obs_data: Union[dict, list]) -> pd.DataFrame:\\n series = pd.json_normalize(obs_data[\\\"observations\\\"])\\n series.drop(columns=[\\\"realtime_start\\\", \\\"realtime_end\\\"], inplace=True)\\n series[\\\"value\\\"] = series[\\\"value\\\"].replace(self.nan_char, np.nan)\\n return series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabc39526af7b6e52689bfe97d78ffba\",\n \"score\": \"0.5184404\",\n \"text\": \"def drop_missing(self):\\n self.miss = np.rint(len(self.data.columns)*self.miss)\\n self.data = self.data[self.data.isnull().sum(axis=1)<=self.miss]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6e550833aaa3e203af1bd5115ecd7f9\",\n \"score\": \"0.5179664\",\n \"text\": \"def clean_data(df):\\n data_types = get_missing_columns()\\n \\n for idx in data_types.index:\\n column = idx\\n if column in df.columns:\\n df[column] = df[column].replace(data_types.loc[column]['nan_vals'], np.nan)\\n\\n \\n # remove selected columns\\n df.drop(['AGER_TYP', 'ALTER_HH', 'ALTER_KIND1', 'ALTER_KIND2', 'ALTER_KIND3', 'ALTER_KIND4', 'ALTERSKATEGORIE_FEIN',\\\\\\n 'D19_BANKEN_ONLINE_QUOTE_12', 'D19_GESAMT_ONLINE_QUOTE_12', 'D19_KONSUMTYP', 'D19_LETZTER_KAUF_BRANCHE',\\\\\\n 'D19_LOTTO', 'D19_SOZIALES', 'D19_TELKO_ONLINE_QUOTE_12', 'D19_VERSAND_ONLINE_QUOTE_12',\\\\\\n 'D19_VERSI_ONLINE_QUOTE_12', 'EXTSEL992','GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'TITEL_KZ', 'LNR'],\\n axis=1, inplace=True)\\n \\n print(\\\"Removal of Columns with over 20% null values complete...\\\")\\n \\n # remove selected rows\\n df = df[df.isnull().sum(axis=1) < 9].reset_index(drop=True)\\n \\n # remove selected columns again\\n features_not_in_feat_info_42 = ['AKT_DAT_KL', 'ANZ_KINDER', 'ANZ_STATISTISCHE_HAUSHALTE', 'CJT_KATALOGNUTZER','CJT_TYP_1',\\\\\\n 'CJT_TYP_2', 'CJT_TYP_3', 'CJT_TYP_4', 'CJT_TYP_5', 'CJT_TYP_6', 'DSL_FLAG',\\\\\\n 'EINGEZOGENAM_HH_JAHR','FIRMENDICHTE', 'GEMEINDETYP', 'HH_DELTA_FLAG', 'KBA13_ANTG1',\\\\\\n 'KBA13_ANTG2', 'KBA13_ANTG3','KBA13_ANTG4', 'KBA13_BAUMAX', 'KBA13_CCM_1401_2500',\\\\\\n 'KBA13_GBZ', 'KBA13_HHZ', 'KBA13_KMH_210','KONSUMZELLE', 'MOBI_RASTER', 'RT_KEIN_ANREIZ',\\\\\\n 'RT_SCHNAEPPCHEN', 'RT_UEBERGROESSE', 'STRUKTURTYP','UMFELD_ALT', 'UMFELD_JUNG',\\\\\\n 'UNGLEICHENN_FLAG', 'VERDICHTUNGSRAUM', 'VHA', 'VHN', 'VK_DHT4A','VK_DISTANZ',\\\\\\n 'VK_ZG11','D19_KONSUMTYP_MAX', 'KOMBIALTER','EINGEFUEGT_AM']\\n \\n df.drop(features_not_in_feat_info_42,axis=1, inplace=True)\\n \\n print(\\\"Removal of undocumented columns complete...\\\")\\n \\n \\n # select, re-encode, and engineer column values.\\n # # feature engineering of categorical features\\n\\n df['OST_WEST_KZ'].replace(['W','O'], [1, 0], inplace=True)\\n \\n multi = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CJT_GESAMTTYP', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',\\\\\\n 'D19_BANKEN_DATUM','D19_BANKEN_OFFLINE_DATUM', 'D19_BANKEN_ONLINE_DATUM', 'D19_GESAMT_ANZ_12',\\\\\\n 'D19_GESAMT_ANZ_24', 'D19_GESAMT_DATUM','D19_GESAMT_OFFLINE_DATUM', 'D19_GESAMT_ONLINE_DATUM',\\\\\\n 'D19_TELKO_DATUM', 'D19_TELKO_OFFLINE_DATUM', 'D19_TELKO_ONLINE_DATUM','D19_VERSAND_DATUM',\\\\\\n 'D19_VERSAND_OFFLINE_DATUM', 'D19_VERSAND_ONLINE_DATUM', 'D19_VERSI_DATUM', 'D19_VERSI_OFFLINE_DATUM',\\\\\\n 'D19_VERSI_ONLINE_DATUM', 'FINANZTYP', 'GEBAEUDETYP', 'GFK_URLAUBERTYP','LP_FAMILIE_FEIN','LP_FAMILIE_GROB',\\\\\\n 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP']\\n \\n df = pd.get_dummies(df, columns=multi, prefix=multi)\\n \\n \\n # feature engineering of mixed features\\n df['PRAEGENDE_JUGENDJAHRE_decade'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_decade)\\n df['PRAEGENDE_JUGENDJAHRE_movement'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_movement)\\n df.drop('PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)\\n \\n print(\\\"Feature Engineering PRAEGENDE_JUGENDJAHRE complete...\\\")\\n \\n \\n df['CAMEO_INTL_2015_wealth'] = df['CAMEO_INTL_2015'].apply(make_wealth) \\n df['CAMEO_INTL_2015_life_stage'] = df['CAMEO_INTL_2015'].apply(make_life_stage)\\n df.drop('CAMEO_INTL_2015', axis=1, inplace=True)\\n \\n print(\\\"Feature Engineering CAMEO_INTL_2015 complete...\\\")\\n \\n df['WOHNLAGE_rural'] = df['WOHNLAGE'].map({0:0,1:0,2:0,3:0,4:0,5:0,7:1,8:1})\\n df['WOHNLAGE_rating_class'] = df['WOHNLAGE'].map({0:0,1:1,2:2,3:3,4:4,5:5,7:0,8:0})\\n \\n print(\\\"Feature Engineering WOHNLAGE complete...\\\")\\n \\n df = df.drop(['LP_LEBENSPHASE_FEIN','LP_LEBENSPHASE_GROB','WOHNLAGE','PLZ8_BAUMAX'], axis=1)\\n \\n print(\\\"Finished!\\\")\\n \\n # Return the cleaned dataframe.\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b15f9a5bc9f58c3f1fda20bd23a98b\",\n \"score\": \"0.5174193\",\n \"text\": \"def clean_data(self):\\n # Mask files with lat = 0 or lon = 0\\n mask = np.where(np.logical_or(self.data['VEH_LAT']==0, \\n self.data['VEH_LONG']==0))\\n\\n for k in self.data.keys():\\n if isinstance(self.data[k][0], float):\\n self.data[k][mask] = np.nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fb2a849eb9288c2238968e9235b9752\",\n \"score\": \"0.51717937\",\n \"text\": \"def drop_null_rows(self, x: bool):\\n if x:\\n total_null_rows = self.df[self.target].isin(\\n [' ', 'NULL', np.nan]).sum()\\n if total_null_rows > 0:\\n print(\\\"Dropping rows having [' ', 'NULL', numpy.nan] values\\\")\\n self.df.dropna(inplace=True)\\n self.df.reset_index(drop=True, inplace=True)\\n self.null_values_present = False\\n print_in_red(f\\\"Total Null rows dropped: {total_null_rows}\\\\n\\\")\\n self._start_analysis()\\n else:\\n print(colored(\\\"There is no null rows present.\\\\n\\\", \\\"green\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52449af6e301d2d873cf47553c2b38b8\",\n \"score\": \"0.5167622\",\n \"text\": \"def filter_nan(s, o):\\n data = np.array([s.flatten(), o.flatten()])\\n data = np.transpose(data)\\n data = data[~np.isnan(data).any(1)]\\n return data[:, 0], data[:, 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6512e72d0658c2f1c0378e699cc56170\",\n \"score\": \"0.51613533\",\n \"text\": \"def clean_timeseries(self):\\n if 'tb_incidence' in self.timeseries_log.keys():\\n self.timeseries_log['tb_incidence'][0] = 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"248a5d7d28357321ce39d2c77fe712a7\",\n \"score\": \"0.5160788\",\n \"text\": \"def filter_values_2(self, counter):\\n inf = float(\\\"inf\\\")\\n last_mea = list(self.scan_values_2[counter])\\n for i in range(len(last_mea)):\\n if last_mea[i] == inf:\\n for k in range(1, MEASUREMENTS):\\n if self.scan_values_2[counter-k] != inf:\\n last_mea[i] = self.scan_values_2[k][i]\\n break\\n scan = LaserScan()\\n scan.ranges = last_mea\\n self.laser_filtered_pub.publish(scan)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16cc667bcf39fc41ca499a7580cf48d0\",\n \"score\": \"0.51605296\",\n \"text\": \"def _get_instr_after_remove(self, remove_size_null, remove_adj_cap_nan):\\n if any([remove_size_null, remove_adj_cap_nan]): # will update instr_df\\n instr_df = self.concat_instr_df.copy()\\n else: # nothing will be changed in self.concat_instr_df so no need to copy\\n instr_df = self.concat_instr_df\\n if instr_df.empty:\\n raise Exception('concat_instr_df is empty. Cannot display metrics')\\n if remove_size_null:\\n no_instrs = len(instr_df)\\n idx_size_null = instr_df['stake'] == 0\\n if sum(idx_size_null) > 0:\\n self.logger.warning('Dropping {}/{} instructions with stake 0'.format(\\n sum(idx_size_null), no_instrs))\\n instr_df = instr_df.loc[~idx_size_null]\\n if remove_adj_cap_nan:\\n if 'adj_cap' not in instr_df.columns:\\n raise Exception('concat_instr_df do not have column '\\n 'adj_cap and so cannot remove_adj_cap_nan')\\n no_instrs = len(instr_df)\\n idx_nan = instr_df['adj_cap'].isnull()\\n if sum(idx_nan) > 0:\\n self.logger.warning('Keeping {}/{} instructions with known capital adjustment'\\n .format(no_instrs - sum(idx_nan), no_instrs))\\n # remove nan in adjst_capital\\n instr_df = instr_df.loc[~idx_nan]\\n return instr_df\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":177,"cells":{"query_id":{"kind":"string","value":"014a5481438d0e9b71966a48d4d8bbf6"},"query":{"kind":"string","value":"Generate a NL or LP file from Pyomo, and then do subsequent conversions."},"positive_passages":{"kind":"list like","value":[{"docid":"b50fba180d6eee76d9713189ba7dcb27","score":"0.56642395","text":"def apply(self, *args, **kwds):\n\n import pyomo.scripting.convert\n\n capabilities = kwds.pop(\"capabilities\", None)\n\n # all non-consumed keywords are assumed to be options\n # that should be passed to the writer.\n io_options = {}\n for kwd, value in iteritems(kwds):\n io_options[kwd] = value\n kwds.clear()\n\n # basestring is gone in Python 3.x, merged with str.\n if PY3:\n compare_type = str\n else:\n compare_type = basestring\n\n if isinstance(args[2], compare_type):\n instance = None\n else:\n instance = args[2]\n\n if args[1] == ProblemFormat.cpxlp:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.lp')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.cpxlp,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.cpxlp,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2lp(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2lp conversion \"\n \"returned nonzero error code \"\n \"(%s)\" % ans.errorcode)\n\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n return (problem_filename,),symbol_map\n\n elif args[1] == ProblemFormat.bar:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.bar')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.bar,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.bar,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2bar(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2bar conversion \"\n \"returned nonzero error code \"\n \"(%s)\" % ans.errorcode)\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n return (problem_filename,),symbol_map\n\n elif args[1] in [ProblemFormat.mps, ProblemFormat.nl]:\n if args[1] == ProblemFormat.nl:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.nl')\n if io_options.get(\"symbolic_solver_labels\", False):\n pyutilib.services.TempfileManager.add_tempfile(\n problem_filename[:-3]+\".row\",\n exists=False)\n pyutilib.services.TempfileManager.add_tempfile(\n problem_filename[:-3]+\".col\",\n exists=False)\n else:\n assert args[1] == ProblemFormat.mps\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.mps')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=args[1],\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=args[1],\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2nl(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2nl conversion \"\n \"returned nonzero error \"\n \"code (%s)\" % ans.errorcode)\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n\n if args[1] == ProblemFormat.nl:\n return (problem_filename,),symbol_map\n #\n # Convert from NL to MPS\n #\n # TBD: We don't support a variable map file when going\n # from NL to MPS within the PICO converter.\n # NOTE: this is a problem with the MPS writer that is\n # provided by COIN-OR\n # NOTE: we should generalize this so it doesn't strictly\n # depend on the PICO converter utility.\n #\n ans = self.pico_converter.apply(ProblemFormat.nl,\n ProblemFormat.mps,\n problem_filename)\n os.remove(problem_filename)\n return ans\n\n elif args[1] == ProblemFormat.osil:\n if False:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix='pyomo.osil')\n if instance:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.osil,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.osil,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), None\n else:\n raise NotImplementedError(\n \"There is currently no \"\n \"script conversion available from \"\n \"Pyomo to OSiL format.\")","title":""}],"string":"[\n {\n \"docid\": \"b50fba180d6eee76d9713189ba7dcb27\",\n \"score\": \"0.56642395\",\n \"text\": \"def apply(self, *args, **kwds):\\n\\n import pyomo.scripting.convert\\n\\n capabilities = kwds.pop(\\\"capabilities\\\", None)\\n\\n # all non-consumed keywords are assumed to be options\\n # that should be passed to the writer.\\n io_options = {}\\n for kwd, value in iteritems(kwds):\\n io_options[kwd] = value\\n kwds.clear()\\n\\n # basestring is gone in Python 3.x, merged with str.\\n if PY3:\\n compare_type = str\\n else:\\n compare_type = basestring\\n\\n if isinstance(args[2], compare_type):\\n instance = None\\n else:\\n instance = args[2]\\n\\n if args[1] == ProblemFormat.cpxlp:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.lp')\\n if instance is not None:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=ProblemFormat.cpxlp,\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=ProblemFormat.cpxlp,\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), symbol_map_id\\n else:\\n\\n #\\n # I'm simply exposing a fatal issue with\\n # this code path. How would we convert the\\n # collected keywords into command-line\\n # arguments that can be sent to the writer?\\n #\\n if len(io_options):\\n raise ValueError(\\n \\\"The following io_options will be ignored \\\"\\n \\\"(please create a bug report):\\\\n\\\\t\\\" +\\n \\\"\\\\n\\\\t\\\".join(\\\"%s = %s\\\" % (k,v)\\n for k,v in iteritems(io_options)))\\n\\n ans = pyomo.scripting.convert.\\\\\\n pyomo2lp(['--output',problem_filename,args[2]])\\n if ans.errorcode:\\n raise RuntimeError(\\\"pyomo2lp conversion \\\"\\n \\\"returned nonzero error code \\\"\\n \\\"(%s)\\\" % ans.errorcode)\\n\\n model = ans.retval\\n problem_filename = model.filename\\n symbol_map = model.symbol_map\\n return (problem_filename,),symbol_map\\n\\n elif args[1] == ProblemFormat.bar:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.bar')\\n if instance is not None:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=ProblemFormat.bar,\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=ProblemFormat.bar,\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), symbol_map_id\\n else:\\n\\n #\\n # I'm simply exposing a fatal issue with\\n # this code path. How would we convert the\\n # collected keywords into command-line\\n # arguments that can be sent to the writer?\\n #\\n if len(io_options):\\n raise ValueError(\\n \\\"The following io_options will be ignored \\\"\\n \\\"(please create a bug report):\\\\n\\\\t\\\" +\\n \\\"\\\\n\\\\t\\\".join(\\\"%s = %s\\\" % (k,v)\\n for k,v in iteritems(io_options)))\\n\\n ans = pyomo.scripting.convert.\\\\\\n pyomo2bar(['--output',problem_filename,args[2]])\\n if ans.errorcode:\\n raise RuntimeError(\\\"pyomo2bar conversion \\\"\\n \\\"returned nonzero error code \\\"\\n \\\"(%s)\\\" % ans.errorcode)\\n model = ans.retval\\n problem_filename = model.filename\\n symbol_map = model.symbol_map\\n return (problem_filename,),symbol_map\\n\\n elif args[1] in [ProblemFormat.mps, ProblemFormat.nl]:\\n if args[1] == ProblemFormat.nl:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.nl')\\n if io_options.get(\\\"symbolic_solver_labels\\\", False):\\n pyutilib.services.TempfileManager.add_tempfile(\\n problem_filename[:-3]+\\\".row\\\",\\n exists=False)\\n pyutilib.services.TempfileManager.add_tempfile(\\n problem_filename[:-3]+\\\".col\\\",\\n exists=False)\\n else:\\n assert args[1] == ProblemFormat.mps\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.mps')\\n if instance is not None:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=args[1],\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=args[1],\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), symbol_map_id\\n else:\\n\\n #\\n # I'm simply exposing a fatal issue with\\n # this code path. How would we convert the\\n # collected keywords into command-line\\n # arguments that can be sent to the writer?\\n #\\n if len(io_options):\\n raise ValueError(\\n \\\"The following io_options will be ignored \\\"\\n \\\"(please create a bug report):\\\\n\\\\t\\\" +\\n \\\"\\\\n\\\\t\\\".join(\\\"%s = %s\\\" % (k,v)\\n for k,v in iteritems(io_options)))\\n\\n ans = pyomo.scripting.convert.\\\\\\n pyomo2nl(['--output',problem_filename,args[2]])\\n if ans.errorcode:\\n raise RuntimeError(\\\"pyomo2nl conversion \\\"\\n \\\"returned nonzero error \\\"\\n \\\"code (%s)\\\" % ans.errorcode)\\n model = ans.retval\\n problem_filename = model.filename\\n symbol_map = model.symbol_map\\n\\n if args[1] == ProblemFormat.nl:\\n return (problem_filename,),symbol_map\\n #\\n # Convert from NL to MPS\\n #\\n # TBD: We don't support a variable map file when going\\n # from NL to MPS within the PICO converter.\\n # NOTE: this is a problem with the MPS writer that is\\n # provided by COIN-OR\\n # NOTE: we should generalize this so it doesn't strictly\\n # depend on the PICO converter utility.\\n #\\n ans = self.pico_converter.apply(ProblemFormat.nl,\\n ProblemFormat.mps,\\n problem_filename)\\n os.remove(problem_filename)\\n return ans\\n\\n elif args[1] == ProblemFormat.osil:\\n if False:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix='pyomo.osil')\\n if instance:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=ProblemFormat.osil,\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=ProblemFormat.osil,\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), None\\n else:\\n raise NotImplementedError(\\n \\\"There is currently no \\\"\\n \\\"script conversion available from \\\"\\n \\\"Pyomo to OSiL format.\\\")\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"67db92a1a3821a68008dc249af915482","score":"0.5871873","text":"def create_model(self):\r\n\r\n self.try_load_model()\r\n\r\n self.set_correct_problem_instance_name()\r\n\r\n self.add_objective_and_budget_constraints()\r\n self.add_starting_ending_nodes_constraints()\r\n\r\n self.docplex_model.print_information()\r\n\r\n # DEBUG\r\n # for constraint in self.docplex_model.iter_constraints():\r\n # print(constraint)\r","title":""},{"docid":"95e1197a8a29532e1bb1bc651f420051","score":"0.5701343","text":"def formula_generator(known_args, other_args): # pylint: disable=W0613\n poly = Poly(*known_args.format)\n\n if known_args.smt2:\n with open(known_args.smt2, 'w') as smt2_file:\n with redirect_stdout(smt2_file):\n poly.problem(known_args.degree)\n else:\n poly.problem(known_args.degree)","title":""},{"docid":"1717949860fb45cd73863fd5f97acc0e","score":"0.56868994","text":"def __init__(self, model, output_path, wanted_lorentz = [],\n wanted_couplings = [], replace_dict={}):\n\n self.model = model\n self.model_name = export_cpp.ProcessExporterCPP.get_model_name(model['name'])\n self.aloha_model = create_aloha.AbstractALOHAModel(self.model_name)\n\n\n self.dir_path = output_path\n self.default_replace_dict = dict(replace_dict)\n # List of needed ALOHA routines\n self.wanted_lorentz = wanted_lorentz\n self.wanted_couplings = wanted_couplings\n\n # For dependent couplings, only want to update the ones\n # actually used in each process. For other couplings and\n # parameters, just need a list of all.\n self.coups_dep = {} # name -> base_objects.ModelVariable\n self.coups_indep = [] # base_objects.ModelVariable\n self.params_dep = [] # base_objects.ModelVariable\n self.params_indep = [] # base_objects.ModelVariable\n self.p_to_cpp = None \n\n # Prepare parameters and couplings for writeout in C++\n self.prepare_parameters()\n self.prepare_couplings(wanted_couplings)","title":""},{"docid":"c5525bfc1f28e0a667830ec014bdb725","score":"0.56353825","text":"def convertProcessToLcia(O,p):\r\n \r\n # the players\r\n P = O.findUuid(os.path.splitext(p)[0])\r\n print 'Importing: ' + P.El('baseName').text\r\n L = O.createLciaFromTemplate()\r\n \r\n lcia_ns = L.getroot().nsmap\r\n \r\n # the basic data\r\n L.El('name','common').text = P.El('baseName').text\r\n L.El('methodology').text = 'TRACI 2.0'\r\n L.El('impactCategory').text = selectFromList(impactCategories)\r\n L.El('impactIndicator').text = raw_input('Impact Indicator: ')\r\n L.El('referenceYear').text = P.El('referenceYear','common').text\r\n L.El('duration').text = 'indefinite'\r\n L.El('interventionLocation').text = 'US'\r\n L.El('impactLocation').text = 'US'\r\n\r\n L.El('typeOfDataSet').text = selectFromList(indicatorTypes)\r\n L.El('normalisation').text= 'false'\r\n L.El('weighting').text = 'false'\r\n \r\n QR=L.El('quantitativeReference')\r\n CF=L.El('characterisationFactors')\r\n \r\n # the exchanges\r\n Xs = P.El('exchanges').getchildren()\r\n for x in Xs:\r\n flow_el = nonemap(x,'referenceToFlowDataSet')\r\n flow_uuid = flow_el.attrib['refObjectId']\r\n flow_comment = flow_el.getchildren()[0]\r\n if nonemap(x, 'exchangeDirection').text == 'Input':\r\n # Input exchange = reference flow\r\n ref_uuid = getReferenceFlow(O.findUuid(flow_uuid), nsmap = lcia_ns)\r\n RQ = ET.Element('referenceQuantity',\r\n attrib = {'type':'flow property data set',\r\n 'refObjectId':ref_uuid,\r\n 'uri':'../flowproperties/{0}.xml'.format(ref_uuid)},\r\n nsmap = lcia_ns)\r\n RQ.append(flow_comment)\r\n QR.append(RQ)\r\n print 'reference flow: {0} {1}'.format(ref_uuid,flow_comment.text)\r\n \r\n else:\r\n # Output exchange = new factor\r\n mean_value = 1 / float(nonemap(x, 'resultingAmount').text) # note inversion!\r\n F = ET.Element('factor',nsmap = lcia_ns)\r\n FL = ET.Element('referenceToFlowDataSet', \r\n attrib={'type':'flow data set',\r\n 'refObjectId':flow_uuid,\r\n 'uri':'../flows/{0}.xml'.format(flow_uuid)},\r\n nsmap = lcia_ns)\r\n FL.append(flow_comment)\r\n F.append(FL)\r\n ET.SubElement(F, 'exchangeDirection',nsmap = lcia_ns).text='Output'\r\n ET.SubElement(F, 'meanValue', nsmap = lcia_ns).text = str( mean_value ) \r\n CF.append(F)\r\n print 'exchange: {0} {1}'.format(flow_uuid,flow_comment.text)\r\n\r\n return L","title":""},{"docid":"b0f2020c341a65e6214adebd68e7903e","score":"0.56281185","text":"def generate_and_save_all_interpretations_for_model(pci_obj, X, y, mdl, mdl_name):\n \n ## Generate dataset-level interpretation for Logistic Regression model\n print('Dataset-level interpretation generation is in process for ' + mdl_name + '.')\n dataset_level_interpretation = pci_obj.dataset_level_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_dataset_level_interpretation_' + mdl_name + '.txt', dataset_level_interpretation, fmt='%1.8e')\n print('Dataset-level interpretation generation is finished for' + mdl_name + '.')\n \n \n ## Generate whole dataset signed interpretation for Logistic Regression model\n print('Whole dataset signed interpretation generation is in process for ' + mdl_name + '.')\n whole_dataset_signed_interpretation = pci_obj.whole_dataset_signed_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_whole_dataset_signed_interpretation_' + mdl_name + '.txt', whole_dataset_signed_interpretation, fmt='%1.8e')\n print('Whole dataset signed interpretation generation is finished for ' + mdl_name + '.')\n \n ## Generate whole dataset signed interpretation for Logistic Regression model\n print('Whole dataset unsigned interpretation generation is in process for ' + mdl_name + '.')\n whole_dataset_unsigned_interpretation = pci_obj.whole_dataset_unsigned_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_whole_dataset_unsigned_interpretation_' + mdl_name + '.txt', whole_dataset_unsigned_interpretation, fmt='%1.8e')\n print('Whole dataset unsigned interpretation generation is finished for ' + mdl_name + '.')","title":""},{"docid":"95c5748803c3d19cc5d921a875e0b086","score":"0.5581633","text":"def convert(prototxt_path, caffemodel_path, phase='test', output_model_name='freeze_graph_model.pb'):\n assert_exist_files(prototxt_path, caffemodel_path)\n\n data_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.mat')\n code_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.py')\n standalone_output_path = os.path.join(os.path.dirname(prototxt_path), output_model_name)\n\n try:\n with tf.Session() as sess:\n transformer = TensorFlowTransformer(prototxt_path, caffemodel_path, phase=phase)\n print_stderr('Converting data...')\n if data_output_path is not None:\n data = transformer.transform_data()\n print_stderr('Saving data...')\n with open(data_output_path, 'wb') as data_out:\n np.save(data_out, data)\n if code_output_path is not None:\n print_stderr('Saving source...')\n with open(code_output_path, 'wb') as src_out:\n src_out.write(transformer.transform_source())\n\n if standalone_output_path:\n filename, _ = os.path.splitext(os.path.basename(standalone_output_path))\n temp_folder = os.path.join(os.path.dirname(standalone_output_path), 'tmp')\n\n if not os.path.exists(temp_folder):\n os.makedirs(temp_folder)\n\n if data_output_path is None:\n data = transformer.transform_data()\n print_stderr('Saving data...')\n data_output_path = os.path.join(temp_folder, filename) + '.npy'\n with open(data_output_path, 'wb') as data_out:\n np.save(data_out, data)\n\n if code_output_path is None:\n print_stderr('Saving source...')\n code_output_path = os.path.join(temp_folder, filename) + '.py'\n with open(code_output_path, 'wb') as src_out:\n src_out.write(transformer.transform_source())\n\n checkpoint_path = os.path.join(temp_folder, filename + '.ckpt')\n graph_name = os.path.basename(standalone_output_path)\n graph_folder = os.path.dirname(standalone_output_path)\n input_node = transformer.graph.nodes[0].name\n output_node = transformer.graph.nodes[-1].name\n tensor_shape = transformer.graph.get_node(input_node).output_shape\n tensor_shape_list = [tensor_shape.batch_size, tensor_shape.height, tensor_shape.width, tensor_shape.channels]\n\n sys.path.append(os.path.dirname(code_output_path))\n module = os.path.splitext(os.path.basename(code_output_path))[0]\n class_name = transformer.graph.name\n KaffeNet = getattr(__import__(module), class_name)\n\n data_placeholder = tf.placeholder(tf.float32, tensor_shape_list, name=input_node)\n net = KaffeNet({input_node: data_placeholder})\n\n # load weights stored in numpy format\n net.load(data_output_path, sess)\n\n print_stderr('Saving checkpoint...')\n\n saver = tf.train.Saver()\n saver.save(sess, checkpoint_path)\n\n print_stderr('Saving graph definition as protobuf...')\n tf.train.write_graph(sess.graph.as_graph_def(), graph_folder, graph_name, as_text=False)\n\n input_graph_path = standalone_output_path\n input_saver_def_path = \"\"\n input_binary = True\n input_checkpoint_path = checkpoint_path\n output_node_names = output_node\n restore_op_name = 'save/restore_all'\n filename_tensor_name = 'save/Const:0'\n output_graph_path = standalone_output_path\n clear_devices = True\n\n print_stderr('Saving standalone model...')\n freeze_graph(input_graph_path, input_saver_def_path,\n input_binary, input_checkpoint_path,\n output_node_names, restore_op_name,\n filename_tensor_name, output_graph_path,\n clear_devices, '')\n\n shutil.rmtree(temp_folder)\n\n print_stderr('Done.')\n except KaffeError as err:\n fatal_error('Error encountered: {}'.format(err))","title":""},{"docid":"870fd0325523f72544b469d902b7c2cc","score":"0.5544181","text":"def convert_odml():\n f_in = join(ROOT, \"tmp.xml\")\n f_out = join(ROOT, \"tmpout.xml\")\n\n o_ver_conv = VersionConverter(f_in)\n\n # either write to file\n o_ver_conv.write_to_file(f_out)\n\n # or pass it to the python xml parser\n doc = ODMLReader(parser='XML').from_string(str(o_ver_conv))\n doc.pprint()","title":""},{"docid":"5d270ed9448ab6ff02368268de72a91b","score":"0.5542094","text":"def compare_lp_files(self, filename, ignored=None, my_om=None):\n if my_om is None:\n om = self.get_om()\n else:\n om = my_om\n tmp_filename = filename.replace('.lp', '') + '_tmp.lp'\n new_filename = ospath.join(self.tmppath, tmp_filename)\n om.write(new_filename, io_options={'symbolic_solver_labels': True})\n logging.info(\"Comparing with file: {0}\".format(filename))\n with open(ospath.join(self.tmppath, tmp_filename)) as generated_file:\n with open(ospath.join(ospath.dirname(ospath.realpath(__file__)),\n \"lp_files\",\n filename)) as expected_file:\n\n def chop_trailing_whitespace(lines):\n return [re.sub(r'\\s*$', '', ln) for ln in lines]\n\n def remove(pattern, lines):\n if not pattern:\n return lines\n return re.subn(pattern, \"\",\n \"\\n\".join(lines))[0].split(\"\\n\")\n\n expected = remove(ignored,\n chop_trailing_whitespace(\n expected_file.readlines()))\n generated = remove(ignored,\n chop_trailing_whitespace(\n generated_file.readlines()))\n\n def normalize_to_positive_results(lines):\n negative_result_indices = [\n n for n, line in enumerate(lines)\n if re.match(\"^= -\", line)]\n equation_start_indices = [\n [n for n in reversed(range(0, nri))\n if re.match('.*:$', lines[n])][0]+1\n for nri in negative_result_indices]\n for (start, end) in zip(\n equation_start_indices,\n negative_result_indices):\n for n in range(start, end):\n lines[n] = (\n '-'\n if lines[n] and lines[n][0] == '+'\n else '+'\n if lines[n]\n else lines[n]) + lines[n][1:]\n lines[end] = '= ' + lines[end][3:]\n return lines\n\n expected = normalize_to_positive_results(expected)\n generated = normalize_to_positive_results(generated)\n\n eq_(generated, expected,\n \"Failed matching expected with generated lp file:\\n\" +\n \"\\n\".join(unified_diff(expected, generated,\n fromfile=ospath.relpath(\n expected_file.name),\n tofile=ospath.basename(\n generated_file.name),\n lineterm=\"\")))","title":""},{"docid":"bffa7248d281833024e2e2237090aa94","score":"0.55059963","text":"def model_to_owl(model, fname):\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\n\n try:\n fileOS = autoclass('java.io.FileOutputStream')(fname)\n except JavaException:\n logger.error('Could not open data file %s' % fname)\n return\n l3_factory = autoclass('org.biopax.paxtools.model.BioPAXLevel').L3.getDefaultFactory()\n model_out = l3_factory.createModel()\n for r in model.getObjects().toArray():\n model_out.add(r)\n io.convertToOWL(model_out, fileOS)\n\n fileOS.close()","title":""},{"docid":"016e6167d18373b2c352dac2e0aac10f","score":"0.54671896","text":"def run(in_obo_filename=IN_OBO_FILENAME, in_pkl_filename=IN_PKL_FILENAME, in_tsv_filename=IN_TSV_FILELANE,\n in_monomers_filename=IN_MONOMERS_FILENAME,\n max_num_proteins=None,\n out_pickle_filename=OUT_PICKLE_FILENAME, out_pickle_filename_2=OUT_PICKLE_FILENAME_2,\n out_tsv_filename=OUT_TSV_FILENAME, out_fasta_filename=OUT_FASTA_FILENAME,\n out_fig_filename=OUT_FIG_FILENAME, out_structure_dirname=OUT_STRUCTURE_DIRNAME,\n out_viz_dirname=OUT_VIZ_DIRNAME):\n # get the PRO ontology and extract the modified proteins from the ontology\n # proteins = get_pro_from_obo(obo_filename=in_obo_filename, pkl_filename=in_pkl_filename, max_num_proteins=max_num_proteins)\n proteins = get_pro_from_tsv(in_tsv_filename, max_num_proteins=max_num_proteins)\n\n # parse the modified proteins and retrieve their sequences\n if not os.path.isfile(out_pickle_filename):\n # parse the modified proteins and retrieve their sequences\n parsed_proteins = []\n for i_protein, protein in enumerate(proteins):\n if i_protein % 100 == 0:\n print('Parsing protein {} of {}'.format(i_protein + 1, len(proteins)))\n parsed_proteins.append(parse_protein(protein))\n\n # save the parsed proteins in pickle format\n with open(out_pickle_filename, 'wb') as file:\n pickle.dump(parsed_proteins, file)\n else:\n # load saved parsed proteins in pickle format\n with open(out_pickle_filename, 'rb') as file:\n parsed_proteins = pickle.load(file)\n\n # read list of monomers\n monomers = {}\n with open(in_monomers_filename, 'r') as file:\n reader = csv.DictReader(file, dialect='excel')\n for row in reader:\n monomers[row['PRO id']] = {\n 'mod': bpforms.protein_alphabet.monomers.get(row['BpForms code'], None),\n 'origin': [],\n }\n if row['Base monomer']:\n monomers[row['PRO id']]['origin'] = row['Base monomer'].split(', ')\n\n # generate list of modified monomeric forms\n for protein in parsed_proteins:\n for modification in protein['modifications']:\n if modification['monomer'] not in monomers:\n monomers[modification['monomer']] = {\n 'mod': None,\n 'origin': [],\n }\n\n # print list of unmapped monomers\n unmapped_monomers = []\n for monomer, code in monomers.items():\n if not code['mod']:\n unmapped_monomers.append(monomer)\n unmapped_monomers.sort()\n if unmapped_monomers:\n print('Several PRO monomeric forms have not been mapped to BpForms monomeric forms:\\n {}'.format(\n '\\n '.join(unmapped_monomers)))\n\n # check for inconsistencies between residue and modified monomeric form\n monomer_codes = {}\n for code, monomer in bpforms.protein_alphabet.monomers.items():\n monomer_codes[monomer] = code\n\n for protein in parsed_proteins:\n for modification in protein.get('modifications', []):\n if modification['residue'] and modification['monomer']:\n monomer = monomers.get(modification['monomer'], None)\n if (monomer['mod'] and monomer['mod'].get_canonical_code(monomer_codes) != modification['residue']) \\\n or (monomer['origin'] and modification['residue'] not in monomer['origin']):\n codes = set(monomer['origin'])\n if monomer['mod']:\n codes.add(monomer['mod'].get_canonical_code(monomer_codes))\n msg = 'Modified monomeric form {} potentially inconsistent with residue {} != {}'.format(\n modification['monomer'], modification['residue'],\n ', '.join(codes))\n print(protein['id'] + ': ' + msg)\n\n # generate BpForms for each protein\n if not os.path.isdir(out_structure_dirname):\n os.mkdir(out_structure_dirname)\n\n if not os.path.isdir(out_viz_dirname):\n os.mkdir(out_viz_dirname)\n\n if not os.path.isfile(out_pickle_filename_2):\n for i_protein, protein in enumerate(parsed_proteins):\n if i_protein % 100 == 0:\n print('Generating BpForms {} of {}'.format(i_protein + 1, len(parsed_proteins)))\n\n protein['modified_seq'] = None\n if not protein['uniprot_id']:\n continue\n if not protein['seq']:\n continue\n if protein['pro_errors']:\n continue\n\n processed_form = gen_bpform(protein, monomers, monomer_codes, apply_modifications=False)\n protein['processed_seq'] = str(processed_form)\n if not processed_form.validate():\n processed_formula = processed_form.get_formula()\n protein['processed_formula'] = str(processed_formula)\n protein['processed_mol_wt'] = processed_form.get_mol_wt()\n protein['processed_charge'] = processed_form.get_charge()\n\n if not protein['modifications']:\n continue\n\n modified_form = gen_bpform(protein, monomers, monomer_codes, include_annotations=False)\n protein['modified_seq'] = str(modified_form)\n\n modified_form = gen_bpform(protein, monomers, monomer_codes)\n if not modified_form.validate():\n modified_formula = modified_form.get_formula()\n protein['modified_full_seq'] = str(modified_form)\n protein['modified_formula'] = str(modified_formula)\n protein['modified_mol_wt'] = modified_form.get_mol_wt()\n protein['modified_charge'] = modified_form.get_charge()\n\n protein['modifications_formula'] = str(modified_formula - processed_formula)\n protein['modifications_mol_wt'] = protein['modified_mol_wt'] - protein['processed_mol_wt']\n protein['modifications_charge'] = protein['modified_charge'] - protein['processed_charge']\n\n # with open(os.path.join(out_structure_dirname, protein['id'] + '.cml'), 'w') as file:\n # file.write(modified_form.export('cml'))\n\n form = gen_bpform(protein, monomers, monomer_codes,\n apply_processing=False, include_annotations=True)\n seq_features = []\n if protein['processing']:\n seq_features.append({\n 'label': 'Processed',\n 'color': '#cccccc',\n 'positions': [],\n })\n last = 0\n for p in protein['processing']:\n seq_features[0]['positions'].append([last + 1, p['start'] - 1])\n last = p['end']\n seq_features[0]['positions'].append([\n protein['processing'][-1]['end'] + 1,\n len(form.seq),\n ])\n\n if protein['processing'][0]['start'] == 1:\n seq_features[0]['positions'].pop(0)\n if protein['processing'][-1]['end'] == len(form.seq):\n seq_features[0]['positions'].pop(len(seq_features[0]['positions']) - 1)\n\n with open(os.path.join(out_viz_dirname, protein['id'] + '.svg'), 'w') as file:\n file.write(form.get_genomic_image(seq_features, width=910))\n\n if modified_form.get_canonical_seq(monomer_codes) != protein['processed_seq']:\n protein['pro_errors'].append('Modified sequence for {} not compatible with the processed sequence'.format(\n protein['id']))\n\n # save the parsed proteins in pickle format\n with open(out_pickle_filename_2, 'wb') as file:\n pickle.dump(parsed_proteins, file)\n else:\n with open(out_pickle_filename_2, 'rb') as file:\n parsed_proteins = pickle.load(file)\n\n # save the proteoforms in TSV format\n with open(out_tsv_filename, 'w') as file:\n writer = csv.writer(file, dialect='excel-tab')\n writer.writerow(['PRO id', 'UniProt id', 'Organism',\n 'Unmodified sequence (IUBMB)',\n 'Processing', 'Deletions', 'Processsed sequence (IUBMB)', 'Processsed formula',\n 'Processsed molecular weight', 'Processsed charge',\n 'Modifications', 'Crosslinks', 'Modified sequence (abbreviated BpForms)', 'Modified sequence (BpForms)',\n 'Is modified sequence concrete', 'Modified formula', 'Modified molecular weight', 'Modified charge',\n 'Modifications formula', 'Modifications molecular weight', 'Modifications charge',\n 'PRO issues', 'Monomeric form issues'])\n\n for parsed_protein in parsed_proteins:\n if parsed_protein.get('pro_errors', None):\n pro_errors = '. '.join(parsed_protein['pro_errors']) + '.'\n else:\n pro_errors = None\n\n if parsed_protein.get('modified_errors', None):\n modified_errors = '. '.join(parsed_protein['modified_errors']) + '.'\n else:\n modified_errors = None\n\n writer.writerow([\n parsed_protein['id'],\n parsed_protein.get('uniprot_id', None),\n parsed_protein.get('organism', None),\n parsed_protein.get('seq', None),\n ', '.join('{}-{}'.format(p['start'], p['end']) for p in parsed_protein['processing']),\n ', '.join('{}-{}'.format(deletion[0], deletion[1]) for deletion in parsed_protein.get('deletions', [])),\n parsed_protein.get('processed_seq', None),\n parsed_protein.get('processed_formula', None),\n parsed_protein.get('processed_mol_wt', None),\n parsed_protein.get('processed_charge', None),\n ', '.join('{} --> {} ({})'.format(m['residue'] or '?', m['monomer'], ', '.join(str(p) for p in m['positions']))\n for m in parsed_protein['modifications']),\n ', '.join('{}{}-{}{}'.format(xlink[0][1], xlink[0][0], xlink[1][1], xlink[1][0])\n for xlink in parsed_protein.get('crosslinks', [])),\n parsed_protein.get('modified_seq', None),\n parsed_protein.get('modified_full_seq', None),\n parsed_protein.get('modified_concrete', False),\n parsed_protein.get('modified_formula', None),\n parsed_protein.get('modified_mol_wt', None),\n parsed_protein.get('modified_charge', None),\n parsed_protein.get('modifications_formula', None),\n parsed_protein.get('modifications_mol_wt', None),\n parsed_protein.get('modifications_charge', None),\n pro_errors,\n modified_errors,\n ])\n\n # save the proteoforms in FASTA format\n seqs = (SeqRecord(id='{} | {}'.format(protein['id'], protein['uniprot_id']),\n seq=Seq(protein['modified_seq']),\n description='')\n for protein in parsed_proteins\n if protein['modified_seq'])\n SeqIO.write(seqs, out_fasta_filename, \"fasta\")\n\n # analyze frequency of modifications\n plot_modifications(parsed_proteins, fig_filename=out_fig_filename)\n\n # return proteins\n return proteins, parsed_proteins","title":""},{"docid":"776ae1cf149e78ae0a4114b21438d138","score":"0.54315245","text":"def build_LM(in_file):\r\n print 'building language models...'\r\n # This is an empty method\r\n # Pls implement your code in below\r\n\r\n # Open in_file and split into a list of sentences\r\n input_file = open(in_file, 'r')\r\n train_input_list = input_file.read().split(\"\\r\\n\")\r\n\r\n # Set up dictionaries to store the 4grams generated from input.train\r\n # ngram_count stores the total number of ngrams each language has generated\r\n four_gram_table = {}\r\n ngram_count = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\r\n\r\n '''\r\n NGRAM GENERATION\r\n Multiple for loops through each sentence and then each character for\r\n 4gram generation. Each sentence is padded with 3 'START's and 3 'END's\r\n '''\r\n for sentence in train_input_list:\r\n sentence_parts = sentence.split(None, 1)\r\n # To handle possible empty strings or weird strings in training input\r\n if (len(sentence_parts) != 2):\r\n break\r\n language = sentence_parts[0]\r\n sentence_body = sentence_parts[1]\r\n char_list = list(sentence_body)\r\n for index in range(3):\r\n char_list.insert(0, 'START')\r\n char_list.append('END')\r\n for index in range(len(char_list) - 4):\r\n four_gram = (char_list[index], char_list[index + 1],\r\n char_list[index + 2], char_list[index + 3])\r\n if four_gram in four_gram_table:\r\n four_gram_table[four_gram][language] += 1\r\n ngram_count[language] += 1\r\n else:\r\n # Add-one smoothing for new ngrams\r\n four_gram_table[four_gram] = {'malaysian': 1, 'indonesian': 1,\r\n 'tamil': 1}\r\n for value in ngram_count.itervalues():\r\n value += 1\r\n four_gram_table[four_gram][language] += 1\r\n ngram_count[language] += 1\r\n return [four_gram_table, ngram_count]\r\n\r\n input_file.close()","title":""},{"docid":"8776c3c0636ee64e48b9236afde29d96","score":"0.54051876","text":"def run(args: argparse.Namespace) -> None:\n assert WaymoDataFileReader is not None, (\n \"Please install the requirements in scripts/optional.txt to use\"\n \"Waymo conversion.\"\n )\n if not os.path.exists(args.output):\n os.makedirs(args.output)\n result = from_waymo(\n args.input,\n args.output,\n args.save_images,\n args.use_lidar_labels,\n args.nproc,\n )\n save(os.path.join(args.output, \"scalabel_anns.json\"), result)","title":""},{"docid":"89aa08012b425e9517160341b3b4c2b8","score":"0.5397848","text":"def __init__(self,pedfname = None,title='title',outfname='rgMakeGenModel.phe',\n xtradir=\"/foo/bar\", linkage=True, logf = None, model=additive,\n labels=['FounderNonAff','FounderAff','OffspringNonAff','OffspringAff']):\n ehet = '1'\n ehom1 = '0'\n ehom2 = '2'\n missval = '.'\n missing = ['N','0','.','-']\n if model == additive: # we have ehom2 = 2 major alleles\n modeldict = {ehom1:'2',ehet:'1',ehom2:'0',missval:missval}\n elif model == zadditive:\n modeldict = {ehom1:'1',ehet:'0',ehom2:'-1',missval:missval}\n elif model == dominant:\n modeldict = {ehom1:'1',ehet:'1',ehom2:'0',missval:missval}\n else:\n modeldict = {ehom1:'2',ehet:'0',ehom2:'0',missval:missval}\n # model code translation dicts\n\n swapdict = {ehom1:ehom2,ehom2:ehom1,ehet:ehet,missval:missval} # if initial ref allele was wrong, use these to swap\n mdict = dict(zip(missing,missing))\n pedf = '%s.ped' % pedfname\n f = file(pedf,'r')\n if linkage: # read map file\n map = readMap(pedfname)\n rslist = [x[1] for x in map] # get rs numbers\n else:\n head = f.next().strip()\n rslist = head.split()\n nrs = len(rslist) # number of markers\n elen = 2*nrs + 6 # expected # elements on each line\n logf.write('%s %s: found %d for nrs\\n' % (thisprog,timenow(),nrs))\n gm = {}\n gm['founders'] = [] # array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\n gm['offspring'] = [] # [array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\n adicts = [{} for x in xrange(nrs)] # count of alleles in a dict for each marker\n refallele = [None for x in xrange(nrs)] # list of first observed alleles\n nsubj = 0\n indiv = {'founders':[],'offspring':[]}\n for lnum,l in enumerate(f):\n ll = l.strip().split()\n if (lnum+1) % 200 == 0:\n logf.write('%s %s: Processing line %d\\n' % (thisprog, timenow(),lnum+1))\n if len(ll) < elen: # ? short ?\n logf.write('%s %s: Line %d is %d long, expected %d\\n' % (thisprog, timenow(),\n lnum,len(ll),elen))\n else:\n garray = array.array('c',[missval,]*nrs)\n nsubj += 1\n sid = '%s_%s' % (ll[0],ll[1])\n if sid == '1_1': # eesh\n sid = '%d_%d' % (nsubj,nsubj)\n isFounder = isOff = False\n status = labels[0] # founder unaff\n if ll[2] <> '0' and ll[3] <> '0': # has parent ids\n iclass = 'offspring'\n status = labels[2] # unaffected offspring\n if ll[5] == '2':\n status = labels[3] # affected offspring\n else:\n iclass = 'founders'\n if ll[5] == '2':\n status = labels[1] #change from unaff to aff founder label\n gender = 'M'\n if ll[4] == '2':\n gender = 'F'\n ped = ll[:6]\n indiv[iclass].append(ped) # for row wise output\n for snp in xrange(nrs):\n pos = 2*snp + 6 # first\n g1,g2 = ll[pos],ll[pos+1] # pair of genos\n if mdict.get(g1,None) or mdict.get(g2,None): # one or both missing\n esnp = missval # missing value\n else:\n if not refallele[snp]:\n refallele[snp] = g1 # first one we saw!\n for g in (g1,g2):\n n = adicts[snp].get(g,0)\n n += 1\n adicts[snp][g] = n\n if g1 == g2: # hom\n if g1 == refallele[snp]:\n esnp = ehom2 # 2 copies of current reference allele\n else:\n esnp = ehom1 # no copies\n else:\n esnp = ehet # het - always has one copy of reference allele\n garray[snp] = esnp\n gm[iclass].append(garray) # append genos for this new subject\n for ek in gm.keys():\n lek = len(gm[ek])\n if len(gm[ek]) > 0:\n lek0 = len(gm[ek][0])\n s = 'for %s, have %d subjects with %d markers' % (ek,lek,lek0)\n print s\n logf.write(s)\n for x in range(lek):\n if len(gm[ek][x]) <> lek0:\n s = 'for row %d, len = %d, not %d' % (x, len(gm[ek][x]),lek0)\n print s\n logf.write(s)\n logf.write('%s %s: Now checking major allele assignment and fixing as needed\\n' % (thisprog,timenow()))\n for iclass in gm.keys():\n for subject in xrange(len(gm[iclass])): # for each subject\n for snp in xrange(nrs): # now check to see if reference = major allele\n major = majAllele(adicts[snp])\n if major <> refallele[snp]: # either None or we need to change all the codes\n if major <> None:\n gm[iclass][subject][snp] = swapdict[gm[iclass][subject][snp]]\n for iclass in gm.keys():\n for subject in xrange(len(gm[iclass])): # now convert to genetic model wanted\n gm[iclass][subject] = [modeldict[x] for x in gm[iclass][subject]] # translate to model\n self.gm = gm\n self.model = model\n self.indiv = indiv\n self.nrs = nrs\n self.nsubj = nsubj\n self.logf = logf\n self.basename = title\n self.outfname = outfname\n self.rslist = rslist\n pedhead = 'famid\\tiid\\tfid\\tmid\\tgender\\taffection\\t'\n rs = '\\t'.join(rslist)\n self.outhead = '%s%s\\n' % (pedhead,rs)","title":""},{"docid":"ce63391b0ba53484bcdbfa3c177f5093","score":"0.5392235","text":"def test_LM(in_file, out_file, LM):\n print(\"testing language models...\")\n\n # Parse inputs\n data = read_by_line(in_file)\n model = LM[0]\n languages = LM[1]\n\n # Create an empty probability reference sheet\n basic_probability_ref = dict()\n for l in languages:\n basic_probability_ref[l] = np.float64(1)\n\n # Clear file of any results, to prepare the output file for eval.py\n open(out_file, 'w').close()\n \n with open(out_file, 'a') as f:\n # Process each sentence and make a guess\n for sentence in data:\n p_ref = basic_probability_ref.copy()\n tokens = tokenise(sentence, token_size)\n num_undetected = 0\n\n # Multiple token probabilities for each token\n for t in tokens:\n if t in model:\n # Multiply the respective probability of each language\n counts = model[t]\n for l in languages:\n p_ref[l] += counts[l]\n else:\n num_undetected += 1\n\n # Find the best prediction using the maximum product\n if num_undetected / len(tokens) > other_threshold:\n prediction = other_language\n else:\n prediction = max(p_ref.items(), key=operator.itemgetter(1))[0]\n\n # Write the prediction to the output file\n f.write(prediction + \" \" + sentence + \"\\n\")","title":""},{"docid":"11937e2179eb9e5be7829ede94d45adb","score":"0.5387432","text":"def test_issue4707():\n nlp = English()\n nlp.add_pipe(\"sentencizer\")\n nlp.add_pipe(\"entity_ruler\")\n assert nlp.pipe_names == [\"sentencizer\", \"entity_ruler\"]\n exclude = [\"tokenizer\", \"sentencizer\"]\n with make_tempdir() as tmpdir:\n nlp.to_disk(tmpdir, exclude=exclude)\n new_nlp = load_model_from_path(tmpdir, disable=exclude)\n assert \"sentencizer\" not in new_nlp.pipe_names\n assert \"entity_ruler\" in new_nlp.pipe_names","title":""},{"docid":"fe5676fbca6769a80c88f1d8e1109454","score":"0.5377364","text":"def main(argv):\n parser = argparse.ArgumentParser(\n description='Create all possible determinizations of this domain.')\n parser.add_argument('-d', '--domain', required=True)\n parser.add_argument('-o', '--output', required=True) \n args = parser.parse_args()\n domain_file_name = args.domain\n output_file_name = args.output\n \n # Reading domain file.\n domain_str = ''\n try:\n with open(domain_file_name, 'r') as domain_file:\n for line in domain_file:\n if line.startswith(';;') or line.isspace():\n continue;\n domain_str += line\n except IOError:\n print \"Could not read file:\", domain_file_name\n sys.exit(-1) \n \n # Parsing the domain tree.\n domain_tree = ppddl_util.parse_sexp(domain_str)\n all_prob_effects_list = []\n \n # Getting all possible probabilistic effects.\n ppddl_util.get_all_probabilistic_effects(domain_tree, all_prob_effects_list)\n \n # Getting the most likely outcome determinization.\n mlo_determinization = []\n for probabilistic_effect_info in all_prob_effects_list:\n mlo_determinization.append(\n ppddl_util.get_mlo_determinization_effect(probabilistic_effect_info))\n \n # Writing the MLO determinization to a PPDDL file.\n determinization_ppddl_tree = copy.deepcopy(domain_tree)\n description_text = \"\"\n for effect_info in mlo_determinization:\n description_text += \"%s %d\\n\" % (effect_info[0], effect_info[1][0])\n ppddl_util.determinize_tree(mlo_determinization, determinization_ppddl_tree)\n ppddl_util.clean_up_tree(determinization_ppddl_tree)\n f = open('%s_mlo_det.pddl' % (output_file_name), 'w')\n fd = open('%s_mlo_det.desc' % (output_file_name), 'w')\n f.write(ppddl_util.make_str(determinization_ppddl_tree[0]))\n fd.write(description_text)\n f.close()\n fd.close()\n \n print 'Created MLO determinization for %s' % domain_file_name","title":""},{"docid":"2b6f6722ce9ac5c08f4612e25ef7aec0","score":"0.5376005","text":"def make_model(pbm):\n for stmt in pbm.statements:\n try:\n # Skip statements with no subject\n if stmt.agent_list()[0] is None and \\\n not isinstance(stmt, Conversion):\n continue\n # Assemble statements\n if isinstance(stmt, Modification):\n pbm._assemble_modification(stmt)\n elif isinstance(stmt, RegulateActivity):\n pbm._assemble_regulate_activity(stmt)\n elif isinstance(stmt, RegulateAmount):\n pbm._assemble_regulate_amount(stmt)\n elif isinstance(stmt, Gef):\n pbm._assemble_gef(stmt)\n elif isinstance(stmt, Gap):\n pbm._assemble_gap(stmt)\n elif isinstance(stmt, ActiveForm):\n pbm._assemble_active_form(stmt)\n elif isinstance(stmt, Complex):\n pbm._assemble_complex(stmt)\n elif isinstance(stmt, Conversion):\n pbm._assemble_conversion(stmt)\n elif isinstance(stmt, Autophosphorylation):\n pbm._assemble_autophosphorylation(stmt)\n elif isinstance(stmt, Transphosphorylation):\n pbm._assemble_transphosphorylation(stmt)\n else:\n logger.info('Unhandled statement: %s' % stmt)\n except Exception:\n pass\n return pbm.model","title":""},{"docid":"9045c7c73ca55f814b38834d2149df8b","score":"0.5342216","text":"def try_load_model(self):\r\n filename = 'constraints_problem_size_{0}'.format(self.get_real_n())\r\n path = '../cache_constraints/'\r\n\r\n solution_is_loop = self.sol_is_loop()\r\n\r\n if solution_is_loop:\r\n filename += '_loop'\r\n path_filename = path + filename + '.lp'\r\n\r\n model_reader = ModelReader()\r\n\r\n self.outer_model_interface.check_dir(path)\r\n\r\n try:\r\n # Load the constraints\r\n self.docplex_model = model_reader.read_model(path_filename)\r\n except:\r\n # If they are not cached, create them\r\n print(\"\\nFile {} not found :(\\nI'm gonna create it! :D\".format(path_filename))\r\n self.docplex_model = self.generate_general_constraints(path, filename)","title":""},{"docid":"3c879101600ab140261ce79e742c8479","score":"0.5335524","text":"def generateModel(self):\n\t\tglobal startLoadModel, doneLoadModel\n\t\tprint(\"model type:\" + self.modeltype)\n\t\tstartLoadModel = time.time()\n\t\tif not os.path.exists(self.dictionaryfile):\n\t\t\tprint(\"creating dictionary \" + self.dictionaryfile)\n\t\t\tself.dictionary = self.makeDictionaryMemoryFriendly(self.corpusfile)\n\t\telse:\n\t\t\tprint(\"reading dictionary \" + self.dictionaryfile)\n\t\t\tself.dictionary = self.loadDictionary(self.dictionaryfile)\n\t\tif not os.path.exists(self.corpusmatrixfile):\n\t\t\tprint(\"serializing corpus \" + self.corpusmatrixfile)\n\t\t\tself.serializeCorpusMemoryFriendly(self.corpusfile, self.dictionary)\n\t\tif self.modeltype == \"LDA\":\n\t\t\tprint(\"creating lda model with \" + str(self.topics) + \" topics.\")\n\t\t\tif self.online:\n\t\t\t\tprint(\"Running in online mode with chunksize: \" + str(self.chunksize))\n\t\t\telse:\n\t\t\t\tprint(\"Running in offline mode with \" + str(self.passes) + \" passes\")\n\t\t\tif self.distributed:\n\t\t\t\tprint(\"Running in distributed mode\")\n\t\t\tself.generateLDAModel(self.topics, self.passes, self.distributed)\n\t\telif self.modeltype == \"LSI\":\n\t\t\tprint(\"creating lsi model\")\n\t\t\tself.generateLSIModel(topics)\n\t\telif self.modeltype == \"HDP\":\n\t\t\tprint(\"creating hdp model\")\n\t\t\tself.generateHDPModel()\n\t\tdoneLoadModel = time.time()\n\t\tprint(\"Time to generate model:\" + str(doneLoadModel - startLoadModel))","title":""},{"docid":"91c5431c7473abefa61a82391d7c11c8","score":"0.5323186","text":"def test_LM(in_file, out_file, LM):\r\n print \"testing language models...\"\r\n # This is an empty method\r\n # Pls implement your code in below\r\n\r\n # Setting up data provided from given LM\r\n four_gram_table = LM[0]\r\n ngram_count = LM[1]\r\n\r\n # Open in_file and split into a list of sentences\r\n # Open out_file for writing predictions\r\n input_file = open(in_file, 'r')\r\n test_input_list = input_file.read().split(\"\\r\\n\")\r\n output_file = open(out_file, 'w')\r\n\r\n for sentence in test_input_list:\r\n # To handle possible empty strings or invalid input\r\n if sentence == \"\":\r\n break\r\n # Ngram probability score for current sentence\r\n ngram_score = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\r\n # Counters to keep track of number of unencountered ngrams generated by\r\n # the current sentence.\r\n invalid_ngram_count = 0\r\n total_ngram_count = 0\r\n # Sentence is padded with 3 'START's and 3 'END's\r\n char_list = list(sentence)\r\n for index in range(3):\r\n char_list.insert(0, 'START')\r\n char_list.append('END')\r\n for index in range(len(char_list) - 4):\r\n four_gram = (char_list[index], char_list[index + 1],\r\n char_list[index + 2], char_list[index + 3])\r\n # Log10 is used to prevent underflow of individual ngram_scores\r\n if four_gram in four_gram_table:\r\n for language, count in four_gram_table[four_gram].iteritems():\r\n ngram_score[language] += math.log10(count/ngram_count[language])\r\n else:\r\n invalid_ngram_count += 1\r\n total_ngram_count += 1\r\n # invalid_ngram_ratio represents the proportion of unencountered ngrams\r\n # out of all ngrams generated from current sentence\r\n invalid_ngram_ratio = invalid_ngram_count / total_ngram_count\r\n # The set threshold of 0.6 indicates that the sentence might not be a\r\n # valid language in the given LM\r\n if max(ngram_score.itervalues()) is 0 or invalid_ngram_ratio > 0.6:\r\n predicted_language = 'other'\r\n else:\r\n predicted_language = max(ngram_score.iterkeys(), key=(lambda language: ngram_score[language]))\r\n output_file.write(predicted_language + \" \" + sentence + \"\\n\")\r\n input_file.close\r\n output_file.close","title":""},{"docid":"3c8230e8f56aabea2a2d18b33a22dab9","score":"0.5304721","text":"def generate(args):\n if args.cuda and torch.cuda.is_available():\n device = 0\n use_cuda = True\n elif args.cuda and not torch.cuda.is_available():\n print(\"You do not have CUDA, turning cuda off\")\n device = -1 \n use_cuda = False\n else:\n device = -1\n use_cuda=False\n\n #Load the vocab\n vocab = du.load_vocab(args.vocab)\n eos_id = vocab.stoi[EOS_TOK]\n pad_id = vocab.stoi[PAD_TOK]\n\n if args.ranking: # default is HARD one, the 'Inverse Narrative Cloze' in the paper\n dataset = du.NarrativeClozeDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, LM=False)\n # Batch size during decoding is set to 1\n batches = BatchIter(dataset, 1, sort_key=lambda x:len(x.actual), train=False, device=-1)\n else:\n dataset = du.SentenceDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, add_eos=False) #put in filter pred later\n # Batch size during decoding is set to 1\n batches = BatchIter(dataset, args.batch_size, sort_key=lambda x:len(x.text), train=False, device=-1)\n\n data_len = len(dataset)\n\n #Create the model\n with open(args.load, 'rb') as fi:\n if not use_cuda:\n model = torch.load(fi, map_location=lambda storage, loc : storage)\n else:\n model = torch.load(fi, map_location=torch.device('cuda'))\n\n if not hasattr(model.latent_root, 'nohier'):\n model.latent_root.set_nohier(args.nohier) #for backwards compatibility\n\n model.decoder.eval()\n model.set_use_cuda(use_cuda)\n\n #For reconstruction\n if args.perplexity:\n loss = calc_perplexity(args, model, batches, vocab, data_len)\n print(\"Loss = {}\".format(loss))\n elif args.schema:\n generate_from_seed(args, model, batches, vocab, data_len)\n elif args.ranking:\n do_ranking(args, model, batches, vocab, data_len, use_cuda)\n else:\n# sample_outputs(model, vocab)\n reconstruct(args, model, batches, vocab)","title":""},{"docid":"6b20588231ce0fcb6d4f7434d73e68c3","score":"0.52846843","text":"def generateModel(modelName, pack):\n\n (params, run, cost, optim_date, var_set) = pack\n\n with open('%s_optimized.mo' % modelName, 'w') as file:\n header = \"\"\"model {mn}_optimized \"Generated by PostProcess/postprocess_optim.py {d} with cost {c} lowest at run {r}\"\n extends {mn} (\n \\n\"\"\".format(mn=modelName, d = optim_date, c = '%.6f' % cost, r = '%d' % run)\n footer = ');\\nend %s_optimized;\\n' % modelName\n\n param_lines = (' %s = %.6e' % param for param in params)\n # use join to avoid placing the comma after the last\n lines = ',\\n'.join(param_lines)\n\n\n file.write(header)\n file.writelines(lines)\n file.write(footer)","title":""},{"docid":"4d6897ba3959406b0ee3b87b094e823e","score":"0.5258285","text":"def load_model(length=MAX_UTTERENCE_LENGTH):\n return torch.load(os.path.join(PATH_TO_SAVE, 'generator_{}.pt'.format(length))).to(DEVICE)","title":""},{"docid":"078acbdaf7a42c95ccc2c6c684279376","score":"0.5254087","text":"def compile_po_file(po_path: Path) -> None:\n output_path = po_path.parent\n\n target_json = po_path.with_suffix(\".json\")\n if target_json.exists():\n target_json.unlink()\n convert_catalog_to_json(po_path, output_path, po_path.stem)\n\n target_mo = po_path.with_suffix(\".mo\")\n if target_mo.exists():\n target_mo.unlink()\n compile_to_mo(po_path)","title":""},{"docid":"b3a0443fbd71bfab66c479b31441d52a","score":"0.52386135","text":"def test_LM(in_file, out_file, LM):\r\n print \"testing language models...\"\r\n # This is an empty method\r\n # Pls implement your code in below\r\n malay_LM = LM['malaysian']\r\n indon_LM = LM['indonesian']\r\n tamil_LM = LM['tamil']\r\n\r\n FD_in = open(in_file, 'r')\r\n FD_out = open(out_file, 'w')\r\n\r\n lines = FD_in.read()\r\n lines_array = lines.replace('\\r', ' ').split('\\n')\r\n lines_array.pop(len(lines_array) - 1)\r\n\r\n for line in lines_array:\r\n malay_count = 1\r\n indon_count = 1\r\n tamil_count = 1\r\n\r\n total_count = 0\r\n miss_count = 0\r\n\r\n four_char_array = get_four_char_array(line)\r\n\r\n for four_char in four_char_array:\r\n # key in malayLM = key in all LMs due to add one smoothing\r\n if four_char in malay_LM:\r\n malay_count += math.log10(malay_LM[four_char])\r\n indon_count += math.log10(indon_LM[four_char])\r\n tamil_count += math.log10(tamil_LM[four_char])\r\n else:\r\n miss_count += 1\r\n total_count += 1\r\n\r\n if (miss_count / total_count > 0.7):\r\n FD_out.write(\"other \" + line + \"\\n\")\r\n elif malay_count > indon_count and malay_count > tamil_count:\r\n FD_out.write(\"malaysian \" + line + \"\\n\")\r\n elif indon_count > malay_count and indon_count > tamil_count:\r\n FD_out.write(\"indonesian \" + line + \"\\n\")\r\n else:\r\n FD_out.write(\"tamil \" + line + \"\\n\")\r\n\r\n FD_in.close()\r\n FD_out.close()","title":""},{"docid":"5a76f0d38ddb8ab17ec18597b44bc149","score":"0.5236154","text":"def to_lalinference(self, **kwargs):\n return self.write(file_format=\"lalinference\", package=\"gw\", **kwargs)","title":""},{"docid":"35a86959eb9fc6a9c3356b7be538a93d","score":"0.5229652","text":"def make_input(\n wel_qin,\n wel_qin_1,\n wel_qout,\n wel_qout_1,\n wel_coordsin,\n wel_coordsin_1,\n wel_coordsout,\n wel_coordsout_1,\n model_path,\n name,\n exe_name='mf6',\n verbosity_level=0):\n # created to load, build and save the simulation\n sim = flopy.mf6.MFSimulation(\n sim_name=name,\n sim_ws=model_path, #working folder\n exe_name=exe_name,\n verbosity_level=verbosity_level, #standard output to be written - error messages should be included (=1)\n )\n # time discretization package\n times = (10.0, 120, 1.0)\n tdis_rc = [(1.0, 1, 1.0), times, times, times]\n flopy.mf6.ModflowTdis(\n sim, pname=\"tdis\",\n time_units=\"DAYS\",\n nper=4, # number of stress periods\n perioddata=tdis_rc, # [perlen(len stress period), nstp(time-steps in the stressperiod), tsmult(multiplier of of the lenght of sucessive time-steps]\n )\n #iterative model solution package\n flopy.mf6.ModflowIms(sim) # default solver - nearly linear models - confined or a single unconfined layer that is tick enough to contain the water table within a single layer\n # groundwater flow model\n gwf = flopy.mf6.ModflowGwf(sim, modelname=name, save_flows=True)\n # spatial discretization package\n flopy.mf6.ModflowGwfdis(gwf, nrow=10, ncol=10) # number ir rows and cols since the model has only one layer\n # Initial conditions package\n flopy.mf6.ModflowGwfic(gwf) #models using steady state in the beginning won't be affected\n # node property flow package pane - default is harmonic mean\n flopy.mf6.ModflowGwfnpf(\n gwf,\n save_flows=True,\n save_specific_discharge=True,\n icelltype=[0], # 0 unconfined, -1 confined\n k=[0.5], # initial value of k\n k33=[0.1], # vertical anisotropy\n )\n # storage package specifying sy - specific yield bigger than 0 means the cell is convertible\n sy = flopy.mf6.ModflowGwfsto.sy.empty(\n gwf,\n default_value=0.2 # convertible cells\n )\n #stirage package specifying ss - specific storage\n ss = flopy.mf6.ModflowGwfsto.ss.empty(\n gwf, default_value=0.000001\n )\n # storage package\n flopy.mf6.ModflowGwfsto(\n gwf,\n pname=\"sto\",\n save_flows=True,\n save_specific_discharge=True,\n iconvert=1, # flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top\n ss=ss,\n sy=sy,\n transient={0: True}, # keyword to indicate that stress period IPER is transient. Transient conditions will apply until the STEADY-STATE keyword is specified in a subsequent BEGIN PERIOD block\n )\n #dictionary of boundaries Each well is defined through definition of layer (int), row (int), column (int), flux (float). The simplest form is a dictionary with a lists of boundaries for each stress period, where each list of boundaries itself is a list of boundaries. Indices of the dictionary are the numbers of the stress period\n # if the number of stress periods is larger than the dic than the last speficied will apply until the end\n stress_period_data = {\n 1: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\n 2: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\n 3: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0]\n }\n # defines the well package\n flopy.mf6.ModflowGwfwel(\n gwf,\n stress_period_data=stress_period_data,\n )\n # constant head package\n flopy.mf6.ModflowGwfchd(\n gwf,\n stress_period_data=[\n [(0, 0, 0), 1.], # top left cell [cell_id, head, aux, boundname]\n [(0, 9, 9), 1.]], # bottom right cell\n )\n # variable for the output control\n budget_file = name + '.bud'\n head_file = name + '.hds'\n # output control package\n flopy.mf6.ModflowGwfoc(\n gwf,\n budget_filerecord=budget_file,\n head_filerecord=head_file,\n saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL')])\n sim.write_simulation()","title":""},{"docid":"ef942e4bbae34ac5e0e916536c3fd820","score":"0.5222133","text":"def main():\n args = parse_arguments()\n\n if args.output_name:\n output_name = args.output_name\n else:\n output_name = args.xodr_file.rpartition(\".\")[0]\n output_name = f\"{output_name}.xml\" # only name of file\n\n if os.path.isfile(output_name) and not args.force_overwrite:\n print(\n \"Not converting because file exists and option 'force-overwrite' not active\",\n file=sys.stderr,\n )\n sys.exit(-1)\n\n with open(\"{}\".format(args.xodr_file), \"r\") as file_in:\n opendrive = parse_opendrive(etree.parse(file_in).getroot())\n\n scenario = convert_opendrive(opendrive)\n\n if not args.osm:\n writer = ExtendedCommonRoadFileWriter(\n scenario, source=\"OpenDRIVE 2 Lanelet Converter\"\n )\n\n with open(f\"{output_name}\", \"w\") as file_out:\n writer.write_scenario_to_file_io(file_out)\n\n else:\n l2osm = OSMConverter(args.osm)\n osm = l2osm(scenario)\n with open(f\"{output_name}\", \"w\") as file_out:\n file_out.write(etree.tostring(osm, encoding=\"unicode\", pretty_print=True))","title":""},{"docid":"f23cd82b5a7d336e4b8f27a862f78256","score":"0.52209806","text":"def main(model=Path(\"L:\\\\BigData\\\\Env\\\\SavedModels\"), output_dir=Path(\"L:\\\\BigData\\\\Env\\\\SavedModels\")):\n if model is not None:\n nlp = spacy.load(model) # load existing spaCy model\n print(\"Loaded model '%s'\" % model)\n\n ner = nlp.get_pipe(\"ner\")\n\n ner.add_label(\"Email\")# add new entity label to entity recognizer\n ner.add_label(\"Full Name\")\n ner.add_label(\"First Name\")\n ner.add_label(\"Updatedtime\")\n ner.add_label(\"City\")\n ner.add_label(\"Postcode\")\n ner.add_label(\"Date\")\n ner.add_label(\"Phone\")\n ner.add_label(\"State\")\n ner.add_label(\"Country\")\n ner.add_label(\"Street\")\n ner.add_label(\"Id\")\n ner.add_label(\"Last Name\")\n ner.add_label(\"username\")\n ner.add_label(\"Transaction Id\")\n\n move_names = list(ner.move_names)\n\n # test the trained model\n test_text = \"Carlie@marguerite.ca , Glenna Olson , Corbin , 1999-07-05T12:33:47.066Z , Kingshire 77787 1/8/2013 | (695)464-6165 x9467 , Iowa Indonesia 038 Schumm Walks 0 Ortiz Frida 362 \"\n doc = nlp(test_text)\n print(\"Entities in '%s'\" % test_text)\n for ent in doc.ents:\n print(ent.label_, ent.text)\n\n # save model to output directory\n if output_dir is not None:\n output_dir = Path(output_dir)\n if not output_dir.exists():\n output_dir.mkdir() # rename model\n nlp.to_disk(output_dir)\n print(\"Saved model to\", output_dir)\n\n # test the saved model\n print(\"Loading from\", output_dir)\n nlp2 = spacy.load(output_dir)\n # Check the classes have loaded back consistently\n assert nlp2.get_pipe(\"ner\").move_names == move_names\n doc2 = nlp2(test_text)\n for ent in doc2.ents:\n print(ent.label_, ent.text)","title":""},{"docid":"ba506d586a69c49c3403983dbd1f5025","score":"0.52202463","text":"def GenFile(DSLModel, Table, View):","title":""},{"docid":"8666e838db88654723d809adce0d3d19","score":"0.52056324","text":"def generateModel(self,startPoint,start,end,tol,vel):\r\n\t\t\r\n\t\t(code,levels,cantPoints) = Loader().load(self.path,startPoint)\r\n\t\thaveParser = False\r\n\r\n\t\tadded = 0\r\n\t\tself.start = start\r\n\t\tself.end = end\r\n\t\t\r\n\t\tif( tol > 0):\t\t\r\n\t\t\tself.parser = PointsAdder(code,levels,cantPoints,tol)\r\n\t\t\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\r\n\t\t\thaveParser = True\r\n\t\t\t\r\n\t\tif( vel > 0):\t\t\r\n\t\t\tself.parser = VelocityChanger(code,levels,cantPoints,vel)\r\n\t\t\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\r\n\t\t\thaveParser = True\r\n\t\t\t\r\n\t\tif (not haveParser):\r\n\t\t\tself.parser = Parser(code,levels,cantPoints)\r\n\t\t\t(code,levels,cantPoints) = self.parser.parse()\t\r\n\t\r\n\t\tself.modifiedGCode = code\r\n\t\tself.levels = levels\r\n\t\tself.cantPoints = cantPoints","title":""},{"docid":"e9a39b116920aebf857a1c3b136837b0","score":"0.5203123","text":"def compile():\n\n m = dir(modules)\n\n words = []\n for module_name in m:\n try:\n eval(\"words.extend(modules.%s.WORDS)\" % module_name)\n except:\n pass # module probably doesn't have the property\n\n words = list(set(words))\n\n # for spotify module\n words.extend([\"MUSIC\",\"SPOTIFY\"])\n\n # create the dictionary\n pronounced = g2p.translateWords(words)\n zipped = zip(words, pronounced)\n lines = [\"%s %s\" % (x, y) for x, y in zipped]\n\n with open(\"../client/dictionary.dic\", \"w\") as f:\n f.write(\"\\n\".join(lines) + \"\\n\")\n\n # create the language model\n with open(\"../client/sentences.txt\", \"w\") as f:\n f.write(\"\\n\".join(words) + \"\\n\")\n f.write(\" \\n \\n\")\n f.close()\n\n # make language model\n os.system(\n \"text2idngram -vocab ../client/sentences.txt < ../client/sentences.txt -idngram temp.idngram\")\n os.system(\n \"idngram2lm -idngram temp.idngram -vocab ../client/sentences.txt -arpa ../client/languagemodel.lm\")","title":""},{"docid":"49e798a22449fe4ec327a89f2378b7cd","score":"0.51998425","text":"def main() -> None:\n\n args = get_args()\n package = Package()\n ontology = get_ontology(args.ontology_file)\n\n for i, fh in enumerate(args.data_file, start=1):\n print(f'{i:3}: {os.path.basename(fh.name)}')\n fh.close()\n package.infer(fh.name)\n\n for res in package.resources:\n if args.missing_values:\n res.descriptor['schema']['missingValues'].extend(\n args.missing_values)\n\n for field in res.descriptor['schema']['fields']:\n name = field.get('name')\n if not name:\n continue\n\n ont = ontology.get(name)\n if not ont:\n continue\n\n data_type = ont.get('frictionless type', field.get('type'))\n if data_type:\n field['type'] = data_type\n\n data_format = ont.get('frictionless format', field.get('format'))\n if data_format:\n field['format'] = data_format\n\n rdf_type = ont.get('rdf type purl', field.get('rdfType'))\n if rdf_type:\n field['rdfType'] = rdf_type\n\n units = ont.get('units purl', field.get('pm:unitRdfType'))\n if units:\n field['pm:unitRdfType'] = units\n\n source_url = ont.get('pm:source url', field.get('pm:sourceUrl'))\n if source_url:\n field['pm:source url'] = source_url\n\n measure_source = ont.get('measurement source purl',\n field.get('pm:measurementSourceRdfType'))\n if measure_source:\n field['pm:measurementSourceRdfType'] = measure_source\n\n measure_url = ont.get('pm:measurement source protocol',\n field.get('pm:measurement source protocol'))\n if measure_url:\n field['pm:measurementSourceProtocolUrl'] = measure_url\n\n # TODO: Not sure about this\n # is_searchable = ont.get('pm:searchable', field.get('pm:searchable'))\n # if is_searchable:\n # field['pm:searchable'] = is_searchable\n\n package.remove_resource(res.name)\n package.add_resource(res.descriptor)\n\n package.save(args.out_file)\n\n print(f'Done, see \"{args.out_file}\"')","title":""},{"docid":"a3fe6b4a1d97a21eaef6d6f4d02a796d","score":"0.51963997","text":"def actualSolve(self, lp):\n\n if not self.executable(self.path):\n raise PulpSolverError(\"PuLP: cannot execute \" + self.path)\n tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, \"lp\", \"sol\", \"mst\")\n vs = lp.writeLP(tmpLp, writeSOS=1)\n try:\n os.remove(tmpSol)\n except:\n pass\n cmd = self.path\n options = self.options + self.getOptions()\n if self.timeLimit is not None:\n options.append((\"TimeLimit\", self.timeLimit))\n cmd += \" \" + \" \".join([f\"{key}={value}\" for key, value in options])\n cmd += f\" ResultFile={tmpSol}\"\n if self.optionsDict.get(\"warmStart\", False):\n self.writesol(filename=tmpMst, vs=vs)\n cmd += f\" InputFile={tmpMst}\"\n\n if lp.isMIP():\n if not self.mip:\n warnings.warn(\"GUROBI_CMD does not allow a problem to be relaxed\")\n cmd += f\" {tmpLp}\"\n if self.msg:\n pipe = None\n else:\n pipe = open(os.devnull, \"w\")\n\n return_code = subprocess.call(cmd.split(), stdout=pipe, stderr=pipe)\n\n # Close the pipe now if we used it.\n if pipe is not None:\n pipe.close()\n\n if return_code != 0:\n raise PulpSolverError(\"PuLP: Error while trying to execute \" + self.path)\n if not os.path.exists(tmpSol):\n # TODO: the status should be infeasible here, I think\n status = constants.LpStatusNotSolved\n values = reducedCosts = shadowPrices = slacks = None\n else:\n # TODO: the status should be infeasible here, I think\n status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)\n self.delete_tmp_files(tmpLp, tmpMst, tmpSol, \"gurobi.log\")\n if status != constants.LpStatusInfeasible:\n lp.assignVarsVals(values)\n lp.assignVarsDj(reducedCosts)\n lp.assignConsPi(shadowPrices)\n lp.assignConsSlack(slacks)\n lp.assignStatus(status)\n return status","title":""},{"docid":"148556ac6767aa89118518bfbccc312b","score":"0.5196136","text":"def create_pyomo_model(self, debug_mode=False, debug_cost=2e7):\n\n # work on a local copy of the dataframe\n if not debug_mode and self.df.index.str.contains('DBUG').any():\n # previously ran in debug mode, but now done\n df = self.remove_debug_links()\n df.to_csv(self.linksfile + '-final.csv')\n else:\n df = self.df\n\n self.log.info('Creating Pyomo Model (debug=%s)' % debug_mode)\n\n model = ConcreteModel()\n\n model.N = Set(initialize=self.nodes)\n model.k = Set(initialize=range(15))\n model.A = Set(within=model.N*model.N*model.k, \n initialize=self.links, ordered=True)\n model.source = Param(initialize='SOURCE')\n model.sink = Param(initialize='SINK')\n\n def init_params(p):\n if p == 'cost' and debug_mode:\n return (lambda model,i,j,k: debug_cost \n if ('DBUG' in str(i)+'_'+str(j))\n else 1.0)\n else:\n return lambda model,i,j,k: df.loc[str(i)+'_'+str(j)+'_'+str(k)][p]\n\n model.u = Param(model.A, initialize=init_params('upper_bound'), mutable=True)\n model.l = Param(model.A, initialize=init_params('lower_bound'), mutable=True)\n model.a = Param(model.A, initialize=init_params('amplitude'))\n model.c = Param(model.A, initialize=init_params('cost'))\n\n # The flow over each arc\n model.X = Var(model.A, within=Reals)\n\n # Minimize total cost\n def obj_fxn(model):\n return sum(model.c[i,j,k]*model.X[i,j,k] for (i,j,k) in model.A)\n model.total = Objective(rule=obj_fxn, sense=minimize)\n\n # Enforce an upper bound limit on the flow across each arc\n def limit_rule_upper(model, i, j, k):\n return model.X[i,j,k] <= model.u[i,j,k]\n model.limit_upper = Constraint(model.A, rule=limit_rule_upper)\n\n # Enforce a lower bound limit on the flow across each arc\n def limit_rule_lower(model, i, j, k):\n return model.X[i,j,k] >= model.l[i,j,k]\n model.limit_lower = Constraint(model.A, rule=limit_rule_lower)\n\n # To speed up creating the mass balance constraints, first\n # create dictionaries of arcs_in and arcs_out of every node\n # These are NOT Pyomo data, and Pyomo does not use \"model._\" at all\n arcs_in = {}\n arcs_out = {}\n\n def arc_list_hack(model, i,j,k):\n if j not in arcs_in:\n arcs_in[j] = []\n arcs_in[j].append((i,j,k))\n\n if i not in arcs_out:\n arcs_out[i] = []\n arcs_out[i].append((i,j,k))\n return [0]\n\n model._ = Set(model.A, initialize=arc_list_hack)\n\n # Enforce flow through each node (mass balance)\n def flow_rule(model, node):\n if node in [value(model.source), value(model.sink)]:\n return Constraint.Skip\n outflow = sum(model.X[i,j,k]/model.a[i,j,k] for i,j,k in arcs_out[node])\n inflow = sum(model.X[i,j,k] for i,j,k in arcs_in[node])\n return inflow == outflow\n model.flow = Constraint(model.N, rule=flow_rule)\n\n model.dual = Suffix(direction=Suffix.IMPORT)\n\n self.model = model","title":""},{"docid":"f1d525b72f37243510024f58395907b3","score":"0.5173822","text":"def makeLibSource(self):\n\n # Make vector field (and event) file for compilation\n # This sets the field self._eventNames\n self._prepareEventSpecs()\n\n # Write the model.m file\n allfilestr = self._prepareModelContents()\n modelfile = os.path.join(self._target_dir, self._model_file)\n try:\n file = open(modelfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._model_file+\" for writing\")\n raise IOError(e)\n\n # Write the events.m file\n if len(self._eventNames) > 0:\n allfilestr = self._prepareEventsFileContents() + self._prepareAuxContents()\n eventsfile = os.path.join(self._target_dir, self._events_file)\n try:\n file = open(eventsfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._events_file+\" for writing\")\n raise IOError(e)\n\n\n # Write the initialconditions.m file\n allfilestr = self._prepareICContents()\n icfile = os.path.join(self._target_dir, self._ic_file)\n try:\n file = open(icfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._ic_file+\" for writing\")\n raise IOError(e)\n\n # Write the pars.m file\n allfilestr = self._prepareParamContents()\n paramfile = os.path.join(self._target_dir, self._param_file)\n try:\n file = open(paramfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._param_file+\" for writing\")\n raise IOError(e)\n\n # Write the get.m file\n allfilestr = self._prepareGetFileContents()\n getfile = os.path.join(self._target_dir, self._get_file)\n try:\n file = open(getfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._get_file+\" for writing\")\n raise IOError(e)\n\n # Write the set.m file\n allfilestr = self._prepareSetFileContents()\n setfile = os.path.join(self._target_dir, self._set_file)\n try:\n file = open(setfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._set_file+\" for writing\")\n raise IOError(e)\n\n # Write the vfield.m file\n# vfdefines = self._prepareVfieldDefines()\n# allfilestr = self._prepareVfieldContents(vfdefines)\n allfilestr = self.funcspec.spec[0] + self._prepareAuxContents()\n vffile = os.path.join(self._target_dir, self._vfield_file)\n try:\n file = open(vffile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._vfield_file+\" for writing\")\n raise IOError(e)","title":""},{"docid":"870c6fd27ca456de9582a6aa9f045d53","score":"0.51735485","text":"def buildSolverModel(self, lp):\n log.debug(\"create the gurobi model\")\n lp.solverModel = gurobipy.Model(lp.name)\n log.debug(\"set the sense of the problem\")\n if lp.sense == constants.LpMaximize:\n lp.solverModel.setAttr(\"ModelSense\", -1)\n if self.timeLimit:\n lp.solverModel.setParam(\"TimeLimit\", self.timeLimit)\n gapRel = self.optionsDict.get(\"gapRel\")\n logPath = self.optionsDict.get(\"logPath\")\n if gapRel:\n lp.solverModel.setParam(\"MIPGap\", gapRel)\n if logPath:\n lp.solverModel.setParam(\"LogFile\", logPath)\n\n log.debug(\"add the variables to the problem\")\n for var in lp.variables():\n lowBound = var.lowBound\n if lowBound is None:\n lowBound = -gurobipy.GRB.INFINITY\n upBound = var.upBound\n if upBound is None:\n upBound = gurobipy.GRB.INFINITY\n obj = lp.objective.get(var, 0.0)\n varType = gurobipy.GRB.CONTINUOUS\n if var.cat == constants.LpInteger and self.mip:\n varType = gurobipy.GRB.INTEGER\n var.solverVar = lp.solverModel.addVar(\n lowBound, upBound, vtype=varType, obj=obj, name=var.name\n )\n if self.optionsDict.get(\"warmStart\", False):\n # Once lp.variables() has been used at least once in the building of the model.\n # we can use the lp._variables with the cache.\n for var in lp._variables:\n if var.varValue is not None:\n var.solverVar.start = var.varValue\n\n lp.solverModel.update()\n log.debug(\"add the Constraints to the problem\")\n for name, constraint in lp.constraints.items():\n # build the expression\n expr = gurobipy.LinExpr(\n list(constraint.values()), [v.solverVar for v in constraint.keys()]\n )\n if constraint.sense == constants.LpConstraintLE:\n relation = gurobipy.GRB.LESS_EQUAL\n elif constraint.sense == constants.LpConstraintGE:\n relation = gurobipy.GRB.GREATER_EQUAL\n elif constraint.sense == constants.LpConstraintEQ:\n relation = gurobipy.GRB.EQUAL\n else:\n raise PulpSolverError(\"Detected an invalid constraint type\")\n constraint.solverConstraint = lp.solverModel.addConstr(\n expr, relation, -constraint.constant, name\n )\n lp.solverModel.update()","title":""},{"docid":"f807685d80245425900a66f5f81872f5","score":"0.5164706","text":"def import_model(self, file):\n felems = os.path.splitext(file)\n ext = felems[1].lower()\n if ext in (\".gz\", \".zip\",):\n ext = os.path.splitext(felems[0])[1].lower()\n\n if ext == \".cpo\":\n import docplex.cp.cpo.cpo_parser as cpo_parser\n prs = cpo_parser.CpoParser(self)\n prs.parse(file)\n\n elif ext == \".fzn\":\n import docplex.cp.fzn.fzn_parser as fzn_parser\n prs = fzn_parser.FznParser(self)\n prs.parse(file)\n # Get model to force compilation\n prs.get_model()\n\n elif ext == \".lp\":\n import docplex.cp.lp.lp_parser as lp_parser\n prs = lp_parser.LpParser(self)\n prs.parse(file)\n\n else:\n raise CpoException(\"Unknown '{}' file format. Only .cpo, .fzn and .lp are supported.\".format(ext))","title":""},{"docid":"a3c04ce9f517817a518f872f20d83633","score":"0.51574755","text":"def to_automl(data, path='.', name='autodata'):\n # check if folder exists\n dir = os.path.join(path, name+'_automl')\n if not os.path.exists(dir):\n # create folder\n os.mkdir(dir)\n data.descriptors().to_csv(os.path.join(dir, name+'.info'), header=True) # some information\n if data.has_class():\n pd.DataFrame(data.indexes['X']).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\n pd.DataFrame(data.indexes['y']).to_csv(os.path.join(dir, name+'_label.name'), index=False, header=False) # label name\n if 'train' in data.indexes: # train/test and X/y splits\n data.get_data('X_train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\n data.get_data('X_test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\n data.get_data('y_train').to_csv(os.path.join(dir, name+'_train.solution'), sep=' ', index=False, header=False) # train solution\n data.get_data('y_test').to_csv(os.path.join(dir, name+'_test.solution'), sep=' ', index=False, header=False) # test solution\n else: # only X/y split\n data.get_data('X').to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data\n data.get_data('y').to_csv(os.path.join(dir, name+'.solution'), sep=' ', index=False, header=False) # solution\n else:\n pd.DataFrame(data.columns).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\n if 'train' in data.indexes: # only train/test split\n data.get_data('train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\n data.get_data('test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\n else: # no split at all\n data.to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data","title":""},{"docid":"311b06e5d22497504730712619f4da06","score":"0.5155293","text":"def _make_input_file(self, teff_lims, teff_step, logg_lims, logg_step,\n feh_lims, feh_step, vsini_lims, vsini_step, \n vmicro_lims, vmicro_step, resolution):\n output_string = '{:.1f} {:.0f} {:.1f}\\n'.format(teff_lims[0], \n teff_step, \n teff_lims[-1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(logg_lims[0],\n logg_step,\n logg_lims[1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(vmicro_lims[0],\n vmicro_step,\n vmicro_lims[1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(vsini_lims[0],\n vsini_step,\n vsini_lims[1])\n\n output_string += \"skip 0.03 0.02 0.07 !dilution factor\\n\"\n\n output_string += 'skip {:.1f} {:.1f} {:.1f}\\n'.format(feh_lims[0],\n feh_step,\n feh_lims[1])\n \n output_string += 'He 0.04 0.005 0.06 ! Individual abundance\\n'\n\n output_string += '0.0 {:.0f}\\n'.format(resolution)\n\n output_string += '{}\\n{}\\n'.format(self.abundance_table, self.model_dir)\n\n output_string += '2 1 !atmosphere model vmicro and mass\\n'\n output_string += 'ST ! model atmosphere chemical composition flag\\n'\n\n dx = self.data.x[1] - self.data.x[0]\n output_string += '1 {:.5f} fit\\n'.format(dx)\n\n output_string += 'data_sets/{}.txt\\n'.format(self.output_basename)\n\n output_string += '0.5 0.99 0.0 adjust ! RV determination stuff\\n'\n\n xmin, xmax = self.data.x[0]-1, self.data.x[-1]+1\n output_string += '{:.1f} {:.1f}\\n'.format(xmin, xmax)\n\n outfilename = '{}.inp'.format(self.output_basename)\n with open(outfilename, 'w') as outfile:\n outfile.write(output_string)\n\n return outfilename","title":""},{"docid":"8ae6ddb4fe2249629c719de91f2cabee","score":"0.51552427","text":"def write(self, filename='pflotran.in'):\n\n if filename:\n self.filename = filename\n outfile = open(self.filename, 'w')\n\n # Presumes simulation.simulation_type is required\n if self.simulation.simulation_type:\n self._write_simulation(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'simulation is required, it is currently reading as empty')\n\n if self.simulation.subsurface_flow or \\\n self.simulation.subsurface_transport:\n self._write_subsurface_simulation_begin(outfile)\n\n if self.reference_pressure:\n self._write_reference_pressure(outfile)\n\n if self.reference_temperature:\n self._write_reference_temperature(outfile)\n\n if self.co2_database:\n self._write_co2_database(outfile)\n\n if self.regression.cells \\\n or self.regression.cells_per_process \\\n or self.regression.all_cells:\n self._write_regression(outfile)\n\n if self.uniform_velocity.value_list:\n self._write_uniform_velocity(outfile)\n\n if self.specified_velocity:\n self._write_specified_velocity(outfile)\n\n if self.update_flow_permeability:\n self._write_update_flow_permeability(outfile)\n\n if self.reference_liquid_density:\n self._write_reference_liquid_density(outfile)\n\n if self.minimum_hydrostatic_pressure:\n self._write_minimum_hydrostatic_pressure(outfile)\n\n if self.reference_stress_state.value_list:\n self._write_reference_stress_state(outfile)\n\n if self.eoslist:\n self._write_eos(outfile)\n\n if self.klinkenberg_effect:\n self._write_klinkenberg_effect(outfile)\n\n if self.creep_closure_table:\n self._write_creep_closure_table(outfile)\n\n if self.nonuniform_velocity.filename:\n self._write_nonuniform_velocity(outfile)\n\n if self.simulation.mode.upper() == 'MPHASE' and self.co2_database:\n self._write_co2_database(outfile)\n\n if self.multiple_continuum:\n self._write_multiple_continuum(outfile)\n\n if self.overwrite_restart_flow_params:\n self._write_overwrite_restart_flow(outfile)\n\n if self.overwrite_restart_transport:\n self._write_overwrite_restart_transport(outfile)\n\n if self.initialize_flow_from_file is not None:\n self._write_initialize_flow_from_file(outfile)\n\n if self.initialize_transport_from_file is not None:\n self._write_initialize_transport_from_file(outfile)\n\n if self.isothermal:\n self._write_isothermal(outfile)\n\n if self.reference_porosity:\n self._write_reference_porosity(outfile)\n\n if self.datasetlist:\n self._write_dataset(outfile)\n # else: print 'info: dataset name not detected\\n'\n\n if self.chemistry:\n self._write_chemistry(outfile)\n # else: print 'info: chemistry not detected\\n'\n\n if self.grid:\n self._write_grid(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'grid is required, it is currently reading as empty!')\n\n if self.timestepper_flow or self.timestepper_transport:\n self._write_timestepper(outfile)\n # else: print 'info: timestepper not detected\\n'\n\n if self.time:\n self._write_time(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'time is required, it is currently reading as empty!')\n\n if self.proplist:\n self._write_prop(outfile)\n else:\n PyFLOTRAN_WARNING(\n 'material property list is empty! ' +\n ' Using default material property settings')\n self.add(pmaterial())\n self._write_prop(outfile)\n\n if self.lsolverlist:\n self._write_lsolver(outfile)\n # else: print 'info: lsolverlist (linear solver list) not detected\\n'\n\n if self.nsolverlist:\n self._write_nsolver(outfile)\n # else: print 'info: nsolverlist (newton solver list) not detected\\n'\n\n if self.output:\n self._write_output(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'output is required, it is currently reading as empty!')\n\n if self.fluidlist:\n self._write_fluid(outfile)\n\n if self.saturationlist:\n self._write_saturation(outfile)\n elif self.charlist:\n self._write_characteristic_curves(outfile)\n else:\n if self.simulation.subsurface_flow:\n self.add(pcharacteristic_curves())\n self._write_characteristic_curves(outfile)\n PyFLOTRAN_WARNING(\n 'characteristic_curves list or saturation list ' +\n 'is required, it is currently reading as empty! ' +\n ' Using default characteristic_curves settings')\n\n if self.regionlist:\n self._write_region(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'regionlist is required, it is currently reading as empty!')\n\n if self.integral_flux_list:\n self._write_integral_flux(outfile)\n\n if self.observation_list:\n self._write_observation(outfile)\n\n if self.flowlist:\n self._write_flow(outfile)\n\n if self.transportlist:\n self._write_transport(outfile)\n\n if self.initial_condition_list:\n self._write_initial_condition(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'initial_condition_list is required,' +\n 'it is currently reading as empty!')\n\n if self.boundary_condition_list:\n self._write_boundary_condition(outfile)\n\n if self.source_sink_list:\n self._write_source_sink(outfile)\n\n if self.source_sink_sandbox_list:\n self._write_source_sink_sandbox(outfile)\n\n if self.reference_saturation:\n self._write_reference_saturation(outfile)\n\n if self.strata_list:\n self._write_strata(outfile)\n else:\n PyFLOTRAN_WARNING(\n 'stratigraphy_coupler is required, ' +\n 'it is currently reading as empty! ' +\n 'Using default settings')\n self.add(pstrata())\n self._write_strata(outfile)\n\n if self.constraint_list:\n self._write_constraint(outfile)\n\n if self.rt_mass_transfer_list:\n self._write_rt_mass_transfer(outfile)\n\n if self.flow_mass_transfer_list:\n self._write_flow_mass_transfer(outfile)\n\n if self.simulation.subsurface_flow or \\\n self.simulation.subsurface_transport:\n self._write_subsurface_simulation_end(outfile)\n\n\n # -----------------------------------------------\n\n if self.simulation.surface_subsurface:\n self._write_surface_subsurface_begin(outfile)\n\n if self.surface_flow:\n self._write_surface_flow(outfile)\n\n self._write_surface_subsurface_end(outfile)\n\n # -----------------------------------------------\n\n if self.simulation.simulation_type == 'hydroquake':\n self._write_hydroquake(outfile)\n\n if self.simulation.simulation_type.lower() == 'geomechanics_subsurface':\n self._write_geomechanics(outfile)\n\n if self.waste_form_general:\n self._write_wasteform_general(outfile)\n\n if self.ufd_decay:\n self._write_ufd_decay(outfile)\n\n if self.ufd_biosphere:\n self._write_ufd_biosphere(outfile)\n\n if self.wipp_source_sink:\n self._write_wipp_source_sink(outfile)\n\n outfile.close()","title":""},{"docid":"6217bff10682c1129e9efb6eb05446d3","score":"0.5149605","text":"def write(solution):\n trimmed_solution = solution\n input_file_name_stripped = trimmed_solution.name\n cwd = os.getcwd()\n output_file_name = os.path.join(cwd,\n 'pym_' +\n input_file_name_stripped +\n '.inp')\n f = open(output_file_name, 'w+')\n\n #Work functions\n\n calories_constant = 4184.0 #number of calories in 1000 Joules of energy\n def eliminate(input_string, char_to_replace, spaces='single'):\n \"\"\"\n Eliminate characters from a string\n\n :param input_string\n string to be modified\n :param char_to_replace\n array of character strings to be removed\n \"\"\"\n for char in char_to_replace:\n input_string = input_string.replace(char, \"\")\n if spaces == 'double':\n input_string = input_string.replace(\" \", \" \")\n return input_string\n\n def section_break(title):\n \"\"\"\n Insert break and new section title into cti file\n\n :param title:\n title string for next section_break\n \"\"\"\n f.write('!'+ \"-\"*75 + '\\n')\n f.write('! ' + title +'\\n')\n f.write('!'+ \"-\"*75 + '\\n')\n\n def replace_multiple(input_string, replace_list):\n \"\"\"\n Replace multiple characters in a string\n\n :param input_string\n string to be modified\n :param replace list\n list containing items to be replaced (value replaces key)\n \"\"\"\n for original_character, new_character in replace_list.items():\n input_string = input_string.replace(original_character,\n new_character)\n return input_string\n\n def build_arrhenius(equation_object, equation_type):\n \"\"\"\n Builds Arrhenius coefficient string\n\n :param equation_objects\n cantera equation object\n :param equation_type:\n string of equation type\n \"\"\"\n coeff_sum = sum(equation_object.reactants.values())\n pre_exponential_factor = equation_object.rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.rate.activation_energy / calories_constant)\n if equation_type == 'ElementaryReaction':\n if coeff_sum == 1:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor))\n if coeff_sum == 2:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**3))\n if coeff_sum == 3:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**6))\n if equation_type == 'ThreeBodyReaction':\n if coeff_sum == 1:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**3))\n if coeff_sum == 2:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**6))\n if (equation_type != 'ElementaryReaction'\n and equation_type != 'ThreeBodyReaction'):\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor))\n arrhenius = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius\n\n def build_modified_arrhenius(equation_object, t_range):\n \"\"\"\n Builds Arrhenius coefficient strings for high and low temperature ranges\n\n :param equation_objects\n cantera equation object\n :param t_range:\n simple string ('high' or 'low') to designate temperature range\n \"\"\"\n if t_range == 'high':\n pre_exponential_factor = equation_object.high_rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.high_rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.high_rate.activation_energy/calories_constant)\n if len(equation_object.products) == 1:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**3))\n else:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor))\n arrhenius_high = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius_high\n if t_range == 'low':\n\n pre_exponential_factor = equation_object.low_rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.low_rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.low_rate.activation_energy/calories_constant)\n if len(equation_object.products) == 1:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**6))\n else:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**3))\n\n arrhenius_low = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius_low\n\n\n def build_nasa(nasa_coeffs, row):\n \"\"\"\n Creates string of nasa polynomial coefficients\n\n :param nasa_coeffs\n cantera species thermo coefficients object\n :param row\n which row to write coefficients in\n \"\"\"\n line_coeffs = ''\n lines = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]]\n line_index = lines[row-2]\n for ix, c in enumerate(nasa_coeffs):\n if ix in line_index:\n if c >= 0:\n line_coeffs += ' '\n line_coeffs += str('{:.8e}'.format(c))\n return line_coeffs\n\n def build_species_string():\n \"\"\"\n formats species string for writing\n \"\"\"\n species_list_string = ''\n line = 1\n for sp_index, sp_string in enumerate(trimmed_solution.species_names):\n sp = ' '\n #get length of string next species is added\n length_new = len(sp_string)\n length_string = len(species_list_string)\n total = length_new + length_string + 3\n #if string will go over width, wrap to new line\n if total >= 70*line:\n species_list_string += '\\n'\n line += 1\n species_list_string += sp_string + ((16-len(sp_string))*sp)\n return species_list_string\n\n #Write title block to file\n section_break('Chemkin File converted from Solution Object by pyMARS')\n\n #Write phase definition to file\n element_names = eliminate(str(trimmed_solution.element_names),\n ['[', ']', '\\'', ','])\n element_string = Template(\n 'ELEMENTS\\n' +\n '$element_names\\n' +\n 'END\\n')\n f.write(element_string.substitute(element_names=element_names))\n species_names = build_species_string()\n species_string = Template(\n 'SPECIES\\n' +\n '$species_names\\n'+\n 'END\\n')\n f.write(species_string.substitute(species_names=species_names))\n\n #Write species to file\n section_break('Species data')\n f.write('THERMO ALL' + '\\n' +\n ' 300.000 1000.000 5000.000' +'\\n')\n phase_unknown_list = []\n\n #write data for each species in the Solution object\n for sp_index in xrange(len(trimmed_solution.species_names)):\n d = 3.33564e-30 #1 debye = d coulomb-meters\n species = trimmed_solution.species(sp_index)\n name = str(trimmed_solution.species(sp_index).name)\n nasa_coeffs = trimmed_solution.species(sp_index).thermo.coeffs\n #Species attributes from trimmed solution object\n t_low = '{0:.3f}'.format(species.thermo.min_temp)\n t_max = '{0:.3f}'.format(species.thermo.max_temp)\n t_mid = '{0:.3f}'.format(species.thermo.coeffs[0])\n temp_range = str(t_low) + ' ' + str(t_max) + ' ' + t_mid\n species_comp = ''\n for atom in species.composition:\n species_comp += '{:<4}'.format(atom)\n species_comp += str(int(species.composition[atom]))\n if type(species.transport).__name__ == 'GasTransportData':\n species_phase = 'G'\n else:\n phase_unknown_list.append(name)\n species_phase = 'G'\n line_1 = (\n '{:<18}'.format(name) +\n '{:<6}'.format(' ') +\n '{:<20}'.format(species_comp) +\n '{:<4}'.format(species_phase) +\n '{:<31}'.format(temp_range) +\n '{:<1}'.format('1') +\n '\\n')\n f.write(line_1)\n line_2_coeffs = build_nasa(nasa_coeffs, 2)\n line_2 = line_2_coeffs + ' 2\\n'\n f.write(line_2)\n line_3_coeffs = build_nasa(nasa_coeffs, 3)\n line_3 = line_3_coeffs + ' 3\\n'\n f.write(line_3)\n line_4_coeffs = build_nasa(nasa_coeffs, 4)\n line_4 = line_4_coeffs + ' 4\\n'\n f.write(line_4)\n\n f.write('END\\n')\n\n #Write reactions to file\n section_break('Reaction Data')\n f.write('REACTIONS\\n')\n #write data for each reaction in the Solution Object\n for reac_index in xrange(len(trimmed_solution.reaction_equations())):\n equation_string = str(trimmed_solution.reaction_equation(reac_index))\n equation_string = eliminate(equation_string, ' ', 'single')\n equation_object = trimmed_solution.reaction(reac_index)\n equation_type = type(equation_object).__name__\n m = str(reac_index+1)\n if equation_type == 'ThreeBodyReaction':\n arrhenius = build_arrhenius(equation_object, equation_type)\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arrhenius[0]) +\n '{:>9}'.format(arrhenius[1]) +\n '{:>11}'.format(arrhenius[2]) +\n '\\n')\n f.write(main_line)\n #trimms efficiencies list\n efficiencies = equation_object.efficiencies\n trimmed_efficiencies = equation_object.efficiencies\n for s in efficiencies:\n if s not in trimmed_solution.species_names:\n del trimmed_efficiencies[s]\n replace_list_2 = {\n '{':'',\n '}':'/',\n '\\'':'',\n ':':'/',\n ',':'/'}\n efficiencies_string = replace_multiple(\n str(trimmed_efficiencies),\n replace_list_2)\n secondary_line = str(efficiencies_string) + '\\n'\n if bool(efficiencies) is True:\n f.write(secondary_line)\n if equation_type == 'ElementaryReaction':\n arrhenius = build_arrhenius(equation_object, equation_type)\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arrhenius[0]) +\n '{:>9}'.format(arrhenius[1]) +\n '{:>11}'.format(arrhenius[2]) +\n '\\n')\n f.write(main_line)\n if equation_type == 'FalloffReaction':\n arr_high = build_modified_arrhenius(equation_object, 'high')\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arr_high[0]) +\n '{:>9}'.format(arr_high[1]) +\n '{:>11}'.format(arr_high[2]) +\n '\\n')\n f.write(main_line)\n arr_low = build_modified_arrhenius(equation_object, 'low')\n second_line = (\n ' LOW /' +\n ' ' + arr_low[0] +\n ' ' + arr_low[1] +\n ' ' + arr_low[2] + '/\\n')\n f.write(second_line)\n j = equation_object.falloff.parameters\n #If optional Arrhenius data included:\n try:\n third_line = (\n ' TROE/' +\n ' ' + str(j[0]) +\n ' ' + str(j[1]) +\n ' ' + str(j[2]) +\n ' ' + str(j[3]) +' /\\n')\n f.write(third_line)\n except IndexError:\n pass\n #trimms efficiencies list\n efficiencies = equation_object.efficiencies\n trimmed_efficiencies = equation_object.efficiencies\n for s in efficiencies:\n if s not in trimmed_solution.species_names:\n del trimmed_efficiencies[s]\n replace_list_2 = {\n '{':'',\n '}':'/',\n '\\'':'',\n ':':'/',\n ',':'/'}\n efficiencies_string = replace_multiple(\n str(trimmed_efficiencies),\n replace_list_2)\n\n fourth_line = str(efficiencies_string) + '\\n'\n if bool(efficiencies) is True:\n f.write(fourth_line)\n #dupluicate option\n if equation_object.duplicate is True:\n duplicate_line = ' DUPLICATE' +'\\n'\n f.write(duplicate_line)\n f.write('END')\n f.close()\n\n #Test mechanism file\n\n original_solution = solution\n #convert written chemkin file to cti, and get solution\n parser = ck2cti.Parser()\n outName = 'test_file.cti'\n parser.convertMech(output_file_name, outName=outName)\n new_solution = ct.Solution(outName)\n\n #test new solution vs original solutoin\n #test(original_solution, new_solution)\n os.remove(outName)\n return output_file_name\n\n\n \"\"\"\n def build_falloff(j):\n\n Creates falloff reaction Troe parameter string\n\n param j:\n Cantera falloff parameters object\n\n falloff_str = str(',\\n falloff = Troe(' +\n 'A = ' + str(j[0]) +\n ', T3 = ' + str(j[1]) +\n ', T1 = ' + str(j[2]) +\n ', T2 = ' + str(j[3]) +') )\\n\\n')\n return falloff_str\n \"\"\"","title":""},{"docid":"bb9fe475543f8a500b17238e2c01e2a8","score":"0.5131358","text":"def writeDataFile(self):\n model = pe.PEChainBuilder(self.lenPa, self.numPa, self.lenPc, self.numPc,\\\n self.volRatio, self.chargeFraction)\n model.genChains()\n \n \n model.writeFiles(self.directory)\n self.dataFileName = \"data.pe.la{0}.na{1}.lc{2}.nc{3}.rho{4}.r{5}.lammps\".\\\n format(self.lenPa, self.numPa, self.lenPc, self.numPc, \\\n self.volRatio, self.chargeRepeat)","title":""},{"docid":"dc59f59e013fe69fd099020e5707bc62","score":"0.51224095","text":"def createPrismModel(states, transitions, target, mode, nr_actions, reset_transitions, reset_cost):\n out_file = open(TMP_MODEL_PATH, 'w')\n # module\n out_file.write(\"mdp\\n\\nmodule tmp\\n\\n\")\n\n # number of state and initial state\n out_file.write(\"\\ts : [0..\" + str(len(states)) + \"] init \" + str(abs(mode)) + \";\\n\\n\")\n\n # transitions\n for state in range(len(transitions)):\n for action in range(len(transitions[state])):\n if sum(transitions[state][action]) != 0:\n out_file.write(\"\\t[\" + chr(97 + action) + \"] s=\" + str(state) + \"-> \")\n destinations = []\n for dest in range(len(transitions[state][action])):\n if transitions[state][action][dest] != 0:\n destinations.append(str(transitions[state][action][dest]) + \":(s'=\" + str(dest) + \")\")\n out_file.write(\" + \".join(destinations))\n out_file.write(\";\\n\")\n out_file.write(\"\\nendmodule\\n\\n\")\n\n # label target\n out_file.write('label \"target\" = ')\n target = list(set(target))\n target = [\"(s=\" + str(x) + \")\" for x in target]\n if len(target) == 0:\n out_file.write(\"(s=\" + str(len(states) + 1) + \");\\n\")\n else:\n out_file.write(\" | \".join(target))\n out_file.write(\";\\n\")\n\n # label states\n if mode == MODE_MAX:\n out_file.write('label \"sink\" = (s=0);\\n')\n\n for i in range(mode, len(states)):\n out_file.write('label \"s' + str(states[i][0]) + '_obs' + str(states[i][1]) + '\" = (s=' + str(i) + ');\\n')\n\n if mode == MODE_MIN:\n out_file.write(\"\\nrewards\\n\")\n if reset_cost == 1:\n out_file.write(\"\\ttrue:1;\\n\")\n else:\n for i in range(len(states)):\n for j in range(nr_actions):\n if j in reset_transitions[i]:\n out_file.write(\"\\t[\" + chr(97 + j) + \"] (s=\" + str(i) + \") : \" + str(reset_cost) + \";\\n\")\n else:\n out_file.write(\"\\t[\" + chr(97 + j) + \"] (s=\" + str(i) + \") : 1;\\n\")\n\n out_file.write(\"endrewards\\n\")\n\n out_file.close()","title":""},{"docid":"f391c50f49d5b0eae5ebbf6c5f12961a","score":"0.5118555","text":"def build_LM(in_file):\r\n print 'building language models...'\r\n # This is an empty method\r\n # Pls implement your code in below\r\n LMs = {'malaysian': {}, 'indonesian': {}, 'tamil': {}}\r\n malay_LM = LMs['malaysian']\r\n indon_LM = LMs['indonesian']\r\n tamil_LM = LMs['tamil']\r\n\r\n FD = open(in_file, 'r')\r\n\r\n lines = FD.read()\r\n lines_array = lines.replace('\\r', ' ').split('\\n')\r\n lines_array.pop(len(lines_array) - 1)\r\n\r\n for line in lines_array:\r\n line_array = line.split(' ', 1)\r\n lang = line_array[0]\r\n text = line_array[1]\r\n current_LM = LMs[lang]\r\n four_char_array = get_four_char_array(text)\r\n\r\n for four_char in four_char_array:\r\n if four_char not in current_LM:\r\n # performs add one smoothing\r\n malay_LM[four_char] = 1\r\n indon_LM[four_char] = 1\r\n tamil_LM[four_char] = 1\r\n current_LM[four_char] += 1\r\n\r\n # calculates ratio of set frequency over total frequency\r\n # for all sets in each of the three LMs\r\n for LM in LMs.values():\r\n count = 0\r\n for freq_value in LM.values():\r\n count += freq_value\r\n for four_char_key in LM.keys():\r\n LM[four_char_key] /= count\r\n return LMs\r\n\r\n FD.close()","title":""},{"docid":"c862647fcd5ba7f408d748abaf7ab617","score":"0.5115003","text":"def test_ImportUFONoSideEffectNLO(self):\n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('loop_sm'),False)\n original_all_particles = copy.copy(ufo_model.all_particles)\n original_all_vertices = copy.copy(ufo_model.all_vertices)\n original_all_couplings = copy.copy(ufo_model.all_couplings)\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\n original_all_parameters = copy.copy(ufo_model.all_parameters)\n original_all_orders = copy.copy(ufo_model.all_orders)\n original_all_functions = copy.copy(ufo_model.all_functions)\n original_all_CTvertices = copy.copy(ufo_model.all_CTvertices)\n original_all_CTparameters = copy.copy(ufo_model.all_CTparameters)\n\n\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\n model = ufo2mg5_converter.load_model()\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \n\n self.assertEqual(original_all_particles,ufo_model.all_particles)\n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\n# self.assertEqual(original_all_couplings,ufo_model.all_couplings)\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\n self.assertEqual(original_all_orders,ufo_model.all_orders)\n self.assertEqual(original_all_functions,ufo_model.all_functions)\n self.assertEqual(original_all_CTvertices,ufo_model.all_CTvertices)\n self.assertEqual(original_all_CTparameters,ufo_model.all_CTparameters)","title":""},{"docid":"dfe119951be54a560fcefe9057a921e1","score":"0.5112634","text":"def main(models, output_dir):\n\tfor model in models:\n\t\tobj = ParseOmsa(model)\n\t\tobj.bios_version()\n\t\tobj.perc_versions()\n\t\tobj.write_yaml(output_dir)","title":""},{"docid":"00e7eaf7ed485ca9233fa3bbe1a1b96e","score":"0.5110691","text":"def solve_lp(self):\n self._initialize_model()\n self._initialize_node_variables_lp()\n self._initialize_edge_variables_lp()\n self._initialize_objective()\n self._initialize_contraint_nodes()\n self._initialize_constraint_edges()\n self._initialize_constraint_terminals()\n self._run_solver()\n self._calculate_cut_value()\n self._calculate_source_sets()\n self._calculate_possible_terminals_by_node_weak()\n self._calculate_possible_terminals_by_node_strong()","title":""},{"docid":"6586aa3830f43b54dce72f69cbc0963d","score":"0.5104416","text":"def loadLoopToyModel():\n \n mypartlist = base_objects.ParticleList()\n myinterlist = base_objects.InteractionList()\n myloopmodel = loop_base_objects.LoopModel()\n\n # A gluon\n mypartlist.append(base_objects.Particle({'name':'g',\n 'antiname':'g',\n 'spin':3,\n 'color':8,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'G',\n #'antitexname':'G',\n 'line':'curly',\n 'charge':0.,\n 'pdg_code':21,\n #'propagating':True,\n 'propagator':0, \n 'is_part':True,\n 'counterterm':{('QCD', ((6,),)): {0: 'UVWfct_G_1', -1: 'UVWfct_G_1_1eps'}, ('QCD', ((5,),)): {0: 'UVWfct_G_0', -1: 'UVWfct_G_0_1eps'}},\n 'self_antipart':True}))\n \n # A quark U and its antiparticle\n mypartlist.append(base_objects.Particle({'name':'u',\n 'antiname':'u~',\n 'spin':2,\n 'color':3,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'u',\n #'antitexname':'u',\n 'line':'straight',\n 'charge':2. / 3.,\n 'pdg_code':2,\n #'propagating':True,\n 'propagator':'', \n 'is_part':True,\n 'self_antipart':False}))\n antiu = copy.copy(mypartlist[1])\n antiu.set('is_part', False)\n\n # A quark D and its antiparticle\n mypartlist.append(base_objects.Particle({'name':'d',\n 'antiname':'d~',\n 'spin':2,\n 'color':3,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'d',\n #'antitexname':'d',\n 'line':'straight',\n 'charge':-1. / 3.,\n 'pdg_code':1,\n #'propagating':True,\n 'propagator':'',\n 'is_part':True,\n 'self_antipart':False}))\n antid = copy.copy(mypartlist[2])\n antid.set('is_part', False)\n\n myloopmodel.set('particles', mypartlist)\n myloopmodel.set('couplings', ['QCD']) \n myloopmodel.set('interactions', myinterlist)\n myloopmodel.set('perturbation_couplings', ['QCD'])\n myloopmodel.set('order_hierarchy', {'QCD':1})\n\n return myloopmodel","title":""},{"docid":"695e98b499f902207ba35ccac8891bda","score":"0.5104137","text":"def generate(genconf_model, project_folder):\n\n gendesc = get_generator_desc(genconf_model.gen_name)\n meta = get_language(gendesc.lang)\n\n # Path for templates overrides\n templates_path = os.path.join(project_folder, 'templates',\n genconf_model.gen_name)\n\n output_root = os.path.join(project_folder, genconf_model.output)\n\n def _create_folder(output_file):\n try:\n os.makedirs(os.path.dirname(output_file))\n except FileExistsError:\n pass\n\n # For each model configured in the current genconf\n for model_path in genconf_model.models:\n\n click.echo('Processing model \"{}\"'.format(model_path))\n model = meta.model_from_file(os.path.join(project_folder, 'model',\n model_path))\n\n # Validate model using generator specific validation.\n if gendesc.validate:\n gendesc.validate(model)\n\n params = {}\n # Adding generator params\n for p in genconf_model.params:\n params[p.name] = p.value\n\n # Processing all rules\n for rule in genconf_model.rules:\n\n # Sanity check\n if len(rule.types) != len(rule.var_names):\n raise TextXToolsError('Number of variables don\\'t match'\n ' number of types in rule \"{}\"'\n .format(rule.name))\n\n # Collect all object of each given type\n type_objs = []\n for t in rule.types:\n type_objs.append(children_of_type(model, t))\n\n if rule.all:\n context = {'__builtins__':{}}\n for ind, obj in enumerate(type_objs):\n params[rule.var_names[ind]] = obj\n context.update(params)\n for t in rule.trans:\n target_path = eval(t.python_path_expr, context)\n output_file = os.path.join(output_root, target_path)\n\n click.echo(\"Generating {}\".format(output_file))\n\n _create_folder(output_file)\n with open(output_file, 'w') as f:\n f.write(gendesc.render(t.template_path, params,\n templates_path))\n else:\n if len(rule.types) > 1:\n raise TextXToolsError('Multiple types/variables are not'\n ' possible for \"non-all\" rules.')\n for obj in type_objs[0]:\n params[rule.var_names[0]] = obj\n context = {'__builtins__': {}}\n context.update(params)\n for t in rule.trans:\n target_path = eval(t.python_path_expr, context)\n output_file = os.path.join(output_root, target_path)\n click.echo(\"Generating {}\".format(output_file))\n _create_folder(output_file)\n\n with open(output_file, 'w') as f:\n f.write(gendesc.render(t.template_path, params,\n templates_path))","title":""},{"docid":"8a1835d042cf66486ed099ce14f71906","score":"0.51036656","text":"def generate_pymods(the_parsed_topology_xml, xml_filename, opt):\n if the_parsed_topology_xml.get_namespace():\n if VERBOSE:\n print(\n \"Generating pymods for topology %s::%s\"\n % (\n the_parsed_topology_xml.get_namespace(),\n the_parsed_topology_xml.get_name(),\n )\n )\n else:\n if VERBOSE:\n print(\n \"Generating pymods for topology %s\"\n % (the_parsed_topology_xml.get_name())\n )\n model = TopoFactory.TopoFactory.getInstance()\n topology_model = model.create(the_parsed_topology_xml)\n\n # create list of used parsed component xmls\n parsed_xml_dict = {}\n for comp in the_parsed_topology_xml.get_instances():\n if comp.get_type() in topology_model.get_base_id_dict():\n parsed_xml_dict[comp.get_type()] = comp.get_comp_xml()\n else:\n if VERBOSE:\n print(\n \"Components with type {} aren't in the topology model.\".format(\n comp.get_type()\n )\n )\n\n #\n # Hack to set up deployment path for instanced dictionaries (if one exists remove old one)\n #\n if opt.dict_dir is None:\n os.environ[\"DICT_DIR\"] = os.getcwd()\n else:\n os.environ[\"DICT_DIR\"] = opt.dict_dir\n\n xml_list = []\n for parsed_xml_type in parsed_xml_dict:\n if parsed_xml_dict[parsed_xml_type] is None:\n print(\n \"ERROR: XML of type {} is being used, but has not been parsed correctly. Check if file exists or add xml file with the 'import_component_type' tag to the Topology file.\".format(\n parsed_xml_type\n )\n )\n raise Exception()\n xml_list.append(parsed_xml_dict[parsed_xml_type])\n temp_comp = parsed_xml_dict[parsed_xml_type].get_component()\n if VERBOSE:\n print(\n \"Generating component dicts for %s::%s\"\n % (temp_comp.get_namespace(), temp_comp.get_name())\n )\n write_pymods_from_comp(parsed_xml_dict[parsed_xml_type], opt, topology_model)\n if VERBOSE:\n print(\n \"Generated component dicts for %s::%s\"\n % (temp_comp.get_namespace(), temp_comp.get_name())\n )\n\n topology_model.set_instance_xml_list(xml_list)\n\n if the_parsed_topology_xml.get_namespace():\n if VERBOSE:\n print(\n \"Generated pymods for topology %s::%s\"\n % (\n the_parsed_topology_xml.get_namespace(),\n the_parsed_topology_xml.get_name(),\n )\n )\n else:\n if VERBOSE:\n print(\n \"Generated pymods for topology %s\"\n % (the_parsed_topology_xml.get_name())\n )","title":""},{"docid":"8b7d04b25bee59ebf17f61215a787437","score":"0.51035786","text":"def main(input_dir: str, output_dir: str) -> None:\n input_dir = Path(input_dir)\n output_dir = Path(output_dir)\n output_dir.mkdir(parents=True, exist_ok=True)\n\n spacy_formatted_data = {\"train\": [], \"test\": []}\n\n for partition in spacy_formatted_data:\n note_dir = input_dir / partition / f\"{partition}_note\"\n if partition == \"train\":\n norm_dir = input_dir / partition / f\"{partition}_norm\"\n else:\n # TODO (John): Is this the name of the directory in the official download?\n norm_dir = input_dir / partition / f\"{partition}_norm_cui_replaced_with_unk\"\n\n for note_filepath in note_dir.iterdir():\n if not note_filepath.name.endswith(\".txt\"):\n continue\n\n norm_filepath = norm_dir / f\"{note_filepath.stem}.norm\"\n\n with open(note_filepath, \"r\") as f:\n spacy_formatted_data[partition].append((f.read(), {\"entities\": []}))\n with open(norm_filepath, \"r\") as f:\n for line in f:\n start, end = tuple(map(int, line.strip().split(\"||\")[-2:]))\n spacy_formatted_data[partition][-1][-1][\"entities\"].append((start, end, ENTITY_LABEL))\n\n with open(output_dir / \"spacy_formatted_data.pickle\", \"wb\") as f:\n pickle.dump(spacy_formatted_data, f)","title":""},{"docid":"df99f942c3c7b8f03f573780597898e3","score":"0.5099381","text":"def pmodel(self):\r\n if not hasattr(self, '_pmodel'):\r\n pmodelname = self.get('pmodel-name')\r\n if pmodelname == 'ISO9613':\r\n # create environment\r\n G = tuple([float(x) for x in self.get('pmodel-iso9613-ground-coeffs').strip('()').split(',')])\r\n p = self.getFloat('pmodel-iso9613-atmospheric-pressure')\r\n t = self.getFloat('pmodel-iso9613-atmospheric-temperature')\r\n r = self.getFloat('pmodel-iso9613-atmospheric-humidity')\r\n self._environment = ISO9613Environment(G = G, p = p, t = t, r = r)\r\n # create propagation model\r\n self._pmodel = ISO9613Model(environment = self._environment)\r\n self._pmodel.correction['geometricDivergence'] = self.getBool('pmodel-iso9613-flag-geometric-divergence')\r\n self._pmodel.correction['atmosphericAbsorption'] = self.getBool('pmodel-iso9613-flag-atmospheric-absorption')\r\n self._pmodel.correction['groundEffect'] = self.getBool('pmodel-iso9613-flag-ground-effect')\r\n self._pmodel.correction['sourceDirectivity'] = self.getBool('pmodel-iso9613-flag-source-directivity')\r\n else:\r\n raise Exception('configuration file: Propagation model \"%s\" not known - use \"ISO9613\"' % pmodelname)\r\n return self._pmodel","title":""},{"docid":"7bb2d23548d39b59677cfcfc9082e026","score":"0.50863206","text":"def main():\n\n parser = argparse.ArgumentParser(description='Duolingo shared task baseline model')\n parser.add_argument('--language', default='en_es', help='choose from [es_en, en_es, fr_en]', required=False)\n parser.add_argument('--dataset_path', default='../data/%s/', required=False)\n parser.add_argument('--outputs_path', default='./outputs/', required=False)\n args = parser.parse_args()\n\n dataset_path = args.dataset_path % args.language\n assert os.path.isdir(dataset_path)\n\n train_path = dataset_path + '%s.slam.20190204.train' % args.language\n dev_path = dataset_path + '%s.slam.20190204.dev' % args.language\n test_path = dataset_path + '%s.slam.20190204.test' % args.language\n assert os.path.isfile(train_path)\n assert os.path.isfile(dev_path)\n assert os.path.isfile(test_path)\n\n if not os.path.isdir(args.outputs_path): os.mkdir(args.outputs_path)\n\n # ============================== Hyper Parameter ==============================\n dbg = False\n from_path = None\n # from_path = './saved_model/seq2seq_nomlp_20'\n # from_path = './saved_model/seq2seq_exp_20'\n # from_path = './saved_model/attention_v2_20'\n # from_path = './saved_model/cnn_3'\n # from_path = './saved_model/seq2seq_c_7'\n # from_path = './saved_model/transformer_1'\n epochs = 10 if dbg else 10\n lang = Lang()\n\n # ============================== Data Loading ==============================\n\n print('Begin Data Loading')\n start_time = time.time()\n training_data, training_labels = load_data(train_path, lang, dbg=dbg, use_all_features=True)\n dev_data = load_data(dev_path, lang, use_all_features=True)\n test_data = load_data(test_path, lang, use_all_features=True)\n users = list(get_users(training_data).union(get_users(dev_data)).union(get_users(test_data)))\n lang.addUsers(users)\n for i in range(len(lang.letters)): lang.letter2Index[lang.letters[i]] = i\n end_time = time.time()\n print('Data Loaded\\t Time Taken %0.2fm' % ((end_time - start_time)/60))\n\n model = Model(lang)\n\n # ============================== Training ==============================\n if from_path == None:\n print('Begin Training')\n train_loader = get_dataloader(training_data, lang, training_labels)\n model.train(train_loader, epochs)\n\n # ============================== Inference ==============================\n # print('Begin Inference-Dev', end=' ')\n # start_time = time.time()\n # dev_loader = get_dataloader(dev_data, lang)\n # predictions = model.predict_for_set(dev_loader, from_path)\n # with open(args.outputs_path + '%s_dev_predictions.pred' % args.language, 'wt') as f:\n # for instance_id, prediction in iteritems(predictions):\n # f.write(instance_id + ' ' + str(prediction) + '\\n')\n # end_time = time.time()\n # print('| %0.2fm' % ((end_time-start_time)/60))\n \n print('Begin Inference-Test', end=' ')\n start_time = time.time()\n test_loader = get_dataloader(test_data, lang)\n predictions = model.predict_for_set(test_loader, from_path)\n with open(args.outputs_path + '%s_test_predictions.pred' % args.language, 'wt') as f:\n for instance_id, prediction in predictions.items():\n f.write(instance_id + ' ' + str(prediction) + '\\n')\n end_time = time.time()\n print('| %0.2fm' % ((end_time-start_time)/60))","title":""},{"docid":"aa7982cf4b80468889131323fa94e9d7","score":"0.50660026","text":"def generate_language_model(**kwargs):\n\n lm = Session.query(model.MorphemeLanguageModel).get(kwargs['morpheme_language_model_id'])\n trie_path = lm.get_file_path('trie')\n trie_mod_time = lm.get_modification_time(trie_path)\n lm.generate_succeeded = False\n try:\n lm.write_corpus()\n except Exception, e:\n lm.generate_message = u'Error writing the corpus file. %s' % e\n try:\n lm.write_vocabulary()\n except Exception, e:\n lm.generate_message = u'Error writing the vocabulary file. %s' % e\n try:\n lm.write_arpa(kwargs['timeout'])\n except Exception, e:\n lm.generate_message = u'Error writing the ARPA file. %s' % e\n try:\n lm.generate_trie()\n except Exception, e:\n lm.generate_message = u'Error generating the LMTrie instance. %s' % e\n else:\n if lm.get_modification_time(trie_path) != trie_mod_time:\n lm.generate_succeeded = True\n lm.generate_message = u'Language model successfully generated.'\n else:\n lm.generate_message = u'Error generating the LMTrie instance.'\n lm.generate_attempt = unicode(uuid4())\n lm.modifier_id = kwargs['user_id']\n lm.datetime_modified = h.now()\n Session.commit()","title":""},{"docid":"2cff1d19062392a49928573882a530bd","score":"0.5064492","text":"def load_model(model_path):\r\n nlp = spacy.blank('en')\r\n if 'ner' not in nlp.pipe_names:\r\n ner = nlp.create_pipe('ner')\r\n nlp.add_pipe(ner)\r\n\r\n ner = nlp.from_disk(model_path)\r\n return ner","title":""},{"docid":"fd82b28979eb3edddc68ff36415c4e0c","score":"0.5058911","text":"def _write_imports(\n cls,\n pickle_type: Optional[str] = None,\n mojo_model: Optional[bool] = False,\n binary_h2o_model: Optional[bool] = False,\n tf_model: Optional[bool] = False,\n binary_string: Optional[str] = None,\n ) -> None:\n pickle_type = pickle_type if pickle_type else \"pickle\"\n cls.score_code += (\n f\"import math\\nimport {pickle_type}\\nimport pandas as pd\\n\"\n \"import numpy as np\\nfrom pathlib import Path\\n\\n\"\n )\n \"\"\"\nimport math\nimport pickle\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\n\n \"\"\"\n\n try:\n if current_session().version_info() != 3.5:\n cls.score_code += \"import settings\\n\\n\"\n \"\"\"\nimport settings\n \n \n \"\"\"\n except AttributeError:\n warn(\n \"No current session connection was found to a SAS Viya server. Score \"\n \"code will be written under the assumption that the target server is \"\n \"SAS Viya 4.\"\n )\n\n if mojo_model or binary_h2o_model:\n cls.score_code += \"import h2o\\n\\nh2o.init()\\n\\n\"\n \"\"\"\nimport h2o\n\nh2o.init()\n\n \"\"\"\n elif tf_model:\n cls.score_code += \"import tensorflow as tf\\n\\n\"\n \"\"\"\nimport tensorflow as tf\n\n \"\"\"\n elif binary_string:\n cls.score_code += (\n f'import codecs\\n\\nbinary_string = \"{binary_string}\"'\n f\"\\nmodel = {pickle_type}.loads(codecs.decode(binary_string\"\n '.encode(), \"base64\"))\\n\\n'\n )\n \"\"\"\nimport codecs\n\nbinary_string = \"\"\nmodel = pickle.load(codecs.decode(binary_string.encode(), \"base64\"))\n\n \"\"\"","title":""},{"docid":"2c1108513704f5cbba7b473aa2906d50","score":"0.50567424","text":"def write_pymods_from_comp(the_parsed_component_xml, opt, topology_model):\n global BUILD_ROOT\n global DEPLOYMENT\n global VERBOSE\n\n parsed_port_xml_list = []\n parsed_serializable_xml_list = []\n # uses the topology model to process the items\n # checks if the topology model exists\n if topology_model is None:\n PRINT.info(\n \"Topology model was not specified. Please also input a topology model when running this command.\"\n )\n raise OSError\n\n port_type_files_list = the_parsed_component_xml.get_port_type_files()\n\n for port_file in port_type_files_list:\n port_file = search_for_file(\"Port\", port_file)\n xml_parser_obj = XmlPortsParser.XmlPortsParser(port_file)\n parsed_port_xml_list.append(xml_parser_obj)\n del xml_parser_obj\n\n serializable_type_files_list = (\n the_parsed_component_xml.get_serializable_type_files()\n )\n for serializable_file in serializable_type_files_list:\n serializable_file = search_for_file(\"Serializable\", serializable_file)\n xml_parser_obj = XmlSerializeParser.XmlSerializeParser(\n serializable_file\n ) # Telemetry/Params can only use generated serializable types\n # check to make sure that the serializables don't have things that channels and parameters can't have\n # can't have external non-xml members\n if len(xml_parser_obj.get_include_header_files()):\n print(\n \"ERROR: Component include serializables cannot use user-defined types. file: \"\n % serializable_file\n )\n sys.exit(-1)\n\n parsed_serializable_xml_list.append(xml_parser_obj)\n del xml_parser_obj\n\n model = CompFactory.CompFactory.getInstance()\n component_model = model.create(\n the_parsed_component_xml, parsed_port_xml_list, parsed_serializable_xml_list\n )\n\n instChannelWriter = InstChannelWriter.InstChannelWriter()\n instCommandWriter = InstCommandWriter.InstCommandWriter()\n instEventWriter = InstEventWriter.InstEventWriter()\n\n if opt.dict_dir is None:\n if VERBOSE:\n print(\"Dictionary output directory not specified!, defaulting to cwd\")\n opt.dict_dir = os.getcwd()\n os.environ[\"DICT_DIR\"] = opt.dict_dir\n\n # iterate through command instances\n for command_model in component_model.get_commands():\n if VERBOSE:\n print(\"Generating command dict %s\" % command_model.get_mnemonic())\n instCommandWriter.DictStartWrite(command_model, topology_model)\n instCommandWriter.DictHeaderWrite(command_model, topology_model)\n instCommandWriter.DictBodyWrite(command_model, topology_model)\n\n for parameter_model in component_model.get_parameters():\n if VERBOSE:\n print(\"Generating parameter dict %s\" % parameter_model.get_name())\n instCommandWriter.DictStartWrite(parameter_model, topology_model)\n instCommandWriter.DictHeaderWrite(parameter_model, topology_model)\n instCommandWriter.DictBodyWrite(parameter_model, topology_model)\n\n # iterate through command instances\n for event_model in component_model.get_events():\n if VERBOSE:\n print(\"Generating event dict %s\" % event_model.get_name())\n instEventWriter.DictStartWrite(event_model, topology_model)\n instEventWriter.DictHeaderWrite(event_model, topology_model)\n instEventWriter.DictBodyWrite(event_model, topology_model)\n\n # iterate through command instances\n for channel_model in component_model.get_channels():\n if VERBOSE:\n print(\"Generating channel dict %s\" % channel_model.get_name())\n instChannelWriter.DictStartWrite(channel_model, topology_model)\n instChannelWriter.DictHeaderWrite(channel_model, topology_model)\n instChannelWriter.DictBodyWrite(channel_model, topology_model)","title":""},{"docid":"dbac7e9dbefed924c07d9e4bb763e9cb","score":"0.5051691","text":"def convert(self, path, version, target):\n source = self.comparer.get_representation(path)\n lines = [ '# \\n'.format(version) ]\n\n for line in self.comparer.template.contents[version].preamble:\n lines.append(line.write(source.preamble, source.version, source.stored) + \"\\n\")\n\n for line in self.comparer.template.contents[version].body:\n for valueset in source.body:\n lines.append(line.write(valueset, source.version, source.stored) + \"\\n\")\n\n with open(os.path.expanduser(target), 'w') as f:\n f.writelines(lines)","title":""},{"docid":"34c2bef9339db6de5f9e061ddef3fb47","score":"0.5050992","text":"def convert_to_onnx(pytorch_model, input_shape, output_file, input_names, output_names):\n\n pytorch_model.eval()\n onnx_input_shape = torch.randn(input_shape)\n torch.onnx.export(pytorch_model, onnx_input_shape, output_file,\n verbose=True, input_names=input_names, output_names=output_names)\n\n # Model check after conversion\n import onnx\n model_from_onnx = onnx.load(output_file)\n try:\n onnx.checker.check_model(model_from_onnx)\n print('ONNX check passed successfully.')\n except onnx.onnx_cpp2py_export.checker.ValidationError as exc:\n sys.exit('ONNX check failed with error: ' + str(exc))","title":""},{"docid":"5f4a2a2fd203b35f20cc76a1f1693031","score":"0.5048336","text":"def _ppo(self, m):\n\n m[\"ppo1\"] = m[\"MemoryOp <: po_loc :> Write\"]\n m[\"ppo2\"] = m[\"rdw\"]\n m[\"ppo3\"] = m[\"(AMO + StoreConditional) <: (rf & po)\"]\n m[\"ppo4rr\"] = m[\"(Read <: po :> (FencePR & FenceSR)).(po :> Read)\"]\n m[\"ppo4rw\"] = m[\"(Read <: po :> (FencePR & FenceSW)).(po :> Write)\"]\n m[\"ppo4wr\"] = m[\"(Write <: po :> (FencePW & FenceSR)).(po :> Read)\"]\n m[\"ppo4ww\"] = m[\"(Write <: po :> (FencePW & FenceSW)).(po :> Write)\"]\n m[\"ppo4tso1\"] = m[\"(Read <: po :> (FenceTSO)).(po :> MemoryOp)\"]\n m[\"ppo4tso2\"] = m[\"(MemoryOp <: po :> (FenceTSO)).(po :> Write)\"]\n m[\"ppo4\"] = m[\"ppo4rr + ppo4rw + ppo4wr + ppo4ww + ppo4tso1 + ppo4tso2\"]\n m[\"ppo5\"] = m[\"Aq <: po :> MemoryOp\"]\n m[\"ppo6\"] = m[\"MemoryOp <: po :> Rl\"]\n m[\"ppo7\"] = m[\"RCsc <: po :> RCsc\"]\n m[\"ppo8\"] = m[\"pair\"]\n m[\"ppo9\"] = m[\"addrdep\"]\n m[\"ppo10\"] = m[\"datadep\"]\n m[\"ppo11\"] = m[\"ctrldep.po :> Write\"]\n m[\"ppo12\"] = m[\"(addrdep + datadep).(rf & po)\"]\n m[\"ppo13\"] = m[\"addrdep.po :> Write\"]\n\n return m[\n \"ppo1 + ppo2 + ppo3 + ppo4 + ppo5 + ppo6 + ppo7 + \"\n \"ppo8 + ppo9 + ppo10 + ppo11 + ppo12 + ppo13\"\n ]","title":""},{"docid":"7ac89b0a71c9943d58dd2e15032afde8","score":"0.504813","text":"def makeFile(m, model):\n tempfile = ['#include \"lime.h\"\\n', '#include \"math.h\"\\n',\n '#include \"stdio.h\"\\n', '#include \"stdlib.h\"\\n',\n '#include \"%s\"\\n\\n' % model.header.fn]\n writeModelProperties(tempfile, model)\n writeWeighting(tempfile, model)\n writeImageParameters(tempfile, m, model)\n writeInterpolationFuncs(tempfile, model)\n writeDensity(tempfile, model)\n writeTemperatures(tempfile, model)\n writeAbundance(tempfile, model)\n if model.dust is not None:\n writeGastoDust(tempfile, model)\n writeDoppler(tempfile, model)\n writeVelocityStructure(tempfile, model)\n with open('model_%d.c' % m, 'w') as tosave:\n for line in tempfile:\n tosave.write('%s' % line)\n return","title":""},{"docid":"06ef0a20cae196e2de5c67f6c87df504","score":"0.5047686","text":"def _save_prototxt(self):\n solver_path = osp.dirname(self._solver_prototxt)\n if not osp.exists(solver_path):\n os.makedirs(solver_path)\n with open(self._solver_prototxt, 'w') as f:\n f.write(text_format.MessageToString(self._solver))","title":""},{"docid":"9127df10abf73188e578bcc4dcd4d09c","score":"0.50475544","text":"def owl_to_model(fname):\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\n\n try:\n file_is = autoclass('java.io.FileInputStream')(fname)\n except JavaException:\n logger.error('Could not open data file %s' % fname)\n return\n try:\n biopax_model = io.convertFromOWL(file_is)\n except JavaException as e:\n logger.error('Could not convert data file %s to BioPax model' % fname)\n logger.error(e)\n return\n\n file_is.close()\n\n return biopax_model","title":""},{"docid":"b084295606442608e4e95a1201da837c","score":"0.5046919","text":"def export_model(self, out=None, **kwargs):\n # Remove all code transformations but respect those provided explicitly\n kwargs.setdefault('length_for_alias', None)\n kwargs.setdefault('name_all_constraints', False)\n\n CpoCompiler(self, **kwargs).write(out)","title":""},{"docid":"380e97eb5048bd83b762af621acf5fd7","score":"0.5038669","text":"def main(model=None, output_dir=None, n_iter=100):\n if model is not None:\n nlp = spacy.load(model) # load existing spaCy model\n print(\"Loaded model '%s'\" % model)\n else:\n nlp = spacy.blank(\"en\") # create blank Language class\n print(\"Created blank 'en' model\")\n \n # create the built-in pipeline components and add them to the pipeline\n # nlp.create_pipe works for built-ins that are registered with spaCy\n if \"ner\" not in nlp.pipe_names:\n ner = nlp.create_pipe(\"ner\")\n nlp.add_pipe(ner, last=True)\n # otherwise, get it so we can add labels\n else:\n ner = nlp.get_pipe(\"ner\")\n \n # add labels\n for _, annotations in TRAIN_DATA:\n for ent in annotations.get(\"entities\"):\n ner.add_label(ent[2])\n\n # get names of other pipes to disable them during training\n other_pipes = [pipe for pipe in nlp.pipe_names if pipe != \"ner\"]\n with nlp.disable_pipes(*other_pipes): # only train NER\n # reset and initialize the weights randomly – but only if we're\n # training a new model\n if model is None:\n nlp.begin_training()\n for itn in range(n_iter):\n random.shuffle(TRAIN_DATA)\n losses = {}\n # batch up the examples using spaCy's minibatch\n batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))\n for batch in batches:\n texts, annotations = zip(*batch)\n nlp.update(\n texts, # batch of texts\n annotations, # batch of annotations\n drop=0.5, # dropout - make it harder to memorise data\n losses=losses,\n )\n #print(\"Losses\", losses)\n\n # test the trained model\n for text in TEST_DATA:\n doc = nlp(text)\n print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])\n print(\"Tokens\", [(t.text, t.ent_type_, t.ent_iob) for t in doc])\n \n # save model to output directory\n if output_dir is not None:\n output_dir = Path(output_dir)\n if not output_dir.exists():\n output_dir.mkdir()\n nlp.to_disk(output_dir)\n print(\"Saved model to\", output_dir)\n \n # test the saved model\n print(\"Loading from\", output_dir)\n nlp2 = spacy.load(output_dir)\n for text, _ in TRAIN_DATA:\n doc = nlp2(text)\n print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])","title":""},{"docid":"4dfa7950183441b0441fb0c52fd47749","score":"0.5037608","text":"def convert2or():\n # parse converter arguments\n args = get_args_convert2or()\n\n # input data parameters\n try:\n workspace_dir, exec_file_name, data_uri, data_path, output_path = get_params()\n entry_filename = os.path.splitext(os.path.basename(exec_file_name))[0]\n except Exception as e:\n print('Invalid arguments, {}'.format(e))\n sys.exit(1)\n\n else:\n # Generate requirements.txt\n try:\n import pipreqs\n except ImportError as e:\n raise RuntimeError('Package installation failed: {}'.format(e))\n\n else:\n time.sleep(1)\n try:\n p = subprocess.Popen([\"pipreqs\", \"--force\", workspace_dir])\n p.wait()\n time.sleep(1)\n\n # fix the bug raising from 'tensorflow', 'tensorflow_gpu'\n filename = os.path.join(workspace_dir, \"requirements.txt\")\n\n rw_file(filename, matplotlib=\"matplotlib\", tensorflow_gpu=\"\", tensorflow=\"tensorflow-gpu\")\n remove_empty_lines(filename)\n print(\"Generated 'requirements.txt' successfully!\")\n\n except Exception as e:\n raise RuntimeError(\"Generating 'requirements.txt' failed: {}\".format(e))\n\n else:\n # Generate params.json\n try:\n exec_file_name_v = os.path.relpath(exec_file_name, start=workspace_dir)\n data_path_v = \"\" if data_path == \"\" else os.path.relpath(data_path, start=workspace_dir)\n output_path_v = \"\" if output_path == \"\" else os.path.relpath(output_path, start=workspace_dir)\n params_json = json.dumps({\"exec_file_name\": exec_file_name_v,\n \"data_uri\": data_uri,\n \"data_path\": data_path_v,\n \"output_path\": output_path_v,\n })\n with open(os.path.join(workspace_dir, \"params.json\"), 'w+') as f:\n f.write(params_json)\n print(\"Generated 'params.json' successfully!\")\n\n except Exception as e:\n raise IOError(\"Generating 'params.json' failed: {}\".format(e))\n\n else:\n time.sleep(2)\n try:\n zip_folder_path = os.path.join(workspace_dir, os.pardir, \"task_files\")\n\n if not os.path.exists(zip_folder_path):\n os.makedirs(zip_folder_path)\n\n output_filename = str(entry_filename) + \"_orion.zip\"\n zip_folder(workspace_dir, os.path.join(zip_folder_path, output_filename))\n print('Zipped files successfully!')\n except Exception as e:\n raise RuntimeError('Zipping files failed: {}'.format(e))\n else:\n try:\n os.remove(os.path.join(workspace_dir, \"params.json\"))\n os.remove(os.path.join(workspace_dir, \"requirements.txt\"))\n except Exception as e:\n raise RuntimeError('Removing files failed: {}'.format(e))","title":""},{"docid":"78aca13d4302fac740b8dddb5e1eb7e6","score":"0.5032639","text":"def descout_to_input( # noqa: C901 - fxn too complex\n outfile,\n infile,\n objective=\"force\",\n optimizer=\"lsq-exact\",\n header=\"#DESC-generated input file\",\n ftol=1e-2,\n xtol=1e-6,\n gtol=1e-6,\n maxiter=100,\n ):\n f = open(outfile, \"w+\")\n\n f.seek(0)\n\n eq = load(infile)\n try:\n eq0 = eq[-1]\n except TypeError:\n eq0 = eq\n\n f.write(header + \"\\n\")\n\n f.write(\"# global parameters\\n\")\n f.write(\"sym = {:1d} \\n\".format(eq0.sym))\n f.write(\"NFP = {:3d} \\n\".format(int(eq0.NFP)))\n f.write(\"Psi = {:.8f} \\n\".format(eq0.Psi))\n\n f.write(\"\\n# spectral resolution\\n\")\n for key, val in {\n \"L_rad\": \"L\",\n \"M_pol\": \"M\",\n \"N_tor\": \"N\",\n \"L_grid\": \"L_grid\",\n \"M_grid\": \"M_grid\",\n \"N_grid\": \"N_grid\",\n }.items():\n f.write(f\"{key} = {getattr(eq0, val)}\\n\")\n\n f.write(\"\\n\\n# solver tolerances\\n\")\n f.write(f\"ftol = {ftol}\\n\")\n f.write(f\"xtol = {xtol}\\n\")\n f.write(f\"gtol = {gtol}\\n\")\n f.write(f\"maxiter = {maxiter}\\n\")\n\n f.write(\"\\n\\n# solver methods\\n\")\n f.write(f\"optimizer = {optimizer}\\n\")\n f.write(f\"objective = {objective}\\n\")\n f.write(\"spectral_indexing = {}\\n\".format(eq0._spectral_indexing))\n f.write(\"node_pattern = {}\\n\".format(eq0._node_pattern))\n\n f.write(\"\\n# pressure and rotational transform/current profiles\\n\")\n\n if eq0.iota:\n assert (\n eq0.pressure.__class__.__name__ == \"PowerSeriesProfile\"\n and eq0.iota.__class__.__name__ == \"PowerSeriesProfile\"\n ), \"Equilibrium must have power series profiles for ascii io\"\n char = \"i\"\n iseven_pres = int(eq0._pressure.basis.sym == \"even\") + 1\n iseven_iota = int(eq0._iota.basis.sym == \"even\") + 1\n pres_profile = np.zeros((eq0.L + 1,))\n iota_profile = np.zeros((eq0.L + 1,))\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\n iota_profile[: eq0.L + 1 : iseven_iota] = eq0._iota.params\n\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\n for l in idxs:\n f.write(\n \"l: {:3d}\\tp = {:16.8E}\\t{} = {:16.8E}\\n\".format(\n int(l), pres_profile[l], char, iota_profile[l]\n )\n )\n else:\n assert (\n eq0.pressure.__class__.__name__ == \"PowerSeriesProfile\"\n and eq0.current.__class__.__name__ == \"PowerSeriesProfile\"\n ), \"Equilibrium must have power series profiles for ascii io\"\n char = \"c\"\n iseven_pres = int(eq0._pressure.basis.sym == \"even\") + 1\n iseven_curr = int(eq0._current.basis.sym == \"even\") + 1\n pres_profile = np.zeros((eq0.L + 1,))\n curr_profile = np.zeros((eq0.L + 1,))\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\n curr_profile[: eq0.L + 1 : iseven_curr] = eq0._current.params\n\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\n for l in idxs:\n f.write(\n \"l: {:3d}\\tp = {:16.8E}\\t{} = {:16.8E}\\n\".format(\n int(l), pres_profile[l], char, curr_profile[l]\n )\n )\n\n f.write(\"\\n\")\n\n f.write(\"\\n# fixed-boundary surface shape\\n\")\n # boundary paramters\n if eq0.sym:\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\n if abs(eq0.Rb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, eq0.Rb_lmn[k], 0\n )\n )\n for k, (l, m, n) in enumerate(eq0.surface.Z_basis.modes):\n if abs(eq0.Zb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, 0, eq0.Zb_lmn[k]\n )\n )\n else:\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\n if abs(eq0.Rb_lmn[k]) > 1e-8 or abs(eq0.Zb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, eq0.Rb_lmn[k], eq0.Zb_lmn[k]\n )\n )\n\n f.close()","title":""},{"docid":"aac8d9a34c982c21d64ee69aa9b8aada","score":"0.50321543","text":"def models(self):\r\n # make model directory, removing the old one if necessary\r\n rm_rf(self.cfg.model_dir)\r\n mkdir_p(self.cfg.model_dir)\r\n\r\n # run generator script\r\n gen_script = os.path.join(self._prj_root, 'gen.py')\r\n\r\n if 'PYTHON_MSDSL' in os.environ:\r\n python_name = os.environ['PYTHON_MSDSL']\r\n else:\r\n python_name = which('python')\r\n \r\n call([python_name, gen_script, '-o', self.cfg.model_dir, '--dt', str(self.dt)])","title":""},{"docid":"37e59d1ec3cd22e83b1a98ae5bb15330","score":"0.502148","text":"def write_model_to_file(self) -> str:\n\n model_file_content = self.interpreter.get_model_file_content()\n\n db_logger.info(f\"Writing {self.model_file_url}\")\n\n with open(self.model_file_location, 'w') as model:\n model.write(model_file_content)\n\n file_name = os.path.basename(self.model_file_location)\n project_dir = self.model_file_location.replace(file_name, '')\n\n helpers.process_includes(\n self.model_file_url, project_dir, self.interpreter\n )\n\n if not self.no_score:\n self.model_file_location = self.setup_protocol(self.score)\n\n return self.model_file_location","title":""},{"docid":"5a4533cb8662178a596b89ed1d73420b","score":"0.50117064","text":"def test_ImportUFONoSideEffectLO(self): \n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('sm'),False)\n original_all_particles = copy.copy(ufo_model.all_particles)\n original_all_vertices = copy.copy(ufo_model.all_vertices)\n original_all_couplings = copy.copy(ufo_model.all_couplings)\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\n original_all_parameters = copy.copy(ufo_model.all_parameters)\n original_all_orders = copy.copy(ufo_model.all_orders)\n original_all_functions = copy.copy(ufo_model.all_functions)\n\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\n model = ufo2mg5_converter.load_model()\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \n\n self.assertEqual(original_all_particles,ufo_model.all_particles) \n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\n self.assertEqual(original_all_couplings,ufo_model.all_couplings)\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\n self.assertEqual(original_all_orders,ufo_model.all_orders)\n self.assertEqual(original_all_functions,ufo_model.all_functions)","title":""},{"docid":"7e8c2e02b0ee94ddd321ca2db4033cd0","score":"0.50004613","text":"def exe_dyn_mdl(fv, usr):\n\n exe = os.chdir(prg_path + '/dynamic_mdl-0.1') \n exe = os.getcwd() \n pcs = sbp.Popen([exe + '/a.out', str(fv)], stdout=sbp.PIPE, stderr=sbp.PIPE)\n out, error = pcs.communicate()\n if out:\n print('OK', out)\n if error:\n print('Error', error.strip()) \n if not pcs.poll():\n print(\"Dynamic Module execute finish\")\n p_nme = 'Dynamic'\n dest_pth = fles_dir + usr + '/' + p_nme\n dest_pth = dest_pth + '/' + time.get_date_time_hm()\n dummy_sys_file.crt_dir(dest_pth)\n \"\"\"Creating directory for program execution\"\"\"\n\n \"\"\"Names of the generated files\"\"\"\n fle_name1 = 'sk'\n fle_name2 = 'f_self'\n fle_name3 = 'coefficient'\n \"\"\"full path of the outputs of program\"\"\"\n out_fle1 = exe + '/' + 'sk.dat'\n out_fle2 = exe + '/' + 'fself.dat'\n out_fle3 = exe + '/' + 'coeficiente.dat' \n dummy_sys_file.cpy_file_to_pth_dir(out_fle1, dest_pth, fle_name1)\n dummy_sys_file.cpy_file_to_pth_dir(out_fle2, dest_pth, fle_name2)\n dummy_sys_file.cpy_file_to_pth_dir(out_fle3, dest_pth, fle_name3)","title":""},{"docid":"10bf29315de0dab52975584c75ef2dfb","score":"0.49911758","text":"def generate_package(self):\n\n # Create a directory (mymodel)\n cwd = Path(self.dir)\n directory=cwd/'csharp_model'\n if (directory).isdir() :\n self.dir = directory\n else:\n self.dir = directory.mkdir()\n\n files = []\n count = 0\n\n for model in self.models:\n\n\n self.generate_component(model)\n\n ext = '' if count == 0 else str(count)\n filename = self.dir/\"%s.cs\"%signature(model)\n\n with open(filename, \"w\") as python_file:\n python_file.write(self.code.encode('utf-8','ignore'))\n files.append(filename)\n\n model.module_name = str(Path(filename).namebase)\n\n count += 1\n\n return files","title":""},{"docid":"22f27813e63fc901b230aaee14b21dfe","score":"0.49885112","text":"def new_IDP_gen(subj,LUT_txt): #,fix4melviewtxt\n\n\n #remove trailing forward slashes in subject paths\n if subj.endswith(\"/\"):\n subj = subj[:-1]\n\n if not os.path.exists(subj + \"/IDP_files/\"):\n os.makedirs(subj + \"/IDP_files/\")\n\n subjName = subj[subj.rfind(\"/\") + 1 :]\n\n\n # IDP_FC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_FC_dist.txt\")\n # IDP_SC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_SC_dist.txt\")\n # IDP_MELODIC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_MELODIC_SNR.txt\")\n # IDP_MCFLIRT_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_MCFLIRT_disp.txt\")\n # IDP_homotopic_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_homotopic.txt\")\n # new_IDP_list_file = os.path.join(subj + \"/IDP_files/\", \"tvb_new_IDPs.txt\")\n\n new_IDP_file = os.path.join(subj + \"/IDP_files/\", \"tvb_new_IDP.txt\")\n\n\n # IDP_output_files = [IDP_FC_file,IDP_SC_file,IDP_MELODIC_file,IDP_MCFLIRT_file,IDP_homotopic_file]\n\n # for file in IDP_output_files:\n # with open(file, 'w') as fp:\n # pass\n\n with open(new_IDP_file, 'w') as fp:\n line = '\\t'.join([\"short\",\"num\",\"category\",\"num_in_cat\",\"long\",\"unit\",\"dtype\",\"description\",\"value\"])\n\n fp.write(line)\n\n\n\n fix4melviewtxt=\"\"\n\n FC_distribution(subj)\n SC_distribution(subj)\n MELODIC_SNR(subj,fix4melviewtxt)\n MCFLIRT_displacement(subj) \n\n homotopic(subj,LUT_txt)\n \n\n\n\n\n #TODO TL/distance, or ts.txt","title":""},{"docid":"cde366ecb10bf14e7b028bb5eeb70a26","score":"0.497974","text":"def produce_layout(eoi, graph):\n\n subprocess.run((\"/usr/bin/dot\", \"{}\".format(graph.filename), \"-o\", \n \"{}/layout.dot\".format(eoi)))","title":""},{"docid":"42db4bc248db8077aaa00be0cc702c70","score":"0.4975401","text":"def load_model_output(text):\r\n # uncomment when working in linux and remove subsequent two lines\r\n # nlp = spacy.load(\"../models/quick-spacy/\")\r\n model_path = Path(__file__).parent.absolute() / \"models/quick-spacy/\"\r\n nlp = spacy.load(model_path)\r\n doc = nlp(text)\r\n sentence = [ent.text for ent in doc.ents]\r\n labels = [ent.label_ for ent in doc.ents]\r\n return sentence, labels","title":""},{"docid":"d71b59de5a8a54084e51dfb95444b719","score":"0.49555877","text":"def generate_theory(\n grammar,\n config,\n theory_op_file,\n theorem_prover,\n max_depth,\n min_depth\n):\n # Get Theorem Prover Config and initialize Theorem Prover\n theorem_prover_config = TheoremProverConfig(\n grammar, **config[\"theory\"][\"theorem_prover\"]\n )\n\n num_examples = config[\"theory\"][\"num_examples\"]\n min_num_positive_examples = config[\"theory\"][\"min_num_positive_examples\"]\n max_num_negative_examples = num_examples - min_num_positive_examples\n statement_types = config[\"theory\"][\"statement_types_per_example\"]\n assertion_start_symbol = config[\"assertion\"][\"start_symbol\"]\n example_id_prefix = config.get(\"example_id_prefix\", \"\")\n\n # Generate examples for every required type of statement (Start Symbol type)\n num_true_labels = 0\n num_false_labels = 0\n curr_num_examples = 0\n progress_tracker = tqdm(total=num_examples)\n progress_tracker.set_description(desc=\"Generating Examples...\")\n while (\n curr_num_examples < num_examples and num_true_labels < min_num_positive_examples\n ):\n example = generate_random_example(\n curr_num_examples + 1,\n example_id_prefix,\n grammar,\n theorem_prover_config,\n statement_types,\n assertion_start_symbol,\n theorem_prover,\n max_depth,\n min_depth\n )\n if example is not None:\n if example.theory_assertion_instance.label:\n num_true_labels += 1\n else:\n if num_false_labels == max_num_negative_examples:\n continue\n else:\n num_false_labels += 1\n json.dump(example.to_json(), theory_op_file)\n theory_op_file.write(\"\\n\")\n curr_num_examples += 1\n progress_tracker.update()\n progress_tracker.close()\n print(f\"Generated {curr_num_examples} examples.\")\n print(f\" No. with True label: {num_true_labels}\")\n print(f\" No. with False label: {num_false_labels}\")","title":""},{"docid":"6abcb45148b5a15227491491ca106d5b","score":"0.49505714","text":"def polish_and_save(model_filename,\n save_filename='',\n suffix='.polished',\n *args,\n **kwargs):\n\n save_filename = save_filename or model_filename.replace(\n '.onnx', suffix + '.onnx')\n\n model = onnx.load(model_filename)\n model = polish_model(model, *args, **kwargs)\n onnx.save(model, save_filename)\n logger.info('polished model saved to: %s', save_filename)\n return save_filename","title":""},{"docid":"c70ae932b14d6c0ac6415942b0994419","score":"0.49496683","text":"def output_perplexity(task_letter):\n output_file = open(\"output/group02.perplexity\" + task_letter, mode=\"w\")\n word_dictionary = get_word_dict()\n test_sentences = open(\"data/sentences.eval\", mode=\"r\")\n\n for test_sentence in test_sentences.readlines():\n # predicted_softmax_vecs: 30 X 20.000 X 1 Output Vector with softmax probabilities\n predicted_softmax_vecs = eval_neural_network(test_sentence)\n perp = perplexity(predicted_softmax_vecs, test_sentence, word_dictionary)\n output_file.write(str(perp) + \"\\n\")\n\n output_file.close()","title":""},{"docid":"a208cbaa76b296741b5c20e88440925f","score":"0.49453568","text":"def __init__(self,\n fileName,\n realFileName=None,\n prequelFileName=None,\n preErrorMessages=(), #TODO: check the type\n readFileLater=False,\n fillImportBoxLater=False,\n parseFileLater=False,\n noSymbolChecking=False,\n allowedFeatures=(),\n recognizeUSEOCLNativeModelDefinition=False):\n #type: (Text, Optional[Text], Optional[Text], List[Any], bool, bool, bool, bool, List[Text], bool) -> None\n\n\n # if readFileLater or fillImportBoxLater or parseFileLater:\n # assert finalizeLater\n\n # Create an empty model\n # Not to be moved after super\n # This should be done in all case so that\n # the model attribute always exist even if there\n # are some error in reading the file\n self.model = self.emptyModel() # type: Model\n\n\n\n\n # Call the super class, read the file or not\n try:\n # This can raise an exception for instance if\n # there is a problem reading the file\n super(ModelSourceFile, self).__init__(\n fileName=fileName,\n realFileName=realFileName,\n prequelFileName=prequelFileName,\n preErrorMessages=preErrorMessages,\n doNotReadFiles=readFileLater,\n allowedFeatures=allowedFeatures\n )\n except FatalError:\n pass # an error as already been registered\n\n from modelscripts.megamodels import Megamodel\n Megamodel.registerSource(self)\n Megamodel.registerModel(self.model)\n\n\n # Backward link\n self.model.source=self\n\n # Link issue box\n self.model._issueBox.addParent(self._issueBox)\n\n # Source to ModelElement Mapping\n self._modelMapping=_ModelSourceMapping()\n\n\n # Create first an empty ImportBox.\n self.importBox=ImportBox(self)\n\n # Then fill it by reading megamodel statements,\n # unless specified.\n try:\n if not fillImportBoxLater:\n parseToFillImportBox(\n self,\n noSymbolChecking,\n recognizeUSEOCLNativeModelDefinition)\n\n if not parseFileLater:\n self.parseToFillModel()\n self.finalize()\n\n except FatalError:\n pass # nothing to do, the issue has been registered","title":""},{"docid":"2e5aa18489143959b9fdd129d63b51d0","score":"0.49399826","text":"def preflight(self):\n self.params[\"translations\"][\"mgf_input_file\"] = os.path.join(\n self.params[\"input_dir_path\"], self.params[\"input_file\"]\n )\n self.params[\"translations\"][\"output_file_incl_path\"] = os.path.join(\n self.params[\"output_dir_path\"], self.params[\"output_file\"]\n )\n self.param_file_name = os.path.join(\n self.params[\"translations\"][\"output_file_incl_path\"].strip(\".csv\")\n + \"pipi_params.def\"\n )\n self.created_tmp_files.append(self.param_file_name)\n\n # pprint.pprint(self.params['translations']['_grouped_by_translated_key'])\n # pprint.pprint(self.params)\n # sys.exit(1)\n self.params_to_write = {\n \"version\": self.META_INFO[\"version\"],\n \"output_percolator_input\": 1,\n \"mod10\": \"0.0@X?\",\n \"pepNterm\": 0.0,\n \"pepCterm\": 0.0,\n \"proNterm\": 0.0,\n \"proCterm\": 0.0,\n }\n symbols = [\"~\", \"!\", \"%\", \"^\", \"&\", \"*\", \"+\", \"<\", \">\"]\n for x, element in enumerate(symbols):\n self.params_to_write[\"mod0{0}\".format(x + 1)] = \"0.0@X{0}\".format(element)\n for aa in \"ACDEFGHIKLMNOPQRSTUVWYnc\":\n self.params_to_write[aa] = 0\n\n write_exclusion_list = [\n # 'label',\n \"-Xmx\",\n \"frag_mass_tolerance_unit\",\n \"base_mz\",\n # 'header_translations',\n # 'validation_score_field'\n ]\n\n # additional_15N_modifications = []\n # if self.params['translations']['label'] == '15N':\n # for aminoacid, N15_Diff in ursgal.ukb.DICT_15N_DIFF.items():\n # existing = False\n # for mod_dict in self.params['mods']['fix']:\n # if aminoacid == mod_dict['aa']:\n # mod_dict['mass'] += N15_Diff\n # mod_dict['name'] += '_15N_{0}'.format(aminoacid)\n # existing = True\n # if existing == True:\n # continue\n # else:\n # self.params['mods']['fix'].append(\n # {\n # 'aa': aminoacid,\n # 'mass': N15_Diff,\n # 'name': '_15N_{0}'.format(aminoacid),\n # 'pos': 'any',\n # }\n # )\n\n if self.params[\"translations\"][\"frag_mass_tolerance_unit\"] == \"ppm\":\n self.params[\"translations\"][\"_grouped_by_translated_key\"][\n \"ms2_tolerance\"\n ] = {\n \"frag_mass_tolerance\": ursgal.ucore.convert_ppm_to_dalton(\n self.params[\"translations\"][\"frag_mass_tolerance\"],\n base_mz=self.params[\"base_mz\"],\n )\n }\n\n for pipi_param_name in self.params[\"translations\"][\n \"_grouped_by_translated_key\"\n ].keys():\n for ursgal_param_name, param_value in self.params[\"translations\"][\n \"_grouped_by_translated_key\"\n ][pipi_param_name].items():\n if pipi_param_name in write_exclusion_list:\n continue\n elif pipi_param_name == \"frag_clear_mz_range\":\n min_mz, max_mz = param_value\n self.params_to_write[\"min_clear_mz\"] = min_mz\n self.params_to_write[\"max_clear_mz\"] = max_mz\n elif type(pipi_param_name) is tuple:\n for pn in pipi_param_name:\n self.params_to_write[pn] = param_value\n elif pipi_param_name == \"enzyme\":\n enz, site, aa, inh = param_value.split(\";\")\n self.params_to_write[\"enzyme\"] = \"{0} {1} {2} {3}\".format(\n enz, site, aa, inh\n )\n elif pipi_param_name == \"modifications\":\n \"\"\"\n # specify additional mass and amino acid. DO NOT change the last character.\n # maximum number is 10\n # empty entries must start with 0.0\n mod01 = 79.966331@S~ # Phosphorylation\n mod02 = 79.966331@T! # Phosphorylation\n mod03 = 79.966331@Y% # Phosphorylation\n mod04 = 42.010565@K^ # Acetylation\n mod05 = 15.994915@M& # Oxidation\n mod06 = 14.015650@K* # Methylation\n mod07 = 0.0@X+\n mod08 = 0.0@X<\n mod09 = 0.0@X>\n mod10 = 0.0@X?\n \"\"\"\n n = 0\n for mod_dict in self.params[\"mods\"][\"opt\"]:\n \"\"\"\n {'_id': 0,\n 'aa': '*',\n 'composition': {'C': 2, 'H': 2, 'O': 1},\n 'id': '1',\n 'mass': 42.010565,\n 'name': 'Acetyl',\n 'org': '*,opt,Prot-N-term,Acetyl',\n 'pos': 'Prot-N-term',\n 'unimod': True},\n \"\"\"\n if mod_dict[\"pos\"] == \"Prot-N-term\":\n self.params_to_write[\"proNterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"Prot-C-term\":\n self.params_to_write[\"proCterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"N-term\":\n self.params_to_write[\"pepNterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"C-term\":\n self.params_to_write[\"pepCterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"any\":\n pass\n else:\n print(\n \"\"\"\n Unknown positional argument for given modification:\n {0}\n PIPI cannot deal with this, please use one of the follwing:\n any, Prot-N-term, Prot-C-term, N-term, C-term\n \"\"\".format(\n mod_dict[\"org\"]\n )\n )\n sys.exit(1)\n n += 1\n assert (\n n <= 10\n ), \"\"\"\n A maximum of 10 optional modifications is allowed in PIPI.\n You specified more than 10.\n \"\"\"\n if n < 10:\n mod_n = \"mod0{0}\".format(n)\n elif n == 10:\n mod_n = \"mod{0}\".format(n)\n else:\n print(\n \"\"\"\n A maximum of 10 optional modifications is allowed in PIPI.\n You specified more than 10.\n \"\"\"\n )\n sys.exit(1)\n self.params_to_write[mod_n] = \"{0}@{1}{2}\".format(\n mod_dict[\"mass\"], mod_dict[\"aa\"], symbols[n - 1]\n )\n\n for mod_dict in self.params[\"mods\"][\"fix\"]:\n if \"N-term\" in mod_dict[\"pos\"]:\n self.params_to_write[\"n\"] = mod_dict[\"mass\"]\n elif \"C-term\" in mod_dict[\"pos\"]:\n self.params_to_write[\"c\"] = mod_dict[\"mass\"]\n else:\n self.params_to_write[mod_dict[\"aa\"]] = mod_dict[\"mass\"]\n else:\n self.params_to_write[pipi_param_name] = param_value\n self.write_params_file()\n\n self.params[\"command_list\"] = [\n \"java\",\n \"-Xmx{0}\".format(\n self.params[\"translations\"][\"_grouped_by_translated_key\"][\"-Xmx\"][\n \"-xmx\"\n ]\n ),\n \"-jar\",\n self.exe,\n self.param_file_name,\n self.params[\"translations\"][\"mgf_input_file\"],\n ]\n\n return self.params","title":""},{"docid":"45b7bcbb2c25384bb8bd0c4ba1275413","score":"0.49382108","text":"def gen_pov(ldraw_path, pov_path):\n model, parts = get_model(ldraw_path)\n\n with open(pov_path, \"w\") as pov_file:\n pov_file.write(POV_HEADER + \"\\n\")\n writer = POVRayWriter(parts, pov_file)\n writer.write(model)\n pov_file.write(POV_TRAILER + \"\\n\")","title":""},{"docid":"b0f8306823fe036ca17dd5e9585dab03","score":"0.49359792","text":"def django_run(target, opt=\"XTAL\"):\r\n # Set up the OpenBaebel conversion modules\r\n sdconv = OBConversion()\r\n ligref = OBMol()\r\n # Define the residues and the proteisn to analyse\r\n if os.path.isfile(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')):\r\n \tres_d = [trans_res(x) for x in ast.literal_eval(open(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')).read())[target.title].split()]\r\n print res_d\r\n# Molecules\r\n\t # Now read in the ligand\r\n plif_method = PlifMethod()\r\n plif_method.text= \"PYPLIF\"\r\n feature_list = [\"POLAR\",\"FACE\",\"EDGE\",\"ACCEPTOR\",\"DONOR\",\"NEGATIVE\",\"POSITIVE\"]\r\n try:\r\n plif_method.validate_unique()\r\n plif_method.save()\r\n except ValidationError:\r\n \t plif_method = PlifMethod.objects.get(text=\"PYPLIF\")\r\n out_d = {}\r\n counter = 0\r\n# Create a file for the protein\r\n t = tempfile.NamedTemporaryFile(suffix=\".pdb\",delete=False)\r\n my_prot = Protein.objects.get(code=target.title+\"TEMP\")\r\n t.write(my_prot.pdb_info.name)\r\n t.close()\r\n protref = read_prot(t.name, res_d)\r\n t = tempfile.NamedTemporaryFile(suffix=\".sdf\",delete=False)\r\n t.close()\r\n sdconv.SetInFormat(\"sdf\")\r\n if opt == \"XTAL\":\r\n mols = Molecule.objects.exclude(prot_id__code__contains=target.title).filter(prot_id__target_id=target)\r\n elif opt == \"LLOOMMPPAA\":\r\n \t mols = []\r\n \t sps = SynthPoint.objects.filter(target_id=target)\r\n \t for s in sps:\r\n \t \t mols.extend([m for m in s.mol_id.all()])\r\n else:\r\n \t print \"UNKNOWN OPTION\"\r\n \t return\r\n for dj_mol in mols:\r\n \t out_sd = Chem.SDWriter(t.name)\r\n \t out_sd.write(Chem.MolFromMolBlock(str(dj_mol.sdf_info)))\r\n \t out_sd.close()\r\n \t sdconv.ReadFile(ligref, t.name)\r\n \t # Now make the new plif\r\n \t new_plif = Plif()\r\n \t new_plif.mol_id = dj_mol\r\n \t new_plif.prot_id = my_prot\r\n \t new_plif.method_id = plif_method\r\n \t try:\r\n \t \t new_plif.validate_unique()\r\n \t \t new_plif.save()\r\n \t except ValidationError:\r\n \t \t new_plif = Plif.objects.get(mol_id=dj_mol,prot_id=my_prot,method_id=plif_method)\r\n \t lig_name = ligref.GetTitle().strip(\",\")\r\n \t prot_name = lig_name.split(\"_\")[0]\r\n \t ligref.AddHydrogens()\r\n \t counter +=1\r\n \t refresdict = pp.getresiduedict(protref, res_d)\r\n \t new_d = get_fp(protref,ligref, res_d)\r\n \t for res in new_d:\r\n \t \t new_res = PlifRes()\r\n \t \t new_res.res_name = res[:3]\r\n \t \t new_res.res_num = int(res[3:])\r\n \t \t new_res.prot_id = my_prot\r\n \t \t try:\r\n \t \t \t new_res.validate_unique()\r\n \t \t \t new_res.save()\r\n \t \t except ValidationError:\r\n \t \t \t new_res = PlifRes.objects.get(res_name=res[:3],res_num=int(res[3:]),prot_id=my_prot)\r\n \t \t new_plif.res_id.add(new_res)\r\n \t \t for bit_num, bit in enumerate(new_d[res]):\r\n \t \t \t new_bit = PlifBit()\r\n \t \t \t new_bit.feature = feature_list[bit_num]\r\n \t \t \t new_bit.method_id = plif_method\r\n \t \t \t new_bit.res_id = new_res\r\n \t \t \t try:\r\n \t \t \t \t new_bit.validate_unique()\r\n \t \t \t \t new_bit.save()\r\n \t \t \t \t my_fun(dj_mol,new_bit,new_plif,bit)\r\n \t \t \t except ValidationError:\r\n\t\t\t\t\t\t\t\t\t\tnew_bit = PlifBit.objects.get(feature=feature_list[bit_num],method_id=plif_method,res_id=new_res)\r\n\t\t\t\t\t\t\t\t\t\tnew_bit.save()\r\n\t\t\t\t\t\t\t\t\t\tnew_plif.bit_id.add(new_bit)\r\n\t\t\t\t\t\t\t\t\t\tmy_fun(dj_mol,new_bit,new_plif,bit)\r\n\t\t\t\t\t\t\t\t\t\t\r\n \t ligref = OBMol()\r\n \t notatend = sdconv.Read(ligref)","title":""},{"docid":"2cea0abf4708ac80030bc376d139c8b2","score":"0.49342123","text":"def writeLP(item_count, capacity, items):\n lpfile = checkpath + '/knapsack.lp'\n f = open(lpfile, 'w')\n f.write(\"Maximize\\n\")\n f.write(\"Objective:\\n\")\n cns = \"\"\n cnt = 0\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n cns = cns + \" + \" + str(items[i].value) + \" \" + xVar\n cnt = cnt + 1\n if cnt % 5 == 4:\n f.write(cns + \"\\n\")\n cns = \"\"\n cnt = 0\n f.write(cns + \"\\n\")\n\n f.write(\"Subject To\\n\")\n cns = \"\"\n cnt = 0\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n cns = cns + \" + \" + str(items[i].weight) + \" \" + xVar\n cnt = cnt + 1\n if cnt % 5 == 4:\n f.write(cns + \"\\n\")\n cns = \"\"\n cnt = 0\n f.write(cns + \" <= \" + str(capacity) + \"\\n\")\n\n f.write(\"Binaries\\n\")\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n f.write(xVar + \"\\n\")\n f.write(\"End\")\n f.close()","title":""},{"docid":"aa711305cbfff319951e389f1f16131b","score":"0.49325135","text":"def createModelHandler(self):\n className = self.model.name.upper()\n filename = self.model.name + \".pk\"\n\n return f\"\"\"\n# Copyright 2017 Battelle Energy Alliance, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport sys\nimport pickle\nimport os\nfrom pythonfmu.fmi2slave import Fmi2Type, Fmi2Slave, Fmi2Causality, Fmi2Variability, Integer, Real, Boolean, String\n\nclass {className}(Fmi2Slave):\n #\n # RAVEN (raven.inl.gov) Model-based Python-driven simulator\n #\n author = \"RAVEN Team\"\n description = \"RAVEN Model-based Python-driven simulator\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.inputVariables = {self.inputVars}\n self.outputVariables = {self.outVars}\n # set path to raven and the serialized model\n self.raven_path = r\"{self.ravenDir}\"\n # model_path is by default the path to this model that is exported as FMU (serialized). It is stored in the resource folder\n self.model_path = self.resources + \"/\" + \"{filename}\"\n #os.path.sep\n sys.path.append(self.model_path)\n # this flag activates the initialization at the begin of the solve\n self.initialized = False\n # register raven_path variables if needed to be changed\n self.register_variable(String(\"raven_path\", causality=Fmi2Causality.parameter, variability=Fmi2Variability.tunable))\n # register input variables\n for var in self.inputVariables:\n # set var\n self.__dict__[var] = 0.0\n self.register_variable(Real(var, causality=Fmi2Causality.input))\n for var in self.outputVariables:\n # set var\n self.__dict__[var] = 0.0\n self.register_variable(Real(var, causality=Fmi2Causality.output))\n\n def setup_experiment(self, start_time: float):\n self.start_time = start_time\n if not self.initialized:\n sys.path.append(self.raven_path)\n # find the RAVEN framework\n if os.path.isdir(os.path.join(self.raven_path,\"ravenframework\")):\n # we import the Driver to load the RAVEN enviroment for the un-pickling\n try:\n import ravenframework.Driver\n except RuntimeError as ae:\n # we try to add the framework directory\n raise RuntimeError(\"Importing or RAVEN failed with error:\" +str(ae))\n else:\n print(\"framework not found in\",self.raven_path)\n # de-serialize the model\n print(\"model_path\", self.model_path)\n self.model = pickle.load(open(self.model_path, mode='rb'))\n self.initialized = True\n\n def do_step(self, current_time: float, step_size: float) -> bool:\n request = dict()\n for var in self.inputVariables:\n request[var] = self.__dict__[var]\n request['current_time'] = current_time\n request['step_size'] = step_size\n\n return_var = self.model.{self.executeMethod}(request)\n outs = return_var if isinstance(return_var,dict) else return_var[{self.indexReturnDict}]\n\n for var in outs:\n self.__dict__[var] = outs[var]\n return True\n\n\"\"\"","title":""},{"docid":"bfc1b2a35b7e5fd939b8a36de23ec2a2","score":"0.49318784","text":"def buildSolverModel(self, lp):\n log.debug(\"create the glpk model\")\n prob = glpk.glp_create_prob()\n glpk.glp_set_prob_name(prob, lp.name)\n log.debug(\"set the sense of the problem\")\n if lp.sense == constants.LpMaximize:\n glpk.glp_set_obj_dir(prob, glpk.GLP_MAX)\n log.debug(\"add the constraints to the problem\")\n glpk.glp_add_rows(prob, len(list(lp.constraints.keys())))\n for i, v in enumerate(lp.constraints.items(), start=1):\n name, constraint = v\n glpk.glp_set_row_name(prob, i, name)\n if constraint.sense == constants.LpConstraintLE:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_UP, 0.0, -constraint.constant\n )\n elif constraint.sense == constants.LpConstraintGE:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_LO, -constraint.constant, 0.0\n )\n elif constraint.sense == constants.LpConstraintEQ:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_FX, -constraint.constant, -constraint.constant\n )\n else:\n raise PulpSolverError(\"Detected an invalid constraint type\")\n constraint.glpk_index = i\n log.debug(\"add the variables to the problem\")\n glpk.glp_add_cols(prob, len(lp.variables()))\n for j, var in enumerate(lp.variables(), start=1):\n glpk.glp_set_col_name(prob, j, var.name)\n lb = 0.0\n ub = 0.0\n t = glpk.GLP_FR\n if not var.lowBound is None:\n lb = var.lowBound\n t = glpk.GLP_LO\n if not var.upBound is None:\n ub = var.upBound\n t = glpk.GLP_UP\n if not var.upBound is None and not var.lowBound is None:\n if ub == lb:\n t = glpk.GLP_FX\n else:\n t = glpk.GLP_DB\n glpk.glp_set_col_bnds(prob, j, t, lb, ub)\n if var.cat == constants.LpInteger:\n glpk.glp_set_col_kind(prob, j, glpk.GLP_IV)\n assert glpk.glp_get_col_kind(prob, j) == glpk.GLP_IV\n var.glpk_index = j\n log.debug(\"set the objective function\")\n for var in lp.variables():\n value = lp.objective.get(var)\n if value:\n glpk.glp_set_obj_coef(prob, var.glpk_index, value)\n log.debug(\"set the problem matrix\")\n for constraint in lp.constraints.values():\n l = len(list(constraint.items()))\n ind = glpk.intArray(l + 1)\n val = glpk.doubleArray(l + 1)\n for j, v in enumerate(constraint.items(), start=1):\n var, value = v\n ind[j] = var.glpk_index\n val[j] = value\n glpk.glp_set_mat_row(prob, constraint.glpk_index, l, ind, val)\n lp.solverModel = prob\n # glpk.glp_write_lp(prob, None, \"glpk.lp\")","title":""},{"docid":"90dd741a4cce0ae2de8a4b272b8dd9f3","score":"0.4929237","text":"def to_modflow(model, fname):\n try:\n import flopy\n except ImportError as e:\n raise type(e)(str(e) + \", modflow output not possible!\")\n # For modflow we want to create a new folder instead of only a file. The folder name is the base\n # name of the passed filename\n realization_dir = pathlib.Path(fname).parent\n runname = pathlib.Path(fname).name\n mfdir = realization_dir / 'MODFLOW'\n mfdir.mkdir(parents=True, exist_ok=True)\n mfname = str(mfdir / runname)\n\n # Assign name and create modflow model object\n mf = flopy.modflow.Modflow(mfname, exe_name='mf2005')\n\n # Create the discretization object\n ztop = model.grid.z0 + model.grid.lz\n zbot = model.grid.z0\n botm = np.linspace(ztop, zbot, model.grid.nz + 1)\n dis = flopy.modflow.ModflowDis(mf, model.grid.nz, model.grid.nx, model.grid.ny, \n delr=model.grid.dx, delc=model.grid.dy,\n top=ztop, botm=botm[1:])\n\n # Variables for the BAS package\n ibound = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.int32)\n ibound[:, :, 0] = -1\n ibound[:, :, -1] = -1\n\n strt = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.float32)\n strt[:, :, 0] = model.flowtrans['hin'][0]\n strt[:, :, -1] = model.flowtrans['hout'][0]\n\n bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=strt)\n\n # Assign hydraulic conductivity\n hyvr_hk = np.transpose(model.data['k_iso'], (2, 0, 1))\n hyvr_layvka = 1 # VKA dataset is ratio of horizontal K\n if 'anirat' in model.data.keys():\n hyvr_vka = np.transpose(model.data['anirat'], (2, 0, 1))\n\n # Add LPF package to the MODFLOW model\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\n hk=hyvr_hk, # Horizontal hydraulic conductivity\n layvka=hyvr_layvka, # Flag for each layer of anisotropic ratio\n vka=hyvr_vka) # Anisotropy ratios.\n else:\n # Add LPF package to the MODFLOW model\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\n hk=hyvr_hk) # Horizontal hydraulic conductivity\n\n oc = flopy.modflow.ModflowOc(mf) # Add OC package to the MODFLOW model\n pcg = flopy.modflow.ModflowPcg(mf) # Add PCG package to the MODFLOW model\n mf.write_input() # Write the MODFLOW model input files","title":""},{"docid":"486cabbe9fb6d10c75ea06f7ee5cf1a7","score":"0.49249116","text":"def write_production_mdp(prefix, rep, temp):\n\n fn = prefix + '_%d.mdp'%rep\n fout = open(fn, 'w')\n fout.write(\"\"\"title = prod_GBSA\ncpp = /lib/cpp\ninclude = -I../top\ndefine = \nintegrator = sd\ndt = 0.002\nnsteps = 25000000 ; 50 ns\nnstxout = 500000 ; every 1 ns \nnstvout = 500000\nnstlog = 5000 ; every 10 ps\nnstxtcout = 5000 ; every 10 ps\nnstenergy = 5000 ; every 10 ps\n\n\nimplicit_solvent = GBSA\ngb_algorithm = OBC\nnstgbradii = 1\ngb_epsilon_solvent = 80.0\nsa_algorithm = Ace-approximation\n\ncomm_mode = ANGULAR\n\nrgbradii = 0.9\ncoulombtype = Cut-off\nrvdw = 0.9\nrlist = 0.9\nrcoulomb = 0.9\n\n; CONSTRAINTS\nconstraints = hbonds\nconstraint_algorithm = LINCS\n\n; other stuff\nbd_fric = 1.0\nnstcomm = 10\ncomm_grps = System\nxtc_grps = System\nenergygrps = System\nnstlist = 10\nns_type = grid\ntc-grps = System \ntau_t = 0.0109\n\nref_t = %d \ncompressibility = 4.5e-5\nref_p = 1.0\ngen_vel = yes\ngen_temp = %d \ngen_seed = 1729\npbc = no\"\"\"%(temp, temp))\n\n fout.close()\n\n return fn","title":""},{"docid":"6bfcf30894e9237e9a45c1cbf4059801","score":"0.49182045","text":"def write_aloha_routines(self):\n \n\n self.aloha_model.add_Lorentz_object(self.model.get('lorentz'))\n self.aloha_model.compute_subset(self.wanted_lorentz)\n # Write out the aloha routines in Python\n aloha_routines = []\n \n # First add the default external wavefunction routines\n wavefunction_routines = open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read() \n open(pjoin(self.dir_path,'wavefunctions.py'),'w').write(\n 'from __future__ import division\\n'+wavefunction_routines)\n\n #aloha_routines.append(open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read())\n\n # Now write the process-depenent Feynman rules ones\n for routine in self.aloha_model.values():\n aloha_routines.append(routine.write(output_dir = None, \n mode='mg5',\n language = 'Python'))\n\n for routine in self.aloha_model.external_routines:\n aloha_routines.append(\n open(self.aloha_model.locate_external(routine, 'Python')).read())\n\n # Now collect imports\n python_imports = []\n new_aloha_routines = []\n for aloha_routine in aloha_routines:\n new_aloha_routine = []\n for line in aloha_routine.split('\\n'):\n if any(line.startswith(token) for token in ['from','import']):\n if line not in python_imports:\n python_imports.append(line)\n else:\n new_aloha_routine.append(line)\n new_aloha_routines.append('\\n'.join(new_aloha_routine))\n aloha_routines = new_aloha_routines\n \n # Veto some imports\n vetoed_imports = ['import aloha.template_files.wavefunctions as wavefunctions']\n python_imports = [pi for pi in python_imports if pi not in vetoed_imports]\n python_imports.insert(0, 'import wavefunctions')\n\n aloha_output = open(pjoin(self.dir_path,'aloha_methods.py'),'w')\n aloha_output.write('from __future__ import division\\n')\n # Write imports\n aloha_output.write('\\n'.join(python_imports))\n aloha_output.write('\\n'*2)\n\n # Write routines\n aloha_output.write('\\n'.join(aloha_routines))\n\n aloha_output.close()","title":""},{"docid":"f73793368d062ed8010c45c9ae955d77","score":"0.49119902","text":"def run(self):\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\n self._openScriptFile()\n self.scriptf.write(\"from phycas import *\\n\\n\")\n\n if self.opts.seed > 0:\n self.scriptf.write('setMasterSeed(%d)\\n\\n' % self.opts.seed)\n\n if self.opts.model == 'jc':\n self.model_jc(0)\n elif self.opts.model == 'jc+i':\n self.model_jc(1)\n elif self.opts.model == 'jc+g':\n self.model_jc(2)\n elif self.opts.model == 'jc+i+g':\n self.model_jc(3)\n elif self.opts.model == 'hky':\n self.model_hky(0)\n elif self.opts.model == 'hky+i':\n self.model_hky(1)\n elif self.opts.model == 'hky+g':\n self.model_hky(2)\n elif self.opts.model == 'hky+i+g':\n self.model_hky(3)\n elif self.opts.model == 'gtr':\n self.model_gtr(0)\n elif self.opts.model == 'gtr+i':\n self.model_gtr(1)\n elif self.opts.model == 'gtr+g':\n self.model_gtr(2)\n elif self.opts.model == 'gtr+i+g':\n self.model_gtr(3)\n elif self.opts.model == 'codon':\n self.model_codon()\n\n self.model_edgelen()\n\n if self.opts.datafile is None:\n self.scriptf.write(\"mcmc.data_source = None\\n\")\n elif self.opts.datafile == 'sample.nex':\n self.saveSampleData()\n self.scriptf.write(\"mcmc.data_source = '%s'\\n\\n\" % self.opts.datafile)\n else:\n self.scriptf.write(\"mcmc.data_source = '%s'\\n\\n\" % self.opts.datafile)\n\n if self.opts.analysis == 'mcmc':\n self.mcmc_analysis()\n self.sump_analysis()\n self.sumt_analysis()\n if self.opts.analysis == 'poly':\n self.polytomy_analysis()\n elif self.opts.analysis == 'cpo':\n self.cpo_analysis()\n #elif self.opts.analysis == 'idr':\n # self.idr_analysis()\n elif self.opts.analysis == 'ss':\n self.steppingstone_analysis()\n\n self._closeScriptFile()","title":""},{"docid":"c3e42d57ee79ba479dae9b3b96b226d2","score":"0.49086118","text":"def build_model(out: ModelOutput = ModelOutput(\"malt/swemalt-1.7.2.mco\"),\n _maltjar: Binary = Binary(\"[malt.jar]\")):\n out.download(\"http://maltparser.org/mco/swedish_parser/swemalt-1.7.2.mco\")","title":""},{"docid":"e9a026d51eff7c21f70c849e56481fa5","score":"0.49045718","text":"def to_alien(self, file_pointer, format='opb', comments=None):\n\n if self.atms and format == 'smt':\n raise NotImplementedError('SMT-LIB2 does not support PB constraints directly; you may want to use Z3\\'s API instead')\n\n cchars = {'lp': '\\\\', 'opb': '*', 'smt': ';'}\n\n # saving formula's internal comments\n for c in self.comments:\n print(cchars[format], c, file=file_pointer)\n\n # saving externally specified comments\n if comments:\n for c in comments:\n print(cchars[format], c, file=file_pointer)\n\n # normalized soft clauses\n soft, hard = [], []\n topv = self.nv + 1\n for cl in self.soft:\n if len(cl) == 1:\n soft.append(cl)\n else:\n hard.append([topv] + cl)\n soft.append([topv])\n topv += 1\n\n if format == 'opb':\n print('* #variable= {0} #constraint= {1}'.format(self.nv, len(self.hard) + len(hard)),\n file=file_pointer)\n print('min:',\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\n ';', file=file_pointer)\n elif format == 'lp':\n print('Minimize', file=file_pointer)\n print('obj:',\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\n file=file_pointer)\n print('Subject To', file=file_pointer)\n elif format == 'smt':\n for v in range(1, self.nv + 1):\n print('(declare-fun x{0} () Bool)'.format(v), file=file_pointer)\n\n for i, cl in enumerate(self.hard + hard, 1):\n line, neg = [], 0\n for l in cl:\n if l > 0:\n if format == 'smt':\n line.append('x{0}'.format(l))\n else:\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', l))\n else:\n if format == 'smt':\n line.append('(not x{0})'.format(-l))\n else:\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', -l))\n neg += 1\n\n if format == 'smt':\n print('(assert (or {0}))'.format(' '.join(line)), file=file_pointer)\n else:\n print('{0}{1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}: '.format(i),\n ' '.join(l for l in line),\n 1 - neg, ';' if format == 'opb' else ''),\n file=file_pointer)\n\n for i, am in enumerate(self.atms, len(self.hard) + len(hard) + 1):\n line, neg = [], 0\n for l in am[0]:\n if l > 0:\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', l))\n neg += 1\n else:\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', -l))\n\n print('{0} {1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}:'.format(i),\n ' '.join(l for l in line),\n len(am[0]) - am[1] - neg, ';' if format == 'opb' else ''),\n file=file_pointer)\n\n if format == 'lp':\n print('Bounds', file=file_pointer)\n for v in range(1, topv):\n print('0 <= x{0} <= 1'.format(v), file=file_pointer)\n print('Binary', file=file_pointer)\n for v in range(1, topv):\n print('x{0}'.format(v), file=file_pointer)\n print('End', file=file_pointer)\n elif format == 'smt':\n for cl, w in zip(soft, self.wght):\n l = 'x{0}'.format(cl[0]) if cl[0] > 0 else '(not x{0})'.format(-cl[0])\n print('(assert-soft {0} :weight {1})'.format(l, w), file=file_pointer)\n\n print('(check-sat)', file=file_pointer)\n print('(get-model)', file=file_pointer)\n print('(get-objectives)', file=file_pointer)\n print('(exit)', file=file_pointer)","title":""},{"docid":"1bed09b7d57987e01641bd6feb172467","score":"0.4902589","text":"def copy_files_export_model(geomodel_dir, geomodeller_file, new_dir, nx, ny, nz, **kwds):\n \n \n \n os.chdir(geomodel_dir)\n \n \n # first step: determine all Geomodeller files in original directory\n geomodel_files = []\n for l in os.listdir(\".\"):\n if os.path.splitext(l)[1] == \".sec\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".s3d\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".md5\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".wsp\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".xml\":\n # This is the original file - keep for later\n geomodel_xml_ori = l\n \n print geomodel_xml_ori\n os.chdir(new_dir)\n\n for f in geomodel_files:\n shutil.copyfile(os.path.join(geomodel_dir, f), os.path.join(\".\",f))\n\n for l2 in os.listdir(\".\"):\n if l2 == geomodeller_file:\n print l2\n shutil.copyfile(l2,os.path.join(\".\", geomodel_xml_ori))\n if \"original\" in l2:\n new_grid = \"exported_grid_original.txt\"\n new_delxyz = \"delxyz_original.txt\"\n else:\n new_grid = \"exported_grid_\" + os.path.splitext(l2)[0] + \".txt\"\n new_delxyz = \"delxyz_\" + os.path.splitext(l2)[0] + \".txt\"\n \n if kwds.has_key('nz_list'): # cell discretization study\n try:\n i = int(l2[-7:-4])\n except ValueError:\n os.chdir('..') \n continue # original model\n subprocess.call(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, kwds['nz_list'][i]), shell=True)\n else:\n\n while True:\n proc = subprocess.Popen(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\n for i in range(10):\n time.sleep(5)\n if proc.poll() == 0:\n print(\"Process finished successfully\")\n break\n if proc.poll() == None:\n proc.kill()\n print(\"\\n\\n\\tProcess killed, restart!\\n\\n\")\n continue\n elif proc.poll() == 0:\n print(\"Process finished successfully\")\n break\n print(\"\\n\\n\\tProcess not finished correctly!\\n\\n\\n\")\n break\n # subprocess.call(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\n shutil.copyfile(\"exported_grid.txt\", os.path.join(\".\", new_grid))\n shutil.copyfile(\"delxyz.txt\", os.path.join(\".\", new_delxyz))","title":""},{"docid":"8dc902536351b3d417ec8d2e8f7e209e","score":"0.48992693","text":"def convert2lite(save_dir,\n lite_valid_places=\"arm\",\n lite_model_type=\"naive_buffer\"):\n\n from paddlelite.lite import Opt\n opt = Opt()\n opt.set_model_dir(save_dir + \"/inference_model\")\n opt.set_valid_places(lite_valid_places)\n opt.set_model_type(lite_model_type)\n opt.set_optimize_out(save_dir + \"/opt\")\n opt.run()","title":""},{"docid":"38860f271edd9ad00978731b25db1a2b","score":"0.48990908","text":"def makefile2dot(**kwargs):\n\n direction = kwargs.get('direction', \"BT\")\n if direction not in [\"LR\", \"RL\", \"BT\", \"TB\"]:\n raise ValueError('direction must be one of \"BT\", \"TB\", \"LR\", RL\"')\n\n output = kwargs.get('output', '')\n view = kwargs.get('view', False)\n\n graph = build_graph(stream_database(), direction=direction)\n if output == \"\":\n if view:\n graph.view()\n else:\n print(graph)\n else:\n with open(output, 'w') as file:\n file.write(str(graph))\n if view:\n graph.view()","title":""},{"docid":"7c4d103572b36b3f5587fd6cccb6325d","score":"0.48969853","text":"def load_model(fp: str):\n pass","title":""},{"docid":"6128a8fd86ce81b50e836d0234155722","score":"0.48924553","text":"def writemodelfile(self, classpy=None):\n if not classpy is None:\n classpy.close()\n pyfile = '%s.py' % os.path.join(self.modelspath, self.thismodule)\n classpy = open(pyfile, 'r')\n lines = classpy.readlines()\n lines.insert(2, '\\n')\n classpy.close()\n froms = list()\n for line in lines:\n prefix = ''\n if 'ForeignKey' in line or 'OneToOneField' in line \\\n or 'ManyToManyField' in line:\n idx = lines.index(line)\n third = ''\n paren = False\n comma = False\n first, second = line.split('(', 1)\n if not 'self' in second: # do nothing if it is\n if ',' in second: # some options exist\n second, third = second.split(',', 1)\n third = ', %s' % third\n if ')' in second: # that's all in the line\n paren = True # so it can be put back\n second = second.replace(')', '').strip()\n if \"'\" in second:\n second = second.replace(\"'\", '').strip()\n if '\"' in second:\n second = second.replace('\"', '').strip()\n relatedmodel = second # totally naked\n assert len(second.split()) == 1\n module = self.checkthisimport(relatedmodel)\n if not module is None:\n if module.startswith('.'):\n prefix = '#'\n line = \"%s('%s'%s\" % (first, second, third)\n else:\n line = \"%s(%s%s\" % (first, second, third)\n if paren:\n line = '%s)\\n' % line.rstrip()\n if not line.endswith('\\n'):\n line = '%s\\n' % line\n if idx:\n lines[idx] = line\n if module:\n impline = '%sfrom %s import %s\\n' % (prefix, module,\n relatedmodel)\n else:\n impline = 'import %s\\n' % model\n if not impline in froms:\n #print(impline)\n froms.insert(0, impline)\n # now find all the other imported items\n chunk = line.replace('=', ' ')\n chunk = chunk.replace('[', ' ')\n chunk = chunk.replace('(', ' ')\n chunk = chunk.replace(')', ' ')\n chunk = chunk.replace('.', ' ')\n chunk = chunk.replace(']', ' ')\n chunk = chunk.replace(',', ' ')\n bits = chunk.split()\n for bit in bits:\n if self.seemsok(bit, line):\n module = self.checkthisimport(bit)\n if not module is None:\n if module == '':\n impline = 'import %s\\n' % bit\n else:\n impline = 'from %s import %s\\n' % (module, bit)\n if not impline in froms:\n froms.insert(0, impline)\n\n clean = list()\n for item in froms:\n if not '__future__' in item:\n if not item in clean:\n clean.insert(0, item)\n cleaner = self.removethismodule(clean)\n del(clean)\n cleaner.sort()\n cleaner.reverse()\n if aggregate:\n cleanest = self.aggregatefroms(cleaner)\n else:\n cleanest = cleaner\n for item in cleanest:\n lines.insert(self.startline, item)\n classpy = open(pyfile, 'w')\n classpy.writelines(lines)\n classpy.close()","title":""},{"docid":"6b24a82db4e8a7271fd5aaa542794f09","score":"0.48900214","text":"def _translate_and_simulate(self, simulate):\n import os\n import shutil\n import jinja2\n import datetime\n\n # Delete output files\n self.deleteOutputFiles()\n\n worDir = self._outputDir_\n\n # Construct the model instance with all parameter values\n # and the package redeclarations\n dec = self._declare_parameters()\n dec.extend(self._modelModifiers_)\n\n if len(dec) == 0:\n model_modifier = \"\"\n else:\n model_modifier = '{dec}'.format(mn=self.modelName, dec=','.join(dec))\n\n file_name = \"{}.py\".format(self.modelName.replace(\".\", \"_\"))\n## self._time_stamp_old_files = datetime.datetime.now()\n with open(os.path.join(worDir, file_name), mode=\"w\", encoding=\"utf-8\") as fil:\n path_to_template = os.path.join(\n os.path.dirname(__file__), os.path.pardir, \"development\")\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(path_to_template))\n\n template = env.get_template(\"optimica_run.template\")\n\n # Note that filter argument must respect glob syntax ([ is escaped with []]) + OPTIMICA mat file\n # stores matrix variables with no space e.g. [1,1].\n txt = template.render(\n model=self.modelName,\n model_modifier=model_modifier,\n ncp=self._simulator_.get('numberOfIntervals'),\n rtol=self._simulator_.get('eps'),\n solver=self._simulator_.get('solver'),\n start_time=self._simulator_.get('t0') if self._simulator_.get(\n 't0') is not None else 'mod.get_default_experiment_start_time()',\n final_time=self._simulator_.get('t1') if self._simulator_.get(\n 't1') is not None else 'mod.get_default_experiment_stop_time()',\n result_file_name=f\"{self._simulator_.get('resultFile')}.mat\",\n simulate=simulate,\n time_out=self._simulator_.get('timeout'),\n filter=self._result_filter,\n generate_html_diagnostics=self._generate_html_diagnostics,\n debug_solver=self._debug_solver,\n debug_solver_interactive_mode=self._debug_solver_interactive_mode)\n\n fil.write(txt)\n shutil.copyfile(\n os.path.join(\n os.path.dirname(__file__),\n \"OutputGrabber.py\"),\n os.path.join(\n worDir,\n \"OutputGrabber.py\"))\n\n try:\n super()._runSimulation([\"jm_ipython.sh\", file_name],\n self._simulator_.get('timeout'),\n worDir)\n\n self._check_simulation_errors(worDir=worDir, simulate=simulate)\n# self._copyNewFiles(worDir)\n# self._deleteTemporaryDirectory(worDir)\n\n except Exception as e: # Catch all possible exceptions\n em = f\"Simulation failed in '{worDir}'\\n Exception: {e}.\\n You need to delete the directory manually.\\n\"\n self._reporter.writeError(em)\n raise e\n os.remove(os.path.join(worDir, \"OutputGrabber.py\"))","title":""},{"docid":"989010fe3938e83cdafa116524121a61","score":"0.4889075","text":"def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):\n from pathlib import Path\n\n from stamp_processing.module.yolov5.models.experimental import attempt_load\n from stamp_processing.module.yolov5.models.yolo import Model\n from stamp_processing.module.yolov5.utils.torch_utils import select_device\n\n file = Path(__file__).absolute()\n\n save_dir = Path(\"\") if str(name).endswith(\".pt\") else file.parent\n path = (save_dir / name).with_suffix(\".pt\") # checkpoint path\n try:\n device = select_device((\"0\" if torch.cuda.is_available() else \"cpu\") if device is None else device)\n\n if pretrained and channels == 3 and classes == 80:\n model = attempt_load(path, map_location=device) # download/load FP32 model\n else:\n cfg = list((Path(__file__).parent / \"models\").rglob(f\"{name}.yaml\"))[0] # model.yaml path\n model = Model(cfg, channels, classes) # create model\n if autoshape:\n model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS\n return model.to(device)\n\n except Exception as e:\n help_url = \"https://github.com/ultralytics/yolov5/issues/36\"\n s = \"Cache may be out of date, try `force_reload=True`. See %s for help.\" % help_url\n raise Exception(s) from e","title":""},{"docid":"1163e643e5b668503b7ff3340a328791","score":"0.48880547","text":"def make_dot(aut_fname, dot_fname):\n coll = collection.Collection()\n aut = load_automaton(coll, aut_fname, False, False)\n\n make_backup_file(dot_fname)\n\n dot_handle = open(dot_fname, 'w')\n dot_handle.write(aut.to_dot())\n dot_handle.close()","title":""}],"string":"[\n {\n \"docid\": \"67db92a1a3821a68008dc249af915482\",\n \"score\": \"0.5871873\",\n \"text\": \"def create_model(self):\\r\\n\\r\\n self.try_load_model()\\r\\n\\r\\n self.set_correct_problem_instance_name()\\r\\n\\r\\n self.add_objective_and_budget_constraints()\\r\\n self.add_starting_ending_nodes_constraints()\\r\\n\\r\\n self.docplex_model.print_information()\\r\\n\\r\\n # DEBUG\\r\\n # for constraint in self.docplex_model.iter_constraints():\\r\\n # print(constraint)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e1197a8a29532e1bb1bc651f420051\",\n \"score\": \"0.5701343\",\n \"text\": \"def formula_generator(known_args, other_args): # pylint: disable=W0613\\n poly = Poly(*known_args.format)\\n\\n if known_args.smt2:\\n with open(known_args.smt2, 'w') as smt2_file:\\n with redirect_stdout(smt2_file):\\n poly.problem(known_args.degree)\\n else:\\n poly.problem(known_args.degree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1717949860fb45cd73863fd5f97acc0e\",\n \"score\": \"0.56868994\",\n \"text\": \"def __init__(self, model, output_path, wanted_lorentz = [],\\n wanted_couplings = [], replace_dict={}):\\n\\n self.model = model\\n self.model_name = export_cpp.ProcessExporterCPP.get_model_name(model['name'])\\n self.aloha_model = create_aloha.AbstractALOHAModel(self.model_name)\\n\\n\\n self.dir_path = output_path\\n self.default_replace_dict = dict(replace_dict)\\n # List of needed ALOHA routines\\n self.wanted_lorentz = wanted_lorentz\\n self.wanted_couplings = wanted_couplings\\n\\n # For dependent couplings, only want to update the ones\\n # actually used in each process. For other couplings and\\n # parameters, just need a list of all.\\n self.coups_dep = {} # name -> base_objects.ModelVariable\\n self.coups_indep = [] # base_objects.ModelVariable\\n self.params_dep = [] # base_objects.ModelVariable\\n self.params_indep = [] # base_objects.ModelVariable\\n self.p_to_cpp = None \\n\\n # Prepare parameters and couplings for writeout in C++\\n self.prepare_parameters()\\n self.prepare_couplings(wanted_couplings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5525bfc1f28e0a667830ec014bdb725\",\n \"score\": \"0.56353825\",\n \"text\": \"def convertProcessToLcia(O,p):\\r\\n \\r\\n # the players\\r\\n P = O.findUuid(os.path.splitext(p)[0])\\r\\n print 'Importing: ' + P.El('baseName').text\\r\\n L = O.createLciaFromTemplate()\\r\\n \\r\\n lcia_ns = L.getroot().nsmap\\r\\n \\r\\n # the basic data\\r\\n L.El('name','common').text = P.El('baseName').text\\r\\n L.El('methodology').text = 'TRACI 2.0'\\r\\n L.El('impactCategory').text = selectFromList(impactCategories)\\r\\n L.El('impactIndicator').text = raw_input('Impact Indicator: ')\\r\\n L.El('referenceYear').text = P.El('referenceYear','common').text\\r\\n L.El('duration').text = 'indefinite'\\r\\n L.El('interventionLocation').text = 'US'\\r\\n L.El('impactLocation').text = 'US'\\r\\n\\r\\n L.El('typeOfDataSet').text = selectFromList(indicatorTypes)\\r\\n L.El('normalisation').text= 'false'\\r\\n L.El('weighting').text = 'false'\\r\\n \\r\\n QR=L.El('quantitativeReference')\\r\\n CF=L.El('characterisationFactors')\\r\\n \\r\\n # the exchanges\\r\\n Xs = P.El('exchanges').getchildren()\\r\\n for x in Xs:\\r\\n flow_el = nonemap(x,'referenceToFlowDataSet')\\r\\n flow_uuid = flow_el.attrib['refObjectId']\\r\\n flow_comment = flow_el.getchildren()[0]\\r\\n if nonemap(x, 'exchangeDirection').text == 'Input':\\r\\n # Input exchange = reference flow\\r\\n ref_uuid = getReferenceFlow(O.findUuid(flow_uuid), nsmap = lcia_ns)\\r\\n RQ = ET.Element('referenceQuantity',\\r\\n attrib = {'type':'flow property data set',\\r\\n 'refObjectId':ref_uuid,\\r\\n 'uri':'../flowproperties/{0}.xml'.format(ref_uuid)},\\r\\n nsmap = lcia_ns)\\r\\n RQ.append(flow_comment)\\r\\n QR.append(RQ)\\r\\n print 'reference flow: {0} {1}'.format(ref_uuid,flow_comment.text)\\r\\n \\r\\n else:\\r\\n # Output exchange = new factor\\r\\n mean_value = 1 / float(nonemap(x, 'resultingAmount').text) # note inversion!\\r\\n F = ET.Element('factor',nsmap = lcia_ns)\\r\\n FL = ET.Element('referenceToFlowDataSet', \\r\\n attrib={'type':'flow data set',\\r\\n 'refObjectId':flow_uuid,\\r\\n 'uri':'../flows/{0}.xml'.format(flow_uuid)},\\r\\n nsmap = lcia_ns)\\r\\n FL.append(flow_comment)\\r\\n F.append(FL)\\r\\n ET.SubElement(F, 'exchangeDirection',nsmap = lcia_ns).text='Output'\\r\\n ET.SubElement(F, 'meanValue', nsmap = lcia_ns).text = str( mean_value ) \\r\\n CF.append(F)\\r\\n print 'exchange: {0} {1}'.format(flow_uuid,flow_comment.text)\\r\\n\\r\\n return L\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f2020c341a65e6214adebd68e7903e\",\n \"score\": \"0.56281185\",\n \"text\": \"def generate_and_save_all_interpretations_for_model(pci_obj, X, y, mdl, mdl_name):\\n \\n ## Generate dataset-level interpretation for Logistic Regression model\\n print('Dataset-level interpretation generation is in process for ' + mdl_name + '.')\\n dataset_level_interpretation = pci_obj.dataset_level_interpretation(X, y, mdl)\\n np.savetxt('./result/' + 'ACES_dataset_level_interpretation_' + mdl_name + '.txt', dataset_level_interpretation, fmt='%1.8e')\\n print('Dataset-level interpretation generation is finished for' + mdl_name + '.')\\n \\n \\n ## Generate whole dataset signed interpretation for Logistic Regression model\\n print('Whole dataset signed interpretation generation is in process for ' + mdl_name + '.')\\n whole_dataset_signed_interpretation = pci_obj.whole_dataset_signed_interpretation(X, y, mdl)\\n np.savetxt('./result/' + 'ACES_whole_dataset_signed_interpretation_' + mdl_name + '.txt', whole_dataset_signed_interpretation, fmt='%1.8e')\\n print('Whole dataset signed interpretation generation is finished for ' + mdl_name + '.')\\n \\n ## Generate whole dataset signed interpretation for Logistic Regression model\\n print('Whole dataset unsigned interpretation generation is in process for ' + mdl_name + '.')\\n whole_dataset_unsigned_interpretation = pci_obj.whole_dataset_unsigned_interpretation(X, y, mdl)\\n np.savetxt('./result/' + 'ACES_whole_dataset_unsigned_interpretation_' + mdl_name + '.txt', whole_dataset_unsigned_interpretation, fmt='%1.8e')\\n print('Whole dataset unsigned interpretation generation is finished for ' + mdl_name + '.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c5748803c3d19cc5d921a875e0b086\",\n \"score\": \"0.5581633\",\n \"text\": \"def convert(prototxt_path, caffemodel_path, phase='test', output_model_name='freeze_graph_model.pb'):\\n assert_exist_files(prototxt_path, caffemodel_path)\\n\\n data_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.mat')\\n code_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.py')\\n standalone_output_path = os.path.join(os.path.dirname(prototxt_path), output_model_name)\\n\\n try:\\n with tf.Session() as sess:\\n transformer = TensorFlowTransformer(prototxt_path, caffemodel_path, phase=phase)\\n print_stderr('Converting data...')\\n if data_output_path is not None:\\n data = transformer.transform_data()\\n print_stderr('Saving data...')\\n with open(data_output_path, 'wb') as data_out:\\n np.save(data_out, data)\\n if code_output_path is not None:\\n print_stderr('Saving source...')\\n with open(code_output_path, 'wb') as src_out:\\n src_out.write(transformer.transform_source())\\n\\n if standalone_output_path:\\n filename, _ = os.path.splitext(os.path.basename(standalone_output_path))\\n temp_folder = os.path.join(os.path.dirname(standalone_output_path), 'tmp')\\n\\n if not os.path.exists(temp_folder):\\n os.makedirs(temp_folder)\\n\\n if data_output_path is None:\\n data = transformer.transform_data()\\n print_stderr('Saving data...')\\n data_output_path = os.path.join(temp_folder, filename) + '.npy'\\n with open(data_output_path, 'wb') as data_out:\\n np.save(data_out, data)\\n\\n if code_output_path is None:\\n print_stderr('Saving source...')\\n code_output_path = os.path.join(temp_folder, filename) + '.py'\\n with open(code_output_path, 'wb') as src_out:\\n src_out.write(transformer.transform_source())\\n\\n checkpoint_path = os.path.join(temp_folder, filename + '.ckpt')\\n graph_name = os.path.basename(standalone_output_path)\\n graph_folder = os.path.dirname(standalone_output_path)\\n input_node = transformer.graph.nodes[0].name\\n output_node = transformer.graph.nodes[-1].name\\n tensor_shape = transformer.graph.get_node(input_node).output_shape\\n tensor_shape_list = [tensor_shape.batch_size, tensor_shape.height, tensor_shape.width, tensor_shape.channels]\\n\\n sys.path.append(os.path.dirname(code_output_path))\\n module = os.path.splitext(os.path.basename(code_output_path))[0]\\n class_name = transformer.graph.name\\n KaffeNet = getattr(__import__(module), class_name)\\n\\n data_placeholder = tf.placeholder(tf.float32, tensor_shape_list, name=input_node)\\n net = KaffeNet({input_node: data_placeholder})\\n\\n # load weights stored in numpy format\\n net.load(data_output_path, sess)\\n\\n print_stderr('Saving checkpoint...')\\n\\n saver = tf.train.Saver()\\n saver.save(sess, checkpoint_path)\\n\\n print_stderr('Saving graph definition as protobuf...')\\n tf.train.write_graph(sess.graph.as_graph_def(), graph_folder, graph_name, as_text=False)\\n\\n input_graph_path = standalone_output_path\\n input_saver_def_path = \\\"\\\"\\n input_binary = True\\n input_checkpoint_path = checkpoint_path\\n output_node_names = output_node\\n restore_op_name = 'save/restore_all'\\n filename_tensor_name = 'save/Const:0'\\n output_graph_path = standalone_output_path\\n clear_devices = True\\n\\n print_stderr('Saving standalone model...')\\n freeze_graph(input_graph_path, input_saver_def_path,\\n input_binary, input_checkpoint_path,\\n output_node_names, restore_op_name,\\n filename_tensor_name, output_graph_path,\\n clear_devices, '')\\n\\n shutil.rmtree(temp_folder)\\n\\n print_stderr('Done.')\\n except KaffeError as err:\\n fatal_error('Error encountered: {}'.format(err))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870fd0325523f72544b469d902b7c2cc\",\n \"score\": \"0.5544181\",\n \"text\": \"def convert_odml():\\n f_in = join(ROOT, \\\"tmp.xml\\\")\\n f_out = join(ROOT, \\\"tmpout.xml\\\")\\n\\n o_ver_conv = VersionConverter(f_in)\\n\\n # either write to file\\n o_ver_conv.write_to_file(f_out)\\n\\n # or pass it to the python xml parser\\n doc = ODMLReader(parser='XML').from_string(str(o_ver_conv))\\n doc.pprint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d270ed9448ab6ff02368268de72a91b\",\n \"score\": \"0.5542094\",\n \"text\": \"def compare_lp_files(self, filename, ignored=None, my_om=None):\\n if my_om is None:\\n om = self.get_om()\\n else:\\n om = my_om\\n tmp_filename = filename.replace('.lp', '') + '_tmp.lp'\\n new_filename = ospath.join(self.tmppath, tmp_filename)\\n om.write(new_filename, io_options={'symbolic_solver_labels': True})\\n logging.info(\\\"Comparing with file: {0}\\\".format(filename))\\n with open(ospath.join(self.tmppath, tmp_filename)) as generated_file:\\n with open(ospath.join(ospath.dirname(ospath.realpath(__file__)),\\n \\\"lp_files\\\",\\n filename)) as expected_file:\\n\\n def chop_trailing_whitespace(lines):\\n return [re.sub(r'\\\\s*$', '', ln) for ln in lines]\\n\\n def remove(pattern, lines):\\n if not pattern:\\n return lines\\n return re.subn(pattern, \\\"\\\",\\n \\\"\\\\n\\\".join(lines))[0].split(\\\"\\\\n\\\")\\n\\n expected = remove(ignored,\\n chop_trailing_whitespace(\\n expected_file.readlines()))\\n generated = remove(ignored,\\n chop_trailing_whitespace(\\n generated_file.readlines()))\\n\\n def normalize_to_positive_results(lines):\\n negative_result_indices = [\\n n for n, line in enumerate(lines)\\n if re.match(\\\"^= -\\\", line)]\\n equation_start_indices = [\\n [n for n in reversed(range(0, nri))\\n if re.match('.*:$', lines[n])][0]+1\\n for nri in negative_result_indices]\\n for (start, end) in zip(\\n equation_start_indices,\\n negative_result_indices):\\n for n in range(start, end):\\n lines[n] = (\\n '-'\\n if lines[n] and lines[n][0] == '+'\\n else '+'\\n if lines[n]\\n else lines[n]) + lines[n][1:]\\n lines[end] = '= ' + lines[end][3:]\\n return lines\\n\\n expected = normalize_to_positive_results(expected)\\n generated = normalize_to_positive_results(generated)\\n\\n eq_(generated, expected,\\n \\\"Failed matching expected with generated lp file:\\\\n\\\" +\\n \\\"\\\\n\\\".join(unified_diff(expected, generated,\\n fromfile=ospath.relpath(\\n expected_file.name),\\n tofile=ospath.basename(\\n generated_file.name),\\n lineterm=\\\"\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bffa7248d281833024e2e2237090aa94\",\n \"score\": \"0.55059963\",\n \"text\": \"def model_to_owl(model, fname):\\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\\n\\n try:\\n fileOS = autoclass('java.io.FileOutputStream')(fname)\\n except JavaException:\\n logger.error('Could not open data file %s' % fname)\\n return\\n l3_factory = autoclass('org.biopax.paxtools.model.BioPAXLevel').L3.getDefaultFactory()\\n model_out = l3_factory.createModel()\\n for r in model.getObjects().toArray():\\n model_out.add(r)\\n io.convertToOWL(model_out, fileOS)\\n\\n fileOS.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"016e6167d18373b2c352dac2e0aac10f\",\n \"score\": \"0.54671896\",\n \"text\": \"def run(in_obo_filename=IN_OBO_FILENAME, in_pkl_filename=IN_PKL_FILENAME, in_tsv_filename=IN_TSV_FILELANE,\\n in_monomers_filename=IN_MONOMERS_FILENAME,\\n max_num_proteins=None,\\n out_pickle_filename=OUT_PICKLE_FILENAME, out_pickle_filename_2=OUT_PICKLE_FILENAME_2,\\n out_tsv_filename=OUT_TSV_FILENAME, out_fasta_filename=OUT_FASTA_FILENAME,\\n out_fig_filename=OUT_FIG_FILENAME, out_structure_dirname=OUT_STRUCTURE_DIRNAME,\\n out_viz_dirname=OUT_VIZ_DIRNAME):\\n # get the PRO ontology and extract the modified proteins from the ontology\\n # proteins = get_pro_from_obo(obo_filename=in_obo_filename, pkl_filename=in_pkl_filename, max_num_proteins=max_num_proteins)\\n proteins = get_pro_from_tsv(in_tsv_filename, max_num_proteins=max_num_proteins)\\n\\n # parse the modified proteins and retrieve their sequences\\n if not os.path.isfile(out_pickle_filename):\\n # parse the modified proteins and retrieve their sequences\\n parsed_proteins = []\\n for i_protein, protein in enumerate(proteins):\\n if i_protein % 100 == 0:\\n print('Parsing protein {} of {}'.format(i_protein + 1, len(proteins)))\\n parsed_proteins.append(parse_protein(protein))\\n\\n # save the parsed proteins in pickle format\\n with open(out_pickle_filename, 'wb') as file:\\n pickle.dump(parsed_proteins, file)\\n else:\\n # load saved parsed proteins in pickle format\\n with open(out_pickle_filename, 'rb') as file:\\n parsed_proteins = pickle.load(file)\\n\\n # read list of monomers\\n monomers = {}\\n with open(in_monomers_filename, 'r') as file:\\n reader = csv.DictReader(file, dialect='excel')\\n for row in reader:\\n monomers[row['PRO id']] = {\\n 'mod': bpforms.protein_alphabet.monomers.get(row['BpForms code'], None),\\n 'origin': [],\\n }\\n if row['Base monomer']:\\n monomers[row['PRO id']]['origin'] = row['Base monomer'].split(', ')\\n\\n # generate list of modified monomeric forms\\n for protein in parsed_proteins:\\n for modification in protein['modifications']:\\n if modification['monomer'] not in monomers:\\n monomers[modification['monomer']] = {\\n 'mod': None,\\n 'origin': [],\\n }\\n\\n # print list of unmapped monomers\\n unmapped_monomers = []\\n for monomer, code in monomers.items():\\n if not code['mod']:\\n unmapped_monomers.append(monomer)\\n unmapped_monomers.sort()\\n if unmapped_monomers:\\n print('Several PRO monomeric forms have not been mapped to BpForms monomeric forms:\\\\n {}'.format(\\n '\\\\n '.join(unmapped_monomers)))\\n\\n # check for inconsistencies between residue and modified monomeric form\\n monomer_codes = {}\\n for code, monomer in bpforms.protein_alphabet.monomers.items():\\n monomer_codes[monomer] = code\\n\\n for protein in parsed_proteins:\\n for modification in protein.get('modifications', []):\\n if modification['residue'] and modification['monomer']:\\n monomer = monomers.get(modification['monomer'], None)\\n if (monomer['mod'] and monomer['mod'].get_canonical_code(monomer_codes) != modification['residue']) \\\\\\n or (monomer['origin'] and modification['residue'] not in monomer['origin']):\\n codes = set(monomer['origin'])\\n if monomer['mod']:\\n codes.add(monomer['mod'].get_canonical_code(monomer_codes))\\n msg = 'Modified monomeric form {} potentially inconsistent with residue {} != {}'.format(\\n modification['monomer'], modification['residue'],\\n ', '.join(codes))\\n print(protein['id'] + ': ' + msg)\\n\\n # generate BpForms for each protein\\n if not os.path.isdir(out_structure_dirname):\\n os.mkdir(out_structure_dirname)\\n\\n if not os.path.isdir(out_viz_dirname):\\n os.mkdir(out_viz_dirname)\\n\\n if not os.path.isfile(out_pickle_filename_2):\\n for i_protein, protein in enumerate(parsed_proteins):\\n if i_protein % 100 == 0:\\n print('Generating BpForms {} of {}'.format(i_protein + 1, len(parsed_proteins)))\\n\\n protein['modified_seq'] = None\\n if not protein['uniprot_id']:\\n continue\\n if not protein['seq']:\\n continue\\n if protein['pro_errors']:\\n continue\\n\\n processed_form = gen_bpform(protein, monomers, monomer_codes, apply_modifications=False)\\n protein['processed_seq'] = str(processed_form)\\n if not processed_form.validate():\\n processed_formula = processed_form.get_formula()\\n protein['processed_formula'] = str(processed_formula)\\n protein['processed_mol_wt'] = processed_form.get_mol_wt()\\n protein['processed_charge'] = processed_form.get_charge()\\n\\n if not protein['modifications']:\\n continue\\n\\n modified_form = gen_bpform(protein, monomers, monomer_codes, include_annotations=False)\\n protein['modified_seq'] = str(modified_form)\\n\\n modified_form = gen_bpform(protein, monomers, monomer_codes)\\n if not modified_form.validate():\\n modified_formula = modified_form.get_formula()\\n protein['modified_full_seq'] = str(modified_form)\\n protein['modified_formula'] = str(modified_formula)\\n protein['modified_mol_wt'] = modified_form.get_mol_wt()\\n protein['modified_charge'] = modified_form.get_charge()\\n\\n protein['modifications_formula'] = str(modified_formula - processed_formula)\\n protein['modifications_mol_wt'] = protein['modified_mol_wt'] - protein['processed_mol_wt']\\n protein['modifications_charge'] = protein['modified_charge'] - protein['processed_charge']\\n\\n # with open(os.path.join(out_structure_dirname, protein['id'] + '.cml'), 'w') as file:\\n # file.write(modified_form.export('cml'))\\n\\n form = gen_bpform(protein, monomers, monomer_codes,\\n apply_processing=False, include_annotations=True)\\n seq_features = []\\n if protein['processing']:\\n seq_features.append({\\n 'label': 'Processed',\\n 'color': '#cccccc',\\n 'positions': [],\\n })\\n last = 0\\n for p in protein['processing']:\\n seq_features[0]['positions'].append([last + 1, p['start'] - 1])\\n last = p['end']\\n seq_features[0]['positions'].append([\\n protein['processing'][-1]['end'] + 1,\\n len(form.seq),\\n ])\\n\\n if protein['processing'][0]['start'] == 1:\\n seq_features[0]['positions'].pop(0)\\n if protein['processing'][-1]['end'] == len(form.seq):\\n seq_features[0]['positions'].pop(len(seq_features[0]['positions']) - 1)\\n\\n with open(os.path.join(out_viz_dirname, protein['id'] + '.svg'), 'w') as file:\\n file.write(form.get_genomic_image(seq_features, width=910))\\n\\n if modified_form.get_canonical_seq(monomer_codes) != protein['processed_seq']:\\n protein['pro_errors'].append('Modified sequence for {} not compatible with the processed sequence'.format(\\n protein['id']))\\n\\n # save the parsed proteins in pickle format\\n with open(out_pickle_filename_2, 'wb') as file:\\n pickle.dump(parsed_proteins, file)\\n else:\\n with open(out_pickle_filename_2, 'rb') as file:\\n parsed_proteins = pickle.load(file)\\n\\n # save the proteoforms in TSV format\\n with open(out_tsv_filename, 'w') as file:\\n writer = csv.writer(file, dialect='excel-tab')\\n writer.writerow(['PRO id', 'UniProt id', 'Organism',\\n 'Unmodified sequence (IUBMB)',\\n 'Processing', 'Deletions', 'Processsed sequence (IUBMB)', 'Processsed formula',\\n 'Processsed molecular weight', 'Processsed charge',\\n 'Modifications', 'Crosslinks', 'Modified sequence (abbreviated BpForms)', 'Modified sequence (BpForms)',\\n 'Is modified sequence concrete', 'Modified formula', 'Modified molecular weight', 'Modified charge',\\n 'Modifications formula', 'Modifications molecular weight', 'Modifications charge',\\n 'PRO issues', 'Monomeric form issues'])\\n\\n for parsed_protein in parsed_proteins:\\n if parsed_protein.get('pro_errors', None):\\n pro_errors = '. '.join(parsed_protein['pro_errors']) + '.'\\n else:\\n pro_errors = None\\n\\n if parsed_protein.get('modified_errors', None):\\n modified_errors = '. '.join(parsed_protein['modified_errors']) + '.'\\n else:\\n modified_errors = None\\n\\n writer.writerow([\\n parsed_protein['id'],\\n parsed_protein.get('uniprot_id', None),\\n parsed_protein.get('organism', None),\\n parsed_protein.get('seq', None),\\n ', '.join('{}-{}'.format(p['start'], p['end']) for p in parsed_protein['processing']),\\n ', '.join('{}-{}'.format(deletion[0], deletion[1]) for deletion in parsed_protein.get('deletions', [])),\\n parsed_protein.get('processed_seq', None),\\n parsed_protein.get('processed_formula', None),\\n parsed_protein.get('processed_mol_wt', None),\\n parsed_protein.get('processed_charge', None),\\n ', '.join('{} --> {} ({})'.format(m['residue'] or '?', m['monomer'], ', '.join(str(p) for p in m['positions']))\\n for m in parsed_protein['modifications']),\\n ', '.join('{}{}-{}{}'.format(xlink[0][1], xlink[0][0], xlink[1][1], xlink[1][0])\\n for xlink in parsed_protein.get('crosslinks', [])),\\n parsed_protein.get('modified_seq', None),\\n parsed_protein.get('modified_full_seq', None),\\n parsed_protein.get('modified_concrete', False),\\n parsed_protein.get('modified_formula', None),\\n parsed_protein.get('modified_mol_wt', None),\\n parsed_protein.get('modified_charge', None),\\n parsed_protein.get('modifications_formula', None),\\n parsed_protein.get('modifications_mol_wt', None),\\n parsed_protein.get('modifications_charge', None),\\n pro_errors,\\n modified_errors,\\n ])\\n\\n # save the proteoforms in FASTA format\\n seqs = (SeqRecord(id='{} | {}'.format(protein['id'], protein['uniprot_id']),\\n seq=Seq(protein['modified_seq']),\\n description='')\\n for protein in parsed_proteins\\n if protein['modified_seq'])\\n SeqIO.write(seqs, out_fasta_filename, \\\"fasta\\\")\\n\\n # analyze frequency of modifications\\n plot_modifications(parsed_proteins, fig_filename=out_fig_filename)\\n\\n # return proteins\\n return proteins, parsed_proteins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776ae1cf149e78ae0a4114b21438d138\",\n \"score\": \"0.54315245\",\n \"text\": \"def build_LM(in_file):\\r\\n print 'building language models...'\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n\\r\\n # Open in_file and split into a list of sentences\\r\\n input_file = open(in_file, 'r')\\r\\n train_input_list = input_file.read().split(\\\"\\\\r\\\\n\\\")\\r\\n\\r\\n # Set up dictionaries to store the 4grams generated from input.train\\r\\n # ngram_count stores the total number of ngrams each language has generated\\r\\n four_gram_table = {}\\r\\n ngram_count = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\\r\\n\\r\\n '''\\r\\n NGRAM GENERATION\\r\\n Multiple for loops through each sentence and then each character for\\r\\n 4gram generation. Each sentence is padded with 3 'START's and 3 'END's\\r\\n '''\\r\\n for sentence in train_input_list:\\r\\n sentence_parts = sentence.split(None, 1)\\r\\n # To handle possible empty strings or weird strings in training input\\r\\n if (len(sentence_parts) != 2):\\r\\n break\\r\\n language = sentence_parts[0]\\r\\n sentence_body = sentence_parts[1]\\r\\n char_list = list(sentence_body)\\r\\n for index in range(3):\\r\\n char_list.insert(0, 'START')\\r\\n char_list.append('END')\\r\\n for index in range(len(char_list) - 4):\\r\\n four_gram = (char_list[index], char_list[index + 1],\\r\\n char_list[index + 2], char_list[index + 3])\\r\\n if four_gram in four_gram_table:\\r\\n four_gram_table[four_gram][language] += 1\\r\\n ngram_count[language] += 1\\r\\n else:\\r\\n # Add-one smoothing for new ngrams\\r\\n four_gram_table[four_gram] = {'malaysian': 1, 'indonesian': 1,\\r\\n 'tamil': 1}\\r\\n for value in ngram_count.itervalues():\\r\\n value += 1\\r\\n four_gram_table[four_gram][language] += 1\\r\\n ngram_count[language] += 1\\r\\n return [four_gram_table, ngram_count]\\r\\n\\r\\n input_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8776c3c0636ee64e48b9236afde29d96\",\n \"score\": \"0.54051876\",\n \"text\": \"def run(args: argparse.Namespace) -> None:\\n assert WaymoDataFileReader is not None, (\\n \\\"Please install the requirements in scripts/optional.txt to use\\\"\\n \\\"Waymo conversion.\\\"\\n )\\n if not os.path.exists(args.output):\\n os.makedirs(args.output)\\n result = from_waymo(\\n args.input,\\n args.output,\\n args.save_images,\\n args.use_lidar_labels,\\n args.nproc,\\n )\\n save(os.path.join(args.output, \\\"scalabel_anns.json\\\"), result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89aa08012b425e9517160341b3b4c2b8\",\n \"score\": \"0.5397848\",\n \"text\": \"def __init__(self,pedfname = None,title='title',outfname='rgMakeGenModel.phe',\\n xtradir=\\\"/foo/bar\\\", linkage=True, logf = None, model=additive,\\n labels=['FounderNonAff','FounderAff','OffspringNonAff','OffspringAff']):\\n ehet = '1'\\n ehom1 = '0'\\n ehom2 = '2'\\n missval = '.'\\n missing = ['N','0','.','-']\\n if model == additive: # we have ehom2 = 2 major alleles\\n modeldict = {ehom1:'2',ehet:'1',ehom2:'0',missval:missval}\\n elif model == zadditive:\\n modeldict = {ehom1:'1',ehet:'0',ehom2:'-1',missval:missval}\\n elif model == dominant:\\n modeldict = {ehom1:'1',ehet:'1',ehom2:'0',missval:missval}\\n else:\\n modeldict = {ehom1:'2',ehet:'0',ehom2:'0',missval:missval}\\n # model code translation dicts\\n\\n swapdict = {ehom1:ehom2,ehom2:ehom1,ehet:ehet,missval:missval} # if initial ref allele was wrong, use these to swap\\n mdict = dict(zip(missing,missing))\\n pedf = '%s.ped' % pedfname\\n f = file(pedf,'r')\\n if linkage: # read map file\\n map = readMap(pedfname)\\n rslist = [x[1] for x in map] # get rs numbers\\n else:\\n head = f.next().strip()\\n rslist = head.split()\\n nrs = len(rslist) # number of markers\\n elen = 2*nrs + 6 # expected # elements on each line\\n logf.write('%s %s: found %d for nrs\\\\n' % (thisprog,timenow(),nrs))\\n gm = {}\\n gm['founders'] = [] # array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\\n gm['offspring'] = [] # [array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\\n adicts = [{} for x in xrange(nrs)] # count of alleles in a dict for each marker\\n refallele = [None for x in xrange(nrs)] # list of first observed alleles\\n nsubj = 0\\n indiv = {'founders':[],'offspring':[]}\\n for lnum,l in enumerate(f):\\n ll = l.strip().split()\\n if (lnum+1) % 200 == 0:\\n logf.write('%s %s: Processing line %d\\\\n' % (thisprog, timenow(),lnum+1))\\n if len(ll) < elen: # ? short ?\\n logf.write('%s %s: Line %d is %d long, expected %d\\\\n' % (thisprog, timenow(),\\n lnum,len(ll),elen))\\n else:\\n garray = array.array('c',[missval,]*nrs)\\n nsubj += 1\\n sid = '%s_%s' % (ll[0],ll[1])\\n if sid == '1_1': # eesh\\n sid = '%d_%d' % (nsubj,nsubj)\\n isFounder = isOff = False\\n status = labels[0] # founder unaff\\n if ll[2] <> '0' and ll[3] <> '0': # has parent ids\\n iclass = 'offspring'\\n status = labels[2] # unaffected offspring\\n if ll[5] == '2':\\n status = labels[3] # affected offspring\\n else:\\n iclass = 'founders'\\n if ll[5] == '2':\\n status = labels[1] #change from unaff to aff founder label\\n gender = 'M'\\n if ll[4] == '2':\\n gender = 'F'\\n ped = ll[:6]\\n indiv[iclass].append(ped) # for row wise output\\n for snp in xrange(nrs):\\n pos = 2*snp + 6 # first\\n g1,g2 = ll[pos],ll[pos+1] # pair of genos\\n if mdict.get(g1,None) or mdict.get(g2,None): # one or both missing\\n esnp = missval # missing value\\n else:\\n if not refallele[snp]:\\n refallele[snp] = g1 # first one we saw!\\n for g in (g1,g2):\\n n = adicts[snp].get(g,0)\\n n += 1\\n adicts[snp][g] = n\\n if g1 == g2: # hom\\n if g1 == refallele[snp]:\\n esnp = ehom2 # 2 copies of current reference allele\\n else:\\n esnp = ehom1 # no copies\\n else:\\n esnp = ehet # het - always has one copy of reference allele\\n garray[snp] = esnp\\n gm[iclass].append(garray) # append genos for this new subject\\n for ek in gm.keys():\\n lek = len(gm[ek])\\n if len(gm[ek]) > 0:\\n lek0 = len(gm[ek][0])\\n s = 'for %s, have %d subjects with %d markers' % (ek,lek,lek0)\\n print s\\n logf.write(s)\\n for x in range(lek):\\n if len(gm[ek][x]) <> lek0:\\n s = 'for row %d, len = %d, not %d' % (x, len(gm[ek][x]),lek0)\\n print s\\n logf.write(s)\\n logf.write('%s %s: Now checking major allele assignment and fixing as needed\\\\n' % (thisprog,timenow()))\\n for iclass in gm.keys():\\n for subject in xrange(len(gm[iclass])): # for each subject\\n for snp in xrange(nrs): # now check to see if reference = major allele\\n major = majAllele(adicts[snp])\\n if major <> refallele[snp]: # either None or we need to change all the codes\\n if major <> None:\\n gm[iclass][subject][snp] = swapdict[gm[iclass][subject][snp]]\\n for iclass in gm.keys():\\n for subject in xrange(len(gm[iclass])): # now convert to genetic model wanted\\n gm[iclass][subject] = [modeldict[x] for x in gm[iclass][subject]] # translate to model\\n self.gm = gm\\n self.model = model\\n self.indiv = indiv\\n self.nrs = nrs\\n self.nsubj = nsubj\\n self.logf = logf\\n self.basename = title\\n self.outfname = outfname\\n self.rslist = rslist\\n pedhead = 'famid\\\\tiid\\\\tfid\\\\tmid\\\\tgender\\\\taffection\\\\t'\\n rs = '\\\\t'.join(rslist)\\n self.outhead = '%s%s\\\\n' % (pedhead,rs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce63391b0ba53484bcdbfa3c177f5093\",\n \"score\": \"0.5392235\",\n \"text\": \"def test_LM(in_file, out_file, LM):\\n print(\\\"testing language models...\\\")\\n\\n # Parse inputs\\n data = read_by_line(in_file)\\n model = LM[0]\\n languages = LM[1]\\n\\n # Create an empty probability reference sheet\\n basic_probability_ref = dict()\\n for l in languages:\\n basic_probability_ref[l] = np.float64(1)\\n\\n # Clear file of any results, to prepare the output file for eval.py\\n open(out_file, 'w').close()\\n \\n with open(out_file, 'a') as f:\\n # Process each sentence and make a guess\\n for sentence in data:\\n p_ref = basic_probability_ref.copy()\\n tokens = tokenise(sentence, token_size)\\n num_undetected = 0\\n\\n # Multiple token probabilities for each token\\n for t in tokens:\\n if t in model:\\n # Multiply the respective probability of each language\\n counts = model[t]\\n for l in languages:\\n p_ref[l] += counts[l]\\n else:\\n num_undetected += 1\\n\\n # Find the best prediction using the maximum product\\n if num_undetected / len(tokens) > other_threshold:\\n prediction = other_language\\n else:\\n prediction = max(p_ref.items(), key=operator.itemgetter(1))[0]\\n\\n # Write the prediction to the output file\\n f.write(prediction + \\\" \\\" + sentence + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11937e2179eb9e5be7829ede94d45adb\",\n \"score\": \"0.5387432\",\n \"text\": \"def test_issue4707():\\n nlp = English()\\n nlp.add_pipe(\\\"sentencizer\\\")\\n nlp.add_pipe(\\\"entity_ruler\\\")\\n assert nlp.pipe_names == [\\\"sentencizer\\\", \\\"entity_ruler\\\"]\\n exclude = [\\\"tokenizer\\\", \\\"sentencizer\\\"]\\n with make_tempdir() as tmpdir:\\n nlp.to_disk(tmpdir, exclude=exclude)\\n new_nlp = load_model_from_path(tmpdir, disable=exclude)\\n assert \\\"sentencizer\\\" not in new_nlp.pipe_names\\n assert \\\"entity_ruler\\\" in new_nlp.pipe_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5676fbca6769a80c88f1d8e1109454\",\n \"score\": \"0.5377364\",\n \"text\": \"def main(argv):\\n parser = argparse.ArgumentParser(\\n description='Create all possible determinizations of this domain.')\\n parser.add_argument('-d', '--domain', required=True)\\n parser.add_argument('-o', '--output', required=True) \\n args = parser.parse_args()\\n domain_file_name = args.domain\\n output_file_name = args.output\\n \\n # Reading domain file.\\n domain_str = ''\\n try:\\n with open(domain_file_name, 'r') as domain_file:\\n for line in domain_file:\\n if line.startswith(';;') or line.isspace():\\n continue;\\n domain_str += line\\n except IOError:\\n print \\\"Could not read file:\\\", domain_file_name\\n sys.exit(-1) \\n \\n # Parsing the domain tree.\\n domain_tree = ppddl_util.parse_sexp(domain_str)\\n all_prob_effects_list = []\\n \\n # Getting all possible probabilistic effects.\\n ppddl_util.get_all_probabilistic_effects(domain_tree, all_prob_effects_list)\\n \\n # Getting the most likely outcome determinization.\\n mlo_determinization = []\\n for probabilistic_effect_info in all_prob_effects_list:\\n mlo_determinization.append(\\n ppddl_util.get_mlo_determinization_effect(probabilistic_effect_info))\\n \\n # Writing the MLO determinization to a PPDDL file.\\n determinization_ppddl_tree = copy.deepcopy(domain_tree)\\n description_text = \\\"\\\"\\n for effect_info in mlo_determinization:\\n description_text += \\\"%s %d\\\\n\\\" % (effect_info[0], effect_info[1][0])\\n ppddl_util.determinize_tree(mlo_determinization, determinization_ppddl_tree)\\n ppddl_util.clean_up_tree(determinization_ppddl_tree)\\n f = open('%s_mlo_det.pddl' % (output_file_name), 'w')\\n fd = open('%s_mlo_det.desc' % (output_file_name), 'w')\\n f.write(ppddl_util.make_str(determinization_ppddl_tree[0]))\\n fd.write(description_text)\\n f.close()\\n fd.close()\\n \\n print 'Created MLO determinization for %s' % domain_file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b6f6722ce9ac5c08f4612e25ef7aec0\",\n \"score\": \"0.5376005\",\n \"text\": \"def make_model(pbm):\\n for stmt in pbm.statements:\\n try:\\n # Skip statements with no subject\\n if stmt.agent_list()[0] is None and \\\\\\n not isinstance(stmt, Conversion):\\n continue\\n # Assemble statements\\n if isinstance(stmt, Modification):\\n pbm._assemble_modification(stmt)\\n elif isinstance(stmt, RegulateActivity):\\n pbm._assemble_regulate_activity(stmt)\\n elif isinstance(stmt, RegulateAmount):\\n pbm._assemble_regulate_amount(stmt)\\n elif isinstance(stmt, Gef):\\n pbm._assemble_gef(stmt)\\n elif isinstance(stmt, Gap):\\n pbm._assemble_gap(stmt)\\n elif isinstance(stmt, ActiveForm):\\n pbm._assemble_active_form(stmt)\\n elif isinstance(stmt, Complex):\\n pbm._assemble_complex(stmt)\\n elif isinstance(stmt, Conversion):\\n pbm._assemble_conversion(stmt)\\n elif isinstance(stmt, Autophosphorylation):\\n pbm._assemble_autophosphorylation(stmt)\\n elif isinstance(stmt, Transphosphorylation):\\n pbm._assemble_transphosphorylation(stmt)\\n else:\\n logger.info('Unhandled statement: %s' % stmt)\\n except Exception:\\n pass\\n return pbm.model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9045c7c73ca55f814b38834d2149df8b\",\n \"score\": \"0.5342216\",\n \"text\": \"def try_load_model(self):\\r\\n filename = 'constraints_problem_size_{0}'.format(self.get_real_n())\\r\\n path = '../cache_constraints/'\\r\\n\\r\\n solution_is_loop = self.sol_is_loop()\\r\\n\\r\\n if solution_is_loop:\\r\\n filename += '_loop'\\r\\n path_filename = path + filename + '.lp'\\r\\n\\r\\n model_reader = ModelReader()\\r\\n\\r\\n self.outer_model_interface.check_dir(path)\\r\\n\\r\\n try:\\r\\n # Load the constraints\\r\\n self.docplex_model = model_reader.read_model(path_filename)\\r\\n except:\\r\\n # If they are not cached, create them\\r\\n print(\\\"\\\\nFile {} not found :(\\\\nI'm gonna create it! :D\\\".format(path_filename))\\r\\n self.docplex_model = self.generate_general_constraints(path, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c879101600ab140261ce79e742c8479\",\n \"score\": \"0.5335524\",\n \"text\": \"def generateModel(self):\\n\\t\\tglobal startLoadModel, doneLoadModel\\n\\t\\tprint(\\\"model type:\\\" + self.modeltype)\\n\\t\\tstartLoadModel = time.time()\\n\\t\\tif not os.path.exists(self.dictionaryfile):\\n\\t\\t\\tprint(\\\"creating dictionary \\\" + self.dictionaryfile)\\n\\t\\t\\tself.dictionary = self.makeDictionaryMemoryFriendly(self.corpusfile)\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"reading dictionary \\\" + self.dictionaryfile)\\n\\t\\t\\tself.dictionary = self.loadDictionary(self.dictionaryfile)\\n\\t\\tif not os.path.exists(self.corpusmatrixfile):\\n\\t\\t\\tprint(\\\"serializing corpus \\\" + self.corpusmatrixfile)\\n\\t\\t\\tself.serializeCorpusMemoryFriendly(self.corpusfile, self.dictionary)\\n\\t\\tif self.modeltype == \\\"LDA\\\":\\n\\t\\t\\tprint(\\\"creating lda model with \\\" + str(self.topics) + \\\" topics.\\\")\\n\\t\\t\\tif self.online:\\n\\t\\t\\t\\tprint(\\\"Running in online mode with chunksize: \\\" + str(self.chunksize))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"Running in offline mode with \\\" + str(self.passes) + \\\" passes\\\")\\n\\t\\t\\tif self.distributed:\\n\\t\\t\\t\\tprint(\\\"Running in distributed mode\\\")\\n\\t\\t\\tself.generateLDAModel(self.topics, self.passes, self.distributed)\\n\\t\\telif self.modeltype == \\\"LSI\\\":\\n\\t\\t\\tprint(\\\"creating lsi model\\\")\\n\\t\\t\\tself.generateLSIModel(topics)\\n\\t\\telif self.modeltype == \\\"HDP\\\":\\n\\t\\t\\tprint(\\\"creating hdp model\\\")\\n\\t\\t\\tself.generateHDPModel()\\n\\t\\tdoneLoadModel = time.time()\\n\\t\\tprint(\\\"Time to generate model:\\\" + str(doneLoadModel - startLoadModel))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91c5431c7473abefa61a82391d7c11c8\",\n \"score\": \"0.5323186\",\n \"text\": \"def test_LM(in_file, out_file, LM):\\r\\n print \\\"testing language models...\\\"\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n\\r\\n # Setting up data provided from given LM\\r\\n four_gram_table = LM[0]\\r\\n ngram_count = LM[1]\\r\\n\\r\\n # Open in_file and split into a list of sentences\\r\\n # Open out_file for writing predictions\\r\\n input_file = open(in_file, 'r')\\r\\n test_input_list = input_file.read().split(\\\"\\\\r\\\\n\\\")\\r\\n output_file = open(out_file, 'w')\\r\\n\\r\\n for sentence in test_input_list:\\r\\n # To handle possible empty strings or invalid input\\r\\n if sentence == \\\"\\\":\\r\\n break\\r\\n # Ngram probability score for current sentence\\r\\n ngram_score = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\\r\\n # Counters to keep track of number of unencountered ngrams generated by\\r\\n # the current sentence.\\r\\n invalid_ngram_count = 0\\r\\n total_ngram_count = 0\\r\\n # Sentence is padded with 3 'START's and 3 'END's\\r\\n char_list = list(sentence)\\r\\n for index in range(3):\\r\\n char_list.insert(0, 'START')\\r\\n char_list.append('END')\\r\\n for index in range(len(char_list) - 4):\\r\\n four_gram = (char_list[index], char_list[index + 1],\\r\\n char_list[index + 2], char_list[index + 3])\\r\\n # Log10 is used to prevent underflow of individual ngram_scores\\r\\n if four_gram in four_gram_table:\\r\\n for language, count in four_gram_table[four_gram].iteritems():\\r\\n ngram_score[language] += math.log10(count/ngram_count[language])\\r\\n else:\\r\\n invalid_ngram_count += 1\\r\\n total_ngram_count += 1\\r\\n # invalid_ngram_ratio represents the proportion of unencountered ngrams\\r\\n # out of all ngrams generated from current sentence\\r\\n invalid_ngram_ratio = invalid_ngram_count / total_ngram_count\\r\\n # The set threshold of 0.6 indicates that the sentence might not be a\\r\\n # valid language in the given LM\\r\\n if max(ngram_score.itervalues()) is 0 or invalid_ngram_ratio > 0.6:\\r\\n predicted_language = 'other'\\r\\n else:\\r\\n predicted_language = max(ngram_score.iterkeys(), key=(lambda language: ngram_score[language]))\\r\\n output_file.write(predicted_language + \\\" \\\" + sentence + \\\"\\\\n\\\")\\r\\n input_file.close\\r\\n output_file.close\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8230e8f56aabea2a2d18b33a22dab9\",\n \"score\": \"0.5304721\",\n \"text\": \"def generate(args):\\n if args.cuda and torch.cuda.is_available():\\n device = 0\\n use_cuda = True\\n elif args.cuda and not torch.cuda.is_available():\\n print(\\\"You do not have CUDA, turning cuda off\\\")\\n device = -1 \\n use_cuda = False\\n else:\\n device = -1\\n use_cuda=False\\n\\n #Load the vocab\\n vocab = du.load_vocab(args.vocab)\\n eos_id = vocab.stoi[EOS_TOK]\\n pad_id = vocab.stoi[PAD_TOK]\\n\\n if args.ranking: # default is HARD one, the 'Inverse Narrative Cloze' in the paper\\n dataset = du.NarrativeClozeDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, LM=False)\\n # Batch size during decoding is set to 1\\n batches = BatchIter(dataset, 1, sort_key=lambda x:len(x.actual), train=False, device=-1)\\n else:\\n dataset = du.SentenceDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, add_eos=False) #put in filter pred later\\n # Batch size during decoding is set to 1\\n batches = BatchIter(dataset, args.batch_size, sort_key=lambda x:len(x.text), train=False, device=-1)\\n\\n data_len = len(dataset)\\n\\n #Create the model\\n with open(args.load, 'rb') as fi:\\n if not use_cuda:\\n model = torch.load(fi, map_location=lambda storage, loc : storage)\\n else:\\n model = torch.load(fi, map_location=torch.device('cuda'))\\n\\n if not hasattr(model.latent_root, 'nohier'):\\n model.latent_root.set_nohier(args.nohier) #for backwards compatibility\\n\\n model.decoder.eval()\\n model.set_use_cuda(use_cuda)\\n\\n #For reconstruction\\n if args.perplexity:\\n loss = calc_perplexity(args, model, batches, vocab, data_len)\\n print(\\\"Loss = {}\\\".format(loss))\\n elif args.schema:\\n generate_from_seed(args, model, batches, vocab, data_len)\\n elif args.ranking:\\n do_ranking(args, model, batches, vocab, data_len, use_cuda)\\n else:\\n# sample_outputs(model, vocab)\\n reconstruct(args, model, batches, vocab)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b20588231ce0fcb6d4f7434d73e68c3\",\n \"score\": \"0.52846843\",\n \"text\": \"def generateModel(modelName, pack):\\n\\n (params, run, cost, optim_date, var_set) = pack\\n\\n with open('%s_optimized.mo' % modelName, 'w') as file:\\n header = \\\"\\\"\\\"model {mn}_optimized \\\"Generated by PostProcess/postprocess_optim.py {d} with cost {c} lowest at run {r}\\\"\\n extends {mn} (\\n \\\\n\\\"\\\"\\\".format(mn=modelName, d = optim_date, c = '%.6f' % cost, r = '%d' % run)\\n footer = ');\\\\nend %s_optimized;\\\\n' % modelName\\n\\n param_lines = (' %s = %.6e' % param for param in params)\\n # use join to avoid placing the comma after the last\\n lines = ',\\\\n'.join(param_lines)\\n\\n\\n file.write(header)\\n file.writelines(lines)\\n file.write(footer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d6897ba3959406b0ee3b87b094e823e\",\n \"score\": \"0.5258285\",\n \"text\": \"def load_model(length=MAX_UTTERENCE_LENGTH):\\n return torch.load(os.path.join(PATH_TO_SAVE, 'generator_{}.pt'.format(length))).to(DEVICE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078acbdaf7a42c95ccc2c6c684279376\",\n \"score\": \"0.5254087\",\n \"text\": \"def compile_po_file(po_path: Path) -> None:\\n output_path = po_path.parent\\n\\n target_json = po_path.with_suffix(\\\".json\\\")\\n if target_json.exists():\\n target_json.unlink()\\n convert_catalog_to_json(po_path, output_path, po_path.stem)\\n\\n target_mo = po_path.with_suffix(\\\".mo\\\")\\n if target_mo.exists():\\n target_mo.unlink()\\n compile_to_mo(po_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a0443fbd71bfab66c479b31441d52a\",\n \"score\": \"0.52386135\",\n \"text\": \"def test_LM(in_file, out_file, LM):\\r\\n print \\\"testing language models...\\\"\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n malay_LM = LM['malaysian']\\r\\n indon_LM = LM['indonesian']\\r\\n tamil_LM = LM['tamil']\\r\\n\\r\\n FD_in = open(in_file, 'r')\\r\\n FD_out = open(out_file, 'w')\\r\\n\\r\\n lines = FD_in.read()\\r\\n lines_array = lines.replace('\\\\r', ' ').split('\\\\n')\\r\\n lines_array.pop(len(lines_array) - 1)\\r\\n\\r\\n for line in lines_array:\\r\\n malay_count = 1\\r\\n indon_count = 1\\r\\n tamil_count = 1\\r\\n\\r\\n total_count = 0\\r\\n miss_count = 0\\r\\n\\r\\n four_char_array = get_four_char_array(line)\\r\\n\\r\\n for four_char in four_char_array:\\r\\n # key in malayLM = key in all LMs due to add one smoothing\\r\\n if four_char in malay_LM:\\r\\n malay_count += math.log10(malay_LM[four_char])\\r\\n indon_count += math.log10(indon_LM[four_char])\\r\\n tamil_count += math.log10(tamil_LM[four_char])\\r\\n else:\\r\\n miss_count += 1\\r\\n total_count += 1\\r\\n\\r\\n if (miss_count / total_count > 0.7):\\r\\n FD_out.write(\\\"other \\\" + line + \\\"\\\\n\\\")\\r\\n elif malay_count > indon_count and malay_count > tamil_count:\\r\\n FD_out.write(\\\"malaysian \\\" + line + \\\"\\\\n\\\")\\r\\n elif indon_count > malay_count and indon_count > tamil_count:\\r\\n FD_out.write(\\\"indonesian \\\" + line + \\\"\\\\n\\\")\\r\\n else:\\r\\n FD_out.write(\\\"tamil \\\" + line + \\\"\\\\n\\\")\\r\\n\\r\\n FD_in.close()\\r\\n FD_out.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a76f0d38ddb8ab17ec18597b44bc149\",\n \"score\": \"0.5236154\",\n \"text\": \"def to_lalinference(self, **kwargs):\\n return self.write(file_format=\\\"lalinference\\\", package=\\\"gw\\\", **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a86959eb9fc6a9c3356b7be538a93d\",\n \"score\": \"0.5229652\",\n \"text\": \"def make_input(\\n wel_qin,\\n wel_qin_1,\\n wel_qout,\\n wel_qout_1,\\n wel_coordsin,\\n wel_coordsin_1,\\n wel_coordsout,\\n wel_coordsout_1,\\n model_path,\\n name,\\n exe_name='mf6',\\n verbosity_level=0):\\n # created to load, build and save the simulation\\n sim = flopy.mf6.MFSimulation(\\n sim_name=name,\\n sim_ws=model_path, #working folder\\n exe_name=exe_name,\\n verbosity_level=verbosity_level, #standard output to be written - error messages should be included (=1)\\n )\\n # time discretization package\\n times = (10.0, 120, 1.0)\\n tdis_rc = [(1.0, 1, 1.0), times, times, times]\\n flopy.mf6.ModflowTdis(\\n sim, pname=\\\"tdis\\\",\\n time_units=\\\"DAYS\\\",\\n nper=4, # number of stress periods\\n perioddata=tdis_rc, # [perlen(len stress period), nstp(time-steps in the stressperiod), tsmult(multiplier of of the lenght of sucessive time-steps]\\n )\\n #iterative model solution package\\n flopy.mf6.ModflowIms(sim) # default solver - nearly linear models - confined or a single unconfined layer that is tick enough to contain the water table within a single layer\\n # groundwater flow model\\n gwf = flopy.mf6.ModflowGwf(sim, modelname=name, save_flows=True)\\n # spatial discretization package\\n flopy.mf6.ModflowGwfdis(gwf, nrow=10, ncol=10) # number ir rows and cols since the model has only one layer\\n # Initial conditions package\\n flopy.mf6.ModflowGwfic(gwf) #models using steady state in the beginning won't be affected\\n # node property flow package pane - default is harmonic mean\\n flopy.mf6.ModflowGwfnpf(\\n gwf,\\n save_flows=True,\\n save_specific_discharge=True,\\n icelltype=[0], # 0 unconfined, -1 confined\\n k=[0.5], # initial value of k\\n k33=[0.1], # vertical anisotropy\\n )\\n # storage package specifying sy - specific yield bigger than 0 means the cell is convertible\\n sy = flopy.mf6.ModflowGwfsto.sy.empty(\\n gwf,\\n default_value=0.2 # convertible cells\\n )\\n #stirage package specifying ss - specific storage\\n ss = flopy.mf6.ModflowGwfsto.ss.empty(\\n gwf, default_value=0.000001\\n )\\n # storage package\\n flopy.mf6.ModflowGwfsto(\\n gwf,\\n pname=\\\"sto\\\",\\n save_flows=True,\\n save_specific_discharge=True,\\n iconvert=1, # flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top\\n ss=ss,\\n sy=sy,\\n transient={0: True}, # keyword to indicate that stress period IPER is transient. Transient conditions will apply until the STEADY-STATE keyword is specified in a subsequent BEGIN PERIOD block\\n )\\n #dictionary of boundaries Each well is defined through definition of layer (int), row (int), column (int), flux (float). The simplest form is a dictionary with a lists of boundaries for each stress period, where each list of boundaries itself is a list of boundaries. Indices of the dictionary are the numbers of the stress period\\n # if the number of stress periods is larger than the dic than the last speficied will apply until the end\\n stress_period_data = {\\n 1: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\\n 2: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\\n 3: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0]\\n }\\n # defines the well package\\n flopy.mf6.ModflowGwfwel(\\n gwf,\\n stress_period_data=stress_period_data,\\n )\\n # constant head package\\n flopy.mf6.ModflowGwfchd(\\n gwf,\\n stress_period_data=[\\n [(0, 0, 0), 1.], # top left cell [cell_id, head, aux, boundname]\\n [(0, 9, 9), 1.]], # bottom right cell\\n )\\n # variable for the output control\\n budget_file = name + '.bud'\\n head_file = name + '.hds'\\n # output control package\\n flopy.mf6.ModflowGwfoc(\\n gwf,\\n budget_filerecord=budget_file,\\n head_filerecord=head_file,\\n saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL')])\\n sim.write_simulation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef942e4bbae34ac5e0e916536c3fd820\",\n \"score\": \"0.5222133\",\n \"text\": \"def main():\\n args = parse_arguments()\\n\\n if args.output_name:\\n output_name = args.output_name\\n else:\\n output_name = args.xodr_file.rpartition(\\\".\\\")[0]\\n output_name = f\\\"{output_name}.xml\\\" # only name of file\\n\\n if os.path.isfile(output_name) and not args.force_overwrite:\\n print(\\n \\\"Not converting because file exists and option 'force-overwrite' not active\\\",\\n file=sys.stderr,\\n )\\n sys.exit(-1)\\n\\n with open(\\\"{}\\\".format(args.xodr_file), \\\"r\\\") as file_in:\\n opendrive = parse_opendrive(etree.parse(file_in).getroot())\\n\\n scenario = convert_opendrive(opendrive)\\n\\n if not args.osm:\\n writer = ExtendedCommonRoadFileWriter(\\n scenario, source=\\\"OpenDRIVE 2 Lanelet Converter\\\"\\n )\\n\\n with open(f\\\"{output_name}\\\", \\\"w\\\") as file_out:\\n writer.write_scenario_to_file_io(file_out)\\n\\n else:\\n l2osm = OSMConverter(args.osm)\\n osm = l2osm(scenario)\\n with open(f\\\"{output_name}\\\", \\\"w\\\") as file_out:\\n file_out.write(etree.tostring(osm, encoding=\\\"unicode\\\", pretty_print=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23cd82b5a7d336e4b8f27a862f78256\",\n \"score\": \"0.52209806\",\n \"text\": \"def main(model=Path(\\\"L:\\\\\\\\BigData\\\\\\\\Env\\\\\\\\SavedModels\\\"), output_dir=Path(\\\"L:\\\\\\\\BigData\\\\\\\\Env\\\\\\\\SavedModels\\\")):\\n if model is not None:\\n nlp = spacy.load(model) # load existing spaCy model\\n print(\\\"Loaded model '%s'\\\" % model)\\n\\n ner = nlp.get_pipe(\\\"ner\\\")\\n\\n ner.add_label(\\\"Email\\\")# add new entity label to entity recognizer\\n ner.add_label(\\\"Full Name\\\")\\n ner.add_label(\\\"First Name\\\")\\n ner.add_label(\\\"Updatedtime\\\")\\n ner.add_label(\\\"City\\\")\\n ner.add_label(\\\"Postcode\\\")\\n ner.add_label(\\\"Date\\\")\\n ner.add_label(\\\"Phone\\\")\\n ner.add_label(\\\"State\\\")\\n ner.add_label(\\\"Country\\\")\\n ner.add_label(\\\"Street\\\")\\n ner.add_label(\\\"Id\\\")\\n ner.add_label(\\\"Last Name\\\")\\n ner.add_label(\\\"username\\\")\\n ner.add_label(\\\"Transaction Id\\\")\\n\\n move_names = list(ner.move_names)\\n\\n # test the trained model\\n test_text = \\\"Carlie@marguerite.ca , Glenna Olson , Corbin , 1999-07-05T12:33:47.066Z , Kingshire 77787 1/8/2013 | (695)464-6165 x9467 , Iowa Indonesia 038 Schumm Walks 0 Ortiz Frida 362 \\\"\\n doc = nlp(test_text)\\n print(\\\"Entities in '%s'\\\" % test_text)\\n for ent in doc.ents:\\n print(ent.label_, ent.text)\\n\\n # save model to output directory\\n if output_dir is not None:\\n output_dir = Path(output_dir)\\n if not output_dir.exists():\\n output_dir.mkdir() # rename model\\n nlp.to_disk(output_dir)\\n print(\\\"Saved model to\\\", output_dir)\\n\\n # test the saved model\\n print(\\\"Loading from\\\", output_dir)\\n nlp2 = spacy.load(output_dir)\\n # Check the classes have loaded back consistently\\n assert nlp2.get_pipe(\\\"ner\\\").move_names == move_names\\n doc2 = nlp2(test_text)\\n for ent in doc2.ents:\\n print(ent.label_, ent.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba506d586a69c49c3403983dbd1f5025\",\n \"score\": \"0.52202463\",\n \"text\": \"def GenFile(DSLModel, Table, View):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8666e838db88654723d809adce0d3d19\",\n \"score\": \"0.52056324\",\n \"text\": \"def generateModel(self,startPoint,start,end,tol,vel):\\r\\n\\t\\t\\r\\n\\t\\t(code,levels,cantPoints) = Loader().load(self.path,startPoint)\\r\\n\\t\\thaveParser = False\\r\\n\\r\\n\\t\\tadded = 0\\r\\n\\t\\tself.start = start\\r\\n\\t\\tself.end = end\\r\\n\\t\\t\\r\\n\\t\\tif( tol > 0):\\t\\t\\r\\n\\t\\t\\tself.parser = PointsAdder(code,levels,cantPoints,tol)\\r\\n\\t\\t\\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\\r\\n\\t\\t\\thaveParser = True\\r\\n\\t\\t\\t\\r\\n\\t\\tif( vel > 0):\\t\\t\\r\\n\\t\\t\\tself.parser = VelocityChanger(code,levels,cantPoints,vel)\\r\\n\\t\\t\\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\\r\\n\\t\\t\\thaveParser = True\\r\\n\\t\\t\\t\\r\\n\\t\\tif (not haveParser):\\r\\n\\t\\t\\tself.parser = Parser(code,levels,cantPoints)\\r\\n\\t\\t\\t(code,levels,cantPoints) = self.parser.parse()\\t\\r\\n\\t\\r\\n\\t\\tself.modifiedGCode = code\\r\\n\\t\\tself.levels = levels\\r\\n\\t\\tself.cantPoints = cantPoints\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9a39b116920aebf857a1c3b136837b0\",\n \"score\": \"0.5203123\",\n \"text\": \"def compile():\\n\\n m = dir(modules)\\n\\n words = []\\n for module_name in m:\\n try:\\n eval(\\\"words.extend(modules.%s.WORDS)\\\" % module_name)\\n except:\\n pass # module probably doesn't have the property\\n\\n words = list(set(words))\\n\\n # for spotify module\\n words.extend([\\\"MUSIC\\\",\\\"SPOTIFY\\\"])\\n\\n # create the dictionary\\n pronounced = g2p.translateWords(words)\\n zipped = zip(words, pronounced)\\n lines = [\\\"%s %s\\\" % (x, y) for x, y in zipped]\\n\\n with open(\\\"../client/dictionary.dic\\\", \\\"w\\\") as f:\\n f.write(\\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\")\\n\\n # create the language model\\n with open(\\\"../client/sentences.txt\\\", \\\"w\\\") as f:\\n f.write(\\\"\\\\n\\\".join(words) + \\\"\\\\n\\\")\\n f.write(\\\" \\\\n \\\\n\\\")\\n f.close()\\n\\n # make language model\\n os.system(\\n \\\"text2idngram -vocab ../client/sentences.txt < ../client/sentences.txt -idngram temp.idngram\\\")\\n os.system(\\n \\\"idngram2lm -idngram temp.idngram -vocab ../client/sentences.txt -arpa ../client/languagemodel.lm\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e798a22449fe4ec327a89f2378b7cd\",\n \"score\": \"0.51998425\",\n \"text\": \"def main() -> None:\\n\\n args = get_args()\\n package = Package()\\n ontology = get_ontology(args.ontology_file)\\n\\n for i, fh in enumerate(args.data_file, start=1):\\n print(f'{i:3}: {os.path.basename(fh.name)}')\\n fh.close()\\n package.infer(fh.name)\\n\\n for res in package.resources:\\n if args.missing_values:\\n res.descriptor['schema']['missingValues'].extend(\\n args.missing_values)\\n\\n for field in res.descriptor['schema']['fields']:\\n name = field.get('name')\\n if not name:\\n continue\\n\\n ont = ontology.get(name)\\n if not ont:\\n continue\\n\\n data_type = ont.get('frictionless type', field.get('type'))\\n if data_type:\\n field['type'] = data_type\\n\\n data_format = ont.get('frictionless format', field.get('format'))\\n if data_format:\\n field['format'] = data_format\\n\\n rdf_type = ont.get('rdf type purl', field.get('rdfType'))\\n if rdf_type:\\n field['rdfType'] = rdf_type\\n\\n units = ont.get('units purl', field.get('pm:unitRdfType'))\\n if units:\\n field['pm:unitRdfType'] = units\\n\\n source_url = ont.get('pm:source url', field.get('pm:sourceUrl'))\\n if source_url:\\n field['pm:source url'] = source_url\\n\\n measure_source = ont.get('measurement source purl',\\n field.get('pm:measurementSourceRdfType'))\\n if measure_source:\\n field['pm:measurementSourceRdfType'] = measure_source\\n\\n measure_url = ont.get('pm:measurement source protocol',\\n field.get('pm:measurement source protocol'))\\n if measure_url:\\n field['pm:measurementSourceProtocolUrl'] = measure_url\\n\\n # TODO: Not sure about this\\n # is_searchable = ont.get('pm:searchable', field.get('pm:searchable'))\\n # if is_searchable:\\n # field['pm:searchable'] = is_searchable\\n\\n package.remove_resource(res.name)\\n package.add_resource(res.descriptor)\\n\\n package.save(args.out_file)\\n\\n print(f'Done, see \\\"{args.out_file}\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3fe6b4a1d97a21eaef6d6f4d02a796d\",\n \"score\": \"0.51963997\",\n \"text\": \"def actualSolve(self, lp):\\n\\n if not self.executable(self.path):\\n raise PulpSolverError(\\\"PuLP: cannot execute \\\" + self.path)\\n tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, \\\"lp\\\", \\\"sol\\\", \\\"mst\\\")\\n vs = lp.writeLP(tmpLp, writeSOS=1)\\n try:\\n os.remove(tmpSol)\\n except:\\n pass\\n cmd = self.path\\n options = self.options + self.getOptions()\\n if self.timeLimit is not None:\\n options.append((\\\"TimeLimit\\\", self.timeLimit))\\n cmd += \\\" \\\" + \\\" \\\".join([f\\\"{key}={value}\\\" for key, value in options])\\n cmd += f\\\" ResultFile={tmpSol}\\\"\\n if self.optionsDict.get(\\\"warmStart\\\", False):\\n self.writesol(filename=tmpMst, vs=vs)\\n cmd += f\\\" InputFile={tmpMst}\\\"\\n\\n if lp.isMIP():\\n if not self.mip:\\n warnings.warn(\\\"GUROBI_CMD does not allow a problem to be relaxed\\\")\\n cmd += f\\\" {tmpLp}\\\"\\n if self.msg:\\n pipe = None\\n else:\\n pipe = open(os.devnull, \\\"w\\\")\\n\\n return_code = subprocess.call(cmd.split(), stdout=pipe, stderr=pipe)\\n\\n # Close the pipe now if we used it.\\n if pipe is not None:\\n pipe.close()\\n\\n if return_code != 0:\\n raise PulpSolverError(\\\"PuLP: Error while trying to execute \\\" + self.path)\\n if not os.path.exists(tmpSol):\\n # TODO: the status should be infeasible here, I think\\n status = constants.LpStatusNotSolved\\n values = reducedCosts = shadowPrices = slacks = None\\n else:\\n # TODO: the status should be infeasible here, I think\\n status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)\\n self.delete_tmp_files(tmpLp, tmpMst, tmpSol, \\\"gurobi.log\\\")\\n if status != constants.LpStatusInfeasible:\\n lp.assignVarsVals(values)\\n lp.assignVarsDj(reducedCosts)\\n lp.assignConsPi(shadowPrices)\\n lp.assignConsSlack(slacks)\\n lp.assignStatus(status)\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148556ac6767aa89118518bfbccc312b\",\n \"score\": \"0.5196136\",\n \"text\": \"def create_pyomo_model(self, debug_mode=False, debug_cost=2e7):\\n\\n # work on a local copy of the dataframe\\n if not debug_mode and self.df.index.str.contains('DBUG').any():\\n # previously ran in debug mode, but now done\\n df = self.remove_debug_links()\\n df.to_csv(self.linksfile + '-final.csv')\\n else:\\n df = self.df\\n\\n self.log.info('Creating Pyomo Model (debug=%s)' % debug_mode)\\n\\n model = ConcreteModel()\\n\\n model.N = Set(initialize=self.nodes)\\n model.k = Set(initialize=range(15))\\n model.A = Set(within=model.N*model.N*model.k, \\n initialize=self.links, ordered=True)\\n model.source = Param(initialize='SOURCE')\\n model.sink = Param(initialize='SINK')\\n\\n def init_params(p):\\n if p == 'cost' and debug_mode:\\n return (lambda model,i,j,k: debug_cost \\n if ('DBUG' in str(i)+'_'+str(j))\\n else 1.0)\\n else:\\n return lambda model,i,j,k: df.loc[str(i)+'_'+str(j)+'_'+str(k)][p]\\n\\n model.u = Param(model.A, initialize=init_params('upper_bound'), mutable=True)\\n model.l = Param(model.A, initialize=init_params('lower_bound'), mutable=True)\\n model.a = Param(model.A, initialize=init_params('amplitude'))\\n model.c = Param(model.A, initialize=init_params('cost'))\\n\\n # The flow over each arc\\n model.X = Var(model.A, within=Reals)\\n\\n # Minimize total cost\\n def obj_fxn(model):\\n return sum(model.c[i,j,k]*model.X[i,j,k] for (i,j,k) in model.A)\\n model.total = Objective(rule=obj_fxn, sense=minimize)\\n\\n # Enforce an upper bound limit on the flow across each arc\\n def limit_rule_upper(model, i, j, k):\\n return model.X[i,j,k] <= model.u[i,j,k]\\n model.limit_upper = Constraint(model.A, rule=limit_rule_upper)\\n\\n # Enforce a lower bound limit on the flow across each arc\\n def limit_rule_lower(model, i, j, k):\\n return model.X[i,j,k] >= model.l[i,j,k]\\n model.limit_lower = Constraint(model.A, rule=limit_rule_lower)\\n\\n # To speed up creating the mass balance constraints, first\\n # create dictionaries of arcs_in and arcs_out of every node\\n # These are NOT Pyomo data, and Pyomo does not use \\\"model._\\\" at all\\n arcs_in = {}\\n arcs_out = {}\\n\\n def arc_list_hack(model, i,j,k):\\n if j not in arcs_in:\\n arcs_in[j] = []\\n arcs_in[j].append((i,j,k))\\n\\n if i not in arcs_out:\\n arcs_out[i] = []\\n arcs_out[i].append((i,j,k))\\n return [0]\\n\\n model._ = Set(model.A, initialize=arc_list_hack)\\n\\n # Enforce flow through each node (mass balance)\\n def flow_rule(model, node):\\n if node in [value(model.source), value(model.sink)]:\\n return Constraint.Skip\\n outflow = sum(model.X[i,j,k]/model.a[i,j,k] for i,j,k in arcs_out[node])\\n inflow = sum(model.X[i,j,k] for i,j,k in arcs_in[node])\\n return inflow == outflow\\n model.flow = Constraint(model.N, rule=flow_rule)\\n\\n model.dual = Suffix(direction=Suffix.IMPORT)\\n\\n self.model = model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1d525b72f37243510024f58395907b3\",\n \"score\": \"0.5173822\",\n \"text\": \"def makeLibSource(self):\\n\\n # Make vector field (and event) file for compilation\\n # This sets the field self._eventNames\\n self._prepareEventSpecs()\\n\\n # Write the model.m file\\n allfilestr = self._prepareModelContents()\\n modelfile = os.path.join(self._target_dir, self._model_file)\\n try:\\n file = open(modelfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._model_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the events.m file\\n if len(self._eventNames) > 0:\\n allfilestr = self._prepareEventsFileContents() + self._prepareAuxContents()\\n eventsfile = os.path.join(self._target_dir, self._events_file)\\n try:\\n file = open(eventsfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._events_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n\\n # Write the initialconditions.m file\\n allfilestr = self._prepareICContents()\\n icfile = os.path.join(self._target_dir, self._ic_file)\\n try:\\n file = open(icfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._ic_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the pars.m file\\n allfilestr = self._prepareParamContents()\\n paramfile = os.path.join(self._target_dir, self._param_file)\\n try:\\n file = open(paramfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._param_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the get.m file\\n allfilestr = self._prepareGetFileContents()\\n getfile = os.path.join(self._target_dir, self._get_file)\\n try:\\n file = open(getfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._get_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the set.m file\\n allfilestr = self._prepareSetFileContents()\\n setfile = os.path.join(self._target_dir, self._set_file)\\n try:\\n file = open(setfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._set_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the vfield.m file\\n# vfdefines = self._prepareVfieldDefines()\\n# allfilestr = self._prepareVfieldContents(vfdefines)\\n allfilestr = self.funcspec.spec[0] + self._prepareAuxContents()\\n vffile = os.path.join(self._target_dir, self._vfield_file)\\n try:\\n file = open(vffile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._vfield_file+\\\" for writing\\\")\\n raise IOError(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870c6fd27ca456de9582a6aa9f045d53\",\n \"score\": \"0.51735485\",\n \"text\": \"def buildSolverModel(self, lp):\\n log.debug(\\\"create the gurobi model\\\")\\n lp.solverModel = gurobipy.Model(lp.name)\\n log.debug(\\\"set the sense of the problem\\\")\\n if lp.sense == constants.LpMaximize:\\n lp.solverModel.setAttr(\\\"ModelSense\\\", -1)\\n if self.timeLimit:\\n lp.solverModel.setParam(\\\"TimeLimit\\\", self.timeLimit)\\n gapRel = self.optionsDict.get(\\\"gapRel\\\")\\n logPath = self.optionsDict.get(\\\"logPath\\\")\\n if gapRel:\\n lp.solverModel.setParam(\\\"MIPGap\\\", gapRel)\\n if logPath:\\n lp.solverModel.setParam(\\\"LogFile\\\", logPath)\\n\\n log.debug(\\\"add the variables to the problem\\\")\\n for var in lp.variables():\\n lowBound = var.lowBound\\n if lowBound is None:\\n lowBound = -gurobipy.GRB.INFINITY\\n upBound = var.upBound\\n if upBound is None:\\n upBound = gurobipy.GRB.INFINITY\\n obj = lp.objective.get(var, 0.0)\\n varType = gurobipy.GRB.CONTINUOUS\\n if var.cat == constants.LpInteger and self.mip:\\n varType = gurobipy.GRB.INTEGER\\n var.solverVar = lp.solverModel.addVar(\\n lowBound, upBound, vtype=varType, obj=obj, name=var.name\\n )\\n if self.optionsDict.get(\\\"warmStart\\\", False):\\n # Once lp.variables() has been used at least once in the building of the model.\\n # we can use the lp._variables with the cache.\\n for var in lp._variables:\\n if var.varValue is not None:\\n var.solverVar.start = var.varValue\\n\\n lp.solverModel.update()\\n log.debug(\\\"add the Constraints to the problem\\\")\\n for name, constraint in lp.constraints.items():\\n # build the expression\\n expr = gurobipy.LinExpr(\\n list(constraint.values()), [v.solverVar for v in constraint.keys()]\\n )\\n if constraint.sense == constants.LpConstraintLE:\\n relation = gurobipy.GRB.LESS_EQUAL\\n elif constraint.sense == constants.LpConstraintGE:\\n relation = gurobipy.GRB.GREATER_EQUAL\\n elif constraint.sense == constants.LpConstraintEQ:\\n relation = gurobipy.GRB.EQUAL\\n else:\\n raise PulpSolverError(\\\"Detected an invalid constraint type\\\")\\n constraint.solverConstraint = lp.solverModel.addConstr(\\n expr, relation, -constraint.constant, name\\n )\\n lp.solverModel.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f807685d80245425900a66f5f81872f5\",\n \"score\": \"0.5164706\",\n \"text\": \"def import_model(self, file):\\n felems = os.path.splitext(file)\\n ext = felems[1].lower()\\n if ext in (\\\".gz\\\", \\\".zip\\\",):\\n ext = os.path.splitext(felems[0])[1].lower()\\n\\n if ext == \\\".cpo\\\":\\n import docplex.cp.cpo.cpo_parser as cpo_parser\\n prs = cpo_parser.CpoParser(self)\\n prs.parse(file)\\n\\n elif ext == \\\".fzn\\\":\\n import docplex.cp.fzn.fzn_parser as fzn_parser\\n prs = fzn_parser.FznParser(self)\\n prs.parse(file)\\n # Get model to force compilation\\n prs.get_model()\\n\\n elif ext == \\\".lp\\\":\\n import docplex.cp.lp.lp_parser as lp_parser\\n prs = lp_parser.LpParser(self)\\n prs.parse(file)\\n\\n else:\\n raise CpoException(\\\"Unknown '{}' file format. Only .cpo, .fzn and .lp are supported.\\\".format(ext))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c04ce9f517817a518f872f20d83633\",\n \"score\": \"0.51574755\",\n \"text\": \"def to_automl(data, path='.', name='autodata'):\\n # check if folder exists\\n dir = os.path.join(path, name+'_automl')\\n if not os.path.exists(dir):\\n # create folder\\n os.mkdir(dir)\\n data.descriptors().to_csv(os.path.join(dir, name+'.info'), header=True) # some information\\n if data.has_class():\\n pd.DataFrame(data.indexes['X']).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\\n pd.DataFrame(data.indexes['y']).to_csv(os.path.join(dir, name+'_label.name'), index=False, header=False) # label name\\n if 'train' in data.indexes: # train/test and X/y splits\\n data.get_data('X_train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\\n data.get_data('X_test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\\n data.get_data('y_train').to_csv(os.path.join(dir, name+'_train.solution'), sep=' ', index=False, header=False) # train solution\\n data.get_data('y_test').to_csv(os.path.join(dir, name+'_test.solution'), sep=' ', index=False, header=False) # test solution\\n else: # only X/y split\\n data.get_data('X').to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data\\n data.get_data('y').to_csv(os.path.join(dir, name+'.solution'), sep=' ', index=False, header=False) # solution\\n else:\\n pd.DataFrame(data.columns).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\\n if 'train' in data.indexes: # only train/test split\\n data.get_data('train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\\n data.get_data('test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\\n else: # no split at all\\n data.to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311b06e5d22497504730712619f4da06\",\n \"score\": \"0.5155293\",\n \"text\": \"def _make_input_file(self, teff_lims, teff_step, logg_lims, logg_step,\\n feh_lims, feh_step, vsini_lims, vsini_step, \\n vmicro_lims, vmicro_step, resolution):\\n output_string = '{:.1f} {:.0f} {:.1f}\\\\n'.format(teff_lims[0], \\n teff_step, \\n teff_lims[-1])\\n\\n output_string += '{:.1f} {:.1f} {:.1f}\\\\n'.format(logg_lims[0],\\n logg_step,\\n logg_lims[1])\\n\\n output_string += '{:.1f} {:.1f} {:.1f}\\\\n'.format(vmicro_lims[0],\\n vmicro_step,\\n vmicro_lims[1])\\n\\n output_string += '{:.1f} {:.1f} {:.1f}\\\\n'.format(vsini_lims[0],\\n vsini_step,\\n vsini_lims[1])\\n\\n output_string += \\\"skip 0.03 0.02 0.07 !dilution factor\\\\n\\\"\\n\\n output_string += 'skip {:.1f} {:.1f} {:.1f}\\\\n'.format(feh_lims[0],\\n feh_step,\\n feh_lims[1])\\n \\n output_string += 'He 0.04 0.005 0.06 ! Individual abundance\\\\n'\\n\\n output_string += '0.0 {:.0f}\\\\n'.format(resolution)\\n\\n output_string += '{}\\\\n{}\\\\n'.format(self.abundance_table, self.model_dir)\\n\\n output_string += '2 1 !atmosphere model vmicro and mass\\\\n'\\n output_string += 'ST ! model atmosphere chemical composition flag\\\\n'\\n\\n dx = self.data.x[1] - self.data.x[0]\\n output_string += '1 {:.5f} fit\\\\n'.format(dx)\\n\\n output_string += 'data_sets/{}.txt\\\\n'.format(self.output_basename)\\n\\n output_string += '0.5 0.99 0.0 adjust ! RV determination stuff\\\\n'\\n\\n xmin, xmax = self.data.x[0]-1, self.data.x[-1]+1\\n output_string += '{:.1f} {:.1f}\\\\n'.format(xmin, xmax)\\n\\n outfilename = '{}.inp'.format(self.output_basename)\\n with open(outfilename, 'w') as outfile:\\n outfile.write(output_string)\\n\\n return outfilename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae6ddb4fe2249629c719de91f2cabee\",\n \"score\": \"0.51552427\",\n \"text\": \"def write(self, filename='pflotran.in'):\\n\\n if filename:\\n self.filename = filename\\n outfile = open(self.filename, 'w')\\n\\n # Presumes simulation.simulation_type is required\\n if self.simulation.simulation_type:\\n self._write_simulation(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'simulation is required, it is currently reading as empty')\\n\\n if self.simulation.subsurface_flow or \\\\\\n self.simulation.subsurface_transport:\\n self._write_subsurface_simulation_begin(outfile)\\n\\n if self.reference_pressure:\\n self._write_reference_pressure(outfile)\\n\\n if self.reference_temperature:\\n self._write_reference_temperature(outfile)\\n\\n if self.co2_database:\\n self._write_co2_database(outfile)\\n\\n if self.regression.cells \\\\\\n or self.regression.cells_per_process \\\\\\n or self.regression.all_cells:\\n self._write_regression(outfile)\\n\\n if self.uniform_velocity.value_list:\\n self._write_uniform_velocity(outfile)\\n\\n if self.specified_velocity:\\n self._write_specified_velocity(outfile)\\n\\n if self.update_flow_permeability:\\n self._write_update_flow_permeability(outfile)\\n\\n if self.reference_liquid_density:\\n self._write_reference_liquid_density(outfile)\\n\\n if self.minimum_hydrostatic_pressure:\\n self._write_minimum_hydrostatic_pressure(outfile)\\n\\n if self.reference_stress_state.value_list:\\n self._write_reference_stress_state(outfile)\\n\\n if self.eoslist:\\n self._write_eos(outfile)\\n\\n if self.klinkenberg_effect:\\n self._write_klinkenberg_effect(outfile)\\n\\n if self.creep_closure_table:\\n self._write_creep_closure_table(outfile)\\n\\n if self.nonuniform_velocity.filename:\\n self._write_nonuniform_velocity(outfile)\\n\\n if self.simulation.mode.upper() == 'MPHASE' and self.co2_database:\\n self._write_co2_database(outfile)\\n\\n if self.multiple_continuum:\\n self._write_multiple_continuum(outfile)\\n\\n if self.overwrite_restart_flow_params:\\n self._write_overwrite_restart_flow(outfile)\\n\\n if self.overwrite_restart_transport:\\n self._write_overwrite_restart_transport(outfile)\\n\\n if self.initialize_flow_from_file is not None:\\n self._write_initialize_flow_from_file(outfile)\\n\\n if self.initialize_transport_from_file is not None:\\n self._write_initialize_transport_from_file(outfile)\\n\\n if self.isothermal:\\n self._write_isothermal(outfile)\\n\\n if self.reference_porosity:\\n self._write_reference_porosity(outfile)\\n\\n if self.datasetlist:\\n self._write_dataset(outfile)\\n # else: print 'info: dataset name not detected\\\\n'\\n\\n if self.chemistry:\\n self._write_chemistry(outfile)\\n # else: print 'info: chemistry not detected\\\\n'\\n\\n if self.grid:\\n self._write_grid(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'grid is required, it is currently reading as empty!')\\n\\n if self.timestepper_flow or self.timestepper_transport:\\n self._write_timestepper(outfile)\\n # else: print 'info: timestepper not detected\\\\n'\\n\\n if self.time:\\n self._write_time(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'time is required, it is currently reading as empty!')\\n\\n if self.proplist:\\n self._write_prop(outfile)\\n else:\\n PyFLOTRAN_WARNING(\\n 'material property list is empty! ' +\\n ' Using default material property settings')\\n self.add(pmaterial())\\n self._write_prop(outfile)\\n\\n if self.lsolverlist:\\n self._write_lsolver(outfile)\\n # else: print 'info: lsolverlist (linear solver list) not detected\\\\n'\\n\\n if self.nsolverlist:\\n self._write_nsolver(outfile)\\n # else: print 'info: nsolverlist (newton solver list) not detected\\\\n'\\n\\n if self.output:\\n self._write_output(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'output is required, it is currently reading as empty!')\\n\\n if self.fluidlist:\\n self._write_fluid(outfile)\\n\\n if self.saturationlist:\\n self._write_saturation(outfile)\\n elif self.charlist:\\n self._write_characteristic_curves(outfile)\\n else:\\n if self.simulation.subsurface_flow:\\n self.add(pcharacteristic_curves())\\n self._write_characteristic_curves(outfile)\\n PyFLOTRAN_WARNING(\\n 'characteristic_curves list or saturation list ' +\\n 'is required, it is currently reading as empty! ' +\\n ' Using default characteristic_curves settings')\\n\\n if self.regionlist:\\n self._write_region(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'regionlist is required, it is currently reading as empty!')\\n\\n if self.integral_flux_list:\\n self._write_integral_flux(outfile)\\n\\n if self.observation_list:\\n self._write_observation(outfile)\\n\\n if self.flowlist:\\n self._write_flow(outfile)\\n\\n if self.transportlist:\\n self._write_transport(outfile)\\n\\n if self.initial_condition_list:\\n self._write_initial_condition(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'initial_condition_list is required,' +\\n 'it is currently reading as empty!')\\n\\n if self.boundary_condition_list:\\n self._write_boundary_condition(outfile)\\n\\n if self.source_sink_list:\\n self._write_source_sink(outfile)\\n\\n if self.source_sink_sandbox_list:\\n self._write_source_sink_sandbox(outfile)\\n\\n if self.reference_saturation:\\n self._write_reference_saturation(outfile)\\n\\n if self.strata_list:\\n self._write_strata(outfile)\\n else:\\n PyFLOTRAN_WARNING(\\n 'stratigraphy_coupler is required, ' +\\n 'it is currently reading as empty! ' +\\n 'Using default settings')\\n self.add(pstrata())\\n self._write_strata(outfile)\\n\\n if self.constraint_list:\\n self._write_constraint(outfile)\\n\\n if self.rt_mass_transfer_list:\\n self._write_rt_mass_transfer(outfile)\\n\\n if self.flow_mass_transfer_list:\\n self._write_flow_mass_transfer(outfile)\\n\\n if self.simulation.subsurface_flow or \\\\\\n self.simulation.subsurface_transport:\\n self._write_subsurface_simulation_end(outfile)\\n\\n\\n # -----------------------------------------------\\n\\n if self.simulation.surface_subsurface:\\n self._write_surface_subsurface_begin(outfile)\\n\\n if self.surface_flow:\\n self._write_surface_flow(outfile)\\n\\n self._write_surface_subsurface_end(outfile)\\n\\n # -----------------------------------------------\\n\\n if self.simulation.simulation_type == 'hydroquake':\\n self._write_hydroquake(outfile)\\n\\n if self.simulation.simulation_type.lower() == 'geomechanics_subsurface':\\n self._write_geomechanics(outfile)\\n\\n if self.waste_form_general:\\n self._write_wasteform_general(outfile)\\n\\n if self.ufd_decay:\\n self._write_ufd_decay(outfile)\\n\\n if self.ufd_biosphere:\\n self._write_ufd_biosphere(outfile)\\n\\n if self.wipp_source_sink:\\n self._write_wipp_source_sink(outfile)\\n\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6217bff10682c1129e9efb6eb05446d3\",\n \"score\": \"0.5149605\",\n \"text\": \"def write(solution):\\n trimmed_solution = solution\\n input_file_name_stripped = trimmed_solution.name\\n cwd = os.getcwd()\\n output_file_name = os.path.join(cwd,\\n 'pym_' +\\n input_file_name_stripped +\\n '.inp')\\n f = open(output_file_name, 'w+')\\n\\n #Work functions\\n\\n calories_constant = 4184.0 #number of calories in 1000 Joules of energy\\n def eliminate(input_string, char_to_replace, spaces='single'):\\n \\\"\\\"\\\"\\n Eliminate characters from a string\\n\\n :param input_string\\n string to be modified\\n :param char_to_replace\\n array of character strings to be removed\\n \\\"\\\"\\\"\\n for char in char_to_replace:\\n input_string = input_string.replace(char, \\\"\\\")\\n if spaces == 'double':\\n input_string = input_string.replace(\\\" \\\", \\\" \\\")\\n return input_string\\n\\n def section_break(title):\\n \\\"\\\"\\\"\\n Insert break and new section title into cti file\\n\\n :param title:\\n title string for next section_break\\n \\\"\\\"\\\"\\n f.write('!'+ \\\"-\\\"*75 + '\\\\n')\\n f.write('! ' + title +'\\\\n')\\n f.write('!'+ \\\"-\\\"*75 + '\\\\n')\\n\\n def replace_multiple(input_string, replace_list):\\n \\\"\\\"\\\"\\n Replace multiple characters in a string\\n\\n :param input_string\\n string to be modified\\n :param replace list\\n list containing items to be replaced (value replaces key)\\n \\\"\\\"\\\"\\n for original_character, new_character in replace_list.items():\\n input_string = input_string.replace(original_character,\\n new_character)\\n return input_string\\n\\n def build_arrhenius(equation_object, equation_type):\\n \\\"\\\"\\\"\\n Builds Arrhenius coefficient string\\n\\n :param equation_objects\\n cantera equation object\\n :param equation_type:\\n string of equation type\\n \\\"\\\"\\\"\\n coeff_sum = sum(equation_object.reactants.values())\\n pre_exponential_factor = equation_object.rate.pre_exponential_factor\\n temperature_exponent = '{:.3f}'.format(equation_object.rate.temperature_exponent)\\n activation_energy = '{:.2f}'.format(equation_object.rate.activation_energy / calories_constant)\\n if equation_type == 'ElementaryReaction':\\n if coeff_sum == 1:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor))\\n if coeff_sum == 2:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**3))\\n if coeff_sum == 3:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**6))\\n if equation_type == 'ThreeBodyReaction':\\n if coeff_sum == 1:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**3))\\n if coeff_sum == 2:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**6))\\n if (equation_type != 'ElementaryReaction'\\n and equation_type != 'ThreeBodyReaction'):\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor))\\n arrhenius = [pre_exponential_factor,\\n temperature_exponent,\\n activation_energy]\\n return arrhenius\\n\\n def build_modified_arrhenius(equation_object, t_range):\\n \\\"\\\"\\\"\\n Builds Arrhenius coefficient strings for high and low temperature ranges\\n\\n :param equation_objects\\n cantera equation object\\n :param t_range:\\n simple string ('high' or 'low') to designate temperature range\\n \\\"\\\"\\\"\\n if t_range == 'high':\\n pre_exponential_factor = equation_object.high_rate.pre_exponential_factor\\n temperature_exponent = '{:.3f}'.format(equation_object.high_rate.temperature_exponent)\\n activation_energy = '{:.2f}'.format(equation_object.high_rate.activation_energy/calories_constant)\\n if len(equation_object.products) == 1:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor*10**3))\\n else:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor))\\n arrhenius_high = [pre_exponential_factor,\\n temperature_exponent,\\n activation_energy]\\n return arrhenius_high\\n if t_range == 'low':\\n\\n pre_exponential_factor = equation_object.low_rate.pre_exponential_factor\\n temperature_exponent = '{:.3f}'.format(equation_object.low_rate.temperature_exponent)\\n activation_energy = '{:.2f}'.format(equation_object.low_rate.activation_energy/calories_constant)\\n if len(equation_object.products) == 1:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor*10**6))\\n else:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor*10**3))\\n\\n arrhenius_low = [pre_exponential_factor,\\n temperature_exponent,\\n activation_energy]\\n return arrhenius_low\\n\\n\\n def build_nasa(nasa_coeffs, row):\\n \\\"\\\"\\\"\\n Creates string of nasa polynomial coefficients\\n\\n :param nasa_coeffs\\n cantera species thermo coefficients object\\n :param row\\n which row to write coefficients in\\n \\\"\\\"\\\"\\n line_coeffs = ''\\n lines = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]]\\n line_index = lines[row-2]\\n for ix, c in enumerate(nasa_coeffs):\\n if ix in line_index:\\n if c >= 0:\\n line_coeffs += ' '\\n line_coeffs += str('{:.8e}'.format(c))\\n return line_coeffs\\n\\n def build_species_string():\\n \\\"\\\"\\\"\\n formats species string for writing\\n \\\"\\\"\\\"\\n species_list_string = ''\\n line = 1\\n for sp_index, sp_string in enumerate(trimmed_solution.species_names):\\n sp = ' '\\n #get length of string next species is added\\n length_new = len(sp_string)\\n length_string = len(species_list_string)\\n total = length_new + length_string + 3\\n #if string will go over width, wrap to new line\\n if total >= 70*line:\\n species_list_string += '\\\\n'\\n line += 1\\n species_list_string += sp_string + ((16-len(sp_string))*sp)\\n return species_list_string\\n\\n #Write title block to file\\n section_break('Chemkin File converted from Solution Object by pyMARS')\\n\\n #Write phase definition to file\\n element_names = eliminate(str(trimmed_solution.element_names),\\n ['[', ']', '\\\\'', ','])\\n element_string = Template(\\n 'ELEMENTS\\\\n' +\\n '$element_names\\\\n' +\\n 'END\\\\n')\\n f.write(element_string.substitute(element_names=element_names))\\n species_names = build_species_string()\\n species_string = Template(\\n 'SPECIES\\\\n' +\\n '$species_names\\\\n'+\\n 'END\\\\n')\\n f.write(species_string.substitute(species_names=species_names))\\n\\n #Write species to file\\n section_break('Species data')\\n f.write('THERMO ALL' + '\\\\n' +\\n ' 300.000 1000.000 5000.000' +'\\\\n')\\n phase_unknown_list = []\\n\\n #write data for each species in the Solution object\\n for sp_index in xrange(len(trimmed_solution.species_names)):\\n d = 3.33564e-30 #1 debye = d coulomb-meters\\n species = trimmed_solution.species(sp_index)\\n name = str(trimmed_solution.species(sp_index).name)\\n nasa_coeffs = trimmed_solution.species(sp_index).thermo.coeffs\\n #Species attributes from trimmed solution object\\n t_low = '{0:.3f}'.format(species.thermo.min_temp)\\n t_max = '{0:.3f}'.format(species.thermo.max_temp)\\n t_mid = '{0:.3f}'.format(species.thermo.coeffs[0])\\n temp_range = str(t_low) + ' ' + str(t_max) + ' ' + t_mid\\n species_comp = ''\\n for atom in species.composition:\\n species_comp += '{:<4}'.format(atom)\\n species_comp += str(int(species.composition[atom]))\\n if type(species.transport).__name__ == 'GasTransportData':\\n species_phase = 'G'\\n else:\\n phase_unknown_list.append(name)\\n species_phase = 'G'\\n line_1 = (\\n '{:<18}'.format(name) +\\n '{:<6}'.format(' ') +\\n '{:<20}'.format(species_comp) +\\n '{:<4}'.format(species_phase) +\\n '{:<31}'.format(temp_range) +\\n '{:<1}'.format('1') +\\n '\\\\n')\\n f.write(line_1)\\n line_2_coeffs = build_nasa(nasa_coeffs, 2)\\n line_2 = line_2_coeffs + ' 2\\\\n'\\n f.write(line_2)\\n line_3_coeffs = build_nasa(nasa_coeffs, 3)\\n line_3 = line_3_coeffs + ' 3\\\\n'\\n f.write(line_3)\\n line_4_coeffs = build_nasa(nasa_coeffs, 4)\\n line_4 = line_4_coeffs + ' 4\\\\n'\\n f.write(line_4)\\n\\n f.write('END\\\\n')\\n\\n #Write reactions to file\\n section_break('Reaction Data')\\n f.write('REACTIONS\\\\n')\\n #write data for each reaction in the Solution Object\\n for reac_index in xrange(len(trimmed_solution.reaction_equations())):\\n equation_string = str(trimmed_solution.reaction_equation(reac_index))\\n equation_string = eliminate(equation_string, ' ', 'single')\\n equation_object = trimmed_solution.reaction(reac_index)\\n equation_type = type(equation_object).__name__\\n m = str(reac_index+1)\\n if equation_type == 'ThreeBodyReaction':\\n arrhenius = build_arrhenius(equation_object, equation_type)\\n main_line = (\\n '{:<51}'.format(equation_string) +\\n '{:>9}'.format(arrhenius[0]) +\\n '{:>9}'.format(arrhenius[1]) +\\n '{:>11}'.format(arrhenius[2]) +\\n '\\\\n')\\n f.write(main_line)\\n #trimms efficiencies list\\n efficiencies = equation_object.efficiencies\\n trimmed_efficiencies = equation_object.efficiencies\\n for s in efficiencies:\\n if s not in trimmed_solution.species_names:\\n del trimmed_efficiencies[s]\\n replace_list_2 = {\\n '{':'',\\n '}':'/',\\n '\\\\'':'',\\n ':':'/',\\n ',':'/'}\\n efficiencies_string = replace_multiple(\\n str(trimmed_efficiencies),\\n replace_list_2)\\n secondary_line = str(efficiencies_string) + '\\\\n'\\n if bool(efficiencies) is True:\\n f.write(secondary_line)\\n if equation_type == 'ElementaryReaction':\\n arrhenius = build_arrhenius(equation_object, equation_type)\\n main_line = (\\n '{:<51}'.format(equation_string) +\\n '{:>9}'.format(arrhenius[0]) +\\n '{:>9}'.format(arrhenius[1]) +\\n '{:>11}'.format(arrhenius[2]) +\\n '\\\\n')\\n f.write(main_line)\\n if equation_type == 'FalloffReaction':\\n arr_high = build_modified_arrhenius(equation_object, 'high')\\n main_line = (\\n '{:<51}'.format(equation_string) +\\n '{:>9}'.format(arr_high[0]) +\\n '{:>9}'.format(arr_high[1]) +\\n '{:>11}'.format(arr_high[2]) +\\n '\\\\n')\\n f.write(main_line)\\n arr_low = build_modified_arrhenius(equation_object, 'low')\\n second_line = (\\n ' LOW /' +\\n ' ' + arr_low[0] +\\n ' ' + arr_low[1] +\\n ' ' + arr_low[2] + '/\\\\n')\\n f.write(second_line)\\n j = equation_object.falloff.parameters\\n #If optional Arrhenius data included:\\n try:\\n third_line = (\\n ' TROE/' +\\n ' ' + str(j[0]) +\\n ' ' + str(j[1]) +\\n ' ' + str(j[2]) +\\n ' ' + str(j[3]) +' /\\\\n')\\n f.write(third_line)\\n except IndexError:\\n pass\\n #trimms efficiencies list\\n efficiencies = equation_object.efficiencies\\n trimmed_efficiencies = equation_object.efficiencies\\n for s in efficiencies:\\n if s not in trimmed_solution.species_names:\\n del trimmed_efficiencies[s]\\n replace_list_2 = {\\n '{':'',\\n '}':'/',\\n '\\\\'':'',\\n ':':'/',\\n ',':'/'}\\n efficiencies_string = replace_multiple(\\n str(trimmed_efficiencies),\\n replace_list_2)\\n\\n fourth_line = str(efficiencies_string) + '\\\\n'\\n if bool(efficiencies) is True:\\n f.write(fourth_line)\\n #dupluicate option\\n if equation_object.duplicate is True:\\n duplicate_line = ' DUPLICATE' +'\\\\n'\\n f.write(duplicate_line)\\n f.write('END')\\n f.close()\\n\\n #Test mechanism file\\n\\n original_solution = solution\\n #convert written chemkin file to cti, and get solution\\n parser = ck2cti.Parser()\\n outName = 'test_file.cti'\\n parser.convertMech(output_file_name, outName=outName)\\n new_solution = ct.Solution(outName)\\n\\n #test new solution vs original solutoin\\n #test(original_solution, new_solution)\\n os.remove(outName)\\n return output_file_name\\n\\n\\n \\\"\\\"\\\"\\n def build_falloff(j):\\n\\n Creates falloff reaction Troe parameter string\\n\\n param j:\\n Cantera falloff parameters object\\n\\n falloff_str = str(',\\\\n falloff = Troe(' +\\n 'A = ' + str(j[0]) +\\n ', T3 = ' + str(j[1]) +\\n ', T1 = ' + str(j[2]) +\\n ', T2 = ' + str(j[3]) +') )\\\\n\\\\n')\\n return falloff_str\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9fe475543f8a500b17238e2c01e2a8\",\n \"score\": \"0.5131358\",\n \"text\": \"def writeDataFile(self):\\n model = pe.PEChainBuilder(self.lenPa, self.numPa, self.lenPc, self.numPc,\\\\\\n self.volRatio, self.chargeFraction)\\n model.genChains()\\n \\n \\n model.writeFiles(self.directory)\\n self.dataFileName = \\\"data.pe.la{0}.na{1}.lc{2}.nc{3}.rho{4}.r{5}.lammps\\\".\\\\\\n format(self.lenPa, self.numPa, self.lenPc, self.numPc, \\\\\\n self.volRatio, self.chargeRepeat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc59f59e013fe69fd099020e5707bc62\",\n \"score\": \"0.51224095\",\n \"text\": \"def createPrismModel(states, transitions, target, mode, nr_actions, reset_transitions, reset_cost):\\n out_file = open(TMP_MODEL_PATH, 'w')\\n # module\\n out_file.write(\\\"mdp\\\\n\\\\nmodule tmp\\\\n\\\\n\\\")\\n\\n # number of state and initial state\\n out_file.write(\\\"\\\\ts : [0..\\\" + str(len(states)) + \\\"] init \\\" + str(abs(mode)) + \\\";\\\\n\\\\n\\\")\\n\\n # transitions\\n for state in range(len(transitions)):\\n for action in range(len(transitions[state])):\\n if sum(transitions[state][action]) != 0:\\n out_file.write(\\\"\\\\t[\\\" + chr(97 + action) + \\\"] s=\\\" + str(state) + \\\"-> \\\")\\n destinations = []\\n for dest in range(len(transitions[state][action])):\\n if transitions[state][action][dest] != 0:\\n destinations.append(str(transitions[state][action][dest]) + \\\":(s'=\\\" + str(dest) + \\\")\\\")\\n out_file.write(\\\" + \\\".join(destinations))\\n out_file.write(\\\";\\\\n\\\")\\n out_file.write(\\\"\\\\nendmodule\\\\n\\\\n\\\")\\n\\n # label target\\n out_file.write('label \\\"target\\\" = ')\\n target = list(set(target))\\n target = [\\\"(s=\\\" + str(x) + \\\")\\\" for x in target]\\n if len(target) == 0:\\n out_file.write(\\\"(s=\\\" + str(len(states) + 1) + \\\");\\\\n\\\")\\n else:\\n out_file.write(\\\" | \\\".join(target))\\n out_file.write(\\\";\\\\n\\\")\\n\\n # label states\\n if mode == MODE_MAX:\\n out_file.write('label \\\"sink\\\" = (s=0);\\\\n')\\n\\n for i in range(mode, len(states)):\\n out_file.write('label \\\"s' + str(states[i][0]) + '_obs' + str(states[i][1]) + '\\\" = (s=' + str(i) + ');\\\\n')\\n\\n if mode == MODE_MIN:\\n out_file.write(\\\"\\\\nrewards\\\\n\\\")\\n if reset_cost == 1:\\n out_file.write(\\\"\\\\ttrue:1;\\\\n\\\")\\n else:\\n for i in range(len(states)):\\n for j in range(nr_actions):\\n if j in reset_transitions[i]:\\n out_file.write(\\\"\\\\t[\\\" + chr(97 + j) + \\\"] (s=\\\" + str(i) + \\\") : \\\" + str(reset_cost) + \\\";\\\\n\\\")\\n else:\\n out_file.write(\\\"\\\\t[\\\" + chr(97 + j) + \\\"] (s=\\\" + str(i) + \\\") : 1;\\\\n\\\")\\n\\n out_file.write(\\\"endrewards\\\\n\\\")\\n\\n out_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f391c50f49d5b0eae5ebbf6c5f12961a\",\n \"score\": \"0.5118555\",\n \"text\": \"def build_LM(in_file):\\r\\n print 'building language models...'\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n LMs = {'malaysian': {}, 'indonesian': {}, 'tamil': {}}\\r\\n malay_LM = LMs['malaysian']\\r\\n indon_LM = LMs['indonesian']\\r\\n tamil_LM = LMs['tamil']\\r\\n\\r\\n FD = open(in_file, 'r')\\r\\n\\r\\n lines = FD.read()\\r\\n lines_array = lines.replace('\\\\r', ' ').split('\\\\n')\\r\\n lines_array.pop(len(lines_array) - 1)\\r\\n\\r\\n for line in lines_array:\\r\\n line_array = line.split(' ', 1)\\r\\n lang = line_array[0]\\r\\n text = line_array[1]\\r\\n current_LM = LMs[lang]\\r\\n four_char_array = get_four_char_array(text)\\r\\n\\r\\n for four_char in four_char_array:\\r\\n if four_char not in current_LM:\\r\\n # performs add one smoothing\\r\\n malay_LM[four_char] = 1\\r\\n indon_LM[four_char] = 1\\r\\n tamil_LM[four_char] = 1\\r\\n current_LM[four_char] += 1\\r\\n\\r\\n # calculates ratio of set frequency over total frequency\\r\\n # for all sets in each of the three LMs\\r\\n for LM in LMs.values():\\r\\n count = 0\\r\\n for freq_value in LM.values():\\r\\n count += freq_value\\r\\n for four_char_key in LM.keys():\\r\\n LM[four_char_key] /= count\\r\\n return LMs\\r\\n\\r\\n FD.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c862647fcd5ba7f408d748abaf7ab617\",\n \"score\": \"0.5115003\",\n \"text\": \"def test_ImportUFONoSideEffectNLO(self):\\n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('loop_sm'),False)\\n original_all_particles = copy.copy(ufo_model.all_particles)\\n original_all_vertices = copy.copy(ufo_model.all_vertices)\\n original_all_couplings = copy.copy(ufo_model.all_couplings)\\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\\n original_all_parameters = copy.copy(ufo_model.all_parameters)\\n original_all_orders = copy.copy(ufo_model.all_orders)\\n original_all_functions = copy.copy(ufo_model.all_functions)\\n original_all_CTvertices = copy.copy(ufo_model.all_CTvertices)\\n original_all_CTparameters = copy.copy(ufo_model.all_CTparameters)\\n\\n\\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\\n model = ufo2mg5_converter.load_model()\\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \\n\\n self.assertEqual(original_all_particles,ufo_model.all_particles)\\n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\\n# self.assertEqual(original_all_couplings,ufo_model.all_couplings)\\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\\n self.assertEqual(original_all_orders,ufo_model.all_orders)\\n self.assertEqual(original_all_functions,ufo_model.all_functions)\\n self.assertEqual(original_all_CTvertices,ufo_model.all_CTvertices)\\n self.assertEqual(original_all_CTparameters,ufo_model.all_CTparameters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfe119951be54a560fcefe9057a921e1\",\n \"score\": \"0.5112634\",\n \"text\": \"def main(models, output_dir):\\n\\tfor model in models:\\n\\t\\tobj = ParseOmsa(model)\\n\\t\\tobj.bios_version()\\n\\t\\tobj.perc_versions()\\n\\t\\tobj.write_yaml(output_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e7eaf7ed485ca9233fa3bbe1a1b96e\",\n \"score\": \"0.5110691\",\n \"text\": \"def solve_lp(self):\\n self._initialize_model()\\n self._initialize_node_variables_lp()\\n self._initialize_edge_variables_lp()\\n self._initialize_objective()\\n self._initialize_contraint_nodes()\\n self._initialize_constraint_edges()\\n self._initialize_constraint_terminals()\\n self._run_solver()\\n self._calculate_cut_value()\\n self._calculate_source_sets()\\n self._calculate_possible_terminals_by_node_weak()\\n self._calculate_possible_terminals_by_node_strong()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6586aa3830f43b54dce72f69cbc0963d\",\n \"score\": \"0.5104416\",\n \"text\": \"def loadLoopToyModel():\\n \\n mypartlist = base_objects.ParticleList()\\n myinterlist = base_objects.InteractionList()\\n myloopmodel = loop_base_objects.LoopModel()\\n\\n # A gluon\\n mypartlist.append(base_objects.Particle({'name':'g',\\n 'antiname':'g',\\n 'spin':3,\\n 'color':8,\\n 'mass':'ZERO',\\n 'width':'ZERO',\\n #'texname':'G',\\n #'antitexname':'G',\\n 'line':'curly',\\n 'charge':0.,\\n 'pdg_code':21,\\n #'propagating':True,\\n 'propagator':0, \\n 'is_part':True,\\n 'counterterm':{('QCD', ((6,),)): {0: 'UVWfct_G_1', -1: 'UVWfct_G_1_1eps'}, ('QCD', ((5,),)): {0: 'UVWfct_G_0', -1: 'UVWfct_G_0_1eps'}},\\n 'self_antipart':True}))\\n \\n # A quark U and its antiparticle\\n mypartlist.append(base_objects.Particle({'name':'u',\\n 'antiname':'u~',\\n 'spin':2,\\n 'color':3,\\n 'mass':'ZERO',\\n 'width':'ZERO',\\n #'texname':'u',\\n #'antitexname':'u',\\n 'line':'straight',\\n 'charge':2. / 3.,\\n 'pdg_code':2,\\n #'propagating':True,\\n 'propagator':'', \\n 'is_part':True,\\n 'self_antipart':False}))\\n antiu = copy.copy(mypartlist[1])\\n antiu.set('is_part', False)\\n\\n # A quark D and its antiparticle\\n mypartlist.append(base_objects.Particle({'name':'d',\\n 'antiname':'d~',\\n 'spin':2,\\n 'color':3,\\n 'mass':'ZERO',\\n 'width':'ZERO',\\n #'texname':'d',\\n #'antitexname':'d',\\n 'line':'straight',\\n 'charge':-1. / 3.,\\n 'pdg_code':1,\\n #'propagating':True,\\n 'propagator':'',\\n 'is_part':True,\\n 'self_antipart':False}))\\n antid = copy.copy(mypartlist[2])\\n antid.set('is_part', False)\\n\\n myloopmodel.set('particles', mypartlist)\\n myloopmodel.set('couplings', ['QCD']) \\n myloopmodel.set('interactions', myinterlist)\\n myloopmodel.set('perturbation_couplings', ['QCD'])\\n myloopmodel.set('order_hierarchy', {'QCD':1})\\n\\n return myloopmodel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695e98b499f902207ba35ccac8891bda\",\n \"score\": \"0.5104137\",\n \"text\": \"def generate(genconf_model, project_folder):\\n\\n gendesc = get_generator_desc(genconf_model.gen_name)\\n meta = get_language(gendesc.lang)\\n\\n # Path for templates overrides\\n templates_path = os.path.join(project_folder, 'templates',\\n genconf_model.gen_name)\\n\\n output_root = os.path.join(project_folder, genconf_model.output)\\n\\n def _create_folder(output_file):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except FileExistsError:\\n pass\\n\\n # For each model configured in the current genconf\\n for model_path in genconf_model.models:\\n\\n click.echo('Processing model \\\"{}\\\"'.format(model_path))\\n model = meta.model_from_file(os.path.join(project_folder, 'model',\\n model_path))\\n\\n # Validate model using generator specific validation.\\n if gendesc.validate:\\n gendesc.validate(model)\\n\\n params = {}\\n # Adding generator params\\n for p in genconf_model.params:\\n params[p.name] = p.value\\n\\n # Processing all rules\\n for rule in genconf_model.rules:\\n\\n # Sanity check\\n if len(rule.types) != len(rule.var_names):\\n raise TextXToolsError('Number of variables don\\\\'t match'\\n ' number of types in rule \\\"{}\\\"'\\n .format(rule.name))\\n\\n # Collect all object of each given type\\n type_objs = []\\n for t in rule.types:\\n type_objs.append(children_of_type(model, t))\\n\\n if rule.all:\\n context = {'__builtins__':{}}\\n for ind, obj in enumerate(type_objs):\\n params[rule.var_names[ind]] = obj\\n context.update(params)\\n for t in rule.trans:\\n target_path = eval(t.python_path_expr, context)\\n output_file = os.path.join(output_root, target_path)\\n\\n click.echo(\\\"Generating {}\\\".format(output_file))\\n\\n _create_folder(output_file)\\n with open(output_file, 'w') as f:\\n f.write(gendesc.render(t.template_path, params,\\n templates_path))\\n else:\\n if len(rule.types) > 1:\\n raise TextXToolsError('Multiple types/variables are not'\\n ' possible for \\\"non-all\\\" rules.')\\n for obj in type_objs[0]:\\n params[rule.var_names[0]] = obj\\n context = {'__builtins__': {}}\\n context.update(params)\\n for t in rule.trans:\\n target_path = eval(t.python_path_expr, context)\\n output_file = os.path.join(output_root, target_path)\\n click.echo(\\\"Generating {}\\\".format(output_file))\\n _create_folder(output_file)\\n\\n with open(output_file, 'w') as f:\\n f.write(gendesc.render(t.template_path, params,\\n templates_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a1835d042cf66486ed099ce14f71906\",\n \"score\": \"0.51036656\",\n \"text\": \"def generate_pymods(the_parsed_topology_xml, xml_filename, opt):\\n if the_parsed_topology_xml.get_namespace():\\n if VERBOSE:\\n print(\\n \\\"Generating pymods for topology %s::%s\\\"\\n % (\\n the_parsed_topology_xml.get_namespace(),\\n the_parsed_topology_xml.get_name(),\\n )\\n )\\n else:\\n if VERBOSE:\\n print(\\n \\\"Generating pymods for topology %s\\\"\\n % (the_parsed_topology_xml.get_name())\\n )\\n model = TopoFactory.TopoFactory.getInstance()\\n topology_model = model.create(the_parsed_topology_xml)\\n\\n # create list of used parsed component xmls\\n parsed_xml_dict = {}\\n for comp in the_parsed_topology_xml.get_instances():\\n if comp.get_type() in topology_model.get_base_id_dict():\\n parsed_xml_dict[comp.get_type()] = comp.get_comp_xml()\\n else:\\n if VERBOSE:\\n print(\\n \\\"Components with type {} aren't in the topology model.\\\".format(\\n comp.get_type()\\n )\\n )\\n\\n #\\n # Hack to set up deployment path for instanced dictionaries (if one exists remove old one)\\n #\\n if opt.dict_dir is None:\\n os.environ[\\\"DICT_DIR\\\"] = os.getcwd()\\n else:\\n os.environ[\\\"DICT_DIR\\\"] = opt.dict_dir\\n\\n xml_list = []\\n for parsed_xml_type in parsed_xml_dict:\\n if parsed_xml_dict[parsed_xml_type] is None:\\n print(\\n \\\"ERROR: XML of type {} is being used, but has not been parsed correctly. Check if file exists or add xml file with the 'import_component_type' tag to the Topology file.\\\".format(\\n parsed_xml_type\\n )\\n )\\n raise Exception()\\n xml_list.append(parsed_xml_dict[parsed_xml_type])\\n temp_comp = parsed_xml_dict[parsed_xml_type].get_component()\\n if VERBOSE:\\n print(\\n \\\"Generating component dicts for %s::%s\\\"\\n % (temp_comp.get_namespace(), temp_comp.get_name())\\n )\\n write_pymods_from_comp(parsed_xml_dict[parsed_xml_type], opt, topology_model)\\n if VERBOSE:\\n print(\\n \\\"Generated component dicts for %s::%s\\\"\\n % (temp_comp.get_namespace(), temp_comp.get_name())\\n )\\n\\n topology_model.set_instance_xml_list(xml_list)\\n\\n if the_parsed_topology_xml.get_namespace():\\n if VERBOSE:\\n print(\\n \\\"Generated pymods for topology %s::%s\\\"\\n % (\\n the_parsed_topology_xml.get_namespace(),\\n the_parsed_topology_xml.get_name(),\\n )\\n )\\n else:\\n if VERBOSE:\\n print(\\n \\\"Generated pymods for topology %s\\\"\\n % (the_parsed_topology_xml.get_name())\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7d04b25bee59ebf17f61215a787437\",\n \"score\": \"0.51035786\",\n \"text\": \"def main(input_dir: str, output_dir: str) -> None:\\n input_dir = Path(input_dir)\\n output_dir = Path(output_dir)\\n output_dir.mkdir(parents=True, exist_ok=True)\\n\\n spacy_formatted_data = {\\\"train\\\": [], \\\"test\\\": []}\\n\\n for partition in spacy_formatted_data:\\n note_dir = input_dir / partition / f\\\"{partition}_note\\\"\\n if partition == \\\"train\\\":\\n norm_dir = input_dir / partition / f\\\"{partition}_norm\\\"\\n else:\\n # TODO (John): Is this the name of the directory in the official download?\\n norm_dir = input_dir / partition / f\\\"{partition}_norm_cui_replaced_with_unk\\\"\\n\\n for note_filepath in note_dir.iterdir():\\n if not note_filepath.name.endswith(\\\".txt\\\"):\\n continue\\n\\n norm_filepath = norm_dir / f\\\"{note_filepath.stem}.norm\\\"\\n\\n with open(note_filepath, \\\"r\\\") as f:\\n spacy_formatted_data[partition].append((f.read(), {\\\"entities\\\": []}))\\n with open(norm_filepath, \\\"r\\\") as f:\\n for line in f:\\n start, end = tuple(map(int, line.strip().split(\\\"||\\\")[-2:]))\\n spacy_formatted_data[partition][-1][-1][\\\"entities\\\"].append((start, end, ENTITY_LABEL))\\n\\n with open(output_dir / \\\"spacy_formatted_data.pickle\\\", \\\"wb\\\") as f:\\n pickle.dump(spacy_formatted_data, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df99f942c3c7b8f03f573780597898e3\",\n \"score\": \"0.5099381\",\n \"text\": \"def pmodel(self):\\r\\n if not hasattr(self, '_pmodel'):\\r\\n pmodelname = self.get('pmodel-name')\\r\\n if pmodelname == 'ISO9613':\\r\\n # create environment\\r\\n G = tuple([float(x) for x in self.get('pmodel-iso9613-ground-coeffs').strip('()').split(',')])\\r\\n p = self.getFloat('pmodel-iso9613-atmospheric-pressure')\\r\\n t = self.getFloat('pmodel-iso9613-atmospheric-temperature')\\r\\n r = self.getFloat('pmodel-iso9613-atmospheric-humidity')\\r\\n self._environment = ISO9613Environment(G = G, p = p, t = t, r = r)\\r\\n # create propagation model\\r\\n self._pmodel = ISO9613Model(environment = self._environment)\\r\\n self._pmodel.correction['geometricDivergence'] = self.getBool('pmodel-iso9613-flag-geometric-divergence')\\r\\n self._pmodel.correction['atmosphericAbsorption'] = self.getBool('pmodel-iso9613-flag-atmospheric-absorption')\\r\\n self._pmodel.correction['groundEffect'] = self.getBool('pmodel-iso9613-flag-ground-effect')\\r\\n self._pmodel.correction['sourceDirectivity'] = self.getBool('pmodel-iso9613-flag-source-directivity')\\r\\n else:\\r\\n raise Exception('configuration file: Propagation model \\\"%s\\\" not known - use \\\"ISO9613\\\"' % pmodelname)\\r\\n return self._pmodel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb2d23548d39b59677cfcfc9082e026\",\n \"score\": \"0.50863206\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser(description='Duolingo shared task baseline model')\\n parser.add_argument('--language', default='en_es', help='choose from [es_en, en_es, fr_en]', required=False)\\n parser.add_argument('--dataset_path', default='../data/%s/', required=False)\\n parser.add_argument('--outputs_path', default='./outputs/', required=False)\\n args = parser.parse_args()\\n\\n dataset_path = args.dataset_path % args.language\\n assert os.path.isdir(dataset_path)\\n\\n train_path = dataset_path + '%s.slam.20190204.train' % args.language\\n dev_path = dataset_path + '%s.slam.20190204.dev' % args.language\\n test_path = dataset_path + '%s.slam.20190204.test' % args.language\\n assert os.path.isfile(train_path)\\n assert os.path.isfile(dev_path)\\n assert os.path.isfile(test_path)\\n\\n if not os.path.isdir(args.outputs_path): os.mkdir(args.outputs_path)\\n\\n # ============================== Hyper Parameter ==============================\\n dbg = False\\n from_path = None\\n # from_path = './saved_model/seq2seq_nomlp_20'\\n # from_path = './saved_model/seq2seq_exp_20'\\n # from_path = './saved_model/attention_v2_20'\\n # from_path = './saved_model/cnn_3'\\n # from_path = './saved_model/seq2seq_c_7'\\n # from_path = './saved_model/transformer_1'\\n epochs = 10 if dbg else 10\\n lang = Lang()\\n\\n # ============================== Data Loading ==============================\\n\\n print('Begin Data Loading')\\n start_time = time.time()\\n training_data, training_labels = load_data(train_path, lang, dbg=dbg, use_all_features=True)\\n dev_data = load_data(dev_path, lang, use_all_features=True)\\n test_data = load_data(test_path, lang, use_all_features=True)\\n users = list(get_users(training_data).union(get_users(dev_data)).union(get_users(test_data)))\\n lang.addUsers(users)\\n for i in range(len(lang.letters)): lang.letter2Index[lang.letters[i]] = i\\n end_time = time.time()\\n print('Data Loaded\\\\t Time Taken %0.2fm' % ((end_time - start_time)/60))\\n\\n model = Model(lang)\\n\\n # ============================== Training ==============================\\n if from_path == None:\\n print('Begin Training')\\n train_loader = get_dataloader(training_data, lang, training_labels)\\n model.train(train_loader, epochs)\\n\\n # ============================== Inference ==============================\\n # print('Begin Inference-Dev', end=' ')\\n # start_time = time.time()\\n # dev_loader = get_dataloader(dev_data, lang)\\n # predictions = model.predict_for_set(dev_loader, from_path)\\n # with open(args.outputs_path + '%s_dev_predictions.pred' % args.language, 'wt') as f:\\n # for instance_id, prediction in iteritems(predictions):\\n # f.write(instance_id + ' ' + str(prediction) + '\\\\n')\\n # end_time = time.time()\\n # print('| %0.2fm' % ((end_time-start_time)/60))\\n \\n print('Begin Inference-Test', end=' ')\\n start_time = time.time()\\n test_loader = get_dataloader(test_data, lang)\\n predictions = model.predict_for_set(test_loader, from_path)\\n with open(args.outputs_path + '%s_test_predictions.pred' % args.language, 'wt') as f:\\n for instance_id, prediction in predictions.items():\\n f.write(instance_id + ' ' + str(prediction) + '\\\\n')\\n end_time = time.time()\\n print('| %0.2fm' % ((end_time-start_time)/60))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7982cf4b80468889131323fa94e9d7\",\n \"score\": \"0.50660026\",\n \"text\": \"def generate_language_model(**kwargs):\\n\\n lm = Session.query(model.MorphemeLanguageModel).get(kwargs['morpheme_language_model_id'])\\n trie_path = lm.get_file_path('trie')\\n trie_mod_time = lm.get_modification_time(trie_path)\\n lm.generate_succeeded = False\\n try:\\n lm.write_corpus()\\n except Exception, e:\\n lm.generate_message = u'Error writing the corpus file. %s' % e\\n try:\\n lm.write_vocabulary()\\n except Exception, e:\\n lm.generate_message = u'Error writing the vocabulary file. %s' % e\\n try:\\n lm.write_arpa(kwargs['timeout'])\\n except Exception, e:\\n lm.generate_message = u'Error writing the ARPA file. %s' % e\\n try:\\n lm.generate_trie()\\n except Exception, e:\\n lm.generate_message = u'Error generating the LMTrie instance. %s' % e\\n else:\\n if lm.get_modification_time(trie_path) != trie_mod_time:\\n lm.generate_succeeded = True\\n lm.generate_message = u'Language model successfully generated.'\\n else:\\n lm.generate_message = u'Error generating the LMTrie instance.'\\n lm.generate_attempt = unicode(uuid4())\\n lm.modifier_id = kwargs['user_id']\\n lm.datetime_modified = h.now()\\n Session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cff1d19062392a49928573882a530bd\",\n \"score\": \"0.5064492\",\n \"text\": \"def load_model(model_path):\\r\\n nlp = spacy.blank('en')\\r\\n if 'ner' not in nlp.pipe_names:\\r\\n ner = nlp.create_pipe('ner')\\r\\n nlp.add_pipe(ner)\\r\\n\\r\\n ner = nlp.from_disk(model_path)\\r\\n return ner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd82b28979eb3edddc68ff36415c4e0c\",\n \"score\": \"0.5058911\",\n \"text\": \"def _write_imports(\\n cls,\\n pickle_type: Optional[str] = None,\\n mojo_model: Optional[bool] = False,\\n binary_h2o_model: Optional[bool] = False,\\n tf_model: Optional[bool] = False,\\n binary_string: Optional[str] = None,\\n ) -> None:\\n pickle_type = pickle_type if pickle_type else \\\"pickle\\\"\\n cls.score_code += (\\n f\\\"import math\\\\nimport {pickle_type}\\\\nimport pandas as pd\\\\n\\\"\\n \\\"import numpy as np\\\\nfrom pathlib import Path\\\\n\\\\n\\\"\\n )\\n \\\"\\\"\\\"\\nimport math\\nimport pickle\\nimport pandas as pd\\nimport numpy as np\\nfrom pathlib import Path\\n\\n\\n \\\"\\\"\\\"\\n\\n try:\\n if current_session().version_info() != 3.5:\\n cls.score_code += \\\"import settings\\\\n\\\\n\\\"\\n \\\"\\\"\\\"\\nimport settings\\n \\n \\n \\\"\\\"\\\"\\n except AttributeError:\\n warn(\\n \\\"No current session connection was found to a SAS Viya server. Score \\\"\\n \\\"code will be written under the assumption that the target server is \\\"\\n \\\"SAS Viya 4.\\\"\\n )\\n\\n if mojo_model or binary_h2o_model:\\n cls.score_code += \\\"import h2o\\\\n\\\\nh2o.init()\\\\n\\\\n\\\"\\n \\\"\\\"\\\"\\nimport h2o\\n\\nh2o.init()\\n\\n \\\"\\\"\\\"\\n elif tf_model:\\n cls.score_code += \\\"import tensorflow as tf\\\\n\\\\n\\\"\\n \\\"\\\"\\\"\\nimport tensorflow as tf\\n\\n \\\"\\\"\\\"\\n elif binary_string:\\n cls.score_code += (\\n f'import codecs\\\\n\\\\nbinary_string = \\\"{binary_string}\\\"'\\n f\\\"\\\\nmodel = {pickle_type}.loads(codecs.decode(binary_string\\\"\\n '.encode(), \\\"base64\\\"))\\\\n\\\\n'\\n )\\n \\\"\\\"\\\"\\nimport codecs\\n\\nbinary_string = \\\"\\\"\\nmodel = pickle.load(codecs.decode(binary_string.encode(), \\\"base64\\\"))\\n\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1108513704f5cbba7b473aa2906d50\",\n \"score\": \"0.50567424\",\n \"text\": \"def write_pymods_from_comp(the_parsed_component_xml, opt, topology_model):\\n global BUILD_ROOT\\n global DEPLOYMENT\\n global VERBOSE\\n\\n parsed_port_xml_list = []\\n parsed_serializable_xml_list = []\\n # uses the topology model to process the items\\n # checks if the topology model exists\\n if topology_model is None:\\n PRINT.info(\\n \\\"Topology model was not specified. Please also input a topology model when running this command.\\\"\\n )\\n raise OSError\\n\\n port_type_files_list = the_parsed_component_xml.get_port_type_files()\\n\\n for port_file in port_type_files_list:\\n port_file = search_for_file(\\\"Port\\\", port_file)\\n xml_parser_obj = XmlPortsParser.XmlPortsParser(port_file)\\n parsed_port_xml_list.append(xml_parser_obj)\\n del xml_parser_obj\\n\\n serializable_type_files_list = (\\n the_parsed_component_xml.get_serializable_type_files()\\n )\\n for serializable_file in serializable_type_files_list:\\n serializable_file = search_for_file(\\\"Serializable\\\", serializable_file)\\n xml_parser_obj = XmlSerializeParser.XmlSerializeParser(\\n serializable_file\\n ) # Telemetry/Params can only use generated serializable types\\n # check to make sure that the serializables don't have things that channels and parameters can't have\\n # can't have external non-xml members\\n if len(xml_parser_obj.get_include_header_files()):\\n print(\\n \\\"ERROR: Component include serializables cannot use user-defined types. file: \\\"\\n % serializable_file\\n )\\n sys.exit(-1)\\n\\n parsed_serializable_xml_list.append(xml_parser_obj)\\n del xml_parser_obj\\n\\n model = CompFactory.CompFactory.getInstance()\\n component_model = model.create(\\n the_parsed_component_xml, parsed_port_xml_list, parsed_serializable_xml_list\\n )\\n\\n instChannelWriter = InstChannelWriter.InstChannelWriter()\\n instCommandWriter = InstCommandWriter.InstCommandWriter()\\n instEventWriter = InstEventWriter.InstEventWriter()\\n\\n if opt.dict_dir is None:\\n if VERBOSE:\\n print(\\\"Dictionary output directory not specified!, defaulting to cwd\\\")\\n opt.dict_dir = os.getcwd()\\n os.environ[\\\"DICT_DIR\\\"] = opt.dict_dir\\n\\n # iterate through command instances\\n for command_model in component_model.get_commands():\\n if VERBOSE:\\n print(\\\"Generating command dict %s\\\" % command_model.get_mnemonic())\\n instCommandWriter.DictStartWrite(command_model, topology_model)\\n instCommandWriter.DictHeaderWrite(command_model, topology_model)\\n instCommandWriter.DictBodyWrite(command_model, topology_model)\\n\\n for parameter_model in component_model.get_parameters():\\n if VERBOSE:\\n print(\\\"Generating parameter dict %s\\\" % parameter_model.get_name())\\n instCommandWriter.DictStartWrite(parameter_model, topology_model)\\n instCommandWriter.DictHeaderWrite(parameter_model, topology_model)\\n instCommandWriter.DictBodyWrite(parameter_model, topology_model)\\n\\n # iterate through command instances\\n for event_model in component_model.get_events():\\n if VERBOSE:\\n print(\\\"Generating event dict %s\\\" % event_model.get_name())\\n instEventWriter.DictStartWrite(event_model, topology_model)\\n instEventWriter.DictHeaderWrite(event_model, topology_model)\\n instEventWriter.DictBodyWrite(event_model, topology_model)\\n\\n # iterate through command instances\\n for channel_model in component_model.get_channels():\\n if VERBOSE:\\n print(\\\"Generating channel dict %s\\\" % channel_model.get_name())\\n instChannelWriter.DictStartWrite(channel_model, topology_model)\\n instChannelWriter.DictHeaderWrite(channel_model, topology_model)\\n instChannelWriter.DictBodyWrite(channel_model, topology_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbac7e9dbefed924c07d9e4bb763e9cb\",\n \"score\": \"0.5051691\",\n \"text\": \"def convert(self, path, version, target):\\n source = self.comparer.get_representation(path)\\n lines = [ '# \\\\n'.format(version) ]\\n\\n for line in self.comparer.template.contents[version].preamble:\\n lines.append(line.write(source.preamble, source.version, source.stored) + \\\"\\\\n\\\")\\n\\n for line in self.comparer.template.contents[version].body:\\n for valueset in source.body:\\n lines.append(line.write(valueset, source.version, source.stored) + \\\"\\\\n\\\")\\n\\n with open(os.path.expanduser(target), 'w') as f:\\n f.writelines(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c2bef9339db6de5f9e061ddef3fb47\",\n \"score\": \"0.5050992\",\n \"text\": \"def convert_to_onnx(pytorch_model, input_shape, output_file, input_names, output_names):\\n\\n pytorch_model.eval()\\n onnx_input_shape = torch.randn(input_shape)\\n torch.onnx.export(pytorch_model, onnx_input_shape, output_file,\\n verbose=True, input_names=input_names, output_names=output_names)\\n\\n # Model check after conversion\\n import onnx\\n model_from_onnx = onnx.load(output_file)\\n try:\\n onnx.checker.check_model(model_from_onnx)\\n print('ONNX check passed successfully.')\\n except onnx.onnx_cpp2py_export.checker.ValidationError as exc:\\n sys.exit('ONNX check failed with error: ' + str(exc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4a2a2fd203b35f20cc76a1f1693031\",\n \"score\": \"0.5048336\",\n \"text\": \"def _ppo(self, m):\\n\\n m[\\\"ppo1\\\"] = m[\\\"MemoryOp <: po_loc :> Write\\\"]\\n m[\\\"ppo2\\\"] = m[\\\"rdw\\\"]\\n m[\\\"ppo3\\\"] = m[\\\"(AMO + StoreConditional) <: (rf & po)\\\"]\\n m[\\\"ppo4rr\\\"] = m[\\\"(Read <: po :> (FencePR & FenceSR)).(po :> Read)\\\"]\\n m[\\\"ppo4rw\\\"] = m[\\\"(Read <: po :> (FencePR & FenceSW)).(po :> Write)\\\"]\\n m[\\\"ppo4wr\\\"] = m[\\\"(Write <: po :> (FencePW & FenceSR)).(po :> Read)\\\"]\\n m[\\\"ppo4ww\\\"] = m[\\\"(Write <: po :> (FencePW & FenceSW)).(po :> Write)\\\"]\\n m[\\\"ppo4tso1\\\"] = m[\\\"(Read <: po :> (FenceTSO)).(po :> MemoryOp)\\\"]\\n m[\\\"ppo4tso2\\\"] = m[\\\"(MemoryOp <: po :> (FenceTSO)).(po :> Write)\\\"]\\n m[\\\"ppo4\\\"] = m[\\\"ppo4rr + ppo4rw + ppo4wr + ppo4ww + ppo4tso1 + ppo4tso2\\\"]\\n m[\\\"ppo5\\\"] = m[\\\"Aq <: po :> MemoryOp\\\"]\\n m[\\\"ppo6\\\"] = m[\\\"MemoryOp <: po :> Rl\\\"]\\n m[\\\"ppo7\\\"] = m[\\\"RCsc <: po :> RCsc\\\"]\\n m[\\\"ppo8\\\"] = m[\\\"pair\\\"]\\n m[\\\"ppo9\\\"] = m[\\\"addrdep\\\"]\\n m[\\\"ppo10\\\"] = m[\\\"datadep\\\"]\\n m[\\\"ppo11\\\"] = m[\\\"ctrldep.po :> Write\\\"]\\n m[\\\"ppo12\\\"] = m[\\\"(addrdep + datadep).(rf & po)\\\"]\\n m[\\\"ppo13\\\"] = m[\\\"addrdep.po :> Write\\\"]\\n\\n return m[\\n \\\"ppo1 + ppo2 + ppo3 + ppo4 + ppo5 + ppo6 + ppo7 + \\\"\\n \\\"ppo8 + ppo9 + ppo10 + ppo11 + ppo12 + ppo13\\\"\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac89b0a71c9943d58dd2e15032afde8\",\n \"score\": \"0.504813\",\n \"text\": \"def makeFile(m, model):\\n tempfile = ['#include \\\"lime.h\\\"\\\\n', '#include \\\"math.h\\\"\\\\n',\\n '#include \\\"stdio.h\\\"\\\\n', '#include \\\"stdlib.h\\\"\\\\n',\\n '#include \\\"%s\\\"\\\\n\\\\n' % model.header.fn]\\n writeModelProperties(tempfile, model)\\n writeWeighting(tempfile, model)\\n writeImageParameters(tempfile, m, model)\\n writeInterpolationFuncs(tempfile, model)\\n writeDensity(tempfile, model)\\n writeTemperatures(tempfile, model)\\n writeAbundance(tempfile, model)\\n if model.dust is not None:\\n writeGastoDust(tempfile, model)\\n writeDoppler(tempfile, model)\\n writeVelocityStructure(tempfile, model)\\n with open('model_%d.c' % m, 'w') as tosave:\\n for line in tempfile:\\n tosave.write('%s' % line)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ef0a20cae196e2de5c67f6c87df504\",\n \"score\": \"0.5047686\",\n \"text\": \"def _save_prototxt(self):\\n solver_path = osp.dirname(self._solver_prototxt)\\n if not osp.exists(solver_path):\\n os.makedirs(solver_path)\\n with open(self._solver_prototxt, 'w') as f:\\n f.write(text_format.MessageToString(self._solver))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9127df10abf73188e578bcc4dcd4d09c\",\n \"score\": \"0.50475544\",\n \"text\": \"def owl_to_model(fname):\\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\\n\\n try:\\n file_is = autoclass('java.io.FileInputStream')(fname)\\n except JavaException:\\n logger.error('Could not open data file %s' % fname)\\n return\\n try:\\n biopax_model = io.convertFromOWL(file_is)\\n except JavaException as e:\\n logger.error('Could not convert data file %s to BioPax model' % fname)\\n logger.error(e)\\n return\\n\\n file_is.close()\\n\\n return biopax_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b084295606442608e4e95a1201da837c\",\n \"score\": \"0.5046919\",\n \"text\": \"def export_model(self, out=None, **kwargs):\\n # Remove all code transformations but respect those provided explicitly\\n kwargs.setdefault('length_for_alias', None)\\n kwargs.setdefault('name_all_constraints', False)\\n\\n CpoCompiler(self, **kwargs).write(out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380e97eb5048bd83b762af621acf5fd7\",\n \"score\": \"0.5038669\",\n \"text\": \"def main(model=None, output_dir=None, n_iter=100):\\n if model is not None:\\n nlp = spacy.load(model) # load existing spaCy model\\n print(\\\"Loaded model '%s'\\\" % model)\\n else:\\n nlp = spacy.blank(\\\"en\\\") # create blank Language class\\n print(\\\"Created blank 'en' model\\\")\\n \\n # create the built-in pipeline components and add them to the pipeline\\n # nlp.create_pipe works for built-ins that are registered with spaCy\\n if \\\"ner\\\" not in nlp.pipe_names:\\n ner = nlp.create_pipe(\\\"ner\\\")\\n nlp.add_pipe(ner, last=True)\\n # otherwise, get it so we can add labels\\n else:\\n ner = nlp.get_pipe(\\\"ner\\\")\\n \\n # add labels\\n for _, annotations in TRAIN_DATA:\\n for ent in annotations.get(\\\"entities\\\"):\\n ner.add_label(ent[2])\\n\\n # get names of other pipes to disable them during training\\n other_pipes = [pipe for pipe in nlp.pipe_names if pipe != \\\"ner\\\"]\\n with nlp.disable_pipes(*other_pipes): # only train NER\\n # reset and initialize the weights randomly – but only if we're\\n # training a new model\\n if model is None:\\n nlp.begin_training()\\n for itn in range(n_iter):\\n random.shuffle(TRAIN_DATA)\\n losses = {}\\n # batch up the examples using spaCy's minibatch\\n batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))\\n for batch in batches:\\n texts, annotations = zip(*batch)\\n nlp.update(\\n texts, # batch of texts\\n annotations, # batch of annotations\\n drop=0.5, # dropout - make it harder to memorise data\\n losses=losses,\\n )\\n #print(\\\"Losses\\\", losses)\\n\\n # test the trained model\\n for text in TEST_DATA:\\n doc = nlp(text)\\n print(\\\"Entities\\\", [(ent.text, ent.label_) for ent in doc.ents])\\n print(\\\"Tokens\\\", [(t.text, t.ent_type_, t.ent_iob) for t in doc])\\n \\n # save model to output directory\\n if output_dir is not None:\\n output_dir = Path(output_dir)\\n if not output_dir.exists():\\n output_dir.mkdir()\\n nlp.to_disk(output_dir)\\n print(\\\"Saved model to\\\", output_dir)\\n \\n # test the saved model\\n print(\\\"Loading from\\\", output_dir)\\n nlp2 = spacy.load(output_dir)\\n for text, _ in TRAIN_DATA:\\n doc = nlp2(text)\\n print(\\\"Entities\\\", [(ent.text, ent.label_) for ent in doc.ents])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dfa7950183441b0441fb0c52fd47749\",\n \"score\": \"0.5037608\",\n \"text\": \"def convert2or():\\n # parse converter arguments\\n args = get_args_convert2or()\\n\\n # input data parameters\\n try:\\n workspace_dir, exec_file_name, data_uri, data_path, output_path = get_params()\\n entry_filename = os.path.splitext(os.path.basename(exec_file_name))[0]\\n except Exception as e:\\n print('Invalid arguments, {}'.format(e))\\n sys.exit(1)\\n\\n else:\\n # Generate requirements.txt\\n try:\\n import pipreqs\\n except ImportError as e:\\n raise RuntimeError('Package installation failed: {}'.format(e))\\n\\n else:\\n time.sleep(1)\\n try:\\n p = subprocess.Popen([\\\"pipreqs\\\", \\\"--force\\\", workspace_dir])\\n p.wait()\\n time.sleep(1)\\n\\n # fix the bug raising from 'tensorflow', 'tensorflow_gpu'\\n filename = os.path.join(workspace_dir, \\\"requirements.txt\\\")\\n\\n rw_file(filename, matplotlib=\\\"matplotlib\\\", tensorflow_gpu=\\\"\\\", tensorflow=\\\"tensorflow-gpu\\\")\\n remove_empty_lines(filename)\\n print(\\\"Generated 'requirements.txt' successfully!\\\")\\n\\n except Exception as e:\\n raise RuntimeError(\\\"Generating 'requirements.txt' failed: {}\\\".format(e))\\n\\n else:\\n # Generate params.json\\n try:\\n exec_file_name_v = os.path.relpath(exec_file_name, start=workspace_dir)\\n data_path_v = \\\"\\\" if data_path == \\\"\\\" else os.path.relpath(data_path, start=workspace_dir)\\n output_path_v = \\\"\\\" if output_path == \\\"\\\" else os.path.relpath(output_path, start=workspace_dir)\\n params_json = json.dumps({\\\"exec_file_name\\\": exec_file_name_v,\\n \\\"data_uri\\\": data_uri,\\n \\\"data_path\\\": data_path_v,\\n \\\"output_path\\\": output_path_v,\\n })\\n with open(os.path.join(workspace_dir, \\\"params.json\\\"), 'w+') as f:\\n f.write(params_json)\\n print(\\\"Generated 'params.json' successfully!\\\")\\n\\n except Exception as e:\\n raise IOError(\\\"Generating 'params.json' failed: {}\\\".format(e))\\n\\n else:\\n time.sleep(2)\\n try:\\n zip_folder_path = os.path.join(workspace_dir, os.pardir, \\\"task_files\\\")\\n\\n if not os.path.exists(zip_folder_path):\\n os.makedirs(zip_folder_path)\\n\\n output_filename = str(entry_filename) + \\\"_orion.zip\\\"\\n zip_folder(workspace_dir, os.path.join(zip_folder_path, output_filename))\\n print('Zipped files successfully!')\\n except Exception as e:\\n raise RuntimeError('Zipping files failed: {}'.format(e))\\n else:\\n try:\\n os.remove(os.path.join(workspace_dir, \\\"params.json\\\"))\\n os.remove(os.path.join(workspace_dir, \\\"requirements.txt\\\"))\\n except Exception as e:\\n raise RuntimeError('Removing files failed: {}'.format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78aca13d4302fac740b8dddb5e1eb7e6\",\n \"score\": \"0.5032639\",\n \"text\": \"def descout_to_input( # noqa: C901 - fxn too complex\\n outfile,\\n infile,\\n objective=\\\"force\\\",\\n optimizer=\\\"lsq-exact\\\",\\n header=\\\"#DESC-generated input file\\\",\\n ftol=1e-2,\\n xtol=1e-6,\\n gtol=1e-6,\\n maxiter=100,\\n ):\\n f = open(outfile, \\\"w+\\\")\\n\\n f.seek(0)\\n\\n eq = load(infile)\\n try:\\n eq0 = eq[-1]\\n except TypeError:\\n eq0 = eq\\n\\n f.write(header + \\\"\\\\n\\\")\\n\\n f.write(\\\"# global parameters\\\\n\\\")\\n f.write(\\\"sym = {:1d} \\\\n\\\".format(eq0.sym))\\n f.write(\\\"NFP = {:3d} \\\\n\\\".format(int(eq0.NFP)))\\n f.write(\\\"Psi = {:.8f} \\\\n\\\".format(eq0.Psi))\\n\\n f.write(\\\"\\\\n# spectral resolution\\\\n\\\")\\n for key, val in {\\n \\\"L_rad\\\": \\\"L\\\",\\n \\\"M_pol\\\": \\\"M\\\",\\n \\\"N_tor\\\": \\\"N\\\",\\n \\\"L_grid\\\": \\\"L_grid\\\",\\n \\\"M_grid\\\": \\\"M_grid\\\",\\n \\\"N_grid\\\": \\\"N_grid\\\",\\n }.items():\\n f.write(f\\\"{key} = {getattr(eq0, val)}\\\\n\\\")\\n\\n f.write(\\\"\\\\n\\\\n# solver tolerances\\\\n\\\")\\n f.write(f\\\"ftol = {ftol}\\\\n\\\")\\n f.write(f\\\"xtol = {xtol}\\\\n\\\")\\n f.write(f\\\"gtol = {gtol}\\\\n\\\")\\n f.write(f\\\"maxiter = {maxiter}\\\\n\\\")\\n\\n f.write(\\\"\\\\n\\\\n# solver methods\\\\n\\\")\\n f.write(f\\\"optimizer = {optimizer}\\\\n\\\")\\n f.write(f\\\"objective = {objective}\\\\n\\\")\\n f.write(\\\"spectral_indexing = {}\\\\n\\\".format(eq0._spectral_indexing))\\n f.write(\\\"node_pattern = {}\\\\n\\\".format(eq0._node_pattern))\\n\\n f.write(\\\"\\\\n# pressure and rotational transform/current profiles\\\\n\\\")\\n\\n if eq0.iota:\\n assert (\\n eq0.pressure.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n and eq0.iota.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n ), \\\"Equilibrium must have power series profiles for ascii io\\\"\\n char = \\\"i\\\"\\n iseven_pres = int(eq0._pressure.basis.sym == \\\"even\\\") + 1\\n iseven_iota = int(eq0._iota.basis.sym == \\\"even\\\") + 1\\n pres_profile = np.zeros((eq0.L + 1,))\\n iota_profile = np.zeros((eq0.L + 1,))\\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\\n iota_profile[: eq0.L + 1 : iseven_iota] = eq0._iota.params\\n\\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\\n for l in idxs:\\n f.write(\\n \\\"l: {:3d}\\\\tp = {:16.8E}\\\\t{} = {:16.8E}\\\\n\\\".format(\\n int(l), pres_profile[l], char, iota_profile[l]\\n )\\n )\\n else:\\n assert (\\n eq0.pressure.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n and eq0.current.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n ), \\\"Equilibrium must have power series profiles for ascii io\\\"\\n char = \\\"c\\\"\\n iseven_pres = int(eq0._pressure.basis.sym == \\\"even\\\") + 1\\n iseven_curr = int(eq0._current.basis.sym == \\\"even\\\") + 1\\n pres_profile = np.zeros((eq0.L + 1,))\\n curr_profile = np.zeros((eq0.L + 1,))\\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\\n curr_profile[: eq0.L + 1 : iseven_curr] = eq0._current.params\\n\\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\\n for l in idxs:\\n f.write(\\n \\\"l: {:3d}\\\\tp = {:16.8E}\\\\t{} = {:16.8E}\\\\n\\\".format(\\n int(l), pres_profile[l], char, curr_profile[l]\\n )\\n )\\n\\n f.write(\\\"\\\\n\\\")\\n\\n f.write(\\\"\\\\n# fixed-boundary surface shape\\\\n\\\")\\n # boundary paramters\\n if eq0.sym:\\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\\n if abs(eq0.Rb_lmn[k]) > 1e-8:\\n f.write(\\n \\\"l: {:3d}\\\\tm: {:3d}\\\\tn: {:3d}\\\\tR1 = {:16.8E}\\\\t\\\\\\n Z1 = {:16.8E}\\\\n\\\".format(\\n int(0), m, n, eq0.Rb_lmn[k], 0\\n )\\n )\\n for k, (l, m, n) in enumerate(eq0.surface.Z_basis.modes):\\n if abs(eq0.Zb_lmn[k]) > 1e-8:\\n f.write(\\n \\\"l: {:3d}\\\\tm: {:3d}\\\\tn: {:3d}\\\\tR1 = {:16.8E}\\\\t\\\\\\n Z1 = {:16.8E}\\\\n\\\".format(\\n int(0), m, n, 0, eq0.Zb_lmn[k]\\n )\\n )\\n else:\\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\\n if abs(eq0.Rb_lmn[k]) > 1e-8 or abs(eq0.Zb_lmn[k]) > 1e-8:\\n f.write(\\n \\\"l: {:3d}\\\\tm: {:3d}\\\\tn: {:3d}\\\\tR1 = {:16.8E}\\\\t\\\\\\n Z1 = {:16.8E}\\\\n\\\".format(\\n int(0), m, n, eq0.Rb_lmn[k], eq0.Zb_lmn[k]\\n )\\n )\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac8d9a34c982c21d64ee69aa9b8aada\",\n \"score\": \"0.50321543\",\n \"text\": \"def models(self):\\r\\n # make model directory, removing the old one if necessary\\r\\n rm_rf(self.cfg.model_dir)\\r\\n mkdir_p(self.cfg.model_dir)\\r\\n\\r\\n # run generator script\\r\\n gen_script = os.path.join(self._prj_root, 'gen.py')\\r\\n\\r\\n if 'PYTHON_MSDSL' in os.environ:\\r\\n python_name = os.environ['PYTHON_MSDSL']\\r\\n else:\\r\\n python_name = which('python')\\r\\n \\r\\n call([python_name, gen_script, '-o', self.cfg.model_dir, '--dt', str(self.dt)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e59d1ec3cd22e83b1a98ae5bb15330\",\n \"score\": \"0.502148\",\n \"text\": \"def write_model_to_file(self) -> str:\\n\\n model_file_content = self.interpreter.get_model_file_content()\\n\\n db_logger.info(f\\\"Writing {self.model_file_url}\\\")\\n\\n with open(self.model_file_location, 'w') as model:\\n model.write(model_file_content)\\n\\n file_name = os.path.basename(self.model_file_location)\\n project_dir = self.model_file_location.replace(file_name, '')\\n\\n helpers.process_includes(\\n self.model_file_url, project_dir, self.interpreter\\n )\\n\\n if not self.no_score:\\n self.model_file_location = self.setup_protocol(self.score)\\n\\n return self.model_file_location\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4533cb8662178a596b89ed1d73420b\",\n \"score\": \"0.50117064\",\n \"text\": \"def test_ImportUFONoSideEffectLO(self): \\n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('sm'),False)\\n original_all_particles = copy.copy(ufo_model.all_particles)\\n original_all_vertices = copy.copy(ufo_model.all_vertices)\\n original_all_couplings = copy.copy(ufo_model.all_couplings)\\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\\n original_all_parameters = copy.copy(ufo_model.all_parameters)\\n original_all_orders = copy.copy(ufo_model.all_orders)\\n original_all_functions = copy.copy(ufo_model.all_functions)\\n\\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\\n model = ufo2mg5_converter.load_model()\\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \\n\\n self.assertEqual(original_all_particles,ufo_model.all_particles) \\n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\\n self.assertEqual(original_all_couplings,ufo_model.all_couplings)\\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\\n self.assertEqual(original_all_orders,ufo_model.all_orders)\\n self.assertEqual(original_all_functions,ufo_model.all_functions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e8c2e02b0ee94ddd321ca2db4033cd0\",\n \"score\": \"0.50004613\",\n \"text\": \"def exe_dyn_mdl(fv, usr):\\n\\n exe = os.chdir(prg_path + '/dynamic_mdl-0.1') \\n exe = os.getcwd() \\n pcs = sbp.Popen([exe + '/a.out', str(fv)], stdout=sbp.PIPE, stderr=sbp.PIPE)\\n out, error = pcs.communicate()\\n if out:\\n print('OK', out)\\n if error:\\n print('Error', error.strip()) \\n if not pcs.poll():\\n print(\\\"Dynamic Module execute finish\\\")\\n p_nme = 'Dynamic'\\n dest_pth = fles_dir + usr + '/' + p_nme\\n dest_pth = dest_pth + '/' + time.get_date_time_hm()\\n dummy_sys_file.crt_dir(dest_pth)\\n \\\"\\\"\\\"Creating directory for program execution\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"Names of the generated files\\\"\\\"\\\"\\n fle_name1 = 'sk'\\n fle_name2 = 'f_self'\\n fle_name3 = 'coefficient'\\n \\\"\\\"\\\"full path of the outputs of program\\\"\\\"\\\"\\n out_fle1 = exe + '/' + 'sk.dat'\\n out_fle2 = exe + '/' + 'fself.dat'\\n out_fle3 = exe + '/' + 'coeficiente.dat' \\n dummy_sys_file.cpy_file_to_pth_dir(out_fle1, dest_pth, fle_name1)\\n dummy_sys_file.cpy_file_to_pth_dir(out_fle2, dest_pth, fle_name2)\\n dummy_sys_file.cpy_file_to_pth_dir(out_fle3, dest_pth, fle_name3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10bf29315de0dab52975584c75ef2dfb\",\n \"score\": \"0.49911758\",\n \"text\": \"def generate_package(self):\\n\\n # Create a directory (mymodel)\\n cwd = Path(self.dir)\\n directory=cwd/'csharp_model'\\n if (directory).isdir() :\\n self.dir = directory\\n else:\\n self.dir = directory.mkdir()\\n\\n files = []\\n count = 0\\n\\n for model in self.models:\\n\\n\\n self.generate_component(model)\\n\\n ext = '' if count == 0 else str(count)\\n filename = self.dir/\\\"%s.cs\\\"%signature(model)\\n\\n with open(filename, \\\"w\\\") as python_file:\\n python_file.write(self.code.encode('utf-8','ignore'))\\n files.append(filename)\\n\\n model.module_name = str(Path(filename).namebase)\\n\\n count += 1\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f27813e63fc901b230aaee14b21dfe\",\n \"score\": \"0.49885112\",\n \"text\": \"def new_IDP_gen(subj,LUT_txt): #,fix4melviewtxt\\n\\n\\n #remove trailing forward slashes in subject paths\\n if subj.endswith(\\\"/\\\"):\\n subj = subj[:-1]\\n\\n if not os.path.exists(subj + \\\"/IDP_files/\\\"):\\n os.makedirs(subj + \\\"/IDP_files/\\\")\\n\\n subjName = subj[subj.rfind(\\\"/\\\") + 1 :]\\n\\n\\n # IDP_FC_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_FC_dist.txt\\\")\\n # IDP_SC_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_SC_dist.txt\\\")\\n # IDP_MELODIC_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_MELODIC_SNR.txt\\\")\\n # IDP_MCFLIRT_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_MCFLIRT_disp.txt\\\")\\n # IDP_homotopic_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_homotopic.txt\\\")\\n # new_IDP_list_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_new_IDPs.txt\\\")\\n\\n new_IDP_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_new_IDP.txt\\\")\\n\\n\\n # IDP_output_files = [IDP_FC_file,IDP_SC_file,IDP_MELODIC_file,IDP_MCFLIRT_file,IDP_homotopic_file]\\n\\n # for file in IDP_output_files:\\n # with open(file, 'w') as fp:\\n # pass\\n\\n with open(new_IDP_file, 'w') as fp:\\n line = '\\\\t'.join([\\\"short\\\",\\\"num\\\",\\\"category\\\",\\\"num_in_cat\\\",\\\"long\\\",\\\"unit\\\",\\\"dtype\\\",\\\"description\\\",\\\"value\\\"])\\n\\n fp.write(line)\\n\\n\\n\\n fix4melviewtxt=\\\"\\\"\\n\\n FC_distribution(subj)\\n SC_distribution(subj)\\n MELODIC_SNR(subj,fix4melviewtxt)\\n MCFLIRT_displacement(subj) \\n\\n homotopic(subj,LUT_txt)\\n \\n\\n\\n\\n\\n #TODO TL/distance, or ts.txt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde366ecb10bf14e7b028bb5eeb70a26\",\n \"score\": \"0.497974\",\n \"text\": \"def produce_layout(eoi, graph):\\n\\n subprocess.run((\\\"/usr/bin/dot\\\", \\\"{}\\\".format(graph.filename), \\\"-o\\\", \\n \\\"{}/layout.dot\\\".format(eoi)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42db4bc248db8077aaa00be0cc702c70\",\n \"score\": \"0.4975401\",\n \"text\": \"def load_model_output(text):\\r\\n # uncomment when working in linux and remove subsequent two lines\\r\\n # nlp = spacy.load(\\\"../models/quick-spacy/\\\")\\r\\n model_path = Path(__file__).parent.absolute() / \\\"models/quick-spacy/\\\"\\r\\n nlp = spacy.load(model_path)\\r\\n doc = nlp(text)\\r\\n sentence = [ent.text for ent in doc.ents]\\r\\n labels = [ent.label_ for ent in doc.ents]\\r\\n return sentence, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d71b59de5a8a54084e51dfb95444b719\",\n \"score\": \"0.49555877\",\n \"text\": \"def generate_theory(\\n grammar,\\n config,\\n theory_op_file,\\n theorem_prover,\\n max_depth,\\n min_depth\\n):\\n # Get Theorem Prover Config and initialize Theorem Prover\\n theorem_prover_config = TheoremProverConfig(\\n grammar, **config[\\\"theory\\\"][\\\"theorem_prover\\\"]\\n )\\n\\n num_examples = config[\\\"theory\\\"][\\\"num_examples\\\"]\\n min_num_positive_examples = config[\\\"theory\\\"][\\\"min_num_positive_examples\\\"]\\n max_num_negative_examples = num_examples - min_num_positive_examples\\n statement_types = config[\\\"theory\\\"][\\\"statement_types_per_example\\\"]\\n assertion_start_symbol = config[\\\"assertion\\\"][\\\"start_symbol\\\"]\\n example_id_prefix = config.get(\\\"example_id_prefix\\\", \\\"\\\")\\n\\n # Generate examples for every required type of statement (Start Symbol type)\\n num_true_labels = 0\\n num_false_labels = 0\\n curr_num_examples = 0\\n progress_tracker = tqdm(total=num_examples)\\n progress_tracker.set_description(desc=\\\"Generating Examples...\\\")\\n while (\\n curr_num_examples < num_examples and num_true_labels < min_num_positive_examples\\n ):\\n example = generate_random_example(\\n curr_num_examples + 1,\\n example_id_prefix,\\n grammar,\\n theorem_prover_config,\\n statement_types,\\n assertion_start_symbol,\\n theorem_prover,\\n max_depth,\\n min_depth\\n )\\n if example is not None:\\n if example.theory_assertion_instance.label:\\n num_true_labels += 1\\n else:\\n if num_false_labels == max_num_negative_examples:\\n continue\\n else:\\n num_false_labels += 1\\n json.dump(example.to_json(), theory_op_file)\\n theory_op_file.write(\\\"\\\\n\\\")\\n curr_num_examples += 1\\n progress_tracker.update()\\n progress_tracker.close()\\n print(f\\\"Generated {curr_num_examples} examples.\\\")\\n print(f\\\" No. with True label: {num_true_labels}\\\")\\n print(f\\\" No. with False label: {num_false_labels}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abcb45148b5a15227491491ca106d5b\",\n \"score\": \"0.49505714\",\n \"text\": \"def polish_and_save(model_filename,\\n save_filename='',\\n suffix='.polished',\\n *args,\\n **kwargs):\\n\\n save_filename = save_filename or model_filename.replace(\\n '.onnx', suffix + '.onnx')\\n\\n model = onnx.load(model_filename)\\n model = polish_model(model, *args, **kwargs)\\n onnx.save(model, save_filename)\\n logger.info('polished model saved to: %s', save_filename)\\n return save_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70ae932b14d6c0ac6415942b0994419\",\n \"score\": \"0.49496683\",\n \"text\": \"def output_perplexity(task_letter):\\n output_file = open(\\\"output/group02.perplexity\\\" + task_letter, mode=\\\"w\\\")\\n word_dictionary = get_word_dict()\\n test_sentences = open(\\\"data/sentences.eval\\\", mode=\\\"r\\\")\\n\\n for test_sentence in test_sentences.readlines():\\n # predicted_softmax_vecs: 30 X 20.000 X 1 Output Vector with softmax probabilities\\n predicted_softmax_vecs = eval_neural_network(test_sentence)\\n perp = perplexity(predicted_softmax_vecs, test_sentence, word_dictionary)\\n output_file.write(str(perp) + \\\"\\\\n\\\")\\n\\n output_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a208cbaa76b296741b5c20e88440925f\",\n \"score\": \"0.49453568\",\n \"text\": \"def __init__(self,\\n fileName,\\n realFileName=None,\\n prequelFileName=None,\\n preErrorMessages=(), #TODO: check the type\\n readFileLater=False,\\n fillImportBoxLater=False,\\n parseFileLater=False,\\n noSymbolChecking=False,\\n allowedFeatures=(),\\n recognizeUSEOCLNativeModelDefinition=False):\\n #type: (Text, Optional[Text], Optional[Text], List[Any], bool, bool, bool, bool, List[Text], bool) -> None\\n\\n\\n # if readFileLater or fillImportBoxLater or parseFileLater:\\n # assert finalizeLater\\n\\n # Create an empty model\\n # Not to be moved after super\\n # This should be done in all case so that\\n # the model attribute always exist even if there\\n # are some error in reading the file\\n self.model = self.emptyModel() # type: Model\\n\\n\\n\\n\\n # Call the super class, read the file or not\\n try:\\n # This can raise an exception for instance if\\n # there is a problem reading the file\\n super(ModelSourceFile, self).__init__(\\n fileName=fileName,\\n realFileName=realFileName,\\n prequelFileName=prequelFileName,\\n preErrorMessages=preErrorMessages,\\n doNotReadFiles=readFileLater,\\n allowedFeatures=allowedFeatures\\n )\\n except FatalError:\\n pass # an error as already been registered\\n\\n from modelscripts.megamodels import Megamodel\\n Megamodel.registerSource(self)\\n Megamodel.registerModel(self.model)\\n\\n\\n # Backward link\\n self.model.source=self\\n\\n # Link issue box\\n self.model._issueBox.addParent(self._issueBox)\\n\\n # Source to ModelElement Mapping\\n self._modelMapping=_ModelSourceMapping()\\n\\n\\n # Create first an empty ImportBox.\\n self.importBox=ImportBox(self)\\n\\n # Then fill it by reading megamodel statements,\\n # unless specified.\\n try:\\n if not fillImportBoxLater:\\n parseToFillImportBox(\\n self,\\n noSymbolChecking,\\n recognizeUSEOCLNativeModelDefinition)\\n\\n if not parseFileLater:\\n self.parseToFillModel()\\n self.finalize()\\n\\n except FatalError:\\n pass # nothing to do, the issue has been registered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5aa18489143959b9fdd129d63b51d0\",\n \"score\": \"0.49399826\",\n \"text\": \"def preflight(self):\\n self.params[\\\"translations\\\"][\\\"mgf_input_file\\\"] = os.path.join(\\n self.params[\\\"input_dir_path\\\"], self.params[\\\"input_file\\\"]\\n )\\n self.params[\\\"translations\\\"][\\\"output_file_incl_path\\\"] = os.path.join(\\n self.params[\\\"output_dir_path\\\"], self.params[\\\"output_file\\\"]\\n )\\n self.param_file_name = os.path.join(\\n self.params[\\\"translations\\\"][\\\"output_file_incl_path\\\"].strip(\\\".csv\\\")\\n + \\\"pipi_params.def\\\"\\n )\\n self.created_tmp_files.append(self.param_file_name)\\n\\n # pprint.pprint(self.params['translations']['_grouped_by_translated_key'])\\n # pprint.pprint(self.params)\\n # sys.exit(1)\\n self.params_to_write = {\\n \\\"version\\\": self.META_INFO[\\\"version\\\"],\\n \\\"output_percolator_input\\\": 1,\\n \\\"mod10\\\": \\\"0.0@X?\\\",\\n \\\"pepNterm\\\": 0.0,\\n \\\"pepCterm\\\": 0.0,\\n \\\"proNterm\\\": 0.0,\\n \\\"proCterm\\\": 0.0,\\n }\\n symbols = [\\\"~\\\", \\\"!\\\", \\\"%\\\", \\\"^\\\", \\\"&\\\", \\\"*\\\", \\\"+\\\", \\\"<\\\", \\\">\\\"]\\n for x, element in enumerate(symbols):\\n self.params_to_write[\\\"mod0{0}\\\".format(x + 1)] = \\\"0.0@X{0}\\\".format(element)\\n for aa in \\\"ACDEFGHIKLMNOPQRSTUVWYnc\\\":\\n self.params_to_write[aa] = 0\\n\\n write_exclusion_list = [\\n # 'label',\\n \\\"-Xmx\\\",\\n \\\"frag_mass_tolerance_unit\\\",\\n \\\"base_mz\\\",\\n # 'header_translations',\\n # 'validation_score_field'\\n ]\\n\\n # additional_15N_modifications = []\\n # if self.params['translations']['label'] == '15N':\\n # for aminoacid, N15_Diff in ursgal.ukb.DICT_15N_DIFF.items():\\n # existing = False\\n # for mod_dict in self.params['mods']['fix']:\\n # if aminoacid == mod_dict['aa']:\\n # mod_dict['mass'] += N15_Diff\\n # mod_dict['name'] += '_15N_{0}'.format(aminoacid)\\n # existing = True\\n # if existing == True:\\n # continue\\n # else:\\n # self.params['mods']['fix'].append(\\n # {\\n # 'aa': aminoacid,\\n # 'mass': N15_Diff,\\n # 'name': '_15N_{0}'.format(aminoacid),\\n # 'pos': 'any',\\n # }\\n # )\\n\\n if self.params[\\\"translations\\\"][\\\"frag_mass_tolerance_unit\\\"] == \\\"ppm\\\":\\n self.params[\\\"translations\\\"][\\\"_grouped_by_translated_key\\\"][\\n \\\"ms2_tolerance\\\"\\n ] = {\\n \\\"frag_mass_tolerance\\\": ursgal.ucore.convert_ppm_to_dalton(\\n self.params[\\\"translations\\\"][\\\"frag_mass_tolerance\\\"],\\n base_mz=self.params[\\\"base_mz\\\"],\\n )\\n }\\n\\n for pipi_param_name in self.params[\\\"translations\\\"][\\n \\\"_grouped_by_translated_key\\\"\\n ].keys():\\n for ursgal_param_name, param_value in self.params[\\\"translations\\\"][\\n \\\"_grouped_by_translated_key\\\"\\n ][pipi_param_name].items():\\n if pipi_param_name in write_exclusion_list:\\n continue\\n elif pipi_param_name == \\\"frag_clear_mz_range\\\":\\n min_mz, max_mz = param_value\\n self.params_to_write[\\\"min_clear_mz\\\"] = min_mz\\n self.params_to_write[\\\"max_clear_mz\\\"] = max_mz\\n elif type(pipi_param_name) is tuple:\\n for pn in pipi_param_name:\\n self.params_to_write[pn] = param_value\\n elif pipi_param_name == \\\"enzyme\\\":\\n enz, site, aa, inh = param_value.split(\\\";\\\")\\n self.params_to_write[\\\"enzyme\\\"] = \\\"{0} {1} {2} {3}\\\".format(\\n enz, site, aa, inh\\n )\\n elif pipi_param_name == \\\"modifications\\\":\\n \\\"\\\"\\\"\\n # specify additional mass and amino acid. DO NOT change the last character.\\n # maximum number is 10\\n # empty entries must start with 0.0\\n mod01 = 79.966331@S~ # Phosphorylation\\n mod02 = 79.966331@T! # Phosphorylation\\n mod03 = 79.966331@Y% # Phosphorylation\\n mod04 = 42.010565@K^ # Acetylation\\n mod05 = 15.994915@M& # Oxidation\\n mod06 = 14.015650@K* # Methylation\\n mod07 = 0.0@X+\\n mod08 = 0.0@X<\\n mod09 = 0.0@X>\\n mod10 = 0.0@X?\\n \\\"\\\"\\\"\\n n = 0\\n for mod_dict in self.params[\\\"mods\\\"][\\\"opt\\\"]:\\n \\\"\\\"\\\"\\n {'_id': 0,\\n 'aa': '*',\\n 'composition': {'C': 2, 'H': 2, 'O': 1},\\n 'id': '1',\\n 'mass': 42.010565,\\n 'name': 'Acetyl',\\n 'org': '*,opt,Prot-N-term,Acetyl',\\n 'pos': 'Prot-N-term',\\n 'unimod': True},\\n \\\"\\\"\\\"\\n if mod_dict[\\\"pos\\\"] == \\\"Prot-N-term\\\":\\n self.params_to_write[\\\"proNterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"Prot-C-term\\\":\\n self.params_to_write[\\\"proCterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"N-term\\\":\\n self.params_to_write[\\\"pepNterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"C-term\\\":\\n self.params_to_write[\\\"pepCterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"any\\\":\\n pass\\n else:\\n print(\\n \\\"\\\"\\\"\\n Unknown positional argument for given modification:\\n {0}\\n PIPI cannot deal with this, please use one of the follwing:\\n any, Prot-N-term, Prot-C-term, N-term, C-term\\n \\\"\\\"\\\".format(\\n mod_dict[\\\"org\\\"]\\n )\\n )\\n sys.exit(1)\\n n += 1\\n assert (\\n n <= 10\\n ), \\\"\\\"\\\"\\n A maximum of 10 optional modifications is allowed in PIPI.\\n You specified more than 10.\\n \\\"\\\"\\\"\\n if n < 10:\\n mod_n = \\\"mod0{0}\\\".format(n)\\n elif n == 10:\\n mod_n = \\\"mod{0}\\\".format(n)\\n else:\\n print(\\n \\\"\\\"\\\"\\n A maximum of 10 optional modifications is allowed in PIPI.\\n You specified more than 10.\\n \\\"\\\"\\\"\\n )\\n sys.exit(1)\\n self.params_to_write[mod_n] = \\\"{0}@{1}{2}\\\".format(\\n mod_dict[\\\"mass\\\"], mod_dict[\\\"aa\\\"], symbols[n - 1]\\n )\\n\\n for mod_dict in self.params[\\\"mods\\\"][\\\"fix\\\"]:\\n if \\\"N-term\\\" in mod_dict[\\\"pos\\\"]:\\n self.params_to_write[\\\"n\\\"] = mod_dict[\\\"mass\\\"]\\n elif \\\"C-term\\\" in mod_dict[\\\"pos\\\"]:\\n self.params_to_write[\\\"c\\\"] = mod_dict[\\\"mass\\\"]\\n else:\\n self.params_to_write[mod_dict[\\\"aa\\\"]] = mod_dict[\\\"mass\\\"]\\n else:\\n self.params_to_write[pipi_param_name] = param_value\\n self.write_params_file()\\n\\n self.params[\\\"command_list\\\"] = [\\n \\\"java\\\",\\n \\\"-Xmx{0}\\\".format(\\n self.params[\\\"translations\\\"][\\\"_grouped_by_translated_key\\\"][\\\"-Xmx\\\"][\\n \\\"-xmx\\\"\\n ]\\n ),\\n \\\"-jar\\\",\\n self.exe,\\n self.param_file_name,\\n self.params[\\\"translations\\\"][\\\"mgf_input_file\\\"],\\n ]\\n\\n return self.params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b7bcbb2c25384bb8bd0c4ba1275413\",\n \"score\": \"0.49382108\",\n \"text\": \"def gen_pov(ldraw_path, pov_path):\\n model, parts = get_model(ldraw_path)\\n\\n with open(pov_path, \\\"w\\\") as pov_file:\\n pov_file.write(POV_HEADER + \\\"\\\\n\\\")\\n writer = POVRayWriter(parts, pov_file)\\n writer.write(model)\\n pov_file.write(POV_TRAILER + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f8306823fe036ca17dd5e9585dab03\",\n \"score\": \"0.49359792\",\n \"text\": \"def django_run(target, opt=\\\"XTAL\\\"):\\r\\n # Set up the OpenBaebel conversion modules\\r\\n sdconv = OBConversion()\\r\\n ligref = OBMol()\\r\\n # Define the residues and the proteisn to analyse\\r\\n if os.path.isfile(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')):\\r\\n \\tres_d = [trans_res(x) for x in ast.literal_eval(open(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')).read())[target.title].split()]\\r\\n print res_d\\r\\n# Molecules\\r\\n\\t # Now read in the ligand\\r\\n plif_method = PlifMethod()\\r\\n plif_method.text= \\\"PYPLIF\\\"\\r\\n feature_list = [\\\"POLAR\\\",\\\"FACE\\\",\\\"EDGE\\\",\\\"ACCEPTOR\\\",\\\"DONOR\\\",\\\"NEGATIVE\\\",\\\"POSITIVE\\\"]\\r\\n try:\\r\\n plif_method.validate_unique()\\r\\n plif_method.save()\\r\\n except ValidationError:\\r\\n \\t plif_method = PlifMethod.objects.get(text=\\\"PYPLIF\\\")\\r\\n out_d = {}\\r\\n counter = 0\\r\\n# Create a file for the protein\\r\\n t = tempfile.NamedTemporaryFile(suffix=\\\".pdb\\\",delete=False)\\r\\n my_prot = Protein.objects.get(code=target.title+\\\"TEMP\\\")\\r\\n t.write(my_prot.pdb_info.name)\\r\\n t.close()\\r\\n protref = read_prot(t.name, res_d)\\r\\n t = tempfile.NamedTemporaryFile(suffix=\\\".sdf\\\",delete=False)\\r\\n t.close()\\r\\n sdconv.SetInFormat(\\\"sdf\\\")\\r\\n if opt == \\\"XTAL\\\":\\r\\n mols = Molecule.objects.exclude(prot_id__code__contains=target.title).filter(prot_id__target_id=target)\\r\\n elif opt == \\\"LLOOMMPPAA\\\":\\r\\n \\t mols = []\\r\\n \\t sps = SynthPoint.objects.filter(target_id=target)\\r\\n \\t for s in sps:\\r\\n \\t \\t mols.extend([m for m in s.mol_id.all()])\\r\\n else:\\r\\n \\t print \\\"UNKNOWN OPTION\\\"\\r\\n \\t return\\r\\n for dj_mol in mols:\\r\\n \\t out_sd = Chem.SDWriter(t.name)\\r\\n \\t out_sd.write(Chem.MolFromMolBlock(str(dj_mol.sdf_info)))\\r\\n \\t out_sd.close()\\r\\n \\t sdconv.ReadFile(ligref, t.name)\\r\\n \\t # Now make the new plif\\r\\n \\t new_plif = Plif()\\r\\n \\t new_plif.mol_id = dj_mol\\r\\n \\t new_plif.prot_id = my_prot\\r\\n \\t new_plif.method_id = plif_method\\r\\n \\t try:\\r\\n \\t \\t new_plif.validate_unique()\\r\\n \\t \\t new_plif.save()\\r\\n \\t except ValidationError:\\r\\n \\t \\t new_plif = Plif.objects.get(mol_id=dj_mol,prot_id=my_prot,method_id=plif_method)\\r\\n \\t lig_name = ligref.GetTitle().strip(\\\",\\\")\\r\\n \\t prot_name = lig_name.split(\\\"_\\\")[0]\\r\\n \\t ligref.AddHydrogens()\\r\\n \\t counter +=1\\r\\n \\t refresdict = pp.getresiduedict(protref, res_d)\\r\\n \\t new_d = get_fp(protref,ligref, res_d)\\r\\n \\t for res in new_d:\\r\\n \\t \\t new_res = PlifRes()\\r\\n \\t \\t new_res.res_name = res[:3]\\r\\n \\t \\t new_res.res_num = int(res[3:])\\r\\n \\t \\t new_res.prot_id = my_prot\\r\\n \\t \\t try:\\r\\n \\t \\t \\t new_res.validate_unique()\\r\\n \\t \\t \\t new_res.save()\\r\\n \\t \\t except ValidationError:\\r\\n \\t \\t \\t new_res = PlifRes.objects.get(res_name=res[:3],res_num=int(res[3:]),prot_id=my_prot)\\r\\n \\t \\t new_plif.res_id.add(new_res)\\r\\n \\t \\t for bit_num, bit in enumerate(new_d[res]):\\r\\n \\t \\t \\t new_bit = PlifBit()\\r\\n \\t \\t \\t new_bit.feature = feature_list[bit_num]\\r\\n \\t \\t \\t new_bit.method_id = plif_method\\r\\n \\t \\t \\t new_bit.res_id = new_res\\r\\n \\t \\t \\t try:\\r\\n \\t \\t \\t \\t new_bit.validate_unique()\\r\\n \\t \\t \\t \\t new_bit.save()\\r\\n \\t \\t \\t \\t my_fun(dj_mol,new_bit,new_plif,bit)\\r\\n \\t \\t \\t except ValidationError:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnew_bit = PlifBit.objects.get(feature=feature_list[bit_num],method_id=plif_method,res_id=new_res)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnew_bit.save()\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnew_plif.bit_id.add(new_bit)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmy_fun(dj_mol,new_bit,new_plif,bit)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\r\\n \\t ligref = OBMol()\\r\\n \\t notatend = sdconv.Read(ligref)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cea0abf4708ac80030bc376d139c8b2\",\n \"score\": \"0.49342123\",\n \"text\": \"def writeLP(item_count, capacity, items):\\n lpfile = checkpath + '/knapsack.lp'\\n f = open(lpfile, 'w')\\n f.write(\\\"Maximize\\\\n\\\")\\n f.write(\\\"Objective:\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n for i in range(0, item_count):\\n xVar = \\\"x_\\\"+str(i)\\n cns = cns + \\\" + \\\" + str(items[i].value) + \\\" \\\" + xVar\\n cnt = cnt + 1\\n if cnt % 5 == 4:\\n f.write(cns + \\\"\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n f.write(cns + \\\"\\\\n\\\")\\n\\n f.write(\\\"Subject To\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n for i in range(0, item_count):\\n xVar = \\\"x_\\\"+str(i)\\n cns = cns + \\\" + \\\" + str(items[i].weight) + \\\" \\\" + xVar\\n cnt = cnt + 1\\n if cnt % 5 == 4:\\n f.write(cns + \\\"\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n f.write(cns + \\\" <= \\\" + str(capacity) + \\\"\\\\n\\\")\\n\\n f.write(\\\"Binaries\\\\n\\\")\\n for i in range(0, item_count):\\n xVar = \\\"x_\\\"+str(i)\\n f.write(xVar + \\\"\\\\n\\\")\\n f.write(\\\"End\\\")\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa711305cbfff319951e389f1f16131b\",\n \"score\": \"0.49325135\",\n \"text\": \"def createModelHandler(self):\\n className = self.model.name.upper()\\n filename = self.model.name + \\\".pk\\\"\\n\\n return f\\\"\\\"\\\"\\n# Copyright 2017 Battelle Energy Alliance, LLC\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\nimport sys\\nimport pickle\\nimport os\\nfrom pythonfmu.fmi2slave import Fmi2Type, Fmi2Slave, Fmi2Causality, Fmi2Variability, Integer, Real, Boolean, String\\n\\nclass {className}(Fmi2Slave):\\n #\\n # RAVEN (raven.inl.gov) Model-based Python-driven simulator\\n #\\n author = \\\"RAVEN Team\\\"\\n description = \\\"RAVEN Model-based Python-driven simulator\\\"\\n\\n def __init__(self, **kwargs):\\n super().__init__(**kwargs)\\n self.inputVariables = {self.inputVars}\\n self.outputVariables = {self.outVars}\\n # set path to raven and the serialized model\\n self.raven_path = r\\\"{self.ravenDir}\\\"\\n # model_path is by default the path to this model that is exported as FMU (serialized). It is stored in the resource folder\\n self.model_path = self.resources + \\\"/\\\" + \\\"{filename}\\\"\\n #os.path.sep\\n sys.path.append(self.model_path)\\n # this flag activates the initialization at the begin of the solve\\n self.initialized = False\\n # register raven_path variables if needed to be changed\\n self.register_variable(String(\\\"raven_path\\\", causality=Fmi2Causality.parameter, variability=Fmi2Variability.tunable))\\n # register input variables\\n for var in self.inputVariables:\\n # set var\\n self.__dict__[var] = 0.0\\n self.register_variable(Real(var, causality=Fmi2Causality.input))\\n for var in self.outputVariables:\\n # set var\\n self.__dict__[var] = 0.0\\n self.register_variable(Real(var, causality=Fmi2Causality.output))\\n\\n def setup_experiment(self, start_time: float):\\n self.start_time = start_time\\n if not self.initialized:\\n sys.path.append(self.raven_path)\\n # find the RAVEN framework\\n if os.path.isdir(os.path.join(self.raven_path,\\\"ravenframework\\\")):\\n # we import the Driver to load the RAVEN enviroment for the un-pickling\\n try:\\n import ravenframework.Driver\\n except RuntimeError as ae:\\n # we try to add the framework directory\\n raise RuntimeError(\\\"Importing or RAVEN failed with error:\\\" +str(ae))\\n else:\\n print(\\\"framework not found in\\\",self.raven_path)\\n # de-serialize the model\\n print(\\\"model_path\\\", self.model_path)\\n self.model = pickle.load(open(self.model_path, mode='rb'))\\n self.initialized = True\\n\\n def do_step(self, current_time: float, step_size: float) -> bool:\\n request = dict()\\n for var in self.inputVariables:\\n request[var] = self.__dict__[var]\\n request['current_time'] = current_time\\n request['step_size'] = step_size\\n\\n return_var = self.model.{self.executeMethod}(request)\\n outs = return_var if isinstance(return_var,dict) else return_var[{self.indexReturnDict}]\\n\\n for var in outs:\\n self.__dict__[var] = outs[var]\\n return True\\n\\n\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc1b2a35b7e5fd939b8a36de23ec2a2\",\n \"score\": \"0.49318784\",\n \"text\": \"def buildSolverModel(self, lp):\\n log.debug(\\\"create the glpk model\\\")\\n prob = glpk.glp_create_prob()\\n glpk.glp_set_prob_name(prob, lp.name)\\n log.debug(\\\"set the sense of the problem\\\")\\n if lp.sense == constants.LpMaximize:\\n glpk.glp_set_obj_dir(prob, glpk.GLP_MAX)\\n log.debug(\\\"add the constraints to the problem\\\")\\n glpk.glp_add_rows(prob, len(list(lp.constraints.keys())))\\n for i, v in enumerate(lp.constraints.items(), start=1):\\n name, constraint = v\\n glpk.glp_set_row_name(prob, i, name)\\n if constraint.sense == constants.LpConstraintLE:\\n glpk.glp_set_row_bnds(\\n prob, i, glpk.GLP_UP, 0.0, -constraint.constant\\n )\\n elif constraint.sense == constants.LpConstraintGE:\\n glpk.glp_set_row_bnds(\\n prob, i, glpk.GLP_LO, -constraint.constant, 0.0\\n )\\n elif constraint.sense == constants.LpConstraintEQ:\\n glpk.glp_set_row_bnds(\\n prob, i, glpk.GLP_FX, -constraint.constant, -constraint.constant\\n )\\n else:\\n raise PulpSolverError(\\\"Detected an invalid constraint type\\\")\\n constraint.glpk_index = i\\n log.debug(\\\"add the variables to the problem\\\")\\n glpk.glp_add_cols(prob, len(lp.variables()))\\n for j, var in enumerate(lp.variables(), start=1):\\n glpk.glp_set_col_name(prob, j, var.name)\\n lb = 0.0\\n ub = 0.0\\n t = glpk.GLP_FR\\n if not var.lowBound is None:\\n lb = var.lowBound\\n t = glpk.GLP_LO\\n if not var.upBound is None:\\n ub = var.upBound\\n t = glpk.GLP_UP\\n if not var.upBound is None and not var.lowBound is None:\\n if ub == lb:\\n t = glpk.GLP_FX\\n else:\\n t = glpk.GLP_DB\\n glpk.glp_set_col_bnds(prob, j, t, lb, ub)\\n if var.cat == constants.LpInteger:\\n glpk.glp_set_col_kind(prob, j, glpk.GLP_IV)\\n assert glpk.glp_get_col_kind(prob, j) == glpk.GLP_IV\\n var.glpk_index = j\\n log.debug(\\\"set the objective function\\\")\\n for var in lp.variables():\\n value = lp.objective.get(var)\\n if value:\\n glpk.glp_set_obj_coef(prob, var.glpk_index, value)\\n log.debug(\\\"set the problem matrix\\\")\\n for constraint in lp.constraints.values():\\n l = len(list(constraint.items()))\\n ind = glpk.intArray(l + 1)\\n val = glpk.doubleArray(l + 1)\\n for j, v in enumerate(constraint.items(), start=1):\\n var, value = v\\n ind[j] = var.glpk_index\\n val[j] = value\\n glpk.glp_set_mat_row(prob, constraint.glpk_index, l, ind, val)\\n lp.solverModel = prob\\n # glpk.glp_write_lp(prob, None, \\\"glpk.lp\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90dd741a4cce0ae2de8a4b272b8dd9f3\",\n \"score\": \"0.4929237\",\n \"text\": \"def to_modflow(model, fname):\\n try:\\n import flopy\\n except ImportError as e:\\n raise type(e)(str(e) + \\\", modflow output not possible!\\\")\\n # For modflow we want to create a new folder instead of only a file. The folder name is the base\\n # name of the passed filename\\n realization_dir = pathlib.Path(fname).parent\\n runname = pathlib.Path(fname).name\\n mfdir = realization_dir / 'MODFLOW'\\n mfdir.mkdir(parents=True, exist_ok=True)\\n mfname = str(mfdir / runname)\\n\\n # Assign name and create modflow model object\\n mf = flopy.modflow.Modflow(mfname, exe_name='mf2005')\\n\\n # Create the discretization object\\n ztop = model.grid.z0 + model.grid.lz\\n zbot = model.grid.z0\\n botm = np.linspace(ztop, zbot, model.grid.nz + 1)\\n dis = flopy.modflow.ModflowDis(mf, model.grid.nz, model.grid.nx, model.grid.ny, \\n delr=model.grid.dx, delc=model.grid.dy,\\n top=ztop, botm=botm[1:])\\n\\n # Variables for the BAS package\\n ibound = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.int32)\\n ibound[:, :, 0] = -1\\n ibound[:, :, -1] = -1\\n\\n strt = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.float32)\\n strt[:, :, 0] = model.flowtrans['hin'][0]\\n strt[:, :, -1] = model.flowtrans['hout'][0]\\n\\n bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=strt)\\n\\n # Assign hydraulic conductivity\\n hyvr_hk = np.transpose(model.data['k_iso'], (2, 0, 1))\\n hyvr_layvka = 1 # VKA dataset is ratio of horizontal K\\n if 'anirat' in model.data.keys():\\n hyvr_vka = np.transpose(model.data['anirat'], (2, 0, 1))\\n\\n # Add LPF package to the MODFLOW model\\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\\n hk=hyvr_hk, # Horizontal hydraulic conductivity\\n layvka=hyvr_layvka, # Flag for each layer of anisotropic ratio\\n vka=hyvr_vka) # Anisotropy ratios.\\n else:\\n # Add LPF package to the MODFLOW model\\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\\n hk=hyvr_hk) # Horizontal hydraulic conductivity\\n\\n oc = flopy.modflow.ModflowOc(mf) # Add OC package to the MODFLOW model\\n pcg = flopy.modflow.ModflowPcg(mf) # Add PCG package to the MODFLOW model\\n mf.write_input() # Write the MODFLOW model input files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486cabbe9fb6d10c75ea06f7ee5cf1a7\",\n \"score\": \"0.49249116\",\n \"text\": \"def write_production_mdp(prefix, rep, temp):\\n\\n fn = prefix + '_%d.mdp'%rep\\n fout = open(fn, 'w')\\n fout.write(\\\"\\\"\\\"title = prod_GBSA\\ncpp = /lib/cpp\\ninclude = -I../top\\ndefine = \\nintegrator = sd\\ndt = 0.002\\nnsteps = 25000000 ; 50 ns\\nnstxout = 500000 ; every 1 ns \\nnstvout = 500000\\nnstlog = 5000 ; every 10 ps\\nnstxtcout = 5000 ; every 10 ps\\nnstenergy = 5000 ; every 10 ps\\n\\n\\nimplicit_solvent = GBSA\\ngb_algorithm = OBC\\nnstgbradii = 1\\ngb_epsilon_solvent = 80.0\\nsa_algorithm = Ace-approximation\\n\\ncomm_mode = ANGULAR\\n\\nrgbradii = 0.9\\ncoulombtype = Cut-off\\nrvdw = 0.9\\nrlist = 0.9\\nrcoulomb = 0.9\\n\\n; CONSTRAINTS\\nconstraints = hbonds\\nconstraint_algorithm = LINCS\\n\\n; other stuff\\nbd_fric = 1.0\\nnstcomm = 10\\ncomm_grps = System\\nxtc_grps = System\\nenergygrps = System\\nnstlist = 10\\nns_type = grid\\ntc-grps = System \\ntau_t = 0.0109\\n\\nref_t = %d \\ncompressibility = 4.5e-5\\nref_p = 1.0\\ngen_vel = yes\\ngen_temp = %d \\ngen_seed = 1729\\npbc = no\\\"\\\"\\\"%(temp, temp))\\n\\n fout.close()\\n\\n return fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bfcf30894e9237e9a45c1cbf4059801\",\n \"score\": \"0.49182045\",\n \"text\": \"def write_aloha_routines(self):\\n \\n\\n self.aloha_model.add_Lorentz_object(self.model.get('lorentz'))\\n self.aloha_model.compute_subset(self.wanted_lorentz)\\n # Write out the aloha routines in Python\\n aloha_routines = []\\n \\n # First add the default external wavefunction routines\\n wavefunction_routines = open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read() \\n open(pjoin(self.dir_path,'wavefunctions.py'),'w').write(\\n 'from __future__ import division\\\\n'+wavefunction_routines)\\n\\n #aloha_routines.append(open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read())\\n\\n # Now write the process-depenent Feynman rules ones\\n for routine in self.aloha_model.values():\\n aloha_routines.append(routine.write(output_dir = None, \\n mode='mg5',\\n language = 'Python'))\\n\\n for routine in self.aloha_model.external_routines:\\n aloha_routines.append(\\n open(self.aloha_model.locate_external(routine, 'Python')).read())\\n\\n # Now collect imports\\n python_imports = []\\n new_aloha_routines = []\\n for aloha_routine in aloha_routines:\\n new_aloha_routine = []\\n for line in aloha_routine.split('\\\\n'):\\n if any(line.startswith(token) for token in ['from','import']):\\n if line not in python_imports:\\n python_imports.append(line)\\n else:\\n new_aloha_routine.append(line)\\n new_aloha_routines.append('\\\\n'.join(new_aloha_routine))\\n aloha_routines = new_aloha_routines\\n \\n # Veto some imports\\n vetoed_imports = ['import aloha.template_files.wavefunctions as wavefunctions']\\n python_imports = [pi for pi in python_imports if pi not in vetoed_imports]\\n python_imports.insert(0, 'import wavefunctions')\\n\\n aloha_output = open(pjoin(self.dir_path,'aloha_methods.py'),'w')\\n aloha_output.write('from __future__ import division\\\\n')\\n # Write imports\\n aloha_output.write('\\\\n'.join(python_imports))\\n aloha_output.write('\\\\n'*2)\\n\\n # Write routines\\n aloha_output.write('\\\\n'.join(aloha_routines))\\n\\n aloha_output.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73793368d062ed8010c45c9ae955d77\",\n \"score\": \"0.49119902\",\n \"text\": \"def run(self):\\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\\n self._openScriptFile()\\n self.scriptf.write(\\\"from phycas import *\\\\n\\\\n\\\")\\n\\n if self.opts.seed > 0:\\n self.scriptf.write('setMasterSeed(%d)\\\\n\\\\n' % self.opts.seed)\\n\\n if self.opts.model == 'jc':\\n self.model_jc(0)\\n elif self.opts.model == 'jc+i':\\n self.model_jc(1)\\n elif self.opts.model == 'jc+g':\\n self.model_jc(2)\\n elif self.opts.model == 'jc+i+g':\\n self.model_jc(3)\\n elif self.opts.model == 'hky':\\n self.model_hky(0)\\n elif self.opts.model == 'hky+i':\\n self.model_hky(1)\\n elif self.opts.model == 'hky+g':\\n self.model_hky(2)\\n elif self.opts.model == 'hky+i+g':\\n self.model_hky(3)\\n elif self.opts.model == 'gtr':\\n self.model_gtr(0)\\n elif self.opts.model == 'gtr+i':\\n self.model_gtr(1)\\n elif self.opts.model == 'gtr+g':\\n self.model_gtr(2)\\n elif self.opts.model == 'gtr+i+g':\\n self.model_gtr(3)\\n elif self.opts.model == 'codon':\\n self.model_codon()\\n\\n self.model_edgelen()\\n\\n if self.opts.datafile is None:\\n self.scriptf.write(\\\"mcmc.data_source = None\\\\n\\\")\\n elif self.opts.datafile == 'sample.nex':\\n self.saveSampleData()\\n self.scriptf.write(\\\"mcmc.data_source = '%s'\\\\n\\\\n\\\" % self.opts.datafile)\\n else:\\n self.scriptf.write(\\\"mcmc.data_source = '%s'\\\\n\\\\n\\\" % self.opts.datafile)\\n\\n if self.opts.analysis == 'mcmc':\\n self.mcmc_analysis()\\n self.sump_analysis()\\n self.sumt_analysis()\\n if self.opts.analysis == 'poly':\\n self.polytomy_analysis()\\n elif self.opts.analysis == 'cpo':\\n self.cpo_analysis()\\n #elif self.opts.analysis == 'idr':\\n # self.idr_analysis()\\n elif self.opts.analysis == 'ss':\\n self.steppingstone_analysis()\\n\\n self._closeScriptFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e42d57ee79ba479dae9b3b96b226d2\",\n \"score\": \"0.49086118\",\n \"text\": \"def build_model(out: ModelOutput = ModelOutput(\\\"malt/swemalt-1.7.2.mco\\\"),\\n _maltjar: Binary = Binary(\\\"[malt.jar]\\\")):\\n out.download(\\\"http://maltparser.org/mco/swedish_parser/swemalt-1.7.2.mco\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9a026d51eff7c21f70c849e56481fa5\",\n \"score\": \"0.49045718\",\n \"text\": \"def to_alien(self, file_pointer, format='opb', comments=None):\\n\\n if self.atms and format == 'smt':\\n raise NotImplementedError('SMT-LIB2 does not support PB constraints directly; you may want to use Z3\\\\'s API instead')\\n\\n cchars = {'lp': '\\\\\\\\', 'opb': '*', 'smt': ';'}\\n\\n # saving formula's internal comments\\n for c in self.comments:\\n print(cchars[format], c, file=file_pointer)\\n\\n # saving externally specified comments\\n if comments:\\n for c in comments:\\n print(cchars[format], c, file=file_pointer)\\n\\n # normalized soft clauses\\n soft, hard = [], []\\n topv = self.nv + 1\\n for cl in self.soft:\\n if len(cl) == 1:\\n soft.append(cl)\\n else:\\n hard.append([topv] + cl)\\n soft.append([topv])\\n topv += 1\\n\\n if format == 'opb':\\n print('* #variable= {0} #constraint= {1}'.format(self.nv, len(self.hard) + len(hard)),\\n file=file_pointer)\\n print('min:',\\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\\n ';', file=file_pointer)\\n elif format == 'lp':\\n print('Minimize', file=file_pointer)\\n print('obj:',\\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\\n file=file_pointer)\\n print('Subject To', file=file_pointer)\\n elif format == 'smt':\\n for v in range(1, self.nv + 1):\\n print('(declare-fun x{0} () Bool)'.format(v), file=file_pointer)\\n\\n for i, cl in enumerate(self.hard + hard, 1):\\n line, neg = [], 0\\n for l in cl:\\n if l > 0:\\n if format == 'smt':\\n line.append('x{0}'.format(l))\\n else:\\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', l))\\n else:\\n if format == 'smt':\\n line.append('(not x{0})'.format(-l))\\n else:\\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', -l))\\n neg += 1\\n\\n if format == 'smt':\\n print('(assert (or {0}))'.format(' '.join(line)), file=file_pointer)\\n else:\\n print('{0}{1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}: '.format(i),\\n ' '.join(l for l in line),\\n 1 - neg, ';' if format == 'opb' else ''),\\n file=file_pointer)\\n\\n for i, am in enumerate(self.atms, len(self.hard) + len(hard) + 1):\\n line, neg = [], 0\\n for l in am[0]:\\n if l > 0:\\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', l))\\n neg += 1\\n else:\\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', -l))\\n\\n print('{0} {1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}:'.format(i),\\n ' '.join(l for l in line),\\n len(am[0]) - am[1] - neg, ';' if format == 'opb' else ''),\\n file=file_pointer)\\n\\n if format == 'lp':\\n print('Bounds', file=file_pointer)\\n for v in range(1, topv):\\n print('0 <= x{0} <= 1'.format(v), file=file_pointer)\\n print('Binary', file=file_pointer)\\n for v in range(1, topv):\\n print('x{0}'.format(v), file=file_pointer)\\n print('End', file=file_pointer)\\n elif format == 'smt':\\n for cl, w in zip(soft, self.wght):\\n l = 'x{0}'.format(cl[0]) if cl[0] > 0 else '(not x{0})'.format(-cl[0])\\n print('(assert-soft {0} :weight {1})'.format(l, w), file=file_pointer)\\n\\n print('(check-sat)', file=file_pointer)\\n print('(get-model)', file=file_pointer)\\n print('(get-objectives)', file=file_pointer)\\n print('(exit)', file=file_pointer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bed09b7d57987e01641bd6feb172467\",\n \"score\": \"0.4902589\",\n \"text\": \"def copy_files_export_model(geomodel_dir, geomodeller_file, new_dir, nx, ny, nz, **kwds):\\n \\n \\n \\n os.chdir(geomodel_dir)\\n \\n \\n # first step: determine all Geomodeller files in original directory\\n geomodel_files = []\\n for l in os.listdir(\\\".\\\"):\\n if os.path.splitext(l)[1] == \\\".sec\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".s3d\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".md5\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".wsp\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".xml\\\":\\n # This is the original file - keep for later\\n geomodel_xml_ori = l\\n \\n print geomodel_xml_ori\\n os.chdir(new_dir)\\n\\n for f in geomodel_files:\\n shutil.copyfile(os.path.join(geomodel_dir, f), os.path.join(\\\".\\\",f))\\n\\n for l2 in os.listdir(\\\".\\\"):\\n if l2 == geomodeller_file:\\n print l2\\n shutil.copyfile(l2,os.path.join(\\\".\\\", geomodel_xml_ori))\\n if \\\"original\\\" in l2:\\n new_grid = \\\"exported_grid_original.txt\\\"\\n new_delxyz = \\\"delxyz_original.txt\\\"\\n else:\\n new_grid = \\\"exported_grid_\\\" + os.path.splitext(l2)[0] + \\\".txt\\\"\\n new_delxyz = \\\"delxyz_\\\" + os.path.splitext(l2)[0] + \\\".txt\\\"\\n \\n if kwds.has_key('nz_list'): # cell discretization study\\n try:\\n i = int(l2[-7:-4])\\n except ValueError:\\n os.chdir('..') \\n continue # original model\\n subprocess.call(\\\"/home/flo/bin/export_model %s %d %d %d\\\" % (geomodel_xml_ori, nx, ny, kwds['nz_list'][i]), shell=True)\\n else:\\n\\n while True:\\n proc = subprocess.Popen(\\\"/home/flo/bin/export_model %s %d %d %d\\\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\\n for i in range(10):\\n time.sleep(5)\\n if proc.poll() == 0:\\n print(\\\"Process finished successfully\\\")\\n break\\n if proc.poll() == None:\\n proc.kill()\\n print(\\\"\\\\n\\\\n\\\\tProcess killed, restart!\\\\n\\\\n\\\")\\n continue\\n elif proc.poll() == 0:\\n print(\\\"Process finished successfully\\\")\\n break\\n print(\\\"\\\\n\\\\n\\\\tProcess not finished correctly!\\\\n\\\\n\\\\n\\\")\\n break\\n # subprocess.call(\\\"/home/flo/bin/export_model %s %d %d %d\\\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\\n shutil.copyfile(\\\"exported_grid.txt\\\", os.path.join(\\\".\\\", new_grid))\\n shutil.copyfile(\\\"delxyz.txt\\\", os.path.join(\\\".\\\", new_delxyz))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dc902536351b3d417ec8d2e8f7e209e\",\n \"score\": \"0.48992693\",\n \"text\": \"def convert2lite(save_dir,\\n lite_valid_places=\\\"arm\\\",\\n lite_model_type=\\\"naive_buffer\\\"):\\n\\n from paddlelite.lite import Opt\\n opt = Opt()\\n opt.set_model_dir(save_dir + \\\"/inference_model\\\")\\n opt.set_valid_places(lite_valid_places)\\n opt.set_model_type(lite_model_type)\\n opt.set_optimize_out(save_dir + \\\"/opt\\\")\\n opt.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38860f271edd9ad00978731b25db1a2b\",\n \"score\": \"0.48990908\",\n \"text\": \"def makefile2dot(**kwargs):\\n\\n direction = kwargs.get('direction', \\\"BT\\\")\\n if direction not in [\\\"LR\\\", \\\"RL\\\", \\\"BT\\\", \\\"TB\\\"]:\\n raise ValueError('direction must be one of \\\"BT\\\", \\\"TB\\\", \\\"LR\\\", RL\\\"')\\n\\n output = kwargs.get('output', '')\\n view = kwargs.get('view', False)\\n\\n graph = build_graph(stream_database(), direction=direction)\\n if output == \\\"\\\":\\n if view:\\n graph.view()\\n else:\\n print(graph)\\n else:\\n with open(output, 'w') as file:\\n file.write(str(graph))\\n if view:\\n graph.view()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4d103572b36b3f5587fd6cccb6325d\",\n \"score\": \"0.48969853\",\n \"text\": \"def load_model(fp: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6128a8fd86ce81b50e836d0234155722\",\n \"score\": \"0.48924553\",\n \"text\": \"def writemodelfile(self, classpy=None):\\n if not classpy is None:\\n classpy.close()\\n pyfile = '%s.py' % os.path.join(self.modelspath, self.thismodule)\\n classpy = open(pyfile, 'r')\\n lines = classpy.readlines()\\n lines.insert(2, '\\\\n')\\n classpy.close()\\n froms = list()\\n for line in lines:\\n prefix = ''\\n if 'ForeignKey' in line or 'OneToOneField' in line \\\\\\n or 'ManyToManyField' in line:\\n idx = lines.index(line)\\n third = ''\\n paren = False\\n comma = False\\n first, second = line.split('(', 1)\\n if not 'self' in second: # do nothing if it is\\n if ',' in second: # some options exist\\n second, third = second.split(',', 1)\\n third = ', %s' % third\\n if ')' in second: # that's all in the line\\n paren = True # so it can be put back\\n second = second.replace(')', '').strip()\\n if \\\"'\\\" in second:\\n second = second.replace(\\\"'\\\", '').strip()\\n if '\\\"' in second:\\n second = second.replace('\\\"', '').strip()\\n relatedmodel = second # totally naked\\n assert len(second.split()) == 1\\n module = self.checkthisimport(relatedmodel)\\n if not module is None:\\n if module.startswith('.'):\\n prefix = '#'\\n line = \\\"%s('%s'%s\\\" % (first, second, third)\\n else:\\n line = \\\"%s(%s%s\\\" % (first, second, third)\\n if paren:\\n line = '%s)\\\\n' % line.rstrip()\\n if not line.endswith('\\\\n'):\\n line = '%s\\\\n' % line\\n if idx:\\n lines[idx] = line\\n if module:\\n impline = '%sfrom %s import %s\\\\n' % (prefix, module,\\n relatedmodel)\\n else:\\n impline = 'import %s\\\\n' % model\\n if not impline in froms:\\n #print(impline)\\n froms.insert(0, impline)\\n # now find all the other imported items\\n chunk = line.replace('=', ' ')\\n chunk = chunk.replace('[', ' ')\\n chunk = chunk.replace('(', ' ')\\n chunk = chunk.replace(')', ' ')\\n chunk = chunk.replace('.', ' ')\\n chunk = chunk.replace(']', ' ')\\n chunk = chunk.replace(',', ' ')\\n bits = chunk.split()\\n for bit in bits:\\n if self.seemsok(bit, line):\\n module = self.checkthisimport(bit)\\n if not module is None:\\n if module == '':\\n impline = 'import %s\\\\n' % bit\\n else:\\n impline = 'from %s import %s\\\\n' % (module, bit)\\n if not impline in froms:\\n froms.insert(0, impline)\\n\\n clean = list()\\n for item in froms:\\n if not '__future__' in item:\\n if not item in clean:\\n clean.insert(0, item)\\n cleaner = self.removethismodule(clean)\\n del(clean)\\n cleaner.sort()\\n cleaner.reverse()\\n if aggregate:\\n cleanest = self.aggregatefroms(cleaner)\\n else:\\n cleanest = cleaner\\n for item in cleanest:\\n lines.insert(self.startline, item)\\n classpy = open(pyfile, 'w')\\n classpy.writelines(lines)\\n classpy.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b24a82db4e8a7271fd5aaa542794f09\",\n \"score\": \"0.48900214\",\n \"text\": \"def _translate_and_simulate(self, simulate):\\n import os\\n import shutil\\n import jinja2\\n import datetime\\n\\n # Delete output files\\n self.deleteOutputFiles()\\n\\n worDir = self._outputDir_\\n\\n # Construct the model instance with all parameter values\\n # and the package redeclarations\\n dec = self._declare_parameters()\\n dec.extend(self._modelModifiers_)\\n\\n if len(dec) == 0:\\n model_modifier = \\\"\\\"\\n else:\\n model_modifier = '{dec}'.format(mn=self.modelName, dec=','.join(dec))\\n\\n file_name = \\\"{}.py\\\".format(self.modelName.replace(\\\".\\\", \\\"_\\\"))\\n## self._time_stamp_old_files = datetime.datetime.now()\\n with open(os.path.join(worDir, file_name), mode=\\\"w\\\", encoding=\\\"utf-8\\\") as fil:\\n path_to_template = os.path.join(\\n os.path.dirname(__file__), os.path.pardir, \\\"development\\\")\\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(path_to_template))\\n\\n template = env.get_template(\\\"optimica_run.template\\\")\\n\\n # Note that filter argument must respect glob syntax ([ is escaped with []]) + OPTIMICA mat file\\n # stores matrix variables with no space e.g. [1,1].\\n txt = template.render(\\n model=self.modelName,\\n model_modifier=model_modifier,\\n ncp=self._simulator_.get('numberOfIntervals'),\\n rtol=self._simulator_.get('eps'),\\n solver=self._simulator_.get('solver'),\\n start_time=self._simulator_.get('t0') if self._simulator_.get(\\n 't0') is not None else 'mod.get_default_experiment_start_time()',\\n final_time=self._simulator_.get('t1') if self._simulator_.get(\\n 't1') is not None else 'mod.get_default_experiment_stop_time()',\\n result_file_name=f\\\"{self._simulator_.get('resultFile')}.mat\\\",\\n simulate=simulate,\\n time_out=self._simulator_.get('timeout'),\\n filter=self._result_filter,\\n generate_html_diagnostics=self._generate_html_diagnostics,\\n debug_solver=self._debug_solver,\\n debug_solver_interactive_mode=self._debug_solver_interactive_mode)\\n\\n fil.write(txt)\\n shutil.copyfile(\\n os.path.join(\\n os.path.dirname(__file__),\\n \\\"OutputGrabber.py\\\"),\\n os.path.join(\\n worDir,\\n \\\"OutputGrabber.py\\\"))\\n\\n try:\\n super()._runSimulation([\\\"jm_ipython.sh\\\", file_name],\\n self._simulator_.get('timeout'),\\n worDir)\\n\\n self._check_simulation_errors(worDir=worDir, simulate=simulate)\\n# self._copyNewFiles(worDir)\\n# self._deleteTemporaryDirectory(worDir)\\n\\n except Exception as e: # Catch all possible exceptions\\n em = f\\\"Simulation failed in '{worDir}'\\\\n Exception: {e}.\\\\n You need to delete the directory manually.\\\\n\\\"\\n self._reporter.writeError(em)\\n raise e\\n os.remove(os.path.join(worDir, \\\"OutputGrabber.py\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"989010fe3938e83cdafa116524121a61\",\n \"score\": \"0.4889075\",\n \"text\": \"def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):\\n from pathlib import Path\\n\\n from stamp_processing.module.yolov5.models.experimental import attempt_load\\n from stamp_processing.module.yolov5.models.yolo import Model\\n from stamp_processing.module.yolov5.utils.torch_utils import select_device\\n\\n file = Path(__file__).absolute()\\n\\n save_dir = Path(\\\"\\\") if str(name).endswith(\\\".pt\\\") else file.parent\\n path = (save_dir / name).with_suffix(\\\".pt\\\") # checkpoint path\\n try:\\n device = select_device((\\\"0\\\" if torch.cuda.is_available() else \\\"cpu\\\") if device is None else device)\\n\\n if pretrained and channels == 3 and classes == 80:\\n model = attempt_load(path, map_location=device) # download/load FP32 model\\n else:\\n cfg = list((Path(__file__).parent / \\\"models\\\").rglob(f\\\"{name}.yaml\\\"))[0] # model.yaml path\\n model = Model(cfg, channels, classes) # create model\\n if autoshape:\\n model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS\\n return model.to(device)\\n\\n except Exception as e:\\n help_url = \\\"https://github.com/ultralytics/yolov5/issues/36\\\"\\n s = \\\"Cache may be out of date, try `force_reload=True`. See %s for help.\\\" % help_url\\n raise Exception(s) from e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1163e643e5b668503b7ff3340a328791\",\n \"score\": \"0.48880547\",\n \"text\": \"def make_dot(aut_fname, dot_fname):\\n coll = collection.Collection()\\n aut = load_automaton(coll, aut_fname, False, False)\\n\\n make_backup_file(dot_fname)\\n\\n dot_handle = open(dot_fname, 'w')\\n dot_handle.write(aut.to_dot())\\n dot_handle.close()\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":178,"cells":{"query_id":{"kind":"string","value":"129bf3d37fc164404295d1647290238f"},"query":{"kind":"string","value":"Set an instance variable to test that this method gets called"},"positive_passages":{"kind":"list like","value":[{"docid":"abe8787ed3fa3d0a67f544bcbdd156df","score":"0.0","text":"def __parent_setup_2(self):\r\n self.parent_setup_exists += 1","title":""}],"string":"[\n {\n \"docid\": \"abe8787ed3fa3d0a67f544bcbdd156df\",\n \"score\": \"0.0\",\n \"text\": \"def __parent_setup_2(self):\\r\\n self.parent_setup_exists += 1\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"de81fdce13d62ec47641ea1a8c6ee8c7","score":"0.73396957","text":"def before_tester_run(self) -> None:","title":""},{"docid":"f3b2d6e10065ff6b983c1edd3630c152","score":"0.7324083","text":"def classSetUp(self):\r\n assert self.something == True\r\n self.something = False","title":""},{"docid":"51262b0e50f19bfeabbd206ecbc400f8","score":"0.72828907","text":"def _test(self):\n pass","title":""},{"docid":"f755fb8af7ee7ec95e19b5587ea6f098","score":"0.7067964","text":"def testing(self):","title":""},{"docid":"808425a98f90a1aaa5ee11daef2130fd","score":"0.70527375","text":"def _mockme(self):\n pass","title":""},{"docid":"ccc77d904ab17a7c854c8f5789312938","score":"0.69012713","text":"def setup_method(self, method):\n assert method","title":""},{"docid":"f7529441c82c36857491679fc0ad7bba","score":"0.6900921","text":"def test_(self):\n pass","title":""},{"docid":"0714b55a928f10390dd112e4a71d64e7","score":"0.6892391","text":"def test_init(self):\n pass","title":""},{"docid":"6395a0447babd542b1cb1cb13ec3ce90","score":"0.68894523","text":"def before_test_run(self) -> None:","title":""},{"docid":"eaa2604b3e54d4eaa66788fed871de75","score":"0.68563145","text":"def testInit(self):","title":""},{"docid":"39d1e4b8a393b823fec5ed0de63b0ff3","score":"0.6824386","text":"def test(cls):\n pass","title":""},{"docid":"0645a56473c801c578eebf4001ceec3b","score":"0.68234134","text":"def test(self):\n # TODO\n pass","title":""},{"docid":"343d0cce8356ac3e92c0de489f4efcd1","score":"0.67005354","text":"def test(self):\n pass","title":""},{"docid":"343d0cce8356ac3e92c0de489f4efcd1","score":"0.67005354","text":"def test(self):\n pass","title":""},{"docid":"675dfa93100212ef6375295a1a1ce439","score":"0.66937464","text":"def setup_method(self):\n self.out = None\n return","title":""},{"docid":"1b1a5d7ff04d8016d90ae97a5edbdd07","score":"0.6687489","text":"def after_tester_run(self) -> None:","title":""},{"docid":"82815e8f330845c57d8826a78a373780","score":"0.664704","text":"def setUp(self):\n self.obj = SomeClass()","title":""},{"docid":"1996573f8fd08abd8c20cb78f5d337bd","score":"0.6622737","text":"def self_test():","title":""},{"docid":"9f668c50b248a875b1d26bf7f593c018","score":"0.6618984","text":"def __init__(self):\n self.verbose = False\n self.simulate = False\n self.debug = False","title":""},{"docid":"ad8da9c4f89cf541e07ee70f4417550c","score":"0.65945566","text":"def test(self):\n return True","title":""},{"docid":"87ab336d1d39a975629372b0424f8642","score":"0.6570382","text":"def setup_method(self):\n\n reload(pysat.instruments.pysat_testing)\n self.name = 'testing'\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\n return","title":""},{"docid":"e3b18d01113aa19349cad235b54ecf01","score":"0.6537312","text":"def SetUp(self):\n pass","title":""},{"docid":"f9d58dbcc9463628df8a2b5dcadfc8fb","score":"0.6458454","text":"def _on_test_begin(self):\n pass","title":""},{"docid":"12415e51a45655613acc98a6dd303b6a","score":"0.6443678","text":"def setUp(self):\n self.result = ''","title":""},{"docid":"d388939ba349a300a7a20adb599c127d","score":"0.64378744","text":"def __call__(self):\n pass","title":""},{"docid":"d388939ba349a300a7a20adb599c127d","score":"0.64378744","text":"def __call__(self):\n pass","title":""},{"docid":"cb74abd832f67445757a9df7a3220729","score":"0.6430383","text":"def test_set_instance_field(self):\n pass","title":""},{"docid":"06618ec00d1e59a1aa3d17ad542fd572","score":"0.6405326","text":"def setup_method(self):\n\n warnings.simplefilter(\"always\", DeprecationWarning)\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\n self.warn_msgs = []\n self.war = \"\"\n return","title":""},{"docid":"1020f9278cdfaa93e4732c3cd4517798","score":"0.6402218","text":"def test_init(self):\n self.assertTrue(hasattr(self.test, \"mouse_is_on\"))\n self.assertTrue(hasattr(self.test, \"hover\"))\n self.assertTrue(hasattr(self.test, \"request_list\"))\n self.assertTrue(hasattr(self.test, \"command_list\"))","title":""},{"docid":"51f35c96fe367ace0d686a1971bd1021","score":"0.6395603","text":"def test_instrument_init(self):\n\n assert self.testInst.new_thing\n return","title":""},{"docid":"3304793c6d84b318dab1a82509cae13e","score":"0.63714534","text":"def setup_class(self):","title":""},{"docid":"3304793c6d84b318dab1a82509cae13e","score":"0.63714534","text":"def setup_class(self):","title":""},{"docid":"4d89e4e206ecb876b22b77a273dfebbe","score":"0.6368773","text":"def initiate(self):","title":""},{"docid":"0b0e1f4643bfe57e20fa45988b64a3cc","score":"0.63375026","text":"def init(self):\n\t\tpass","title":""},{"docid":"4dcfc871867931eeed4399775081280b","score":"0.63354313","text":"def setUp(self):\n self.options = Options()","title":""},{"docid":"3795754c68868d6a9865aab795995b9d","score":"0.6334769","text":"def setup(self) -> None:","title":""},{"docid":"b269306d60acc935f8f503117a22bcd6","score":"0.6329245","text":"def run(self):\n assert_equal(self, 1, 1, 'test')\n assert_true(self, False, 'test')\n return","title":""},{"docid":"db3d57cbf37ee951907cbac117eeb09b","score":"0.6328691","text":"def test_init(self, hotp):\n hotp.assert_not_called()\n hotp.return_value.verify.assert_not_called()","title":""},{"docid":"f0868c574190f7892a5ed88ea2afca7d","score":"0.63255155","text":"def init(self):\n return True","title":""},{"docid":"cf419d90fd2a0cb0ae308b3de97c90b5","score":"0.63226557","text":"def __init__(self):\r\n self.initialized = False","title":""},{"docid":"cf419d90fd2a0cb0ae308b3de97c90b5","score":"0.63226557","text":"def __init__(self):\r\n self.initialized = False","title":""},{"docid":"19eaeecab013f1002b22a8666ddd1919","score":"0.6319818","text":"def call(self):\n pass","title":""},{"docid":"801be9e54b91396497442b025caecff4","score":"0.6318234","text":"def __init__(self):\n self.initialized = False","title":""},{"docid":"801be9e54b91396497442b025caecff4","score":"0.6318234","text":"def __init__(self):\n self.initialized = False","title":""},{"docid":"801be9e54b91396497442b025caecff4","score":"0.6318234","text":"def __init__(self):\n self.initialized = False","title":""},{"docid":"ab0f4e081e9a97e6d579fb34b09337ac","score":"0.6315708","text":"def test_something(self):\n pass","title":""},{"docid":"ab0f4e081e9a97e6d579fb34b09337ac","score":"0.6315708","text":"def test_something(self):\n pass","title":""},{"docid":"20e315badfcd5ea2fbd2adeb30f65c3d","score":"0.63003427","text":"def init(self, ):\n pass","title":""},{"docid":"fcafb07835deeb463e5bda11509ea028","score":"0.62935686","text":"def test_method(self):","title":""},{"docid":"ff31b1e94630d6d5ec1d74bc327ce48a","score":"0.6287083","text":"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]","title":""},{"docid":"ff31b1e94630d6d5ec1d74bc327ce48a","score":"0.6287083","text":"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]","title":""},{"docid":"4034806bcb3ae3ad4c90b6a3eb9844a0","score":"0.62859535","text":"def setUp(self):\n print(\"setUp\")\n print(\"===========\")","title":""},{"docid":"cb3cfadca177693c1d29f5e79bdd34cd","score":"0.6281448","text":"def setUp(self):\n self.supvisors = MockedSupvisors()","title":""},{"docid":"4acc340f00371ec4a826c5450a6971f1","score":"0.6276333","text":"def test_init(self, hotp):\n hotp.assert_not_called\n hotp.return_value.verify.assert_not_called()","title":""},{"docid":"afa0cd3753bfed9ca350c54f4c15ff2c","score":"0.6259981","text":"def setUpAttr(cls):\n pass","title":""},{"docid":"3c963aade5e955a41445071162555445","score":"0.6255766","text":"def test(self, test):\n\n self._test = test","title":""},{"docid":"3c963aade5e955a41445071162555445","score":"0.6255766","text":"def test(self, test):\n\n self._test = test","title":""},{"docid":"2c82da39e2f0c091b2a1b647b101d2ef","score":"0.6237032","text":"def verify(self):\n raise Exception(\"abstact class called unexpectedly\")","title":""},{"docid":"b931cb4adffb6ce8764de896d8ea98f8","score":"0.6232571","text":"def method(self):\n pass","title":""},{"docid":"19c9f152b244481fe2a33153022c0f29","score":"0.6231853","text":"def setUpClass(cls):\n cls.token = generate_v2_test_token(username='bob')\n addr.const = MagicMock()\n addr.const.VLAB_IPAM_LOG_LEVEL = 'INFO'\n addr.const.VLAB_VERIFY_TOKEN = False","title":""},{"docid":"ac72173d9df6ed7e73a1b9e8adc5e667","score":"0.6227319","text":"def setUp(cls):\n pass","title":""},{"docid":"d7cf04ce463176249d361d722146083a","score":"0.6224186","text":"def setup_method(self):\n\n self.name = 'ndtesting'\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\n return","title":""},{"docid":"40cbf579c77d25fc0bfdb693242b1beb","score":"0.62184924","text":"def __init__ (self):\n\t\tpass","title":""},{"docid":"a5c013bdd1962d473bc29d8ef9f1594c","score":"0.62147206","text":"def test(self):","title":""},{"docid":"6d80dfe989aa65877ae3dc3d77fb57db","score":"0.6212095","text":"def set(self) -> None:\n ...","title":""},{"docid":"0d3a7974a6fa0f3868f469c68d2757c6","score":"0.62030315","text":"def run(self):\n # Subclass and overload\n assert(False)","title":""},{"docid":"403c4dfdd692c82ed5514985279b1191","score":"0.61971235","text":"def setUp(self):\n self.context = Context()","title":""},{"docid":"403c4dfdd692c82ed5514985279b1191","score":"0.61971235","text":"def setUp(self):\n self.context = Context()","title":""},{"docid":"206755d80bf9e86ccc213a1fbc09e89b","score":"0.6192788","text":"def setUp(self):\n\t\tpass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"82ed2740386a8b33a8212e5597c25781","score":"0.6189197","text":"def setUpClass(cls):\n cls.review = inspect.getmembers(review, inspect.isfunction)","title":""},{"docid":"e1d913a5d348c714c776c3a28899f32b","score":"0.61843926","text":"def test_bad_init(self):","title":""},{"docid":"8a12d13bb0d51fc9b1dc9016725da181","score":"0.6181193","text":"def setUp(self):\n self.amity = Amity()","title":""},{"docid":"fc8f2d6201521d534f054d29a2847b9b","score":"0.6176834","text":"def __post_init__(self) -> None:","title":""},{"docid":"dab9d16dbf4b0efe8d5bf45a9b1de6d4","score":"0.6171858","text":"def test(self):\r\n return False","title":""},{"docid":"7f6ca4697e61e917d06607e64687bb0f","score":"0.61702687","text":"def run_test(self):\n pass","title":""},{"docid":"3aea05982b9de22fb76c4c5f2fdcd5f2","score":"0.6170054","text":"def setup(self) -> bool:\n ...\n\n return True","title":""},{"docid":"8cbc5b40487aa2d2d7445d47ccfdbd26","score":"0.61607534","text":"def pre_test_response(self):","title":""},{"docid":"66342ab51d8d53981de648ca89724675","score":"0.6158772","text":"def test_something(self):\r\n return True","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"1e19df646aed902a37f593713904d3a0","score":"0.6147413","text":"def test_run(self):\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6147379","text":"def initialize(self):\r\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6147379","text":"def initialize(self):\r\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6147379","text":"def initialize(self):\r\n pass","title":""}],"string":"[\n {\n \"docid\": \"de81fdce13d62ec47641ea1a8c6ee8c7\",\n \"score\": \"0.73396957\",\n \"text\": \"def before_tester_run(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b2d6e10065ff6b983c1edd3630c152\",\n \"score\": \"0.7324083\",\n \"text\": \"def classSetUp(self):\\r\\n assert self.something == True\\r\\n self.something = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51262b0e50f19bfeabbd206ecbc400f8\",\n \"score\": \"0.72828907\",\n \"text\": \"def _test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f755fb8af7ee7ec95e19b5587ea6f098\",\n \"score\": \"0.7067964\",\n \"text\": \"def testing(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808425a98f90a1aaa5ee11daef2130fd\",\n \"score\": \"0.70527375\",\n \"text\": \"def _mockme(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc77d904ab17a7c854c8f5789312938\",\n \"score\": \"0.69012713\",\n \"text\": \"def setup_method(self, method):\\n assert method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7529441c82c36857491679fc0ad7bba\",\n \"score\": \"0.6900921\",\n \"text\": \"def test_(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0714b55a928f10390dd112e4a71d64e7\",\n \"score\": \"0.6892391\",\n \"text\": \"def test_init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6395a0447babd542b1cb1cb13ec3ce90\",\n \"score\": \"0.68894523\",\n \"text\": \"def before_test_run(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa2604b3e54d4eaa66788fed871de75\",\n \"score\": \"0.68563145\",\n \"text\": \"def testInit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d1e4b8a393b823fec5ed0de63b0ff3\",\n \"score\": \"0.6824386\",\n \"text\": \"def test(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0645a56473c801c578eebf4001ceec3b\",\n \"score\": \"0.68234134\",\n \"text\": \"def test(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"343d0cce8356ac3e92c0de489f4efcd1\",\n \"score\": \"0.67005354\",\n \"text\": \"def test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"343d0cce8356ac3e92c0de489f4efcd1\",\n \"score\": \"0.67005354\",\n \"text\": \"def test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675dfa93100212ef6375295a1a1ce439\",\n \"score\": \"0.66937464\",\n \"text\": \"def setup_method(self):\\n self.out = None\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1a5d7ff04d8016d90ae97a5edbdd07\",\n \"score\": \"0.6687489\",\n \"text\": \"def after_tester_run(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82815e8f330845c57d8826a78a373780\",\n \"score\": \"0.664704\",\n \"text\": \"def setUp(self):\\n self.obj = SomeClass()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1996573f8fd08abd8c20cb78f5d337bd\",\n \"score\": \"0.6622737\",\n \"text\": \"def self_test():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f668c50b248a875b1d26bf7f593c018\",\n \"score\": \"0.6618984\",\n \"text\": \"def __init__(self):\\n self.verbose = False\\n self.simulate = False\\n self.debug = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8da9c4f89cf541e07ee70f4417550c\",\n \"score\": \"0.65945566\",\n \"text\": \"def test(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ab336d1d39a975629372b0424f8642\",\n \"score\": \"0.6570382\",\n \"text\": \"def setup_method(self):\\n\\n reload(pysat.instruments.pysat_testing)\\n self.name = 'testing'\\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b18d01113aa19349cad235b54ecf01\",\n \"score\": \"0.6537312\",\n \"text\": \"def SetUp(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d58dbcc9463628df8a2b5dcadfc8fb\",\n \"score\": \"0.6458454\",\n \"text\": \"def _on_test_begin(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12415e51a45655613acc98a6dd303b6a\",\n \"score\": \"0.6443678\",\n \"text\": \"def setUp(self):\\n self.result = ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d388939ba349a300a7a20adb599c127d\",\n \"score\": \"0.64378744\",\n \"text\": \"def __call__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d388939ba349a300a7a20adb599c127d\",\n \"score\": \"0.64378744\",\n \"text\": \"def __call__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb74abd832f67445757a9df7a3220729\",\n \"score\": \"0.6430383\",\n \"text\": \"def test_set_instance_field(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06618ec00d1e59a1aa3d17ad542fd572\",\n \"score\": \"0.6405326\",\n \"text\": \"def setup_method(self):\\n\\n warnings.simplefilter(\\\"always\\\", DeprecationWarning)\\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\\n self.warn_msgs = []\\n self.war = \\\"\\\"\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1020f9278cdfaa93e4732c3cd4517798\",\n \"score\": \"0.6402218\",\n \"text\": \"def test_init(self):\\n self.assertTrue(hasattr(self.test, \\\"mouse_is_on\\\"))\\n self.assertTrue(hasattr(self.test, \\\"hover\\\"))\\n self.assertTrue(hasattr(self.test, \\\"request_list\\\"))\\n self.assertTrue(hasattr(self.test, \\\"command_list\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f35c96fe367ace0d686a1971bd1021\",\n \"score\": \"0.6395603\",\n \"text\": \"def test_instrument_init(self):\\n\\n assert self.testInst.new_thing\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3304793c6d84b318dab1a82509cae13e\",\n \"score\": \"0.63714534\",\n \"text\": \"def setup_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3304793c6d84b318dab1a82509cae13e\",\n \"score\": \"0.63714534\",\n \"text\": \"def setup_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d89e4e206ecb876b22b77a273dfebbe\",\n \"score\": \"0.6368773\",\n \"text\": \"def initiate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b0e1f4643bfe57e20fa45988b64a3cc\",\n \"score\": \"0.63375026\",\n \"text\": \"def init(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dcfc871867931eeed4399775081280b\",\n \"score\": \"0.63354313\",\n \"text\": \"def setUp(self):\\n self.options = Options()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3795754c68868d6a9865aab795995b9d\",\n \"score\": \"0.6334769\",\n \"text\": \"def setup(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b269306d60acc935f8f503117a22bcd6\",\n \"score\": \"0.6329245\",\n \"text\": \"def run(self):\\n assert_equal(self, 1, 1, 'test')\\n assert_true(self, False, 'test')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3d57cbf37ee951907cbac117eeb09b\",\n \"score\": \"0.6328691\",\n \"text\": \"def test_init(self, hotp):\\n hotp.assert_not_called()\\n hotp.return_value.verify.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0868c574190f7892a5ed88ea2afca7d\",\n \"score\": \"0.63255155\",\n \"text\": \"def init(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf419d90fd2a0cb0ae308b3de97c90b5\",\n \"score\": \"0.63226557\",\n \"text\": \"def __init__(self):\\r\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf419d90fd2a0cb0ae308b3de97c90b5\",\n \"score\": \"0.63226557\",\n \"text\": \"def __init__(self):\\r\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19eaeecab013f1002b22a8666ddd1919\",\n \"score\": \"0.6319818\",\n \"text\": \"def call(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801be9e54b91396497442b025caecff4\",\n \"score\": \"0.6318234\",\n \"text\": \"def __init__(self):\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801be9e54b91396497442b025caecff4\",\n \"score\": \"0.6318234\",\n \"text\": \"def __init__(self):\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801be9e54b91396497442b025caecff4\",\n \"score\": \"0.6318234\",\n \"text\": \"def __init__(self):\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0f4e081e9a97e6d579fb34b09337ac\",\n \"score\": \"0.6315708\",\n \"text\": \"def test_something(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0f4e081e9a97e6d579fb34b09337ac\",\n \"score\": \"0.6315708\",\n \"text\": \"def test_something(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e315badfcd5ea2fbd2adeb30f65c3d\",\n \"score\": \"0.63003427\",\n \"text\": \"def init(self, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcafb07835deeb463e5bda11509ea028\",\n \"score\": \"0.62935686\",\n \"text\": \"def test_method(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff31b1e94630d6d5ec1d74bc327ce48a\",\n \"score\": \"0.6287083\",\n \"text\": \"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff31b1e94630d6d5ec1d74bc327ce48a\",\n \"score\": \"0.6287083\",\n \"text\": \"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4034806bcb3ae3ad4c90b6a3eb9844a0\",\n \"score\": \"0.62859535\",\n \"text\": \"def setUp(self):\\n print(\\\"setUp\\\")\\n print(\\\"===========\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3cfadca177693c1d29f5e79bdd34cd\",\n \"score\": \"0.6281448\",\n \"text\": \"def setUp(self):\\n self.supvisors = MockedSupvisors()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acc340f00371ec4a826c5450a6971f1\",\n \"score\": \"0.6276333\",\n \"text\": \"def test_init(self, hotp):\\n hotp.assert_not_called\\n hotp.return_value.verify.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa0cd3753bfed9ca350c54f4c15ff2c\",\n \"score\": \"0.6259981\",\n \"text\": \"def setUpAttr(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c963aade5e955a41445071162555445\",\n \"score\": \"0.6255766\",\n \"text\": \"def test(self, test):\\n\\n self._test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c963aade5e955a41445071162555445\",\n \"score\": \"0.6255766\",\n \"text\": \"def test(self, test):\\n\\n self._test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c82da39e2f0c091b2a1b647b101d2ef\",\n \"score\": \"0.6237032\",\n \"text\": \"def verify(self):\\n raise Exception(\\\"abstact class called unexpectedly\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b931cb4adffb6ce8764de896d8ea98f8\",\n \"score\": \"0.6232571\",\n \"text\": \"def method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c9f152b244481fe2a33153022c0f29\",\n \"score\": \"0.6231853\",\n \"text\": \"def setUpClass(cls):\\n cls.token = generate_v2_test_token(username='bob')\\n addr.const = MagicMock()\\n addr.const.VLAB_IPAM_LOG_LEVEL = 'INFO'\\n addr.const.VLAB_VERIFY_TOKEN = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac72173d9df6ed7e73a1b9e8adc5e667\",\n \"score\": \"0.6227319\",\n \"text\": \"def setUp(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7cf04ce463176249d361d722146083a\",\n \"score\": \"0.6224186\",\n \"text\": \"def setup_method(self):\\n\\n self.name = 'ndtesting'\\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40cbf579c77d25fc0bfdb693242b1beb\",\n \"score\": \"0.62184924\",\n \"text\": \"def __init__ (self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c013bdd1962d473bc29d8ef9f1594c\",\n \"score\": \"0.62147206\",\n \"text\": \"def test(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d80dfe989aa65877ae3dc3d77fb57db\",\n \"score\": \"0.6212095\",\n \"text\": \"def set(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3a7974a6fa0f3868f469c68d2757c6\",\n \"score\": \"0.62030315\",\n \"text\": \"def run(self):\\n # Subclass and overload\\n assert(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403c4dfdd692c82ed5514985279b1191\",\n \"score\": \"0.61971235\",\n \"text\": \"def setUp(self):\\n self.context = Context()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403c4dfdd692c82ed5514985279b1191\",\n \"score\": \"0.61971235\",\n \"text\": \"def setUp(self):\\n self.context = Context()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"206755d80bf9e86ccc213a1fbc09e89b\",\n \"score\": \"0.6192788\",\n \"text\": \"def setUp(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ed2740386a8b33a8212e5597c25781\",\n \"score\": \"0.6189197\",\n \"text\": \"def setUpClass(cls):\\n cls.review = inspect.getmembers(review, inspect.isfunction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d913a5d348c714c776c3a28899f32b\",\n \"score\": \"0.61843926\",\n \"text\": \"def test_bad_init(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a12d13bb0d51fc9b1dc9016725da181\",\n \"score\": \"0.6181193\",\n \"text\": \"def setUp(self):\\n self.amity = Amity()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc8f2d6201521d534f054d29a2847b9b\",\n \"score\": \"0.6176834\",\n \"text\": \"def __post_init__(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab9d16dbf4b0efe8d5bf45a9b1de6d4\",\n \"score\": \"0.6171858\",\n \"text\": \"def test(self):\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6ca4697e61e917d06607e64687bb0f\",\n \"score\": \"0.61702687\",\n \"text\": \"def run_test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aea05982b9de22fb76c4c5f2fdcd5f2\",\n \"score\": \"0.6170054\",\n \"text\": \"def setup(self) -> bool:\\n ...\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbc5b40487aa2d2d7445d47ccfdbd26\",\n \"score\": \"0.61607534\",\n \"text\": \"def pre_test_response(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66342ab51d8d53981de648ca89724675\",\n \"score\": \"0.6158772\",\n \"text\": \"def test_something(self):\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e19df646aed902a37f593713904d3a0\",\n \"score\": \"0.6147413\",\n \"text\": \"def test_run(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6147379\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6147379\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6147379\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":179,"cells":{"query_id":{"kind":"string","value":"dde81fd75e8dab25708f015fee2011b9"},"query":{"kind":"string","value":"If the same item appears in the list, sums the quantities and create a unique Food object."},"positive_passages":{"kind":"list like","value":[{"docid":"fc122672b927400040c2b8b6fa8b33a7","score":"0.5547891","text":"def eraseDubloons(foodList):\n resList = []\n foodNames = [f.codsougr_name for f in foodList]\n uniqueFoodNames = list(set(foodNames))\n for name in uniqueFoodNames:\n duplicates = [f for f in foodList if f.codsougr_name == name]\n qte_nette = sum([f.qte_nette for f in duplicates])\n codsougr = [f.codsougr for f in duplicates][0]\n \n newFood = Food(codsougr, name, qte_nette)\n resList.append(newFood)\n return resList","title":""}],"string":"[\n {\n \"docid\": \"fc122672b927400040c2b8b6fa8b33a7\",\n \"score\": \"0.5547891\",\n \"text\": \"def eraseDubloons(foodList):\\n resList = []\\n foodNames = [f.codsougr_name for f in foodList]\\n uniqueFoodNames = list(set(foodNames))\\n for name in uniqueFoodNames:\\n duplicates = [f for f in foodList if f.codsougr_name == name]\\n qte_nette = sum([f.qte_nette for f in duplicates])\\n codsougr = [f.codsougr for f in duplicates][0]\\n \\n newFood = Food(codsougr, name, qte_nette)\\n resList.append(newFood)\\n return resList\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"4f021ae31218d83063fc7b7fe95882c6","score":"0.6519353","text":"def __add_muilt(self,food_name,quantity):\n\t\tif (not food_name in self.items):\n\t\t\tself.items[food_name] =0\n\t\tself.items[food_name] = self.items[food_name] + quantity","title":""},{"docid":"232d5321f12a8823e62ae2f05ee7bab2","score":"0.5817523","text":"def addFood(self, num):\n self.camp.data[\"foodQuantity\"] += int(num)","title":""},{"docid":"3f7d71c67883675ad8cf3a3be6f640a0","score":"0.575201","text":"def add(self, name, quantity):\n item = Item(name, quantity)\n if item in self.items:\n # we already have this, so add to it\n self.items[self.items.index(item)] += item\n else:\n self.items.append(item)","title":""},{"docid":"5d5bf92df7dcf5cd7b9da5461a553cbf","score":"0.5631031","text":"def condense_grocery_list(list_of_grocery_items):\n condensed_list=[]\n condensed_dict=dict()\n for grocery_item in list_of_grocery_items:\n dict_key=grocery_item.unit+\"-\"+grocery_item.name\n if dict_key in condensed_dict:\n condensed_dict[dict_key].number = str(float(condensed_dict[dict_key].number) + float(grocery_item.number))\n else:\n condensed_dict[dict_key]=grocery_item\n\n for item in condensed_dict:\n condensed_list.append(condensed_dict[item])\n\n condensed_list.sort(key=lambda x: x.name)\n condensed_dict={}\n for item in condensed_list:\n if item.name in condensed_dict:\n condensed_dict[item.name] = condensed_dict[item.name] + \", \" + item.number.strip() + \" \" + item.unit.strip()\n else:\n condensed_dict[item.name]=item.name + \": \" + item.number.strip() + \" \" + item.unit.strip()\n condensed_list=[]\n for item in condensed_dict:\n new_item=shopping_item()\n new_item.name=item\n new_item.grocery_list_line=condensed_dict[item]\n condensed_list.append(new_item)\n\n return condensed_list","title":""},{"docid":"615ee899602c1129b4b2df5f3f783058","score":"0.5625155","text":"def createMeal(jour, tyrep, itemsName, itemsDict, minItem, maxItem, meanQuantities):\n nItems = random.randint(minItem,maxItem)\n items = random.sample(itemsName, nItems)\n foodItems = [Food(itemsDict[x], x, meanQuantities[x]) for x in items]\n meal = Meal(foodItems, jour=jour, tyrep=tyrep)\n meal.reorder()\n return meal","title":""},{"docid":"ff3c0498b926385879c47b0567a49a81","score":"0.5587279","text":"def consume_food(self, food):\n quantity = self.__items['food'].get(food, 0)\n if quantity > 0:\n self.__items['food'][food] = quantity - 1","title":""},{"docid":"0139cce57cee1ffab79707a72f831baf","score":"0.55362856","text":"def add_to_inventory_v2(inventory, added_items):\n\n for item in added_items:\n if item not in inventory:\n my_inventory.setdefault(item, 1)\n else:\n my_inventory[item] += 1\n return my_inventory","title":""},{"docid":"7d1cce71848b886fcbf253344f3c75a8","score":"0.5494977","text":"def add_to_inventory(inventory, added_items):\n for key in list(inventory):\n for item in added_items:\n if key == item:\n inventory[key] += 1\n else:\n inventory.setdefault(item, 1)\n\n return my_inventory","title":""},{"docid":"21510822cd79307c80383341d84fa004","score":"0.53571904","text":"def add(self, item1, item2):\n for sumField in self.sumFields:\n item1[sumField] = item1[sumField] + item2[sumField]","title":""},{"docid":"ae3d284c0f2807c357fd971d08a6f7f8","score":"0.5330261","text":"def addDish(self,dish):\r\n\t\tself.items[dish.name] = dish\r\n\t\tfor nutrient in TARGET_NUTRIENTS:\r\n\t\t\tself.totals[nutrient]+=dish.nutrition[nutrient]","title":""},{"docid":"f1159106f3c256affb95ba2576b99a40","score":"0.5326817","text":"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\n pass","title":""},{"docid":"9fa86c34f680edfb331c7e63841be1d7","score":"0.5321849","text":"def update_world_totals(new_item):\n # TODO: Can definitely optimize this search if performance becomes an issue\n for item in world_inv:\n if new_item.equals(item):\n item.set_count(item.count + new_item.count)\n return\n\n if (new_item.damage != 0 and (new_item.full_name in degradeable_tools or new_item.full_name in degradeable_armor)):\n world_inv.append(Item(new_item.full_name, new_item.count, -1, new_item.lore))\n else:\n world_inv.append(Item(new_item.full_name, new_item.count, new_item.damage, new_item.lore))","title":""},{"docid":"90ff6a56e8a808fc9686f492e012e077","score":"0.52610236","text":"def feeding_animal(self):\n for food in self.get_food_set():\n for animal in self.get_animal_set():\n if (abs(animal.position[0] - food.position[0]) < Constants.ANIMAL_WIDTH / 2 + Constants.FOOD_WIDTH / 2 and \\\n abs(animal.position[1] - food.position[1]) < Constants.ANIMAL_HEIGHT / 2 + Constants.FOOD_HEIGHT / 2):\n self.animal_set.remove(animal)\n self.money += 1\n food.used = True\n if food.used:\n self.food_set.remove(food)","title":""},{"docid":"f300c9707bf682e3a6b1d5677eba5aa9","score":"0.52460706","text":"def execute_item_creation(self)->None:\n [ingedient.use_quantity(quantity) for ingedient,quantity in self.ingredient_quantity]","title":""},{"docid":"70b286ea3b1c733d10122c24d15eb782","score":"0.52455187","text":"def digest_fat(self):\n can_move = min(self.population - self.food, self.fat_storage)\n self.fat_storage -= can_move\n self.food += can_move","title":""},{"docid":"ae9736db106bb68027158dacec819627","score":"0.5245462","text":"def add_fruit(inventory, fruit, quantity=0):\n #print fruit, quantity\n inventory[fruit]=quantity\n #return inventory","title":""},{"docid":"bf0a041ec85a5bcb7a4fa271d76f533e","score":"0.5186193","text":"def add_item(self, item_name, item_price):\n if 'none' in self.items_with_price:\n self.items_with_price.clear()\n self.items_with_price.update({str(item_name): float(item_price)})","title":""},{"docid":"379782037ed753eb6be9b48c0ea5b3b7","score":"0.51835597","text":"def add_to_inventory(inventory, added_items):\n for i in added_items:\n if i in inventory.keys():\n inventory[i] += 1\n else:\n inventory[i] = 1\n return inventory","title":""},{"docid":"8fd6eb79281380c997d0ddf13a3b1137","score":"0.51719004","text":"def updateCart(self, replaceItem):\n\t\ttopFoods = self.dashFilter(replaceItem)\n\t\trestOfCart = [item for item in self.foods if item[\"id\"] != replaceItem]\n\t\t#topCombo = self.comboFilter(topFoods, restOfCart)\n\t\ttopCombo = self.randomFilter(topFoods)\n\t\tself.foods = [{\"id\":topCombo[\"id\"],\"name\":topCombo['name'],\"groups\":item['groups'],\"combos\":topCombo['combos']} if item[\"id\"] == replaceItem else item for item in self.foods]\n\t\treturn {'id':topCombo['id'],'name':topCombo['name'].upper(),'img':topCombo['id']+'.jpg'}","title":""},{"docid":"242d58e8f298df51464e9f6224d850aa","score":"0.51659745","text":"def addFood(surface) -> Food:\n\n newFood = Food(\n coord=[randint(0, screen_resolution[0]), randint(0, screen_resolution[1])],\n energy=2,\n surface=surface\n )\n foodHolder.append(newFood)\n\n return newFood","title":""},{"docid":"3c319e9376a5acfcd6692426a6106298","score":"0.51233023","text":"def test_bulk_inventory_quantity_combined(self):\n category = ['Clothing', 'Unisex Adult Clothing', 'Pants']\n\n expected_left_bulk_header = 'Individual Quantity'\n\n new_quantities = ['200', '300', '400', '500']\n\n expected_product_offerings = [\n ['1', '500.00', '550', '200', 't', 'L', '1', 'Black', '1'],\n ['1', '500.00', '550', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['1', '500.00', '550', '400', 't', 'M', '2', 'Black', '1'],\n ['1', '500.00', '550', '500', 't', 'M', '2', 'Pinkish', '2'],\n ['2', '1.00', '111', '200', 't', 'L', '1', 'Black', '1'],\n ['2', '1.00', '111', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['2', '1.00', '111', '400', 't', 'M', '2', 'Black', '1'],\n ['2', '1.00', '111', '500', 't', 'M', '2', 'Pinkish', '2'],\n ['3', '10.00', '222', '200', 't', 'L', '1', 'Black', '1'],\n ['3', '10.00', '222', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['3', '10.00', '222', '400', 't', 'M', '2', 'Black', '1'],\n ['3', '10.00', '222', '500', 't', 'M', '2', 'Pinkish', '2']\n ]\n\n expected_variation_properties = [\n ['1', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['2', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['3', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\n ]\n\n expected_variation_options = [\n ['1', 't', '2119', 'L', '1'],\n ['1', 't', '2116', 'M', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '', 'Pinkish', '2'],\n ['2', 't', '2119', 'L', '1'],\n ['2', 't', '2116', 'M', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '', 'Pinkish', '2'],\n ['3', 't', '2119', 'L', '1'],\n ['3', 't', '2116', 'M', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '', 'Pinkish', '2']\n ]\n\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\n\n # Select category in bulk edit area\n bpiv.select_category(category)\n\n bulk_row = bpiv.bulk_edit_row\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\n\n # Set first variation property\n bpiv.set_property(bulk_row, 0, 'Size', \"Men's\")\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 0, 'L', displayed_name=\"L Men's\")\n bpiv.add_option(bulk_row, 0, 'M', displayed_name=\"M Men's\")\n\n # Set second variation property\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 1, 'Black')\n bpiv.add_custom_option(bulk_row, 1, 'Pinkish')\n\n bpiv.select_inventory_tab(bulk_row, \"Quantity\")\n\n # Switch to individual quantities, check the header is changed\n click(bpiv.bulk_header_checkbox(bulk_row, i=0))\n click(bpiv.bulk_header_checkbox(bulk_row, i=1), delay=0.5)\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\n\n # Enter individual quantities for the property combinations\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=0)\n for i, quantity in enumerate(new_quantities):\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\n\n # Check that only titles are in listing preview as preview is disabled\n check_simplified_preview(bpiv)\n\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\n\n # Apply\n assert bpiv.operation_apply().is_enabled() is True\n click(bpiv.operation_apply())\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\n 'Sync button is not enabled')\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\'t show up'\n\n # Check bulk edit area texts and that Apply button is disabled\n check_bulk_properties_disabled(bpiv)\n\n # Sync updates\n click(bpiv.sync_updates_button())\n\n # Check data in DB\n check_db_state(expected_product_offerings, expected_variation_properties, expected_variation_options)","title":""},{"docid":"8ea81c57ff2db743346ab37434f24574","score":"0.51203","text":"def calculateAmount(self):\n return sum([item.price for item in self.items])","title":""},{"docid":"e5115be39649d3f3029e91969f33dab0","score":"0.5108897","text":"def add_to_inventory(player_inventory, added_items):\n # For each new piece of loot...\n for new_loot_piece in added_items:\n if new_loot_piece in player_inventory:\n # if the loot already existed in inventory, increment it's count\n player_inventory[new_loot_piece] += 1\n else:\n # If the loot was not already in the inventory, create a new piece of loot\n # in the inventory and se it's default value to 1.\n player_inventory.setdefault(new_loot_piece, 1)\n\n return player_inventory","title":""},{"docid":"5e4b677c435c587a9cfddc38115a3c4f","score":"0.5098413","text":"def test_bulk_inventory_single_quantity_individual(self, property_number):\n category = ['Home & Living', 'Furniture']\n\n expected_left_bulk_header = 'Individual Quantity'\n\n new_quantities = ['22', '33']\n\n expected_product_offerings = [\n [\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'White', '2'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'White', '2'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'White', '2'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\n ],\n [\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'wool', '1', 'White', '2'],\n ['1', '500.00', '550', '22', 't', 'cotton', '2', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'wool', '1', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'cotton', '2', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'wool', '1', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'cotton', '2', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\n ]\n ]\n\n expected_variation_properties = [\n [\n ['1', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\n ['2', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\n ['3', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f']\n ],\n [\n ['1', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['2', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['3', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\n ]\n ]\n\n expected_variation_options = [\n [\n ['1', 't', '', 'wool', '1'],\n ['1', 't', '', 'cotton', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '10', 'White', '2'],\n ['2', 't', '', 'wool', '1'],\n ['2', 't', '', 'cotton', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '10', 'White', '2'],\n ['3', 't', '', 'wool', '1'],\n ['3', 't', '', 'cotton', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '10', 'White', '2']\n ],\n [\n ['1', 't', '', 'wool', '1'],\n ['1', 't', '', 'cotton', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '10', 'White', '2'],\n ['2', 't', '', 'wool', '1'],\n ['2', 't', '', 'cotton', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '10', 'White', '2'],\n ['3', 't', '', 'wool', '1'],\n ['3', 't', '', 'cotton', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '10', 'White', '2']\n ]\n ]\n\n\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\n\n # Select category in bulk edit area\n bpiv.select_category(category)\n\n bulk_row = bpiv.bulk_edit_row\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\n\n # Set first variation property\n bpiv.set_property(bulk_row, 0, 'Fabric')\n # Add two options to first property, one by one\n bpiv.add_custom_option(bulk_row, 0, 'wool')\n bpiv.add_custom_option(bulk_row, 0, 'cotton')\n\n # Set second variation property\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 1, 'Black')\n bpiv.add_option(bulk_row, 1, 'White')\n\n bpiv.select_inventory_tab(bulk_row, \"Quantity\")\n\n # Switch to individual quantities, check the header is changed\n click(bpiv.bulk_header_checkbox(bulk_row, i=property_number), delay=0.5)\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\n\n # Enter individual quantities\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=property_number)\n for i, quantity in enumerate(new_quantities):\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\n\n # Check that only titles are in listing preview as preview is disabled\n check_simplified_preview(bpiv)\n\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\n\n # Apply\n assert bpiv.operation_apply().is_enabled() is True\n click(bpiv.operation_apply())\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\n 'Sync button is not enabled')\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\'t show up'\n\n # Check bulk edit area texts and that Apply button is disabled\n check_bulk_properties_disabled(bpiv)\n\n # Sync updates\n click(bpiv.sync_updates_button())\n\n # Check data in DB\n check_db_state(expected_product_offerings[property_number],\n expected_variation_properties[property_number],\n expected_variation_options[property_number])","title":""},{"docid":"ddde5fc2882d117d910fb6854f8c507f","score":"0.50927","text":"def concatenate_items(self) -> dict:\n test = []\n for text in self.receipt_text:\n if self.text_is_number(text) | self.remove_pound_symbol(text) | self.change_o_to_zero(text):\n text = text[1:]\n else:\n test.append(text)\n\n if self.text_is_number(text):\n if test[-1] in self.dict_to_return:\n self.dict_to_return[test[-1]]['quantity'] += 1\n else:\n self.dict_to_return[test[-1]] = {'name': text, 'quantity': 1}\n test = []\n\n return self.dict_to_return","title":""},{"docid":"28e1c26cb444d691a6d757c89aed7d2b","score":"0.5092597","text":"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\n self.ingredient_quantity.append((ingredient,quantity))","title":""},{"docid":"0a68a1b31438ff6224ac04a9bb4f05d1","score":"0.50659823","text":"def scan_onto_shelf(self):\n if hasattr(self, \"shelf\"):\n items_added = []\n for item in self.items_stored:\n for product in self.shelf.compartments:\n if product[\"Product Number\"] == item.number:\n if product[\"Items Stored\"] is None:\n product[\"Items Stored\"] = []\n product[\"Items Stored\"].append(item)\n product[\"Quantity\"] += 1\n items_added.append(item)\n Item.sorted_items.append(item)\n Item.shipment.remove(item)\n for item in items_added:\n self.items_stored.remove(item)","title":""},{"docid":"484270c6a6c051655cc77f321d243802","score":"0.50581247","text":"def add_to_inventory(inventory, added_items):\n if added_items in inventory:\n inventory[added_items] += 1\n else:\n inventory[element] = 1","title":""},{"docid":"a4d21353320ad10efa03007bf3481510","score":"0.50539666","text":"def add(self, product, quantity):\n product_id = product.id\n if product_id not in self.basket:\n self.basket[product_id] = {\"price\": float(product.regular_price), \"quantity\": quantity}\n else:\n self.basket[product_id][\"quantity\"] = quantity\n self.save()","title":""},{"docid":"35cf195a68cb0eebf880a0671ed3bb9d","score":"0.50376934","text":"def _add_dict_inventory(self, d, item_pouch): # item_pouch used to find which dictionary to look in\r\n for group in d:\r\n if group in item_pouch:\r\n if item_pouch[group] is None:\r\n item_pouch[group] = d[group]\r\n elif isinstance(item_pouch[group], list):\r\n item_pouch[group].extend(d[group])\r\n elif isinstance(item_pouch[group], dict):\r\n self._add_dict_inventory(d[group], item_pouch[group])\r\n elif isinstance(item_pouch[group], int):\r\n item_pouch[group] = item_pouch.setdefault(group, 0)+d[group]\r\n else:\r\n item_pouch[group] = d[group]","title":""},{"docid":"3ca2e8c63e53f9ab69061677cf963375","score":"0.50190413","text":"def test_adding_item_to_order_model_is_add_one_to_quantity(self):\n cart_item_1 = CartItem.objects.create(order=self.order, item=self.item_1)\n cart_item_1.Add() # quantity = 1 ** payout = self.item_1.price * 1\n cart_item_1.Add() # quantity = 2 ** payout = self.item_1.price * 2\n\n self.assertEqual(CartItem.objects.get(order=self.order, item=self.item_1).quantity, 2)\n self.assertEqual(cart_item_1.total_price, self.item_1.price * 2)\n self.assertEqual(self.order.payout, self.item_1.price * 2)","title":""},{"docid":"f019318a90a39d1d4baa58c66b55a7e8","score":"0.50127226","text":"def uniq_add(my_list=[]):\n\n return sum(set(my_list))","title":""},{"docid":"85016ea7f6bcc15054e712a84e7aaa66","score":"0.5005258","text":"def create_order_item(self, food_item, quantity):\n OrderItem.objects.create(\n user=self.customer,\n food_item=food_item,\n quantity=quantity,\n )","title":""},{"docid":"3f95d8bb1a0a822a0a89287235821bd2","score":"0.50050193","text":"def calculateAmount(self):\n summa = 0\n for item in self.items:\n summa += item.price\n return summa","title":""},{"docid":"0a975cec92256ad6ac4119086b89bcaf","score":"0.50043887","text":"def add(self, product, quantity=None, update_quantity=False):\n if product.price_with_taxes > 0 and quantity != \"0\":\n self.cart[str(product.id)] = {\n 'user_id': self.request.user.id,\n 'article_id': str(product.id),\n 'name': product.name,\n 'quantity': quantity,\n 'price_type': product.price_type,\n 'price_with_taxes': str(product.price_with_taxes),\n 'article_code': product.article_code,\n }\n self.save()","title":""},{"docid":"e946d19a98dd4708d5ea172fdab839a3","score":"0.49940917","text":"def fortune(player):\n random.seed()\n \n # (inventory item, amount received)\n treasures = [\n ('food',random.randint(100,500)),\n ('money',random.randint(200,500)),\n ('bullets',random.randint(30,100)),\n ('kits',random.randint(2,3)),\n ('parts',random.randint(2,3)),\n ]\n \n # Determine random item.\n found = random.choice(treasures)\n \n # Handle special message for food.\n if found[0] == 'food':\n current_food = player.get_from_inventory('food')\n # Handle food inventory limit.\n if current_food + found[1] > 1000:\n added_food = 1000 - current_food\n print(\"You found {} pounds of food in an abandoned wagon\".format(found[1]))\n print(\"You can only add {} pounds to your wagon\".format(added_food))\n player.update_inventory('food',1000)\n else:\n print(\"You found {} pounds of food in an abandoned wagon\".format(found[1]))\n player.add_to_inventory('food',found[1])\n \n # Handle special message for money.\n elif found[0] == 'money':\n print(\"You found ${} in an abandoned wagon\".format(\"%.2f\" %found[1]))\n player.add_to_inventory('money',found[1])\n \n # Handle all other items.\n else:\n print(\"You found {} {} in an abandoned wagon\".format(found[1],found[0]))\n player.add_to_inventory(found[0],found[1])","title":""},{"docid":"1e056e6e443126ef305397e1edf9f00e","score":"0.49848992","text":"def calc_items_total(cls, items):\n shipping_charge = 0\n subtotal = 0\n ad_charge_amt = 0\n ad_charge_breakup = {}\n\n for item in items:\n shipping_charge += item.shipping_charge\n subtotal += item.subtotal\n ad_charge_amt += item.additional_charge\n\n for charge in item.cost_breakup['additional_charge']:\n k = slugify(charge.get('name')).replace('-', '_')\n v = charge.get('amt')\n # Initialize value\n if ad_charge_breakup.get(k, None) is None:\n ad_charge_breakup[k] = 0.0\n ad_charge_breakup[k] += v\n\n return {\n 'shipping_charge': helper.round_off(shipping_charge),\n 'subtotal': helper.round_off(subtotal),\n 'additional_charge': helper.round_off(ad_charge_amt),\n 'cost_breakup': {\n 'additional_charge': ad_charge_breakup\n }\n }","title":""},{"docid":"5ba05c3eea1691cbb2cf7dd27f2811a3","score":"0.49668226","text":"def add(self, comida, quantity=1, update_quantity=False):\n product_id = str(comida.id)\n if product_id not in self.cart:\n self.cart[product_id] = {'quantity': 0,\n 'precio': str(comida.precio)}\n if update_quantity:\n self.cart[product_id]['quantity'] = quantity\n else:\n self.cart[product_id]['quantity'] += quantity\n self.save()","title":""},{"docid":"2a55875101d7602488fcb4f6054d62ad","score":"0.49663317","text":"def initializeFoods(self):\n for i in range(0, self.numFoods):\n self.addFood()","title":""},{"docid":"9da1b12f109a9a6aef2dab62544176d0","score":"0.49556127","text":"def add(self, product, qty):\n product_id = str(product.id)\n\n if product_id in self.basket:\n self.basket[product_id][\"qty\"] = qty\n else:\n self.basket[product_id] = {\"price\": str(product.regular_price), \"qty\": qty}\n\n self.save()","title":""},{"docid":"ffc752d2ca3326fdbd563d1bbeac4d05","score":"0.49489328","text":"def add(self, product, quantity=1, update_quantity=False):\n product_id = str(product.id) # converted to string because JSON use only string data\n if product_id not in self.cart:\n self.cart[product_id] = {'quantity': 0,\n 'price': str(product.price)\n }\n if update_quantity:\n self.cart[product_id]['quantity'] = quantity\n else:\n self.cart[product_id]['quantity'] += quantity # increment the quantity\n self.save() # saving the state of the quantity after update","title":""},{"docid":"3e122449c4307f51ed5cb01667c9a9d1","score":"0.49486324","text":"def eatFood(self):\n # if a prey is in contact with a food, then eat the food by removing the food and increment hunger.\n objIDInContactList = super().getContactObjects()\n # if there are objects in contact with this prey, then determine if they are food and if so eat them!\n foodEaten = 0\n if objIDInContactList:\n for objID in objIDInContactList:\n food = hsm.objIDToObject[objID]\n if food in hsm.foodList:\n hsm.foodList.remove(food)\n hsm.destroy(objID)\n foodEaten += 1\n # incrememnt hunger accordingly\n self.hunger += mv.PREY_INCREMENT_HUNGER \n # throw away excess, prey can only eat so much food at a time.\n if self.hunger > mv.PREY_MAX_HUNGER:\n self.hunger = mv.PREY_MAX_HUNGER\n if foodEaten > 0:\n self.foodTimeStamps.append([hsm.frameCount, foodEaten])","title":""},{"docid":"892adb19f630ba769f49adfe54589f4c","score":"0.4945769","text":"def add_item(self, x):\n self._items_with_price.update(x)","title":""},{"docid":"8b3b75d718473bad062e7134b4e43dd2","score":"0.49438444","text":"def add_item_to_cart(self, product_id, quantity):\n product = Product.objects.filter(product_id=product_id, store=self.customer_id.store_id, deleted_at=None).first()\n if product is None or product.inventory < quantity:\n raise ProductNotFoundException\n\n product.inventory -= quantity\n product.save()\n\n existing_cart_item = CartItem.objects.filter(cart_id=self.cart_id, product_id=product.product_id\n , removed_at=None).first()\n\n self.customer_id.last_cart_activity_at = datetime.now()\n self.customer_id.updated_at = datetime.now()\n self.customer_id.save()\n if existing_cart_item:\n existing_cart_item.quantity += quantity\n existing_cart_item.save()\n return existing_cart_item\n else:\n cart_item = CartItem(\n cart_item_id=str(uuid.uuid4().int),\n product_id=product.product_id,\n cart_id=self.cart_id,\n quantity=quantity\n ).save()\n return cart_item","title":""},{"docid":"106331337e2a2c1d307ba926e7184437","score":"0.49325597","text":"def calculate_total_nutrition_fulldayofeating2(\n specificingredient2_dict_list,\n all_nutrients_and_default_units,\n set_to_zero_if_none,\n):\n\n # Initialize the dictionary which will store the results\n result_total_nutrition_fulldayofeating = {}\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n new_dict = {nutrient_name: 0}\n result_total_nutrition_fulldayofeating.update(\n new_dict\n )\n\n # Go through all the SpecificIngredients related to the FullDayOfEating.\n # For each SpecificIngredient, calculate its Nutrition by\n # multiplying the calculated_amount divided by the reference amount\n # multiplied by each nutrient value in the associated RawIngredient.\n\n # Sum up the total nutrition\n for k in range(len(specificingredient2_dict_list)):\n # For each nutrient, calculate the amount of that nutrient contained\n # in the calculated_amount of the SpecificIngredient\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n result_total_nutrition_fulldayofeating[nutrient_name] = \\\n result_total_nutrition_fulldayofeating[nutrient_name] \\\n + set_to_zero_if_none(specificingredient2_dict_list[k][\n 'raw_ingredient'][nutrient_name]) \\\n * float(specificingredient2_dict_list[k]['calculated_amount'])\\\n / float(specificingredient2_dict_list[k][\n 'raw_ingredient']['reference_amount'])\n\n # Round the values in the result_total_nutrition_fulldayofeating\n # Initialize the dictionary containing the rounded values.\n result_total_nutrition_fulldayofeating_rounded = {}\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n new_dict = {nutrient_name: 0}\n result_total_nutrition_fulldayofeating_rounded.update(\n new_dict\n )\n\n # Round the values and save them to the respective dictionary.\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n result_total_nutrition_fulldayofeating_rounded[nutrient_name] = \\\n round(result_total_nutrition_fulldayofeating[nutrient_name], 1)\n\n return result_total_nutrition_fulldayofeating,\\\n result_total_nutrition_fulldayofeating_rounded","title":""},{"docid":"6e950cf94ff01f7be16cc47e54da41ba","score":"0.4931627","text":"def _update_total_nutrition_values(self, unique_id, date, plan, add):\n table = self.dynamodb.Table(c.TABLE_NUTRITIONAL_NEEDS_DAY)\n week = form.get_week_by_date(date)\n\n item_nutrients_for_week = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.WEEK: week\n },\n ProjectionExpression='#toplevel',\n ExpressionAttributeNames={\"#toplevel\": c.NUTRIENTS_FOR_WEEK}\n )\n\n item_nutrients_for_day = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date\n },\n ProjectionExpression='#toplevel',\n ExpressionAttributeNames={\"#toplevel\": c.NUTRIENTS_FOR_DAY}\n )\n\n for container, container_content in plan.iteritems():\n item_nutrients_for_container = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id, c.DATE: date},\n ProjectionExpression='#toplevel.#container',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_CONTAINER,\n \"#container\": container\n }\n )\n for meal_key, meal_content in container_content.iteritems():\n\n\n\n for n in params.nutrientList:\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\n (1 if add else -1)\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\n (1 if add else -1)\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['VAL'] += \\\n meal_content['nutrients'][n]['VAL'] * (1 if add else -1)\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['UNIT'] = \\\n meal_content['nutrients'][n]['UNIT']\n\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date},\n UpdateExpression='SET #toplevel.#cat = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_CONTAINER,\n \"#cat\": container\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container]\n }\n )\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date},\n UpdateExpression='SET #toplevel = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_DAY\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY]\n }\n )\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.WEEK: week\n },\n UpdateExpression='SET #toplevel = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_WEEK\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK]\n }\n )","title":""},{"docid":"c7dfb948b8301a0430651d0b5bb487bb","score":"0.49241003","text":"def player_eats_food(self, snake, food):\n head = snake[0]\n head_x, head_y = head\n for food_item in food:\n food_x, food_y = food_item\n #Return whether or not the player's received food, adds fat if they have\n if head_x == food_x and head_y == food_y:\n #We've eaten food, update our score\n self.score += int(150 + int(len(snake)/10)*5)\n\n #Add more fat to the user if they already have some or they have a decent score\n self.fat += 8 + int(self.fat/4) + int(self.score/500)\n return True\n return False","title":""},{"docid":"7e5cd209b264fb2dd585ad69bbc06220","score":"0.49199784","text":"def createConsumptionSeq(df):\n mealList = []\n M = {} #Contient les aliments d'un repas \n nomen = df.nomen.iloc[0]\n jour = df.jour.iloc[0]\n tyrep = df.tyrep.iloc[0]\n \n for index, row in df.iterrows(): # Scan tous les aliments de consommation\n #print(row.nomen, row.jour, row.tyrep)\n if (row.nomen == nomen) & (row.jour == jour) & (row.tyrep == tyrep):\n name = str(row.nomen) + '_' + str(index)\n M[name] = Food(row.codsougr, row.codsougr_name, row.qte_nette) # Crée l'objet FoodItem\n else:\n m = list(set(M.values())) #Set of food items in a meal\n uniqueM = eraseDubloons(m)\n meal = Meal(uniqueM, jour = jour, tyrep = tyrep)\n meal.reorder()\n logging.debug('{}'.format(meal.nameTuple))\n if meal:\n mealList.append(meal)\n\n M = {}\n nomen = row.nomen\n jour = row.jour\n tyrep = row.tyrep\n \n meals = [m for m in mealList if m]\n return meals","title":""},{"docid":"c830b9fef7a64c1f072cecdf121412c0","score":"0.4918213","text":"def create_dico(item_list):\n assert type(item_list) is list\n dico = {}\n for items in item_list:\n for item in items:\n if item not in dico:\n dico[item] = 1\n else:\n dico[item] += 1\n return dico","title":""},{"docid":"c830b9fef7a64c1f072cecdf121412c0","score":"0.4918213","text":"def create_dico(item_list):\n assert type(item_list) is list\n dico = {}\n for items in item_list:\n for item in items:\n if item not in dico:\n dico[item] = 1\n else:\n dico[item] += 1\n return dico","title":""},{"docid":"6ddaa4351c2892173251704a2ad04d77","score":"0.49181578","text":"def sum_list(new_list):\n return sum(new_list)\n # return None","title":""},{"docid":"9ee3ad439b54ccc0e9043295f07ea1cd","score":"0.49134657","text":"def saved_meal_item_add(\n self, meal_id, food_id, food_entry_name, serving_id, num_units\n ):\n params = {\n \"method\": \"saved_meal_item.add\",\n \"format\": \"json\",\n \"saved_meal_id\": meal_id,\n \"food_id\": food_id,\n \"food_entry_name\": food_entry_name,\n \"serving_id\": serving_id,\n \"number_of_units\": num_units,\n }\n\n response = self.session.get(self.api_url, params)\n return self.valid_response(response)","title":""},{"docid":"a46b885d595c7bea38ce23fc1d6daa4d","score":"0.4910791","text":"def calculateInventory(self):\n from collections import Counter\n orders = self.model[0]\n shipping = self.model[1]\n inventory = {key:dict() for key in shipping}\n citys = {o.dest for o in orders}\n months = {o.orderTime.month for o in orders}\n products = {o.pro for o in orders}\n #print shipping\n for pro in products:\n for city in citys:\n inventory[pro][city] = Counter()\n for o in orders:\n o.ship = self.chooseBestShipping(o,citys)\n\n inventory[o.pro][o.ship][o.orderTime.month] += o.num\n #self.view.showInventory(inventory)\n self.inventory = inventory\n self.citys = citys\n self.months = months\n self.products = products\n return inventory","title":""},{"docid":"dcbca7ea38a938ec591a33b34ace0ede","score":"0.49077475","text":"def save(self, *args, **kwargs):\n if self.pk is None:\n item = self.item\n item.quantity += self.quantity\n item.save()\n return super(Entry, self).save(*args, **kwargs)","title":""},{"docid":"cbf4880010aa81907064f104b9d60f03","score":"0.49064472","text":"def subtotal(self):\n sub = 0.0\n for i in self.items:\n sub += i.quantity * INVENTORY[i.name]\n return sub","title":""},{"docid":"442dae3041bd85957abe5451209e318b","score":"0.48951203","text":"def _initilizeItemInventory(self):\n for itemName in itemPrice.keys():\n for j in range(5):\n self._itemInventory.add(itemName)","title":""},{"docid":"2bf1960258a00848bdd160a6a903b421","score":"0.48922408","text":"def find_food(self):\n \n if self.check_knife(1):\n self.knife_dur -= 1\n self.food += 1","title":""},{"docid":"4280fe8c4dcd8681a0e6d972b181946f","score":"0.48690817","text":"def createFoodDict(food_item_list):\n food_dict = {}\n for food_item in food_item_list:\n words = food_item.split(' ')\n first_word = words[0]\n food_dict[first_word] = food_item\n return food_dict","title":""},{"docid":"31e19cfc7f9cc7ac082b4629f8df71cb","score":"0.48465908","text":"def scan_onto_cart(self):\n if hasattr(self, \"shelf\"):\n for item in Item.shipment:\n if len(self.items_stored) < self.max_capacity:\n for product in self.shelf.compartments:\n if item.number == product[\"Product Number\"]:\n self.items_stored.append(item)","title":""},{"docid":"bb607bc5bfc19f80144df53d7174c4b2","score":"0.4835511","text":"def returnfrequentitemsets(itemSet, transactionList, minSupport, freqSet):\n\t\t_itemSet = set()\n\t\tlocalSet = defaultdict(int)\n\n\t\tfor item in itemSet:\n\t\t\tfor transaction in transactionList:\n\t\t\t\tif item.issubset(transaction):\n\t\t\t\t\tfreqSet[item] += 1\n\t\t\t\t\tlocalSet[item] += 1\n\n\t\tfor item, count in localSet.items():\n\t\t\t\tsupport = float(count)/len(transactionList)\n\n\t\t\t\tif support >= minSupport:\n\t\t\t\t\t\t_itemSet.add(item)\n\n\t\treturn _itemSet","title":""},{"docid":"f64d95b9e2dbd365c3e3c875f63c4ec1","score":"0.48311788","text":"def add_item(self, item):\n self.items_with_price.update(item)","title":""},{"docid":"b1dea7e98dd8eb0a30e38b7120376a69","score":"0.4829422","text":"def process_item(self, item, spider):\n # looks for a duplicate\n duplicate_check = self.db[self.collection_name].find({'name': item['name'], 'store': item['store'], 'category': item['category'], 'animal': item['animal']}).count()\n if duplicate_check == 0:\n self.db[self.collection_name].insert(dict(item))\n logging.debug('Product added to MongoDB')\n return item\n else:\n # logging.debug('Duplicate found. Skipping product')\n raise DropItem('Duplicate found. Skipping product: %s' % item['name'])\n # return item","title":""},{"docid":"616c81c4094a7fd6dab55793794e5f42","score":"0.48247102","text":"def generate_food(self):\n num_food = ENV_N_FOOD\n\n pos = [(random() * self.W, random() * self.H) for i in range(num_food)]\n\n return [Food(x, y, self.W, self.H) for (x, y) in pos]","title":""},{"docid":"0bdff644a55ea47546734621b9954d60","score":"0.4823412","text":"def budget(data,productsIds, quantity):\r\n \r\n data=data.fillna(0)\r\n budg=0\r\n products=productsIds\r\n quantity=quantity\r\n \r\n dataFilter = data[data['No. Item'].isin(products)]\r\n prices=dataFilter['Precio Unitario'].tolist()\r\n \r\n for i in range(len(quantity)):\r\n budg+=quantity[i]*prices[i]\r\n return budg","title":""},{"docid":"b9b6f3b617d4f2917091b725f1b1b61a","score":"0.48086756","text":"def get_total(self):\n\n base_price = 5\n if self.species == \"Christmas melon\":\n base_price = base_price * 1.5\n\n total = (1 + self.tax) * self.qty * base_price\n \n # if qty is less than ten melons and international, then +$3 to international_order\n if self.qty < 10 and self.order_type == 'international':\n total = total + 3\n\n return total","title":""},{"docid":"a15150d7171093c8a2769cd73df5a8ab","score":"0.4804091","text":"def fruits(fruit_list, fruit):\n\n updated_fruits_list = []\n for item in fruit_list:\n updated_fruits_list.append(item)\n updated_fruits_list.append(fruit)\n\n return updated_fruits_list","title":""},{"docid":"17ff7340eee41c80ad913f6610332c1b","score":"0.47999802","text":"def add_to_cart(self, selection_id, selection_qty):\n # read the data from data storage\n for keys, values in ShoppingCart.__ecommerce_data.items():\n # check if the product contain necessary data before proceed\n if \"products\" and \"sku\" and \"prices\" and \"stock\" in values:\n # check if it is a correct sku code from the data storage\n if selection_id in values[\"sku\"]:\n # add product information to the instance variables\n self.__seller_id.append(keys)\n product_id = values[\"sku\"].index(selection_id)\n self.__seller.append(values[\"first_name\"] + \" \" + values[\"last_name\"])\n self.__sku.append(values[\"sku\"][product_id])\n self.__price.append(values[\"prices\"][product_id])\n self.__product_name.append(values[\"products\"][product_id])\n self.__pos.append(product_id)\n self.__check_out_qty.append(selection_qty)","title":""},{"docid":"c1ac6afebd277ad77e422efa246f2ad9","score":"0.47989768","text":"def addItem(self, itemId, itemQty):\r\n self.items[itemId] += itemQty\r\n return self","title":""},{"docid":"d10ce6669d3c4fce854bab14bf47b1b7","score":"0.47970992","text":"def add_item(self, item):\n LOG.info('Add Product')\n LOG.info(\n \"Add the item: %s , price: %s, quantity: %s\"\n , item.get_name(), item.get_price(), item.get_quantity())\n if not item.get_name() in self.store_stock:\n self.db_manager.insert_element(item)\n else:\n current_item_quantity = \\\n self.store_stock[item.get_name()].get_quantity() \\\n + item.get_quantity()\n self.db_manager.update_quantity_field(quantity=current_item_quantity,\n id=self.store_stock[item.get_name()].get_id())\n LOG.info(\"Complete to add a item successful\")","title":""},{"docid":"1f5be3b43a6445bef2e20fac0d8943eb","score":"0.4792217","text":"def combine_the_anon_cart(self, request):\n cart = request.session.get('cart')\n if cart:\n for item in cart.items():\n product = Product.objects.get(id=item[0])\n if product.user == request.user:\n messages.error(request, 'You can\\'t buy your product')\n else:\n quantity = item[1]['quantity']\n self.add(product, quantity)\n del request.session['cart']","title":""},{"docid":"5f77055a0765db17ee93d22d7bb315d6","score":"0.47913775","text":"def total_items_sold(self, given_date):\n # ensure given_date is a date\n assert isinstance(given_date, date), \"given_date must be datetime.date()\"\n\n # get quantity from order_lines for given_date\n quantities = [\n i[0]\n for i in cur.execute(\"\"\"\n SELECT quantity\n FROM orders\n INNER JOIN order_lines ON orders.id=order_lines.order_id\n WHERE created_at LIKE ?\n \"\"\", (given_date.isoformat()+'%',))\n ]\n\n # sum quantity\n return sum(quantities)","title":""},{"docid":"2a07d888277c16437ea1ed144e02e719","score":"0.47881973","text":"def oh_quantity_by_location(self):\n location_list = self.transactions.all().values(\"location\").distinct().order_by(\"location\")\n my_dict = dict()\n for l in location_list:\n location = Location.objects.get(pk=l[\"location\"])\n my_dict[location] = self.get_oh_quantity(location)\n return my_dict","title":""},{"docid":"b7a2e2d6e3ad23e24d00b97541db3187","score":"0.47833753","text":"def returnItemsWithMinSupport(itemSet, transactionList, minSupport, freqSet):\n _itemSet = set()\n localSet = defaultdict(int)\n\n for item in itemSet:\n for transaction in transactionList:\n if item.issubset(transaction):\n freqSet[item] += 1\n localSet[item] += 1\n\n # print 'localSet',localSet\n for item, count in localSet.items():\n # print item,count\n support = float(count)/len(transactionList)\n\n if support >= minSupport:\n _itemSet.add(item)\n\n return _itemSet","title":""},{"docid":"8e7d505ef1815ef26d24544b025773ca","score":"0.47816607","text":"def main():\r\n group_of_list = []\r\n group = {}\r\n count = 0\r\n dict_input_food_and_price = input()\r\n dict_input_food_and_price = json.loads(dict_input_food_and_price)\r\n while True:\r\n yourname = input()\r\n if yourname == \"End\":\r\n break\r\n group[yourname] = 0\r\n for food, price in dict_input_food_and_price.items():\r\n while True:\r\n yourname = input()\r\n if yourname == \"End\":\r\n break\r\n group_of_list.append(yourname)\r\n count += 1\r\n total = price/count\r\n for food in group_of_list:\r\n group[food] = group.get(food)+total\r\n count = 0\r\n group_of_list = []\r\n print(group)","title":""},{"docid":"57fee8f9f7f940bfc0f162909b7ee2b2","score":"0.47804844","text":"def reducer(L):\n\tdistribution = {}\n\tfor item in L:\n\t\tfor record in item:\n\t\t\tif record in distribution:\n\t\t\t\tdistribution[record] += item[record]\n\t\t\telse:\n\t\t\t\tdistribution[record] = item[record]\n\t\titem.clear()\n\treturn distribution","title":""},{"docid":"7dbbe3574385be484fb2e72fb6016bb1","score":"0.47785863","text":"def add_item(self, new_item):\n self.items_with_price.update(new_item)","title":""},{"docid":"301be53ed8845a4768b33a75ed332e78","score":"0.4775439","text":"def getFood( self, amount ) :\n\t\treturn self.getSupply( amount )","title":""},{"docid":"6e98031becdd3b82c96afc040c16348c","score":"0.47707295","text":"def get_total(self, **kwargs):\n return self.get_price_per_item(**kwargs) * self.get_quantity(**kwargs)","title":""},{"docid":"99bf9dc41926c19f7c1798b9a962799d","score":"0.47650927","text":"def cardlist_sum(cardlist1, cardlist2):\n cards = deepcopy(cardlist1)\n for name, amount in cardlist2.items():\n if name not in cards:\n cards[name] = 0\n cards[name] = cards[name] + amount\n return cards","title":""},{"docid":"af3e40827b1596cd982bb10623718ab9","score":"0.47612268","text":"def food_add_favorite(self, food_id, serving_id=None, number_of_units=None):\n\n params = {\"method\": \"food.add_favorite\", \"format\": \"json\", \"food_id\": food_id}\n\n if serving_id and number_of_units:\n params[\"serving_id\"] = serving_id\n params[\"number_of_units\"] = number_of_units\n\n response = self.session.get(self.api_url, params=params)\n return self.valid_response(response)","title":""},{"docid":"46d8b637023ae9e022ee3a1e96d883cd","score":"0.47490332","text":"def food_insert(self, name, calories, protein, fat, carbs, fiber):\n pass","title":""},{"docid":"7d8c0504c732838f7986103ecf4d9fb0","score":"0.47435322","text":"def add(self, product, quantity=1, update_quantity=False):\n product_vencode = str(product.vencode)\n if product_vencode not in self.cart:\n self.cart[product_vencode] = {'quantity': 0,\n 'price': str(product.price)}\n if update_quantity:\n self.cart[product_vencode]['quantity'] = quantity\n else:\n self.cart[product_vencode]['quantity'] += quantity\n self.save()","title":""},{"docid":"abd12d614dd01fa7d1dc8d540d13a970","score":"0.4740142","text":"def add(self, item):","title":""},{"docid":"96d3c09b955b186c27327828c5c875b7","score":"0.47401217","text":"def test_unique_quantities(self):\n collection = Collection(['air__temperature', 'water__temperature'])\n\n quantities = collection.quantities()\n\n self.assertEqual(len(quantities), 1)\n self.assertTrue('temperature' in quantities)","title":""},{"docid":"3f0904ee6421b4e1fba7c11b177be256","score":"0.47290277","text":"def get_sum(items=[]):\n\t\tresult = 0\n\t\tif items:\n\t\t\tresult = reduce(lambda x, y: x+y, items)\n\t\treturn result","title":""},{"docid":"baa64214b750820c4bb4b85c71e8520d","score":"0.47211656","text":"def _amount_all(self):\n for order in self:\n amount_untaxed = 0.0\n for line in order.depence:\n amount_untaxed += line.montant\n\n order.update({\n\n 'Montant_total': amount_untaxed,\n })","title":""},{"docid":"105d02d685d7278913bf8246ba61d913","score":"0.4696378","text":"def union(a, b):\n c = a.copy()\n for item, quantity in b.items():\n if item in c:\n c[item] += quantity\n else:\n c[item] = quantity\n return c","title":""},{"docid":"43a8f10d46c49f4fc02930e22a6c1f53","score":"0.46955162","text":"def sum_list(input_list: typing.List[float]) -> float:\n return sum(input_list)","title":""},{"docid":"6b6714d61699a90caccf507a1439ba0b","score":"0.46904996","text":"def add_food(self, food: FoodProp) -> None:\n if not self.can_place_food(food):\n raise Exception('Cannot place a food with that color on the colorful plate')\n\n self.foods.append(food)","title":""},{"docid":"68e5cbf07cac2a1423f45e8e5ac6e692","score":"0.46781105","text":"def __len__(self):\n return sum(item[\"qty\"] for item in self.basket.values())","title":""},{"docid":"0b38093e769550aa59eb195f38c23eb6","score":"0.46772686","text":"def add_item(self, product, price):\n if not product in self.items_in_cart:\n self.items_in_cart[product] = price\n print (product + \" added.\")\n else:\n print (product + \" is already in the cart.\")","title":""},{"docid":"1690b8513ce9e630d2fbdaaf6b81ca8d","score":"0.4674813","text":"def sum_mixed_list(input_list: List[Union[int, float]]) -> float:\n return reduce(lambda x, y: x+y, input_list)","title":""},{"docid":"8d97e4fa24429dc883f373bd0ca99355","score":"0.46693996","text":"def _product_association(self):\n # Find orders that bought the target item\n orders = self.data[self.data[self.item_col]==self.target][self.order_col]\n \n # Find all items in those orders\n items = self.data[self.data[self.order_col].isin(orders)]\n \n # Recommend items frequently bought with the target item \n self._find_recs(items.groupby(self.item_col).size())\n self._print_recs(\"popular items other customers bought\")","title":""},{"docid":"e92b39dd58dbb549782499edade31dac","score":"0.46683574","text":"def autoPopulateInventory(self):\n if not self._permanent:\n self.clearInventory()\n\n # Randomly select the itemCount\n itemCount = int(getRandomItemFromList(self._numOfItemsCarried))\n\n if not itemCount: # 0 items carried\n return True\n\n for itemNum in range(1, itemCount + 1): # +1 due to exclusive ranges\n itemId = getRandomItemFromList(self._itemCatalog)\n if not itemId:\n continue\n if \"/\" not in itemId:\n continue\n\n dLog(\"creature.autoPopulateInventory: obj = \" + itemId)\n oType, oNum = itemId.split(\"/\")\n obj1 = ObjectFactory(oType, oNum)\n obj1.load()\n self.addToInventory(obj1)\n return True","title":""},{"docid":"4025849a832f2176dc523dd3048e8ffc","score":"0.46618393","text":"def raw_ingredients_exact(item_set):\n remaining = item_set.copy()\n raw_ingredients = {}\n while any(remaining):\n item, quantity = list(remaining.items())[0]\n item_object = items[item]\n del remaining[item]\n if item_object.raw:\n raw_ingredients = union(raw_ingredients, {item: quantity})\n else:\n item_list = item_object.recipe.ingredients.items()\n for ingredient, ingredient_quantity in item_list:\n remaining = union(\n remaining,\n multiply(\n {ingredient: ingredient_quantity},\n quantity / item_object.recipe.quantity,\n ),\n )\n return raw_ingredients","title":""},{"docid":"5a586b690c5f4ad80786ce24861767f9","score":"0.46590447","text":"def frequentOneItem(self):\n\n candidate = {}\n # global finalPatterns, minSup, Database\n # self.minSup = self.minSup\n for i in range(len(self.Database)):\n for j in range(len(self.Database[i])):\n if self.Database[i][j] not in candidate:\n candidate[self.Database[i][j]] = 1\n else:\n candidate[self.Database[i][j]] += 1\n self.finalPatterns = {keys: value for keys, value in candidate.items() if value >= self.minSup}","title":""},{"docid":"cac81ccf764ad1c0b428a32da2a2677e","score":"0.46445563","text":"def add_item(request):\n\n if request.method == \"GET\":\n return HttpResponseRedirect(reverse(\"index\"))\n \n # Variable definition\n try:\n dish = Dish.objects.get(pk=(request.POST[\"dish\"]))\n except:\n return HttpResponseRedirect(reverse(\"index\"))\n\n extra = request.POST.getlist('extra')\n\n # No toppings Pizza validator\n if (dish.pk == 1 or dish.pk == 2 or dish.pk == 11 or dish.pk == 12) and len(extra) != 0:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 1 topping Pizza validator\n if (dish.pk == 3 or dish.pk == 4 or dish.pk == 13 or dish.pk == 14) and len(extra) != 1:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 2 toppings Pizza validator\n if (dish.pk == 5 or dish.pk == 6 or dish.pk == 15 or dish.pk == 16) and len(extra) != 2:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 3 toppings Pizza validator\n if (dish.pk == 7 or dish.pk == 8 or dish.pk == 17 or dish.pk == 18) and len(extra) != 3:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # Special Pizza Pizza validator\n if (dish.pk == 9 or dish.pk == 10 or dish.pk == 19 or dish.pk == 20) and len(extra) != 5:\n return HttpResponseRedirect(reverse(\"index\"))\n \n # Item variables\n extras = []\n price = dish.price\n\n for item in extra:\n new_extra = Extra.objects.get(pk=(item))\n extras.append(new_extra)\n price += new_extra.price\n\n new_item = Item(dish=dish, price=price)\n new_item.save()\n for item in extras:\n new_item.extras.add(item)\n\n # Cart Handling\n try:\n cart = Cart.objects.get(user=request.user)\n except:\n cart = Cart(user=request.user)\n cart.save()\n cart.items.add(new_item)\n cart.save()\n\n return HttpResponseRedirect(reverse(\"index\"))","title":""},{"docid":"a923dccac387cb688c107df06e72d941","score":"0.4643559","text":"def sum(items):\n if items is None or len(items) == 0:\n return None\n try:\n return sum(items)\n except:\n msg = \"Error while addition: %s\" %sys.exc_info()[0]\n sys.stderr.write(msg)\n raise","title":""},{"docid":"e5e463dda3a19d276a1fe9f6ee356c72","score":"0.46426684","text":"def test_add_item(self):\n before = len(self.shopping_list_1.items)\n self.shopping_list_1.add_item(self.item_3)\n after = len(self.shopping_list_1.items)\n self.assertEqual(1, after - before,\n msg='The object should add an item to the instance')","title":""},{"docid":"a0d6261296989b18c47c219f2b8c7f83","score":"0.46423283","text":"def get_total(self):\n\n # self.base_price = self.get_base_price()\n\n if self.species == \"Christmas\":\n self.base_price *= 1.5\n total = (1 + self.tax) * self.qty * self.base_price\n return total","title":""}],"string":"[\n {\n \"docid\": \"4f021ae31218d83063fc7b7fe95882c6\",\n \"score\": \"0.6519353\",\n \"text\": \"def __add_muilt(self,food_name,quantity):\\n\\t\\tif (not food_name in self.items):\\n\\t\\t\\tself.items[food_name] =0\\n\\t\\tself.items[food_name] = self.items[food_name] + quantity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232d5321f12a8823e62ae2f05ee7bab2\",\n \"score\": \"0.5817523\",\n \"text\": \"def addFood(self, num):\\n self.camp.data[\\\"foodQuantity\\\"] += int(num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f7d71c67883675ad8cf3a3be6f640a0\",\n \"score\": \"0.575201\",\n \"text\": \"def add(self, name, quantity):\\n item = Item(name, quantity)\\n if item in self.items:\\n # we already have this, so add to it\\n self.items[self.items.index(item)] += item\\n else:\\n self.items.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5bf92df7dcf5cd7b9da5461a553cbf\",\n \"score\": \"0.5631031\",\n \"text\": \"def condense_grocery_list(list_of_grocery_items):\\n condensed_list=[]\\n condensed_dict=dict()\\n for grocery_item in list_of_grocery_items:\\n dict_key=grocery_item.unit+\\\"-\\\"+grocery_item.name\\n if dict_key in condensed_dict:\\n condensed_dict[dict_key].number = str(float(condensed_dict[dict_key].number) + float(grocery_item.number))\\n else:\\n condensed_dict[dict_key]=grocery_item\\n\\n for item in condensed_dict:\\n condensed_list.append(condensed_dict[item])\\n\\n condensed_list.sort(key=lambda x: x.name)\\n condensed_dict={}\\n for item in condensed_list:\\n if item.name in condensed_dict:\\n condensed_dict[item.name] = condensed_dict[item.name] + \\\", \\\" + item.number.strip() + \\\" \\\" + item.unit.strip()\\n else:\\n condensed_dict[item.name]=item.name + \\\": \\\" + item.number.strip() + \\\" \\\" + item.unit.strip()\\n condensed_list=[]\\n for item in condensed_dict:\\n new_item=shopping_item()\\n new_item.name=item\\n new_item.grocery_list_line=condensed_dict[item]\\n condensed_list.append(new_item)\\n\\n return condensed_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"615ee899602c1129b4b2df5f3f783058\",\n \"score\": \"0.5625155\",\n \"text\": \"def createMeal(jour, tyrep, itemsName, itemsDict, minItem, maxItem, meanQuantities):\\n nItems = random.randint(minItem,maxItem)\\n items = random.sample(itemsName, nItems)\\n foodItems = [Food(itemsDict[x], x, meanQuantities[x]) for x in items]\\n meal = Meal(foodItems, jour=jour, tyrep=tyrep)\\n meal.reorder()\\n return meal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3c0498b926385879c47b0567a49a81\",\n \"score\": \"0.5587279\",\n \"text\": \"def consume_food(self, food):\\n quantity = self.__items['food'].get(food, 0)\\n if quantity > 0:\\n self.__items['food'][food] = quantity - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0139cce57cee1ffab79707a72f831baf\",\n \"score\": \"0.55362856\",\n \"text\": \"def add_to_inventory_v2(inventory, added_items):\\n\\n for item in added_items:\\n if item not in inventory:\\n my_inventory.setdefault(item, 1)\\n else:\\n my_inventory[item] += 1\\n return my_inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d1cce71848b886fcbf253344f3c75a8\",\n \"score\": \"0.5494977\",\n \"text\": \"def add_to_inventory(inventory, added_items):\\n for key in list(inventory):\\n for item in added_items:\\n if key == item:\\n inventory[key] += 1\\n else:\\n inventory.setdefault(item, 1)\\n\\n return my_inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21510822cd79307c80383341d84fa004\",\n \"score\": \"0.53571904\",\n \"text\": \"def add(self, item1, item2):\\n for sumField in self.sumFields:\\n item1[sumField] = item1[sumField] + item2[sumField]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3d284c0f2807c357fd971d08a6f7f8\",\n \"score\": \"0.5330261\",\n \"text\": \"def addDish(self,dish):\\r\\n\\t\\tself.items[dish.name] = dish\\r\\n\\t\\tfor nutrient in TARGET_NUTRIENTS:\\r\\n\\t\\t\\tself.totals[nutrient]+=dish.nutrition[nutrient]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1159106f3c256affb95ba2576b99a40\",\n \"score\": \"0.5326817\",\n \"text\": \"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa86c34f680edfb331c7e63841be1d7\",\n \"score\": \"0.5321849\",\n \"text\": \"def update_world_totals(new_item):\\n # TODO: Can definitely optimize this search if performance becomes an issue\\n for item in world_inv:\\n if new_item.equals(item):\\n item.set_count(item.count + new_item.count)\\n return\\n\\n if (new_item.damage != 0 and (new_item.full_name in degradeable_tools or new_item.full_name in degradeable_armor)):\\n world_inv.append(Item(new_item.full_name, new_item.count, -1, new_item.lore))\\n else:\\n world_inv.append(Item(new_item.full_name, new_item.count, new_item.damage, new_item.lore))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ff6a56e8a808fc9686f492e012e077\",\n \"score\": \"0.52610236\",\n \"text\": \"def feeding_animal(self):\\n for food in self.get_food_set():\\n for animal in self.get_animal_set():\\n if (abs(animal.position[0] - food.position[0]) < Constants.ANIMAL_WIDTH / 2 + Constants.FOOD_WIDTH / 2 and \\\\\\n abs(animal.position[1] - food.position[1]) < Constants.ANIMAL_HEIGHT / 2 + Constants.FOOD_HEIGHT / 2):\\n self.animal_set.remove(animal)\\n self.money += 1\\n food.used = True\\n if food.used:\\n self.food_set.remove(food)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f300c9707bf682e3a6b1d5677eba5aa9\",\n \"score\": \"0.52460706\",\n \"text\": \"def execute_item_creation(self)->None:\\n [ingedient.use_quantity(quantity) for ingedient,quantity in self.ingredient_quantity]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70b286ea3b1c733d10122c24d15eb782\",\n \"score\": \"0.52455187\",\n \"text\": \"def digest_fat(self):\\n can_move = min(self.population - self.food, self.fat_storage)\\n self.fat_storage -= can_move\\n self.food += can_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9736db106bb68027158dacec819627\",\n \"score\": \"0.5245462\",\n \"text\": \"def add_fruit(inventory, fruit, quantity=0):\\n #print fruit, quantity\\n inventory[fruit]=quantity\\n #return inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0a041ec85a5bcb7a4fa271d76f533e\",\n \"score\": \"0.5186193\",\n \"text\": \"def add_item(self, item_name, item_price):\\n if 'none' in self.items_with_price:\\n self.items_with_price.clear()\\n self.items_with_price.update({str(item_name): float(item_price)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379782037ed753eb6be9b48c0ea5b3b7\",\n \"score\": \"0.51835597\",\n \"text\": \"def add_to_inventory(inventory, added_items):\\n for i in added_items:\\n if i in inventory.keys():\\n inventory[i] += 1\\n else:\\n inventory[i] = 1\\n return inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd6eb79281380c997d0ddf13a3b1137\",\n \"score\": \"0.51719004\",\n \"text\": \"def updateCart(self, replaceItem):\\n\\t\\ttopFoods = self.dashFilter(replaceItem)\\n\\t\\trestOfCart = [item for item in self.foods if item[\\\"id\\\"] != replaceItem]\\n\\t\\t#topCombo = self.comboFilter(topFoods, restOfCart)\\n\\t\\ttopCombo = self.randomFilter(topFoods)\\n\\t\\tself.foods = [{\\\"id\\\":topCombo[\\\"id\\\"],\\\"name\\\":topCombo['name'],\\\"groups\\\":item['groups'],\\\"combos\\\":topCombo['combos']} if item[\\\"id\\\"] == replaceItem else item for item in self.foods]\\n\\t\\treturn {'id':topCombo['id'],'name':topCombo['name'].upper(),'img':topCombo['id']+'.jpg'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"242d58e8f298df51464e9f6224d850aa\",\n \"score\": \"0.51659745\",\n \"text\": \"def addFood(surface) -> Food:\\n\\n newFood = Food(\\n coord=[randint(0, screen_resolution[0]), randint(0, screen_resolution[1])],\\n energy=2,\\n surface=surface\\n )\\n foodHolder.append(newFood)\\n\\n return newFood\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c319e9376a5acfcd6692426a6106298\",\n \"score\": \"0.51233023\",\n \"text\": \"def test_bulk_inventory_quantity_combined(self):\\n category = ['Clothing', 'Unisex Adult Clothing', 'Pants']\\n\\n expected_left_bulk_header = 'Individual Quantity'\\n\\n new_quantities = ['200', '300', '400', '500']\\n\\n expected_product_offerings = [\\n ['1', '500.00', '550', '200', 't', 'L', '1', 'Black', '1'],\\n ['1', '500.00', '550', '300', 't', 'L', '1', 'Pinkish', '2'],\\n ['1', '500.00', '550', '400', 't', 'M', '2', 'Black', '1'],\\n ['1', '500.00', '550', '500', 't', 'M', '2', 'Pinkish', '2'],\\n ['2', '1.00', '111', '200', 't', 'L', '1', 'Black', '1'],\\n ['2', '1.00', '111', '300', 't', 'L', '1', 'Pinkish', '2'],\\n ['2', '1.00', '111', '400', 't', 'M', '2', 'Black', '1'],\\n ['2', '1.00', '111', '500', 't', 'M', '2', 'Pinkish', '2'],\\n ['3', '10.00', '222', '200', 't', 'L', '1', 'Black', '1'],\\n ['3', '10.00', '222', '300', 't', 'L', '1', 'Pinkish', '2'],\\n ['3', '10.00', '222', '400', 't', 'M', '2', 'Black', '1'],\\n ['3', '10.00', '222', '500', 't', 'M', '2', 'Pinkish', '2']\\n ]\\n\\n expected_variation_properties = [\\n ['1', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['2', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['3', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\\n ]\\n\\n expected_variation_options = [\\n ['1', 't', '2119', 'L', '1'],\\n ['1', 't', '2116', 'M', '2'],\\n ['1', 'f', '1', 'Black', '1'],\\n ['1', 'f', '', 'Pinkish', '2'],\\n ['2', 't', '2119', 'L', '1'],\\n ['2', 't', '2116', 'M', '2'],\\n ['2', 'f', '1', 'Black', '1'],\\n ['2', 'f', '', 'Pinkish', '2'],\\n ['3', 't', '2119', 'L', '1'],\\n ['3', 't', '2116', 'M', '2'],\\n ['3', 'f', '1', 'Black', '1'],\\n ['3', 'f', '', 'Pinkish', '2']\\n ]\\n\\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\\n\\n # Select category in bulk edit area\\n bpiv.select_category(category)\\n\\n bulk_row = bpiv.bulk_edit_row\\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\\n\\n # Set first variation property\\n bpiv.set_property(bulk_row, 0, 'Size', \\\"Men's\\\")\\n # Add two options to first property, one by one\\n bpiv.add_option(bulk_row, 0, 'L', displayed_name=\\\"L Men's\\\")\\n bpiv.add_option(bulk_row, 0, 'M', displayed_name=\\\"M Men's\\\")\\n\\n # Set second variation property\\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\\n # Add two options to first property, one by one\\n bpiv.add_option(bulk_row, 1, 'Black')\\n bpiv.add_custom_option(bulk_row, 1, 'Pinkish')\\n\\n bpiv.select_inventory_tab(bulk_row, \\\"Quantity\\\")\\n\\n # Switch to individual quantities, check the header is changed\\n click(bpiv.bulk_header_checkbox(bulk_row, i=0))\\n click(bpiv.bulk_header_checkbox(bulk_row, i=1), delay=0.5)\\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\\n\\n # Enter individual quantities for the property combinations\\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=0)\\n for i, quantity in enumerate(new_quantities):\\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\\n\\n # Check that only titles are in listing preview as preview is disabled\\n check_simplified_preview(bpiv)\\n\\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\\n\\n # Apply\\n assert bpiv.operation_apply().is_enabled() is True\\n click(bpiv.operation_apply())\\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\\n 'Sync button is not enabled')\\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\\\'t show up'\\n\\n # Check bulk edit area texts and that Apply button is disabled\\n check_bulk_properties_disabled(bpiv)\\n\\n # Sync updates\\n click(bpiv.sync_updates_button())\\n\\n # Check data in DB\\n check_db_state(expected_product_offerings, expected_variation_properties, expected_variation_options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea81c57ff2db743346ab37434f24574\",\n \"score\": \"0.51203\",\n \"text\": \"def calculateAmount(self):\\n return sum([item.price for item in self.items])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5115be39649d3f3029e91969f33dab0\",\n \"score\": \"0.5108897\",\n \"text\": \"def add_to_inventory(player_inventory, added_items):\\n # For each new piece of loot...\\n for new_loot_piece in added_items:\\n if new_loot_piece in player_inventory:\\n # if the loot already existed in inventory, increment it's count\\n player_inventory[new_loot_piece] += 1\\n else:\\n # If the loot was not already in the inventory, create a new piece of loot\\n # in the inventory and se it's default value to 1.\\n player_inventory.setdefault(new_loot_piece, 1)\\n\\n return player_inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4b677c435c587a9cfddc38115a3c4f\",\n \"score\": \"0.5098413\",\n \"text\": \"def test_bulk_inventory_single_quantity_individual(self, property_number):\\n category = ['Home & Living', 'Furniture']\\n\\n expected_left_bulk_header = 'Individual Quantity'\\n\\n new_quantities = ['22', '33']\\n\\n expected_product_offerings = [\\n [\\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'White', '2'],\\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'Black', '1'],\\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'White', '2'],\\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'Black', '1'],\\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'White', '2'],\\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'Black', '1'],\\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\\n ],\\n [\\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\\n ['1', '500.00', '550', '33', 't', 'wool', '1', 'White', '2'],\\n ['1', '500.00', '550', '22', 't', 'cotton', '2', 'Black', '1'],\\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\\n ['2', '1.00', '111', '33', 't', 'wool', '1', 'White', '2'],\\n ['2', '1.00', '111', '22', 't', 'cotton', '2', 'Black', '1'],\\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\\n ['3', '10.00', '222', '33', 't', 'wool', '1', 'White', '2'],\\n ['3', '10.00', '222', '22', 't', 'cotton', '2', 'Black', '1'],\\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\\n ]\\n ]\\n\\n expected_variation_properties = [\\n [\\n ['1', 't', '502', 'Fabric', '', 'f', 't', 'f'],\\n ['1', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\\n ['2', 't', '502', 'Fabric', '', 'f', 't', 'f'],\\n ['2', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\\n ['3', 't', '502', 'Fabric', '', 'f', 't', 'f'],\\n ['3', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f']\\n ],\\n [\\n ['1', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['2', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['3', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\\n ]\\n ]\\n\\n expected_variation_options = [\\n [\\n ['1', 't', '', 'wool', '1'],\\n ['1', 't', '', 'cotton', '2'],\\n ['1', 'f', '1', 'Black', '1'],\\n ['1', 'f', '10', 'White', '2'],\\n ['2', 't', '', 'wool', '1'],\\n ['2', 't', '', 'cotton', '2'],\\n ['2', 'f', '1', 'Black', '1'],\\n ['2', 'f', '10', 'White', '2'],\\n ['3', 't', '', 'wool', '1'],\\n ['3', 't', '', 'cotton', '2'],\\n ['3', 'f', '1', 'Black', '1'],\\n ['3', 'f', '10', 'White', '2']\\n ],\\n [\\n ['1', 't', '', 'wool', '1'],\\n ['1', 't', '', 'cotton', '2'],\\n ['1', 'f', '1', 'Black', '1'],\\n ['1', 'f', '10', 'White', '2'],\\n ['2', 't', '', 'wool', '1'],\\n ['2', 't', '', 'cotton', '2'],\\n ['2', 'f', '1', 'Black', '1'],\\n ['2', 'f', '10', 'White', '2'],\\n ['3', 't', '', 'wool', '1'],\\n ['3', 't', '', 'cotton', '2'],\\n ['3', 'f', '1', 'Black', '1'],\\n ['3', 'f', '10', 'White', '2']\\n ]\\n ]\\n\\n\\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\\n\\n # Select category in bulk edit area\\n bpiv.select_category(category)\\n\\n bulk_row = bpiv.bulk_edit_row\\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\\n\\n # Set first variation property\\n bpiv.set_property(bulk_row, 0, 'Fabric')\\n # Add two options to first property, one by one\\n bpiv.add_custom_option(bulk_row, 0, 'wool')\\n bpiv.add_custom_option(bulk_row, 0, 'cotton')\\n\\n # Set second variation property\\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\\n # Add two options to first property, one by one\\n bpiv.add_option(bulk_row, 1, 'Black')\\n bpiv.add_option(bulk_row, 1, 'White')\\n\\n bpiv.select_inventory_tab(bulk_row, \\\"Quantity\\\")\\n\\n # Switch to individual quantities, check the header is changed\\n click(bpiv.bulk_header_checkbox(bulk_row, i=property_number), delay=0.5)\\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\\n\\n # Enter individual quantities\\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=property_number)\\n for i, quantity in enumerate(new_quantities):\\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\\n\\n # Check that only titles are in listing preview as preview is disabled\\n check_simplified_preview(bpiv)\\n\\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\\n\\n # Apply\\n assert bpiv.operation_apply().is_enabled() is True\\n click(bpiv.operation_apply())\\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\\n 'Sync button is not enabled')\\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\\\'t show up'\\n\\n # Check bulk edit area texts and that Apply button is disabled\\n check_bulk_properties_disabled(bpiv)\\n\\n # Sync updates\\n click(bpiv.sync_updates_button())\\n\\n # Check data in DB\\n check_db_state(expected_product_offerings[property_number],\\n expected_variation_properties[property_number],\\n expected_variation_options[property_number])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddde5fc2882d117d910fb6854f8c507f\",\n \"score\": \"0.50927\",\n \"text\": \"def concatenate_items(self) -> dict:\\n test = []\\n for text in self.receipt_text:\\n if self.text_is_number(text) | self.remove_pound_symbol(text) | self.change_o_to_zero(text):\\n text = text[1:]\\n else:\\n test.append(text)\\n\\n if self.text_is_number(text):\\n if test[-1] in self.dict_to_return:\\n self.dict_to_return[test[-1]]['quantity'] += 1\\n else:\\n self.dict_to_return[test[-1]] = {'name': text, 'quantity': 1}\\n test = []\\n\\n return self.dict_to_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e1c26cb444d691a6d757c89aed7d2b\",\n \"score\": \"0.5092597\",\n \"text\": \"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\\n self.ingredient_quantity.append((ingredient,quantity))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a68a1b31438ff6224ac04a9bb4f05d1\",\n \"score\": \"0.50659823\",\n \"text\": \"def scan_onto_shelf(self):\\n if hasattr(self, \\\"shelf\\\"):\\n items_added = []\\n for item in self.items_stored:\\n for product in self.shelf.compartments:\\n if product[\\\"Product Number\\\"] == item.number:\\n if product[\\\"Items Stored\\\"] is None:\\n product[\\\"Items Stored\\\"] = []\\n product[\\\"Items Stored\\\"].append(item)\\n product[\\\"Quantity\\\"] += 1\\n items_added.append(item)\\n Item.sorted_items.append(item)\\n Item.shipment.remove(item)\\n for item in items_added:\\n self.items_stored.remove(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"484270c6a6c051655cc77f321d243802\",\n \"score\": \"0.50581247\",\n \"text\": \"def add_to_inventory(inventory, added_items):\\n if added_items in inventory:\\n inventory[added_items] += 1\\n else:\\n inventory[element] = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d21353320ad10efa03007bf3481510\",\n \"score\": \"0.50539666\",\n \"text\": \"def add(self, product, quantity):\\n product_id = product.id\\n if product_id not in self.basket:\\n self.basket[product_id] = {\\\"price\\\": float(product.regular_price), \\\"quantity\\\": quantity}\\n else:\\n self.basket[product_id][\\\"quantity\\\"] = quantity\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35cf195a68cb0eebf880a0671ed3bb9d\",\n \"score\": \"0.50376934\",\n \"text\": \"def _add_dict_inventory(self, d, item_pouch): # item_pouch used to find which dictionary to look in\\r\\n for group in d:\\r\\n if group in item_pouch:\\r\\n if item_pouch[group] is None:\\r\\n item_pouch[group] = d[group]\\r\\n elif isinstance(item_pouch[group], list):\\r\\n item_pouch[group].extend(d[group])\\r\\n elif isinstance(item_pouch[group], dict):\\r\\n self._add_dict_inventory(d[group], item_pouch[group])\\r\\n elif isinstance(item_pouch[group], int):\\r\\n item_pouch[group] = item_pouch.setdefault(group, 0)+d[group]\\r\\n else:\\r\\n item_pouch[group] = d[group]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ca2e8c63e53f9ab69061677cf963375\",\n \"score\": \"0.50190413\",\n \"text\": \"def test_adding_item_to_order_model_is_add_one_to_quantity(self):\\n cart_item_1 = CartItem.objects.create(order=self.order, item=self.item_1)\\n cart_item_1.Add() # quantity = 1 ** payout = self.item_1.price * 1\\n cart_item_1.Add() # quantity = 2 ** payout = self.item_1.price * 2\\n\\n self.assertEqual(CartItem.objects.get(order=self.order, item=self.item_1).quantity, 2)\\n self.assertEqual(cart_item_1.total_price, self.item_1.price * 2)\\n self.assertEqual(self.order.payout, self.item_1.price * 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f019318a90a39d1d4baa58c66b55a7e8\",\n \"score\": \"0.50127226\",\n \"text\": \"def uniq_add(my_list=[]):\\n\\n return sum(set(my_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85016ea7f6bcc15054e712a84e7aaa66\",\n \"score\": \"0.5005258\",\n \"text\": \"def create_order_item(self, food_item, quantity):\\n OrderItem.objects.create(\\n user=self.customer,\\n food_item=food_item,\\n quantity=quantity,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f95d8bb1a0a822a0a89287235821bd2\",\n \"score\": \"0.50050193\",\n \"text\": \"def calculateAmount(self):\\n summa = 0\\n for item in self.items:\\n summa += item.price\\n return summa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a975cec92256ad6ac4119086b89bcaf\",\n \"score\": \"0.50043887\",\n \"text\": \"def add(self, product, quantity=None, update_quantity=False):\\n if product.price_with_taxes > 0 and quantity != \\\"0\\\":\\n self.cart[str(product.id)] = {\\n 'user_id': self.request.user.id,\\n 'article_id': str(product.id),\\n 'name': product.name,\\n 'quantity': quantity,\\n 'price_type': product.price_type,\\n 'price_with_taxes': str(product.price_with_taxes),\\n 'article_code': product.article_code,\\n }\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e946d19a98dd4708d5ea172fdab839a3\",\n \"score\": \"0.49940917\",\n \"text\": \"def fortune(player):\\n random.seed()\\n \\n # (inventory item, amount received)\\n treasures = [\\n ('food',random.randint(100,500)),\\n ('money',random.randint(200,500)),\\n ('bullets',random.randint(30,100)),\\n ('kits',random.randint(2,3)),\\n ('parts',random.randint(2,3)),\\n ]\\n \\n # Determine random item.\\n found = random.choice(treasures)\\n \\n # Handle special message for food.\\n if found[0] == 'food':\\n current_food = player.get_from_inventory('food')\\n # Handle food inventory limit.\\n if current_food + found[1] > 1000:\\n added_food = 1000 - current_food\\n print(\\\"You found {} pounds of food in an abandoned wagon\\\".format(found[1]))\\n print(\\\"You can only add {} pounds to your wagon\\\".format(added_food))\\n player.update_inventory('food',1000)\\n else:\\n print(\\\"You found {} pounds of food in an abandoned wagon\\\".format(found[1]))\\n player.add_to_inventory('food',found[1])\\n \\n # Handle special message for money.\\n elif found[0] == 'money':\\n print(\\\"You found ${} in an abandoned wagon\\\".format(\\\"%.2f\\\" %found[1]))\\n player.add_to_inventory('money',found[1])\\n \\n # Handle all other items.\\n else:\\n print(\\\"You found {} {} in an abandoned wagon\\\".format(found[1],found[0]))\\n player.add_to_inventory(found[0],found[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e056e6e443126ef305397e1edf9f00e\",\n \"score\": \"0.49848992\",\n \"text\": \"def calc_items_total(cls, items):\\n shipping_charge = 0\\n subtotal = 0\\n ad_charge_amt = 0\\n ad_charge_breakup = {}\\n\\n for item in items:\\n shipping_charge += item.shipping_charge\\n subtotal += item.subtotal\\n ad_charge_amt += item.additional_charge\\n\\n for charge in item.cost_breakup['additional_charge']:\\n k = slugify(charge.get('name')).replace('-', '_')\\n v = charge.get('amt')\\n # Initialize value\\n if ad_charge_breakup.get(k, None) is None:\\n ad_charge_breakup[k] = 0.0\\n ad_charge_breakup[k] += v\\n\\n return {\\n 'shipping_charge': helper.round_off(shipping_charge),\\n 'subtotal': helper.round_off(subtotal),\\n 'additional_charge': helper.round_off(ad_charge_amt),\\n 'cost_breakup': {\\n 'additional_charge': ad_charge_breakup\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba05c3eea1691cbb2cf7dd27f2811a3\",\n \"score\": \"0.49668226\",\n \"text\": \"def add(self, comida, quantity=1, update_quantity=False):\\n product_id = str(comida.id)\\n if product_id not in self.cart:\\n self.cart[product_id] = {'quantity': 0,\\n 'precio': str(comida.precio)}\\n if update_quantity:\\n self.cart[product_id]['quantity'] = quantity\\n else:\\n self.cart[product_id]['quantity'] += quantity\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a55875101d7602488fcb4f6054d62ad\",\n \"score\": \"0.49663317\",\n \"text\": \"def initializeFoods(self):\\n for i in range(0, self.numFoods):\\n self.addFood()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9da1b12f109a9a6aef2dab62544176d0\",\n \"score\": \"0.49556127\",\n \"text\": \"def add(self, product, qty):\\n product_id = str(product.id)\\n\\n if product_id in self.basket:\\n self.basket[product_id][\\\"qty\\\"] = qty\\n else:\\n self.basket[product_id] = {\\\"price\\\": str(product.regular_price), \\\"qty\\\": qty}\\n\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc752d2ca3326fdbd563d1bbeac4d05\",\n \"score\": \"0.49489328\",\n \"text\": \"def add(self, product, quantity=1, update_quantity=False):\\n product_id = str(product.id) # converted to string because JSON use only string data\\n if product_id not in self.cart:\\n self.cart[product_id] = {'quantity': 0,\\n 'price': str(product.price)\\n }\\n if update_quantity:\\n self.cart[product_id]['quantity'] = quantity\\n else:\\n self.cart[product_id]['quantity'] += quantity # increment the quantity\\n self.save() # saving the state of the quantity after update\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e122449c4307f51ed5cb01667c9a9d1\",\n \"score\": \"0.49486324\",\n \"text\": \"def eatFood(self):\\n # if a prey is in contact with a food, then eat the food by removing the food and increment hunger.\\n objIDInContactList = super().getContactObjects()\\n # if there are objects in contact with this prey, then determine if they are food and if so eat them!\\n foodEaten = 0\\n if objIDInContactList:\\n for objID in objIDInContactList:\\n food = hsm.objIDToObject[objID]\\n if food in hsm.foodList:\\n hsm.foodList.remove(food)\\n hsm.destroy(objID)\\n foodEaten += 1\\n # incrememnt hunger accordingly\\n self.hunger += mv.PREY_INCREMENT_HUNGER \\n # throw away excess, prey can only eat so much food at a time.\\n if self.hunger > mv.PREY_MAX_HUNGER:\\n self.hunger = mv.PREY_MAX_HUNGER\\n if foodEaten > 0:\\n self.foodTimeStamps.append([hsm.frameCount, foodEaten])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892adb19f630ba769f49adfe54589f4c\",\n \"score\": \"0.4945769\",\n \"text\": \"def add_item(self, x):\\n self._items_with_price.update(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b3b75d718473bad062e7134b4e43dd2\",\n \"score\": \"0.49438444\",\n \"text\": \"def add_item_to_cart(self, product_id, quantity):\\n product = Product.objects.filter(product_id=product_id, store=self.customer_id.store_id, deleted_at=None).first()\\n if product is None or product.inventory < quantity:\\n raise ProductNotFoundException\\n\\n product.inventory -= quantity\\n product.save()\\n\\n existing_cart_item = CartItem.objects.filter(cart_id=self.cart_id, product_id=product.product_id\\n , removed_at=None).first()\\n\\n self.customer_id.last_cart_activity_at = datetime.now()\\n self.customer_id.updated_at = datetime.now()\\n self.customer_id.save()\\n if existing_cart_item:\\n existing_cart_item.quantity += quantity\\n existing_cart_item.save()\\n return existing_cart_item\\n else:\\n cart_item = CartItem(\\n cart_item_id=str(uuid.uuid4().int),\\n product_id=product.product_id,\\n cart_id=self.cart_id,\\n quantity=quantity\\n ).save()\\n return cart_item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106331337e2a2c1d307ba926e7184437\",\n \"score\": \"0.49325597\",\n \"text\": \"def calculate_total_nutrition_fulldayofeating2(\\n specificingredient2_dict_list,\\n all_nutrients_and_default_units,\\n set_to_zero_if_none,\\n):\\n\\n # Initialize the dictionary which will store the results\\n result_total_nutrition_fulldayofeating = {}\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n new_dict = {nutrient_name: 0}\\n result_total_nutrition_fulldayofeating.update(\\n new_dict\\n )\\n\\n # Go through all the SpecificIngredients related to the FullDayOfEating.\\n # For each SpecificIngredient, calculate its Nutrition by\\n # multiplying the calculated_amount divided by the reference amount\\n # multiplied by each nutrient value in the associated RawIngredient.\\n\\n # Sum up the total nutrition\\n for k in range(len(specificingredient2_dict_list)):\\n # For each nutrient, calculate the amount of that nutrient contained\\n # in the calculated_amount of the SpecificIngredient\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n result_total_nutrition_fulldayofeating[nutrient_name] = \\\\\\n result_total_nutrition_fulldayofeating[nutrient_name] \\\\\\n + set_to_zero_if_none(specificingredient2_dict_list[k][\\n 'raw_ingredient'][nutrient_name]) \\\\\\n * float(specificingredient2_dict_list[k]['calculated_amount'])\\\\\\n / float(specificingredient2_dict_list[k][\\n 'raw_ingredient']['reference_amount'])\\n\\n # Round the values in the result_total_nutrition_fulldayofeating\\n # Initialize the dictionary containing the rounded values.\\n result_total_nutrition_fulldayofeating_rounded = {}\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n new_dict = {nutrient_name: 0}\\n result_total_nutrition_fulldayofeating_rounded.update(\\n new_dict\\n )\\n\\n # Round the values and save them to the respective dictionary.\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n result_total_nutrition_fulldayofeating_rounded[nutrient_name] = \\\\\\n round(result_total_nutrition_fulldayofeating[nutrient_name], 1)\\n\\n return result_total_nutrition_fulldayofeating,\\\\\\n result_total_nutrition_fulldayofeating_rounded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e950cf94ff01f7be16cc47e54da41ba\",\n \"score\": \"0.4931627\",\n \"text\": \"def _update_total_nutrition_values(self, unique_id, date, plan, add):\\n table = self.dynamodb.Table(c.TABLE_NUTRITIONAL_NEEDS_DAY)\\n week = form.get_week_by_date(date)\\n\\n item_nutrients_for_week = table.get_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\\n Key={\\n c.UNIQUE_IDENTIFIER: unique_id,\\n c.WEEK: week\\n },\\n ProjectionExpression='#toplevel',\\n ExpressionAttributeNames={\\\"#toplevel\\\": c.NUTRIENTS_FOR_WEEK}\\n )\\n\\n item_nutrients_for_day = table.get_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={\\n c.UNIQUE_IDENTIFIER: unique_id,\\n c.DATE: date\\n },\\n ProjectionExpression='#toplevel',\\n ExpressionAttributeNames={\\\"#toplevel\\\": c.NUTRIENTS_FOR_DAY}\\n )\\n\\n for container, container_content in plan.iteritems():\\n item_nutrients_for_container = table.get_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={c.UNIQUE_IDENTIFIER: unique_id, c.DATE: date},\\n ProjectionExpression='#toplevel.#container',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_CONTAINER,\\n \\\"#container\\\": container\\n }\\n )\\n for meal_key, meal_content in container_content.iteritems():\\n\\n\\n\\n for n in params.nutrientList:\\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\\\\n (1 if add else -1)\\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\\\\n (1 if add else -1)\\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['VAL'] += \\\\\\n meal_content['nutrients'][n]['VAL'] * (1 if add else -1)\\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['UNIT'] = \\\\\\n meal_content['nutrients'][n]['UNIT']\\n\\n\\n table.update_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={c.UNIQUE_IDENTIFIER: unique_id,\\n c.DATE: date},\\n UpdateExpression='SET #toplevel.#cat = :value',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_CONTAINER,\\n \\\"#cat\\\": container\\n },\\n ExpressionAttributeValues={\\n \\\":value\\\": item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container]\\n }\\n )\\n\\n table.update_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={c.UNIQUE_IDENTIFIER: unique_id,\\n c.DATE: date},\\n UpdateExpression='SET #toplevel = :value',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_DAY\\n },\\n ExpressionAttributeValues={\\n \\\":value\\\": item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY]\\n }\\n )\\n\\n table.update_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\\n Key={\\n c.UNIQUE_IDENTIFIER: unique_id,\\n c.WEEK: week\\n },\\n UpdateExpression='SET #toplevel = :value',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_WEEK\\n },\\n ExpressionAttributeValues={\\n \\\":value\\\": item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK]\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7dfb948b8301a0430651d0b5bb487bb\",\n \"score\": \"0.49241003\",\n \"text\": \"def player_eats_food(self, snake, food):\\n head = snake[0]\\n head_x, head_y = head\\n for food_item in food:\\n food_x, food_y = food_item\\n #Return whether or not the player's received food, adds fat if they have\\n if head_x == food_x and head_y == food_y:\\n #We've eaten food, update our score\\n self.score += int(150 + int(len(snake)/10)*5)\\n\\n #Add more fat to the user if they already have some or they have a decent score\\n self.fat += 8 + int(self.fat/4) + int(self.score/500)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e5cd209b264fb2dd585ad69bbc06220\",\n \"score\": \"0.49199784\",\n \"text\": \"def createConsumptionSeq(df):\\n mealList = []\\n M = {} #Contient les aliments d'un repas \\n nomen = df.nomen.iloc[0]\\n jour = df.jour.iloc[0]\\n tyrep = df.tyrep.iloc[0]\\n \\n for index, row in df.iterrows(): # Scan tous les aliments de consommation\\n #print(row.nomen, row.jour, row.tyrep)\\n if (row.nomen == nomen) & (row.jour == jour) & (row.tyrep == tyrep):\\n name = str(row.nomen) + '_' + str(index)\\n M[name] = Food(row.codsougr, row.codsougr_name, row.qte_nette) # Crée l'objet FoodItem\\n else:\\n m = list(set(M.values())) #Set of food items in a meal\\n uniqueM = eraseDubloons(m)\\n meal = Meal(uniqueM, jour = jour, tyrep = tyrep)\\n meal.reorder()\\n logging.debug('{}'.format(meal.nameTuple))\\n if meal:\\n mealList.append(meal)\\n\\n M = {}\\n nomen = row.nomen\\n jour = row.jour\\n tyrep = row.tyrep\\n \\n meals = [m for m in mealList if m]\\n return meals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c830b9fef7a64c1f072cecdf121412c0\",\n \"score\": \"0.4918213\",\n \"text\": \"def create_dico(item_list):\\n assert type(item_list) is list\\n dico = {}\\n for items in item_list:\\n for item in items:\\n if item not in dico:\\n dico[item] = 1\\n else:\\n dico[item] += 1\\n return dico\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c830b9fef7a64c1f072cecdf121412c0\",\n \"score\": \"0.4918213\",\n \"text\": \"def create_dico(item_list):\\n assert type(item_list) is list\\n dico = {}\\n for items in item_list:\\n for item in items:\\n if item not in dico:\\n dico[item] = 1\\n else:\\n dico[item] += 1\\n return dico\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ddaa4351c2892173251704a2ad04d77\",\n \"score\": \"0.49181578\",\n \"text\": \"def sum_list(new_list):\\n return sum(new_list)\\n # return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee3ad439b54ccc0e9043295f07ea1cd\",\n \"score\": \"0.49134657\",\n \"text\": \"def saved_meal_item_add(\\n self, meal_id, food_id, food_entry_name, serving_id, num_units\\n ):\\n params = {\\n \\\"method\\\": \\\"saved_meal_item.add\\\",\\n \\\"format\\\": \\\"json\\\",\\n \\\"saved_meal_id\\\": meal_id,\\n \\\"food_id\\\": food_id,\\n \\\"food_entry_name\\\": food_entry_name,\\n \\\"serving_id\\\": serving_id,\\n \\\"number_of_units\\\": num_units,\\n }\\n\\n response = self.session.get(self.api_url, params)\\n return self.valid_response(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a46b885d595c7bea38ce23fc1d6daa4d\",\n \"score\": \"0.4910791\",\n \"text\": \"def calculateInventory(self):\\n from collections import Counter\\n orders = self.model[0]\\n shipping = self.model[1]\\n inventory = {key:dict() for key in shipping}\\n citys = {o.dest for o in orders}\\n months = {o.orderTime.month for o in orders}\\n products = {o.pro for o in orders}\\n #print shipping\\n for pro in products:\\n for city in citys:\\n inventory[pro][city] = Counter()\\n for o in orders:\\n o.ship = self.chooseBestShipping(o,citys)\\n\\n inventory[o.pro][o.ship][o.orderTime.month] += o.num\\n #self.view.showInventory(inventory)\\n self.inventory = inventory\\n self.citys = citys\\n self.months = months\\n self.products = products\\n return inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcbca7ea38a938ec591a33b34ace0ede\",\n \"score\": \"0.49077475\",\n \"text\": \"def save(self, *args, **kwargs):\\n if self.pk is None:\\n item = self.item\\n item.quantity += self.quantity\\n item.save()\\n return super(Entry, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf4880010aa81907064f104b9d60f03\",\n \"score\": \"0.49064472\",\n \"text\": \"def subtotal(self):\\n sub = 0.0\\n for i in self.items:\\n sub += i.quantity * INVENTORY[i.name]\\n return sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442dae3041bd85957abe5451209e318b\",\n \"score\": \"0.48951203\",\n \"text\": \"def _initilizeItemInventory(self):\\n for itemName in itemPrice.keys():\\n for j in range(5):\\n self._itemInventory.add(itemName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bf1960258a00848bdd160a6a903b421\",\n \"score\": \"0.48922408\",\n \"text\": \"def find_food(self):\\n \\n if self.check_knife(1):\\n self.knife_dur -= 1\\n self.food += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4280fe8c4dcd8681a0e6d972b181946f\",\n \"score\": \"0.48690817\",\n \"text\": \"def createFoodDict(food_item_list):\\n food_dict = {}\\n for food_item in food_item_list:\\n words = food_item.split(' ')\\n first_word = words[0]\\n food_dict[first_word] = food_item\\n return food_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31e19cfc7f9cc7ac082b4629f8df71cb\",\n \"score\": \"0.48465908\",\n \"text\": \"def scan_onto_cart(self):\\n if hasattr(self, \\\"shelf\\\"):\\n for item in Item.shipment:\\n if len(self.items_stored) < self.max_capacity:\\n for product in self.shelf.compartments:\\n if item.number == product[\\\"Product Number\\\"]:\\n self.items_stored.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb607bc5bfc19f80144df53d7174c4b2\",\n \"score\": \"0.4835511\",\n \"text\": \"def returnfrequentitemsets(itemSet, transactionList, minSupport, freqSet):\\n\\t\\t_itemSet = set()\\n\\t\\tlocalSet = defaultdict(int)\\n\\n\\t\\tfor item in itemSet:\\n\\t\\t\\tfor transaction in transactionList:\\n\\t\\t\\t\\tif item.issubset(transaction):\\n\\t\\t\\t\\t\\tfreqSet[item] += 1\\n\\t\\t\\t\\t\\tlocalSet[item] += 1\\n\\n\\t\\tfor item, count in localSet.items():\\n\\t\\t\\t\\tsupport = float(count)/len(transactionList)\\n\\n\\t\\t\\t\\tif support >= minSupport:\\n\\t\\t\\t\\t\\t\\t_itemSet.add(item)\\n\\n\\t\\treturn _itemSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64d95b9e2dbd365c3e3c875f63c4ec1\",\n \"score\": \"0.48311788\",\n \"text\": \"def add_item(self, item):\\n self.items_with_price.update(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1dea7e98dd8eb0a30e38b7120376a69\",\n \"score\": \"0.4829422\",\n \"text\": \"def process_item(self, item, spider):\\n # looks for a duplicate\\n duplicate_check = self.db[self.collection_name].find({'name': item['name'], 'store': item['store'], 'category': item['category'], 'animal': item['animal']}).count()\\n if duplicate_check == 0:\\n self.db[self.collection_name].insert(dict(item))\\n logging.debug('Product added to MongoDB')\\n return item\\n else:\\n # logging.debug('Duplicate found. Skipping product')\\n raise DropItem('Duplicate found. Skipping product: %s' % item['name'])\\n # return item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616c81c4094a7fd6dab55793794e5f42\",\n \"score\": \"0.48247102\",\n \"text\": \"def generate_food(self):\\n num_food = ENV_N_FOOD\\n\\n pos = [(random() * self.W, random() * self.H) for i in range(num_food)]\\n\\n return [Food(x, y, self.W, self.H) for (x, y) in pos]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bdff644a55ea47546734621b9954d60\",\n \"score\": \"0.4823412\",\n \"text\": \"def budget(data,productsIds, quantity):\\r\\n \\r\\n data=data.fillna(0)\\r\\n budg=0\\r\\n products=productsIds\\r\\n quantity=quantity\\r\\n \\r\\n dataFilter = data[data['No. Item'].isin(products)]\\r\\n prices=dataFilter['Precio Unitario'].tolist()\\r\\n \\r\\n for i in range(len(quantity)):\\r\\n budg+=quantity[i]*prices[i]\\r\\n return budg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b6f3b617d4f2917091b725f1b1b61a\",\n \"score\": \"0.48086756\",\n \"text\": \"def get_total(self):\\n\\n base_price = 5\\n if self.species == \\\"Christmas melon\\\":\\n base_price = base_price * 1.5\\n\\n total = (1 + self.tax) * self.qty * base_price\\n \\n # if qty is less than ten melons and international, then +$3 to international_order\\n if self.qty < 10 and self.order_type == 'international':\\n total = total + 3\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15150d7171093c8a2769cd73df5a8ab\",\n \"score\": \"0.4804091\",\n \"text\": \"def fruits(fruit_list, fruit):\\n\\n updated_fruits_list = []\\n for item in fruit_list:\\n updated_fruits_list.append(item)\\n updated_fruits_list.append(fruit)\\n\\n return updated_fruits_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ff7340eee41c80ad913f6610332c1b\",\n \"score\": \"0.47999802\",\n \"text\": \"def add_to_cart(self, selection_id, selection_qty):\\n # read the data from data storage\\n for keys, values in ShoppingCart.__ecommerce_data.items():\\n # check if the product contain necessary data before proceed\\n if \\\"products\\\" and \\\"sku\\\" and \\\"prices\\\" and \\\"stock\\\" in values:\\n # check if it is a correct sku code from the data storage\\n if selection_id in values[\\\"sku\\\"]:\\n # add product information to the instance variables\\n self.__seller_id.append(keys)\\n product_id = values[\\\"sku\\\"].index(selection_id)\\n self.__seller.append(values[\\\"first_name\\\"] + \\\" \\\" + values[\\\"last_name\\\"])\\n self.__sku.append(values[\\\"sku\\\"][product_id])\\n self.__price.append(values[\\\"prices\\\"][product_id])\\n self.__product_name.append(values[\\\"products\\\"][product_id])\\n self.__pos.append(product_id)\\n self.__check_out_qty.append(selection_qty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1ac6afebd277ad77e422efa246f2ad9\",\n \"score\": \"0.47989768\",\n \"text\": \"def addItem(self, itemId, itemQty):\\r\\n self.items[itemId] += itemQty\\r\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10ce6669d3c4fce854bab14bf47b1b7\",\n \"score\": \"0.47970992\",\n \"text\": \"def add_item(self, item):\\n LOG.info('Add Product')\\n LOG.info(\\n \\\"Add the item: %s , price: %s, quantity: %s\\\"\\n , item.get_name(), item.get_price(), item.get_quantity())\\n if not item.get_name() in self.store_stock:\\n self.db_manager.insert_element(item)\\n else:\\n current_item_quantity = \\\\\\n self.store_stock[item.get_name()].get_quantity() \\\\\\n + item.get_quantity()\\n self.db_manager.update_quantity_field(quantity=current_item_quantity,\\n id=self.store_stock[item.get_name()].get_id())\\n LOG.info(\\\"Complete to add a item successful\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5be3b43a6445bef2e20fac0d8943eb\",\n \"score\": \"0.4792217\",\n \"text\": \"def combine_the_anon_cart(self, request):\\n cart = request.session.get('cart')\\n if cart:\\n for item in cart.items():\\n product = Product.objects.get(id=item[0])\\n if product.user == request.user:\\n messages.error(request, 'You can\\\\'t buy your product')\\n else:\\n quantity = item[1]['quantity']\\n self.add(product, quantity)\\n del request.session['cart']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f77055a0765db17ee93d22d7bb315d6\",\n \"score\": \"0.47913775\",\n \"text\": \"def total_items_sold(self, given_date):\\n # ensure given_date is a date\\n assert isinstance(given_date, date), \\\"given_date must be datetime.date()\\\"\\n\\n # get quantity from order_lines for given_date\\n quantities = [\\n i[0]\\n for i in cur.execute(\\\"\\\"\\\"\\n SELECT quantity\\n FROM orders\\n INNER JOIN order_lines ON orders.id=order_lines.order_id\\n WHERE created_at LIKE ?\\n \\\"\\\"\\\", (given_date.isoformat()+'%',))\\n ]\\n\\n # sum quantity\\n return sum(quantities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a07d888277c16437ea1ed144e02e719\",\n \"score\": \"0.47881973\",\n \"text\": \"def oh_quantity_by_location(self):\\n location_list = self.transactions.all().values(\\\"location\\\").distinct().order_by(\\\"location\\\")\\n my_dict = dict()\\n for l in location_list:\\n location = Location.objects.get(pk=l[\\\"location\\\"])\\n my_dict[location] = self.get_oh_quantity(location)\\n return my_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7a2e2d6e3ad23e24d00b97541db3187\",\n \"score\": \"0.47833753\",\n \"text\": \"def returnItemsWithMinSupport(itemSet, transactionList, minSupport, freqSet):\\n _itemSet = set()\\n localSet = defaultdict(int)\\n\\n for item in itemSet:\\n for transaction in transactionList:\\n if item.issubset(transaction):\\n freqSet[item] += 1\\n localSet[item] += 1\\n\\n # print 'localSet',localSet\\n for item, count in localSet.items():\\n # print item,count\\n support = float(count)/len(transactionList)\\n\\n if support >= minSupport:\\n _itemSet.add(item)\\n\\n return _itemSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e7d505ef1815ef26d24544b025773ca\",\n \"score\": \"0.47816607\",\n \"text\": \"def main():\\r\\n group_of_list = []\\r\\n group = {}\\r\\n count = 0\\r\\n dict_input_food_and_price = input()\\r\\n dict_input_food_and_price = json.loads(dict_input_food_and_price)\\r\\n while True:\\r\\n yourname = input()\\r\\n if yourname == \\\"End\\\":\\r\\n break\\r\\n group[yourname] = 0\\r\\n for food, price in dict_input_food_and_price.items():\\r\\n while True:\\r\\n yourname = input()\\r\\n if yourname == \\\"End\\\":\\r\\n break\\r\\n group_of_list.append(yourname)\\r\\n count += 1\\r\\n total = price/count\\r\\n for food in group_of_list:\\r\\n group[food] = group.get(food)+total\\r\\n count = 0\\r\\n group_of_list = []\\r\\n print(group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57fee8f9f7f940bfc0f162909b7ee2b2\",\n \"score\": \"0.47804844\",\n \"text\": \"def reducer(L):\\n\\tdistribution = {}\\n\\tfor item in L:\\n\\t\\tfor record in item:\\n\\t\\t\\tif record in distribution:\\n\\t\\t\\t\\tdistribution[record] += item[record]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdistribution[record] = item[record]\\n\\t\\titem.clear()\\n\\treturn distribution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dbbe3574385be484fb2e72fb6016bb1\",\n \"score\": \"0.47785863\",\n \"text\": \"def add_item(self, new_item):\\n self.items_with_price.update(new_item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301be53ed8845a4768b33a75ed332e78\",\n \"score\": \"0.4775439\",\n \"text\": \"def getFood( self, amount ) :\\n\\t\\treturn self.getSupply( amount )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e98031becdd3b82c96afc040c16348c\",\n \"score\": \"0.47707295\",\n \"text\": \"def get_total(self, **kwargs):\\n return self.get_price_per_item(**kwargs) * self.get_quantity(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bf9dc41926c19f7c1798b9a962799d\",\n \"score\": \"0.47650927\",\n \"text\": \"def cardlist_sum(cardlist1, cardlist2):\\n cards = deepcopy(cardlist1)\\n for name, amount in cardlist2.items():\\n if name not in cards:\\n cards[name] = 0\\n cards[name] = cards[name] + amount\\n return cards\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3e40827b1596cd982bb10623718ab9\",\n \"score\": \"0.47612268\",\n \"text\": \"def food_add_favorite(self, food_id, serving_id=None, number_of_units=None):\\n\\n params = {\\\"method\\\": \\\"food.add_favorite\\\", \\\"format\\\": \\\"json\\\", \\\"food_id\\\": food_id}\\n\\n if serving_id and number_of_units:\\n params[\\\"serving_id\\\"] = serving_id\\n params[\\\"number_of_units\\\"] = number_of_units\\n\\n response = self.session.get(self.api_url, params=params)\\n return self.valid_response(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d8b637023ae9e022ee3a1e96d883cd\",\n \"score\": \"0.47490332\",\n \"text\": \"def food_insert(self, name, calories, protein, fat, carbs, fiber):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d8c0504c732838f7986103ecf4d9fb0\",\n \"score\": \"0.47435322\",\n \"text\": \"def add(self, product, quantity=1, update_quantity=False):\\n product_vencode = str(product.vencode)\\n if product_vencode not in self.cart:\\n self.cart[product_vencode] = {'quantity': 0,\\n 'price': str(product.price)}\\n if update_quantity:\\n self.cart[product_vencode]['quantity'] = quantity\\n else:\\n self.cart[product_vencode]['quantity'] += quantity\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd12d614dd01fa7d1dc8d540d13a970\",\n \"score\": \"0.4740142\",\n \"text\": \"def add(self, item):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d3c09b955b186c27327828c5c875b7\",\n \"score\": \"0.47401217\",\n \"text\": \"def test_unique_quantities(self):\\n collection = Collection(['air__temperature', 'water__temperature'])\\n\\n quantities = collection.quantities()\\n\\n self.assertEqual(len(quantities), 1)\\n self.assertTrue('temperature' in quantities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f0904ee6421b4e1fba7c11b177be256\",\n \"score\": \"0.47290277\",\n \"text\": \"def get_sum(items=[]):\\n\\t\\tresult = 0\\n\\t\\tif items:\\n\\t\\t\\tresult = reduce(lambda x, y: x+y, items)\\n\\t\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa64214b750820c4bb4b85c71e8520d\",\n \"score\": \"0.47211656\",\n \"text\": \"def _amount_all(self):\\n for order in self:\\n amount_untaxed = 0.0\\n for line in order.depence:\\n amount_untaxed += line.montant\\n\\n order.update({\\n\\n 'Montant_total': amount_untaxed,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105d02d685d7278913bf8246ba61d913\",\n \"score\": \"0.4696378\",\n \"text\": \"def union(a, b):\\n c = a.copy()\\n for item, quantity in b.items():\\n if item in c:\\n c[item] += quantity\\n else:\\n c[item] = quantity\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a8f10d46c49f4fc02930e22a6c1f53\",\n \"score\": \"0.46955162\",\n \"text\": \"def sum_list(input_list: typing.List[float]) -> float:\\n return sum(input_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b6714d61699a90caccf507a1439ba0b\",\n \"score\": \"0.46904996\",\n \"text\": \"def add_food(self, food: FoodProp) -> None:\\n if not self.can_place_food(food):\\n raise Exception('Cannot place a food with that color on the colorful plate')\\n\\n self.foods.append(food)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e5cbf07cac2a1423f45e8e5ac6e692\",\n \"score\": \"0.46781105\",\n \"text\": \"def __len__(self):\\n return sum(item[\\\"qty\\\"] for item in self.basket.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b38093e769550aa59eb195f38c23eb6\",\n \"score\": \"0.46772686\",\n \"text\": \"def add_item(self, product, price):\\n if not product in self.items_in_cart:\\n self.items_in_cart[product] = price\\n print (product + \\\" added.\\\")\\n else:\\n print (product + \\\" is already in the cart.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1690b8513ce9e630d2fbdaaf6b81ca8d\",\n \"score\": \"0.4674813\",\n \"text\": \"def sum_mixed_list(input_list: List[Union[int, float]]) -> float:\\n return reduce(lambda x, y: x+y, input_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d97e4fa24429dc883f373bd0ca99355\",\n \"score\": \"0.46693996\",\n \"text\": \"def _product_association(self):\\n # Find orders that bought the target item\\n orders = self.data[self.data[self.item_col]==self.target][self.order_col]\\n \\n # Find all items in those orders\\n items = self.data[self.data[self.order_col].isin(orders)]\\n \\n # Recommend items frequently bought with the target item \\n self._find_recs(items.groupby(self.item_col).size())\\n self._print_recs(\\\"popular items other customers bought\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92b39dd58dbb549782499edade31dac\",\n \"score\": \"0.46683574\",\n \"text\": \"def autoPopulateInventory(self):\\n if not self._permanent:\\n self.clearInventory()\\n\\n # Randomly select the itemCount\\n itemCount = int(getRandomItemFromList(self._numOfItemsCarried))\\n\\n if not itemCount: # 0 items carried\\n return True\\n\\n for itemNum in range(1, itemCount + 1): # +1 due to exclusive ranges\\n itemId = getRandomItemFromList(self._itemCatalog)\\n if not itemId:\\n continue\\n if \\\"/\\\" not in itemId:\\n continue\\n\\n dLog(\\\"creature.autoPopulateInventory: obj = \\\" + itemId)\\n oType, oNum = itemId.split(\\\"/\\\")\\n obj1 = ObjectFactory(oType, oNum)\\n obj1.load()\\n self.addToInventory(obj1)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4025849a832f2176dc523dd3048e8ffc\",\n \"score\": \"0.46618393\",\n \"text\": \"def raw_ingredients_exact(item_set):\\n remaining = item_set.copy()\\n raw_ingredients = {}\\n while any(remaining):\\n item, quantity = list(remaining.items())[0]\\n item_object = items[item]\\n del remaining[item]\\n if item_object.raw:\\n raw_ingredients = union(raw_ingredients, {item: quantity})\\n else:\\n item_list = item_object.recipe.ingredients.items()\\n for ingredient, ingredient_quantity in item_list:\\n remaining = union(\\n remaining,\\n multiply(\\n {ingredient: ingredient_quantity},\\n quantity / item_object.recipe.quantity,\\n ),\\n )\\n return raw_ingredients\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a586b690c5f4ad80786ce24861767f9\",\n \"score\": \"0.46590447\",\n \"text\": \"def frequentOneItem(self):\\n\\n candidate = {}\\n # global finalPatterns, minSup, Database\\n # self.minSup = self.minSup\\n for i in range(len(self.Database)):\\n for j in range(len(self.Database[i])):\\n if self.Database[i][j] not in candidate:\\n candidate[self.Database[i][j]] = 1\\n else:\\n candidate[self.Database[i][j]] += 1\\n self.finalPatterns = {keys: value for keys, value in candidate.items() if value >= self.minSup}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cac81ccf764ad1c0b428a32da2a2677e\",\n \"score\": \"0.46445563\",\n \"text\": \"def add_item(request):\\n\\n if request.method == \\\"GET\\\":\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n \\n # Variable definition\\n try:\\n dish = Dish.objects.get(pk=(request.POST[\\\"dish\\\"]))\\n except:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n extra = request.POST.getlist('extra')\\n\\n # No toppings Pizza validator\\n if (dish.pk == 1 or dish.pk == 2 or dish.pk == 11 or dish.pk == 12) and len(extra) != 0:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # 1 topping Pizza validator\\n if (dish.pk == 3 or dish.pk == 4 or dish.pk == 13 or dish.pk == 14) and len(extra) != 1:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # 2 toppings Pizza validator\\n if (dish.pk == 5 or dish.pk == 6 or dish.pk == 15 or dish.pk == 16) and len(extra) != 2:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # 3 toppings Pizza validator\\n if (dish.pk == 7 or dish.pk == 8 or dish.pk == 17 or dish.pk == 18) and len(extra) != 3:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # Special Pizza Pizza validator\\n if (dish.pk == 9 or dish.pk == 10 or dish.pk == 19 or dish.pk == 20) and len(extra) != 5:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n \\n # Item variables\\n extras = []\\n price = dish.price\\n\\n for item in extra:\\n new_extra = Extra.objects.get(pk=(item))\\n extras.append(new_extra)\\n price += new_extra.price\\n\\n new_item = Item(dish=dish, price=price)\\n new_item.save()\\n for item in extras:\\n new_item.extras.add(item)\\n\\n # Cart Handling\\n try:\\n cart = Cart.objects.get(user=request.user)\\n except:\\n cart = Cart(user=request.user)\\n cart.save()\\n cart.items.add(new_item)\\n cart.save()\\n\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a923dccac387cb688c107df06e72d941\",\n \"score\": \"0.4643559\",\n \"text\": \"def sum(items):\\n if items is None or len(items) == 0:\\n return None\\n try:\\n return sum(items)\\n except:\\n msg = \\\"Error while addition: %s\\\" %sys.exc_info()[0]\\n sys.stderr.write(msg)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e463dda3a19d276a1fe9f6ee356c72\",\n \"score\": \"0.46426684\",\n \"text\": \"def test_add_item(self):\\n before = len(self.shopping_list_1.items)\\n self.shopping_list_1.add_item(self.item_3)\\n after = len(self.shopping_list_1.items)\\n self.assertEqual(1, after - before,\\n msg='The object should add an item to the instance')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d6261296989b18c47c219f2b8c7f83\",\n \"score\": \"0.46423283\",\n \"text\": \"def get_total(self):\\n\\n # self.base_price = self.get_base_price()\\n\\n if self.species == \\\"Christmas\\\":\\n self.base_price *= 1.5\\n total = (1 + self.tax) * self.qty * self.base_price\\n return total\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":180,"cells":{"query_id":{"kind":"string","value":"17ff9d02a0b166bd8a9c253d3fe795a0"},"query":{"kind":"string","value":"Update details of an Experiment. Update details of an Experiment with specific Experiment Id."},"positive_passages":{"kind":"list like","value":[{"docid":"10d33500639a40dacacd744c0f51f89c","score":"0.64625514","text":"async def update(\n self,\n subscription_id: str,\n resource_group_name: str,\n workspace_name: str,\n experiment_id: str,\n body: Optional[\"_models.ModifyExperiment\"] = None,\n **kwargs: Any\n ) -> \"_models.Experiment\":\n cls = kwargs.pop('cls', None) # type: ClsType[\"_models.Experiment\"]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n\n content_type = kwargs.pop('content_type', \"application/json\") # type: Optional[str]\n\n if body is not None:\n _json = self._serialize.body(body, 'ModifyExperiment')\n else:\n _json = None\n\n request = build_update_request(\n subscription_id=subscription_id,\n resource_group_name=resource_group_name,\n workspace_name=workspace_name,\n experiment_id=experiment_id,\n content_type=content_type,\n json=_json,\n template_url=self.update.metadata['url'],\n )\n request = _convert_request(request)\n request.url = self._client.format_url(request.url)\n\n pipeline_response = await self._client._pipeline.run( # pylint: disable=protected-access\n request,\n stream=False,\n **kwargs\n )\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\n\n deserialized = self._deserialize('Experiment', pipeline_response)\n\n if cls:\n return cls(pipeline_response, deserialized, {})\n\n return deserialized","title":""}],"string":"[\n {\n \"docid\": \"10d33500639a40dacacd744c0f51f89c\",\n \"score\": \"0.64625514\",\n \"text\": \"async def update(\\n self,\\n subscription_id: str,\\n resource_group_name: str,\\n workspace_name: str,\\n experiment_id: str,\\n body: Optional[\\\"_models.ModifyExperiment\\\"] = None,\\n **kwargs: Any\\n ) -> \\\"_models.Experiment\\\":\\n cls = kwargs.pop('cls', None) # type: ClsType[\\\"_models.Experiment\\\"]\\n error_map = {\\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\\n }\\n error_map.update(kwargs.pop('error_map', {}))\\n\\n content_type = kwargs.pop('content_type', \\\"application/json\\\") # type: Optional[str]\\n\\n if body is not None:\\n _json = self._serialize.body(body, 'ModifyExperiment')\\n else:\\n _json = None\\n\\n request = build_update_request(\\n subscription_id=subscription_id,\\n resource_group_name=resource_group_name,\\n workspace_name=workspace_name,\\n experiment_id=experiment_id,\\n content_type=content_type,\\n json=_json,\\n template_url=self.update.metadata['url'],\\n )\\n request = _convert_request(request)\\n request.url = self._client.format_url(request.url)\\n\\n pipeline_response = await self._client._pipeline.run( # pylint: disable=protected-access\\n request,\\n stream=False,\\n **kwargs\\n )\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)\\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\\n\\n deserialized = self._deserialize('Experiment', pipeline_response)\\n\\n if cls:\\n return cls(pipeline_response, deserialized, {})\\n\\n return deserialized\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"a90932de5b0dd8cc3450221c5e50b85a","score":"0.7416664","text":"def update_experiment(request, pk):\n\n required_data = request.data.get(\"requiredData\")\n tests_config = request.data.get(\"testsConfig\")\n expiration = request.data.get(\"expiration\")\n allow_multiple_answers = request.data.get(\"allowMultipleAnswers\")\n disabled = request.data.get(\"disabled\")\n name = request.data.get(\"name\")\n\n try:\n print('ENTERED')\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if tests_config is None or len(tests_config) == 0:\n return Response('At least one test must be provided', status=HTTP_400_BAD_REQUEST)\n\n if expiration is None:\n return Response('Valid expiration date must be provided', status=HTTP_400_BAD_REQUEST)\n\n try:\n experiment.name = name\n experiment.requiredDataConfig = required_data\n experiment.testsConfig = tests_config\n experiment.expiration = expiration\n experiment.allowMultipleAnswers = allow_multiple_answers\n experiment.disabled = disabled\n experiment.save()\n except Exception as e:\n return Response(str(e), HTTP_400_BAD_REQUEST)\n\n serialized = ExperimentSerializer(experiment).data\n\n return Response(serialized, HTTP_200_OK)","title":""},{"docid":"0aace5c2053a4192cc2f99b73768ad86","score":"0.67339313","text":"def update_experiment_metadata(\n writer_client, experiment_id, name=None, description=None\n):\n logger.info(\"Modifying experiment %r\", experiment_id)\n request = write_service_pb2.UpdateExperimentRequest()\n request.experiment.experiment_id = experiment_id\n if name is not None:\n logger.info(\"Setting exp %r name to %r\", experiment_id, name)\n request.experiment.name = name\n request.experiment_mask.name = True\n if description is not None:\n logger.info(\n \"Setting exp %r description to %r\", experiment_id, description\n )\n request.experiment.description = description\n request.experiment_mask.description = True\n try:\n grpc_util.call_with_retries(writer_client.UpdateExperiment, request)\n except grpc.RpcError as e:\n if e.code() == grpc.StatusCode.NOT_FOUND:\n raise ExperimentNotFoundError()\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\n raise PermissionDeniedError()\n if e.code() == grpc.StatusCode.INVALID_ARGUMENT:\n raise InvalidArgumentError(e.details())\n raise","title":""},{"docid":"546f21b219d724d0964dc0327c6c25f2","score":"0.66571933","text":"def test_submit_edit_experiment(self):\n experiment = self.create_test_experiment(name=\"old_name\")\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.name, \"new_name\")","title":""},{"docid":"3cfc1af097dea409e1e342c357e44dae","score":"0.61671877","text":"def refresh(self) -> None:\n self.update_from_remote_data(self._api_client.experiment_get(self.uuid))","title":""},{"docid":"9e7efd2d39f68200aa5173115ccf47fd","score":"0.6142266","text":"def test_edit_experiment_view(self):\n experiment = self.create_test_experiment()\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")","title":""},{"docid":"6c08855f31c71f29263100fbc5d63505","score":"0.59430104","text":"def update_emp(db, id, hoursTrained, trainedStatus):\r\n trainingInfo = {\"Hours Trained\" : hoursTrained,\r\n \"Training Completed\" : trainedStatus}\r\n\r\n db.collection(\"Employees\").document(id).set(trainingInfo)","title":""},{"docid":"58e74d8d26fab10630d7b37236175eee","score":"0.5902595","text":"def test_submit_edit_started_experiment_changes_name_and_notes(self):\n experiment = self.create_test_experiment(name=\"old_name\", notes=\"old_notes\",\n tracks_finalized=True)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n old_track = self.create_test_track(experiment=experiment, name=\"old_name_track\")\n experiment_json = {\n \"name\": \"new_name\", \"notes\": \"new_notes\", \"tracks\": [{\"id\": old_track.id,\n \"name\": \"new_track_name\"}],\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment_json)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.name, \"new_name\")\n self.assertEquals(experiment.notes, \"new_notes\")\n self.assertEquals(experiment.tracks.all()[0].name, \"new_track_name\")","title":""},{"docid":"5b3b67f1d59d87115dd9fc81a90aab86","score":"0.58990973","text":"def update(self, collab_id: str, project_id: str, expt_id: str, **updates):\n return self._execute_operation(\n operation=\"put\",\n url=self._generate_single_url(\n collab_id=collab_id,\n project_id=project_id, \n expt_id=expt_id\n ),\n payload=updates\n )","title":""},{"docid":"4272881909e59c29f6e7e94aae595276","score":"0.5827913","text":"def update(id_exp=None):\n\n current_app.logger.info(\"PUT /sync route with id: %s\" % id_exp)\n\n (gfg_id, exp_name, session_id), session, resp = parse_id_exp(id_exp)\n\n # Check JSON validity\n if utils.check_valid_json(request.get_data()):\n valid_json = json.loads(request.get_data())\n # session.datastring = valid_json\n else:\n resp = {\"status\": \"bad request\"}\n current_app.logger.error(\"Invalid JSON\")\n\n current_app.logger.info(\n \"Current trial: %s, unique_id: %s, experiment name: %s, session id: %s \" % (valid_json['currenttrial'],\n valid_json['uniqueid'], valid_json['experimentname'], valid_json['sessionid']))\n\n # For each trial, pass to appropriate parser, if not in db\n for json_trial in valid_json['data']:\n if exp_name == \"category_switch\":\n experiment_class = CategorySwitch\n elif exp_name == \"keep_track\":\n experiment_class = KeepTrack\n else:\n current_app.logger.error(\"%s does not exist\" % (exp_name))\n resp = {\"status\": \"bad request\"}\n\n db_trial, new = db_utils.get_or_create(db.session,\n experiment_class, gfg_id=gfg_id, session_id=session_id,\n trial_num=json_trial['current_trial'])\n\n # If the trial is new, add data\n if new:\n db_trial.add_json_data(json_trial)\n db.session.commit()\n\n # For each event, pass to parser, if not in db\n for json_event in valid_json['eventdata']:\n db_event, new = db_utils.get_or_create(db.session, EventData,\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name, \n timestamp = utils.convert_timestamp(json_event['timestamp']))\n\n if new:\n db_event.add_json_data(json_event)\n db.session.commit()\n\n # For the QuestionData, pass to parser, if not in db\n for json_ques in valid_json['questiondata']:\n db_ques, new = db_utils.get_or_create(db.session, QuestionData,\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name)\n\n if new:\n db_ques.add_json_data(json_ques)\n db.session.commit()\n\n if resp is None:\n resp = {\"status\": \"user data saved\"}\n return jsonify(**resp)","title":""},{"docid":"6b05deea5431803e1eac4738e4cc6a36","score":"0.5811839","text":"def update(self, request, pk=None):\n selected_routine = request.query_params.get('routine', None) \n\n try: \n exercise = Exercise.objects.get(pk=pk)\n\n exercise.routine = Routine.objects.get(pk=selected_routine)\n exercise.description = request.data['description']\n exercise.save()\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)\n \n except ValidationError as ex:\n return Response({\"reason\": ex.message}, status=status.HTTP_400_BAD_REQUEST)","title":""},{"docid":"e1ea28e67ff7a830d61e1851c7da9f33","score":"0.56713605","text":"async def edit_training_examples(\n intent: str,\n id: str,\n request_data: TextData,\n current_user: User = Depends(auth.get_current_user),\n):\n mongo_processor.edit_training_example(\n id, request_data.data, intent.lower(), current_user.get_bot(), current_user.get_user()\n )\n return {\"message\": \"Training Example updated!\"}","title":""},{"docid":"b610548abb62bf8777e59ea744a47f65","score":"0.5661236","text":"def put(self, exploration_id):\n exploration = exp_services.get_exploration_by_id(exploration_id)\n version = self.payload.get('version')\n _require_valid_version(version, exploration.version)\n\n commit_message = self.payload.get('commit_message')\n change_list = self.payload.get('change_list')\n\n try:\n exp_services.update_exploration(\n self.user_id, exploration_id, change_list, commit_message)\n except utils.ValidationError as e:\n raise self.InvalidInputException(e)\n\n self.values.update(self._get_exploration_data(exploration_id))\n self.render_json(self.values)","title":""},{"docid":"4f444f6fc62b19e72dd10f8ea281178b","score":"0.5650311","text":"def update(self, entity_id, details, delete_existing):","title":""},{"docid":"829b7025e64765315044ed5f1182fe55","score":"0.5622866","text":"def causx_edit_an_entity(id):\n project = get_project()\n updated_entry = request.get_json()[\"updated_entry\"]\n entity = causx_set_variable(project, id, updated_entry)\n response=dict(entity=entity)\n calc_params = get_calc_params(project, data_required=False)\n if calc_params:\n response[\"layers\"] = get_qnodes_layer(calc_params)\n return response, 200","title":""},{"docid":"d3a677e8ac2b6e770914054e27e7250c","score":"0.56208825","text":"def test_submit_edit_started_experiment_changes_existing_tracks(self):\n experiment = self.create_test_experiment(name=\"old_name\", tracks_finalized=False,\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n track1 = self.create_test_track(experiment=experiment, name=\"A\")\n track2 = self.create_test_track(experiment=experiment, name=\"B\")\n self.create_test_track_weight(experiment=experiment, track=track1)\n self.create_test_track_weight(experiment=experiment, track=track2)\n track_count = experiment.tracks.count()\n experiment_json = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": False,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": track1.id, \"weighting\": 30, \"name\": \"C\"},\n {\"id\": track2.id, \"weighting\": 70, \"name\": \"D\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment.id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment_json)\n )\n self.assertOkay(response)\n experiment = Experiment.objects.get(id=experiment.id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.tracks.count(), track_count)\n track1 = experiment.tracks.get(id=track1.id)\n track2 = experiment.tracks.get(id=track2.id)\n self.assertEquals(track1.name, \"C\") #Checks name has changed\n self.assertEquals(track2.name, \"D\")\n self.assertEquals(track1.weight.weighting, 30) #Checks weighting has changed\n self.assertEquals(track2.weight.weighting, 70)","title":""},{"docid":"02d99f571b3fa51ed6afda42fff2838c","score":"0.56005096","text":"def put(self, id):\n data = request.get_json()\n return update_a_reporter(id=id, data=data)","title":""},{"docid":"3eca505c37f3bd0881bd1ed010599a0a","score":"0.559187","text":"def test_edit_experiment_view_started_experiment(self):\n experiment = self.create_test_experiment()\n experiment.tracks_finalized = True\n experiment.save()\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")","title":""},{"docid":"e11f93fd1499f93fcb17b45a21891a2d","score":"0.55208725","text":"def test_submit_edit_started_experiment_does_not_change_tracks(self):\n experiment = self.create_test_experiment(name=\"old_name\", tracks_finalized=True,\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_tracks = experiment.tracks.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"},\n {\"id\": None, \"weighting\": None, \"name\": \"B\"},\n {\"id\": None, \"weighting\": None, \"name\": \"C\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.tracks.count(), no_tracks)","title":""},{"docid":"2db940d31dbabec99f670e73eaba4b31","score":"0.5507888","text":"def test_submit_edit_experiment_nonexistent(self):\n experiment_id = NONEXISTENT_EXPERIMENT_ID\n experiment = {\"name\": \"new_name\", \"notes\": \"\"}\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertEquals(response.status_code, 404)","title":""},{"docid":"214e821c70df6edb4046bfd747a7248d","score":"0.5484202","text":"def edit(project):\n if g.user:\n session['exp'] = project\n abstract = s.get_experiment_data(g.user, project, 'abstract')\n gl = s.get_experiment_data(g.user, project, 'gl')\n ec = s.get_experiment_data(g.user, project, 'ec')\n fn = s.get_experiment_data(g.user, project, 'fn')\n return render_template('edit.html', project=project, abstract=abstract, gl=gl, ec=ec, fn=fn)\n return redirect(url_for('login'))","title":""},{"docid":"f70ff7a5bde05f7ffc13d2ef2cf7f6ab","score":"0.5455811","text":"def update(self, entity_id, details, delete_existing=False):\n self._validate_entity_id(entity_id)\n\n if not isinstance(details, dict):\n msg = (_(\"Provided details %s is not valid dict.\")\n % details)\n raise ValueError(msg)\n\n return self._storage.update(\n entity_id, details, delete_existing)","title":""},{"docid":"95f73067644b0b8d431330b8b573696d","score":"0.54527766","text":"def update(self, event_id, event_time=None, **kwargs):\n pass","title":""},{"docid":"95f73067644b0b8d431330b8b573696d","score":"0.54527766","text":"def update(self, event_id, event_time=None, **kwargs):\n pass","title":""},{"docid":"95f73067644b0b8d431330b8b573696d","score":"0.54527766","text":"def update(self, event_id, event_time=None, **kwargs):\n pass","title":""},{"docid":"5ba50b0f354cac4d2eb92581c8b79298","score":"0.5451184","text":"def update(self, id, data):\n return self.make_request('update', '/' + id, data)","title":""},{"docid":"53e08e1e5e63528ecaa3e670768710da","score":"0.54415154","text":"def put(user_id,self):\n form = ResearchInfoUpdateForm(request.form)\n if form.validate():\n \n research_info = ResearchInformation.query.filter((ResearchInformation.id == form.research_id.data) & (ResearchInformation.user_id == user_id))\n if research_info is None:\n return make_response(jsonify({'error':'Please give an appropriate research ID'}),400)\n update_dict = {}\n for key, value in form.data.items():\n if value and key != 'research_id':\n update_dict[key] = value\n try:\n research_info.update(update_dict)\n db.session.commit()\n except:\n return make_response(jsonify({'error' : 'Database Connection Problem'}),500)\n return make_response(jsonify({'msg' : ' Successfully changed'}),201)\n\n else:\n return make_response(jsonify({'error':'Wrong input format'}),400)","title":""},{"docid":"83b67ad4ce2e3e31eb6207122f1a66f3","score":"0.5403792","text":"def put(self):\n return employee_service.edit_employee(get_jwt_identity(), get_uuid(request), **api.payload), 201","title":""},{"docid":"d2ac49e54bcfac440fb624d369239316","score":"0.53886133","text":"def _add_experiment(self, experiment_key, variation_id):\n\n experiment_id = self.config.get_experiment_id(experiment_key)\n self.params[EXPERIMENT_PARAM_FORMAT.format(experiment_prefix=Params.EXPERIMENT_PREFIX,\n experiment_id=experiment_id)] = variation_id","title":""},{"docid":"0fd8bb0e660cd252ad87d2e7d500422a","score":"0.53784376","text":"def put(self, id):\n\t\ttoken_parser.parse_args()\n\t\trecord = Record.query.get(id)\n\t\tif not record:\n\t\t\treturn error_response('RECORD NOT FOUND', 404) \n\t\t# PERMISSION_DENIED because don't want user to know this record id exists\n\t\tif record.runner.id!=int(get_jwt_identity()) and not min_access_level(self, ROLES['admin']):\n\t\t\treturn error_response('RECORD NOT FOUND', 404) \n\n\t\tdata = request.get_json() or {}\n\t\ttry:\n\t\t\trecord.update(data)\n\t\texcept ValueError as error:\n\t\t\treturn error_response('RECORD NOT FOUND', 404)\n\t\texcept Exception as error:\n\t\t\treturn error_response('PERMISSION DENIED', 403)\n\t\tdb.session.commit()\n\t\treturn make_response(jsonify(record.to_dict()), 200)","title":""},{"docid":"7a8c7feb5e4f073a780db8763a8c3a47","score":"0.53570676","text":"def test_edit_report(self):\n report = self.create_report()\n pk = report[0].pk\n my_url = report_url(pk)\n response = self.client.put(\n my_url,\n self.updated_report,\n **self.headers,\n format='json'\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)","title":""},{"docid":"3c6b8e8628b2258d34781373f3756603","score":"0.5355502","text":"def put(self, id):\n # Get the json object from the request body\n data = request.get_json()\n # Check all fields for availability and get the new data if the field is available\n if 'email' not in data:\n email = None\n else:\n email = data[\"email\"]\n if 'first_name' not in data:\n first_name = None\n else:\n first_name = data[\"first_name\"]\n if 'last_name' not in data:\n last_name = None\n else:\n last_name = data[\"last_name\"]\n if 'availability' not in data:\n availability = None\n else:\n availability = data[\"availability\"]\n\n # Update all requested fields of the requested interviewer, if there is an interviewer with the id, else return\n # an error\n interviewer = interviewer_ctrl.update_interviewer(id, email, first_name, last_name, availability)\n if not interviewer:\n response = \"Interviewer with id %s does not exist or email %s is already taken by another interviewer\" % (\n id, email), 400\n else:\n response = interviewer, 200\n return response","title":""},{"docid":"15c1f7722e5e0c8697db10072db3f404","score":"0.53356797","text":"def put(self, id):\n data = request.json\n args = request.args\n return update_unit(id, data, args)","title":""},{"docid":"f5a0bd39cf438b04b13e0fd2dad2b75f","score":"0.5282175","text":"def update_project(id=None, name=None, description=None):\n pass","title":""},{"docid":"7da3e669c6d3439e143564ac0ff1622b","score":"0.5282065","text":"def test_edit_experiment_view_last_modified_updated(self):\n experiment = self.create_test_experiment()\n experiment.name += \" (updated)\"\n response = self.client.post(reverse(\"ab_testing_tool_submit_edit_experiment\",\n args=(experiment.id,)),\n content_type=\"application/json\",\n data=experiment.to_json())\n self.assertEquals(response.content, \"success\")\n updated_experiment = Experiment.objects.get(id=experiment.id)\n self.assertLess(experiment.updated_on, updated_experiment.updated_on,\n response)","title":""},{"docid":"a63400c45bfcc9bce89953c8ae6b3701","score":"0.5281295","text":"def put(self, id):\n data = request.get_json()\n if 'email' not in data:\n email = None\n else:\n email = data[\"email\"]\n if 'first_name' not in data:\n first_name = None\n else:\n first_name = data[\"first_name\"]\n if 'last_name' not in data:\n last_name = None\n else:\n last_name = data[\"last_name\"]\n if 'availability' not in data:\n availability = None\n else:\n availability = data[\"availability\"]\n interviewee = interviewee_ctrl.update_interviewee(id, email, first_name, last_name, availability)\n if not interviewee:\n response = \"Interviewee with id %s does not exist or email %s is already taken by another interviewee\" % (\n id, email), 400\n else:\n response = interviewee, 200\n return response","title":""},{"docid":"f85bb5f0f4fbddd046d215c53c2b171b","score":"0.52504665","text":"def put(self,request,employee_id):\n try:\n get_data = Employee.objects.get(pk=employee_id)\n employee_data = EmployeeSerializer(get_data,data=request.data)\n if not(employee_data.is_valid()):\n return ApiResponse().error(\"Error while update employee details\",400)\n employee_data.save()\n return ApiResponse().success(employee_data.data,200)\n except Exception as err:\n print(err)\n return ApiResponse().success(\"Error\",500)","title":""},{"docid":"ecba54809858f5347c6dc258ddbd143b","score":"0.5228171","text":"def update_testimonial():\n data = request.get_json()\n description = data.get('description')\n title = data.get('title')\n image = data.get('image')\n rank = data.get('rank')\n testimonial_id = data.get('id')\n\n if (not title) or (not description) or (not rank) or (not testimonial_id):\n return jsonify(result={'failed_msg': \"Unanle to save testimonial with missing fields\"})\n\n return jsonify(result=save_testimonial(title, description, rank, image, _id=testimonial_id))","title":""},{"docid":"997ffa7f45afa19ce3c55b4b84f758f6","score":"0.5227724","text":"async def update(\n self,\n id: int,\n data: EnvironmentUpdateSchema\n ) -> Record:\n\n async with self.database.transaction():\n query = (\n environments_table.update()\n .where(environments_table.c.id == id)\n .values(\n name=data.name,\n description=data.description,\n updated_at=datetime.now()\n )\n .returning(\n environments_table.c.id,\n environments_table.c.name,\n environments_table.c.code,\n environments_table.c.description,\n environments_table.c.created_at,\n environments_table.c.updated_at,\n environments_table.c.deleted_at,\n environments_table.c.is_deleted\n )\n )\n\n return await self.database.fetch_one(query)","title":""},{"docid":"1b7edbfabd11852be3e16d3e6569fbe4","score":"0.52266294","text":"def update(self, model, id, update_with):\n raise NotImplementedError('You have not implemented an update func in your client class')","title":""},{"docid":"15f18479a5de36268b4534b705d906ee","score":"0.5196483","text":"def test_put_experiment_valid_permission(self):\n url = '/' + version + '/collection/unittestcol/experiment/unittestexp/'\n data = {'description': 'A new experiment for unit tests. Updated'}\n\n response = self.client.put(url, data=data)\n self.assertEqual(response.status_code, 200)","title":""},{"docid":"cf3a3156e9bbff73bb66d428cba3cc4a","score":"0.5194518","text":"def test_submit_edit_experiment_changes_assignment_method_to_weighted(self):\n experiment = self.create_test_experiment(name=\"old_name\")\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_track_weights = experiment.track_probabilites.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": False,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": 20, \"name\": \"A\"},\n {\"id\": None, \"weighting\": 80, \"name\": \"B\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.track_probabilites.count(), no_track_weights + 2)","title":""},{"docid":"3ca66dab826730162433142988260ed8","score":"0.5188264","text":"def test_submit_edit_experiment_changes_assignment_method_to_uniform(self):\n experiment = self.create_test_experiment(\n name=\"old_name\", assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_tracks = experiment.tracks.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"},\n {\"id\": None, \"weighting\": None, \"name\": \"B\"},\n {\"id\": None, \"weighting\": None, \"name\": \"C\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.UNIFORM_RANDOM)\n self.assertEquals(experiment.tracks.count(), no_tracks + 3)","title":""},{"docid":"7565e22ff55c1a4dbfaef25b7830ba89","score":"0.5184008","text":"def put(self, id):\n report = self.mydb.get_by_id(id)\n \n if report:\n data = request.get_json()\n report.createdBy = data['username']\n report.type = data['flag']\n report.location = data['location']\n report.status = data['status']\n report.image = data['image']\n report.video = data['video']\n report.comment = ['comment']\n return make_response(jsonify({\n \"message\":\"Report edited successfully\",\n \"data\": report.serialize()\n }), 201)\n else:\n return make_response(jsonify({\n \"message\": \"ID invalid, no report found\"\n }),400)","title":""},{"docid":"160cb1102a31e22433b0a95d1b19c287","score":"0.5183066","text":"def upload_experiment(self, experiment: Experiment) -> None:\n data = {\n 'device_name': experiment.backend_name,\n 'type': experiment.type,\n 'extra': experiment.extra,\n }\n if experiment.start_datetime:\n data['start_time'] = local_to_utc_str(experiment.start_datetime)\n if experiment.tags:\n data['tags'] = experiment.tags\n if experiment.uuid:\n data['uuid'] = experiment.uuid\n response_data = self._api_client.experiment_upload(data)\n experiment.update_from_remote_data(response_data)","title":""},{"docid":"a0650d7cb60a6083e8c9359cba962c35","score":"0.517982","text":"def update(self, typ, id, **kwargs):\n return self._load(self._request(self._url(typ, id), 'PUT', kwargs))","title":""},{"docid":"8b5e1688a20dad535a152c61484ada45","score":"0.517151","text":"def update(self, step_name, worker_id):\n record = ReportClient().get_record(step_name, worker_id)\n logging.debug(\"Get Record=%s\", str(record))\n self.search_alg.update(record.serialize())\n ParameterSharing().remove()\n logging.info(f\"Update Success. step_name={step_name}, worker_id={worker_id}\")\n logging.info(\"Best values: %s\", ReportServer().print_best(step_name=General.step_name))","title":""},{"docid":"4cc0ccaf21cc95495b47366271ce2579","score":"0.51659554","text":"def post_experiment(self):\n if not self.has_prepared_out:\n self.prepare_experiment()\n\n for exp, prod, desc in self.experiment.overview:\n print exp, prod, desc\n dt = json.dumps(\\\n {'name': exp,\n 'desc': desc})\n resp = requests.post(\n self.server_url + 'experiments/',\n data=dt,\n headers={'Content-type':'application/json'})\n print resp","title":""},{"docid":"3a1a25ea3994e5d6663400ef4dc22a54","score":"0.5152818","text":"def edit_quiz(request_ctx, course_id, id, quiz_notify_of_update, **request_kwargs):\n\n path = '/v1/courses/{course_id}/quizzes/{id}'\n payload = {\n 'quiz[notify_of_update]' : quiz_notify_of_update,\n }\n url = request_ctx.base_api_url + path.format(course_id=course_id, id=id)\n response = client.put(request_ctx, url, payload=payload, **request_kwargs)\n\n return response","title":""},{"docid":"76aca4be1ce01e6f0e21a2963567cc01","score":"0.5147998","text":"def test_edit_experiment_view_nonexistent(self):\n e_id = NONEXISTENT_EXPERIMENT_ID\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(e_id,)))\n self.assertTemplateNotUsed(response, \"ab_tool/edit_experiment.html\")\n self.assertEquals(response.status_code, 404)","title":""},{"docid":"ddb24b14a3442c8472d5c6d09956b062","score":"0.51298386","text":"def update_project(self, id: str, name: str = None, description: str = None) -> Dict:\n pass","title":""},{"docid":"9609d770f7c1fe4197f40d9c80ed4b91","score":"0.5129037","text":"def update_environment(context: click.Context, application_id: str, environment_id: str, name: str, url: str) -> None:\n api = context.obj\n application = api.application_by_id(application_id)\n environment = api.environment_by_id(application, environment_id)\n updated_environment = api.update_environment(application, environment, name, url)\n table = [[\"ID\", \"Name\", \"URL\"], [updated_environment.id, updated_environment.name, updated_environment.url]]\n click.echo(tabulate.tabulate(table, headers=\"firstrow\"))","title":""},{"docid":"da739f61d8dd8506648f0f235b47402d","score":"0.51289636","text":"def edit_experience(request, id):\r\n experience = Experience.objects.get(pk=id)\r\n context = {\r\n 'experience': experience\r\n }\r\n if request.method == 'POST':\r\n experience.user_id = request.POST['user_id' or None]\r\n experience.company_name = request.POST['company_name' or None]\r\n experience.job_title = request.POST['job_title' or None]\r\n experience.job_description = request.POST['job_description' or None]\r\n experience.year_from = request.POST['year_from' or None]\r\n experience.year_to = request.POST['year_to' or None]\r\n\r\n experience.save()\r\n messages.success(request, 'Experience Updated Successfully')\r\n return redirect('dashboard', request.user.pk)\r\n else:\r\n return render(request, 'accounts/edit_experience.html', context)","title":""},{"docid":"1d62bd10f0ddf13b60680d431d1214af","score":"0.5126918","text":"def update_teacher(email=None, zipcode=None, bio=None, \n mobile_number=None, days_of_week=None,teaching_experience_in_hours=None,\n pay_rate_per_hour=None, pod_id=None, img_url=None, covid_risk_profile_id=None):\n\n teacher = db.session.query(Teacher).filter(Teacher.email==email).one()\n teacher.zipcode=zipcode \n teacher.bio=bio \n teacher.mobile_number=mobile_number\n teacher.days_of_week=days_of_week\n teacher.teaching_experience_in_hours=teaching_experience_in_hours\n teacher.pay_rate_per_hour=pay_rate_per_hour\n teacher.pod_id=pod_id\n teacher.img_url=img_url\n teacher.covid_risk_profile_id=covid_risk_profile_id\n\n db.session.commit()\n\n return teacher","title":""},{"docid":"608457875b3bc5272855818c41796e9c","score":"0.5124847","text":"def edit(obj, index, description, duration, code, task_id, project_id):\n with ots_filestore(obj) as timesheet_storage:\n timesheet_edited = timesheet_storage.edit_timesheet(\n index,\n description=description,\n duration=duration,\n task_code=code,\n task_id=task_id,\n project_id=project_id,\n )\n if timesheet_edited:\n click.echo('Timesheet updated.')\n else:\n click.echo('Nothing to update.')","title":""},{"docid":"3cbff9690ae2f28c9373856792e9b190","score":"0.50883085","text":"def update(self, data, id):\n with db.session.begin_nested():\n self.query.filter_by(id=id).update(data)","title":""},{"docid":"f165c0fcebdc404642a446e6277bbe24","score":"0.5067855","text":"def test_submit_edit_experiment_wrong_course(self):\n experiment = self.create_test_experiment(name=\"old_name\",\n course_id=TEST_OTHER_COURSE_ID)\n data = {\"name\": \"new_name\", \"notes\": \"\"}\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment.id,)),\n content_type=\"application/json\", data=json.dumps(data)\n )\n self.assertError(response, UNAUTHORIZED_ACCESS)","title":""},{"docid":"a61125735c80419b89b3c2ef8fdb6cc7","score":"0.50544035","text":"def update_suite(suiteId):\n\t res = requests.post('https://api.ghostinspector.com/v1/suites/'+suiteId+'/?apiKey='+apiKey)\n\t return res","title":""},{"docid":"67143faae1b601cdc1166defe338b29d","score":"0.505348","text":"def step_impl(context, post_id):\n context.payload = constants.POST_INFORMATION_TO_UPDATE\n context.response = request.put(context.url + \"/%s\" % post_id, context.payload)","title":""},{"docid":"c923e8f9fc820942ebc8185747323eb5","score":"0.5051238","text":"def get_experiment(experiment_id):\n req = requests.get(\"{}experiments/{}/?format=json\".format(__base_url__, experiment_id))\n metadata = req.json()\n if metadata['status'] != 'error':\n save_metadata(metadata)\n else:\n print 'Error fetching metadata for {}'.format(experiment_id)","title":""},{"docid":"0c7f7bf6fb01612d985fa2b143d8134b","score":"0.5051149","text":"def wxt_update_person(person_id, data, myheaders):\n update_person_url = 'https://api.ciscospark.com/v1/people/{0}'\n response = requests.put(update_person_url.format(person_id),\n data=json.dumps(data),\n headers=myheaders)\n return response","title":""},{"docid":"4a558e28f4fcb1556894539bd864dab7","score":"0.5049372","text":"def experiment_detail(request, exp_name):\n exp = get_object_or_404(Experiment, name=exp_name)\n reports = ExperimentReport.objects.filter(experiment=exp)\n\n return render_to_response(\n \"splango/experiment_detail.html\",\n {\"title\": exp.name, \"exp\": exp, \"reports\": reports},\n RequestContext(request))","title":""},{"docid":"0758bd2dc137efe10c77c05015e4b113","score":"0.50357556","text":"def update_movie_details(db: Session, sl_id: int, details: schema.UpdateMovie):\n db.query(model.Movies).filter(model.Movies.id == sl_id).update(vars(details))\n db.commit()\n return db.query(model.Movies).filter(model.Movies.id == sl_id).first()","title":""},{"docid":"e912a37963fc2345ab045aa5222960ba","score":"0.5034493","text":"def update_by_id(self, id=None, **kwargs):\n if not id:\n raise PardotAPIArgumentError('id is required to update a prospect.')\n response = self._post(path='/do/update/id/{id}'.format(id=id), params=kwargs)\n return response","title":""},{"docid":"bec9d8e407070c9a77a984f35bc91736","score":"0.5032699","text":"def update_test_run(self, run_update_model, project, run_id):\n route_values = {}\n if project is not None:\n route_values['project'] = self._serialize.url('project', project, 'str')\n if run_id is not None:\n route_values['runId'] = self._serialize.url('run_id', run_id, 'int')\n content = self._serialize.body(run_update_model, 'RunUpdateModel')\n response = self._send(http_method='PATCH',\n location_id='cadb3810-d47d-4a3c-a234-fe5f3be50138',\n version='5.0',\n route_values=route_values,\n content=content)\n return self._deserialize('TestRun', response)","title":""},{"docid":"41e64bd368f6696cc8782c9dac2067f6","score":"0.50115055","text":"def update(self, request, pk=None):\n player = Player.objects.get(user=request.auth.user)\n suspect = Suspect.objects.get(pk=pk)\n suspect.player = player\n suspect.name = request.data[\"name\"]\n suspect.description = request.data[\"description\"]\n guilty = Guilty.objects.get(pk=request.data[\"guiltyId\"])\n suspect.guilty = guilty\n movie = Movie.objects.get(pk=request.data[\"movieId\"])\n suspect.suspect_image_url = request.data[\"suspectImageUrl\"]\n suspect.movie = movie\n \n suspect.save()\n\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)","title":""},{"docid":"489dce730c6e0515e0c8902d35851c78","score":"0.49720645","text":"def update_test(testId):\n\t res = requests.post('https://api.ghostinspector.com/v1/tests/'+testId+'/?apiKey='+apiKey)\n\t return res","title":""},{"docid":"6b1c0e0826bdbdb476676fb3b9967ce7","score":"0.49566084","text":"def test_put_experiment_no_permissions(self):\n url = '/' + version + '/collection/col1/experiment/exp1/'\n data = {'description': 'A new experiment for unit tests. Updated'}\n\n response = self.client.put(url, data=data)\n self.assertEqual(response.status_code, 403)","title":""},{"docid":"60ee48539f966381bc6e042c165a54a8","score":"0.49527475","text":"def put(self, id):\n return DAO.update(id, api.payload)","title":""},{"docid":"3c8a67ef451a5b9d3cfbff45c9f08289","score":"0.49498895","text":"def delete_experiment(writer_client, experiment_id):\n logger.info(\"Deleting experiment %r\", experiment_id)\n request = write_service_pb2.DeleteExperimentRequest()\n request.experiment_id = experiment_id\n try:\n grpc_util.call_with_retries(writer_client.DeleteExperiment, request)\n except grpc.RpcError as e:\n if e.code() == grpc.StatusCode.NOT_FOUND:\n raise ExperimentNotFoundError()\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\n raise PermissionDeniedError()\n raise","title":""},{"docid":"60ebeb2bb4b736d0d454c31d04209770","score":"0.49268797","text":"def test_edit_experiment_view_with_tracks_weights(self):\n experiment = self.create_test_experiment()\n experiment.assignment_method = Experiment.WEIGHTED_PROBABILITY_RANDOM\n track1 = self.create_test_track(name=\"track1\", experiment=experiment)\n track2 = self.create_test_track(name=\"track2\", experiment=experiment)\n self.create_test_track_weight(experiment=experiment, track=track1)\n self.create_test_track_weight(experiment=experiment, track=track2)\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")","title":""},{"docid":"1a83bb2f35b27541623f09db41fce462","score":"0.49165675","text":"def update_results(student_id, experiment_id, student_response):\n\n replies = get_experiment_replies(experiment_id)\n replies_dict = json.loads(replies)\n\n replies_dict['replies'].append({\"id\": int(student_id), \"response\": student_response})\n\n new_replies = json.dumps(replies_dict)\n\n con = sql.connect(DB_PATH)\n cur = con.cursor()\n\n cur.execute(\"UPDATE experiments SET replies = ? WHERE id = ?\", (new_replies, experiment_id))\n\n cur.execute(\"DELETE FROM experiments_students \"\n \"WHERE student_id = ? AND experiment_id = ?\", (student_id, experiment_id))\n\n con.commit()\n\n con.close()","title":""},{"docid":"d8b1a5247263b3eaa3ed81d6c8455d38","score":"0.49110433","text":"def update(self, kwargs):\n if self._status in [DEFAULT, DETACHED]:\n if any([k not in self.param_keys() and k in self._specials for k in kwargs]):\n raise ValueError('Key not recognised!')\n else:\n self.__dict__.update(kwargs)\n else:\n raise ValueError('Parameters of a created experiment cannot be updated.')","title":""},{"docid":"b058a3ccc0c492a3a2054e916ba606a3","score":"0.4910217","text":"def update(self, model, rec_id, data):\n\n url = self.base_url + model + '/' + str(rec_id)\n data = format_write_data(data)\n return self.session.put(url, data=data)","title":""},{"docid":"9c3a3f85820ec6698cda066db2a4dae5","score":"0.4900522","text":"def detail_put(self, request, id, *args, **kwargs):\n raise NotAllowed()","title":""},{"docid":"84ef2def248ce0213ba3a195c608662a","score":"0.48950928","text":"def edit_education(request, id):\r\n education = Education.objects.get(pk=id)\r\n context = {\r\n 'education': education\r\n }\r\n if request.method == 'POST':\r\n education.user_id = request.POST['user_id' or None]\r\n education.school = request.POST['school' or None]\r\n education.field_of_study = request.POST['field_of_study' or None]\r\n education.qualification = request.POST['qualification' or None]\r\n education.year = request.POST['year' or None]\r\n\r\n education.save()\r\n messages.success(request, 'Education Updated Successfully, you may add another !')\r\n return redirect('dashboard', request.user.pk)\r\n else:\r\n return render(request, 'accounts/edit_education.html', context)\r\n\r\n # DELETE EDUCATION\r","title":""},{"docid":"1f9b95546a734ec60c96a828783d5784","score":"0.4894346","text":"def test_experiment_specification(self, client, ephemeral_storage):\n\n storage = ephemeral_storage.storage\n\n _add_experiment(storage, name=\"a\", version=1, _id=1)\n _add_trial(storage, experiment=1, id_override=\"ae8\", status=\"completed\")\n\n response = client.simulate_get(\"/experiments/status/a\")\n assert response.status == \"200 OK\"\n assert response.json == {\n \"trials_completed\": 1,\n \"best_trials_id\": \"7bbfdb8c684aa5f4be324a09d7da94af\",\n \"best_evaluation\": 0.05,\n \"start_time\": \"0001-01-01 00:00:00\",\n \"finish_time\": \"0001-01-02 00:00:00\",\n \"max_trials\": 10,\n \"nb_trials\": 1,\n \"progress\": 0.1,\n \"trial_status_count\": {\"completed\": 1},\n \"elapsed_time\": \"23:59:50\",\n \"sum_of_trials_time\": \"23:59:50\",\n \"eta\": \"8 days, 23:58:30\",\n \"eta_milliseconds\": 777510000.0,\n }","title":""},{"docid":"527afa47e52ca91f15e0bf2975749899","score":"0.48926717","text":"def edit(id):\n\n pet = Pet.query.get_or_404(id)\n form = EditPetForm(obj=pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.data[\"photo_url\"]\n pet.notes = form.data[\"notes\"]\n pet.available = form.data['available']\n db.session.commit()\n flash(f\"{pet.name} updated.\", 'success')\n return redirect(url_for('index'))\n\n else:\n return render_template(\"edit_pet_form.html\", form=form, pet=pet)","title":""},{"docid":"f73c9c211624982ebce62505144b3717","score":"0.48906887","text":"def update(self):\n response = self.api(self.id).get()\n self._update_fields(response)","title":""},{"docid":"3aac6ddcfb59a9b9059b7ceec9897878","score":"0.48723197","text":"def put(self, id):\n data = request.json\n if data[\"entityID\"] != id:\n return {\"message\": \"entityID property in the incoming json object and id parameter in the URL path are\"\n \"not matched\"}, 400\n evn = EventService.get_by_id(id)\n if not evn:\n return {\"message\": \"The event does not exist\"}, 404\n else:\n return EventService.update(data, id)","title":""},{"docid":"4ee013b5fc5b6473163ae2bdd9f275cd","score":"0.4865083","text":"def test_experiment_specification(self, client, ephemeral_storage):\n\n storage = ephemeral_storage.storage\n\n _add_experiment(storage, name=\"a\", version=1, _id=1)\n _add_trial(storage, experiment=1, id_override=\"ae8\", status=\"completed\")\n\n response = client.simulate_get(\"/experiments/a\")\n\n assert response.status == \"200 OK\"\n\n assert response.json[\"name\"] == \"a\"\n assert response.json[\"version\"] == 1\n assert response.json[\"status\"] == \"not done\"\n assert response.json[\"trialsCompleted\"] == 1\n assert response.json[\"startTime\"] == \"0001-01-01 00:00:00\" # TODO\n assert response.json[\"endTime\"] == \"0001-01-02 00:00:00\" # TODO\n assert len(response.json[\"user\"])\n assert response.json[\"orionVersion\"] == \"x.y.z\"\n\n _assert_config(response.json[\"config\"])\n _assert_best_trial(response.json[\"bestTrial\"])","title":""},{"docid":"d7d6f7bd49709c1e26e4af92b13dda56","score":"0.48624727","text":"def api_employee_edit(request):\r\n\r\n if request.method.lower() == 'post':\r\n\r\n data = request.POST.dict()\r\n employee_id = int(request.POST['id'])\r\n\r\n try:\r\n employee = Employee.objects.get(id=employee_id)\r\n\r\n form_employee = EmployeeEditForm(data=data, instance=employee)\r\n\r\n if form_employee.is_valid():\r\n form_employee.save()\r\n return ApiResponse.http(status=ApiResponse.ST_SUCCESS, message='Employee successfully updated!', employee_id=employee_id)\r\n else:\r\n errors = dict(form_employee.errors)\r\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Validation errors.', errors=errors)\r\n except Employee.DoesNotExist:\r\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Invalid employee.')\r\n else:\r\n return ApiResponse.http(status=ApiResponse.ST_FORBIDDEN, message='Use Post!')","title":""},{"docid":"9bcea40c6b9b0fdaa251a9fb9aff1687","score":"0.4860615","text":"def delete_experiment(request, pk):\n\n try:\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.deleted is True:\n return Response('Experiment with given ID is already deleted', status=HTTP_400_BAD_REQUEST)\n\n experiment.deleted = True;\n experiment.save()\n\n return Response(HTTP_200_OK)","title":""},{"docid":"c3d2f2168376364b60de974564c9a455","score":"0.48573977","text":"def patch(self, incident_id):\n data = request.get_json()\n\n # script to allow only location request.body\n key_list = [*data]\n if (len(key_list) == 1 and key_list[0] == \"location\"):\n\n new_incident_instance = IncidentsModel()\n res = new_incident_instance.edit_incident(incident_id, data)\n\n if res:\n return {\n \"status\": 200,\n \"data\": [{\n \"id\": res[\"id\"],\n \"message\": \"incident record has been updated\"\n }]\n }, 200\n else:\n return {\n \"status\": 404,\n \"error\": \"Not found for id {}\".format(incident_id)\n }, 404\n else:\n return {\n \"status\": 403,\n \"error\": \"Bad Request only location is allowed\"\n }, 403","title":""},{"docid":"b76af9e574ad062068ef873994d3d48a","score":"0.48331708","text":"def update_teste_cmd(teste_id, **teste_properties):\n return UpdateTesteCommand(teste_id, **teste_properties)","title":""},{"docid":"b77d5e6d015b83efb6dd8d19931b53fe","score":"0.48324943","text":"def get_experiment(request, pk):\n\n try:\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.deleted is True:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.disabled is True:\n return Response('This experiment is currently unavailable', status=HTTP_423_LOCKED)\n\n if experiment.expiration < timezone.now():\n return Response('Experiment with given ID already expired', status=HTTP_400_BAD_REQUEST)\n\n serialized = ExperimentSerializer(experiment).data\n\n return Response(serialized, status=HTTP_200_OK)","title":""},{"docid":"834ae376dc46243ee0e99aa11693c662","score":"0.48319104","text":"def test_update_resolution(self):\n self.client = APIClient()\n login = self.client.login(username=self.username, password=self.password)\n self.assertEqual(login, True)\n url = reverse('resolution-detail', args=[str(self.upd_pk)])\n\n data = {\n 'description': 'Updated description',\n }\n response = self.client.put(url, data=data, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution update Failed')\n self.assertDictContainsSubset(data, response.data, \"Modified Resolution didn't contain the updated description\")\n\n #lets doublecheck we get updated data back\n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution failed to be Retrieved')\n self.assertDictContainsSubset(data, response.data, \"Resolution didn't contain the updated description\")","title":""},{"docid":"4601be0b78971f6a8aa160d438fd449c","score":"0.48254716","text":"def test_modify_report(self):\n print \"(\"+self.test_modify_report.__name__+\")\", self.test_modify_report.__doc__\n resp = self.client.put(self.url,\n data=json.dumps(self.report_1_req),\n headers={\"Content-Type\": JSON})\n self.assertEquals(resp.status_code, 204)\n #Check that the report has been modified\n resp2 = self.client.get(self.url)\n self.assertEquals(resp2.status_code, 200)\n data = json.loads(resp2.data)\n #Check that the report has been modified with the new data\n self.assertEquals(data[\"details\"], self.report_1_req[\"details\"])","title":""},{"docid":"747712a9fe8b9ca2a54838b34b33ff28","score":"0.48183075","text":"def edit(id):\n sensor = Sensor.query.get(id)\n form = SensorForm(request.form, obj=sensor)\n if request.method == 'POST':\n form.populate_obj(sensor)\n if form.validate():\n db.session.commit()\n flash('You have successfully modified the sensor.', 'success')\n return redirect(url_for('sensors.edit', id=sensor.id))\n return render_template('sensors/edit.html', form=form, sensor=sensor)","title":""},{"docid":"a19c62d0f4ff880e9eb69dbb30f04d74","score":"0.48122746","text":"def update(self, obj): \n if isinstance(obj, Entity):\n url_filter = \"v1/ai/skillEntity/name/{}\"\n else:\n url_filter = \"v1/ai/skill/name/{}\"\n url_filter = url_filter.format(obj.name)\n obj._update_json_data()\n response = self.carol.call_api(url_filter, method = 'PUT', data=obj._json_data)\n return response","title":""},{"docid":"6175d3e3097744ca2912d63fc9461e1a","score":"0.48087907","text":"def put(self, id):\n pass","title":""},{"docid":"07dd4375dada7f132f3e9aad7897d557","score":"0.48071754","text":"def update(ctx, name, image, ingredients, id):\n repo = getDrinkRepo()\n\n updateArgs = {}\n if image is not None:\n updateArgs[\"image\"] = image\n\n if ingredients is not None:\n updateArgs[\"ingredients\"] = ingredients\n\n if name is not None and name != \"\":\n updateArgs[\"name\"] = name\n\n success = False if len(updateArgs) <= 0 else repo.updateDrink(id, **updateArgs)\n\n if success:\n ctx.invoke(describe, drink=id)\n else:\n click.echo(\"Unable to update drink. Make sure the ID is right.\")","title":""},{"docid":"568612e39e13d479cbcd2c0e0e449a7d","score":"0.4804053","text":"def update_research_plan(self, id, title, description, assigned_to_id, objective, evaluation_process):\n if (id is None or id == '' or id == 0) or \\\n (title is None or title == '') or \\\n (assigned_to_id is None or assigned_to_id == '' or assigned_to_id == 0):\n raise Exception('id and title, assigned_to_id are required.')\n\n op = Operation(silverpond_schema.Mutation)\n parent = op.update_research_plan(id=id, title=title, description=description, assigned_to_id=assigned_to_id,\n objective=objective, evaluation_process=evaluation_process)\n\n # Field Set\n parent.errors()\n researchplan = parent.research_plan()\n fieldset_research_plan(researchplan)\n fieldset_experiment(researchplan.experiments())\n fieldset_research_plan_metrics(researchplan.research_plan_metrics())\n\n ql_endpoint = HTTPEndpoint(self.GRAPHQL_API_ENDPOINT, self.headers)\n data = ql_endpoint(op)\n obj = op + data\n if len(obj.update_research_plan.errors) > 0:\n raise Exception(obj.update_research_plan.errors[0])\n return obj.update_research_plan.research_plan","title":""},{"docid":"9b0ef8b4263c6629412f32c2845466ca","score":"0.47978824","text":"def edit_recipe(id):\n recipe = mongo.db.recipes.find_one({\"_id\": ObjectId(id)})\n\n if session[\"user\"] != recipe['created_by']:\n return render_template(\"403.html\")\n\n if request.method == \"POST\":\n edit = {\n \"created_by\": session[\"user\"],\n \"image_url\": request.form.get(\"image_url\"),\n \"name\": request.form.get(\"name\"),\n \"desc\": request.form.get(\"desc\"),\n \"instructions\": request.form.getlist(\"step\"),\n \"ingredients\": request.form.getlist(\"ingredient\"),\n \"amount\": request.form.getlist(\"amount\"),\n \"measure\": request.form.getlist(\"measurement\"),\n }\n mongo.db.recipes.update({\"_id\": ObjectId(id)}, edit)\n flash(\"Changes Successfully Saved\")\n\n return render_template(\"edit_recipe.html\", recipe=recipe)","title":""},{"docid":"f62bedbce7972cef7d5388864810ea47","score":"0.47965991","text":"def update(self, request, pk=None):\n raise MethodNotAllowed(method=\"PUT\")","title":""},{"docid":"f62bedbce7972cef7d5388864810ea47","score":"0.47965991","text":"def update(self, request, pk=None):\n raise MethodNotAllowed(method=\"PUT\")","title":""},{"docid":"db18595a922a3022c241d41ec0dd8953","score":"0.47930816","text":"def put(self, id):\n\n data = request.json\n update_service(id, data)\n return None, 204","title":""},{"docid":"0e53e28a07124d9371460d81cf2a1079","score":"0.4790036","text":"def get_experiment_id(self):\n return self.experiment_id","title":""},{"docid":"53e553e91fdc6114550e464616fdb09f","score":"0.47860163","text":"def experiment_id(self) -> str:\n return self._experiment_id","title":""},{"docid":"8f101fff17f778b28c18fdd5714ef7e6","score":"0.47858354","text":"def _update_details(self, details, obj):\n pass","title":""},{"docid":"8a81408420c0f78ee0daaee351debc12","score":"0.4765387","text":"def put(self, id):\n data = request.json\n Team.query.filter(Team.id == id).update({'name': data.get('name')})\n team = Team.query.filter(Team.id == id).one()\n enrollments = data.get('enrollments')\n team = add_enrollments(enrollments, team)\n\n db.session.commit()\n return team, 204","title":""}],"string":"[\n {\n \"docid\": \"a90932de5b0dd8cc3450221c5e50b85a\",\n \"score\": \"0.7416664\",\n \"text\": \"def update_experiment(request, pk):\\n\\n required_data = request.data.get(\\\"requiredData\\\")\\n tests_config = request.data.get(\\\"testsConfig\\\")\\n expiration = request.data.get(\\\"expiration\\\")\\n allow_multiple_answers = request.data.get(\\\"allowMultipleAnswers\\\")\\n disabled = request.data.get(\\\"disabled\\\")\\n name = request.data.get(\\\"name\\\")\\n\\n try:\\n print('ENTERED')\\n experiment = Experiment.objects.get(pk=pk)\\n except Experiment.DoesNotExist:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if tests_config is None or len(tests_config) == 0:\\n return Response('At least one test must be provided', status=HTTP_400_BAD_REQUEST)\\n\\n if expiration is None:\\n return Response('Valid expiration date must be provided', status=HTTP_400_BAD_REQUEST)\\n\\n try:\\n experiment.name = name\\n experiment.requiredDataConfig = required_data\\n experiment.testsConfig = tests_config\\n experiment.expiration = expiration\\n experiment.allowMultipleAnswers = allow_multiple_answers\\n experiment.disabled = disabled\\n experiment.save()\\n except Exception as e:\\n return Response(str(e), HTTP_400_BAD_REQUEST)\\n\\n serialized = ExperimentSerializer(experiment).data\\n\\n return Response(serialized, HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aace5c2053a4192cc2f99b73768ad86\",\n \"score\": \"0.67339313\",\n \"text\": \"def update_experiment_metadata(\\n writer_client, experiment_id, name=None, description=None\\n):\\n logger.info(\\\"Modifying experiment %r\\\", experiment_id)\\n request = write_service_pb2.UpdateExperimentRequest()\\n request.experiment.experiment_id = experiment_id\\n if name is not None:\\n logger.info(\\\"Setting exp %r name to %r\\\", experiment_id, name)\\n request.experiment.name = name\\n request.experiment_mask.name = True\\n if description is not None:\\n logger.info(\\n \\\"Setting exp %r description to %r\\\", experiment_id, description\\n )\\n request.experiment.description = description\\n request.experiment_mask.description = True\\n try:\\n grpc_util.call_with_retries(writer_client.UpdateExperiment, request)\\n except grpc.RpcError as e:\\n if e.code() == grpc.StatusCode.NOT_FOUND:\\n raise ExperimentNotFoundError()\\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\\n raise PermissionDeniedError()\\n if e.code() == grpc.StatusCode.INVALID_ARGUMENT:\\n raise InvalidArgumentError(e.details())\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546f21b219d724d0964dc0327c6c25f2\",\n \"score\": \"0.66571933\",\n \"text\": \"def test_submit_edit_experiment(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\")\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": True,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"A\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.name, \\\"new_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfc1af097dea409e1e342c357e44dae\",\n \"score\": \"0.61671877\",\n \"text\": \"def refresh(self) -> None:\\n self.update_from_remote_data(self._api_client.experiment_get(self.uuid))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7efd2d39f68200aa5173115ccf47fd\",\n \"score\": \"0.6142266\",\n \"text\": \"def test_edit_experiment_view(self):\\n experiment = self.create_test_experiment()\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(experiment.id,)))\\n self.assertTemplateUsed(response, \\\"ab_tool/edit_experiment.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c08855f31c71f29263100fbc5d63505\",\n \"score\": \"0.59430104\",\n \"text\": \"def update_emp(db, id, hoursTrained, trainedStatus):\\r\\n trainingInfo = {\\\"Hours Trained\\\" : hoursTrained,\\r\\n \\\"Training Completed\\\" : trainedStatus}\\r\\n\\r\\n db.collection(\\\"Employees\\\").document(id).set(trainingInfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e74d8d26fab10630d7b37236175eee\",\n \"score\": \"0.5902595\",\n \"text\": \"def test_submit_edit_started_experiment_changes_name_and_notes(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\", notes=\\\"old_notes\\\",\\n tracks_finalized=True)\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n old_track = self.create_test_track(experiment=experiment, name=\\\"old_name_track\\\")\\n experiment_json = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"new_notes\\\", \\\"tracks\\\": [{\\\"id\\\": old_track.id,\\n \\\"name\\\": \\\"new_track_name\\\"}],\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment_json)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.name, \\\"new_name\\\")\\n self.assertEquals(experiment.notes, \\\"new_notes\\\")\\n self.assertEquals(experiment.tracks.all()[0].name, \\\"new_track_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3b67f1d59d87115dd9fc81a90aab86\",\n \"score\": \"0.58990973\",\n \"text\": \"def update(self, collab_id: str, project_id: str, expt_id: str, **updates):\\n return self._execute_operation(\\n operation=\\\"put\\\",\\n url=self._generate_single_url(\\n collab_id=collab_id,\\n project_id=project_id, \\n expt_id=expt_id\\n ),\\n payload=updates\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4272881909e59c29f6e7e94aae595276\",\n \"score\": \"0.5827913\",\n \"text\": \"def update(id_exp=None):\\n\\n current_app.logger.info(\\\"PUT /sync route with id: %s\\\" % id_exp)\\n\\n (gfg_id, exp_name, session_id), session, resp = parse_id_exp(id_exp)\\n\\n # Check JSON validity\\n if utils.check_valid_json(request.get_data()):\\n valid_json = json.loads(request.get_data())\\n # session.datastring = valid_json\\n else:\\n resp = {\\\"status\\\": \\\"bad request\\\"}\\n current_app.logger.error(\\\"Invalid JSON\\\")\\n\\n current_app.logger.info(\\n \\\"Current trial: %s, unique_id: %s, experiment name: %s, session id: %s \\\" % (valid_json['currenttrial'],\\n valid_json['uniqueid'], valid_json['experimentname'], valid_json['sessionid']))\\n\\n # For each trial, pass to appropriate parser, if not in db\\n for json_trial in valid_json['data']:\\n if exp_name == \\\"category_switch\\\":\\n experiment_class = CategorySwitch\\n elif exp_name == \\\"keep_track\\\":\\n experiment_class = KeepTrack\\n else:\\n current_app.logger.error(\\\"%s does not exist\\\" % (exp_name))\\n resp = {\\\"status\\\": \\\"bad request\\\"}\\n\\n db_trial, new = db_utils.get_or_create(db.session,\\n experiment_class, gfg_id=gfg_id, session_id=session_id,\\n trial_num=json_trial['current_trial'])\\n\\n # If the trial is new, add data\\n if new:\\n db_trial.add_json_data(json_trial)\\n db.session.commit()\\n\\n # For each event, pass to parser, if not in db\\n for json_event in valid_json['eventdata']:\\n db_event, new = db_utils.get_or_create(db.session, EventData,\\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name, \\n timestamp = utils.convert_timestamp(json_event['timestamp']))\\n\\n if new:\\n db_event.add_json_data(json_event)\\n db.session.commit()\\n\\n # For the QuestionData, pass to parser, if not in db\\n for json_ques in valid_json['questiondata']:\\n db_ques, new = db_utils.get_or_create(db.session, QuestionData,\\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name)\\n\\n if new:\\n db_ques.add_json_data(json_ques)\\n db.session.commit()\\n\\n if resp is None:\\n resp = {\\\"status\\\": \\\"user data saved\\\"}\\n return jsonify(**resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b05deea5431803e1eac4738e4cc6a36\",\n \"score\": \"0.5811839\",\n \"text\": \"def update(self, request, pk=None):\\n selected_routine = request.query_params.get('routine', None) \\n\\n try: \\n exercise = Exercise.objects.get(pk=pk)\\n\\n exercise.routine = Routine.objects.get(pk=selected_routine)\\n exercise.description = request.data['description']\\n exercise.save()\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\\n \\n except ValidationError as ex:\\n return Response({\\\"reason\\\": ex.message}, status=status.HTTP_400_BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ea28e67ff7a830d61e1851c7da9f33\",\n \"score\": \"0.56713605\",\n \"text\": \"async def edit_training_examples(\\n intent: str,\\n id: str,\\n request_data: TextData,\\n current_user: User = Depends(auth.get_current_user),\\n):\\n mongo_processor.edit_training_example(\\n id, request_data.data, intent.lower(), current_user.get_bot(), current_user.get_user()\\n )\\n return {\\\"message\\\": \\\"Training Example updated!\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b610548abb62bf8777e59ea744a47f65\",\n \"score\": \"0.5661236\",\n \"text\": \"def put(self, exploration_id):\\n exploration = exp_services.get_exploration_by_id(exploration_id)\\n version = self.payload.get('version')\\n _require_valid_version(version, exploration.version)\\n\\n commit_message = self.payload.get('commit_message')\\n change_list = self.payload.get('change_list')\\n\\n try:\\n exp_services.update_exploration(\\n self.user_id, exploration_id, change_list, commit_message)\\n except utils.ValidationError as e:\\n raise self.InvalidInputException(e)\\n\\n self.values.update(self._get_exploration_data(exploration_id))\\n self.render_json(self.values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f444f6fc62b19e72dd10f8ea281178b\",\n \"score\": \"0.5650311\",\n \"text\": \"def update(self, entity_id, details, delete_existing):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"829b7025e64765315044ed5f1182fe55\",\n \"score\": \"0.5622866\",\n \"text\": \"def causx_edit_an_entity(id):\\n project = get_project()\\n updated_entry = request.get_json()[\\\"updated_entry\\\"]\\n entity = causx_set_variable(project, id, updated_entry)\\n response=dict(entity=entity)\\n calc_params = get_calc_params(project, data_required=False)\\n if calc_params:\\n response[\\\"layers\\\"] = get_qnodes_layer(calc_params)\\n return response, 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a677e8ac2b6e770914054e27e7250c\",\n \"score\": \"0.56208825\",\n \"text\": \"def test_submit_edit_started_experiment_changes_existing_tracks(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\", tracks_finalized=False,\\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n track1 = self.create_test_track(experiment=experiment, name=\\\"A\\\")\\n track2 = self.create_test_track(experiment=experiment, name=\\\"B\\\")\\n self.create_test_track_weight(experiment=experiment, track=track1)\\n self.create_test_track_weight(experiment=experiment, track=track2)\\n track_count = experiment.tracks.count()\\n experiment_json = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": False,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": track1.id, \\\"weighting\\\": 30, \\\"name\\\": \\\"C\\\"},\\n {\\\"id\\\": track2.id, \\\"weighting\\\": 70, \\\"name\\\": \\\"D\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment.id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment_json)\\n )\\n self.assertOkay(response)\\n experiment = Experiment.objects.get(id=experiment.id)\\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n self.assertEquals(experiment.tracks.count(), track_count)\\n track1 = experiment.tracks.get(id=track1.id)\\n track2 = experiment.tracks.get(id=track2.id)\\n self.assertEquals(track1.name, \\\"C\\\") #Checks name has changed\\n self.assertEquals(track2.name, \\\"D\\\")\\n self.assertEquals(track1.weight.weighting, 30) #Checks weighting has changed\\n self.assertEquals(track2.weight.weighting, 70)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d99f571b3fa51ed6afda42fff2838c\",\n \"score\": \"0.56005096\",\n \"text\": \"def put(self, id):\\n data = request.get_json()\\n return update_a_reporter(id=id, data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eca505c37f3bd0881bd1ed010599a0a\",\n \"score\": \"0.559187\",\n \"text\": \"def test_edit_experiment_view_started_experiment(self):\\n experiment = self.create_test_experiment()\\n experiment.tracks_finalized = True\\n experiment.save()\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(experiment.id,)))\\n self.assertTemplateUsed(response, \\\"ab_tool/edit_experiment.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e11f93fd1499f93fcb17b45a21891a2d\",\n \"score\": \"0.55208725\",\n \"text\": \"def test_submit_edit_started_experiment_does_not_change_tracks(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\", tracks_finalized=True,\\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n no_tracks = experiment.tracks.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": True,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"A\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"B\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"C\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n self.assertEquals(experiment.tracks.count(), no_tracks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db940d31dbabec99f670e73eaba4b31\",\n \"score\": \"0.5507888\",\n \"text\": \"def test_submit_edit_experiment_nonexistent(self):\\n experiment_id = NONEXISTENT_EXPERIMENT_ID\\n experiment = {\\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"\\\"}\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertEquals(response.status_code, 404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214e821c70df6edb4046bfd747a7248d\",\n \"score\": \"0.5484202\",\n \"text\": \"def edit(project):\\n if g.user:\\n session['exp'] = project\\n abstract = s.get_experiment_data(g.user, project, 'abstract')\\n gl = s.get_experiment_data(g.user, project, 'gl')\\n ec = s.get_experiment_data(g.user, project, 'ec')\\n fn = s.get_experiment_data(g.user, project, 'fn')\\n return render_template('edit.html', project=project, abstract=abstract, gl=gl, ec=ec, fn=fn)\\n return redirect(url_for('login'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70ff7a5bde05f7ffc13d2ef2cf7f6ab\",\n \"score\": \"0.5455811\",\n \"text\": \"def update(self, entity_id, details, delete_existing=False):\\n self._validate_entity_id(entity_id)\\n\\n if not isinstance(details, dict):\\n msg = (_(\\\"Provided details %s is not valid dict.\\\")\\n % details)\\n raise ValueError(msg)\\n\\n return self._storage.update(\\n entity_id, details, delete_existing)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f73067644b0b8d431330b8b573696d\",\n \"score\": \"0.54527766\",\n \"text\": \"def update(self, event_id, event_time=None, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f73067644b0b8d431330b8b573696d\",\n \"score\": \"0.54527766\",\n \"text\": \"def update(self, event_id, event_time=None, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f73067644b0b8d431330b8b573696d\",\n \"score\": \"0.54527766\",\n \"text\": \"def update(self, event_id, event_time=None, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba50b0f354cac4d2eb92581c8b79298\",\n \"score\": \"0.5451184\",\n \"text\": \"def update(self, id, data):\\n return self.make_request('update', '/' + id, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e08e1e5e63528ecaa3e670768710da\",\n \"score\": \"0.54415154\",\n \"text\": \"def put(user_id,self):\\n form = ResearchInfoUpdateForm(request.form)\\n if form.validate():\\n \\n research_info = ResearchInformation.query.filter((ResearchInformation.id == form.research_id.data) & (ResearchInformation.user_id == user_id))\\n if research_info is None:\\n return make_response(jsonify({'error':'Please give an appropriate research ID'}),400)\\n update_dict = {}\\n for key, value in form.data.items():\\n if value and key != 'research_id':\\n update_dict[key] = value\\n try:\\n research_info.update(update_dict)\\n db.session.commit()\\n except:\\n return make_response(jsonify({'error' : 'Database Connection Problem'}),500)\\n return make_response(jsonify({'msg' : ' Successfully changed'}),201)\\n\\n else:\\n return make_response(jsonify({'error':'Wrong input format'}),400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b67ad4ce2e3e31eb6207122f1a66f3\",\n \"score\": \"0.5403792\",\n \"text\": \"def put(self):\\n return employee_service.edit_employee(get_jwt_identity(), get_uuid(request), **api.payload), 201\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ac49e54bcfac440fb624d369239316\",\n \"score\": \"0.53886133\",\n \"text\": \"def _add_experiment(self, experiment_key, variation_id):\\n\\n experiment_id = self.config.get_experiment_id(experiment_key)\\n self.params[EXPERIMENT_PARAM_FORMAT.format(experiment_prefix=Params.EXPERIMENT_PREFIX,\\n experiment_id=experiment_id)] = variation_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd8bb0e660cd252ad87d2e7d500422a\",\n \"score\": \"0.53784376\",\n \"text\": \"def put(self, id):\\n\\t\\ttoken_parser.parse_args()\\n\\t\\trecord = Record.query.get(id)\\n\\t\\tif not record:\\n\\t\\t\\treturn error_response('RECORD NOT FOUND', 404) \\n\\t\\t# PERMISSION_DENIED because don't want user to know this record id exists\\n\\t\\tif record.runner.id!=int(get_jwt_identity()) and not min_access_level(self, ROLES['admin']):\\n\\t\\t\\treturn error_response('RECORD NOT FOUND', 404) \\n\\n\\t\\tdata = request.get_json() or {}\\n\\t\\ttry:\\n\\t\\t\\trecord.update(data)\\n\\t\\texcept ValueError as error:\\n\\t\\t\\treturn error_response('RECORD NOT FOUND', 404)\\n\\t\\texcept Exception as error:\\n\\t\\t\\treturn error_response('PERMISSION DENIED', 403)\\n\\t\\tdb.session.commit()\\n\\t\\treturn make_response(jsonify(record.to_dict()), 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a8c7feb5e4f073a780db8763a8c3a47\",\n \"score\": \"0.53570676\",\n \"text\": \"def test_edit_report(self):\\n report = self.create_report()\\n pk = report[0].pk\\n my_url = report_url(pk)\\n response = self.client.put(\\n my_url,\\n self.updated_report,\\n **self.headers,\\n format='json'\\n )\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6b8e8628b2258d34781373f3756603\",\n \"score\": \"0.5355502\",\n \"text\": \"def put(self, id):\\n # Get the json object from the request body\\n data = request.get_json()\\n # Check all fields for availability and get the new data if the field is available\\n if 'email' not in data:\\n email = None\\n else:\\n email = data[\\\"email\\\"]\\n if 'first_name' not in data:\\n first_name = None\\n else:\\n first_name = data[\\\"first_name\\\"]\\n if 'last_name' not in data:\\n last_name = None\\n else:\\n last_name = data[\\\"last_name\\\"]\\n if 'availability' not in data:\\n availability = None\\n else:\\n availability = data[\\\"availability\\\"]\\n\\n # Update all requested fields of the requested interviewer, if there is an interviewer with the id, else return\\n # an error\\n interviewer = interviewer_ctrl.update_interviewer(id, email, first_name, last_name, availability)\\n if not interviewer:\\n response = \\\"Interviewer with id %s does not exist or email %s is already taken by another interviewer\\\" % (\\n id, email), 400\\n else:\\n response = interviewer, 200\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c1f7722e5e0c8697db10072db3f404\",\n \"score\": \"0.53356797\",\n \"text\": \"def put(self, id):\\n data = request.json\\n args = request.args\\n return update_unit(id, data, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a0bd39cf438b04b13e0fd2dad2b75f\",\n \"score\": \"0.5282175\",\n \"text\": \"def update_project(id=None, name=None, description=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da3e669c6d3439e143564ac0ff1622b\",\n \"score\": \"0.5282065\",\n \"text\": \"def test_edit_experiment_view_last_modified_updated(self):\\n experiment = self.create_test_experiment()\\n experiment.name += \\\" (updated)\\\"\\n response = self.client.post(reverse(\\\"ab_testing_tool_submit_edit_experiment\\\",\\n args=(experiment.id,)),\\n content_type=\\\"application/json\\\",\\n data=experiment.to_json())\\n self.assertEquals(response.content, \\\"success\\\")\\n updated_experiment = Experiment.objects.get(id=experiment.id)\\n self.assertLess(experiment.updated_on, updated_experiment.updated_on,\\n response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63400c45bfcc9bce89953c8ae6b3701\",\n \"score\": \"0.5281295\",\n \"text\": \"def put(self, id):\\n data = request.get_json()\\n if 'email' not in data:\\n email = None\\n else:\\n email = data[\\\"email\\\"]\\n if 'first_name' not in data:\\n first_name = None\\n else:\\n first_name = data[\\\"first_name\\\"]\\n if 'last_name' not in data:\\n last_name = None\\n else:\\n last_name = data[\\\"last_name\\\"]\\n if 'availability' not in data:\\n availability = None\\n else:\\n availability = data[\\\"availability\\\"]\\n interviewee = interviewee_ctrl.update_interviewee(id, email, first_name, last_name, availability)\\n if not interviewee:\\n response = \\\"Interviewee with id %s does not exist or email %s is already taken by another interviewee\\\" % (\\n id, email), 400\\n else:\\n response = interviewee, 200\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85bb5f0f4fbddd046d215c53c2b171b\",\n \"score\": \"0.52504665\",\n \"text\": \"def put(self,request,employee_id):\\n try:\\n get_data = Employee.objects.get(pk=employee_id)\\n employee_data = EmployeeSerializer(get_data,data=request.data)\\n if not(employee_data.is_valid()):\\n return ApiResponse().error(\\\"Error while update employee details\\\",400)\\n employee_data.save()\\n return ApiResponse().success(employee_data.data,200)\\n except Exception as err:\\n print(err)\\n return ApiResponse().success(\\\"Error\\\",500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecba54809858f5347c6dc258ddbd143b\",\n \"score\": \"0.5228171\",\n \"text\": \"def update_testimonial():\\n data = request.get_json()\\n description = data.get('description')\\n title = data.get('title')\\n image = data.get('image')\\n rank = data.get('rank')\\n testimonial_id = data.get('id')\\n\\n if (not title) or (not description) or (not rank) or (not testimonial_id):\\n return jsonify(result={'failed_msg': \\\"Unanle to save testimonial with missing fields\\\"})\\n\\n return jsonify(result=save_testimonial(title, description, rank, image, _id=testimonial_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"997ffa7f45afa19ce3c55b4b84f758f6\",\n \"score\": \"0.5227724\",\n \"text\": \"async def update(\\n self,\\n id: int,\\n data: EnvironmentUpdateSchema\\n ) -> Record:\\n\\n async with self.database.transaction():\\n query = (\\n environments_table.update()\\n .where(environments_table.c.id == id)\\n .values(\\n name=data.name,\\n description=data.description,\\n updated_at=datetime.now()\\n )\\n .returning(\\n environments_table.c.id,\\n environments_table.c.name,\\n environments_table.c.code,\\n environments_table.c.description,\\n environments_table.c.created_at,\\n environments_table.c.updated_at,\\n environments_table.c.deleted_at,\\n environments_table.c.is_deleted\\n )\\n )\\n\\n return await self.database.fetch_one(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b7edbfabd11852be3e16d3e6569fbe4\",\n \"score\": \"0.52266294\",\n \"text\": \"def update(self, model, id, update_with):\\n raise NotImplementedError('You have not implemented an update func in your client class')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f18479a5de36268b4534b705d906ee\",\n \"score\": \"0.5196483\",\n \"text\": \"def test_put_experiment_valid_permission(self):\\n url = '/' + version + '/collection/unittestcol/experiment/unittestexp/'\\n data = {'description': 'A new experiment for unit tests. Updated'}\\n\\n response = self.client.put(url, data=data)\\n self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3a3156e9bbff73bb66d428cba3cc4a\",\n \"score\": \"0.5194518\",\n \"text\": \"def test_submit_edit_experiment_changes_assignment_method_to_weighted(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\")\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n no_track_weights = experiment.track_probabilites.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": False,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": 20, \\\"name\\\": \\\"A\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": 80, \\\"name\\\": \\\"B\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n self.assertEquals(experiment.track_probabilites.count(), no_track_weights + 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ca66dab826730162433142988260ed8\",\n \"score\": \"0.5188264\",\n \"text\": \"def test_submit_edit_experiment_changes_assignment_method_to_uniform(self):\\n experiment = self.create_test_experiment(\\n name=\\\"old_name\\\", assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n no_tracks = experiment.tracks.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": True,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"A\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"B\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"C\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.assignment_method, Experiment.UNIFORM_RANDOM)\\n self.assertEquals(experiment.tracks.count(), no_tracks + 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7565e22ff55c1a4dbfaef25b7830ba89\",\n \"score\": \"0.5184008\",\n \"text\": \"def put(self, id):\\n report = self.mydb.get_by_id(id)\\n \\n if report:\\n data = request.get_json()\\n report.createdBy = data['username']\\n report.type = data['flag']\\n report.location = data['location']\\n report.status = data['status']\\n report.image = data['image']\\n report.video = data['video']\\n report.comment = ['comment']\\n return make_response(jsonify({\\n \\\"message\\\":\\\"Report edited successfully\\\",\\n \\\"data\\\": report.serialize()\\n }), 201)\\n else:\\n return make_response(jsonify({\\n \\\"message\\\": \\\"ID invalid, no report found\\\"\\n }),400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"160cb1102a31e22433b0a95d1b19c287\",\n \"score\": \"0.5183066\",\n \"text\": \"def upload_experiment(self, experiment: Experiment) -> None:\\n data = {\\n 'device_name': experiment.backend_name,\\n 'type': experiment.type,\\n 'extra': experiment.extra,\\n }\\n if experiment.start_datetime:\\n data['start_time'] = local_to_utc_str(experiment.start_datetime)\\n if experiment.tags:\\n data['tags'] = experiment.tags\\n if experiment.uuid:\\n data['uuid'] = experiment.uuid\\n response_data = self._api_client.experiment_upload(data)\\n experiment.update_from_remote_data(response_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0650d7cb60a6083e8c9359cba962c35\",\n \"score\": \"0.517982\",\n \"text\": \"def update(self, typ, id, **kwargs):\\n return self._load(self._request(self._url(typ, id), 'PUT', kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b5e1688a20dad535a152c61484ada45\",\n \"score\": \"0.517151\",\n \"text\": \"def update(self, step_name, worker_id):\\n record = ReportClient().get_record(step_name, worker_id)\\n logging.debug(\\\"Get Record=%s\\\", str(record))\\n self.search_alg.update(record.serialize())\\n ParameterSharing().remove()\\n logging.info(f\\\"Update Success. step_name={step_name}, worker_id={worker_id}\\\")\\n logging.info(\\\"Best values: %s\\\", ReportServer().print_best(step_name=General.step_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc0ccaf21cc95495b47366271ce2579\",\n \"score\": \"0.51659554\",\n \"text\": \"def post_experiment(self):\\n if not self.has_prepared_out:\\n self.prepare_experiment()\\n\\n for exp, prod, desc in self.experiment.overview:\\n print exp, prod, desc\\n dt = json.dumps(\\\\\\n {'name': exp,\\n 'desc': desc})\\n resp = requests.post(\\n self.server_url + 'experiments/',\\n data=dt,\\n headers={'Content-type':'application/json'})\\n print resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1a25ea3994e5d6663400ef4dc22a54\",\n \"score\": \"0.5152818\",\n \"text\": \"def edit_quiz(request_ctx, course_id, id, quiz_notify_of_update, **request_kwargs):\\n\\n path = '/v1/courses/{course_id}/quizzes/{id}'\\n payload = {\\n 'quiz[notify_of_update]' : quiz_notify_of_update,\\n }\\n url = request_ctx.base_api_url + path.format(course_id=course_id, id=id)\\n response = client.put(request_ctx, url, payload=payload, **request_kwargs)\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76aca4be1ce01e6f0e21a2963567cc01\",\n \"score\": \"0.5147998\",\n \"text\": \"def test_edit_experiment_view_nonexistent(self):\\n e_id = NONEXISTENT_EXPERIMENT_ID\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(e_id,)))\\n self.assertTemplateNotUsed(response, \\\"ab_tool/edit_experiment.html\\\")\\n self.assertEquals(response.status_code, 404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb24b14a3442c8472d5c6d09956b062\",\n \"score\": \"0.51298386\",\n \"text\": \"def update_project(self, id: str, name: str = None, description: str = None) -> Dict:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9609d770f7c1fe4197f40d9c80ed4b91\",\n \"score\": \"0.5129037\",\n \"text\": \"def update_environment(context: click.Context, application_id: str, environment_id: str, name: str, url: str) -> None:\\n api = context.obj\\n application = api.application_by_id(application_id)\\n environment = api.environment_by_id(application, environment_id)\\n updated_environment = api.update_environment(application, environment, name, url)\\n table = [[\\\"ID\\\", \\\"Name\\\", \\\"URL\\\"], [updated_environment.id, updated_environment.name, updated_environment.url]]\\n click.echo(tabulate.tabulate(table, headers=\\\"firstrow\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da739f61d8dd8506648f0f235b47402d\",\n \"score\": \"0.51289636\",\n \"text\": \"def edit_experience(request, id):\\r\\n experience = Experience.objects.get(pk=id)\\r\\n context = {\\r\\n 'experience': experience\\r\\n }\\r\\n if request.method == 'POST':\\r\\n experience.user_id = request.POST['user_id' or None]\\r\\n experience.company_name = request.POST['company_name' or None]\\r\\n experience.job_title = request.POST['job_title' or None]\\r\\n experience.job_description = request.POST['job_description' or None]\\r\\n experience.year_from = request.POST['year_from' or None]\\r\\n experience.year_to = request.POST['year_to' or None]\\r\\n\\r\\n experience.save()\\r\\n messages.success(request, 'Experience Updated Successfully')\\r\\n return redirect('dashboard', request.user.pk)\\r\\n else:\\r\\n return render(request, 'accounts/edit_experience.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d62bd10f0ddf13b60680d431d1214af\",\n \"score\": \"0.5126918\",\n \"text\": \"def update_teacher(email=None, zipcode=None, bio=None, \\n mobile_number=None, days_of_week=None,teaching_experience_in_hours=None,\\n pay_rate_per_hour=None, pod_id=None, img_url=None, covid_risk_profile_id=None):\\n\\n teacher = db.session.query(Teacher).filter(Teacher.email==email).one()\\n teacher.zipcode=zipcode \\n teacher.bio=bio \\n teacher.mobile_number=mobile_number\\n teacher.days_of_week=days_of_week\\n teacher.teaching_experience_in_hours=teaching_experience_in_hours\\n teacher.pay_rate_per_hour=pay_rate_per_hour\\n teacher.pod_id=pod_id\\n teacher.img_url=img_url\\n teacher.covid_risk_profile_id=covid_risk_profile_id\\n\\n db.session.commit()\\n\\n return teacher\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608457875b3bc5272855818c41796e9c\",\n \"score\": \"0.5124847\",\n \"text\": \"def edit(obj, index, description, duration, code, task_id, project_id):\\n with ots_filestore(obj) as timesheet_storage:\\n timesheet_edited = timesheet_storage.edit_timesheet(\\n index,\\n description=description,\\n duration=duration,\\n task_code=code,\\n task_id=task_id,\\n project_id=project_id,\\n )\\n if timesheet_edited:\\n click.echo('Timesheet updated.')\\n else:\\n click.echo('Nothing to update.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbff9690ae2f28c9373856792e9b190\",\n \"score\": \"0.50883085\",\n \"text\": \"def update(self, data, id):\\n with db.session.begin_nested():\\n self.query.filter_by(id=id).update(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f165c0fcebdc404642a446e6277bbe24\",\n \"score\": \"0.5067855\",\n \"text\": \"def test_submit_edit_experiment_wrong_course(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\",\\n course_id=TEST_OTHER_COURSE_ID)\\n data = {\\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"\\\"}\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment.id,)),\\n content_type=\\\"application/json\\\", data=json.dumps(data)\\n )\\n self.assertError(response, UNAUTHORIZED_ACCESS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61125735c80419b89b3c2ef8fdb6cc7\",\n \"score\": \"0.50544035\",\n \"text\": \"def update_suite(suiteId):\\n\\t res = requests.post('https://api.ghostinspector.com/v1/suites/'+suiteId+'/?apiKey='+apiKey)\\n\\t return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67143faae1b601cdc1166defe338b29d\",\n \"score\": \"0.505348\",\n \"text\": \"def step_impl(context, post_id):\\n context.payload = constants.POST_INFORMATION_TO_UPDATE\\n context.response = request.put(context.url + \\\"/%s\\\" % post_id, context.payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c923e8f9fc820942ebc8185747323eb5\",\n \"score\": \"0.5051238\",\n \"text\": \"def get_experiment(experiment_id):\\n req = requests.get(\\\"{}experiments/{}/?format=json\\\".format(__base_url__, experiment_id))\\n metadata = req.json()\\n if metadata['status'] != 'error':\\n save_metadata(metadata)\\n else:\\n print 'Error fetching metadata for {}'.format(experiment_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c7f7bf6fb01612d985fa2b143d8134b\",\n \"score\": \"0.5051149\",\n \"text\": \"def wxt_update_person(person_id, data, myheaders):\\n update_person_url = 'https://api.ciscospark.com/v1/people/{0}'\\n response = requests.put(update_person_url.format(person_id),\\n data=json.dumps(data),\\n headers=myheaders)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a558e28f4fcb1556894539bd864dab7\",\n \"score\": \"0.5049372\",\n \"text\": \"def experiment_detail(request, exp_name):\\n exp = get_object_or_404(Experiment, name=exp_name)\\n reports = ExperimentReport.objects.filter(experiment=exp)\\n\\n return render_to_response(\\n \\\"splango/experiment_detail.html\\\",\\n {\\\"title\\\": exp.name, \\\"exp\\\": exp, \\\"reports\\\": reports},\\n RequestContext(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0758bd2dc137efe10c77c05015e4b113\",\n \"score\": \"0.50357556\",\n \"text\": \"def update_movie_details(db: Session, sl_id: int, details: schema.UpdateMovie):\\n db.query(model.Movies).filter(model.Movies.id == sl_id).update(vars(details))\\n db.commit()\\n return db.query(model.Movies).filter(model.Movies.id == sl_id).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e912a37963fc2345ab045aa5222960ba\",\n \"score\": \"0.5034493\",\n \"text\": \"def update_by_id(self, id=None, **kwargs):\\n if not id:\\n raise PardotAPIArgumentError('id is required to update a prospect.')\\n response = self._post(path='/do/update/id/{id}'.format(id=id), params=kwargs)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec9d8e407070c9a77a984f35bc91736\",\n \"score\": \"0.5032699\",\n \"text\": \"def update_test_run(self, run_update_model, project, run_id):\\n route_values = {}\\n if project is not None:\\n route_values['project'] = self._serialize.url('project', project, 'str')\\n if run_id is not None:\\n route_values['runId'] = self._serialize.url('run_id', run_id, 'int')\\n content = self._serialize.body(run_update_model, 'RunUpdateModel')\\n response = self._send(http_method='PATCH',\\n location_id='cadb3810-d47d-4a3c-a234-fe5f3be50138',\\n version='5.0',\\n route_values=route_values,\\n content=content)\\n return self._deserialize('TestRun', response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e64bd368f6696cc8782c9dac2067f6\",\n \"score\": \"0.50115055\",\n \"text\": \"def update(self, request, pk=None):\\n player = Player.objects.get(user=request.auth.user)\\n suspect = Suspect.objects.get(pk=pk)\\n suspect.player = player\\n suspect.name = request.data[\\\"name\\\"]\\n suspect.description = request.data[\\\"description\\\"]\\n guilty = Guilty.objects.get(pk=request.data[\\\"guiltyId\\\"])\\n suspect.guilty = guilty\\n movie = Movie.objects.get(pk=request.data[\\\"movieId\\\"])\\n suspect.suspect_image_url = request.data[\\\"suspectImageUrl\\\"]\\n suspect.movie = movie\\n \\n suspect.save()\\n\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489dce730c6e0515e0c8902d35851c78\",\n \"score\": \"0.49720645\",\n \"text\": \"def update_test(testId):\\n\\t res = requests.post('https://api.ghostinspector.com/v1/tests/'+testId+'/?apiKey='+apiKey)\\n\\t return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b1c0e0826bdbdb476676fb3b9967ce7\",\n \"score\": \"0.49566084\",\n \"text\": \"def test_put_experiment_no_permissions(self):\\n url = '/' + version + '/collection/col1/experiment/exp1/'\\n data = {'description': 'A new experiment for unit tests. Updated'}\\n\\n response = self.client.put(url, data=data)\\n self.assertEqual(response.status_code, 403)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ee48539f966381bc6e042c165a54a8\",\n \"score\": \"0.49527475\",\n \"text\": \"def put(self, id):\\n return DAO.update(id, api.payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8a67ef451a5b9d3cfbff45c9f08289\",\n \"score\": \"0.49498895\",\n \"text\": \"def delete_experiment(writer_client, experiment_id):\\n logger.info(\\\"Deleting experiment %r\\\", experiment_id)\\n request = write_service_pb2.DeleteExperimentRequest()\\n request.experiment_id = experiment_id\\n try:\\n grpc_util.call_with_retries(writer_client.DeleteExperiment, request)\\n except grpc.RpcError as e:\\n if e.code() == grpc.StatusCode.NOT_FOUND:\\n raise ExperimentNotFoundError()\\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\\n raise PermissionDeniedError()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ebeb2bb4b736d0d454c31d04209770\",\n \"score\": \"0.49268797\",\n \"text\": \"def test_edit_experiment_view_with_tracks_weights(self):\\n experiment = self.create_test_experiment()\\n experiment.assignment_method = Experiment.WEIGHTED_PROBABILITY_RANDOM\\n track1 = self.create_test_track(name=\\\"track1\\\", experiment=experiment)\\n track2 = self.create_test_track(name=\\\"track2\\\", experiment=experiment)\\n self.create_test_track_weight(experiment=experiment, track=track1)\\n self.create_test_track_weight(experiment=experiment, track=track2)\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(experiment.id,)))\\n self.assertTemplateUsed(response, \\\"ab_tool/edit_experiment.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a83bb2f35b27541623f09db41fce462\",\n \"score\": \"0.49165675\",\n \"text\": \"def update_results(student_id, experiment_id, student_response):\\n\\n replies = get_experiment_replies(experiment_id)\\n replies_dict = json.loads(replies)\\n\\n replies_dict['replies'].append({\\\"id\\\": int(student_id), \\\"response\\\": student_response})\\n\\n new_replies = json.dumps(replies_dict)\\n\\n con = sql.connect(DB_PATH)\\n cur = con.cursor()\\n\\n cur.execute(\\\"UPDATE experiments SET replies = ? WHERE id = ?\\\", (new_replies, experiment_id))\\n\\n cur.execute(\\\"DELETE FROM experiments_students \\\"\\n \\\"WHERE student_id = ? AND experiment_id = ?\\\", (student_id, experiment_id))\\n\\n con.commit()\\n\\n con.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b1a5247263b3eaa3ed81d6c8455d38\",\n \"score\": \"0.49110433\",\n \"text\": \"def update(self, kwargs):\\n if self._status in [DEFAULT, DETACHED]:\\n if any([k not in self.param_keys() and k in self._specials for k in kwargs]):\\n raise ValueError('Key not recognised!')\\n else:\\n self.__dict__.update(kwargs)\\n else:\\n raise ValueError('Parameters of a created experiment cannot be updated.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b058a3ccc0c492a3a2054e916ba606a3\",\n \"score\": \"0.4910217\",\n \"text\": \"def update(self, model, rec_id, data):\\n\\n url = self.base_url + model + '/' + str(rec_id)\\n data = format_write_data(data)\\n return self.session.put(url, data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3a3f85820ec6698cda066db2a4dae5\",\n \"score\": \"0.4900522\",\n \"text\": \"def detail_put(self, request, id, *args, **kwargs):\\n raise NotAllowed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ef2def248ce0213ba3a195c608662a\",\n \"score\": \"0.48950928\",\n \"text\": \"def edit_education(request, id):\\r\\n education = Education.objects.get(pk=id)\\r\\n context = {\\r\\n 'education': education\\r\\n }\\r\\n if request.method == 'POST':\\r\\n education.user_id = request.POST['user_id' or None]\\r\\n education.school = request.POST['school' or None]\\r\\n education.field_of_study = request.POST['field_of_study' or None]\\r\\n education.qualification = request.POST['qualification' or None]\\r\\n education.year = request.POST['year' or None]\\r\\n\\r\\n education.save()\\r\\n messages.success(request, 'Education Updated Successfully, you may add another !')\\r\\n return redirect('dashboard', request.user.pk)\\r\\n else:\\r\\n return render(request, 'accounts/edit_education.html', context)\\r\\n\\r\\n # DELETE EDUCATION\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9b95546a734ec60c96a828783d5784\",\n \"score\": \"0.4894346\",\n \"text\": \"def test_experiment_specification(self, client, ephemeral_storage):\\n\\n storage = ephemeral_storage.storage\\n\\n _add_experiment(storage, name=\\\"a\\\", version=1, _id=1)\\n _add_trial(storage, experiment=1, id_override=\\\"ae8\\\", status=\\\"completed\\\")\\n\\n response = client.simulate_get(\\\"/experiments/status/a\\\")\\n assert response.status == \\\"200 OK\\\"\\n assert response.json == {\\n \\\"trials_completed\\\": 1,\\n \\\"best_trials_id\\\": \\\"7bbfdb8c684aa5f4be324a09d7da94af\\\",\\n \\\"best_evaluation\\\": 0.05,\\n \\\"start_time\\\": \\\"0001-01-01 00:00:00\\\",\\n \\\"finish_time\\\": \\\"0001-01-02 00:00:00\\\",\\n \\\"max_trials\\\": 10,\\n \\\"nb_trials\\\": 1,\\n \\\"progress\\\": 0.1,\\n \\\"trial_status_count\\\": {\\\"completed\\\": 1},\\n \\\"elapsed_time\\\": \\\"23:59:50\\\",\\n \\\"sum_of_trials_time\\\": \\\"23:59:50\\\",\\n \\\"eta\\\": \\\"8 days, 23:58:30\\\",\\n \\\"eta_milliseconds\\\": 777510000.0,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527afa47e52ca91f15e0bf2975749899\",\n \"score\": \"0.48926717\",\n \"text\": \"def edit(id):\\n\\n pet = Pet.query.get_or_404(id)\\n form = EditPetForm(obj=pet)\\n\\n if form.validate_on_submit():\\n pet.photo_url = form.data[\\\"photo_url\\\"]\\n pet.notes = form.data[\\\"notes\\\"]\\n pet.available = form.data['available']\\n db.session.commit()\\n flash(f\\\"{pet.name} updated.\\\", 'success')\\n return redirect(url_for('index'))\\n\\n else:\\n return render_template(\\\"edit_pet_form.html\\\", form=form, pet=pet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73c9c211624982ebce62505144b3717\",\n \"score\": \"0.48906887\",\n \"text\": \"def update(self):\\n response = self.api(self.id).get()\\n self._update_fields(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aac6ddcfb59a9b9059b7ceec9897878\",\n \"score\": \"0.48723197\",\n \"text\": \"def put(self, id):\\n data = request.json\\n if data[\\\"entityID\\\"] != id:\\n return {\\\"message\\\": \\\"entityID property in the incoming json object and id parameter in the URL path are\\\"\\n \\\"not matched\\\"}, 400\\n evn = EventService.get_by_id(id)\\n if not evn:\\n return {\\\"message\\\": \\\"The event does not exist\\\"}, 404\\n else:\\n return EventService.update(data, id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee013b5fc5b6473163ae2bdd9f275cd\",\n \"score\": \"0.4865083\",\n \"text\": \"def test_experiment_specification(self, client, ephemeral_storage):\\n\\n storage = ephemeral_storage.storage\\n\\n _add_experiment(storage, name=\\\"a\\\", version=1, _id=1)\\n _add_trial(storage, experiment=1, id_override=\\\"ae8\\\", status=\\\"completed\\\")\\n\\n response = client.simulate_get(\\\"/experiments/a\\\")\\n\\n assert response.status == \\\"200 OK\\\"\\n\\n assert response.json[\\\"name\\\"] == \\\"a\\\"\\n assert response.json[\\\"version\\\"] == 1\\n assert response.json[\\\"status\\\"] == \\\"not done\\\"\\n assert response.json[\\\"trialsCompleted\\\"] == 1\\n assert response.json[\\\"startTime\\\"] == \\\"0001-01-01 00:00:00\\\" # TODO\\n assert response.json[\\\"endTime\\\"] == \\\"0001-01-02 00:00:00\\\" # TODO\\n assert len(response.json[\\\"user\\\"])\\n assert response.json[\\\"orionVersion\\\"] == \\\"x.y.z\\\"\\n\\n _assert_config(response.json[\\\"config\\\"])\\n _assert_best_trial(response.json[\\\"bestTrial\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d6f7bd49709c1e26e4af92b13dda56\",\n \"score\": \"0.48624727\",\n \"text\": \"def api_employee_edit(request):\\r\\n\\r\\n if request.method.lower() == 'post':\\r\\n\\r\\n data = request.POST.dict()\\r\\n employee_id = int(request.POST['id'])\\r\\n\\r\\n try:\\r\\n employee = Employee.objects.get(id=employee_id)\\r\\n\\r\\n form_employee = EmployeeEditForm(data=data, instance=employee)\\r\\n\\r\\n if form_employee.is_valid():\\r\\n form_employee.save()\\r\\n return ApiResponse.http(status=ApiResponse.ST_SUCCESS, message='Employee successfully updated!', employee_id=employee_id)\\r\\n else:\\r\\n errors = dict(form_employee.errors)\\r\\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Validation errors.', errors=errors)\\r\\n except Employee.DoesNotExist:\\r\\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Invalid employee.')\\r\\n else:\\r\\n return ApiResponse.http(status=ApiResponse.ST_FORBIDDEN, message='Use Post!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcea40c6b9b0fdaa251a9fb9aff1687\",\n \"score\": \"0.4860615\",\n \"text\": \"def delete_experiment(request, pk):\\n\\n try:\\n experiment = Experiment.objects.get(pk=pk)\\n except Experiment.DoesNotExist:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if experiment.deleted is True:\\n return Response('Experiment with given ID is already deleted', status=HTTP_400_BAD_REQUEST)\\n\\n experiment.deleted = True;\\n experiment.save()\\n\\n return Response(HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3d2f2168376364b60de974564c9a455\",\n \"score\": \"0.48573977\",\n \"text\": \"def patch(self, incident_id):\\n data = request.get_json()\\n\\n # script to allow only location request.body\\n key_list = [*data]\\n if (len(key_list) == 1 and key_list[0] == \\\"location\\\"):\\n\\n new_incident_instance = IncidentsModel()\\n res = new_incident_instance.edit_incident(incident_id, data)\\n\\n if res:\\n return {\\n \\\"status\\\": 200,\\n \\\"data\\\": [{\\n \\\"id\\\": res[\\\"id\\\"],\\n \\\"message\\\": \\\"incident record has been updated\\\"\\n }]\\n }, 200\\n else:\\n return {\\n \\\"status\\\": 404,\\n \\\"error\\\": \\\"Not found for id {}\\\".format(incident_id)\\n }, 404\\n else:\\n return {\\n \\\"status\\\": 403,\\n \\\"error\\\": \\\"Bad Request only location is allowed\\\"\\n }, 403\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b76af9e574ad062068ef873994d3d48a\",\n \"score\": \"0.48331708\",\n \"text\": \"def update_teste_cmd(teste_id, **teste_properties):\\n return UpdateTesteCommand(teste_id, **teste_properties)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b77d5e6d015b83efb6dd8d19931b53fe\",\n \"score\": \"0.48324943\",\n \"text\": \"def get_experiment(request, pk):\\n\\n try:\\n experiment = Experiment.objects.get(pk=pk)\\n except Experiment.DoesNotExist:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if experiment.deleted is True:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if experiment.disabled is True:\\n return Response('This experiment is currently unavailable', status=HTTP_423_LOCKED)\\n\\n if experiment.expiration < timezone.now():\\n return Response('Experiment with given ID already expired', status=HTTP_400_BAD_REQUEST)\\n\\n serialized = ExperimentSerializer(experiment).data\\n\\n return Response(serialized, status=HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"834ae376dc46243ee0e99aa11693c662\",\n \"score\": \"0.48319104\",\n \"text\": \"def test_update_resolution(self):\\n self.client = APIClient()\\n login = self.client.login(username=self.username, password=self.password)\\n self.assertEqual(login, True)\\n url = reverse('resolution-detail', args=[str(self.upd_pk)])\\n\\n data = {\\n 'description': 'Updated description',\\n }\\n response = self.client.put(url, data=data, format='json')\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution update Failed')\\n self.assertDictContainsSubset(data, response.data, \\\"Modified Resolution didn't contain the updated description\\\")\\n\\n #lets doublecheck we get updated data back\\n response = self.client.get(url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution failed to be Retrieved')\\n self.assertDictContainsSubset(data, response.data, \\\"Resolution didn't contain the updated description\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4601be0b78971f6a8aa160d438fd449c\",\n \"score\": \"0.48254716\",\n \"text\": \"def test_modify_report(self):\\n print \\\"(\\\"+self.test_modify_report.__name__+\\\")\\\", self.test_modify_report.__doc__\\n resp = self.client.put(self.url,\\n data=json.dumps(self.report_1_req),\\n headers={\\\"Content-Type\\\": JSON})\\n self.assertEquals(resp.status_code, 204)\\n #Check that the report has been modified\\n resp2 = self.client.get(self.url)\\n self.assertEquals(resp2.status_code, 200)\\n data = json.loads(resp2.data)\\n #Check that the report has been modified with the new data\\n self.assertEquals(data[\\\"details\\\"], self.report_1_req[\\\"details\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"747712a9fe8b9ca2a54838b34b33ff28\",\n \"score\": \"0.48183075\",\n \"text\": \"def edit(id):\\n sensor = Sensor.query.get(id)\\n form = SensorForm(request.form, obj=sensor)\\n if request.method == 'POST':\\n form.populate_obj(sensor)\\n if form.validate():\\n db.session.commit()\\n flash('You have successfully modified the sensor.', 'success')\\n return redirect(url_for('sensors.edit', id=sensor.id))\\n return render_template('sensors/edit.html', form=form, sensor=sensor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19c62d0f4ff880e9eb69dbb30f04d74\",\n \"score\": \"0.48122746\",\n \"text\": \"def update(self, obj): \\n if isinstance(obj, Entity):\\n url_filter = \\\"v1/ai/skillEntity/name/{}\\\"\\n else:\\n url_filter = \\\"v1/ai/skill/name/{}\\\"\\n url_filter = url_filter.format(obj.name)\\n obj._update_json_data()\\n response = self.carol.call_api(url_filter, method = 'PUT', data=obj._json_data)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6175d3e3097744ca2912d63fc9461e1a\",\n \"score\": \"0.48087907\",\n \"text\": \"def put(self, id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07dd4375dada7f132f3e9aad7897d557\",\n \"score\": \"0.48071754\",\n \"text\": \"def update(ctx, name, image, ingredients, id):\\n repo = getDrinkRepo()\\n\\n updateArgs = {}\\n if image is not None:\\n updateArgs[\\\"image\\\"] = image\\n\\n if ingredients is not None:\\n updateArgs[\\\"ingredients\\\"] = ingredients\\n\\n if name is not None and name != \\\"\\\":\\n updateArgs[\\\"name\\\"] = name\\n\\n success = False if len(updateArgs) <= 0 else repo.updateDrink(id, **updateArgs)\\n\\n if success:\\n ctx.invoke(describe, drink=id)\\n else:\\n click.echo(\\\"Unable to update drink. Make sure the ID is right.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"568612e39e13d479cbcd2c0e0e449a7d\",\n \"score\": \"0.4804053\",\n \"text\": \"def update_research_plan(self, id, title, description, assigned_to_id, objective, evaluation_process):\\n if (id is None or id == '' or id == 0) or \\\\\\n (title is None or title == '') or \\\\\\n (assigned_to_id is None or assigned_to_id == '' or assigned_to_id == 0):\\n raise Exception('id and title, assigned_to_id are required.')\\n\\n op = Operation(silverpond_schema.Mutation)\\n parent = op.update_research_plan(id=id, title=title, description=description, assigned_to_id=assigned_to_id,\\n objective=objective, evaluation_process=evaluation_process)\\n\\n # Field Set\\n parent.errors()\\n researchplan = parent.research_plan()\\n fieldset_research_plan(researchplan)\\n fieldset_experiment(researchplan.experiments())\\n fieldset_research_plan_metrics(researchplan.research_plan_metrics())\\n\\n ql_endpoint = HTTPEndpoint(self.GRAPHQL_API_ENDPOINT, self.headers)\\n data = ql_endpoint(op)\\n obj = op + data\\n if len(obj.update_research_plan.errors) > 0:\\n raise Exception(obj.update_research_plan.errors[0])\\n return obj.update_research_plan.research_plan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b0ef8b4263c6629412f32c2845466ca\",\n \"score\": \"0.47978824\",\n \"text\": \"def edit_recipe(id):\\n recipe = mongo.db.recipes.find_one({\\\"_id\\\": ObjectId(id)})\\n\\n if session[\\\"user\\\"] != recipe['created_by']:\\n return render_template(\\\"403.html\\\")\\n\\n if request.method == \\\"POST\\\":\\n edit = {\\n \\\"created_by\\\": session[\\\"user\\\"],\\n \\\"image_url\\\": request.form.get(\\\"image_url\\\"),\\n \\\"name\\\": request.form.get(\\\"name\\\"),\\n \\\"desc\\\": request.form.get(\\\"desc\\\"),\\n \\\"instructions\\\": request.form.getlist(\\\"step\\\"),\\n \\\"ingredients\\\": request.form.getlist(\\\"ingredient\\\"),\\n \\\"amount\\\": request.form.getlist(\\\"amount\\\"),\\n \\\"measure\\\": request.form.getlist(\\\"measurement\\\"),\\n }\\n mongo.db.recipes.update({\\\"_id\\\": ObjectId(id)}, edit)\\n flash(\\\"Changes Successfully Saved\\\")\\n\\n return render_template(\\\"edit_recipe.html\\\", recipe=recipe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62bedbce7972cef7d5388864810ea47\",\n \"score\": \"0.47965991\",\n \"text\": \"def update(self, request, pk=None):\\n raise MethodNotAllowed(method=\\\"PUT\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62bedbce7972cef7d5388864810ea47\",\n \"score\": \"0.47965991\",\n \"text\": \"def update(self, request, pk=None):\\n raise MethodNotAllowed(method=\\\"PUT\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db18595a922a3022c241d41ec0dd8953\",\n \"score\": \"0.47930816\",\n \"text\": \"def put(self, id):\\n\\n data = request.json\\n update_service(id, data)\\n return None, 204\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e53e28a07124d9371460d81cf2a1079\",\n \"score\": \"0.4790036\",\n \"text\": \"def get_experiment_id(self):\\n return self.experiment_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e553e91fdc6114550e464616fdb09f\",\n \"score\": \"0.47860163\",\n \"text\": \"def experiment_id(self) -> str:\\n return self._experiment_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f101fff17f778b28c18fdd5714ef7e6\",\n \"score\": \"0.47858354\",\n \"text\": \"def _update_details(self, details, obj):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a81408420c0f78ee0daaee351debc12\",\n \"score\": \"0.4765387\",\n \"text\": \"def put(self, id):\\n data = request.json\\n Team.query.filter(Team.id == id).update({'name': data.get('name')})\\n team = Team.query.filter(Team.id == id).one()\\n enrollments = data.get('enrollments')\\n team = add_enrollments(enrollments, team)\\n\\n db.session.commit()\\n return team, 204\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":181,"cells":{"query_id":{"kind":"string","value":"97251e022090c1c096acfb522379fe37"},"query":{"kind":"string","value":"Check to see if the tolerance has been met. Returns False if tolerance is met."},"positive_passages":{"kind":"list like","value":[{"docid":"18dae62af5d8fcb325b8c556954aefee","score":"0.0","text":"def check_tol(g, tol, log_scale_param):\n s = np.exp(log_scale_param)\n for tolerance, gradient in zip(tol, g):\n if abs(gradient) > tolerance / s:\n return True\n return False","title":""}],"string":"[\n {\n \"docid\": \"18dae62af5d8fcb325b8c556954aefee\",\n \"score\": \"0.0\",\n \"text\": \"def check_tol(g, tol, log_scale_param):\\n s = np.exp(log_scale_param)\\n for tolerance, gradient in zip(tol, g):\\n if abs(gradient) > tolerance / s:\\n return True\\n return False\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"ce055bab6287f8377db1128076fa364f","score":"0.8071129","text":"def _tolerance_check(self, tolerance, value):\n if tolerance != None and np.linalg.norm(value) < tolerance:\n print(\"Variable update tolerance was reached. Terminating Search.\")\n return True\n return False","title":""},{"docid":"352bced7ca72b726560ef38f52549699","score":"0.7769892","text":"def __bool__(self):\n return bool(abs(self) > tolerance)","title":""},{"docid":"a1971746879f68f3648bf7f1b42f064d","score":"0.76606834","text":"def reached_tolerance(self):\n if sum(self.dp) < self.tolerance:\n rospy.logwarn('[%s] Tolerance of %f achieved kp=%f, ki=%f, kd=%f with best error: %f',\n self.name, self.tolerance, self.p[0], self.p[1], self.p[2], self.best_error)\n return True\n return False","title":""},{"docid":"f482c72478d2317b312759080e7312ba","score":"0.7599516","text":"def passed(self):\n return self.value_diff <= self.tolerance","title":""},{"docid":"5e0066709419ead4f4919127aa2b7f3d","score":"0.7546813","text":"def tolerance(computedValue,targetValue,tolerance):\n\n if abs(computedValue-targetValue) bool:","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"fdc569d7b54c21a1edbc947696b74106","score":"0.6949747","text":"def CheckTolerance(self, value):\n tolerance = self._unitop.GetTolerance()\n error = self.CalculateError(value)\n if error > tolerance:\n self._unitop.PushConsistencyError(self, value)","title":""},{"docid":"5bb76f3f9c3cbbc265d428d33cda9b7b","score":"0.67973673","text":"def is_satisfied(self, tol=1.0e-6):\n return abs(self()) < tol","title":""},{"docid":"01eed1f9e6d4c494b70631c89a5020eb","score":"0.67568636","text":"def check_tolerance(self, last_obj, new_obj=0.0):\n if last_obj is not None:\n delta_obj = abs(float(new_obj) - float(last_obj))\n self.mention('delta obj ratio: %.2e' % (delta_obj / self.TOLERANCE))\n return delta_obj < self.TOLERANCE\n return False","title":""},{"docid":"1f484cc19e63f22b759133eb18b11f88","score":"0.6700461","text":"def inTolerance(self):\n ...","title":""},{"docid":"78cb818baf81b4d40eeb15a3d8f3427e","score":"0.6625738","text":"def within(a, b, tolerance=1e-5):\n if a==0 and b==0:\n return True\n elif a!=0 and b==0:\n #Avoid divide by zero\n return False\n else:\n return abs((a/b)-1) < tolerance","title":""},{"docid":"df68bc8c4fad2af46f1f98168c37eb99","score":"0.6526349","text":"def check_difference(shift1, shift2, tolerance=0.05):\r\n mean_diff = (shift1[1] - shift2[1]) ** 2\r\n sigma_diff = (shift1[2] + shift2[2]) ** 2\r\n res = mean_diff > sigma_diff\r\n if abs(shift1[1] - shift2[1]) < tolerance:\r\n res = False\r\n return res","title":""},{"docid":"052e05a351af76d3d07aabb0d11920b3","score":"0.6519439","text":"def check_difference(shift1, shift2, tolerance=0.05):\n mean_diff = (shift1[1] - shift2[1]) ** 2\n sigma_diff = (shift1[2] + shift2[2]) ** 2\n res = mean_diff > sigma_diff\n if abs(shift1[1] - shift2[1]) < tolerance:\n res = False\n return res","title":""},{"docid":"54b7b3c683e3287d731d3fc658b0b49e","score":"0.65151095","text":"def approx_eq(x, y, tolerance=1e-5):\n return abs(x - y) < tolerance","title":""},{"docid":"b21b7db657d408a524edb16b0a76a8c5","score":"0.64969355","text":"def relaxed_allclose(x, y, atol=0.01, pct_wrong_allowable=0.1):\n res = torch.isclose(x, y, atol=atol)\n n_wrong = res.numel() - res.sum()\n n_wrong_allowable = pct_wrong_allowable * res.numel()\n return n_wrong <= n_wrong_allowable","title":""},{"docid":"c98b6d633c65381e53c549fda9118dc5","score":"0.64799714","text":"def match(self, other, tolerance=3, amtdev=0.00):\n if (tolerance == ()):\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev)\n else:\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev) and \\\n (abs(self.date-other.date) <= datetime.timedelta(days=tolerance))","title":""},{"docid":"66b0b45511093d926ef07d9c350ee676","score":"0.64740145","text":"def check_convergence(self):\n return False","title":""},{"docid":"f179475d3fa9daa37883a197df0fde02","score":"0.647181","text":"def _check_convergence(self, tol):\n if hasattr(self, 'coeffs_prev'):\n if torch.abs(self.coeffs - self.coeffs_prev).max() < tol:\n return True\n self.coeffs_prev = self.coeffs.detach().clone()\n return False","title":""},{"docid":"cafa9423f1cf921e7a02d68925ddd74a","score":"0.6433718","text":"def check_convergence(new_measure, old_measure, direction, threshold):\n\n sign = 1.0 if direction == 'higher' else -1.0\n\n if sign * (new_measure - old_measure) / old_measure < threshold:\n return True\n else:\n return False","title":""},{"docid":"c897e612f977361f4bbbb71eeb1adec3","score":"0.6422954","text":"def converged(self):\n\n top_lnls = [abs(x['lnL']) for x in self.estimates()][0:2]\n\n # dif = (top_lnls[0] - top_lnls[1]) / (0.5 * (top_lnls[0] + top_lnls[1]))\n dif = top_lnls[0] - top_lnls[1]\n\n if abs(dif) < 0.1:\n return True\n\n else:\n return False","title":""},{"docid":"fae09a0e92a3b5f68c494935dbbc3b46","score":"0.6420245","text":"def tolerance(*args, **kwargs):\n pass","title":""},{"docid":"dfa65081f2e310884cc7d5b8c35c0003","score":"0.63993156","text":"def _check_tolerance(self, content, name, value, mos_file):\n\n if (name + \"=\" == \"tolerance=\" and float(value) > 1e-6):\n self._wrong_parameter(mos_file, name, value)","title":""},{"docid":"97ba0fccb0fd64a1b60f0c57d8f1ae49","score":"0.6392067","text":"def is_not_close_to(self, other, tolerance):\n self._validate_close_to_args(self.val, other, tolerance)\n\n if self.val >= (other-tolerance) and self.val <= (other+tolerance):\n if type(self.val) is datetime.datetime:\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\n h, rem = divmod(tolerance_seconds, 3600)\n m, s = divmod(rem, 60)\n self._err('Expected <%s> to not be close to <%s> within tolerance <%d:%02d:%02d>, but was.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\n else:\n self._err('Expected <%s> to not be close to <%s> within tolerance <%s>, but was.' % (self.val, other, tolerance))\n return self","title":""},{"docid":"598e173b3b57da6b873a58422c1b89d2","score":"0.6380607","text":"def check_below_threshold(distance, epsilon):\n return distance < epsilon","title":""},{"docid":"ad4e1852afe57b0efaf7eb41c4d73868","score":"0.63688385","text":"def test_equal_time(t1, t2, tol=1E-3):\n if abs(t1) <= tol:\n res = abs(t1 - t2) <= tol\n else:\n res = abs(t1 - t2) <= tol * abs(t1)\n return res","title":""},{"docid":"55450f03a699b012ba500303f637fc20","score":"0.6359971","text":"def almost_less(x, y, rtol=1e-14, atol=1e-14):\n return x - y <= atol + np.abs(y) * rtol","title":""},{"docid":"f0ba06fef13eccb4c5e30f8990ea616b","score":"0.6354376","text":"def get_tolerance():\n return parameters.TOLERANCE","title":""},{"docid":"ea156158ab9f5d2aaf9fc4bdbf001e45","score":"0.6347623","text":"def tolerance(self, tol):\n self._tolerance = tol","title":""},{"docid":"ea156158ab9f5d2aaf9fc4bdbf001e45","score":"0.6347623","text":"def tolerance(self, tol):\n self._tolerance = tol","title":""},{"docid":"25409b090e1ef08c7cd44cce91d0d556","score":"0.6339426","text":"def require_accurate_evaluation(self, noisy_val):\n # Check if the point improves over existing points, \n # or if it could be optimal according to tolerances. \n # In this case, perform a double evaluation.\n best_possible = self.fmin\n if ((noisy_val <= best_possible -\n self.l_settings.eps_impr*max(1.0, abs(best_possible))) or\n (noisy_val <= self.l_settings.target_objval +\n self.l_settings.eps_opt*abs(self.l_settings.target_objval))):\n return True\n else:\n return False","title":""},{"docid":"c7e85303ea78fbed7177461921ba142e","score":"0.63144785","text":"def assertNowish(self, timestamp, tolerance=1):\n delta = datetime.now() - timestamp \n self.assertTrue(delta.seconds <= tolerance)","title":""},{"docid":"66fe57c29b7f2dc32be82220469ad628","score":"0.63110167","text":"def is_almost_equal(num_1, num_2):\n threshold = 0.03\n if abs(num_1 - num_2) < threshold:\n return True\n else:\n return False","title":""},{"docid":"e96566cf853f4af55a1845bc40f16ae7","score":"0.6305576","text":"def close_to(self, other, tol=1e-3):\n for diff in self.iter_differences(other):\n if abs(diff) > tol:\n return False\n return True","title":""},{"docid":"97ec08d14fe9c90904c986522080af42","score":"0.62932694","text":"def validate(self, maze, tolerance):\n pos = maze.turtlebot_pos\n\n # all negative positions are invalid\n if any(np.asarray(pos) < 0):\n return False\n elif self.state_pixel is None:\n return True\n # if the position does not change dramatically, then it is valid\n elif np.sum(abs(self.state_pixel - pos)) < tolerance:\n return True\n else:\n return False","title":""},{"docid":"c729962a8c959955b7ce8295b5bcdf55","score":"0.6289345","text":"def tolerance(self):\n return self.settings.hu_tolerance","title":""},{"docid":"c729962a8c959955b7ce8295b5bcdf55","score":"0.6289345","text":"def tolerance(self):\n return self.settings.hu_tolerance","title":""},{"docid":"59c0b8ed0b1ce02b75778415e3dc7bb9","score":"0.628875","text":"def tol(self):\n\n return self._tol","title":""},{"docid":"4e00eb39f18c5d9c9b7cfb437e6f9f4d","score":"0.62810886","text":"def is_goal(self, tolerance):\n \n # if self.goal_pixel is not None:\n # print(\"Distance to goal: \", np.linalg.norm(self.goal_pixel - self.state_pixel))\n return (self.goal_pixel is not None) and np.linalg.norm(self.goal_pixel - self.state_pixel) < tolerance","title":""},{"docid":"e0b5989ad0bf9e75eb987728af518a83","score":"0.6276838","text":"def make_check_values(self, value_calculated:np.array, value_returned:np.array) -> np.ndarray:\n return all(abs(x-y) <= self.eps for x, y in zip(value_calculated, value_returned))","title":""},{"docid":"4e4c8aba4006b0dfda076ed8c32293b3","score":"0.62742877","text":"def converged(self, rtol, atol):\n return self.err < rtol * abs(self.mean) + atol","title":""},{"docid":"81a8fd864474efc6c9860a1953ce10be","score":"0.6266887","text":"def checkAnswer(comment,value,expected,tol=1e-7,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True","title":""},{"docid":"7c499c9f239a76df6adfde8157564e6c","score":"0.6262354","text":"def close_to_exceeding(self) -> bool:\n if self.max_cuts is not None and self.num_cuts >= self.max_cuts:\n return True\n\n thresh = self.longest_seen\n\n if self.max_duration is not None:\n return self.current + thresh >= self.max_duration - 1e-3 # float precision\n return False","title":""},{"docid":"ca048b2f7eed20824b9ec4952b296ff1","score":"0.626226","text":"def testEqualityInvalid(self):\n self.assertNotEqual(mox.IsAlmost(1.899), 1.9)","title":""},{"docid":"3aa76e22b72faa026c4886b1c1042820","score":"0.62560344","text":"def approx_less_eq(number1, number2):\n if number1 < number2 or abs(number1-number2) < 0.0001:\n return True\n else:\n return False","title":""},{"docid":"b20ed3b6e514e81eed5fc1f427ae2ed8","score":"0.62403435","text":"def is_fulfilled(self, tolerance=1e-2) -> bool:\r\n self.satisfied = True\r\n ianionsite = 0\r\n for isite, site in enumerate(self.lse.structure):\r\n if self.lse.valences[isite] < 0:\r\n if (abs(self.anions_bond_strengths[ianionsite]['bond_strengths_sum'] - 2.0)) > tolerance:\r\n self.satisfied = False\r\n\r\n ianionsite = ianionsite + 1\r\n\r\n return self.satisfied","title":""},{"docid":"7ba8649b0078c87521af46929105746a","score":"0.6238806","text":"def should_stop(cls, interval_delta, middle_point, epsilon_rel, epsilon_abs):\n return abs(interval_delta) <= epsilon_rel * abs(middle_point) + epsilon_abs","title":""},{"docid":"16a938f0aa9ae53b0660ef67c3505ce2","score":"0.6237505","text":"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True","title":""},{"docid":"16a938f0aa9ae53b0660ef67c3505ce2","score":"0.6237505","text":"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True","title":""},{"docid":"bf8e86ebc3665ab1a22e210725724cc2","score":"0.6235275","text":"def first_failure(self) -> bool:\n if self.error_count == (self._tolerance + 1):\n return True\n return False","title":""},{"docid":"862ceb422d3e20a1aa4297ca1f61debc","score":"0.6231477","text":"def overTolerance(self):\n ...","title":""},{"docid":"761e8922afafb75e52b936db13da0302","score":"0.62229717","text":"def _check_convergence(self, ll_old = None, ll_new = None):\n\t\tif np.isnan(ll_old) or np.isnan(ll_new):\n\t\t\tif np.sum(np.fabs(self.score)) < self.tol:\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0\n\t\telse:\n\t\t\tif np.abs(ll_old - ll_new) < self.tol:\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0","title":""},{"docid":"42818455aef80a63675e73aa5aa1ce48","score":"0.62129897","text":"def passed(self):\n return self.nominal_length_mm - self.tolerance < self.length_mm < self.nominal_length_mm + self.tolerance","title":""},{"docid":"ff6845c40999f73e9c0068282d2d8d90","score":"0.6206111","text":"def satisfied(self):\n if self.current_t is None:\n return False\n return self.current_t >= self.target_t","title":""},{"docid":"9e43890b5778c1c8e01bd2caef100059","score":"0.62052995","text":"def check_within_epsilon(true_values, estimated_values, epsilon):\n for i in range(len(true_values)):\n if not is_within_epsilon(true_values[i], estimated_values[i], epsilon):\n return False\n return True","title":""},{"docid":"6003c157d1137bfc396a53043442dd09","score":"0.6186162","text":"def _check_termination(self):\n convergence_radius = 0.1\n\n abs_dist = self._get_dist_to_goal()\n if (abs_dist < convergence_radius):\n logging.debug(\"done - success!\")\n return True\n if (self.num_steps > self.MAX_STEPS):\n logging.debug(\"done - max steps reached\")\n logging.debug(\"final delta to goal \\t{}\".format(abs_dist))\n return True\n else:\n return False","title":""},{"docid":"c3968786a87d2361ac9a3411b5ce4ad9","score":"0.6183106","text":"def is_convergence(self, eps=1e-6):\n return 1.0 - self.convergence() < eps","title":""},{"docid":"81a478e3f5c74597d60defb839359e63","score":"0.6168033","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n\treturn abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"6297d904a6c5ecb1de360644656c890f","score":"0.61669457","text":"def check(self, point):\n value = self.calculate_value(point)\n\n if self._equation:\n if math.fabs(value) > EPSILON:\n return False\n return True\n else:\n if value >= 0:\n return True\n return False","title":""},{"docid":"3c466c5bf1a782b9ebeae9e830d5d875","score":"0.61604327","text":"def result_is_close_enough(self, result):\n return (np.linalg.norm(result) / np.linalg.norm(self.b)) < self.precision","title":""},{"docid":"4648393a7a9f827347aea27d99f85684","score":"0.6160404","text":"def converged(self):\n # This insures that the cost has been computed at least twice without checking iterations\n try:\n is_converged = abs(self.cost[-2] - self.cost[-1]) <= self.tol\n except IndexError:\n is_converged = False\n return is_converged","title":""},{"docid":"3ce06aa43bb5ad73239467f4103b9ed0","score":"0.6154088","text":"def withinEpsilon(x, y, epsilon):\r\n return abs(x - y) <= epsilon","title":""},{"docid":"b2fc040f3290c78528ec01bdab21f953","score":"0.6151508","text":"def check_answer(self, x, ftol):\n if (self.lb is not None and np.any(x < self.lb) or\n self.ub is not None and np.any(x > self.ub)):\n return False\n\n f = np.sum(self.fun(x) ** 2)\n return f < (1 + ftol) * self.fopt","title":""},{"docid":"3751932a2738cc6efda700136490a635","score":"0.6150774","text":"def withinToleranceOfGoal(self, possible_node, goal_point, tolerance_m):\n dx, dy = goal_point.x - possible_node.point.x, goal_point.y - possible_node.point.y\n dist = math.sqrt(dx * dx + dy * dy)\n if dist <= tolerance_m:\n return True\n return False","title":""},{"docid":"7877ad34d7fdb9f8246a47f4d0f4651c","score":"0.6150306","text":"def pass_tol(self):\n pass_tol = self.stat < self.tol\n return pass_tol","title":""},{"docid":"9b5665e2b8bf3a5e9a7c07c7467ccc21","score":"0.6146118","text":"def on_curve(self, curve, tol=1e-6):\n return self.distance_to_point(curve.closest_point(self)) < tol","title":""},{"docid":"d7d2de72638ff94b890520db4a3ee527","score":"0.614382","text":"def is_close_to(self, other, tolerance):\n self._validate_close_to_args(self.val, other, tolerance)\n\n if self.val < (other-tolerance) or self.val > (other+tolerance):\n if type(self.val) is datetime.datetime:\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\n h, rem = divmod(tolerance_seconds, 3600)\n m, s = divmod(rem, 60)\n self._err('Expected <%s> to be close to <%s> within tolerance <%d:%02d:%02d>, but was not.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\n else:\n self._err('Expected <%s> to be close to <%s> within tolerance <%s>, but was not.' % (self.val, other, tolerance))\n return self","title":""},{"docid":"1d3859dbee4ccea0f8a16f0893fceca7","score":"0.61364233","text":"def tolerance(focuser):\n return 0","title":""},{"docid":"ea3c1cea00539b49b223f0de89c48038","score":"0.6133284","text":"def win(self):\n return abs(self) < 0.4","title":""},{"docid":"e0075ced987e952c0fe93c9be7232991","score":"0.6107463","text":"def is_satisfied(\n self,\n x: \"CartesianCoordinates\",\n tol: float = 1e-4,\n ) -> bool:\n return abs(self.delta(x)) < tol","title":""},{"docid":"b7d54b5fe2b7b828ebba13bce6f9f414","score":"0.61056906","text":"def approx_equal(x, y, atol=.01):\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol","title":""},{"docid":"b7d54b5fe2b7b828ebba13bce6f9f414","score":"0.61056906","text":"def approx_equal(x, y, atol=.01):\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol","title":""},{"docid":"3a1f4bdc249cb1e97c3ff44c4588e500","score":"0.6102711","text":"def check_convergence(\n prev_loss,\n cur_loss,\n cur_step,\n iter_below_tol,\n abs_loss_chg_tol=0.001,\n min_num_iter=100,\n max_iter_below_tol=50\n):\n has_converged = False\n\n # Check if we have reached the desired loss tolerance.\n loss_diff = abs(prev_loss - cur_loss)\n if loss_diff < abs_loss_chg_tol:\n iter_below_tol += 1\n else:\n iter_below_tol = 0\n\n if iter_below_tol >= max_iter_below_tol:\n has_converged = True\n\n if cur_step < min_num_iter:\n has_converged = False\n\n return has_converged, iter_below_tol","title":""},{"docid":"eff80409fd268c1870d26d9ad330a405","score":"0.6100981","text":"def same(x,y):\n print x,y\n if abs(x-y) < 0.000001:\n return True\n else:\n return False","title":""},{"docid":"666dc76b0995415179aa619056de6e3d","score":"0.6099752","text":"def test_rmsd_convergence(self):\n\n # http://en.wikipedia.org/wiki/\n # Non-linear_least_squares#Convergence_criteria\n try:\n r1 = self.history[\"rmsd\"][-1]\n r2 = self.history[\"rmsd\"][-2]\n except IndexError:\n return False\n\n tests = [\n abs((r2[0] - r1[0]) / r2[0]) < self._rmsd_tolerance if r2[0] > 0 else True\n ]\n\n return all(tests)","title":""},{"docid":"f56d59064290fa3d889a3e8c5b3948dc","score":"0.6090564","text":"def withinEpsilon(x, y, epsilon):\n\t\t return abs(x - y) <= epsilon","title":""},{"docid":"3b136615e4b36f758caaa630be12e493","score":"0.60889125","text":"def is_time_around(expected_time, loop=None, delta=.01):\n now = loop.time() if loop else time.time()\n return (expected_time - delta) <= now <= (expected_time + delta)","title":""},{"docid":"53bc44057f3e8cfc7bf7c71e9074e729","score":"0.608647","text":"def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:\n return abs(value_1 - value_2) <= delta","title":""},{"docid":"5a4e870bbde087be74dc9e5b654a6cda","score":"0.6082421","text":"def check_below_threshold(distance, epsilon):\n\n accepted = False\n for i in range(len(epsilon)):\n if epsilon[i] >= distance[i] >= 0:\n accepted = True\n else:\n accepted = False\n break\n return accepted","title":""},{"docid":"fcd33f8d6dc55e034f7e449659c687d8","score":"0.60819095","text":"def isViolated(self):\n upVar = self._findValue(\"upVar\")\n lowVar = self._findValue(\"lowVar\")\n freeVar = self._findValue(\"freeVar\")\n result = abs(upVar + lowVar) >= const.EPS\n if result:\n log.debug(\n \"isViolated %s, upVar %s, lowVar %s, freeVar %s result %s\"\n % (self.name, upVar, lowVar, freeVar, result)\n )\n log.debug(f\"isViolated value lhs {self.findLHSValue()} constant {self.RHS}\")\n return result","title":""},{"docid":"3e153b1e868caa0e2263a65e3d9fd63d","score":"0.60777473","text":"def check_pass_or_fail(self,\n epsilon_failure_tolerance =np.nan, epsilon_failure_tolerance_default =None,\n non_finite_data_tolerance =np.nan, non_finite_data_tolerance_default =None,\n total_data_failure_tolerance=np.nan, total_data_failure_tolerance_default=None,\n min_acceptable_r_squared =np.nan, min_acceptable_r_squared_default =None\n ) :\n\n passValues = [ ]\n \n # test the epsilon value tolerance\n \n # get the tolerance for failures compared to epsilon\n epsilonTolerance = epsilon_failure_tolerance if epsilon_failure_tolerance is not np.nan else epsilon_failure_tolerance_default\n\n # did we fail based on the epsilon?\n failed_fraction = self.comparison.diff_outside_epsilon_fraction\n passed_epsilon = None if (epsilonTolerance is None) else (failed_fraction <= epsilonTolerance)\n passValues.append(passed_epsilon)\n\n # test the nonfinite tolerance\n \n # get the tolerance for failures in amount of nonfinite data (in spatially valid areas)\n nonfiniteTolerance = non_finite_data_tolerance if non_finite_data_tolerance is not np.nan else non_finite_data_tolerance_default\n \n # did we fail based on nonfinite data\n non_finite_diff_fraction = self.finiteData.finite_in_only_one_fraction\n passed_nonfinite = None if (nonfiniteTolerance is None) else (non_finite_diff_fraction <= nonfiniteTolerance)\n passValues.append(passed_nonfinite)\n\n # test if the total failed percentage is acceptable\n \n # get the total percentage of failed data that is acceptable\n totalFailTolerance = total_data_failure_tolerance if total_data_failure_tolerance is not np.nan else total_data_failure_tolerance_default\n \n # did we fail based on all data failures?\n passed_all_percentage = None if (totalFailTolerance is None) else ((non_finite_diff_fraction + failed_fraction) <= totalFailTolerance)\n passValues.append(passed_all_percentage)\n\n # test the r-squared correlation coefficent\n \n # get the minimum acceptable r-squared correlation coefficient\n min_r_squared = min_acceptable_r_squared if (min_acceptable_r_squared is not np.nan) else min_acceptable_r_squared_default\n\n # did we fail based on the r-squared correlation coefficient?\n r_squared_value = None if (min_r_squared is None) else self.comparison.r_squared_correlation\n passed_r_squared = None if (min_r_squared is None) else (r_squared_value >= min_r_squared)\n passValues.append(passed_r_squared)\n\n # figure out the overall pass/fail result\n didPass = None\n for passValue in passValues :\n # if passValue isn't none, we need to update didPass\n if passValue is not None :\n if didPass is not None :\n didPass = passValue and didPass\n else :\n didPass = passValue\n \n return didPass, failed_fraction, non_finite_diff_fraction, r_squared_value","title":""},{"docid":"dd3456078c758c8906845f50e0b61fcb","score":"0.60761505","text":"def check_distractor_distance_threshold(self, goal, gripper):\n self.current_norm_distance = self.calc_distance(goal, gripper)\n threshold = 0.1\n return self.current_norm_distance < threshold","title":""},{"docid":"3355029114166bf81231fd0d2d801c5d","score":"0.6064035","text":"def isclose(a, b, rel_tol=1e-04, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"32e334610a88894d3aaa5bac1639704b","score":"0.6063143","text":"def testEqualityValid(self):\n self.assertEqual(mox.IsAlmost(1.8999999999), 1.9)","title":""},{"docid":"92458285d0dafe3180d047c506589c65","score":"0.6051033","text":"def assert_approximately_equal(computed_potential, expected_potential, tolerance=ENERGY_TOLERANCE):\n\n # Compute error.\n error = (computed_potential - expected_potential)\n\n # Raise an exception if the error is larger than the tolerance.\n if abs(error) > tolerance:\n raise Exception(\"Computed potential %s, expected %s. Error %s is larger than acceptable tolerance of %s.\" % (computed_potential, expected_potential, error, tolerance))\n \n return","title":""},{"docid":"a1b3eccca005b97c8d678bf85206ff99","score":"0.6048701","text":"def approx_equal(x,y):\n if abs(x-y) < 0.00001:\n return True\n else:\n return False","title":""},{"docid":"fcbdd67840da82380b3179d13588b9e7","score":"0.60423213","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"fcbdd67840da82380b3179d13588b9e7","score":"0.60423213","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"bb6d4c5d6a88c6cdeaabba4604521bb3","score":"0.6019731","text":"def is_almost_equal(x, y, epsilon=1 * 10 ** (-8)):\r\n return abs(x - y) <= epsilon","title":""},{"docid":"c6f0f831144f4f4bc4e2b444dda49c07","score":"0.6016614","text":"def terminate(fitness, tolerance):\n # for i in fitness:\n # if abs((2**(1.0 / 2)) - i) < tolerance:\n # return True\n return False","title":""},{"docid":"4736fe1b1e1ae33fa99ac5411b20e523","score":"0.6011596","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"45f49e14c0c463fd5b66be61780bae72","score":"0.60109454","text":"def isViolated(self):\n if abs(value(self.denominator)) >= const.EPS:\n if self.lowTarget is not None:\n if self.lowTarget > self.findLHSValue():\n return True\n if self.upTarget is not None:\n if self.findLHSValue() > self.upTarget:\n return True\n else:\n # if the denominator is zero the constraint is satisfied\n return False","title":""},{"docid":"2f14dead98b6ba6800efd45cdbe674d5","score":"0.6010444","text":"def float_cmp(x, y, rtol=1e-14, atol=1e-14):\n return np.abs(x - y) <= atol + np.abs(y) * rtol","title":""},{"docid":"636a908b2b82f32903e61aae0d60938c","score":"0.5998174","text":"def approx_equal(a, b, tol=1e-9):\n return fabs(a-b) <= max(fabs(a), fabs(b)) * tol","title":""},{"docid":"ced2eec7247f91ef01d988715d9d6ab9","score":"0.5985774","text":"def is_close_enough(x, y):\n\n return abs(x - y) < 0.0001","title":""},{"docid":"4d7d4ae19a201d3f531de04477e5da05","score":"0.5979146","text":"def _check_epsilon(epsilon):\n if not (isinstance(epsilon, float) and 1e-5 <= epsilon <= 0.3):\n print(\"> ERROR: epsilon must be a float between 0.3 and 10^-5\")\n return False\n\n return True","title":""},{"docid":"ffcf0711a0d3f286558a560e3298c417","score":"0.59740263","text":"def getTolerance(self):\n ...","title":""}],"string":"[\n {\n \"docid\": \"ce055bab6287f8377db1128076fa364f\",\n \"score\": \"0.8071129\",\n \"text\": \"def _tolerance_check(self, tolerance, value):\\n if tolerance != None and np.linalg.norm(value) < tolerance:\\n print(\\\"Variable update tolerance was reached. Terminating Search.\\\")\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352bced7ca72b726560ef38f52549699\",\n \"score\": \"0.7769892\",\n \"text\": \"def __bool__(self):\\n return bool(abs(self) > tolerance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1971746879f68f3648bf7f1b42f064d\",\n \"score\": \"0.76606834\",\n \"text\": \"def reached_tolerance(self):\\n if sum(self.dp) < self.tolerance:\\n rospy.logwarn('[%s] Tolerance of %f achieved kp=%f, ki=%f, kd=%f with best error: %f',\\n self.name, self.tolerance, self.p[0], self.p[1], self.p[2], self.best_error)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f482c72478d2317b312759080e7312ba\",\n \"score\": \"0.7599516\",\n \"text\": \"def passed(self):\\n return self.value_diff <= self.tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0066709419ead4f4919127aa2b7f3d\",\n \"score\": \"0.7546813\",\n \"text\": \"def tolerance(computedValue,targetValue,tolerance):\\n\\n if abs(computedValue-targetValue) bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc569d7b54c21a1edbc947696b74106\",\n \"score\": \"0.6949747\",\n \"text\": \"def CheckTolerance(self, value):\\n tolerance = self._unitop.GetTolerance()\\n error = self.CalculateError(value)\\n if error > tolerance:\\n self._unitop.PushConsistencyError(self, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb76f3f9c3cbbc265d428d33cda9b7b\",\n \"score\": \"0.67973673\",\n \"text\": \"def is_satisfied(self, tol=1.0e-6):\\n return abs(self()) < tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01eed1f9e6d4c494b70631c89a5020eb\",\n \"score\": \"0.67568636\",\n \"text\": \"def check_tolerance(self, last_obj, new_obj=0.0):\\n if last_obj is not None:\\n delta_obj = abs(float(new_obj) - float(last_obj))\\n self.mention('delta obj ratio: %.2e' % (delta_obj / self.TOLERANCE))\\n return delta_obj < self.TOLERANCE\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f484cc19e63f22b759133eb18b11f88\",\n \"score\": \"0.6700461\",\n \"text\": \"def inTolerance(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78cb818baf81b4d40eeb15a3d8f3427e\",\n \"score\": \"0.6625738\",\n \"text\": \"def within(a, b, tolerance=1e-5):\\n if a==0 and b==0:\\n return True\\n elif a!=0 and b==0:\\n #Avoid divide by zero\\n return False\\n else:\\n return abs((a/b)-1) < tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df68bc8c4fad2af46f1f98168c37eb99\",\n \"score\": \"0.6526349\",\n \"text\": \"def check_difference(shift1, shift2, tolerance=0.05):\\r\\n mean_diff = (shift1[1] - shift2[1]) ** 2\\r\\n sigma_diff = (shift1[2] + shift2[2]) ** 2\\r\\n res = mean_diff > sigma_diff\\r\\n if abs(shift1[1] - shift2[1]) < tolerance:\\r\\n res = False\\r\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052e05a351af76d3d07aabb0d11920b3\",\n \"score\": \"0.6519439\",\n \"text\": \"def check_difference(shift1, shift2, tolerance=0.05):\\n mean_diff = (shift1[1] - shift2[1]) ** 2\\n sigma_diff = (shift1[2] + shift2[2]) ** 2\\n res = mean_diff > sigma_diff\\n if abs(shift1[1] - shift2[1]) < tolerance:\\n res = False\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b7b3c683e3287d731d3fc658b0b49e\",\n \"score\": \"0.65151095\",\n \"text\": \"def approx_eq(x, y, tolerance=1e-5):\\n return abs(x - y) < tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21b7db657d408a524edb16b0a76a8c5\",\n \"score\": \"0.64969355\",\n \"text\": \"def relaxed_allclose(x, y, atol=0.01, pct_wrong_allowable=0.1):\\n res = torch.isclose(x, y, atol=atol)\\n n_wrong = res.numel() - res.sum()\\n n_wrong_allowable = pct_wrong_allowable * res.numel()\\n return n_wrong <= n_wrong_allowable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98b6d633c65381e53c549fda9118dc5\",\n \"score\": \"0.64799714\",\n \"text\": \"def match(self, other, tolerance=3, amtdev=0.00):\\n if (tolerance == ()):\\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev)\\n else:\\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev) and \\\\\\n (abs(self.date-other.date) <= datetime.timedelta(days=tolerance))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b0b45511093d926ef07d9c350ee676\",\n \"score\": \"0.64740145\",\n \"text\": \"def check_convergence(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f179475d3fa9daa37883a197df0fde02\",\n \"score\": \"0.647181\",\n \"text\": \"def _check_convergence(self, tol):\\n if hasattr(self, 'coeffs_prev'):\\n if torch.abs(self.coeffs - self.coeffs_prev).max() < tol:\\n return True\\n self.coeffs_prev = self.coeffs.detach().clone()\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cafa9423f1cf921e7a02d68925ddd74a\",\n \"score\": \"0.6433718\",\n \"text\": \"def check_convergence(new_measure, old_measure, direction, threshold):\\n\\n sign = 1.0 if direction == 'higher' else -1.0\\n\\n if sign * (new_measure - old_measure) / old_measure < threshold:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c897e612f977361f4bbbb71eeb1adec3\",\n \"score\": \"0.6422954\",\n \"text\": \"def converged(self):\\n\\n top_lnls = [abs(x['lnL']) for x in self.estimates()][0:2]\\n\\n # dif = (top_lnls[0] - top_lnls[1]) / (0.5 * (top_lnls[0] + top_lnls[1]))\\n dif = top_lnls[0] - top_lnls[1]\\n\\n if abs(dif) < 0.1:\\n return True\\n\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae09a0e92a3b5f68c494935dbbc3b46\",\n \"score\": \"0.6420245\",\n \"text\": \"def tolerance(*args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfa65081f2e310884cc7d5b8c35c0003\",\n \"score\": \"0.63993156\",\n \"text\": \"def _check_tolerance(self, content, name, value, mos_file):\\n\\n if (name + \\\"=\\\" == \\\"tolerance=\\\" and float(value) > 1e-6):\\n self._wrong_parameter(mos_file, name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ba0fccb0fd64a1b60f0c57d8f1ae49\",\n \"score\": \"0.6392067\",\n \"text\": \"def is_not_close_to(self, other, tolerance):\\n self._validate_close_to_args(self.val, other, tolerance)\\n\\n if self.val >= (other-tolerance) and self.val <= (other+tolerance):\\n if type(self.val) is datetime.datetime:\\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\\n h, rem = divmod(tolerance_seconds, 3600)\\n m, s = divmod(rem, 60)\\n self._err('Expected <%s> to not be close to <%s> within tolerance <%d:%02d:%02d>, but was.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\\n else:\\n self._err('Expected <%s> to not be close to <%s> within tolerance <%s>, but was.' % (self.val, other, tolerance))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598e173b3b57da6b873a58422c1b89d2\",\n \"score\": \"0.6380607\",\n \"text\": \"def check_below_threshold(distance, epsilon):\\n return distance < epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad4e1852afe57b0efaf7eb41c4d73868\",\n \"score\": \"0.63688385\",\n \"text\": \"def test_equal_time(t1, t2, tol=1E-3):\\n if abs(t1) <= tol:\\n res = abs(t1 - t2) <= tol\\n else:\\n res = abs(t1 - t2) <= tol * abs(t1)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55450f03a699b012ba500303f637fc20\",\n \"score\": \"0.6359971\",\n \"text\": \"def almost_less(x, y, rtol=1e-14, atol=1e-14):\\n return x - y <= atol + np.abs(y) * rtol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ba06fef13eccb4c5e30f8990ea616b\",\n \"score\": \"0.6354376\",\n \"text\": \"def get_tolerance():\\n return parameters.TOLERANCE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea156158ab9f5d2aaf9fc4bdbf001e45\",\n \"score\": \"0.6347623\",\n \"text\": \"def tolerance(self, tol):\\n self._tolerance = tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea156158ab9f5d2aaf9fc4bdbf001e45\",\n \"score\": \"0.6347623\",\n \"text\": \"def tolerance(self, tol):\\n self._tolerance = tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25409b090e1ef08c7cd44cce91d0d556\",\n \"score\": \"0.6339426\",\n \"text\": \"def require_accurate_evaluation(self, noisy_val):\\n # Check if the point improves over existing points, \\n # or if it could be optimal according to tolerances. \\n # In this case, perform a double evaluation.\\n best_possible = self.fmin\\n if ((noisy_val <= best_possible -\\n self.l_settings.eps_impr*max(1.0, abs(best_possible))) or\\n (noisy_val <= self.l_settings.target_objval +\\n self.l_settings.eps_opt*abs(self.l_settings.target_objval))):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e85303ea78fbed7177461921ba142e\",\n \"score\": \"0.63144785\",\n \"text\": \"def assertNowish(self, timestamp, tolerance=1):\\n delta = datetime.now() - timestamp \\n self.assertTrue(delta.seconds <= tolerance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fe57c29b7f2dc32be82220469ad628\",\n \"score\": \"0.63110167\",\n \"text\": \"def is_almost_equal(num_1, num_2):\\n threshold = 0.03\\n if abs(num_1 - num_2) < threshold:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96566cf853f4af55a1845bc40f16ae7\",\n \"score\": \"0.6305576\",\n \"text\": \"def close_to(self, other, tol=1e-3):\\n for diff in self.iter_differences(other):\\n if abs(diff) > tol:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ec08d14fe9c90904c986522080af42\",\n \"score\": \"0.62932694\",\n \"text\": \"def validate(self, maze, tolerance):\\n pos = maze.turtlebot_pos\\n\\n # all negative positions are invalid\\n if any(np.asarray(pos) < 0):\\n return False\\n elif self.state_pixel is None:\\n return True\\n # if the position does not change dramatically, then it is valid\\n elif np.sum(abs(self.state_pixel - pos)) < tolerance:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c729962a8c959955b7ce8295b5bcdf55\",\n \"score\": \"0.6289345\",\n \"text\": \"def tolerance(self):\\n return self.settings.hu_tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c729962a8c959955b7ce8295b5bcdf55\",\n \"score\": \"0.6289345\",\n \"text\": \"def tolerance(self):\\n return self.settings.hu_tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c0b8ed0b1ce02b75778415e3dc7bb9\",\n \"score\": \"0.628875\",\n \"text\": \"def tol(self):\\n\\n return self._tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e00eb39f18c5d9c9b7cfb437e6f9f4d\",\n \"score\": \"0.62810886\",\n \"text\": \"def is_goal(self, tolerance):\\n \\n # if self.goal_pixel is not None:\\n # print(\\\"Distance to goal: \\\", np.linalg.norm(self.goal_pixel - self.state_pixel))\\n return (self.goal_pixel is not None) and np.linalg.norm(self.goal_pixel - self.state_pixel) < tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b5989ad0bf9e75eb987728af518a83\",\n \"score\": \"0.6276838\",\n \"text\": \"def make_check_values(self, value_calculated:np.array, value_returned:np.array) -> np.ndarray:\\n return all(abs(x-y) <= self.eps for x, y in zip(value_calculated, value_returned))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e4c8aba4006b0dfda076ed8c32293b3\",\n \"score\": \"0.62742877\",\n \"text\": \"def converged(self, rtol, atol):\\n return self.err < rtol * abs(self.mean) + atol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a8fd864474efc6c9860a1953ce10be\",\n \"score\": \"0.6266887\",\n \"text\": \"def checkAnswer(comment,value,expected,tol=1e-7,updateResults=True):\\n if abs(value - expected) > tol:\\n print(\\\"checking answer\\\",comment,value,\\\"!=\\\",expected)\\n if updateResults:\\n results[\\\"fail\\\"] += 1\\n return False\\n else:\\n if updateResults:\\n results[\\\"pass\\\"] += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c499c9f239a76df6adfde8157564e6c\",\n \"score\": \"0.6262354\",\n \"text\": \"def close_to_exceeding(self) -> bool:\\n if self.max_cuts is not None and self.num_cuts >= self.max_cuts:\\n return True\\n\\n thresh = self.longest_seen\\n\\n if self.max_duration is not None:\\n return self.current + thresh >= self.max_duration - 1e-3 # float precision\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca048b2f7eed20824b9ec4952b296ff1\",\n \"score\": \"0.626226\",\n \"text\": \"def testEqualityInvalid(self):\\n self.assertNotEqual(mox.IsAlmost(1.899), 1.9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa76e22b72faa026c4886b1c1042820\",\n \"score\": \"0.62560344\",\n \"text\": \"def approx_less_eq(number1, number2):\\n if number1 < number2 or abs(number1-number2) < 0.0001:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20ed3b6e514e81eed5fc1f427ae2ed8\",\n \"score\": \"0.62403435\",\n \"text\": \"def is_fulfilled(self, tolerance=1e-2) -> bool:\\r\\n self.satisfied = True\\r\\n ianionsite = 0\\r\\n for isite, site in enumerate(self.lse.structure):\\r\\n if self.lse.valences[isite] < 0:\\r\\n if (abs(self.anions_bond_strengths[ianionsite]['bond_strengths_sum'] - 2.0)) > tolerance:\\r\\n self.satisfied = False\\r\\n\\r\\n ianionsite = ianionsite + 1\\r\\n\\r\\n return self.satisfied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba8649b0078c87521af46929105746a\",\n \"score\": \"0.6238806\",\n \"text\": \"def should_stop(cls, interval_delta, middle_point, epsilon_rel, epsilon_abs):\\n return abs(interval_delta) <= epsilon_rel * abs(middle_point) + epsilon_abs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a938f0aa9ae53b0660ef67c3505ce2\",\n \"score\": \"0.6237505\",\n \"text\": \"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\\n if abs(value - expected) > tol:\\n print(\\\"checking answer\\\",comment,value,\\\"!=\\\",expected)\\n if updateResults:\\n results[\\\"fail\\\"] += 1\\n return False\\n else:\\n if updateResults:\\n results[\\\"pass\\\"] += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a938f0aa9ae53b0660ef67c3505ce2\",\n \"score\": \"0.6237505\",\n \"text\": \"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\\n if abs(value - expected) > tol:\\n print(\\\"checking answer\\\",comment,value,\\\"!=\\\",expected)\\n if updateResults:\\n results[\\\"fail\\\"] += 1\\n return False\\n else:\\n if updateResults:\\n results[\\\"pass\\\"] += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8e86ebc3665ab1a22e210725724cc2\",\n \"score\": \"0.6235275\",\n \"text\": \"def first_failure(self) -> bool:\\n if self.error_count == (self._tolerance + 1):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862ceb422d3e20a1aa4297ca1f61debc\",\n \"score\": \"0.6231477\",\n \"text\": \"def overTolerance(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"761e8922afafb75e52b936db13da0302\",\n \"score\": \"0.62229717\",\n \"text\": \"def _check_convergence(self, ll_old = None, ll_new = None):\\n\\t\\tif np.isnan(ll_old) or np.isnan(ll_new):\\n\\t\\t\\tif np.sum(np.fabs(self.score)) < self.tol:\\n\\t\\t\\t\\treturn 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\tif np.abs(ll_old - ll_new) < self.tol:\\n\\t\\t\\t\\treturn 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42818455aef80a63675e73aa5aa1ce48\",\n \"score\": \"0.62129897\",\n \"text\": \"def passed(self):\\n return self.nominal_length_mm - self.tolerance < self.length_mm < self.nominal_length_mm + self.tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6845c40999f73e9c0068282d2d8d90\",\n \"score\": \"0.6206111\",\n \"text\": \"def satisfied(self):\\n if self.current_t is None:\\n return False\\n return self.current_t >= self.target_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e43890b5778c1c8e01bd2caef100059\",\n \"score\": \"0.62052995\",\n \"text\": \"def check_within_epsilon(true_values, estimated_values, epsilon):\\n for i in range(len(true_values)):\\n if not is_within_epsilon(true_values[i], estimated_values[i], epsilon):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6003c157d1137bfc396a53043442dd09\",\n \"score\": \"0.6186162\",\n \"text\": \"def _check_termination(self):\\n convergence_radius = 0.1\\n\\n abs_dist = self._get_dist_to_goal()\\n if (abs_dist < convergence_radius):\\n logging.debug(\\\"done - success!\\\")\\n return True\\n if (self.num_steps > self.MAX_STEPS):\\n logging.debug(\\\"done - max steps reached\\\")\\n logging.debug(\\\"final delta to goal \\\\t{}\\\".format(abs_dist))\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3968786a87d2361ac9a3411b5ce4ad9\",\n \"score\": \"0.6183106\",\n \"text\": \"def is_convergence(self, eps=1e-6):\\n return 1.0 - self.convergence() < eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a478e3f5c74597d60defb839359e63\",\n \"score\": \"0.6168033\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n\\treturn abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6297d904a6c5ecb1de360644656c890f\",\n \"score\": \"0.61669457\",\n \"text\": \"def check(self, point):\\n value = self.calculate_value(point)\\n\\n if self._equation:\\n if math.fabs(value) > EPSILON:\\n return False\\n return True\\n else:\\n if value >= 0:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c466c5bf1a782b9ebeae9e830d5d875\",\n \"score\": \"0.61604327\",\n \"text\": \"def result_is_close_enough(self, result):\\n return (np.linalg.norm(result) / np.linalg.norm(self.b)) < self.precision\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4648393a7a9f827347aea27d99f85684\",\n \"score\": \"0.6160404\",\n \"text\": \"def converged(self):\\n # This insures that the cost has been computed at least twice without checking iterations\\n try:\\n is_converged = abs(self.cost[-2] - self.cost[-1]) <= self.tol\\n except IndexError:\\n is_converged = False\\n return is_converged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce06aa43bb5ad73239467f4103b9ed0\",\n \"score\": \"0.6154088\",\n \"text\": \"def withinEpsilon(x, y, epsilon):\\r\\n return abs(x - y) <= epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2fc040f3290c78528ec01bdab21f953\",\n \"score\": \"0.6151508\",\n \"text\": \"def check_answer(self, x, ftol):\\n if (self.lb is not None and np.any(x < self.lb) or\\n self.ub is not None and np.any(x > self.ub)):\\n return False\\n\\n f = np.sum(self.fun(x) ** 2)\\n return f < (1 + ftol) * self.fopt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3751932a2738cc6efda700136490a635\",\n \"score\": \"0.6150774\",\n \"text\": \"def withinToleranceOfGoal(self, possible_node, goal_point, tolerance_m):\\n dx, dy = goal_point.x - possible_node.point.x, goal_point.y - possible_node.point.y\\n dist = math.sqrt(dx * dx + dy * dy)\\n if dist <= tolerance_m:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7877ad34d7fdb9f8246a47f4d0f4651c\",\n \"score\": \"0.6150306\",\n \"text\": \"def pass_tol(self):\\n pass_tol = self.stat < self.tol\\n return pass_tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b5665e2b8bf3a5e9a7c07c7467ccc21\",\n \"score\": \"0.6146118\",\n \"text\": \"def on_curve(self, curve, tol=1e-6):\\n return self.distance_to_point(curve.closest_point(self)) < tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d2de72638ff94b890520db4a3ee527\",\n \"score\": \"0.614382\",\n \"text\": \"def is_close_to(self, other, tolerance):\\n self._validate_close_to_args(self.val, other, tolerance)\\n\\n if self.val < (other-tolerance) or self.val > (other+tolerance):\\n if type(self.val) is datetime.datetime:\\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\\n h, rem = divmod(tolerance_seconds, 3600)\\n m, s = divmod(rem, 60)\\n self._err('Expected <%s> to be close to <%s> within tolerance <%d:%02d:%02d>, but was not.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\\n else:\\n self._err('Expected <%s> to be close to <%s> within tolerance <%s>, but was not.' % (self.val, other, tolerance))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3859dbee4ccea0f8a16f0893fceca7\",\n \"score\": \"0.61364233\",\n \"text\": \"def tolerance(focuser):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3c1cea00539b49b223f0de89c48038\",\n \"score\": \"0.6133284\",\n \"text\": \"def win(self):\\n return abs(self) < 0.4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0075ced987e952c0fe93c9be7232991\",\n \"score\": \"0.6107463\",\n \"text\": \"def is_satisfied(\\n self,\\n x: \\\"CartesianCoordinates\\\",\\n tol: float = 1e-4,\\n ) -> bool:\\n return abs(self.delta(x)) < tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d54b5fe2b7b828ebba13bce6f9f414\",\n \"score\": \"0.61056906\",\n \"text\": \"def approx_equal(x, y, atol=.01):\\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d54b5fe2b7b828ebba13bce6f9f414\",\n \"score\": \"0.61056906\",\n \"text\": \"def approx_equal(x, y, atol=.01):\\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1f4bdc249cb1e97c3ff44c4588e500\",\n \"score\": \"0.6102711\",\n \"text\": \"def check_convergence(\\n prev_loss,\\n cur_loss,\\n cur_step,\\n iter_below_tol,\\n abs_loss_chg_tol=0.001,\\n min_num_iter=100,\\n max_iter_below_tol=50\\n):\\n has_converged = False\\n\\n # Check if we have reached the desired loss tolerance.\\n loss_diff = abs(prev_loss - cur_loss)\\n if loss_diff < abs_loss_chg_tol:\\n iter_below_tol += 1\\n else:\\n iter_below_tol = 0\\n\\n if iter_below_tol >= max_iter_below_tol:\\n has_converged = True\\n\\n if cur_step < min_num_iter:\\n has_converged = False\\n\\n return has_converged, iter_below_tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff80409fd268c1870d26d9ad330a405\",\n \"score\": \"0.6100981\",\n \"text\": \"def same(x,y):\\n print x,y\\n if abs(x-y) < 0.000001:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"666dc76b0995415179aa619056de6e3d\",\n \"score\": \"0.6099752\",\n \"text\": \"def test_rmsd_convergence(self):\\n\\n # http://en.wikipedia.org/wiki/\\n # Non-linear_least_squares#Convergence_criteria\\n try:\\n r1 = self.history[\\\"rmsd\\\"][-1]\\n r2 = self.history[\\\"rmsd\\\"][-2]\\n except IndexError:\\n return False\\n\\n tests = [\\n abs((r2[0] - r1[0]) / r2[0]) < self._rmsd_tolerance if r2[0] > 0 else True\\n ]\\n\\n return all(tests)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f56d59064290fa3d889a3e8c5b3948dc\",\n \"score\": \"0.6090564\",\n \"text\": \"def withinEpsilon(x, y, epsilon):\\n\\t\\t return abs(x - y) <= epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b136615e4b36f758caaa630be12e493\",\n \"score\": \"0.60889125\",\n \"text\": \"def is_time_around(expected_time, loop=None, delta=.01):\\n now = loop.time() if loop else time.time()\\n return (expected_time - delta) <= now <= (expected_time + delta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53bc44057f3e8cfc7bf7c71e9074e729\",\n \"score\": \"0.608647\",\n \"text\": \"def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:\\n return abs(value_1 - value_2) <= delta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4e870bbde087be74dc9e5b654a6cda\",\n \"score\": \"0.6082421\",\n \"text\": \"def check_below_threshold(distance, epsilon):\\n\\n accepted = False\\n for i in range(len(epsilon)):\\n if epsilon[i] >= distance[i] >= 0:\\n accepted = True\\n else:\\n accepted = False\\n break\\n return accepted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd33f8d6dc55e034f7e449659c687d8\",\n \"score\": \"0.60819095\",\n \"text\": \"def isViolated(self):\\n upVar = self._findValue(\\\"upVar\\\")\\n lowVar = self._findValue(\\\"lowVar\\\")\\n freeVar = self._findValue(\\\"freeVar\\\")\\n result = abs(upVar + lowVar) >= const.EPS\\n if result:\\n log.debug(\\n \\\"isViolated %s, upVar %s, lowVar %s, freeVar %s result %s\\\"\\n % (self.name, upVar, lowVar, freeVar, result)\\n )\\n log.debug(f\\\"isViolated value lhs {self.findLHSValue()} constant {self.RHS}\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e153b1e868caa0e2263a65e3d9fd63d\",\n \"score\": \"0.60777473\",\n \"text\": \"def check_pass_or_fail(self,\\n epsilon_failure_tolerance =np.nan, epsilon_failure_tolerance_default =None,\\n non_finite_data_tolerance =np.nan, non_finite_data_tolerance_default =None,\\n total_data_failure_tolerance=np.nan, total_data_failure_tolerance_default=None,\\n min_acceptable_r_squared =np.nan, min_acceptable_r_squared_default =None\\n ) :\\n\\n passValues = [ ]\\n \\n # test the epsilon value tolerance\\n \\n # get the tolerance for failures compared to epsilon\\n epsilonTolerance = epsilon_failure_tolerance if epsilon_failure_tolerance is not np.nan else epsilon_failure_tolerance_default\\n\\n # did we fail based on the epsilon?\\n failed_fraction = self.comparison.diff_outside_epsilon_fraction\\n passed_epsilon = None if (epsilonTolerance is None) else (failed_fraction <= epsilonTolerance)\\n passValues.append(passed_epsilon)\\n\\n # test the nonfinite tolerance\\n \\n # get the tolerance for failures in amount of nonfinite data (in spatially valid areas)\\n nonfiniteTolerance = non_finite_data_tolerance if non_finite_data_tolerance is not np.nan else non_finite_data_tolerance_default\\n \\n # did we fail based on nonfinite data\\n non_finite_diff_fraction = self.finiteData.finite_in_only_one_fraction\\n passed_nonfinite = None if (nonfiniteTolerance is None) else (non_finite_diff_fraction <= nonfiniteTolerance)\\n passValues.append(passed_nonfinite)\\n\\n # test if the total failed percentage is acceptable\\n \\n # get the total percentage of failed data that is acceptable\\n totalFailTolerance = total_data_failure_tolerance if total_data_failure_tolerance is not np.nan else total_data_failure_tolerance_default\\n \\n # did we fail based on all data failures?\\n passed_all_percentage = None if (totalFailTolerance is None) else ((non_finite_diff_fraction + failed_fraction) <= totalFailTolerance)\\n passValues.append(passed_all_percentage)\\n\\n # test the r-squared correlation coefficent\\n \\n # get the minimum acceptable r-squared correlation coefficient\\n min_r_squared = min_acceptable_r_squared if (min_acceptable_r_squared is not np.nan) else min_acceptable_r_squared_default\\n\\n # did we fail based on the r-squared correlation coefficient?\\n r_squared_value = None if (min_r_squared is None) else self.comparison.r_squared_correlation\\n passed_r_squared = None if (min_r_squared is None) else (r_squared_value >= min_r_squared)\\n passValues.append(passed_r_squared)\\n\\n # figure out the overall pass/fail result\\n didPass = None\\n for passValue in passValues :\\n # if passValue isn't none, we need to update didPass\\n if passValue is not None :\\n if didPass is not None :\\n didPass = passValue and didPass\\n else :\\n didPass = passValue\\n \\n return didPass, failed_fraction, non_finite_diff_fraction, r_squared_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd3456078c758c8906845f50e0b61fcb\",\n \"score\": \"0.60761505\",\n \"text\": \"def check_distractor_distance_threshold(self, goal, gripper):\\n self.current_norm_distance = self.calc_distance(goal, gripper)\\n threshold = 0.1\\n return self.current_norm_distance < threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3355029114166bf81231fd0d2d801c5d\",\n \"score\": \"0.6064035\",\n \"text\": \"def isclose(a, b, rel_tol=1e-04, abs_tol=0.0):\\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e334610a88894d3aaa5bac1639704b\",\n \"score\": \"0.6063143\",\n \"text\": \"def testEqualityValid(self):\\n self.assertEqual(mox.IsAlmost(1.8999999999), 1.9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92458285d0dafe3180d047c506589c65\",\n \"score\": \"0.6051033\",\n \"text\": \"def assert_approximately_equal(computed_potential, expected_potential, tolerance=ENERGY_TOLERANCE):\\n\\n # Compute error.\\n error = (computed_potential - expected_potential)\\n\\n # Raise an exception if the error is larger than the tolerance.\\n if abs(error) > tolerance:\\n raise Exception(\\\"Computed potential %s, expected %s. Error %s is larger than acceptable tolerance of %s.\\\" % (computed_potential, expected_potential, error, tolerance))\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1b3eccca005b97c8d678bf85206ff99\",\n \"score\": \"0.6048701\",\n \"text\": \"def approx_equal(x,y):\\n if abs(x-y) < 0.00001:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbdd67840da82380b3179d13588b9e7\",\n \"score\": \"0.60423213\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbdd67840da82380b3179d13588b9e7\",\n \"score\": \"0.60423213\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb6d4c5d6a88c6cdeaabba4604521bb3\",\n \"score\": \"0.6019731\",\n \"text\": \"def is_almost_equal(x, y, epsilon=1 * 10 ** (-8)):\\r\\n return abs(x - y) <= epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f0f831144f4f4bc4e2b444dda49c07\",\n \"score\": \"0.6016614\",\n \"text\": \"def terminate(fitness, tolerance):\\n # for i in fitness:\\n # if abs((2**(1.0 / 2)) - i) < tolerance:\\n # return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4736fe1b1e1ae33fa99ac5411b20e523\",\n \"score\": \"0.6011596\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f49e14c0c463fd5b66be61780bae72\",\n \"score\": \"0.60109454\",\n \"text\": \"def isViolated(self):\\n if abs(value(self.denominator)) >= const.EPS:\\n if self.lowTarget is not None:\\n if self.lowTarget > self.findLHSValue():\\n return True\\n if self.upTarget is not None:\\n if self.findLHSValue() > self.upTarget:\\n return True\\n else:\\n # if the denominator is zero the constraint is satisfied\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f14dead98b6ba6800efd45cdbe674d5\",\n \"score\": \"0.6010444\",\n \"text\": \"def float_cmp(x, y, rtol=1e-14, atol=1e-14):\\n return np.abs(x - y) <= atol + np.abs(y) * rtol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636a908b2b82f32903e61aae0d60938c\",\n \"score\": \"0.5998174\",\n \"text\": \"def approx_equal(a, b, tol=1e-9):\\n return fabs(a-b) <= max(fabs(a), fabs(b)) * tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced2eec7247f91ef01d988715d9d6ab9\",\n \"score\": \"0.5985774\",\n \"text\": \"def is_close_enough(x, y):\\n\\n return abs(x - y) < 0.0001\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d7d4ae19a201d3f531de04477e5da05\",\n \"score\": \"0.5979146\",\n \"text\": \"def _check_epsilon(epsilon):\\n if not (isinstance(epsilon, float) and 1e-5 <= epsilon <= 0.3):\\n print(\\\"> ERROR: epsilon must be a float between 0.3 and 10^-5\\\")\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcf0711a0d3f286558a560e3298c417\",\n \"score\": \"0.59740263\",\n \"text\": \"def getTolerance(self):\\n ...\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":182,"cells":{"query_id":{"kind":"string","value":"3267fcb6ce40a8d0595e6979dd8f0ae1"},"query":{"kind":"string","value":"Return filenames that have a spcified set of extensions."},"positive_passages":{"kind":"list like","value":[{"docid":"841a54f260628de898d042118a6d2804","score":"0.671288","text":"def filenameMatchesAListOfExtensions(filename, extensionList=None):\n if extensionList is not None:\n for currExt in extensionList:\n if filename.lower().endswith(currExt.lower()):\n return True\n return False","title":""}],"string":"[\n {\n \"docid\": \"841a54f260628de898d042118a6d2804\",\n \"score\": \"0.671288\",\n \"text\": \"def filenameMatchesAListOfExtensions(filename, extensionList=None):\\n if extensionList is not None:\\n for currExt in extensionList:\\n if filename.lower().endswith(currExt.lower()):\\n return True\\n return False\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"980d3d0380131c6070595da9574d0e44","score":"0.7548063","text":"def get_files(dirname, extensions=['.png', '.tif', '.jpg']):\n dir_path = Path(dirname)\n\n files = dir_path.glob('**/*')\n\n files = [path.resolve() for path in files]\n\n match = [f for f in files if f.suffix in extensions]\n return match","title":""},{"docid":"756bf2887c3883a51543e44a722ba14b","score":"0.74919146","text":"def ext_filter(directory, exts, ABS_PATHS = True):\n if isinstance(exts,list) or isinstance(exts,tuple):\n exts = list(exts)\n if ABS_PATHS:\n return [opj(directory,file) for file in os.listdir(directory) if \n os.path.splitext(file)[1] in exts]\n else:\n return [file for file in os.listdir(directory) if \n os.path.splitext(file)[1] in exts]","title":""},{"docid":"59861aab08ffbe5d5f4534e9f1ede444","score":"0.73087174","text":"def filter_by_extension(ext):\n def filt(filename):\n return filename.endswith(ext)\n return filt","title":""},{"docid":"1418fdb60218040b473b134833d3322a","score":"0.7305053","text":"def get_files_with_extension(dir, names, ext):\n list = []\n for name in names:\n e = os.path.splitext(name)[1]\n if e == ext:\n list.append(name)\n list.sort()\n return [os.path.join(dir, n) for n in list]","title":""},{"docid":"b4aa8be8863df9601ac1abf7504c66b8","score":"0.726645","text":"def _get_allowed_extensions():\n return [\n ext.lower()\n for ext, file_type in Image.EXTENSION.items()\n if file_type.upper() in MIME_TYPE_TO_PIL_IDENTIFIER.values()\n ]","title":""},{"docid":"aaae5bf503325a9a14f507df67b18904","score":"0.7261796","text":"def filter_by_suffix(files):\n if includesuffix:\n for insuffix in includesuffix:\n extname = '*.' + insuffix\n if fnmatch.fnmatch(files, extname):\n return True\n\n if excludesuffix:\n for exsuffix in excludesuffix:\n extname = '*.' + exsuffix\n if fnmatch.fnmatch(files, extname):\n return False\n\n if includedefault:\n return True\n else:\n return False","title":""},{"docid":"f6ad56e049784f05cd7a715e0f302633","score":"0.7199362","text":"def filter_files(directory, extensions):\n\n return map(lambda x: os.path.join(directory, x),\n filter(lambda x: re.search('\\.({0})$'.format(\n '|'.join(extensions)), x), sorted(os.listdir(directory))))","title":""},{"docid":"069c2ef98f7e6ab26e42a762fee47e2c","score":"0.71652","text":"def allowed_file_exts(files: List, extensions: List[str]) -> bool:\n for f in files:\n ext = get_file_extension(f.filename)\n if ext not in extensions:\n return False\n return True","title":""},{"docid":"1cf1ff4052794423d8f2d4f6e16fb27f","score":"0.71020085","text":"def file_extensions_get(fname_list):\n return [os.path.splitext(fname)[-1] for fname in fname_list]","title":""},{"docid":"df91497740c894b23886e74235c15f45","score":"0.69904","text":"def list_files_with_extensions(directory, ext_list):\n filepath_list = sum(\n [\n glob(\"{0}/*{1}\".format(directory, ext))\n for ext in ext_list\n ],\n [],\n )\n filenames = [os.path.basename(filepath) for filepath in filepath_list]\n return filenames","title":""},{"docid":"8c7239a42c8c11c31617909d28b183ed","score":"0.6975149","text":"def get_file_list(path, ext):\n return [f for f in os.listdir(path) if \n os.path.isfile(os.path.join(path, f)) and f.endswith(ext)]","title":""},{"docid":"65c3dee1bd54f7da56b4079011cad679","score":"0.69076073","text":"def getFileNames():\n included_extenstions = ['json' ]\n file_names = [fn for fn in os.listdir(os.getcwd()) \n if any([fn.endswith(ext) \n for ext in included_extenstions])] \n return file_names","title":""},{"docid":"276a32c3bedac93c824d12a57fa12d6c","score":"0.6899496","text":"def _generate_filename_list(self):\n fq_filename, extension = os.path.splitext(self._fq_infilename)\n\n la_ext_list = [\n 'la{0}'.format(char) for char in string.lowercase]\n da_ext_list = [\n 'da{0}'.format(char) for char in string.lowercase]\n\n extension_list = la_ext_list + da_ext_list\n\n filename_list = [fq_filename + '.{0}'.format(ext)\n for ext in extension_list\n if os.path.exists(fq_filename + '.{0}'.format(ext))]\n\n return filename_list","title":""},{"docid":"f8ec45f3da344c3e14e5a94841f8ca67","score":"0.68790245","text":"def _file_has_extensions(self, filestr, extensions):\r\n for extension in extensions:\r\n if filestr.find(extension) > -1:\r\n return True\r\n return False","title":""},{"docid":"1dd879bf8ef32a15b5c6accde3a3c28d","score":"0.6872849","text":"def get_exts(filepath, all_exts=True):\n \n ext = 42 # initialize and set to non-0 value\n out = [] # the output array of extensions\n \n while ext:\n filepath, ext = path.splitext(filepath)\n\n if ext:\n clean_ext = ext[1:] # strip the \".\" off the front of ext\n out = [clean_ext] + out # append to output array\n if not all_exts: break # if all_exts is False then end the\n # loop after grabbing the first ext\n return out","title":""},{"docid":"b01cc22ff929e71d1968875d4d7497db","score":"0.6780839","text":"def has_file_allowed_extension(filename, extensions):\r\n return filename.lower().endswith(extensions)","title":""},{"docid":"799cf33845c24e316c0f6012022c9d39","score":"0.6763899","text":"def get_files(dir_path, extension):\n files = []\n for file in os.listdir(dir_path):\n if file.endswith(f\".{extension}\"):\n files.append(file)\n return files","title":""},{"docid":"dc9a2178e41285df4b777ad0ee69655b","score":"0.6762181","text":"def test_find_files_returns_only_files_with_correct_extensions(tmpdir):\n folder = tmpdir.mkdir('sub')\n txt = folder.join('file1.txt').write('foo') #noqa\n py = folder.join('file2.py').write('foo') # noqa\n\n exts = ('.txt')\n files = find_files(folder, exts=exts)\n\n # only keep the extensions - makes the assert simpler.\n files = [f[-4:] for f in files]\n\n assert '.txt' in files","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"2c50aae55f171a1e973c614e2ca4de44","score":"0.67118245","text":"def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\r\n return filename.lower().endswith(extensions)","title":""},{"docid":"5421761bd07d486d4c06a376bbeae8b2","score":"0.67003095","text":"def list_files_with_extension(directory, extension):\n tmp = os.listdir(directory)\n logging.debug(tmp)\n extens = [f for f in tmp if os.path.splitext(f)[1] == \".\"+extension]\n return [os.path.join(directory, f) for f in extens]","title":""},{"docid":"f5eab32a1febb229cc347dceb867e23d","score":"0.667502","text":"def getFiles(self):\n fileList = [os.path.normcase(f) for f in os.listdir(self.directory)]\n fileList = [os.path.join(self.directory, f) for f in fileList if os.path.splitext(f)[1] in self.extList ]\n return [self.getAssertion(f) for f in fileList]","title":""},{"docid":"19726e185e58dfb46ca6cecbfa3fab44","score":"0.66600364","text":"def list():\r\n return [stem for stem, extension in [\r\n os.path.splitext(filename) for filename in os.listdir(_dirname)\r\n ] if extension == '.dat' and stem != 'root']","title":""},{"docid":"81ebd23d3bf8322b15b144d2fafb50b7","score":"0.6659508","text":"def list_of_files_in_dir_for_ext(dir, ext='.xtc') :\n if dir is None : return []\n if not os.path.exists(dir) : return [] \n return sorted([f for f in os.listdir(dir) if os.path.splitext(f)[1] == ext])","title":""},{"docid":"645da5d74ffa9a97b65fa0142e44c844","score":"0.6638439","text":"def file_list(directory, extension='jpg'):\n return [os.path.join(directory,f) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory,f)) and f.endswith(\".{}\".format(extension))]","title":""},{"docid":"7916ced72f4dfb18ecf328bee7073f74","score":"0.6617648","text":"def remove_unwanted_files(files: List[str], extensions: List[str]=['.DS_Store', '.gitignore']):\n for source_file in files:\n for ex in extensions:\n if ex in source_file:\n files.remove(source_file)\n break\n return files","title":""},{"docid":"4318629eeb540df122d4835547b3180e","score":"0.6605068","text":"def has_file_allowed_extension(filename, extensions):\r\n filename_lower = filename.lower()\r\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"acef0f93bcfae5beca5c606154193b84","score":"0.66009","text":"def filter_srt(files):\n\n out = []\n for filename in files:\n splitted = os.path.splitext(filename)\n srt_identifier = \".srt\"\n if splitted[1] == srt_identifier: \n out.append(filename)\n\n return out","title":""},{"docid":"b0f7c84fe8b6dfd6c72f8c698d1a9204","score":"0.6594698","text":"def search_file(path, ext):\n to_search = []\n for local_files in os.listdir(path):\n if local_files.endswith(ext):\n to_search.append(local_files)\n return to_search","title":""},{"docid":"8bd9f2583a013c4869bd150f748a48cf","score":"0.6586023","text":"def file_extensions_valid(ext_list):\n ext_list_valid = ['.csv','.txt','.xls','.xlsx']\n return ext_list[0] in ext_list_valid","title":""},{"docid":"7658e9c1622a6e670e73581653f33cb5","score":"0.657985","text":"def has_file_allowed_extension(filename: PATH_TYPE, extensions: Tuple[str, ...]) -> bool:\n return str(filename).lower().endswith(extensions)","title":""},{"docid":"e5d429d8ce86144952e4aeb00b3126b7","score":"0.65746367","text":"def get_file_names(path: str, extension: Union[List, str]) -> List:\n if isinstance(extension, List):\n \n images: list = []\n for ext in extension:\n request = os.path.join(path, f'*.{ext}')\n images += glob(request) # to avoid merging opperation after\n\n return images\n\n elif isinstance(extension, str):\n\n request = os.path.join(path, f'*.{extension}')\n return glob(request) #list\n\n else:\n\n raise TypeError('Only list or str are expected')","title":""},{"docid":"300624ac6baeeb201c602b5784d31828","score":"0.6562819","text":"def FileNameList(basepath='.', name='*.*', extension=True, excludename=''):\n files = fnmatch.filter(os.listdir(basepath), name)\n\n if not extension:\n files = [file.split('.')[0] for file in files]\n\n files = ExcludeFromList(files, excludename)\n\n return sorted(files)","title":""},{"docid":"8d86f8d2d9d70ddaf014e1bd0ced7a58","score":"0.65608174","text":"def has_file_allowed_extension(filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"8d86f8d2d9d70ddaf014e1bd0ced7a58","score":"0.65608174","text":"def has_file_allowed_extension(filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"6ba5686840208032086ef18a90ec8b73","score":"0.6553543","text":"def get_filenames_names_only(*filenames: str) -> List[str]:\n return [os.path.splitext(file)[0] for file in filenames]","title":""},{"docid":"0e5acc64ff532175cfdabccea7afe32a","score":"0.6542695","text":"def _find_files(self, chapter, extension):\n find_pattern = os.path.join(build_config.IMAGES_DIRECTORY,\n chapter, '*%s' % extension)\n found_files = Glob(find_pattern, strings=True)\n # Strip off the extensions.\n length_extension = len(extension)\n found_files = [x[x.index(os.sep) + 1:-length_extension]\n for x in found_files]\n return found_files","title":""},{"docid":"91091c1aac2c6f91219737ba2f01e1a4","score":"0.65398926","text":"def list_files(path, extension=\".cpp\", exclude=\"S.cpp\"):\n return [\"%s/%s\" % (path, f) for f in listdir(path) if f.endswith(extension) and (not f.endswith(exclude))]","title":""},{"docid":"7b35943bad645aa56528ba618476ed27","score":"0.6533559","text":"def _get_files(paths, types):\n files = set()\n for path in paths:\n for dirpath, dirnames, filenames in os.walk(path):\n for filename in filenames:\n try:\n # try-except because [1] and [1:] can fail\n file_ext = os.path.splitext(filename)[1].decode('utf8')[1:]\n if file_ext.lower() in types:\n full_path = os.path.join(dirpath, filename)\n files.add(full_path)\n except:\n pass\n return files","title":""},{"docid":"5077294894066df8af31e946ce04b105","score":"0.6518748","text":"def get_dir_filenames(dir_path, extension='.txt'):\n return [ i for i in listdir(dir_path) if i.find(extension) > 0 ]","title":""},{"docid":"ff5fc702b37c7878ca58605e8048dea1","score":"0.65176266","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.split('.')[-1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"c2ede6286900cf09fee7270c3fa7bda3","score":"0.64979035","text":"def check_extensions(x, elist):\n if not elist: # if no qualifier then we are good\n return True\n\n extension = os.path.splitext(x)[1].lower()\n\n for y in elist: # for each extension we just have to match one\n if '.'+ y == extension:\n return True\n return False","title":""},{"docid":"6abc209052dcc275c09192e3081b8755","score":"0.6495629","text":"def get_filenames(self, prefix, ext='gz'):\n if self.langs:\n return [f'{prefix}.{lang}.{ext}' for lang in self.langs]\n num = len(self.files)\n return [f'{prefix}.{i}.{ext}' for i in range(1, num + 1)]","title":""},{"docid":"651ee5bc19ff5d41c7290c1fb1ddd1c7","score":"0.64897215","text":"def list_of_pathes_in_dir_for_ext(dir, ext='.xtc') :\n return [os.path.join(dir,f) for f in list_of_files_in_dir_for_ext(dir, ext)]","title":""},{"docid":"f3ba78dd868419a763fabf267dbd907d","score":"0.6488085","text":"def get_files(fpath,extensions,recursive=False):\n p = Path(fpath)\n if recursive:\n filelist = [str(x) for x in p.rglob('*') if x.suffix in extensions]\n else:\n filelist = [str(x) for x in p.glob('*') if x.suffix in extensions]\n assert len(filelist) > 0, f\"No files matching extensions {extensions} found in {fpath}\"\n return(filelist)","title":""},{"docid":"1bf7d95d9cbbf7cbbb7487588a5548c1","score":"0.6485401","text":"def filter_lst_files(files):\r\n return [lst_file for lst_file in files if lst_file.endswith(\".lst\")]","title":""},{"docid":"d75973d2539760dbb06cee8f1505f558","score":"0.6484452","text":"def listFiles(dir, ext, ignoreExt=None):\n matches = []\n for root, dirs, files in os.walk(dir):\n for f in files:\n if f.endswith(ext):\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\n matches.append(os.path.join(root, f))\n return matches","title":""},{"docid":"d75973d2539760dbb06cee8f1505f558","score":"0.6484452","text":"def listFiles(dir, ext, ignoreExt=None):\n matches = []\n for root, dirs, files in os.walk(dir):\n for f in files:\n if f.endswith(ext):\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\n matches.append(os.path.join(root, f))\n return matches","title":""},{"docid":"0bd1deb96d18b65d580a0899b7268dec","score":"0.64773446","text":"def mfilter(f):\n return os.path.isfile(f) and f.endswith(ext)","title":""},{"docid":"afbcff433711999ef61b24b30b431e91","score":"0.64706737","text":"def _list_dir(ext):\n return [os.path.join(upload_dir, name) for name in os.listdir(upload_dir) if\n os.path.isfile(os.path.join(upload_dir, name)) and name.endswith(ext)]","title":""},{"docid":"75cee534d7e9514a87d45c139de47294","score":"0.6459723","text":"def get_file_extension_list(self, tpc_type=None):\n file_names = []\n file_extensions = []\n for data_file_name in self.get_data_file_list(tpc_type=tpc_type):\n datum = data_file_name.split(\".\")\n if datum[0] is None or datum[1] is None or datum[0] == \"\" or datum[1] == \"\":\n raise ValueError(\"An attempt was made to parse non eligible files!\")\n file_names.append(datum[0])\n file_extensions.append(datum[1])\n if len(file_names) != len(file_extensions):\n raise ValueError(\"File name list does not match list of file extensions!\")\n return file_names, file_extensions","title":""},{"docid":"954e5050c09733909745fff458aaed44","score":"0.6458051","text":"def lookupExtension(filename):","title":""},{"docid":"d3194b2c6b22e636b9ed62b4210f7497","score":"0.6446683","text":"def _get_fortran_files(srcfiles, extensions=False):\n files_out = []\n for srcfile in srcfiles:\n ext = os.path.splitext(srcfile)[1]\n if ext.lower() in [\".f\", \".for\", \".f90\", \".fpp\"]:\n if extensions:\n # save unique extension\n if ext not in files_out:\n files_out.append(ext)\n else:\n files_out.append(srcfile)\n if len(files_out) < 1:\n files_out = None\n return files_out","title":""},{"docid":"f4291c27f6503ca61ac99590c229b061","score":"0.64121467","text":"def ext_ambiguous_file_list(fn, ext, do_sub_dirs = None) :\n\n ext = ext or \"\"\n if ext and (ext[0] != '.') :\n ext = '.' + ext\n\n if os.path.isdir(fn) :\n fn = os.path.join(fn, '*' + ext)\n else :\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs)\n if not len(fns) : # if there are no ambiguous files to find,\n fn += '*' + ext # then add the extension in case it's not there.\n pass\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs) # find the files\n\n return(fns)","title":""},{"docid":"42aa6b0a35ee1c729ca08ab4246b9dec","score":"0.6408415","text":"def list_all_files(dir_name, exts=[\"jpg\", \"bmp\", \"png\"]):\n result = []\n for dir_, subdirs, file_names in os.walk(dir_name):\n for file_name in file_names:\n if any(file_name.endswith(ext) for ext in exts):\n result.append(os.path.join(dir_, file_name)) \n return result","title":""},{"docid":"c86969a31ae3ab540e6cf0fb67d500bc","score":"0.6398939","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"c86969a31ae3ab540e6cf0fb67d500bc","score":"0.6398939","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"c86969a31ae3ab540e6cf0fb67d500bc","score":"0.6398939","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"4693c015a2f86460acdecbf08217851b","score":"0.63641214","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"e45a64d233e1122b068e3d50407b9620","score":"0.63629353","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1] \\\n in ALLOWED_EXTENSIONS","title":""},{"docid":"f107566b36eb4d52c54343511a9eb42f","score":"0.63594925","text":"def test_filter_to_preferred_ext(image_files):\n tiff_files = list(Path(image_files).glob(\"*.tif\"))\n assert len(tiff_files) == 2000\n jpeg_files = list(Path(image_files).glob(\"*.jpg\"))\n assert len(jpeg_files) == 1000\n image_files = list(Path(image_files).iterdir())\n return_files = list(core.filter_to_preferred_ext(image_files, [\".jpg\"]))\n assert len(return_files) == 2000","title":""},{"docid":"d5f2936129dd4078f5c2d4712324b785","score":"0.6341543","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"d5f2936129dd4078f5c2d4712324b785","score":"0.6341543","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"d5f2936129dd4078f5c2d4712324b785","score":"0.6341543","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"530c663dd2481cfb9df2f98b2e798f24","score":"0.633947","text":"def has_file_allowed_extension(self, filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"9b9cc413fd83ab2c29e4e9db0769890a","score":"0.633573","text":"def _kwik_filenames(filename):\n basename, ext = op.splitext(filename)\n return {ext: '{basename}.{ext}'.format(basename=basename, ext=ext)\n for ext in _KWIK_EXTENSIONS}","title":""},{"docid":"e3472c2429f376eda94d88e246754f0d","score":"0.63155866","text":"def allowed_file_extension(filename):\n\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['UPLOAD_EXTENSIONS']","title":""},{"docid":"d3ec7c26f73886c8002746eda9cbb2fd","score":"0.63105655","text":"def allowedFile(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"1ec4943a2176c4c5d643e1c0a952924b","score":"0.63059044","text":"def get_files(path, extensions='*'):\n if path[-1] is not '/' and path[-1] is not '\\\\':\n path = path + '/'\n\n if isinstance(extensions, str) or isinstance(extensions, unicode):\n extensions = [extensions]\n\n all_files = [os.path.join(root, filename)\n for root, dirnames, filenames in os.walk(path)\n for filename in filenames]\n\n filtered_files = [\n filename for filename in all_files\n if get_extension(filename) in extensions or '*' in extensions\n ]\n\n return filtered_files","title":""},{"docid":"2a4add0b8a0cf1a8a2dcb24bf22e8f9a","score":"0.6276475","text":"def GetImageExtWildcard():","title":""},{"docid":"b93b2a34639ddd4c3bf1281667a59826","score":"0.62707496","text":"def file_extensions_all_equal(ext_list):\n return len(set(ext_list))==1","title":""},{"docid":"e55ab259acc0438e8e1a7c98a5e1a18c","score":"0.62676054","text":"def find_by_ext(folder, extension):\n found_files = [path.join(dirpath, f)\n for dirpath, dirnames, files in walk(folder)\n for f in files if f.endswith('.' + extension)]\n return found_files","title":""},{"docid":"6a65a86abf2079431459ba72d2a3cf19","score":"0.6267129","text":"def is_valid_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\n\n return any(filename.lower().endswith(ext) for ext in extensions)","title":""},{"docid":"1afcce354f31a092af483f465d296d42","score":"0.62572294","text":"def split_matching_extensions_and_other(self, srcs, exts):\n\n matches = []\n leftovers = []\n\n for src in (srcs or []):\n base, ext = os.path.splitext(src)\n if ext in exts:\n matches.append(src)\n else:\n leftovers.append(src)\n\n return (matches, leftovers)","title":""},{"docid":"8fa9688926f891714b18fb38bf371e1c","score":"0.6253049","text":"def list_files_with_ext(*paths, ext, maxdepth=-1):\n needed_files = []\n for path in paths:\n if ext in path:\n needed_files.append(path)\n continue\n if not os.path.isdir(path):\n print_red(\n qte(path) + \" is not a directory nor does it contain \" +\n qte(ext) + \". Skipping.\"\n )\n continue\n path = os.path.realpath(path)\n start_level = path.count(os.sep)\n # pylint: disable=unused-variable\n # Need dirs to iterate\n for root, dirs, files in os.walk(path):\n depth = root.count(os.sep) - start_level\n if depth > maxdepth and (maxdepth != -1):\n break\n for filename in files:\n if ext in filename:\n needed_files.append(os.path.join(root, filename))\n return needed_files","title":""},{"docid":"b6340c4e87b1f4f711576f1699c15b5c","score":"0.62498426","text":"def get_filtered_files(root_folder, extensions, keywords, strict=False):\n result = []\n unfiltered_files = UtilityBox.get_all_files(root_folder, *extensions)\n for file_path in unfiltered_files:\n if (not strict and any(kw.lower() in file_path.lower() for kw in keywords))\\\n or (strict and all(kw.lower() in file_path.lower() for kw in keywords)):\n result.append(file_path)\n return result","title":""},{"docid":"9073547ed295dc0f959d4a012820c136","score":"0.6249226","text":"def get_paths_by_ext(directory, exts=['JPEG']):\n if not directory.endswith('/'):\n directory += '/'\n\n img_paths = []\n for ext in exts:\n img_paths = img_paths + glob(os.path.join(directory) + '**/*.' + ext, recursive=True)\n return sorted(img_paths)","title":""},{"docid":"e54f73d0236ebe7aad1f4c86f4fca73f","score":"0.624537","text":"def get_file_info(d, extensions):\n for key in extensions:\n for f in os.listdir(d):\n if f.endswith(key):\n pass","title":""},{"docid":"ff03cf7eb8834954b337babf077d85a7","score":"0.622903","text":"def get_related_files(filename):\n related_files = []\n if filename.endswith(\".img\") or filename.endswith(\".hdr\"):\n path, name, ext = split_filename(filename)\n for ext in ['.hdr', '.img', '.mat']:\n related_files.append(os.path.join(path, name + ext))\n elif filename.endswith(\".BRIK\") or filename.endswith(\".HEAD\"):\n path, name, ext = split_filename(filename)\n for ext in ['.BRIK', '.HEAD']:\n related_files.append(os.path.join(path, name + ext))\n if not len(related_files):\n related_files = [filename]\n return related_files","title":""},{"docid":"73a2dceed81f4f2da1fc9fb3e5c52545","score":"0.62266505","text":"def grab_names():\n\n output = []\n original = os.listdir()\n \n for filename in original:\n if \".\" in filename:\n continue\n else:\n output.append(filename)\n\n return output","title":""},{"docid":"d767db9cf7334383a8d7456127fc741b","score":"0.6209032","text":"def get_all_files(dir, ext):\n extension_suffix = f\".{ext}\"\n files = []\n for file in os.listdir(dir):\n if file.endswith(extension_suffix):\n files.append(os.path.join(dir, file))\n return files","title":""},{"docid":"e47ab99363540050623a47b24d81fc9a","score":"0.62019986","text":"def load_images_without_extention(path):\n images = []\n valid_images = [\".jpg\", \".png\", \".jpeg\"]\n for f in os.listdir(path):\n file, ext = os.path.splitext(f)\n if ext.lower() not in valid_images:\n continue\n images.append(file)\n return images","title":""},{"docid":"7eb177ef54ee82e41b2fb88a46dfe5bf","score":"0.6201061","text":"def read_directory(extensions):\n for file in os.listdir('.'):\n filenames = file.split('.')\n if os.path.isdir(file):\n continue\n if filenames[1] not in extensions:\n extensions.append(filenames[1])\n print(extensions)","title":""},{"docid":"8755751c0c0eede4b772f14be8436f93","score":"0.6200504","text":"def get_filenames(format: str = 'json') -> List[str]:\n filenames = []\n for type in get_filetypes(format):\n filenames.extend(glob.glob(f'*.{type}'))\n return filenames","title":""},{"docid":"2cd5d5d20c08c7a82fb7c096ac33b951","score":"0.6196302","text":"def get_files(directory):\n files = []\n for item in os.listdir(directory):\n item = os.path.join(directory, item)\n if not os.path.isfile(item):\n continue\n parts = os.path.splitext(item)\n if parts and len(parts) > 1 and parts[1] == '.py'\\\n and parts[0].lower().find(\"test\") == -1:\n files.append(item)\n return files","title":""},{"docid":"fceba3ab8b472f6765e4facaea1c5bee","score":"0.61914957","text":"def _collect_files(self, path, extensions):\n path_list = []\n for dirpath, _, filenames in os.walk(path):\n path_list.extend(\n [\n os.path.join(dirpath, filename)\n for filename in filenames\n if os.path.splitext(filename)[1].lower() in extensions\n ]\n )\n return path_list","title":""},{"docid":"feecc2c670c46c78eacd50b912e03f26","score":"0.61866087","text":"def filter_files(pattern, file_names):\n p = re.compile(pattern)\n return [p.match(f) for f in file_names if p.match(f) is not None]","title":""},{"docid":"f73372929bb991939b97752d0da98eda","score":"0.6159966","text":"def get_files(dir, suffix):\n files = []\n for file in os.listdir(dir):\n if file.endswith(suffix):\n files.append(file)\n return files","title":""},{"docid":"f73372929bb991939b97752d0da98eda","score":"0.6159966","text":"def get_files(dir, suffix):\n files = []\n for file in os.listdir(dir):\n if file.endswith(suffix):\n files.append(file)\n return files","title":""},{"docid":"ddf7989ea21b31799113ffb7259206cc","score":"0.6147999","text":"def stringsWithImageFileExtensions(listOfStrings):\r\n\r\n #todo: string comparison should ignore case\r\n result = []\r\n for s in listOfStrings:\r\n if (s.find('.tif') != -1) or\\\r\n (s.find('.bmp') != -1) or\\\r\n (s.find('.pgm') != -1) or\\\r\n (s.find('.gif') != -1) or\\\r\n (s.find('.png') != -1):\r\n result.append(s)\r\n return result","title":""},{"docid":"9ea904a64b978053bd910323a78d3c9e","score":"0.6144758","text":"def _get_c_files(srcfiles, extensions=False):\n files_out = []\n for srcfile in srcfiles:\n ext = os.path.splitext(srcfile)[1]\n if ext.lower() in [\".c\", \".cpp\"]:\n if extensions:\n if ext not in files_out:\n files_out.append(ext)\n else:\n files_out.append(srcfile)\n if len(files_out) < 1:\n files_out = None\n return files_out","title":""},{"docid":"00a19e8793e3621beb428a0710c1a5af","score":"0.6140286","text":"def get_file_list(path, extensions):\n if not os.path.isdir(path):\n log_error_exit('Directory invalid.')\n else:\n files_list = []\n # The FITS files are named in numerically ascending order with an optional\n for file in os.listdir(path):\n if file.split('.')[-1] in extensions:\n files_list.append(os.path.join(path, file))\n if len(files_list) == 0:\n log_error_exit('No files with given extensins found !')\n return sorted(files_list)","title":""},{"docid":"c7430a703d144531116f0ef4a00b716f","score":"0.6116649","text":"def get_files_with_suffix(dir_name, suffixes):\n import os\n found_filenames = []\n for localpath, directories, filenames in os.walk(dir_name):\n for filename in filenames:\n for suffix in suffixes:\n if filename.endswith(suffix):\n found_filenames.append(os.path.join(localpath, filename))\n found_filenames.sort()\n return found_filenames","title":""},{"docid":"8227919c4a92055b6e0f0aae097b9067","score":"0.6115884","text":"def _splitext(fname):\n dir, filename = os.path.split(fname)\n for special_ext in ['.nii.gz', '.tar.gz']:\n if filename.endswith(special_ext):\n stem, ext = filename[:-len(special_ext)], special_ext\n return os.path.join(dir, stem), ext\n # If no special case, behaves like the regular splitext\n stem, ext = os.path.splitext(filename)\n return os.path.join(dir, stem), ext","title":""},{"docid":"c98eb8adabba7af1cf0a28c40a232498","score":"0.6114838","text":"def listfiles(mypath, mytype):\n result = []\n # Match Extension.\n for foundfile in Path(mypath).glob(\"**/*.*\"):\n filetype = str(foundfile).split(\".\")[-1]\n if filetype in mytype:\n # Force string type was Posix(path)\n result.append(str(foundfile))\n return result","title":""},{"docid":"73d0dc0b8cca82a9ec101f45f6470ac3","score":"0.61128","text":"def remove_all_file_extensions(filepath: Path, extensions: list = None):\n\n def _remove_next_suffix(f):\n if extensions is not None and len(extensions) > 0:\n return f.suffix in extensions\n return len(f.suffix) > 0\n\n while _remove_next_suffix(filepath):\n filepath = filepath.with_suffix(\"\")\n\n return filepath","title":""},{"docid":"81288495451141c1b16e070d8a7535db","score":"0.61059415","text":"def list_all_extension_files(directory_path, extension='.txt'):\n files_paths = []\n for r, d, f in os.walk(directory_path):\n f = [os.path.join(r,file) for file in f if file.find(extension) != -1]\n files_paths.append(f)\n\n files_paths = list(np.hstack(files_paths))\n files_paths = [tuple((in_file, in_file.replace(\"text_volumes\",\"speeches\").replace(\".txt\",\".csv\"))) for in_file in files_paths]\n\n files_paths = [tuple((in_file,out_file)) for in_file, out_file in files_paths if not os.path.exists(out_file)]\n return files_paths","title":""}],"string":"[\n {\n \"docid\": \"980d3d0380131c6070595da9574d0e44\",\n \"score\": \"0.7548063\",\n \"text\": \"def get_files(dirname, extensions=['.png', '.tif', '.jpg']):\\n dir_path = Path(dirname)\\n\\n files = dir_path.glob('**/*')\\n\\n files = [path.resolve() for path in files]\\n\\n match = [f for f in files if f.suffix in extensions]\\n return match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756bf2887c3883a51543e44a722ba14b\",\n \"score\": \"0.74919146\",\n \"text\": \"def ext_filter(directory, exts, ABS_PATHS = True):\\n if isinstance(exts,list) or isinstance(exts,tuple):\\n exts = list(exts)\\n if ABS_PATHS:\\n return [opj(directory,file) for file in os.listdir(directory) if \\n os.path.splitext(file)[1] in exts]\\n else:\\n return [file for file in os.listdir(directory) if \\n os.path.splitext(file)[1] in exts]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59861aab08ffbe5d5f4534e9f1ede444\",\n \"score\": \"0.73087174\",\n \"text\": \"def filter_by_extension(ext):\\n def filt(filename):\\n return filename.endswith(ext)\\n return filt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1418fdb60218040b473b134833d3322a\",\n \"score\": \"0.7305053\",\n \"text\": \"def get_files_with_extension(dir, names, ext):\\n list = []\\n for name in names:\\n e = os.path.splitext(name)[1]\\n if e == ext:\\n list.append(name)\\n list.sort()\\n return [os.path.join(dir, n) for n in list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4aa8be8863df9601ac1abf7504c66b8\",\n \"score\": \"0.726645\",\n \"text\": \"def _get_allowed_extensions():\\n return [\\n ext.lower()\\n for ext, file_type in Image.EXTENSION.items()\\n if file_type.upper() in MIME_TYPE_TO_PIL_IDENTIFIER.values()\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaae5bf503325a9a14f507df67b18904\",\n \"score\": \"0.7261796\",\n \"text\": \"def filter_by_suffix(files):\\n if includesuffix:\\n for insuffix in includesuffix:\\n extname = '*.' + insuffix\\n if fnmatch.fnmatch(files, extname):\\n return True\\n\\n if excludesuffix:\\n for exsuffix in excludesuffix:\\n extname = '*.' + exsuffix\\n if fnmatch.fnmatch(files, extname):\\n return False\\n\\n if includedefault:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ad56e049784f05cd7a715e0f302633\",\n \"score\": \"0.7199362\",\n \"text\": \"def filter_files(directory, extensions):\\n\\n return map(lambda x: os.path.join(directory, x),\\n filter(lambda x: re.search('\\\\.({0})$'.format(\\n '|'.join(extensions)), x), sorted(os.listdir(directory))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069c2ef98f7e6ab26e42a762fee47e2c\",\n \"score\": \"0.71652\",\n \"text\": \"def allowed_file_exts(files: List, extensions: List[str]) -> bool:\\n for f in files:\\n ext = get_file_extension(f.filename)\\n if ext not in extensions:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf1ff4052794423d8f2d4f6e16fb27f\",\n \"score\": \"0.71020085\",\n \"text\": \"def file_extensions_get(fname_list):\\n return [os.path.splitext(fname)[-1] for fname in fname_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df91497740c894b23886e74235c15f45\",\n \"score\": \"0.69904\",\n \"text\": \"def list_files_with_extensions(directory, ext_list):\\n filepath_list = sum(\\n [\\n glob(\\\"{0}/*{1}\\\".format(directory, ext))\\n for ext in ext_list\\n ],\\n [],\\n )\\n filenames = [os.path.basename(filepath) for filepath in filepath_list]\\n return filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7239a42c8c11c31617909d28b183ed\",\n \"score\": \"0.6975149\",\n \"text\": \"def get_file_list(path, ext):\\n return [f for f in os.listdir(path) if \\n os.path.isfile(os.path.join(path, f)) and f.endswith(ext)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c3dee1bd54f7da56b4079011cad679\",\n \"score\": \"0.69076073\",\n \"text\": \"def getFileNames():\\n included_extenstions = ['json' ]\\n file_names = [fn for fn in os.listdir(os.getcwd()) \\n if any([fn.endswith(ext) \\n for ext in included_extenstions])] \\n return file_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"276a32c3bedac93c824d12a57fa12d6c\",\n \"score\": \"0.6899496\",\n \"text\": \"def _generate_filename_list(self):\\n fq_filename, extension = os.path.splitext(self._fq_infilename)\\n\\n la_ext_list = [\\n 'la{0}'.format(char) for char in string.lowercase]\\n da_ext_list = [\\n 'da{0}'.format(char) for char in string.lowercase]\\n\\n extension_list = la_ext_list + da_ext_list\\n\\n filename_list = [fq_filename + '.{0}'.format(ext)\\n for ext in extension_list\\n if os.path.exists(fq_filename + '.{0}'.format(ext))]\\n\\n return filename_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8ec45f3da344c3e14e5a94841f8ca67\",\n \"score\": \"0.68790245\",\n \"text\": \"def _file_has_extensions(self, filestr, extensions):\\r\\n for extension in extensions:\\r\\n if filestr.find(extension) > -1:\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd879bf8ef32a15b5c6accde3a3c28d\",\n \"score\": \"0.6872849\",\n \"text\": \"def get_exts(filepath, all_exts=True):\\n \\n ext = 42 # initialize and set to non-0 value\\n out = [] # the output array of extensions\\n \\n while ext:\\n filepath, ext = path.splitext(filepath)\\n\\n if ext:\\n clean_ext = ext[1:] # strip the \\\".\\\" off the front of ext\\n out = [clean_ext] + out # append to output array\\n if not all_exts: break # if all_exts is False then end the\\n # loop after grabbing the first ext\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b01cc22ff929e71d1968875d4d7497db\",\n \"score\": \"0.6780839\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\r\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"799cf33845c24e316c0f6012022c9d39\",\n \"score\": \"0.6763899\",\n \"text\": \"def get_files(dir_path, extension):\\n files = []\\n for file in os.listdir(dir_path):\\n if file.endswith(f\\\".{extension}\\\"):\\n files.append(file)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9a2178e41285df4b777ad0ee69655b\",\n \"score\": \"0.6762181\",\n \"text\": \"def test_find_files_returns_only_files_with_correct_extensions(tmpdir):\\n folder = tmpdir.mkdir('sub')\\n txt = folder.join('file1.txt').write('foo') #noqa\\n py = folder.join('file2.py').write('foo') # noqa\\n\\n exts = ('.txt')\\n files = find_files(folder, exts=exts)\\n\\n # only keep the extensions - makes the assert simpler.\\n files = [f[-4:] for f in files]\\n\\n assert '.txt' in files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c50aae55f171a1e973c614e2ca4de44\",\n \"score\": \"0.67118245\",\n \"text\": \"def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\\r\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5421761bd07d486d4c06a376bbeae8b2\",\n \"score\": \"0.67003095\",\n \"text\": \"def list_files_with_extension(directory, extension):\\n tmp = os.listdir(directory)\\n logging.debug(tmp)\\n extens = [f for f in tmp if os.path.splitext(f)[1] == \\\".\\\"+extension]\\n return [os.path.join(directory, f) for f in extens]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5eab32a1febb229cc347dceb867e23d\",\n \"score\": \"0.667502\",\n \"text\": \"def getFiles(self):\\n fileList = [os.path.normcase(f) for f in os.listdir(self.directory)]\\n fileList = [os.path.join(self.directory, f) for f in fileList if os.path.splitext(f)[1] in self.extList ]\\n return [self.getAssertion(f) for f in fileList]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19726e185e58dfb46ca6cecbfa3fab44\",\n \"score\": \"0.66600364\",\n \"text\": \"def list():\\r\\n return [stem for stem, extension in [\\r\\n os.path.splitext(filename) for filename in os.listdir(_dirname)\\r\\n ] if extension == '.dat' and stem != 'root']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81ebd23d3bf8322b15b144d2fafb50b7\",\n \"score\": \"0.6659508\",\n \"text\": \"def list_of_files_in_dir_for_ext(dir, ext='.xtc') :\\n if dir is None : return []\\n if not os.path.exists(dir) : return [] \\n return sorted([f for f in os.listdir(dir) if os.path.splitext(f)[1] == ext])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645da5d74ffa9a97b65fa0142e44c844\",\n \"score\": \"0.6638439\",\n \"text\": \"def file_list(directory, extension='jpg'):\\n return [os.path.join(directory,f) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory,f)) and f.endswith(\\\".{}\\\".format(extension))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7916ced72f4dfb18ecf328bee7073f74\",\n \"score\": \"0.6617648\",\n \"text\": \"def remove_unwanted_files(files: List[str], extensions: List[str]=['.DS_Store', '.gitignore']):\\n for source_file in files:\\n for ex in extensions:\\n if ex in source_file:\\n files.remove(source_file)\\n break\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4318629eeb540df122d4835547b3180e\",\n \"score\": \"0.6605068\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\r\\n filename_lower = filename.lower()\\r\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acef0f93bcfae5beca5c606154193b84\",\n \"score\": \"0.66009\",\n \"text\": \"def filter_srt(files):\\n\\n out = []\\n for filename in files:\\n splitted = os.path.splitext(filename)\\n srt_identifier = \\\".srt\\\"\\n if splitted[1] == srt_identifier: \\n out.append(filename)\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f7c84fe8b6dfd6c72f8c698d1a9204\",\n \"score\": \"0.6594698\",\n \"text\": \"def search_file(path, ext):\\n to_search = []\\n for local_files in os.listdir(path):\\n if local_files.endswith(ext):\\n to_search.append(local_files)\\n return to_search\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd9f2583a013c4869bd150f748a48cf\",\n \"score\": \"0.6586023\",\n \"text\": \"def file_extensions_valid(ext_list):\\n ext_list_valid = ['.csv','.txt','.xls','.xlsx']\\n return ext_list[0] in ext_list_valid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7658e9c1622a6e670e73581653f33cb5\",\n \"score\": \"0.657985\",\n \"text\": \"def has_file_allowed_extension(filename: PATH_TYPE, extensions: Tuple[str, ...]) -> bool:\\n return str(filename).lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d429d8ce86144952e4aeb00b3126b7\",\n \"score\": \"0.65746367\",\n \"text\": \"def get_file_names(path: str, extension: Union[List, str]) -> List:\\n if isinstance(extension, List):\\n \\n images: list = []\\n for ext in extension:\\n request = os.path.join(path, f'*.{ext}')\\n images += glob(request) # to avoid merging opperation after\\n\\n return images\\n\\n elif isinstance(extension, str):\\n\\n request = os.path.join(path, f'*.{extension}')\\n return glob(request) #list\\n\\n else:\\n\\n raise TypeError('Only list or str are expected')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"300624ac6baeeb201c602b5784d31828\",\n \"score\": \"0.6562819\",\n \"text\": \"def FileNameList(basepath='.', name='*.*', extension=True, excludename=''):\\n files = fnmatch.filter(os.listdir(basepath), name)\\n\\n if not extension:\\n files = [file.split('.')[0] for file in files]\\n\\n files = ExcludeFromList(files, excludename)\\n\\n return sorted(files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d86f8d2d9d70ddaf014e1bd0ced7a58\",\n \"score\": \"0.65608174\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n filename_lower = filename.lower()\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d86f8d2d9d70ddaf014e1bd0ced7a58\",\n \"score\": \"0.65608174\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n filename_lower = filename.lower()\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba5686840208032086ef18a90ec8b73\",\n \"score\": \"0.6553543\",\n \"text\": \"def get_filenames_names_only(*filenames: str) -> List[str]:\\n return [os.path.splitext(file)[0] for file in filenames]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5acc64ff532175cfdabccea7afe32a\",\n \"score\": \"0.6542695\",\n \"text\": \"def _find_files(self, chapter, extension):\\n find_pattern = os.path.join(build_config.IMAGES_DIRECTORY,\\n chapter, '*%s' % extension)\\n found_files = Glob(find_pattern, strings=True)\\n # Strip off the extensions.\\n length_extension = len(extension)\\n found_files = [x[x.index(os.sep) + 1:-length_extension]\\n for x in found_files]\\n return found_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91091c1aac2c6f91219737ba2f01e1a4\",\n \"score\": \"0.65398926\",\n \"text\": \"def list_files(path, extension=\\\".cpp\\\", exclude=\\\"S.cpp\\\"):\\n return [\\\"%s/%s\\\" % (path, f) for f in listdir(path) if f.endswith(extension) and (not f.endswith(exclude))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b35943bad645aa56528ba618476ed27\",\n \"score\": \"0.6533559\",\n \"text\": \"def _get_files(paths, types):\\n files = set()\\n for path in paths:\\n for dirpath, dirnames, filenames in os.walk(path):\\n for filename in filenames:\\n try:\\n # try-except because [1] and [1:] can fail\\n file_ext = os.path.splitext(filename)[1].decode('utf8')[1:]\\n if file_ext.lower() in types:\\n full_path = os.path.join(dirpath, filename)\\n files.add(full_path)\\n except:\\n pass\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5077294894066df8af31e946ce04b105\",\n \"score\": \"0.6518748\",\n \"text\": \"def get_dir_filenames(dir_path, extension='.txt'):\\n return [ i for i in listdir(dir_path) if i.find(extension) > 0 ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5fc702b37c7878ca58605e8048dea1\",\n \"score\": \"0.65176266\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.split('.')[-1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ede6286900cf09fee7270c3fa7bda3\",\n \"score\": \"0.64979035\",\n \"text\": \"def check_extensions(x, elist):\\n if not elist: # if no qualifier then we are good\\n return True\\n\\n extension = os.path.splitext(x)[1].lower()\\n\\n for y in elist: # for each extension we just have to match one\\n if '.'+ y == extension:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abc209052dcc275c09192e3081b8755\",\n \"score\": \"0.6495629\",\n \"text\": \"def get_filenames(self, prefix, ext='gz'):\\n if self.langs:\\n return [f'{prefix}.{lang}.{ext}' for lang in self.langs]\\n num = len(self.files)\\n return [f'{prefix}.{i}.{ext}' for i in range(1, num + 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"651ee5bc19ff5d41c7290c1fb1ddd1c7\",\n \"score\": \"0.64897215\",\n \"text\": \"def list_of_pathes_in_dir_for_ext(dir, ext='.xtc') :\\n return [os.path.join(dir,f) for f in list_of_files_in_dir_for_ext(dir, ext)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ba78dd868419a763fabf267dbd907d\",\n \"score\": \"0.6488085\",\n \"text\": \"def get_files(fpath,extensions,recursive=False):\\n p = Path(fpath)\\n if recursive:\\n filelist = [str(x) for x in p.rglob('*') if x.suffix in extensions]\\n else:\\n filelist = [str(x) for x in p.glob('*') if x.suffix in extensions]\\n assert len(filelist) > 0, f\\\"No files matching extensions {extensions} found in {fpath}\\\"\\n return(filelist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bf7d95d9cbbf7cbbb7487588a5548c1\",\n \"score\": \"0.6485401\",\n \"text\": \"def filter_lst_files(files):\\r\\n return [lst_file for lst_file in files if lst_file.endswith(\\\".lst\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75973d2539760dbb06cee8f1505f558\",\n \"score\": \"0.6484452\",\n \"text\": \"def listFiles(dir, ext, ignoreExt=None):\\n matches = []\\n for root, dirs, files in os.walk(dir):\\n for f in files:\\n if f.endswith(ext):\\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\\n matches.append(os.path.join(root, f))\\n return matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75973d2539760dbb06cee8f1505f558\",\n \"score\": \"0.6484452\",\n \"text\": \"def listFiles(dir, ext, ignoreExt=None):\\n matches = []\\n for root, dirs, files in os.walk(dir):\\n for f in files:\\n if f.endswith(ext):\\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\\n matches.append(os.path.join(root, f))\\n return matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd1deb96d18b65d580a0899b7268dec\",\n \"score\": \"0.64773446\",\n \"text\": \"def mfilter(f):\\n return os.path.isfile(f) and f.endswith(ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afbcff433711999ef61b24b30b431e91\",\n \"score\": \"0.64706737\",\n \"text\": \"def _list_dir(ext):\\n return [os.path.join(upload_dir, name) for name in os.listdir(upload_dir) if\\n os.path.isfile(os.path.join(upload_dir, name)) and name.endswith(ext)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75cee534d7e9514a87d45c139de47294\",\n \"score\": \"0.6459723\",\n \"text\": \"def get_file_extension_list(self, tpc_type=None):\\n file_names = []\\n file_extensions = []\\n for data_file_name in self.get_data_file_list(tpc_type=tpc_type):\\n datum = data_file_name.split(\\\".\\\")\\n if datum[0] is None or datum[1] is None or datum[0] == \\\"\\\" or datum[1] == \\\"\\\":\\n raise ValueError(\\\"An attempt was made to parse non eligible files!\\\")\\n file_names.append(datum[0])\\n file_extensions.append(datum[1])\\n if len(file_names) != len(file_extensions):\\n raise ValueError(\\\"File name list does not match list of file extensions!\\\")\\n return file_names, file_extensions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954e5050c09733909745fff458aaed44\",\n \"score\": \"0.6458051\",\n \"text\": \"def lookupExtension(filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3194b2c6b22e636b9ed62b4210f7497\",\n \"score\": \"0.6446683\",\n \"text\": \"def _get_fortran_files(srcfiles, extensions=False):\\n files_out = []\\n for srcfile in srcfiles:\\n ext = os.path.splitext(srcfile)[1]\\n if ext.lower() in [\\\".f\\\", \\\".for\\\", \\\".f90\\\", \\\".fpp\\\"]:\\n if extensions:\\n # save unique extension\\n if ext not in files_out:\\n files_out.append(ext)\\n else:\\n files_out.append(srcfile)\\n if len(files_out) < 1:\\n files_out = None\\n return files_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4291c27f6503ca61ac99590c229b061\",\n \"score\": \"0.64121467\",\n \"text\": \"def ext_ambiguous_file_list(fn, ext, do_sub_dirs = None) :\\n\\n ext = ext or \\\"\\\"\\n if ext and (ext[0] != '.') :\\n ext = '.' + ext\\n\\n if os.path.isdir(fn) :\\n fn = os.path.join(fn, '*' + ext)\\n else :\\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs)\\n if not len(fns) : # if there are no ambiguous files to find,\\n fn += '*' + ext # then add the extension in case it's not there.\\n pass\\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs) # find the files\\n\\n return(fns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42aa6b0a35ee1c729ca08ab4246b9dec\",\n \"score\": \"0.6408415\",\n \"text\": \"def list_all_files(dir_name, exts=[\\\"jpg\\\", \\\"bmp\\\", \\\"png\\\"]):\\n result = []\\n for dir_, subdirs, file_names in os.walk(dir_name):\\n for file_name in file_names:\\n if any(file_name.endswith(ext) for ext in exts):\\n result.append(os.path.join(dir_, file_name)) \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86969a31ae3ab540e6cf0fb67d500bc\",\n \"score\": \"0.6398939\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86969a31ae3ab540e6cf0fb67d500bc\",\n \"score\": \"0.6398939\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86969a31ae3ab540e6cf0fb67d500bc\",\n \"score\": \"0.6398939\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4693c015a2f86460acdecbf08217851b\",\n \"score\": \"0.63641214\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45a64d233e1122b068e3d50407b9620\",\n \"score\": \"0.63629353\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1] \\\\\\n in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f107566b36eb4d52c54343511a9eb42f\",\n \"score\": \"0.63594925\",\n \"text\": \"def test_filter_to_preferred_ext(image_files):\\n tiff_files = list(Path(image_files).glob(\\\"*.tif\\\"))\\n assert len(tiff_files) == 2000\\n jpeg_files = list(Path(image_files).glob(\\\"*.jpg\\\"))\\n assert len(jpeg_files) == 1000\\n image_files = list(Path(image_files).iterdir())\\n return_files = list(core.filter_to_preferred_ext(image_files, [\\\".jpg\\\"]))\\n assert len(return_files) == 2000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f2936129dd4078f5c2d4712324b785\",\n \"score\": \"0.6341543\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f2936129dd4078f5c2d4712324b785\",\n \"score\": \"0.6341543\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f2936129dd4078f5c2d4712324b785\",\n \"score\": \"0.6341543\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"530c663dd2481cfb9df2f98b2e798f24\",\n \"score\": \"0.633947\",\n \"text\": \"def has_file_allowed_extension(self, filename, extensions):\\n filename_lower = filename.lower()\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9cc413fd83ab2c29e4e9db0769890a\",\n \"score\": \"0.633573\",\n \"text\": \"def _kwik_filenames(filename):\\n basename, ext = op.splitext(filename)\\n return {ext: '{basename}.{ext}'.format(basename=basename, ext=ext)\\n for ext in _KWIK_EXTENSIONS}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3472c2429f376eda94d88e246754f0d\",\n \"score\": \"0.63155866\",\n \"text\": \"def allowed_file_extension(filename):\\n\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['UPLOAD_EXTENSIONS']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ec7c26f73886c8002746eda9cbb2fd\",\n \"score\": \"0.63105655\",\n \"text\": \"def allowedFile(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ec4943a2176c4c5d643e1c0a952924b\",\n \"score\": \"0.63059044\",\n \"text\": \"def get_files(path, extensions='*'):\\n if path[-1] is not '/' and path[-1] is not '\\\\\\\\':\\n path = path + '/'\\n\\n if isinstance(extensions, str) or isinstance(extensions, unicode):\\n extensions = [extensions]\\n\\n all_files = [os.path.join(root, filename)\\n for root, dirnames, filenames in os.walk(path)\\n for filename in filenames]\\n\\n filtered_files = [\\n filename for filename in all_files\\n if get_extension(filename) in extensions or '*' in extensions\\n ]\\n\\n return filtered_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4add0b8a0cf1a8a2dcb24bf22e8f9a\",\n \"score\": \"0.6276475\",\n \"text\": \"def GetImageExtWildcard():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93b2a34639ddd4c3bf1281667a59826\",\n \"score\": \"0.62707496\",\n \"text\": \"def file_extensions_all_equal(ext_list):\\n return len(set(ext_list))==1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55ab259acc0438e8e1a7c98a5e1a18c\",\n \"score\": \"0.62676054\",\n \"text\": \"def find_by_ext(folder, extension):\\n found_files = [path.join(dirpath, f)\\n for dirpath, dirnames, files in walk(folder)\\n for f in files if f.endswith('.' + extension)]\\n return found_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a65a86abf2079431459ba72d2a3cf19\",\n \"score\": \"0.6267129\",\n \"text\": \"def is_valid_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\\n\\n return any(filename.lower().endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afcce354f31a092af483f465d296d42\",\n \"score\": \"0.62572294\",\n \"text\": \"def split_matching_extensions_and_other(self, srcs, exts):\\n\\n matches = []\\n leftovers = []\\n\\n for src in (srcs or []):\\n base, ext = os.path.splitext(src)\\n if ext in exts:\\n matches.append(src)\\n else:\\n leftovers.append(src)\\n\\n return (matches, leftovers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa9688926f891714b18fb38bf371e1c\",\n \"score\": \"0.6253049\",\n \"text\": \"def list_files_with_ext(*paths, ext, maxdepth=-1):\\n needed_files = []\\n for path in paths:\\n if ext in path:\\n needed_files.append(path)\\n continue\\n if not os.path.isdir(path):\\n print_red(\\n qte(path) + \\\" is not a directory nor does it contain \\\" +\\n qte(ext) + \\\". Skipping.\\\"\\n )\\n continue\\n path = os.path.realpath(path)\\n start_level = path.count(os.sep)\\n # pylint: disable=unused-variable\\n # Need dirs to iterate\\n for root, dirs, files in os.walk(path):\\n depth = root.count(os.sep) - start_level\\n if depth > maxdepth and (maxdepth != -1):\\n break\\n for filename in files:\\n if ext in filename:\\n needed_files.append(os.path.join(root, filename))\\n return needed_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6340c4e87b1f4f711576f1699c15b5c\",\n \"score\": \"0.62498426\",\n \"text\": \"def get_filtered_files(root_folder, extensions, keywords, strict=False):\\n result = []\\n unfiltered_files = UtilityBox.get_all_files(root_folder, *extensions)\\n for file_path in unfiltered_files:\\n if (not strict and any(kw.lower() in file_path.lower() for kw in keywords))\\\\\\n or (strict and all(kw.lower() in file_path.lower() for kw in keywords)):\\n result.append(file_path)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9073547ed295dc0f959d4a012820c136\",\n \"score\": \"0.6249226\",\n \"text\": \"def get_paths_by_ext(directory, exts=['JPEG']):\\n if not directory.endswith('/'):\\n directory += '/'\\n\\n img_paths = []\\n for ext in exts:\\n img_paths = img_paths + glob(os.path.join(directory) + '**/*.' + ext, recursive=True)\\n return sorted(img_paths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54f73d0236ebe7aad1f4c86f4fca73f\",\n \"score\": \"0.624537\",\n \"text\": \"def get_file_info(d, extensions):\\n for key in extensions:\\n for f in os.listdir(d):\\n if f.endswith(key):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff03cf7eb8834954b337babf077d85a7\",\n \"score\": \"0.622903\",\n \"text\": \"def get_related_files(filename):\\n related_files = []\\n if filename.endswith(\\\".img\\\") or filename.endswith(\\\".hdr\\\"):\\n path, name, ext = split_filename(filename)\\n for ext in ['.hdr', '.img', '.mat']:\\n related_files.append(os.path.join(path, name + ext))\\n elif filename.endswith(\\\".BRIK\\\") or filename.endswith(\\\".HEAD\\\"):\\n path, name, ext = split_filename(filename)\\n for ext in ['.BRIK', '.HEAD']:\\n related_files.append(os.path.join(path, name + ext))\\n if not len(related_files):\\n related_files = [filename]\\n return related_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a2dceed81f4f2da1fc9fb3e5c52545\",\n \"score\": \"0.62266505\",\n \"text\": \"def grab_names():\\n\\n output = []\\n original = os.listdir()\\n \\n for filename in original:\\n if \\\".\\\" in filename:\\n continue\\n else:\\n output.append(filename)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d767db9cf7334383a8d7456127fc741b\",\n \"score\": \"0.6209032\",\n \"text\": \"def get_all_files(dir, ext):\\n extension_suffix = f\\\".{ext}\\\"\\n files = []\\n for file in os.listdir(dir):\\n if file.endswith(extension_suffix):\\n files.append(os.path.join(dir, file))\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e47ab99363540050623a47b24d81fc9a\",\n \"score\": \"0.62019986\",\n \"text\": \"def load_images_without_extention(path):\\n images = []\\n valid_images = [\\\".jpg\\\", \\\".png\\\", \\\".jpeg\\\"]\\n for f in os.listdir(path):\\n file, ext = os.path.splitext(f)\\n if ext.lower() not in valid_images:\\n continue\\n images.append(file)\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eb177ef54ee82e41b2fb88a46dfe5bf\",\n \"score\": \"0.6201061\",\n \"text\": \"def read_directory(extensions):\\n for file in os.listdir('.'):\\n filenames = file.split('.')\\n if os.path.isdir(file):\\n continue\\n if filenames[1] not in extensions:\\n extensions.append(filenames[1])\\n print(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8755751c0c0eede4b772f14be8436f93\",\n \"score\": \"0.6200504\",\n \"text\": \"def get_filenames(format: str = 'json') -> List[str]:\\n filenames = []\\n for type in get_filetypes(format):\\n filenames.extend(glob.glob(f'*.{type}'))\\n return filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd5d5d20c08c7a82fb7c096ac33b951\",\n \"score\": \"0.6196302\",\n \"text\": \"def get_files(directory):\\n files = []\\n for item in os.listdir(directory):\\n item = os.path.join(directory, item)\\n if not os.path.isfile(item):\\n continue\\n parts = os.path.splitext(item)\\n if parts and len(parts) > 1 and parts[1] == '.py'\\\\\\n and parts[0].lower().find(\\\"test\\\") == -1:\\n files.append(item)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fceba3ab8b472f6765e4facaea1c5bee\",\n \"score\": \"0.61914957\",\n \"text\": \"def _collect_files(self, path, extensions):\\n path_list = []\\n for dirpath, _, filenames in os.walk(path):\\n path_list.extend(\\n [\\n os.path.join(dirpath, filename)\\n for filename in filenames\\n if os.path.splitext(filename)[1].lower() in extensions\\n ]\\n )\\n return path_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feecc2c670c46c78eacd50b912e03f26\",\n \"score\": \"0.61866087\",\n \"text\": \"def filter_files(pattern, file_names):\\n p = re.compile(pattern)\\n return [p.match(f) for f in file_names if p.match(f) is not None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73372929bb991939b97752d0da98eda\",\n \"score\": \"0.6159966\",\n \"text\": \"def get_files(dir, suffix):\\n files = []\\n for file in os.listdir(dir):\\n if file.endswith(suffix):\\n files.append(file)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73372929bb991939b97752d0da98eda\",\n \"score\": \"0.6159966\",\n \"text\": \"def get_files(dir, suffix):\\n files = []\\n for file in os.listdir(dir):\\n if file.endswith(suffix):\\n files.append(file)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf7989ea21b31799113ffb7259206cc\",\n \"score\": \"0.6147999\",\n \"text\": \"def stringsWithImageFileExtensions(listOfStrings):\\r\\n\\r\\n #todo: string comparison should ignore case\\r\\n result = []\\r\\n for s in listOfStrings:\\r\\n if (s.find('.tif') != -1) or\\\\\\r\\n (s.find('.bmp') != -1) or\\\\\\r\\n (s.find('.pgm') != -1) or\\\\\\r\\n (s.find('.gif') != -1) or\\\\\\r\\n (s.find('.png') != -1):\\r\\n result.append(s)\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea904a64b978053bd910323a78d3c9e\",\n \"score\": \"0.6144758\",\n \"text\": \"def _get_c_files(srcfiles, extensions=False):\\n files_out = []\\n for srcfile in srcfiles:\\n ext = os.path.splitext(srcfile)[1]\\n if ext.lower() in [\\\".c\\\", \\\".cpp\\\"]:\\n if extensions:\\n if ext not in files_out:\\n files_out.append(ext)\\n else:\\n files_out.append(srcfile)\\n if len(files_out) < 1:\\n files_out = None\\n return files_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a19e8793e3621beb428a0710c1a5af\",\n \"score\": \"0.6140286\",\n \"text\": \"def get_file_list(path, extensions):\\n if not os.path.isdir(path):\\n log_error_exit('Directory invalid.')\\n else:\\n files_list = []\\n # The FITS files are named in numerically ascending order with an optional\\n for file in os.listdir(path):\\n if file.split('.')[-1] in extensions:\\n files_list.append(os.path.join(path, file))\\n if len(files_list) == 0:\\n log_error_exit('No files with given extensins found !')\\n return sorted(files_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7430a703d144531116f0ef4a00b716f\",\n \"score\": \"0.6116649\",\n \"text\": \"def get_files_with_suffix(dir_name, suffixes):\\n import os\\n found_filenames = []\\n for localpath, directories, filenames in os.walk(dir_name):\\n for filename in filenames:\\n for suffix in suffixes:\\n if filename.endswith(suffix):\\n found_filenames.append(os.path.join(localpath, filename))\\n found_filenames.sort()\\n return found_filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8227919c4a92055b6e0f0aae097b9067\",\n \"score\": \"0.6115884\",\n \"text\": \"def _splitext(fname):\\n dir, filename = os.path.split(fname)\\n for special_ext in ['.nii.gz', '.tar.gz']:\\n if filename.endswith(special_ext):\\n stem, ext = filename[:-len(special_ext)], special_ext\\n return os.path.join(dir, stem), ext\\n # If no special case, behaves like the regular splitext\\n stem, ext = os.path.splitext(filename)\\n return os.path.join(dir, stem), ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98eb8adabba7af1cf0a28c40a232498\",\n \"score\": \"0.6114838\",\n \"text\": \"def listfiles(mypath, mytype):\\n result = []\\n # Match Extension.\\n for foundfile in Path(mypath).glob(\\\"**/*.*\\\"):\\n filetype = str(foundfile).split(\\\".\\\")[-1]\\n if filetype in mytype:\\n # Force string type was Posix(path)\\n result.append(str(foundfile))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d0dc0b8cca82a9ec101f45f6470ac3\",\n \"score\": \"0.61128\",\n \"text\": \"def remove_all_file_extensions(filepath: Path, extensions: list = None):\\n\\n def _remove_next_suffix(f):\\n if extensions is not None and len(extensions) > 0:\\n return f.suffix in extensions\\n return len(f.suffix) > 0\\n\\n while _remove_next_suffix(filepath):\\n filepath = filepath.with_suffix(\\\"\\\")\\n\\n return filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81288495451141c1b16e070d8a7535db\",\n \"score\": \"0.61059415\",\n \"text\": \"def list_all_extension_files(directory_path, extension='.txt'):\\n files_paths = []\\n for r, d, f in os.walk(directory_path):\\n f = [os.path.join(r,file) for file in f if file.find(extension) != -1]\\n files_paths.append(f)\\n\\n files_paths = list(np.hstack(files_paths))\\n files_paths = [tuple((in_file, in_file.replace(\\\"text_volumes\\\",\\\"speeches\\\").replace(\\\".txt\\\",\\\".csv\\\"))) for in_file in files_paths]\\n\\n files_paths = [tuple((in_file,out_file)) for in_file, out_file in files_paths if not os.path.exists(out_file)]\\n return files_paths\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":183,"cells":{"query_id":{"kind":"string","value":"dfc4c0b6369e884b99877afc55bfd359"},"query":{"kind":"string","value":"Build a Cypher query based on given parameters."},"positive_passages":{"kind":"list like","value":[{"docid":"d75bc1828ce83ede1c9f954653f24192","score":"0.5487266","text":"def _build_dependencies_query(\n cls, team_id, topic, label, node, filter_on_config=False, impacted=False\n ):\n where = \"\"\n order = \"(n)<-[r]-(m)\" if impacted else \"(n)-[r]->(m)\"\n query = (\n \"MATCH(n:{topic}_{label}{{name: '{name}'}}) \"\n \"OPTIONAL MATCH {order} {where}\"\n \"RETURN n,r,m ORDER BY m.name LIMIT 10\"\n )\n\n # Filter the dependencies using the labels declared in the configuration\n if filter_on_config:\n label_config = ConfigController.get_label_config(team_id, label)\n regex = r\"^.*(\\[[A-Za-z]+(, [A-Za-z]+)*?\\])$\"\n\n match = re.search(regex, label_config[\"qos\"])\n if match:\n deps = match.group(1)[1:-1].split(\", \")\n where += \"WHERE '{}_{}' IN LABELS(m) \".format(topic, deps[0])\n\n for dep in deps[1:]:\n where += \"OR '{}_{}' IN LABELS(m) \".format(topic, dep)\n\n return query.format(\n where=where, order=order, topic=topic, label=label, name=node\n )","title":""}],"string":"[\n {\n \"docid\": \"d75bc1828ce83ede1c9f954653f24192\",\n \"score\": \"0.5487266\",\n \"text\": \"def _build_dependencies_query(\\n cls, team_id, topic, label, node, filter_on_config=False, impacted=False\\n ):\\n where = \\\"\\\"\\n order = \\\"(n)<-[r]-(m)\\\" if impacted else \\\"(n)-[r]->(m)\\\"\\n query = (\\n \\\"MATCH(n:{topic}_{label}{{name: '{name}'}}) \\\"\\n \\\"OPTIONAL MATCH {order} {where}\\\"\\n \\\"RETURN n,r,m ORDER BY m.name LIMIT 10\\\"\\n )\\n\\n # Filter the dependencies using the labels declared in the configuration\\n if filter_on_config:\\n label_config = ConfigController.get_label_config(team_id, label)\\n regex = r\\\"^.*(\\\\[[A-Za-z]+(, [A-Za-z]+)*?\\\\])$\\\"\\n\\n match = re.search(regex, label_config[\\\"qos\\\"])\\n if match:\\n deps = match.group(1)[1:-1].split(\\\", \\\")\\n where += \\\"WHERE '{}_{}' IN LABELS(m) \\\".format(topic, deps[0])\\n\\n for dep in deps[1:]:\\n where += \\\"OR '{}_{}' IN LABELS(m) \\\".format(topic, dep)\\n\\n return query.format(\\n where=where, order=order, topic=topic, label=label, name=node\\n )\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"72ebf75d0424beb0d9398dd9dcc5f821","score":"0.7439297","text":"def build_query(self, params):\n pass","title":""},{"docid":"a95e410d68282d18b99170eb9c871fae","score":"0.70802385","text":"def _build_query(self, params, boolean='AND'):\n query = []\n for p in params:\n if isinstance(p, str):\n # direct query string\n query.append(p)\n\n elif isinstance(p, tuple) and len(p) == 3:\n # triplet\n (field, op, value) = p\n op_ = '-' if op == '!=' else ''\n\n if isinstance(value, list):\n value_ = '(\"' + '\",\"'.join(value) + '\")'\n elif isinstance(value, tuple):\n value_ = '[%s TO %s]' % value\n else:\n quote = '\"' if not '*' in str(value) else ''\n value_ = quote + str(value) + quote\n\n query.append('%s%s:%s' % (op_, field, value_))\n\n elif isinstance(p, tuple) and len(p) == 2:\n # group/subquery with boolean operator\n (op, subquery) = p\n query.append('(' + self._build_query(subquery, op) + ')')\n\n return (' '+boolean+' ').join(query)","title":""},{"docid":"2c96f74b26acea483825aadcbb969353","score":"0.6558855","text":"def build_query(self, params):\n phrase = params[\"phrase\"]\n slop = params[\"slop\"]\n\n query = {}\n query[\"query\"] = {\"match\":{}}\n\n infl_form = {}\n infl_form[\"type\"] = \"phrase\"\n infl_form[\"query\"] = phrase\n infl_form[\"slop\"] = slop\n query[\"query\"][\"match\"][\"inflected_form\"] = infl_form\n\n query = json.dumps(query)\n return query","title":""},{"docid":"2a8397a2a8e9edc0608094386c96404c","score":"0.653392","text":"def _build_query_string(cls, parameters: dict) -> str:\n\t\tquery_string = '?'\n\n\t\t# Builds a list of tuples for easy iteration like:\n\t\t# [('channel', 'channel-1'), ('channel', 'channel-2'), ('assetId', 'asset-1'), ...]\n\t\tparameter_tuples = [(parameter_name, parameter_value) \n\t\t\tfor parameter_name, parameter_values in parameters.items()\n\t\t\t\tfor parameter_value in parameter_values]\n\n\t\tfor index, parameter_tuple in enumerate(parameter_tuples):\n\t\t\tname, value = parameter_tuple\n\t\t\tvalue = quote(value.encode()) if type(value) == str else value\n\n\t\t\tif name == 'orderBy':\n\t\t\t\tvalue = cls._convert_order_by(value)\n\n\t\t\tquery_string += f'{name}={value}' if index == 0 else f'&{name}={value}'\n\n\t\treturn query_string","title":""},{"docid":"0a18169ab89094b2a6bab17605534b4f","score":"0.6477496","text":"def _build_query(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"76313a0e15cad414416780ca9e335fb0","score":"0.6379244","text":"def _build_search(query, params):\n #PLACEHOLDER -- only searches the objects table and does the simplest\n #search possible for now\n query_terms = params.copy() if( params and params != {} ) else _translate_query(query)\n arguments = _args()\n keywords = \"\"\n query_string = \"\"\n\n if 'query_string' in query_terms:\n query_string = query_terms['query_string']\n arguments.update({'query_string':query_string})\n del query_terms['query_string']\n if 'keywords' in query_terms:\n arguments.update({'keywords':query_terms['keywords']})\n del query_terms['keywords']\n # TODO: need to do all the query string processing here \n # but, for now just a simple search assumption will work by default\n\n if query_string != \"\":\n keywords += query_string\n \n return \"ca_objects?q=\"+keywords, arguments","title":""},{"docid":"c157292ed4cc40e351d63d9d0af82576","score":"0.6359702","text":"def generate_query(self):\n return ''.join(['SELECT ',\n ', '.join(self.columns),\n ' ',\n 'FROM molecule ',\n ' '.join(self.joins),\n ' ',\n 'WHERE ',\n '(', ') AND ('.join(self.wheres), ')'])","title":""},{"docid":"cba63cfec2c1ae532de6e05ad75c5ffe","score":"0.6332386","text":"def _set_sql_query(self, query_params):\n query = f'SELECT * FROM {CATS_TABLE}'\n # attributes in query string\n if query_params.get('attribute'):\n query += ' ORDER BY '\n # may be 1>= attrs\n for attr in query_params['attribute']:\n query += f'{attr}, '\n query = query.rstrip(', ')\n\n # order in query string\n if query_params.get('order'):\n order = query_params['order'][0].upper()\n query += f' {order}'\n\n # offset in query string\n if query_params.get('offset'):\n offset = query_params['offset'][0]\n query += f' OFFSET {offset}'\n\n # limit in query string\n if query_params.get('limit'):\n limit = query_params['limit'][0]\n query += f' LIMIT {limit}'\n\n return query","title":""},{"docid":"6aafa03146dc86896e8b4203a577f391","score":"0.6295957","text":"def make_query(self, **kw):\n query = kw.pop(\"query\", {})\n\n query.update(self.get_request_query())\n query.update(self.get_custom_query())\n query.update(self.get_keyword_query(**kw))\n\n sort_on, sort_order = self.get_sort_spec()\n if sort_on and \"sort_on\" not in query:\n query.update({\"sort_on\": sort_on})\n if sort_order and \"sort_order\" not in query:\n query.update({\"sort_order\": sort_order})\n\n logger.info(\"make_query:: query={} | catalog={}\".format(\n query, self.catalog))\n\n return query","title":""},{"docid":"1216cd9552ca67c038d89c1a33795cb6","score":"0.6221052","text":"def build_query(self):\n\n # initalize config\n self.config.update_config()\n\n # get the base query from the config\n query = self.config.get_base_query()\n return query","title":""},{"docid":"d11b2046780debe8f975dcb0277e234c","score":"0.62166256","text":"def buildQuery(self):\n if not self.Valid:\n print(\"Invalid usage: cannot build.\")\n return False\n if not self.needsBuilt:\n return self.queryString\n\n filterDict = dict()\n theFilters = dict()\n if len(self._andFilters) > 0:\n theFilters[\"and\"] = self._andFilters\n if len(self._orFilters) > 0:\n theFilters[\"or\"] = self._orFilters\n filterDict[\"filter\"] = theFilters\n queryString = json.dumps(filterDict)\n\n self.queryString = queryString\n return queryString","title":""},{"docid":"bb15855e6fd47d6e9d3a76d89a8b5ace","score":"0.6071394","text":"def _build_query(symbol: str, start_date: str, end_date: str, order_by: str):\n query = \"select * from stock_price where symbol = '{symbol}'\".format(symbol=symbol)\n if start_date:\n query += \" and time >= '{}'\".format(start_date)\n if end_date:\n query += \" and time <= '{}'\".format(end_date)\n\n if order_by == 'ASC':\n # Default\n pass\n elif order_by == 'DESC':\n query += ' order by time desc'\n\n return query","title":""},{"docid":"d46b3852c7f93eb9f1037b46961e8edb","score":"0.60509795","text":"def query_generator(self, kwargs):\n\n query_addition = \"\"\n\n if \"quarter\" in kwargs:\n query_addition += f\" & qtr == {kwargs['quarter']}\"\n if \"down\" in kwargs:\n query_addition += f\" & down == {kwargs['down']}\"\n if \"half\" in kwargs:\n query_addition += f\" & game_half == 'Half{kwargs['half']}'\"\n if \"close\" in kwargs:\n if kwargs['close'] is True:\n query_addition += f\" & score_differential >= -12 & score_differential <= 12\"\n else:\n query_addition += f\" & (score_differential < -12 | score_differential > 12)\"\n if \"rz\" in kwargs:\n if kwargs['rz'] is True:\n query_addition += f\" & yardline_100 <= 25\"\n else:\n query_addition += f\" & yardline_100 > 25\"\n if \"where\" in kwargs:\n if kwargs['where'] is 'home':\n query_addition += f\" & home_team == '{self.team}'\"\n else:\n query_addition += f\" & away_team == '{self.team}'\"\n if \"play\" in kwargs:\n if kwargs['play'] is 'run':\n query_addition += f\" & rush_attempt == 1\"\n else:\n query_addition += f\" & pass_attempt == 1\"\n if \"since\" in kwargs:\n if kwargs['since'] is 'sep':\n query_addition += f\" & (game_date.str.slice(5, 7) == '09' | game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n elif kwargs['since'] is 'oct':\n query_addition += f\" & (game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n elif kwargs['since'] is 'nov':\n query_addition += f\" & (game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n else:\n query_addition += f\" & game_date.str.slice(5, 7) == '12'\"\n\n return query_addition","title":""},{"docid":"c570052933c609ad72ebabe6e69a8bfd","score":"0.598974","text":"def data_query(self, parents=None):\n filter_q = self.filter_query(parents=parents)\n q = db.session.query()\n stmt, q = self._add_statement(q)\n\n filter_sq = filter_q.subquery()\n q = q.filter(stmt.subject == filter_sq.c.subject)\n q = q.filter(stmt._attribute.in_(self.project()))\n\n q = q.add_column(stmt.subject.label('id'))\n q = q.add_column(stmt._attribute.label('attribute'))\n q = q.add_column(stmt._value.label('value'))\n\n if parents is not None and self.node.attribute:\n parent_stmt, q = self._add_statement(q)\n q = q.filter(stmt.subject == parent_stmt._value)\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\n q = q.add_column(parent_stmt.subject.label('parent_id'))\n\n q = q.order_by(filter_sq.c.subject.desc())\n q = q.order_by(stmt.created_at.asc())\n return q","title":""},{"docid":"dd1bf23e219921f97adc765b4e477518","score":"0.5976437","text":"def make_query(columns, filter):\n if columns is not None:\n # the user only wants to report a subset of the columns\n query = \"SELECT \" + columns + \" FROM variants\"\n# elif cancers != 'none':\n# query = \"SELECT \" + columns + \",civic_gene_abbreviations,cgi_gene_abbreviations FROM variants\"\n else:\n # report the kitchen sink\n query = \"SELECT * FROM variants\"\n if filter is not None:\n # add any non-genotype column limits to the where clause\n query += \" WHERE \" + filter\n return query","title":""},{"docid":"3662e9f0259cba0673eaafff569ccaaa","score":"0.59676003","text":"def make_query(request, **kw):\n\n # build the catalog query\n query = {\n \"sort_limit\": get_sort_limit(request),\n \"sort_on\": get_sort_on(request),\n \"sort_order\": get_sort_order(request),\n \"SearchableText\": get_query(request),\n }\n\n # inject keyword args\n query.update(kw)\n\n # inject the creator if given\n if get_creator(request):\n query[\"Creator\"] = get_creator(request)\n\n logger.info(\"Catalog Query --> %r\", query)\n return query","title":""},{"docid":"a0196bcfee0829e1a676506cb8f483df","score":"0.59151345","text":"def query_from_params(self, params):\n q = {}\n start, rows = 0, 10\n if \"start\" in params:\n start = int(params[\"start\"])\n if \"rows\" in params:\n rows = int(params[\"rows\"])\n\n if \"lat\" in params and \"lon\" in params:\n lat = float(params[\"lat\"])\n lon = float(params[\"lon\"])\n q[\"loc\"] = {\n \"$near\": [lat, lon]\n }\n for atr in [\"city\", \"state\", \"country\"]:\n if atr in params:\n q[atr] = params[atr]\n return self.query(start=start, rows=rows, **q)","title":""},{"docid":"324d683b973caa2910387cddae4780e5","score":"0.5909418","text":"def _create_query_params(self, kwargs):\n if None == kwargs or 0 == len(kwargs):\n return (\"\", [])\n\n if not isinstance(kwargs, basestring):\n pairs = kwargs.items()\n stmt = \" WHERE \" + \\\n \" AND \".join([ x[0] + (None is x[1] and \" IS NULL\" or \" = ?\")\n for x in pairs ])\n\n args = [x[1] for x in filter(lambda x: None is not x[1], pairs)]\n else:\n stmt = \" WHERE \" + kwargs\n args = []\n return (stmt, args)","title":""},{"docid":"49409b5c49174457e063fc4a1f2caf74","score":"0.5909326","text":"def build_query(cols=None, tables=None, group_by=None, order_by=None, **kwargs):\n assert len(tables)\n\n # Create SELECT line\n cols_str = \", \".join(cols) if cols else \"*\"\n query = f'SELECT {cols_str} FROM '\n\n # Create FROM line\n # Automatically add bracelets around strings with space inside them. Useful for nested queries.\n query += ' NATURAL JOIN '.join(f'({table})' if ' ' in table else table for table in tables)\n\n # Apply all filters in the kwargs dict\n constraints = []\n for col_name, value in kwargs.items():\n # Empty filters don't need to be applied\n if value not in (None, ''):\n # Use \"LIKE\" for strings, and \"==\" otherwise\n if isinstance(value, str):\n constraints.append(f'{col_name} LIKE \"{value}\"')\n else:\n constraints.append(f'{col_name} == {value}')\n\n # Create WHERE line if there are constraints\n if constraints:\n query += ' WHERE ' + ' AND '.join(constraints)\n\n # Create GROUP BY line if needed\n if group_by:\n query += ' GROUP BY ' + group_by\n\n # Create ORDER BY line if needed\n if order_by:\n query += ' ORDER BY ' + order_by\n\n return query","title":""},{"docid":"1fb4cc87b5249fb99610ec14d3f62cf8","score":"0.58879614","text":"def GenerateQuery(unused_ref, args, request):\n customer_id = ConvertOrgIdToObfuscatedCustomerId(args.organization)\n labels = FilterLabels(args.labels)\n labels_str = ','.join(labels)\n request.query = 'parent==\\\"customerId/{0}\\\" && \\\"{1}\\\" in labels'.format(\n customer_id, labels_str)\n\n return request","title":""},{"docid":"ff8ba2a0ee0c22e8f3b0dc0581901893","score":"0.5856376","text":"def make_query(self, ns):\n if issubclass(self.model_class, db.Model):\n query = db.Query(self.model_class, namespace=ns)\n for f in self.filters:\n query.filter(\"%s %s\" % (f[0], f[1]), f[2])\n else:\n query = self.model_class.query(namespace=ns)\n for f in self.filters:\n query = query.filter(ndb.FilterNode(*f))\n return query","title":""},{"docid":"a5aeac92141db6643f5dae341690653e","score":"0.5830022","text":"def build_query(\n self,\n regions=None,\n genes=None,\n effect_types=None,\n family_ids=None,\n person_ids=None,\n inheritance=None,\n roles=None,\n sexes=None,\n variant_type=None,\n real_attr_filter=None,\n ultra_rare=None,\n frequency_filter=None,\n return_reference=None,\n return_unknown=None,\n limit=None,\n pedigree_fields=None):\n # pylint: disable=too-many-arguments\n self.query_builder.reset_product()\n\n self.query_builder.build_select()\n\n self.query_builder.build_from()\n\n self.query_builder.build_join()\n\n self.query_builder.build_where(\n regions=regions,\n genes=genes,\n effect_types=effect_types,\n family_ids=family_ids,\n person_ids=person_ids,\n inheritance=inheritance,\n roles=roles,\n sexes=sexes,\n variant_type=variant_type,\n real_attr_filter=real_attr_filter,\n ultra_rare=ultra_rare,\n frequency_filter=frequency_filter,\n return_reference=return_reference,\n return_unknown=return_unknown,\n pedigree_fields=pedigree_fields,\n )\n\n self.query_builder.build_group_by()\n self.query_builder.build_limit(limit)","title":""},{"docid":"9bf3e320668377af9f499baa5b818919","score":"0.5816653","text":"def make_query(config):\n\n query = {}\n\n items = ['object', 'radius', 'max_out', 'source', 'output', 'mime', 'sort']\n\n for item in items\n query[item] = str(config[item])\n\n return query","title":""},{"docid":"2159f3098a64c4db73cdb716ccac6fd2","score":"0.57821774","text":"def make_where_statement(self, table_name, params):\n if not params:\n return (\" 1 \", ())\n column_names = [row[1] for row in self.get_rows(\"PRAGMA table_info(%s)\" % table_name)]\n for column in params.keys():\n if column not in column_names:\n raise KeyError(\"Error: Database column %s not in our database\" % column)\n # Construct our SQL statement\n where_statement = ' AND '.join(['\"%s\" = ?' % column for (column, value) in sorted(params.items())])\n where_values = tuple([value for (column, value) in sorted(params.items())])\n return (where_statement, where_values)","title":""},{"docid":"dd14224ced22e5c08b3414302d16b36f","score":"0.5775194","text":"def build_query(self) -> Query:\n logger.info(\n {\n \"message\": f\"Start building query for resource {self.analysis.definition_id}\",\n \"resource_id\": self.analysis.resource_id,\n },\n )\n\n # We don't need to have columns duplicated in the dataframe\n # so we keep the one we've already seen here.\n # Note that the primary key column is added at the query initialization.\n self._cur_query_columns = {self.analysis.primary_key_column}\n # To avoid duplicated joins, we keep them here\n self._cur_query_join_tables = {}\n\n query = self.session.query(self.get_column(self.analysis.primary_key_column, self._sqlalchemy_pk_table))\n\n # Add attributes to query\n for attribute in self.analysis.attributes:\n query = self.add_attribute_to_query(query, attribute)\n\n # Add filters to query\n query = self.apply_filters(query)\n\n logger.info(\n {\n \"message\": f\"Built query for resource {self.analysis.definition_id}: {query.statement}\",\n \"resource_id\": self.analysis.resource_id,\n },\n )\n return query","title":""},{"docid":"deb662581d7042718da96ca7baef1b0e","score":"0.5773945","text":"def query(self):\n if 'order' in self.config['database']:\n order = 'ORDER BY {order}'.format(**self.config['database'])\n else:\n order = ''\n\n if isinstance(self.cols, tuple):\n cols = self.cols\n else:\n cols = (self.cols,)\n\n where, params = parse_queries(self.selection, self.mapping)\n if where:\n where = 'WHERE {conditions}'.format(conditions=' AND '.join(where))\n else:\n where = ''\n limit = (self.slice[0].stop or sys.maxint) - (self.slice[0].start or 0)\n\n sql = \"\"\"\n SELECT {cols} FROM {table}\n {where}\n {order}\n LIMIT {limit}\n OFFSET {offset}\n \"\"\".format(\n cols=', '.join(self.config[key]['col'] for key in cols),\n table=self.config['database']['table'],\n where=where, order=order, limit=limit,\n offset=self.slice[0].start or 0)\n\n return sql, params","title":""},{"docid":"86f87622ad6a714500a7b6ccb0df8944","score":"0.5773749","text":"def sql_with_params(self):\r\n return self.get_compiler(DEFAULT_DB_ALIAS).as_sql()","title":""},{"docid":"a12abdcbed66b2452ab47d4061b8ce7d","score":"0.5761608","text":"def build_query(self, query_dict):\n\n # Loop over keys in request dictionary, match them up to database fields\n # Build query accounting for the exceptions\n qry_sci_name = ''\n qry_common_name = ''\n qry_group = ''\n qry_native = ''\n qry_pollut_tol = ''\n qry_rarity = ''\n qry_range = ''\n qry_max_age = ''\n qry_mean_weight = ''\n qry_mean_length = ''\n qry_max_length = ''\n qry_habitat = ''\n qry_ubiquity = ''\n qry_extent = ''\n qry_tolerance = ''\n qry_robustness = ''\n\n #Used in a couple of places to remove a trailing 'or '\n trailing_or = ' or'\n trailing_and = ' and'\n\n #This will be used later for habitat related fields that contain 1 for true. E.g. caves=1\n set_keys = set(query_dict.keys())\n\n for req_key, req_val in query_dict.items():\n # Is request param a valid query param\n if req_key in self.attrib:\n\n if (req_key.lower() == 'scientific_name'):\n words = req_val.split('_')\n if len(words) == 1:\n qry_sci_name = (\"( genus LIKE INITCAP('%%{0}%%') or species LIKE LOWER('%%{0}%%'))\")\n qry_sci_name = str.format(qry_sci_name, words[0])\n elif len(words) == 2:\n qry_sci_name = (\" ((genus LIKE INITCAP('%%{0}%%') and species LIKE LOWER('%%{1}%%')) or (species LIKE LOWER('%%{0}%%') and genus LIKE INITCAP('%%{1}%%')))\")\n qry_sci_name = str.format(qry_sci_name, words[0], words[1])\n\n if req_key.lower() == 'common_name':\n words = req_val.split('_')\n if len(words) == 1:\n qry_common_name = (\" commonname LIKE INITCAP('%%{0}%%')\")\n qry_common_name = str.format(qry_common_name, words[0])\n elif len(words) == 2:\n qry_common_name = (\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%'))\")\n qry_common_name = str.format(qry_common_name, words[0], words[1])\n elif len(words) == 3:\n qry_common_name = (\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%') and commonname LIKE INITCAP('%%{2}%%'))\")\n qry_common_name = str.format(qry_common_name, words[0], words[1], words[2])\n\n\n # Can be multiple groups\n #e.g. grp='Black Bass' or grp='Mullet'\n if (req_key.lower() == 'group'):\n words = req_val.lower()\n words = words.replace('_', ' ')\n words = words.split(',')\n for idx, grp in enumerate(words):\n if idx != 0:\n qry_group += \" or \"\n qry_group += str.format(\" lower(grp) = '{0}'\", grp.lower())\n continue\n\n if (req_key.lower() == 'native'):\n qry_native = str.format(\" native='{0}'\",req_val)\n continue\n\n # pollution tolerance can be: \"I\", \"T\", \"M\", or \"U\"\n # if (req_key.lower() == 'pollut_tol'):\n # if (req_key.lower() == 'tolerance'):\n # words = req_val.lower()\n # words = words.split(',')\n # for idx, pollut in enumerate(words):\n # if idx != 0:\n # qry_pollut_tol += \" or \"\n # qry_pollut_tol += str.format(\" lower(pollut_tol) = '{0}'\", pollut)\n # continue\n\n if (req_key.lower() == 'rarity'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_rarity = str.format(\" rarity>={0} and rarity<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'max_age'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_max_age = str.format(\" max_age>={0} and max_age<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'mean_weight'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_mean_weight = str.format(\" mean_weight>={0} and mean_weight<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'mean_length'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_mean_length = str.format(\" mean_length>={0} and mean_length<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'max_length'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_max_length = str.format(\" max_length>={0} and max_length<={1}\", range[0], range[1])\n continue\n\n ubiquity_range = [0, 75]\n if (req_key.lower() == 'ubiquity'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != ubiquity_range[0] or int(range[1]) != ubiquity_range[1]):\n qry_ubiquity = str.format(\" ubiquity>={0} and ubiquity<={1}\", range[0], range[1])\n continue\n\n extent_range = [0, 77]\n if (req_key.lower() == 'extent'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != extent_range[0] or int(range[1]) != extent_range[1]):\n qry_extent = str.format(\" extent>={0} and extent<={1}\", range[0], range[1])\n continue\n\n tolerance_range = [0, 94]\n if (req_key.lower() == 'tolerance'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != tolerance_range[0] or int(range[1]) != tolerance_range[1]):\n qry_tolerance = str.format(\" tolerance>={0} and tolerance<={1}\", range[0], range[1])\n continue\n\n robustness_range = [0, 47]\n if (req_key.lower() == 'robustness'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != robustness_range[0] or int(range[1]) != robustness_range[1]):\n qry_robustness = str.format(\" robustness>={0} and robustness<={1}\", range[0], range[1])\n continue\n\n if qry_habitat.endswith(trailing_and):\n qry_habitat = qry_habitat[:-len(trailing_and)]\n\n # The rest of the query parameters are are used if value == 1\n # e.g. caves=1, springs=1, headwaters=1\n for cat in self.categories:\n intersect = set_keys & self.categories[cat]\n if len(intersect) > 0:\n print(cat)\n print(intersect)\n for param in intersect:\n val = query_dict[param]\n if (val == '1'):\n self.query_categories[cat].append(param)\n\n qry_habitat = ''\n query_cat = ''\n for category in self.query_categories:\n query_cat = ''\n for val in self.query_categories[category]:\n query_cat += str.format(\" {0}='1' or\", val)\n\n if query_cat.endswith(trailing_or):\n query_cat = query_cat[:-len(trailing_or)]\n\n if len(query_cat) > 0:\n qry_habitat += ' (' + query_cat + ') and'\n print(qry_habitat)\n\n if qry_habitat.endswith(trailing_and):\n qry_habitat = qry_habitat[:-len(trailing_and)]\n\n query = \"select * from fishproperties where\"\n first_condition = True\n\n if qry_sci_name != '':\n query += qry_sci_name\n first_condition = False\n\n if qry_common_name != '':\n if not first_condition:\n query += ' and'\n query += qry_common_name\n first_condition = False\n\n if qry_group != '':\n if not first_condition:\n query += ' and'\n query += qry_group\n first_condition = False\n\n if qry_native != '':\n if not first_condition:\n query += ' and'\n query += qry_native\n first_condition = False\n\n if qry_pollut_tol != '':\n if not first_condition:\n query += ' and'\n query += qry_pollut_tol\n first_condition = False\n\n if qry_rarity != '':\n if not first_condition:\n query += ' and'\n query += qry_rarity\n first_condition = False\n\n if qry_range != '':\n if not first_condition:\n query += ' and'\n query += qry_range\n first_condition = False\n\n if qry_max_age != '':\n if not first_condition:\n query += ' and'\n query += qry_max_age\n first_condition = False\n\n if qry_mean_weight != '':\n if not first_condition:\n query += ' and'\n query += qry_mean_weight\n first_condition = False\n\n if qry_mean_length != '':\n if not first_condition:\n query += ' and'\n query += qry_mean_length\n first_condition = False\n\n if qry_max_length != '':\n if not first_condition:\n query += ' and'\n query += qry_max_length\n first_condition = False\n\n if qry_ubiquity != '':\n if not first_condition:\n query += ' and'\n query += qry_ubiquity\n first_condition = False\n\n if qry_extent != '':\n if not first_condition:\n query += ' and'\n query += qry_extent\n first_condition = False\n\n if qry_tolerance != '':\n if not first_condition:\n query += ' and'\n query += qry_tolerance\n first_condition = False\n\n if qry_robustness != '':\n if not first_condition:\n query += ' and'\n query += qry_robustness\n first_condition = False\n\n if qry_habitat != '':\n if not first_condition:\n query += ' and'\n query += qry_habitat\n first_condition = False\n\n print(query)\n\n return query","title":""},{"docid":"c45b3d40d8a26bb7193088e942d5a655","score":"0.5736782","text":"def _construct_query(self, query_chain):\n # here we take the query_chain and convert to a real sql sentence\n res_dict = query_chain.pop(0)\n\n for q in query_chain:\n if q[\"action\"] == \"_db__where\":\n if res_dict[\"action\"] == \"_db__select_all\":\n res_dict.update({\"action\": \"_db__select\"})\n\n condition = res_dict.get(\"condition\", \"\")\n if condition:\n condition = \" AND \".join([condition, q[\"condition\"]])\n else:\n condition = q[\"condition\"]\n\n res_dict.update({\"condition\": condition})\n elif q[\"action\"] == \"_db__select_related\":\n for model_join in q[\"fields\"]:\n join_const = getattr(self, q[\"action\"]).format(**model_join)\n res_dict[\"join\"] += join_const\n\n select = res_dict[\"select\"][:]\n\n if select == \"COUNT(*)\":\n pass\n elif select == \"*\":\n select = select.replace(\n \"*\",\n \"{left_table}.*, {f_formatter}\".format(\n left_table=model_join[\"left_table\"], f_formatter=model_join[\"fields_formatter\"]\n ),\n )\n res_dict[\"select\"] = select\n else:\n res_dict[\"select\"] += \", \" + model_join[\"fields_formatter\"]\n\n # if we are not counting, then we can assign ordering\n operations = [\"COUNT\", \"MAX\", \"MIN\", \"SUM\", \"AVG\", \"STDDEV\"]\n\n # we must get error, if we set only one field and update instance\n if res_dict[\"action\"] == \"_db__update\" and len(res_dict[\"field_values\"]) == 1:\n res_dict.update({\"action\": \"_db__update_single_field\"})\n\n if res_dict.get(\"select\", \"\").split(\"(\")[0] not in operations:\n res_dict[\"ordering\"] = self._ordering_syntax(res_dict.get(\"ordering\", []))\n else:\n res_dict[\"ordering\"] = \"\"\n query = getattr(self, res_dict[\"action\"]).format(**res_dict)\n query = self._query_clean(query)\n\n logger.debug(\"QUERY: {}, VALUES: {}\".format(query, res_dict.get(\"field_values\")))\n return query, res_dict.get(\"field_values\")","title":""},{"docid":"0c13ae92a74d4f3943d1cd2abdd2d58f","score":"0.572707","text":"def _build_query(env, start, end, certname=None):\n query = ExtractOperator()\n query.add_field(FunctionOperator('count'))\n query.add_field('status')\n subquery = AndOperator()\n subquery.add(GreaterEqualOperator('producer_timestamp', start))\n subquery.add(LessOperator('producer_timestamp', end))\n if certname is not None:\n subquery.add(EqualsOperator('certname', certname))\n if env != '*':\n subquery.add(EqualsOperator('environment', env))\n query.add_query(subquery)\n query.add_group_by(\"status\")\n return query","title":""},{"docid":"6a1dd0e6509804a1d36f57e0e6c5750a","score":"0.5720286","text":"def query() -> str:\n return \"\"\"\n MATCH (doc:Publication)<-[:located_in]-(subject)-[association:chemical_to_disease_or_phenotypic_feature_association]->(obj)-[:located_in]->(sec_doc:Publication)\n WHERE association.relation <> 'null' and obj.name <> 'null' and subject.name <> 'null' and doc.pmid in {pmids} and association.relation <> 'null' and type(association) <> 'could_refer_to' and type(association) <> 'located_in' and sec_doc.pmid = doc.pmid\n RETURN DISTINCT subject.name as subject, subject.synonym as subj_syn, association.relation as predicate, obj.name as obj, obj.synonym as disease_synonym, type(association) as rel_type, doc.pmid as pmid, doc.abstract as abstract\n \"\"\"","title":""},{"docid":"6002db82a2bae451e4ed335385a5f2a2","score":"0.57197946","text":"def build_where_clause(args: dict, table_args_dict: dict) -> str:\n where_clause: str = ''\n for key in args.keys():\n if key in table_args_dict.keys():\n if key == 'query':\n # if query arg is supplied than we just need to parse it and only it\n return args[key].strip()\n else:\n values_list: list = argToList(args[key])\n for raw_value in values_list:\n for field in table_args_dict[key]:\n value = raw_value\n if key == 'url':\n value = f'*{raw_value}*'\n if not where_clause:\n # the beginning of the where part should start without OR\n where_clause += f\"{field}'{value}'\"\n else:\n where_clause += f\" OR {field}'{value}'\"\n return where_clause","title":""},{"docid":"fd48cb6a7ac2b019d07866613d70b519","score":"0.5693227","text":"def generate_where_clause(state=None, product=None,\n issue=None, min_complaints=None):\n where_inner = []\n where_outer = []\n\n if state and not state == \"All\":\n where_inner.append(\"ccdb.state = '{}'\".format(state))\n\n if product and not product == \"All\":\n where_inner.append(\"ccdb.product = '{}'\".format(product))\n\n if issue and not issue == \"All\":\n where_inner.append(\"ccdb.issue = '{}'\".format(issue))\n\n if min_complaints:\n where_outer.append(\"complaint_count > {}\".format(min_complaints))\n\n if len(where_inner) > 0:\n where_inner = \"WHERE \" + \" AND \".join(where_inner)\n else:\n where_inner = \"\"\n\n if len(where_outer) > 0:\n where_outer = \"WHERE \" + \" AND \".join(where_outer)\n else:\n where_outer = \"\"\n\n return where_inner, where_outer","title":""},{"docid":"a7e6e63e44f78e69cf88a741ac17d2fb","score":"0.5682573","text":"def build_query(self, params):\n term = params[\"term\"]\n size = params[\"size\"]\n page = params[\"page\"]\n\n query = {}\n query[\"query\"] = {\"match\":{}}\n query[\"query\"][\"match\"][\"inflected_form\"] = term\n\n query[\"sort\"] = []\n query[\"sort\"].append({\"_score\": \"desc\"})\n query[\"sort\"].append({\"word_count\": \"asc\"})\n\n query[\"from\"] = (page - 1) * size\n query[\"size\"] = size\n\n query = json.dumps(query)\n return query","title":""},{"docid":"930b18d9b4e9205bd3e078001ad05e29","score":"0.5674045","text":"def generate(self):\n result = self.query\n for param in self.params:\n result = result.replace(param.raw, param.sql_value())\n\n return result","title":""},{"docid":"2aa19aae92a018fc0f26673cb0f78bfb","score":"0.56575936","text":"def filter_query(self, parents=None):\n q = db.session.query()\n stmt, q = self._add_statement(q)\n q = q.add_column(stmt.subject)\n\n if parents is not None and self.node.attribute:\n parent_stmt, q = self._add_statement(q)\n q = q.filter(stmt.subject == parent_stmt._value)\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\n q = q.filter(parent_stmt.subject.in_(parents))\n\n q = self.filter(q, stmt)\n q = q.group_by(stmt.subject)\n q = q.order_by(stmt.subject.asc())\n\n if self.node.root:\n q = q.limit(self.node.limit)\n q = q.offset(self.node.offset)\n\n return q","title":""},{"docid":"ddd52219e9c4b5adb4afd6a139da0d56","score":"0.56358266","text":"def derived(self, statement, params=(), replace=False):\n return Query(\n db=self.db,\n sql=(statement,) if replace else self.sql + (statement,),\n params=self.params + params,\n )","title":""},{"docid":"2cd8fe36d01097c20060583de2e8bc83","score":"0.562555","text":"def make_query(self):","title":""},{"docid":"9fe5061d0a503021cd5b78f56d96ffed","score":"0.5612804","text":"def build_query(media_type=None, tags=None, sort=None, max_duration=None, published_after=None, categories=None,\n region=None, random=False, limit=constants.DEFAULT_QUERY_LIMIT,\n offset=constants.DEFAULT_QUERY_OFFSET):\n # NOTE: query time skyrockets with added OPTIONAL patterns, see:\n # https://stackoverflow.com/questions/25609691/alternative-for-optional-keyword-in-sparql-queries\n #\n # NOTE: query assumes ordering of group concat operations are same across tag and source - have eyeballed and\n # looks good\n if published_after is not None:\n raise NotImplementedError('The parameter `publishedAfter` is not yet implemented')\n if region is not None:\n raise NotImplementedError('The parameter `region` is not yet implemented')\n if sort is not None and random:\n raise InvalidInputParameterCombination('Cannot specify both `sort` and `random`.')\n\n regular_fields = '?title ?image ?version ?programme ?pid ?media ?duration ?publicationDate ?masterBrand'\n query_string = f\"\"\"\n SELECT\n {regular_fields}\n {build_tags_select_statement()}\n {build_genres_select_statement()}\n\n WHERE {{\n {build_regular_fields_pattern_statement()}\n {build_tags_pattern_statement()}\n {build_genres_pattern_statement()}\n\n {build_sparql_anding_statement(\n object_list=tags,\n predicate_str='datalab:tag/datalab:tagValue',\n object_binding='programme'\n )}\n {build_values_oring_statement('media', media_type)}\n {build_filter_statement('duration', '<', max_duration)}\n {build_filter_statement('published_date', '>', published_after)}\n {build_sparql_anding_statement(\n object_list=categories,\n predicate_str='po:genre/datalab:genreKey',\n object_binding='programme'\n )}\n }}\n GROUP BY {regular_fields}\n {'ORDER BY RAND()' if random else 'ORDER BY ' + build_sort_statement(sort) if sort is not None else ''}\n LIMIT {limit}\n OFFSET {offset}\n \"\"\"\n\n return query_string","title":""},{"docid":"864d460b0beb79116a83d3be73eff240","score":"0.5609568","text":"def compiledQuery(self, columns='', where='', order_by='',\n distinct='', limit='', offset='',\n group_by='', having='', for_update=False,\n relationDict=None,\n bagFields=False,\n count=False, excludeLogicalDeleted=True,excludeDraft=True,\n ignorePartition=False,ignoreTableOrderBy=False,\n addPkeyColumn=True):\n # get the SqlCompiledQuery: an object that mantains all the informations to build the sql text\n self.cpl = SqlCompiledQuery(self.tblobj.sqlfullname,relationDict=relationDict,maintable_as=self.aliasCode(0))\n distinct = distinct or '' # distinct is a text to be inserted in the sql query string\n \n # aggregate: test if the result will aggregate db rows\n aggregate = bool(distinct or group_by)\n \n # group_by == '*': if all columns are aggregate functions, there will be no GROUP BY columns, \n # but SqlQueryCompiler need to know that result will aggregate db rows\n if group_by == '*':\n group_by = None\n\n if not ignoreTableOrderBy and not aggregate:\n order_by = order_by or self.tblobj.attributes.get('order_by')\n self.init()\n if not 'pkey' in self.cpl.relationDict:\n self.cpl.relationDict['pkey'] = self.tblobj.pkey\n \n # normalize the columns string\n columns = columns or ''\n columns = columns.replace(' ', ' ')\n columns = columns.replace('\\n', '')\n columns = columns.replace(' as ', ' AS ')\n columns = columns.replace(' ,', ',')\n if columns and not columns.endswith(','):\n columns = columns + ','\n \n # expand * and *filters: see self.expandMultipleColumns\n if '*' in columns:\n col_list = [col for col in gnrstring.split(columns, ',') if col]\n new_col_list = []\n for col in col_list:\n col = col.strip()\n if col.startswith('*'):\n new_col_list = new_col_list + self.expandMultipleColumns(col[1:], bagFields)\n else:\n new_col_list.append(col)\n columns = ','.join(new_col_list)\n \n # translate @relname.fldname in $_relname_fldname and add them to the relationDict\n if where:\n where = PERIODFINDER.sub(self.expandPeriod, where)\n \n currentEnv = self.db.currentEnv\n env_conditions = dictExtract(currentEnv,'env_%s_condition_' %self.tblobj.fullname.replace('.','_'))\n if env_conditions:\n wherelist = [where] if where else []\n for condition in env_conditions.values():\n wherelist.append('( %s )' %condition)\n where = ' AND '.join(wherelist)\n\n partition_kwargs = dictExtract(self.tblobj.attributes,'partition_')\n\n if not ignorePartition and partition_kwargs:\n wherelist = [where] if where else []\n for k,v in partition_kwargs.items():\n if currentEnv.get('current_%s' %v):\n wherelist.append('( $%s=:env_current_%s )' % (k,v))\n where = ' AND '.join(wherelist)\n columns = self.updateFieldDict(columns)\n where = self.updateFieldDict(where or '')\n order_by = self.updateFieldDict(order_by or '')\n group_by = self.updateFieldDict(group_by or '')\n having = self.updateFieldDict(having or '')\n \n col_list = uniquify([col for col in gnrstring.split(columns, ',') if col])\n new_col_list = []\n for col in col_list:\n col = col.strip()\n if col.startswith('SUM'):\n aggregate = True\n if not ' AS ' in col:\n if col.startswith('$') and col[1:].replace('_', '').isalnum():\n as_ = col[1:]\n else:\n # replace non word char with _ and check for numbers\n as_ = self.db.colToAs(col)\n col = '%s AS \"%s\"' % (col, as_)\n else:\n colbody, as_ = col.split(' AS ', 1)\n # leave the col as is, but save the AS name to recover the db column original name from selection result\n self.cpl.aliasDict[as_.strip()] = colbody.strip()\n new_col_list.append(col)\n \n # build the clean and complete sql string for the columns, but still all fields are expressed as $fieldname\n columns = ',\\n'.join(new_col_list)\n \n # translate all fields and related fields from $fldname to t0.fldname, t1.fldname... and prepare the JOINs\n colPars = {}\n for key, value in self.cpl.relationDict.items():\n # self._currColKey manage exploding columns in recursive getFieldAlias without add too much parameters\n self._currColKey = key\n colPars[key] = self.getFieldAlias(value)\n if count: # if the query is executed in count mode...\n order_by = '' # sort has no meaning\n if group_by: # the number of rows is defined only from GROUP BY cols, so clean aggregate functions from columns.\n columns = group_by # was 't0.%s' % self.tblobj.pkey # ???? \n elif distinct:\n pass # leave columns as is to calculate distinct values\n else:\n columns = 'count(*) AS gnr_row_count' # use the sql count function istead of load all data\n elif addPkeyColumn and self.tblobj.pkey and not aggregate:\n columns = columns + ',\\n' + '%s.%s AS pkey' % (self.aliasCode(0),self.tblobj.pkey) # when possible add pkey to all selections\n columns = columns.lstrip(',') # if columns was '', now it starts with ','\n else:\n columns = columns.strip('\\n').strip(',')\n \n # replace $fldname with tn.fldname: finally the real SQL columns!\n columns = gnrstring.templateReplace(columns, colPars, safeMode=True)\n \n # replace $fldname with tn.fldname: finally the real SQL where!\n \n where = gnrstring.templateReplace(where, colPars)\n #if excludeLogicalDeleted==True we have additional conditions in the where clause\n logicalDeletionField = self.tblobj.logicalDeletionField\n draftField = self.tblobj.draftField\n if logicalDeletionField:\n if excludeLogicalDeleted is True:\n extracnd = '%s.%s IS NULL' % (self.aliasCode(0),logicalDeletionField)\n if where:\n where = '%s AND %s' % (extracnd, where)\n else:\n where = extracnd\n elif excludeLogicalDeleted == 'mark':\n if not (aggregate or count):\n columns = '%s, %s.%s AS _isdeleted' % (columns, self.aliasCode(0),logicalDeletionField) #add logicalDeletionField\n if draftField:\n if excludeDraft is True:\n extracnd = '%s.%s IS NOT TRUE' %(self.aliasCode(0),draftField)\n if where:\n where = '%s AND %s' % (extracnd, where)\n else:\n where = extracnd\n # add a special joinCondition for the main selection, not for JOINs\n if self.joinConditions:\n extracnd, one_one = self.getJoinCondition('*', '*', self.aliasCode(0))\n if extracnd:\n if where:\n where = '(%s) AND (%s)' % (where, extracnd)\n else:\n where = extracnd\n order_by = gnrstring.templateReplace(order_by, colPars)\n having = gnrstring.templateReplace(having, colPars)\n group_by = gnrstring.templateReplace(group_by, colPars)\n \n if distinct:\n distinct = 'DISTINCT '\n elif distinct is None or distinct == '':\n if self._explodingRows:\n if not aggregate: # if there is not yet a group_by\n distinct = 'DISTINCT ' # add a DISTINCT to remove unusefull rows: eg. a JOIN used only for a where, not for columns\n if order_by:\n xorderby=(('%s '%order_by.lower()).replace(' ascending ','').replace(' descending ','').replace(' asc ','').replace(' desc','')).split(',')\n lowercol=columns.lower()\n for xrd in xorderby:\n if not xrd.strip() in lowercol:\n columns = '%s, \\n%s' % (columns, xrd)\n #order_by=None\n if count:\n columns = '%s.%s' % (self.aliasCode(0),self.tblobj.pkey)\n # Count the DISTINCT maintable pkeys, instead of count(*) which will give the number of JOIN rows.\n # That gives the count of rows on the main table: the result is different from the actual number\n # of rows returned by the query, but it is correct in terms of main table records.\n # It is the right behaviour ???? Yes in some cases: see SqlSelection._aggregateRows\n \n self.cpl.distinct = distinct\n self.cpl.columns = columns\n self.cpl.where = where\n self.cpl.group_by = group_by\n self.cpl.having = having\n self.cpl.order_by = order_by\n self.cpl.limit = limit\n self.cpl.offset = offset\n self.cpl.for_update = for_update\n #raise str(self.cpl.get_sqltext(self.db)) # uncomment it for hard debug\n return self.cpl","title":""},{"docid":"3f2a3634474b6bca3382e5a869663d66","score":"0.5586637","text":"def rebuild_query(self, comments=True, pretty=True):\n if not self.tree:\n self._logger.error('El arbol no ha sido generado todavia. Por tanto, todavia no hay ninguna query que '\n 'reconstruir. Por favor ejecuta la funcion parse_query() para generar el arbol')\n raise LookupError\n\n if not self.__queries:\n self._logger.error('El arbol no ha sido procesado todavia. Por favor ejecuta la funcion rename_tree() para '\n 'procesar el arbol.')\n raise LookupError\n\n query = ' '.join(self.tree.leaves())\n query = (self._untokenize(query)\n .replace(' , ', ', ')\n .replace(' ; ', ';')\n .replace(' ( ', ' (')\n .replace(' ) ', ') ')\n #.replace(' = ', '=')\n .replace(' . ', '.')\n )\n\n if self.udfs:\n for point, scape in zip(self.udfs, self._udfs_norm):\n pattern = re.compile(scape, re.IGNORECASE)\n query = pattern.sub(point, query)\n\n if pretty:\n if comments:\n final = '\\n'.join(self.__comments) + '\\n' + sqlparse.format(query, reindent=True, keyword_case='upper')\n self.__comments = []\n return final\n else:\n return sqlparse.format(query, reindent=True, keyword_case='upper')\n else:\n return query","title":""},{"docid":"4df307799efca220bbeb0a0c969810c8","score":"0.55771285","text":"def construct_query(*args, dialect='MSSS', allow_coalesce=True):\n n = len(args)\n assert all([isinstance(o, UserOption) for o in args]), \"Not all args are UserOptions\"\n invalids = [not o.validate() for o in args]\n assert not any(invalids), \"{:d}/{:d} options have not been validated\".format(\n sum(invalids), n)\n assert all([args[0].context == args[i+1].context for i in range(n-1)]), \"Different\" +\\\n \"contexts associated with the User Opts. Ensure these are the same.\"\n stmt = Statement(args[0].context)\n\n # ==== GET ALL TABLES AND FORM BASIC JOIN SUBTREE ============\n context = args[0].context\n nodes = [o.get_table() for o in args]\n unique_nodes = list(set(nodes))\n join_tree = minimum_subtree(unique_nodes)\n\n # The primary table is considered the \"root node\" of the tree -- edges are undirected.\n primary = [o for o in args if not o.is_secondary]\n assert len(primary) == 1, f'Expecting one primary field. Got {len(primary)}.'\n primary = primary[0]\n primary_tbl = primary.get_table()\n\n # topological sort\n edges = [(k, v[0][0]) for (i,(k,v)) in enumerate(join_tree.items()) if i > 0]\n G = graph.Graph(edges, directed=False)\n sorted_nodes = G.topological_sort(leave_until_last=primary_tbl)\n # (Recreate G since the topological_sort mutates G (ikr - should fix this!))\n G = graph.Graph(edges, directed=False)\n\n # ======= CREATE CTEs WHENEVER WE FIND A NESTED AGGREGATION ============\n field_tbl_lkp = defaultdict(list)\n for arg in args:\n field_tbl_lkp[arg.get_table()].append(arg)\n all_fields = dict()\n\n # Traverse the join tree of the selected tables, making CTEs wherever\n # there exists secondary aggregation (i.e. not of the primary variable).\n for i, v in enumerate(sorted_nodes[:-1]):\n v_fields = field_tbl_lkp[v]\n any_v_has_agg = any([arg.has_aggregation for arg in v_fields])\n\n # Get 'child tables' and 'parent tables' (wrt topological sort)\n child_tbls = G._graph[v] - set(sorted_nodes[i:])\n parent_tbl = join_tree[v][0][0]\n parent_fks, v_pks = join_tree[v][0][1], join_tree[v][1][1]\n\n # The current table contains an aggregation, so create a CTE.\n if any_v_has_agg:\n # Get all arguments for table which are not aggregated\n f_non_agg = [arg for arg in v_fields if not arg.has_aggregation]\n f_non_agg += flatten([all_fields[w] for w in child_tbls])\n\n # Add in table PKs if not already included in `f_non_agg`.\n f_non_agg_names = [f.sql_fieldname for f in f_non_agg]\n pk_names_to_add = [f for f in v_pks if f not in f_non_agg_names]\n pks_to_add = [construct_simple_field(f, v, context) for f in pk_names_to_add]\n\n # Make a copy of all non-aggregated AND aggregated fields.\n f_non_agg_cte = [f.copy() for f in f_non_agg]\n f_agg_cte = [arg for arg in v_fields if arg.has_aggregation]\n f_agg = [f.copy() for f in f_agg_cte]\n\n # consolidate CTE fields and add primary designator to (any) field in root\n cte_fields = pks_to_add + f_non_agg_cte + f_agg_cte\n for field in cte_fields:\n if field.sql_fieldname in v_pks:\n field.is_secondary = False\n break\n\n # Defer lookups until the final query\n for field in cte_fields:\n field.perform_lkp = False\n\n # Construct CTE\n cte = CTENode([parent_tbl], # parent\n v_pks, # pk\n cte_fields) # cte_fields\n stmt.ctes.append(cte)\n\n # Point the [references to the aggregations] outside the CTE to the CTE field\n for f in f_agg:\n f.field_alias = f.field_alias # this is *not* a noop! (@property...)\n f.sql_item = '{alias}' + f.field_alias\n f.table = cte\n f.set_aggregation(None, force=True)\n f.set_transform(None, force=True)\n if allow_coalesce:\n f.coalesce = 0 # assumes that aggregation is numeric\n\n for f in f_non_agg:\n f.table = cte\n f.sql_item = '{alias}'+f._field_alias_logic(will_perform_lkp=False)\n\n # Push these pointers to within the CTE up to the parent (although not PKs)\n all_fields[v] = f_non_agg + f_agg\n\n # # overwrite the node `v` in the subtree with `cte`\n # join_cond = tree_final[v]\n # join_cond = (join_cond[0], (cte, join_cond[1][1]))\n # tree_final = replace_in_ordered_dict(tree_final, v, cte, join_cond)\n else:\n all_fields[v] = v_fields + flatten([all_fields[w] for w in child_tbls])\n\n # Set args to the args propagated up to the root node\n primary_children = G._graph[primary_tbl]\n args = field_tbl_lkp[primary_tbl] + flatten([all_fields[c] for c in primary_children])\n # Place the resulting tree into the FROM clause of the Statement object.\n\n nodes = [o.get_table() for o in args]\n unique_nodes = list(set(nodes))\n tree_final = minimum_subtree(unique_nodes)\n\n stmt._from.update(tree_final)\n\n # Add dimension tables (if requested to joins)\n lkp_joins = []\n for o in args:\n if o.perform_lkp:\n dtbl = o.dimension_table\n assert not dtbl.is_cte, \"Currently unable to support CTEs for dimension \" + \\\n \"tables. (But it only requires thinking about how \" + \\\n \"to avoid multiple copies.)\"\n fk = o.sql_fieldname\n lkp_joins.append(((o.table, fk), (dtbl, dtbl.pk[0])))\n\n # Generate statement in order to populate aliases dict\n stmt._from.generate_basic_statement(force_alias=(len(lkp_joins) > 0))\n stmt._from.add_lookups_to_statement(lkp_joins)\n\n # === CONSTRUCT SELECT / WHERE ===========\n has_agg = any([arg.has_aggregation for arg in args])\n\n for o in args:\n # Get table alias for field, depending on whether has lookup table or not\n if not o.perform_lkp:\n alias = stmt.aliases[o.get_table()]\n # coalesce is usually None anyway\n coalesce = o.coalesce if allow_coalesce else None\n else:\n dtbl = o.dimension_table\n fk = rm_alias_placeholder(o.sql_item)\n alias = stmt.lkp_aliases[((o.table, fk), (dtbl, dtbl.pk[0]))]\n coalesce = context.coalesce_default if allow_coalesce else None\n\n sel, where = o.sql_transform(alias=alias, dialect=dialect, coalesce=coalesce)\n # SELECT\n stmt.select.append(sel)\n # WHERE\n if len(where) > 0:\n stmt.where.extend(where)\n # GROUP BY\n if has_agg and not o.has_aggregation:\n gby = re.sub('AS [a-zA-Z0-9_]+$', '', sel).strip()\n stmt.groupby.append(gby)\n\n return stmt.generate_statement(dialect=dialect)","title":""},{"docid":"97b15f5e2a70c1eb7088c3897a2bfef2","score":"0.5570735","text":"def _build_query(self, metric, node, ts_from, ts_to):\n resolution = ts_to - ts_from\n if resolution > PROMETHEUS_TS_LIMIT:\n ts_from = ts_to - PROMETHEUS_TS_LIMIT\n\n query_head = \"http://{}:{}/api/v1/query_range?query=\".format(\n self.tsdb_ip, self.tsdb_port)\n query_times = \"&start={}&end={}&step=1s\".format(ts_from, ts_to)\n\n query_selectors = self._get_query_selectors(metric, node)\n query_selector = '{}{{{}}}'.format(metric, query_selectors)\n\n query = \"{}{}{}\".format(query_head, query_selector, query_times)\n return query","title":""},{"docid":"923405ac27fc863e4ecf89c859843768","score":"0.55545264","text":"def get_clean_query(self, additional_params={}):\n # NOTE: right now this makes us pretty vulnerable\n # to SQL Injection. We should make this safer at some point.\n # Graphs likely have the same problem.\n reg = re.compile('(\\{\\{.*?\\}\\})')\n query = self.query\n matches = reg.findall(query)\n if matches:\n for match in matches:\n attr = match[2:len(match)-2]\n if attr in additional_params:\n query = query.replace(match, additional_params[attr])\n else:\n query = query.replace(match, \"\")\n return query","title":""},{"docid":"374044813c297baa727f456ec5a2a772","score":"0.5546569","text":"def buildQueryFromFilters(date, rainfall, temperature, month, year):\n\t# Main part of the query\n\tquery = sql.SQL(\"SELECT * FROM climate\")\n\n\tconditions = []\n\tvalues = []\n\n\t# Check if each filter is actually applied, \n\t# if so, its respective part of the query is appended to the main part\n\tif date != None:\n\t\tclause = sql.SQL(\"date = to_date(%s, 'DD-MM-YYYY')\")\n\t\tconditions.append(clause)\n\t\tvalues.append(date)\n\tif temperature != None:\n\t\tclause = sql.SQL(\"temperature = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(temperature)\n\tif rainfall != None:\n\t\tclause = sql.SQL(\"rainfall = real '%s'\")\n\t\tconditions.append(clause)\n\t\tvalues.append(rainfall)\n\tif month != None:\n\t\tclause = sql.SQL(\"EXTRACT(MONTH FROM date) = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(month)\n\tif year != None:\n\t\tclause = sql.SQL(\"EXTRACT(YEAR FROM date) = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(year)\n\n\t# Put all together\n\tif len(conditions) > 0:\n\t\tquery = sql.SQL(\" \").join([query, sql.SQL(\"WHERE\")])\n\n\t\tconj = sql.SQL(\" AND \").join(conditions)\n\n\t\tquery = sql.SQL(\" \").join([query, conj])\n\n\t\n\treturn query, values","title":""},{"docid":"e9b14da9066124004791a46561a7d775","score":"0.55110025","text":"def query(**kw):","title":""},{"docid":"dfaf1e939ed62d271c7e53d6dfd18c69","score":"0.54942393","text":"def create_query(vaccine=None, age_group=\"adult\", without_registration=None, self_payer=False, monday=None,\n tuesday=None, wednesday=None, thursday=None, friday=None, saturday=None, sunday=None):\n\n open_hours = {'monday': monday, 'tuesday': tuesday, 'wednesday': wednesday, 'thursday': thursday, 'friday': friday,\n 'saturday': saturday, 'sunday': sunday}\n\n query = \"\"\"\n SELECT loc.vacc_id, loc.latitude, loc.longitude\n FROM vacc_center_location as loc\n INNER JOIN vacc_center_type as typ USING(vacc_id)\n \"\"\"\n if age_group not in AGE_GROUP_SET: raise Exception('age_group argument can be only: adult, teenage, child')\n if int(self_payer) not in [0, 1]: raise Exception('self_payer argument can be only: True, False')\n query_where = f\"\"\"WHERE typ.{age_group} = 1\n AND typ.self_payers = {int(self_payer)}\n \"\"\"\n if without_registration is not None:\n if int(without_registration) not in [0, 1]: raise Exception('without_registration argument can be only: True, False, None')\n query_where += f\"AND typ.without_registration = {int(without_registration)} \\n\"\n\n if vaccine is not None:\n if vaccine.lower() not in VACCINES_SET:\n raise Exception('vaccine argument can be only: COMIRNATY, SPIKEVAX, JANSSEN, Vaxzevria, None')\n query += \"INNER JOIN vacc_center_vaccines AS vacc USING(vacc_id) \\n\"\n query_where += f\"AND vacc.{vaccine.lower()} = 1 \\n\"\n\n open_hours = {day: hour for (day, hour) in open_hours.items() if hour is not None}\n if len(open_hours) != 0:\n query += \"INNER JOIN vacc_center_hours as hour USING(vacc_id) \\n\"\n list_hours = []\n for (day, hour) in open_hours.items():\n if not ((hour >= 0) and (hour <= 24)): raise Exception('days arguments can be only set to None or number between 0 to 24')\n list_hours.append(f\"(hour.{day}_open <= {hour} AND hour.{day}_closed >= {hour})\")\n\n query_hours = \"AND (\"\n n = len(list_hours)\n for i in range(n):\n query_hours += list_hours[i]\n if (i+1) == n:\n query_hours += \")\"\n else:\n query_hours += \" OR \"\n query_where += query_hours\n return query + query_where","title":""},{"docid":"3f83fb856d12b26ca2684c9d9c9dfb40","score":"0.5437405","text":"def build_goods_query(\n good_ids: List[str],\n currency_id: str,\n is_searching_for_sellers: bool,\n is_search_query: bool,\n) -> Query:\n if is_search_query:\n # the OEF does not accept attribute names consisting of integers only\n good_ids = [PREFIX + good_id for good_id in good_ids]\n\n data_model = _build_goods_datamodel(\n good_ids=good_ids, is_supply=is_searching_for_sellers\n )\n constraints = [Constraint(good_id, ConstraintType(\">=\", 1)) for good_id in good_ids]\n constraints.append(Constraint(\"currency_id\", ConstraintType(\"==\", currency_id)))\n constraint_expr = cast(List[ConstraintExpr], constraints)\n\n if len(good_ids) > 1:\n constraint_expr = [Or(constraint_expr)]\n\n query = Query(constraint_expr, model=data_model)\n return query","title":""},{"docid":"3716fc1f19951155d518f81b705b75c9","score":"0.54263324","text":"def cypher(self, query, params=None):\r\n path = cypher_path\r\n params = dict(query=query,params=params)\r\n resp = self.request.post(path, params)\r\n\r\n # Cypher data hack\r\n resp.total_size = len(resp.results.data)\r\n resp.results = (Neo4jResult(result[0], self.config) for result in resp.results.data)\r\n return resp","title":""},{"docid":"800590f0ff9faf64788181eb91302965","score":"0.5422505","text":"def buildQuery (self, obj_type, ids=None, params=None, attributes=None):\n\n if type(obj_type) is type(''):\n wrapper = KNOWN_WRAPPERS.get(obj_type.lower(), None)\n if wrapper is None:\n raise KeyError(\"obj_type of %s not supported by getOjbects(). E.g. use 'Image' etc\" % obj_type)\n else:\n raise AttributeError(\"getObjects uses a string to define obj_type, E.g. 'Image'\")\n\n q = self.getQueryService()\n if params is None:\n params = omero.sys.Parameters()\n if params.map is None:\n params.map = {}\n\n # get the base query from the instantiated object itself. E.g \"select obj Project as obj\"\n query = wrapper()._getQueryString()\n\n clauses = []\n # getting object by ids\n if ids != None:\n clauses.append(\"obj.id in (:ids)\")\n params.map[\"ids\"] = rlist([rlong(a) for a in ids])\n\n # support filtering by owner (not for some object types)\n if params.theFilter and params.theFilter.ownerId and obj_type.lower() not in [\"experimentergroup\", \"experimenter\"]:\n clauses.append(\"owner.id = (:eid)\")\n params.map[\"eid\"] = params.theFilter.ownerId\n\n # finding by attributes\n if attributes != None:\n for k,v in attributes.items():\n clauses.append('obj.%s=:%s' % (k, k) )\n params.map[k] = omero_type(v)\n\n if clauses:\n query += \" where \" + (\" and \".join(clauses))\n\n return (query, params, wrapper)","title":""},{"docid":"3193232c6e9b86e2cb3be30784d0c07c","score":"0.5413109","text":"def make_query(store, query, prefix=\"\"):\r\n query = common.parse_query(query)\r\n q = Query()\r\n q.prefix = prefix\r\n q.offset = common.safeint(query.pop('offset', None), 0)\r\n q.limit = common.safeint(query.pop('limit', 20), 20)\r\n if q.limit > 1000:\r\n q.limit = 1000\r\n sort = query.pop('sort', None)\r\n \r\n nested = (prefix != \"\")\r\n \r\n for k, v in query.items():\r\n # key foo can also be written as label:foo\r\n k = k.split(':')[-1]\r\n if v is None:\r\n q.requested[k] = v\r\n elif isinstance(v, dict):\r\n # make sure op is ==\r\n v = dict((k + '.' + key, value) for key, value in v.items())\r\n q2 = make_query(store, v, prefix=prefix + k + \".\")\r\n #@@ Anand: Quick-fix\r\n # dbstore.things looks for key to find whether type is required or not. \r\n q2.key = k \r\n if q2.conditions:\r\n q.conditions.append(q2)\r\n else:\r\n q.requested[k] = q2\r\n else:\r\n k, op = parse_key(k)\r\n q.add_condition(k, op, None, v)\r\n \r\n if not nested:\r\n q.assert_type_required()\r\n \r\n type = get_thing(store, q.get_type())\r\n #assert type is not None, 'Not found: ' + q.get_type()\r\n for c in q.conditions:\r\n if not isinstance(c, Query):\r\n c.datatype = find_datatype(type, c.key, c.value)\r\n \r\n if sort:\r\n parse_key(sort) # to validate key\r\n q.sort = web.storage(key=sort, datatype=find_datatype(type, sort, None))\r\n else:\r\n q.sort = None\r\n \r\n return q","title":""},{"docid":"bac716271d29523c3ec034478d633e21","score":"0.54028887","text":"def create_query(neo4j_entities, entity_value):\n\n def get_value(index):\n return SINONIMOS_NEO4J[entity_value[index]] if SINONIMOS_NEO4J.get(entity_value[index])\\\n else entity_value[index]\n\n get_query_part = {\n \"espacio\": lambda x: \"(n:Room {{ type: '{}' }})\".format(get_value(x)),\n \"sensor\": lambda x: (\"(d:Device)\", f\" '{get_value(x)}' in d.type\"),\n \"orientacion\": lambda x: \" n.orientacion CONTAINS '{}'\".format(get_value(x)),\n \"edificio\": lambda x: (\"(b:Building)\", \" b.name CONTAINS '{}'\".format(get_value(x))),\n \"nombre\": lambda x: (\"(b:Building)\", \" b.name CONTAINS '{}'\".format(get_value(x))),\n \"lugares\": lambda x: \" n.name CONTAINS '{}'\".format(get_value(x))\n }\n\n\n dic = {}\n for entity in neo4j_entities:\n key = entity[1]\n if entity[1] == \"nombre\":\n key = \"edificio\"\n dic[key] = get_query_part[entity[1]](entity[0])\n\n espacio = dic[\"espacio\"] if dic.get(\"espacio\") else \"\"\n sensor = dic[\"sensor\"] if dic.get(\"sensor\") else (\"\", \"\")\n orientacion = dic[\"orientacion\"] if dic.get(\"orientacion\") else \"\"\n edificio = dic[\"edificio\"] if dic.get(\"edificio\") else (\"\", \"\")\n lugares = dic[\"lugares\"] if dic.get(\"lugares\") else \"\"\n ret = []\n\n # crear relaciones que son opcionales en la consulta\n if espacio is not \"\" and sensor != (\"\", \"\"):\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n if espacio is not \"\" and edificio != (\"\", \"\"):\n espacio += \"<-[:HAS]-(f:Floor)<-[:HAS]-\"\n\n #Añadir información a la consulta que no se infica pero que es necesaria para poder recorrer las relaciones en Neo4j.\n \n # Si se pregunta por sensor y lugar pero no por espacio.\n if espacio is \"\" and sensor != (\"\", \"\") and lugares is not \"\":\n espaciocio = \"(n:Room)\"\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n # Si se pregunta por nombre u orientación pero no por espacio.\n if espacio is \"\" and (lugares is not \"\" or orientacion is not \"\"):\n espacio = \"(n:Room)\"\n # Si se pregunta por sensores en un edificio sin especificar un espacio.\n if espacio is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n espacio = \"(n:Room)<-[:HAS]-(f:Floor)<-[:HAS]-\"\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n # Si hay 4\n # orientacion lugares edificio sensor\n # X X X X\n if orientacion is not \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n edificio = (edificio[0], \"AND \" + edificio[1])\n sensor = (sensor[0], \"AND \" + sensor[1])\n\n # Si hay 3\n # orientacion lugares edificio sensor\n # X X X -\n # X - X X\n # - X X X\n # X - X X\n if orientacion is not \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n edificio = (edificio[0], \"AND \" + edificio[1])\n if orientacion is not \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n sensor = (sensor[0], \"AND \" + sensor[1])\n if orientacion is \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"WHERE \" + lugares\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"AND \" + edificio[1])\n if orientacion is not \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n orientacion = \"WHERE \" + orientacion\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"AND \" + edificio[1])\n\n # Si hay 2\n # orientacion lugares edificio sensor\n # X X - -\n # X - X -\n # X - - X\n # - X X -\n # - X - X\n # - - X X\n\n if orientacion is not \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n if orientacion is not \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"AND \" + edificio[1])\n orientacion = \"WHERE \" + orientacion\n if orientacion is not \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n orientacion = \"WHERE \" + orientacion\n if orientacion is \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"AND \" + edificio[1])\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"WHERE \" + edificio[1])\n # Si hay 1\n # orientacion lugares edificio sensor\n # X - - -\n # - X - -\n # - - X -\n # - - - X\n if orientacion is not \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n orientacion = \"WHERE \" + orientacion\n if orientacion is \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"WHERE \" + edificio[1])\n if orientacion is \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"WHERE \" + sensor[1])\n\n\n\n # Preparar los atributos de salida\n if espacio is not \"\":\n ret.append(\"n.name, n.id\")\n if sensor != (\"\", \"\"):\n ret.append(\"d.id, d.type\")\n if orientacion is not \"\":\n ret.append(\"n.orientacion\")\n\n return TEMPLATE_NEO.format(sensor[0], espacio, edificio[0], orientacion,\\\n lugares, edificio[1], sensor[1], \", \".join(ret))","title":""},{"docid":"4e23579f229cdea216279b1841dfff82","score":"0.53795767","text":"def generate_entrez_query(self):\n ending = ') AND ' + self.gene + '[Gene Name] ' + \\\n 'AND 1000:8000[Sequence Length]'\n types = []\n for hpv_type in itertools.chain(self.harmfull, self.safe):\n types.append('\"Human papillomavirus type %d\"[Organism]' % hpv_type)\n self._query = '(' + ' OR '.join(types) + ending\n return self._query","title":""},{"docid":"68cd306f78e9a696fa45ccc17d112056","score":"0.5368679","text":"def sql_query_str(program):\n node_type, node_children = program[0], program[1:]\n assert node_type in sql_entry_tokens and syntax_valid(program, sql_cfg)\n\n if node_type == \"SELECT_AGG\":\n select_exprs_node, joins_node, group_by_exprs_node = node_children\n where_node = None\n elif node_type == \"SELECT_AGG_WHERE\":\n (select_exprs_node,\n joins_node,\n group_by_exprs_node,\n where_node) = node_children\n else:\n raise RuntimeError(\n \"Missing node-type decoding logic for generating SQL query from AST.\"\n )\n\n return query_str(\n select_exprs=list(sql_select_exprs(select_exprs_node)),\n joins=list(sql_joins(joins_node)),\n where_exprs=list(sql_and_exprs_strs(where_node)) if where_node else [],\n group_by_exprs=list(sql_group_by_exprs(group_by_exprs_node)),\n order_by_exprs=[],\n limit_exprs=[],\n )","title":""},{"docid":"f43c43ecaf245aaab63b127c15a173ed","score":"0.53679913","text":"def _getQueryString(self):\n query = \"select obj from Plate as obj \" \\\n \"join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent \"\\\n \"left outer join fetch obj.screenLinks spl \" \\\n \"left outer join fetch spl.parent sc\"\n return query","title":""},{"docid":"51fd823b07a49d65be55acc10cb16971","score":"0.53457093","text":"def _create_qualifiedname_query(self, type_name, *values):\n query = {\n 'typeName': type_name,\n 'excludeDeletedEntities': True,\n 'limit': 10000\n }\n entity_filter = {\n 'condition': 'AND'\n }\n criterion = []\n n = 0\n for v in values:\n criteria = {\n 'attributeName': 'qualifiedName',\n 'operator': 'STARTSWITH' if n == 0 else 'CONTAINS',\n 'attributeValue': v\n }\n n += 1\n criterion.append(criteria)\n if type_name == 'hive_table':\n # Ignore temporary tables.\n criterion.append({'operator': '=', 'attributeName': 'temporary', 'attributeValue': False})\n entity_filter['criterion'] = criterion\n query['entityFilters'] = entity_filter\n return query","title":""},{"docid":"44970fb2e787db5c9720c86d5e16d3f3","score":"0.53439456","text":"def create_query(\n self, query_text, locale=None, language=None, time_zone=None, timestamp=None\n ):\n if not query_text:\n query_text = \"\"\n if isinstance(query_text, (list, tuple)):\n return self._process_list(\n query_text,\n \"create_query\",\n locale=locale,\n language=language,\n time_zone=time_zone,\n timestamp=timestamp,\n )\n return self.resource_loader.query_factory.create_query(\n query_text,\n language=language,\n locale=locale,\n time_zone=time_zone,\n timestamp=timestamp,\n )","title":""},{"docid":"8b4dabb7c03fa99fdfdd73c857a0b88d","score":"0.5328662","text":"def sql_with_where(self):\n e = get_executor(SqlRule)\n where_clause = \"WHERE \"\n where_time_filter = e.compose_where_time_filter(self)\n where_condition = e.compose_where_condition(self)\n if where_time_filter == \"\" and where_condition == \"\":\n where_clause = \"\"\n elif where_time_filter != \"\" and where_condition != \"\":\n where_clause = f\"{where_clause} {where_time_filter} AND {where_condition}\"\n else:\n where_clause = f\"{where_clause} {where_time_filter} {where_condition}\"\n final_sql = f\"{self.sql} {where_clause}\"\n return self.render_sql(final_sql)","title":""},{"docid":"dae062c702cf1a20a574ddd68d4b28aa","score":"0.532052","text":"def construct_url(self\n , query_params: Dict\n , ) -> str:\n url_const = self.faers_ep\n initialized = False\n for param_q, param_val in query_params.items():\n # Need to add '&' to 2nd,3rd,... query_params\n if initialized:\n url_const += '&'\n else:\n initialized = True\n if type(param_val) == str and len(query_params) > 0 \\\n or type(param_val) == int:\n url_const += f'{param_q}={param_val}'\n elif type(param_val) == dict and len(param_val) > 0:\n url_const += f'{param_q}=' + \"+AND+\".join(\n [f'{sub_p_k}:{sub_p_v}' for sub_p_k, sub_p_v in param_val.items()])\n elif type(param_val) == list and len(param_val) > 0:\n url_const += f'{param_q}=' + \"+AND+\".join(param_val)\n else:\n raise TypeError\n return url_const","title":""},{"docid":"f60d6e0b78286e4e35b88227402bb392","score":"0.5318269","text":"def get_custom_query(self):\n query = {}\n\n # searchable text queries\n q = req.get_query()\n if q:\n query[\"SearchableText\"] = q\n\n # physical path queries\n path = req.get_path()\n if path:\n query[\"path\"] = {'query': path, 'depth': req.get_depth()}\n\n # special handling for recent created/modified\n recent_created = req.get_recent_created()\n if recent_created:\n date = api.calculate_delta_date(recent_created)\n query[\"created\"] = {'query': date, 'range': 'min'}\n\n recent_modified = req.get_recent_modified()\n if recent_modified:\n date = api.calculate_delta_date(recent_modified)\n query[\"modified\"] = {'query': date, 'range': 'min'}\n\n return query","title":""},{"docid":"88503be9fbc27ea6562b4928b4a8d6c0","score":"0.530554","text":"def get_where_clause(columns_to_query_lst):\n where_str = \"where \"\n equals_str =[]\n for row in columns_to_query_lst:\n temp_str = \"c.\" + row + \" = t.\" + row\n equals_str.append(temp_str)\n joined_str = \" AND \".join(equals_str)\n where_str = where_str + joined_str\n return where_str","title":""},{"docid":"746bf5ea5425e576e9ff1f9bce37f702","score":"0.52996916","text":"def create_query(processed_request):\n query = {\n \"variant\": {\n \"referenceBases\": \"\" if not processed_request.get(\"referenceBases\") else processed_request.get(\"referenceBases\"),\n \"alternateBases\": \"\" if not processed_request.get(\"alternateBases\") else processed_request.get(\"alternateBases\"),\n \"referenceName\": \"\" if not processed_request.get(\"referenceName\") else processed_request.get(\"referenceName\"),\n \"start\": None if not processed_request.get(\"start\") else processed_request.get(\"start\"),\n \"end\": None if not processed_request.get(\"end\") else processed_request.get(\"end\"),\n \"assemblyId\": \"\" if not processed_request.get(\"assemblyId\") else processed_request.get(\"assemblyId\")\n },\n \"datasets\": {\n \"datasetIds\": None if not processed_request.get(\"datasetIds\") else processed_request.get(\"datasetIds\"),\n \"includeDatasetResponses\": \"ALL\" if not processed_request.get(\"includeDatasetResponses\") else processed_request.get(\"includeDatasetResponses\")\n },\n \"filters\": None if not processed_request.get(\"filters\") else processed_request.get(\"filters\"),\n \"customFilters\": None if not processed_request.get(\"customFilters\") else processed_request.get(\"customFilters\"),\n }\n\n return query","title":""},{"docid":"e8e6478863c1ea4a7a2b784bc3c49cd9","score":"0.529957","text":"def build_where_stmt(self, ident, filters, q_filters=None, source_class=None):\n if q_filters is not None:\n stmts = self._parse_q_filters(ident, q_filters, source_class)\n if stmts:\n self._ast.where.append(stmts)\n else:\n stmts = []\n for row in filters:\n negate = False\n\n # pre-process NOT cases as they are nested dicts\n if \"__NOT__\" in row and len(row) == 1:\n negate = True\n row = row[\"__NOT__\"]\n\n for prop, operator_and_val in row.items():\n operator, val = operator_and_val\n if operator in _UNARY_OPERATORS:\n # unary operators do not have a parameter\n statement = (\n f\"{'NOT' if negate else ''} {ident}.{prop} {operator}\"\n )\n else:\n place_holder = self._register_place_holder(ident + \"_\" + prop)\n statement = f\"{'NOT' if negate else ''} {ident}.{prop} {operator} ${place_holder}\"\n self._query_params[place_holder] = val\n stmts.append(statement)\n\n self._ast.where.append(\" AND \".join(stmts))","title":""},{"docid":"3c5f867e7aef319a9323809c18719262","score":"0.5287673","text":"def __init__ (self, database, var, conditions = (), echo = False):\n\n self.session = buildSession(\n database=database,\n echo=echo)\n if var == \"ncommits\":\n self.query = self.session.query().select_nscmlog([\"commits\",])\n elif var == \"listcommits\":\n self.query = self.session.query().select_listcommits()\n elif var == \"nauthors\":\n self.query = self.session.query().select_nscmlog([\"authors\",])\n elif var == \"listauthors\":\n self.query = self.session.query().select_listauthors()\n for condition in conditions:\n self.query = condition.filter(self.query)","title":""},{"docid":"d298399d7e7d292b986fa091d5abed50","score":"0.5285017","text":"def full_query_pattern(self, include_conclusion: bool = False) -> str:\n query_pattern = \"\"\n for literal in self.premise:\n query_pattern += literal.sparql_patterns()\n\n if include_conclusion:\n query_pattern += self.conclusion.sparql_patterns()\n\n if \"?b\" not in query_pattern and \"?a\" not in query_pattern:\n query_projection = \"ask \"\n else:\n # insert the selectors for subject and object into the select query if they exist in the query pattern\n query_projection = \"select where \"\n\n # the resulting query would look like \"select ?a ?b ...\" if both cases are true\n if \"?b\" in query_pattern:\n query_projection = query_projection.replace(\"select \", \"select ?b \")\n if \"?a\" in query_pattern:\n query_projection = query_projection.replace(\"select \", \"select ?a \")\n\n # build remaining part of the query and execute it\n query_pattern = \"{\" + query_pattern + \"}\"\n return query_projection + query_pattern","title":""},{"docid":"3f90b7aa24950c089ec7f416c7f71163","score":"0.5283903","text":"def _getQueryString(self):\n return \"select obj from %s obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\" % self.OMERO_CLASS","title":""},{"docid":"edec057b919f8d0df46ebd8515dfc0e2","score":"0.5279631","text":"def _getQueryString(self):\n return \"select obj from BooleanAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\"","title":""},{"docid":"5238770c84b4b535af51b45d8d738378","score":"0.52578133","text":"def query(self):\n\n if not hasattr(self, \"_query\"):\n\n # Set up some convenience aliases.\n Query = db.Query\n Condition = Query.Condition\n Or = Query.Or\n\n # Create a query object.\n columns = \"d.id\", \"d.title\"\n self._query = Query(\"document d\", *columns).order(\"d.title\")\n\n # Add the conditions: one for each field with a value.\n conditions = []\n have_doctype = False\n for field in self.fields:\n\n # See if we got a value for this field.\n value = field.var.strip() if field.var else None\n if not value:\n continue\n\n # Remember the criterion for display to the user.\n self.criteria.append(value)\n\n # If 'selectors' is a string, it's a column in `document`.\n value_op = getQueryOp(value)\n if isinstance(field.selectors, str):\n column = f\"d.{field.selectors}\"\n conditions.append(Condition(column, value, value_op))\n continue\n\n # Build up a subquery for the field.\n have_doctype = True\n subquery = Query(\"query_term\", \"doc_id\").unique()\n\n # These are ORd together.\n selector_conditions = []\n for path in field.selectors:\n path_op = \"LIKE\" if \"%\" in path else \"=\"\n\n # Simple case: test for a string stored in the doc.\n if not path.endswith(\"/@cdr:ref[int_val]\"):\n path_test = Condition(\"path\", path, path_op)\n value_test = Condition(\"value\", value, value_op)\n selector_conditions.append((path_test, value_test))\n\n # Trickier case: find values in linked documents.\n else:\n path = path.replace(\"[int_val]\", \"\")\n title_query = Query(\"document\", \"id\").unique()\n args = \"title\", value, value_op\n title_query.where(Condition(*args))\n args = \"int_val\", title_query, \"IN\"\n title_test = Condition(*args)\n path_test = Condition(\"path\", path, path_op)\n selector_conditions.append((path_test, title_test))\n\n # Add the conditions for this field's selectors to the mix.\n subquery.where(Or(*selector_conditions))\n conditions.append(Condition(\"d.id\", subquery, \"IN\"))\n\n # Sanity check.\n if not conditions:\n raise Exception(\"No search conditions specified\")\n\n # If all the selectors are from the document table (or,\n # view, to be precise), then we still need to narrow\n # the search to this document type.\n if not have_doctype:\n self._query.join(\"doc_type t\", \"t.id = d.doc_type\")\n self._query.where(Condition(\"t.name\", self.doctype))\n # Plug the top-level conditions into the query.\n if self.match_all:\n for condition in conditions:\n self._query.where(condition)\n else:\n self._query.where(Or(*conditions))\n\n # All the fields with values have been folded into the query.\n return self._query","title":""},{"docid":"bc3e93874b247c4bb5a6d828028febe0","score":"0.5255788","text":"def query(self):\n self._check_mandatory_inputs()\n return self._gen_query()","title":""},{"docid":"74e698e0dc148ed3565a45d92b4dec8c","score":"0.5251471","text":"def _get_clause(self):\r\n params = [(primary_key, sql.bindparam(None, type_=primary_key.type))\r\n for primary_key in self.primary_key]\r\n return sql.and_(*[k == v for (k, v) in params]), \\\r\n util.column_dict(params)","title":""},{"docid":"bb58117d3fda3c2db7fe7688c4120b96","score":"0.5243088","text":"def build_query_text_request(\n **kwargs # type: Any\n):\n # type: (...) -> HttpRequest\n\n content_type = kwargs.pop('content_type', None) # type: Optional[str]\n\n api_version = \"2021-05-01-preview\"\n accept = \"application/json\"\n # Construct URL\n url = kwargs.pop(\"template_url\", '/:query-text')\n\n # Construct parameters\n query_parameters = kwargs.pop(\"params\", {}) # type: Dict[str, Any]\n query_parameters['api-version'] = _SERIALIZER.query(\"api_version\", api_version, 'str')\n\n # Construct headers\n header_parameters = kwargs.pop(\"headers\", {}) # type: Dict[str, Any]\n if content_type is not None:\n header_parameters['Content-Type'] = _SERIALIZER.header(\"content_type\", content_type, 'str')\n header_parameters['Accept'] = _SERIALIZER.header(\"accept\", accept, 'str')\n\n return HttpRequest(\n method=\"POST\",\n url=url,\n params=query_parameters,\n headers=header_parameters,\n **kwargs\n )","title":""},{"docid":"5e0b0884588b76f9e715cfc138ae0212","score":"0.5239496","text":"def create_complex_query(self):\n columns = copy(self.columns)\n shuffle(columns)\n query = \"\"\"\\\n SELECT\n {col}\n FROM\n datadog_test.dbo.high_cardinality AS hc1\n JOIN (\n SELECT\n id,\n COUNT(*) col12_float\n FROM\n datadog_test.dbo.high_cardinality AS hc2\n WHERE\n hc2.col1_txt LIKE '%-%'\n AND hc2.col14_int > (\n SELECT\n AVG(hc3.col15_int)\n FROM\n datadog_test.dbo.high_cardinality AS hc3)\n GROUP BY\n hc2.id) AS hc4 ON hc4.id = hc1.id\n JOIN datadog_test.dbo.high_cardinality AS hc5 ON hc5.id = hc1.id\n WHERE\n CAST(hc5.col17_date AS VARCHAR)\n IN('2003-04-23', '2043-09-10', '1996-08-08')\n AND hc5.col13_float IS NOT NULL\n AND hc5.col17_date NOT LIKE '21%';\n \"\"\"\n # Select a range of random columns and prefix to match our alias\n return query.format(col=','.join(['hc1.' + col for col in columns[: randint(1, len(columns) - 1)]]))","title":""},{"docid":"23ac357cf50e246b557978088edbcf6b","score":"0.5237348","text":"def __gencondition(self, querylist):\n conditions = []\n for key in self.PROPERTY_SEARCH_LIST:\n if querylist.get(key):\n conditions.append(self.PROPERTY_SEARCH_LIST[key][1] % (querylist.get(key)))\n return \" AND \".join(conditions)","title":""},{"docid":"b90ad42bba7570be5827a9b27873f0c5","score":"0.52357996","text":"def build_sql(self, table, row, known_fkeys={}):\n # Initialize the builder properties\n self.row = row\n self.known_fkeys = known_fkeys\n return self.build_table_sql(table)","title":""},{"docid":"a69e3debc69789bbd12bb2ee0e29ccfd","score":"0.5234567","text":"def __build_where_clause(where_columns_dictionary):\n return ' AND '.join(key + \" = ? \"\n if value is not None\n else key + \" IS NULL \"\n for key, value\n in where_columns_dictionary.items())","title":""},{"docid":"df322a9786ad715a3d5849f5ed411c2d","score":"0.5219735","text":"def concat_conds(*args):\n stmt = \" WHERE 1\"\n for cond in args:\n stmt = stmt + \" AND \" + cond\n return stmt","title":""},{"docid":"b443f879e890682442787a51400a4a67","score":"0.5218033","text":"def build_query(cls, request, model):\n # print(request.data)\n name = request.data.get(\"search_phrase\")\n criteria = request.data.get(\"search_criteria\")\n frequency = request.data.get(\"frequency\")\n age_distribution = request.data.get(\"age_distribution\")\n letters_range = request.data.get(\"letters_range\")\n\n resp = model.objects.filter()\n\n if frequency is not None and len(frequency) > 0:\n resp = model.objects.filter(frequency__in=frequency).defer(\"double_name\", \"number_of_letters\")\n\n double_name = True in letters_range\n\n # resp = resp.filter(double_name=True)\n\n if letters_range is not None and len(letters_range) > 0:\n if double_name:\n letters_range.remove(True)\n resp = resp.filter(Q(number_of_letters__in=letters_range) | Q(double_name=True))\n else:\n resp = resp.filter(double_name=False)\n resp = resp.filter(number_of_letters__in=letters_range)\n\n if age_distribution is not None and len(age_distribution) > 0:\n mask = \"age_distribution_{}__gt\"\n\n # AND age_distribution way\n # dd = {}\n #\n # for d in age_distribution:\n # key = mask.format(d)\n # dd[key] = 0\n # resp = resp.filter(**dd)\n\n # OR age_distribution way\n or_query_set = Q()\n for d in age_distribution:\n tmp = Q(**{mask.format(d): 0})\n or_query_set |= tmp\n\n resp = resp.filter(or_query_set)\n\n if criteria is not None and len(name) > 0:\n if criteria == \"start\":\n resp = resp.filter(name__lower__istartswith=name.lower())\n elif criteria == \"middle\":\n resp = resp.filter(name__lower__icontains=name.lower())\n elif criteria == \"end\":\n resp = resp.filter(name__lower__iendswith=name.lower())\n else:\n resp = resp.filter(name__lower=name)\n\n return resp","title":""},{"docid":"9a699f49d4c0ba175277a65d66a2562a","score":"0.52174175","text":"def query_to_clojure(obj, params):\n # Serialize the query, sorted keys for deterministic substitution, no pretty printing\n data = json.dumps(obj, sort_keys=True)\n # Split the JSON into parts using 'PARAM' as seperator\n parts = data.split(\"PARAM\")\n # Escape each JSON part by serializing as a JSON list and removing the qruare brackets\n escaped_parts = [json.dumps([part])[1:-1] for part in parts]\n # The amount of parts should be one more than the amount ot parameters\n if len(parts) != len(params) + 1:\n raise RuntimeError(\"Param count mismatch: \" +\n str(len(params)) +\n \" given \" +\n str(len(parts) -1) +\n \" needed ; \" +\n data)\n # Interleave the escaped JSON parts and the parameters and put them in a (str ) clojure expression\n rval = \" \".join(list(itertools.chain(*zip([\"(query (str \"] + params, escaped_parts))) + [\") )\"])\n return rval","title":""},{"docid":"c47f0f449170335a3dd6dfeb971f17fa","score":"0.52149814","text":"def process(self):\n q = \"?q=\"\n lst = []\n for field in self.filter_dict:\n for keyword in self.filter_dict[field]:\n for criteria in self.filter_dict[field][keyword]:\n lst.append(\"{0} {1} '{2}'\".format(field, kw_map[keyword], criteria))\n\n q += \" and \".join(lst)\n\n if self.count:\n q +=\"&count=True\"\n self.query = q\n\n return self","title":""},{"docid":"35ea0ee25067279790679c78c36e66c2","score":"0.52111125","text":"def run_sql_statement(self, request, params=()):\n\n # simple string formatting RISK OF SQL INJECTION, DO NOT USE IN REAL DATABASE\n output = request % tuple([\"'\" + p + \"'\" for p in params])\n\n self.output_sql += output + \"\\n\"","title":""},{"docid":"c29abca87c3ec6ed8e7655886a9e9437","score":"0.52013975","text":"def prepare_sql(self, **kwargs):\n source = kwargs.get(\"source\", self.source)\n if not source:\n raise ValueError(\"No source table or view specified.\")\n\n cutoff = kwargs.get(\"cutoff\", self.cutoff)\n start_time = \"start_time_local\" if self.local else \"start_time\"\n end_time = \"end_time_local\" if self.local else \"end_time\"\n\n predicates = kwargs.get(\"predicates\", [])\n predicates = [predicates] if not isinstance(predicates, list) else predicates\n\n provider_name = kwargs.get(\"provider_name\", self.provider_name)\n\n if provider_name:\n predicates.append(f\"lower(provider_name) = lower('{provider_name}')\")\n\n vts = \"'::vehicle_types,'\"\n vehicle_types = kwargs.get(\"vehicle_types\", self.vehicle_types)\n if vehicle_types:\n if not isinstance(vehicle_types, list):\n vehicle_types = [vehicle_types]\n predicates.append(f\"vehicle_type IN ('{vts.join(vehicle_types)}'::vehicle_types)\")\n\n if len(predicates) > 0:\n predicates = \" AND \".join(predicates) + \" AND \"\n else:\n predicates = \"\"\n\n timeranges = [\n f\"({start_time} <= %(start)s AND {end_time} > %(start)s)\",\n f\"({start_time} < %(end)s AND {end_time} >= %(end)s)\",\n f\"({start_time} >= %(start)s AND {end_time} <= %(end)s)\"\n ]\n\n if cutoff > 0:\n where = f\"\"\"\n {predicates}\n ({end_time} IS NULL AND {start_time} between %(start)s - '{cutoff} days'::interval and %(end)s OR\n (date_part('day', {end_time} - {start_time}) <= {cutoff} AND\n ({\" OR \".join(timeranges)})))\n \"\"\"\n else:\n timeranges.append(f\"({start_time} < %(end)s AND {end_time} IS NULL)\")\n where = f\"\"\"\n {predicates}\n ({\" OR \".join(timeranges)})\n \"\"\"\n\n order_by = kwargs.get(\"order_by\", self.order_by)\n if order_by:\n if not isinstance(order_by, list):\n order_by = [order_by]\n order_by = \",\".join(order_by)\n order_by = f\"ORDER BY {order_by}\"\n\n return f\"\"\"\n SELECT\n *\n FROM\n {source}\n WHERE\n {where}\n {order_by};\n \"\"\"","title":""},{"docid":"37809edf146a1f0665c82f677f3754e7","score":"0.51989144","text":"def generate_select_request(self, conditions=None):\n\n if conditions is None:\n return self._SELECT_REQUEST_STRING\n else:\n request = self._SELECT_REQUEST_STRING + \" WHERE {}={}\"\n request = request.format(*conditions.split(\"=\"))\n return request","title":""},{"docid":"d39ef560bb5a4fecbc1c6cd66834ec85","score":"0.5198576","text":"def _getQueryString(self):\n return \"select obj from CommentAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\"","title":""},{"docid":"f80c750afc8c0dee265f318d3f44d2f7","score":"0.51965994","text":"def generate_clause(vars, operator):\n return operator(*vars, evaluate=False)","title":""},{"docid":"84e77ba0a35e57f35fb497a8cc5137a0","score":"0.5192215","text":"def subquery(self, withs, filters=None, optional=False):\n input_with = ', '.join(withs)\n new_withs = withs - {self.collection_alias}\n output_with = ', '.join(new_withs) + ', ' + self.with_statement()\n\n where_string = ''\n if filters is not None:\n relevant = []\n for c in filters:\n if c.involves(self):\n relevant.append(c.for_cypher())\n if relevant:\n where_string = 'WHERE ' + '\\nAND '.join(relevant)\n for_match = self.subquery_match_template.format(anchor_alias=self.anchor_node.alias,\n collection_alias=self.collection_alias)\n kwargs = {'for_match': for_match,\n 'optional': '',\n 'collection_alias': self.collection_alias,\n 'output_with_string': output_with}\n if optional:\n kwargs['optional'] = 'OPTIONAL '\n return self.subquery_template.format(**kwargs)","title":""},{"docid":"0c91288d0f92106eb67c80c495661bb2","score":"0.5183175","text":"def build_query(self):\n # initalize config\n query = super(GlobalTaskTableSource, self).build_query()\n query = query.avoid_duplicates()\n return query","title":""},{"docid":"f4c4035fb5ab40bfe3985d56797e9590","score":"0.51793414","text":"def prepareQuery(queryString, initNs={}, base=None):\n ret = translateQuery(parseQuery(queryString), base, initNs)\n ret._original_args = (queryString, initNs, base)\n return ret","title":""},{"docid":"06ac992c3bdb35350862ce9f31ab0d1b","score":"0.51777846","text":"def buildQuery(self, formInfo, user, query, languages):\r\n return query.options(subqueryload('concepts'))\\\r\n .join(concept_list_concepts)\\\r\n .join(Concept)\\\r\n .join(formInfo.formModel, (formInfo.formModel.language_id == languages.foreign.id) & (formInfo.formModel.concept_id == Concept.id))\\\r\n .outerjoin(Mastery, (Mastery.user_id==user.id) & (getattr(Mastery, formInfo.masteryFieldName) == formInfo.formModel.id))\\\r\n .outerjoin(StalenessPeriod)\\\r\n .order_by(formInfo.listModel.averageRatingFor(user, languages.foreign))","title":""},{"docid":"d1c27b14b20da118bc686538eb54c03e","score":"0.51636916","text":"def concat_query_make(query_ends):\n\n\tquery = result_queries.START\n\tstart = True\n\n\tif query_ends:\n\t\tfor end in query_ends:\n\t\t\tif start:\n\t\t\t\tquery += end\n\t\t\t\t#end is the varying return value to an otherwise identical query\n\n\n\t\t\tif not start:\n\t\t\t\tquery += \" || \\\"DELIM\\\" || \"\n\t\t\t\tquery += end\n\t\n\t\t\tstart = False\n\t\t\t\n\telse:\n\t\tquery = \"(no query selected)\"\n\t\n\treturn query","title":""},{"docid":"0ba8011bc739cf5b088eb97c87937d2b","score":"0.51636404","text":"def evaluate_node_sql(node):\n if 'condition' in node:\n # Node is a condition, get the values of the sub-clauses\n sub_pairs = \\\n [evaluate_node_sql(x) for x in node['rules']]\n\n if not sub_pairs:\n # Nothing has been returned, so it is an empty query\n return '', []\n\n # Now combine\n if node['condition'] == 'AND':\n result = '((' + \\\n ') AND ('.join([x for x, __ in sub_pairs]) + '))'\n else:\n result = '((' + \\\n ') OR ('.join([x for x, __ in sub_pairs]) + '))'\n result_fields = \\\n list(itertools.chain.from_iterable([x for __, x in sub_pairs]))\n\n if node.get('not', False):\n result = '(NOT (' + result + '))'\n\n return result, result_fields\n\n # Get the variable name and duplicate the symbol % in case it is part of\n # the variable name (escape needed for SQL processing)\n varname = fix_pctg_in_name(node['field'])\n\n # Get the operator\n operator = node['operator']\n\n # If the operator is between or not_between, there is a special case,\n # the constant cannot be computed because the node['value'] is a pair\n constant = None\n if node['value'] is not None and not isinstance(node['value'], list):\n # Calculate the constant value depending on the type\n if node['type'] == 'integer':\n constant = int(node['value'])\n elif node['type'] == 'double':\n constant = float(node['value'])\n elif node['type'] == 'boolean':\n constant = node['value'] == '1'\n elif node['type'] == 'string':\n constant = node['value']\n elif node['type'] == 'datetime':\n constant = node['value']\n else:\n raise Exception(\n _('No function to translate type {0}').format(node['type'])\n )\n\n # Terminal Node\n result_fields = []\n if operator == 'equal':\n result = '(\"{0}\"'.format(varname) + \\\n ' = %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [str(constant)]\n\n elif operator == 'not_equal':\n result = '(\"{0}\"'.format(varname) + \\\n '!= %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [str(constant)]\n\n elif operator == 'begins_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [node['value'] + \"%\"]\n\n elif operator == 'not_begins_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [node['value'] + \"%\"]\n\n elif operator == 'contains' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [\"%\" + node['value'] + \"%\"]\n\n elif operator == 'not_contains' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [\"%\" + node['value'] + \"%\"]\n\n elif operator == 'ends_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [\"%\" + node['value']]\n\n elif operator == 'not_ends_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [\"%\" + node['value']]\n\n elif operator == 'is_empty' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n \" = '') OR (\\\"{0}\\\" is null)\".format(varname)\n\n elif operator == 'is_empty' and node['type'] != 'string':\n result = \"(\\\"{0}\\\" is null)\".format(varname)\n\n elif operator == 'is_not_empty' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n \" != '') AND (\\\"{0}\\\" is not null)\".format(varname)\n\n elif operator == 'is_not_empty' and node['type'] != 'string':\n result = \"(\\\"{0}\\\" is not null)\".format(varname)\n\n elif operator == 'less' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' < %s'\n result_fields = [str(constant)]\n\n elif operator == 'less_or_equal' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' <= %s'\n result_fields = [str(constant)]\n\n elif operator == 'greater' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' > %s'\n result_fields = [str(constant)]\n\n elif operator == 'greater_or_equal' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' >= %s'\n result_fields = [str(constant)]\n\n elif operator == 'between' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' BETWEEN %s AND %s'\n result_fields = [str(node['value'][0]), str(node['value'][1])]\n\n elif operator == 'not_between' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' NOT BETWEEN %s AND %s'\n result_fields = [str(node['value'][0]), str(node['value'][1])]\n\n else:\n raise Exception(\n _('Type, operator, field {0}, {1}, {2} not supported yet').format(\n node['type'], operator, varname\n )\n )\n\n if node.get('not', False):\n raise Exception(_('Negation found in unexpected location'))\n\n return result, result_fields","title":""},{"docid":"0dd8285bd0816627e7f7b40210481df8","score":"0.5158893","text":"def query(\n self, strOrQuery, initBindings={},\n initNs={}, base=None, DEBUG=False):\n\n if not isinstance(strOrQuery, Query):\n parsetree = parseQuery(strOrQuery)\n query = translateQuery(parsetree, base, initNs)\n else:\n query = strOrQuery\n\n return evalQuery(self.graph, query, initBindings, base)","title":""},{"docid":"911fc88c4f06f7a15ccb80b7985bca8c","score":"0.51363057","text":"def execute(self, neo4j_session: Session):\n if self.query == \"\":\n raise ValueError(\n \"The query must be generated before this method is called.\"\n )\n try:\n return neo4j_session.write_transaction(lambda tx: tx.run(self.query))\n except:\n print(\"Query not defined error\")\n return None","title":""},{"docid":"5af7e59578232b5286f33142426d48b4","score":"0.51362425","text":"def buildConnectionString(params):\n return \";\".join([\"%s=%s\" % (k, v) for k, v in params.items()])","title":""},{"docid":"250b21f2993d378d79cd2b0b0096170a","score":"0.5131796","text":"def _row_to_query_statement_string(self, row):\n start_node = 'MERGE (start:{state_lab} {{code:\"{start_state}\"}})'.format(\n state_lab=self.labels.state, start_state=row[self.start_state_col]\n )\n\n end_node = 'MERGE (end:{state_lab} {{code:\"{end_state}\"}})'.format(\n state_lab=self.labels.state, end_state=row[self.end_state_col]\n )\n\n transition = \"MERGE (start)<-[:SOURCE]-(trans:{trans_lab})-[:TARGET]->(end)\".format(\n trans_lab=self.labels.transition\n )\n\n def _conditions_str(row, start_state_col, end_state_col):\n \"\"\"Build the string used to express transition conditions.\n \n This is the part in curly braces specifying the Condition node's\n properties.\n \"\"\"\n row = row.drop([start_state_col, end_state_col])\n count = 0\n s = \"\"\n for i, val in row.iteritems():\n s += i + \":\"\n if isinstance(val, six.string_types):\n s += '\"' + val + '\"'\n elif isinstance(val, bool):\n s += str(val).lower()\n else:\n s += str(val)\n if count < len(row) - 1:\n s += \", \"\n count += 1\n\n return \"{\" + s + \"}\"\n\n condition = \"MERGE (cond:{cond_lab} {cond_str})-[:CAUSES]->(trans)\".format(\n cond_lab=self.labels.condition,\n cond_str=_conditions_str(row, self.start_state_col, self.end_state_col),\n )\n\n query_str = (\n start_node + \" \" + end_node + \" \" + transition + \" \" + condition + \";\"\n )\n\n if self.global_params:\n query_str = self._add_global_params_to_query_string(\n query_str, self.global_params\n )\n\n return query_str","title":""},{"docid":"54c7e0913a92b7dfb4e7cc3e5cb349f6","score":"0.5125673","text":"def build_query(url: str, *args, **kwargs) -> str:\n url = url.strip(\"/\")\n args = map(lambda arg: arg.strip(\"/\"), args)\n kwargs = dict(filter(lambda kv: kv[1] is not None, kwargs.items()))\n url = \"/\".join([url, *args]) + \"?\"\n url += \"&\".join([f\"{key}={value}\" for key, value in kwargs.items()])\n return url","title":""},{"docid":"976a7fa212ec09e76082f81572c24b87","score":"0.51216996","text":"def build_query(field, fields, fuzziness=\"AUTO\"):\n val = fields[field]\n if \"*\" in val:\n qtype = \"wildcard\"\n fval = val\n else:\n qtype = \"match\"\n fval = dict(query=val, fuzziness=fuzziness)\n return Q(qtype, **{field: fval})","title":""},{"docid":"27d86e7c41d5fb86b9a53f995bbc51a9","score":"0.5117474","text":"def q(*args: Any, **kwargs: Any) -> \"Query\":\n return Query(*args, **kwargs)","title":""},{"docid":"f0fd368bbe1ee92e7081900909296ef4","score":"0.51087725","text":"def query_on_params(self,\\\n **kwargs):\n if self.check_empty_query(**kwargs):\n return []\n \n elif 'username' in kwargs and len(kwargs) == 1:\n name = kwargs['username'][0]\n return self.query_on_name(\\\n name,\\\n self.all()) \n\n username_s = kwargs['username'][0]\n fullname_s = kwargs['fullname'][0]\n faculty_s = kwargs['faculty'][0]\n program_s = kwargs['program'][0]\n query_set_l = self.all()\n if username_s != '':\n query_set_l = query_set_l.filter(\\\n username__icontains = username_s)\n if fullname_s != '':\n query_set_l = self.query_on_name(\\\n fullname_s,\\\n query_set_l)\n if faculty_s != '':\n query_set_l = query_set_l.filter(\\\n faculty__exact = faculty_s)\n if program_s != '':\n query_set_l = query_set_l.filter(\\\n program__exact = program_s)\n return query_set_l","title":""},{"docid":"a40795caa1ea72b2cdda71b5e0413200","score":"0.51069057","text":"def sql_condition(\n self,\n conditions=tuple(),\n all_inner=True,\n all_outer=True,\n match_inner=True,\n match_outer=True,\n ):\n args = locals()\n # put the conditions into a dict\n conds = {\n pref: args.get(pref)\n for pref in (\n \"all_inner\",\n \"all_outer\",\n \"match_inner\",\n \"match_outer\",\n )\n }\n conds = { # properly cycle all settings\n pref: itertools.cycle(val if hasattr(val, \"__iter__\") else (val,))\n for pref, val in conds.items()\n }\n conds[\"values\"] = (\n (conditions,) if hasattr(conditions, \"items\") else conditions\n )\n sql_conditions, sql_params = [], []\n for cond in listdict_to_dictlist(conds):\n sanitized_values = {\n self.sanitize(k, brutally=True): self.sanitize(\n v, brutally=False\n )\n if isinstance(v, str)\n else v\n for k, v in cond[\"values\"].items()\n if k in self.tableheader\n }\n inner_sql_cond = (\" AND \" if cond[\"all_inner\"] else \" OR \").join(\n map(\n \"{} {} ?\".format,\n sanitized_values.keys(),\n itertools.cycle(\n (\"IS\" if cond[\"match_inner\"] else \"IS NOT\",)\n ),\n )\n ) or \"1=1\"\n sql_conditions.append(\n \"{}({})\".format(\n \"\" if cond[\"match_outer\"] else \"NOT \", inner_sql_cond\n )\n )\n sql_params.extend(tuple(sanitized_values.values()))\n sql_condition = (\n functools.reduce(\n lambda x, y: (\n \" AND \" if next(conds[\"all_outer\"]) else \" OR \"\n ).join((x, y)),\n sql_conditions,\n )\n if sql_conditions\n else \"1=1\"\n )\n return sql_condition, tuple(sql_params)","title":""},{"docid":"c126e83185b3f095e5bc3f7c32cda876","score":"0.51048845","text":"def post(self, statement, parameters=None):\n payload = {\"query\": statement}\n if parameters:\n payload[\"params\"] = {}\n for key, value in parameters.items():\n if isinstance(value, (Node, Rel, Relationship)):\n value = value._id\n payload[\"params\"][key] = value\n log.info(\"execute %r %r\", payload[\"query\"], payload.get(\"params\", {}))\n return self.resource.post(payload)","title":""}],"string":"[\n {\n \"docid\": \"72ebf75d0424beb0d9398dd9dcc5f821\",\n \"score\": \"0.7439297\",\n \"text\": \"def build_query(self, params):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95e410d68282d18b99170eb9c871fae\",\n \"score\": \"0.70802385\",\n \"text\": \"def _build_query(self, params, boolean='AND'):\\n query = []\\n for p in params:\\n if isinstance(p, str):\\n # direct query string\\n query.append(p)\\n\\n elif isinstance(p, tuple) and len(p) == 3:\\n # triplet\\n (field, op, value) = p\\n op_ = '-' if op == '!=' else ''\\n\\n if isinstance(value, list):\\n value_ = '(\\\"' + '\\\",\\\"'.join(value) + '\\\")'\\n elif isinstance(value, tuple):\\n value_ = '[%s TO %s]' % value\\n else:\\n quote = '\\\"' if not '*' in str(value) else ''\\n value_ = quote + str(value) + quote\\n\\n query.append('%s%s:%s' % (op_, field, value_))\\n\\n elif isinstance(p, tuple) and len(p) == 2:\\n # group/subquery with boolean operator\\n (op, subquery) = p\\n query.append('(' + self._build_query(subquery, op) + ')')\\n\\n return (' '+boolean+' ').join(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c96f74b26acea483825aadcbb969353\",\n \"score\": \"0.6558855\",\n \"text\": \"def build_query(self, params):\\n phrase = params[\\\"phrase\\\"]\\n slop = params[\\\"slop\\\"]\\n\\n query = {}\\n query[\\\"query\\\"] = {\\\"match\\\":{}}\\n\\n infl_form = {}\\n infl_form[\\\"type\\\"] = \\\"phrase\\\"\\n infl_form[\\\"query\\\"] = phrase\\n infl_form[\\\"slop\\\"] = slop\\n query[\\\"query\\\"][\\\"match\\\"][\\\"inflected_form\\\"] = infl_form\\n\\n query = json.dumps(query)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8397a2a8e9edc0608094386c96404c\",\n \"score\": \"0.653392\",\n \"text\": \"def _build_query_string(cls, parameters: dict) -> str:\\n\\t\\tquery_string = '?'\\n\\n\\t\\t# Builds a list of tuples for easy iteration like:\\n\\t\\t# [('channel', 'channel-1'), ('channel', 'channel-2'), ('assetId', 'asset-1'), ...]\\n\\t\\tparameter_tuples = [(parameter_name, parameter_value) \\n\\t\\t\\tfor parameter_name, parameter_values in parameters.items()\\n\\t\\t\\t\\tfor parameter_value in parameter_values]\\n\\n\\t\\tfor index, parameter_tuple in enumerate(parameter_tuples):\\n\\t\\t\\tname, value = parameter_tuple\\n\\t\\t\\tvalue = quote(value.encode()) if type(value) == str else value\\n\\n\\t\\t\\tif name == 'orderBy':\\n\\t\\t\\t\\tvalue = cls._convert_order_by(value)\\n\\n\\t\\t\\tquery_string += f'{name}={value}' if index == 0 else f'&{name}={value}'\\n\\n\\t\\treturn query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a18169ab89094b2a6bab17605534b4f\",\n \"score\": \"0.6477496\",\n \"text\": \"def _build_query(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76313a0e15cad414416780ca9e335fb0\",\n \"score\": \"0.6379244\",\n \"text\": \"def _build_search(query, params):\\n #PLACEHOLDER -- only searches the objects table and does the simplest\\n #search possible for now\\n query_terms = params.copy() if( params and params != {} ) else _translate_query(query)\\n arguments = _args()\\n keywords = \\\"\\\"\\n query_string = \\\"\\\"\\n\\n if 'query_string' in query_terms:\\n query_string = query_terms['query_string']\\n arguments.update({'query_string':query_string})\\n del query_terms['query_string']\\n if 'keywords' in query_terms:\\n arguments.update({'keywords':query_terms['keywords']})\\n del query_terms['keywords']\\n # TODO: need to do all the query string processing here \\n # but, for now just a simple search assumption will work by default\\n\\n if query_string != \\\"\\\":\\n keywords += query_string\\n \\n return \\\"ca_objects?q=\\\"+keywords, arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c157292ed4cc40e351d63d9d0af82576\",\n \"score\": \"0.6359702\",\n \"text\": \"def generate_query(self):\\n return ''.join(['SELECT ',\\n ', '.join(self.columns),\\n ' ',\\n 'FROM molecule ',\\n ' '.join(self.joins),\\n ' ',\\n 'WHERE ',\\n '(', ') AND ('.join(self.wheres), ')'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba63cfec2c1ae532de6e05ad75c5ffe\",\n \"score\": \"0.6332386\",\n \"text\": \"def _set_sql_query(self, query_params):\\n query = f'SELECT * FROM {CATS_TABLE}'\\n # attributes in query string\\n if query_params.get('attribute'):\\n query += ' ORDER BY '\\n # may be 1>= attrs\\n for attr in query_params['attribute']:\\n query += f'{attr}, '\\n query = query.rstrip(', ')\\n\\n # order in query string\\n if query_params.get('order'):\\n order = query_params['order'][0].upper()\\n query += f' {order}'\\n\\n # offset in query string\\n if query_params.get('offset'):\\n offset = query_params['offset'][0]\\n query += f' OFFSET {offset}'\\n\\n # limit in query string\\n if query_params.get('limit'):\\n limit = query_params['limit'][0]\\n query += f' LIMIT {limit}'\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aafa03146dc86896e8b4203a577f391\",\n \"score\": \"0.6295957\",\n \"text\": \"def make_query(self, **kw):\\n query = kw.pop(\\\"query\\\", {})\\n\\n query.update(self.get_request_query())\\n query.update(self.get_custom_query())\\n query.update(self.get_keyword_query(**kw))\\n\\n sort_on, sort_order = self.get_sort_spec()\\n if sort_on and \\\"sort_on\\\" not in query:\\n query.update({\\\"sort_on\\\": sort_on})\\n if sort_order and \\\"sort_order\\\" not in query:\\n query.update({\\\"sort_order\\\": sort_order})\\n\\n logger.info(\\\"make_query:: query={} | catalog={}\\\".format(\\n query, self.catalog))\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1216cd9552ca67c038d89c1a33795cb6\",\n \"score\": \"0.6221052\",\n \"text\": \"def build_query(self):\\n\\n # initalize config\\n self.config.update_config()\\n\\n # get the base query from the config\\n query = self.config.get_base_query()\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11b2046780debe8f975dcb0277e234c\",\n \"score\": \"0.62166256\",\n \"text\": \"def buildQuery(self):\\n if not self.Valid:\\n print(\\\"Invalid usage: cannot build.\\\")\\n return False\\n if not self.needsBuilt:\\n return self.queryString\\n\\n filterDict = dict()\\n theFilters = dict()\\n if len(self._andFilters) > 0:\\n theFilters[\\\"and\\\"] = self._andFilters\\n if len(self._orFilters) > 0:\\n theFilters[\\\"or\\\"] = self._orFilters\\n filterDict[\\\"filter\\\"] = theFilters\\n queryString = json.dumps(filterDict)\\n\\n self.queryString = queryString\\n return queryString\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb15855e6fd47d6e9d3a76d89a8b5ace\",\n \"score\": \"0.6071394\",\n \"text\": \"def _build_query(symbol: str, start_date: str, end_date: str, order_by: str):\\n query = \\\"select * from stock_price where symbol = '{symbol}'\\\".format(symbol=symbol)\\n if start_date:\\n query += \\\" and time >= '{}'\\\".format(start_date)\\n if end_date:\\n query += \\\" and time <= '{}'\\\".format(end_date)\\n\\n if order_by == 'ASC':\\n # Default\\n pass\\n elif order_by == 'DESC':\\n query += ' order by time desc'\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46b3852c7f93eb9f1037b46961e8edb\",\n \"score\": \"0.60509795\",\n \"text\": \"def query_generator(self, kwargs):\\n\\n query_addition = \\\"\\\"\\n\\n if \\\"quarter\\\" in kwargs:\\n query_addition += f\\\" & qtr == {kwargs['quarter']}\\\"\\n if \\\"down\\\" in kwargs:\\n query_addition += f\\\" & down == {kwargs['down']}\\\"\\n if \\\"half\\\" in kwargs:\\n query_addition += f\\\" & game_half == 'Half{kwargs['half']}'\\\"\\n if \\\"close\\\" in kwargs:\\n if kwargs['close'] is True:\\n query_addition += f\\\" & score_differential >= -12 & score_differential <= 12\\\"\\n else:\\n query_addition += f\\\" & (score_differential < -12 | score_differential > 12)\\\"\\n if \\\"rz\\\" in kwargs:\\n if kwargs['rz'] is True:\\n query_addition += f\\\" & yardline_100 <= 25\\\"\\n else:\\n query_addition += f\\\" & yardline_100 > 25\\\"\\n if \\\"where\\\" in kwargs:\\n if kwargs['where'] is 'home':\\n query_addition += f\\\" & home_team == '{self.team}'\\\"\\n else:\\n query_addition += f\\\" & away_team == '{self.team}'\\\"\\n if \\\"play\\\" in kwargs:\\n if kwargs['play'] is 'run':\\n query_addition += f\\\" & rush_attempt == 1\\\"\\n else:\\n query_addition += f\\\" & pass_attempt == 1\\\"\\n if \\\"since\\\" in kwargs:\\n if kwargs['since'] is 'sep':\\n query_addition += f\\\" & (game_date.str.slice(5, 7) == '09' | game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\\\"\\n elif kwargs['since'] is 'oct':\\n query_addition += f\\\" & (game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\\\"\\n elif kwargs['since'] is 'nov':\\n query_addition += f\\\" & (game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\\\"\\n else:\\n query_addition += f\\\" & game_date.str.slice(5, 7) == '12'\\\"\\n\\n return query_addition\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c570052933c609ad72ebabe6e69a8bfd\",\n \"score\": \"0.598974\",\n \"text\": \"def data_query(self, parents=None):\\n filter_q = self.filter_query(parents=parents)\\n q = db.session.query()\\n stmt, q = self._add_statement(q)\\n\\n filter_sq = filter_q.subquery()\\n q = q.filter(stmt.subject == filter_sq.c.subject)\\n q = q.filter(stmt._attribute.in_(self.project()))\\n\\n q = q.add_column(stmt.subject.label('id'))\\n q = q.add_column(stmt._attribute.label('attribute'))\\n q = q.add_column(stmt._value.label('value'))\\n\\n if parents is not None and self.node.attribute:\\n parent_stmt, q = self._add_statement(q)\\n q = q.filter(stmt.subject == parent_stmt._value)\\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\\n q = q.add_column(parent_stmt.subject.label('parent_id'))\\n\\n q = q.order_by(filter_sq.c.subject.desc())\\n q = q.order_by(stmt.created_at.asc())\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd1bf23e219921f97adc765b4e477518\",\n \"score\": \"0.5976437\",\n \"text\": \"def make_query(columns, filter):\\n if columns is not None:\\n # the user only wants to report a subset of the columns\\n query = \\\"SELECT \\\" + columns + \\\" FROM variants\\\"\\n# elif cancers != 'none':\\n# query = \\\"SELECT \\\" + columns + \\\",civic_gene_abbreviations,cgi_gene_abbreviations FROM variants\\\"\\n else:\\n # report the kitchen sink\\n query = \\\"SELECT * FROM variants\\\"\\n if filter is not None:\\n # add any non-genotype column limits to the where clause\\n query += \\\" WHERE \\\" + filter\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3662e9f0259cba0673eaafff569ccaaa\",\n \"score\": \"0.59676003\",\n \"text\": \"def make_query(request, **kw):\\n\\n # build the catalog query\\n query = {\\n \\\"sort_limit\\\": get_sort_limit(request),\\n \\\"sort_on\\\": get_sort_on(request),\\n \\\"sort_order\\\": get_sort_order(request),\\n \\\"SearchableText\\\": get_query(request),\\n }\\n\\n # inject keyword args\\n query.update(kw)\\n\\n # inject the creator if given\\n if get_creator(request):\\n query[\\\"Creator\\\"] = get_creator(request)\\n\\n logger.info(\\\"Catalog Query --> %r\\\", query)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0196bcfee0829e1a676506cb8f483df\",\n \"score\": \"0.59151345\",\n \"text\": \"def query_from_params(self, params):\\n q = {}\\n start, rows = 0, 10\\n if \\\"start\\\" in params:\\n start = int(params[\\\"start\\\"])\\n if \\\"rows\\\" in params:\\n rows = int(params[\\\"rows\\\"])\\n\\n if \\\"lat\\\" in params and \\\"lon\\\" in params:\\n lat = float(params[\\\"lat\\\"])\\n lon = float(params[\\\"lon\\\"])\\n q[\\\"loc\\\"] = {\\n \\\"$near\\\": [lat, lon]\\n }\\n for atr in [\\\"city\\\", \\\"state\\\", \\\"country\\\"]:\\n if atr in params:\\n q[atr] = params[atr]\\n return self.query(start=start, rows=rows, **q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324d683b973caa2910387cddae4780e5\",\n \"score\": \"0.5909418\",\n \"text\": \"def _create_query_params(self, kwargs):\\n if None == kwargs or 0 == len(kwargs):\\n return (\\\"\\\", [])\\n\\n if not isinstance(kwargs, basestring):\\n pairs = kwargs.items()\\n stmt = \\\" WHERE \\\" + \\\\\\n \\\" AND \\\".join([ x[0] + (None is x[1] and \\\" IS NULL\\\" or \\\" = ?\\\")\\n for x in pairs ])\\n\\n args = [x[1] for x in filter(lambda x: None is not x[1], pairs)]\\n else:\\n stmt = \\\" WHERE \\\" + kwargs\\n args = []\\n return (stmt, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49409b5c49174457e063fc4a1f2caf74\",\n \"score\": \"0.5909326\",\n \"text\": \"def build_query(cols=None, tables=None, group_by=None, order_by=None, **kwargs):\\n assert len(tables)\\n\\n # Create SELECT line\\n cols_str = \\\", \\\".join(cols) if cols else \\\"*\\\"\\n query = f'SELECT {cols_str} FROM '\\n\\n # Create FROM line\\n # Automatically add bracelets around strings with space inside them. Useful for nested queries.\\n query += ' NATURAL JOIN '.join(f'({table})' if ' ' in table else table for table in tables)\\n\\n # Apply all filters in the kwargs dict\\n constraints = []\\n for col_name, value in kwargs.items():\\n # Empty filters don't need to be applied\\n if value not in (None, ''):\\n # Use \\\"LIKE\\\" for strings, and \\\"==\\\" otherwise\\n if isinstance(value, str):\\n constraints.append(f'{col_name} LIKE \\\"{value}\\\"')\\n else:\\n constraints.append(f'{col_name} == {value}')\\n\\n # Create WHERE line if there are constraints\\n if constraints:\\n query += ' WHERE ' + ' AND '.join(constraints)\\n\\n # Create GROUP BY line if needed\\n if group_by:\\n query += ' GROUP BY ' + group_by\\n\\n # Create ORDER BY line if needed\\n if order_by:\\n query += ' ORDER BY ' + order_by\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb4cc87b5249fb99610ec14d3f62cf8\",\n \"score\": \"0.58879614\",\n \"text\": \"def GenerateQuery(unused_ref, args, request):\\n customer_id = ConvertOrgIdToObfuscatedCustomerId(args.organization)\\n labels = FilterLabels(args.labels)\\n labels_str = ','.join(labels)\\n request.query = 'parent==\\\\\\\"customerId/{0}\\\\\\\" && \\\\\\\"{1}\\\\\\\" in labels'.format(\\n customer_id, labels_str)\\n\\n return request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8ba2a0ee0c22e8f3b0dc0581901893\",\n \"score\": \"0.5856376\",\n \"text\": \"def make_query(self, ns):\\n if issubclass(self.model_class, db.Model):\\n query = db.Query(self.model_class, namespace=ns)\\n for f in self.filters:\\n query.filter(\\\"%s %s\\\" % (f[0], f[1]), f[2])\\n else:\\n query = self.model_class.query(namespace=ns)\\n for f in self.filters:\\n query = query.filter(ndb.FilterNode(*f))\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5aeac92141db6643f5dae341690653e\",\n \"score\": \"0.5830022\",\n \"text\": \"def build_query(\\n self,\\n regions=None,\\n genes=None,\\n effect_types=None,\\n family_ids=None,\\n person_ids=None,\\n inheritance=None,\\n roles=None,\\n sexes=None,\\n variant_type=None,\\n real_attr_filter=None,\\n ultra_rare=None,\\n frequency_filter=None,\\n return_reference=None,\\n return_unknown=None,\\n limit=None,\\n pedigree_fields=None):\\n # pylint: disable=too-many-arguments\\n self.query_builder.reset_product()\\n\\n self.query_builder.build_select()\\n\\n self.query_builder.build_from()\\n\\n self.query_builder.build_join()\\n\\n self.query_builder.build_where(\\n regions=regions,\\n genes=genes,\\n effect_types=effect_types,\\n family_ids=family_ids,\\n person_ids=person_ids,\\n inheritance=inheritance,\\n roles=roles,\\n sexes=sexes,\\n variant_type=variant_type,\\n real_attr_filter=real_attr_filter,\\n ultra_rare=ultra_rare,\\n frequency_filter=frequency_filter,\\n return_reference=return_reference,\\n return_unknown=return_unknown,\\n pedigree_fields=pedigree_fields,\\n )\\n\\n self.query_builder.build_group_by()\\n self.query_builder.build_limit(limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf3e320668377af9f499baa5b818919\",\n \"score\": \"0.5816653\",\n \"text\": \"def make_query(config):\\n\\n query = {}\\n\\n items = ['object', 'radius', 'max_out', 'source', 'output', 'mime', 'sort']\\n\\n for item in items\\n query[item] = str(config[item])\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2159f3098a64c4db73cdb716ccac6fd2\",\n \"score\": \"0.57821774\",\n \"text\": \"def make_where_statement(self, table_name, params):\\n if not params:\\n return (\\\" 1 \\\", ())\\n column_names = [row[1] for row in self.get_rows(\\\"PRAGMA table_info(%s)\\\" % table_name)]\\n for column in params.keys():\\n if column not in column_names:\\n raise KeyError(\\\"Error: Database column %s not in our database\\\" % column)\\n # Construct our SQL statement\\n where_statement = ' AND '.join(['\\\"%s\\\" = ?' % column for (column, value) in sorted(params.items())])\\n where_values = tuple([value for (column, value) in sorted(params.items())])\\n return (where_statement, where_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd14224ced22e5c08b3414302d16b36f\",\n \"score\": \"0.5775194\",\n \"text\": \"def build_query(self) -> Query:\\n logger.info(\\n {\\n \\\"message\\\": f\\\"Start building query for resource {self.analysis.definition_id}\\\",\\n \\\"resource_id\\\": self.analysis.resource_id,\\n },\\n )\\n\\n # We don't need to have columns duplicated in the dataframe\\n # so we keep the one we've already seen here.\\n # Note that the primary key column is added at the query initialization.\\n self._cur_query_columns = {self.analysis.primary_key_column}\\n # To avoid duplicated joins, we keep them here\\n self._cur_query_join_tables = {}\\n\\n query = self.session.query(self.get_column(self.analysis.primary_key_column, self._sqlalchemy_pk_table))\\n\\n # Add attributes to query\\n for attribute in self.analysis.attributes:\\n query = self.add_attribute_to_query(query, attribute)\\n\\n # Add filters to query\\n query = self.apply_filters(query)\\n\\n logger.info(\\n {\\n \\\"message\\\": f\\\"Built query for resource {self.analysis.definition_id}: {query.statement}\\\",\\n \\\"resource_id\\\": self.analysis.resource_id,\\n },\\n )\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb662581d7042718da96ca7baef1b0e\",\n \"score\": \"0.5773945\",\n \"text\": \"def query(self):\\n if 'order' in self.config['database']:\\n order = 'ORDER BY {order}'.format(**self.config['database'])\\n else:\\n order = ''\\n\\n if isinstance(self.cols, tuple):\\n cols = self.cols\\n else:\\n cols = (self.cols,)\\n\\n where, params = parse_queries(self.selection, self.mapping)\\n if where:\\n where = 'WHERE {conditions}'.format(conditions=' AND '.join(where))\\n else:\\n where = ''\\n limit = (self.slice[0].stop or sys.maxint) - (self.slice[0].start or 0)\\n\\n sql = \\\"\\\"\\\"\\n SELECT {cols} FROM {table}\\n {where}\\n {order}\\n LIMIT {limit}\\n OFFSET {offset}\\n \\\"\\\"\\\".format(\\n cols=', '.join(self.config[key]['col'] for key in cols),\\n table=self.config['database']['table'],\\n where=where, order=order, limit=limit,\\n offset=self.slice[0].start or 0)\\n\\n return sql, params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f87622ad6a714500a7b6ccb0df8944\",\n \"score\": \"0.5773749\",\n \"text\": \"def sql_with_params(self):\\r\\n return self.get_compiler(DEFAULT_DB_ALIAS).as_sql()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a12abdcbed66b2452ab47d4061b8ce7d\",\n \"score\": \"0.5761608\",\n \"text\": \"def build_query(self, query_dict):\\n\\n # Loop over keys in request dictionary, match them up to database fields\\n # Build query accounting for the exceptions\\n qry_sci_name = ''\\n qry_common_name = ''\\n qry_group = ''\\n qry_native = ''\\n qry_pollut_tol = ''\\n qry_rarity = ''\\n qry_range = ''\\n qry_max_age = ''\\n qry_mean_weight = ''\\n qry_mean_length = ''\\n qry_max_length = ''\\n qry_habitat = ''\\n qry_ubiquity = ''\\n qry_extent = ''\\n qry_tolerance = ''\\n qry_robustness = ''\\n\\n #Used in a couple of places to remove a trailing 'or '\\n trailing_or = ' or'\\n trailing_and = ' and'\\n\\n #This will be used later for habitat related fields that contain 1 for true. E.g. caves=1\\n set_keys = set(query_dict.keys())\\n\\n for req_key, req_val in query_dict.items():\\n # Is request param a valid query param\\n if req_key in self.attrib:\\n\\n if (req_key.lower() == 'scientific_name'):\\n words = req_val.split('_')\\n if len(words) == 1:\\n qry_sci_name = (\\\"( genus LIKE INITCAP('%%{0}%%') or species LIKE LOWER('%%{0}%%'))\\\")\\n qry_sci_name = str.format(qry_sci_name, words[0])\\n elif len(words) == 2:\\n qry_sci_name = (\\\" ((genus LIKE INITCAP('%%{0}%%') and species LIKE LOWER('%%{1}%%')) or (species LIKE LOWER('%%{0}%%') and genus LIKE INITCAP('%%{1}%%')))\\\")\\n qry_sci_name = str.format(qry_sci_name, words[0], words[1])\\n\\n if req_key.lower() == 'common_name':\\n words = req_val.split('_')\\n if len(words) == 1:\\n qry_common_name = (\\\" commonname LIKE INITCAP('%%{0}%%')\\\")\\n qry_common_name = str.format(qry_common_name, words[0])\\n elif len(words) == 2:\\n qry_common_name = (\\\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%'))\\\")\\n qry_common_name = str.format(qry_common_name, words[0], words[1])\\n elif len(words) == 3:\\n qry_common_name = (\\\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%') and commonname LIKE INITCAP('%%{2}%%'))\\\")\\n qry_common_name = str.format(qry_common_name, words[0], words[1], words[2])\\n\\n\\n # Can be multiple groups\\n #e.g. grp='Black Bass' or grp='Mullet'\\n if (req_key.lower() == 'group'):\\n words = req_val.lower()\\n words = words.replace('_', ' ')\\n words = words.split(',')\\n for idx, grp in enumerate(words):\\n if idx != 0:\\n qry_group += \\\" or \\\"\\n qry_group += str.format(\\\" lower(grp) = '{0}'\\\", grp.lower())\\n continue\\n\\n if (req_key.lower() == 'native'):\\n qry_native = str.format(\\\" native='{0}'\\\",req_val)\\n continue\\n\\n # pollution tolerance can be: \\\"I\\\", \\\"T\\\", \\\"M\\\", or \\\"U\\\"\\n # if (req_key.lower() == 'pollut_tol'):\\n # if (req_key.lower() == 'tolerance'):\\n # words = req_val.lower()\\n # words = words.split(',')\\n # for idx, pollut in enumerate(words):\\n # if idx != 0:\\n # qry_pollut_tol += \\\" or \\\"\\n # qry_pollut_tol += str.format(\\\" lower(pollut_tol) = '{0}'\\\", pollut)\\n # continue\\n\\n if (req_key.lower() == 'rarity'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_rarity = str.format(\\\" rarity>={0} and rarity<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'max_age'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_max_age = str.format(\\\" max_age>={0} and max_age<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'mean_weight'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_mean_weight = str.format(\\\" mean_weight>={0} and mean_weight<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'mean_length'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_mean_length = str.format(\\\" mean_length>={0} and mean_length<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'max_length'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_max_length = str.format(\\\" max_length>={0} and max_length<={1}\\\", range[0], range[1])\\n continue\\n\\n ubiquity_range = [0, 75]\\n if (req_key.lower() == 'ubiquity'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != ubiquity_range[0] or int(range[1]) != ubiquity_range[1]):\\n qry_ubiquity = str.format(\\\" ubiquity>={0} and ubiquity<={1}\\\", range[0], range[1])\\n continue\\n\\n extent_range = [0, 77]\\n if (req_key.lower() == 'extent'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != extent_range[0] or int(range[1]) != extent_range[1]):\\n qry_extent = str.format(\\\" extent>={0} and extent<={1}\\\", range[0], range[1])\\n continue\\n\\n tolerance_range = [0, 94]\\n if (req_key.lower() == 'tolerance'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != tolerance_range[0] or int(range[1]) != tolerance_range[1]):\\n qry_tolerance = str.format(\\\" tolerance>={0} and tolerance<={1}\\\", range[0], range[1])\\n continue\\n\\n robustness_range = [0, 47]\\n if (req_key.lower() == 'robustness'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != robustness_range[0] or int(range[1]) != robustness_range[1]):\\n qry_robustness = str.format(\\\" robustness>={0} and robustness<={1}\\\", range[0], range[1])\\n continue\\n\\n if qry_habitat.endswith(trailing_and):\\n qry_habitat = qry_habitat[:-len(trailing_and)]\\n\\n # The rest of the query parameters are are used if value == 1\\n # e.g. caves=1, springs=1, headwaters=1\\n for cat in self.categories:\\n intersect = set_keys & self.categories[cat]\\n if len(intersect) > 0:\\n print(cat)\\n print(intersect)\\n for param in intersect:\\n val = query_dict[param]\\n if (val == '1'):\\n self.query_categories[cat].append(param)\\n\\n qry_habitat = ''\\n query_cat = ''\\n for category in self.query_categories:\\n query_cat = ''\\n for val in self.query_categories[category]:\\n query_cat += str.format(\\\" {0}='1' or\\\", val)\\n\\n if query_cat.endswith(trailing_or):\\n query_cat = query_cat[:-len(trailing_or)]\\n\\n if len(query_cat) > 0:\\n qry_habitat += ' (' + query_cat + ') and'\\n print(qry_habitat)\\n\\n if qry_habitat.endswith(trailing_and):\\n qry_habitat = qry_habitat[:-len(trailing_and)]\\n\\n query = \\\"select * from fishproperties where\\\"\\n first_condition = True\\n\\n if qry_sci_name != '':\\n query += qry_sci_name\\n first_condition = False\\n\\n if qry_common_name != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_common_name\\n first_condition = False\\n\\n if qry_group != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_group\\n first_condition = False\\n\\n if qry_native != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_native\\n first_condition = False\\n\\n if qry_pollut_tol != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_pollut_tol\\n first_condition = False\\n\\n if qry_rarity != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_rarity\\n first_condition = False\\n\\n if qry_range != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_range\\n first_condition = False\\n\\n if qry_max_age != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_max_age\\n first_condition = False\\n\\n if qry_mean_weight != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_mean_weight\\n first_condition = False\\n\\n if qry_mean_length != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_mean_length\\n first_condition = False\\n\\n if qry_max_length != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_max_length\\n first_condition = False\\n\\n if qry_ubiquity != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_ubiquity\\n first_condition = False\\n\\n if qry_extent != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_extent\\n first_condition = False\\n\\n if qry_tolerance != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_tolerance\\n first_condition = False\\n\\n if qry_robustness != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_robustness\\n first_condition = False\\n\\n if qry_habitat != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_habitat\\n first_condition = False\\n\\n print(query)\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45b3d40d8a26bb7193088e942d5a655\",\n \"score\": \"0.5736782\",\n \"text\": \"def _construct_query(self, query_chain):\\n # here we take the query_chain and convert to a real sql sentence\\n res_dict = query_chain.pop(0)\\n\\n for q in query_chain:\\n if q[\\\"action\\\"] == \\\"_db__where\\\":\\n if res_dict[\\\"action\\\"] == \\\"_db__select_all\\\":\\n res_dict.update({\\\"action\\\": \\\"_db__select\\\"})\\n\\n condition = res_dict.get(\\\"condition\\\", \\\"\\\")\\n if condition:\\n condition = \\\" AND \\\".join([condition, q[\\\"condition\\\"]])\\n else:\\n condition = q[\\\"condition\\\"]\\n\\n res_dict.update({\\\"condition\\\": condition})\\n elif q[\\\"action\\\"] == \\\"_db__select_related\\\":\\n for model_join in q[\\\"fields\\\"]:\\n join_const = getattr(self, q[\\\"action\\\"]).format(**model_join)\\n res_dict[\\\"join\\\"] += join_const\\n\\n select = res_dict[\\\"select\\\"][:]\\n\\n if select == \\\"COUNT(*)\\\":\\n pass\\n elif select == \\\"*\\\":\\n select = select.replace(\\n \\\"*\\\",\\n \\\"{left_table}.*, {f_formatter}\\\".format(\\n left_table=model_join[\\\"left_table\\\"], f_formatter=model_join[\\\"fields_formatter\\\"]\\n ),\\n )\\n res_dict[\\\"select\\\"] = select\\n else:\\n res_dict[\\\"select\\\"] += \\\", \\\" + model_join[\\\"fields_formatter\\\"]\\n\\n # if we are not counting, then we can assign ordering\\n operations = [\\\"COUNT\\\", \\\"MAX\\\", \\\"MIN\\\", \\\"SUM\\\", \\\"AVG\\\", \\\"STDDEV\\\"]\\n\\n # we must get error, if we set only one field and update instance\\n if res_dict[\\\"action\\\"] == \\\"_db__update\\\" and len(res_dict[\\\"field_values\\\"]) == 1:\\n res_dict.update({\\\"action\\\": \\\"_db__update_single_field\\\"})\\n\\n if res_dict.get(\\\"select\\\", \\\"\\\").split(\\\"(\\\")[0] not in operations:\\n res_dict[\\\"ordering\\\"] = self._ordering_syntax(res_dict.get(\\\"ordering\\\", []))\\n else:\\n res_dict[\\\"ordering\\\"] = \\\"\\\"\\n query = getattr(self, res_dict[\\\"action\\\"]).format(**res_dict)\\n query = self._query_clean(query)\\n\\n logger.debug(\\\"QUERY: {}, VALUES: {}\\\".format(query, res_dict.get(\\\"field_values\\\")))\\n return query, res_dict.get(\\\"field_values\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c13ae92a74d4f3943d1cd2abdd2d58f\",\n \"score\": \"0.572707\",\n \"text\": \"def _build_query(env, start, end, certname=None):\\n query = ExtractOperator()\\n query.add_field(FunctionOperator('count'))\\n query.add_field('status')\\n subquery = AndOperator()\\n subquery.add(GreaterEqualOperator('producer_timestamp', start))\\n subquery.add(LessOperator('producer_timestamp', end))\\n if certname is not None:\\n subquery.add(EqualsOperator('certname', certname))\\n if env != '*':\\n subquery.add(EqualsOperator('environment', env))\\n query.add_query(subquery)\\n query.add_group_by(\\\"status\\\")\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1dd0e6509804a1d36f57e0e6c5750a\",\n \"score\": \"0.5720286\",\n \"text\": \"def query() -> str:\\n return \\\"\\\"\\\"\\n MATCH (doc:Publication)<-[:located_in]-(subject)-[association:chemical_to_disease_or_phenotypic_feature_association]->(obj)-[:located_in]->(sec_doc:Publication)\\n WHERE association.relation <> 'null' and obj.name <> 'null' and subject.name <> 'null' and doc.pmid in {pmids} and association.relation <> 'null' and type(association) <> 'could_refer_to' and type(association) <> 'located_in' and sec_doc.pmid = doc.pmid\\n RETURN DISTINCT subject.name as subject, subject.synonym as subj_syn, association.relation as predicate, obj.name as obj, obj.synonym as disease_synonym, type(association) as rel_type, doc.pmid as pmid, doc.abstract as abstract\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6002db82a2bae451e4ed335385a5f2a2\",\n \"score\": \"0.57197946\",\n \"text\": \"def build_where_clause(args: dict, table_args_dict: dict) -> str:\\n where_clause: str = ''\\n for key in args.keys():\\n if key in table_args_dict.keys():\\n if key == 'query':\\n # if query arg is supplied than we just need to parse it and only it\\n return args[key].strip()\\n else:\\n values_list: list = argToList(args[key])\\n for raw_value in values_list:\\n for field in table_args_dict[key]:\\n value = raw_value\\n if key == 'url':\\n value = f'*{raw_value}*'\\n if not where_clause:\\n # the beginning of the where part should start without OR\\n where_clause += f\\\"{field}'{value}'\\\"\\n else:\\n where_clause += f\\\" OR {field}'{value}'\\\"\\n return where_clause\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd48cb6a7ac2b019d07866613d70b519\",\n \"score\": \"0.5693227\",\n \"text\": \"def generate_where_clause(state=None, product=None,\\n issue=None, min_complaints=None):\\n where_inner = []\\n where_outer = []\\n\\n if state and not state == \\\"All\\\":\\n where_inner.append(\\\"ccdb.state = '{}'\\\".format(state))\\n\\n if product and not product == \\\"All\\\":\\n where_inner.append(\\\"ccdb.product = '{}'\\\".format(product))\\n\\n if issue and not issue == \\\"All\\\":\\n where_inner.append(\\\"ccdb.issue = '{}'\\\".format(issue))\\n\\n if min_complaints:\\n where_outer.append(\\\"complaint_count > {}\\\".format(min_complaints))\\n\\n if len(where_inner) > 0:\\n where_inner = \\\"WHERE \\\" + \\\" AND \\\".join(where_inner)\\n else:\\n where_inner = \\\"\\\"\\n\\n if len(where_outer) > 0:\\n where_outer = \\\"WHERE \\\" + \\\" AND \\\".join(where_outer)\\n else:\\n where_outer = \\\"\\\"\\n\\n return where_inner, where_outer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e6e63e44f78e69cf88a741ac17d2fb\",\n \"score\": \"0.5682573\",\n \"text\": \"def build_query(self, params):\\n term = params[\\\"term\\\"]\\n size = params[\\\"size\\\"]\\n page = params[\\\"page\\\"]\\n\\n query = {}\\n query[\\\"query\\\"] = {\\\"match\\\":{}}\\n query[\\\"query\\\"][\\\"match\\\"][\\\"inflected_form\\\"] = term\\n\\n query[\\\"sort\\\"] = []\\n query[\\\"sort\\\"].append({\\\"_score\\\": \\\"desc\\\"})\\n query[\\\"sort\\\"].append({\\\"word_count\\\": \\\"asc\\\"})\\n\\n query[\\\"from\\\"] = (page - 1) * size\\n query[\\\"size\\\"] = size\\n\\n query = json.dumps(query)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930b18d9b4e9205bd3e078001ad05e29\",\n \"score\": \"0.5674045\",\n \"text\": \"def generate(self):\\n result = self.query\\n for param in self.params:\\n result = result.replace(param.raw, param.sql_value())\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa19aae92a018fc0f26673cb0f78bfb\",\n \"score\": \"0.56575936\",\n \"text\": \"def filter_query(self, parents=None):\\n q = db.session.query()\\n stmt, q = self._add_statement(q)\\n q = q.add_column(stmt.subject)\\n\\n if parents is not None and self.node.attribute:\\n parent_stmt, q = self._add_statement(q)\\n q = q.filter(stmt.subject == parent_stmt._value)\\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\\n q = q.filter(parent_stmt.subject.in_(parents))\\n\\n q = self.filter(q, stmt)\\n q = q.group_by(stmt.subject)\\n q = q.order_by(stmt.subject.asc())\\n\\n if self.node.root:\\n q = q.limit(self.node.limit)\\n q = q.offset(self.node.offset)\\n\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd52219e9c4b5adb4afd6a139da0d56\",\n \"score\": \"0.56358266\",\n \"text\": \"def derived(self, statement, params=(), replace=False):\\n return Query(\\n db=self.db,\\n sql=(statement,) if replace else self.sql + (statement,),\\n params=self.params + params,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd8fe36d01097c20060583de2e8bc83\",\n \"score\": \"0.562555\",\n \"text\": \"def make_query(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe5061d0a503021cd5b78f56d96ffed\",\n \"score\": \"0.5612804\",\n \"text\": \"def build_query(media_type=None, tags=None, sort=None, max_duration=None, published_after=None, categories=None,\\n region=None, random=False, limit=constants.DEFAULT_QUERY_LIMIT,\\n offset=constants.DEFAULT_QUERY_OFFSET):\\n # NOTE: query time skyrockets with added OPTIONAL patterns, see:\\n # https://stackoverflow.com/questions/25609691/alternative-for-optional-keyword-in-sparql-queries\\n #\\n # NOTE: query assumes ordering of group concat operations are same across tag and source - have eyeballed and\\n # looks good\\n if published_after is not None:\\n raise NotImplementedError('The parameter `publishedAfter` is not yet implemented')\\n if region is not None:\\n raise NotImplementedError('The parameter `region` is not yet implemented')\\n if sort is not None and random:\\n raise InvalidInputParameterCombination('Cannot specify both `sort` and `random`.')\\n\\n regular_fields = '?title ?image ?version ?programme ?pid ?media ?duration ?publicationDate ?masterBrand'\\n query_string = f\\\"\\\"\\\"\\n SELECT\\n {regular_fields}\\n {build_tags_select_statement()}\\n {build_genres_select_statement()}\\n\\n WHERE {{\\n {build_regular_fields_pattern_statement()}\\n {build_tags_pattern_statement()}\\n {build_genres_pattern_statement()}\\n\\n {build_sparql_anding_statement(\\n object_list=tags,\\n predicate_str='datalab:tag/datalab:tagValue',\\n object_binding='programme'\\n )}\\n {build_values_oring_statement('media', media_type)}\\n {build_filter_statement('duration', '<', max_duration)}\\n {build_filter_statement('published_date', '>', published_after)}\\n {build_sparql_anding_statement(\\n object_list=categories,\\n predicate_str='po:genre/datalab:genreKey',\\n object_binding='programme'\\n )}\\n }}\\n GROUP BY {regular_fields}\\n {'ORDER BY RAND()' if random else 'ORDER BY ' + build_sort_statement(sort) if sort is not None else ''}\\n LIMIT {limit}\\n OFFSET {offset}\\n \\\"\\\"\\\"\\n\\n return query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"864d460b0beb79116a83d3be73eff240\",\n \"score\": \"0.5609568\",\n \"text\": \"def compiledQuery(self, columns='', where='', order_by='',\\n distinct='', limit='', offset='',\\n group_by='', having='', for_update=False,\\n relationDict=None,\\n bagFields=False,\\n count=False, excludeLogicalDeleted=True,excludeDraft=True,\\n ignorePartition=False,ignoreTableOrderBy=False,\\n addPkeyColumn=True):\\n # get the SqlCompiledQuery: an object that mantains all the informations to build the sql text\\n self.cpl = SqlCompiledQuery(self.tblobj.sqlfullname,relationDict=relationDict,maintable_as=self.aliasCode(0))\\n distinct = distinct or '' # distinct is a text to be inserted in the sql query string\\n \\n # aggregate: test if the result will aggregate db rows\\n aggregate = bool(distinct or group_by)\\n \\n # group_by == '*': if all columns are aggregate functions, there will be no GROUP BY columns, \\n # but SqlQueryCompiler need to know that result will aggregate db rows\\n if group_by == '*':\\n group_by = None\\n\\n if not ignoreTableOrderBy and not aggregate:\\n order_by = order_by or self.tblobj.attributes.get('order_by')\\n self.init()\\n if not 'pkey' in self.cpl.relationDict:\\n self.cpl.relationDict['pkey'] = self.tblobj.pkey\\n \\n # normalize the columns string\\n columns = columns or ''\\n columns = columns.replace(' ', ' ')\\n columns = columns.replace('\\\\n', '')\\n columns = columns.replace(' as ', ' AS ')\\n columns = columns.replace(' ,', ',')\\n if columns and not columns.endswith(','):\\n columns = columns + ','\\n \\n # expand * and *filters: see self.expandMultipleColumns\\n if '*' in columns:\\n col_list = [col for col in gnrstring.split(columns, ',') if col]\\n new_col_list = []\\n for col in col_list:\\n col = col.strip()\\n if col.startswith('*'):\\n new_col_list = new_col_list + self.expandMultipleColumns(col[1:], bagFields)\\n else:\\n new_col_list.append(col)\\n columns = ','.join(new_col_list)\\n \\n # translate @relname.fldname in $_relname_fldname and add them to the relationDict\\n if where:\\n where = PERIODFINDER.sub(self.expandPeriod, where)\\n \\n currentEnv = self.db.currentEnv\\n env_conditions = dictExtract(currentEnv,'env_%s_condition_' %self.tblobj.fullname.replace('.','_'))\\n if env_conditions:\\n wherelist = [where] if where else []\\n for condition in env_conditions.values():\\n wherelist.append('( %s )' %condition)\\n where = ' AND '.join(wherelist)\\n\\n partition_kwargs = dictExtract(self.tblobj.attributes,'partition_')\\n\\n if not ignorePartition and partition_kwargs:\\n wherelist = [where] if where else []\\n for k,v in partition_kwargs.items():\\n if currentEnv.get('current_%s' %v):\\n wherelist.append('( $%s=:env_current_%s )' % (k,v))\\n where = ' AND '.join(wherelist)\\n columns = self.updateFieldDict(columns)\\n where = self.updateFieldDict(where or '')\\n order_by = self.updateFieldDict(order_by or '')\\n group_by = self.updateFieldDict(group_by or '')\\n having = self.updateFieldDict(having or '')\\n \\n col_list = uniquify([col for col in gnrstring.split(columns, ',') if col])\\n new_col_list = []\\n for col in col_list:\\n col = col.strip()\\n if col.startswith('SUM'):\\n aggregate = True\\n if not ' AS ' in col:\\n if col.startswith('$') and col[1:].replace('_', '').isalnum():\\n as_ = col[1:]\\n else:\\n # replace non word char with _ and check for numbers\\n as_ = self.db.colToAs(col)\\n col = '%s AS \\\"%s\\\"' % (col, as_)\\n else:\\n colbody, as_ = col.split(' AS ', 1)\\n # leave the col as is, but save the AS name to recover the db column original name from selection result\\n self.cpl.aliasDict[as_.strip()] = colbody.strip()\\n new_col_list.append(col)\\n \\n # build the clean and complete sql string for the columns, but still all fields are expressed as $fieldname\\n columns = ',\\\\n'.join(new_col_list)\\n \\n # translate all fields and related fields from $fldname to t0.fldname, t1.fldname... and prepare the JOINs\\n colPars = {}\\n for key, value in self.cpl.relationDict.items():\\n # self._currColKey manage exploding columns in recursive getFieldAlias without add too much parameters\\n self._currColKey = key\\n colPars[key] = self.getFieldAlias(value)\\n if count: # if the query is executed in count mode...\\n order_by = '' # sort has no meaning\\n if group_by: # the number of rows is defined only from GROUP BY cols, so clean aggregate functions from columns.\\n columns = group_by # was 't0.%s' % self.tblobj.pkey # ???? \\n elif distinct:\\n pass # leave columns as is to calculate distinct values\\n else:\\n columns = 'count(*) AS gnr_row_count' # use the sql count function istead of load all data\\n elif addPkeyColumn and self.tblobj.pkey and not aggregate:\\n columns = columns + ',\\\\n' + '%s.%s AS pkey' % (self.aliasCode(0),self.tblobj.pkey) # when possible add pkey to all selections\\n columns = columns.lstrip(',') # if columns was '', now it starts with ','\\n else:\\n columns = columns.strip('\\\\n').strip(',')\\n \\n # replace $fldname with tn.fldname: finally the real SQL columns!\\n columns = gnrstring.templateReplace(columns, colPars, safeMode=True)\\n \\n # replace $fldname with tn.fldname: finally the real SQL where!\\n \\n where = gnrstring.templateReplace(where, colPars)\\n #if excludeLogicalDeleted==True we have additional conditions in the where clause\\n logicalDeletionField = self.tblobj.logicalDeletionField\\n draftField = self.tblobj.draftField\\n if logicalDeletionField:\\n if excludeLogicalDeleted is True:\\n extracnd = '%s.%s IS NULL' % (self.aliasCode(0),logicalDeletionField)\\n if where:\\n where = '%s AND %s' % (extracnd, where)\\n else:\\n where = extracnd\\n elif excludeLogicalDeleted == 'mark':\\n if not (aggregate or count):\\n columns = '%s, %s.%s AS _isdeleted' % (columns, self.aliasCode(0),logicalDeletionField) #add logicalDeletionField\\n if draftField:\\n if excludeDraft is True:\\n extracnd = '%s.%s IS NOT TRUE' %(self.aliasCode(0),draftField)\\n if where:\\n where = '%s AND %s' % (extracnd, where)\\n else:\\n where = extracnd\\n # add a special joinCondition for the main selection, not for JOINs\\n if self.joinConditions:\\n extracnd, one_one = self.getJoinCondition('*', '*', self.aliasCode(0))\\n if extracnd:\\n if where:\\n where = '(%s) AND (%s)' % (where, extracnd)\\n else:\\n where = extracnd\\n order_by = gnrstring.templateReplace(order_by, colPars)\\n having = gnrstring.templateReplace(having, colPars)\\n group_by = gnrstring.templateReplace(group_by, colPars)\\n \\n if distinct:\\n distinct = 'DISTINCT '\\n elif distinct is None or distinct == '':\\n if self._explodingRows:\\n if not aggregate: # if there is not yet a group_by\\n distinct = 'DISTINCT ' # add a DISTINCT to remove unusefull rows: eg. a JOIN used only for a where, not for columns\\n if order_by:\\n xorderby=(('%s '%order_by.lower()).replace(' ascending ','').replace(' descending ','').replace(' asc ','').replace(' desc','')).split(',')\\n lowercol=columns.lower()\\n for xrd in xorderby:\\n if not xrd.strip() in lowercol:\\n columns = '%s, \\\\n%s' % (columns, xrd)\\n #order_by=None\\n if count:\\n columns = '%s.%s' % (self.aliasCode(0),self.tblobj.pkey)\\n # Count the DISTINCT maintable pkeys, instead of count(*) which will give the number of JOIN rows.\\n # That gives the count of rows on the main table: the result is different from the actual number\\n # of rows returned by the query, but it is correct in terms of main table records.\\n # It is the right behaviour ???? Yes in some cases: see SqlSelection._aggregateRows\\n \\n self.cpl.distinct = distinct\\n self.cpl.columns = columns\\n self.cpl.where = where\\n self.cpl.group_by = group_by\\n self.cpl.having = having\\n self.cpl.order_by = order_by\\n self.cpl.limit = limit\\n self.cpl.offset = offset\\n self.cpl.for_update = for_update\\n #raise str(self.cpl.get_sqltext(self.db)) # uncomment it for hard debug\\n return self.cpl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2a3634474b6bca3382e5a869663d66\",\n \"score\": \"0.5586637\",\n \"text\": \"def rebuild_query(self, comments=True, pretty=True):\\n if not self.tree:\\n self._logger.error('El arbol no ha sido generado todavia. Por tanto, todavia no hay ninguna query que '\\n 'reconstruir. Por favor ejecuta la funcion parse_query() para generar el arbol')\\n raise LookupError\\n\\n if not self.__queries:\\n self._logger.error('El arbol no ha sido procesado todavia. Por favor ejecuta la funcion rename_tree() para '\\n 'procesar el arbol.')\\n raise LookupError\\n\\n query = ' '.join(self.tree.leaves())\\n query = (self._untokenize(query)\\n .replace(' , ', ', ')\\n .replace(' ; ', ';')\\n .replace(' ( ', ' (')\\n .replace(' ) ', ') ')\\n #.replace(' = ', '=')\\n .replace(' . ', '.')\\n )\\n\\n if self.udfs:\\n for point, scape in zip(self.udfs, self._udfs_norm):\\n pattern = re.compile(scape, re.IGNORECASE)\\n query = pattern.sub(point, query)\\n\\n if pretty:\\n if comments:\\n final = '\\\\n'.join(self.__comments) + '\\\\n' + sqlparse.format(query, reindent=True, keyword_case='upper')\\n self.__comments = []\\n return final\\n else:\\n return sqlparse.format(query, reindent=True, keyword_case='upper')\\n else:\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4df307799efca220bbeb0a0c969810c8\",\n \"score\": \"0.55771285\",\n \"text\": \"def construct_query(*args, dialect='MSSS', allow_coalesce=True):\\n n = len(args)\\n assert all([isinstance(o, UserOption) for o in args]), \\\"Not all args are UserOptions\\\"\\n invalids = [not o.validate() for o in args]\\n assert not any(invalids), \\\"{:d}/{:d} options have not been validated\\\".format(\\n sum(invalids), n)\\n assert all([args[0].context == args[i+1].context for i in range(n-1)]), \\\"Different\\\" +\\\\\\n \\\"contexts associated with the User Opts. Ensure these are the same.\\\"\\n stmt = Statement(args[0].context)\\n\\n # ==== GET ALL TABLES AND FORM BASIC JOIN SUBTREE ============\\n context = args[0].context\\n nodes = [o.get_table() for o in args]\\n unique_nodes = list(set(nodes))\\n join_tree = minimum_subtree(unique_nodes)\\n\\n # The primary table is considered the \\\"root node\\\" of the tree -- edges are undirected.\\n primary = [o for o in args if not o.is_secondary]\\n assert len(primary) == 1, f'Expecting one primary field. Got {len(primary)}.'\\n primary = primary[0]\\n primary_tbl = primary.get_table()\\n\\n # topological sort\\n edges = [(k, v[0][0]) for (i,(k,v)) in enumerate(join_tree.items()) if i > 0]\\n G = graph.Graph(edges, directed=False)\\n sorted_nodes = G.topological_sort(leave_until_last=primary_tbl)\\n # (Recreate G since the topological_sort mutates G (ikr - should fix this!))\\n G = graph.Graph(edges, directed=False)\\n\\n # ======= CREATE CTEs WHENEVER WE FIND A NESTED AGGREGATION ============\\n field_tbl_lkp = defaultdict(list)\\n for arg in args:\\n field_tbl_lkp[arg.get_table()].append(arg)\\n all_fields = dict()\\n\\n # Traverse the join tree of the selected tables, making CTEs wherever\\n # there exists secondary aggregation (i.e. not of the primary variable).\\n for i, v in enumerate(sorted_nodes[:-1]):\\n v_fields = field_tbl_lkp[v]\\n any_v_has_agg = any([arg.has_aggregation for arg in v_fields])\\n\\n # Get 'child tables' and 'parent tables' (wrt topological sort)\\n child_tbls = G._graph[v] - set(sorted_nodes[i:])\\n parent_tbl = join_tree[v][0][0]\\n parent_fks, v_pks = join_tree[v][0][1], join_tree[v][1][1]\\n\\n # The current table contains an aggregation, so create a CTE.\\n if any_v_has_agg:\\n # Get all arguments for table which are not aggregated\\n f_non_agg = [arg for arg in v_fields if not arg.has_aggregation]\\n f_non_agg += flatten([all_fields[w] for w in child_tbls])\\n\\n # Add in table PKs if not already included in `f_non_agg`.\\n f_non_agg_names = [f.sql_fieldname for f in f_non_agg]\\n pk_names_to_add = [f for f in v_pks if f not in f_non_agg_names]\\n pks_to_add = [construct_simple_field(f, v, context) for f in pk_names_to_add]\\n\\n # Make a copy of all non-aggregated AND aggregated fields.\\n f_non_agg_cte = [f.copy() for f in f_non_agg]\\n f_agg_cte = [arg for arg in v_fields if arg.has_aggregation]\\n f_agg = [f.copy() for f in f_agg_cte]\\n\\n # consolidate CTE fields and add primary designator to (any) field in root\\n cte_fields = pks_to_add + f_non_agg_cte + f_agg_cte\\n for field in cte_fields:\\n if field.sql_fieldname in v_pks:\\n field.is_secondary = False\\n break\\n\\n # Defer lookups until the final query\\n for field in cte_fields:\\n field.perform_lkp = False\\n\\n # Construct CTE\\n cte = CTENode([parent_tbl], # parent\\n v_pks, # pk\\n cte_fields) # cte_fields\\n stmt.ctes.append(cte)\\n\\n # Point the [references to the aggregations] outside the CTE to the CTE field\\n for f in f_agg:\\n f.field_alias = f.field_alias # this is *not* a noop! (@property...)\\n f.sql_item = '{alias}' + f.field_alias\\n f.table = cte\\n f.set_aggregation(None, force=True)\\n f.set_transform(None, force=True)\\n if allow_coalesce:\\n f.coalesce = 0 # assumes that aggregation is numeric\\n\\n for f in f_non_agg:\\n f.table = cte\\n f.sql_item = '{alias}'+f._field_alias_logic(will_perform_lkp=False)\\n\\n # Push these pointers to within the CTE up to the parent (although not PKs)\\n all_fields[v] = f_non_agg + f_agg\\n\\n # # overwrite the node `v` in the subtree with `cte`\\n # join_cond = tree_final[v]\\n # join_cond = (join_cond[0], (cte, join_cond[1][1]))\\n # tree_final = replace_in_ordered_dict(tree_final, v, cte, join_cond)\\n else:\\n all_fields[v] = v_fields + flatten([all_fields[w] for w in child_tbls])\\n\\n # Set args to the args propagated up to the root node\\n primary_children = G._graph[primary_tbl]\\n args = field_tbl_lkp[primary_tbl] + flatten([all_fields[c] for c in primary_children])\\n # Place the resulting tree into the FROM clause of the Statement object.\\n\\n nodes = [o.get_table() for o in args]\\n unique_nodes = list(set(nodes))\\n tree_final = minimum_subtree(unique_nodes)\\n\\n stmt._from.update(tree_final)\\n\\n # Add dimension tables (if requested to joins)\\n lkp_joins = []\\n for o in args:\\n if o.perform_lkp:\\n dtbl = o.dimension_table\\n assert not dtbl.is_cte, \\\"Currently unable to support CTEs for dimension \\\" + \\\\\\n \\\"tables. (But it only requires thinking about how \\\" + \\\\\\n \\\"to avoid multiple copies.)\\\"\\n fk = o.sql_fieldname\\n lkp_joins.append(((o.table, fk), (dtbl, dtbl.pk[0])))\\n\\n # Generate statement in order to populate aliases dict\\n stmt._from.generate_basic_statement(force_alias=(len(lkp_joins) > 0))\\n stmt._from.add_lookups_to_statement(lkp_joins)\\n\\n # === CONSTRUCT SELECT / WHERE ===========\\n has_agg = any([arg.has_aggregation for arg in args])\\n\\n for o in args:\\n # Get table alias for field, depending on whether has lookup table or not\\n if not o.perform_lkp:\\n alias = stmt.aliases[o.get_table()]\\n # coalesce is usually None anyway\\n coalesce = o.coalesce if allow_coalesce else None\\n else:\\n dtbl = o.dimension_table\\n fk = rm_alias_placeholder(o.sql_item)\\n alias = stmt.lkp_aliases[((o.table, fk), (dtbl, dtbl.pk[0]))]\\n coalesce = context.coalesce_default if allow_coalesce else None\\n\\n sel, where = o.sql_transform(alias=alias, dialect=dialect, coalesce=coalesce)\\n # SELECT\\n stmt.select.append(sel)\\n # WHERE\\n if len(where) > 0:\\n stmt.where.extend(where)\\n # GROUP BY\\n if has_agg and not o.has_aggregation:\\n gby = re.sub('AS [a-zA-Z0-9_]+$', '', sel).strip()\\n stmt.groupby.append(gby)\\n\\n return stmt.generate_statement(dialect=dialect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b15f5e2a70c1eb7088c3897a2bfef2\",\n \"score\": \"0.5570735\",\n \"text\": \"def _build_query(self, metric, node, ts_from, ts_to):\\n resolution = ts_to - ts_from\\n if resolution > PROMETHEUS_TS_LIMIT:\\n ts_from = ts_to - PROMETHEUS_TS_LIMIT\\n\\n query_head = \\\"http://{}:{}/api/v1/query_range?query=\\\".format(\\n self.tsdb_ip, self.tsdb_port)\\n query_times = \\\"&start={}&end={}&step=1s\\\".format(ts_from, ts_to)\\n\\n query_selectors = self._get_query_selectors(metric, node)\\n query_selector = '{}{{{}}}'.format(metric, query_selectors)\\n\\n query = \\\"{}{}{}\\\".format(query_head, query_selector, query_times)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923405ac27fc863e4ecf89c859843768\",\n \"score\": \"0.55545264\",\n \"text\": \"def get_clean_query(self, additional_params={}):\\n # NOTE: right now this makes us pretty vulnerable\\n # to SQL Injection. We should make this safer at some point.\\n # Graphs likely have the same problem.\\n reg = re.compile('(\\\\{\\\\{.*?\\\\}\\\\})')\\n query = self.query\\n matches = reg.findall(query)\\n if matches:\\n for match in matches:\\n attr = match[2:len(match)-2]\\n if attr in additional_params:\\n query = query.replace(match, additional_params[attr])\\n else:\\n query = query.replace(match, \\\"\\\")\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"374044813c297baa727f456ec5a2a772\",\n \"score\": \"0.5546569\",\n \"text\": \"def buildQueryFromFilters(date, rainfall, temperature, month, year):\\n\\t# Main part of the query\\n\\tquery = sql.SQL(\\\"SELECT * FROM climate\\\")\\n\\n\\tconditions = []\\n\\tvalues = []\\n\\n\\t# Check if each filter is actually applied, \\n\\t# if so, its respective part of the query is appended to the main part\\n\\tif date != None:\\n\\t\\tclause = sql.SQL(\\\"date = to_date(%s, 'DD-MM-YYYY')\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(date)\\n\\tif temperature != None:\\n\\t\\tclause = sql.SQL(\\\"temperature = (%s)\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(temperature)\\n\\tif rainfall != None:\\n\\t\\tclause = sql.SQL(\\\"rainfall = real '%s'\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(rainfall)\\n\\tif month != None:\\n\\t\\tclause = sql.SQL(\\\"EXTRACT(MONTH FROM date) = (%s)\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(month)\\n\\tif year != None:\\n\\t\\tclause = sql.SQL(\\\"EXTRACT(YEAR FROM date) = (%s)\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(year)\\n\\n\\t# Put all together\\n\\tif len(conditions) > 0:\\n\\t\\tquery = sql.SQL(\\\" \\\").join([query, sql.SQL(\\\"WHERE\\\")])\\n\\n\\t\\tconj = sql.SQL(\\\" AND \\\").join(conditions)\\n\\n\\t\\tquery = sql.SQL(\\\" \\\").join([query, conj])\\n\\n\\t\\n\\treturn query, values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b14da9066124004791a46561a7d775\",\n \"score\": \"0.55110025\",\n \"text\": \"def query(**kw):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfaf1e939ed62d271c7e53d6dfd18c69\",\n \"score\": \"0.54942393\",\n \"text\": \"def create_query(vaccine=None, age_group=\\\"adult\\\", without_registration=None, self_payer=False, monday=None,\\n tuesday=None, wednesday=None, thursday=None, friday=None, saturday=None, sunday=None):\\n\\n open_hours = {'monday': monday, 'tuesday': tuesday, 'wednesday': wednesday, 'thursday': thursday, 'friday': friday,\\n 'saturday': saturday, 'sunday': sunday}\\n\\n query = \\\"\\\"\\\"\\n SELECT loc.vacc_id, loc.latitude, loc.longitude\\n FROM vacc_center_location as loc\\n INNER JOIN vacc_center_type as typ USING(vacc_id)\\n \\\"\\\"\\\"\\n if age_group not in AGE_GROUP_SET: raise Exception('age_group argument can be only: adult, teenage, child')\\n if int(self_payer) not in [0, 1]: raise Exception('self_payer argument can be only: True, False')\\n query_where = f\\\"\\\"\\\"WHERE typ.{age_group} = 1\\n AND typ.self_payers = {int(self_payer)}\\n \\\"\\\"\\\"\\n if without_registration is not None:\\n if int(without_registration) not in [0, 1]: raise Exception('without_registration argument can be only: True, False, None')\\n query_where += f\\\"AND typ.without_registration = {int(without_registration)} \\\\n\\\"\\n\\n if vaccine is not None:\\n if vaccine.lower() not in VACCINES_SET:\\n raise Exception('vaccine argument can be only: COMIRNATY, SPIKEVAX, JANSSEN, Vaxzevria, None')\\n query += \\\"INNER JOIN vacc_center_vaccines AS vacc USING(vacc_id) \\\\n\\\"\\n query_where += f\\\"AND vacc.{vaccine.lower()} = 1 \\\\n\\\"\\n\\n open_hours = {day: hour for (day, hour) in open_hours.items() if hour is not None}\\n if len(open_hours) != 0:\\n query += \\\"INNER JOIN vacc_center_hours as hour USING(vacc_id) \\\\n\\\"\\n list_hours = []\\n for (day, hour) in open_hours.items():\\n if not ((hour >= 0) and (hour <= 24)): raise Exception('days arguments can be only set to None or number between 0 to 24')\\n list_hours.append(f\\\"(hour.{day}_open <= {hour} AND hour.{day}_closed >= {hour})\\\")\\n\\n query_hours = \\\"AND (\\\"\\n n = len(list_hours)\\n for i in range(n):\\n query_hours += list_hours[i]\\n if (i+1) == n:\\n query_hours += \\\")\\\"\\n else:\\n query_hours += \\\" OR \\\"\\n query_where += query_hours\\n return query + query_where\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f83fb856d12b26ca2684c9d9c9dfb40\",\n \"score\": \"0.5437405\",\n \"text\": \"def build_goods_query(\\n good_ids: List[str],\\n currency_id: str,\\n is_searching_for_sellers: bool,\\n is_search_query: bool,\\n) -> Query:\\n if is_search_query:\\n # the OEF does not accept attribute names consisting of integers only\\n good_ids = [PREFIX + good_id for good_id in good_ids]\\n\\n data_model = _build_goods_datamodel(\\n good_ids=good_ids, is_supply=is_searching_for_sellers\\n )\\n constraints = [Constraint(good_id, ConstraintType(\\\">=\\\", 1)) for good_id in good_ids]\\n constraints.append(Constraint(\\\"currency_id\\\", ConstraintType(\\\"==\\\", currency_id)))\\n constraint_expr = cast(List[ConstraintExpr], constraints)\\n\\n if len(good_ids) > 1:\\n constraint_expr = [Or(constraint_expr)]\\n\\n query = Query(constraint_expr, model=data_model)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3716fc1f19951155d518f81b705b75c9\",\n \"score\": \"0.54263324\",\n \"text\": \"def cypher(self, query, params=None):\\r\\n path = cypher_path\\r\\n params = dict(query=query,params=params)\\r\\n resp = self.request.post(path, params)\\r\\n\\r\\n # Cypher data hack\\r\\n resp.total_size = len(resp.results.data)\\r\\n resp.results = (Neo4jResult(result[0], self.config) for result in resp.results.data)\\r\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800590f0ff9faf64788181eb91302965\",\n \"score\": \"0.5422505\",\n \"text\": \"def buildQuery (self, obj_type, ids=None, params=None, attributes=None):\\n\\n if type(obj_type) is type(''):\\n wrapper = KNOWN_WRAPPERS.get(obj_type.lower(), None)\\n if wrapper is None:\\n raise KeyError(\\\"obj_type of %s not supported by getOjbects(). E.g. use 'Image' etc\\\" % obj_type)\\n else:\\n raise AttributeError(\\\"getObjects uses a string to define obj_type, E.g. 'Image'\\\")\\n\\n q = self.getQueryService()\\n if params is None:\\n params = omero.sys.Parameters()\\n if params.map is None:\\n params.map = {}\\n\\n # get the base query from the instantiated object itself. E.g \\\"select obj Project as obj\\\"\\n query = wrapper()._getQueryString()\\n\\n clauses = []\\n # getting object by ids\\n if ids != None:\\n clauses.append(\\\"obj.id in (:ids)\\\")\\n params.map[\\\"ids\\\"] = rlist([rlong(a) for a in ids])\\n\\n # support filtering by owner (not for some object types)\\n if params.theFilter and params.theFilter.ownerId and obj_type.lower() not in [\\\"experimentergroup\\\", \\\"experimenter\\\"]:\\n clauses.append(\\\"owner.id = (:eid)\\\")\\n params.map[\\\"eid\\\"] = params.theFilter.ownerId\\n\\n # finding by attributes\\n if attributes != None:\\n for k,v in attributes.items():\\n clauses.append('obj.%s=:%s' % (k, k) )\\n params.map[k] = omero_type(v)\\n\\n if clauses:\\n query += \\\" where \\\" + (\\\" and \\\".join(clauses))\\n\\n return (query, params, wrapper)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3193232c6e9b86e2cb3be30784d0c07c\",\n \"score\": \"0.5413109\",\n \"text\": \"def make_query(store, query, prefix=\\\"\\\"):\\r\\n query = common.parse_query(query)\\r\\n q = Query()\\r\\n q.prefix = prefix\\r\\n q.offset = common.safeint(query.pop('offset', None), 0)\\r\\n q.limit = common.safeint(query.pop('limit', 20), 20)\\r\\n if q.limit > 1000:\\r\\n q.limit = 1000\\r\\n sort = query.pop('sort', None)\\r\\n \\r\\n nested = (prefix != \\\"\\\")\\r\\n \\r\\n for k, v in query.items():\\r\\n # key foo can also be written as label:foo\\r\\n k = k.split(':')[-1]\\r\\n if v is None:\\r\\n q.requested[k] = v\\r\\n elif isinstance(v, dict):\\r\\n # make sure op is ==\\r\\n v = dict((k + '.' + key, value) for key, value in v.items())\\r\\n q2 = make_query(store, v, prefix=prefix + k + \\\".\\\")\\r\\n #@@ Anand: Quick-fix\\r\\n # dbstore.things looks for key to find whether type is required or not. \\r\\n q2.key = k \\r\\n if q2.conditions:\\r\\n q.conditions.append(q2)\\r\\n else:\\r\\n q.requested[k] = q2\\r\\n else:\\r\\n k, op = parse_key(k)\\r\\n q.add_condition(k, op, None, v)\\r\\n \\r\\n if not nested:\\r\\n q.assert_type_required()\\r\\n \\r\\n type = get_thing(store, q.get_type())\\r\\n #assert type is not None, 'Not found: ' + q.get_type()\\r\\n for c in q.conditions:\\r\\n if not isinstance(c, Query):\\r\\n c.datatype = find_datatype(type, c.key, c.value)\\r\\n \\r\\n if sort:\\r\\n parse_key(sort) # to validate key\\r\\n q.sort = web.storage(key=sort, datatype=find_datatype(type, sort, None))\\r\\n else:\\r\\n q.sort = None\\r\\n \\r\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac716271d29523c3ec034478d633e21\",\n \"score\": \"0.54028887\",\n \"text\": \"def create_query(neo4j_entities, entity_value):\\n\\n def get_value(index):\\n return SINONIMOS_NEO4J[entity_value[index]] if SINONIMOS_NEO4J.get(entity_value[index])\\\\\\n else entity_value[index]\\n\\n get_query_part = {\\n \\\"espacio\\\": lambda x: \\\"(n:Room {{ type: '{}' }})\\\".format(get_value(x)),\\n \\\"sensor\\\": lambda x: (\\\"(d:Device)\\\", f\\\" '{get_value(x)}' in d.type\\\"),\\n \\\"orientacion\\\": lambda x: \\\" n.orientacion CONTAINS '{}'\\\".format(get_value(x)),\\n \\\"edificio\\\": lambda x: (\\\"(b:Building)\\\", \\\" b.name CONTAINS '{}'\\\".format(get_value(x))),\\n \\\"nombre\\\": lambda x: (\\\"(b:Building)\\\", \\\" b.name CONTAINS '{}'\\\".format(get_value(x))),\\n \\\"lugares\\\": lambda x: \\\" n.name CONTAINS '{}'\\\".format(get_value(x))\\n }\\n\\n\\n dic = {}\\n for entity in neo4j_entities:\\n key = entity[1]\\n if entity[1] == \\\"nombre\\\":\\n key = \\\"edificio\\\"\\n dic[key] = get_query_part[entity[1]](entity[0])\\n\\n espacio = dic[\\\"espacio\\\"] if dic.get(\\\"espacio\\\") else \\\"\\\"\\n sensor = dic[\\\"sensor\\\"] if dic.get(\\\"sensor\\\") else (\\\"\\\", \\\"\\\")\\n orientacion = dic[\\\"orientacion\\\"] if dic.get(\\\"orientacion\\\") else \\\"\\\"\\n edificio = dic[\\\"edificio\\\"] if dic.get(\\\"edificio\\\") else (\\\"\\\", \\\"\\\")\\n lugares = dic[\\\"lugares\\\"] if dic.get(\\\"lugares\\\") else \\\"\\\"\\n ret = []\\n\\n # crear relaciones que son opcionales en la consulta\\n if espacio is not \\\"\\\" and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0]+\\\"<-[:HAS]-\\\", sensor[1])\\n if espacio is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\"):\\n espacio += \\\"<-[:HAS]-(f:Floor)<-[:HAS]-\\\"\\n\\n #Añadir información a la consulta que no se infica pero que es necesaria para poder recorrer las relaciones en Neo4j.\\n \\n # Si se pregunta por sensor y lugar pero no por espacio.\\n if espacio is \\\"\\\" and sensor != (\\\"\\\", \\\"\\\") and lugares is not \\\"\\\":\\n espaciocio = \\\"(n:Room)\\\"\\n sensor = (sensor[0]+\\\"<-[:HAS]-\\\", sensor[1])\\n # Si se pregunta por nombre u orientación pero no por espacio.\\n if espacio is \\\"\\\" and (lugares is not \\\"\\\" or orientacion is not \\\"\\\"):\\n espacio = \\\"(n:Room)\\\"\\n # Si se pregunta por sensores en un edificio sin especificar un espacio.\\n if espacio is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n espacio = \\\"(n:Room)<-[:HAS]-(f:Floor)<-[:HAS]-\\\"\\n sensor = (sensor[0]+\\\"<-[:HAS]-\\\", sensor[1])\\n # Si hay 4\\n # orientacion lugares edificio sensor\\n # X X X X\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n\\n # Si hay 3\\n # orientacion lugares edificio sensor\\n # X X X -\\n # X - X X\\n # - X X X\\n # X - X X\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n lugares = \\\"WHERE \\\" + lugares\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n orientacion = \\\"WHERE \\\" + orientacion\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n\\n # Si hay 2\\n # orientacion lugares edificio sensor\\n # X X - -\\n # X - X -\\n # X - - X\\n # - X X -\\n # - X - X\\n # - - X X\\n\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n lugares = \\\"WHERE \\\" + lugares\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n lugares = \\\"WHERE \\\" + lugares\\n if orientacion is \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n edificio = (edificio[0], \\\"WHERE \\\" + edificio[1])\\n # Si hay 1\\n # orientacion lugares edificio sensor\\n # X - - -\\n # - X - -\\n # - - X -\\n # - - - X\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n lugares = \\\"WHERE \\\" + lugares\\n if orientacion is \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n edificio = (edificio[0], \\\"WHERE \\\" + edificio[1])\\n if orientacion is \\\"\\\" and lugares is \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"WHERE \\\" + sensor[1])\\n\\n\\n\\n # Preparar los atributos de salida\\n if espacio is not \\\"\\\":\\n ret.append(\\\"n.name, n.id\\\")\\n if sensor != (\\\"\\\", \\\"\\\"):\\n ret.append(\\\"d.id, d.type\\\")\\n if orientacion is not \\\"\\\":\\n ret.append(\\\"n.orientacion\\\")\\n\\n return TEMPLATE_NEO.format(sensor[0], espacio, edificio[0], orientacion,\\\\\\n lugares, edificio[1], sensor[1], \\\", \\\".join(ret))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e23579f229cdea216279b1841dfff82\",\n \"score\": \"0.53795767\",\n \"text\": \"def generate_entrez_query(self):\\n ending = ') AND ' + self.gene + '[Gene Name] ' + \\\\\\n 'AND 1000:8000[Sequence Length]'\\n types = []\\n for hpv_type in itertools.chain(self.harmfull, self.safe):\\n types.append('\\\"Human papillomavirus type %d\\\"[Organism]' % hpv_type)\\n self._query = '(' + ' OR '.join(types) + ending\\n return self._query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68cd306f78e9a696fa45ccc17d112056\",\n \"score\": \"0.5368679\",\n \"text\": \"def sql_query_str(program):\\n node_type, node_children = program[0], program[1:]\\n assert node_type in sql_entry_tokens and syntax_valid(program, sql_cfg)\\n\\n if node_type == \\\"SELECT_AGG\\\":\\n select_exprs_node, joins_node, group_by_exprs_node = node_children\\n where_node = None\\n elif node_type == \\\"SELECT_AGG_WHERE\\\":\\n (select_exprs_node,\\n joins_node,\\n group_by_exprs_node,\\n where_node) = node_children\\n else:\\n raise RuntimeError(\\n \\\"Missing node-type decoding logic for generating SQL query from AST.\\\"\\n )\\n\\n return query_str(\\n select_exprs=list(sql_select_exprs(select_exprs_node)),\\n joins=list(sql_joins(joins_node)),\\n where_exprs=list(sql_and_exprs_strs(where_node)) if where_node else [],\\n group_by_exprs=list(sql_group_by_exprs(group_by_exprs_node)),\\n order_by_exprs=[],\\n limit_exprs=[],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f43c43ecaf245aaab63b127c15a173ed\",\n \"score\": \"0.53679913\",\n \"text\": \"def _getQueryString(self):\\n query = \\\"select obj from Plate as obj \\\" \\\\\\n \\\"join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent \\\"\\\\\\n \\\"left outer join fetch obj.screenLinks spl \\\" \\\\\\n \\\"left outer join fetch spl.parent sc\\\"\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51fd823b07a49d65be55acc10cb16971\",\n \"score\": \"0.53457093\",\n \"text\": \"def _create_qualifiedname_query(self, type_name, *values):\\n query = {\\n 'typeName': type_name,\\n 'excludeDeletedEntities': True,\\n 'limit': 10000\\n }\\n entity_filter = {\\n 'condition': 'AND'\\n }\\n criterion = []\\n n = 0\\n for v in values:\\n criteria = {\\n 'attributeName': 'qualifiedName',\\n 'operator': 'STARTSWITH' if n == 0 else 'CONTAINS',\\n 'attributeValue': v\\n }\\n n += 1\\n criterion.append(criteria)\\n if type_name == 'hive_table':\\n # Ignore temporary tables.\\n criterion.append({'operator': '=', 'attributeName': 'temporary', 'attributeValue': False})\\n entity_filter['criterion'] = criterion\\n query['entityFilters'] = entity_filter\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44970fb2e787db5c9720c86d5e16d3f3\",\n \"score\": \"0.53439456\",\n \"text\": \"def create_query(\\n self, query_text, locale=None, language=None, time_zone=None, timestamp=None\\n ):\\n if not query_text:\\n query_text = \\\"\\\"\\n if isinstance(query_text, (list, tuple)):\\n return self._process_list(\\n query_text,\\n \\\"create_query\\\",\\n locale=locale,\\n language=language,\\n time_zone=time_zone,\\n timestamp=timestamp,\\n )\\n return self.resource_loader.query_factory.create_query(\\n query_text,\\n language=language,\\n locale=locale,\\n time_zone=time_zone,\\n timestamp=timestamp,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b4dabb7c03fa99fdfdd73c857a0b88d\",\n \"score\": \"0.5328662\",\n \"text\": \"def sql_with_where(self):\\n e = get_executor(SqlRule)\\n where_clause = \\\"WHERE \\\"\\n where_time_filter = e.compose_where_time_filter(self)\\n where_condition = e.compose_where_condition(self)\\n if where_time_filter == \\\"\\\" and where_condition == \\\"\\\":\\n where_clause = \\\"\\\"\\n elif where_time_filter != \\\"\\\" and where_condition != \\\"\\\":\\n where_clause = f\\\"{where_clause} {where_time_filter} AND {where_condition}\\\"\\n else:\\n where_clause = f\\\"{where_clause} {where_time_filter} {where_condition}\\\"\\n final_sql = f\\\"{self.sql} {where_clause}\\\"\\n return self.render_sql(final_sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae062c702cf1a20a574ddd68d4b28aa\",\n \"score\": \"0.532052\",\n \"text\": \"def construct_url(self\\n , query_params: Dict\\n , ) -> str:\\n url_const = self.faers_ep\\n initialized = False\\n for param_q, param_val in query_params.items():\\n # Need to add '&' to 2nd,3rd,... query_params\\n if initialized:\\n url_const += '&'\\n else:\\n initialized = True\\n if type(param_val) == str and len(query_params) > 0 \\\\\\n or type(param_val) == int:\\n url_const += f'{param_q}={param_val}'\\n elif type(param_val) == dict and len(param_val) > 0:\\n url_const += f'{param_q}=' + \\\"+AND+\\\".join(\\n [f'{sub_p_k}:{sub_p_v}' for sub_p_k, sub_p_v in param_val.items()])\\n elif type(param_val) == list and len(param_val) > 0:\\n url_const += f'{param_q}=' + \\\"+AND+\\\".join(param_val)\\n else:\\n raise TypeError\\n return url_const\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60d6e0b78286e4e35b88227402bb392\",\n \"score\": \"0.5318269\",\n \"text\": \"def get_custom_query(self):\\n query = {}\\n\\n # searchable text queries\\n q = req.get_query()\\n if q:\\n query[\\\"SearchableText\\\"] = q\\n\\n # physical path queries\\n path = req.get_path()\\n if path:\\n query[\\\"path\\\"] = {'query': path, 'depth': req.get_depth()}\\n\\n # special handling for recent created/modified\\n recent_created = req.get_recent_created()\\n if recent_created:\\n date = api.calculate_delta_date(recent_created)\\n query[\\\"created\\\"] = {'query': date, 'range': 'min'}\\n\\n recent_modified = req.get_recent_modified()\\n if recent_modified:\\n date = api.calculate_delta_date(recent_modified)\\n query[\\\"modified\\\"] = {'query': date, 'range': 'min'}\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88503be9fbc27ea6562b4928b4a8d6c0\",\n \"score\": \"0.530554\",\n \"text\": \"def get_where_clause(columns_to_query_lst):\\n where_str = \\\"where \\\"\\n equals_str =[]\\n for row in columns_to_query_lst:\\n temp_str = \\\"c.\\\" + row + \\\" = t.\\\" + row\\n equals_str.append(temp_str)\\n joined_str = \\\" AND \\\".join(equals_str)\\n where_str = where_str + joined_str\\n return where_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746bf5ea5425e576e9ff1f9bce37f702\",\n \"score\": \"0.52996916\",\n \"text\": \"def create_query(processed_request):\\n query = {\\n \\\"variant\\\": {\\n \\\"referenceBases\\\": \\\"\\\" if not processed_request.get(\\\"referenceBases\\\") else processed_request.get(\\\"referenceBases\\\"),\\n \\\"alternateBases\\\": \\\"\\\" if not processed_request.get(\\\"alternateBases\\\") else processed_request.get(\\\"alternateBases\\\"),\\n \\\"referenceName\\\": \\\"\\\" if not processed_request.get(\\\"referenceName\\\") else processed_request.get(\\\"referenceName\\\"),\\n \\\"start\\\": None if not processed_request.get(\\\"start\\\") else processed_request.get(\\\"start\\\"),\\n \\\"end\\\": None if not processed_request.get(\\\"end\\\") else processed_request.get(\\\"end\\\"),\\n \\\"assemblyId\\\": \\\"\\\" if not processed_request.get(\\\"assemblyId\\\") else processed_request.get(\\\"assemblyId\\\")\\n },\\n \\\"datasets\\\": {\\n \\\"datasetIds\\\": None if not processed_request.get(\\\"datasetIds\\\") else processed_request.get(\\\"datasetIds\\\"),\\n \\\"includeDatasetResponses\\\": \\\"ALL\\\" if not processed_request.get(\\\"includeDatasetResponses\\\") else processed_request.get(\\\"includeDatasetResponses\\\")\\n },\\n \\\"filters\\\": None if not processed_request.get(\\\"filters\\\") else processed_request.get(\\\"filters\\\"),\\n \\\"customFilters\\\": None if not processed_request.get(\\\"customFilters\\\") else processed_request.get(\\\"customFilters\\\"),\\n }\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e6478863c1ea4a7a2b784bc3c49cd9\",\n \"score\": \"0.529957\",\n \"text\": \"def build_where_stmt(self, ident, filters, q_filters=None, source_class=None):\\n if q_filters is not None:\\n stmts = self._parse_q_filters(ident, q_filters, source_class)\\n if stmts:\\n self._ast.where.append(stmts)\\n else:\\n stmts = []\\n for row in filters:\\n negate = False\\n\\n # pre-process NOT cases as they are nested dicts\\n if \\\"__NOT__\\\" in row and len(row) == 1:\\n negate = True\\n row = row[\\\"__NOT__\\\"]\\n\\n for prop, operator_and_val in row.items():\\n operator, val = operator_and_val\\n if operator in _UNARY_OPERATORS:\\n # unary operators do not have a parameter\\n statement = (\\n f\\\"{'NOT' if negate else ''} {ident}.{prop} {operator}\\\"\\n )\\n else:\\n place_holder = self._register_place_holder(ident + \\\"_\\\" + prop)\\n statement = f\\\"{'NOT' if negate else ''} {ident}.{prop} {operator} ${place_holder}\\\"\\n self._query_params[place_holder] = val\\n stmts.append(statement)\\n\\n self._ast.where.append(\\\" AND \\\".join(stmts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5f867e7aef319a9323809c18719262\",\n \"score\": \"0.5287673\",\n \"text\": \"def __init__ (self, database, var, conditions = (), echo = False):\\n\\n self.session = buildSession(\\n database=database,\\n echo=echo)\\n if var == \\\"ncommits\\\":\\n self.query = self.session.query().select_nscmlog([\\\"commits\\\",])\\n elif var == \\\"listcommits\\\":\\n self.query = self.session.query().select_listcommits()\\n elif var == \\\"nauthors\\\":\\n self.query = self.session.query().select_nscmlog([\\\"authors\\\",])\\n elif var == \\\"listauthors\\\":\\n self.query = self.session.query().select_listauthors()\\n for condition in conditions:\\n self.query = condition.filter(self.query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d298399d7e7d292b986fa091d5abed50\",\n \"score\": \"0.5285017\",\n \"text\": \"def full_query_pattern(self, include_conclusion: bool = False) -> str:\\n query_pattern = \\\"\\\"\\n for literal in self.premise:\\n query_pattern += literal.sparql_patterns()\\n\\n if include_conclusion:\\n query_pattern += self.conclusion.sparql_patterns()\\n\\n if \\\"?b\\\" not in query_pattern and \\\"?a\\\" not in query_pattern:\\n query_projection = \\\"ask \\\"\\n else:\\n # insert the selectors for subject and object into the select query if they exist in the query pattern\\n query_projection = \\\"select where \\\"\\n\\n # the resulting query would look like \\\"select ?a ?b ...\\\" if both cases are true\\n if \\\"?b\\\" in query_pattern:\\n query_projection = query_projection.replace(\\\"select \\\", \\\"select ?b \\\")\\n if \\\"?a\\\" in query_pattern:\\n query_projection = query_projection.replace(\\\"select \\\", \\\"select ?a \\\")\\n\\n # build remaining part of the query and execute it\\n query_pattern = \\\"{\\\" + query_pattern + \\\"}\\\"\\n return query_projection + query_pattern\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f90b7aa24950c089ec7f416c7f71163\",\n \"score\": \"0.5283903\",\n \"text\": \"def _getQueryString(self):\\n return \\\"select obj from %s obj join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent\\\" % self.OMERO_CLASS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edec057b919f8d0df46ebd8515dfc0e2\",\n \"score\": \"0.5279631\",\n \"text\": \"def _getQueryString(self):\\n return \\\"select obj from BooleanAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5238770c84b4b535af51b45d8d738378\",\n \"score\": \"0.52578133\",\n \"text\": \"def query(self):\\n\\n if not hasattr(self, \\\"_query\\\"):\\n\\n # Set up some convenience aliases.\\n Query = db.Query\\n Condition = Query.Condition\\n Or = Query.Or\\n\\n # Create a query object.\\n columns = \\\"d.id\\\", \\\"d.title\\\"\\n self._query = Query(\\\"document d\\\", *columns).order(\\\"d.title\\\")\\n\\n # Add the conditions: one for each field with a value.\\n conditions = []\\n have_doctype = False\\n for field in self.fields:\\n\\n # See if we got a value for this field.\\n value = field.var.strip() if field.var else None\\n if not value:\\n continue\\n\\n # Remember the criterion for display to the user.\\n self.criteria.append(value)\\n\\n # If 'selectors' is a string, it's a column in `document`.\\n value_op = getQueryOp(value)\\n if isinstance(field.selectors, str):\\n column = f\\\"d.{field.selectors}\\\"\\n conditions.append(Condition(column, value, value_op))\\n continue\\n\\n # Build up a subquery for the field.\\n have_doctype = True\\n subquery = Query(\\\"query_term\\\", \\\"doc_id\\\").unique()\\n\\n # These are ORd together.\\n selector_conditions = []\\n for path in field.selectors:\\n path_op = \\\"LIKE\\\" if \\\"%\\\" in path else \\\"=\\\"\\n\\n # Simple case: test for a string stored in the doc.\\n if not path.endswith(\\\"/@cdr:ref[int_val]\\\"):\\n path_test = Condition(\\\"path\\\", path, path_op)\\n value_test = Condition(\\\"value\\\", value, value_op)\\n selector_conditions.append((path_test, value_test))\\n\\n # Trickier case: find values in linked documents.\\n else:\\n path = path.replace(\\\"[int_val]\\\", \\\"\\\")\\n title_query = Query(\\\"document\\\", \\\"id\\\").unique()\\n args = \\\"title\\\", value, value_op\\n title_query.where(Condition(*args))\\n args = \\\"int_val\\\", title_query, \\\"IN\\\"\\n title_test = Condition(*args)\\n path_test = Condition(\\\"path\\\", path, path_op)\\n selector_conditions.append((path_test, title_test))\\n\\n # Add the conditions for this field's selectors to the mix.\\n subquery.where(Or(*selector_conditions))\\n conditions.append(Condition(\\\"d.id\\\", subquery, \\\"IN\\\"))\\n\\n # Sanity check.\\n if not conditions:\\n raise Exception(\\\"No search conditions specified\\\")\\n\\n # If all the selectors are from the document table (or,\\n # view, to be precise), then we still need to narrow\\n # the search to this document type.\\n if not have_doctype:\\n self._query.join(\\\"doc_type t\\\", \\\"t.id = d.doc_type\\\")\\n self._query.where(Condition(\\\"t.name\\\", self.doctype))\\n # Plug the top-level conditions into the query.\\n if self.match_all:\\n for condition in conditions:\\n self._query.where(condition)\\n else:\\n self._query.where(Or(*conditions))\\n\\n # All the fields with values have been folded into the query.\\n return self._query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3e93874b247c4bb5a6d828028febe0\",\n \"score\": \"0.5255788\",\n \"text\": \"def query(self):\\n self._check_mandatory_inputs()\\n return self._gen_query()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74e698e0dc148ed3565a45d92b4dec8c\",\n \"score\": \"0.5251471\",\n \"text\": \"def _get_clause(self):\\r\\n params = [(primary_key, sql.bindparam(None, type_=primary_key.type))\\r\\n for primary_key in self.primary_key]\\r\\n return sql.and_(*[k == v for (k, v) in params]), \\\\\\r\\n util.column_dict(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb58117d3fda3c2db7fe7688c4120b96\",\n \"score\": \"0.5243088\",\n \"text\": \"def build_query_text_request(\\n **kwargs # type: Any\\n):\\n # type: (...) -> HttpRequest\\n\\n content_type = kwargs.pop('content_type', None) # type: Optional[str]\\n\\n api_version = \\\"2021-05-01-preview\\\"\\n accept = \\\"application/json\\\"\\n # Construct URL\\n url = kwargs.pop(\\\"template_url\\\", '/:query-text')\\n\\n # Construct parameters\\n query_parameters = kwargs.pop(\\\"params\\\", {}) # type: Dict[str, Any]\\n query_parameters['api-version'] = _SERIALIZER.query(\\\"api_version\\\", api_version, 'str')\\n\\n # Construct headers\\n header_parameters = kwargs.pop(\\\"headers\\\", {}) # type: Dict[str, Any]\\n if content_type is not None:\\n header_parameters['Content-Type'] = _SERIALIZER.header(\\\"content_type\\\", content_type, 'str')\\n header_parameters['Accept'] = _SERIALIZER.header(\\\"accept\\\", accept, 'str')\\n\\n return HttpRequest(\\n method=\\\"POST\\\",\\n url=url,\\n params=query_parameters,\\n headers=header_parameters,\\n **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0b0884588b76f9e715cfc138ae0212\",\n \"score\": \"0.5239496\",\n \"text\": \"def create_complex_query(self):\\n columns = copy(self.columns)\\n shuffle(columns)\\n query = \\\"\\\"\\\"\\\\\\n SELECT\\n {col}\\n FROM\\n datadog_test.dbo.high_cardinality AS hc1\\n JOIN (\\n SELECT\\n id,\\n COUNT(*) col12_float\\n FROM\\n datadog_test.dbo.high_cardinality AS hc2\\n WHERE\\n hc2.col1_txt LIKE '%-%'\\n AND hc2.col14_int > (\\n SELECT\\n AVG(hc3.col15_int)\\n FROM\\n datadog_test.dbo.high_cardinality AS hc3)\\n GROUP BY\\n hc2.id) AS hc4 ON hc4.id = hc1.id\\n JOIN datadog_test.dbo.high_cardinality AS hc5 ON hc5.id = hc1.id\\n WHERE\\n CAST(hc5.col17_date AS VARCHAR)\\n IN('2003-04-23', '2043-09-10', '1996-08-08')\\n AND hc5.col13_float IS NOT NULL\\n AND hc5.col17_date NOT LIKE '21%';\\n \\\"\\\"\\\"\\n # Select a range of random columns and prefix to match our alias\\n return query.format(col=','.join(['hc1.' + col for col in columns[: randint(1, len(columns) - 1)]]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23ac357cf50e246b557978088edbcf6b\",\n \"score\": \"0.5237348\",\n \"text\": \"def __gencondition(self, querylist):\\n conditions = []\\n for key in self.PROPERTY_SEARCH_LIST:\\n if querylist.get(key):\\n conditions.append(self.PROPERTY_SEARCH_LIST[key][1] % (querylist.get(key)))\\n return \\\" AND \\\".join(conditions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90ad42bba7570be5827a9b27873f0c5\",\n \"score\": \"0.52357996\",\n \"text\": \"def build_sql(self, table, row, known_fkeys={}):\\n # Initialize the builder properties\\n self.row = row\\n self.known_fkeys = known_fkeys\\n return self.build_table_sql(table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a69e3debc69789bbd12bb2ee0e29ccfd\",\n \"score\": \"0.5234567\",\n \"text\": \"def __build_where_clause(where_columns_dictionary):\\n return ' AND '.join(key + \\\" = ? \\\"\\n if value is not None\\n else key + \\\" IS NULL \\\"\\n for key, value\\n in where_columns_dictionary.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df322a9786ad715a3d5849f5ed411c2d\",\n \"score\": \"0.5219735\",\n \"text\": \"def concat_conds(*args):\\n stmt = \\\" WHERE 1\\\"\\n for cond in args:\\n stmt = stmt + \\\" AND \\\" + cond\\n return stmt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b443f879e890682442787a51400a4a67\",\n \"score\": \"0.5218033\",\n \"text\": \"def build_query(cls, request, model):\\n # print(request.data)\\n name = request.data.get(\\\"search_phrase\\\")\\n criteria = request.data.get(\\\"search_criteria\\\")\\n frequency = request.data.get(\\\"frequency\\\")\\n age_distribution = request.data.get(\\\"age_distribution\\\")\\n letters_range = request.data.get(\\\"letters_range\\\")\\n\\n resp = model.objects.filter()\\n\\n if frequency is not None and len(frequency) > 0:\\n resp = model.objects.filter(frequency__in=frequency).defer(\\\"double_name\\\", \\\"number_of_letters\\\")\\n\\n double_name = True in letters_range\\n\\n # resp = resp.filter(double_name=True)\\n\\n if letters_range is not None and len(letters_range) > 0:\\n if double_name:\\n letters_range.remove(True)\\n resp = resp.filter(Q(number_of_letters__in=letters_range) | Q(double_name=True))\\n else:\\n resp = resp.filter(double_name=False)\\n resp = resp.filter(number_of_letters__in=letters_range)\\n\\n if age_distribution is not None and len(age_distribution) > 0:\\n mask = \\\"age_distribution_{}__gt\\\"\\n\\n # AND age_distribution way\\n # dd = {}\\n #\\n # for d in age_distribution:\\n # key = mask.format(d)\\n # dd[key] = 0\\n # resp = resp.filter(**dd)\\n\\n # OR age_distribution way\\n or_query_set = Q()\\n for d in age_distribution:\\n tmp = Q(**{mask.format(d): 0})\\n or_query_set |= tmp\\n\\n resp = resp.filter(or_query_set)\\n\\n if criteria is not None and len(name) > 0:\\n if criteria == \\\"start\\\":\\n resp = resp.filter(name__lower__istartswith=name.lower())\\n elif criteria == \\\"middle\\\":\\n resp = resp.filter(name__lower__icontains=name.lower())\\n elif criteria == \\\"end\\\":\\n resp = resp.filter(name__lower__iendswith=name.lower())\\n else:\\n resp = resp.filter(name__lower=name)\\n\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a699f49d4c0ba175277a65d66a2562a\",\n \"score\": \"0.52174175\",\n \"text\": \"def query_to_clojure(obj, params):\\n # Serialize the query, sorted keys for deterministic substitution, no pretty printing\\n data = json.dumps(obj, sort_keys=True)\\n # Split the JSON into parts using 'PARAM' as seperator\\n parts = data.split(\\\"PARAM\\\")\\n # Escape each JSON part by serializing as a JSON list and removing the qruare brackets\\n escaped_parts = [json.dumps([part])[1:-1] for part in parts]\\n # The amount of parts should be one more than the amount ot parameters\\n if len(parts) != len(params) + 1:\\n raise RuntimeError(\\\"Param count mismatch: \\\" +\\n str(len(params)) +\\n \\\" given \\\" +\\n str(len(parts) -1) +\\n \\\" needed ; \\\" +\\n data)\\n # Interleave the escaped JSON parts and the parameters and put them in a (str ) clojure expression\\n rval = \\\" \\\".join(list(itertools.chain(*zip([\\\"(query (str \\\"] + params, escaped_parts))) + [\\\") )\\\"])\\n return rval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47f0f449170335a3dd6dfeb971f17fa\",\n \"score\": \"0.52149814\",\n \"text\": \"def process(self):\\n q = \\\"?q=\\\"\\n lst = []\\n for field in self.filter_dict:\\n for keyword in self.filter_dict[field]:\\n for criteria in self.filter_dict[field][keyword]:\\n lst.append(\\\"{0} {1} '{2}'\\\".format(field, kw_map[keyword], criteria))\\n\\n q += \\\" and \\\".join(lst)\\n\\n if self.count:\\n q +=\\\"&count=True\\\"\\n self.query = q\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ea0ee25067279790679c78c36e66c2\",\n \"score\": \"0.52111125\",\n \"text\": \"def run_sql_statement(self, request, params=()):\\n\\n # simple string formatting RISK OF SQL INJECTION, DO NOT USE IN REAL DATABASE\\n output = request % tuple([\\\"'\\\" + p + \\\"'\\\" for p in params])\\n\\n self.output_sql += output + \\\"\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29abca87c3ec6ed8e7655886a9e9437\",\n \"score\": \"0.52013975\",\n \"text\": \"def prepare_sql(self, **kwargs):\\n source = kwargs.get(\\\"source\\\", self.source)\\n if not source:\\n raise ValueError(\\\"No source table or view specified.\\\")\\n\\n cutoff = kwargs.get(\\\"cutoff\\\", self.cutoff)\\n start_time = \\\"start_time_local\\\" if self.local else \\\"start_time\\\"\\n end_time = \\\"end_time_local\\\" if self.local else \\\"end_time\\\"\\n\\n predicates = kwargs.get(\\\"predicates\\\", [])\\n predicates = [predicates] if not isinstance(predicates, list) else predicates\\n\\n provider_name = kwargs.get(\\\"provider_name\\\", self.provider_name)\\n\\n if provider_name:\\n predicates.append(f\\\"lower(provider_name) = lower('{provider_name}')\\\")\\n\\n vts = \\\"'::vehicle_types,'\\\"\\n vehicle_types = kwargs.get(\\\"vehicle_types\\\", self.vehicle_types)\\n if vehicle_types:\\n if not isinstance(vehicle_types, list):\\n vehicle_types = [vehicle_types]\\n predicates.append(f\\\"vehicle_type IN ('{vts.join(vehicle_types)}'::vehicle_types)\\\")\\n\\n if len(predicates) > 0:\\n predicates = \\\" AND \\\".join(predicates) + \\\" AND \\\"\\n else:\\n predicates = \\\"\\\"\\n\\n timeranges = [\\n f\\\"({start_time} <= %(start)s AND {end_time} > %(start)s)\\\",\\n f\\\"({start_time} < %(end)s AND {end_time} >= %(end)s)\\\",\\n f\\\"({start_time} >= %(start)s AND {end_time} <= %(end)s)\\\"\\n ]\\n\\n if cutoff > 0:\\n where = f\\\"\\\"\\\"\\n {predicates}\\n ({end_time} IS NULL AND {start_time} between %(start)s - '{cutoff} days'::interval and %(end)s OR\\n (date_part('day', {end_time} - {start_time}) <= {cutoff} AND\\n ({\\\" OR \\\".join(timeranges)})))\\n \\\"\\\"\\\"\\n else:\\n timeranges.append(f\\\"({start_time} < %(end)s AND {end_time} IS NULL)\\\")\\n where = f\\\"\\\"\\\"\\n {predicates}\\n ({\\\" OR \\\".join(timeranges)})\\n \\\"\\\"\\\"\\n\\n order_by = kwargs.get(\\\"order_by\\\", self.order_by)\\n if order_by:\\n if not isinstance(order_by, list):\\n order_by = [order_by]\\n order_by = \\\",\\\".join(order_by)\\n order_by = f\\\"ORDER BY {order_by}\\\"\\n\\n return f\\\"\\\"\\\"\\n SELECT\\n *\\n FROM\\n {source}\\n WHERE\\n {where}\\n {order_by};\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37809edf146a1f0665c82f677f3754e7\",\n \"score\": \"0.51989144\",\n \"text\": \"def generate_select_request(self, conditions=None):\\n\\n if conditions is None:\\n return self._SELECT_REQUEST_STRING\\n else:\\n request = self._SELECT_REQUEST_STRING + \\\" WHERE {}={}\\\"\\n request = request.format(*conditions.split(\\\"=\\\"))\\n return request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d39ef560bb5a4fecbc1c6cd66834ec85\",\n \"score\": \"0.5198576\",\n \"text\": \"def _getQueryString(self):\\n return \\\"select obj from CommentAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80c750afc8c0dee265f318d3f44d2f7\",\n \"score\": \"0.51965994\",\n \"text\": \"def generate_clause(vars, operator):\\n return operator(*vars, evaluate=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e77ba0a35e57f35fb497a8cc5137a0\",\n \"score\": \"0.5192215\",\n \"text\": \"def subquery(self, withs, filters=None, optional=False):\\n input_with = ', '.join(withs)\\n new_withs = withs - {self.collection_alias}\\n output_with = ', '.join(new_withs) + ', ' + self.with_statement()\\n\\n where_string = ''\\n if filters is not None:\\n relevant = []\\n for c in filters:\\n if c.involves(self):\\n relevant.append(c.for_cypher())\\n if relevant:\\n where_string = 'WHERE ' + '\\\\nAND '.join(relevant)\\n for_match = self.subquery_match_template.format(anchor_alias=self.anchor_node.alias,\\n collection_alias=self.collection_alias)\\n kwargs = {'for_match': for_match,\\n 'optional': '',\\n 'collection_alias': self.collection_alias,\\n 'output_with_string': output_with}\\n if optional:\\n kwargs['optional'] = 'OPTIONAL '\\n return self.subquery_template.format(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c91288d0f92106eb67c80c495661bb2\",\n \"score\": \"0.5183175\",\n \"text\": \"def build_query(self):\\n # initalize config\\n query = super(GlobalTaskTableSource, self).build_query()\\n query = query.avoid_duplicates()\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c4035fb5ab40bfe3985d56797e9590\",\n \"score\": \"0.51793414\",\n \"text\": \"def prepareQuery(queryString, initNs={}, base=None):\\n ret = translateQuery(parseQuery(queryString), base, initNs)\\n ret._original_args = (queryString, initNs, base)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ac992c3bdb35350862ce9f31ab0d1b\",\n \"score\": \"0.51777846\",\n \"text\": \"def buildQuery(self, formInfo, user, query, languages):\\r\\n return query.options(subqueryload('concepts'))\\\\\\r\\n .join(concept_list_concepts)\\\\\\r\\n .join(Concept)\\\\\\r\\n .join(formInfo.formModel, (formInfo.formModel.language_id == languages.foreign.id) & (formInfo.formModel.concept_id == Concept.id))\\\\\\r\\n .outerjoin(Mastery, (Mastery.user_id==user.id) & (getattr(Mastery, formInfo.masteryFieldName) == formInfo.formModel.id))\\\\\\r\\n .outerjoin(StalenessPeriod)\\\\\\r\\n .order_by(formInfo.listModel.averageRatingFor(user, languages.foreign))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1c27b14b20da118bc686538eb54c03e\",\n \"score\": \"0.51636916\",\n \"text\": \"def concat_query_make(query_ends):\\n\\n\\tquery = result_queries.START\\n\\tstart = True\\n\\n\\tif query_ends:\\n\\t\\tfor end in query_ends:\\n\\t\\t\\tif start:\\n\\t\\t\\t\\tquery += end\\n\\t\\t\\t\\t#end is the varying return value to an otherwise identical query\\n\\n\\n\\t\\t\\tif not start:\\n\\t\\t\\t\\tquery += \\\" || \\\\\\\"DELIM\\\\\\\" || \\\"\\n\\t\\t\\t\\tquery += end\\n\\t\\n\\t\\t\\tstart = False\\n\\t\\t\\t\\n\\telse:\\n\\t\\tquery = \\\"(no query selected)\\\"\\n\\t\\n\\treturn query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba8011bc739cf5b088eb97c87937d2b\",\n \"score\": \"0.51636404\",\n \"text\": \"def evaluate_node_sql(node):\\n if 'condition' in node:\\n # Node is a condition, get the values of the sub-clauses\\n sub_pairs = \\\\\\n [evaluate_node_sql(x) for x in node['rules']]\\n\\n if not sub_pairs:\\n # Nothing has been returned, so it is an empty query\\n return '', []\\n\\n # Now combine\\n if node['condition'] == 'AND':\\n result = '((' + \\\\\\n ') AND ('.join([x for x, __ in sub_pairs]) + '))'\\n else:\\n result = '((' + \\\\\\n ') OR ('.join([x for x, __ in sub_pairs]) + '))'\\n result_fields = \\\\\\n list(itertools.chain.from_iterable([x for __, x in sub_pairs]))\\n\\n if node.get('not', False):\\n result = '(NOT (' + result + '))'\\n\\n return result, result_fields\\n\\n # Get the variable name and duplicate the symbol % in case it is part of\\n # the variable name (escape needed for SQL processing)\\n varname = fix_pctg_in_name(node['field'])\\n\\n # Get the operator\\n operator = node['operator']\\n\\n # If the operator is between or not_between, there is a special case,\\n # the constant cannot be computed because the node['value'] is a pair\\n constant = None\\n if node['value'] is not None and not isinstance(node['value'], list):\\n # Calculate the constant value depending on the type\\n if node['type'] == 'integer':\\n constant = int(node['value'])\\n elif node['type'] == 'double':\\n constant = float(node['value'])\\n elif node['type'] == 'boolean':\\n constant = node['value'] == '1'\\n elif node['type'] == 'string':\\n constant = node['value']\\n elif node['type'] == 'datetime':\\n constant = node['value']\\n else:\\n raise Exception(\\n _('No function to translate type {0}').format(node['type'])\\n )\\n\\n # Terminal Node\\n result_fields = []\\n if operator == 'equal':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' = %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [str(constant)]\\n\\n elif operator == 'not_equal':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n '!= %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [str(constant)]\\n\\n elif operator == 'begins_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' LIKE %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [node['value'] + \\\"%\\\"]\\n\\n elif operator == 'not_begins_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' NOT LIKE %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [node['value'] + \\\"%\\\"]\\n\\n elif operator == 'contains' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' LIKE %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value'] + \\\"%\\\"]\\n\\n elif operator == 'not_contains' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' NOT LIKE %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value'] + \\\"%\\\"]\\n\\n elif operator == 'ends_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' LIKE %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value']]\\n\\n elif operator == 'not_ends_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' NOT LIKE %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value']]\\n\\n elif operator == 'is_empty' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n \\\" = '') OR (\\\\\\\"{0}\\\\\\\" is null)\\\".format(varname)\\n\\n elif operator == 'is_empty' and node['type'] != 'string':\\n result = \\\"(\\\\\\\"{0}\\\\\\\" is null)\\\".format(varname)\\n\\n elif operator == 'is_not_empty' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n \\\" != '') AND (\\\\\\\"{0}\\\\\\\" is not null)\\\".format(varname)\\n\\n elif operator == 'is_not_empty' and node['type'] != 'string':\\n result = \\\"(\\\\\\\"{0}\\\\\\\" is not null)\\\".format(varname)\\n\\n elif operator == 'less' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' < %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'less_or_equal' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' <= %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'greater' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' > %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'greater_or_equal' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' >= %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'between' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' BETWEEN %s AND %s'\\n result_fields = [str(node['value'][0]), str(node['value'][1])]\\n\\n elif operator == 'not_between' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' NOT BETWEEN %s AND %s'\\n result_fields = [str(node['value'][0]), str(node['value'][1])]\\n\\n else:\\n raise Exception(\\n _('Type, operator, field {0}, {1}, {2} not supported yet').format(\\n node['type'], operator, varname\\n )\\n )\\n\\n if node.get('not', False):\\n raise Exception(_('Negation found in unexpected location'))\\n\\n return result, result_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd8285bd0816627e7f7b40210481df8\",\n \"score\": \"0.5158893\",\n \"text\": \"def query(\\n self, strOrQuery, initBindings={},\\n initNs={}, base=None, DEBUG=False):\\n\\n if not isinstance(strOrQuery, Query):\\n parsetree = parseQuery(strOrQuery)\\n query = translateQuery(parsetree, base, initNs)\\n else:\\n query = strOrQuery\\n\\n return evalQuery(self.graph, query, initBindings, base)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911fc88c4f06f7a15ccb80b7985bca8c\",\n \"score\": \"0.51363057\",\n \"text\": \"def execute(self, neo4j_session: Session):\\n if self.query == \\\"\\\":\\n raise ValueError(\\n \\\"The query must be generated before this method is called.\\\"\\n )\\n try:\\n return neo4j_session.write_transaction(lambda tx: tx.run(self.query))\\n except:\\n print(\\\"Query not defined error\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af7e59578232b5286f33142426d48b4\",\n \"score\": \"0.51362425\",\n \"text\": \"def buildConnectionString(params):\\n return \\\";\\\".join([\\\"%s=%s\\\" % (k, v) for k, v in params.items()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"250b21f2993d378d79cd2b0b0096170a\",\n \"score\": \"0.5131796\",\n \"text\": \"def _row_to_query_statement_string(self, row):\\n start_node = 'MERGE (start:{state_lab} {{code:\\\"{start_state}\\\"}})'.format(\\n state_lab=self.labels.state, start_state=row[self.start_state_col]\\n )\\n\\n end_node = 'MERGE (end:{state_lab} {{code:\\\"{end_state}\\\"}})'.format(\\n state_lab=self.labels.state, end_state=row[self.end_state_col]\\n )\\n\\n transition = \\\"MERGE (start)<-[:SOURCE]-(trans:{trans_lab})-[:TARGET]->(end)\\\".format(\\n trans_lab=self.labels.transition\\n )\\n\\n def _conditions_str(row, start_state_col, end_state_col):\\n \\\"\\\"\\\"Build the string used to express transition conditions.\\n \\n This is the part in curly braces specifying the Condition node's\\n properties.\\n \\\"\\\"\\\"\\n row = row.drop([start_state_col, end_state_col])\\n count = 0\\n s = \\\"\\\"\\n for i, val in row.iteritems():\\n s += i + \\\":\\\"\\n if isinstance(val, six.string_types):\\n s += '\\\"' + val + '\\\"'\\n elif isinstance(val, bool):\\n s += str(val).lower()\\n else:\\n s += str(val)\\n if count < len(row) - 1:\\n s += \\\", \\\"\\n count += 1\\n\\n return \\\"{\\\" + s + \\\"}\\\"\\n\\n condition = \\\"MERGE (cond:{cond_lab} {cond_str})-[:CAUSES]->(trans)\\\".format(\\n cond_lab=self.labels.condition,\\n cond_str=_conditions_str(row, self.start_state_col, self.end_state_col),\\n )\\n\\n query_str = (\\n start_node + \\\" \\\" + end_node + \\\" \\\" + transition + \\\" \\\" + condition + \\\";\\\"\\n )\\n\\n if self.global_params:\\n query_str = self._add_global_params_to_query_string(\\n query_str, self.global_params\\n )\\n\\n return query_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c7e0913a92b7dfb4e7cc3e5cb349f6\",\n \"score\": \"0.5125673\",\n \"text\": \"def build_query(url: str, *args, **kwargs) -> str:\\n url = url.strip(\\\"/\\\")\\n args = map(lambda arg: arg.strip(\\\"/\\\"), args)\\n kwargs = dict(filter(lambda kv: kv[1] is not None, kwargs.items()))\\n url = \\\"/\\\".join([url, *args]) + \\\"?\\\"\\n url += \\\"&\\\".join([f\\\"{key}={value}\\\" for key, value in kwargs.items()])\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976a7fa212ec09e76082f81572c24b87\",\n \"score\": \"0.51216996\",\n \"text\": \"def build_query(field, fields, fuzziness=\\\"AUTO\\\"):\\n val = fields[field]\\n if \\\"*\\\" in val:\\n qtype = \\\"wildcard\\\"\\n fval = val\\n else:\\n qtype = \\\"match\\\"\\n fval = dict(query=val, fuzziness=fuzziness)\\n return Q(qtype, **{field: fval})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27d86e7c41d5fb86b9a53f995bbc51a9\",\n \"score\": \"0.5117474\",\n \"text\": \"def q(*args: Any, **kwargs: Any) -> \\\"Query\\\":\\n return Query(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0fd368bbe1ee92e7081900909296ef4\",\n \"score\": \"0.51087725\",\n \"text\": \"def query_on_params(self,\\\\\\n **kwargs):\\n if self.check_empty_query(**kwargs):\\n return []\\n \\n elif 'username' in kwargs and len(kwargs) == 1:\\n name = kwargs['username'][0]\\n return self.query_on_name(\\\\\\n name,\\\\\\n self.all()) \\n\\n username_s = kwargs['username'][0]\\n fullname_s = kwargs['fullname'][0]\\n faculty_s = kwargs['faculty'][0]\\n program_s = kwargs['program'][0]\\n query_set_l = self.all()\\n if username_s != '':\\n query_set_l = query_set_l.filter(\\\\\\n username__icontains = username_s)\\n if fullname_s != '':\\n query_set_l = self.query_on_name(\\\\\\n fullname_s,\\\\\\n query_set_l)\\n if faculty_s != '':\\n query_set_l = query_set_l.filter(\\\\\\n faculty__exact = faculty_s)\\n if program_s != '':\\n query_set_l = query_set_l.filter(\\\\\\n program__exact = program_s)\\n return query_set_l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a40795caa1ea72b2cdda71b5e0413200\",\n \"score\": \"0.51069057\",\n \"text\": \"def sql_condition(\\n self,\\n conditions=tuple(),\\n all_inner=True,\\n all_outer=True,\\n match_inner=True,\\n match_outer=True,\\n ):\\n args = locals()\\n # put the conditions into a dict\\n conds = {\\n pref: args.get(pref)\\n for pref in (\\n \\\"all_inner\\\",\\n \\\"all_outer\\\",\\n \\\"match_inner\\\",\\n \\\"match_outer\\\",\\n )\\n }\\n conds = { # properly cycle all settings\\n pref: itertools.cycle(val if hasattr(val, \\\"__iter__\\\") else (val,))\\n for pref, val in conds.items()\\n }\\n conds[\\\"values\\\"] = (\\n (conditions,) if hasattr(conditions, \\\"items\\\") else conditions\\n )\\n sql_conditions, sql_params = [], []\\n for cond in listdict_to_dictlist(conds):\\n sanitized_values = {\\n self.sanitize(k, brutally=True): self.sanitize(\\n v, brutally=False\\n )\\n if isinstance(v, str)\\n else v\\n for k, v in cond[\\\"values\\\"].items()\\n if k in self.tableheader\\n }\\n inner_sql_cond = (\\\" AND \\\" if cond[\\\"all_inner\\\"] else \\\" OR \\\").join(\\n map(\\n \\\"{} {} ?\\\".format,\\n sanitized_values.keys(),\\n itertools.cycle(\\n (\\\"IS\\\" if cond[\\\"match_inner\\\"] else \\\"IS NOT\\\",)\\n ),\\n )\\n ) or \\\"1=1\\\"\\n sql_conditions.append(\\n \\\"{}({})\\\".format(\\n \\\"\\\" if cond[\\\"match_outer\\\"] else \\\"NOT \\\", inner_sql_cond\\n )\\n )\\n sql_params.extend(tuple(sanitized_values.values()))\\n sql_condition = (\\n functools.reduce(\\n lambda x, y: (\\n \\\" AND \\\" if next(conds[\\\"all_outer\\\"]) else \\\" OR \\\"\\n ).join((x, y)),\\n sql_conditions,\\n )\\n if sql_conditions\\n else \\\"1=1\\\"\\n )\\n return sql_condition, tuple(sql_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c126e83185b3f095e5bc3f7c32cda876\",\n \"score\": \"0.51048845\",\n \"text\": \"def post(self, statement, parameters=None):\\n payload = {\\\"query\\\": statement}\\n if parameters:\\n payload[\\\"params\\\"] = {}\\n for key, value in parameters.items():\\n if isinstance(value, (Node, Rel, Relationship)):\\n value = value._id\\n payload[\\\"params\\\"][key] = value\\n log.info(\\\"execute %r %r\\\", payload[\\\"query\\\"], payload.get(\\\"params\\\", {}))\\n return self.resource.post(payload)\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":184,"cells":{"query_id":{"kind":"string","value":"152c3edbb3aa0b05d956c5dcb6acb205"},"query":{"kind":"string","value":"Initialize the Decomposable OperatorValued Kernel."},"positive_passages":{"kind":"list like","value":[{"docid":"f09118c547cd865e208584e4cfd55bdd","score":"0.0","text":"def __init__(self, X, gamma):\n super(RBFDivFreeKernelMap, self).__init__(gamma)\n self.n = X.shape[0]\n self.d = X.shape[1]\n self.p = X.shape[1]\n self.X = X\n self.Gs_train = None","title":""}],"string":"[\n {\n \"docid\": \"f09118c547cd865e208584e4cfd55bdd\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self, X, gamma):\\n super(RBFDivFreeKernelMap, self).__init__(gamma)\\n self.n = X.shape[0]\\n self.d = X.shape[1]\\n self.p = X.shape[1]\\n self.X = X\\n self.Gs_train = None\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"31c1f825722e935a9f2040878ee62b3c","score":"0.6014059","text":"def init_op(self):\n return self._init_op","title":""},{"docid":"933dc4e128744f5c26c4a36acf306da6","score":"0.59667915","text":"def __init__(self,kernel_fn,*args,order=2,**kwargs):\n super().__init__(*args,**kwargs)\n self.dkernel_fn = DiscretizedKernelFN(kernel_fn,order)","title":""},{"docid":"6d9845f2e29e914f28540bd4faadf50b","score":"0.5955899","text":"def __init__(self, X, A, scalar_kernel, scalar_kernel_params):\n super(DecomposableKernelMap, self).__init__(A, scalar_kernel,\n scalar_kernel_params)\n self.n = X.shape[0]\n self.d = X.shape[1]\n self.X = X\n self.Gs_train = None","title":""},{"docid":"4fca6d74bf6efc98b6afae92b5d813d2","score":"0.594024","text":"def __init__(self):\n # set the kernel. One can play with the parameters.\n self.set_kernel(1, 2.0, 0.98)","title":""},{"docid":"0ab1359409ab9f3bea19235d57510e45","score":"0.5931874","text":"def __init__(self):\n self.d = dd(lambda : 0)","title":""},{"docid":"679da9cba2b0e04380ef1933feec9920","score":"0.579953","text":"def _setup_primitive_kernel(request) -> SDEKernel:\n return _create_markovflow_primitive_kernel(request.param)","title":""},{"docid":"32cd9d7e5acbca27580e7960d902ce69","score":"0.56664544","text":"def __init__(self, kernel, adopt_thresh, state, target, maxsize, adaptive=True, forget_rate=0.0):\n\t\tself.dp = KernelDict(kernel, adopt_thresh, state, target, maxsize, adaptive, forget_rate)\n\t\tself.P = [[1]]\n\t\tself.Alpha = np.dot(self.dp.Kinv, target)","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"5acf0ce085e1d9c8d75204b2a828c8ad","score":"0.56514794","text":"def _init_op(new_op):\n\n def _impl(inputs, attrs):\n assert len(inputs) == 0\n shape = attrs.get_int_tuple(\"shape\")\n dtype = attrs.get_str(\"dtype\", \"float32\")\n return new_op(shape=shape, dtype=dtype)\n\n return _impl","title":""},{"docid":"d18640e6b96ae414bc449aa49d579501","score":"0.5644447","text":"def __init__(self, kernel=None, noise_var=None, exact_feval=False, optimizer='bfgs',\n max_iters=1000, optimize_restarts=5, sparse=None, num_inducing=10,\n verbose=False, ARD=False, seed=42, normalize_Y=True, n_subspaces=12, update_freq=5):\n self.kernel = kernel\n self.noise_var = noise_var\n self.exact_feval = exact_feval\n self.optimize_restarts = optimize_restarts\n self.optimizer = optimizer\n self.max_iters = max_iters\n self.verbose = verbose\n self.sparse = sparse\n self.sparse_surrogate = False\n self.num_inducing = num_inducing\n self.model = None\n self.ARD = ARD\n self.seed = seed\n self.update_interval = update_freq\n self.normalize_Y = normalize_Y\n self.n_sub = n_subspaces\n self.n_decomp = 20\n np.random.rand(self.seed)","title":""},{"docid":"abb773c00e94f7cd4d00189b8b26809d","score":"0.5515068","text":"def __call__(self):\r\n new_node = Op.__call__(self)\r\n new_node.inputs = []\r\n new_node.name = \"Global_Variables_Initializer\"\r\n return new_node","title":""},{"docid":"c8983388747a5dd2343aaa072cff2509","score":"0.5513527","text":"def kernel(self, value):\n\n if value is not None:\n assert hasattr(\n value,\n '__call__'), ('\"{0}\" attribute: \"{1}\" is not callable!'.format(\n 'kernel', value))\n\n self._kernel = value","title":""},{"docid":"a05329e78028c09e4d065fb146db76c7","score":"0.5498108","text":"def __init__(\n self,\n params: grid_parametrization.GridParametrization,\n kernel_op: get_kernel_fn.ApplyKernelOp,\n solver_option: poisson_solver_pb2.PoissonSolver,\n ):\n super().__init__(params, kernel_op, solver_option)\n\n self._kernel_op.add_kernel({'weighted_sum_121': ([1.0, 2.0, 1.0], 1)})\n self._omega = solver_option.jacobi.omega\n self._num_iters = solver_option.jacobi.max_iterations\n self._halo_width = solver_option.jacobi.halo_width\n\n self._factor_b = 0.5 / (params.dx**-2 + params.dy**-2 + params.dz**-2)\n self._factor_x = params.dx**2 / self._factor_b\n self._factor_y = params.dy**2 / self._factor_b\n self._factor_z = params.dz**2 / self._factor_b","title":""},{"docid":"1dfd0e455ad0824814bf1628e531aa99","score":"0.5469124","text":"def __init__(self, initial_value, *args, dtype=None, **kwargs):\n # Variables by default use the current device scope for placement. This\n # wrapper has them follow the initial value's placement instead (which will\n # be the DTensor device if the initial value has a layout).\n\n # Pop layout from kwargs since keras make_variable may pass a 'layout'\n # keyword argument. We need to pop it because we are passing kwargs to\n # super class constructor.\n layout = kwargs.pop('layout', None)\n shape = kwargs.get('shape', None)\n\n if callable(initial_value):\n unwrapped = initial_value\n if issubclass(type(initial_value), functools.partial):\n unwrapped = initial_value.func\n\n # If wrapped is a CheckpointInitialValueCallable, this means that\n # we are creating a Variable during a checkpoint restore.\n # Thus the restore will happen now through this callable\n # and we will create the DVariable with the restored dtensor.\n if issubclass(type(unwrapped), trackable.CheckpointInitialValueCallable):\n if not shape or not layout:\n raise ValueError('Expected shape and layout to be not None.')\n\n # CheckpointInitialValueCallable will call an eager tf.RestoreV2,\n # which does not have any shape information or layout information\n # attached. Thus we will do two things to have them correctly specified:\n #\n # The default layout scope allows us to correctly specify the output\n # layout of the tf.RestoreV2 that will be called\n #\n # Passing shard_info with the correct shape allows the tf.RestoreV2\n # ShapeInference to extract the shape.\n initial_value = api.call_with_layout(\n initial_value,\n layout,\n shard_info=trackable.ShardInfo(\n shape=shape, offset=[0] * len(shape)))\n else:\n initial_value = initial_value()\n\n # When the initial value came from a Checkpoint restoration, fetch tensor.\n if isinstance(initial_value, trackable.CheckpointInitialValue):\n initial_value = initial_value.wrapped_value\n\n initial_value = ops.convert_to_tensor(initial_value, dtype=dtype)\n variable_device = initial_value.device\n self._save_as_bf16 = False\n # TODO(b/159035705): The following code enables variable creation inside\n # a tf.function. However, it requires a global dtensor device.\n # if not variable_device and not tf.executing_eagerly():\n # try:\n # initial_value.op.get_attr(\"_layout\")\n # except ValueError:\n # pass\n # else:\n # # The initial value is a DTensor, but because the DTensor device is\n # # only active during eager execution at the moment we need to\n # # translate that into a placement for the eager VarHandleOp.\n # variable_device = _dtensor_device().name\n with ops.device(variable_device):\n # If initial tensor assigned to DVariable is DTensor, record the layout of\n # the resource so that this can be queried.\n if context.executing_eagerly():\n if api.is_dtensor(initial_value):\n value_layout = api.fetch_layout(initial_value)\n if layout is not None and layout != value_layout:\n raise errors_impl.InvalidArgumentError(\n None,\n None,\n 'Conflicting layout are provided for initial '\n f'value layout ({value_layout}) and variable ({layout}).',\n )\n layout = value_layout\n elif layout is not None:\n initial_value = api.relayout(initial_value, layout)\n else:\n raise errors_impl.InvalidArgumentError(\n None,\n None,\n 'Neither layout nor DTensor initial value are provided.',\n )\n self.layout = layout\n with api.default_mesh(layout.mesh):\n super(DVariable, self).__init__(\n initial_value, *args, dtype=dtype, **kwargs\n )\n else:\n # FIXME(175928457): Record value layout in graph mode.\n if layout is not None:\n initial_value = api.relayout(initial_value, layout)\n super(DVariable, self).__init__(\n initial_value, *args, dtype=dtype, **kwargs)","title":""},{"docid":"c3de15c5a768f66fe4fbaed3a7d68cc4","score":"0.54562724","text":"def __init__(self, kernel=None, hp=None):\n\n self.kernel = kernel\n self.hp = hp\n self.name = kernel.__name__","title":""},{"docid":"95f099c9ceb72599c3eb191e27acebee","score":"0.5444765","text":"def __init__(\n self,\n kernel='linear', # kernel type\n max_iter=180, # maximum iteration times\n conv=1e-3, # convergence criterion\n beta=1e3, # initial beta\n bias_used=True # bias\n ):\n self.kernel = kernel\n self.n_iter = max_iter\n self.conv = conv\n self.beta = beta\n self.bias_used = bias_used\n self.alpha, self.alpha_, self.alpha_old, self.beta_, self.relevance, self.phi, self.phi_,\\\n self.y, self.mean_, self.sigma_, self.bias = [None]*11","title":""},{"docid":"2fa80d6f5fdd7a6d6ca7a2bed06d3ca9","score":"0.5426685","text":"def __init__(self, model):\n self.model = model\n self.opcache = {} # a cache of operators, which should get initialized by from_vector calls","title":""},{"docid":"4b823c3cb49da97065fb9a3afeeb8a89","score":"0.53927","text":"def __call__(self, initial_value, dtype = None, shape = None, name = \"Variable\"):\r\n new_node = Op.__call__(self)\r\n if shape is not None:\r\n assert shape == initial_value.shape\r\n if dtype is not None:\r\n if isinstance(initial_value, np.ndarray):\r\n global_variables[new_node] = initial_value.astype(dtype)\r\n else:\r\n global_variables[new_node] = np.array(initial_value).astype(dtype)\r\n else:\r\n global_variables[new_node] = initial_value\r\n new_node.name = name\r\n return new_node","title":""},{"docid":"303057902312f28c3b3789db58ed8b76","score":"0.5379558","text":"def __init__(self, kernel_size):\n\n self.stride = kernel_size\n self.kernel_size = kernel_size","title":""},{"docid":"75b72de45026680e384ba08990058900","score":"0.5367593","text":"def __init__(self, **kwargs):\n\n LOG.info(\"%s: initing...\" % str(self))\n\n super(Generic2dOscillator, self).__init__(**kwargs)\n\n #self._state_variables = [\"V\", \"W\"]\n self._nvar = 2\n self.cvar = numpy.array([0], dtype=numpy.int32)\n\n LOG.debug(\"%s: inited.\" % repr(self))","title":""},{"docid":"b7aab43ddc245a9aeebd561fb21486f2","score":"0.53663516","text":"def _setup_impl(self):\n\n self.bpm_program = OCLProgram(absPath(\"kernels/bpm_3d_kernels.cl\"))\n\n Nx, Ny, Nz = self.size\n\n self._plan = fft_plan((Ny,Nx))\n\n\n self._H_g = OCLArray.from_array(self._H.astype(np.complex64))\n\n if not self.dn is None and self.n_volumes==1:\n self.dn_g = OCLArray.from_array(self.dn)\n\n\n self.scatter_weights_g = OCLArray.from_array(self.scatter_weights.astype(np.float32))\n self.gfactor_weights_g = OCLArray.from_array(self.gfactor_weights.astype(np.float32))\n\n self.scatter_cross_sec_g = OCLArray.zeros(Nz,\"float32\")\n self.gfactor_g = OCLArray.zeros(Nz,\"float32\")\n\n\n\n # self.reduce_kernel = OCLReductionKernel(\n # np.float32, neutral=\"0\",\n # reduce_expr=\"a+b\",\n # map_expr=\"weights[i]*cfloat_abs(field[i]-(i==0)*plain)*cfloat_abs(field[i]-(i==0)*plain)\",\n # arguments=\"__global cfloat_t *field, __global float * weights,cfloat_t plain\")","title":""},{"docid":"5fdf5263c27b7114fd643c548c5c86cc","score":"0.5359017","text":"def __init__(self, value):\n\t\tsuper(Functor, self).__init__(value)","title":""},{"docid":"6bb8bab55f68310770b6b4a2897fe8af","score":"0.53568095","text":"def __init__(self, kernel_type=None):\r\n self.kernel_type = kernel_type\r\n self.gamma = 0\r\n self.coef = 0\r\n self.degree = 0\r\n self.num_classes = 0\r\n self.labels = None\r\n self.total_sv = 0\r\n self.rho = 0\r\n self.sv = []","title":""},{"docid":"0967a0baddc13a0d6db87af07c84175b","score":"0.5346729","text":"def initialize_operator(self, operator=None, matrix=False, eval_at_once=False):\n # TODO: Make this more efficient, only compute values needed at each (r,c) step.\n # For this, 'operator' must support the 'component=(r,c)' option.\n # Operator is None is interpreted as identity transformation\n if operator is None:\n self._operator = lambda nodes, dummy, entry=None: ones((1, nodes.shape[1])) if entry[0] == entry[1] else zeros((1, nodes.shape[1]))\n else:\n # Wrap the operator inside a lambda ignoring the dummy parameter\n # This allows to use the same nsd code for quadrature and matrix construction.\n if matrix is False:\n self._operator = lambda nodes, dummy, entry=None: operator(nodes, entry=entry)\n else:\n self._operator = operator\n self._eval_at_once = eval_at_once","title":""},{"docid":"b68ca023fe7092aeaef64e1da8254009","score":"0.5345871","text":"def _init_routine(self):\n if self.is_initialized is False:\n # decide the routine\n if self.sn_def['op'] in {'d', 'project'}:\n # for d kernel_shape [num_in, num_out]; for project, kernel shape [num_class, num_in]\n assert len(self.kernel_shape) == 2, \\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\n num_in, num_out = self.kernel_shape\n # self.use_u = True\n self.use_u = True if num_in <= num_out else False\n x_shape = [1, num_in] if self.use_u else [1, num_out]\n self.forward = self._dense_ if self.use_u else self._dense_t_\n self.backward = self._dense_t_ if self.use_u else self._dense_\n elif self.sn_def['op'] in {'cd'}: # kernel_shape [num_class, num_in, num_out]\n assert len(self.kernel_shape) == 3, \\\n '{}: kernel shape {} does not have length 3'.format(self.name_in_err, self.kernel_shape)\n num_class, num_in, num_out = self.kernel_shape\n self.use_u = True if num_in <= num_out else False\n x_shape = [num_class, 1, num_in] if self.use_u else [num_class, 1, num_out]\n self.forward = self._dense_ if self.use_u else self._dense_t_\n self.backward = self._dense_t_ if self.use_u else self._dense_\n elif self.sn_def['op'] in {'dck'}: # convolution * conditional scale\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\n assert len(self.kernel_shape[0]) == 2 and len(self.kernel_shape[1]) == 2, \\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\n num_in, num_out = self.kernel_shape[0]\n num_class = self.kernel_shape[1][0]\n self.use_u = True if num_in <= num_out else False\n x_shape = [num_class, num_in] if self.use_u else [num_class, num_out]\n self.forward = (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0)) \\\n if self.use_u else (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0))\n self.backward = (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0)) \\\n if self.use_u else (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0))\n elif self.sn_def['op'] in {'c', 'tc'}:\n assert len(self.kernel_shape) == 4, \\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\n # self.use_u = True\n self.use_u = True \\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\n else False\n if self.sn_def['op'] in {'c'}: # input / output shape NCHW or NHWC\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\n x_shape[0] = 1\n y_shape = self.sn_def['input_shape'].copy()\n y_shape[0] = 1\n elif self.sn_def['op'] in {'tc'}: # tc\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\n x_shape[0] = 1\n y_shape = self.sn_def['output_shape'].copy()\n y_shape[0] = 1\n else:\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\n self.forward = self._conv_ if self.use_u else (lambda y: self._conv_t_(y, x_shape=y_shape))\n self.backward = (lambda y: self._conv_t_(y, x_shape=y_shape)) if self.use_u else self._conv_\n elif self.sn_def['op'] in {'cck', 'tcck'}: # convolution * conditional scale\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\n assert len(self.kernel_shape[0]) == 4 and len(self.kernel_shape[1]) == 4, \\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\n self.use_u = True \\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\n else False\n num_class = self.kernel_shape[1][0]\n if self.sn_def['op'] in {'cck'}: # input / output shape NCHW or NHWC\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\n x_shape[0] = num_class\n y_shape = self.sn_def['input_shape'].copy()\n y_shape[0] = num_class\n self.forward = (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0)) \\\n if self.use_u \\\n else (lambda y: self._conv_t_(self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0))\n self.backward = (lambda y: self._conv_t_(\n self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0)) \\\n if self.use_u else (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0))\n elif self.sn_def['op'] in {'tcck'}: # tcck\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\n x_shape[0] = num_class\n y_shape = self.sn_def['output_shape'].copy()\n y_shape[0] = num_class\n self.forward = (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0)) \\\n if self.use_u \\\n else (lambda y: self._scalar_(self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0))\n self.backward = (lambda y: self._scalar_(\n self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0)) \\\n if self.use_u else (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0))\n else:\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\n else:\n raise NotImplementedError('{}: {} is not implemented.'.format(self.name_in_err, self.sn_def['op']))\n\n self.x = tf.compat.v1.get_variable(\n 'in_rand', shape=x_shape, dtype=tf.float32,\n initializer=tf.truncated_normal_initializer(), trainable=False)\n\n self.is_initialized = True","title":""},{"docid":"f2a6967944c915daed81a3fe3422a498","score":"0.5284371","text":"def __init__(self, shape, k, filters, kernels):\n self.dims = shape\n self.k = k\n self.filters = filters\n self.kernels = kernels","title":""},{"docid":"3d489be3b563e75c12cba4f31c651635","score":"0.52758646","text":"def __init__(self, var, accum, linear, grad, indices,\n lr, lr_power, l1, l2, l2_shrinkage, kernel_name):\n super().__init__(var, grad, indices, kernel_name)\n self.lr = lr\n self.lr_power = lr_power\n self.l1 = l1\n self.l2 = l2\n self.l2_shrinkage = l2_shrinkage\n self.lr_vrec = 1.0 / self.lr\n self.var_shape = var.get(\"shape\")\n self.var_dtype = var.get(\"dtype\").lower()\n self.accum_shape = accum.get(\"shape\")\n self.accum_dtype = accum.get(\"dtype\").lower()\n self.linear_shape = linear.get(\"shape\")\n self.linear_dtype = linear.get(\"dtype\").lower()\n self.check_param()","title":""},{"docid":"a0686a0db70ea3e597fcc88adaa8c3f5","score":"0.52641636","text":"def __init__(self):\n self.da_val = DynamicArray()\n self.da_max = DynamicArray()","title":""},{"docid":"b24468922248ebc17217ff925e070f54","score":"0.526289","text":"def __init__(self, D, kernel_gamma, m, step_size, gamma2=0.1, schedule=None, acc_star=0.234,\n update_kernel_gamma=None, update_kernel_gamma_schedule=None, update_kernel_gamma_tol=0.1):\n self.kernel_gamma = kernel_gamma\n self.m = m\n self.D = D\n self.gamma2 = gamma2\n self.step_size = step_size\n self.schedule = schedule\n self.acc_star = acc_star\n self.update_kernel_gamma = update_kernel_gamma\n self.update_kernel_gamma_schedule = update_kernel_gamma_schedule\n self.update_kernel_gamma_tol = update_kernel_gamma_tol\n \n # scaling parameter evolution might be collected to assess convergence\n self.nu2s = [step_size]\n \n # some sanity checks\n if acc_star is not None:\n assert acc_star > 0 and acc_star < 1\n \n if schedule is not None:\n lmbdas = np.array([schedule(t) for t in np.arange(100)])\n assert np.all(lmbdas > 0)\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\n \n if self.update_kernel_gamma:\n self.past_samples = collections.deque()\n \n if self.update_kernel_gamma_schedule is not None:\n lmbdas = np.array([update_kernel_gamma_schedule(t) for t in np.arange(100)])\n assert np.all(lmbdas > 0)\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\n \n self._initialise()","title":""},{"docid":"260cd30aa6404a0db73c4f2663e7a1d5","score":"0.5251513","text":"def __init__(self, value):\n self.np_value = value","title":""},{"docid":"1b98432c1827a929281f2a7f25c397f1","score":"0.52501065","text":"def __init__(self, initializer: ArrayOp, index_op: IndexOp):\n self.initializer = initializer\n self.index_op = index_op","title":""},{"docid":"a6498103bfa75e7a4a24ec77f577d87d","score":"0.52426285","text":"def __init__(self, value):\n self._v = value","title":""},{"docid":"601f3074c3929a4c542d73770542d2a7","score":"0.52349836","text":"def __init__(self, *args, **kwargs):\n super(DPTuner, self).__init__(*args, **kwargs)\n self._num_states = self._max_num_states = None\n self._stage_dict = {}\n self._dep_dict = {}\n self._counted_nodes_set = set()\n\n self._global_data_dict = {\n \"dtype\": self._dtype,\n \"counted_nodes_set\": self._counted_nodes_set,\n \"stage_dict\": self._stage_dict,\n \"in_nodes_dict\": self._in_nodes_dict,\n \"out_nodes_dict\": self._out_nodes_dict,\n \"dep_dict\": self._dep_dict,\n \"node_list\": self._node_list,\n \"input_shapes\": self._input_shapes,\n \"layout_transform_interlayer_cost\": self._layout_transform_interlayer_cost,\n }","title":""},{"docid":"fe05bf701bd73f4f90dc69bea2b7c5e7","score":"0.5229092","text":"def __init__(self, pars=[], kernel=\"\"):\n\n self._pars = pars\n self._kernel = kernel","title":""},{"docid":"86c32e4f4c1420ad4bad9df9f953f1cf","score":"0.52148443","text":"def init_vector(ocp):\n v_init = np.ndarray((0, 1))\n\n # For states\n for i_phase in range(len(ocp.nlp)):\n current_nlp = ocp.nlp[i_phase]\n\n repeat = 1\n if current_nlp.ode_solver.is_direct_collocation:\n repeat += current_nlp.ode_solver.polynomial_degree\n\n nlp = ocp.nlp[current_nlp.use_states_from_phase_idx]\n OptimizationVectorHelper._set_node_index(nlp, 0)\n for key in nlp.states:\n if key in nlp.x_init.keys():\n n_points = OptimizationVectorHelper._nb_points(nlp, nlp.x_init[key].type)\n nlp.x_init[key].check_and_adjust_dimensions(nlp.states[key].cx.shape[0], n_points)\n\n for k in range(nlp.ns + 1):\n OptimizationVectorHelper._set_node_index(nlp, k)\n for p in range(repeat if k != nlp.ns else 1):\n point = k if k != 0 else 0 if p == 0 else 1\n\n collapsed_values = np.ndarray((nlp.states.shape, 1))\n for key in nlp.states:\n if key in nlp.x_init.keys():\n point_to_eval = point\n if nlp.x_init[key].type == InterpolationType.ALL_POINTS:\n point_to_eval = k * repeat + p\n value = (\n nlp.x_init[key].init.evaluate_at(shooting_point=point_to_eval, repeat=repeat)\n / nlp.x_scaling[key].scaling\n )[:, np.newaxis]\n else:\n value = 0\n # Organize the controls according to the correct indices\n collapsed_values[nlp.states[key].index, :] = value\n\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n # For controls\n for i_phase in range(len(ocp.nlp)):\n current_nlp = ocp.nlp[i_phase]\n\n nlp = ocp.nlp[current_nlp.use_controls_from_phase_idx]\n OptimizationVectorHelper._set_node_index(nlp, 0)\n if nlp.control_type in (ControlType.CONSTANT, ControlType.NONE):\n ns = nlp.ns\n elif nlp.control_type in (ControlType.LINEAR_CONTINUOUS, ControlType.CONSTANT_WITH_LAST_NODE):\n ns = nlp.ns + 1\n else:\n raise NotImplementedError(f\"Multiple shooting problem not implemented yet for {nlp.control_type}\")\n\n for key in nlp.controls.keys():\n if key in nlp.u_init.keys():\n nlp.u_init[key].check_and_adjust_dimensions(nlp.controls[key].cx.shape[0], ns - 1)\n\n for k in range(ns):\n OptimizationVectorHelper._set_node_index(nlp, k)\n collapsed_values = np.ndarray((nlp.controls.shape, 1))\n for key in nlp.controls:\n if key in nlp.u_init.keys():\n value = nlp.u_init[key].init.evaluate_at(shooting_point=k) / nlp.u_scaling[key].scaling\n else:\n value = 0\n\n # Organize the controls according to the correct indices\n collapsed_values[nlp.controls[key].index, 0] = value\n\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n # For parameters\n collapsed_values = np.zeros((ocp.parameters.shape, 1))\n for key in ocp.parameters.keys():\n if key not in ocp.parameter_init.keys():\n v_init = np.concatenate((v_init, np.zeros((ocp.parameters[key].size, 1))))\n continue\n\n scaled_init = ocp.parameter_init[key].scale(ocp.parameters[key].scaling)\n collapsed_values[ocp.parameters[key].index, :] = scaled_init.init\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n # For stochastic variables\n for i_phase in range(len(ocp.nlp)):\n nlp = ocp.nlp[i_phase]\n OptimizationVectorHelper._set_node_index(nlp, 0)\n\n for key in nlp.stochastic_variables.keys():\n if key in nlp.s_init.keys():\n nlp.s_init[key].check_and_adjust_dimensions(nlp.stochastic_variables[key].cx.shape[0], nlp.ns)\n\n for k in range(nlp.ns + 1):\n OptimizationVectorHelper._set_node_index(nlp, k)\n collapsed_values = np.ndarray((nlp.stochastic_variables.shape, 1))\n for key in nlp.stochastic_variables:\n if key in nlp.s_init.keys():\n value = nlp.s_init[key].init.evaluate_at(shooting_point=k) / nlp.s_scaling[key].scaling\n else:\n value = 0\n\n # Organize the stochastic variables according to the correct indices\n collapsed_values[nlp.stochastic_variables[key].index, 0] = value\n\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n for i_phase in range(len(ocp.nlp)):\n nlp = ocp.nlp[i_phase]\n if nlp.motor_noise is not None:\n n_motor_noise = nlp.motor_noise.shape[0]\n n_sensory_noise = nlp.sensory_noise.shape[0]\n v_init = np.concatenate((v_init, np.zeros((n_motor_noise, 1))))\n v_init = np.concatenate((v_init, np.zeros((n_sensory_noise, 1))))\n\n return v_init","title":""},{"docid":"f21f06197faa3e88badb2b42da306fe1","score":"0.52086794","text":"def __init__(self):\r\n self.inputs = []\r\n self.op = None\r\n self.const_attr = None\r\n self.name = \"\"","title":""},{"docid":"7d475a0425781c6d7fa1542d8463f437","score":"0.5186119","text":"def __init__(self, name: tp.Optional[str] = None, dtype: np.dtype = jnp.float32):\n self.name = name if name else utils.lower_snake_case(self.__class__.__name__)\n self.dtype = dtype\n self._params = {}\n self._states = []\n self._submodules = []\n self._dynamic_submodules = []\n self._initialized = False\n self._trainable = True\n\n _init = self.init\n\n def init(*args, **kwargs):\n return _init(*args, **kwargs)\n\n self.init = init\n\n utils.wraps(self.call)(self.init)\n utils.wraps(self.call)(self)\n\n self._jit_functions()","title":""},{"docid":"ede1473867441e7e3658e63982b4fb4e","score":"0.5181392","text":"def Do_kernel(self):\n return self.Kernel","title":""},{"docid":"ec7c2366c48398d16ed6ca58d56cb137","score":"0.5176585","text":"def __init__(self, x=None):\n self.v = x\n self.cl = None\n self.cr = None\n return None","title":""},{"docid":"89a718b8ed355be4f8f776d69cf7d2da","score":"0.51614505","text":"def __init__(self, pyo, t=None):\n if type(pyo) is Numeric.ArrayType:\n k = _arraytok(pyo) \n elif type(pyo) is _K:\n k = pyo\n if t and t != k.t:\n raise NotImplemented\n else:\n if not t:\n t = _guesst(pyo)\n else:\n if t > 0:\n pyt = _pyt[t-1]\n pyo = pyt(pyo)\n elif t < 0:\n pyt = _pyt[-t-1]\n pyo = map(pyt, pyo)\n kgen = _kgen(t)\n k = kgen(pyo)\n _K.__init__(self, k)","title":""},{"docid":"b870f3d7f5b6706d9c9597f891c73f95","score":"0.5157406","text":"def _v__init__(self):","title":""},{"docid":"0ba1f95b84ca10b7b07a5b2ccf6e80a2","score":"0.514581","text":"def __init__(self, shape, dtype):\n self._dtype = dtype\n self._sum = tf.Variable(lambda: tf.zeros(shape, dtype), False)\n self._count = tf.Variable(lambda: 0, trainable=False)","title":""},{"docid":"ff5d8627423d0ced3926413340e0e896","score":"0.51323915","text":"def __init__(self, master=None, value=None, name=None):\n _TK.Variable.__init__(self, master, value, name)","title":""},{"docid":"0bb7d41df8f841d7b93509b616075367","score":"0.5131436","text":"def __init__(self,kernel,args,invocations=None,type='VERTICAL'):\n\t\tself.args = []\n\t\tself.kernel = \"\"\n\t\tif(invocations == None):\n\t\t\ttemp = kernel.getSub(\"clEnqueueNDRangeKernel\")\n\t\t\tif(temp is not None):\n\t\t\t\tself.kernel = temp\n\t\t\telse:\n\t\t\t\tprint \"Invalid kernel statement\"\n\t\t\tfor arg in args:\n\t\t\t\ttemp = arg.getSub(\"clSetKernelArg\")\n\t\t\t\tif(temp is not None):\n\t\t\t\t\tif(temp.children[0] == self.kernel.children[1]):\n\t\t\t\t\t\tself.args.append((temp.children[2],temp.children[3]))\n\t\t\t\telse:\n\t\t\t\t\tprint \"invalid kernel argument\"\n\t\telse:\n\t\t\tself.kernel = copy.deepcopy(invocations[0].kernel)\n\t\t\tself.kernel.children[1].tokens[0].value = \"\"\n\t\t\tfor invocation in invocations:\n\t\t\t\tself.kernel.children[1].tokens[0].value += invocation.kernel.children[1].tokens[0].value.split('_')[0] + \"_\"\n\t\t\t\tself.args += invocation.args\n\t\t\tself.kernel.children[1].tokens[0].value += \"kernel\"\n\t\t\tif(type == 'HORIZONTAL'):\n\t\t\t\t\"\"\"Append an h\"\"\"\n\t\t\t\tself.kernel.children[1].tokens[0].value +='h'\t\n\t\t\tself.args = removeDuplicates2(self.args)","title":""},{"docid":"429e749ed7cc10f710ceb9a638caf71f","score":"0.5110538","text":"def __init__(self, device_params = DEFAULT_DEVICE_PARAMS):\n self.device_names = list(device_params.keys())\n self.dirichlet_params = list(device_params.values())\n self.p_vector = dirichlet(self.dirichlet_params).rvs().reshape(-1,)","title":""},{"docid":"e245d650938297578a09e6890a46c981","score":"0.51071066","text":"def __init__(self, potential, flow_operator_mask_v, flow_operator_mask_k):\n self._potential = potential\n self._v = potential.without_weights()\n self._k = potential.kinetic_energy()\n self._lam = potential.lam\n self._flow_op_mask_v = flow_operator_mask_v\n self._flow_op_mask_k = flow_operator_mask_k\n self._flow = 'lambda'","title":""},{"docid":"07d03dfd8f3467b96acfbfa3c3a0d282","score":"0.5101611","text":"def __init__(\n self,\n kernel_op: get_kernel_fn.ApplyKernelOp,\n params: parameters_lib.SwirlLMParameters,\n scalar_name: str,\n thermodynamics: thermodynamics_manager.ThermodynamicsManager,\n ):\n self._kernel_op = kernel_op\n self._params = params\n self._bc_types = copy.deepcopy(params.bc_type)\n self._scalar_name = scalar_name\n self._thermodynamics = thermodynamics\n\n self._h = (self._params.dx, self._params.dy, self._params.dz)\n\n self._scalar_params = None\n for scalar in self._params.scalars:\n if scalar.name == self._scalar_name:\n self._scalar_params = scalar\n break\n assert (\n self._scalar_params is not None\n ), f'{self._scalar_name} is not configured.'\n\n for override_bc in self._scalar_params.override_bc_type:\n dim = override_bc.dim\n face = override_bc.face\n logging.info('BC type for scalar: %s '\n 'will be reset to %s (originally %s) for dimension %d at '\n 'face %d', self._scalar_name,\n str(boundary_condition_utils.BoundaryType.UNKNOWN),\n str(self._bc_types[dim][face]), dim, face)\n self._bc_types[dim][face] = (\n boundary_condition_utils.BoundaryType.UNKNOWN)\n\n # Find the direction of gravity. Only vector along a particular dimension is\n # considered currently.\n self._g_vec = (\n self._params.gravity_direction if self._params.gravity_direction else [\n 0.0,\n ] * 3)\n self._g_dim = None\n for i in range(3):\n if np.abs(np.abs(self._g_vec[i]) - 1.0) < _G_THRESHOLD:\n self._g_dim = i\n break\n\n # Prepare diffusion related models.\n self._diffusion_fn = diffusion.diffusion_scalar(self._params)\n self._use_sgs = self._params.use_sgs\n filter_widths = (self._params.dx, self._params.dy, self._params.dz)\n if self._use_sgs:\n self._sgs_model = sgs_model.SgsModel(self._kernel_op, filter_widths,\n params.sgs_model)","title":""},{"docid":"c6c3eaa187c603279b9eacd627bd1e8a","score":"0.5097064","text":"def __init__(self, K, delta, c, np = None):\n\n self.K = float(K)\n self.K_int = int(K)\n self.delta = delta\n self.c = c\n\n S = self.calc_S()\n cdf, Z = gen_rsd_cdf(K, S, delta)\n self.cdf = cdf\n self.Z = Z\n\n #self.inter = inter.interp1d(np.concatenate(([0], cdf)), range(0,K+1))\n self.np_rand = RandomState(1)\n self.np = np\n\n self.state = 1","title":""},{"docid":"b025b34a1936ef033d369b14517a6320","score":"0.5088485","text":"def __init__(self, ntok, ndim, init=df.init.ortho_svd()):\n df.Module.__init__(self)\n\n self.ndim = ndim\n self.W = self._addparam((ntok, ndim), init, name='Wemb_{}x{}'.format(ntok, ndim))","title":""},{"docid":"670b1ee85ca5f25840cd22b783692716","score":"0.5082001","text":"def __init__(self, k: float = 1.0, x_shift: float = 0.0, y_shift: float = 0.0):\n\n self.constants = {self.k: k, self.x_shift: x_shift, self.y_shift: y_shift}\n self.V = self.V_functional.subs(self.constants)\n self.dVdpos = sp.diff(self.V, self.position)\n\n super().__init__()","title":""},{"docid":"5ed8bb627d644964e64e4280b08c7088","score":"0.50803864","text":"def __init__(self, target: \"Expr\", operator: UnOp) -> None:\n self.target = target\n self.operator = operator","title":""},{"docid":"1d3bce030f98c517a2d9c05a1062aa7d","score":"0.5075545","text":"def _kernel_init(scale=1.0, seed=None):\n scale = 2. * scale\n return tf.keras.initializers.VarianceScaling(\n scale=scale, mode='fan_in', distribution='truncated_normal', seed=seed)","title":""},{"docid":"2e3b771b5507abee7a05e1de11e5d55b","score":"0.50715685","text":"def __init__(self, batch_size, embedding_size, k=None, device=None):\n super(SimpleStruct, self).__init__(batch_size, embedding_size)\n operations = [Operation.push, Operation.pop]\n self._reg_trackers = [None for _ in operations]\n self._read_strength = k\n self._values = []\n self._strengths = []\n self._device = device","title":""},{"docid":"14767bc57bcafb626a78c7673062cca6","score":"0.50543296","text":"def __init__(self, tsr):\n super(DenseLinearOperator, self).__init__(tsr)\n self.tensor = tsr","title":""},{"docid":"b638c603a0064f8e19d723f0d6a1a7fb","score":"0.5053925","text":"def _initialise_state(self) -> None:\n super()._initialise_state()\n self.test_window = tf.gather(self.x_ref_eff, self.init_test_inds)\n self.k_xtc = self.kernel(self.test_window, self.kernel_centers)","title":""},{"docid":"1f8713d2cff480f49e5a16bb5caa601b","score":"0.5046923","text":"def __init__(self,\n kernel,\n batch_size,\n max_num_context,\n x_size=1,\n y_size=1,\n testing=False,\n device = torch.device(\"cpu\")):\n self.kernel = kernel\n self._batch_size = batch_size\n self._max_num_context = max_num_context\n self._x_size = x_size\n self._y_size = y_size\n self._testing = testing\n self.device = device","title":""},{"docid":"827ed719975ed277602a055df1fe7bc9","score":"0.50449395","text":"def __init__(self, initializer_params={}, verbose=True, seed=0):\n\n super(Constant, self).__init__(initializer_params, verbose, seed)","title":""},{"docid":"5277911cc013a07ef6cceb5bcfc2fc05","score":"0.5040688","text":"def __init__(self, vec, evotype=\"auto\"):\n vec = SPAMVec.convert_to_vector(vec)\n if evotype == \"auto\":\n evotype = \"statevec\" if _np.iscomplexobj(vec) else \"densitymx\"\n assert(evotype in (\"statevec\", \"densitymx\")), \\\n \"Invalid evolution type '%s' for %s\" % (evotype, self.__class__.__name__)\n DenseSPAMVec.__init__(self, vec, evotype)","title":""},{"docid":"8f2a5100d4a03294b66cfeb709965644","score":"0.5024744","text":"def kernel(self, *args):\n raise NotImplementedError()","title":""},{"docid":"f077d60cbbc17c88aa5fb673dd71f0c3","score":"0.5020952","text":"def __init__(self, vec, evotype=\"auto\"):\n vec = SPAMVec.convert_to_vector(vec)\n if evotype == \"auto\":\n evotype = \"statevec\" if _np.iscomplexobj(vec) else \"densitymx\"\n elif evotype == \"statevec\":\n vec = _np.asarray(vec, complex) # ensure all statevec vecs are complex (densitymx could be either?)\n\n assert(evotype in (\"statevec\", \"densitymx\")), \\\n \"Invalid evolution type '%s' for %s\" % (evotype, self.__class__.__name__)\n DenseSPAMVec.__init__(self, vec, evotype)","title":""},{"docid":"5b73f58df49cdc4fb04ea592cd5a8341","score":"0.5013608","text":"def _define_kernel(self):\n def k(x,y,\n dist=self.dist,\n commonKernel=self.commonKernel,\n blockKernel=self.blockKernel):\n # Geodesic distance. \n d=dist(x[1:],y[1:])\n\n # Calculate the underlying kernel for all subjects.\n cov=commonKernel(d)\n\n # Add individual specific covariance.\n if x[0]==y[0]:\n cov+=blockKernel[int(x[0])](d)\n return cov\n \n self.kernel=k\n \n # Update GPR under-the-hood that relies on this kernel.\n self._update_gp()","title":""},{"docid":"c357b6703e4e6f0eb142d7347a729a71","score":"0.50009483","text":"def _init_variable(self, name, val):\n # return tf.get_variable(name, (), dtype=tf.float32, trainable=False,\n # initializer=tf.constant_initializer(val))\n return tf.constant(name=name, shape=(), dtype=tf.float32, value=val)","title":""},{"docid":"b119ae4642062f2847a8127122d8ef4f","score":"0.50004977","text":"def __init__(self, n_features, kernel):\n self.n_features = n_features\n self.kernel = kernel","title":""},{"docid":"77a92b259d5be88049f3e71e4212ebd7","score":"0.49999592","text":"def __init__(self, kernel, itspace_extents, *args, **kwargs):\n if self._initialized:\n return\n self._parloop = kwargs.get('parloop')\n self.comm = itspace_extents.comm\n self._kernel = self._parloop._kernel\n self._config = kwargs.get('config')\n self._initialized = True","title":""},{"docid":"5ad8752674ae08b5ffe6f6e6d4d2aa61","score":"0.49982068","text":"def __init__(self\n , **kwargs\n ):\n self.t_functorArgs = {}\n self.t_cstArgs = {}\n \n # The data to which the functor is applied\n self.o_vectorDataSet = None\n \n # The functor returned data\n self.x_fData = None\n \n # First get T_FUNCTOR_ARGS\n for t_funcArgTuple in self.__class__.getFunctorArgs():\n \n s_funcArgKey = t_funcArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\n b_required = t_funcArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\n \n if not kwargs.has_key( s_funcArgKey ):\n if b_required:\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_funcArgKey )\n else:\n self.t_functorArgs[ s_funcArgKey ] = None \n else:\n # Get the QArkFunctorArg\n o_functorArg = kwargs[ s_funcArgKey ]\n self.t_functorArgs[ s_funcArgKey ] = o_functorArg.getValue()\n \n # Get the constant value parameters\n for t_cstArgTuple in self.__class__.getCstArgs():\n \n s_cstArgKey = t_cstArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\n b_required = t_cstArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\n \n if not kwargs.has_key( s_cstArgKey ):\n if b_required:\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_cstArgKey )\n else:\n self.t_cstArgs[ s_cstArgKey ] = None \n else:\n # Get the QArkFunctorArg\n o_cstArg = kwargs[ s_cstArgKey ]\n self.t_cstArgs[ s_cstArgKey ] = o_cstArg.getValue()","title":""},{"docid":"2dea2f57a1f90dc4b827a67ba680564f","score":"0.4976893","text":"def __init__(self, env, k):\n gym.Wrapper.__init__(self, env)\n self.k = k\n self.states = deque([], maxlen=k)\n shp = self.observation_space.n\n self.observation_space = (k, shp)","title":""},{"docid":"3b767c6f0501614ef6a73b62c8e8eaa9","score":"0.49731472","text":"def init_op_pattern():\n fusion_manager.init_current_op_pattern()","title":""},{"docid":"1e53c3d8b3fa5a4ff22e11b3435270ca","score":"0.49591565","text":"def init(self, net_dim, state_dim, action_dim):","title":""},{"docid":"1e53c3d8b3fa5a4ff22e11b3435270ca","score":"0.49591565","text":"def init(self, net_dim, state_dim, action_dim):","title":""},{"docid":"49a81ef952dca4e2e241406f697edc54","score":"0.49527246","text":"def __init__(self, X, mu, p):\n super(DotProductKernelMap, self).__init__(mu, p)\n self.n = X.shape[0]\n self.d = X.shape[1]\n self.X = X\n self.Gs_train = None","title":""},{"docid":"79466e46f7a122d0032ad903f386dd53","score":"0.4948092","text":"def init_accumulators(self):\n torch.nn.init.constant_(self.mu_numerator, self.eps)\n torch.nn.init.constant_(self.mu_denominator, self.eps * self.C)\n torch.nn.init.constant_(self.var_numerator, self.eps)\n torch.nn.init.constant_(self.var_denominator, self.eps * self.C)","title":""},{"docid":"251831a91ce68da00733b1975980b5f5","score":"0.49430963","text":"def __init__(self, **kwargs):\n LOG.info(\"%s: initing...\" % str(self))\n super(JansenRitDavid, self).__init__(**kwargs)\n\n #self._state_variables = [\"y0\", \"y1\", \"y2\", \"y3\", \"y4\", \"y5\"]\n self._nvar = 8\n\n self.cvar = numpy.array([1,2], dtype=numpy.int32)\n\n #TODO: adding an update_derived_parameters method to remove some of the\n # redundant parameter multiplication in dfun should gain about 7%\n # maybe not worth it... The three exp() kill us at ~90 times *\n #self.nu_max2 = None #2.0 * self.nu_max\n #self.Aa = None # self.A * self.a\n #self.Bb = None # self.B * self.b\n #self.aa = None # self.a**2\n #self.a2 = None # 2.0 * self.a\n #self.b2 = None # 2.0 * self.b\n #self.a_1J = None # self.a_1 * self.J\n #self.a_2J = None # self.a_2 * self.J\n #self.a_3J = None # self.a_3 * self.J\n #self.a_4J = None # self.a_4 * self.J\n\n LOG.debug('%s: inited.' % repr(self))","title":""},{"docid":"8b8907041e456b0c34a14714a13a8534","score":"0.4932093","text":"def init(self):\n self._model = {}\n self._value_function = {}\n self._terminal_state_set = set()\n self._state_action = None","title":""},{"docid":"5399a96be429116cc61fcfd03f89ba41","score":"0.49313113","text":"def _do_data_dependent_init():\n w_fc_normalized = tf.nn.l2_normalize(w_fc.read_value(), [0])\n output_init = tf.matmul(embeddings, w_fc_normalized)\n mean_init, var_init = tf.nn.moments(output_init, [0])\n # Data-dependent init values.\n g_init_value = 1. / tf.sqrt(var_init + 1e-10)\n ops = [tf.assign(g, g_init_value)]\n if not cosine_classifier:\n # Also initialize a bias in a data-dependent way.\n b_fc_init_value = -mean_init * g_init_value\n ops.append(tf.assign(b_fc, b_fc_init_value))\n # Mark that the data-dependent initialization is done to prevent it from\n # happening again in the future.\n ops.append(tf.assign(data_dependent_init_done, 1))\n return tf.group(*ops)","title":""},{"docid":"44c5613c40d97e6454db8adf43bf569c","score":"0.49255443","text":"def init_variable(v, init, name=\"init\"):\n with ops.name_scope(None, v.op.name + \"/\", [v, init]):\n with ops.name_scope(name) as scope:\n with ops.colocate_with(v):\n if callable(init):\n assert v.get_shape().is_fully_defined(), \"Variable shape unknown.\"\n # TODO(mrry): Convert to v.shape when the property and\n # accessor are reconciled (and all initializers support\n # tf.TensorShape objects).\n value = init(v.get_shape().as_list(), v.dtype.base_dtype)\n value = ops.convert_to_tensor(value, name=\"value\")\n return gen_state_ops.assign(v, value, name=scope)\n else:\n init = ops.convert_to_tensor(init, name=\"init\")\n return gen_state_ops.assign(v, init, name=scope)","title":""},{"docid":"7ad5823f394b26d1fc23bd233430ddc7","score":"0.4921869","text":"def __init__(self, component, name, shape, dtype):\n self._name = name\n self._shape = shape\n self._component = component\n beta = tf.get_variable(\n 'beta_%s' % name,\n shape=shape,\n dtype=dtype,\n initializer=tf.zeros_initializer())\n gamma = tf.get_variable(\n 'gamma_%s' % name,\n shape=shape,\n dtype=dtype,\n initializer=tf.ones_initializer())\n self._params = [beta, gamma]","title":""},{"docid":"0729f28466be1f8662a2650d69888e6c","score":"0.49182227","text":"def __init__(self, name, **okwargs):\n assert name in AVAILABLE_KERNELS, \"Unknown kernel %s\" % name\n super(SparseKernel, self).__init__(name, **okwargs)\n self.use_rbf = okwargs.get('use_rbf', name in RBF_KERNELS)\n if name not in RBF_KERNELS and self.use_rbf:\n raise ValueError('Cannot use RBF with non-RBF kernel {}'.format(name))\n # get the module used to compute the sparse distances (FIXME ugly)\n spdist = None\n exec('import sparse_distances.sparse_%s as spdist' % name)\n # define \"raw\" distance functions between sparse vectors (from C library)\n self._v2v_f = spdist.v2v # raw dist. between vectors\n self._v2m_f = spdist.v2m # raw dist. between a vector and a matrix\n self._m2m_f = spdist.m2m # raw dist. between a (test) matrix and a (train) matrix\n self._gram_f = spdist.gram # raw gram matrix of a list of vectors\n if name in ('linear', 'intersection'):\n self.is_additive = True","title":""},{"docid":"696f0b83531e773d2f9e7679ffa66996","score":"0.49122977","text":"def __init__(self, dataset, kernel, model=Exact(), mean=None, name=None, rescale_x=False):\n \n if not isinstance(dataset, DataSet):\n dataset = DataSet(dataset)\n if dataset.get_output_dims() == 0:\n raise ValueError(\"dataset must have at least one channel\")\n names = [name for name in dataset.get_names() if name is not None]\n if len(set(names)) != len(names):\n raise ValueError(\"all data channels must have unique names\")\n\n if rescale_x:\n dataset.rescale_x()\n else:\n for channel in dataset:\n for dim in range(channel.get_input_dims()):\n xran = np.max(channel.X[dim].transformed) - np.min(channel.X[dim].transformed)\n if xran < 1e-3:\n logger.warning(\"Very small X range may give problems, it is suggested to scale up your X axis\")\n elif 1e4 < xran:\n logger.warning(\"Very large X range may give problems, it is suggested to scale down your X axis\")\n\n self.name = name\n self.dataset = dataset\n self.kernel = kernel\n\n X = [[x[channel.mask] for x in channel.X] for channel in self.dataset]\n Y = [np.array(channel.Y[channel.mask]) for channel in self.dataset]\n x, y = self._to_kernel_format(X, Y)\n\n self.model = model.build(kernel, x, y, mean, name)\n if issubclass(type(kernel), MultiOutputKernel) and issubclass(type(model), Exact):\n self.model.variance.assign(0.0, lower=0.0, trainable=False) # handled by MultiOutputKernel","title":""},{"docid":"8cf3117e76c5fabe7b753fb2c1e95073","score":"0.49072382","text":"def __init__(self,oscillators,comb_op):\n self.oscillators=oscillators\n self.comb_op=comb_op","title":""},{"docid":"6a80a836cd045a2779e2c83a489f40bd","score":"0.4900156","text":"def __init__(self, **kwargs):\n LOG.info('%s: initing...' % str(self))\n super(LileySteynRoss, self).__init__(**kwargs)\n #self._state_variables = [\"E\", \"I\"]\n self._nvar = 2\n self.cvar = numpy.array([0, 1], dtype=numpy.int32)\n LOG.debug('%s: inited.' % repr(self))","title":""},{"docid":"a26bdf4c22d69c8784e808227fb4785a","score":"0.4893577","text":"def __init__(self, name, copy=0, val=0, size=1, **kwargs):\r\n self.value = val\r\n if isinstance(self.value, numpy.ndarray):\r\n self.size = self.value.shape[0]\r\n elif isinstance(self.value, list):\r\n self.size = len(self.value)\r\n else:\r\n self.size = size\r\n\r\n super(IndVar, self).__init__(name, copy, **kwargs)\r\n \r\n if 'u_scal' in self.kwargs:\r\n self.u_scal = self.kwargs['u_scal']\r\n else:\r\n self.u_scal = 1.0\r\n if 'f_scal' in self.kwargs:\r\n self.f_scal = self.kwargs['f_scal']\r\n else:\r\n self.f_scal = 1.0","title":""},{"docid":"7f13d984fb9004cdb7e465aa197e71b8","score":"0.4890825","text":"def __init__(self, pool_size, stride, padding=\"VALID\"):\n super().__init__()\n self.kernel_shape = pool_size\n self.stride = stride\n\n self.padding_mode = padding\n self.padding = None\n\n self.input = None\n self.X = None","title":""},{"docid":"20d37517a9cbdc62dadf4417a61d5731","score":"0.48837817","text":"def __init__(self):\n self.da = DynamicArray()","title":""},{"docid":"b0d23029791c70659c983e74c8c7aa81","score":"0.48798135","text":"def setKernel(self, k):\n self.__kernel = k","title":""},{"docid":"2b365597f362f5204053091ab359913b","score":"0.48740718","text":"def __init__(\n self,\n dtensor_components: Tuple[tensor.Tensor],\n global_element_spec: tensor_spec.TensorSpec,\n layouts: Any):\n # dtensor_components is expected to be a single-element tuple.\n [self._iterator_resource_dtensor] = dtensor_components\n self._global_element_spec = global_element_spec\n self._layouts = layouts\n self._layouts_str = nest.map_structure(\n lambda layout: layout.to_string(), layouts)\n\n super().__init__(\n components=dtensor_components, element_spec=global_element_spec)","title":""},{"docid":"0a7fe2b29bb7123c470f655eedadb589","score":"0.48736146","text":"def __init__(self, stride_shape, convolution_shape, num_kernels):\n\n # Inherit parent attributes\n super().__init__()\n\n self.stride_shape = stride_shape\n self.stride_y, self.stride_x = 0, 0\n\n self.convolution_shape = convolution_shape\n self.input_tensor_shape = None\n self.input_tensor = None\n\n self.num_kernels = num_kernels\n\n # Initialize the filter uniformly random in the range [0, 1) as tensor of size: [Nr_kernels x C x M (x N)].\n self.weights = np.random.uniform(size=(self.num_kernels, *self.convolution_shape))\n\n # Initialize the bias uniformly random in the range [0, 1) for each kernel as a single value.\n self.bias = np.random.uniform(size=self.num_kernels)\n\n self.gradient_weights = None # Gradient with respect to the weights.\n self.gradient_bias = None # Gradient with respect to the bias.\n\n self.optimizer = None # Optimizer of this layer.","title":""},{"docid":"c1c2bb027440f1d4c616441288b1acd3","score":"0.48729536","text":"def __init__(self, *args):\n this = _openmm.new_VariableVerletIntegrator(*args)\n try:\n self.this.append(this)\n except:\n self.this = this","title":""},{"docid":"a66eeb8f71b64a13fbd03d9940288a98","score":"0.48687768","text":"def _scalar_op(self, op, o):\n new_parts = []\n new_terms = {term: getattr(term, op)(o) for term in self.expgate_sets.keys()}\n new_parts = ({targets: new_terms[term]\n for targets, term in part.items()}\n for part in self.parts)\n new = self.__class__(*new_parts)\n if self._dense is not None:\n new.dense = getattr(self.dense, op)(o)\n if self._circuit is not None:\n new._circuit = self._circuit\n new._circuit.dt = None\n new.expgate_sets = {new_terms[term]: gate_set\n for term, gate_set in self.expgate_sets.items()}\n return new","title":""},{"docid":"03d9055aa5ebd994aa9b36d5ecc6417d","score":"0.4867485","text":"def __init__(self):\n self.node: Node[K, V] = EmptyNode()","title":""},{"docid":"00babe8f5fa91fe8a9f10ef7c506e6ef","score":"0.4864531","text":"def __init__(self, rho: float, batch_multiplier: float, lambda_vae: float, lambda_cos: float, lambda_l1: float):\n self.rho = rho\n self.lambda_vae = lambda_vae\n self.lambda_cos = lambda_cos\n self.lambda_l1 = lambda_l1\n self.batch_multiplier = batch_multiplier","title":""},{"docid":"665007793f12574d1c320285c82d3a00","score":"0.48640746","text":"def __init__(self, target, key, values=0, active=0):\n \n #Initialized variables\n self.target = target\n self.key = key\n self.values = values\n self.active = active","title":""},{"docid":"6f42e931ee21cb79a96e0b09c1f99d14","score":"0.48613185","text":"def __init__(self, dim): #, length_scale, length_scale_bounds=()):\n# assert isinstance(column, (list, tuple, int)), \"must be int or list of ints\"\n# self.column = [column] if isinstance(column, int) else column\n# assert all(isinstance(i, int) for i in self.column), \"must be integers\"\n self.dim = dim\n \n kernels = [Projection(RBF(), [c]) for c in range(dim)]\n\n # combine the kernels into a single product kernel\n self.kernel = reduce(lambda k0, k1 : k0 * k1, kernels)","title":""},{"docid":"91dee435523b3f227ca893f9e528764f","score":"0.48593938","text":"def __init__(self, operator_id):\n\n _base.Base.__init__(self, _types.COMPONENT_TYPE_OPERATOR)\n self.__op_id = operator_id","title":""},{"docid":"54500e01c5648df0d8f588ea9a0ff3b6","score":"0.48528546","text":"def init(self) -> None:\n self.layout = self.attrs['data_layout']\n\n logger.info(\"Init {} layer: {}\".format(\n 'Conv2D' if self.kernel_groups == 1 else 'DepthWiseConv2D',\n self.layout))\n logger.info(self.input_shapes)\n\n input_shapes, kernel, biases = \\\n self.input_shapes, self.kernel, self.biases\n if kernel is not None:\n kernel = \\\n tf.compat.v1.placeholder_with_default(kernel, kernel.shape)\n else:\n kernel = \\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\n shape=input_shapes[1])\n\n if biases is not None:\n biases = \\\n tf.compat.v1.placeholder_with_default(biases, biases.shape)\n else:\n biases = \\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\n shape=input_shapes[2])\n\n inpt = \\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\n shape=input_shapes[0])\n\n self.inpts = [inpt, kernel, biases]\n\n self.quant_output = self._get_conv_tensor(self.inpts)\n self.res = self.get_output_tensors(self.inpts)[0]\n logger.info(\"Res shape: {}\".format(self.res.shape))","title":""},{"docid":"f3c96da5ae9c96c7d103042f8d26c505","score":"0.4852539","text":"def glv_init(self):\n pass","title":""},{"docid":"453f264a9edca02a883467f14ebb4701","score":"0.4848578","text":"def __init__(self, sg_values): \n self.__a = {} \n self.__init_coefs(sg_values)","title":""},{"docid":"ca7dd23f379b010e3624e42573da451f","score":"0.48449957","text":"def __init__(self):\n self.U, self.s, self.V = (None,None,None)\n self.user_encoder = None\n self.item_encoder = None\n self.mat = None\n self.decomp = False","title":""},{"docid":"159c7de3ad234f5ac44629ce829f08c0","score":"0.48449928","text":"def __init__(self, *args):\n _gp.gp_Vec2d_swiginit(self,_gp.new_gp_Vec2d(*args))","title":""}],"string":"[\n {\n \"docid\": \"31c1f825722e935a9f2040878ee62b3c\",\n \"score\": \"0.6014059\",\n \"text\": \"def init_op(self):\\n return self._init_op\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933dc4e128744f5c26c4a36acf306da6\",\n \"score\": \"0.59667915\",\n \"text\": \"def __init__(self,kernel_fn,*args,order=2,**kwargs):\\n super().__init__(*args,**kwargs)\\n self.dkernel_fn = DiscretizedKernelFN(kernel_fn,order)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d9845f2e29e914f28540bd4faadf50b\",\n \"score\": \"0.5955899\",\n \"text\": \"def __init__(self, X, A, scalar_kernel, scalar_kernel_params):\\n super(DecomposableKernelMap, self).__init__(A, scalar_kernel,\\n scalar_kernel_params)\\n self.n = X.shape[0]\\n self.d = X.shape[1]\\n self.X = X\\n self.Gs_train = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fca6d74bf6efc98b6afae92b5d813d2\",\n \"score\": \"0.594024\",\n \"text\": \"def __init__(self):\\n # set the kernel. One can play with the parameters.\\n self.set_kernel(1, 2.0, 0.98)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab1359409ab9f3bea19235d57510e45\",\n \"score\": \"0.5931874\",\n \"text\": \"def __init__(self):\\n self.d = dd(lambda : 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679da9cba2b0e04380ef1933feec9920\",\n \"score\": \"0.579953\",\n \"text\": \"def _setup_primitive_kernel(request) -> SDEKernel:\\n return _create_markovflow_primitive_kernel(request.param)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32cd9d7e5acbca27580e7960d902ce69\",\n \"score\": \"0.56664544\",\n \"text\": \"def __init__(self, kernel, adopt_thresh, state, target, maxsize, adaptive=True, forget_rate=0.0):\\n\\t\\tself.dp = KernelDict(kernel, adopt_thresh, state, target, maxsize, adaptive, forget_rate)\\n\\t\\tself.P = [[1]]\\n\\t\\tself.Alpha = np.dot(self.dp.Kinv, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5acf0ce085e1d9c8d75204b2a828c8ad\",\n \"score\": \"0.56514794\",\n \"text\": \"def _init_op(new_op):\\n\\n def _impl(inputs, attrs):\\n assert len(inputs) == 0\\n shape = attrs.get_int_tuple(\\\"shape\\\")\\n dtype = attrs.get_str(\\\"dtype\\\", \\\"float32\\\")\\n return new_op(shape=shape, dtype=dtype)\\n\\n return _impl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d18640e6b96ae414bc449aa49d579501\",\n \"score\": \"0.5644447\",\n \"text\": \"def __init__(self, kernel=None, noise_var=None, exact_feval=False, optimizer='bfgs',\\n max_iters=1000, optimize_restarts=5, sparse=None, num_inducing=10,\\n verbose=False, ARD=False, seed=42, normalize_Y=True, n_subspaces=12, update_freq=5):\\n self.kernel = kernel\\n self.noise_var = noise_var\\n self.exact_feval = exact_feval\\n self.optimize_restarts = optimize_restarts\\n self.optimizer = optimizer\\n self.max_iters = max_iters\\n self.verbose = verbose\\n self.sparse = sparse\\n self.sparse_surrogate = False\\n self.num_inducing = num_inducing\\n self.model = None\\n self.ARD = ARD\\n self.seed = seed\\n self.update_interval = update_freq\\n self.normalize_Y = normalize_Y\\n self.n_sub = n_subspaces\\n self.n_decomp = 20\\n np.random.rand(self.seed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb773c00e94f7cd4d00189b8b26809d\",\n \"score\": \"0.5515068\",\n \"text\": \"def __call__(self):\\r\\n new_node = Op.__call__(self)\\r\\n new_node.inputs = []\\r\\n new_node.name = \\\"Global_Variables_Initializer\\\"\\r\\n return new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8983388747a5dd2343aaa072cff2509\",\n \"score\": \"0.5513527\",\n \"text\": \"def kernel(self, value):\\n\\n if value is not None:\\n assert hasattr(\\n value,\\n '__call__'), ('\\\"{0}\\\" attribute: \\\"{1}\\\" is not callable!'.format(\\n 'kernel', value))\\n\\n self._kernel = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05329e78028c09e4d065fb146db76c7\",\n \"score\": \"0.5498108\",\n \"text\": \"def __init__(\\n self,\\n params: grid_parametrization.GridParametrization,\\n kernel_op: get_kernel_fn.ApplyKernelOp,\\n solver_option: poisson_solver_pb2.PoissonSolver,\\n ):\\n super().__init__(params, kernel_op, solver_option)\\n\\n self._kernel_op.add_kernel({'weighted_sum_121': ([1.0, 2.0, 1.0], 1)})\\n self._omega = solver_option.jacobi.omega\\n self._num_iters = solver_option.jacobi.max_iterations\\n self._halo_width = solver_option.jacobi.halo_width\\n\\n self._factor_b = 0.5 / (params.dx**-2 + params.dy**-2 + params.dz**-2)\\n self._factor_x = params.dx**2 / self._factor_b\\n self._factor_y = params.dy**2 / self._factor_b\\n self._factor_z = params.dz**2 / self._factor_b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dfd0e455ad0824814bf1628e531aa99\",\n \"score\": \"0.5469124\",\n \"text\": \"def __init__(self, initial_value, *args, dtype=None, **kwargs):\\n # Variables by default use the current device scope for placement. This\\n # wrapper has them follow the initial value's placement instead (which will\\n # be the DTensor device if the initial value has a layout).\\n\\n # Pop layout from kwargs since keras make_variable may pass a 'layout'\\n # keyword argument. We need to pop it because we are passing kwargs to\\n # super class constructor.\\n layout = kwargs.pop('layout', None)\\n shape = kwargs.get('shape', None)\\n\\n if callable(initial_value):\\n unwrapped = initial_value\\n if issubclass(type(initial_value), functools.partial):\\n unwrapped = initial_value.func\\n\\n # If wrapped is a CheckpointInitialValueCallable, this means that\\n # we are creating a Variable during a checkpoint restore.\\n # Thus the restore will happen now through this callable\\n # and we will create the DVariable with the restored dtensor.\\n if issubclass(type(unwrapped), trackable.CheckpointInitialValueCallable):\\n if not shape or not layout:\\n raise ValueError('Expected shape and layout to be not None.')\\n\\n # CheckpointInitialValueCallable will call an eager tf.RestoreV2,\\n # which does not have any shape information or layout information\\n # attached. Thus we will do two things to have them correctly specified:\\n #\\n # The default layout scope allows us to correctly specify the output\\n # layout of the tf.RestoreV2 that will be called\\n #\\n # Passing shard_info with the correct shape allows the tf.RestoreV2\\n # ShapeInference to extract the shape.\\n initial_value = api.call_with_layout(\\n initial_value,\\n layout,\\n shard_info=trackable.ShardInfo(\\n shape=shape, offset=[0] * len(shape)))\\n else:\\n initial_value = initial_value()\\n\\n # When the initial value came from a Checkpoint restoration, fetch tensor.\\n if isinstance(initial_value, trackable.CheckpointInitialValue):\\n initial_value = initial_value.wrapped_value\\n\\n initial_value = ops.convert_to_tensor(initial_value, dtype=dtype)\\n variable_device = initial_value.device\\n self._save_as_bf16 = False\\n # TODO(b/159035705): The following code enables variable creation inside\\n # a tf.function. However, it requires a global dtensor device.\\n # if not variable_device and not tf.executing_eagerly():\\n # try:\\n # initial_value.op.get_attr(\\\"_layout\\\")\\n # except ValueError:\\n # pass\\n # else:\\n # # The initial value is a DTensor, but because the DTensor device is\\n # # only active during eager execution at the moment we need to\\n # # translate that into a placement for the eager VarHandleOp.\\n # variable_device = _dtensor_device().name\\n with ops.device(variable_device):\\n # If initial tensor assigned to DVariable is DTensor, record the layout of\\n # the resource so that this can be queried.\\n if context.executing_eagerly():\\n if api.is_dtensor(initial_value):\\n value_layout = api.fetch_layout(initial_value)\\n if layout is not None and layout != value_layout:\\n raise errors_impl.InvalidArgumentError(\\n None,\\n None,\\n 'Conflicting layout are provided for initial '\\n f'value layout ({value_layout}) and variable ({layout}).',\\n )\\n layout = value_layout\\n elif layout is not None:\\n initial_value = api.relayout(initial_value, layout)\\n else:\\n raise errors_impl.InvalidArgumentError(\\n None,\\n None,\\n 'Neither layout nor DTensor initial value are provided.',\\n )\\n self.layout = layout\\n with api.default_mesh(layout.mesh):\\n super(DVariable, self).__init__(\\n initial_value, *args, dtype=dtype, **kwargs\\n )\\n else:\\n # FIXME(175928457): Record value layout in graph mode.\\n if layout is not None:\\n initial_value = api.relayout(initial_value, layout)\\n super(DVariable, self).__init__(\\n initial_value, *args, dtype=dtype, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3de15c5a768f66fe4fbaed3a7d68cc4\",\n \"score\": \"0.54562724\",\n \"text\": \"def __init__(self, kernel=None, hp=None):\\n\\n self.kernel = kernel\\n self.hp = hp\\n self.name = kernel.__name__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f099c9ceb72599c3eb191e27acebee\",\n \"score\": \"0.5444765\",\n \"text\": \"def __init__(\\n self,\\n kernel='linear', # kernel type\\n max_iter=180, # maximum iteration times\\n conv=1e-3, # convergence criterion\\n beta=1e3, # initial beta\\n bias_used=True # bias\\n ):\\n self.kernel = kernel\\n self.n_iter = max_iter\\n self.conv = conv\\n self.beta = beta\\n self.bias_used = bias_used\\n self.alpha, self.alpha_, self.alpha_old, self.beta_, self.relevance, self.phi, self.phi_,\\\\\\n self.y, self.mean_, self.sigma_, self.bias = [None]*11\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa80d6f5fdd7a6d6ca7a2bed06d3ca9\",\n \"score\": \"0.5426685\",\n \"text\": \"def __init__(self, model):\\n self.model = model\\n self.opcache = {} # a cache of operators, which should get initialized by from_vector calls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b823c3cb49da97065fb9a3afeeb8a89\",\n \"score\": \"0.53927\",\n \"text\": \"def __call__(self, initial_value, dtype = None, shape = None, name = \\\"Variable\\\"):\\r\\n new_node = Op.__call__(self)\\r\\n if shape is not None:\\r\\n assert shape == initial_value.shape\\r\\n if dtype is not None:\\r\\n if isinstance(initial_value, np.ndarray):\\r\\n global_variables[new_node] = initial_value.astype(dtype)\\r\\n else:\\r\\n global_variables[new_node] = np.array(initial_value).astype(dtype)\\r\\n else:\\r\\n global_variables[new_node] = initial_value\\r\\n new_node.name = name\\r\\n return new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"303057902312f28c3b3789db58ed8b76\",\n \"score\": \"0.5379558\",\n \"text\": \"def __init__(self, kernel_size):\\n\\n self.stride = kernel_size\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b72de45026680e384ba08990058900\",\n \"score\": \"0.5367593\",\n \"text\": \"def __init__(self, **kwargs):\\n\\n LOG.info(\\\"%s: initing...\\\" % str(self))\\n\\n super(Generic2dOscillator, self).__init__(**kwargs)\\n\\n #self._state_variables = [\\\"V\\\", \\\"W\\\"]\\n self._nvar = 2\\n self.cvar = numpy.array([0], dtype=numpy.int32)\\n\\n LOG.debug(\\\"%s: inited.\\\" % repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7aab43ddc245a9aeebd561fb21486f2\",\n \"score\": \"0.53663516\",\n \"text\": \"def _setup_impl(self):\\n\\n self.bpm_program = OCLProgram(absPath(\\\"kernels/bpm_3d_kernels.cl\\\"))\\n\\n Nx, Ny, Nz = self.size\\n\\n self._plan = fft_plan((Ny,Nx))\\n\\n\\n self._H_g = OCLArray.from_array(self._H.astype(np.complex64))\\n\\n if not self.dn is None and self.n_volumes==1:\\n self.dn_g = OCLArray.from_array(self.dn)\\n\\n\\n self.scatter_weights_g = OCLArray.from_array(self.scatter_weights.astype(np.float32))\\n self.gfactor_weights_g = OCLArray.from_array(self.gfactor_weights.astype(np.float32))\\n\\n self.scatter_cross_sec_g = OCLArray.zeros(Nz,\\\"float32\\\")\\n self.gfactor_g = OCLArray.zeros(Nz,\\\"float32\\\")\\n\\n\\n\\n # self.reduce_kernel = OCLReductionKernel(\\n # np.float32, neutral=\\\"0\\\",\\n # reduce_expr=\\\"a+b\\\",\\n # map_expr=\\\"weights[i]*cfloat_abs(field[i]-(i==0)*plain)*cfloat_abs(field[i]-(i==0)*plain)\\\",\\n # arguments=\\\"__global cfloat_t *field, __global float * weights,cfloat_t plain\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fdf5263c27b7114fd643c548c5c86cc\",\n \"score\": \"0.5359017\",\n \"text\": \"def __init__(self, value):\\n\\t\\tsuper(Functor, self).__init__(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb8bab55f68310770b6b4a2897fe8af\",\n \"score\": \"0.53568095\",\n \"text\": \"def __init__(self, kernel_type=None):\\r\\n self.kernel_type = kernel_type\\r\\n self.gamma = 0\\r\\n self.coef = 0\\r\\n self.degree = 0\\r\\n self.num_classes = 0\\r\\n self.labels = None\\r\\n self.total_sv = 0\\r\\n self.rho = 0\\r\\n self.sv = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0967a0baddc13a0d6db87af07c84175b\",\n \"score\": \"0.5346729\",\n \"text\": \"def initialize_operator(self, operator=None, matrix=False, eval_at_once=False):\\n # TODO: Make this more efficient, only compute values needed at each (r,c) step.\\n # For this, 'operator' must support the 'component=(r,c)' option.\\n # Operator is None is interpreted as identity transformation\\n if operator is None:\\n self._operator = lambda nodes, dummy, entry=None: ones((1, nodes.shape[1])) if entry[0] == entry[1] else zeros((1, nodes.shape[1]))\\n else:\\n # Wrap the operator inside a lambda ignoring the dummy parameter\\n # This allows to use the same nsd code for quadrature and matrix construction.\\n if matrix is False:\\n self._operator = lambda nodes, dummy, entry=None: operator(nodes, entry=entry)\\n else:\\n self._operator = operator\\n self._eval_at_once = eval_at_once\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b68ca023fe7092aeaef64e1da8254009\",\n \"score\": \"0.5345871\",\n \"text\": \"def _init_routine(self):\\n if self.is_initialized is False:\\n # decide the routine\\n if self.sn_def['op'] in {'d', 'project'}:\\n # for d kernel_shape [num_in, num_out]; for project, kernel shape [num_class, num_in]\\n assert len(self.kernel_shape) == 2, \\\\\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\\n num_in, num_out = self.kernel_shape\\n # self.use_u = True\\n self.use_u = True if num_in <= num_out else False\\n x_shape = [1, num_in] if self.use_u else [1, num_out]\\n self.forward = self._dense_ if self.use_u else self._dense_t_\\n self.backward = self._dense_t_ if self.use_u else self._dense_\\n elif self.sn_def['op'] in {'cd'}: # kernel_shape [num_class, num_in, num_out]\\n assert len(self.kernel_shape) == 3, \\\\\\n '{}: kernel shape {} does not have length 3'.format(self.name_in_err, self.kernel_shape)\\n num_class, num_in, num_out = self.kernel_shape\\n self.use_u = True if num_in <= num_out else False\\n x_shape = [num_class, 1, num_in] if self.use_u else [num_class, 1, num_out]\\n self.forward = self._dense_ if self.use_u else self._dense_t_\\n self.backward = self._dense_t_ if self.use_u else self._dense_\\n elif self.sn_def['op'] in {'dck'}: # convolution * conditional scale\\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\\\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\\n assert len(self.kernel_shape[0]) == 2 and len(self.kernel_shape[1]) == 2, \\\\\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\\n num_in, num_out = self.kernel_shape[0]\\n num_class = self.kernel_shape[1][0]\\n self.use_u = True if num_in <= num_out else False\\n x_shape = [num_class, num_in] if self.use_u else [num_class, num_out]\\n self.forward = (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0)) \\\\\\n if self.use_u else (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0))\\n self.backward = (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0)) \\\\\\n if self.use_u else (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0))\\n elif self.sn_def['op'] in {'c', 'tc'}:\\n assert len(self.kernel_shape) == 4, \\\\\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\\n # self.use_u = True\\n self.use_u = True \\\\\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\\\\n else False\\n if self.sn_def['op'] in {'c'}: # input / output shape NCHW or NHWC\\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\\n x_shape[0] = 1\\n y_shape = self.sn_def['input_shape'].copy()\\n y_shape[0] = 1\\n elif self.sn_def['op'] in {'tc'}: # tc\\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\\n x_shape[0] = 1\\n y_shape = self.sn_def['output_shape'].copy()\\n y_shape[0] = 1\\n else:\\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\\n self.forward = self._conv_ if self.use_u else (lambda y: self._conv_t_(y, x_shape=y_shape))\\n self.backward = (lambda y: self._conv_t_(y, x_shape=y_shape)) if self.use_u else self._conv_\\n elif self.sn_def['op'] in {'cck', 'tcck'}: # convolution * conditional scale\\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\\\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\\n assert len(self.kernel_shape[0]) == 4 and len(self.kernel_shape[1]) == 4, \\\\\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\\n self.use_u = True \\\\\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\\\\n else False\\n num_class = self.kernel_shape[1][0]\\n if self.sn_def['op'] in {'cck'}: # input / output shape NCHW or NHWC\\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\\n x_shape[0] = num_class\\n y_shape = self.sn_def['input_shape'].copy()\\n y_shape[0] = num_class\\n self.forward = (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0)) \\\\\\n if self.use_u \\\\\\n else (lambda y: self._conv_t_(self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0))\\n self.backward = (lambda y: self._conv_t_(\\n self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0)) \\\\\\n if self.use_u else (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0))\\n elif self.sn_def['op'] in {'tcck'}: # tcck\\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\\n x_shape[0] = num_class\\n y_shape = self.sn_def['output_shape'].copy()\\n y_shape[0] = num_class\\n self.forward = (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0)) \\\\\\n if self.use_u \\\\\\n else (lambda y: self._scalar_(self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0))\\n self.backward = (lambda y: self._scalar_(\\n self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0)) \\\\\\n if self.use_u else (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0))\\n else:\\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\\n else:\\n raise NotImplementedError('{}: {} is not implemented.'.format(self.name_in_err, self.sn_def['op']))\\n\\n self.x = tf.compat.v1.get_variable(\\n 'in_rand', shape=x_shape, dtype=tf.float32,\\n initializer=tf.truncated_normal_initializer(), trainable=False)\\n\\n self.is_initialized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2a6967944c915daed81a3fe3422a498\",\n \"score\": \"0.5284371\",\n \"text\": \"def __init__(self, shape, k, filters, kernels):\\n self.dims = shape\\n self.k = k\\n self.filters = filters\\n self.kernels = kernels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d489be3b563e75c12cba4f31c651635\",\n \"score\": \"0.52758646\",\n \"text\": \"def __init__(self, var, accum, linear, grad, indices,\\n lr, lr_power, l1, l2, l2_shrinkage, kernel_name):\\n super().__init__(var, grad, indices, kernel_name)\\n self.lr = lr\\n self.lr_power = lr_power\\n self.l1 = l1\\n self.l2 = l2\\n self.l2_shrinkage = l2_shrinkage\\n self.lr_vrec = 1.0 / self.lr\\n self.var_shape = var.get(\\\"shape\\\")\\n self.var_dtype = var.get(\\\"dtype\\\").lower()\\n self.accum_shape = accum.get(\\\"shape\\\")\\n self.accum_dtype = accum.get(\\\"dtype\\\").lower()\\n self.linear_shape = linear.get(\\\"shape\\\")\\n self.linear_dtype = linear.get(\\\"dtype\\\").lower()\\n self.check_param()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0686a0db70ea3e597fcc88adaa8c3f5\",\n \"score\": \"0.52641636\",\n \"text\": \"def __init__(self):\\n self.da_val = DynamicArray()\\n self.da_max = DynamicArray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24468922248ebc17217ff925e070f54\",\n \"score\": \"0.526289\",\n \"text\": \"def __init__(self, D, kernel_gamma, m, step_size, gamma2=0.1, schedule=None, acc_star=0.234,\\n update_kernel_gamma=None, update_kernel_gamma_schedule=None, update_kernel_gamma_tol=0.1):\\n self.kernel_gamma = kernel_gamma\\n self.m = m\\n self.D = D\\n self.gamma2 = gamma2\\n self.step_size = step_size\\n self.schedule = schedule\\n self.acc_star = acc_star\\n self.update_kernel_gamma = update_kernel_gamma\\n self.update_kernel_gamma_schedule = update_kernel_gamma_schedule\\n self.update_kernel_gamma_tol = update_kernel_gamma_tol\\n \\n # scaling parameter evolution might be collected to assess convergence\\n self.nu2s = [step_size]\\n \\n # some sanity checks\\n if acc_star is not None:\\n assert acc_star > 0 and acc_star < 1\\n \\n if schedule is not None:\\n lmbdas = np.array([schedule(t) for t in np.arange(100)])\\n assert np.all(lmbdas > 0)\\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\\n \\n if self.update_kernel_gamma:\\n self.past_samples = collections.deque()\\n \\n if self.update_kernel_gamma_schedule is not None:\\n lmbdas = np.array([update_kernel_gamma_schedule(t) for t in np.arange(100)])\\n assert np.all(lmbdas > 0)\\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\\n \\n self._initialise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260cd30aa6404a0db73c4f2663e7a1d5\",\n \"score\": \"0.5251513\",\n \"text\": \"def __init__(self, value):\\n self.np_value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b98432c1827a929281f2a7f25c397f1\",\n \"score\": \"0.52501065\",\n \"text\": \"def __init__(self, initializer: ArrayOp, index_op: IndexOp):\\n self.initializer = initializer\\n self.index_op = index_op\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6498103bfa75e7a4a24ec77f577d87d\",\n \"score\": \"0.52426285\",\n \"text\": \"def __init__(self, value):\\n self._v = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601f3074c3929a4c542d73770542d2a7\",\n \"score\": \"0.52349836\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super(DPTuner, self).__init__(*args, **kwargs)\\n self._num_states = self._max_num_states = None\\n self._stage_dict = {}\\n self._dep_dict = {}\\n self._counted_nodes_set = set()\\n\\n self._global_data_dict = {\\n \\\"dtype\\\": self._dtype,\\n \\\"counted_nodes_set\\\": self._counted_nodes_set,\\n \\\"stage_dict\\\": self._stage_dict,\\n \\\"in_nodes_dict\\\": self._in_nodes_dict,\\n \\\"out_nodes_dict\\\": self._out_nodes_dict,\\n \\\"dep_dict\\\": self._dep_dict,\\n \\\"node_list\\\": self._node_list,\\n \\\"input_shapes\\\": self._input_shapes,\\n \\\"layout_transform_interlayer_cost\\\": self._layout_transform_interlayer_cost,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe05bf701bd73f4f90dc69bea2b7c5e7\",\n \"score\": \"0.5229092\",\n \"text\": \"def __init__(self, pars=[], kernel=\\\"\\\"):\\n\\n self._pars = pars\\n self._kernel = kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c32e4f4c1420ad4bad9df9f953f1cf\",\n \"score\": \"0.52148443\",\n \"text\": \"def init_vector(ocp):\\n v_init = np.ndarray((0, 1))\\n\\n # For states\\n for i_phase in range(len(ocp.nlp)):\\n current_nlp = ocp.nlp[i_phase]\\n\\n repeat = 1\\n if current_nlp.ode_solver.is_direct_collocation:\\n repeat += current_nlp.ode_solver.polynomial_degree\\n\\n nlp = ocp.nlp[current_nlp.use_states_from_phase_idx]\\n OptimizationVectorHelper._set_node_index(nlp, 0)\\n for key in nlp.states:\\n if key in nlp.x_init.keys():\\n n_points = OptimizationVectorHelper._nb_points(nlp, nlp.x_init[key].type)\\n nlp.x_init[key].check_and_adjust_dimensions(nlp.states[key].cx.shape[0], n_points)\\n\\n for k in range(nlp.ns + 1):\\n OptimizationVectorHelper._set_node_index(nlp, k)\\n for p in range(repeat if k != nlp.ns else 1):\\n point = k if k != 0 else 0 if p == 0 else 1\\n\\n collapsed_values = np.ndarray((nlp.states.shape, 1))\\n for key in nlp.states:\\n if key in nlp.x_init.keys():\\n point_to_eval = point\\n if nlp.x_init[key].type == InterpolationType.ALL_POINTS:\\n point_to_eval = k * repeat + p\\n value = (\\n nlp.x_init[key].init.evaluate_at(shooting_point=point_to_eval, repeat=repeat)\\n / nlp.x_scaling[key].scaling\\n )[:, np.newaxis]\\n else:\\n value = 0\\n # Organize the controls according to the correct indices\\n collapsed_values[nlp.states[key].index, :] = value\\n\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n # For controls\\n for i_phase in range(len(ocp.nlp)):\\n current_nlp = ocp.nlp[i_phase]\\n\\n nlp = ocp.nlp[current_nlp.use_controls_from_phase_idx]\\n OptimizationVectorHelper._set_node_index(nlp, 0)\\n if nlp.control_type in (ControlType.CONSTANT, ControlType.NONE):\\n ns = nlp.ns\\n elif nlp.control_type in (ControlType.LINEAR_CONTINUOUS, ControlType.CONSTANT_WITH_LAST_NODE):\\n ns = nlp.ns + 1\\n else:\\n raise NotImplementedError(f\\\"Multiple shooting problem not implemented yet for {nlp.control_type}\\\")\\n\\n for key in nlp.controls.keys():\\n if key in nlp.u_init.keys():\\n nlp.u_init[key].check_and_adjust_dimensions(nlp.controls[key].cx.shape[0], ns - 1)\\n\\n for k in range(ns):\\n OptimizationVectorHelper._set_node_index(nlp, k)\\n collapsed_values = np.ndarray((nlp.controls.shape, 1))\\n for key in nlp.controls:\\n if key in nlp.u_init.keys():\\n value = nlp.u_init[key].init.evaluate_at(shooting_point=k) / nlp.u_scaling[key].scaling\\n else:\\n value = 0\\n\\n # Organize the controls according to the correct indices\\n collapsed_values[nlp.controls[key].index, 0] = value\\n\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n # For parameters\\n collapsed_values = np.zeros((ocp.parameters.shape, 1))\\n for key in ocp.parameters.keys():\\n if key not in ocp.parameter_init.keys():\\n v_init = np.concatenate((v_init, np.zeros((ocp.parameters[key].size, 1))))\\n continue\\n\\n scaled_init = ocp.parameter_init[key].scale(ocp.parameters[key].scaling)\\n collapsed_values[ocp.parameters[key].index, :] = scaled_init.init\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n # For stochastic variables\\n for i_phase in range(len(ocp.nlp)):\\n nlp = ocp.nlp[i_phase]\\n OptimizationVectorHelper._set_node_index(nlp, 0)\\n\\n for key in nlp.stochastic_variables.keys():\\n if key in nlp.s_init.keys():\\n nlp.s_init[key].check_and_adjust_dimensions(nlp.stochastic_variables[key].cx.shape[0], nlp.ns)\\n\\n for k in range(nlp.ns + 1):\\n OptimizationVectorHelper._set_node_index(nlp, k)\\n collapsed_values = np.ndarray((nlp.stochastic_variables.shape, 1))\\n for key in nlp.stochastic_variables:\\n if key in nlp.s_init.keys():\\n value = nlp.s_init[key].init.evaluate_at(shooting_point=k) / nlp.s_scaling[key].scaling\\n else:\\n value = 0\\n\\n # Organize the stochastic variables according to the correct indices\\n collapsed_values[nlp.stochastic_variables[key].index, 0] = value\\n\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n for i_phase in range(len(ocp.nlp)):\\n nlp = ocp.nlp[i_phase]\\n if nlp.motor_noise is not None:\\n n_motor_noise = nlp.motor_noise.shape[0]\\n n_sensory_noise = nlp.sensory_noise.shape[0]\\n v_init = np.concatenate((v_init, np.zeros((n_motor_noise, 1))))\\n v_init = np.concatenate((v_init, np.zeros((n_sensory_noise, 1))))\\n\\n return v_init\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f21f06197faa3e88badb2b42da306fe1\",\n \"score\": \"0.52086794\",\n \"text\": \"def __init__(self):\\r\\n self.inputs = []\\r\\n self.op = None\\r\\n self.const_attr = None\\r\\n self.name = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d475a0425781c6d7fa1542d8463f437\",\n \"score\": \"0.5186119\",\n \"text\": \"def __init__(self, name: tp.Optional[str] = None, dtype: np.dtype = jnp.float32):\\n self.name = name if name else utils.lower_snake_case(self.__class__.__name__)\\n self.dtype = dtype\\n self._params = {}\\n self._states = []\\n self._submodules = []\\n self._dynamic_submodules = []\\n self._initialized = False\\n self._trainable = True\\n\\n _init = self.init\\n\\n def init(*args, **kwargs):\\n return _init(*args, **kwargs)\\n\\n self.init = init\\n\\n utils.wraps(self.call)(self.init)\\n utils.wraps(self.call)(self)\\n\\n self._jit_functions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede1473867441e7e3658e63982b4fb4e\",\n \"score\": \"0.5181392\",\n \"text\": \"def Do_kernel(self):\\n return self.Kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7c2366c48398d16ed6ca58d56cb137\",\n \"score\": \"0.5176585\",\n \"text\": \"def __init__(self, x=None):\\n self.v = x\\n self.cl = None\\n self.cr = None\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a718b8ed355be4f8f776d69cf7d2da\",\n \"score\": \"0.51614505\",\n \"text\": \"def __init__(self, pyo, t=None):\\n if type(pyo) is Numeric.ArrayType:\\n k = _arraytok(pyo) \\n elif type(pyo) is _K:\\n k = pyo\\n if t and t != k.t:\\n raise NotImplemented\\n else:\\n if not t:\\n t = _guesst(pyo)\\n else:\\n if t > 0:\\n pyt = _pyt[t-1]\\n pyo = pyt(pyo)\\n elif t < 0:\\n pyt = _pyt[-t-1]\\n pyo = map(pyt, pyo)\\n kgen = _kgen(t)\\n k = kgen(pyo)\\n _K.__init__(self, k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b870f3d7f5b6706d9c9597f891c73f95\",\n \"score\": \"0.5157406\",\n \"text\": \"def _v__init__(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba1f95b84ca10b7b07a5b2ccf6e80a2\",\n \"score\": \"0.514581\",\n \"text\": \"def __init__(self, shape, dtype):\\n self._dtype = dtype\\n self._sum = tf.Variable(lambda: tf.zeros(shape, dtype), False)\\n self._count = tf.Variable(lambda: 0, trainable=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5d8627423d0ced3926413340e0e896\",\n \"score\": \"0.51323915\",\n \"text\": \"def __init__(self, master=None, value=None, name=None):\\n _TK.Variable.__init__(self, master, value, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bb7d41df8f841d7b93509b616075367\",\n \"score\": \"0.5131436\",\n \"text\": \"def __init__(self,kernel,args,invocations=None,type='VERTICAL'):\\n\\t\\tself.args = []\\n\\t\\tself.kernel = \\\"\\\"\\n\\t\\tif(invocations == None):\\n\\t\\t\\ttemp = kernel.getSub(\\\"clEnqueueNDRangeKernel\\\")\\n\\t\\t\\tif(temp is not None):\\n\\t\\t\\t\\tself.kernel = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint \\\"Invalid kernel statement\\\"\\n\\t\\t\\tfor arg in args:\\n\\t\\t\\t\\ttemp = arg.getSub(\\\"clSetKernelArg\\\")\\n\\t\\t\\t\\tif(temp is not None):\\n\\t\\t\\t\\t\\tif(temp.children[0] == self.kernel.children[1]):\\n\\t\\t\\t\\t\\t\\tself.args.append((temp.children[2],temp.children[3]))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint \\\"invalid kernel argument\\\"\\n\\t\\telse:\\n\\t\\t\\tself.kernel = copy.deepcopy(invocations[0].kernel)\\n\\t\\t\\tself.kernel.children[1].tokens[0].value = \\\"\\\"\\n\\t\\t\\tfor invocation in invocations:\\n\\t\\t\\t\\tself.kernel.children[1].tokens[0].value += invocation.kernel.children[1].tokens[0].value.split('_')[0] + \\\"_\\\"\\n\\t\\t\\t\\tself.args += invocation.args\\n\\t\\t\\tself.kernel.children[1].tokens[0].value += \\\"kernel\\\"\\n\\t\\t\\tif(type == 'HORIZONTAL'):\\n\\t\\t\\t\\t\\\"\\\"\\\"Append an h\\\"\\\"\\\"\\n\\t\\t\\t\\tself.kernel.children[1].tokens[0].value +='h'\\t\\n\\t\\t\\tself.args = removeDuplicates2(self.args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429e749ed7cc10f710ceb9a638caf71f\",\n \"score\": \"0.5110538\",\n \"text\": \"def __init__(self, device_params = DEFAULT_DEVICE_PARAMS):\\n self.device_names = list(device_params.keys())\\n self.dirichlet_params = list(device_params.values())\\n self.p_vector = dirichlet(self.dirichlet_params).rvs().reshape(-1,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e245d650938297578a09e6890a46c981\",\n \"score\": \"0.51071066\",\n \"text\": \"def __init__(self, potential, flow_operator_mask_v, flow_operator_mask_k):\\n self._potential = potential\\n self._v = potential.without_weights()\\n self._k = potential.kinetic_energy()\\n self._lam = potential.lam\\n self._flow_op_mask_v = flow_operator_mask_v\\n self._flow_op_mask_k = flow_operator_mask_k\\n self._flow = 'lambda'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d03dfd8f3467b96acfbfa3c3a0d282\",\n \"score\": \"0.5101611\",\n \"text\": \"def __init__(\\n self,\\n kernel_op: get_kernel_fn.ApplyKernelOp,\\n params: parameters_lib.SwirlLMParameters,\\n scalar_name: str,\\n thermodynamics: thermodynamics_manager.ThermodynamicsManager,\\n ):\\n self._kernel_op = kernel_op\\n self._params = params\\n self._bc_types = copy.deepcopy(params.bc_type)\\n self._scalar_name = scalar_name\\n self._thermodynamics = thermodynamics\\n\\n self._h = (self._params.dx, self._params.dy, self._params.dz)\\n\\n self._scalar_params = None\\n for scalar in self._params.scalars:\\n if scalar.name == self._scalar_name:\\n self._scalar_params = scalar\\n break\\n assert (\\n self._scalar_params is not None\\n ), f'{self._scalar_name} is not configured.'\\n\\n for override_bc in self._scalar_params.override_bc_type:\\n dim = override_bc.dim\\n face = override_bc.face\\n logging.info('BC type for scalar: %s '\\n 'will be reset to %s (originally %s) for dimension %d at '\\n 'face %d', self._scalar_name,\\n str(boundary_condition_utils.BoundaryType.UNKNOWN),\\n str(self._bc_types[dim][face]), dim, face)\\n self._bc_types[dim][face] = (\\n boundary_condition_utils.BoundaryType.UNKNOWN)\\n\\n # Find the direction of gravity. Only vector along a particular dimension is\\n # considered currently.\\n self._g_vec = (\\n self._params.gravity_direction if self._params.gravity_direction else [\\n 0.0,\\n ] * 3)\\n self._g_dim = None\\n for i in range(3):\\n if np.abs(np.abs(self._g_vec[i]) - 1.0) < _G_THRESHOLD:\\n self._g_dim = i\\n break\\n\\n # Prepare diffusion related models.\\n self._diffusion_fn = diffusion.diffusion_scalar(self._params)\\n self._use_sgs = self._params.use_sgs\\n filter_widths = (self._params.dx, self._params.dy, self._params.dz)\\n if self._use_sgs:\\n self._sgs_model = sgs_model.SgsModel(self._kernel_op, filter_widths,\\n params.sgs_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6c3eaa187c603279b9eacd627bd1e8a\",\n \"score\": \"0.5097064\",\n \"text\": \"def __init__(self, K, delta, c, np = None):\\n\\n self.K = float(K)\\n self.K_int = int(K)\\n self.delta = delta\\n self.c = c\\n\\n S = self.calc_S()\\n cdf, Z = gen_rsd_cdf(K, S, delta)\\n self.cdf = cdf\\n self.Z = Z\\n\\n #self.inter = inter.interp1d(np.concatenate(([0], cdf)), range(0,K+1))\\n self.np_rand = RandomState(1)\\n self.np = np\\n\\n self.state = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b025b34a1936ef033d369b14517a6320\",\n \"score\": \"0.5088485\",\n \"text\": \"def __init__(self, ntok, ndim, init=df.init.ortho_svd()):\\n df.Module.__init__(self)\\n\\n self.ndim = ndim\\n self.W = self._addparam((ntok, ndim), init, name='Wemb_{}x{}'.format(ntok, ndim))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670b1ee85ca5f25840cd22b783692716\",\n \"score\": \"0.5082001\",\n \"text\": \"def __init__(self, k: float = 1.0, x_shift: float = 0.0, y_shift: float = 0.0):\\n\\n self.constants = {self.k: k, self.x_shift: x_shift, self.y_shift: y_shift}\\n self.V = self.V_functional.subs(self.constants)\\n self.dVdpos = sp.diff(self.V, self.position)\\n\\n super().__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed8bb627d644964e64e4280b08c7088\",\n \"score\": \"0.50803864\",\n \"text\": \"def __init__(self, target: \\\"Expr\\\", operator: UnOp) -> None:\\n self.target = target\\n self.operator = operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3bce030f98c517a2d9c05a1062aa7d\",\n \"score\": \"0.5075545\",\n \"text\": \"def _kernel_init(scale=1.0, seed=None):\\n scale = 2. * scale\\n return tf.keras.initializers.VarianceScaling(\\n scale=scale, mode='fan_in', distribution='truncated_normal', seed=seed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3b771b5507abee7a05e1de11e5d55b\",\n \"score\": \"0.50715685\",\n \"text\": \"def __init__(self, batch_size, embedding_size, k=None, device=None):\\n super(SimpleStruct, self).__init__(batch_size, embedding_size)\\n operations = [Operation.push, Operation.pop]\\n self._reg_trackers = [None for _ in operations]\\n self._read_strength = k\\n self._values = []\\n self._strengths = []\\n self._device = device\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14767bc57bcafb626a78c7673062cca6\",\n \"score\": \"0.50543296\",\n \"text\": \"def __init__(self, tsr):\\n super(DenseLinearOperator, self).__init__(tsr)\\n self.tensor = tsr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b638c603a0064f8e19d723f0d6a1a7fb\",\n \"score\": \"0.5053925\",\n \"text\": \"def _initialise_state(self) -> None:\\n super()._initialise_state()\\n self.test_window = tf.gather(self.x_ref_eff, self.init_test_inds)\\n self.k_xtc = self.kernel(self.test_window, self.kernel_centers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f8713d2cff480f49e5a16bb5caa601b\",\n \"score\": \"0.5046923\",\n \"text\": \"def __init__(self,\\n kernel,\\n batch_size,\\n max_num_context,\\n x_size=1,\\n y_size=1,\\n testing=False,\\n device = torch.device(\\\"cpu\\\")):\\n self.kernel = kernel\\n self._batch_size = batch_size\\n self._max_num_context = max_num_context\\n self._x_size = x_size\\n self._y_size = y_size\\n self._testing = testing\\n self.device = device\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827ed719975ed277602a055df1fe7bc9\",\n \"score\": \"0.50449395\",\n \"text\": \"def __init__(self, initializer_params={}, verbose=True, seed=0):\\n\\n super(Constant, self).__init__(initializer_params, verbose, seed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5277911cc013a07ef6cceb5bcfc2fc05\",\n \"score\": \"0.5040688\",\n \"text\": \"def __init__(self, vec, evotype=\\\"auto\\\"):\\n vec = SPAMVec.convert_to_vector(vec)\\n if evotype == \\\"auto\\\":\\n evotype = \\\"statevec\\\" if _np.iscomplexobj(vec) else \\\"densitymx\\\"\\n assert(evotype in (\\\"statevec\\\", \\\"densitymx\\\")), \\\\\\n \\\"Invalid evolution type '%s' for %s\\\" % (evotype, self.__class__.__name__)\\n DenseSPAMVec.__init__(self, vec, evotype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2a5100d4a03294b66cfeb709965644\",\n \"score\": \"0.5024744\",\n \"text\": \"def kernel(self, *args):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f077d60cbbc17c88aa5fb673dd71f0c3\",\n \"score\": \"0.5020952\",\n \"text\": \"def __init__(self, vec, evotype=\\\"auto\\\"):\\n vec = SPAMVec.convert_to_vector(vec)\\n if evotype == \\\"auto\\\":\\n evotype = \\\"statevec\\\" if _np.iscomplexobj(vec) else \\\"densitymx\\\"\\n elif evotype == \\\"statevec\\\":\\n vec = _np.asarray(vec, complex) # ensure all statevec vecs are complex (densitymx could be either?)\\n\\n assert(evotype in (\\\"statevec\\\", \\\"densitymx\\\")), \\\\\\n \\\"Invalid evolution type '%s' for %s\\\" % (evotype, self.__class__.__name__)\\n DenseSPAMVec.__init__(self, vec, evotype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b73f58df49cdc4fb04ea592cd5a8341\",\n \"score\": \"0.5013608\",\n \"text\": \"def _define_kernel(self):\\n def k(x,y,\\n dist=self.dist,\\n commonKernel=self.commonKernel,\\n blockKernel=self.blockKernel):\\n # Geodesic distance. \\n d=dist(x[1:],y[1:])\\n\\n # Calculate the underlying kernel for all subjects.\\n cov=commonKernel(d)\\n\\n # Add individual specific covariance.\\n if x[0]==y[0]:\\n cov+=blockKernel[int(x[0])](d)\\n return cov\\n \\n self.kernel=k\\n \\n # Update GPR under-the-hood that relies on this kernel.\\n self._update_gp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c357b6703e4e6f0eb142d7347a729a71\",\n \"score\": \"0.50009483\",\n \"text\": \"def _init_variable(self, name, val):\\n # return tf.get_variable(name, (), dtype=tf.float32, trainable=False,\\n # initializer=tf.constant_initializer(val))\\n return tf.constant(name=name, shape=(), dtype=tf.float32, value=val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b119ae4642062f2847a8127122d8ef4f\",\n \"score\": \"0.50004977\",\n \"text\": \"def __init__(self, n_features, kernel):\\n self.n_features = n_features\\n self.kernel = kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77a92b259d5be88049f3e71e4212ebd7\",\n \"score\": \"0.49999592\",\n \"text\": \"def __init__(self, kernel, itspace_extents, *args, **kwargs):\\n if self._initialized:\\n return\\n self._parloop = kwargs.get('parloop')\\n self.comm = itspace_extents.comm\\n self._kernel = self._parloop._kernel\\n self._config = kwargs.get('config')\\n self._initialized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad8752674ae08b5ffe6f6e6d4d2aa61\",\n \"score\": \"0.49982068\",\n \"text\": \"def __init__(self\\n , **kwargs\\n ):\\n self.t_functorArgs = {}\\n self.t_cstArgs = {}\\n \\n # The data to which the functor is applied\\n self.o_vectorDataSet = None\\n \\n # The functor returned data\\n self.x_fData = None\\n \\n # First get T_FUNCTOR_ARGS\\n for t_funcArgTuple in self.__class__.getFunctorArgs():\\n \\n s_funcArgKey = t_funcArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\\n b_required = t_funcArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\\n \\n if not kwargs.has_key( s_funcArgKey ):\\n if b_required:\\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_funcArgKey )\\n else:\\n self.t_functorArgs[ s_funcArgKey ] = None \\n else:\\n # Get the QArkFunctorArg\\n o_functorArg = kwargs[ s_funcArgKey ]\\n self.t_functorArgs[ s_funcArgKey ] = o_functorArg.getValue()\\n \\n # Get the constant value parameters\\n for t_cstArgTuple in self.__class__.getCstArgs():\\n \\n s_cstArgKey = t_cstArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\\n b_required = t_cstArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\\n \\n if not kwargs.has_key( s_cstArgKey ):\\n if b_required:\\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_cstArgKey )\\n else:\\n self.t_cstArgs[ s_cstArgKey ] = None \\n else:\\n # Get the QArkFunctorArg\\n o_cstArg = kwargs[ s_cstArgKey ]\\n self.t_cstArgs[ s_cstArgKey ] = o_cstArg.getValue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dea2f57a1f90dc4b827a67ba680564f\",\n \"score\": \"0.4976893\",\n \"text\": \"def __init__(self, env, k):\\n gym.Wrapper.__init__(self, env)\\n self.k = k\\n self.states = deque([], maxlen=k)\\n shp = self.observation_space.n\\n self.observation_space = (k, shp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b767c6f0501614ef6a73b62c8e8eaa9\",\n \"score\": \"0.49731472\",\n \"text\": \"def init_op_pattern():\\n fusion_manager.init_current_op_pattern()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e53c3d8b3fa5a4ff22e11b3435270ca\",\n \"score\": \"0.49591565\",\n \"text\": \"def init(self, net_dim, state_dim, action_dim):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e53c3d8b3fa5a4ff22e11b3435270ca\",\n \"score\": \"0.49591565\",\n \"text\": \"def init(self, net_dim, state_dim, action_dim):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a81ef952dca4e2e241406f697edc54\",\n \"score\": \"0.49527246\",\n \"text\": \"def __init__(self, X, mu, p):\\n super(DotProductKernelMap, self).__init__(mu, p)\\n self.n = X.shape[0]\\n self.d = X.shape[1]\\n self.X = X\\n self.Gs_train = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79466e46f7a122d0032ad903f386dd53\",\n \"score\": \"0.4948092\",\n \"text\": \"def init_accumulators(self):\\n torch.nn.init.constant_(self.mu_numerator, self.eps)\\n torch.nn.init.constant_(self.mu_denominator, self.eps * self.C)\\n torch.nn.init.constant_(self.var_numerator, self.eps)\\n torch.nn.init.constant_(self.var_denominator, self.eps * self.C)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251831a91ce68da00733b1975980b5f5\",\n \"score\": \"0.49430963\",\n \"text\": \"def __init__(self, **kwargs):\\n LOG.info(\\\"%s: initing...\\\" % str(self))\\n super(JansenRitDavid, self).__init__(**kwargs)\\n\\n #self._state_variables = [\\\"y0\\\", \\\"y1\\\", \\\"y2\\\", \\\"y3\\\", \\\"y4\\\", \\\"y5\\\"]\\n self._nvar = 8\\n\\n self.cvar = numpy.array([1,2], dtype=numpy.int32)\\n\\n #TODO: adding an update_derived_parameters method to remove some of the\\n # redundant parameter multiplication in dfun should gain about 7%\\n # maybe not worth it... The three exp() kill us at ~90 times *\\n #self.nu_max2 = None #2.0 * self.nu_max\\n #self.Aa = None # self.A * self.a\\n #self.Bb = None # self.B * self.b\\n #self.aa = None # self.a**2\\n #self.a2 = None # 2.0 * self.a\\n #self.b2 = None # 2.0 * self.b\\n #self.a_1J = None # self.a_1 * self.J\\n #self.a_2J = None # self.a_2 * self.J\\n #self.a_3J = None # self.a_3 * self.J\\n #self.a_4J = None # self.a_4 * self.J\\n\\n LOG.debug('%s: inited.' % repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b8907041e456b0c34a14714a13a8534\",\n \"score\": \"0.4932093\",\n \"text\": \"def init(self):\\n self._model = {}\\n self._value_function = {}\\n self._terminal_state_set = set()\\n self._state_action = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5399a96be429116cc61fcfd03f89ba41\",\n \"score\": \"0.49313113\",\n \"text\": \"def _do_data_dependent_init():\\n w_fc_normalized = tf.nn.l2_normalize(w_fc.read_value(), [0])\\n output_init = tf.matmul(embeddings, w_fc_normalized)\\n mean_init, var_init = tf.nn.moments(output_init, [0])\\n # Data-dependent init values.\\n g_init_value = 1. / tf.sqrt(var_init + 1e-10)\\n ops = [tf.assign(g, g_init_value)]\\n if not cosine_classifier:\\n # Also initialize a bias in a data-dependent way.\\n b_fc_init_value = -mean_init * g_init_value\\n ops.append(tf.assign(b_fc, b_fc_init_value))\\n # Mark that the data-dependent initialization is done to prevent it from\\n # happening again in the future.\\n ops.append(tf.assign(data_dependent_init_done, 1))\\n return tf.group(*ops)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c5613c40d97e6454db8adf43bf569c\",\n \"score\": \"0.49255443\",\n \"text\": \"def init_variable(v, init, name=\\\"init\\\"):\\n with ops.name_scope(None, v.op.name + \\\"/\\\", [v, init]):\\n with ops.name_scope(name) as scope:\\n with ops.colocate_with(v):\\n if callable(init):\\n assert v.get_shape().is_fully_defined(), \\\"Variable shape unknown.\\\"\\n # TODO(mrry): Convert to v.shape when the property and\\n # accessor are reconciled (and all initializers support\\n # tf.TensorShape objects).\\n value = init(v.get_shape().as_list(), v.dtype.base_dtype)\\n value = ops.convert_to_tensor(value, name=\\\"value\\\")\\n return gen_state_ops.assign(v, value, name=scope)\\n else:\\n init = ops.convert_to_tensor(init, name=\\\"init\\\")\\n return gen_state_ops.assign(v, init, name=scope)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad5823f394b26d1fc23bd233430ddc7\",\n \"score\": \"0.4921869\",\n \"text\": \"def __init__(self, component, name, shape, dtype):\\n self._name = name\\n self._shape = shape\\n self._component = component\\n beta = tf.get_variable(\\n 'beta_%s' % name,\\n shape=shape,\\n dtype=dtype,\\n initializer=tf.zeros_initializer())\\n gamma = tf.get_variable(\\n 'gamma_%s' % name,\\n shape=shape,\\n dtype=dtype,\\n initializer=tf.ones_initializer())\\n self._params = [beta, gamma]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0729f28466be1f8662a2650d69888e6c\",\n \"score\": \"0.49182227\",\n \"text\": \"def __init__(self, name, **okwargs):\\n assert name in AVAILABLE_KERNELS, \\\"Unknown kernel %s\\\" % name\\n super(SparseKernel, self).__init__(name, **okwargs)\\n self.use_rbf = okwargs.get('use_rbf', name in RBF_KERNELS)\\n if name not in RBF_KERNELS and self.use_rbf:\\n raise ValueError('Cannot use RBF with non-RBF kernel {}'.format(name))\\n # get the module used to compute the sparse distances (FIXME ugly)\\n spdist = None\\n exec('import sparse_distances.sparse_%s as spdist' % name)\\n # define \\\"raw\\\" distance functions between sparse vectors (from C library)\\n self._v2v_f = spdist.v2v # raw dist. between vectors\\n self._v2m_f = spdist.v2m # raw dist. between a vector and a matrix\\n self._m2m_f = spdist.m2m # raw dist. between a (test) matrix and a (train) matrix\\n self._gram_f = spdist.gram # raw gram matrix of a list of vectors\\n if name in ('linear', 'intersection'):\\n self.is_additive = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"696f0b83531e773d2f9e7679ffa66996\",\n \"score\": \"0.49122977\",\n \"text\": \"def __init__(self, dataset, kernel, model=Exact(), mean=None, name=None, rescale_x=False):\\n \\n if not isinstance(dataset, DataSet):\\n dataset = DataSet(dataset)\\n if dataset.get_output_dims() == 0:\\n raise ValueError(\\\"dataset must have at least one channel\\\")\\n names = [name for name in dataset.get_names() if name is not None]\\n if len(set(names)) != len(names):\\n raise ValueError(\\\"all data channels must have unique names\\\")\\n\\n if rescale_x:\\n dataset.rescale_x()\\n else:\\n for channel in dataset:\\n for dim in range(channel.get_input_dims()):\\n xran = np.max(channel.X[dim].transformed) - np.min(channel.X[dim].transformed)\\n if xran < 1e-3:\\n logger.warning(\\\"Very small X range may give problems, it is suggested to scale up your X axis\\\")\\n elif 1e4 < xran:\\n logger.warning(\\\"Very large X range may give problems, it is suggested to scale down your X axis\\\")\\n\\n self.name = name\\n self.dataset = dataset\\n self.kernel = kernel\\n\\n X = [[x[channel.mask] for x in channel.X] for channel in self.dataset]\\n Y = [np.array(channel.Y[channel.mask]) for channel in self.dataset]\\n x, y = self._to_kernel_format(X, Y)\\n\\n self.model = model.build(kernel, x, y, mean, name)\\n if issubclass(type(kernel), MultiOutputKernel) and issubclass(type(model), Exact):\\n self.model.variance.assign(0.0, lower=0.0, trainable=False) # handled by MultiOutputKernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf3117e76c5fabe7b753fb2c1e95073\",\n \"score\": \"0.49072382\",\n \"text\": \"def __init__(self,oscillators,comb_op):\\n self.oscillators=oscillators\\n self.comb_op=comb_op\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a80a836cd045a2779e2c83a489f40bd\",\n \"score\": \"0.4900156\",\n \"text\": \"def __init__(self, **kwargs):\\n LOG.info('%s: initing...' % str(self))\\n super(LileySteynRoss, self).__init__(**kwargs)\\n #self._state_variables = [\\\"E\\\", \\\"I\\\"]\\n self._nvar = 2\\n self.cvar = numpy.array([0, 1], dtype=numpy.int32)\\n LOG.debug('%s: inited.' % repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a26bdf4c22d69c8784e808227fb4785a\",\n \"score\": \"0.4893577\",\n \"text\": \"def __init__(self, name, copy=0, val=0, size=1, **kwargs):\\r\\n self.value = val\\r\\n if isinstance(self.value, numpy.ndarray):\\r\\n self.size = self.value.shape[0]\\r\\n elif isinstance(self.value, list):\\r\\n self.size = len(self.value)\\r\\n else:\\r\\n self.size = size\\r\\n\\r\\n super(IndVar, self).__init__(name, copy, **kwargs)\\r\\n \\r\\n if 'u_scal' in self.kwargs:\\r\\n self.u_scal = self.kwargs['u_scal']\\r\\n else:\\r\\n self.u_scal = 1.0\\r\\n if 'f_scal' in self.kwargs:\\r\\n self.f_scal = self.kwargs['f_scal']\\r\\n else:\\r\\n self.f_scal = 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f13d984fb9004cdb7e465aa197e71b8\",\n \"score\": \"0.4890825\",\n \"text\": \"def __init__(self, pool_size, stride, padding=\\\"VALID\\\"):\\n super().__init__()\\n self.kernel_shape = pool_size\\n self.stride = stride\\n\\n self.padding_mode = padding\\n self.padding = None\\n\\n self.input = None\\n self.X = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d37517a9cbdc62dadf4417a61d5731\",\n \"score\": \"0.48837817\",\n \"text\": \"def __init__(self):\\n self.da = DynamicArray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d23029791c70659c983e74c8c7aa81\",\n \"score\": \"0.48798135\",\n \"text\": \"def setKernel(self, k):\\n self.__kernel = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b365597f362f5204053091ab359913b\",\n \"score\": \"0.48740718\",\n \"text\": \"def __init__(\\n self,\\n dtensor_components: Tuple[tensor.Tensor],\\n global_element_spec: tensor_spec.TensorSpec,\\n layouts: Any):\\n # dtensor_components is expected to be a single-element tuple.\\n [self._iterator_resource_dtensor] = dtensor_components\\n self._global_element_spec = global_element_spec\\n self._layouts = layouts\\n self._layouts_str = nest.map_structure(\\n lambda layout: layout.to_string(), layouts)\\n\\n super().__init__(\\n components=dtensor_components, element_spec=global_element_spec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7fe2b29bb7123c470f655eedadb589\",\n \"score\": \"0.48736146\",\n \"text\": \"def __init__(self, stride_shape, convolution_shape, num_kernels):\\n\\n # Inherit parent attributes\\n super().__init__()\\n\\n self.stride_shape = stride_shape\\n self.stride_y, self.stride_x = 0, 0\\n\\n self.convolution_shape = convolution_shape\\n self.input_tensor_shape = None\\n self.input_tensor = None\\n\\n self.num_kernels = num_kernels\\n\\n # Initialize the filter uniformly random in the range [0, 1) as tensor of size: [Nr_kernels x C x M (x N)].\\n self.weights = np.random.uniform(size=(self.num_kernels, *self.convolution_shape))\\n\\n # Initialize the bias uniformly random in the range [0, 1) for each kernel as a single value.\\n self.bias = np.random.uniform(size=self.num_kernels)\\n\\n self.gradient_weights = None # Gradient with respect to the weights.\\n self.gradient_bias = None # Gradient with respect to the bias.\\n\\n self.optimizer = None # Optimizer of this layer.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c2bb027440f1d4c616441288b1acd3\",\n \"score\": \"0.48729536\",\n \"text\": \"def __init__(self, *args):\\n this = _openmm.new_VariableVerletIntegrator(*args)\\n try:\\n self.this.append(this)\\n except:\\n self.this = this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66eeb8f71b64a13fbd03d9940288a98\",\n \"score\": \"0.48687768\",\n \"text\": \"def _scalar_op(self, op, o):\\n new_parts = []\\n new_terms = {term: getattr(term, op)(o) for term in self.expgate_sets.keys()}\\n new_parts = ({targets: new_terms[term]\\n for targets, term in part.items()}\\n for part in self.parts)\\n new = self.__class__(*new_parts)\\n if self._dense is not None:\\n new.dense = getattr(self.dense, op)(o)\\n if self._circuit is not None:\\n new._circuit = self._circuit\\n new._circuit.dt = None\\n new.expgate_sets = {new_terms[term]: gate_set\\n for term, gate_set in self.expgate_sets.items()}\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03d9055aa5ebd994aa9b36d5ecc6417d\",\n \"score\": \"0.4867485\",\n \"text\": \"def __init__(self):\\n self.node: Node[K, V] = EmptyNode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00babe8f5fa91fe8a9f10ef7c506e6ef\",\n \"score\": \"0.4864531\",\n \"text\": \"def __init__(self, rho: float, batch_multiplier: float, lambda_vae: float, lambda_cos: float, lambda_l1: float):\\n self.rho = rho\\n self.lambda_vae = lambda_vae\\n self.lambda_cos = lambda_cos\\n self.lambda_l1 = lambda_l1\\n self.batch_multiplier = batch_multiplier\",\n \"title\": \"\"\n },\n {\n \"docid\": \"665007793f12574d1c320285c82d3a00\",\n \"score\": \"0.48640746\",\n \"text\": \"def __init__(self, target, key, values=0, active=0):\\n \\n #Initialized variables\\n self.target = target\\n self.key = key\\n self.values = values\\n self.active = active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f42e931ee21cb79a96e0b09c1f99d14\",\n \"score\": \"0.48613185\",\n \"text\": \"def __init__(self, dim): #, length_scale, length_scale_bounds=()):\\n# assert isinstance(column, (list, tuple, int)), \\\"must be int or list of ints\\\"\\n# self.column = [column] if isinstance(column, int) else column\\n# assert all(isinstance(i, int) for i in self.column), \\\"must be integers\\\"\\n self.dim = dim\\n \\n kernels = [Projection(RBF(), [c]) for c in range(dim)]\\n\\n # combine the kernels into a single product kernel\\n self.kernel = reduce(lambda k0, k1 : k0 * k1, kernels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91dee435523b3f227ca893f9e528764f\",\n \"score\": \"0.48593938\",\n \"text\": \"def __init__(self, operator_id):\\n\\n _base.Base.__init__(self, _types.COMPONENT_TYPE_OPERATOR)\\n self.__op_id = operator_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54500e01c5648df0d8f588ea9a0ff3b6\",\n \"score\": \"0.48528546\",\n \"text\": \"def init(self) -> None:\\n self.layout = self.attrs['data_layout']\\n\\n logger.info(\\\"Init {} layer: {}\\\".format(\\n 'Conv2D' if self.kernel_groups == 1 else 'DepthWiseConv2D',\\n self.layout))\\n logger.info(self.input_shapes)\\n\\n input_shapes, kernel, biases = \\\\\\n self.input_shapes, self.kernel, self.biases\\n if kernel is not None:\\n kernel = \\\\\\n tf.compat.v1.placeholder_with_default(kernel, kernel.shape)\\n else:\\n kernel = \\\\\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\\n shape=input_shapes[1])\\n\\n if biases is not None:\\n biases = \\\\\\n tf.compat.v1.placeholder_with_default(biases, biases.shape)\\n else:\\n biases = \\\\\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\\n shape=input_shapes[2])\\n\\n inpt = \\\\\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\\n shape=input_shapes[0])\\n\\n self.inpts = [inpt, kernel, biases]\\n\\n self.quant_output = self._get_conv_tensor(self.inpts)\\n self.res = self.get_output_tensors(self.inpts)[0]\\n logger.info(\\\"Res shape: {}\\\".format(self.res.shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c96da5ae9c96c7d103042f8d26c505\",\n \"score\": \"0.4852539\",\n \"text\": \"def glv_init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"453f264a9edca02a883467f14ebb4701\",\n \"score\": \"0.4848578\",\n \"text\": \"def __init__(self, sg_values): \\n self.__a = {} \\n self.__init_coefs(sg_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7dd23f379b010e3624e42573da451f\",\n \"score\": \"0.48449957\",\n \"text\": \"def __init__(self):\\n self.U, self.s, self.V = (None,None,None)\\n self.user_encoder = None\\n self.item_encoder = None\\n self.mat = None\\n self.decomp = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"159c7de3ad234f5ac44629ce829f08c0\",\n \"score\": \"0.48449928\",\n \"text\": \"def __init__(self, *args):\\n _gp.gp_Vec2d_swiginit(self,_gp.new_gp_Vec2d(*args))\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":185,"cells":{"query_id":{"kind":"string","value":"b385b0580c451d27151a0b7f7ebf8293"},"query":{"kind":"string","value":"Return a restricted zscore."},"positive_passages":{"kind":"list like","value":[{"docid":"d9b8dbd9271cb251514b6863c4b30b2c","score":"0.70605326","text":"def zscoreOtherRestricted(measure, power, median, variationCoefficient, computeFinalZScore):\n zscoreNorm = zscore(measure, power, median, variationCoefficient)\n if math.fabs(zscoreNorm) > 3 and computeFinalZScore:\n if zscoreNorm > 3:\n std3Pos = cutoff(3, power, median, variationCoefficient)\n std23Pos = std3Pos - cutoff(2, power, median, variationCoefficient)\n zscoreNorm = 3 + ((measure - std3Pos) / std23Pos)\n elif zscoreNorm < 3:\n std3Neg = cutoff(-3, power, median, variationCoefficient)\n std23Neg = cutoff(-2, power, median, variationCoefficient) - std3Neg\n zscoreNorm = -3 + ((measure - std3Neg) / std23Neg)\n return zscoreNorm","title":""}],"string":"[\n {\n \"docid\": \"d9b8dbd9271cb251514b6863c4b30b2c\",\n \"score\": \"0.70605326\",\n \"text\": \"def zscoreOtherRestricted(measure, power, median, variationCoefficient, computeFinalZScore):\\n zscoreNorm = zscore(measure, power, median, variationCoefficient)\\n if math.fabs(zscoreNorm) > 3 and computeFinalZScore:\\n if zscoreNorm > 3:\\n std3Pos = cutoff(3, power, median, variationCoefficient)\\n std23Pos = std3Pos - cutoff(2, power, median, variationCoefficient)\\n zscoreNorm = 3 + ((measure - std3Pos) / std23Pos)\\n elif zscoreNorm < 3:\\n std3Neg = cutoff(-3, power, median, variationCoefficient)\\n std23Neg = cutoff(-2, power, median, variationCoefficient) - std3Neg\\n zscoreNorm = -3 + ((measure - std3Neg) / std23Neg)\\n return zscoreNorm\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"9ef0a40399c631949df3b99157924f0d","score":"0.72016364","text":"def get_zscore(self, zscore_value):\n\n s = self.get_score(self.prov_types['zscore'], zscore_value)\n return s","title":""},{"docid":"953a1114c01dd842fe0a8694202b17b3","score":"0.7117339","text":"def zscore(x):\n return (x - x.mean()) / x.std()","title":""},{"docid":"9394d33a1d8cdcc699a9e78d16db20d9","score":"0.6993641","text":"def zscore(rate, mean, std):\n zscore = (rate - mean) / std\n return zscore","title":""},{"docid":"5460d67193924f289c030ec4183feda1","score":"0.6947195","text":"def compute_z_score(self):\n\t\tx_scaled = (self.x - self.x.mean())/self.x.std()\n\t\treturn x_scaled","title":""},{"docid":"af3719b97714cf1fbb5f7b1abf8762cc","score":"0.6893497","text":"def zscore(raw, mean, stddev):\n\n zscore = (raw - mean) / stddev\n\n return zscore","title":""},{"docid":"23dac8796a09bd083c06ea7a94804d96","score":"0.6865544","text":"def zscore(x, mu, std):\n\n return (x-mu)/std","title":""},{"docid":"d3d988f044779167aeff2c0b2dd226e9","score":"0.6859767","text":"def z_score(self, x):\n return (x - self.mean) / self.stddev","title":""},{"docid":"d3d988f044779167aeff2c0b2dd226e9","score":"0.6859767","text":"def z_score(self, x):\n return (x - self.mean) / self.stddev","title":""},{"docid":"d3d988f044779167aeff2c0b2dd226e9","score":"0.6859767","text":"def z_score(self, x):\n return (x - self.mean) / self.stddev","title":""},{"docid":"d0b7c03dfe37befe5b0e7529dbc69e0f","score":"0.67672366","text":"def zscore(self, *args):\n if self._cluster:\n return self.execute(u'ZSCORE', *args, shard_key=args[0])\n return self.execute(u'ZSCORE', *args)","title":""},{"docid":"963aafe67d75b527d31b4529d57ce7a3","score":"0.66174215","text":"def get_worst_zscore(self):\n worst_zscore = None\n for indicator in VisitStatistics.INDICATORS:\n if hasattr(self, indicator):\n zandp = getattr(self, indicator)\n if zandp and not healthdb.util.isNaN(zandp.zscore):\n if worst_zscore is None or worst_zscore > zandp.zscore:\n# logging.info(\"new worst_zscore = %s\" % zandp.zscore)\n worst_zscore = zandp.zscore\n# logging.info(\"worst_zscore = %s\" % worst_zscore)\n return worst_zscore","title":""},{"docid":"dd34f7cb38892869ec81637ad9eb5f5b","score":"0.64894617","text":"def _max_score(self):\n try:\n return self.zscore(self.__getitem__(-1))\n except IndexError:\n return None","title":""},{"docid":"79af348f22e96b9985b97269f8c1892b","score":"0.6463926","text":"def test_getZScores(self):\n values = [12,13,9,18,7,9,14,16,10,12,7,13,14,19,10,16,12,16,19,11]\n arr = numpy.array(values,dtype=numpy.float64)\n\n zscore = Stats.getZscore(arr)\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)\n\n modZ = Stats.getModifiedZscore(arr)\n self.assertAlmostEqual(1.23658, modZ[4], places = 4)\n self.assertAlmostEqual(0.33725, modZ[6], places = 4)\n\n # Test the sorted argument still works. Remove this when the function is removed\n # sorted=True only ever affected the order\n zscore = Stats.getZscore(arr, sorted=True)\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)","title":""},{"docid":"0940662b45e0cde7a986b1275d9724c5","score":"0.6352521","text":"def _min_score(self):\n try:\n return self.zscore(self.__getitem__(0))\n except IndexError:\n return None","title":""},{"docid":"20f5abc792709c0f3acd896f348248a3","score":"0.6240699","text":"def zscore(self, elem):\n return self.redis.zscore(self.key, self.serialize(elem))","title":""},{"docid":"80a138434df927a55d202d6a4c6817ea","score":"0.62341267","text":"def zscore(data):\n datalength = len(data)\n try:\n data = (data - np.mean(data)) / np.std(data)\n except:\n data = np.zeros(datalen)\n\n return data","title":""},{"docid":"a3d418bed63651d05af4a8c8dd922b7b","score":"0.62231916","text":"def _impaired_or_not(z_score, cutoff):\n if z_score <= cutoff:\n return 1\n else:\n return 0","title":""},{"docid":"ac30978fc86ba5d8a99918673b987d7b","score":"0.6182298","text":"def compute_zscore(self, data, slope, intercept):\n spread = (data['PEP'].price - (slope * data['KO'].price + intercept))\n self.spreads.append(spread)\n spread_wind = self.spreads[-self.window_length:]\n zscore = (spread - np.mean(spread_wind)) / np.std(spread_wind)\n return zscore","title":""},{"docid":"d5f1571d2b2bce9a8648d59f5f8a68e9","score":"0.6143231","text":"def get_highest_z(self) -> float:\n assert False, \"get_highest_z only supported on engine core\"","title":""},{"docid":"24308779304fdd0ed05600ca107a58aa","score":"0.6113914","text":"def z_score_xr(dr):\n other_dims = [d for d in dr.dims if d != 'neuron']\n return (dr - dr.mean(other_dims)) / dr.std(other_dims)","title":""},{"docid":"3df5e6e4b48aeafa746a58814735697b","score":"0.61133456","text":"def z_score(x, lst):\n if not lst or not len(lst):\n return nan\n mu = mean(lst)\n sdv = sd(lst)\n return (x - mu) / sdv","title":""},{"docid":"1e236c46b9707f1430564c47d6d8750d","score":"0.61009943","text":"def score(self, member):\n return self.zscore(member)","title":""},{"docid":"9dbe17dd42a08fcd259adccf4b4b02c6","score":"0.609521","text":"def scale_zscore(zscore, mean, std):\n zscaled = zscore * std + mean\n return zscaled","title":""},{"docid":"2523ad7970c4346a9b39754236d4f142","score":"0.6089465","text":"def z(inlist, score):\n z = (score - mean(inlist)) / samplestdev(inlist)\n return z","title":""},{"docid":"022687a206d6cdb454293405fc51f052","score":"0.6046075","text":"def _to_z_score(scaled_score, expected_score, test):\n denominator_dict = {'sdmt': 2.790,\n 'bvmt': 2.793,\n 'cvlt': 2.801}\n\n denominator = denominator_dict.get(test)\n\n z_score = (scaled_score - expected_score)/denominator\n\n return z_score","title":""},{"docid":"db42d74e4ba1ed69cf6c45ab5c7544ee","score":"0.6023797","text":"def get_score(self) -> float:\n pass","title":""},{"docid":"7a2947259caa75efb1d8d35827aa97bb","score":"0.60219604","text":"def score(self, member):\r\n return self.client.zscore(self.name, member)","title":""},{"docid":"c6f3a0bf0ba08e71441d9017bef9d20b","score":"0.6010244","text":"def z_score(self):\n control = VariantStat(self.experiment.control, self.experiment)\n\n alternative = self\n\n if control.name == alternative.name:\n return 'N/A'\n\n conv_c = control.conversion_rate\n conv_a = alternative.conversion_rate\n\n num_c = control.participant_count\n num_a = alternative.participant_count\n\n if conv_c == 0 or conv_a == 0:\n return 0\n\n numerator = conv_a - conv_c\n\n frac_c = (conv_c * (1 - conv_c)) / float(num_c)\n frac_a = (conv_a * (1 - conv_a)) / float(num_a)\n\n if frac_c + frac_a == 0:\n # square root of 0 is 0, so no need to calculate\n return 0\n elif frac_c + frac_a < 0:\n # can't take a square root of a negative number,\n # so return 'Invalid'\n return 'Invalid'\n\n return numerator / math.sqrt((frac_c + frac_a))","title":""},{"docid":"db9623e3168adac427129f947467fe01","score":"0.59958607","text":"def SquaredZScoreInWorldSpace(self, point: 'itkPointD3') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInWorldSpace(self, point)","title":""},{"docid":"d414d8a0b9dac5c06073d50832ad5685","score":"0.59949404","text":"def SquaredZScoreInObjectSpace(self, point: 'itkPointD3') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInObjectSpace(self, point)","title":""},{"docid":"cbeb0aa9fca4b72da8f4275f00e9ab99","score":"0.59595954","text":"def Zscore(self, kmer):\n mean = self.eValue(kmer)\n N = self.N\n p = mean/N #finds p from the mean.\n stdDev = (N*p*(1 - p))**0.5 #finds stdDeviation\n\n Ztop = self.kmerCounts[kmer][0] - mean\n \n return Ztop/stdDev","title":""},{"docid":"35d13d515807810bbea8099b2ee5a663","score":"0.59485126","text":"def z_score(dist, num):\n mu = float(sum((key * val for (key, val)\n in zip(dist.keys(),\n dist.values())))) / 1000\n\n # print show_hist(dist)\n print \"mean is: \" + str(mu)\n sigma = math.sqrt(float(sum(((key - mu) ** 2) * val for (key, val)\n in zip(dist.keys(),\n dist.values()))) / 1000)\n\n print \"standart is: \" + str(sigma)\n print \"tripple st dev:\" + str(mu + 3 * sigma)\n return (num - mu) / sigma","title":""},{"docid":"9e774de9fc5eaa8a9a96da62a24700f2","score":"0.5948267","text":"def set_zscores(self):\n pass","title":""},{"docid":"4f76f124efa1c1489560ceaaa1b1057f","score":"0.5924716","text":"def SquaredZScoreInObjectSpace(self, point: 'itkPointD2') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInObjectSpace(self, point)","title":""},{"docid":"ecd7b02f7a5dde6074639eceee4fb1a3","score":"0.59235656","text":"def sorted_score(self, k: str, m: str) -> Union[float, None]:\n conn = self.get_conn()\n return conn.zscore(k, m)","title":""},{"docid":"94d7ebb287e80fdfa23f90a6c66fe585","score":"0.59227055","text":"def SquaredZScoreInWorldSpace(self, point: 'itkPointD2') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInWorldSpace(self, point)","title":""},{"docid":"db854da7e2241d1c9618ba95aa0b6a8b","score":"0.5864021","text":"def z_eval(self):\n if not hasattr(self, '_zeval'):\n\n if self._args['evaluate_mc_at_zlens']:\n self._zeval = self.z\n else:\n self._zeval = self.z_infall\n\n return self._zeval","title":""},{"docid":"81c40100f9e55858b0ab51e81aa4ce62","score":"0.5846473","text":"def z(self) -> float:\n return self._z","title":""},{"docid":"8ac6c28e2e253ff235afab6da251fe3a","score":"0.58452505","text":"def threshold_by_zscore(zscored_data, time, minimum_duration=0.015, zscore_threshold=2):\n is_above_mean = zscored_data >= 0\n is_above_threshold = zscored_data >= zscore_threshold\n\n return extend_threshold_to_mean(\n is_above_mean, is_above_threshold, time, minimum_duration=minimum_duration\n )","title":""},{"docid":"5f68c7214412be2bd4790b674ebe0ad7","score":"0.5815507","text":"def get_score(self):\n pass","title":""},{"docid":"e18da72954e262bc2bc0eb509ab11a46","score":"0.5778263","text":"def wZ(self, z):\r\n return self.w0 * np.sqrt(1 + (z / self.zR) ** 2)","title":""},{"docid":"1b3ce039d4fce67e51f63cdff71b27f1","score":"0.5752515","text":"def z_critical_value(cls, alpha, dir):\n if dir == cls.TWO_TAILED_TEST:\n alpha /= 2.0\n\n z = norm.ppf(1 - alpha)\n if dir == StatTool.ONE_TAILED_NEGATIVE_TEST:\n z = 0 - z\n return z","title":""},{"docid":"a84a09df14c8964e5f8753ec4498895f","score":"0.57146937","text":"def normalize_zscore(data, z=2, offset=0.5, clip=False):\n mean = np.mean(data)\n std = np.std(data)\n img = ((data - mean) / (2 * std * z) + offset) \n if clip:\n # print ('Before')\n # print (np.min(img), np.max(img))\n img = np.clip(img, -0.0, 1.0)\n # print ('After clip')\n # print (np.min(img), np.max(img))\n return img","title":""},{"docid":"0ac26db3b124e3947506297470d4ed5f","score":"0.571298","text":"def z_get(self, proxy, key):\n return self._client.zscore(key, proxy)","title":""},{"docid":"3c8f389df059e4999a4a9b0369104525","score":"0.57076114","text":"def probability_for_z(cls, z_score, dir):\n assert dir in cls.valid_dirs\n\n # cdv is the probability that any sample will have LESS than z_score\n cdv = norm.cdf(z_score)\n\n if dir == cls.TWO_TAILED_TEST:\n if z_score < 0:\n dir = cls.ONE_TAILED_NEGATIVE_TEST\n else:\n dir = cls.ONE_TAILED_POSITIVE_TEST\n\n if dir == cls.ONE_TAILED_POSITIVE_TEST:\n return 1 - cdv\n else:\n return cdv","title":""},{"docid":"ddff10cdc4fcf7c3ae8aa9d0dea252a6","score":"0.5698196","text":"def var_modified(r, level = 5):\n #compute the Z score assuming it was Gausian\n z= norm.ppf(level/100)\n #if modified:\n # modify the Z score based on observed skewness and kurtosis\n s=skewness(r)\n k=kurtosis(r)\n z=(z +\n (z**2-1)*s/6 + \n (z**3 -3*z)*(k-3)/24 -\n (2*z**3 - 5*z)*(s**2)/36\n )\n return -(r.mean() + z*r.std(ddof = 0))","title":""},{"docid":"2f433132958d74f2171254fe150c2033","score":"0.56827646","text":"def zmax(self):\n if self._zmax is None:\n self._zmax = self.rmax\n return self._zmax","title":""},{"docid":"bbb52be9a9b0ab7ada2fffe4dc821349","score":"0.56279254","text":"def get_score(self):\n return self._score","title":""},{"docid":"86513526f07539ac8651bf91d21c69e8","score":"0.56194705","text":"def z_score(tableau, ur):\n zScore = 0\n for j in tableau[ur]:\n zScore += tableau[ur][j][2]\n return zScore","title":""},{"docid":"05717db3354e3172f361963721c1d58a","score":"0.5546481","text":"def z_value(x: float, mean: float, std: float) -> float:\n return (x-mean) / std","title":""},{"docid":"4479528e768431bd5e61a0d9eb3d352d","score":"0.55305403","text":"def R2(self, z, zpred):\n\n\n zmean = np.average(z)\n\n return 1 - np.sum((z - zpred)**2)/np.sum((z - zmean)**2)","title":""},{"docid":"12e94247201b5761983384f234e6755d","score":"0.5527806","text":"def zscore(A, axis=None):\n return (A - np.mean(A,axis=axis)) / np.std(A,axis=axis,ddof=1)","title":""},{"docid":"66559194e54019dbd45ca832f8553605","score":"0.55251133","text":"def outliers_modified_z_score(self, ys, y):\n threshold = 3.5\n\n median_y = np.median(ys)\n median_absolute_deviation_y = np.median([np.abs(y - median_y) for y in ys])\n modified_z_scores = 0.6745 * (y - median_y) / median_absolute_deviation_y\n return np.where(np.abs(modified_z_scores) > threshold)","title":""},{"docid":"437de78c57999e8bc2d3967e81698b4d","score":"0.5522258","text":"def z_eval(self):\n return self.z","title":""},{"docid":"d2258ec09fbf7d74eb4dc3878cee20a5","score":"0.55171925","text":"def get_a_from_z(self, z):\n z = np.array(z)\n if np.any(z < 0.0):\n raise ValueError(\n \"Cannot convert negative redshift to scale factor\")\n return 1.0/(1.0+z)","title":""},{"docid":"ef7e30711e78b06da46136ccf1bc31d2","score":"0.54868895","text":"def get_clinical_Z_range(): \n return (-105.0, 100000000.0) # in mm","title":""},{"docid":"bf3accf9bcd511a1b5a157bdbb04cfa6","score":"0.54471064","text":"def raw_score(self):\n return self._raw_score","title":""},{"docid":"58c3cbe7285b3c0cba21e9786e7c813b","score":"0.5436805","text":"def get_score(self):\n raise NotImplementedError(\"This method must be overridden\")","title":""},{"docid":"aafa86dc8337437eff19f347d4e7db14","score":"0.542814","text":"def zscoreToPercentile(zscore):\n retVal = healthdb.util.NaN\n # WHO technical specs chapter 7: \"However, a restriction was imposed on\n # all indicators to enable the derivation of percentiles only within\n # the interval corresponding to z-scores between -3 and 3. The\n # underlying reasoning is that percentiles beyond +-3 SD are invariant\n # to changes in equivalent z-scores. The loss accruing to this\n # restriction is small since the inclusion range corresponds to the\n # 0.135th to 99.865th percentiles.\"\n if math.fabs(zscore) <= 3:\n absVal = math.fabs(zscore)\n P1 = (1 - 1 / math.sqrt(2 * math.pi) * math.exp(-math.pow(absVal, 2) / 2)\n * (\n 0.31938 * (1 / (1 + 0.2316419 * absVal))\n - 0.356563782 * math.pow((1 / (1 + 0.2316419 * absVal)), 2)\n + 1.781477937 * math.pow((1 / (1 + 0.2316419 * absVal)), 3)\n - 1.82125 * math.pow((1 / (1 + 0.2316419 * absVal)), 4)\n + 1.330274429 * math.pow((1 / (1 + 0.2316419 * absVal)), 5)\n ))\n \n if zscore > 0:\n P1 = P1 * 100\n else:\n P1 = 100 - P1 * 100\n \n if 0 <= P1 and P1 <= 100:\n retVal = P1\n return retVal","title":""},{"docid":"a19b11413fb191d6925413b7ebf74b01","score":"0.53977555","text":"def getZScore(self, index):\n # Calculate mean and standard deviation of the list of values\n mu = np.mean(self.matrix[:, index], axis=0)\n sd = np.std(self.matrix[:, index], axis=0)\n # Calculate Z-score for the given column for each gene\n zscore = (self.matrix[:, index] - mu) / sd\n mygenes = {}\n for (name, ndx) in self.genes.items():\n try:\n mygenes[name] = zscore[ndx, :]\n except IndexError:\n mygenes[name] = zscore[ndx]\n # Return the dictionary of Z-scores\n return mygenes","title":""},{"docid":"e326cfd1ca90e434102efeb46af2d1f6","score":"0.53955096","text":"def get_score(self, value):\n try:\n encodedvalue = PickleEncoder().encode(value)\n except Exception, e:\n raise RedisError(\"encode error:%s\" % e)\n\n try:\n score = self._db.zscore(self.namespace, encodedvalue)\n return score\n except Exception, e:\n raise RedisError(\"redis error:%s\" % e)","title":""},{"docid":"ed457a87f10952f48019978b8e1e1d36","score":"0.5376343","text":"def zscoreFromAttribute(anthroConfig):\n for row in csv.DictReader(open(anthroConfig.fileName)):\n if 'age' in row:\n dataAgeHeightOrLength = row['age']\n elif 'length' in row:\n dataAgeHeightOrLength = row['length']\n elif 'height' in row:\n dataAgeHeightOrLength = row['height']\n if int(row['sex']) == anthroConfig.sex and float(dataAgeHeightOrLength) == anthroConfig.ageHeightOrLength:\n return zscoreOtherRestricted(anthroConfig.measureKey, float(row['l']), float(row['m']), float(row['s']), True) \n return healthdb.util.NaN","title":""},{"docid":"fad2d8b9197199d7edc5fda8187214db","score":"0.5350002","text":"def score(self):\n pos = self.scores.filter(value=1).count()\n n = self.scores.count()\n return wilson_ci_lower_bound(n, pos)","title":""},{"docid":"bfed7dbefd669d898835324cabe153d1","score":"0.53445476","text":"def NO_SCORE() -> int:\n return -1","title":""},{"docid":"8538005b30ce8cfd3ed977e86d84b93a","score":"0.5340626","text":"def Z(self) -> int:\r\n return self._Z","title":""},{"docid":"ea26fb8ca8fcf09b92a47eeba81c975a","score":"0.5333083","text":"def get_best_score(self):\n return self.best_score","title":""},{"docid":"1716b29e9d7a608b2938d8cc1634e9c6","score":"0.5331177","text":"def compute(cls, observation, prediction):\n assert type(observation) is dict\n assert type(prediction) is dict\n try:\n p_value = prediction['mean'] # Use the prediction's mean. \n except (TypeError,KeyError): # If there isn't one...\n try:\n p_value = prediction['value'] # Use the prediction's value. \n except TypeError: # If there isn't one...\n p_value = prediction # Use the prediction (assume it is numeric).\n o_mean = observation['mean']\n o_std = observation['std']\n value = (p_value - o_mean)/o_std\n value = utils.assert_dimensionless(value)\n return ZScore(value)","title":""},{"docid":"100ea5e8e6e0474083b076a4a68719d7","score":"0.53240174","text":"def compute_best_risk(self):\n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \n mask = (has_chi2) & (self.zbest > self.zgrid[0])\n \n zbest_grid = np.dot(self.zbest[mask, None],\n np.ones_like(self.zgrid)[None,:])\n L = self._loss((zbest_grid-self.zgrid)/(1+self.zgrid))\n #dz = np.gradient(self.zgrid)\n \n zbest_risk = np.zeros(self.NOBJ, dtype=self.ARRAY_DTYPE)-1\n zbest_risk[mask] = np.dot(np.exp(self.lnp[mask,:])*L, self.trdz)\n \n del(has_chi2)\n del(mask)\n del(zbest_grid)\n del(L)\n \n return zbest_risk","title":""},{"docid":"c70832f4b5008dc1a9bab31f1fb77d2c","score":"0.5320918","text":"def score_one(self, x: dict) -> float:","title":""},{"docid":"c70832f4b5008dc1a9bab31f1fb77d2c","score":"0.5320918","text":"def score_one(self, x: dict) -> float:","title":""},{"docid":"00b65cb58654be9d87f5140f9f692e8d","score":"0.5320872","text":"def wZ(w0, z, zR):\r\n return w0 * np.sqrt(1 + (z / zR) ** 2)","title":""},{"docid":"57bd528109934590edcc37520b8aecc6","score":"0.53161615","text":"def get_score(self):\n return self._points","title":""},{"docid":"d145a7dc3c8c2db905526386567322af","score":"0.5306321","text":"def norm_score(self):\n return self.score","title":""},{"docid":"10ba4834d38dd73afa4faf78179c59de","score":"0.53024745","text":"def compute_full_risk(self):\n zsq = np.dot(self.zgrid[:,None], np.ones_like(self.zgrid)[None,:])\n L = self._loss((zsq-self.zgrid)/(1+self.zgrid))\n \n Rz = self.lnp*0.\n \n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \n hasz = self.zbest > self.zgrid[0]\n idx = self.idx[hasz & (has_chi2)]\n \n for i in idx:\n Rz[i,:] = np.dot(np.exp(self.lnp[i,:])*L, self.trdz)\n \n del(zsq)\n del(L)\n del(has_chi2)\n del(hasz)\n del(idx) \n return Rz\n \n #self.full_risk = Rz\n #self.min_risk = self.zgrid[np.argmin(Rz, axis=1)]","title":""},{"docid":"35b8f1cc00b5b07453048380c9183176","score":"0.5287981","text":"def getScore(self):\n return self._score","title":""},{"docid":"35b8f1cc00b5b07453048380c9183176","score":"0.5287981","text":"def getScore(self):\n return self._score","title":""},{"docid":"dcb862fa989f902036a49cc1625a4f90","score":"0.528489","text":"def max_score(self):\n return self.weight if self.has_score else None","title":""},{"docid":"3440f70adf1c7fd88bdefc2f668b642d","score":"0.5280114","text":"def constant_score(self):\n return self._constant_score","title":""},{"docid":"8dc96d30401cecf5b2207a98a0df1a10","score":"0.5262474","text":"def get_score(self):\n score = 1\n if self.answer:\n score = 3\n if not self.correct:\n score *= -1\n return score","title":""},{"docid":"7c7e62a3d3390f0de2732aa78ed1d28e","score":"0.5253121","text":"def __call__(self, Z):\n\n return tf.math.maximum(Z, 0)","title":""},{"docid":"1836f7e1518a3851013eb49b808aa889","score":"0.52435356","text":"def score(self):\n return self.__score","title":""},{"docid":"3de3634c605416248a0eb6a0e5726904","score":"0.52232313","text":"def score(self):\n check_is_fitted(self, [\"lower_bound_\"])\n return self.lower_bound_","title":""},{"docid":"0a79fbce580c66f934eacea64394c344","score":"0.52211654","text":"def z(self):\n return self._vct.z","title":""},{"docid":"58de9a1a700da120b9ef3b0822a54958","score":"0.5218734","text":"def saclhitsrate(self) :\n try :\n return self._saclhitsrate\n except Exception as e:\n raise e","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"953ef2864b7c9b9b26aa003dab0961b4","score":"0.5205887","text":"def fowlkes_mallows_index(y_true: np.array, y_score: np.array) -> float:\n ppv = positive_predictive_value(y_true, y_score)\n tpr = true_positive_rate(y_true, y_score)\n fm = (ppv * tpr) ** 0.5\n return fm","title":""},{"docid":"a542313a7482d565193ff9df0cef3138","score":"0.5202119","text":"def detect_outlier_zscore(df):\n\tthreshold = 3\n\tz = np.abs(stats.zscore(df))\n\toutliers = np.where(z > threshold)\n\treturn outliers","title":""},{"docid":"6f99c4afcdafcff521b56c5a458723e4","score":"0.5199307","text":"def getZonePercentageFeatureValue(self):\n Nz = self.coefficients['Nz']\n Np = self.coefficients['Np']\n\n zp = Nz / Np\n return zp","title":""},{"docid":"81da7f92b66bb19c61c2617346cad4fe","score":"0.5194981","text":"def zprob(z):\n Z_MAX = 6.0 # maximum meaningful z-value\n if z == 0.0:\n x = 0.0\n else:\n y = 0.5 * math.fabs(z)\n if y >= (Z_MAX * 0.5):\n x = 1.0\n elif (y < 1.0):\n w = y * y\n x = ((((((((0.000124818987 * w\n - 0.001075204047) * w + 0.005198775019) * w\n - 0.019198292004) * w + 0.059054035642) * w\n - 0.151968751364) * w + 0.319152932694) * w\n - 0.531923007300) * w + 0.797884560593) * y * 2.0\n else:\n y = y - 2.0\n x = (((((((((((((-0.000045255659 * y\n + 0.000152529290) * y - 0.000019538132) * y\n - 0.000676904986) * y + 0.001390604284) * y\n - 0.000794620820) * y - 0.002034254874) * y\n + 0.006549791214) * y - 0.010557625006) * y\n + 0.011630447319) * y - 0.009279453341) * y\n + 0.005353579108) * y - 0.002141268741) * y\n + 0.000535310849) * y + 0.999936657524\n if z > 0.0:\n prob = ((x + 1.0) * 0.5)\n else:\n prob = ((1.0 - x) * 0.5)\n return prob","title":""},{"docid":"23444449548c751d57eb8a7312433623","score":"0.5192021","text":"def credit_score(self):\n return self._credit_score","title":""},{"docid":"1706217b6bb470a64b395196f3a35f76","score":"0.5191078","text":"def res_function(X, Z):\n X_col = X.reshape((-1, 1))\n Z_row = Z.reshape((1, -1))\n return np.maximum(Z_row - X_col + margin, 0).mean()","title":""},{"docid":"5b4d0a2f8fbc43bd9a9ce40b6891f4a6","score":"0.5191033","text":"def lcz(self, zbest=None):\n if zbest is None:\n zbest = self.zbest\n \n _lcz = np.dot(1/(1+zbest[:, np.newaxis]), self.pivot[np.newaxis,:])\n return _lcz","title":""},{"docid":"350f7ffdbf74204002d650b60f84004b","score":"0.5189782","text":"def Score(cls):\n\n return ScoreMask()","title":""},{"docid":"595a292a563ab3746659d808dd8b2ba1","score":"0.51834446","text":"def custom_score(game, player):\n # TODO: finish this function!\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n def calculate_score(moves):\n if len(moves) == 0:\n return 0\n centered_locs = np.array(moves) - (game.height/2.0, game.width/2.0)\n #print(centered_locs)\n return np.sum(1/LA.norm(centered_locs,ord=2,axis=1))\n\n own_moves = game.get_legal_moves(player)\n opp_moves = game.get_legal_moves(game.get_opponent(player))\n\n return float(calculate_score(own_moves)-calculate_score(opp_moves))","title":""}],"string":"[\n {\n \"docid\": \"9ef0a40399c631949df3b99157924f0d\",\n \"score\": \"0.72016364\",\n \"text\": \"def get_zscore(self, zscore_value):\\n\\n s = self.get_score(self.prov_types['zscore'], zscore_value)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"953a1114c01dd842fe0a8694202b17b3\",\n \"score\": \"0.7117339\",\n \"text\": \"def zscore(x):\\n return (x - x.mean()) / x.std()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9394d33a1d8cdcc699a9e78d16db20d9\",\n \"score\": \"0.6993641\",\n \"text\": \"def zscore(rate, mean, std):\\n zscore = (rate - mean) / std\\n return zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5460d67193924f289c030ec4183feda1\",\n \"score\": \"0.6947195\",\n \"text\": \"def compute_z_score(self):\\n\\t\\tx_scaled = (self.x - self.x.mean())/self.x.std()\\n\\t\\treturn x_scaled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3719b97714cf1fbb5f7b1abf8762cc\",\n \"score\": \"0.6893497\",\n \"text\": \"def zscore(raw, mean, stddev):\\n\\n zscore = (raw - mean) / stddev\\n\\n return zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dac8796a09bd083c06ea7a94804d96\",\n \"score\": \"0.6865544\",\n \"text\": \"def zscore(x, mu, std):\\n\\n return (x-mu)/std\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d988f044779167aeff2c0b2dd226e9\",\n \"score\": \"0.6859767\",\n \"text\": \"def z_score(self, x):\\n return (x - self.mean) / self.stddev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d988f044779167aeff2c0b2dd226e9\",\n \"score\": \"0.6859767\",\n \"text\": \"def z_score(self, x):\\n return (x - self.mean) / self.stddev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d988f044779167aeff2c0b2dd226e9\",\n \"score\": \"0.6859767\",\n \"text\": \"def z_score(self, x):\\n return (x - self.mean) / self.stddev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0b7c03dfe37befe5b0e7529dbc69e0f\",\n \"score\": \"0.67672366\",\n \"text\": \"def zscore(self, *args):\\n if self._cluster:\\n return self.execute(u'ZSCORE', *args, shard_key=args[0])\\n return self.execute(u'ZSCORE', *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963aafe67d75b527d31b4529d57ce7a3\",\n \"score\": \"0.66174215\",\n \"text\": \"def get_worst_zscore(self):\\n worst_zscore = None\\n for indicator in VisitStatistics.INDICATORS:\\n if hasattr(self, indicator):\\n zandp = getattr(self, indicator)\\n if zandp and not healthdb.util.isNaN(zandp.zscore):\\n if worst_zscore is None or worst_zscore > zandp.zscore:\\n# logging.info(\\\"new worst_zscore = %s\\\" % zandp.zscore)\\n worst_zscore = zandp.zscore\\n# logging.info(\\\"worst_zscore = %s\\\" % worst_zscore)\\n return worst_zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd34f7cb38892869ec81637ad9eb5f5b\",\n \"score\": \"0.64894617\",\n \"text\": \"def _max_score(self):\\n try:\\n return self.zscore(self.__getitem__(-1))\\n except IndexError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79af348f22e96b9985b97269f8c1892b\",\n \"score\": \"0.6463926\",\n \"text\": \"def test_getZScores(self):\\n values = [12,13,9,18,7,9,14,16,10,12,7,13,14,19,10,16,12,16,19,11]\\n arr = numpy.array(values,dtype=numpy.float64)\\n\\n zscore = Stats.getZscore(arr)\\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)\\n\\n modZ = Stats.getModifiedZscore(arr)\\n self.assertAlmostEqual(1.23658, modZ[4], places = 4)\\n self.assertAlmostEqual(0.33725, modZ[6], places = 4)\\n\\n # Test the sorted argument still works. Remove this when the function is removed\\n # sorted=True only ever affected the order\\n zscore = Stats.getZscore(arr, sorted=True)\\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0940662b45e0cde7a986b1275d9724c5\",\n \"score\": \"0.6352521\",\n \"text\": \"def _min_score(self):\\n try:\\n return self.zscore(self.__getitem__(0))\\n except IndexError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f5abc792709c0f3acd896f348248a3\",\n \"score\": \"0.6240699\",\n \"text\": \"def zscore(self, elem):\\n return self.redis.zscore(self.key, self.serialize(elem))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80a138434df927a55d202d6a4c6817ea\",\n \"score\": \"0.62341267\",\n \"text\": \"def zscore(data):\\n datalength = len(data)\\n try:\\n data = (data - np.mean(data)) / np.std(data)\\n except:\\n data = np.zeros(datalen)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d418bed63651d05af4a8c8dd922b7b\",\n \"score\": \"0.62231916\",\n \"text\": \"def _impaired_or_not(z_score, cutoff):\\n if z_score <= cutoff:\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac30978fc86ba5d8a99918673b987d7b\",\n \"score\": \"0.6182298\",\n \"text\": \"def compute_zscore(self, data, slope, intercept):\\n spread = (data['PEP'].price - (slope * data['KO'].price + intercept))\\n self.spreads.append(spread)\\n spread_wind = self.spreads[-self.window_length:]\\n zscore = (spread - np.mean(spread_wind)) / np.std(spread_wind)\\n return zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f1571d2b2bce9a8648d59f5f8a68e9\",\n \"score\": \"0.6143231\",\n \"text\": \"def get_highest_z(self) -> float:\\n assert False, \\\"get_highest_z only supported on engine core\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24308779304fdd0ed05600ca107a58aa\",\n \"score\": \"0.6113914\",\n \"text\": \"def z_score_xr(dr):\\n other_dims = [d for d in dr.dims if d != 'neuron']\\n return (dr - dr.mean(other_dims)) / dr.std(other_dims)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3df5e6e4b48aeafa746a58814735697b\",\n \"score\": \"0.61133456\",\n \"text\": \"def z_score(x, lst):\\n if not lst or not len(lst):\\n return nan\\n mu = mean(lst)\\n sdv = sd(lst)\\n return (x - mu) / sdv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e236c46b9707f1430564c47d6d8750d\",\n \"score\": \"0.61009943\",\n \"text\": \"def score(self, member):\\n return self.zscore(member)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbe17dd42a08fcd259adccf4b4b02c6\",\n \"score\": \"0.609521\",\n \"text\": \"def scale_zscore(zscore, mean, std):\\n zscaled = zscore * std + mean\\n return zscaled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2523ad7970c4346a9b39754236d4f142\",\n \"score\": \"0.6089465\",\n \"text\": \"def z(inlist, score):\\n z = (score - mean(inlist)) / samplestdev(inlist)\\n return z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"022687a206d6cdb454293405fc51f052\",\n \"score\": \"0.6046075\",\n \"text\": \"def _to_z_score(scaled_score, expected_score, test):\\n denominator_dict = {'sdmt': 2.790,\\n 'bvmt': 2.793,\\n 'cvlt': 2.801}\\n\\n denominator = denominator_dict.get(test)\\n\\n z_score = (scaled_score - expected_score)/denominator\\n\\n return z_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db42d74e4ba1ed69cf6c45ab5c7544ee\",\n \"score\": \"0.6023797\",\n \"text\": \"def get_score(self) -> float:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a2947259caa75efb1d8d35827aa97bb\",\n \"score\": \"0.60219604\",\n \"text\": \"def score(self, member):\\r\\n return self.client.zscore(self.name, member)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f3a0bf0ba08e71441d9017bef9d20b\",\n \"score\": \"0.6010244\",\n \"text\": \"def z_score(self):\\n control = VariantStat(self.experiment.control, self.experiment)\\n\\n alternative = self\\n\\n if control.name == alternative.name:\\n return 'N/A'\\n\\n conv_c = control.conversion_rate\\n conv_a = alternative.conversion_rate\\n\\n num_c = control.participant_count\\n num_a = alternative.participant_count\\n\\n if conv_c == 0 or conv_a == 0:\\n return 0\\n\\n numerator = conv_a - conv_c\\n\\n frac_c = (conv_c * (1 - conv_c)) / float(num_c)\\n frac_a = (conv_a * (1 - conv_a)) / float(num_a)\\n\\n if frac_c + frac_a == 0:\\n # square root of 0 is 0, so no need to calculate\\n return 0\\n elif frac_c + frac_a < 0:\\n # can't take a square root of a negative number,\\n # so return 'Invalid'\\n return 'Invalid'\\n\\n return numerator / math.sqrt((frac_c + frac_a))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db9623e3168adac427129f947467fe01\",\n \"score\": \"0.59958607\",\n \"text\": \"def SquaredZScoreInWorldSpace(self, point: 'itkPointD3') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInWorldSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d414d8a0b9dac5c06073d50832ad5685\",\n \"score\": \"0.59949404\",\n \"text\": \"def SquaredZScoreInObjectSpace(self, point: 'itkPointD3') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInObjectSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbeb0aa9fca4b72da8f4275f00e9ab99\",\n \"score\": \"0.59595954\",\n \"text\": \"def Zscore(self, kmer):\\n mean = self.eValue(kmer)\\n N = self.N\\n p = mean/N #finds p from the mean.\\n stdDev = (N*p*(1 - p))**0.5 #finds stdDeviation\\n\\n Ztop = self.kmerCounts[kmer][0] - mean\\n \\n return Ztop/stdDev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d13d515807810bbea8099b2ee5a663\",\n \"score\": \"0.59485126\",\n \"text\": \"def z_score(dist, num):\\n mu = float(sum((key * val for (key, val)\\n in zip(dist.keys(),\\n dist.values())))) / 1000\\n\\n # print show_hist(dist)\\n print \\\"mean is: \\\" + str(mu)\\n sigma = math.sqrt(float(sum(((key - mu) ** 2) * val for (key, val)\\n in zip(dist.keys(),\\n dist.values()))) / 1000)\\n\\n print \\\"standart is: \\\" + str(sigma)\\n print \\\"tripple st dev:\\\" + str(mu + 3 * sigma)\\n return (num - mu) / sigma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e774de9fc5eaa8a9a96da62a24700f2\",\n \"score\": \"0.5948267\",\n \"text\": \"def set_zscores(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f76f124efa1c1489560ceaaa1b1057f\",\n \"score\": \"0.5924716\",\n \"text\": \"def SquaredZScoreInObjectSpace(self, point: 'itkPointD2') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInObjectSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd7b02f7a5dde6074639eceee4fb1a3\",\n \"score\": \"0.59235656\",\n \"text\": \"def sorted_score(self, k: str, m: str) -> Union[float, None]:\\n conn = self.get_conn()\\n return conn.zscore(k, m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d7ebb287e80fdfa23f90a6c66fe585\",\n \"score\": \"0.59227055\",\n \"text\": \"def SquaredZScoreInWorldSpace(self, point: 'itkPointD2') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInWorldSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db854da7e2241d1c9618ba95aa0b6a8b\",\n \"score\": \"0.5864021\",\n \"text\": \"def z_eval(self):\\n if not hasattr(self, '_zeval'):\\n\\n if self._args['evaluate_mc_at_zlens']:\\n self._zeval = self.z\\n else:\\n self._zeval = self.z_infall\\n\\n return self._zeval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c40100f9e55858b0ab51e81aa4ce62\",\n \"score\": \"0.5846473\",\n \"text\": \"def z(self) -> float:\\n return self._z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac6c28e2e253ff235afab6da251fe3a\",\n \"score\": \"0.58452505\",\n \"text\": \"def threshold_by_zscore(zscored_data, time, minimum_duration=0.015, zscore_threshold=2):\\n is_above_mean = zscored_data >= 0\\n is_above_threshold = zscored_data >= zscore_threshold\\n\\n return extend_threshold_to_mean(\\n is_above_mean, is_above_threshold, time, minimum_duration=minimum_duration\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f68c7214412be2bd4790b674ebe0ad7\",\n \"score\": \"0.5815507\",\n \"text\": \"def get_score(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18da72954e262bc2bc0eb509ab11a46\",\n \"score\": \"0.5778263\",\n \"text\": \"def wZ(self, z):\\r\\n return self.w0 * np.sqrt(1 + (z / self.zR) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3ce039d4fce67e51f63cdff71b27f1\",\n \"score\": \"0.5752515\",\n \"text\": \"def z_critical_value(cls, alpha, dir):\\n if dir == cls.TWO_TAILED_TEST:\\n alpha /= 2.0\\n\\n z = norm.ppf(1 - alpha)\\n if dir == StatTool.ONE_TAILED_NEGATIVE_TEST:\\n z = 0 - z\\n return z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a84a09df14c8964e5f8753ec4498895f\",\n \"score\": \"0.57146937\",\n \"text\": \"def normalize_zscore(data, z=2, offset=0.5, clip=False):\\n mean = np.mean(data)\\n std = np.std(data)\\n img = ((data - mean) / (2 * std * z) + offset) \\n if clip:\\n # print ('Before')\\n # print (np.min(img), np.max(img))\\n img = np.clip(img, -0.0, 1.0)\\n # print ('After clip')\\n # print (np.min(img), np.max(img))\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac26db3b124e3947506297470d4ed5f\",\n \"score\": \"0.571298\",\n \"text\": \"def z_get(self, proxy, key):\\n return self._client.zscore(key, proxy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8f389df059e4999a4a9b0369104525\",\n \"score\": \"0.57076114\",\n \"text\": \"def probability_for_z(cls, z_score, dir):\\n assert dir in cls.valid_dirs\\n\\n # cdv is the probability that any sample will have LESS than z_score\\n cdv = norm.cdf(z_score)\\n\\n if dir == cls.TWO_TAILED_TEST:\\n if z_score < 0:\\n dir = cls.ONE_TAILED_NEGATIVE_TEST\\n else:\\n dir = cls.ONE_TAILED_POSITIVE_TEST\\n\\n if dir == cls.ONE_TAILED_POSITIVE_TEST:\\n return 1 - cdv\\n else:\\n return cdv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddff10cdc4fcf7c3ae8aa9d0dea252a6\",\n \"score\": \"0.5698196\",\n \"text\": \"def var_modified(r, level = 5):\\n #compute the Z score assuming it was Gausian\\n z= norm.ppf(level/100)\\n #if modified:\\n # modify the Z score based on observed skewness and kurtosis\\n s=skewness(r)\\n k=kurtosis(r)\\n z=(z +\\n (z**2-1)*s/6 + \\n (z**3 -3*z)*(k-3)/24 -\\n (2*z**3 - 5*z)*(s**2)/36\\n )\\n return -(r.mean() + z*r.std(ddof = 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f433132958d74f2171254fe150c2033\",\n \"score\": \"0.56827646\",\n \"text\": \"def zmax(self):\\n if self._zmax is None:\\n self._zmax = self.rmax\\n return self._zmax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb52be9a9b0ab7ada2fffe4dc821349\",\n \"score\": \"0.56279254\",\n \"text\": \"def get_score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86513526f07539ac8651bf91d21c69e8\",\n \"score\": \"0.56194705\",\n \"text\": \"def z_score(tableau, ur):\\n zScore = 0\\n for j in tableau[ur]:\\n zScore += tableau[ur][j][2]\\n return zScore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05717db3354e3172f361963721c1d58a\",\n \"score\": \"0.5546481\",\n \"text\": \"def z_value(x: float, mean: float, std: float) -> float:\\n return (x-mean) / std\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4479528e768431bd5e61a0d9eb3d352d\",\n \"score\": \"0.55305403\",\n \"text\": \"def R2(self, z, zpred):\\n\\n\\n zmean = np.average(z)\\n\\n return 1 - np.sum((z - zpred)**2)/np.sum((z - zmean)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e94247201b5761983384f234e6755d\",\n \"score\": \"0.5527806\",\n \"text\": \"def zscore(A, axis=None):\\n return (A - np.mean(A,axis=axis)) / np.std(A,axis=axis,ddof=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66559194e54019dbd45ca832f8553605\",\n \"score\": \"0.55251133\",\n \"text\": \"def outliers_modified_z_score(self, ys, y):\\n threshold = 3.5\\n\\n median_y = np.median(ys)\\n median_absolute_deviation_y = np.median([np.abs(y - median_y) for y in ys])\\n modified_z_scores = 0.6745 * (y - median_y) / median_absolute_deviation_y\\n return np.where(np.abs(modified_z_scores) > threshold)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437de78c57999e8bc2d3967e81698b4d\",\n \"score\": \"0.5522258\",\n \"text\": \"def z_eval(self):\\n return self.z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2258ec09fbf7d74eb4dc3878cee20a5\",\n \"score\": \"0.55171925\",\n \"text\": \"def get_a_from_z(self, z):\\n z = np.array(z)\\n if np.any(z < 0.0):\\n raise ValueError(\\n \\\"Cannot convert negative redshift to scale factor\\\")\\n return 1.0/(1.0+z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7e30711e78b06da46136ccf1bc31d2\",\n \"score\": \"0.54868895\",\n \"text\": \"def get_clinical_Z_range(): \\n return (-105.0, 100000000.0) # in mm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3accf9bcd511a1b5a157bdbb04cfa6\",\n \"score\": \"0.54471064\",\n \"text\": \"def raw_score(self):\\n return self._raw_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c3cbe7285b3c0cba21e9786e7c813b\",\n \"score\": \"0.5436805\",\n \"text\": \"def get_score(self):\\n raise NotImplementedError(\\\"This method must be overridden\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aafa86dc8337437eff19f347d4e7db14\",\n \"score\": \"0.542814\",\n \"text\": \"def zscoreToPercentile(zscore):\\n retVal = healthdb.util.NaN\\n # WHO technical specs chapter 7: \\\"However, a restriction was imposed on\\n # all indicators to enable the derivation of percentiles only within\\n # the interval corresponding to z-scores between -3 and 3. The\\n # underlying reasoning is that percentiles beyond +-3 SD are invariant\\n # to changes in equivalent z-scores. The loss accruing to this\\n # restriction is small since the inclusion range corresponds to the\\n # 0.135th to 99.865th percentiles.\\\"\\n if math.fabs(zscore) <= 3:\\n absVal = math.fabs(zscore)\\n P1 = (1 - 1 / math.sqrt(2 * math.pi) * math.exp(-math.pow(absVal, 2) / 2)\\n * (\\n 0.31938 * (1 / (1 + 0.2316419 * absVal))\\n - 0.356563782 * math.pow((1 / (1 + 0.2316419 * absVal)), 2)\\n + 1.781477937 * math.pow((1 / (1 + 0.2316419 * absVal)), 3)\\n - 1.82125 * math.pow((1 / (1 + 0.2316419 * absVal)), 4)\\n + 1.330274429 * math.pow((1 / (1 + 0.2316419 * absVal)), 5)\\n ))\\n \\n if zscore > 0:\\n P1 = P1 * 100\\n else:\\n P1 = 100 - P1 * 100\\n \\n if 0 <= P1 and P1 <= 100:\\n retVal = P1\\n return retVal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19b11413fb191d6925413b7ebf74b01\",\n \"score\": \"0.53977555\",\n \"text\": \"def getZScore(self, index):\\n # Calculate mean and standard deviation of the list of values\\n mu = np.mean(self.matrix[:, index], axis=0)\\n sd = np.std(self.matrix[:, index], axis=0)\\n # Calculate Z-score for the given column for each gene\\n zscore = (self.matrix[:, index] - mu) / sd\\n mygenes = {}\\n for (name, ndx) in self.genes.items():\\n try:\\n mygenes[name] = zscore[ndx, :]\\n except IndexError:\\n mygenes[name] = zscore[ndx]\\n # Return the dictionary of Z-scores\\n return mygenes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e326cfd1ca90e434102efeb46af2d1f6\",\n \"score\": \"0.53955096\",\n \"text\": \"def get_score(self, value):\\n try:\\n encodedvalue = PickleEncoder().encode(value)\\n except Exception, e:\\n raise RedisError(\\\"encode error:%s\\\" % e)\\n\\n try:\\n score = self._db.zscore(self.namespace, encodedvalue)\\n return score\\n except Exception, e:\\n raise RedisError(\\\"redis error:%s\\\" % e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed457a87f10952f48019978b8e1e1d36\",\n \"score\": \"0.5376343\",\n \"text\": \"def zscoreFromAttribute(anthroConfig):\\n for row in csv.DictReader(open(anthroConfig.fileName)):\\n if 'age' in row:\\n dataAgeHeightOrLength = row['age']\\n elif 'length' in row:\\n dataAgeHeightOrLength = row['length']\\n elif 'height' in row:\\n dataAgeHeightOrLength = row['height']\\n if int(row['sex']) == anthroConfig.sex and float(dataAgeHeightOrLength) == anthroConfig.ageHeightOrLength:\\n return zscoreOtherRestricted(anthroConfig.measureKey, float(row['l']), float(row['m']), float(row['s']), True) \\n return healthdb.util.NaN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad2d8b9197199d7edc5fda8187214db\",\n \"score\": \"0.5350002\",\n \"text\": \"def score(self):\\n pos = self.scores.filter(value=1).count()\\n n = self.scores.count()\\n return wilson_ci_lower_bound(n, pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfed7dbefd669d898835324cabe153d1\",\n \"score\": \"0.53445476\",\n \"text\": \"def NO_SCORE() -> int:\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8538005b30ce8cfd3ed977e86d84b93a\",\n \"score\": \"0.5340626\",\n \"text\": \"def Z(self) -> int:\\r\\n return self._Z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea26fb8ca8fcf09b92a47eeba81c975a\",\n \"score\": \"0.5333083\",\n \"text\": \"def get_best_score(self):\\n return self.best_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1716b29e9d7a608b2938d8cc1634e9c6\",\n \"score\": \"0.5331177\",\n \"text\": \"def compute(cls, observation, prediction):\\n assert type(observation) is dict\\n assert type(prediction) is dict\\n try:\\n p_value = prediction['mean'] # Use the prediction's mean. \\n except (TypeError,KeyError): # If there isn't one...\\n try:\\n p_value = prediction['value'] # Use the prediction's value. \\n except TypeError: # If there isn't one...\\n p_value = prediction # Use the prediction (assume it is numeric).\\n o_mean = observation['mean']\\n o_std = observation['std']\\n value = (p_value - o_mean)/o_std\\n value = utils.assert_dimensionless(value)\\n return ZScore(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100ea5e8e6e0474083b076a4a68719d7\",\n \"score\": \"0.53240174\",\n \"text\": \"def compute_best_risk(self):\\n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \\n mask = (has_chi2) & (self.zbest > self.zgrid[0])\\n \\n zbest_grid = np.dot(self.zbest[mask, None],\\n np.ones_like(self.zgrid)[None,:])\\n L = self._loss((zbest_grid-self.zgrid)/(1+self.zgrid))\\n #dz = np.gradient(self.zgrid)\\n \\n zbest_risk = np.zeros(self.NOBJ, dtype=self.ARRAY_DTYPE)-1\\n zbest_risk[mask] = np.dot(np.exp(self.lnp[mask,:])*L, self.trdz)\\n \\n del(has_chi2)\\n del(mask)\\n del(zbest_grid)\\n del(L)\\n \\n return zbest_risk\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70832f4b5008dc1a9bab31f1fb77d2c\",\n \"score\": \"0.5320918\",\n \"text\": \"def score_one(self, x: dict) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70832f4b5008dc1a9bab31f1fb77d2c\",\n \"score\": \"0.5320918\",\n \"text\": \"def score_one(self, x: dict) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b65cb58654be9d87f5140f9f692e8d\",\n \"score\": \"0.5320872\",\n \"text\": \"def wZ(w0, z, zR):\\r\\n return w0 * np.sqrt(1 + (z / zR) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57bd528109934590edcc37520b8aecc6\",\n \"score\": \"0.53161615\",\n \"text\": \"def get_score(self):\\n return self._points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d145a7dc3c8c2db905526386567322af\",\n \"score\": \"0.5306321\",\n \"text\": \"def norm_score(self):\\n return self.score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ba4834d38dd73afa4faf78179c59de\",\n \"score\": \"0.53024745\",\n \"text\": \"def compute_full_risk(self):\\n zsq = np.dot(self.zgrid[:,None], np.ones_like(self.zgrid)[None,:])\\n L = self._loss((zsq-self.zgrid)/(1+self.zgrid))\\n \\n Rz = self.lnp*0.\\n \\n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \\n hasz = self.zbest > self.zgrid[0]\\n idx = self.idx[hasz & (has_chi2)]\\n \\n for i in idx:\\n Rz[i,:] = np.dot(np.exp(self.lnp[i,:])*L, self.trdz)\\n \\n del(zsq)\\n del(L)\\n del(has_chi2)\\n del(hasz)\\n del(idx) \\n return Rz\\n \\n #self.full_risk = Rz\\n #self.min_risk = self.zgrid[np.argmin(Rz, axis=1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b8f1cc00b5b07453048380c9183176\",\n \"score\": \"0.5287981\",\n \"text\": \"def getScore(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b8f1cc00b5b07453048380c9183176\",\n \"score\": \"0.5287981\",\n \"text\": \"def getScore(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb862fa989f902036a49cc1625a4f90\",\n \"score\": \"0.528489\",\n \"text\": \"def max_score(self):\\n return self.weight if self.has_score else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3440f70adf1c7fd88bdefc2f668b642d\",\n \"score\": \"0.5280114\",\n \"text\": \"def constant_score(self):\\n return self._constant_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dc96d30401cecf5b2207a98a0df1a10\",\n \"score\": \"0.5262474\",\n \"text\": \"def get_score(self):\\n score = 1\\n if self.answer:\\n score = 3\\n if not self.correct:\\n score *= -1\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7e62a3d3390f0de2732aa78ed1d28e\",\n \"score\": \"0.5253121\",\n \"text\": \"def __call__(self, Z):\\n\\n return tf.math.maximum(Z, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1836f7e1518a3851013eb49b808aa889\",\n \"score\": \"0.52435356\",\n \"text\": \"def score(self):\\n return self.__score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de3634c605416248a0eb6a0e5726904\",\n \"score\": \"0.52232313\",\n \"text\": \"def score(self):\\n check_is_fitted(self, [\\\"lower_bound_\\\"])\\n return self.lower_bound_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a79fbce580c66f934eacea64394c344\",\n \"score\": \"0.52211654\",\n \"text\": \"def z(self):\\n return self._vct.z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58de9a1a700da120b9ef3b0822a54958\",\n \"score\": \"0.5218734\",\n \"text\": \"def saclhitsrate(self) :\\n try :\\n return self._saclhitsrate\\n except Exception as e:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"953ef2864b7c9b9b26aa003dab0961b4\",\n \"score\": \"0.5205887\",\n \"text\": \"def fowlkes_mallows_index(y_true: np.array, y_score: np.array) -> float:\\n ppv = positive_predictive_value(y_true, y_score)\\n tpr = true_positive_rate(y_true, y_score)\\n fm = (ppv * tpr) ** 0.5\\n return fm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a542313a7482d565193ff9df0cef3138\",\n \"score\": \"0.5202119\",\n \"text\": \"def detect_outlier_zscore(df):\\n\\tthreshold = 3\\n\\tz = np.abs(stats.zscore(df))\\n\\toutliers = np.where(z > threshold)\\n\\treturn outliers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f99c4afcdafcff521b56c5a458723e4\",\n \"score\": \"0.5199307\",\n \"text\": \"def getZonePercentageFeatureValue(self):\\n Nz = self.coefficients['Nz']\\n Np = self.coefficients['Np']\\n\\n zp = Nz / Np\\n return zp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81da7f92b66bb19c61c2617346cad4fe\",\n \"score\": \"0.5194981\",\n \"text\": \"def zprob(z):\\n Z_MAX = 6.0 # maximum meaningful z-value\\n if z == 0.0:\\n x = 0.0\\n else:\\n y = 0.5 * math.fabs(z)\\n if y >= (Z_MAX * 0.5):\\n x = 1.0\\n elif (y < 1.0):\\n w = y * y\\n x = ((((((((0.000124818987 * w\\n - 0.001075204047) * w + 0.005198775019) * w\\n - 0.019198292004) * w + 0.059054035642) * w\\n - 0.151968751364) * w + 0.319152932694) * w\\n - 0.531923007300) * w + 0.797884560593) * y * 2.0\\n else:\\n y = y - 2.0\\n x = (((((((((((((-0.000045255659 * y\\n + 0.000152529290) * y - 0.000019538132) * y\\n - 0.000676904986) * y + 0.001390604284) * y\\n - 0.000794620820) * y - 0.002034254874) * y\\n + 0.006549791214) * y - 0.010557625006) * y\\n + 0.011630447319) * y - 0.009279453341) * y\\n + 0.005353579108) * y - 0.002141268741) * y\\n + 0.000535310849) * y + 0.999936657524\\n if z > 0.0:\\n prob = ((x + 1.0) * 0.5)\\n else:\\n prob = ((1.0 - x) * 0.5)\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23444449548c751d57eb8a7312433623\",\n \"score\": \"0.5192021\",\n \"text\": \"def credit_score(self):\\n return self._credit_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1706217b6bb470a64b395196f3a35f76\",\n \"score\": \"0.5191078\",\n \"text\": \"def res_function(X, Z):\\n X_col = X.reshape((-1, 1))\\n Z_row = Z.reshape((1, -1))\\n return np.maximum(Z_row - X_col + margin, 0).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4d0a2f8fbc43bd9a9ce40b6891f4a6\",\n \"score\": \"0.5191033\",\n \"text\": \"def lcz(self, zbest=None):\\n if zbest is None:\\n zbest = self.zbest\\n \\n _lcz = np.dot(1/(1+zbest[:, np.newaxis]), self.pivot[np.newaxis,:])\\n return _lcz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350f7ffdbf74204002d650b60f84004b\",\n \"score\": \"0.5189782\",\n \"text\": \"def Score(cls):\\n\\n return ScoreMask()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595a292a563ab3746659d808dd8b2ba1\",\n \"score\": \"0.51834446\",\n \"text\": \"def custom_score(game, player):\\n # TODO: finish this function!\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n def calculate_score(moves):\\n if len(moves) == 0:\\n return 0\\n centered_locs = np.array(moves) - (game.height/2.0, game.width/2.0)\\n #print(centered_locs)\\n return np.sum(1/LA.norm(centered_locs,ord=2,axis=1))\\n\\n own_moves = game.get_legal_moves(player)\\n opp_moves = game.get_legal_moves(game.get_opponent(player))\\n\\n return float(calculate_score(own_moves)-calculate_score(opp_moves))\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":186,"cells":{"query_id":{"kind":"string","value":"bdeff234398c45a1b8d7776f2ff0c2a2"},"query":{"kind":"string","value":"Class method, which allows the user to populate the Database"},"positive_passages":{"kind":"list like","value":[{"docid":"5e4f589475625e693b789b714ddcc790","score":"0.0","text":"def populate(self):\n data = api_research_off()\n for product in data['products']:\n product_name = self.get_product_name(product)\n nutriscore = self.get_nutriscore(product)\n if nutriscore is None:\n continue\n nutritional_list = self.get_nutritional_mark_for_100g(product)\n if nutritional_list is None:\n continue\n url_page = self.get_url_page(product)\n url_image = self.get_url_image(product)\n if(not url_image):\n continue\n id_product = self.get_id_product(product)\n if(not id_product):\n continue\n last_modified_date = self.get_last_modified(product)\n food = Food(name=product_name,\n nutriscore=nutriscore,\n url=url_page,\n url_picture=url_image,\n fat_100g=nutritional_list[0],\n saturated_fat_100g=nutritional_list[2],\n sugars_100g=nutritional_list[4],\n salt_100g=nutritional_list[6],\n fat_level=nutritional_list[1],\n salt_level=nutritional_list[7],\n saturated_fat_level=nutritional_list[3],\n sugars_level=nutritional_list[5],\n last_modified=last_modified_date,\n openff_id=id_product)\n food.save()\n self.get_and_insert_categories_in_db(product, food)","title":""}],"string":"[\n {\n \"docid\": \"5e4f589475625e693b789b714ddcc790\",\n \"score\": \"0.0\",\n \"text\": \"def populate(self):\\n data = api_research_off()\\n for product in data['products']:\\n product_name = self.get_product_name(product)\\n nutriscore = self.get_nutriscore(product)\\n if nutriscore is None:\\n continue\\n nutritional_list = self.get_nutritional_mark_for_100g(product)\\n if nutritional_list is None:\\n continue\\n url_page = self.get_url_page(product)\\n url_image = self.get_url_image(product)\\n if(not url_image):\\n continue\\n id_product = self.get_id_product(product)\\n if(not id_product):\\n continue\\n last_modified_date = self.get_last_modified(product)\\n food = Food(name=product_name,\\n nutriscore=nutriscore,\\n url=url_page,\\n url_picture=url_image,\\n fat_100g=nutritional_list[0],\\n saturated_fat_100g=nutritional_list[2],\\n sugars_100g=nutritional_list[4],\\n salt_100g=nutritional_list[6],\\n fat_level=nutritional_list[1],\\n salt_level=nutritional_list[7],\\n saturated_fat_level=nutritional_list[3],\\n sugars_level=nutritional_list[5],\\n last_modified=last_modified_date,\\n openff_id=id_product)\\n food.save()\\n self.get_and_insert_categories_in_db(product, food)\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"11c5c10dcb7519f823633e95d691706e","score":"0.7409577","text":"def _populateTables(cls):\n for method in dir(Populate):\n if not method.startswith('__'):\n populateTable = getattr(Populate, method)\n print 'Inserting data for %s ...' % method\n populateTable()\n\n db.session.commit()","title":""},{"docid":"dda5e8e7e0ff2e9f2e1e78366231c75b","score":"0.7352905","text":"def __call__(self):\n self.create_database()","title":""},{"docid":"e04dc2fb4584516abc29cd5b12401ab9","score":"0.7279164","text":"def populate():","title":""},{"docid":"3738361c8de7ca311743c12b407f463c","score":"0.7264388","text":"def populate_db_command():\n populate_db()\n click.echo(\"Created dummy data.\")","title":""},{"docid":"aeb3e562c42462ac59c49c99c1f6c53e","score":"0.72629017","text":"def handle(self, *args, **options):\n self._populate_db()","title":""},{"docid":"e755fbb017c46d5659f9eac88663f994","score":"0.72245425","text":"def populate_db():\n database.create_tables([Donor, Donation])\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n #database = SqliteDatabase('donor.db')\n\n logger.info('Working with Person class')\n logger.info('Note how I use constants and a list of tuples as a simple schema')\n logger.info('Normally you probably will have prompted for this from a user')\n\n# NAME = 0\n# DONATION = 1\n# \n donors = [('Karl', [100, 200, 300.55]),\n ('Luis', [400, 500, 600]),\n ('Woody', [700, 800, 900]),\n ('Mary', [455.99])]\n \n logger.info('Creating Donor records: iterate through the list of tuples')\n logger.info('Prepare to explain any errors with exceptions')\n logger.info('and the transaction tells the database to fail on error')\n \n for users, donations in donors:\n user = Donor.create(username=users)\n for donation in donations:\n Donation.create(user=user, donation=float(donation))","title":""},{"docid":"13029ab8865339e29a5c71c47127fda5","score":"0.71562296","text":"def data_into_database():\n pass","title":""},{"docid":"d20c034a8c3cbc0832dc74bb4da1b0f3","score":"0.71474314","text":"def fromDB(self):\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n pass","title":""},{"docid":"fdc0072cd1eefb95b2048c8061190efe","score":"0.7144774","text":"def initiate(cls):\n cls._purgeTables()\n cls._populateTables()\n print 'DB initialization finished!!'","title":""},{"docid":"8f9911dbbe0284a285ab8f29ced0957c","score":"0.7093239","text":"def populate(self, connection, system):","title":""},{"docid":"7efc43d24ebff7e863c3941c631b63d4","score":"0.704847","text":"def init_db():\n\n with app.app_context():\n data = json.loads(urllib2.urlopen(DATA_URL).read())\n ds = DataSource(data['data'], data['meta']['view']['columns'])\n headers = ds.get_headers(\n set([\n 'Applicant', \n 'Address', \n 'FoodItems', \n 'Latitude', \n 'Longitude'\n ]),\n 'name'\n )\n db = get_db()\n db.init_database()\n for item in ds.gen_items(headers):\n try:\n db.add_row(\n item['Applicant'], \n item['Address'], \n item['FoodItems'], \n item['Latitude'], \n item['Longitude']\n )\n except psycopg2.IntegrityError:\n pass","title":""},{"docid":"8a8560696f49cd0ff48c227dff9a1f49","score":"0.69914603","text":"def populate_db2(self):\n db.create_all()\n\n informantRelationships = self.create_informant_relationships()\n users = self.create_users()\n finalCodes = self.create_final_codes()\n states = self.create_states()\n abstractStatuses = self.create_abstract_statuses()\n sexes = self.create_sexes()\n races = self.create_races()\n ethnicities = self.create_ethnicities()\n vitals = self.create_vital_statuses()\n contacts = self.create_contacts()\n inactives = self.create_inactives()\n ucrReportTypes = self.create_ucr_report_types()\n physicianStatuses = self.create_physician_statuses()\n physFacilityStatuses = self.create_physician_facility_statuses()\n phoneTypes = self.create_phone_types()\n irbHolders = self.create_irb_holders()\n projectTypes = self.create_project_types()\n contactStatuses = self.create_contact_statuses()\n contactSources = self.create_contact_sources()\n grantStatuses = self.create_grant_statuses()\n fundingSources = self.create_funding_sources()\n reviewCommitteeStatuses = self.create_review_committee_statuses()\n projectStatuses = self.create_project_statuses()\n logTypes = self.create_log_subjects()\n reviewCommittees = self.create_review_committees()\n staffRoles = self.create_staff_roles()\n projectPhases = self.create_project_phases()\n hsts = self.create_human_subject_trainings()\n tracingSources = self.create_tracing_sources()\n contactTypes = self.create_contact_types()\n ucrRoles = self.create_ucr_roles()\n giftCards = self.create_gift_cards()\n\n project1 = models.Project(\n projectTypeID=1,\n irbHolderID=1,\n projectTitle=\"Test Project\",\n shortTitle=\"Test Project\",\n projectSummary=\"Summary\",\n sop=\"sop\",\n ucrProposal=\"ucr_proposal\",\n budgetDoc=\"budget_doc\",\n ucrFee=\"no\",\n ucrNoFee=\"yes\",\n previousShortTitle=\"t short\",\n dateAdded=datetime(2016, 2, 2),\n finalRecruitmentReport=\"report\",\n ongoingContact=True,\n activityStartDate=datetime(2016, 2, 2),\n activityEndDate=datetime(2016, 2, 2))\n\n project2 = models.Project(\n projectTypeID=1,\n irbHolderID=1,\n projectTitle=\"Test Project\",\n shortTitle=\"Test Project\",\n projectSummary=\"Summary\",\n sop=\"sop\",\n ucrProposal=\"ucr_proposal\",\n budgetDoc=\"budget_doc\",\n ucrFee=\"no\",\n ucrNoFee=\"yes\",\n previousShortTitle=\"t short\",\n dateAdded=datetime(2016, 2, 2),\n finalRecruitmentReport=\"report\",\n ongoingContact=True,\n activityStartDate=datetime(2016, 2, 2),\n activityEndDate=datetime(2016, 2, 2))\n\n budget1 = models.Budget(\n projectID=1,\n numPeriods=1,\n periodStart=datetime(2016, 2, 2),\n periodEnd=datetime(2016, 2, 2),\n periodTotal=1.23,\n periodComment=\"comment\")\n\n rc = models.ReviewCommittee(\n projectID=1,\n reviewCommitteeStatusID=1,\n reviewCommitteeLUTID=1,\n reviewCommitteeNumber=\"1\",\n dateInitialReview=datetime(2016, 2, 2),\n dateExpires=datetime(2016, 2, 2),\n rcNote=\"rc_note\",\n rcProtocol=\"rc_proto\",\n rcApproval=\"rc_approval\")\n\n ucr = models.UCRReport(\n projectID=1,\n reportTypeID=1,\n reportSubmitted=datetime(2016, 2, 2),\n reportDue=datetime(2016, 2, 2),\n reportDoc=\"doc\"\n )\n arcReview = models.ArcReview(\n projectID=1,\n reviewType=1,\n dateSentToReviewer=datetime(2016, 2, 2),\n reviewer1=1,\n reviewer1Rec=1,\n reviewer1SigDate=datetime(2016, 2, 2),\n reviewer1Comments=\"test comment\",\n reviewer2=2,\n reviewer2Rec=2,\n reviewer2SigDate=datetime(2016, 2, 2),\n reviewer2Comments=\"test comment\",\n research=1,\n linkage=False,\n contact=True,\n engaged=True,\n nonPublicData=True)\n\n funding = models.Funding(\n grantStatusID=1,\n projectID=1,\n fundingSourceID=1,\n primaryFundingSource=\"pfs\",\n secondaryFundingSource=\"sfs\",\n fundingNumber=\"number\",\n grantTitle=\"title\",\n dateStatus=datetime(2016, 2, 2),\n grantPi=1,\n primaryChartfield=\"pcf\",\n secondaryChartfield=\"scf\"\n )\n\n staff = models.Staff(\n userID=1,\n firstName=\"Aaron\",\n lastName=\"Thomas\",\n middleName=\"Pulver\",\n email=\"aaron.pulver@utah.edu\",\n phoneNumber=\"phone\",\n phoneComment=\"phoneComment\",\n institution=\"institution\",\n department=\"department\",\n position=\"position\",\n credentials=\"credentials\",\n street=\"street\",\n city=\"city\",\n stateID=1,\n ucrRoleID=1\n )\n staff2 = models.Staff(\n userID=2,\n firstName=\"Phoebe\",\n lastName=\"\",\n middleName=\"McNeally\",\n email=\"email\",\n phoneNumber=\"phone\",\n phoneComment=\"phoneComment\",\n institution=\"institution\",\n department=\"department\",\n position=\"position\",\n credentials=\"credentials\",\n street=\"street\",\n city=\"city\",\n stateID=2,\n ucrRoleID=1\n )\n\n projStatus = models.ProjectStatus(\n projectStatusTypeID=1,\n projectID=1,\n staffID=1,\n statusDate=datetime(2016, 2, 2),\n statusNotes=\"notes\"\n )\n\n preApp = models.PreApplication(\n projectID=1,\n piFirstName=\"pi_fname\",\n piLastName=\"pi_lname\",\n piEmail=\"pi_email\",\n piPhone=\"pi_phone\",\n contactFirstName=\"contact_fname\",\n contactLastName=\"contact_lname\",\n contactPhone=\"contact_phone\",\n contactEmail=\"contact_email\",\n institution=\"institution\",\n institution2=\"institution2\",\n uid=\"uid\",\n udoh=1,\n projectTitle=\"project_title\",\n purpose=\"purpose\",\n irb0=True,\n irb1=True,\n irb2=True,\n irb3=True,\n irb4=True,\n otherIrb=\"other_irb\",\n updb=True,\n ptContact=True,\n startDate=datetime(2016, 2, 2),\n link=True,\n deliveryDate=datetime(2016, 2, 2),\n description=\"description\"\n )\n log = models.Log(\n logSubjectID=1,\n projectID=1,\n staffID=1,\n phaseStatusID=1,\n note=\"note\",\n date=datetime(2016, 2, 2)\n )\n projectStaff = models.ProjectStaff(\n staffRoleID=1,\n projectID=1,\n staffID=1,\n datePledge=datetime(2016, 2, 2),\n dateRevoked=datetime(2016, 2, 2),\n contactID=1,\n inactiveID=1\n )\n staffTraining = models.StaffTraining(\n staffID=1,\n humanSubjectTrainingID=1,\n dateTaken=datetime(2016, 2, 2),\n dateExpires=datetime(2016, 2, 2)\n )\n patient = models.Patient(\n patID=\"1\",\n ucrDistID=1,\n UPDBID=1,\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"mname\",\n maidenName=\"maiden_name\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle\",\n dobDay=15,\n dobMonth=2,\n dobYear=1990,\n SSN=\"999999999\",\n sexID=2,\n raceID=1,\n ethnicityID=1,\n vitalStatusID=1\n )\n patient2 = models.Patient(\n patID=\"1\",\n ucrDistID=1,\n UPDBID=1,\n firstName=\"fname2\",\n lastName=\"lname2\",\n middleName=\"mname2\",\n maidenName=\"maiden_name\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle\",\n dobDay=26,\n dobMonth=4,\n dobYear=1970,\n SSN=\"999999999\",\n sexID=1,\n raceID=2,\n ethnicityID=1,\n vitalStatusID=2\n )\n\n patientAddress = models.PatientAddress(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=1,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n\n patientEmail = models.PatientEmail(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n email=\"email\",\n emailStatusDate=datetime(2016, 2, 2)\n )\n patientPhone = models.PatientPhone(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n patientPhone2 = models.PatientPhone(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n informant1 = models.Informant(\n participantID=1,\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n informantPrimary=True,\n informantRelationshipID=1,\n notes=\"notes\"\n )\n informant2 = models.Informant(\n participantID=1,\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n informantPrimary=True,\n informantRelationshipID=1,\n notes=\"notes\"\n )\n informantAddress = models.InformantAddress(\n contactInfoSourceID=1,\n informantID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=2,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n informantPhone = models.InformantPhone(\n contactInfoSourceID=1,\n informantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n informantPhone2 = models.InformantPhone(\n contactInfoSourceID=1,\n informantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n ctc1 = models.CTC(\n participantID=1,\n dxDateDay=2,\n dxDateMonth=7,\n dxDateYear=1988,\n site=\"Site 2\",\n histology=\"histology\",\n behavior=\"behavior\",\n ctcSequence=\"sequence\",\n stage=\"stage\",\n dxAge=1,\n dxStreet1=\"street1\",\n dxStreet2=\"street2\",\n dxCity=\"city\",\n dxStateID=1,\n dxZip=99999,\n dxCounty=\"county\",\n dnc=\"dnc\",\n dncReason=\"dnc_reason\",\n recordID=\"abc321\"\n )\n ctc2 = models.CTC(\n participantID=1,\n dxDateDay=3,\n dxDateMonth=10,\n dxDateYear=1958,\n site=\"Site 1\",\n histology=\"histology\",\n behavior=\"behavior\",\n ctcSequence=\"sequence\",\n stage=\"stage\",\n dxAge=1,\n dxStreet1=\"street1\",\n dxStreet2=\"street2\",\n dxCity=\"city\",\n dxStateID=2,\n dxZip=99999,\n dxCounty=\"county\",\n dnc=\"dnc\",\n dncReason=\"dnc_reason\",\n recordID=\"abc123\"\n )\n projectPatient = models.ProjectPatient(\n projectID=1,\n staffID=1,\n ctcID=1,\n currentAge=1,\n batch=1,\n siteGrp=1,\n finalCodeID=1,\n finalCodeDate=datetime(2016, 2, 2),\n enrollmentDate=datetime(2016, 2, 2),\n dateCoordSigned=datetime(2016, 2, 2),\n importDate=datetime(2016, 2, 2),\n finalCodeStaffID=1,\n enrollmentStaffID=1,\n dateCoordSignedStaffID=1,\n abstractStatusID=1,\n abstractStatusDate=datetime(2016, 2, 2),\n abstractStatusStaffID=1,\n sentToAbstractorDate=datetime(2016, 2, 2),\n sentToAbstractorStaffID=1,\n abstractedDate=datetime(2016, 2, 2),\n abstractorStaffID=1,\n researcherDate=datetime(2016, 2, 2),\n researcherStaffID=1,\n consentLink=\"link\",\n medRecordReleaseSigned=True,\n medRecordReleaseLink=\"link\",\n medRecordReleaseStaffID=1,\n medRecordReleaseDate=datetime(2016, 2, 2),\n surveyToResearcher=datetime(2016, 2, 2),\n surveyToResearcherStaffID=1,\n qualityControl=True,\n )\n\n projectPatient2 = models.ProjectPatient(\n projectID=1,\n staffID=1,\n ctcID=1,\n currentAge=1,\n batch=1,\n siteGrp=1,\n finalCodeID=1,\n finalCodeDate=datetime(2016, 2, 2),\n enrollmentDate=datetime(2016, 2, 2),\n dateCoordSigned=datetime(2016, 2, 2),\n importDate=datetime(2016, 2, 2),\n finalCodeStaffID=1,\n enrollmentStaffID=1,\n dateCoordSignedStaffID=1,\n abstractStatusID=1,\n abstractStatusDate=datetime(2016, 2, 2),\n abstractStatusStaffID=1,\n sentToAbstractorDate=datetime(2016, 2, 2),\n sentToAbstractorStaffID=1,\n abstractedDate=datetime(2016, 2, 2),\n abstractorStaffID=1,\n researcherDate=datetime(2016, 2, 2),\n researcherStaffID=1,\n consentLink=\"link\",\n medRecordReleaseSigned=True,\n medRecordReleaseLink=\"link\",\n medRecordReleaseStaffID=1,\n medRecordReleaseDate=datetime(2016, 2, 2),\n surveyToResearcher=datetime(2016, 2, 2),\n surveyToResearcherStaffID=1,\n qualityControl=False\n )\n\n tracing = models.Tracing(\n tracingSourceID=1,\n participantID=1,\n date=datetime(2016, 2, 2),\n staffID=1,\n notes=\"notes\"\n )\n physician = models.Physician(\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n credentials=\"credentials\",\n specialty=\"specialty\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle_name\",\n physicianStatusID=1,\n physicianStatusDate=datetime(2016, 2, 2),\n )\n\n physician2 = models.Physician(\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n credentials=\"credentials\",\n specialty=\"specialty\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle_name\",\n physicianStatusID=1,\n physicianStatusDate=datetime(2016, 2, 2),\n )\n physicianAddress = models.PhysicianAddress(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=1,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n\n physicianEmail = models.PhysicianEmail(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n email=\"email\",\n emailStatusDate=datetime(2016, 2, 2)\n )\n\n physicianPhone = models.PhysicianPhone(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n phoneNumber=\"phone\",\n phoneTypeID=1,\n phoneStatusDate=datetime(2016, 2, 2)\n )\n physicianPhone2 = models.PhysicianPhone(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n phoneNumber=\"phone\",\n phoneTypeID=1,\n phoneStatusDate=datetime(2016, 2, 2)\n )\n physicianToCTC = models.PhysicianToCTC(\n physicianID=1,\n ctcID=1\n )\n facility1 = models.Facility(\n facilityName=\"name\",\n contactFirstName=\"fname\",\n contactLastName=\"lname\",\n facilityStatus=1,\n facilityStatusDate=datetime(2016, 2, 2),\n contact2FirstName=\"fname\",\n contact2LastName=\"lname\"\n )\n facility2 = models.Facility(\n facilityName=\"name\",\n contactFirstName=\"fname\",\n contactLastName=\"lname\",\n facilityStatus=1,\n facilityStatusDate=datetime(2016, 2, 2),\n contact2FirstName=\"fname\",\n contact2LastName=\"lname\"\n )\n facilityAddress = models.FacilityAddress(\n contactInfoSourceID=1,\n facilityID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=1,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n\n facilityPhone = models.FacilityPhone(\n contactInfoSourceID=1,\n facilityID=1,\n contactInfoStatusID=1,\n clinicName=\"clinic\",\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n facilityPhone2 = models.FacilityPhone(\n contactInfoSourceID=1,\n facilityID=1,\n contactInfoStatusID=1,\n clinicName=\"clinic\",\n phoneTypeID=1,\n phoneNumber=\"phone2\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n patientProjectStatusType1 = models.PatientProjectStatusLUT(\n statusDescription=\"desc\"\n )\n patientProjectStatusType2 = models.PatientProjectStatusLUT(\n statusDescription=\"desc\"\n )\n patientProjectStatus = models.PatientProjectStatus(\n patientProjectStatusTypeID=1,\n participantID=1,\n statusDate=datetime(2016,2,2)\n )\n physicianFacility = models.PhysicianFacility(\n facilityID=1,\n physicianID=1,\n physFacilityStatusID=1,\n physFacilityStatusDate=datetime(2016, 2, 2)\n )\n contact = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n informantID=1,\n informantPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n contact2 = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n facilityID=1,\n facilityPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n contact3 = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n physicianID=1,\n physicianPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n contact4 = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n patientPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n ctcFacility = models.CTCFacility(\n ctcID=1,\n facilityID=1,\n coc=123\n )\n incentive = models.Incentive(\n participantID=1,\n incentiveDescription=\"desc\",\n barcode=\"123456789\",\n dateGiven=datetime(2016, 4, 3)\n )\n db.session.add_all(ucrRoles)\n db.session.add_all(informantRelationships)\n db.session.add_all(users)\n db.session.add_all(states)\n db.session.add_all(finalCodes)\n db.session.add_all(sexes)\n db.session.add_all(abstractStatuses)\n db.session.add_all(races)\n db.session.add_all(ethnicities)\n db.session.add_all(vitals)\n db.session.add_all(contacts)\n db.session.add_all(inactives)\n db.session.add_all(ucrReportTypes)\n db.session.add_all(physicianStatuses)\n db.session.add_all(physFacilityStatuses)\n db.session.add_all(phoneTypes)\n db.session.add_all(irbHolders)\n db.session.add_all(projectTypes)\n db.session.add_all(contactStatuses)\n db.session.add_all(contactSources)\n db.session.add_all(grantStatuses)\n db.session.add_all(fundingSources)\n db.session.add_all(reviewCommitteeStatuses)\n db.session.add_all(projectStatuses)\n db.session.add_all(logTypes)\n db.session.add_all(reviewCommittees)\n db.session.add_all(staffRoles)\n db.session.add_all(projectPhases)\n db.session.add_all(hsts)\n db.session.add_all(tracingSources)\n db.session.add_all(contactTypes)\n db.session.add_all(giftCards)\n db.session.add(staff)\n db.session.add(staff2)\n db.session.add(project1)\n db.session.add(project2)\n db.session.add(funding)\n db.session.add(budget1)\n db.session.add(rc)\n db.session.add(ucr)\n db.session.add(arcReview)\n db.session.add(preApp)\n db.session.add(log)\n db.session.add(projectStaff)\n db.session.add(staffTraining)\n db.session.add(patient)\n db.session.add(patient2)\n db.session.add(patientAddress)\n db.session.add(patientEmail)\n db.session.add(patientPhone)\n db.session.add(patientPhone2)\n db.session.add(informant1)\n db.session.add(informant2)\n db.session.add(informantAddress)\n db.session.add(informantPhone)\n db.session.add(informantPhone2)\n db.session.add(ctc1)\n db.session.add(ctc2)\n db.session.add(projectPatient)\n db.session.add(projectPatient2)\n db.session.add(projStatus)\n db.session.add(tracing)\n db.session.add(physician)\n db.session.add(physician2)\n db.session.add(physicianAddress)\n db.session.add(physicianEmail)\n db.session.add(physicianPhone)\n db.session.add(physicianPhone2)\n db.session.add(physicianToCTC)\n db.session.add(facility1)\n db.session.add(facility2)\n db.session.add(facilityAddress)\n db.session.add(facilityPhone)\n db.session.add(facilityPhone2)\n db.session.add(patientProjectStatusType1)\n db.session.add(patientProjectStatusType2)\n db.session.add(patientProjectStatus)\n db.session.add(physicianFacility)\n db.session.add(contact)\n db.session.add(contact2)\n db.session.add(contact3)\n db.session.add(contact4)\n db.session.add(ctcFacility)\n db.session.add(incentive)\n db.session.commit()","title":""},{"docid":"f1f4704030f5c33ae04a6f6ed3fc8c9d","score":"0.699089","text":"def populate(self):\n pass","title":""},{"docid":"77c4cbc11305536344a963ae28dd0f92","score":"0.69689226","text":"def populate_db():\n values = db.load_config()\n logging.info(values)\n logging.info(\"Started import \")\n logging.info(f'Importing into DB: {values[\"database\"]}')\n # host, db, user, password\n conn = db.get_connection_local_pg( values)\n logging.info(\"Finished import \")\n if ( not os.path.isdir(\"data/2014-i2b2-nlp-evaluation-data-txt\") ):\n logging.exception(\"Data not downloaded, run the main.py get_data first \")\n os.sys.exit(1)\n db.import_data(conn, \"data/2014-i2b2-nlp-evaluation-data-txt\", \"data/testing-PHI-Gold-fixed\")","title":""},{"docid":"eab65cea50449a2f2f4271702528c59f","score":"0.69370925","text":"def __init__(self):\n self.db = db\n self.db.create_all()","title":""},{"docid":"53db6e0a06eccebdfecb42251ceff176","score":"0.6923225","text":"def do_populate(self, arg):\n if arg.strip():\n self.print('Bad syntax!')\n return\n\n self.client.populate_db(numElem=50, numVp=5)\n self.print('OK!')","title":""},{"docid":"b0597bdfe794be623b11017593a932e0","score":"0.69071597","text":"def _init_db_structure(self):\n #with self._db as conn: conn.execute('')\n pass","title":""},{"docid":"db9b4b3df1fe7585313948ae354a3a87","score":"0.6852799","text":"def _populate_db():\n data_1 = json.dumps(DATA_1)\n data_2 = json.dumps({\n \"organization\": \"Skynet Papercorp\", \"reported_at\": \"2015-04-22\",\n \"created_at\": \"2015-04-23\",\n \"inventory\": [{\"name\": \"paper\", \"price\": \"4.00\"}]\n })\n report_1 = Report(data=data_1)\n report_2 = Report(data=data_2)\n db.session.add(report_1)\n db.session.add(report_2)\n db.session.commit()","title":""},{"docid":"6a0ccdb9a18f76b2e8cd24c1104a62ad","score":"0.6847385","text":"def update_database(self):","title":""},{"docid":"c47700b5a4ddf0cb55f3bcad8e6ef127","score":"0.6742828","text":"def create_db_samples():\n init_users()\n init_categories()\n init_items()","title":""},{"docid":"f19de7f49a4903a12523b88a31e718e7","score":"0.6733228","text":"def run(self, args):\n super(PopulateCommand, self).run(args)\n try:\n init_conf(\"populate.log\")\n# cfg.CONF.log_file = \"populate.log\"\n# cfg.CONF.use_stderr = True\n LOG = api.LOG = get_logger(\"populate\")\n LOG.info(_(\"Loading environment\"))\n self.load_app()\n LOG.info(_(\"Building schema\"))\n LOG.info(_(\"Starting a transaction...\"))\n models.start()\n\n # FIXME: There's no create_all equivalent for Music.\n\n # Valet\n Group.create_table()\n Placement.create_table()\n Plan.create_table()\n\n # Ostro\n Event.create_table()\n PlacementRequest.create_table()\n PlacementResult.create_table()\n except Exception as ex:\n models.rollback()\n LOG.error(\"Rolling back... %s\" % ex)\n raise\n else:\n LOG.info(_(\"Committing.\"))\n models.commit()","title":""},{"docid":"dda6cc12fabb7bd264b4028d965e6e4f","score":"0.66867036","text":"def insert_data(self):\n # Make a connexion with a mock database\n self.generate_data_collection()","title":""},{"docid":"f9d68f7ddaf994169e9ef7ea55297614","score":"0.664742","text":"def init_db():\n global app\n Supplier.init_db(app)","title":""},{"docid":"1a0f57d7958b9a592b22a30b870e439a","score":"0.66439116","text":"def toDB(self):\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n pass","title":""},{"docid":"9c7a639687c0a13a0d5426588108c1e4","score":"0.66213745","text":"def populate_db():\n from datetime import datetime\n from models import Post, Tag, post_to_tag\n Post.query.delete()\n Tag.query.delete()\n db.session.execute(post_to_tag.delete())\n\n personalwebapp = Tag(name='personalwebapp')\n db.session.add(personalwebapp)\n\n post = Post(title='Hello PersonalWebApp!', content=FIRST_POST, author_id=1, timezone=-5)\n post.tags.append(personalwebapp)\n post.state_id = 2\n post.published_at = datetime.utcnow()\n post.url = 'hello_personal_webapp'\n db.session.add(post)\n\n post = Post(title='Example Draft Post', content='This is the first draft. I\\'ll finish it later.',\n author_id=1, timezone=-5)\n post.url = 'example_draft_post'\n db.session.add(post)\n\n db.session.commit()\n\n print('\"personalwebapp\" tag'\n 'and \"Hello PersonalWebApp!\" published post and \"Example Draft Post\" draft post have been generated.')","title":""},{"docid":"d910fe18fbef4319354befcc35d03628","score":"0.65966296","text":"def create_db(self):\n return None","title":""},{"docid":"ca9c7e87e76ef17c1834e51d3ec5b2fe","score":"0.65857136","text":"def populate_db():\n\n api_url = BOOK_URL\n\n api_response = requests.get(api_url)\n print(api_response.text)\n json_api_response = api_response.json()\n if api_response.status_code == 200:\n for book in json_api_response:\n name = book['name']\n isbn = book[\"isbn\"]\n authors = json.dumps(book[\"authors\"])\n country = book['country']\n number_of_pages = int(book[\"numberOfPages\"])\n publisher = book[\"publisher\"]\n release_date = datetime.strptime(book[\"released\"], \"%Y-%m-%dT%H:%M:%S\").date()\n\n book_insert = Books(None, name, isbn, authors, country, number_of_pages, publisher, release_date, datetime.now(),\n datetime.now())\n db.session.add(book_insert)\n db.session.commit()","title":""},{"docid":"02692e1ea91042a4b1bb734b743fdd70","score":"0.65577847","text":"def populate_database(query, write):\n db = ingest.load_database()\n db = ingest.populate_database(db, query=query, write=write)","title":""},{"docid":"019188738626cd084af97385709e6cb4","score":"0.6507313","text":"def initialize_db():\n # Drop the database and create tables\n setup_database = TestDatabase()\n setup_database.drop()\n setup_database.create()\n setup_database.populate()","title":""},{"docid":"738e02b7d0516b0517e04b9cd2313a1b","score":"0.64941686","text":"def run(self):\n self.db.table('users').insert({\n 'name': 'sjahn',\n 'email': 'asj214@naver.com',\n 'password': generate_password_hash('1234')\n })","title":""},{"docid":"c96b463c4afba4b8874e42597aee6b3b","score":"0.64413","text":"def seed_data():\n typer.echo(\"Creating initial data\")\n db = SessionLocal()\n init_db(db)\n typer.echo(\"Initial data created\")","title":""},{"docid":"a2bc2d10880289fafb45d98b7d9b9758","score":"0.6428364","text":"def dbcreation():\n db.create_all() \n return \"Hi\"","title":""},{"docid":"d23d384becf2768d3e555256fd4cf92f","score":"0.6423991","text":"def populate(self):\n raise NotImplementedError","title":""},{"docid":"ef264dd3df50c3020dbd0dd2de6672e7","score":"0.6420109","text":"def __populate(self):\n self._populate_cli()\n self._populate_model()","title":""},{"docid":"c52a7b7a7c599071e0c42c0dc4163f5e","score":"0.6419514","text":"def setUp(self):\n\t\tdb.create_all()","title":""},{"docid":"d0e326115fecf07a75034ac464aff76c","score":"0.6418617","text":"def initialize():\n db.connect()\n db.create_tables([Product], safe=True)","title":""},{"docid":"22618966909c2b541155c9cccc016691","score":"0.64135474","text":"def initialize():\n db.connect()\n db.create_tables([Entry], safe=True)","title":""},{"docid":"253b0f1c720f5fda12e6a428c5ab086f","score":"0.6398332","text":"def initialisation(self):\n self.cursor.execute('create database if not exists open_food_facts;')\n\n with open(\"creation_bdd.sql\", 'r') as bdd:\n file = bdd.read()\n requetes = file.split(';')\n for line in requetes:\n self.cursor.execute(line)\n\n self.connexion.commit()","title":""},{"docid":"58caf7c1e15f11c58efc8b15d2e8bd2f","score":"0.6390033","text":"def get_database_data(self):\r\n pass","title":""},{"docid":"aa52c5d6fcf41a0a51cbbf90cf49e5f4","score":"0.6380734","text":"def create_db():\n db.create_all()\n db_fill_data()","title":""},{"docid":"739094ba7ea9b2a730020bba9cfc3cab","score":"0.6374668","text":"def init_db_command():\n db = init_db()\n import_parsed_data(db)","title":""},{"docid":"220077a23ec1b23d82b202cd9f0c39c2","score":"0.6373129","text":"def init_db():\n\n create_model_tables([\n Text,\n Citation,\n ], fail_silently=True)","title":""},{"docid":"c5957b336c45f673120ecf164d9b315a","score":"0.63699794","text":"def build_database():\n\tfrom fake_data import setup_initial_database, more_data\n\n\tprint (\"Updating Local Development Database...\")\n\n\tsetup_initial_database()\n\n\tprint (\"Finished!\")\n\n\treturn","title":""},{"docid":"5734b372ff7c325ebedb7ccee68dd464","score":"0.63501185","text":"def initialize_db(self):\r\n click.echo('[OrientModel_initialize_db_%s] Starting process...' % (get_datetime()))\r\n if self.checks['created'] == False:\r\n self.create_db()\r\n if self.checks['open_db'] == False:\r\n self.open_db()\r\n sql = \"\"\r\n for m in self.models:\r\n sql = sql+\"create class %s extends %s;\\n\" % (m, self.models[m]['class'])\r\n for k in self.models[m].keys():\r\n if k != 'class':\r\n sql = sql+\"create property %s.%s %s;\\n\" % (m, k, self.models[m][k])\r\n if 'id' in str(k):\r\n sql = sql + \"create index %s_%s on %s (%s) UNIQUE ;\\n\" % (m, k, m, k)\r\n\r\n sql = sql + \"create sequence idseq type ordered;\"\r\n click.echo('[OrientModel_initialize_db_%s]'\r\n ' Initializing db with following batch statement'\r\n '\\n*************** SQL ***************\\n'\r\n '%s\\n*************** SQL ***************\\n' % (get_datetime(), sql))\r\n self.checks['initialized'] = True\r\n\r\n return self.client.batch(sql)","title":""},{"docid":"b45da97885b18b7a1e9b14a83ba0820f","score":"0.6349823","text":"def creation_database_loading_started(self):\n heading.main_page_loading()\n print('\\t\\tCreation of the Database ... 0%\\n\\n'\n '\\t\\t- - - - - - - - - - - - - - - - - - - - -\\n\\n')","title":""},{"docid":"3f2a1691c8b3e0c77cb7b7cacc2bbcf3","score":"0.63198614","text":"def init_db(self):\n # Connecting to the database file\n conn = sqlite3.connect(DatabaseAccessor.DATABASE_PATH)\n c = conn.cursor()\n\n # Create new tables\n for sql in self.SQL_CREATES:\n c.execute(sql)\n\n # Committing changes and closing the connection to the database file\n conn.commit()\n conn.close()","title":""},{"docid":"06a678067aaeca2b31e147bf8b83af57","score":"0.6313009","text":"def _init_database(self):\r\n\r\n for schema in self._object_schemas:\r\n type_specs = [self._create_sql_for_column(name, schema_item)\r\n for (name, schema_item) in schema.fields]\r\n self.cursor.execute(\"CREATE TABLE %s (%s)\" %\r\n (schema.table_name, ', '.join(type_specs)))\r\n for name, columns in schema.indexes:\r\n self.cursor.execute(\"CREATE INDEX %s ON %s (%s)\" %\r\n (name, schema.table_name, ', '.join(columns)))\r\n for name, columns in schema.unique_indexes:\r\n self.cursor.execute(\"CREATE UNIQUE INDEX %s ON %s (%s)\" %\r\n (name, schema.table_name, ', '.join(columns)))\r\n self._create_variables_table()\r\n self.set_version()\r\n self.setup_fulltext_search()","title":""},{"docid":"bf0c15cd3fe6ac70b7ba41350f4bb2c8","score":"0.63018775","text":"def initialize_database(self):\n self.database = self.loader.request_library(\"common_libs\", \"database\")\n self.database.create_connection(\"production\")\n self.database.load_mappings()\n\n self.migrator = self.loader.request_library(\"database_tools\", \"migrator\")\n self.migrator.migrate()","title":""},{"docid":"46b5beb195eb88b0cf9a0d1aa2d98dc4","score":"0.6297803","text":"def initialize_db():\n print(\"Do init db\")\n db.create_all()\n print(\"init db done\")","title":""},{"docid":"b3e440fe43530ddba4ca0b4a847d00a9","score":"0.62872404","text":"def _create_tables(self):","title":""},{"docid":"1631bc9ae71fad0be54bbace58f0a6ce","score":"0.62842786","text":"def create_database():\n print('Creating database...')\n init_db()","title":""},{"docid":"aca2caa00fa9fc1fbc76530b0e78a022","score":"0.6271357","text":"def database():\n pass","title":""},{"docid":"4c4b447b309d4ac61d1d972e740693d4","score":"0.6265521","text":"def populate_db():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('personjob.db')\n\n logger.info('Working with Person class')\n\n PERSON_NAME = 0\n LIVES_IN_TOWN = 1\n NICKNAME = 2\n\n people = [\n ('Andrew', 'Sumner', 'Andy'),\n ('Peter', 'Seattle', None),\n ('Susan', 'Boston', 'Beannie'),\n ('Pam', 'Coventry', 'PJ'),\n ('Steven', 'Colchester', None),\n ]\n\n JOB_NAME = 0\n START_DATE = 1\n END_DATE = 2\n SALARY = 3\n PERSON_EMPLOYED = 4\n\n jobs = [\n ('Analyst', '2001-09-22', '2003-01-30', 65500, 'Andrew'),\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew'),\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000,\n 'Andrew'),\n ('Admin supervisor', '2012-10-01', '2014-11-10', 45900, 'Peter'),\n ('Admin manager', '2014-11-14', '2018-01-05', 45900, 'Peter')\n ]\n\n DEPARTMENT_NUMBER = 0\n DEPARTMENT_NAME = 1\n DEPARTMENT_MANAGER = 2\n NAME_OF_JOB = 3\n\n departments = [\n ('H153', 'Business Intelligence', 'John Phillips', 'Analyst'),\n ('H567', 'Product Development', 'Rose Adams', 'Senior analyst'),\n ('H195', 'Enterprise Strategy', 'Jonathan Range', 'Senior business '\n 'analyst'),\n ('K976', 'Production Engineering', 'Kathryn Allen', 'Admin '\n 'supervisor'),\n ('L763', 'Reliability Engineering', 'Adam Stunt', 'Admin manager')\n ]\n\n logger.info('Creating Person and Job records: iterate through the list of '\n 'tuples')\n logger.info('Prepare to explain any errors with exceptions')\n logger.info('and the transaction tells the database to fail on error')\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n for person in people:\n with database.transaction():\n new_person = Person.create(\n person_name=person[PERSON_NAME],\n lives_in_town=person[LIVES_IN_TOWN],\n nickname=person[NICKNAME])\n new_person.save()\n logger.info('Database Person add successful')\n\n logger.info('Print the Person records we saved...')\n for saved_person in Person:\n logger.info(f'{saved_person.person_name} lives in '\n f'{saved_person.lives_in_town} ' +\\\n f'and likes to be known as {saved_person.nickname}')\n\n for job in jobs:\n date_end = datetime.datetime.strptime(job[END_DATE],\"%Y-%m-%d\")\n date_start = datetime.datetime.strptime(job[START_DATE],\"%Y-%m-%d\")\n duration = date_end - date_start\n with database.transaction():\n new_job = Job.create(\n job_name=job[JOB_NAME],\n start_date=job[START_DATE],\n end_date=job[END_DATE],\n salary=job[SALARY],\n employment_duration=duration.days,\n person_employed=job[PERSON_EMPLOYED])\n new_job.save()\n logger.info('Database job add successful')\n\n logger.info('Print the Job records we saved...')\n for saved_job in Job:\n logger.info(f'{saved_job.person_employed} held the job of '\n f'{saved_job.job_name} from '\n f'{saved_job.start_date} to '\n f'{saved_job.end_date} with a salary of '\n f'{saved_job.salary}')\n\n for department in departments:\n with database.transaction():\n new_department = Department.create(\n department_number=department[DEPARTMENT_NUMBER],\n department_name=department[DEPARTMENT_NAME],\n department_manager=department[DEPARTMENT_MANAGER],\n name_of_job=department[NAME_OF_JOB])\n new_department.save()\n logger.info('Database department add successful')\n\n logger.info('Print the department records we saved...')\n for saved_department in Department:\n logger.info('Department ' f'{saved_department.department_number} '\n 'is named ' f'{saved_department.department_name}'\n ', managed by ' \n f'{saved_department.department_manager}, and has a '\n f'job of ' f'{saved_department.name_of_job}')\n\n except Exception as e:\n logger.info(f'Error creating = {person[PERSON_NAME]}')\n logger.info(e)\n logger.info('See how the database protects our data')\n\n finally:\n logger.info('database closes')\n database.close()","title":""},{"docid":"74f9e654494a3765f3c2805f2b73e971","score":"0.6265285","text":"def db(self):\n raise NotImplementedError","title":""},{"docid":"86203a90ca15a10a52cec642d40e5c70","score":"0.6262716","text":"def init_db():\n global app\n Recommendation.init_db(app)","title":""},{"docid":"faa54e515457a672171567e91ed392ac","score":"0.62594074","text":"def fill_db():\n dbname = get_database()\n collection_name = dbname[\"templates\"]\n try:\n collection_name.delete_many({})\n except Exception as err:\n logger.debug(f\"ERR: {err}\")\n\n try:\n collection_name.insert_many([*templates_1, *templates_2])\n except Exception as err:\n logger.debug(f\"ERR: {err}\")\n yield\n\n\n try:\n collection_name.delete_many({})\n except Exception as err:\n logger.debug(f\"ERR: {err}\")","title":""},{"docid":"36f62cdc6ff20f32e10aba355418de0a","score":"0.62396115","text":"async def db_create(self, ctx):\n\t\tawait self.rebuild_database()","title":""},{"docid":"75f6c4419807df47b1696e2cf4aaf341","score":"0.62381905","text":"def initializing(self):\n self.cursor.executescript(INIT_USERS_DATABASE)\n self.cursor.executescript(INIT_INVENTORY_DATABASE)","title":""},{"docid":"807dead6cba37df60dcfdc6251d057fe","score":"0.6236994","text":"def populate_database():\n dom = DomainFactory(name=\"test.com\", quota=50)\n admin = UserFactory(\n username=\"admin@test.com\", groups=(\"DomainAdmins\", ),\n password=\"{PLAIN}toto\"\n )\n MailboxFactory(address=\"admin\", domain=dom, user=admin)\n account = UserFactory.create(\n username=\"user@test.com\", groups=(\"SimpleUsers\",),\n )\n MailboxFactory.create(address=\"user\", domain=dom, user=account)\n\n al = AliasFactory.create(\n address=\"forward@test.com\", domain=dom\n )\n AliasRecipientFactory.create(\n address=\"user@external.com\", alias=al)\n\n al = AliasFactory.create(\n address=\"alias@test.com\", domain=dom\n )\n mb = account.mailbox\n AliasRecipientFactory.create(\n address=mb.full_address, alias=al, r_mailbox=mb)\n\n al = AliasFactory.create(\n address=\"postmaster@test.com\", domain=dom\n )\n for address in [\"toto@titi.com\", \"test@truc.fr\"]:\n AliasRecipientFactory.create(address=address, alias=al)\n\n dom.add_admin(admin)\n\n dom2 = DomainFactory.create(name=\"test2.com\", default_mailbox_quota=0)\n admin = UserFactory.create(\n username=\"admin@test2.com\", groups=(\"DomainAdmins\",),\n password=\"{PLAIN}toto\"\n )\n MailboxFactory.create(address=\"admin\", domain=dom2, user=admin)\n u = UserFactory.create(\n username=\"user@test2.com\", groups=(\"SimpleUsers\",)\n )\n MailboxFactory.create(address=\"user\", domain=dom2, user=u)\n dom2.add_admin(admin)","title":""},{"docid":"2c04d5ec3dffc2dcda802bf5284869d1","score":"0.6226869","text":"def example_data():\n\n user_1 = User(email=\"hellohellohello@gmail.com\", password=\"1234\")\n search_1 = Search(search_term=\"testestest\")\n outlet_1 = Outlet(outlet_name='Best News', outlet_popularity=10, outlet_bias='Right-Center')\n user_search_1 = User_Search(user_id=1, search_id=1)\n\n db.session.add_all([user_1, search_1, outlet_1])\n db.session.commit()","title":""},{"docid":"0c41314e33f88a438daf047f9b64236a","score":"0.6223169","text":"def __init__(self, database):\n self.database = database","title":""},{"docid":"7d412440f18ffb4fe6a755293360a1a1","score":"0.62127787","text":"def init_db():\n global app\n Inventory.init_db(app)","title":""},{"docid":"f08630fdffbb4548a9384d670878c7c8","score":"0.6210465","text":"def _populate_data(self):\n\n pass","title":""},{"docid":"b4e83ec014e7a72cfb35bcc9baaa4f77","score":"0.6210141","text":"def setUp(self):\n self.database = Database()","title":""},{"docid":"8d99aa9a5b920aa730a6517506b70c69","score":"0.61997527","text":"def load_data(self, db_name):\r\n create_schema = CreateSchema(db_name)\r\n offices = create_schema.load_offices()\r\n livingspaces = create_schema.load_living_space()\r\n staffs = create_schema.load_staff()\r\n fellows = create_schema.load_fellow()\r\n office_staff_alloc = create_schema.load_office_staff_allocations()\r\n office_fellow_alloc = create_schema.load_office_fellow_allocations()\r\n livingspaces_alloc = create_schema.load_living_space_allocations()\r\n #Adding offices\r\n for office in offices:\r\n self.create_room(\"OFFICE\", office[0])\r\n #Adding living spaces\r\n for livingspace in livingspaces:\r\n self.create_room(\"LIVINGSPACE\", livingspace[0])\r\n #Adding staff\r\n for staff in staffs:\r\n self.add_person(\"STAFF\", staff[0])\r\n #Adding fellows\r\n for fellow in fellows:\r\n self.add_person(\"FELLOW\", fellow[0])\r\n #Creating office staff allocations'\r\n for alloc_office in office_staff_alloc:\r\n value = self.staff_dict[str(alloc_office[1])]\r\n self.allocate_rooms(value, \"OFFICE\")\r\n #Creating office fellow allocations\r\n for alloc_office in office_fellow_alloc:\r\n value = self.fellow_dict[str(alloc_office[2])]\r\n self.allocate_rooms(value, \"OFFICE\")\r\n # Allocating Living space\r\n for alloc_living_space in livingspaces_alloc:\r\n value = self.fellow_dict[str(alloc_living_space[1])]\r\n self.allocate_rooms(value, \"LIVINGSPACE\")","title":""},{"docid":"083997557b55c3c6c5815ab710f0e1e7","score":"0.61968344","text":"def create_database(self):\n self._create_tables()\n self._create_functions()\n self._create_triggers()","title":""},{"docid":"04afa680de176ecafcefb3de5841ad9d","score":"0.6194173","text":"def init():\n try:\n DataBase.create_table()\n DataBase.create_price_table()\n except Exception:\n print(MainMessage.db_critical_error)\n sys.exit(0)\n print(MainMessage.db_init_success)\n if DataBase.read_record_from_pricedb():\n (Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm) = DataBase.read_record_from_pricedb()\n else:\n DataBase.insert_record_to_pricedb(Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm)\n Stakes.change_stakes()","title":""},{"docid":"9355d57b29c60ed9ecb2f2147062f036","score":"0.61881137","text":"def init_database(self):\n self._drop_tables()\n self._create_tables()","title":""},{"docid":"51e9772beb911f40e0222101163fe454","score":"0.6180436","text":"def update_database():\n pass","title":""},{"docid":"c791e0bc0d4578a3d0bbc0f415eed76f","score":"0.6174021","text":"def init_table(self):\n self.create_table()","title":""},{"docid":"b1dcbe9a9c1b48448873415bf2213b59","score":"0.61739","text":"def create_db():\n\n # TODO:\n pass","title":""},{"docid":"959a78bb1df3bc568390da2ab88111ec","score":"0.6173813","text":"def initialize():\r\n db.connect()\r\n db.create_tables([BaseModel], safe=True)\r\n db.create_tables([Records], safe=True)","title":""},{"docid":"1ff935099c2772641552fbf8e0f244b2","score":"0.61727023","text":"def setup_tables(self):","title":""},{"docid":"ff634b9defb5c8b9f625cc35fcecb901","score":"0.61722714","text":"def dbcreate(self):\r\n self.db.createtable()","title":""},{"docid":"79c1b2e3b68a6b4c34d605a6188648f3","score":"0.61717933","text":"def __init__(self, database):\n self.db = database","title":""},{"docid":"92ab14e391589055ac911fad939895a5","score":"0.6170633","text":"def init_db_command():\n from .data.database import init_db\n init_db()\n click.echo('Initialized the database.')","title":""},{"docid":"d492e45f540121a62e6f784eafc01ada","score":"0.61700296","text":"def init_db():\r\n\r\n db = Scraper_DB()\r\n try:\r\n db.create_tables()\r\n except Exception as e:\r\n print(\"{0}\".format(e))","title":""},{"docid":"3286660208fee39a3d20c9ecd4a39791","score":"0.616117","text":"def initialize():\n DATABASE.connect(reuse_if_open=True)\n DATABASE.create_tables([User, ToDo], safe=True)\n DATABASE.close()","title":""},{"docid":"2e163e477fb3afc401b0661de598cf24","score":"0.61580926","text":"def populateInitialDataSet(self):","title":""},{"docid":"96d33114588b0147f80274744c9144f3","score":"0.6151307","text":"def generate_db(self):\n print('Generating LANL Earthquake Prediction Database...')\n # Unzip the database\n self._unzip_db()\n\n # Get test dataset\n self._get_test_db()\n\n # Get train dataset\n self._get_train_db()\n print('Complete!\\n')","title":""},{"docid":"5c47cf554648cdddb92ff0a42d78e2ce","score":"0.6147908","text":"def dbSetUp(self):\n pass","title":""},{"docid":"753de69c4c3126921e551c944d3b7777","score":"0.6147715","text":"def creation_database(self):\n heading.main_page_loading()\n print('\\t\\tPlease, write the name of the database\\n')\n self.choice_creation_database = input('[Database Menu] : #dm\\n'\n '[Quit] : #quit\\n\\n\\t=> ')\n\n return self.choice_creation_database","title":""},{"docid":"0f12a34445caf9eb081b7c0332a02764","score":"0.6136958","text":"def initdb():\n db.create_all()\n print \":creo las tablas de la base de datos:\"\n poblardb()","title":""},{"docid":"641745680596c09f4ccc1753c64609b6","score":"0.6131554","text":"def transfertodb():","title":""},{"docid":"4f32db0d5a6f2dedaeedfadb7aba8090","score":"0.61295503","text":"def setUp(self):\n db.create_all()","title":""},{"docid":"c1041a33217543f2273e244a6771a296","score":"0.61277354","text":"def build_data_base(self):\n\n self.db.create_table(\"STUDENTS\", [[\"Firstname\", \"TEXT\"], [\"LastName\", \"TEXT\"], [\"Id\", \"TEXT\"],\n [\"Gender\", \"TEXT\"], [\"Country\", \"TEXT\"], [\"Age\", \"TEXT\"],\n [\"Username\", \"TEXT\"], [\"Password\", \"TEXT\"]])\n\n self.db.create_table(\"OnlineLessons\", [[\"GeneralSubject\", \"TEXT\"], [\"MainSubject\", \"TEXT\"],\n [\"TargetAudience\", \"TEXT\"], [\"AuthorName\", \"TEXT\"],\n [\"AuthorAge\", \"TEXT\"], [\"LastName\", \"TEXT\"],\n [\"ExceptedLength\", \"TEXT\"]])","title":""},{"docid":"309e67e55289e682909ea0827a4471ad","score":"0.6122299","text":"def init_db():\n db = get_db()\n with app.open_resource('schema.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n with app.open_resource('knowledge.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n with app.open_resource('users.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n \n db.commit()","title":""},{"docid":"38e132347272880dac991ab250774e67","score":"0.6111997","text":"def create_database():\n init_db()\n click.echo(\"Initialized the database.\")","title":""},{"docid":"aa5275a31cd425c5d356faeb8958f47a","score":"0.6109491","text":"def initial_setup():\n db = _get_db()\n db.execute(SCHEMA)\n db.commit()","title":""},{"docid":"d24efa6d151640a258ecc9cece982fdd","score":"0.6107269","text":"def __init__(self):\n # try:\n\n self.filename = os.path.join(CONFIG_DATABASE_PATH,\n CONFIG_DATABASE_NAME)\n self.connection = db.connect(self.filename)\n\n # if not db.Connection.\n # print \"error\"\n \"\"\"creating a cursor\"\"\"\n\n #cursor = self.connection.cursor()\n # cursor.execute(\n # '''INSERT INTO excavation (projectID, projectTitle, projectType, projectSubType, site, studyArea)\n # VALUES (4, 'test', 'field', 'xx', 'ga', 'ta')''')\n #self.connection.commit()\n #\n # cursor = connection.execute('select * from excavation')\n # print cursor.fetchall()\n\n #self.connection.close()","title":""},{"docid":"f4d3fbe5edf0d1582f8979019e09d465","score":"0.6106553","text":"def __init__(self, databaseName):\r\n\r\n print(\"\\n------------| DATABASE CREATION |------------\\n\")\r\n self.database_name = databaseName\r\n\r\n # Create DB\r\n self.conn = sqlite3.connect(self.database_name)\r\n\r\n # Variable to access DB\r\n self.c = self.conn.cursor()\r\n\r\n print(\"> Database\", self.database_name, \"was created successfully\")\r\n self.createTables()\r\n self.closeDB()","title":""},{"docid":"9132cf670cc0bc8535fada9e5d53fa33","score":"0.6106523","text":"def _initializeDatabase(self):\n self.bot = GridBotModel(\n name = self.name,\n symbol = self.symbol,\n exchange = self.exchange.exchange_id,\n starting_balance = self.total_amount,\n current_balance = self.total_amount,\n trade_amount = self.trade_amount,\n trade_step = self.trade_step,\n test_run = self.test_mode)\n self.session.add(self.bot)\n self.session.commit()","title":""},{"docid":"251dc8d2a87a8f12d31b19a5a9953e1f","score":"0.6104041","text":"def init_db():\n print 'Initialize DB'\n from yamp.app import db, engine\n from yamp.models import Base, __all__ as model_files\n from importlib import import_module\n for model_file in model_files:\n import_module('yamp.models.' + model_file, __name__)\n print '* Model \"%s\" loaded.' % model_file\n Base.metadata.create_all(bind=engine)\n db.commit()\n print '... All tables created!'\n raise SystemExit","title":""},{"docid":"e3514015410b72f25ec9f2ccb21c09d3","score":"0.6102784","text":"def init_users():\n UserModel('thinh', '1234').save_to_db()\n UserModel('long', '1234').save_to_db()","title":""},{"docid":"25bd78f2cf3726b2f9fd8f86e7b7d4e7","score":"0.60980797","text":"def setUp(self):\n schema(DBPATH)\n seed(DBPATH)","title":""},{"docid":"6d6902cae012870febfcc1c047d0450c","score":"0.6095673","text":"def _instantiate(self):\n print(\"creating database with table(s) at \" + DB_PATH)\n\n with self._connect() as db_cur:\n\n # create disasters table\n db_cur.execute(\"\"\"CREATE TABLE IF NOT EXISTS slack_calls (\n id integer PRIMARY KEY ,\n team_id text UNIQUE NOT NULL ,\n team_name text ,\n channel_id text UNIQUE NOT NULL ,\n access_token text UNIQUE NOT NULL ,\n bot_access_token text UNIQUE NOT NULL ,\n );\n \"\"\")\n\n # indexing columns for faster access\n db_cur.execute(\"\"\"CREATE UNIQUE INDEX IF NOT EXISTS indexed_team_id ON slack_calls (\n team_id\n );\n \"\"\")","title":""},{"docid":"a1deabb1a8aa6863203ab301bd1ab2db","score":"0.60897547","text":"def manual_enter(self):\n self._dbconnect = sqlite3.connect(self._db_file)\n\n # Set row_factory to access columns by name\n self._dbconnect.row_factory = sqlite3.Row\n\n # Create a cursor to work with the db\n self._cursor = self._dbconnect.cursor()","title":""},{"docid":"ea3633fce200e85f45e90f30ef071775","score":"0.60858333","text":"def initialize():\n db.connect()\n db.create_tables([Task], safe=True)","title":""},{"docid":"a20d295df90cd4681dabefe43ef7696d","score":"0.60857296","text":"def createData(self):\n self.data = self.dbroot['data'] = OOBTree()\n self.meta = self.dbroot['meta'] = Meta('PEAT-DB database')\n self.meta.staticfields = { 'name': 'text', 'Mutations':'text',\n 'Structure':'PDB'}\n self.blobs = self.dbroot['blobs'] = OOBTree() \n #use meta.info for any misc settings specific to the DB\n self.meta.info = PersistentMapping(self.defaultinfo)\n self.commit()\n return","title":""},{"docid":"e776e6da8c21cf8b3eb86d0ad5f64480","score":"0.60816956","text":"def initalize_user_tables(db):\n \n from shotglass2.users.models import init_db as users_init_db \n users_init_db(db)","title":""},{"docid":"dcc46ccf775d7e2ce6ddb240a0fd1e10","score":"0.6078222","text":"def database_access(db):","title":""}],"string":"[\n {\n \"docid\": \"11c5c10dcb7519f823633e95d691706e\",\n \"score\": \"0.7409577\",\n \"text\": \"def _populateTables(cls):\\n for method in dir(Populate):\\n if not method.startswith('__'):\\n populateTable = getattr(Populate, method)\\n print 'Inserting data for %s ...' % method\\n populateTable()\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda5e8e7e0ff2e9f2e1e78366231c75b\",\n \"score\": \"0.7352905\",\n \"text\": \"def __call__(self):\\n self.create_database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04dc2fb4584516abc29cd5b12401ab9\",\n \"score\": \"0.7279164\",\n \"text\": \"def populate():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3738361c8de7ca311743c12b407f463c\",\n \"score\": \"0.7264388\",\n \"text\": \"def populate_db_command():\\n populate_db()\\n click.echo(\\\"Created dummy data.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb3e562c42462ac59c49c99c1f6c53e\",\n \"score\": \"0.72629017\",\n \"text\": \"def handle(self, *args, **options):\\n self._populate_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e755fbb017c46d5659f9eac88663f994\",\n \"score\": \"0.72245425\",\n \"text\": \"def populate_db():\\n database.create_tables([Donor, Donation])\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n #database = SqliteDatabase('donor.db')\\n\\n logger.info('Working with Person class')\\n logger.info('Note how I use constants and a list of tuples as a simple schema')\\n logger.info('Normally you probably will have prompted for this from a user')\\n\\n# NAME = 0\\n# DONATION = 1\\n# \\n donors = [('Karl', [100, 200, 300.55]),\\n ('Luis', [400, 500, 600]),\\n ('Woody', [700, 800, 900]),\\n ('Mary', [455.99])]\\n \\n logger.info('Creating Donor records: iterate through the list of tuples')\\n logger.info('Prepare to explain any errors with exceptions')\\n logger.info('and the transaction tells the database to fail on error')\\n \\n for users, donations in donors:\\n user = Donor.create(username=users)\\n for donation in donations:\\n Donation.create(user=user, donation=float(donation))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13029ab8865339e29a5c71c47127fda5\",\n \"score\": \"0.71562296\",\n \"text\": \"def data_into_database():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d20c034a8c3cbc0832dc74bb4da1b0f3\",\n \"score\": \"0.71474314\",\n \"text\": \"def fromDB(self):\\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc0072cd1eefb95b2048c8061190efe\",\n \"score\": \"0.7144774\",\n \"text\": \"def initiate(cls):\\n cls._purgeTables()\\n cls._populateTables()\\n print 'DB initialization finished!!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f9911dbbe0284a285ab8f29ced0957c\",\n \"score\": \"0.7093239\",\n \"text\": \"def populate(self, connection, system):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efc43d24ebff7e863c3941c631b63d4\",\n \"score\": \"0.704847\",\n \"text\": \"def init_db():\\n\\n with app.app_context():\\n data = json.loads(urllib2.urlopen(DATA_URL).read())\\n ds = DataSource(data['data'], data['meta']['view']['columns'])\\n headers = ds.get_headers(\\n set([\\n 'Applicant', \\n 'Address', \\n 'FoodItems', \\n 'Latitude', \\n 'Longitude'\\n ]),\\n 'name'\\n )\\n db = get_db()\\n db.init_database()\\n for item in ds.gen_items(headers):\\n try:\\n db.add_row(\\n item['Applicant'], \\n item['Address'], \\n item['FoodItems'], \\n item['Latitude'], \\n item['Longitude']\\n )\\n except psycopg2.IntegrityError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a8560696f49cd0ff48c227dff9a1f49\",\n \"score\": \"0.69914603\",\n \"text\": \"def populate_db2(self):\\n db.create_all()\\n\\n informantRelationships = self.create_informant_relationships()\\n users = self.create_users()\\n finalCodes = self.create_final_codes()\\n states = self.create_states()\\n abstractStatuses = self.create_abstract_statuses()\\n sexes = self.create_sexes()\\n races = self.create_races()\\n ethnicities = self.create_ethnicities()\\n vitals = self.create_vital_statuses()\\n contacts = self.create_contacts()\\n inactives = self.create_inactives()\\n ucrReportTypes = self.create_ucr_report_types()\\n physicianStatuses = self.create_physician_statuses()\\n physFacilityStatuses = self.create_physician_facility_statuses()\\n phoneTypes = self.create_phone_types()\\n irbHolders = self.create_irb_holders()\\n projectTypes = self.create_project_types()\\n contactStatuses = self.create_contact_statuses()\\n contactSources = self.create_contact_sources()\\n grantStatuses = self.create_grant_statuses()\\n fundingSources = self.create_funding_sources()\\n reviewCommitteeStatuses = self.create_review_committee_statuses()\\n projectStatuses = self.create_project_statuses()\\n logTypes = self.create_log_subjects()\\n reviewCommittees = self.create_review_committees()\\n staffRoles = self.create_staff_roles()\\n projectPhases = self.create_project_phases()\\n hsts = self.create_human_subject_trainings()\\n tracingSources = self.create_tracing_sources()\\n contactTypes = self.create_contact_types()\\n ucrRoles = self.create_ucr_roles()\\n giftCards = self.create_gift_cards()\\n\\n project1 = models.Project(\\n projectTypeID=1,\\n irbHolderID=1,\\n projectTitle=\\\"Test Project\\\",\\n shortTitle=\\\"Test Project\\\",\\n projectSummary=\\\"Summary\\\",\\n sop=\\\"sop\\\",\\n ucrProposal=\\\"ucr_proposal\\\",\\n budgetDoc=\\\"budget_doc\\\",\\n ucrFee=\\\"no\\\",\\n ucrNoFee=\\\"yes\\\",\\n previousShortTitle=\\\"t short\\\",\\n dateAdded=datetime(2016, 2, 2),\\n finalRecruitmentReport=\\\"report\\\",\\n ongoingContact=True,\\n activityStartDate=datetime(2016, 2, 2),\\n activityEndDate=datetime(2016, 2, 2))\\n\\n project2 = models.Project(\\n projectTypeID=1,\\n irbHolderID=1,\\n projectTitle=\\\"Test Project\\\",\\n shortTitle=\\\"Test Project\\\",\\n projectSummary=\\\"Summary\\\",\\n sop=\\\"sop\\\",\\n ucrProposal=\\\"ucr_proposal\\\",\\n budgetDoc=\\\"budget_doc\\\",\\n ucrFee=\\\"no\\\",\\n ucrNoFee=\\\"yes\\\",\\n previousShortTitle=\\\"t short\\\",\\n dateAdded=datetime(2016, 2, 2),\\n finalRecruitmentReport=\\\"report\\\",\\n ongoingContact=True,\\n activityStartDate=datetime(2016, 2, 2),\\n activityEndDate=datetime(2016, 2, 2))\\n\\n budget1 = models.Budget(\\n projectID=1,\\n numPeriods=1,\\n periodStart=datetime(2016, 2, 2),\\n periodEnd=datetime(2016, 2, 2),\\n periodTotal=1.23,\\n periodComment=\\\"comment\\\")\\n\\n rc = models.ReviewCommittee(\\n projectID=1,\\n reviewCommitteeStatusID=1,\\n reviewCommitteeLUTID=1,\\n reviewCommitteeNumber=\\\"1\\\",\\n dateInitialReview=datetime(2016, 2, 2),\\n dateExpires=datetime(2016, 2, 2),\\n rcNote=\\\"rc_note\\\",\\n rcProtocol=\\\"rc_proto\\\",\\n rcApproval=\\\"rc_approval\\\")\\n\\n ucr = models.UCRReport(\\n projectID=1,\\n reportTypeID=1,\\n reportSubmitted=datetime(2016, 2, 2),\\n reportDue=datetime(2016, 2, 2),\\n reportDoc=\\\"doc\\\"\\n )\\n arcReview = models.ArcReview(\\n projectID=1,\\n reviewType=1,\\n dateSentToReviewer=datetime(2016, 2, 2),\\n reviewer1=1,\\n reviewer1Rec=1,\\n reviewer1SigDate=datetime(2016, 2, 2),\\n reviewer1Comments=\\\"test comment\\\",\\n reviewer2=2,\\n reviewer2Rec=2,\\n reviewer2SigDate=datetime(2016, 2, 2),\\n reviewer2Comments=\\\"test comment\\\",\\n research=1,\\n linkage=False,\\n contact=True,\\n engaged=True,\\n nonPublicData=True)\\n\\n funding = models.Funding(\\n grantStatusID=1,\\n projectID=1,\\n fundingSourceID=1,\\n primaryFundingSource=\\\"pfs\\\",\\n secondaryFundingSource=\\\"sfs\\\",\\n fundingNumber=\\\"number\\\",\\n grantTitle=\\\"title\\\",\\n dateStatus=datetime(2016, 2, 2),\\n grantPi=1,\\n primaryChartfield=\\\"pcf\\\",\\n secondaryChartfield=\\\"scf\\\"\\n )\\n\\n staff = models.Staff(\\n userID=1,\\n firstName=\\\"Aaron\\\",\\n lastName=\\\"Thomas\\\",\\n middleName=\\\"Pulver\\\",\\n email=\\\"aaron.pulver@utah.edu\\\",\\n phoneNumber=\\\"phone\\\",\\n phoneComment=\\\"phoneComment\\\",\\n institution=\\\"institution\\\",\\n department=\\\"department\\\",\\n position=\\\"position\\\",\\n credentials=\\\"credentials\\\",\\n street=\\\"street\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n ucrRoleID=1\\n )\\n staff2 = models.Staff(\\n userID=2,\\n firstName=\\\"Phoebe\\\",\\n lastName=\\\"\\\",\\n middleName=\\\"McNeally\\\",\\n email=\\\"email\\\",\\n phoneNumber=\\\"phone\\\",\\n phoneComment=\\\"phoneComment\\\",\\n institution=\\\"institution\\\",\\n department=\\\"department\\\",\\n position=\\\"position\\\",\\n credentials=\\\"credentials\\\",\\n street=\\\"street\\\",\\n city=\\\"city\\\",\\n stateID=2,\\n ucrRoleID=1\\n )\\n\\n projStatus = models.ProjectStatus(\\n projectStatusTypeID=1,\\n projectID=1,\\n staffID=1,\\n statusDate=datetime(2016, 2, 2),\\n statusNotes=\\\"notes\\\"\\n )\\n\\n preApp = models.PreApplication(\\n projectID=1,\\n piFirstName=\\\"pi_fname\\\",\\n piLastName=\\\"pi_lname\\\",\\n piEmail=\\\"pi_email\\\",\\n piPhone=\\\"pi_phone\\\",\\n contactFirstName=\\\"contact_fname\\\",\\n contactLastName=\\\"contact_lname\\\",\\n contactPhone=\\\"contact_phone\\\",\\n contactEmail=\\\"contact_email\\\",\\n institution=\\\"institution\\\",\\n institution2=\\\"institution2\\\",\\n uid=\\\"uid\\\",\\n udoh=1,\\n projectTitle=\\\"project_title\\\",\\n purpose=\\\"purpose\\\",\\n irb0=True,\\n irb1=True,\\n irb2=True,\\n irb3=True,\\n irb4=True,\\n otherIrb=\\\"other_irb\\\",\\n updb=True,\\n ptContact=True,\\n startDate=datetime(2016, 2, 2),\\n link=True,\\n deliveryDate=datetime(2016, 2, 2),\\n description=\\\"description\\\"\\n )\\n log = models.Log(\\n logSubjectID=1,\\n projectID=1,\\n staffID=1,\\n phaseStatusID=1,\\n note=\\\"note\\\",\\n date=datetime(2016, 2, 2)\\n )\\n projectStaff = models.ProjectStaff(\\n staffRoleID=1,\\n projectID=1,\\n staffID=1,\\n datePledge=datetime(2016, 2, 2),\\n dateRevoked=datetime(2016, 2, 2),\\n contactID=1,\\n inactiveID=1\\n )\\n staffTraining = models.StaffTraining(\\n staffID=1,\\n humanSubjectTrainingID=1,\\n dateTaken=datetime(2016, 2, 2),\\n dateExpires=datetime(2016, 2, 2)\\n )\\n patient = models.Patient(\\n patID=\\\"1\\\",\\n ucrDistID=1,\\n UPDBID=1,\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"mname\\\",\\n maidenName=\\\"maiden_name\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle\\\",\\n dobDay=15,\\n dobMonth=2,\\n dobYear=1990,\\n SSN=\\\"999999999\\\",\\n sexID=2,\\n raceID=1,\\n ethnicityID=1,\\n vitalStatusID=1\\n )\\n patient2 = models.Patient(\\n patID=\\\"1\\\",\\n ucrDistID=1,\\n UPDBID=1,\\n firstName=\\\"fname2\\\",\\n lastName=\\\"lname2\\\",\\n middleName=\\\"mname2\\\",\\n maidenName=\\\"maiden_name\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle\\\",\\n dobDay=26,\\n dobMonth=4,\\n dobYear=1970,\\n SSN=\\\"999999999\\\",\\n sexID=1,\\n raceID=2,\\n ethnicityID=1,\\n vitalStatusID=2\\n )\\n\\n patientAddress = models.PatientAddress(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n\\n patientEmail = models.PatientEmail(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n email=\\\"email\\\",\\n emailStatusDate=datetime(2016, 2, 2)\\n )\\n patientPhone = models.PatientPhone(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n patientPhone2 = models.PatientPhone(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n informant1 = models.Informant(\\n participantID=1,\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n informantPrimary=True,\\n informantRelationshipID=1,\\n notes=\\\"notes\\\"\\n )\\n informant2 = models.Informant(\\n participantID=1,\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n informantPrimary=True,\\n informantRelationshipID=1,\\n notes=\\\"notes\\\"\\n )\\n informantAddress = models.InformantAddress(\\n contactInfoSourceID=1,\\n informantID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=2,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n informantPhone = models.InformantPhone(\\n contactInfoSourceID=1,\\n informantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n informantPhone2 = models.InformantPhone(\\n contactInfoSourceID=1,\\n informantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n ctc1 = models.CTC(\\n participantID=1,\\n dxDateDay=2,\\n dxDateMonth=7,\\n dxDateYear=1988,\\n site=\\\"Site 2\\\",\\n histology=\\\"histology\\\",\\n behavior=\\\"behavior\\\",\\n ctcSequence=\\\"sequence\\\",\\n stage=\\\"stage\\\",\\n dxAge=1,\\n dxStreet1=\\\"street1\\\",\\n dxStreet2=\\\"street2\\\",\\n dxCity=\\\"city\\\",\\n dxStateID=1,\\n dxZip=99999,\\n dxCounty=\\\"county\\\",\\n dnc=\\\"dnc\\\",\\n dncReason=\\\"dnc_reason\\\",\\n recordID=\\\"abc321\\\"\\n )\\n ctc2 = models.CTC(\\n participantID=1,\\n dxDateDay=3,\\n dxDateMonth=10,\\n dxDateYear=1958,\\n site=\\\"Site 1\\\",\\n histology=\\\"histology\\\",\\n behavior=\\\"behavior\\\",\\n ctcSequence=\\\"sequence\\\",\\n stage=\\\"stage\\\",\\n dxAge=1,\\n dxStreet1=\\\"street1\\\",\\n dxStreet2=\\\"street2\\\",\\n dxCity=\\\"city\\\",\\n dxStateID=2,\\n dxZip=99999,\\n dxCounty=\\\"county\\\",\\n dnc=\\\"dnc\\\",\\n dncReason=\\\"dnc_reason\\\",\\n recordID=\\\"abc123\\\"\\n )\\n projectPatient = models.ProjectPatient(\\n projectID=1,\\n staffID=1,\\n ctcID=1,\\n currentAge=1,\\n batch=1,\\n siteGrp=1,\\n finalCodeID=1,\\n finalCodeDate=datetime(2016, 2, 2),\\n enrollmentDate=datetime(2016, 2, 2),\\n dateCoordSigned=datetime(2016, 2, 2),\\n importDate=datetime(2016, 2, 2),\\n finalCodeStaffID=1,\\n enrollmentStaffID=1,\\n dateCoordSignedStaffID=1,\\n abstractStatusID=1,\\n abstractStatusDate=datetime(2016, 2, 2),\\n abstractStatusStaffID=1,\\n sentToAbstractorDate=datetime(2016, 2, 2),\\n sentToAbstractorStaffID=1,\\n abstractedDate=datetime(2016, 2, 2),\\n abstractorStaffID=1,\\n researcherDate=datetime(2016, 2, 2),\\n researcherStaffID=1,\\n consentLink=\\\"link\\\",\\n medRecordReleaseSigned=True,\\n medRecordReleaseLink=\\\"link\\\",\\n medRecordReleaseStaffID=1,\\n medRecordReleaseDate=datetime(2016, 2, 2),\\n surveyToResearcher=datetime(2016, 2, 2),\\n surveyToResearcherStaffID=1,\\n qualityControl=True,\\n )\\n\\n projectPatient2 = models.ProjectPatient(\\n projectID=1,\\n staffID=1,\\n ctcID=1,\\n currentAge=1,\\n batch=1,\\n siteGrp=1,\\n finalCodeID=1,\\n finalCodeDate=datetime(2016, 2, 2),\\n enrollmentDate=datetime(2016, 2, 2),\\n dateCoordSigned=datetime(2016, 2, 2),\\n importDate=datetime(2016, 2, 2),\\n finalCodeStaffID=1,\\n enrollmentStaffID=1,\\n dateCoordSignedStaffID=1,\\n abstractStatusID=1,\\n abstractStatusDate=datetime(2016, 2, 2),\\n abstractStatusStaffID=1,\\n sentToAbstractorDate=datetime(2016, 2, 2),\\n sentToAbstractorStaffID=1,\\n abstractedDate=datetime(2016, 2, 2),\\n abstractorStaffID=1,\\n researcherDate=datetime(2016, 2, 2),\\n researcherStaffID=1,\\n consentLink=\\\"link\\\",\\n medRecordReleaseSigned=True,\\n medRecordReleaseLink=\\\"link\\\",\\n medRecordReleaseStaffID=1,\\n medRecordReleaseDate=datetime(2016, 2, 2),\\n surveyToResearcher=datetime(2016, 2, 2),\\n surveyToResearcherStaffID=1,\\n qualityControl=False\\n )\\n\\n tracing = models.Tracing(\\n tracingSourceID=1,\\n participantID=1,\\n date=datetime(2016, 2, 2),\\n staffID=1,\\n notes=\\\"notes\\\"\\n )\\n physician = models.Physician(\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n credentials=\\\"credentials\\\",\\n specialty=\\\"specialty\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle_name\\\",\\n physicianStatusID=1,\\n physicianStatusDate=datetime(2016, 2, 2),\\n )\\n\\n physician2 = models.Physician(\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n credentials=\\\"credentials\\\",\\n specialty=\\\"specialty\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle_name\\\",\\n physicianStatusID=1,\\n physicianStatusDate=datetime(2016, 2, 2),\\n )\\n physicianAddress = models.PhysicianAddress(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n\\n physicianEmail = models.PhysicianEmail(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n email=\\\"email\\\",\\n emailStatusDate=datetime(2016, 2, 2)\\n )\\n\\n physicianPhone = models.PhysicianPhone(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneTypeID=1,\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n physicianPhone2 = models.PhysicianPhone(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneTypeID=1,\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n physicianToCTC = models.PhysicianToCTC(\\n physicianID=1,\\n ctcID=1\\n )\\n facility1 = models.Facility(\\n facilityName=\\\"name\\\",\\n contactFirstName=\\\"fname\\\",\\n contactLastName=\\\"lname\\\",\\n facilityStatus=1,\\n facilityStatusDate=datetime(2016, 2, 2),\\n contact2FirstName=\\\"fname\\\",\\n contact2LastName=\\\"lname\\\"\\n )\\n facility2 = models.Facility(\\n facilityName=\\\"name\\\",\\n contactFirstName=\\\"fname\\\",\\n contactLastName=\\\"lname\\\",\\n facilityStatus=1,\\n facilityStatusDate=datetime(2016, 2, 2),\\n contact2FirstName=\\\"fname\\\",\\n contact2LastName=\\\"lname\\\"\\n )\\n facilityAddress = models.FacilityAddress(\\n contactInfoSourceID=1,\\n facilityID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n\\n facilityPhone = models.FacilityPhone(\\n contactInfoSourceID=1,\\n facilityID=1,\\n contactInfoStatusID=1,\\n clinicName=\\\"clinic\\\",\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n facilityPhone2 = models.FacilityPhone(\\n contactInfoSourceID=1,\\n facilityID=1,\\n contactInfoStatusID=1,\\n clinicName=\\\"clinic\\\",\\n phoneTypeID=1,\\n phoneNumber=\\\"phone2\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n patientProjectStatusType1 = models.PatientProjectStatusLUT(\\n statusDescription=\\\"desc\\\"\\n )\\n patientProjectStatusType2 = models.PatientProjectStatusLUT(\\n statusDescription=\\\"desc\\\"\\n )\\n patientProjectStatus = models.PatientProjectStatus(\\n patientProjectStatusTypeID=1,\\n participantID=1,\\n statusDate=datetime(2016,2,2)\\n )\\n physicianFacility = models.PhysicianFacility(\\n facilityID=1,\\n physicianID=1,\\n physFacilityStatusID=1,\\n physFacilityStatusDate=datetime(2016, 2, 2)\\n )\\n contact = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n informantID=1,\\n informantPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n contact2 = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n facilityID=1,\\n facilityPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n contact3 = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n physicianID=1,\\n physicianPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n contact4 = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n patientPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n ctcFacility = models.CTCFacility(\\n ctcID=1,\\n facilityID=1,\\n coc=123\\n )\\n incentive = models.Incentive(\\n participantID=1,\\n incentiveDescription=\\\"desc\\\",\\n barcode=\\\"123456789\\\",\\n dateGiven=datetime(2016, 4, 3)\\n )\\n db.session.add_all(ucrRoles)\\n db.session.add_all(informantRelationships)\\n db.session.add_all(users)\\n db.session.add_all(states)\\n db.session.add_all(finalCodes)\\n db.session.add_all(sexes)\\n db.session.add_all(abstractStatuses)\\n db.session.add_all(races)\\n db.session.add_all(ethnicities)\\n db.session.add_all(vitals)\\n db.session.add_all(contacts)\\n db.session.add_all(inactives)\\n db.session.add_all(ucrReportTypes)\\n db.session.add_all(physicianStatuses)\\n db.session.add_all(physFacilityStatuses)\\n db.session.add_all(phoneTypes)\\n db.session.add_all(irbHolders)\\n db.session.add_all(projectTypes)\\n db.session.add_all(contactStatuses)\\n db.session.add_all(contactSources)\\n db.session.add_all(grantStatuses)\\n db.session.add_all(fundingSources)\\n db.session.add_all(reviewCommitteeStatuses)\\n db.session.add_all(projectStatuses)\\n db.session.add_all(logTypes)\\n db.session.add_all(reviewCommittees)\\n db.session.add_all(staffRoles)\\n db.session.add_all(projectPhases)\\n db.session.add_all(hsts)\\n db.session.add_all(tracingSources)\\n db.session.add_all(contactTypes)\\n db.session.add_all(giftCards)\\n db.session.add(staff)\\n db.session.add(staff2)\\n db.session.add(project1)\\n db.session.add(project2)\\n db.session.add(funding)\\n db.session.add(budget1)\\n db.session.add(rc)\\n db.session.add(ucr)\\n db.session.add(arcReview)\\n db.session.add(preApp)\\n db.session.add(log)\\n db.session.add(projectStaff)\\n db.session.add(staffTraining)\\n db.session.add(patient)\\n db.session.add(patient2)\\n db.session.add(patientAddress)\\n db.session.add(patientEmail)\\n db.session.add(patientPhone)\\n db.session.add(patientPhone2)\\n db.session.add(informant1)\\n db.session.add(informant2)\\n db.session.add(informantAddress)\\n db.session.add(informantPhone)\\n db.session.add(informantPhone2)\\n db.session.add(ctc1)\\n db.session.add(ctc2)\\n db.session.add(projectPatient)\\n db.session.add(projectPatient2)\\n db.session.add(projStatus)\\n db.session.add(tracing)\\n db.session.add(physician)\\n db.session.add(physician2)\\n db.session.add(physicianAddress)\\n db.session.add(physicianEmail)\\n db.session.add(physicianPhone)\\n db.session.add(physicianPhone2)\\n db.session.add(physicianToCTC)\\n db.session.add(facility1)\\n db.session.add(facility2)\\n db.session.add(facilityAddress)\\n db.session.add(facilityPhone)\\n db.session.add(facilityPhone2)\\n db.session.add(patientProjectStatusType1)\\n db.session.add(patientProjectStatusType2)\\n db.session.add(patientProjectStatus)\\n db.session.add(physicianFacility)\\n db.session.add(contact)\\n db.session.add(contact2)\\n db.session.add(contact3)\\n db.session.add(contact4)\\n db.session.add(ctcFacility)\\n db.session.add(incentive)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f4704030f5c33ae04a6f6ed3fc8c9d\",\n \"score\": \"0.699089\",\n \"text\": \"def populate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c4cbc11305536344a963ae28dd0f92\",\n \"score\": \"0.69689226\",\n \"text\": \"def populate_db():\\n values = db.load_config()\\n logging.info(values)\\n logging.info(\\\"Started import \\\")\\n logging.info(f'Importing into DB: {values[\\\"database\\\"]}')\\n # host, db, user, password\\n conn = db.get_connection_local_pg( values)\\n logging.info(\\\"Finished import \\\")\\n if ( not os.path.isdir(\\\"data/2014-i2b2-nlp-evaluation-data-txt\\\") ):\\n logging.exception(\\\"Data not downloaded, run the main.py get_data first \\\")\\n os.sys.exit(1)\\n db.import_data(conn, \\\"data/2014-i2b2-nlp-evaluation-data-txt\\\", \\\"data/testing-PHI-Gold-fixed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab65cea50449a2f2f4271702528c59f\",\n \"score\": \"0.69370925\",\n \"text\": \"def __init__(self):\\n self.db = db\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53db6e0a06eccebdfecb42251ceff176\",\n \"score\": \"0.6923225\",\n \"text\": \"def do_populate(self, arg):\\n if arg.strip():\\n self.print('Bad syntax!')\\n return\\n\\n self.client.populate_db(numElem=50, numVp=5)\\n self.print('OK!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0597bdfe794be623b11017593a932e0\",\n \"score\": \"0.69071597\",\n \"text\": \"def _init_db_structure(self):\\n #with self._db as conn: conn.execute('')\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db9b4b3df1fe7585313948ae354a3a87\",\n \"score\": \"0.6852799\",\n \"text\": \"def _populate_db():\\n data_1 = json.dumps(DATA_1)\\n data_2 = json.dumps({\\n \\\"organization\\\": \\\"Skynet Papercorp\\\", \\\"reported_at\\\": \\\"2015-04-22\\\",\\n \\\"created_at\\\": \\\"2015-04-23\\\",\\n \\\"inventory\\\": [{\\\"name\\\": \\\"paper\\\", \\\"price\\\": \\\"4.00\\\"}]\\n })\\n report_1 = Report(data=data_1)\\n report_2 = Report(data=data_2)\\n db.session.add(report_1)\\n db.session.add(report_2)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a0ccdb9a18f76b2e8cd24c1104a62ad\",\n \"score\": \"0.6847385\",\n \"text\": \"def update_database(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47700b5a4ddf0cb55f3bcad8e6ef127\",\n \"score\": \"0.6742828\",\n \"text\": \"def create_db_samples():\\n init_users()\\n init_categories()\\n init_items()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19de7f49a4903a12523b88a31e718e7\",\n \"score\": \"0.6733228\",\n \"text\": \"def run(self, args):\\n super(PopulateCommand, self).run(args)\\n try:\\n init_conf(\\\"populate.log\\\")\\n# cfg.CONF.log_file = \\\"populate.log\\\"\\n# cfg.CONF.use_stderr = True\\n LOG = api.LOG = get_logger(\\\"populate\\\")\\n LOG.info(_(\\\"Loading environment\\\"))\\n self.load_app()\\n LOG.info(_(\\\"Building schema\\\"))\\n LOG.info(_(\\\"Starting a transaction...\\\"))\\n models.start()\\n\\n # FIXME: There's no create_all equivalent for Music.\\n\\n # Valet\\n Group.create_table()\\n Placement.create_table()\\n Plan.create_table()\\n\\n # Ostro\\n Event.create_table()\\n PlacementRequest.create_table()\\n PlacementResult.create_table()\\n except Exception as ex:\\n models.rollback()\\n LOG.error(\\\"Rolling back... %s\\\" % ex)\\n raise\\n else:\\n LOG.info(_(\\\"Committing.\\\"))\\n models.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda6cc12fabb7bd264b4028d965e6e4f\",\n \"score\": \"0.66867036\",\n \"text\": \"def insert_data(self):\\n # Make a connexion with a mock database\\n self.generate_data_collection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d68f7ddaf994169e9ef7ea55297614\",\n \"score\": \"0.664742\",\n \"text\": \"def init_db():\\n global app\\n Supplier.init_db(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0f57d7958b9a592b22a30b870e439a\",\n \"score\": \"0.66439116\",\n \"text\": \"def toDB(self):\\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c7a639687c0a13a0d5426588108c1e4\",\n \"score\": \"0.66213745\",\n \"text\": \"def populate_db():\\n from datetime import datetime\\n from models import Post, Tag, post_to_tag\\n Post.query.delete()\\n Tag.query.delete()\\n db.session.execute(post_to_tag.delete())\\n\\n personalwebapp = Tag(name='personalwebapp')\\n db.session.add(personalwebapp)\\n\\n post = Post(title='Hello PersonalWebApp!', content=FIRST_POST, author_id=1, timezone=-5)\\n post.tags.append(personalwebapp)\\n post.state_id = 2\\n post.published_at = datetime.utcnow()\\n post.url = 'hello_personal_webapp'\\n db.session.add(post)\\n\\n post = Post(title='Example Draft Post', content='This is the first draft. I\\\\'ll finish it later.',\\n author_id=1, timezone=-5)\\n post.url = 'example_draft_post'\\n db.session.add(post)\\n\\n db.session.commit()\\n\\n print('\\\"personalwebapp\\\" tag'\\n 'and \\\"Hello PersonalWebApp!\\\" published post and \\\"Example Draft Post\\\" draft post have been generated.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d910fe18fbef4319354befcc35d03628\",\n \"score\": \"0.65966296\",\n \"text\": \"def create_db(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca9c7e87e76ef17c1834e51d3ec5b2fe\",\n \"score\": \"0.65857136\",\n \"text\": \"def populate_db():\\n\\n api_url = BOOK_URL\\n\\n api_response = requests.get(api_url)\\n print(api_response.text)\\n json_api_response = api_response.json()\\n if api_response.status_code == 200:\\n for book in json_api_response:\\n name = book['name']\\n isbn = book[\\\"isbn\\\"]\\n authors = json.dumps(book[\\\"authors\\\"])\\n country = book['country']\\n number_of_pages = int(book[\\\"numberOfPages\\\"])\\n publisher = book[\\\"publisher\\\"]\\n release_date = datetime.strptime(book[\\\"released\\\"], \\\"%Y-%m-%dT%H:%M:%S\\\").date()\\n\\n book_insert = Books(None, name, isbn, authors, country, number_of_pages, publisher, release_date, datetime.now(),\\n datetime.now())\\n db.session.add(book_insert)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02692e1ea91042a4b1bb734b743fdd70\",\n \"score\": \"0.65577847\",\n \"text\": \"def populate_database(query, write):\\n db = ingest.load_database()\\n db = ingest.populate_database(db, query=query, write=write)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019188738626cd084af97385709e6cb4\",\n \"score\": \"0.6507313\",\n \"text\": \"def initialize_db():\\n # Drop the database and create tables\\n setup_database = TestDatabase()\\n setup_database.drop()\\n setup_database.create()\\n setup_database.populate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"738e02b7d0516b0517e04b9cd2313a1b\",\n \"score\": \"0.64941686\",\n \"text\": \"def run(self):\\n self.db.table('users').insert({\\n 'name': 'sjahn',\\n 'email': 'asj214@naver.com',\\n 'password': generate_password_hash('1234')\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96b463c4afba4b8874e42597aee6b3b\",\n \"score\": \"0.64413\",\n \"text\": \"def seed_data():\\n typer.echo(\\\"Creating initial data\\\")\\n db = SessionLocal()\\n init_db(db)\\n typer.echo(\\\"Initial data created\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2bc2d10880289fafb45d98b7d9b9758\",\n \"score\": \"0.6428364\",\n \"text\": \"def dbcreation():\\n db.create_all() \\n return \\\"Hi\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23d384becf2768d3e555256fd4cf92f\",\n \"score\": \"0.6423991\",\n \"text\": \"def populate(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef264dd3df50c3020dbd0dd2de6672e7\",\n \"score\": \"0.6420109\",\n \"text\": \"def __populate(self):\\n self._populate_cli()\\n self._populate_model()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c52a7b7a7c599071e0c42c0dc4163f5e\",\n \"score\": \"0.6419514\",\n \"text\": \"def setUp(self):\\n\\t\\tdb.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e326115fecf07a75034ac464aff76c\",\n \"score\": \"0.6418617\",\n \"text\": \"def initialize():\\n db.connect()\\n db.create_tables([Product], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22618966909c2b541155c9cccc016691\",\n \"score\": \"0.64135474\",\n \"text\": \"def initialize():\\n db.connect()\\n db.create_tables([Entry], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"253b0f1c720f5fda12e6a428c5ab086f\",\n \"score\": \"0.6398332\",\n \"text\": \"def initialisation(self):\\n self.cursor.execute('create database if not exists open_food_facts;')\\n\\n with open(\\\"creation_bdd.sql\\\", 'r') as bdd:\\n file = bdd.read()\\n requetes = file.split(';')\\n for line in requetes:\\n self.cursor.execute(line)\\n\\n self.connexion.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58caf7c1e15f11c58efc8b15d2e8bd2f\",\n \"score\": \"0.6390033\",\n \"text\": \"def get_database_data(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa52c5d6fcf41a0a51cbbf90cf49e5f4\",\n \"score\": \"0.6380734\",\n \"text\": \"def create_db():\\n db.create_all()\\n db_fill_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739094ba7ea9b2a730020bba9cfc3cab\",\n \"score\": \"0.6374668\",\n \"text\": \"def init_db_command():\\n db = init_db()\\n import_parsed_data(db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220077a23ec1b23d82b202cd9f0c39c2\",\n \"score\": \"0.6373129\",\n \"text\": \"def init_db():\\n\\n create_model_tables([\\n Text,\\n Citation,\\n ], fail_silently=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5957b336c45f673120ecf164d9b315a\",\n \"score\": \"0.63699794\",\n \"text\": \"def build_database():\\n\\tfrom fake_data import setup_initial_database, more_data\\n\\n\\tprint (\\\"Updating Local Development Database...\\\")\\n\\n\\tsetup_initial_database()\\n\\n\\tprint (\\\"Finished!\\\")\\n\\n\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5734b372ff7c325ebedb7ccee68dd464\",\n \"score\": \"0.63501185\",\n \"text\": \"def initialize_db(self):\\r\\n click.echo('[OrientModel_initialize_db_%s] Starting process...' % (get_datetime()))\\r\\n if self.checks['created'] == False:\\r\\n self.create_db()\\r\\n if self.checks['open_db'] == False:\\r\\n self.open_db()\\r\\n sql = \\\"\\\"\\r\\n for m in self.models:\\r\\n sql = sql+\\\"create class %s extends %s;\\\\n\\\" % (m, self.models[m]['class'])\\r\\n for k in self.models[m].keys():\\r\\n if k != 'class':\\r\\n sql = sql+\\\"create property %s.%s %s;\\\\n\\\" % (m, k, self.models[m][k])\\r\\n if 'id' in str(k):\\r\\n sql = sql + \\\"create index %s_%s on %s (%s) UNIQUE ;\\\\n\\\" % (m, k, m, k)\\r\\n\\r\\n sql = sql + \\\"create sequence idseq type ordered;\\\"\\r\\n click.echo('[OrientModel_initialize_db_%s]'\\r\\n ' Initializing db with following batch statement'\\r\\n '\\\\n*************** SQL ***************\\\\n'\\r\\n '%s\\\\n*************** SQL ***************\\\\n' % (get_datetime(), sql))\\r\\n self.checks['initialized'] = True\\r\\n\\r\\n return self.client.batch(sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45da97885b18b7a1e9b14a83ba0820f\",\n \"score\": \"0.6349823\",\n \"text\": \"def creation_database_loading_started(self):\\n heading.main_page_loading()\\n print('\\\\t\\\\tCreation of the Database ... 0%\\\\n\\\\n'\\n '\\\\t\\\\t- - - - - - - - - - - - - - - - - - - - -\\\\n\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2a1691c8b3e0c77cb7b7cacc2bbcf3\",\n \"score\": \"0.63198614\",\n \"text\": \"def init_db(self):\\n # Connecting to the database file\\n conn = sqlite3.connect(DatabaseAccessor.DATABASE_PATH)\\n c = conn.cursor()\\n\\n # Create new tables\\n for sql in self.SQL_CREATES:\\n c.execute(sql)\\n\\n # Committing changes and closing the connection to the database file\\n conn.commit()\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a678067aaeca2b31e147bf8b83af57\",\n \"score\": \"0.6313009\",\n \"text\": \"def _init_database(self):\\r\\n\\r\\n for schema in self._object_schemas:\\r\\n type_specs = [self._create_sql_for_column(name, schema_item)\\r\\n for (name, schema_item) in schema.fields]\\r\\n self.cursor.execute(\\\"CREATE TABLE %s (%s)\\\" %\\r\\n (schema.table_name, ', '.join(type_specs)))\\r\\n for name, columns in schema.indexes:\\r\\n self.cursor.execute(\\\"CREATE INDEX %s ON %s (%s)\\\" %\\r\\n (name, schema.table_name, ', '.join(columns)))\\r\\n for name, columns in schema.unique_indexes:\\r\\n self.cursor.execute(\\\"CREATE UNIQUE INDEX %s ON %s (%s)\\\" %\\r\\n (name, schema.table_name, ', '.join(columns)))\\r\\n self._create_variables_table()\\r\\n self.set_version()\\r\\n self.setup_fulltext_search()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c15cd3fe6ac70b7ba41350f4bb2c8\",\n \"score\": \"0.63018775\",\n \"text\": \"def initialize_database(self):\\n self.database = self.loader.request_library(\\\"common_libs\\\", \\\"database\\\")\\n self.database.create_connection(\\\"production\\\")\\n self.database.load_mappings()\\n\\n self.migrator = self.loader.request_library(\\\"database_tools\\\", \\\"migrator\\\")\\n self.migrator.migrate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b5beb195eb88b0cf9a0d1aa2d98dc4\",\n \"score\": \"0.6297803\",\n \"text\": \"def initialize_db():\\n print(\\\"Do init db\\\")\\n db.create_all()\\n print(\\\"init db done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e440fe43530ddba4ca0b4a847d00a9\",\n \"score\": \"0.62872404\",\n \"text\": \"def _create_tables(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1631bc9ae71fad0be54bbace58f0a6ce\",\n \"score\": \"0.62842786\",\n \"text\": \"def create_database():\\n print('Creating database...')\\n init_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca2caa00fa9fc1fbc76530b0e78a022\",\n \"score\": \"0.6271357\",\n \"text\": \"def database():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4b447b309d4ac61d1d972e740693d4\",\n \"score\": \"0.6265521\",\n \"text\": \"def populate_db():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('personjob.db')\\n\\n logger.info('Working with Person class')\\n\\n PERSON_NAME = 0\\n LIVES_IN_TOWN = 1\\n NICKNAME = 2\\n\\n people = [\\n ('Andrew', 'Sumner', 'Andy'),\\n ('Peter', 'Seattle', None),\\n ('Susan', 'Boston', 'Beannie'),\\n ('Pam', 'Coventry', 'PJ'),\\n ('Steven', 'Colchester', None),\\n ]\\n\\n JOB_NAME = 0\\n START_DATE = 1\\n END_DATE = 2\\n SALARY = 3\\n PERSON_EMPLOYED = 4\\n\\n jobs = [\\n ('Analyst', '2001-09-22', '2003-01-30', 65500, 'Andrew'),\\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew'),\\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000,\\n 'Andrew'),\\n ('Admin supervisor', '2012-10-01', '2014-11-10', 45900, 'Peter'),\\n ('Admin manager', '2014-11-14', '2018-01-05', 45900, 'Peter')\\n ]\\n\\n DEPARTMENT_NUMBER = 0\\n DEPARTMENT_NAME = 1\\n DEPARTMENT_MANAGER = 2\\n NAME_OF_JOB = 3\\n\\n departments = [\\n ('H153', 'Business Intelligence', 'John Phillips', 'Analyst'),\\n ('H567', 'Product Development', 'Rose Adams', 'Senior analyst'),\\n ('H195', 'Enterprise Strategy', 'Jonathan Range', 'Senior business '\\n 'analyst'),\\n ('K976', 'Production Engineering', 'Kathryn Allen', 'Admin '\\n 'supervisor'),\\n ('L763', 'Reliability Engineering', 'Adam Stunt', 'Admin manager')\\n ]\\n\\n logger.info('Creating Person and Job records: iterate through the list of '\\n 'tuples')\\n logger.info('Prepare to explain any errors with exceptions')\\n logger.info('and the transaction tells the database to fail on error')\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n for person in people:\\n with database.transaction():\\n new_person = Person.create(\\n person_name=person[PERSON_NAME],\\n lives_in_town=person[LIVES_IN_TOWN],\\n nickname=person[NICKNAME])\\n new_person.save()\\n logger.info('Database Person add successful')\\n\\n logger.info('Print the Person records we saved...')\\n for saved_person in Person:\\n logger.info(f'{saved_person.person_name} lives in '\\n f'{saved_person.lives_in_town} ' +\\\\\\n f'and likes to be known as {saved_person.nickname}')\\n\\n for job in jobs:\\n date_end = datetime.datetime.strptime(job[END_DATE],\\\"%Y-%m-%d\\\")\\n date_start = datetime.datetime.strptime(job[START_DATE],\\\"%Y-%m-%d\\\")\\n duration = date_end - date_start\\n with database.transaction():\\n new_job = Job.create(\\n job_name=job[JOB_NAME],\\n start_date=job[START_DATE],\\n end_date=job[END_DATE],\\n salary=job[SALARY],\\n employment_duration=duration.days,\\n person_employed=job[PERSON_EMPLOYED])\\n new_job.save()\\n logger.info('Database job add successful')\\n\\n logger.info('Print the Job records we saved...')\\n for saved_job in Job:\\n logger.info(f'{saved_job.person_employed} held the job of '\\n f'{saved_job.job_name} from '\\n f'{saved_job.start_date} to '\\n f'{saved_job.end_date} with a salary of '\\n f'{saved_job.salary}')\\n\\n for department in departments:\\n with database.transaction():\\n new_department = Department.create(\\n department_number=department[DEPARTMENT_NUMBER],\\n department_name=department[DEPARTMENT_NAME],\\n department_manager=department[DEPARTMENT_MANAGER],\\n name_of_job=department[NAME_OF_JOB])\\n new_department.save()\\n logger.info('Database department add successful')\\n\\n logger.info('Print the department records we saved...')\\n for saved_department in Department:\\n logger.info('Department ' f'{saved_department.department_number} '\\n 'is named ' f'{saved_department.department_name}'\\n ', managed by ' \\n f'{saved_department.department_manager}, and has a '\\n f'job of ' f'{saved_department.name_of_job}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {person[PERSON_NAME]}')\\n logger.info(e)\\n logger.info('See how the database protects our data')\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f9e654494a3765f3c2805f2b73e971\",\n \"score\": \"0.6265285\",\n \"text\": \"def db(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86203a90ca15a10a52cec642d40e5c70\",\n \"score\": \"0.6262716\",\n \"text\": \"def init_db():\\n global app\\n Recommendation.init_db(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa54e515457a672171567e91ed392ac\",\n \"score\": \"0.62594074\",\n \"text\": \"def fill_db():\\n dbname = get_database()\\n collection_name = dbname[\\\"templates\\\"]\\n try:\\n collection_name.delete_many({})\\n except Exception as err:\\n logger.debug(f\\\"ERR: {err}\\\")\\n\\n try:\\n collection_name.insert_many([*templates_1, *templates_2])\\n except Exception as err:\\n logger.debug(f\\\"ERR: {err}\\\")\\n yield\\n\\n\\n try:\\n collection_name.delete_many({})\\n except Exception as err:\\n logger.debug(f\\\"ERR: {err}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36f62cdc6ff20f32e10aba355418de0a\",\n \"score\": \"0.62396115\",\n \"text\": \"async def db_create(self, ctx):\\n\\t\\tawait self.rebuild_database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f6c4419807df47b1696e2cf4aaf341\",\n \"score\": \"0.62381905\",\n \"text\": \"def initializing(self):\\n self.cursor.executescript(INIT_USERS_DATABASE)\\n self.cursor.executescript(INIT_INVENTORY_DATABASE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"807dead6cba37df60dcfdc6251d057fe\",\n \"score\": \"0.6236994\",\n \"text\": \"def populate_database():\\n dom = DomainFactory(name=\\\"test.com\\\", quota=50)\\n admin = UserFactory(\\n username=\\\"admin@test.com\\\", groups=(\\\"DomainAdmins\\\", ),\\n password=\\\"{PLAIN}toto\\\"\\n )\\n MailboxFactory(address=\\\"admin\\\", domain=dom, user=admin)\\n account = UserFactory.create(\\n username=\\\"user@test.com\\\", groups=(\\\"SimpleUsers\\\",),\\n )\\n MailboxFactory.create(address=\\\"user\\\", domain=dom, user=account)\\n\\n al = AliasFactory.create(\\n address=\\\"forward@test.com\\\", domain=dom\\n )\\n AliasRecipientFactory.create(\\n address=\\\"user@external.com\\\", alias=al)\\n\\n al = AliasFactory.create(\\n address=\\\"alias@test.com\\\", domain=dom\\n )\\n mb = account.mailbox\\n AliasRecipientFactory.create(\\n address=mb.full_address, alias=al, r_mailbox=mb)\\n\\n al = AliasFactory.create(\\n address=\\\"postmaster@test.com\\\", domain=dom\\n )\\n for address in [\\\"toto@titi.com\\\", \\\"test@truc.fr\\\"]:\\n AliasRecipientFactory.create(address=address, alias=al)\\n\\n dom.add_admin(admin)\\n\\n dom2 = DomainFactory.create(name=\\\"test2.com\\\", default_mailbox_quota=0)\\n admin = UserFactory.create(\\n username=\\\"admin@test2.com\\\", groups=(\\\"DomainAdmins\\\",),\\n password=\\\"{PLAIN}toto\\\"\\n )\\n MailboxFactory.create(address=\\\"admin\\\", domain=dom2, user=admin)\\n u = UserFactory.create(\\n username=\\\"user@test2.com\\\", groups=(\\\"SimpleUsers\\\",)\\n )\\n MailboxFactory.create(address=\\\"user\\\", domain=dom2, user=u)\\n dom2.add_admin(admin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c04d5ec3dffc2dcda802bf5284869d1\",\n \"score\": \"0.6226869\",\n \"text\": \"def example_data():\\n\\n user_1 = User(email=\\\"hellohellohello@gmail.com\\\", password=\\\"1234\\\")\\n search_1 = Search(search_term=\\\"testestest\\\")\\n outlet_1 = Outlet(outlet_name='Best News', outlet_popularity=10, outlet_bias='Right-Center')\\n user_search_1 = User_Search(user_id=1, search_id=1)\\n\\n db.session.add_all([user_1, search_1, outlet_1])\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c41314e33f88a438daf047f9b64236a\",\n \"score\": \"0.6223169\",\n \"text\": \"def __init__(self, database):\\n self.database = database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d412440f18ffb4fe6a755293360a1a1\",\n \"score\": \"0.62127787\",\n \"text\": \"def init_db():\\n global app\\n Inventory.init_db(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f08630fdffbb4548a9384d670878c7c8\",\n \"score\": \"0.6210465\",\n \"text\": \"def _populate_data(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4e83ec014e7a72cfb35bcc9baaa4f77\",\n \"score\": \"0.6210141\",\n \"text\": \"def setUp(self):\\n self.database = Database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d99aa9a5b920aa730a6517506b70c69\",\n \"score\": \"0.61997527\",\n \"text\": \"def load_data(self, db_name):\\r\\n create_schema = CreateSchema(db_name)\\r\\n offices = create_schema.load_offices()\\r\\n livingspaces = create_schema.load_living_space()\\r\\n staffs = create_schema.load_staff()\\r\\n fellows = create_schema.load_fellow()\\r\\n office_staff_alloc = create_schema.load_office_staff_allocations()\\r\\n office_fellow_alloc = create_schema.load_office_fellow_allocations()\\r\\n livingspaces_alloc = create_schema.load_living_space_allocations()\\r\\n #Adding offices\\r\\n for office in offices:\\r\\n self.create_room(\\\"OFFICE\\\", office[0])\\r\\n #Adding living spaces\\r\\n for livingspace in livingspaces:\\r\\n self.create_room(\\\"LIVINGSPACE\\\", livingspace[0])\\r\\n #Adding staff\\r\\n for staff in staffs:\\r\\n self.add_person(\\\"STAFF\\\", staff[0])\\r\\n #Adding fellows\\r\\n for fellow in fellows:\\r\\n self.add_person(\\\"FELLOW\\\", fellow[0])\\r\\n #Creating office staff allocations'\\r\\n for alloc_office in office_staff_alloc:\\r\\n value = self.staff_dict[str(alloc_office[1])]\\r\\n self.allocate_rooms(value, \\\"OFFICE\\\")\\r\\n #Creating office fellow allocations\\r\\n for alloc_office in office_fellow_alloc:\\r\\n value = self.fellow_dict[str(alloc_office[2])]\\r\\n self.allocate_rooms(value, \\\"OFFICE\\\")\\r\\n # Allocating Living space\\r\\n for alloc_living_space in livingspaces_alloc:\\r\\n value = self.fellow_dict[str(alloc_living_space[1])]\\r\\n self.allocate_rooms(value, \\\"LIVINGSPACE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083997557b55c3c6c5815ab710f0e1e7\",\n \"score\": \"0.61968344\",\n \"text\": \"def create_database(self):\\n self._create_tables()\\n self._create_functions()\\n self._create_triggers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04afa680de176ecafcefb3de5841ad9d\",\n \"score\": \"0.6194173\",\n \"text\": \"def init():\\n try:\\n DataBase.create_table()\\n DataBase.create_price_table()\\n except Exception:\\n print(MainMessage.db_critical_error)\\n sys.exit(0)\\n print(MainMessage.db_init_success)\\n if DataBase.read_record_from_pricedb():\\n (Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm) = DataBase.read_record_from_pricedb()\\n else:\\n DataBase.insert_record_to_pricedb(Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm)\\n Stakes.change_stakes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9355d57b29c60ed9ecb2f2147062f036\",\n \"score\": \"0.61881137\",\n \"text\": \"def init_database(self):\\n self._drop_tables()\\n self._create_tables()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e9772beb911f40e0222101163fe454\",\n \"score\": \"0.6180436\",\n \"text\": \"def update_database():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c791e0bc0d4578a3d0bbc0f415eed76f\",\n \"score\": \"0.6174021\",\n \"text\": \"def init_table(self):\\n self.create_table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1dcbe9a9c1b48448873415bf2213b59\",\n \"score\": \"0.61739\",\n \"text\": \"def create_db():\\n\\n # TODO:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"959a78bb1df3bc568390da2ab88111ec\",\n \"score\": \"0.6173813\",\n \"text\": \"def initialize():\\r\\n db.connect()\\r\\n db.create_tables([BaseModel], safe=True)\\r\\n db.create_tables([Records], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff935099c2772641552fbf8e0f244b2\",\n \"score\": \"0.61727023\",\n \"text\": \"def setup_tables(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff634b9defb5c8b9f625cc35fcecb901\",\n \"score\": \"0.61722714\",\n \"text\": \"def dbcreate(self):\\r\\n self.db.createtable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c1b2e3b68a6b4c34d605a6188648f3\",\n \"score\": \"0.61717933\",\n \"text\": \"def __init__(self, database):\\n self.db = database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ab14e391589055ac911fad939895a5\",\n \"score\": \"0.6170633\",\n \"text\": \"def init_db_command():\\n from .data.database import init_db\\n init_db()\\n click.echo('Initialized the database.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d492e45f540121a62e6f784eafc01ada\",\n \"score\": \"0.61700296\",\n \"text\": \"def init_db():\\r\\n\\r\\n db = Scraper_DB()\\r\\n try:\\r\\n db.create_tables()\\r\\n except Exception as e:\\r\\n print(\\\"{0}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3286660208fee39a3d20c9ecd4a39791\",\n \"score\": \"0.616117\",\n \"text\": \"def initialize():\\n DATABASE.connect(reuse_if_open=True)\\n DATABASE.create_tables([User, ToDo], safe=True)\\n DATABASE.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e163e477fb3afc401b0661de598cf24\",\n \"score\": \"0.61580926\",\n \"text\": \"def populateInitialDataSet(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d33114588b0147f80274744c9144f3\",\n \"score\": \"0.6151307\",\n \"text\": \"def generate_db(self):\\n print('Generating LANL Earthquake Prediction Database...')\\n # Unzip the database\\n self._unzip_db()\\n\\n # Get test dataset\\n self._get_test_db()\\n\\n # Get train dataset\\n self._get_train_db()\\n print('Complete!\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c47cf554648cdddb92ff0a42d78e2ce\",\n \"score\": \"0.6147908\",\n \"text\": \"def dbSetUp(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"753de69c4c3126921e551c944d3b7777\",\n \"score\": \"0.6147715\",\n \"text\": \"def creation_database(self):\\n heading.main_page_loading()\\n print('\\\\t\\\\tPlease, write the name of the database\\\\n')\\n self.choice_creation_database = input('[Database Menu] : #dm\\\\n'\\n '[Quit] : #quit\\\\n\\\\n\\\\t=> ')\\n\\n return self.choice_creation_database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f12a34445caf9eb081b7c0332a02764\",\n \"score\": \"0.6136958\",\n \"text\": \"def initdb():\\n db.create_all()\\n print \\\":creo las tablas de la base de datos:\\\"\\n poblardb()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"641745680596c09f4ccc1753c64609b6\",\n \"score\": \"0.6131554\",\n \"text\": \"def transfertodb():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f32db0d5a6f2dedaeedfadb7aba8090\",\n \"score\": \"0.61295503\",\n \"text\": \"def setUp(self):\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1041a33217543f2273e244a6771a296\",\n \"score\": \"0.61277354\",\n \"text\": \"def build_data_base(self):\\n\\n self.db.create_table(\\\"STUDENTS\\\", [[\\\"Firstname\\\", \\\"TEXT\\\"], [\\\"LastName\\\", \\\"TEXT\\\"], [\\\"Id\\\", \\\"TEXT\\\"],\\n [\\\"Gender\\\", \\\"TEXT\\\"], [\\\"Country\\\", \\\"TEXT\\\"], [\\\"Age\\\", \\\"TEXT\\\"],\\n [\\\"Username\\\", \\\"TEXT\\\"], [\\\"Password\\\", \\\"TEXT\\\"]])\\n\\n self.db.create_table(\\\"OnlineLessons\\\", [[\\\"GeneralSubject\\\", \\\"TEXT\\\"], [\\\"MainSubject\\\", \\\"TEXT\\\"],\\n [\\\"TargetAudience\\\", \\\"TEXT\\\"], [\\\"AuthorName\\\", \\\"TEXT\\\"],\\n [\\\"AuthorAge\\\", \\\"TEXT\\\"], [\\\"LastName\\\", \\\"TEXT\\\"],\\n [\\\"ExceptedLength\\\", \\\"TEXT\\\"]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"309e67e55289e682909ea0827a4471ad\",\n \"score\": \"0.6122299\",\n \"text\": \"def init_db():\\n db = get_db()\\n with app.open_resource('schema.sql', mode='r') as f:\\n db.cursor().executescript(f.read())\\n with app.open_resource('knowledge.sql', mode='r') as f:\\n db.cursor().executescript(f.read())\\n with app.open_resource('users.sql', mode='r') as f:\\n db.cursor().executescript(f.read())\\n \\n db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38e132347272880dac991ab250774e67\",\n \"score\": \"0.6111997\",\n \"text\": \"def create_database():\\n init_db()\\n click.echo(\\\"Initialized the database.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5275a31cd425c5d356faeb8958f47a\",\n \"score\": \"0.6109491\",\n \"text\": \"def initial_setup():\\n db = _get_db()\\n db.execute(SCHEMA)\\n db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d24efa6d151640a258ecc9cece982fdd\",\n \"score\": \"0.6107269\",\n \"text\": \"def __init__(self):\\n # try:\\n\\n self.filename = os.path.join(CONFIG_DATABASE_PATH,\\n CONFIG_DATABASE_NAME)\\n self.connection = db.connect(self.filename)\\n\\n # if not db.Connection.\\n # print \\\"error\\\"\\n \\\"\\\"\\\"creating a cursor\\\"\\\"\\\"\\n\\n #cursor = self.connection.cursor()\\n # cursor.execute(\\n # '''INSERT INTO excavation (projectID, projectTitle, projectType, projectSubType, site, studyArea)\\n # VALUES (4, 'test', 'field', 'xx', 'ga', 'ta')''')\\n #self.connection.commit()\\n #\\n # cursor = connection.execute('select * from excavation')\\n # print cursor.fetchall()\\n\\n #self.connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d3fbe5edf0d1582f8979019e09d465\",\n \"score\": \"0.6106553\",\n \"text\": \"def __init__(self, databaseName):\\r\\n\\r\\n print(\\\"\\\\n------------| DATABASE CREATION |------------\\\\n\\\")\\r\\n self.database_name = databaseName\\r\\n\\r\\n # Create DB\\r\\n self.conn = sqlite3.connect(self.database_name)\\r\\n\\r\\n # Variable to access DB\\r\\n self.c = self.conn.cursor()\\r\\n\\r\\n print(\\\"> Database\\\", self.database_name, \\\"was created successfully\\\")\\r\\n self.createTables()\\r\\n self.closeDB()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9132cf670cc0bc8535fada9e5d53fa33\",\n \"score\": \"0.6106523\",\n \"text\": \"def _initializeDatabase(self):\\n self.bot = GridBotModel(\\n name = self.name,\\n symbol = self.symbol,\\n exchange = self.exchange.exchange_id,\\n starting_balance = self.total_amount,\\n current_balance = self.total_amount,\\n trade_amount = self.trade_amount,\\n trade_step = self.trade_step,\\n test_run = self.test_mode)\\n self.session.add(self.bot)\\n self.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251dc8d2a87a8f12d31b19a5a9953e1f\",\n \"score\": \"0.6104041\",\n \"text\": \"def init_db():\\n print 'Initialize DB'\\n from yamp.app import db, engine\\n from yamp.models import Base, __all__ as model_files\\n from importlib import import_module\\n for model_file in model_files:\\n import_module('yamp.models.' + model_file, __name__)\\n print '* Model \\\"%s\\\" loaded.' % model_file\\n Base.metadata.create_all(bind=engine)\\n db.commit()\\n print '... All tables created!'\\n raise SystemExit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3514015410b72f25ec9f2ccb21c09d3\",\n \"score\": \"0.6102784\",\n \"text\": \"def init_users():\\n UserModel('thinh', '1234').save_to_db()\\n UserModel('long', '1234').save_to_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bd78f2cf3726b2f9fd8f86e7b7d4e7\",\n \"score\": \"0.60980797\",\n \"text\": \"def setUp(self):\\n schema(DBPATH)\\n seed(DBPATH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6902cae012870febfcc1c047d0450c\",\n \"score\": \"0.6095673\",\n \"text\": \"def _instantiate(self):\\n print(\\\"creating database with table(s) at \\\" + DB_PATH)\\n\\n with self._connect() as db_cur:\\n\\n # create disasters table\\n db_cur.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS slack_calls (\\n id integer PRIMARY KEY ,\\n team_id text UNIQUE NOT NULL ,\\n team_name text ,\\n channel_id text UNIQUE NOT NULL ,\\n access_token text UNIQUE NOT NULL ,\\n bot_access_token text UNIQUE NOT NULL ,\\n );\\n \\\"\\\"\\\")\\n\\n # indexing columns for faster access\\n db_cur.execute(\\\"\\\"\\\"CREATE UNIQUE INDEX IF NOT EXISTS indexed_team_id ON slack_calls (\\n team_id\\n );\\n \\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1deabb1a8aa6863203ab301bd1ab2db\",\n \"score\": \"0.60897547\",\n \"text\": \"def manual_enter(self):\\n self._dbconnect = sqlite3.connect(self._db_file)\\n\\n # Set row_factory to access columns by name\\n self._dbconnect.row_factory = sqlite3.Row\\n\\n # Create a cursor to work with the db\\n self._cursor = self._dbconnect.cursor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3633fce200e85f45e90f30ef071775\",\n \"score\": \"0.60858333\",\n \"text\": \"def initialize():\\n db.connect()\\n db.create_tables([Task], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a20d295df90cd4681dabefe43ef7696d\",\n \"score\": \"0.60857296\",\n \"text\": \"def createData(self):\\n self.data = self.dbroot['data'] = OOBTree()\\n self.meta = self.dbroot['meta'] = Meta('PEAT-DB database')\\n self.meta.staticfields = { 'name': 'text', 'Mutations':'text',\\n 'Structure':'PDB'}\\n self.blobs = self.dbroot['blobs'] = OOBTree() \\n #use meta.info for any misc settings specific to the DB\\n self.meta.info = PersistentMapping(self.defaultinfo)\\n self.commit()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e776e6da8c21cf8b3eb86d0ad5f64480\",\n \"score\": \"0.60816956\",\n \"text\": \"def initalize_user_tables(db):\\n \\n from shotglass2.users.models import init_db as users_init_db \\n users_init_db(db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc46ccf775d7e2ce6ddb240a0fd1e10\",\n \"score\": \"0.6078222\",\n \"text\": \"def database_access(db):\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":187,"cells":{"query_id":{"kind":"string","value":"28ecbd2ee477b01b0414a979344fe7a1"},"query":{"kind":"string","value":"Function that Cleans the text and handles the superscript and subscript tags in the text"},"positive_passages":{"kind":"list like","value":[{"docid":"bada213dfa474c405bf524914fe10ab8","score":"0.815164","text":"def cleaning(text):\n \n ind = 0\n text = text.replace(\":\",\"\")\n n = len(text)\n\n \"\"\"Replacing sup tag with supercript\"\"\"\n while (ind < len(text)):\n if(text[ind]==\"<\" and ind < n-4 and text[ind+1:ind+4]==\"sup\"):\n curr = ind\n curr += 5\n temp = \"\"\n while(True):\n if(text[curr]==\"<\" and text[curr+1:curr+5]==\"/sup\"):\n break\n temp += text[curr]\n curr += 1\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\n ind = curr + 6\n else:\n ind += 1\n\n\n \"\"\"Replacing sub tag with subcript\"\"\"\n ind = 0\n while (ind < len(text)):\n if(text[ind]==\"<\" and ind < n-4 and text[ind+1:ind+4]==\"sub\"):\n curr = ind\n curr += 5\n temp = \"\"\n while(True):\n if(text[curr]==\"<\" and text[curr+1:curr+5]==\"/sub\"):\n break\n temp += text[curr]\n curr += 1\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\n ind = curr + 6\n else:\n ind += 1\n\n x = text\n y = re.sub('\\\\n', ' ',x)\n y = re.sub('\\\\r', ' ',y)\n y = re.sub('\\\\t', ' ',y)\n y = re.sub('<[^>]*>', ' ',y)\n y = y.strip()\n return y","title":""}],"string":"[\n {\n \"docid\": \"bada213dfa474c405bf524914fe10ab8\",\n \"score\": \"0.815164\",\n \"text\": \"def cleaning(text):\\n \\n ind = 0\\n text = text.replace(\\\":\\\",\\\"\\\")\\n n = len(text)\\n\\n \\\"\\\"\\\"Replacing sup tag with supercript\\\"\\\"\\\"\\n while (ind < len(text)):\\n if(text[ind]==\\\"<\\\" and ind < n-4 and text[ind+1:ind+4]==\\\"sup\\\"):\\n curr = ind\\n curr += 5\\n temp = \\\"\\\"\\n while(True):\\n if(text[curr]==\\\"<\\\" and text[curr+1:curr+5]==\\\"/sup\\\"):\\n break\\n temp += text[curr]\\n curr += 1\\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\\n ind = curr + 6\\n else:\\n ind += 1\\n\\n\\n \\\"\\\"\\\"Replacing sub tag with subcript\\\"\\\"\\\"\\n ind = 0\\n while (ind < len(text)):\\n if(text[ind]==\\\"<\\\" and ind < n-4 and text[ind+1:ind+4]==\\\"sub\\\"):\\n curr = ind\\n curr += 5\\n temp = \\\"\\\"\\n while(True):\\n if(text[curr]==\\\"<\\\" and text[curr+1:curr+5]==\\\"/sub\\\"):\\n break\\n temp += text[curr]\\n curr += 1\\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\\n ind = curr + 6\\n else:\\n ind += 1\\n\\n x = text\\n y = re.sub('\\\\\\\\n', ' ',x)\\n y = re.sub('\\\\\\\\r', ' ',y)\\n y = re.sub('\\\\\\\\t', ' ',y)\\n y = re.sub('<[^>]*>', ' ',y)\\n y = y.strip()\\n return y\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"bcff0b3207f4a1f287474fa3ebeea447","score":"0.70005953","text":"def clean_text(text):\n # Replace newlines by space. We want only one doc vector.\n text = text.replace('\\n', ' ').lower()\n # Remove URLs\n #text = re.sub(r\"http\\S+\", \"\", text)\n # Expand contractions: you're to you are and so on.\n text = contractions.fix(text)\n # Remove stop words\n #text = preprocessing.remove_stopwords(text)\n \n #text = preprocessing.strip_tags(text)\n # Remove punctuation -- all special characters\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_punctuation(text))\n return text","title":""},{"docid":"2da14da586b935e9a7da3e19d0a684ac","score":"0.6929299","text":"def clean_text(text):\n # Replace newlines by space. We want only one doc vector.\n text = text.replace('\\n', ' ').lower()\n # Remove URLs\n text = re.sub(r\"http\\S+\", \"\", text)\n # Expand contractions: you're to you are and so on.\n text = contractions.fix(text)\n # Remove stop words\n text = preprocessing.remove_stopwords(text)\n # Remove punctuation -- all special characters\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_non_alphanum(text))\n return text","title":""},{"docid":"3dc1580629da1f55178692a2c74494d2","score":"0.6823169","text":"def clean_text(text): #cleantext\n replace_char = [\n \"[\",\n \"]\",\n \"u'\",\n \"None\",\n \"Thank you\",\n \"-\",\n \"(\",\n \")\",\n \"#\",\n \"Done\",\n \">\",\n \"<\",\n \"-\",\n \"|\",\n \"/\",\n \"\\\"\",\n \"Hint\",\n \"\\n\",\n \"'\"]\n for l in replace_char:\n text = text.replace(l, \"\")\n text = re.sub(' +', ' ', text)\n return text","title":""},{"docid":"a73ad21d266a49d35abc6fe7f189fdf3","score":"0.6713591","text":"def _clean_text(self, text):\n text = re.sub('[-=+,#/\\?:^$.@*\\\"※~&%ㆍ!』\\\\‘|\\(\\)\\[\\]\\<\\>`…》]', '', text)\n return text","title":""},{"docid":"33cad90681ef72942de02b7463b0bde4","score":"0.6710349","text":"def clean_text(text):\n # try:\n # text = text.encode(\"ascii\", errors=\"ignore\").decode()\n # except:\n # print(\"error in parsing text - \"+ text)\n # pass\n\n text = re.sub(r'\\([^)]*\\)', '', text)\n text = re.sub(r' +', ' ', text)\n\n text = remove_titles(text)\n return text","title":""},{"docid":"439d9bbdc1cdff15c6b24bd045f96de1","score":"0.67070097","text":"def clean_text(text):\n\n text = re.sub('<[^>]+>', '', text)\n text = re.sub(r'[^A-Za-z0-9\\s]+', '', text)\n return text","title":""},{"docid":"8cd7847dced4e22c557ff2b18816f239","score":"0.66308665","text":"def clean_text(self):\n cleanedtext = re.sub(r'\\n\\s*\\n', '', self.text) # extra lines\n cleanedtext = re.sub(r'[^\\w\\s]', '', cleanedtext) # punctuation\n # for numbers\n # cleanedtext = re.sub(r'[^A-Za-z0-9(),!?@\\'\\`\\\"\\_\\n]', ' ', cleanedtext)\n # cleanedtext = re.sub(r'\\w*\\d\\w*','',cleanedtext) # words with number plust letters\n # remove_digits = str.maketrans('', '', digits)\n # cleanedtext = cleanedtext.translate(remove_digits)\n cleanedtext = re.sub(' +', ' ', cleanedtext) # extra white spaces\n cleanedtext = cleanedtext.lower()\n self.cleanedtext = cleanedtext","title":""},{"docid":"be79e585f11ff26f695f9bd4c24a03cc","score":"0.6597903","text":"def clean_text(text: str) -> str:\n fixed_text = re.sub('<[^>]+>', ' ', text) # removing everything inside <>\n fixed_text = re.sub('\\n', ' ', fixed_text)\n fixed_text = re.sub(' ', ' ', fixed_text)\n fixed_text = re.sub('nbsp', ' ', fixed_text)\n fixed_text = re.sub('—', ' ', fixed_text)\n fixed_text = re.sub('\\t', ' ', fixed_text)\n fixed_text = re.sub('\\r', ' ', fixed_text)\n fixed_text = re.sub('\"…', ' ', fixed_text)\n fixed_text = re.sub('>', ' ', fixed_text)\n fixed_text = re.sub(r'[^\\w\\s]', ' ', fixed_text) # removing the punctuation\n fixed_text = re.sub(' +', ' ', fixed_text) # fix multiple spaces\n fixed_text = fixed_text.strip(' ')\n return fixed_text","title":""},{"docid":"de370f2a8e6ba8efac5bacfa16e14e20","score":"0.6591571","text":"def textCleaning(self,text):\n newText = text.replace('\\n',' ').replace('\\r',' ').replace('\\t',' ').replace('
',' ').replace(' ',' ').replace('
',' ').replace('',' ').strip()\n return newText","title":""},{"docid":"46dc40ef3429faddc40feea26c90edce","score":"0.6588975","text":"def cleanText(text):\n # clean: xml tags and mathtype -> spaces\n cleanText = replaceWithSpaces(xmlTagsRe, text)\n cleanText = replaceWithSpaces(mathTypeRe, text)\n # clean: non-letters -> spaces\n cleanText = nonLetterRe.sub(\" \", cleanText)\n cleanText = digitRe.sub(\" \", cleanText)\n return cleanText","title":""},{"docid":"cf7970fb79abc26376642f89ef03a92b","score":"0.65831715","text":"def cleanup_text(text):\n # Remove empty paragraphs\n text = re.sub(r']*?>(\\s| | | |\\xc2\\xa0)*?', '', text,\n flags=re.UNICODE)\n return text","title":""},{"docid":"6bee9bd22cb054c1e24b2fb6ca4026d5","score":"0.65464205","text":"def clean_text(text):\r\n text = text.lower().strip() # lowercase and remove trailing whitespaces\r\n text = re.sub(r\"i'm\", \"i am\", text)\r\n text = re.sub(r\"he's\", \"he is\", text)\r\n text = re.sub(r\"she's\", \"she is\", text)\r\n text = re.sub(r\"it's\", \"it is\", text)\r\n text = re.sub(r\"that's\", \"that is\", text)\r\n text = re.sub(r\"what's\", \"what is\", text)\r\n text = re.sub(r\"where's\", \"where is\", text)\r\n text = re.sub(r\"there's\", \"there is\", text)\r\n text = re.sub(r\"how's\", \"how is\", text)\r\n text = re.sub(r\"\\'ll\", \" will\", text)\r\n text = re.sub(r\"\\'ve\", \" have\", text)\r\n text = re.sub(r\"\\'re\", \" are\", text)\r\n text = re.sub(r\"\\'d\", \" would\", text)\r\n text = re.sub(r\"\\'re\", \" are\", text)\r\n text = re.sub(r\"won't\", \"will not\", text)\r\n text = re.sub(r\"can't\", \"cannot\", text)\r\n text = re.sub(r\"n't\", \" not\", text)\r\n text = re.sub(r\"n'\", \"ng\", text)\r\n text = re.sub(r\"'bout\", \"about\", text)\r\n text = re.sub(r\"'til\", \"until\", text)\r\n text = re.sub(r'[\" \"]+', \" \", text) # remove extra spaces in between\r\n text = re.sub(r\"[-()\\\"#/@;:<>{}`+=~|.!?,]\", \"\", text)\r\n text = ' ' + text + ' '\r\n return text","title":""},{"docid":"04edfda390fef2b3f014fc3061a5fceb","score":"0.6516315","text":"def _remove_any_numbers_and_non_english_characters_from_text(pos_tagged_text):\n for i in xrange(len(pos_tagged_text)):\n word = pos_tagged_text[i][0]\n # Remove numbers\n for digit in string.digits:\n word = word.replace(digit, '')\n # Remove words with non-Latin characters\n try:\n word.decode('ascii')\n except UnicodeDecodeError:\n word = ''\n # Write the preprocessed word back\n pos_tagged_text[i][0] = word\n return pos_tagged_text","title":""},{"docid":"67bf39279b32d413e779e3dad888c4c9","score":"0.65065104","text":"def clean_article_text(text):\r\n text = remove_piece_of_text(text, '')\r\n text = text.replace('', '')\r\n return text","title":""},{"docid":"616fcc6dd08b5608264b3d94abfe134c","score":"0.6477654","text":"def clean_up_text(text):\n # TODO add special characters\n text = re.sub('\\( \\(([^\\)]+)\\)\\)', ' ', text)\n text = re.sub('([a-z])([.!?])([A-Z])', r'\\1\\2 \\3', text)\n text = re.sub('[\\s]+', ' ', text)\n\n return text","title":""},{"docid":"283fc4c63a23b691da69908c290cb32e","score":"0.6468758","text":"def cleantext(text):\n # Convert any non-ASCII characters to XML like 'ꀀabcd޴'\n text = text.encode(\"ascii\", \"xmlcharrefreplace\").decode(\"ascii\")\n lines = [x.strip() for x in text.splitlines()]\n text = \" \".join(lines)\n for match, out in REPLACES:\n text = re.sub(match, out, text)\n text = text.strip() # some spaces may have been generated\n\n return text","title":""},{"docid":"6e0e7484b5ba593baa7ca9e6f233b94e","score":"0.64462006","text":"def cleanText(self):\n text = self.text().strip()\n if self.__prefix and text.startswith(self.__prefix):\n text = text[len(self.__prefix):]\n if self.__suffix and text.endswith(self.__suffix):\n text = text[:-len(self.__suffix)]\n return text.strip()","title":""},{"docid":"6e0e7484b5ba593baa7ca9e6f233b94e","score":"0.64462006","text":"def cleanText(self):\n text = self.text().strip()\n if self.__prefix and text.startswith(self.__prefix):\n text = text[len(self.__prefix):]\n if self.__suffix and text.endswith(self.__suffix):\n text = text[:-len(self.__suffix)]\n return text.strip()","title":""},{"docid":"6476d255266d39d7491fea86957199fc","score":"0.6420874","text":"def clean_text(text):\n \n # remove Unicode characters\n text = re.sub(r'[^\\x00-\\x7F]+', '', text)\n\n # taking sequences of characters with alphanumeric characters separated by other characters\n text = re.sub(r\"[-?!&]\",' ',text)\n text = re.sub(r'''[\"#$%()*+,./:;<=>@[\\]^_`{|}~]''','',text)\n \n # remove all numeric charracters\n text = re.sub(r'[0-9]','',text)\n\n # Remove new line characters\n text = re.sub(r'\\s+', ' ', text)\n\n # remove all numeric charracters\n text = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '',text)\n\n Apos_dict={\"'s\":\" is\",\"n't\":\" not\",\"'m\":\" am\",\"'ll\":\" will\",\n \"'d\":\" would\",\"'ve\":\" have\",\"'re\":\" are\"}\n \n #replace the contractions\n for key,value in Apos_dict.items():\n if key in text:\n text=text.replace(key,value)\n\n # convert to lowercase to maintain consistency\n text = text.lower()\n \n return text","title":""},{"docid":"0f49d845d0d6f6ff334cfea8b6766cbf","score":"0.63663334","text":"def clean_text(text: str):\n position = text.lower().rfind(\"references\")\n text = text[:position] # removing bibliography\n text = text.replace(\"\\n\", \" \") # removing newlines\n text = re.sub(r\"\\[[^\\[^\\]]*\\]\", \"\", text) # removing references\n return text","title":""},{"docid":"9e29448fa18d208e71d63ed06e30ce1b","score":"0.63571817","text":"def _remove_html_tags(self, text):\r\n return re.compile(r'<[^>]+>').sub('', text)","title":""},{"docid":"bc6604b9eac2108048a41f4543e4766b","score":"0.6338481","text":"def strip_markup(wikitext):\n if not wikitext:\n return ''\n\n # remove templates\n text = remove_templates(wikitext)\n\n # remove irrelevant spans\n text = re_comments.sub('', text)\n text = re_ignored_html_tags.sub(r'\\2', text)\n text = re_self_closing_html_tags.sub('', text)\n text = re_dropped_elements.sub('', text)\n text = re_categories.sub('', text)\n text = re_files_images.sub('', text) # TODO: keep file/image captions?\n\n # replace external links with just labels or just URLs\n text = replace_external_links(text)\n\n # drop magic words behavioral switches\n text = re_magic_words.sub('', text)\n\n # replace internal links with just their labels\n text = replace_internal_links(text)\n # text = replace_internal_links(text) # TODO: is this needed?\n\n # remove table markup\n text = text.replace('||', '\\n|').replace('!!', '\\n!') # put each cell on a separate line\n text = re_table_formatting.sub('\\n', text) # remove formatting lines\n text = re_table_cell_formatting.sub('\\n\\\\3', text) # leave only cell content\n\n # strip out text formatting\n text = re_italic_quote.sub(r'\"\\1\"', text)\n text = re_bold_italic.sub(r'\\1', text)\n text = re_quote_quote.sub(r'\"\\1\"', text)\n\n # unescape html entities\n text = ftfy.fixes.unescape_html(text)\n\n # final cleanup\n text = re_headings.sub(r'\\n\\n\\2\\n\\n', text)\n text = re_dots.sub('...', text)\n text = re_brackets.sub(r'', text)\n text = text.replace('[[', '').replace(']]', '')\n text = text.replace('<<', '«').replace('>>', '»')\n text = re_random_cruft.sub(r'\\1', text)\n text = re.sub(r'\\n\\W+?\\n', r'\\n', text, flags=re.UNICODE)\n text = text.replace(',,', ',').replace(',.', '.')\n text = re_spaces.sub(' ', text)\n text = re_linebreaks.sub(r'\\n\\n', text)\n\n return text.strip()","title":""},{"docid":"dd990b81f16b5ea23281fcb150630477","score":"0.63193804","text":"def cleanse_sentence(text):\n \n text = re.sub(hash_regex, hash_repl, text)\n text = re.sub(hndl_regex, hndl_repl, text)\n text = re.sub(url_regex, ' __URL ', text)\n \n for (repl, regx) in emoticons_regex :\n text = re.sub(regx, ' '+repl+' ', text)\n\n text = text.replace('\\'','')\n text = re.sub( word_bound_regex , punctuations_repl, text )\n text = re.sub( rpt_regex, rpt_repl, text )\n \n return text","title":""},{"docid":"43f85bc2347365b9c12ca2580d44a348","score":"0.6308067","text":"def clean_text(text):\n text = re.sub('[-=+,#/\\?:^$.@*\\\"※~&%ㆍ!』\\\\‘|\\(\\)\\[\\]\\<\\>`…》]', '', text)\n return text","title":""},{"docid":"5c7d913e073cac757804fda010b6c80c","score":"0.6296954","text":"def clean_text_1(text, stpwds, valid_tags, stemming=False):\n\tcleaned_text = clean_basic(text)\n\ttokens = cleaned_text.split()\n\ttokens = postagging(tokens, valid_tags)\n\ttokens = remove_stopwords(tokens, stpwds)\n\ttokens = remove_short_tokens(tokens)\n\tif stemming:\n\t\ttokens = stem(tokens)\n\tcleaned_text = remove_hyphen(' '.join(tokens))\n\treturn cleaned_text","title":""},{"docid":"9247a1dbe906adbe476252f075860812","score":"0.6290065","text":"def clean_text(text):\n # remove html tags, lowercase, remove nonsense, remove non-letter\n aff = BeautifulSoup(text, \"lxml\").text \n aff = aff.lower()\n aff = re.sub(r'xa0|#n#‡#n#|#tab#|#r#|\\[|\\]', \"\", aff)\n aff = re.sub(r'[^a-z]+', ' ', aff)\n return aff","title":""},{"docid":"4549a815eef93b30dc8e49e520ea9d4c","score":"0.6278033","text":"def demarkup_text(text):\n\n # keep the opening tag name of certain tags that contains these strings\n # note: are from debian copyright files\n kept_tags = (\n 'lic', 'copy', 'www', 'http', 'auth', 'contr', 'leg', 'inc', '@', \n '', '', '169', 'a9'\n )\n\n # find start and closing tags or the first white space whichever comes first\n # or entities\n # this regex is such that ' '.join(tags.split(a))==a\n\n tags_ents = re.compile(r'(|\\s)?|&[^\\s&]+;|href|[\\'\"]?\\/\\>)', re.IGNORECASE).split\n\n cleaned = []\n for token in tags_ents(text):\n if token.lower().startswith(('<', '&', 'href')) and not any(k in token.lower() for k in kept_tags):\n continue\n else:\n cleaned.append(token)\n return u' '.join(cleaned)","title":""},{"docid":"47cfd092920700f1c2f23d0fba4d528b","score":"0.6265034","text":"def remove_tashkeel(self, input_text):\n\n p_tashkeel = re.compile(r'[\\u0617-\\u061A\\u064B-\\u0652]')\n return re.sub(p_tashkeel,\"\", input_text)","title":""},{"docid":"3afa4198b471da03ad24ac8551e73fa3","score":"0.62647754","text":"def clean(self):\n # Replace double \\n's in case this ever happens on windows for some reason.\n if self.text.__contains__('\\n\\n'):\n self.text = self.text.replace('\\n\\n', '\\n')","title":""},{"docid":"b33f5fa7f977b65ed4d9a97b616ecbba","score":"0.6247174","text":"def preprocess(self):\n self.text = remove_control_characters(self.text)","title":""},{"docid":"3503132af39ea4b05ddf42ac5ed12ffb","score":"0.6229983","text":"def strip_markup(text):\n return (text\n .replace(\"\", \"\")\n .replace(\"\", \"\")\n .replace(\"\", \"\")\n .replace(\"\", \"\")\n .replace(\"\", \"\")\n )","title":""},{"docid":"f27009c1809229c97e68cb97aa67e615","score":"0.62280345","text":"def _del_text(self):\r\n # check whether element already has text node\r\n if self._root.HasChildNodes and \\\r\n self._root.FirstChild and \\\r\n isinstance(self._root.FirstChild, XmlText):\r\n self._root.RemoveChild(self._root.FirstChild)","title":""},{"docid":"16f35e7f296801a447055b16f013b54b","score":"0.6215954","text":"def clean_text(text):\n # lowercase text\n text = text.lower()\n\n # substitute contractions\n # text = re.sub(r\"i'm\", \"i am\", text)\n # text = re.sub(r\"he's\", \"he is\", text)\n # text = re.sub(r\"she's\", \"she is\", text)\n # text = re.sub(r\"it's\", \"it is\", text)\n # text = re.sub(r\"that's\", \"that is\", text)\n # text = re.sub(r\"what's\", \"what is\", text)\n # text = re.sub(r\"where's\", \"where is\", text)\n # text = re.sub(r\"how's\", \"how is\", text)\n # text = re.sub(r\"\\'ll\", \" will\", text)\n # text = re.sub(r\"\\'ve\", \" have\", text)\n # text = re.sub(r\"\\'re\", \" are\", text)\n # text = re.sub(r\"\\'d\", \" would\", text)\n # text = re.sub(r\"\\'re\", \" are\", text)\n # text = re.sub(r\"won't\", \"will not\", text)\n # text = re.sub(r\"can't\", \"cannot\", text)\n # text = re.sub(r\"n't\", \" not\", text)\n # text = re.sub(r\"n'\", \"ng\", text)\n # text = re.sub(r\"'bout\", \"about\", text)\n # text = re.sub(r\"'til\", \"until\", text)\n\n # get rid of username handles\n text = re.sub(r\"(?:@)(\\S+|$)|\", \"\", text)\n\n # get rid of special characters\n text = re.sub(r\"[-()\\\"#/@;:<>{}`+=~|.!?,]\", \"\", text)\n\n return text","title":""},{"docid":"88c982c1d7c70e2702c8b9fafed7d4d5","score":"0.6204147","text":"def strip_tags(text):\n\n return re.sub('<[^<]+?>', '', text)","title":""},{"docid":"0fd6daf8f52ba6aeedce909badebd364","score":"0.6194485","text":"def clean_text(raw_text):\n # Must strip HTML tags out first!\n text = re.sub('<[^<]+?>', ' ', raw_text)\n text = handle_unicode(text)\n text = handle_html_unquote(text)\n text = handle_mac_quotes(text)\n text = handle_text_break_dash(text)\n text = text.lower()\n\n regex_subs = ['\\t', '\\n', '\\r', '\\s+', '&']\n for regex_sub in regex_subs:\n text = re.sub(regex_sub, ' ', text)\n return text","title":""},{"docid":"14b2ba621e89303b47c25fd967a686e4","score":"0.6182298","text":"def _clean_text(source):\n source = str(source)\n source = source.upper()\n source = re.sub('[？|?|！|!|，|,|.|。|#|《|》|<|>|（|）|(|)|〉|、|／|…|-]', '', source)\n source = re.sub('[哈｜嘿｜呵]', '', source)\n source = re.sub('NAN', '', source)\n source = re.sub('\\xa0', '', source)\n return source","title":""},{"docid":"30006085bad7c937637e584674a6cc02","score":"0.6176192","text":"def _clean_bad_chars(self, text):\n return text.replace(\"\\u200b\", \"\")","title":""},{"docid":"1c3428df1fe332453e101d924bcc714b","score":"0.6171018","text":"def text2cleanish(text):\n logger.info(\"Cleaning text ...\")\n text = CLEANER1.sub(r\"\\1 \\2 \\3\", text)\n text = CLEANER2.sub(r\"\\1 ... \\3\", text)\n text = CLEANER3.sub(r\"\\1 ... \", text)\n # text = CLEANER4.sub(r\"\\1 X... \\3\", text)\n return text","title":""},{"docid":"928336714d22728e7f69363c3345b4c8","score":"0.6169784","text":"def clean(self, text = None) -> str:\n return self.normalize(\n self.clean_double_whitespaces(\n self.clean_edge_punctuation(\n self.clean_numeric(\n self.clean_web_tags(\n self.clean_html_tags(\n self.clean_non_ascii(\n text\n )\n )\n )\n )\n )\n )\n )","title":""},{"docid":"cff7fa0a0cef7e0a4aa8555fc2fa3982","score":"0.61514693","text":"def clean_text_remove_puncs(cls, text):\n if isinstance(text, str):\n try:\n text = cls.clean_text(text)\n text = cls._remove_punctuations(text)\n\n except (TypeError, AttributeError):\n logger.warning(\"Error occured in preprocessing text: {}\".format(text))\n sys.exit(0)\n\n return text.strip()\n\n return \"\"","title":""},{"docid":"6400609a86643ee6fd2cb125e5b5aae5","score":"0.61486775","text":"def safe_clean_text(text):\n # strip\n text = text.strip()\n\n # weird words\n text = delete_words(delete_list, text)\n\n # handle \\\\\\'t \\\\\\'ve\n text = text.replace(r\"\\\\\", \"\")\n\n # remove extra spaces\n text = re.sub(r'\\s+', ' ', text)\n\n # fix puncutations\n for p in punctuations:\n text = text.replace(p, p[1:])\n text = re.sub(\" (\\u2018|\\u2019|') \", r\"\\1\", text)\n\n # replace to EN dash\n text = re.sub(u\"\\u2014\", \"-\", text)\n text = re.sub(u\"\\u2013\", \"-\", text)\n\n return text","title":""},{"docid":"6857e424290706ed34fd0100320e8694","score":"0.61465544","text":"def remove_tags(text):\n clean = re.compile('<.*?>')\n return re.sub(clean, '', text)","title":""},{"docid":"5086afb5e199b8a13a21c4f58efbca2f","score":"0.61259377","text":"def remove_smart_quotes(self, text):\r\n return text.replace(u\"\\u201c\",\"\").replace(u\"\\u201d\", \"\")","title":""},{"docid":"49b44d02ed0cc749e2cc7f1d32af1451","score":"0.6104445","text":"def clean_txt(self, text):\n\n text = re.sub(\"'\", \"\", text)\n text = re.sub(\"(\\\\W)+\", \" \", text)\n return text","title":""},{"docid":"faaffdfe0444eca15482157446d68b10","score":"0.6088906","text":"def clean_text(text):\n translation_dict = {\n '’':\"'\",\n '‘':\"'\",\n # \"œ\":'oe',\n }\n # remove control characters not supported by XML.\n text = strip_chars(text)\n text = unicodedata.normalize('NFC', text.strip())\n text = re.sub(r'(\\n\\n )+', ' ', text)\n text = re.sub(r'([a-zA-Zàâçéèêëîïôûùüÿñæœ,;-])(\\n)+([^\\s])', r'\\1 \\3', text)\n return text.translate(str.maketrans(translation_dict))","title":""},{"docid":"ab72be9d87c9f54053505d1b1eaf6b2f","score":"0.6088736","text":"def clearMyText(self):\n self.clearText(self.textName)\n self.clearText(self.textCR)\n self.clearText(self.textAL)\n self.clearText(self.textEC)\n self.clearText(self.textIA)","title":""},{"docid":"b81ac2e011b854386493cc068a9e46dd","score":"0.60839474","text":"def clear_texts(self):\n\n for t in self.texts[:]:\n if not t[4]:\n self.texts.remove(t)","title":""},{"docid":"b3eb01e6647165fa5f3f6cc66a802663","score":"0.6076601","text":"def cleanText(self, s):\n\n\t\tdef _sent_to_lines(sent):\n\t\t\t\"\"\"Converts a string of text to one sentence each line.\"\"\"\n\t\t\tsent = re.sub(r\"\\n\", \" \", sent)\n\t\t\treturn re.sub(r\"(\\.|\\!|\\?|--)+\\s*\", \"\\n\", sent)\n\n\t\tdef _strip_punct(sent):\n\t\t\t\"\"\"Strips away all punctuations from the text.\"\"\"\n\t\t\treturn sent.translate(string.maketrans(\"\",\"\"), string.punctuation)\n\n\t\treturn _strip_punct(_sent_to_lines(s)).lower()","title":""},{"docid":"e1727f2673de7c749d5ef2a767231eba","score":"0.60653853","text":"def clean_message(self, text):\n text = strip_tags(text)\n return text","title":""},{"docid":"a8d8abba63fc621d1b31a7dad4026d4e","score":"0.60498005","text":"def __remove_tags(self, html_text):\n tags_regex = re.compile(\n '<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')\n tag_free_text = re.sub(tags_regex, ' ', html_text)\n\n return tag_free_text","title":""},{"docid":"9ba3be36a740be33aa131f168e63ba4e","score":"0.6047604","text":"def clean_text(text):\n if text:\n text = html2text.html2text(clean_markdown(text))\n return re.sub(r'\\s+', ' ', text).strip()","title":""},{"docid":"9a1fbdfc06773a8ebff8e45493c51bd1","score":"0.603565","text":"def remove_html_tags(text):\r\n clean = re.compile('\\{<.*?>\\}')\r\n return re.sub(clean, '', text)","title":""},{"docid":"4c1f72d624a2444e1814a702ea0cf7e8","score":"0.6031818","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"4c1f72d624a2444e1814a702ea0cf7e8","score":"0.6031818","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"e4682d3a218fa9f846931042e24d0607","score":"0.60222965","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or self._is_control(char):\n continue\n if self._is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"e4f3d2d2e47513b04b3d7b686b294cdd","score":"0.6021114","text":"def _clean_text(self, listing_text):\n if len(listing_text)>0:\n listing_text_clean = ' '.join(listing_text[0].findAll(text=True))\n listing_text_clean = re.sub('\\n|\\t', ' ', listing_text_clean)\n listing_text_clean = re.sub('\\s+', ' ', listing_text_clean)\n listing_text_clean = re.sub(\"'\", '', listing_text_clean)\n listing_text_clean = re.sub('\"', '', listing_text_clean)\n else:\n listing_text_clean = ' ' \n return listing_text_clean","title":""},{"docid":"df88aa6338c651f311f8313abd30f022","score":"0.6015763","text":"def clean(text, tokenizer, stemmer):\n doc = ''.join(text).lower()\n doc = re.sub(r'[<>\\{}/;|\\[\\]-]', ' ', doc)\n doc = re.sub(r'[0-9]', ' ', doc)\n doc = re.sub(r'\\'', ' ', doc)\n doc = re.sub(r'=', ' ', doc)\n doc = re.sub(r':', ' ', doc)\n doc = re.sub(r'\"', ' ', doc)\n doc = re.sub(r'\\s+', ' ', doc)\n doc = re.sub(r'\\(', ' ', doc)\n doc = re.sub(r'\\)', ' ', doc)\n doc = re.sub(r'\\s{2,}', ' ', doc)\n doc = re.sub(r'\\.', '', doc)\n doc = re.sub(r',', '', doc)\n\n return doc","title":""},{"docid":"6d531dc571d409ba0f71d86b949d095b","score":"0.6010883","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"2c801ea1e26a1508d732d0d546871740","score":"0.60044676","text":"def ocr_cleaner(text):\n\n text = text.lower().replace('\\n', ' ').replace('\\r', ' ').replace('\\t', ' ').strip() # replace all whitespace, lower\n text = ' '.join(text.split()) # remove consecutive spaces\n text = re.sub(r'(?<=[a-z]{3})[.]', '.\\n', text)\n text = text.encode('ascii', errors='ignore').decode(encoding='utf8').replace(r'\\\\', '').replace('\"', '')\n text = re.sub(r'(?<=nae)', '\\n', text)\n text = re.sub(r'(?<=dae)', '\\n', text)\n text = text.replace('life histories of cascadia butterflies', '').replace('\\\\', '').replace('|', '')\n text = text.replace('adult biology', '').replace('immature stage biology', '')\n text = text.replace('description of immature stages', '').replace('discussion', '')\n text = re.sub(r'([0-9]){3}', '', text)\n text = re.sub(r'([i])\\b', '', text)\n text = re.sub(r'(family|subfamily).*', '', text)\n text = text.replace('-', ' ').replace('tlie', 'the').replace('ihis', 'this').replace('ditilicult', 'difficult')\n text = re.sub(r'^\\s*$', '', text)\n text = ' '.join(text.split()) # remove consecutive spaces\n text = re.sub(r'(?<=[a-z]{4})[.]', '.\\n', text)\n text = text.strip()\n text = re.sub(r'[.,();\\']', '', text)\n\n return text","title":""},{"docid":"9bbe6034a0e46963cb33decf1d35dfa4","score":"0.59949124","text":"def remove_punc(data):\n \n #All text to lower case\n data = data.lower()\n #Punctuation subbed out for ''\n data = punc.sub( '', data)\n \n return data","title":""},{"docid":"cc5773a680ca43dad9ee13f3609035f2","score":"0.5977697","text":"def _remove_all_other_punctuation(pos_tagged_text):\n for j in xrange(len(pos_tagged_text)):\n word = pos_tagged_text[j][0]\n for symbol in (',', '.', ';', '-'):\n word = word.replace(symbol, ' ')\n # Write the preprocessed word back\n pos_tagged_text[j][0] = word\n return pos_tagged_text","title":""},{"docid":"64bca5555bdd1a051e9978c44b240c42","score":"0.597209","text":"def remove_html(text):\n return html.unescape(re.sub(doubleSpacingRe, ' ', re.sub(nbspRe, ' ', text)))","title":""},{"docid":"f8f082dba8399241d49d19b16ec80984","score":"0.5968257","text":"def get_cleaned_text(self, text):\n cleaned_text = text.replace('\\\"','').replace('\\'','').replace('-',' ')\n cleaned_text = self.remove_non_ascii_chars(cleaned_text)\n \n # retweet\n if re.match(r'RT @[_A-Za-z0-9]+:',cleaned_text): # retweet\n if self.remove_retweets: return ''\n retweet_info = cleaned_text[:cleaned_text.index(':')+2] # 'RT @name: ' will be again added in the text after cleaning\n cleaned_text = cleaned_text[cleaned_text.index(':')+2:]\n else:\n retweet_info = ''\n cleaned_text = self.remove_hyperlinks(cleaned_text)\n cleaned_text = cleaned_text.replace('#','HASHTAGSYMBOL').replace('@','ATSYMBOL') # to avoid being removed while removing punctuations\n \n tokens = [w.translate(self.punc_table) for w in word_tokenize(cleaned_text)] # remove punctuations and tokenize\n tokens = [w for w in tokens if not w.lower() in self.stop_words and len(w)>1] # remove stopwords and single length words\n cleaned_text = ' '.join(tokens)\n \n cleaned_text = cleaned_text.replace('HASHTAGSYMBOL','#').replace('ATSYMBOL','@')\n cleaned_text = retweet_info + cleaned_text\n \n return cleaned_text","title":""},{"docid":"5e0c7d2d36d3f9e03b06bb850b936efa","score":"0.5966438","text":"def remove_char(stringedText):\n\ttext = stringedText\n\t#cleanedText = '' # need a place to store the chars that actually belong\n\t\n\t# new method that skips all the if statement branches\n\t# 1. Put all the special chars in a single list\n\t# 2. Iterate through the list when analyzing each char\n\t# 3. recurs as before\n\n\t# if it's Alice.txt, then use the full list. otherwise use shorter list\n\tif sys.argv[2] == '-2':\n\t\tlistOfSpecialChars = [',', '.', '?', '!', '`', '', '', '', '', '', '', '', '', '', '']\n\telse:\n\t\tlistOfSpecialChars = ['', '', '', '', '', '', '', '', '', '']\n\n\n\tfor item in listOfSpecialChars:\n\t\tif item in text:\n\t\t\ttext = text.replace(item, '')\n\t\t\tremove_char(text) # recursively call until item no longer in text, then fail condition\n\treturn text","title":""},{"docid":"6ce6cdf443d7400675c4686b8fff22d3","score":"0.59407234","text":"def remove_html(self, text):\n soup = BeautifulSoup(text, 'html.parser')\n return soup.get_text(separator=\" \")","title":""},{"docid":"6fadf8ad9b6688d84f136701aeff7897","score":"0.59321564","text":"def clean_text(doc):\n \n words = re.sub(\"< ?/?[a-z]+ ?>|\\n\", \"\", doc)\n words = tokenize_text(words)\n words = lemmatize_text(words)\n words = remove_stopwords(words)\n doc = [w for w in words if w.isalnum()]\n doc = ' '.join(doc)\n \n return doc","title":""},{"docid":"83730e0c1709a337afd4043992bf3a87","score":"0.5919678","text":"def preprocess(self, text):\r\n return text","title":""},{"docid":"895767f9567130d8209cec47ed740cd6","score":"0.5917767","text":"def basic_cleaners(self, text):\n text = self.lowercase(text)\n text = self.collapse_whitespace(text)\n return text","title":""},{"docid":"acefa458000e3f328bffb60228142df1","score":"0.5913148","text":"def clear_text(self):\n self.ax.texts.clear()","title":""},{"docid":"15b60b67b4c52b195697d002d8f248cc","score":"0.59017366","text":"def _do_smart_punctuation(self, text):\r\n if \"'\" in text: # guard for perf\r\n text = self._do_smart_contractions(text)\r\n text = self._opening_single_quote_re.sub(\"‘\", text)\r\n text = self._closing_single_quote_re.sub(\"’\", text)\r\n\r\n if '\"' in text: # guard for perf\r\n text = self._opening_double_quote_re.sub(\"“\", text)\r\n text = self._closing_double_quote_re.sub(\"”\", text)\r\n\r\n text = text.replace(\"---\", \"—\")\r\n text = text.replace(\"--\", \"–\")\r\n text = text.replace(\"...\", \"…\")\r\n text = text.replace(\" . . . \", \"…\")\r\n text = text.replace(\". . .\", \"…\")\r\n return text","title":""},{"docid":"d69ee53fb8109b8eac653e744ce6cf16","score":"0.5900395","text":"def preprocess_text(data):\n if 'Body' in data:\n data['Body'] = sanitise_xml(data['Body'])\n \n if 'Tags' in data:\n data['Tags'] = re.findall('\\<([^>]+)', data['Tags'])\n \n return data","title":""},{"docid":"ee91042b95d6239ece6d61938a4c2c0c","score":"0.589461","text":"def cleanupText(path):\n \n text_cleaned = ''\n try:\n f = open(path)\n raw = f.read().lower()\n text = raw \n text_cleaned = re.sub(r'[-.?!,\":;()/|0-9]','', text)\n \n# splitword = text_cleaned.split(\" \")\n# \n# for word in splitword:\n# if word in stop_word:\n# splitword.remove(word)\n# text_cleaned = \" \".join(splitword)\n# # print \"\\n Word count after:\" + str(len(text_translated.split())) + \"\\n\"\n# text_cleaned = ' '.join([word for word in text_cleaned.split(' ') if (word and len(word) > 1)])\n finally:\n f.close()\n return text_cleaned","title":""},{"docid":"56e9121c9e15b11560fa8798e6959444","score":"0.58939534","text":"def normalize_text(self):\n self.normalized_text = normalize_text(remove_nonwords(self.raw_text))\n return True","title":""},{"docid":"ae4d9363dc24767294beb89a6c113215","score":"0.5891619","text":"def delete_text(self):\n self.in_title.delete(0, \"end\")\n self.in_author.delete(0, \"end\")\n self.answer.configure(text=\"\")\n self.answer.configure(text=\"\")","title":""},{"docid":"9777cfd2650717c6e9d64f74f792a8dc","score":"0.58881503","text":"def clean_text(s: str) -> str:\n s = unicodedata.normalize(\"NFKD\", s) # simplify unicode of string\n s = html.unescape(s) # convert html entities to unicode characters\n s = re.sub(r\"(|)\", \"\\n\", s) # replace p or li close with newline \n s = re.sub(r\"
\", \"• \", s) # replace list item start with bullets\n s = re.sub(r\"<[^<]+?>\", \"\", s) # otherwise strip HTML tags\n return s","title":""},{"docid":"c27e37a99ab23a9aadfc8937ecf3c815","score":"0.5877997","text":"def remove_html_tags(text):\n return HTMLTagsStripper.strip_tags_(text)","title":""},{"docid":"d22683fdf5d6c00468b6eac0349e1afe","score":"0.587424","text":"def remove_meta(text):\n text = re.sub(r'(\\[.*?\\]+)', '', text) # e.g. [one] or [[ two ]]\n text = re.sub(r'(\\{.*?\\}+)', '', text) # e.g. {one} or {{ two }}\n text = re.sub(r'(<.*?>+)', '', text) # e.g. or <>\n return text","title":""},{"docid":"39cb9610b3068c092810921d8c94fb7b","score":"0.5866584","text":"def clean_latex(text):\n # edge case\n text = re.sub(r'\\[math\\]', ' LaTex math ', text)\n text = re.sub(r'\\[\\/math\\]', ' LaTex math ', text)\n text = re.sub(r'\\\\', ' LaTex ', text)\n\n pattern_to_sub = {\n r'\\\\mathrm': ' LaTex math mode ',\n r'\\\\mathbb': ' LaTex math mode ',\n r'\\\\boxed': ' LaTex equation ',\n r'\\\\begin': ' LaTex equation ',\n r'\\\\end': ' LaTex equation ',\n r'\\\\left': ' LaTex equation ',\n r'\\\\right': ' LaTex equation ',\n r'\\\\(over|under)brace': ' LaTex equation ',\n r'\\\\text': ' LaTex equation ',\n r'\\\\vec': ' vector ',\n r'\\\\var': ' variable ',\n r'\\\\theta': ' theta ',\n r'\\\\mu': ' average ',\n r'\\\\min': ' minimum ',\n r'\\\\max': ' maximum ',\n r'\\\\sum': ' + ',\n r'\\\\times': ' * ',\n r'\\\\cdot': ' * ',\n r'\\\\hat': ' ^ ',\n r'\\\\frac': ' / ',\n r'\\\\div': ' / ',\n r'\\\\sin': ' Sine ',\n r'\\\\cos': ' Cosine ',\n r'\\\\tan': ' Tangent ',\n r'\\\\infty': ' infinity ',\n r'\\\\int': ' integer ',\n r'\\\\in': ' in ',\n }\n # post process for look up\n pattern_dict = {k.strip('\\\\'): v for k, v in pattern_to_sub.items()}\n # init re\n patterns = pattern_to_sub.keys()\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\n\n def _replace(match):\n \"\"\"\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\n \"\"\"\n try:\n word = pattern_dict.get(match.group(0).strip('\\\\'))\n except KeyError:\n word = match.group(0)\n print('!!Error: Could Not Find Key: {}'.format(word))\n return word\n return pattern_re.sub(_replace, text)","title":""},{"docid":"39cb9610b3068c092810921d8c94fb7b","score":"0.5866584","text":"def clean_latex(text):\n # edge case\n text = re.sub(r'\\[math\\]', ' LaTex math ', text)\n text = re.sub(r'\\[\\/math\\]', ' LaTex math ', text)\n text = re.sub(r'\\\\', ' LaTex ', text)\n\n pattern_to_sub = {\n r'\\\\mathrm': ' LaTex math mode ',\n r'\\\\mathbb': ' LaTex math mode ',\n r'\\\\boxed': ' LaTex equation ',\n r'\\\\begin': ' LaTex equation ',\n r'\\\\end': ' LaTex equation ',\n r'\\\\left': ' LaTex equation ',\n r'\\\\right': ' LaTex equation ',\n r'\\\\(over|under)brace': ' LaTex equation ',\n r'\\\\text': ' LaTex equation ',\n r'\\\\vec': ' vector ',\n r'\\\\var': ' variable ',\n r'\\\\theta': ' theta ',\n r'\\\\mu': ' average ',\n r'\\\\min': ' minimum ',\n r'\\\\max': ' maximum ',\n r'\\\\sum': ' + ',\n r'\\\\times': ' * ',\n r'\\\\cdot': ' * ',\n r'\\\\hat': ' ^ ',\n r'\\\\frac': ' / ',\n r'\\\\div': ' / ',\n r'\\\\sin': ' Sine ',\n r'\\\\cos': ' Cosine ',\n r'\\\\tan': ' Tangent ',\n r'\\\\infty': ' infinity ',\n r'\\\\int': ' integer ',\n r'\\\\in': ' in ',\n }\n # post process for look up\n pattern_dict = {k.strip('\\\\'): v for k, v in pattern_to_sub.items()}\n # init re\n patterns = pattern_to_sub.keys()\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\n\n def _replace(match):\n \"\"\"\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\n \"\"\"\n try:\n word = pattern_dict.get(match.group(0).strip('\\\\'))\n except KeyError:\n word = match.group(0)\n print('!!Error: Could Not Find Key: {}'.format(word))\n return word\n return pattern_re.sub(_replace, text)","title":""},{"docid":"8c3df43473fa1625aa47e63ad2cfc900","score":"0.58606964","text":"def _clear_tag_string(self, tag_string):\n #clean = re.compile(\".?>\")\n clean = re.compile('.?>|\\\"|.?/>|\", \" \", text)\n return re.sub(r\" +\", \" \", tag_free).strip() # make it nice","title":""},{"docid":"bb4c313f87e01e348ee6179acdfb5eaa","score":"0.58543164","text":"def remove_unicode(text):\n regex = r\"(\\\\u....)\"\n text = re.sub(regex, ' ', text)\n return text","title":""},{"docid":"1d664d3566821921a5d68e2c86a08931","score":"0.5848827","text":"def remove_html_tags(text):\r\n import re\r\n clean = re.compile('<.*?>')\r\n return re.sub(clean, '', text)","title":""},{"docid":"ddd0c2368ecf797bcb9dfcd574fc6ff7","score":"0.584826","text":"def clean_text(self):\n return self.sanitize_html(self.cleaned_data['text'])","title":""},{"docid":"e482c773dc5fd21e1ebd09d9eb3ef6ad","score":"0.58404464","text":"def _process_text(self,\n index: int,\n lower=True,\n delete_whitespace=True,\n delete_urls=True) -> str:\n text = self._texts[index]\n if not text:\n return None\n\n if not isinstance(text, str):\n raise TypeError(\"Text value is not string\")\n\n if lower:\n text = text.lower()\n if delete_whitespace:\n text = re.sub(r'[\\s]+', r' ', text).strip()\n if delete_urls:\n text = URLS.sub('', text)\n return text","title":""},{"docid":"7e4f38466182c733dd2f112000f2c369","score":"0.5832301","text":"def clean(self, text):\n\n return text.replace(\"$=\", \"<=\")","title":""},{"docid":"7db598df2c8505b0d3bd9bcdd77e327c","score":"0.5831346","text":"def textilize(s):\n s = s.replace(\"
\", \" \").replace(' ', ' ')\n return _re_html.sub(\"\", s)","title":""},{"docid":"595bc2579550e46f323aeeb024cfdaea","score":"0.5830553","text":"def character_cleanup(self, char):\n if self.tagcategory:\n char.tags.remove(category=self.tagcategory)\n char.attributes.remove(category=self.tagcategory)","title":""},{"docid":"523d6b90dc8d3f1d665155c824d38e3d","score":"0.5829617","text":"def epub_to_clean_text(ebook):\n out = epub2text(ebook)\n text=\"\"\n for chapter in out : \n text += filter_nonprintable(chapter) \n return text","title":""},{"docid":"15535fe57d25f9aa6661008b09fe0ecc","score":"0.5823075","text":"def remove_html_tags(text):\n\t#print(text)\n\t#First replace linebreaks to something NON-HTML so that it wont be cleared in the next step.\n\ttext = text.replace(\" \", \"BRBRBR\")\n\t#print(text)\n\n\timport re\n\tclean = re.compile('<.*?>')\n\tclean = re.sub(clean, '', text)\n\t#print(clean)\n\t#Again replace back our linebreaks to HTML format\n\tclean = clean.replace(\"BRBRBR\", \" \")\n\t#print(clean)\n\t#print(\"---end---\")\n\treturn Markup(clean)","title":""},{"docid":"40d8dc5dfdcf10201dd1eb69952be726","score":"0.5818737","text":"def text_standardize(text):\n text = text.replace('—', '-')\n text = text.replace('–', '-')\n text = text.replace('―', '-')\n text = text.replace('…', '...')\n text = text.replace('´', \"'\")\n text = re.sub(r'''(-+|~+|!+|\"+|;+|\\?+|\\++|,+|\\)+|\\(+|\\\\+|\\/+|\\*+|\\[+|\\]+|}+|{+|\\|+|_+)''', r' \\1 ', text)\n text = re.sub(r'\\s*\\n\\s*', ' \\n ', text)\n text = re.sub(r'[^\\S\\n]+', ' ', text)\n return text.strip()","title":""},{"docid":"5e09095356905e64ce0cbd9278becd9a","score":"0.581436","text":"def clean_text(text):\n digi_punct = \"[^a-zA-Z.1234567890'# ]\" # used by clean_text(text_corpus)\n text = text.replace(\"´\", \"'\")\n text = re.sub(digi_punct, \" \", text)\n\n # digi_punct = \"[^a-zA-Z.1234567890' ]\"\n text = \" \".join(text.split())\n text = text.lower()\n return text","title":""},{"docid":"6dc88ebeb02bbc3589d10ad07550933f","score":"0.58119494","text":"def remove_speech_meta(text):\n text = re.sub(r'^(\\s*\\([^)]*\\)\\s*)', '', text) # e.g. (to Ray) Hello\n text = re.sub(r'(\\s*\\([^)]*\\)\\s*)$', '', text) # e.g. Hello (motions at Ray)\n return text","title":""},{"docid":"95693e0352cd0d901a0f5a44a8f4cfe8","score":"0.58030546","text":"def clean_tweet_text(tweet):\n tweet = tweet.strip(\"\\n\")\n tweet = tweet.strip(\"\\t\")\n tweet = remove_emojis(tweet)\n tweet = remove_hashtag_symbol(tweet)\n return tweet","title":""},{"docid":"09587b5540da31a7dfd6b6bbd3d7785d","score":"0.5796727","text":"def clean(text: str) -> str:\n\n clean_str = ''\n for char in text:\n if char.isalnum() or char == UNDERSCORE:\n clean_str = clean_str + char\n elif char == HASHTAG_SYMBOL or char == MENTION_SYMBOL:\n clean_str = clean_str + SPACE + char\n else:\n clean_str = clean_str + SPACE\n return clean_str","title":""},{"docid":"4136548aa14d7139072f51ccbad881f9","score":"0.57962835","text":"def _remove_tags(html):\n html = _replace_ampersands(html)\n html = re.sub('\\|\\', '', html)\n html = re.sub('\\|\\', '', html)\n tagrx = re.compile('\\<.+?\\>', flags=re.DOTALL)\n html = tagrx.sub(' ', html)\n return html","title":""},{"docid":"cdd98a8e4b041ee8fc317d9868467d75","score":"0.57946014","text":"def clean_paragraph(p):\n\n\tif p.find_all(\"span\") == None:\n\t\tp_text = p\n\telse:\n\t\tfor span_tag in p.find_all(\"span\"):\n\t\t\tspan_tag.replace_with(\"\")\n\t\tp_text = str(p)\n\tcorrected_p = re.sub(r' \\(.*?\\)', \"\", p_text)\n\treturn corrected_p","title":""},{"docid":"f60bbf591aad17c3da73f1146cab065d","score":"0.5793579","text":"def compact_text(data):\n interim_codes = compact_text_interim(data)\n return (_compact_chunk(chunk) for chunk in chunks(interim_codes, 2))","title":""},{"docid":"7546c60a27ce53548e27a01df5be84cc","score":"0.57929564","text":"def normalize_text(text):\r\n text = text.replace('\\r\\n', '\\n').replace('\\r', '\\n').expandtabs()\r\n if not text.endswith('\\n'):\r\n text += '\\n'\r\n\r\n # ignore BOM chars at the begining of template\r\n BOM = '\\xef\\xbb\\xbf'\r\n if isinstance(text, str) and text.startswith(BOM):\r\n text = text[len(BOM):]\r\n \r\n # support fort \\$ for backward-compatibility \r\n text = text.replace(r'\\$', '$$')\r\n return text","title":""},{"docid":"b56abfb553c3d5584260c5b0cf4c18d3","score":"0.57876676","text":"def delete_chars(text, unwanted_chars):\n for char in unwanted_chars:\n text = text.replace(char, '')\n return text","title":""}],"string":"[\n {\n \"docid\": \"bcff0b3207f4a1f287474fa3ebeea447\",\n \"score\": \"0.70005953\",\n \"text\": \"def clean_text(text):\\n # Replace newlines by space. We want only one doc vector.\\n text = text.replace('\\\\n', ' ').lower()\\n # Remove URLs\\n #text = re.sub(r\\\"http\\\\S+\\\", \\\"\\\", text)\\n # Expand contractions: you're to you are and so on.\\n text = contractions.fix(text)\\n # Remove stop words\\n #text = preprocessing.remove_stopwords(text)\\n \\n #text = preprocessing.strip_tags(text)\\n # Remove punctuation -- all special characters\\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_punctuation(text))\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da14da586b935e9a7da3e19d0a684ac\",\n \"score\": \"0.6929299\",\n \"text\": \"def clean_text(text):\\n # Replace newlines by space. We want only one doc vector.\\n text = text.replace('\\\\n', ' ').lower()\\n # Remove URLs\\n text = re.sub(r\\\"http\\\\S+\\\", \\\"\\\", text)\\n # Expand contractions: you're to you are and so on.\\n text = contractions.fix(text)\\n # Remove stop words\\n text = preprocessing.remove_stopwords(text)\\n # Remove punctuation -- all special characters\\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_non_alphanum(text))\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc1580629da1f55178692a2c74494d2\",\n \"score\": \"0.6823169\",\n \"text\": \"def clean_text(text): #cleantext\\n replace_char = [\\n \\\"[\\\",\\n \\\"]\\\",\\n \\\"u'\\\",\\n \\\"None\\\",\\n \\\"Thank you\\\",\\n \\\"-\\\",\\n \\\"(\\\",\\n \\\")\\\",\\n \\\"#\\\",\\n \\\"Done\\\",\\n \\\">\\\",\\n \\\"<\\\",\\n \\\"-\\\",\\n \\\"|\\\",\\n \\\"/\\\",\\n \\\"\\\\\\\"\\\",\\n \\\"Hint\\\",\\n \\\"\\\\n\\\",\\n \\\"'\\\"]\\n for l in replace_char:\\n text = text.replace(l, \\\"\\\")\\n text = re.sub(' +', ' ', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73ad21d266a49d35abc6fe7f189fdf3\",\n \"score\": \"0.6713591\",\n \"text\": \"def _clean_text(self, text):\\n text = re.sub('[-=+,#/\\\\?:^$.@*\\\\\\\"※~&%ㆍ!』\\\\\\\\‘|\\\\(\\\\)\\\\[\\\\]\\\\<\\\\>`…》]', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33cad90681ef72942de02b7463b0bde4\",\n \"score\": \"0.6710349\",\n \"text\": \"def clean_text(text):\\n # try:\\n # text = text.encode(\\\"ascii\\\", errors=\\\"ignore\\\").decode()\\n # except:\\n # print(\\\"error in parsing text - \\\"+ text)\\n # pass\\n\\n text = re.sub(r'\\\\([^)]*\\\\)', '', text)\\n text = re.sub(r' +', ' ', text)\\n\\n text = remove_titles(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439d9bbdc1cdff15c6b24bd045f96de1\",\n \"score\": \"0.67070097\",\n \"text\": \"def clean_text(text):\\n\\n text = re.sub('<[^>]+>', '', text)\\n text = re.sub(r'[^A-Za-z0-9\\\\s]+', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd7847dced4e22c557ff2b18816f239\",\n \"score\": \"0.66308665\",\n \"text\": \"def clean_text(self):\\n cleanedtext = re.sub(r'\\\\n\\\\s*\\\\n', '', self.text) # extra lines\\n cleanedtext = re.sub(r'[^\\\\w\\\\s]', '', cleanedtext) # punctuation\\n # for numbers\\n # cleanedtext = re.sub(r'[^A-Za-z0-9(),!?@\\\\'\\\\`\\\\\\\"\\\\_\\\\n]', ' ', cleanedtext)\\n # cleanedtext = re.sub(r'\\\\w*\\\\d\\\\w*','',cleanedtext) # words with number plust letters\\n # remove_digits = str.maketrans('', '', digits)\\n # cleanedtext = cleanedtext.translate(remove_digits)\\n cleanedtext = re.sub(' +', ' ', cleanedtext) # extra white spaces\\n cleanedtext = cleanedtext.lower()\\n self.cleanedtext = cleanedtext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be79e585f11ff26f695f9bd4c24a03cc\",\n \"score\": \"0.6597903\",\n \"text\": \"def clean_text(text: str) -> str:\\n fixed_text = re.sub('<[^>]+>', ' ', text) # removing everything inside <>\\n fixed_text = re.sub('\\\\n', ' ', fixed_text)\\n fixed_text = re.sub(' ', ' ', fixed_text)\\n fixed_text = re.sub('nbsp', ' ', fixed_text)\\n fixed_text = re.sub('—', ' ', fixed_text)\\n fixed_text = re.sub('\\\\t', ' ', fixed_text)\\n fixed_text = re.sub('\\\\r', ' ', fixed_text)\\n fixed_text = re.sub('\\\"…', ' ', fixed_text)\\n fixed_text = re.sub('>', ' ', fixed_text)\\n fixed_text = re.sub(r'[^\\\\w\\\\s]', ' ', fixed_text) # removing the punctuation\\n fixed_text = re.sub(' +', ' ', fixed_text) # fix multiple spaces\\n fixed_text = fixed_text.strip(' ')\\n return fixed_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de370f2a8e6ba8efac5bacfa16e14e20\",\n \"score\": \"0.6591571\",\n \"text\": \"def textCleaning(self,text):\\n newText = text.replace('\\\\n',' ').replace('\\\\r',' ').replace('\\\\t',' ').replace('
',' ').replace(' ',' ').replace('
',' ').replace('',' ').strip()\\n return newText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46dc40ef3429faddc40feea26c90edce\",\n \"score\": \"0.6588975\",\n \"text\": \"def cleanText(text):\\n # clean: xml tags and mathtype -> spaces\\n cleanText = replaceWithSpaces(xmlTagsRe, text)\\n cleanText = replaceWithSpaces(mathTypeRe, text)\\n # clean: non-letters -> spaces\\n cleanText = nonLetterRe.sub(\\\" \\\", cleanText)\\n cleanText = digitRe.sub(\\\" \\\", cleanText)\\n return cleanText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf7970fb79abc26376642f89ef03a92b\",\n \"score\": \"0.65831715\",\n \"text\": \"def cleanup_text(text):\\n # Remove empty paragraphs\\n text = re.sub(r']*?>(\\\\s| | | |\\\\xc2\\\\xa0)*?', '', text,\\n flags=re.UNICODE)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bee9bd22cb054c1e24b2fb6ca4026d5\",\n \"score\": \"0.65464205\",\n \"text\": \"def clean_text(text):\\r\\n text = text.lower().strip() # lowercase and remove trailing whitespaces\\r\\n text = re.sub(r\\\"i'm\\\", \\\"i am\\\", text)\\r\\n text = re.sub(r\\\"he's\\\", \\\"he is\\\", text)\\r\\n text = re.sub(r\\\"she's\\\", \\\"she is\\\", text)\\r\\n text = re.sub(r\\\"it's\\\", \\\"it is\\\", text)\\r\\n text = re.sub(r\\\"that's\\\", \\\"that is\\\", text)\\r\\n text = re.sub(r\\\"what's\\\", \\\"what is\\\", text)\\r\\n text = re.sub(r\\\"where's\\\", \\\"where is\\\", text)\\r\\n text = re.sub(r\\\"there's\\\", \\\"there is\\\", text)\\r\\n text = re.sub(r\\\"how's\\\", \\\"how is\\\", text)\\r\\n text = re.sub(r\\\"\\\\'ll\\\", \\\" will\\\", text)\\r\\n text = re.sub(r\\\"\\\\'ve\\\", \\\" have\\\", text)\\r\\n text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\r\\n text = re.sub(r\\\"\\\\'d\\\", \\\" would\\\", text)\\r\\n text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\r\\n text = re.sub(r\\\"won't\\\", \\\"will not\\\", text)\\r\\n text = re.sub(r\\\"can't\\\", \\\"cannot\\\", text)\\r\\n text = re.sub(r\\\"n't\\\", \\\" not\\\", text)\\r\\n text = re.sub(r\\\"n'\\\", \\\"ng\\\", text)\\r\\n text = re.sub(r\\\"'bout\\\", \\\"about\\\", text)\\r\\n text = re.sub(r\\\"'til\\\", \\\"until\\\", text)\\r\\n text = re.sub(r'[\\\" \\\"]+', \\\" \\\", text) # remove extra spaces in between\\r\\n text = re.sub(r\\\"[-()\\\\\\\"#/@;:<>{}`+=~|.!?,]\\\", \\\"\\\", text)\\r\\n text = ' ' + text + ' '\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04edfda390fef2b3f014fc3061a5fceb\",\n \"score\": \"0.6516315\",\n \"text\": \"def _remove_any_numbers_and_non_english_characters_from_text(pos_tagged_text):\\n for i in xrange(len(pos_tagged_text)):\\n word = pos_tagged_text[i][0]\\n # Remove numbers\\n for digit in string.digits:\\n word = word.replace(digit, '')\\n # Remove words with non-Latin characters\\n try:\\n word.decode('ascii')\\n except UnicodeDecodeError:\\n word = ''\\n # Write the preprocessed word back\\n pos_tagged_text[i][0] = word\\n return pos_tagged_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67bf39279b32d413e779e3dad888c4c9\",\n \"score\": \"0.65065104\",\n \"text\": \"def clean_article_text(text):\\r\\n text = remove_piece_of_text(text, '')\\r\\n text = text.replace('', '')\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616fcc6dd08b5608264b3d94abfe134c\",\n \"score\": \"0.6477654\",\n \"text\": \"def clean_up_text(text):\\n # TODO add special characters\\n text = re.sub('\\\\( \\\\(([^\\\\)]+)\\\\)\\\\)', ' ', text)\\n text = re.sub('([a-z])([.!?])([A-Z])', r'\\\\1\\\\2 \\\\3', text)\\n text = re.sub('[\\\\s]+', ' ', text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283fc4c63a23b691da69908c290cb32e\",\n \"score\": \"0.6468758\",\n \"text\": \"def cleantext(text):\\n # Convert any non-ASCII characters to XML like 'ꀀabcd޴'\\n text = text.encode(\\\"ascii\\\", \\\"xmlcharrefreplace\\\").decode(\\\"ascii\\\")\\n lines = [x.strip() for x in text.splitlines()]\\n text = \\\" \\\".join(lines)\\n for match, out in REPLACES:\\n text = re.sub(match, out, text)\\n text = text.strip() # some spaces may have been generated\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0e7484b5ba593baa7ca9e6f233b94e\",\n \"score\": \"0.64462006\",\n \"text\": \"def cleanText(self):\\n text = self.text().strip()\\n if self.__prefix and text.startswith(self.__prefix):\\n text = text[len(self.__prefix):]\\n if self.__suffix and text.endswith(self.__suffix):\\n text = text[:-len(self.__suffix)]\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0e7484b5ba593baa7ca9e6f233b94e\",\n \"score\": \"0.64462006\",\n \"text\": \"def cleanText(self):\\n text = self.text().strip()\\n if self.__prefix and text.startswith(self.__prefix):\\n text = text[len(self.__prefix):]\\n if self.__suffix and text.endswith(self.__suffix):\\n text = text[:-len(self.__suffix)]\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6476d255266d39d7491fea86957199fc\",\n \"score\": \"0.6420874\",\n \"text\": \"def clean_text(text):\\n \\n # remove Unicode characters\\n text = re.sub(r'[^\\\\x00-\\\\x7F]+', '', text)\\n\\n # taking sequences of characters with alphanumeric characters separated by other characters\\n text = re.sub(r\\\"[-?!&]\\\",' ',text)\\n text = re.sub(r'''[\\\"#$%()*+,./:;<=>@[\\\\]^_`{|}~]''','',text)\\n \\n # remove all numeric charracters\\n text = re.sub(r'[0-9]','',text)\\n\\n # Remove new line characters\\n text = re.sub(r'\\\\s+', ' ', text)\\n\\n # remove all numeric charracters\\n text = re.sub(r'^https?:\\\\/\\\\/.*[\\\\r\\\\n]*', '',text)\\n\\n Apos_dict={\\\"'s\\\":\\\" is\\\",\\\"n't\\\":\\\" not\\\",\\\"'m\\\":\\\" am\\\",\\\"'ll\\\":\\\" will\\\",\\n \\\"'d\\\":\\\" would\\\",\\\"'ve\\\":\\\" have\\\",\\\"'re\\\":\\\" are\\\"}\\n \\n #replace the contractions\\n for key,value in Apos_dict.items():\\n if key in text:\\n text=text.replace(key,value)\\n\\n # convert to lowercase to maintain consistency\\n text = text.lower()\\n \\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f49d845d0d6f6ff334cfea8b6766cbf\",\n \"score\": \"0.63663334\",\n \"text\": \"def clean_text(text: str):\\n position = text.lower().rfind(\\\"references\\\")\\n text = text[:position] # removing bibliography\\n text = text.replace(\\\"\\\\n\\\", \\\" \\\") # removing newlines\\n text = re.sub(r\\\"\\\\[[^\\\\[^\\\\]]*\\\\]\\\", \\\"\\\", text) # removing references\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e29448fa18d208e71d63ed06e30ce1b\",\n \"score\": \"0.63571817\",\n \"text\": \"def _remove_html_tags(self, text):\\r\\n return re.compile(r'<[^>]+>').sub('', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc6604b9eac2108048a41f4543e4766b\",\n \"score\": \"0.6338481\",\n \"text\": \"def strip_markup(wikitext):\\n if not wikitext:\\n return ''\\n\\n # remove templates\\n text = remove_templates(wikitext)\\n\\n # remove irrelevant spans\\n text = re_comments.sub('', text)\\n text = re_ignored_html_tags.sub(r'\\\\2', text)\\n text = re_self_closing_html_tags.sub('', text)\\n text = re_dropped_elements.sub('', text)\\n text = re_categories.sub('', text)\\n text = re_files_images.sub('', text) # TODO: keep file/image captions?\\n\\n # replace external links with just labels or just URLs\\n text = replace_external_links(text)\\n\\n # drop magic words behavioral switches\\n text = re_magic_words.sub('', text)\\n\\n # replace internal links with just their labels\\n text = replace_internal_links(text)\\n # text = replace_internal_links(text) # TODO: is this needed?\\n\\n # remove table markup\\n text = text.replace('||', '\\\\n|').replace('!!', '\\\\n!') # put each cell on a separate line\\n text = re_table_formatting.sub('\\\\n', text) # remove formatting lines\\n text = re_table_cell_formatting.sub('\\\\n\\\\\\\\3', text) # leave only cell content\\n\\n # strip out text formatting\\n text = re_italic_quote.sub(r'\\\"\\\\1\\\"', text)\\n text = re_bold_italic.sub(r'\\\\1', text)\\n text = re_quote_quote.sub(r'\\\"\\\\1\\\"', text)\\n\\n # unescape html entities\\n text = ftfy.fixes.unescape_html(text)\\n\\n # final cleanup\\n text = re_headings.sub(r'\\\\n\\\\n\\\\2\\\\n\\\\n', text)\\n text = re_dots.sub('...', text)\\n text = re_brackets.sub(r'', text)\\n text = text.replace('[[', '').replace(']]', '')\\n text = text.replace('<<', '«').replace('>>', '»')\\n text = re_random_cruft.sub(r'\\\\1', text)\\n text = re.sub(r'\\\\n\\\\W+?\\\\n', r'\\\\n', text, flags=re.UNICODE)\\n text = text.replace(',,', ',').replace(',.', '.')\\n text = re_spaces.sub(' ', text)\\n text = re_linebreaks.sub(r'\\\\n\\\\n', text)\\n\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd990b81f16b5ea23281fcb150630477\",\n \"score\": \"0.63193804\",\n \"text\": \"def cleanse_sentence(text):\\n \\n text = re.sub(hash_regex, hash_repl, text)\\n text = re.sub(hndl_regex, hndl_repl, text)\\n text = re.sub(url_regex, ' __URL ', text)\\n \\n for (repl, regx) in emoticons_regex :\\n text = re.sub(regx, ' '+repl+' ', text)\\n\\n text = text.replace('\\\\'','')\\n text = re.sub( word_bound_regex , punctuations_repl, text )\\n text = re.sub( rpt_regex, rpt_repl, text )\\n \\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f85bc2347365b9c12ca2580d44a348\",\n \"score\": \"0.6308067\",\n \"text\": \"def clean_text(text):\\n text = re.sub('[-=+,#/\\\\?:^$.@*\\\\\\\"※~&%ㆍ!』\\\\\\\\‘|\\\\(\\\\)\\\\[\\\\]\\\\<\\\\>`…》]', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c7d913e073cac757804fda010b6c80c\",\n \"score\": \"0.6296954\",\n \"text\": \"def clean_text_1(text, stpwds, valid_tags, stemming=False):\\n\\tcleaned_text = clean_basic(text)\\n\\ttokens = cleaned_text.split()\\n\\ttokens = postagging(tokens, valid_tags)\\n\\ttokens = remove_stopwords(tokens, stpwds)\\n\\ttokens = remove_short_tokens(tokens)\\n\\tif stemming:\\n\\t\\ttokens = stem(tokens)\\n\\tcleaned_text = remove_hyphen(' '.join(tokens))\\n\\treturn cleaned_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9247a1dbe906adbe476252f075860812\",\n \"score\": \"0.6290065\",\n \"text\": \"def clean_text(text):\\n # remove html tags, lowercase, remove nonsense, remove non-letter\\n aff = BeautifulSoup(text, \\\"lxml\\\").text \\n aff = aff.lower()\\n aff = re.sub(r'xa0|#n#‡#n#|#tab#|#r#|\\\\[|\\\\]', \\\"\\\", aff)\\n aff = re.sub(r'[^a-z]+', ' ', aff)\\n return aff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4549a815eef93b30dc8e49e520ea9d4c\",\n \"score\": \"0.6278033\",\n \"text\": \"def demarkup_text(text):\\n\\n # keep the opening tag name of certain tags that contains these strings\\n # note: are from debian copyright files\\n kept_tags = (\\n 'lic', 'copy', 'www', 'http', 'auth', 'contr', 'leg', 'inc', '@', \\n '', '', '169', 'a9'\\n )\\n\\n # find start and closing tags or the first white space whichever comes first\\n # or entities\\n # this regex is such that ' '.join(tags.split(a))==a\\n\\n tags_ents = re.compile(r'(|\\\\s)?|&[^\\\\s&]+;|href|[\\\\'\\\"]?\\\\/\\\\>)', re.IGNORECASE).split\\n\\n cleaned = []\\n for token in tags_ents(text):\\n if token.lower().startswith(('<', '&', 'href')) and not any(k in token.lower() for k in kept_tags):\\n continue\\n else:\\n cleaned.append(token)\\n return u' '.join(cleaned)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cfd092920700f1c2f23d0fba4d528b\",\n \"score\": \"0.6265034\",\n \"text\": \"def remove_tashkeel(self, input_text):\\n\\n p_tashkeel = re.compile(r'[\\\\u0617-\\\\u061A\\\\u064B-\\\\u0652]')\\n return re.sub(p_tashkeel,\\\"\\\", input_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3afa4198b471da03ad24ac8551e73fa3\",\n \"score\": \"0.62647754\",\n \"text\": \"def clean(self):\\n # Replace double \\\\n's in case this ever happens on windows for some reason.\\n if self.text.__contains__('\\\\n\\\\n'):\\n self.text = self.text.replace('\\\\n\\\\n', '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b33f5fa7f977b65ed4d9a97b616ecbba\",\n \"score\": \"0.6247174\",\n \"text\": \"def preprocess(self):\\n self.text = remove_control_characters(self.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3503132af39ea4b05ddf42ac5ed12ffb\",\n \"score\": \"0.6229983\",\n \"text\": \"def strip_markup(text):\\n return (text\\n .replace(\\\"\\\", \\\"\\\")\\n .replace(\\\"\\\", \\\"\\\")\\n .replace(\\\"\\\", \\\"\\\")\\n .replace(\\\"\\\", \\\"\\\")\\n .replace(\\\"\\\", \\\"\\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f27009c1809229c97e68cb97aa67e615\",\n \"score\": \"0.62280345\",\n \"text\": \"def _del_text(self):\\r\\n # check whether element already has text node\\r\\n if self._root.HasChildNodes and \\\\\\r\\n self._root.FirstChild and \\\\\\r\\n isinstance(self._root.FirstChild, XmlText):\\r\\n self._root.RemoveChild(self._root.FirstChild)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f35e7f296801a447055b16f013b54b\",\n \"score\": \"0.6215954\",\n \"text\": \"def clean_text(text):\\n # lowercase text\\n text = text.lower()\\n\\n # substitute contractions\\n # text = re.sub(r\\\"i'm\\\", \\\"i am\\\", text)\\n # text = re.sub(r\\\"he's\\\", \\\"he is\\\", text)\\n # text = re.sub(r\\\"she's\\\", \\\"she is\\\", text)\\n # text = re.sub(r\\\"it's\\\", \\\"it is\\\", text)\\n # text = re.sub(r\\\"that's\\\", \\\"that is\\\", text)\\n # text = re.sub(r\\\"what's\\\", \\\"what is\\\", text)\\n # text = re.sub(r\\\"where's\\\", \\\"where is\\\", text)\\n # text = re.sub(r\\\"how's\\\", \\\"how is\\\", text)\\n # text = re.sub(r\\\"\\\\'ll\\\", \\\" will\\\", text)\\n # text = re.sub(r\\\"\\\\'ve\\\", \\\" have\\\", text)\\n # text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\n # text = re.sub(r\\\"\\\\'d\\\", \\\" would\\\", text)\\n # text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\n # text = re.sub(r\\\"won't\\\", \\\"will not\\\", text)\\n # text = re.sub(r\\\"can't\\\", \\\"cannot\\\", text)\\n # text = re.sub(r\\\"n't\\\", \\\" not\\\", text)\\n # text = re.sub(r\\\"n'\\\", \\\"ng\\\", text)\\n # text = re.sub(r\\\"'bout\\\", \\\"about\\\", text)\\n # text = re.sub(r\\\"'til\\\", \\\"until\\\", text)\\n\\n # get rid of username handles\\n text = re.sub(r\\\"(?:@)(\\\\S+|$)|\\\", \\\"\\\", text)\\n\\n # get rid of special characters\\n text = re.sub(r\\\"[-()\\\\\\\"#/@;:<>{}`+=~|.!?,]\\\", \\\"\\\", text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88c982c1d7c70e2702c8b9fafed7d4d5\",\n \"score\": \"0.6204147\",\n \"text\": \"def strip_tags(text):\\n\\n return re.sub('<[^<]+?>', '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd6daf8f52ba6aeedce909badebd364\",\n \"score\": \"0.6194485\",\n \"text\": \"def clean_text(raw_text):\\n # Must strip HTML tags out first!\\n text = re.sub('<[^<]+?>', ' ', raw_text)\\n text = handle_unicode(text)\\n text = handle_html_unquote(text)\\n text = handle_mac_quotes(text)\\n text = handle_text_break_dash(text)\\n text = text.lower()\\n\\n regex_subs = ['\\\\t', '\\\\n', '\\\\r', '\\\\s+', '&']\\n for regex_sub in regex_subs:\\n text = re.sub(regex_sub, ' ', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b2ba621e89303b47c25fd967a686e4\",\n \"score\": \"0.6182298\",\n \"text\": \"def _clean_text(source):\\n source = str(source)\\n source = source.upper()\\n source = re.sub('[？|?|！|!|，|,|.|。|#|《|》|<|>|（|）|(|)|〉|、|／|…|-]', '', source)\\n source = re.sub('[哈｜嘿｜呵]', '', source)\\n source = re.sub('NAN', '', source)\\n source = re.sub('\\\\xa0', '', source)\\n return source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30006085bad7c937637e584674a6cc02\",\n \"score\": \"0.6176192\",\n \"text\": \"def _clean_bad_chars(self, text):\\n return text.replace(\\\"\\\\u200b\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c3428df1fe332453e101d924bcc714b\",\n \"score\": \"0.6171018\",\n \"text\": \"def text2cleanish(text):\\n logger.info(\\\"Cleaning text ...\\\")\\n text = CLEANER1.sub(r\\\"\\\\1 \\\\2 \\\\3\\\", text)\\n text = CLEANER2.sub(r\\\"\\\\1 ... \\\\3\\\", text)\\n text = CLEANER3.sub(r\\\"\\\\1 ... \\\", text)\\n # text = CLEANER4.sub(r\\\"\\\\1 X... \\\\3\\\", text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"928336714d22728e7f69363c3345b4c8\",\n \"score\": \"0.6169784\",\n \"text\": \"def clean(self, text = None) -> str:\\n return self.normalize(\\n self.clean_double_whitespaces(\\n self.clean_edge_punctuation(\\n self.clean_numeric(\\n self.clean_web_tags(\\n self.clean_html_tags(\\n self.clean_non_ascii(\\n text\\n )\\n )\\n )\\n )\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cff7fa0a0cef7e0a4aa8555fc2fa3982\",\n \"score\": \"0.61514693\",\n \"text\": \"def clean_text_remove_puncs(cls, text):\\n if isinstance(text, str):\\n try:\\n text = cls.clean_text(text)\\n text = cls._remove_punctuations(text)\\n\\n except (TypeError, AttributeError):\\n logger.warning(\\\"Error occured in preprocessing text: {}\\\".format(text))\\n sys.exit(0)\\n\\n return text.strip()\\n\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6400609a86643ee6fd2cb125e5b5aae5\",\n \"score\": \"0.61486775\",\n \"text\": \"def safe_clean_text(text):\\n # strip\\n text = text.strip()\\n\\n # weird words\\n text = delete_words(delete_list, text)\\n\\n # handle \\\\\\\\\\\\'t \\\\\\\\\\\\'ve\\n text = text.replace(r\\\"\\\\\\\\\\\", \\\"\\\")\\n\\n # remove extra spaces\\n text = re.sub(r'\\\\s+', ' ', text)\\n\\n # fix puncutations\\n for p in punctuations:\\n text = text.replace(p, p[1:])\\n text = re.sub(\\\" (\\\\u2018|\\\\u2019|') \\\", r\\\"\\\\1\\\", text)\\n\\n # replace to EN dash\\n text = re.sub(u\\\"\\\\u2014\\\", \\\"-\\\", text)\\n text = re.sub(u\\\"\\\\u2013\\\", \\\"-\\\", text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6857e424290706ed34fd0100320e8694\",\n \"score\": \"0.61465544\",\n \"text\": \"def remove_tags(text):\\n clean = re.compile('<.*?>')\\n return re.sub(clean, '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5086afb5e199b8a13a21c4f58efbca2f\",\n \"score\": \"0.61259377\",\n \"text\": \"def remove_smart_quotes(self, text):\\r\\n return text.replace(u\\\"\\\\u201c\\\",\\\"\\\").replace(u\\\"\\\\u201d\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b44d02ed0cc749e2cc7f1d32af1451\",\n \"score\": \"0.6104445\",\n \"text\": \"def clean_txt(self, text):\\n\\n text = re.sub(\\\"'\\\", \\\"\\\", text)\\n text = re.sub(\\\"(\\\\\\\\W)+\\\", \\\" \\\", text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faaffdfe0444eca15482157446d68b10\",\n \"score\": \"0.6088906\",\n \"text\": \"def clean_text(text):\\n translation_dict = {\\n '’':\\\"'\\\",\\n '‘':\\\"'\\\",\\n # \\\"œ\\\":'oe',\\n }\\n # remove control characters not supported by XML.\\n text = strip_chars(text)\\n text = unicodedata.normalize('NFC', text.strip())\\n text = re.sub(r'(\\\\n\\\\n )+', ' ', text)\\n text = re.sub(r'([a-zA-Zàâçéèêëîïôûùüÿñæœ,;-])(\\\\n)+([^\\\\s])', r'\\\\1 \\\\3', text)\\n return text.translate(str.maketrans(translation_dict))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab72be9d87c9f54053505d1b1eaf6b2f\",\n \"score\": \"0.6088736\",\n \"text\": \"def clearMyText(self):\\n self.clearText(self.textName)\\n self.clearText(self.textCR)\\n self.clearText(self.textAL)\\n self.clearText(self.textEC)\\n self.clearText(self.textIA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b81ac2e011b854386493cc068a9e46dd\",\n \"score\": \"0.60839474\",\n \"text\": \"def clear_texts(self):\\n\\n for t in self.texts[:]:\\n if not t[4]:\\n self.texts.remove(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3eb01e6647165fa5f3f6cc66a802663\",\n \"score\": \"0.6076601\",\n \"text\": \"def cleanText(self, s):\\n\\n\\t\\tdef _sent_to_lines(sent):\\n\\t\\t\\t\\\"\\\"\\\"Converts a string of text to one sentence each line.\\\"\\\"\\\"\\n\\t\\t\\tsent = re.sub(r\\\"\\\\n\\\", \\\" \\\", sent)\\n\\t\\t\\treturn re.sub(r\\\"(\\\\.|\\\\!|\\\\?|--)+\\\\s*\\\", \\\"\\\\n\\\", sent)\\n\\n\\t\\tdef _strip_punct(sent):\\n\\t\\t\\t\\\"\\\"\\\"Strips away all punctuations from the text.\\\"\\\"\\\"\\n\\t\\t\\treturn sent.translate(string.maketrans(\\\"\\\",\\\"\\\"), string.punctuation)\\n\\n\\t\\treturn _strip_punct(_sent_to_lines(s)).lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1727f2673de7c749d5ef2a767231eba\",\n \"score\": \"0.60653853\",\n \"text\": \"def clean_message(self, text):\\n text = strip_tags(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d8abba63fc621d1b31a7dad4026d4e\",\n \"score\": \"0.60498005\",\n \"text\": \"def __remove_tags(self, html_text):\\n tags_regex = re.compile(\\n '<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')\\n tag_free_text = re.sub(tags_regex, ' ', html_text)\\n\\n return tag_free_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba3be36a740be33aa131f168e63ba4e\",\n \"score\": \"0.6047604\",\n \"text\": \"def clean_text(text):\\n if text:\\n text = html2text.html2text(clean_markdown(text))\\n return re.sub(r'\\\\s+', ' ', text).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1fbdfc06773a8ebff8e45493c51bd1\",\n \"score\": \"0.603565\",\n \"text\": \"def remove_html_tags(text):\\r\\n clean = re.compile('\\\\{<.*?>\\\\}')\\r\\n return re.sub(clean, '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1f72d624a2444e1814a702ea0cf7e8\",\n \"score\": \"0.6031818\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1f72d624a2444e1814a702ea0cf7e8\",\n \"score\": \"0.6031818\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4682d3a218fa9f846931042e24d0607\",\n \"score\": \"0.60222965\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or self._is_control(char):\\n continue\\n if self._is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f3d2d2e47513b04b3d7b686b294cdd\",\n \"score\": \"0.6021114\",\n \"text\": \"def _clean_text(self, listing_text):\\n if len(listing_text)>0:\\n listing_text_clean = ' '.join(listing_text[0].findAll(text=True))\\n listing_text_clean = re.sub('\\\\n|\\\\t', ' ', listing_text_clean)\\n listing_text_clean = re.sub('\\\\s+', ' ', listing_text_clean)\\n listing_text_clean = re.sub(\\\"'\\\", '', listing_text_clean)\\n listing_text_clean = re.sub('\\\"', '', listing_text_clean)\\n else:\\n listing_text_clean = ' ' \\n return listing_text_clean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df88aa6338c651f311f8313abd30f022\",\n \"score\": \"0.6015763\",\n \"text\": \"def clean(text, tokenizer, stemmer):\\n doc = ''.join(text).lower()\\n doc = re.sub(r'[<>\\\\{}/;|\\\\[\\\\]-]', ' ', doc)\\n doc = re.sub(r'[0-9]', ' ', doc)\\n doc = re.sub(r'\\\\'', ' ', doc)\\n doc = re.sub(r'=', ' ', doc)\\n doc = re.sub(r':', ' ', doc)\\n doc = re.sub(r'\\\"', ' ', doc)\\n doc = re.sub(r'\\\\s+', ' ', doc)\\n doc = re.sub(r'\\\\(', ' ', doc)\\n doc = re.sub(r'\\\\)', ' ', doc)\\n doc = re.sub(r'\\\\s{2,}', ' ', doc)\\n doc = re.sub(r'\\\\.', '', doc)\\n doc = re.sub(r',', '', doc)\\n\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d531dc571d409ba0f71d86b949d095b\",\n \"score\": \"0.6010883\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c801ea1e26a1508d732d0d546871740\",\n \"score\": \"0.60044676\",\n \"text\": \"def ocr_cleaner(text):\\n\\n text = text.lower().replace('\\\\n', ' ').replace('\\\\r', ' ').replace('\\\\t', ' ').strip() # replace all whitespace, lower\\n text = ' '.join(text.split()) # remove consecutive spaces\\n text = re.sub(r'(?<=[a-z]{3})[.]', '.\\\\n', text)\\n text = text.encode('ascii', errors='ignore').decode(encoding='utf8').replace(r'\\\\\\\\', '').replace('\\\"', '')\\n text = re.sub(r'(?<=nae)', '\\\\n', text)\\n text = re.sub(r'(?<=dae)', '\\\\n', text)\\n text = text.replace('life histories of cascadia butterflies', '').replace('\\\\\\\\', '').replace('|', '')\\n text = text.replace('adult biology', '').replace('immature stage biology', '')\\n text = text.replace('description of immature stages', '').replace('discussion', '')\\n text = re.sub(r'([0-9]){3}', '', text)\\n text = re.sub(r'([i])\\\\b', '', text)\\n text = re.sub(r'(family|subfamily).*', '', text)\\n text = text.replace('-', ' ').replace('tlie', 'the').replace('ihis', 'this').replace('ditilicult', 'difficult')\\n text = re.sub(r'^\\\\s*$', '', text)\\n text = ' '.join(text.split()) # remove consecutive spaces\\n text = re.sub(r'(?<=[a-z]{4})[.]', '.\\\\n', text)\\n text = text.strip()\\n text = re.sub(r'[.,();\\\\']', '', text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbe6034a0e46963cb33decf1d35dfa4\",\n \"score\": \"0.59949124\",\n \"text\": \"def remove_punc(data):\\n \\n #All text to lower case\\n data = data.lower()\\n #Punctuation subbed out for ''\\n data = punc.sub( '', data)\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc5773a680ca43dad9ee13f3609035f2\",\n \"score\": \"0.5977697\",\n \"text\": \"def _remove_all_other_punctuation(pos_tagged_text):\\n for j in xrange(len(pos_tagged_text)):\\n word = pos_tagged_text[j][0]\\n for symbol in (',', '.', ';', '-'):\\n word = word.replace(symbol, ' ')\\n # Write the preprocessed word back\\n pos_tagged_text[j][0] = word\\n return pos_tagged_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64bca5555bdd1a051e9978c44b240c42\",\n \"score\": \"0.597209\",\n \"text\": \"def remove_html(text):\\n return html.unescape(re.sub(doubleSpacingRe, ' ', re.sub(nbspRe, ' ', text)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f082dba8399241d49d19b16ec80984\",\n \"score\": \"0.5968257\",\n \"text\": \"def get_cleaned_text(self, text):\\n cleaned_text = text.replace('\\\\\\\"','').replace('\\\\'','').replace('-',' ')\\n cleaned_text = self.remove_non_ascii_chars(cleaned_text)\\n \\n # retweet\\n if re.match(r'RT @[_A-Za-z0-9]+:',cleaned_text): # retweet\\n if self.remove_retweets: return ''\\n retweet_info = cleaned_text[:cleaned_text.index(':')+2] # 'RT @name: ' will be again added in the text after cleaning\\n cleaned_text = cleaned_text[cleaned_text.index(':')+2:]\\n else:\\n retweet_info = ''\\n cleaned_text = self.remove_hyperlinks(cleaned_text)\\n cleaned_text = cleaned_text.replace('#','HASHTAGSYMBOL').replace('@','ATSYMBOL') # to avoid being removed while removing punctuations\\n \\n tokens = [w.translate(self.punc_table) for w in word_tokenize(cleaned_text)] # remove punctuations and tokenize\\n tokens = [w for w in tokens if not w.lower() in self.stop_words and len(w)>1] # remove stopwords and single length words\\n cleaned_text = ' '.join(tokens)\\n \\n cleaned_text = cleaned_text.replace('HASHTAGSYMBOL','#').replace('ATSYMBOL','@')\\n cleaned_text = retweet_info + cleaned_text\\n \\n return cleaned_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0c7d2d36d3f9e03b06bb850b936efa\",\n \"score\": \"0.5966438\",\n \"text\": \"def remove_char(stringedText):\\n\\ttext = stringedText\\n\\t#cleanedText = '' # need a place to store the chars that actually belong\\n\\t\\n\\t# new method that skips all the if statement branches\\n\\t# 1. Put all the special chars in a single list\\n\\t# 2. Iterate through the list when analyzing each char\\n\\t# 3. recurs as before\\n\\n\\t# if it's Alice.txt, then use the full list. otherwise use shorter list\\n\\tif sys.argv[2] == '-2':\\n\\t\\tlistOfSpecialChars = [',', '.', '?', '!', '`', '', '', '', '', '', '', '', '', '', '']\\n\\telse:\\n\\t\\tlistOfSpecialChars = ['', '', '', '', '', '', '', '', '', '']\\n\\n\\n\\tfor item in listOfSpecialChars:\\n\\t\\tif item in text:\\n\\t\\t\\ttext = text.replace(item, '')\\n\\t\\t\\tremove_char(text) # recursively call until item no longer in text, then fail condition\\n\\treturn text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce6cdf443d7400675c4686b8fff22d3\",\n \"score\": \"0.59407234\",\n \"text\": \"def remove_html(self, text):\\n soup = BeautifulSoup(text, 'html.parser')\\n return soup.get_text(separator=\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fadf8ad9b6688d84f136701aeff7897\",\n \"score\": \"0.59321564\",\n \"text\": \"def clean_text(doc):\\n \\n words = re.sub(\\\"< ?/?[a-z]+ ?>|\\\\n\\\", \\\"\\\", doc)\\n words = tokenize_text(words)\\n words = lemmatize_text(words)\\n words = remove_stopwords(words)\\n doc = [w for w in words if w.isalnum()]\\n doc = ' '.join(doc)\\n \\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83730e0c1709a337afd4043992bf3a87\",\n \"score\": \"0.5919678\",\n \"text\": \"def preprocess(self, text):\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"895767f9567130d8209cec47ed740cd6\",\n \"score\": \"0.5917767\",\n \"text\": \"def basic_cleaners(self, text):\\n text = self.lowercase(text)\\n text = self.collapse_whitespace(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acefa458000e3f328bffb60228142df1\",\n \"score\": \"0.5913148\",\n \"text\": \"def clear_text(self):\\n self.ax.texts.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b60b67b4c52b195697d002d8f248cc\",\n \"score\": \"0.59017366\",\n \"text\": \"def _do_smart_punctuation(self, text):\\r\\n if \\\"'\\\" in text: # guard for perf\\r\\n text = self._do_smart_contractions(text)\\r\\n text = self._opening_single_quote_re.sub(\\\"‘\\\", text)\\r\\n text = self._closing_single_quote_re.sub(\\\"’\\\", text)\\r\\n\\r\\n if '\\\"' in text: # guard for perf\\r\\n text = self._opening_double_quote_re.sub(\\\"“\\\", text)\\r\\n text = self._closing_double_quote_re.sub(\\\"”\\\", text)\\r\\n\\r\\n text = text.replace(\\\"---\\\", \\\"—\\\")\\r\\n text = text.replace(\\\"--\\\", \\\"–\\\")\\r\\n text = text.replace(\\\"...\\\", \\\"…\\\")\\r\\n text = text.replace(\\\" . . . \\\", \\\"…\\\")\\r\\n text = text.replace(\\\". . .\\\", \\\"…\\\")\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69ee53fb8109b8eac653e744ce6cf16\",\n \"score\": \"0.5900395\",\n \"text\": \"def preprocess_text(data):\\n if 'Body' in data:\\n data['Body'] = sanitise_xml(data['Body'])\\n \\n if 'Tags' in data:\\n data['Tags'] = re.findall('\\\\<([^>]+)', data['Tags'])\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee91042b95d6239ece6d61938a4c2c0c\",\n \"score\": \"0.589461\",\n \"text\": \"def cleanupText(path):\\n \\n text_cleaned = ''\\n try:\\n f = open(path)\\n raw = f.read().lower()\\n text = raw \\n text_cleaned = re.sub(r'[-.?!,\\\":;()/|0-9]','', text)\\n \\n# splitword = text_cleaned.split(\\\" \\\")\\n# \\n# for word in splitword:\\n# if word in stop_word:\\n# splitword.remove(word)\\n# text_cleaned = \\\" \\\".join(splitword)\\n# # print \\\"\\\\n Word count after:\\\" + str(len(text_translated.split())) + \\\"\\\\n\\\"\\n# text_cleaned = ' '.join([word for word in text_cleaned.split(' ') if (word and len(word) > 1)])\\n finally:\\n f.close()\\n return text_cleaned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e9121c9e15b11560fa8798e6959444\",\n \"score\": \"0.58939534\",\n \"text\": \"def normalize_text(self):\\n self.normalized_text = normalize_text(remove_nonwords(self.raw_text))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4d9363dc24767294beb89a6c113215\",\n \"score\": \"0.5891619\",\n \"text\": \"def delete_text(self):\\n self.in_title.delete(0, \\\"end\\\")\\n self.in_author.delete(0, \\\"end\\\")\\n self.answer.configure(text=\\\"\\\")\\n self.answer.configure(text=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9777cfd2650717c6e9d64f74f792a8dc\",\n \"score\": \"0.58881503\",\n \"text\": \"def clean_text(s: str) -> str:\\n s = unicodedata.normalize(\\\"NFKD\\\", s) # simplify unicode of string\\n s = html.unescape(s) # convert html entities to unicode characters\\n s = re.sub(r\\\"(|
)\\\", \\\"\\\\n\\\", s) # replace p or li close with newline \\n s = re.sub(r\\\"
\\\", \\\"• \\\", s) # replace list item start with bullets\\n s = re.sub(r\\\"<[^<]+?>\\\", \\\"\\\", s) # otherwise strip HTML tags\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27e37a99ab23a9aadfc8937ecf3c815\",\n \"score\": \"0.5877997\",\n \"text\": \"def remove_html_tags(text):\\n return HTMLTagsStripper.strip_tags_(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22683fdf5d6c00468b6eac0349e1afe\",\n \"score\": \"0.587424\",\n \"text\": \"def remove_meta(text):\\n text = re.sub(r'(\\\\[.*?\\\\]+)', '', text) # e.g. [one] or [[ two ]]\\n text = re.sub(r'(\\\\{.*?\\\\}+)', '', text) # e.g. {one} or {{ two }}\\n text = re.sub(r'(<.*?>+)', '', text) # e.g. or <>\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cb9610b3068c092810921d8c94fb7b\",\n \"score\": \"0.5866584\",\n \"text\": \"def clean_latex(text):\\n # edge case\\n text = re.sub(r'\\\\[math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\[\\\\/math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\\\\\', ' LaTex ', text)\\n\\n pattern_to_sub = {\\n r'\\\\\\\\mathrm': ' LaTex math mode ',\\n r'\\\\\\\\mathbb': ' LaTex math mode ',\\n r'\\\\\\\\boxed': ' LaTex equation ',\\n r'\\\\\\\\begin': ' LaTex equation ',\\n r'\\\\\\\\end': ' LaTex equation ',\\n r'\\\\\\\\left': ' LaTex equation ',\\n r'\\\\\\\\right': ' LaTex equation ',\\n r'\\\\\\\\(over|under)brace': ' LaTex equation ',\\n r'\\\\\\\\text': ' LaTex equation ',\\n r'\\\\\\\\vec': ' vector ',\\n r'\\\\\\\\var': ' variable ',\\n r'\\\\\\\\theta': ' theta ',\\n r'\\\\\\\\mu': ' average ',\\n r'\\\\\\\\min': ' minimum ',\\n r'\\\\\\\\max': ' maximum ',\\n r'\\\\\\\\sum': ' + ',\\n r'\\\\\\\\times': ' * ',\\n r'\\\\\\\\cdot': ' * ',\\n r'\\\\\\\\hat': ' ^ ',\\n r'\\\\\\\\frac': ' / ',\\n r'\\\\\\\\div': ' / ',\\n r'\\\\\\\\sin': ' Sine ',\\n r'\\\\\\\\cos': ' Cosine ',\\n r'\\\\\\\\tan': ' Tangent ',\\n r'\\\\\\\\infty': ' infinity ',\\n r'\\\\\\\\int': ' integer ',\\n r'\\\\\\\\in': ' in ',\\n }\\n # post process for look up\\n pattern_dict = {k.strip('\\\\\\\\'): v for k, v in pattern_to_sub.items()}\\n # init re\\n patterns = pattern_to_sub.keys()\\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\\n\\n def _replace(match):\\n \\\"\\\"\\\"\\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\\n \\\"\\\"\\\"\\n try:\\n word = pattern_dict.get(match.group(0).strip('\\\\\\\\'))\\n except KeyError:\\n word = match.group(0)\\n print('!!Error: Could Not Find Key: {}'.format(word))\\n return word\\n return pattern_re.sub(_replace, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cb9610b3068c092810921d8c94fb7b\",\n \"score\": \"0.5866584\",\n \"text\": \"def clean_latex(text):\\n # edge case\\n text = re.sub(r'\\\\[math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\[\\\\/math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\\\\\', ' LaTex ', text)\\n\\n pattern_to_sub = {\\n r'\\\\\\\\mathrm': ' LaTex math mode ',\\n r'\\\\\\\\mathbb': ' LaTex math mode ',\\n r'\\\\\\\\boxed': ' LaTex equation ',\\n r'\\\\\\\\begin': ' LaTex equation ',\\n r'\\\\\\\\end': ' LaTex equation ',\\n r'\\\\\\\\left': ' LaTex equation ',\\n r'\\\\\\\\right': ' LaTex equation ',\\n r'\\\\\\\\(over|under)brace': ' LaTex equation ',\\n r'\\\\\\\\text': ' LaTex equation ',\\n r'\\\\\\\\vec': ' vector ',\\n r'\\\\\\\\var': ' variable ',\\n r'\\\\\\\\theta': ' theta ',\\n r'\\\\\\\\mu': ' average ',\\n r'\\\\\\\\min': ' minimum ',\\n r'\\\\\\\\max': ' maximum ',\\n r'\\\\\\\\sum': ' + ',\\n r'\\\\\\\\times': ' * ',\\n r'\\\\\\\\cdot': ' * ',\\n r'\\\\\\\\hat': ' ^ ',\\n r'\\\\\\\\frac': ' / ',\\n r'\\\\\\\\div': ' / ',\\n r'\\\\\\\\sin': ' Sine ',\\n r'\\\\\\\\cos': ' Cosine ',\\n r'\\\\\\\\tan': ' Tangent ',\\n r'\\\\\\\\infty': ' infinity ',\\n r'\\\\\\\\int': ' integer ',\\n r'\\\\\\\\in': ' in ',\\n }\\n # post process for look up\\n pattern_dict = {k.strip('\\\\\\\\'): v for k, v in pattern_to_sub.items()}\\n # init re\\n patterns = pattern_to_sub.keys()\\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\\n\\n def _replace(match):\\n \\\"\\\"\\\"\\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\\n \\\"\\\"\\\"\\n try:\\n word = pattern_dict.get(match.group(0).strip('\\\\\\\\'))\\n except KeyError:\\n word = match.group(0)\\n print('!!Error: Could Not Find Key: {}'.format(word))\\n return word\\n return pattern_re.sub(_replace, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3df43473fa1625aa47e63ad2cfc900\",\n \"score\": \"0.58606964\",\n \"text\": \"def _clear_tag_string(self, tag_string):\\n #clean = re.compile(\\\".?>\\\")\\n clean = re.compile('.?>|\\\\\\\"|.?/>|\\\", \\\" \\\", text)\\n return re.sub(r\\\" +\\\", \\\" \\\", tag_free).strip() # make it nice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4c313f87e01e348ee6179acdfb5eaa\",\n \"score\": \"0.58543164\",\n \"text\": \"def remove_unicode(text):\\n regex = r\\\"(\\\\\\\\u....)\\\"\\n text = re.sub(regex, ' ', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d664d3566821921a5d68e2c86a08931\",\n \"score\": \"0.5848827\",\n \"text\": \"def remove_html_tags(text):\\r\\n import re\\r\\n clean = re.compile('<.*?>')\\r\\n return re.sub(clean, '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd0c2368ecf797bcb9dfcd574fc6ff7\",\n \"score\": \"0.584826\",\n \"text\": \"def clean_text(self):\\n return self.sanitize_html(self.cleaned_data['text'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e482c773dc5fd21e1ebd09d9eb3ef6ad\",\n \"score\": \"0.58404464\",\n \"text\": \"def _process_text(self,\\n index: int,\\n lower=True,\\n delete_whitespace=True,\\n delete_urls=True) -> str:\\n text = self._texts[index]\\n if not text:\\n return None\\n\\n if not isinstance(text, str):\\n raise TypeError(\\\"Text value is not string\\\")\\n\\n if lower:\\n text = text.lower()\\n if delete_whitespace:\\n text = re.sub(r'[\\\\s]+', r' ', text).strip()\\n if delete_urls:\\n text = URLS.sub('', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4f38466182c733dd2f112000f2c369\",\n \"score\": \"0.5832301\",\n \"text\": \"def clean(self, text):\\n\\n return text.replace(\\\"$=\\\", \\\"<=\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db598df2c8505b0d3bd9bcdd77e327c\",\n \"score\": \"0.5831346\",\n \"text\": \"def textilize(s):\\n s = s.replace(\\\"
\\\", \\\" \\\").replace(' ', ' ')\\n return _re_html.sub(\\\"\\\", s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595bc2579550e46f323aeeb024cfdaea\",\n \"score\": \"0.5830553\",\n \"text\": \"def character_cleanup(self, char):\\n if self.tagcategory:\\n char.tags.remove(category=self.tagcategory)\\n char.attributes.remove(category=self.tagcategory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523d6b90dc8d3f1d665155c824d38e3d\",\n \"score\": \"0.5829617\",\n \"text\": \"def epub_to_clean_text(ebook):\\n out = epub2text(ebook)\\n text=\\\"\\\"\\n for chapter in out : \\n text += filter_nonprintable(chapter) \\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15535fe57d25f9aa6661008b09fe0ecc\",\n \"score\": \"0.5823075\",\n \"text\": \"def remove_html_tags(text):\\n\\t#print(text)\\n\\t#First replace linebreaks to something NON-HTML so that it wont be cleared in the next step.\\n\\ttext = text.replace(\\\" \\\", \\\"BRBRBR\\\")\\n\\t#print(text)\\n\\n\\timport re\\n\\tclean = re.compile('<.*?>')\\n\\tclean = re.sub(clean, '', text)\\n\\t#print(clean)\\n\\t#Again replace back our linebreaks to HTML format\\n\\tclean = clean.replace(\\\"BRBRBR\\\", \\\" \\\")\\n\\t#print(clean)\\n\\t#print(\\\"---end---\\\")\\n\\treturn Markup(clean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40d8dc5dfdcf10201dd1eb69952be726\",\n \"score\": \"0.5818737\",\n \"text\": \"def text_standardize(text):\\n text = text.replace('—', '-')\\n text = text.replace('–', '-')\\n text = text.replace('―', '-')\\n text = text.replace('…', '...')\\n text = text.replace('´', \\\"'\\\")\\n text = re.sub(r'''(-+|~+|!+|\\\"+|;+|\\\\?+|\\\\++|,+|\\\\)+|\\\\(+|\\\\\\\\+|\\\\/+|\\\\*+|\\\\[+|\\\\]+|}+|{+|\\\\|+|_+)''', r' \\\\1 ', text)\\n text = re.sub(r'\\\\s*\\\\n\\\\s*', ' \\\\n ', text)\\n text = re.sub(r'[^\\\\S\\\\n]+', ' ', text)\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e09095356905e64ce0cbd9278becd9a\",\n \"score\": \"0.581436\",\n \"text\": \"def clean_text(text):\\n digi_punct = \\\"[^a-zA-Z.1234567890'# ]\\\" # used by clean_text(text_corpus)\\n text = text.replace(\\\"´\\\", \\\"'\\\")\\n text = re.sub(digi_punct, \\\" \\\", text)\\n\\n # digi_punct = \\\"[^a-zA-Z.1234567890' ]\\\"\\n text = \\\" \\\".join(text.split())\\n text = text.lower()\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc88ebeb02bbc3589d10ad07550933f\",\n \"score\": \"0.58119494\",\n \"text\": \"def remove_speech_meta(text):\\n text = re.sub(r'^(\\\\s*\\\\([^)]*\\\\)\\\\s*)', '', text) # e.g. (to Ray) Hello\\n text = re.sub(r'(\\\\s*\\\\([^)]*\\\\)\\\\s*)$', '', text) # e.g. Hello (motions at Ray)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95693e0352cd0d901a0f5a44a8f4cfe8\",\n \"score\": \"0.58030546\",\n \"text\": \"def clean_tweet_text(tweet):\\n tweet = tweet.strip(\\\"\\\\n\\\")\\n tweet = tweet.strip(\\\"\\\\t\\\")\\n tweet = remove_emojis(tweet)\\n tweet = remove_hashtag_symbol(tweet)\\n return tweet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09587b5540da31a7dfd6b6bbd3d7785d\",\n \"score\": \"0.5796727\",\n \"text\": \"def clean(text: str) -> str:\\n\\n clean_str = ''\\n for char in text:\\n if char.isalnum() or char == UNDERSCORE:\\n clean_str = clean_str + char\\n elif char == HASHTAG_SYMBOL or char == MENTION_SYMBOL:\\n clean_str = clean_str + SPACE + char\\n else:\\n clean_str = clean_str + SPACE\\n return clean_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4136548aa14d7139072f51ccbad881f9\",\n \"score\": \"0.57962835\",\n \"text\": \"def _remove_tags(html):\\n html = _replace_ampersands(html)\\n html = re.sub('\\\\|\\\\', '', html)\\n html = re.sub('\\\\|\\\\', '', html)\\n tagrx = re.compile('\\\\<.+?\\\\>', flags=re.DOTALL)\\n html = tagrx.sub(' ', html)\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd98a8e4b041ee8fc317d9868467d75\",\n \"score\": \"0.57946014\",\n \"text\": \"def clean_paragraph(p):\\n\\n\\tif p.find_all(\\\"span\\\") == None:\\n\\t\\tp_text = p\\n\\telse:\\n\\t\\tfor span_tag in p.find_all(\\\"span\\\"):\\n\\t\\t\\tspan_tag.replace_with(\\\"\\\")\\n\\t\\tp_text = str(p)\\n\\tcorrected_p = re.sub(r' \\\\(.*?\\\\)', \\\"\\\", p_text)\\n\\treturn corrected_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60bbf591aad17c3da73f1146cab065d\",\n \"score\": \"0.5793579\",\n \"text\": \"def compact_text(data):\\n interim_codes = compact_text_interim(data)\\n return (_compact_chunk(chunk) for chunk in chunks(interim_codes, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7546c60a27ce53548e27a01df5be84cc\",\n \"score\": \"0.57929564\",\n \"text\": \"def normalize_text(text):\\r\\n text = text.replace('\\\\r\\\\n', '\\\\n').replace('\\\\r', '\\\\n').expandtabs()\\r\\n if not text.endswith('\\\\n'):\\r\\n text += '\\\\n'\\r\\n\\r\\n # ignore BOM chars at the begining of template\\r\\n BOM = '\\\\xef\\\\xbb\\\\xbf'\\r\\n if isinstance(text, str) and text.startswith(BOM):\\r\\n text = text[len(BOM):]\\r\\n \\r\\n # support fort \\\\$ for backward-compatibility \\r\\n text = text.replace(r'\\\\$', '$$')\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56abfb553c3d5584260c5b0cf4c18d3\",\n \"score\": \"0.57876676\",\n \"text\": \"def delete_chars(text, unwanted_chars):\\n for char in unwanted_chars:\\n text = text.replace(char, '')\\n return text\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":188,"cells":{"query_id":{"kind":"string","value":"ffb57bc6ba104f32dc98f65948a63fa8"},"query":{"kind":"string","value":"Returns best solution and energy after all runs"},"positive_passages":{"kind":"list like","value":[{"docid":"4e583b5900abb5f15a91f16db7969b24","score":"0.0","text":"def mws(model):\n \n def max_score_local(x_val, k = 0):\n \"\"\"\n Generates best neighbor upon mutating in one random direction\n Input: x_val to be mutated\n Output: neighbor with best score along index k in x_val\n \"\"\"\n steps = 10\n best_neigh = x_val[:]\n mutated_neigh = x_val\n if isinstance(model.max_x, list):\n step_max = model.max_x[k]\n step_min = model.min_x[k]\n else:\n step_max = model.max_x\n step_min = model.min_x\n increment = (step_max - step_min)/steps\n for i in xrange(steps):\n mutated_neigh[k] = step_min + increment*i\n mutated_neigh = model.ok(mutated_neigh)\n mutant_e = model.aggregator(mutated_neigh)\n best_e = model.aggregator(best_neigh)\n if mutant_e > best_e:\n best_neigh = mutated_neigh\n return best_neigh\n \n \n max_tries = 100\n max_changes = 50\n threshold = 100\n p = 0.5\n k = 0\n output = \" \"\n better_count = 0\n confused_count = 0\n best_count = 0\n init_solution, init_score = model.solution()\n #print init_solution, init_score\n print \"#\"*120\n print \"Running MWS for \", type(model).__name__\n print \"#\"*120\n print \"Constraints: \"\n print \"Lower Bound for x= \", model.min_x\n print \"Upper Bound for x= \", model.max_x\n print \"Max trials = %d, Max changes = %d, p = %0.2f, threshold = %d\" % (max_tries, max_changes, p, threshold)\n print \"-\"*120\n for i in range(max_tries):\n \n x_vec, x_score = model.solution()\n for j in range(max_changes):\n c = randrange(0, len(init_solution))\n if x_score > threshold:\n return x_vec\n\n if p < random():\n x_vec, x_score = model.mutate_solution(x_vec, c)\n output += \"?\"\n confused_count += 1\n \n else:\n out_x = max_score_local(x_vec, c)\n if out_x == x_vec:\n output += \".\"\n better_count += 1\n else:\n output += \"+\"\n x_vec = out_x\n best_count += 1\n \n if x_score > init_score:\n init_solution = x_vec\n init_score = x_score\n\n if k % 50 == 0: \n print \"eb = %6f | ? = %d | + = %d | . = %d | %s\" % (init_score, confused_count, best_count, better_count, output)\n output = \" \"\n better_count = 0\n confused_count = 0\n best_count = 0\n k = k + 1\n print \"-\"*120\n print \"Best solution: \", init_solution\n print \"Best energy: \", init_score","title":""}],"string":"[\n {\n \"docid\": \"4e583b5900abb5f15a91f16db7969b24\",\n \"score\": \"0.0\",\n \"text\": \"def mws(model):\\n \\n def max_score_local(x_val, k = 0):\\n \\\"\\\"\\\"\\n Generates best neighbor upon mutating in one random direction\\n Input: x_val to be mutated\\n Output: neighbor with best score along index k in x_val\\n \\\"\\\"\\\"\\n steps = 10\\n best_neigh = x_val[:]\\n mutated_neigh = x_val\\n if isinstance(model.max_x, list):\\n step_max = model.max_x[k]\\n step_min = model.min_x[k]\\n else:\\n step_max = model.max_x\\n step_min = model.min_x\\n increment = (step_max - step_min)/steps\\n for i in xrange(steps):\\n mutated_neigh[k] = step_min + increment*i\\n mutated_neigh = model.ok(mutated_neigh)\\n mutant_e = model.aggregator(mutated_neigh)\\n best_e = model.aggregator(best_neigh)\\n if mutant_e > best_e:\\n best_neigh = mutated_neigh\\n return best_neigh\\n \\n \\n max_tries = 100\\n max_changes = 50\\n threshold = 100\\n p = 0.5\\n k = 0\\n output = \\\" \\\"\\n better_count = 0\\n confused_count = 0\\n best_count = 0\\n init_solution, init_score = model.solution()\\n #print init_solution, init_score\\n print \\\"#\\\"*120\\n print \\\"Running MWS for \\\", type(model).__name__\\n print \\\"#\\\"*120\\n print \\\"Constraints: \\\"\\n print \\\"Lower Bound for x= \\\", model.min_x\\n print \\\"Upper Bound for x= \\\", model.max_x\\n print \\\"Max trials = %d, Max changes = %d, p = %0.2f, threshold = %d\\\" % (max_tries, max_changes, p, threshold)\\n print \\\"-\\\"*120\\n for i in range(max_tries):\\n \\n x_vec, x_score = model.solution()\\n for j in range(max_changes):\\n c = randrange(0, len(init_solution))\\n if x_score > threshold:\\n return x_vec\\n\\n if p < random():\\n x_vec, x_score = model.mutate_solution(x_vec, c)\\n output += \\\"?\\\"\\n confused_count += 1\\n \\n else:\\n out_x = max_score_local(x_vec, c)\\n if out_x == x_vec:\\n output += \\\".\\\"\\n better_count += 1\\n else:\\n output += \\\"+\\\"\\n x_vec = out_x\\n best_count += 1\\n \\n if x_score > init_score:\\n init_solution = x_vec\\n init_score = x_score\\n\\n if k % 50 == 0: \\n print \\\"eb = %6f | ? = %d | + = %d | . = %d | %s\\\" % (init_score, confused_count, best_count, better_count, output)\\n output = \\\" \\\"\\n better_count = 0\\n confused_count = 0\\n best_count = 0\\n k = k + 1\\n print \\\"-\\\"*120\\n print \\\"Best solution: \\\", init_solution\\n print \\\"Best energy: \\\", init_score\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"dc23f9d302e3c5b94328c54bbe84fee4","score":"0.7375325","text":"def solution(self):\n return self.Mbest","title":""},{"docid":"a7a458447c66af157f126dfa185c1878","score":"0.6866638","text":"def optimize(self):\n\n # Initiate particles\n self.__initPart()\n self.listOfPos = []\n\n NFC = 0\n while(NFC < self.maxGen):\n #print \"Run: \" + str(NFC) + \" Best: \" + str(self.globBestFit)\n\n # Perform search\n self.update()\n\n # Acceptably close to solution\n #if self.globBestFit < self.epsError:\n #return self.globBestPos, self.globBestFit\n\n # next gen\n NFC += 1\n self.listOfPos.append(self.globBestFit)\n # Search finished\n return self.globBestPos, self.globBestFit, self.listOfPos","title":""},{"docid":"8f7c273cb7bfaf99e55d77e2de198e94","score":"0.68366396","text":"def solve(self):\r\n\r\n nfev, nit, warning_flag = 0, 0, False\r\n status_message = _status_message['success']\r\n\r\n # calculate energies to start with\r\n for index, candidate in enumerate(self.population):\r\n parameters = self._scale_parameters(candidate)\r\n self.population_energies[index] = self.func(parameters,\r\n *self.args)\r\n nfev += 1\r\n\r\n if nfev > self.maxfun:\r\n warning_flag = True\r\n status_message = _status_message['maxfev']\r\n break\r\n\r\n minval = np.argmin(self.population_energies)\r\n\r\n # put the lowest energy into the best solution position.\r\n lowest_energy = self.population_energies[minval]\r\n self.population_energies[minval] = self.population_energies[0]\r\n self.population_energies[0] = lowest_energy\r\n\r\n self.population[[0, minval], :] = self.population[[minval, 0], :]\r\n\r\n if warning_flag:\r\n return OptimizeResult(\r\n x=self.x,\r\n fun=self.population_energies[0],\r\n nfev=nfev,\r\n nit=nit,\r\n message=status_message,\r\n success=(warning_flag != True))\r\n\r\n # do the optimisation.\r\n for nit in range(1, self.maxiter + 1):\r\n if self.dither is not None:\r\n self.scale = self.random_number_generator.rand(\r\n ) * (self.dither[1] - self.dither[0]) + self.dither[0]\r\n for candidate in range(np.size(self.population, 0)):\r\n if nfev > self.maxfun:\r\n warning_flag = True\r\n status_message = _status_message['maxfev']\r\n break\r\n\r\n trial = self._mutate(candidate)\r\n self._ensure_constraint(trial)\r\n parameters = self._scale_parameters(trial)\r\n\r\n energy = self.func(parameters, *self.args)\r\n nfev += 1\r\n\r\n if energy < self.population_energies[candidate]:\r\n self.population[candidate] = trial\r\n self.population_energies[candidate] = energy\r\n\r\n if energy < self.population_energies[0]:\r\n self.population_energies[0] = energy\r\n self.population[0] = trial\r\n\r\n # stop when the fractional s.d. of the population is less than tol\r\n # of the mean energy\r\n convergence = (np.std(self.population_energies) /\r\n np.abs(np.mean(self.population_energies) +\r\n _MACHEPS))\r\n\r\n if self.disp:\r\n print(\"differential_evolution step %d: f(x)= %g\"\r\n % (nit,\r\n self.population_energies[0]))\r\n\r\n if (self.callback and\r\n self.callback(self._scale_parameters(self.population[0]),\r\n convergence=self.tol / convergence) is True):\r\n\r\n warning_flag = True\r\n status_message = ('callback function requested stop early '\r\n 'by returning True')\r\n break\r\n\r\n if convergence < self.tol or warning_flag:\r\n break\r\n\r\n else:\r\n status_message = _status_message['maxiter']\r\n warning_flag = True\r\n\r\n DE_result = OptimizeResult(\r\n x=self.x,\r\n fun=self.population_energies[0],\r\n nfev=nfev,\r\n nit=nit,\r\n message=status_message,\r\n success=(warning_flag != True))\r\n\r\n if self.polish:\r\n result = minimize(self.func,\r\n np.copy(DE_result.x),\r\n method='L-BFGS-B',\r\n bounds=self.limits.T,\r\n args=self.args)\r\n\r\n nfev += result.nfev\r\n DE_result.nfev = nfev\r\n\r\n if result.fun < DE_result.fun:\r\n DE_result.fun = result.fun\r\n DE_result.x = result.x\r\n DE_result.jac = result.jac\r\n # to keep internal state consistent\r\n self.population_energies[0] = result.fun\r\n self.population[0] = self._unscale_parameters(result.x)\r\n\r\n return DE_result","title":""},{"docid":"a0117546e96810312ac163daf208b031","score":"0.6755187","text":"def search_optimal(self):\n\n for i in range(self.get_nsource()):\n ith_bee_fitness = self.bee[i].get_fitness()\n best_fitness = self.optimal.get_fitness()\n if best_fitness < ith_bee_fitness:\n self.suboptimal.greedy_choice(self.optimal)\n self.optimal.greedy_choice(self.bee[i])\n elif best_fitness == ith_bee_fitness:\n pass # protect updating suboptimal in this case\n elif self.suboptimal.get_fitness() < ith_bee_fitness:\n self.suboptimal.greedy_choice(self.bee[i])","title":""},{"docid":"f474c1ee4cea7c9306c18d7443490471","score":"0.6726404","text":"def _getBestSolution(obj):\n filename = _getOutputFilename(obj.folder_output + BEST_SOLUTION_FILENAME)\n obj.bestSolution = _loadCSV(filename)","title":""},{"docid":"6112d959c74a77caaeb6a8ef2f3e1704","score":"0.6724941","text":"def getstart_temp(self):\n\n assert(not self.dims is None)\n lrange = self.lower\n urange = self.upper\n fmax = _double_min\n fmin = _double_max\n for _ in range(self.Ninit):\n x0 = random.uniform(size=self.dims)*(urange-lrange) + lrange\n fval = self.func(x0, *self.args)\n self.feval += 1\n if fval > fmax:\n fmax = fval\n if fval < fmin:\n fmin = fval\n bestEnergy = fval\n bestSolution = array(x0)\n\n self.T0 = (fmax-fmin)*1.5\n return bestSolution, bestEnergy","title":""},{"docid":"57f818ca9f877589cbc25e77d1157ae1","score":"0.6616518","text":"def get_best():\n\n best_h = 3\n best_not = 3\n runs = 7\n\n np.save(\"H_START_S_BEST.npy\",0)\n np.save(\"NOT_START_S_BEST.npy\",0)\n np.save(\"H_START_S.npy\",0)\n np.save(\"NOT_START_S.npy\",0)\n\n for i in range(runs):\n \n # Run Sim-H\n sim_h.run_main_h()\n # Run Sim-NOT\n sim_not.run_main_not()\n\n hloss = run_waves(\"H\")\n print(\"Loss of H Gate\")\n print(hloss)\n nloss = run_waves(\"N\")\n print(\"Loss of NOT Gate\")\n print(nloss)\n\n # If this is a better H value, update\n if (hloss < best_h):\n best_h = hloss\n overwriteH()\n\n # If this is a better NOT value, update\n if (nloss < best_not):\n best_not = nloss\n overwriteNOT()\n\n\n h_waves_list = np.load(\"H_START_S_BEST.npy\")\n not_waves_list = np.load(\"NOT_START_S_BEST.npy\")\n\n return (h_waves_list, not_waves_list)","title":""},{"docid":"90862e6480a983a07b72ffac8f4a63f1","score":"0.6562416","text":"def solve(self):\n self.runtime = Timer() # Start the timer to measure the runtime\n self.model.min_to_max() # Convert minimization problems to maximization problems\n self.model.construct_payoff() # Construct a payoff table from the objective function values\n self.model.find_obj_range() # Find the range of each objective function\n self.model.convert_prob() # Convert the payoff table\n\n self._find_solutions() # Find all solutions to the optimization problem\n self._process_solutions() # Identify the unique solutions\n self._get_hv_indicator() # Compute the HV indicator\n\n # Save the results to an Excel file if requested\n if self.opts.output_excel:\n self._output_excel()\n\n # Compute the total runtime and print a summary of the results\n self.runtime = round(self.runtime.get(), 2)\n Helper.clear_line()\n print(\n f\"Solved {self.model.models_solved.value()} models for \"\n f\"{self.num_unique_pareto_sols} unique Pareto solutions in \"\n f\"{self.runtime} seconds\"\n )\n\n # Log a summary of the results\n self.logger.info(Helper.separator())\n self.logger.info(f\"Runtime: {self.runtime} seconds\")\n self.logger.info(f\"Models solved: {self.model.models_solved.value()}\")\n self.logger.info(f\"Infeasibilities: {self.model.infeasibilities.value()}\")\n self.logger.info(f\"Solutions: {self.num_sols}\")\n self.logger.info(f\"Unique solutions: {self.num_unique_sols}\")\n self.logger.info(f\"Unique Pareto solutions: {self.num_unique_pareto_sols}\")\n self.logger.info(f\"Hypervolume indicator: {self.hv_indicator}\")","title":""},{"docid":"7f57fe14d76194d4919d36fb73dcce81","score":"0.6554548","text":"def get_best_result(self):\n # self.result_data\n # raise NotImplementedError\n param_names = ['r', 'z', 'phi', 'scale', 'align', 'cloud']\n \n\n for chan in self.chan_list:\n for i in self.index:\n self.best_params[chan][i] = []\n #loops over all the fit waveforms\n # for model_num,single_wf_model in enumerate(self.wf_model):\n model_num = i\n single_wf_model = self.wf_model[i]\n print(\"Calculating best values for {}\".format(model_num))\n\n for j in range(single_wf_model.num_params):\n # loops over all the parameters\n param_values = self.result_data[i][j]\n\n param_avg = np.mean(param_values)\n param_std = np.std(param_values)\n\n self.best_params[model_num].append(param_avg)\n\n print(\"{name}: {avg:4.4f} +/- {std:4.4f}\".format(\n name=param_names[j], \n avg=param_avg, \n std=param_std)\n )","title":""},{"docid":"c22ab22848e4af7f6852a38c863bd374","score":"0.6523312","text":"def solve(self):\n fit_partial = self.fit_partial\n n_jobs = self.n_jobs\n gen = self.gen\n\n # get results in parallel\n all_res = Parallel(n_jobs=n_jobs)(\n delayed(fit_partial)(\n order=order,\n seasonal_order=seasonal_order,\n )\n for order, seasonal_order in gen\n )\n\n sorted_fits = _sort_and_filter_fits(all_res)\n if self.trace and sorted_fits:\n print(f\"\\nBest model: {str(sorted_fits[0])}\")\n\n return sorted_fits","title":""},{"docid":"390db28702298605d2d7b3a6236568e8","score":"0.6472718","text":"def run(self):\n self.time_start = time.time()\n self.step = 0\n self.step_idle = 0\n\n E = self.energy()\n\n prev_state = self.copy_state(self.state)\n prev_energy = E\n\n self.best_state = self.copy_state(self.state)\n self.best_energy = E\n self.best_step = 0\n self.energy_history = [E] * self.history_length\n Ehmean = E\n Ehvar = 0.\n Nvar = float(max(self.history_length - 1, 1))\n\n if self.updates_every > 0:\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\n steps_since_update = 0\n\n while not self.terminate_search() and not self.user_exit:\n self.move()\n E = self.energy()\n\n if E >= prev_energy:\n self.step_idle += 1\n else:\n self.step_idle = 0\n\n v = self.step % self.history_length\n Ev = self.energy_history[v]\n if E < Ev or E <= prev_energy:\n # accept candidate state\n prev_state = self.copy_state(self.state)\n prev_energy = E\n\n if E < self.best_energy:\n self.best_state = self.copy_state(self.state)\n self.best_energy = E\n self.best_step = self.step\n else:\n # restore previous state\n self.state = self.copy_state(prev_state)\n E = prev_energy\n\n if E < Ev:\n # Update energy history\n self.energy_history[v] = E\n\n # and its mean and variance\n dE = E - Ev\n Ehmean_old = Ehmean\n\n Ehmean += dE / self.history_length\n Ehvar += dE * (E-Ehmean+Ev-Ehmean_old) / Nvar\n\n self.step += 1\n steps_since_update += 1\n\n if steps_since_update == self.updates_every:\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\n steps_since_update = 0\n\n self.state = self.copy_state(self.best_state)\n if self.save_state_on_exit:\n self.save_state()\n\n self.time_end = time.time()\n return self.best_state, self.best_energy","title":""},{"docid":"7ec8c15419ad8bc00193cb7e262615f0","score":"0.646268","text":"def optimal(self):\n optimal_check = self.checker(True)\n return optimal_check","title":""},{"docid":"e8707ac8ab5a432499cd686262931a91","score":"0.6461675","text":"def get_best_solution(self):\n print \"getting best solution\"\n greatest_number_of_words = 0\n prelim_best_solutions = []\n for psol in self.potential_solutions:\n l = len(\" \".join(map(lambda x:x['title'],psol)))\n if l == greatest_number_of_words:\n prelim_best_solutions.append(psol)\n elif l > greatest_number_of_words:\n greatest_number_of_words = l\n prelim_best_solutions = [psol]\n best_solution = None\n least_number_of_tracks = 999999\n for bsol in prelim_best_solutions:\n if len(bsol) < least_number_of_tracks:\n least_number_of_tracks = len(bsol)\n best_solution = bsol\n return best_solution","title":""},{"docid":"dbbafe1f015464da4a494c735c33ee07","score":"0.6442411","text":"def getSolution(self):\n self.var_constr_dict = self.mapVarToConstraints()\n\n start = time.time()\n solution = self.solver.getSolution(self)\n self.runtime = time.time() - start\n # solution is an assignment of problem, where the assignments for all variables are filled in. \n return solution, self.getStatistics()","title":""},{"docid":"ede2c84a0104dd19a0e4737190f698ad","score":"0.63996756","text":"def save_best_sol(self):\n\n self.save(result=self.best_solutions_list)","title":""},{"docid":"b4ba86d89bccfe98a869ed480491d257","score":"0.6390985","text":"def optimize(self):\n rankedSol = self._rankSolutions()\n bestSol = rankedSol[0]\n # include best solution in next generation solutions\n newSols = [bestSol]\n\n for s in rankedSol[1:]:\n if np.random.uniform(0.0, 1.0) > 0.5:\n a = self._computeA()\n normA = np.linalg.norm(a)\n if normA < 1.0:\n newS = self._encircle(s, bestSol, a)\n else:\n # ##select random sol\n randomSol = self._sols[np.random.randint(self._sols.shape[0])]\n newS = self._search(s, randomSol, a)\n else:\n newS = self._attack(s, bestSol)\n newSols.append(self._constrain_solution(newS))\n\n self._sols = np.stack(newSols)\n self._a -= self._aStep","title":""},{"docid":"75c1eca33dce422738cb450fdee65c6e","score":"0.63894033","text":"def solver_optimize(self):\n parts = self.feats_obj.feat_partitions\n self.N_s = [None for i in parts]\n self.opt_sol = [None for i in parts]\n self.norm_z = [None for i in parts]\n self.compute_zero_stats()\n\n for i, partition in enumerate(parts):\n self.compute_data_stats(partition, i)\n self.compute_zc(partition, i)\n self.compute_theta(partition, i)\n\n # print('N_s is: ', self.N_s)\n # print('Z_c is: ', self.norm_z)\n # print('Theta is: ', self.opt_sol)","title":""},{"docid":"8d4b156c2e290526811e8a8e8d75a345","score":"0.6357096","text":"def _solve(self):\n #algorithm = self.config['algorithm'](self.config)\n \n self.output = self.algorithm.run(self.config['u_0'],self.config['TOL'],self.config['MAXIT'])\n print('Iterations:' + str(self.output['iter']))","title":""},{"docid":"98e45c5f527db22d70313ad80ec8673d","score":"0.6339497","text":"def solve(self):\n\n while True:\n self.iteration += 1\n\n logger.debug('iteration: n=%s', self.iteration)\n\n self._iterate()\n\n if self._converged():\n break\n\n return self._find_strongest_solution()","title":""},{"docid":"149015545ba83e85aff88252b4aa13f7","score":"0.63234216","text":"def run(self, iterations, run_from_start=True):\n if run_from_start:\n self.run_init()\n for _ in range(iterations):\n self._build_ant_solutions()\n self._update_iteration_best()\n self._update_global_best()\n self.global_pheromone_update(self.global_best)\n self.iteration += 1\n return self.global_best","title":""},{"docid":"7e2f056ef0ae1d598ecbc81e870cab69","score":"0.62895966","text":"def best_fitness(self):\n return self.fitness[self.order[-1]]","title":""},{"docid":"c0f291c79ddcfe9833db29d6e0a3b0f6","score":"0.62888795","text":"def _solve_impl(self):\n\n self.start_time = clock() # Used to show the total time took the process\n bellman_updates = 0 # used to track the performance improvement.\n converged = False\n iteration = 0\n\n num_states = self.representation.num_states_total\n\n while self.has_time() and not converged:\n iteration += 1\n\n # Store the weight vector for comparison\n prev_weight = self.representation.weight.copy()\n\n # Sweep The State Space\n for i in range(num_states):\n\n s = self.representation.stateID2state(i)\n # Sweep through possible actions\n if self.domain.is_terminal(s):\n continue\n for a in self.domain.possible_actions(s):\n\n self.bellman_backup(s, a, ns_samples=self.ns_samples)\n bellman_updates += 1\n\n # Create Log\n if bellman_updates % self.log_interval == 0:\n performance_return, _, _, _ = self.performance_run()\n self._log_updates(performance_return, bellman_updates)\n\n # check for convergence\n weight_diff = l_norm(prev_weight - self.representation.weight)\n converged = weight_diff < self.convergence_threshold\n\n # log the stats\n (\n perf_return,\n perf_steps,\n perf_term,\n perf_disc_return,\n ) = self.performance_run()\n self.logger.info(\n \"PI #%d [%s]: BellmanUpdates=%d, ||delta-weight_vec||=%0.4f, \"\n \"Return=%0.4f, Steps=%d\"\n % (\n iteration,\n hhmmss(deltaT(self.start_time)),\n bellman_updates,\n weight_diff,\n perf_return,\n perf_steps,\n )\n )\n\n # Show the domain and value function\n if self._visualize_mode:\n self.domain.show_learning(self.representation)\n\n # store stats\n self.result[\"bellman_updates\"].append(bellman_updates)\n self.result[\"return\"].append(perf_return)\n self.result[\"planning_time\"].append(deltaT(self.start_time))\n self.result[\"num_features\"].append(self.representation.features_num)\n self.result[\"steps\"].append(perf_steps)\n self.result[\"terminated\"].append(perf_term)\n self.result[\"discounted_return\"].append(perf_disc_return)\n self.result[\"iteration\"].append(iteration)\n\n if converged:\n self.logger.info(\"Converged!\")\n self.log_value()","title":""},{"docid":"d2b1c534c039f242d2a7c458ea6fb951","score":"0.6226838","text":"def calculateBest():\r\n\r\n if var.score>var.best:\r\n setNewBest()\r\n return var.score\r\n else: return var.best\r\n #if the actual score is bigger than the best score\r\n #set the best score to the actual score\r","title":""},{"docid":"c7fe40017c2980b624d976cb8e5d1632","score":"0.6216328","text":"def search(self, config):\n self.iteration = 0\n while self.iteration < self.maxIterations:\n self.GAStep(config)\n self.iteration += 1\n\n print (\"Total iterations: \",self.iteration)\n print (\"Best Solution: \", self.best.getFitness())","title":""},{"docid":"b0ba52f8f0040d777dcae8c696c03ef8","score":"0.6213454","text":"def _solution(self):\n return self.__solution","title":""},{"docid":"07d78b5b7da1a48b218ae692e26ffa46","score":"0.62130654","text":"def solve(self):\n \n # Do the actual run to find the fit and trendline\n self.fitModel = WarmingFitModel(self.dates, self.temps)\n self.constants = self.fitModel.solve()\n self.devs = self.fitModel.deviations()\n \n # Verify the optimality of solutions\n xvariabilities = { \"min\" : [], \"max\" : [] }\n if not self.DISABLE_VARIABILITY_CHECK:\n for op in xvariabilities.keys():\n for xsub in [0, 1]:\n variabilityModel = WarmingVariabilityModel(self.dates, self.temps, xsub, op, self.constants, sum(list(map(abs, self.devs))))\n xvariabilities[op].append(variabilityModel.solve())\n pprint.pprint(xvariabilities)\n \n # Verify the chosen value for solar cycle length\n cycleLengths = [9.7 + 0.2 * i for i in range(11)]\n cycleDevs = []\n if not self.DISABLE_CYCLE_LENGTH_CHECK:\n for cycleLength in cycleLengths:\n solarFitModel = WarmingFitModel(self.dates, self.temps, solarCycle=cycleLength)\n cycleDevs.append(sum([abs(dev) for dev in solarFitModel.deviations()]))\n pprint.pprint(cycleDevs)\n \n stdevs = [0 for x in self.constants]\n if not self.DISABLE_CONFIDENCE_INTERVALS:\n # Do a bunch more iterations to find a confidence interval for the original fit\n ITER_COUNT = 50\n random.seed()\n xstar = []\n for fuzzIter in range(ITER_COUNT):\n fuzzedTemps = [self.temps[i] + random.choice(self.devs) for i in list(range(len(self.dates)))]\n fuzzedModel = WarmingFitModel(self.dates, fuzzedTemps)\n fuzzedConstants = fuzzedModel.solve()\n print(\"Fuzzy constants: \" + str(fuzzedConstants))\n xstar.append(fuzzedConstants)\n \n # Actually compute the confidence interval\n if ITER_COUNT > 0:\n cMeans = [sum(fuzzedX[xpos] for fuzzedX in xstar) / float(ITER_COUNT) for xpos in range(len(self.constants))]\n for pos in range(len(self.constants)):\n cDevs = [(xstar[i][pos] - cMeans[pos]) ** 2 for i in range(ITER_COUNT)]\n cStdDev = math.sqrt(sum(cDevs) / float(len(cDevs)))\n stdevs[pos] = cStdDev\n \n return {\"fit\" : [(self.constants[i], 2 * stdevs[i]) for i in range(len(self.constants))], \\\n \"xvariabilities\" : xvariabilities, \\\n \"cycle\" : (cycleLengths, cycleDevs) }","title":""},{"docid":"21b1caf17582b8c1975b073d829c4e34","score":"0.62079585","text":"def find_solution(self):\n start_time = timer.time()\n result = []\n constraints = []\n \n ##############################\n # Task 2.4 : Addressing Failure\n #reordering agent priority using heuristic costs\n agent_max_cost = []\n for agent in range(self.num_of_agents):\n max_cost = -1\n for loc, cost in self.heuristics[agent].items():\n print(loc, cost)\n if cost > max_cost:\n max_cost = cost\n agent_max_cost.append([cost, agent])\n agent_max_cost.sort()\n agent_max_cost.reverse()\n \n for _, i in agent_max_cost:\n ##############################\n\n # original\n # for i in range(self.num_of_agents): # Find path for each agent\n \n path = a_star(self.my_map, self.starts[i], self.goals[i], self.heuristics[i],\n i, constraints)\n if path is None:\n raise BaseException('No solutions')\n result.append(path)\n \n print('Agent[{}]'.format(i), 'path = ', path)\n \n ##############################\n # Task 2: Add constraints here\n # Useful variables:\n # * path contains the solution path of the current (i'th) agent, e.g., [(1,1),(1,2),(1,3)]\n # * self.num_of_agents has the number of total agents\n # * constraints: array of constraints to consider for future A* searches\n\n # Task 2.1 : adding vertex constraints\n # for node in path:\n # constraint = dict()\n # constraint['agent'] = i\n # constraint['loc'] = [node[0], None]\n # constraint['timestep'] = node[1]\n # constraints.append(constraint)\n \n # build constraint from a path\n for p in range(len(path)):\n curr_loc = path[p]\n next_loc = None\n curr_timestep = p\n \n # vertex constraint\n constraint = dict()\n constraint['agent'] = i\n constraint['loc'] = [curr_loc, next_loc]\n constraint['timestep'] = curr_timestep\n constraints.append(constraint)\n \n # edge constraint except start cell\n if p > 0:\n prev_loc = path[p - 1]\n next_timestep = p\n \n constraint = dict()\n constraint['agent'] = i\n constraint['loc'] = [curr_loc, prev_loc]\n constraint['timestep'] = next_timestep\n constraints.append(constraint)\n \n ##############################\n \n # Task 2.3: additional constraints\n # agent_0 reached its goal cell at timestep 3,\n # but, agent_1 will reach to the goal cell at timestep 7\n # then, agent_0 will be wait \n \n # but, we don't know the value 7\n # so, just estimate this value with the prior knowledge\n # for example, # of a valid cell\n \n ##############################\n \n last_loc = path[-1]\n last_timestep = len(path) -1\n \n max_cells = 0\n \n for x in range(len(self.my_map)):\n for y in range(len(self.my_map[0])):\n if not self.my_map[x][y]:\n max_cells += 1\n \n for timestep in range(last_timestep + 1, max_cells):\n constraint = dict()\n constraint['agent'] = i\n constraint['loc'] = [last_loc, None]\n constraint['timestep'] = timestep\n constraints.append(constraint) \n\n self.CPU_time = timer.time() - start_time\n\n print(\"\\n Found a solution! \\n\")\n print(\"CPU time (s): {:.2f}\".format(self.CPU_time))\n print(\"Sum of costs: {}\".format(get_sum_of_cost(result)))\n print(result)\n return result","title":""},{"docid":"b65a27ab5b4a47ccc18024490760dc46","score":"0.61903894","text":"def result(self):\n return self.best.x","title":""},{"docid":"2e2e8b4ec0309484e14adeec64980705","score":"0.6185776","text":"def getBestSol(sqe, Res, c_ms):\n #---docstring------------------------------------------------------------------\n #---end-docstring--------------------------------------------------------------\n res = dc(Res)\n C_ms = dc(c_ms)\n lsqSc = numpy.zeros(len(res),'d') + numpy.inf \n lsqMu = numpy.zeros(len(res),'d') + numpy.inf \n lsqSl = numpy.zeros(len(res),'d') + numpy.inf\n for i in range(len(res)):\n try:\n zeroCut = sqe.zeroInd + 1\n sqeCut = sqe.zeroInd+res[i][1].cutoffInd\n slopeCut = sqe.slopeCut()\n sqeLin = sqe.se[sqeCut:slopeCut]\n mulSqeLin = nar(nar(res[i][0][1:]))[sqeCut:slopeCut]\n eLin = sqe.e[sqeCut:slopeCut]\n sqeSlope,inter = mfit.poly(eLin,sqeLin ,1)\n mulSlope,inter = mfit.poly(eLin,mulSqeLin,1)\n line = mfit.poly_of_x(eLin,mfit.poly(eLin,sqeLin ,1) )\n lsqSl[i] = (sqeSlope-mulSlope)**2.0\n lsqMu[i] = nar( (mulSqeLin - line )**2.0 /float(len(line)) )\n lsqSc[i] = nar( nar( (sqe.sqe - nar(res[i][0]) )**2.0 ) )\n except:\n pass\n \n start = 0\n while start 0 and self.all_costs[-1] < best_costs:\n best_option = copy.deepcopy(self.batteries)\n best_costs = self.all_costs[-1]\n\n if self.scheme == \"linear\":\n self.temp = self.temp - self.cooling_rate\n elif self.scheme == \"exp\":\n self.temp *= 1 - self.cooling_rate\n\n self.batteries = best_option","title":""},{"docid":"8e51584fbf426cc78699e1fa846fb810","score":"0.61729866","text":"def post_optimization(pdb):\n print('Optimizing...')\n energies = optimize(pdb, os.path.abspath(os.getcwd()))\n print('Energy before optimizing: %s' % str(energies[0][0]))\n print('Energy after optimizing: %s' % str(energies[1][0]))\n print('Model completed')","title":""},{"docid":"5cbbdf5b657c88ce44e3b8c8d148fa37","score":"0.6171333","text":"def guessBest(self):\n #Get the true(past tries excluded) possible values for each value\n search_field = {i:(self.possibleValues[i] - self.impossibleValues[i]) for i in self.possibleValues}\n #Get the counts of times a number is a possible value @deprecated\n #counts = {i:sum(i in search_field[coord_values] for coord_values in search_field if len(search_field[coord_values])) for i in range(10)} \n #Get the coord with the least number of possible values\n bestCoord = min(search_field, key=lambda k: len(search_field[k]) if len(search_field[k]) > 1 else 10)\n #Apply the additional heuristic, sort the chosen coordinates possible values by how often those values could be other solutions @ deprecated\n #vals = sorted(search_field[bestCoord], key = lambda k: counts[k], reverse=True)\n vals = list(search_field[bestCoord])\n #print \"Guessing\", bestCoord, vals[0]\n #self.printData()\n self.setValue(bestCoord, guess=True, guessVal = vals[0])\n self.root = (bestCoord, self.getCoordValue(bestCoord))\n self.possibleValues[bestCoord] = {self.getCoordValue(bestCoord)}","title":""},{"docid":"379f6fcd7411622d26dc90b601fbb81e","score":"0.61595905","text":"def extra_convergence_criteria(self, results):\n # type: (List[Result]) -> bool\n self.iter_count += len(results)\n\n # Skip the setup period\n if not results:\n return False\n\n # Update the best local\n crtl_elapsed_time = 0\n self.best_unchange += len(results)\n valid_result = 0\n iter_best_result = None\n for result in results:\n latency = result.time\n resource = result.size\n crtl_elapsed_time = max(crtl_elapsed_time, result.elapsed)\n if latency == float('inf'):\n continue\n elif resource >= 1.0:\n continue\n\n valid_result += 1\n if (not iter_best_result) or latency < iter_best_result.time:\n iter_best_result = result\n\n self.elapsed_time += crtl_elapsed_time\n\n # Terminate this partition if we cannot get a valid\n # result from the seed design point.\n #if (not self.best_result and self.iter_count >= len(self.seeds)\n # and valid_result == 0):\n # return True\n\n # Update the history\n if iter_best_result:\n if ((not self.best_result) or\n iter_best_result.time < self.best_result.time):\n self.best_result = iter_best_result\n self.best_unchange = -1\n if self.best_result is not None:\n self.log_best(self.iter_count, self.elapsed_time,\n self.best_result.time)\n\n # Keep the best result of current iteration for computing\n # the entropy at the next iteration.\n self.prev_result = iter_best_result\n\n # Update the best global\n count = 1\n new_ranking = float('inf')\n if os.path.exists('.who_is_the_best'):\n with open('.who_is_the_best') as filep:\n for line in filep:\n latency = float(line.replace('\\n', ''))\n if self.best_result:\n if latency == float(self.best_result.time):\n # Found itself\n new_ranking = count\n count += 1\n new_ranking /= count\n\n # Add one thread as the manager requested\n if os.path.exists('.add_threads'):\n with open('.add_threads') as filep:\n line = filep.readline()\n addition_thread = int(line.replace('\\n', ''))\n self.more_parallelism(addition_thread)\n run_command('rm -f .add_threads')\n\n # Skip the warm up period\n if new_ranking == float('inf') or count < 4:\n return False\n\n # Skip the rest criteria to let partitions run as long as possible\n return False","title":""},{"docid":"32bff97f4800c45fadf86ac73a30a350","score":"0.61526996","text":"def get_best_values(self):\n return self.g_best_score, self.g_best_params","title":""},{"docid":"d035d1802dc286f7a043d398d9b8e29d","score":"0.6148896","text":"def optimize():\n stopwatch = StopWatch()\n\n ANNEAL_AREA = False\n logger = LoggerUtils.configure_log(name='Optimization script', use_console=True, use_file=True)\n\n click.clear()\n\n metrics = GeneticAlgorithmConstants();\n\n # Generate initial population\n population = Evolution.populate(initial=True)\n\n # Calculate fitness for each individual and sort them\n ranked_pop = Evolution.adaptability(population)\n \n generation = 0\n same_solution_count = 0\n optimal = {\"genome\": None, \"fitness\": None, \"links\": None}\n\n logger.info('Starting first optimization loop.')\n # The first optimization loop defines the number of nodes for each of the line instalation areas, \n # while keeping squared areas number of nodes constant\n\n # Main loop for the first optimization\n while(generation < metrics.MAX_GENERATIONS and same_solution_count != metrics.MAX_STAGNATED_OPTIMAL):\n generation += 1\n \n # Perform the crossover \n ranked_pop = Evolution.crossover(ranked_pop, metrics.CROSSOVER_PROBABILITY)\n\n # Mutate population\n ranked_pop = Evolution.mutate(ranked_pop, metrics.MUTATION_PROB)\n\n # Next generation\n population = [individual.get('genome') for individual in ranked_pop]\n\n # Calculate fitness for each individual and sort them\n ranked_pop = Evolution.adaptability(population)\n\n # Update the optimal value and count improvements\n if optimal.get('fitness') is None:\n optimal = copy.deepcopy(ranked_pop[0])\n elif ranked_pop[0].get('fitness') < optimal.get('fitness'):\n optimal = copy.deepcopy(ranked_pop[0])\n same_solution_count = 0\n else:\n same_solution_count += 1\n\n # Perform environment pressure\n ranked_pop = Evolution.environment_pressure(ranked_pop)\n \n logger.info(''.join(['[Gen {}] '.format(generation), 'Optimal:{solution: ', str(optimal.get('genome')), ', fitness: ', str(optimal.get('fitness')), \n ', min_links: ', str(optimal.get('links')), '}']))\n \n logger.info('Finished first optimization with %s generations. Total time: %s.' % (generation, stopwatch.read()))\n logger.info('OPTIMAL FOR FIRST OPTIMIZATION: N1: {}, N2: {}, N3: {}, N4: {} '.format(optimal['genome'][0], \n optimal['genome'][1], optimal['genome'][2], optimal['genome'][3]))\n \n # SECOND OPTIMIZATION ROUTINE:\n # Voronoi : generate optimized points distribution in an area.\n # L-BFGS-B : \n # POSSIBILITY: Model the second optimization problem as max f(x) = min SUM distances(i,j) \n\n # GETTING READY FOR SECOND OPTIMIZATION\n\n n1, n2, n3, n4 = optimal['genome']\n # network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\n\n area_nodes = n4\n generation = 0\n\n logger.info('Starting second optimization.')\n # The second optimization cares to find the min number of nodes within squared areas. At this part of the code\n # it's assumed that the min number of nodes for the line areas are known due the first optimization loop.\n\n logger.info('Initial genome [N1 = {}, N2 = {}, N3 = {}, N4 = {}].'. format(n1, n2, n3, n4))\n stopwatch_loop_2 = StopWatch()\n\n\n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4) \n lines = ['N1', 'N2', 'N3', 'N4']\n nodeArray = [network.get('SINK')]\n for line in lines:\n nodeArray += list(network.get(line))\n # Plotter.plot_node_list(nodeArray, title='First Optimization Result', xLabel='Grid Coordinate (m)', yLabel='Grid Coordinate (m)')\n\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'first.geojson')\n\n # Main loop for the second optimization\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 3:\n # First find the min number n of nodes of node at each side of the nxn grid\n \n generation += 1\n area_nodes -= 1\n\n if area_nodes == 0:\n break\n\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, area_nodes)\n\n logger.info('[Gen {}] Fitness for N4 = {}: {}'.format(generation, area_nodes, fitness))\n\n # When the loop ends, area_nodes have a number with invalid fitness for n4.\n # So the correct min number is the result area_nodes + 1.\n n4 = area_nodes + 1\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\n\n logger.info('Finished second optimization with %s generations. Total time: %s.' % (generation, stopwatch_loop_2.read()))\n logger.info('Optimal for second optimization: N1: {}, N2: {}, N3: {}, N4: {} '.format(n1, n2, n3, n4))\n logger.info('Fitness for second optimization: {}'.format(fitness))\n\n # Retrieve network \n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\n\n metrics = SelectiveAneelingConstants()\n\n networkArea = list(network.get('N4'))\n current_optimal_network_area = networkArea \n\n if len(networkArea) >= 2 :\n fitness = NetworkModel.getFitnessForNetwork(networkArea)\n generation = 1\n\n # Define area boundaries\n boundaries = dict()\n boundaries['MIN_X'] = GlobalParameters.N4_DIM.top_left[0]\n boundaries['MAX_X'] = GlobalParameters.N4_DIM.botom_right[0]\n boundaries['MIN_Y'] = GlobalParameters.N4_DIM.botom_right[1]\n boundaries['MAX_Y'] = GlobalParameters.N4_DIM.top_left[1]\n\n if ANNEAL_AREA: logger.info('Starting third optimization. Selective aneeling')\n\n # Finds the optimal number of nodes within area maximizing the distance between them\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 2 and len(networkArea) >= 2 and ANNEAL_AREA:\n\n current_optimal_network_area = networkArea\n networkArea.pop()\n\n # Configuring Annealer\n annealer = DistanceAnnealing(networkArea, boundaries)\n annealer.copy_strategy = metrics.COPY_STRATEGY\n\n # Calibrating Annealer\n click.secho('Calibrating annealer', fg='yellow')\n auto_schedule = annealer.auto(minutes=1)\n annealer.set_schedule(auto_schedule)\n click.secho('Annealer calibrated! Parameters: {}'.format(auto_schedule), fg='green')\n\n networkArea, energy = annealer.anneal()\n \n fitness = NetworkModel.getFitnessForNetwork(networkArea)\n\n logger.info(\"[GEN {}] Number of Links: {} Energy: {}, fitness: {}\".format(len(networkArea), generation, energy, fitness))\n\n generation += 1\n \n \n logger.info('Finished second optimization. Nodes positions inside area defined. {} nodes installed.'.format(len(current_optimal_network_area)))\n\n # Saving the new area node disposition\n network['N4'] = np.array(current_optimal_network_area)\n\n # Adding all nodes of the network in one array\n\n lines = ['N1', 'N2', 'N3', 'N4']\n nodeArray = [network.get('SINK')]\n\n for line in lines:\n nodeArray += list(network.get(line))\n \n Plotter.plot_node_list(nodeArray, add=True, annotate=False)\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'second.geojson')\n \n logger.info('Annealing nodes. Current configuration have %s nodes.' %(len(nodeArray)))\n annealer = PositionAnnealing(nodeArray)\n annealer.copy_strategy = metrics.COPY_STRATEGY\n\n # click.secho('Calibrating annealer.', fg='yellow')\n\n # auto_schedule = annealer.auto(minutes=1)\n # annealer.set_schedule(auto_schedule)\n\n # click.secho('Annealer calibrated!', fg='green')\n # logger.info('Annealer parameters: {}'.format(auto_schedule))\n\n annealer.Tmax=1.5\n annealer.Tmin=1e-10\n\n nodeArray, energy = annealer.anneal()\n\n network[line] = nodeArray\n \n click.secho('Finished Optimization', fg='green')\n logger.info('Finished network optimization. Total elapsed time: {}'.format(stopwatch.read()))\n logger.info('Writing result geoJSON at {}.'.format(OUTPUT_PATH))\n\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'result.geojson')\n\n Plotter.plot_node_list(nodeArray, title='Representação gráfica da Rede', xLabel='Longitude', yLabel='Latitude',\n color='red', annotate=False)","title":""},{"docid":"8a6b2d1b0b101462372338089711edf7","score":"0.61201286","text":"def calc_energy(self, force=True):\n if self.atoms_moved:\n self.timer.switch_on(\"energy\")\n if self.qeq:\n self.timer.switch_to(\"qeq\")\n self.qeq.preopt_q()\n self.timer.switch_to(\"energy\")\n if self.virtual_atoms:\n self.timer.switch_to(\"virt atoms set pos\")\n self.virt_atom_set_pos()\n self.timer.switch_to(\"energy\")\n if self.enforce_nlist_rebuild:\n # if some operation (move atoms/molecules around) could break the neigborlist enforce its rebuild\n self.dlp_nlst.nlst_notest = True\n self.dlp_nlst.nlst_rebuild = True\n ### nullify stress tensor\n self.dlp_conf.stress[:]=0.0\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\n self.dlp.calc_energy_force()\n ### ################################################### ###\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\n if self.QMMM:\n self.timer.switch_to(\"qmmm\")\n self.QMMM_eng = self.QMMM_interface(force)\n self.timer.switch_to(\"energy\")\n # for a QMMM run we report the energies every step\n self.report_energies()\n if self.virtual_atoms:\n self.timer.switch_to(\"virt atoms dist force\")\n self.virt_atom_distribute_force()\n self.timer.switch_to(\"energy\")\n self.atoms_moved = False\n if self.enforce_nlist_rebuild:\n self.dlp_nlst.nlst_notest = False\n self.dlp_nlst.nlst_rebuild = False\n self.enforce_nlist_rebuild = False\n# if self.QMMM:\n# self.timer.switch_to(\"qmmm\")\n# self.QMMM_eng = self.QMMM_interface(force)\n# self.timer.switch_to(\"energy\")\n# # for a QMMM run we report the energies every step\n# self.report_energies()\n if self.extra_term:\n self.timer.switch_to(\"extra term\")\n self.extra_eng = self.extra_term()\n self.timer.switch_to(\"energy\")\n if self.plumed:\n self.timer.switch_to(\"metamd plumed\")\n self.meta_eng = self.plumed_energy()\n self.timer.switch_to(\"energy\")\n if self.constraint:\n self.multiply_force(self.constraint_mask)\n for s in self.extra_systems:\n self.timer.switch_to(s.get_name())\n s.calc_energy()\n self.timer.switch_to(\"energy\")\n self.timer.switch_off()\n # return configuration energy in current units\n energy = self.dlp.engcfg/self.dlp.engunit\n if self.QMMM: energy += self.QMMM_eng\n if self.extra_term: energy += self.extra_eng\n if self.plumed: energy += self.meta_eng\n return energy","title":""},{"docid":"8abf4c4ebfe4d352c7ad6e941203b1e7","score":"0.61065435","text":"def get_solutions_fitness(self, solution):\n value = self.get_solutions_value(solution)\n return self.get_fitness(value)","title":""},{"docid":"9d65270fe6d8d9e8590e16fe04479aff","score":"0.6098882","text":"def compute_solution(self,dataset):\n\t\tpass","title":""},{"docid":"50508a445861c4588a2e65c595bf6459","score":"0.60829246","text":"def getOwnBestFitness(self):\r\n\t\t\r\n\t\treturn self.ownBestFitness","title":""},{"docid":"af4add5b38f8e282631cc6b0b016fc55","score":"0.6068796","text":"def optimal(self):\n return np.max(self.expected_rewards())","title":""},{"docid":"330e6fdf9357f731486615c89f7e2c32","score":"0.6067864","text":"def getBestFit(self):\n self.envLock.acquire()\n best = self.environment.getMostAdapted()\n self.envLock.release()\n return best","title":""},{"docid":"380cba45db1ed8aaf173abe49ca24a3a","score":"0.6064149","text":"def get_solution(self):\n index = -1\n max_f = -math.inf\n\n for i in range(self.N):\n if self.train_v[i] > 1.2 and self.train_f[i] > max_f:\n max_f = self.train_f[i]\n index = i\n return self.train_x[index]","title":""},{"docid":"4596652f65f223725709173384ec358d","score":"0.6045643","text":"def greedy():\n\tglobal table, weights, values, capacity, max_value, best_solution, unit_values\n\tunit_values = [((values[i]*1.0)/weights[i],i,weights[i],values[i]) for i in xrange(len(weights))]\n\tunit_values.sort()\n\tinv_sort_order = [(unit_values[i][1],i) for i in xrange(len(unit_values))]\n\tinv_sort_order.sort()\n\tsort_order = [x[1] for x in inv_sort_order]\n\t\n\tweight = 0\n\tmax_value = 0\n\tbest_solution = [0]*len(weights)\n\ti = len(weights) - 1\n\twhile(weight < capacity and i >= 0):\n\t\tif weight + unit_values[i][2] <= capacity:\n\t\t\tbest_solution[i] = 1\n\t\t\tmax_value += unit_values[i][3]\n\t\t\tweight += unit_values[i][2]\n\t\telse:\n\t\t\tbest_solution[i]=0\n\t\ti -= 1\n\n\tbest_solution = [best_solution[i] for i in sort_order]\n\t\n\treturn {'value':max_value, 'taken':best_solution, 'optimality':0}","title":""},{"docid":"af394f586b631d9fba6859afb2e6cc75","score":"0.60455203","text":"def solve(self):\n convergence = False\n val = 0\n for i in range(self.maxiter):\n grad = self.dR()\n self.x = self.x - self.optStep()*grad\n if np.sum(np.abs(grad)) < self.eps*(np.log(self.x.shape[0])):\n convergence = True\n break\n\n if not convergence:\n print('WARNING: Did not converge. Try increasing maxiter or a smaller eps.') \n\n return(self.eig,self.x)","title":""},{"docid":"dc50367fe11ae1b2a1e4fd8e6b9c5b37","score":"0.60270655","text":"def betterEvaluationFunction(currentGameState):\n \"*** YOUR CODE HERE ***\"\n #more food = worse score\n #closer food = good score\n #closer than 3 ghost = bad score\n \n foodGrid = currentGameState.getFood()\n food = foodGrid.asList()\n pos = currentGameState.getPacmanPosition()\n ghosts = []\n gdists = []\n for i in range(1, currentGameState.getNumAgents()):\n g = currentGameState.getGhostPosition(i)\n ghosts.append(g)\n gdists.append(manhattanDistance(pos, g))\n \n #fterm = len(food)\n fterm = foodGrid.width*foodGrid.height-len(food)\n #gterm = closest(pos, ghosts)[0]\n gterm = 0\n fdist, floc = closest(pos, food)\n \n # breadth first search\n #problem = PositionSearchProblem(currentGameState, goal=floc, start=pos, warn=False)\n #realDist = len(breadthFirstSearch(problem))\n \n dterm = 1/float(fdist+1)\n \n #coefficients\n a = 10\n b = 1\n c = 15\n\n for d in gdists:\n if d<3:\n gterm-=50\n if d<2:\n gterm-=50\n \n total = a*fterm + b*gterm + c*dterm\n \n # break ties\n #if right:\n # total+=2\n #if up:\n # total+=1\n return total","title":""},{"docid":"287e49f8017272b659afe2b7a22ade44","score":"0.60227233","text":"def solve(self):","title":""},{"docid":"80d1d5c90fceaa679fdec499b70a5175","score":"0.60214704","text":"def evaluate_best_agent(self):\n\n eval_eps = Config.EVALUATION_EPISODES\n reward_sum = 0\n log_list = []\n\n print(\"Evaluate The Best Network with Test data: episode \", self.best_episode, \" reward \", self.best_reward)\n self.restore_best_network_to_train_network()\n\n for i in range(eval_eps):\n [reward, dist, trainsize, pred] = self.evaluate(isValidation=False)\n reward_sum = reward_sum + reward\n log = {\n \"episode\": None,\n \"top_reward\": reward,\n \"exp_rate\": None,\n \"trainsize\": trainsize,\n \"top_dist\": dist,\n \"top_pred\": pred\n }\n log_list.append(log)\n\n AgentLogger.print_trianing_results(log)\n\n mean_reward = reward_sum/eval_eps\n print(\"Mean: \", mean_reward)\n AgentLogger.log_evaluation_results(log_list, self.logger, self.best_episode, self.best_reward, mean_reward)","title":""},{"docid":"2147cc31fc145610dbcba7b9c5f6d2ea","score":"0.6020431","text":"def find_best_object_and_behavior(self):\n potential_event_deflection = np.inf\n\n if not (self.model.network_structure is None):\n # check if network structure was passed and only consider\n # candidate objects to which actor has link\n possible_ids = [i for i,link\n in enumerate(self.model.network_structure[self.unique_id])\n if link == 1]\n potential_objects = [ag for ag in self.model.schedule.agents\n if ag.unique_id in possible_ids]\n if self.model.action_on_group:\n potential_objects += [self.model.group]\n else:\n potential_objects = [ag for ag in self.model.schedule.agents\n if ag.unique_id != self.unique_id]\n if self.model.action_on_group:\n potential_objects += [self.model.group]\n\n if self.model.object_choice == \"random\":\n potential_object = np.random.choice(potential_objects)\n potential_action = compute_opt_behavior(self, potential_object)\n potential_transients = compute_transients(\n self.current_transients,\n potential_action,\n potential_object.current_transients,\n self.model.abo_coefficients)\n else:\n np.random.shuffle(potential_objects)\n\n for obj in potential_objects:\n opt_beh = compute_opt_behavior(self, obj)\n transients = compute_transients(self.current_transients,\n opt_beh, obj.current_transients,\n self.model.abo_coefficients)\n\n if self.model.object_choice == \"min event-tension\":\n fundamentals_stack = np.hstack((self.fundamentals, opt_beh,\n obj.fundamentals))\n event_deflection = np.linalg.norm(fundamentals_stack\n - transients)**2\n\n elif self.model.object_choice == \"max deflection-reduction\":\n actor_deflection_diff = (\n np.linalg.norm(self.fundamentals - transients[:3])**2\n - self.personal_deflection\n )\n beh_deflection = np.linalg.norm(opt_beh - transients[3:6])**2\n # we include the actual deflection reduction of all agents\n # in the object choice, not just the group object\n if obj.unique_id == -1:\n object_deflection_diff = (\n np.linalg.norm(obj.fundamentals - transients[6:])**2\n - obj.personal_deflection\n )\n for ag in [agent for agent in self.model.schedule.agents\n if agent.unique_id != self.unique_id]:\n transients_g = compute_transients(\n self.current_transients, opt_beh,\n ag.current_transients,\n self.model.abo_coefficients\n )\n object_deflection_diff+= (\n np.linalg.norm(ag.fundamentals - transients_g[6:])**2\n - ag.personal_deflection\n )\n else:\n object_deflection_diff = (\n np.linalg.norm(obj.fundamentals - transients[6:])**2\n - obj.personal_deflection\n )\n\n event_deflection = ( actor_deflection_diff\n + beh_deflection\n + object_deflection_diff )\n else:\n print(\"unknown or no object selection criterion specified\")\n\n # check if deflection for this behavior is smaller\n # than for other behaviors\n if event_deflection < potential_event_deflection:\n potential_event_deflection = event_deflection\n potential_transients = transients\n potential_object = obj\n potential_action = opt_beh\n\n return (potential_object, potential_action, potential_transients)","title":""},{"docid":"45ccb88b554cf570c6a44f750a0c3ef0","score":"0.60071445","text":"def run(self):\n self.logger.info(\"\\n\\n************************* Genetic Algorithm *************************\")\n self.logger.info(f\"Objective: {self.objective}\")\n self.logger.info(f\"============================ Execution with seed: {self._seed} ============================\")\n self.logger.info(f\"{time.strftime('%d/%m/%Y %H:%M:%S')}\\n\\n\")\n\n random.seed(self._seed)\n np.random.seed(self._seed)\n\n self._generate_initial_population()\n self._population_statistic()\n self._print_population_info(self.new_pop, 0)\n\n best_fitness = self.new_pop.bestFitness\n count_patience = 0\n\n compare_op_obj = partial(ge) if self.objective == 'maximization' else partial(le)\n compare_op_best = partial(gt) if self.objective == 'maximization' else partial(lt)\n\n for gen in range(1, self.nr_max_gen + 1):\n self._generating_new_population()\n self._population_statistic()\n self._print_population_info(self.new_pop, gen)\n\n if compare_op_best(self.new_pop.bestFitness, best_fitness):\n best_fitness = self.new_pop.bestFitness\n count_patience = 0\n else:\n count_patience += 1\n\n if self.objective_fitness is not None:\n if compare_op_obj(self.new_pop.bestFitness, self.objective_fitness):\n self.logger.info(f\"Stop because found the objective fitness {self.objective_fitness}\")\n break\n\n if self.patience > 0:\n if count_patience >= self.patience:\n self.logger.info(f\"Stop because patience limit\")\n break\n\n self.logger.info(f\"Best solution found: {self._statistic[-1]['best_indiv']}\")\n save_statistic(self.output_dir, self._statistic)","title":""},{"docid":"ef7acdb6e0620fe4b000accc940aa89b","score":"0.5998345","text":"def get_energy(self):\n\n D, self.e, self.C = self.build_density(self.H)\n\n E_old = 0.0\n F_old = None\n\n for iteration in range(self.max_iter):\n\n J = np.einsum(\"pqrs, rs -> pq\", self.g, D)\n K = np.einsum(\"prqs, rs -> pq\", self.g, D)\n\n # F = H_pq + 2 * G_pqrs D_rs - G_prqs D_rs\n F = self.H + 2.0 * J - K\n\n if self.damping == 'on' and iteration >= self.damp_start:\n F = self.damp(F_old, F)\n\n F_old = F\n\n if self.diis == 'on' and iteration >= self.diis_start:\n F = diis_helper.diis(F, D, self.diis_start,self.diis_vector)\n\n E_electric = np.sum((F + self.H) * D)\n self.E = E_electric + self.mol.nuclear_repulsion_energy()\n\n E_diff = self.E - E_old\n E_old = self.E\n\n # Build the AO gradient\n grad = F @ D @ self.S - self.S @ D @ F\n grad_rms = np.mean(grad ** 2) ** 0.5\n\n D, self.e, self.C = self.build_density(F)\n\n print(\"Iteration: %3d Energy: % 16.12f Energy difference: % 8.4f Gradient difference: % 8.4f\" % (iteration, self.E, E_diff,grad_rms))\n\n if E_diff < self.e_conv and grad_rms < self.d_conv:\n break\n\n print(\"SCF has finished! \\n\")","title":""},{"docid":"7b24c4d70a946f3b9b496e46c1a8767e","score":"0.5992288","text":"def initPopulation(self):\n for i in range(0, self.popSize):\n individual = Individual(self.genSize, self.data)\n individual.computeFitness()\n self.population.append(individual)\n\n self.best = self.population[0].copy()\n for ind_i in self.population:\n if self.best.getFitness() > ind_i.getFitness():\n self.best = ind_i.copy()\n print (\"Best initial sol: \",self.best.getFitness())","title":""},{"docid":"65ed1251824de1d9a976769dd87cfabf","score":"0.59906185","text":"def _find_solutions(self):\n # Set progress message\n self.model.progress.set_message(\"finding solutions\")\n\n # Generate grid of indices to search for solutions\n grid_range = range(self.opts.gp)\n indices = [tuple([n for n in grid_range]) for _ in self.model.iter_obj2]\n self.cp = list(itertools.product(*indices))\n self.cp = [i[::-1] for i in self.cp]\n\n # Pickle the model and initialize queue and process handlers\n self.model.pickle()\n self.queues = QueueHandler(self.cp, self.opts)\n self.queues.split_work()\n self.procs = ProcessHandler(self.opts, self.model, self.queues)\n\n # Start processes and wait for results\n self.procs.start()\n self.unprocessed_sols = []\n while not self.procs.join():\n self.unprocessed_sols.extend(self.queues.get_result())\n # One last sweep to make sure all results have been collected\n self.unprocessed_sols.extend(self.queues.get_result())\n\n # In case all worker processes were killed, not all work may have been done.\n if self.procs.any_killed:\n raise Exception(\"At least one worker exited prematurely, not all computations may have been done.\")\n\n # Clean the pickled model\n self.model.clean()","title":""},{"docid":"7fb69b984732c6d732aacba92def1e51","score":"0.5982387","text":"def solution_most_iterations_done(self) -> int:\n return int(self.dss_obj.SolutionI(ctypes.c_int32(41), ctypes.c_int32(0)))","title":""},{"docid":"17959bc5f94063bea50630dbccfdc9af","score":"0.59580135","text":"def solve(self, problem):\r\n recommended_solns = []\r\n intermediate_budgets = []\r\n expended_budget = 0\r\n delta_max = self.factors[\"delta_max\"]\r\n gamma_0 = self.factors[\"gamma_0\"]\r\n delta_candidate = [gamma_0 * delta_max, delta_max, delta_max / gamma_0]\r\n #print(delta_candidate)\r\n\r\n # default values\r\n eta_1 = self.factors[\"eta_1\"]\r\n eta_2 = self.factors[\"eta_2\"]\r\n gamma_1 = self.factors[\"gamma_1\"]\r\n gamma_2 = self.factors[\"gamma_2\"]\r\n k = 0 # iteration number\r\n\r\n # Start with the initial solution\r\n new_x = problem.factors[\"initial_solution\"]\r\n new_solution = self.create_new_solution(new_x, problem)\r\n recommended_solns.append(new_solution)\r\n intermediate_budgets.append(expended_budget)\r\n\r\n # Parameter tuning run\r\n tp_final_ob_pt, k, delta, recommended_solns, intermediate_budgets, expended_budget, new_x = self.parameter_tuning(\r\n delta_candidate[0], problem)\r\n for i in range(1, 3):\r\n final_ob_pt, k_pt, delta_pt, recommended_solns_pt, intermediate_budgets_pt, expended_budget_pt, new_x_pt = self.parameter_tuning(\r\n delta_candidate[i], problem)\r\n expended_budget += expended_budget_pt\r\n if -1 * problem.minmax[0] * final_ob_pt < -1 * problem.minmax[0] * tp_final_ob_pt:\r\n k = k_pt\r\n delta = delta_pt\r\n recommended_solns = recommended_solns_pt\r\n intermediate_budgets = intermediate_budgets_pt\r\n new_x = new_x_pt\r\n\r\n intermediate_budgets = (\r\n intermediate_budgets + 2 * np.ones(len(intermediate_budgets)) * problem.factors[\"budget\"] * 0.01).tolist()\r\n intermediate_budgets[0] = 0\r\n\r\n while expended_budget < problem.factors[\"budget\"]:\r\n k += 1\r\n fval, Y, q, grad, Hessian, delta_k, expended_budget = self.model_construction(new_x, delta, k, problem,\r\n expended_budget)\r\n\r\n # Cauchy reduction\r\n if np.matmul(np.matmul(grad, Hessian), grad) <= 0:\r\n tau = 1\r\n else:\r\n tau = min(1, norm(grad) ** 3 / (delta * np.matmul(np.matmul(grad, Hessian), grad)))\r\n\r\n grad = np.reshape(grad, (1, problem.dim))[0]\r\n candidate_x = new_x - tau * delta * grad / norm(grad)\r\n\r\n for i in range(problem.dim):\r\n if candidate_x[i] < problem.lower_bounds[i]:\r\n candidate_x[i] = problem.lower_bounds[i] + 0.01\r\n elif candidate_x[i] > problem.upper_bounds[i]:\r\n candidate_x[i] = problem.upper_bounds[i] - 0.01\r\n\r\n candidate_solution = self.create_new_solution(tuple(candidate_x), problem)\r\n\r\n # adaptive sampling needed\r\n problem.simulate(candidate_solution, 1)\r\n expended_budget += 1\r\n sample_size = 1\r\n\r\n # Adaptive sampling\r\n while True:\r\n problem.simulate(candidate_solution, 1)\r\n expended_budget += 1\r\n sample_size += 1\r\n sig2 = candidate_solution.objectives_var\r\n if sample_size >= self.samplesize(k, sig2, delta_k):\r\n break\r\n\r\n # calculate success ratio\r\n fval_tilde = -1 * problem.minmax[0] * candidate_solution.objectives_mean\r\n\r\n # replace the candidate x if the interpolation set has lower objective function value\r\n if min(fval) < fval_tilde:\r\n minpos = fval.index(min(fval))\r\n fval_tilde = min(fval)\r\n candidate_x = Y[minpos][0]\r\n\r\n if (self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\r\n np.array(candidate_x) - np.array(new_x), q)) == 0:\r\n rho = 0\r\n else:\r\n rho = (fval[0] - fval_tilde) / (\r\n self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\r\n candidate_x - new_x, q));\r\n\r\n if rho >= eta_2: # very successful\r\n new_x = candidate_x\r\n delta_k = min(gamma_1 * delta_k, delta_max)\r\n recommended_solns.append(candidate_solution)\r\n intermediate_budgets.append(expended_budget)\r\n elif rho >= eta_1: # successful\r\n new_x = candidate_x\r\n delta_k = min(delta_k, delta_max)\r\n recommended_solns.append(candidate_solution)\r\n intermediate_budgets.append(expended_budget)\r\n else:\r\n delta_k = min(gamma_2 * delta_k, delta_max)\r\n\r\n return recommended_solns, intermediate_budgets","title":""},{"docid":"2df6e82235fe60278712b06fb8d042ae","score":"0.5955973","text":"def calculate(self):\n # initialize transformation from canonic to energy\n # optimal states (if necessary)\n if self.W_mn is None:\n self.initialize_orbitals(rattle=self.rattle)\n\n # optimize the non-unitary invariant part of the\n # functional\n self.unitary_optimization()\n\n return self.esic, self.ekin","title":""},{"docid":"ad29ddb80d7682684f4cf035f8b96f23","score":"0.59508735","text":"def overall_fitness(self):\n # Use a decyaing function to generate fitness\n # Use two step decaying function\n # First block gives importance to exploration and when as soon\n # food has been found, the next block will focus on dropping\n # the food on hub\n self.individual[0].fitness = (1 - self.beta) * self.delayed_reward \\\n + self.exploration_fitness() + self.carrying_fitness() \\\n + self.food_collected","title":""},{"docid":"faa0eaf976005e56d162f1b3f95a98d0","score":"0.5949581","text":"def solveAll(self):\r\n return None","title":""},{"docid":"b4f00dadad3e1e7502978585f5eeb041","score":"0.5946176","text":"def optimzeGeneralParameters():\n\t#initialize qm energies dictionaries for each molecule\n\tglobal qm_energies\n\tqm_energies = {mol : {} for mol in molecules}\n\n\t#set parameters for minimize function\n\tmaxiter = 5000\n\tpgtol = -1e-17\n\tftol = 1e-17\n\n\t#get parameter bounds for minimization\n\tall_bnds = make_bounds_list(initialValuesList, True)\n\ta_bnds = make_bounds_list(initialValuesList, False)\n\n\t#optimize A parameters for each energy component, store in final_params list\n\tparameterList = initialValuesList\n\tmetaPOInter_initial_list = remove_C_params(parameterList)\n\tglobal fitting_component\n\twhile fitting_component < 4:\n\n\t\t#make list of bounds for component to pass down to POInter. Only include B bound if fitting exchange\n\t\tif fitting_component == 0:\n\t\t\tbnds = all_bnds\n\t\telse:\n\t\t\tbnds = a_bnds\n\n\t\t#make list of parameters for component to pass down to POInter\n\t\t#get only parameters for the component we are fitting\n\n\t\t#TODO: make so user can choose whether or not to fit B params\n\t\t#TODO: make sure input B params are the same for every molecule\n\t\t\t#TODO: use same .exp file ()\n\t\tmetaPOInter_input = get_component_parameters(metaPOInter_initial_list)\n\n\t\tres = minimize(metaPOInter,metaPOInter_input,method='L-BFGS-B',\\\n\t\t\t\t\t\t\tjac=True,\\\n\t\t\t\t\t\t\toptions={'disp':True,'gtol':pgtol,'ftol':ftol,'maxiter':maxiter},\\\n\t\t\t\t\t\t\tbounds = bnds)\n\n\t\tpopt = res.x\n\t\tprint \"Optimized parameters for component \" + str(fitting_component) + \" : \" + str(popt)\n\n\t\tif not res.success:\n\t\t\tprint 'Warning! Optimizer did not terminate successfully, and quit with the following error message:'\n\t\t\tprint\n\t\t\tprint res.message\n\t\t\tprint\n\t\t\treturn\n\n\t\tglobal optimized_params\n\t\toptimized_params[energyComponents[fitting_component]] = popt\n\n\t\t#reset new B params in POInter objects after fitting exchange component\n\t\tif fitting_component == 0:\n\t\t\tupdated_B_params = get_updated_B_from_exchange(popt)\n\t\t\tfor mol in molecules.keys():\n\t\t\t\ti = 0\n\t\t\t\tfor atomtype in allAtomtypes:\n\t\t\t\t\tpointerModel = molecule_POInter_objects[mol]\n\t\t\t\t\tpointerModel.params[atomtype][0]['B'] = updated_B_params[i]\n\t\t\t\t\ti += 1\n\t\t\n\t\tfitting_component += 1\n\n\t#write output JSON file with optimized parameters\n\toptimized_params[\"Dispersion\"] = [1.0 for atomtype in allAtomtypes]\n\tfinal_params_list = write_output_params_to_list(optimized_params)\n\tmap_params(final_params_list, \"optimized_params\")\n\n\t#calc dispersion lsq_error and store\n\tdisp_lsq_error = calc_disp_lsq_error()\n\tcomponent_lsq_error[energyComponents[fitting_component]] = disp_lsq_error\n\n\t#write lsq_error output file\n\twith open(\"component_lsq_error.txt\", 'w') as f:\n\t\tfor component in energyComponents:\n\t\t\tf.write(component + \":\" + '\\t')\n\t\t\tf.write(str(component_lsq_error[component]) + '\\n')\n\n\tprint \"===========================================================================\"\n\tprint \" Optimized parameters successfully written to optimized_params.constraints \"\n\tprint \"===========================================================================\"\n\n\treturn res","title":""},{"docid":"036faa1e19c7270a93947e53c76cf9c0","score":"0.5943793","text":"def get_solution(self):\n\n return self._funcs[\"get_solution\"](self.problem, self.variable_dict)","title":""},{"docid":"469e51f3864b44e39addeb0ee1556f22","score":"0.59420794","text":"def get_analysis(self):\n return self.best_fitness, self.avg_fitness, self.worst_fitness","title":""},{"docid":"88e417ac9a10cd6b79cb7903657a121a","score":"0.593701","text":"def solutions(self):\r\n if self.results is None:\r\n warning('results')\r\n return \r\n if self.is_isoschizomer():\r\n iso = True\r\n else:\r\n iso = False\r\n if type(self.best) == list:\r\n print('Results obtained using the following group of enzymes:')\r\n for e in self.best:\r\n print(e, sep=',')\r\n print('Other possible solutions:')\r\n for el in self.results:\r\n if el != self.best:\r\n print(el)\r\n else:\r\n if iso:\r\n warning('enzyme3')\r\n print('Best enzyme:\\n', self.best)\r\n print('Other possible solutions:')\r\n b = self.results.index(self.best)\r\n print(self.results[0:b]+ self.results[b+1:])\r\n\r\n return","title":""},{"docid":"8f362cc586c58c587ff2150bc663a74b","score":"0.5930106","text":"def optimize(self):\n\n\t\tstill_needed = []\n\t\t# Verify the required information\n\t\tif self.preference_input is None:\n\t\t\tstill_needed.append(\"Preferences\")\n\t\tif self.LP_input is None:\n\t\t\tstill_needed.append(\"LP_input\")\n\t\tif self.teacher_file is None:\n\t\t\tstill_needed.append(\"Teacher File (secondary)\")\n\n\t\ts = \"\"\n\t\tif len(still_needed) > 0:\n\t\t\ts = str(still_needed)\n\t\t\tmessagebox.showerror(\"Error\", \"You are missing the following\\n\\n\" + s)\n\t\t\treturn \n\n\t\tGAP = self.slider.get()\n\t\tprint(GAP)\n\t\t\n\t\tprint(\"All good\")\n\n\t\t# optimize_schedule(self.preference_input, self.LP_input, None,\n\t\t# \tself.teacher_file, GAP, self.requirements, self.save_location)\n\t\t# The none is for Grades, as I just hard coded that in the opt code for now\n\n\t\t# Create optimization instance\n\t\tO = Optimizer(self.preference_input, self.LP_input, None,\n\t\t\t\tself.teacher_file, GAP, self.requirements, self.save_location)\n\t\tO.optimize()\n\n\n\n\n\n\t\t# Things we need to think abou thow to deal with:\n\t\t# \tGrades\n\t\t#\tProximity\n\t\t#\t","title":""},{"docid":"b2320927b5675caba0e0b584d30231a5","score":"0.59246856","text":"def solve(self):\n self.DSSSolution.Solve()","title":""},{"docid":"79aa869a66d8a075a889aaa4a27ba494","score":"0.5923873","text":"def APSP_1(self):\n\n\t\tt_start = time.time()\n\t\tres = float('inf')\n\t\tfor s in self.G.keys():\n\t\t\tprint('Working on node {} of total {} nodes... time elapsed: {:5.2f} minutes.'\\\n\t\t\t\t.format(s, self.n, (time.time() - t_start)/60))\n\t\t\tdp = self.Bellman_Ford(s, self.G)\n\t\t\t\n\t\t\tif dp is None:\n\t\t\t\tprint('\\nThere is a negative cost cycle. Exit the algorithm.')\n\t\t\t\treturn None\n\t\t\tres = min(res, min(dp.values()))\n\n\t\treturn res","title":""},{"docid":"de4dcf4702ef09949cd1b9ec034b0e13","score":"0.59213895","text":"def run(self, max_iterations=100) -> Tuple[List[str], List[float]]:\n\n\n for i in range(max_iterations):\n # Generate Moveset\n moves = self.generate_moveset(self.state)\n\n if len(moves) == 0:\n return (self.state, self.costs)\n\n # Choose S i randomly from Moveset(S)\n m = random.choice(moves)\n\n # Define dV=V(S i )-V(S)\n v_s = self.graph.solution_cost(self.state)\n v_m = self.graph.solution_cost(m)\n dV = v_s - v_m\n\n # If dV>0 then S←S i else with probability p, S←S i\n if dV > 0 or (random.random() <= self.generate_p(dV)):\n self.state = m\n self.costs.append(v_m)\n self.decrease_temperature( i/max_iterations)\n else:\n self.costs.append(v_s)\n # If downhill descent is minimal, terminate\n if abs(dV/v_s) < SimulatedAnnealing.STOPPAGE_VALUE:\n return (self.state, self.costs)\n\n return (self.state, self.costs)","title":""},{"docid":"5913a3f71cbfc93d162a9dbdedf2bd1c","score":"0.5912698","text":"def startSearch(self):\n current = self.initNode\n bestScore = float(\"inf\")\n for i in range(0,100):\n current = self.initNode\n while True:\n neighbors = current.expand()\n if not neighbors:\n break\n current = min(neighbors, key=attrgetter(\"h\"))\n \n # Random minimum\n listOfMins = [x for x in neighbors if current.h == x.h]\n index = random.randint(0, len(listOfMins)-1)\n current = listOfMins[index]\n \n if self.successTest(current.state):\n if current.g < bestScore:\n bestScore = current.g\n bestOne = current\n break\n return self._extractPlan(bestOne)","title":""},{"docid":"a96eb04d01c5e9d4aff74f3f921f6ca9","score":"0.5908016","text":"def best(self):\n if self._best is None:\n raise BestNotFound()\n return self._best","title":""},{"docid":"c72e6d6de196d127dfef8df3d116ae88","score":"0.590735","text":"def employed_bees_phase(self):\n for i in range(self.get_nsource()):\n # nominate neighbour solution except\n k = self.random_nominee_except(i)\n # randomly select soln vector element xi to change\n xi = rand.randint(0,self.get_dimension()-1)\n phi = self.a * rand.uniform(-1,1)\n # get current food source\n eith_src = self.bee[i].get_source()\n # generate a new solution with all vector element in current\n # vector and change the randomly selected parameter only\n enew_src = np.copy(eith_src)\n ekth_src = self.bee[k].get_source()\n # generate new candidate food source\n enew_src[xi] = eith_src[xi] + phi * (eith_src[xi] - ekth_src[xi])\n # update new soln in range of solution lower_limit\n enew_src = self.band_filter(enew_src)\n enew_cost = self.cost_of(enew_src)\n # generate new bee with new_src\n\n enew_bee = food.Nectar(enew_src, enew_cost)\n # greedy selection by current employed bee\n self.bee[i].greedy_choice(enew_bee)","title":""},{"docid":"cd0932f9ee1bdccd14136fe6af53b0f2","score":"0.58952796","text":"def report_best(self):\n lowest_Err = min(self.Err)\n index = self.Err.index(lowest_Err)\n best_x = self.x[index]\n formatted_x = '[' + ', '.join(['%.2f' % xi for xi in best_x]) + ']'\n print 'best x: %s' % formatted_x\n print 'lowest Err: %.3E' % lowest_Err\n return best_x","title":""},{"docid":"3cce4fa387bf2542bc60e2516c287211","score":"0.5893321","text":"def computeOptimalPlays(self):\n\n # Base Cases\n for i in range(self.size):\n # If there is only one pot of gold left, take that pot\n self.cache[(i, i)] = {'score': self.gold[i], 'choice': 'left'}\n for i in range(self.size - 1):\n # If there are two pots of gold left, take the larger one\n self.cache[(i, i+1)] = {\n 'score': max(self.gold[i], self.gold[i+1]),\n 'choice': 'left' if self.gold[i] >= self.gold[i+1] else 'right'\n }\n\n # Inductive Step\n for length in range(2,self.size):\n for j in range(self.size - length):\n k = j + length\n # Here, we decide whether to take from the left (the j side)\n # or the right (the k side). If we take from the left, then\n # (if) our opponent makes the optimal move, we'll be left with\n # the minimum of two pots taken from the left or one pot taken\n # from either side (because it is a zero-sum game).\n # Similarly, if we take from the right, then our worst-case\n # score is the minimum of the subgame situation where either\n # two pots are taken from the right or one from either side.\n # The subgame structure is what makes this a DP problem.\n leftMove = self.gold[j]\n rightMove = self.gold[k]\n afterLeftMove = min(self.score(j+2,k), self.score(j+1,k-1))\n afterRightMove = min(self.score(j+1,k-1), self.score(j,k-2))\n\n if leftMove + afterLeftMove >= rightMove + afterRightMove:\n score = leftMove + afterLeftMove\n choice = 'left'\n else:\n score = rightMove + afterRightMove\n choice = 'right'\n\n self.cache[(j,k)] = {\n 'score': score,\n 'choice': choice\n }","title":""},{"docid":"e515c8f7954ca9a5e4c489dc52a14b0d","score":"0.5886591","text":"def best_ind(self):\n random.seed(64)\n NGEN = 10\n MU = 10\n pop = self.toolbox.population(n=MU)\n hof = tools.ParetoFront()\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", numpy.mean, axis=0)\n stats.register(\"std\", numpy.std, axis=0)\n stats.register(\"min\", numpy.min, axis=0)\n stats.register(\"max\", numpy.max, axis=0)\n # algorithms.eaMuPlusLambda(pop, self.toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats, halloffame=hof)\n algorithms.eaSimple(pop, self.toolbox, cxpb=0.6, mutpb=0.05, ngen=NGEN, halloffame=hof)\n print(\"The best individual is :\", hof[-1])\n # print(\"The best fitness is :\", eval_ind(self, hof[-1]))\n return hof[-1]","title":""},{"docid":"a3d1dea5cd1d94568fde8f01cc0d0532","score":"0.5884986","text":"def optimise(self, verbose=False, guess=None, \n elitism=5, method='GA',cooling_rate=0.1, maxtime=60, maxstallgen=100,\n nswap=3, popsize=50, mut=0.02, maxgens=50, dimension_bits=1):\n # TODO: check length of init guess\n from scipy.optimize import minimize\n from scipy.optimize import basinhopping\n self.models = []\n def eval_func_in(x):\n return self.eval_func(x, prior=[])\n\n N = self.N_reactions\n\n import inspyred\n from random import Random\n rand = Random()\n if method == 'GA':\n ea = inspyred.ec.GA(rand)\n else:\n ea = inspyred.ec.SA(rand)\n self.scores = []\n self.lower = 0\n self.upper = 1\n self.best_score = 1\n\n self.scores_all = []\n def evaluator(candidates, args):\n dimensions = args['dimensions']\n dimension_bits = args['dimension_bits']\n fitness = []\n for cs in enumerate(candidates):\n #if len(params) != dimensions:\n #print(cs, dimensions, dimension_bits)\n if method == 'GA':\n params = self._binary_to_real(cs[1], dimensions, dimension_bits)\n else:\n params = cs[1]\n score = eval_func_in(params)\n #score = eval_func_in(cs)\n fitness.append(score)\n # TODO selection pressure could be added here \n self.scores_all.extend(fitness)\n self.scores.append(min(np.array(fitness)))\n if self.scores[-1] < self.best_score:\n self.best_score = self.scores[-1]\n i = np.argmin(np.array(fitness))\n self.best_individual = candidates[i]\n start = False\n print(self.scores[-1], self.best_score)\n\n return fitness\n dimensions = N\n #dimension_bits = 4 # preceision is 1/(2**n-1)\n def generator(random, args):\n # For the first generation only.\n #print('HERE')\n dimension_bits = args['dimension_bits']\n #guess = args['guess'][:]\n pop = [random.choice([0, 1]) \n for _ in xrange(self.N_reactions * dimension_bits)]\n #if guess is not None:\n # pop[0] = guess\n self.pop = pop\n if hasattr(self, 'best_individual'):\n pop = self.best_individual\n return pop\n\n import inspyred.ec\n import inspyred.ec.ec\n ea.terminator = inspyred.ec.terminators.evaluation_termination\n bounder = inspyred.ec.ec.Bounder(0,1)\n\n self.storage = []\n\n def my_observer(population, num_generations, num_evaluations, args):\n #best = max(population)\n #print('{0:6} -- {1} : '.format(num_generations, best.fitness))\n\n #print(population)\n self.storage.append(population[0].candidate[:])\n for pop in population:\n self.models.append(pop.candidate[:])\n ea.observer = my_observer\n\n\n ea.selector = inspyred.ec.selectors.uniform_selection\n #ea.selector = inspyred.ec.selectors.rank_selection\n #ea.selector = inspyred.ec.selectors.fitness_proportionate_selection\n #ea.selector = inspyred.ec.selectors.tournament_selection\n\n ea.replacer = inspyred.ec.replacers.truncation_replacement\n #ea.replacer = inspyred.ec.replacers.plus_replacement\n self.ea = ea\n\n final = ea.evolve(generator=generator, evaluator=evaluator, \n maximize=False, bounder=bounder, temperature=100, cooling_rate=cooling_rate,\n max_evaluations=popsize*maxgens,dimensions=len(self.model.reactions),\n dimension_bits = dimension_bits, guess=guess, gaussian_mean=0.5, gaussian_std=0.5,\n pop_size=popsize, mutation_rate=mut, num_elites=elitism)\n\n self.ea = ea\n self.final = final\n res = None\n self.best_bitstring = max(self.ea.population).candidate\n print self.best_bitstring\n try:\n self.best_bitstring = self._binary_to_real(self.best_bitstring,\n len(self.model.reactions), dimension_bits)\n except:\n pass","title":""},{"docid":"7a446de06c2fc8fde29effb2ef29f743","score":"0.5871639","text":"def potential_energy_distribution(self):\n\n if len(self.traces) is None:\n raise ValueError(\"Can't predict final energy with just one trace!\")\n\n self.run_all_iterations()\n return self.energy_model.predict_from_trace(self.observed_energies)[0]","title":""},{"docid":"2080004ed77bb58b93101bbcdb5a9740","score":"0.5868522","text":"def result(self):\n return (self.best_mu, self.best_reward,\n self.curr_best_reward, self.sigma)","title":""},{"docid":"99d29e673ca70278dd187ed8c0e891f2","score":"0.5866907","text":"def get_optimal():\n LOG.info(\"Retrieving Optimal with url : %s\", request.url)\n recipe = request.get_json()\n LOG.info(recipe)\n LOG.info(str(recipe['name']))\n current_time = int(time.time())\n workload_name = 'optimal_'+str(current_time)\n # if 'ts_from' in recipe:\n # LOG.debug(recipe['ts_from'])\n # LOG.debug(recipe['ts_to'])\n # workload = Workload(str(recipe['name']), ts_from= recipe['ts_from'], ts_to= recipe['ts_to'])\n # eng = Engine()\n # eng.run('optimal', recipe['name'], recipe['ts_from'], recipe['ts_to'])\n # else:\n # workload = Workload(str(recipe['name']))\n config = {}\n if recipe.get('device_id'):\n config['device_id'] = recipe['device_id'].strip(\n ).lower().replace('-', '_')\n if recipe.get('project'):\n config['project'] = recipe['project']\n if recipe.get('sort_order'):\n config['sort_order'] = recipe['sort_order']\n if recipe.get('telemetry_filter', None) is not None:\n config['telemetry_filter'] = recipe['telemetry_filter']\n else:\n config['telemetry_filter'] = False\n workload = Workload(workload_name, workload_config=config)\n # storing initial recipe\n # TODO: validate recipe format\n recipe_bean = Recipe()\n recipe_bean.from_json(recipe)\n workload.add_recipe(\n int(\"{}{}\".format(int(round(time.time())), '000000000')), recipe_bean)\n pipe_exec = OptimalPipe()\n node_type = 'machine'\n try:\n workload = pipe_exec.run(workload, node_type)\n except KeyError:\n return Response('Service not ready yet, please wait or restart landscape', status=202)\n if workload.get_latest_graph() is None and config.get('device_id') is not None:\n return Response('Device not found', status=404)\n if workload.get_latest_graph() is None:\n return Response('Landscape not ready yet?', status=202)\n results = workload.get_metadata(OptimalFilter.__filter_name__)\n # return Response(results.to_json(), mimetype=MIME)\n # return Response(results.to_dict('results'), mimetype=MIME)\n json_results = json.dumps(results.to_dict('results'))\n return Response(json_results, mimetype=MIME)","title":""},{"docid":"6f76478ca755f6f62b9a4feabf72ea7b","score":"0.58448356","text":"def solve(self):\n start_time = time.time()\n maxdepth = 0\n break_cond = False\n\n while(not break_cond):\n if self.algo == \"bfs\":\n # Pop the leftmost element if doing a bfs (Queue)\n current_state = self.frontier.popleft()\n elif self.algo == \"dfs\":\n # Pop the rightmost element if doing a dfs (Stack)\n current_state = self.frontier.pop()\n else:\n # Get element with highest priority if doing A* (Heap)\n current_state = self.frontier.get()\n self.frontier_set.remove(current_state)\n if (self.isFinalState(current_state)):\n soln = self.get_solution_moves(current_state)\n end_time = time.time()\n stats = {}\n stats[\"nodes_expanded\"] = len(self.explored)\n stats[\"search_depth\"] = current_state.depth\n stats[\"max_search_depth\"] = maxdepth\n stats[\"cost_of_path\"] = len(soln)\n stats[\"time\"] = end_time - start_time\n stats[\"path\"] = soln\n return stats\n neighbors = current_state.generate_possible_states()\n if self.algo == \"dfs\":\n neighbors.reverse()\n\n for neighbor in neighbors:\n if neighbor not in self.explored and neighbor not in self.frontier_set:\n if self.algo == \"bfs\" or self.algo == \"dfs\":\n self.frontier.append(neighbor)\n else:\n self.frontier.put(neighbor)\n self.frontier_set.add(neighbor)\n if neighbor.depth > maxdepth:\n maxdepth = neighbor.depth\n self.explored.add(current_state)\n if self.algo == \"bfs\" or self.algo == \"dfs\":\n frontier_sz = len(self.frontier)\n else:\n frontier_sz = self.frontier.qsize()\n logging.debug(\"Frontier size = \" +\n str(frontier_sz) +\n \"; Explored size = \" +\n str(len(self.explored)))\n if self.algo == \"bfs\" or self.algo == \"dfs\":\n break_cond = len(self.frontier) == 0\n else:\n break_cond = self.frontier.empty()\n logging.error(\"This is an unsolvable board!\")\n return None","title":""},{"docid":"468d623199cf946ade4500c72622bb3f","score":"0.58404225","text":"def doit(self):\n self.reduceSolutions()\n keyz = self.solutions.keys()\n keyz.sort()\n prodarr = self.mergeIdent(keyz)\n keyz += self.chkForMore(keyz,prodarr)\n localans = self.solvePell()\n setx = set(keyz+localans)\n ansvec = list(setx)\n answer = 1+sum(ansvec)\n return answer","title":""},{"docid":"370c27f8417ed8239b7d1a695857c183","score":"0.5814705","text":"def first_state(self):\n new_sol = Solution(\n self.all_ts[0][:1],\n self.all_ys[0][:, :1],\n self.all_models[:1],\n self.all_inputs[:1],\n None,\n None,\n \"final time\",\n )\n new_sol._all_inputs_casadi = self.all_inputs_casadi[:1]\n new_sol._sub_solutions = self.sub_solutions[:1]\n\n new_sol.solve_time = 0\n new_sol.integration_time = 0\n new_sol.set_up_time = 0\n\n return new_sol","title":""},{"docid":"3cc528770f4cb159b4a99b6fd7ff1d8f","score":"0.5813268","text":"def run(self):\n problem = self.testproblem.problem\n results = TestResults(self.testproblem, self.config)\n # results = Test\n try:\n start = time.time() # Time the solve\n print(\"starting\",self.testproblem.id,\"with config\",self.config.id,\"at\",start)\n problem.solve(solver = self.config.solver, verbose = self.config.verbose, **self.config.kwargs)\n print(\"finished solve for\", self.testproblem.id, \"with config\", self.config.id)\n if problem.solver_stats.solve_time is not None:\n results.solve_time = problem.solver_stats.solve_time\n else:\n results.solve_time = time.time() - start\n if problem.solver_stats.setup_time is not None:\n results.setup_time = problem.solver_stats.setup_time\n if problem.solver_stats.num_iters is not None:\n results.num_iters = problem.solver_stats.num_iters\n\n results.status = problem.status\n results.opt_val = problem.value\n except Exception as e:\n print(e)\n # Configuration could not solve the given problem\n results = TestResults(self.testproblem, self.config)\n results.size_metrics = problem.size_metrics\n print(\"failure solving\",self.testproblem.id,\"with config\",self.config.id)\n return results\n\n # Record residual gross stats:\n results.avg_abs_resid, results.max_resid = TestInstance.compute_residual_stats(problem)\n print(\"computed stats for\", self.testproblem.id, \"with config\", self.config.id)\n\n # Record problem metrics:\n results.size_metrics = problem.size_metrics\n\n print(\"finished\",self.testproblem.id,\"with config\",self.config.id,\"at\",time.time()-start)\n return results","title":""},{"docid":"1a4ddcf70a8389e9ebf5384e8f000f5b","score":"0.5812091","text":"def optimize(self):\n self._add_constraints()\n self.Model.update()\n self.Model.optimize()\n if (\n self.Model.Status == 2\n ): # Status = 2 means optimal solution found and model is solved\n self._solved = True\n self._head_results = self._get_head_results()\n print(\"OPTIMIZATION COMPLETE\")\n else:\n self._head_results = None\n print(\"OPTIMIZATION NOT COMPLETE\")","title":""},{"docid":"9d1185bafadfc3331c9f21166a9d22f2","score":"0.5804594","text":"def baseSearch(self, gameState):\n\t\tactions = gameState.getLegalActions(self.index)\n\n\t\t# You can profile your evaluation time by uncommenting these lines\n\t\t# start = time.time()\n\t\tv = []\n\t\tfor action in actions:\n\t\t\tif action == 'Stop':\n\t\t\t\tcontinue\n\t\t\tsuccessor = gameState.generateSuccessor(self.index, action)\n\t\t\tv.append((self.evaluate(successor), action))\n\n\t\t# print 'eval time for agent %d: %.4f' % (self.index, time.time() - start)\n\n\t\t# print \"Values in baseSearch \", v\n\t\tmaxValues = [i for i in v if i == max(v)]\n\t\tmaxValue = random.choice(maxValues)\n\n\t\t# print maxValue\n\t\tbestAction = maxValue[1]\n\t\t# print bestAction\n\t\tfoodLeft = len(self.getFood(gameState).asList())\n\t\t# print foodLeft\n\t\tif foodLeft <= 2:\n\t\t\t# print \"foodl <= 2\"\n\t\t\tbestDist = 9999\n\t\t\tfor action in actions:\n\t\t\t\tif action == 'Stop':\n\t\t\t\t\tcontinue\n\t\t\t\tsuccessor = gameState.generateSuccessor(self.index, action)\n\t\t\t\tpos2 = successor.getAgentPosition(self.index)\n\t\t\t\tdist = self.getMazeDistance(gameState.getAgentPosition(self.index), pos2)\n\t\t\t\t# print \"Comparing {0} < {1}\".format(dist, bestDist)\n\t\t\t\tif dist < bestDist:\n\t\t\t\t\t# print \"Change action to \",action\n\t\t\t\t\tbestAction = action\n\t\t\t\t\tbestDist = dist\n\t\t\t# print \"Base search return with \", bestAction\n\t\t\treturn bestAction\n\t\t# print \"Base search return with \",bestAction\n\t\treturn bestAction","title":""},{"docid":"2e9eb1c81491450c937f6743bbc7fd60","score":"0.5800815","text":"def judge(self):\r\n temp = -1\r\n best_one = None\r\n for individual in self.individuals:\r\n distance = self.get_disctance(individual)\r\n if temp == -1 or distance < temp:\r\n temp = distance\r\n best_one = individual\r\n\r\n self.best_ones.append(best_one)","title":""},{"docid":"9f58e89ad63958879fc3bcc91bf5057c","score":"0.5797411","text":"def optimal_eps(self):\n Network = self.Network\n W = self.get_W()\n B = self._get_B(W)\n N = Network.size()\n eps = np.zeros([N,N])\n for j in range(N):\n eps += self._get_eps_j(j,W[j],B[j])\n return eps","title":""},{"docid":"35d5185a0acaa718fea4eb3c5221e4da","score":"0.5795139","text":"def test_griewangk():\n\n print \"Testing Griewangk:\"\n print \"Expected: x=[0.]*10 and f=0\"\n from mystic.models import griewangk as costfunc\n ndim = 10\n lb = [-400.]*ndim\n ub = [400.]*ndim\n maxiter = 10000\n seed = 123 # Re-seed for each solver to have them all start at same x0\n \n # DifferentialEvolutionSolver\n print \"\\nUsing DifferentialEvolutionSolver:\"\n npop = 50\n random_seed(seed)\n from mystic.solvers import DifferentialEvolutionSolver\n from mystic.termination import ChangeOverGeneration as COG\n from mystic.termination import CandidateRelativeTolerance as CRT\n from mystic.termination import VTR\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\n esow = Monitor()\n ssow = Monitor() \n solver = DifferentialEvolutionSolver(ndim, npop)\n solver.SetRandomInitialPoints(lb, ub)\n solver.SetStrictRanges(lb, ub)\n solver.SetEvaluationLimits(generations=maxiter)\n solver.SetEvaluationMonitor(esow)\n solver.SetGenerationMonitor(ssow)\n solver.enable_signal_handler()\n #term = COG(1e-10)\n #term = CRT()\n term = VTR(0.)\n time1 = time.time() # Is this an ok way of timing?\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\n CrossProbability=0.3, ScalingFactor=1.0)\n sol = solver.Solution()\n time_elapsed = time.time() - time1\n fx = solver.bestEnergy\n print \"Solution: \", sol\n print \"f value: \", fx\n print \"Iterations: \", solver.generations\n print \"Function evaluations: \", len(esow.x)\n print \"Time elapsed: \", time_elapsed, \" seconds\"\n assert almostEqual(fx, 0.0, tol=3e-3)\n\n # DifferentialEvolutionSolver2\n print \"\\nUsing DifferentialEvolutionSolver2:\"\n npop = 50\n random_seed(seed)\n from mystic.solvers import DifferentialEvolutionSolver2\n from mystic.termination import ChangeOverGeneration as COG\n from mystic.termination import CandidateRelativeTolerance as CRT\n from mystic.termination import VTR\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\n esow = Monitor()\n ssow = Monitor() \n solver = DifferentialEvolutionSolver2(ndim, npop)\n solver.SetRandomInitialPoints(lb, ub)\n solver.SetStrictRanges(lb, ub)\n solver.SetEvaluationLimits(generations=maxiter)\n solver.SetEvaluationMonitor(esow)\n solver.SetGenerationMonitor(ssow)\n #term = COG(1e-10)\n #term = CRT()\n term = VTR(0.)\n time1 = time.time() # Is this an ok way of timing?\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\n CrossProbability=0.3, ScalingFactor=1.0)\n sol = solver.Solution()\n time_elapsed = time.time() - time1\n fx = solver.bestEnergy\n print \"Solution: \", sol\n print \"f value: \", fx\n print \"Iterations: \", solver.generations\n print \"Function evaluations: \", len(esow.x)\n print \"Time elapsed: \", time_elapsed, \" seconds\"\n assert almostEqual(fx, 0.0, tol=3e-3)","title":""},{"docid":"cc6b76e94e2da8452d3f6eb07296ad32","score":"0.57891214","text":"def evolutionary_algorithm(self):\n # Creating the Individual Using DEAP\n creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n creator.create(\"Individual\", list, fitness=creator.FitnessMax)\n\n # Creating ToolBox For The DEAP Framework\n toolbox = base.Toolbox()\n toolbox.register(\"attr_bool\", random.randint, 0, 1)\n toolbox.register(\"individual\", tools.initRepeat, creator.Individual, toolbox.attr_bool, self.n_features)\n toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n toolbox.register(\"evaluate\", self.fitness_test)\n toolbox.register(\"mate\", tools.cxOnePoint)\n toolbox.register(\"mutate\", tools.mutFlipBit, indpb=0.05)\n toolbox.register(\"select\", tools.selTournament, tournsize=3)\n\n # Initialize Parameters\n pop = toolbox.population(n=self.n_pop)\n hof = tools.HallOfFame(self.n_pop * self.n_gen)\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", np.mean)\n stats.register(\"min\", np.min)\n stats.register(\"max\", np.max)\n\n # Genetic Algorithm\n pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=self.n_gen, stats=stats, halloffame=hof,verbose=True)\n\n # Return Fall Of Home\n return hof","title":""},{"docid":"763350b629661a962875e4097a558371","score":"0.5786748","text":"def best_model() :\n file_path = os.path.join(os.path.os.getcwd(), cf.PERF_FILE)\n with open(file_path, 'r') as perf_file:\n prec = perf_file.readlines()[0].split(';')\n\n precisions = [t for t in prec if len(t)>0]\n\n models = [t.split(':')[0] for t in precisions]\n models = sorted(models)\n precisions = np.array([float(t.split(':')[1]) for t in precisions])\n index_best_model = np.argmax(precisions)\n #best_model = models[index_best_model]\n\n output_files, model_dict = get_output_files()\n\n chosen_model = model_dict[int(index_best_model)+1]\n chosen_output_file = output_files[int(index_best_model)+1]\n\n print('Best model is ' + chosen_model + ' with precision ' + str(max(precisions)) + '%')\n\n return chosen_model, chosen_output_file","title":""},{"docid":"152015f9a8d342528e38fde1197c42a4","score":"0.57838523","text":"def get_solution(self):\n if self.is_solution_available():\n pass\n raise errors.IllegalState()","title":""},{"docid":"1af224aec567cf6f051e98f8188c816f","score":"0.5780064","text":"def run(self):\n if self.current_control:\n target_value = self.current_density\n else:\n target_value = self.average_cell_voltage * self.stack.n_cells\n if not isinstance(target_value, (list, tuple, np.ndarray)):\n target_value = [target_value]\n cell_voltages = []\n current_densities = []\n local_data_list = []\n global_data_list = []\n for i, tar_value in enumerate(target_value):\n current_errors = []\n temp_errors = []\n convergence_flags = []\n self.timing['simulation'] = 0.0\n self.timing['output'] = 0.0\n simulation_start_time = timeit.default_timer()\n counter = 0\n while True:\n if counter == 0:\n if self.current_control:\n self.stack.update(current_density=tar_value)\n else:\n self.stack.update(voltage=tar_value)\n else:\n self.stack.update()\n if self.stack.break_program:\n break\n current_error, temp_error = self.calc_convergence_criteria()\n current_errors.append(current_error)\n temp_errors.append(temp_error)\n if len(target_value) < 1:\n print(counter)\n counter += 1\n if ((current_error < self.it_crit and temp_error < self.it_crit)\n and counter > self.min_it) or counter > self.max_it:\n break\n if counter > self.max_it:\n convergence_flag = False\n else:\n convergence_flag = True\n convergence_flags.append(convergence_flag)\n simulation_stop_time = timeit.default_timer()\n simulation_time = simulation_stop_time - simulation_start_time\n self.timing['simulation'] += simulation_time\n output_start_time = timeit.default_timer()\n\n if not self.stack.break_program:\n # voltage_loss = self.get_voltage_losses(self.stack)\n cell_voltages.append(np.average([cell.v for cell in\n self.stack.cells]))\n current_densities.append(self.stack.i_cd_avg)\n\n case_name = 'Case'+str(i)\n self.output.save(case_name, self.stack)\n local_data_dict = self.output.get_data(self.stack)\n local_data_dict['Iterations'] = \\\n {'value': [list(range(counter))], 'units': '-'}\n local_data_dict['Current Density Error'] = \\\n {'value': [current_errors], 'units': '-',\n 'xkey': 'Iterations'}\n local_data_dict['Temperature Error'] = \\\n {'value': [current_errors], 'units': '-',\n 'xkey': 'Iterations'}\n\n local_data_list.append(local_data_dict)\n if self.output.save_plot:\n path = os.path.join(self.output.output_dir, case_name,\n 'plots', 'Convergence.png')\n self.output.create_figure(path, list(range(counter)),\n [current_errors, temp_errors],\n xlabels='Iteration',\n ylabels='Error',\n yscale='log',\n legend=['Current Density',\n 'Temperature'])\n else:\n target_value = target_value[0:-i]\n break\n\n average_current_density = \\\n np.average([np.average(cell.i_cd, weights=cell.active_area_dx)\n for cell in self.stack.cells])\n if self.stack.coolant_circuit is None:\n cool_mass_flow = 0.0\n else:\n cool_mass_flow = self.stack.coolant_circuit.mass_flow_in\n global_data = \\\n {\n 'Convergence':\n {'value': convergence_flag, 'units': ' '},\n 'Stack Voltage': {'value': self.stack.v_stack, 'units': 'V'},\n 'Average Cell Voltage':\n {'value': self.stack.v_stack / self.stack.n_cells,\n 'units': 'V'},\n 'Minimum Cell Voltage':\n {'value': np.min(self.stack.v), 'units': 'V'},\n 'Maximum Cell Voltage':\n {'value': np.max(self.stack.v), 'units': 'V'},\n 'Average Current Density':\n {'value': average_current_density, 'units': 'A/m²'},\n 'Stack Power Density':\n {'value': self.stack.v_stack * average_current_density,\n 'units': 'W/m²'},\n 'Stack Power':\n {'value': self.stack.v_stack * average_current_density\n * self.stack.cells[0].active_area,\n 'units': 'W'},\n 'Cooling Mass Flow Rate:':\n {'value': cool_mass_flow,\n 'units': 'kg/s', 'format': '.4E'},\n 'Cathode Mass Flow Rate:':\n {'value': self.stack.fuel_circuits[0].mass_flow_in,\n 'units': 'kg/s', 'format': '.4E'},\n 'Anode Mass Flow Rate:':\n {'value': self.stack.fuel_circuits[1].mass_flow_in,\n 'units': 'kg/s', 'format': '.4E'},\n }\n global_data_list.append(global_data)\n output_stop_time = timeit.default_timer()\n self.timing['output'] += output_stop_time - output_start_time\n self.output.print_global_data(self, global_data)\n self.output.save_global_results(global_data)\n\n output_start_time = timeit.default_timer()\n\n # print polarization curve, if more than one current_density value\n # was provided\n if len(target_value) > 1:\n self.output.write_data(current_densities, cell_voltages,\n 'Current Density [A/m²]', 'Cell Voltage',\n units='V', directory=self.output.output_dir,\n # save_csv=True, save_plot=True,\n write_mode='w')\n\n output_stop_time = timeit.default_timer()\n self.timing['output'] += output_stop_time - output_start_time\n\n return global_data_list, local_data_list","title":""},{"docid":"182e8144cc9cf7df0a32def1d95bd1d8","score":"0.5779991","text":"def _eval_energy(self):\n S = self.S\n M = S.mera\n SIlayer = self.SIlayer # = 2\n SIlayer_p1 = SIlayer + 1\n\n for i in range(self.num_of_SIlayer):\n #这里是反复地从 SIlayer ascending 到 SIlayer_p1, WITH tau changing\n if i>0:\n for o in self.ham_op_names:\n S.__getattribute__(o)[SIlayer][0].data[:] = S.__getattribute__(o)[SIlayer_p1][0].data[:]\n self.ascending_ham(M, S, SIlayer, tau=SIlayer + i)\n #above only update H, no need update rho, as the later is scale-invar not changed in each layer\n \n if 1:\n S.rho_2[SIlayer_p1][0].data[:] = S.rho_2[SIlayer][0].data[:]\n if not S.only_NN:\n S.rho_3[SIlayer_p1][0].data[:] = S.rho_3[SIlayer][0].data[:]\n #in the end eval eng at the layer above the first SIlayer\n S.eng_ham(SIlayer_p1)","title":""},{"docid":"682efc983522090afcf58edc4c64d068","score":"0.5779628","text":"def optimize(self):\n self.open_output_files()\n while (self.n_iter < self.n_maxiter and not self.is_converged):\n self.n_iter += 1\n self.choose_step_direction(self.opt_type)\n self.line_search(-1.0 * self.step_dir)\n self.update_energy()\n self.update_gradient()\n self.traj.append_step(self.mol)\n self.update_criteria()\n self.check_convergence()\n self.print_status()\n self.close_output_files()","title":""},{"docid":"3ec4666d8a6ab245f0cab4066f997667","score":"0.57755536","text":"def optimize_ga(self):\n\n converged = False\n ngens = 1\n generation = 1\n difference = self.tol * 10000.0\n self.solution_history = np.zeros(self.max_generation + 1)\n self.solution_history[0] = np.min(self.parent_fitness)\n\n run_time = 0.0\n start_time = time.time()\n while converged is False and ngens < self.max_generation and \\\n run_time < self.max_time:\n self.crossover()\n self.mutate()\n # determine fitness of offspring\n for i in range(self.population_size):\n self.chromosome_2_variables(self.offspring_population[i])\n self.offspring_fitness[i] = \\\n self.objective_function(self.design_variables)\n\n # rank the total population from best to worst\n total_fitness = np.append(self.parent_fitness,\n self.offspring_fitness)\n ranked_fitness = \\\n np.argsort(total_fitness)[0:int(self.population_size)]\n\n total_population = \\\n np.vstack([self.parent_population, self.offspring_population])\n self.parent_population[:, :] = total_population[ranked_fitness, :]\n self.parent_fitness[:] = total_fitness[ranked_fitness]\n\n # store solution history and wrap up generation\n self.solution_history[generation] = np.min(self.parent_fitness)\n\n if generation > self.convergence_iters:\n difference = \\\n self.solution_history[generation - self.convergence_iters]\\\n - self.solution_history[generation]\n else:\n difference = 1000\n if abs(difference) <= self.tol:\n converged = True\n\n # shuffle up the order of the population\n shuffle_order = np.arange(1, self.population_size)\n np.random.shuffle(shuffle_order)\n shuffle_order = np.append([0], shuffle_order)\n self.parent_population = self.parent_population[shuffle_order]\n self.parent_fitness = self.parent_fitness[shuffle_order]\n\n generation += 1\n ngens += 1\n\n run_time = time.time() - start_time\n\n # Assign final outputs\n self.solution_history = self.solution_history[0:ngens]\n self.optimized_function_value = np.min(self.parent_fitness)\n self.chromosome_2_variables(\n self.parent_population[np.argmin(self.parent_fitness)])\n self.optimized_design_variables = self.design_variables\n\n logger.debug('The GA ran for this many generations: {}'\n .format(ngens))\n logger.debug('The GA ran for this many seconds: {:.3f}'\n .format(run_time))\n logger.debug('The optimized function value was: {:.3e}'\n .format(self.optimized_function_value))\n logger.debug('The optimal design variables were: {}'\n .format(self.optimized_design_variables))","title":""},{"docid":"22f62b19d875ff1dfa2e3d4bfd9a5172","score":"0.57655716","text":"def return_fitness(self, e):\r\n\t\treturn e.fitness","title":""},{"docid":"83fe6af1079e34a1c9740f124d26c109","score":"0.576445","text":"def solve():\n return round(7 * (1 - choose(60, 20) / choose(70, 20)), 10)","title":""},{"docid":"595ecfd9facfe91a02000cddd7224112","score":"0.57595515","text":"def fittest(self):\n return self.best_fit, self.best_fit_fitness","title":""},{"docid":"b1c1e9395f4e30e4b41f6aa74720d615","score":"0.57595515","text":"def test_best_improvement():\n process_list = [1, 2, 4, 1, 3]\n expected_solution = [3, 1, 4, 2, 1]\n current_neighborhood = 0\n\n best_solution = best_improvement(process_list, current_neighborhood, 60)\n assert best_solution == expected_solution","title":""},{"docid":"1774df4c382a9a263ca22e025ab62161","score":"0.5756853","text":"def save_result(self, output_dir):\n self.best_solver.save_tree(output_dir)\n self.best_solver.save_best_solution(output_dir)\n for i in range(self.best_solver.tube_num):\n self.best_solver.visualize_full_search(output_dir, tube_num=i, with_solution=True)\n\n filename = output_dir / \"optimize_result.txt\"\n # noinspection PyTypeChecker\n optimize_result = open(filename, \"w\")\n\n optimize_result.write(f\"Optimizer Success: {self.success}\\n\")\n optimize_result.write(f\"Goal Reached: {self.best_solver.found_solution}\\n\")\n optimize_result.write(f\"Completion Time: {self.completion_time}\\n\")\n optimize_result.write(f\"Number of Iterations: {self.num_iterations}\\n\")\n optimize_result.write(f\"Number of Function Evals: {self.num_function_eval}\\n\")\n q = \", \".join(str(i) for i in self.best_q)\n optimize_result.write(f\"Optimized Q: {q}\\n\")\n\n optimize_result.close()\n\n filename = output_dir / \"optimize_process.txt\"\n # noinspection PyTypeChecker\n optimize_process = open(filename, \"w\")\n\n for i in range(len(self.optimize_process)):\n q = \", \".join(str(j) for j in self.optimize_process[i].get('q'))\n cost = self.optimize_process[i].get('cost')\n optimize_process.write(f\"{q} | {cost}\\n\")\n\n optimize_process.close()","title":""},{"docid":"8d155e5d49b180fe2d888a7e23955477","score":"0.5756708","text":"def betterEvaluationFunction(currentGameState):\n \"*** YOUR CODE HERE ***\"\n foods = currentGameState.getFood()\n newGhostStates=currentGameState.getGhostStates()\n newPos = currentGameState.getPacmanPosition()\n print 'New position:', newPos\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\n\n\n\n optimal_food = []\n optimal_ghost = []\n food = currentGameState.getFood().asList()\n\n for f in food:\n optimal_food.append(manhattanDistance(newPos, f))\n if len(optimal_food) == 0:\n return 0\n a = min(optimal_food)\n\n if a == 0:\n a = currentGameState.getScore()\n cap=[]\n b = max(optimal_food)\n f=currentGameState.getNumFood()\n\n # for g in currentGameState.getGhostPositions():\n # optimal_ghost.append(manhattanDistance(newPos, g))\n # if manhattanDistance(newPos, g) == 0:\n # return -100000.0\n # elif manhattanDistance(newPos, g) > 0.0 and manhattanDistance(newPos, g) <= 1.0:\n # return -100000.0/6\n\n ghost_PM_dist = []\n ghost_dist_nearest = 0\n for ghost in newGhostStates:\n Ghost_Pos = ghost.getPosition()\n GP_dist = manhattanDistance(Ghost_Pos, newPos)\n ghost_PM_dist.append(GP_dist)\n\n if len(ghost_PM_dist) > 0:\n ghost_dist_nearest = min(ghost_PM_dist)\n else:\n ghost_dist_nearest = currentGameState.getScore()\n # print ghost_dist_nearest\n ghost_feature = 1 / (ghost_dist_nearest + 1)\n score = 0\n # c=currentGameState.getCapsules()\n # print 'cap',c\n # for i in c:\n # cap.append(manhattanDistance(newPos,i))\n # if len(optimal_ghost) > 0:\n # ghost_dist_nearest = min(optimal_ghost)\n # else:\n # ghost_dist_nearest = currentGameState.getScore()\n # # print ghost_dist_nearest\n # ghost_feature = 1 / (ghost_dist_nearest + 1)\n # score = 0\n # score=0\n # if currentGameState.isWin():\n # score = 1000000\n return currentGameState.getScore() + 1.0/(a) +100*score+ 2*(ghost_feature)\n\n util.raiseNotDefined()","title":""},{"docid":"14c0ae96cd69e709a3affb6aa640c97d","score":"0.5754821","text":"def best_result(self):\n if self.result is not None:\n return self.result[0]\n return None","title":""},{"docid":"7c226930b9f22f2d5ed885f9483f4c19","score":"0.57431376","text":"def evolve(self):\n #Configure properly each island\n self.__parametrize__()\n\n # Get evolution generator of each island\n self._rc[:].execute(\"gen = gp_engine.sequentially_evolve()\", block=True)\n\n # Loop over all the sessions\n i = 0\n while True:\n logging.info('Launch generation evolution')\n\n #Do generation computing\n self._rc[:].execute(\"chosen = gen.next()\")\n\n #Print best individual\n logging.info('Get best individuals')\n self._rc[:].execute(\"best = gp_engine.get_best_individual()\")\n bests = self._rc[:]['best']\n\n print \"Generation %d\" % i\n print \"=\"*15\n print \"Island\\t| fit\\t\"\n print \"\\n\".join(\\\n [ \"%d\\t%f\" % (j, bests[j][1]) \\\n for j in range(len(bests))])\n\n print \"AVG\\t%f\" %np.mean( [bests[j][1] for j in range(len(bests))])\n i = i + 1\n\n logging.info('Test if end of generation')\n #Test if the process is finished\n self._rc[:].execute('end=gp_engine.__evolver__.is_evolution_ended()')\n\n if np.any( self._rc[:]['end']):\n break\n\n #Operate the migration\n logging.info('Launch migration process')\n self.__migration_operator__.manage_migration()\n\n #loop again\n\n print 'Evolution terminated'\n return bests","title":""},{"docid":"d73f2751a1cb356fcb986044d948ed77","score":"0.5730213","text":"def is_better_then_global_best(self, solution):\n return solution.personal_best.fitness_value < self.best_solution.fitness_value","title":""},{"docid":"6b31563d2c606a0c2b5ebe046d544277","score":"0.5728114","text":"def random_search():\t\t\n\tsim = simulator()\t\t\t\t\t\t\t# simulator object, to be used during this algorithm\n\tnum_search = 20\t\t\t\t\t\t\t\t# number of robots evaluated\n\tnum_tetr_added = 14\t\t\t\t\t\t\t# this is the size of our robots ( 15 tetrahedron total )\n\n\trs_fit = np.zeros(num_search)\t\t\t\t# learning curve array\n\tbest_fit = 0\t\t\t\t\t\t\t\t# best fitness place holder\n\tbest_array = np.empty(8)\t\t\t\t\t# top performing genome place holder\n\t\n\tfor i in range(num_search):\t\t\t\t\t# do the random search\t\n\t\tprint('counter: ', i)\n\n\t\trobot = tetrahedron()\t\t\t\t\t\t\t\t\t\t\t\t\t# make a random robot\n\t\trobot.set_tetr( np.array((0 , 0 , 0)) , np.array((0 , 0 , 0)))\t\t\t# make the seed\n\t\trobot.set_genome(num_tetr_added)\t\t\t\t\t\t\t\t\t\t# generate a random genome\n\t\trobot.build_robot()\t\t\t\t\t\t\t\t\t\t\t\t\t\t# builds the robot \n\t\t\n\t\tmass_list = robot.mass_list\t\t\t\t# center the robot\n\t\tcom = robot.get_com(mass_list)\t\t\t# \t\t....\n\t\trobot.center_object(com)\t\t\t\t# \t\t....\n\n\t\tfitness = calc_fitness( robot , sim)\t# evaluate the robot's fitness\n\n\t\tif fitness > best_fit:\t\t\t\t\t# if the fitness is better than current best\n\t\t\tbest_fit = fitness \t\t\t\t\t# update placeholders\n\t\t\tbest_array = np.copy(robot.genome)\t# \t\t....\n\n\t\trs_fit[i] = best_fit\t\t\t\t\t# add the best fit to the learning curve\n\n\tnp.savetxt(\"rs_robot.csv\", best_array, delimiter=\",\")\n\tnp.savetxt(\"rs_learning.csv\", rs_fit, delimiter=\",\")","title":""}],"string":"[\n {\n \"docid\": \"dc23f9d302e3c5b94328c54bbe84fee4\",\n \"score\": \"0.7375325\",\n \"text\": \"def solution(self):\\n return self.Mbest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a458447c66af157f126dfa185c1878\",\n \"score\": \"0.6866638\",\n \"text\": \"def optimize(self):\\n\\n # Initiate particles\\n self.__initPart()\\n self.listOfPos = []\\n\\n NFC = 0\\n while(NFC < self.maxGen):\\n #print \\\"Run: \\\" + str(NFC) + \\\" Best: \\\" + str(self.globBestFit)\\n\\n # Perform search\\n self.update()\\n\\n # Acceptably close to solution\\n #if self.globBestFit < self.epsError:\\n #return self.globBestPos, self.globBestFit\\n\\n # next gen\\n NFC += 1\\n self.listOfPos.append(self.globBestFit)\\n # Search finished\\n return self.globBestPos, self.globBestFit, self.listOfPos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7c273cb7bfaf99e55d77e2de198e94\",\n \"score\": \"0.68366396\",\n \"text\": \"def solve(self):\\r\\n\\r\\n nfev, nit, warning_flag = 0, 0, False\\r\\n status_message = _status_message['success']\\r\\n\\r\\n # calculate energies to start with\\r\\n for index, candidate in enumerate(self.population):\\r\\n parameters = self._scale_parameters(candidate)\\r\\n self.population_energies[index] = self.func(parameters,\\r\\n *self.args)\\r\\n nfev += 1\\r\\n\\r\\n if nfev > self.maxfun:\\r\\n warning_flag = True\\r\\n status_message = _status_message['maxfev']\\r\\n break\\r\\n\\r\\n minval = np.argmin(self.population_energies)\\r\\n\\r\\n # put the lowest energy into the best solution position.\\r\\n lowest_energy = self.population_energies[minval]\\r\\n self.population_energies[minval] = self.population_energies[0]\\r\\n self.population_energies[0] = lowest_energy\\r\\n\\r\\n self.population[[0, minval], :] = self.population[[minval, 0], :]\\r\\n\\r\\n if warning_flag:\\r\\n return OptimizeResult(\\r\\n x=self.x,\\r\\n fun=self.population_energies[0],\\r\\n nfev=nfev,\\r\\n nit=nit,\\r\\n message=status_message,\\r\\n success=(warning_flag != True))\\r\\n\\r\\n # do the optimisation.\\r\\n for nit in range(1, self.maxiter + 1):\\r\\n if self.dither is not None:\\r\\n self.scale = self.random_number_generator.rand(\\r\\n ) * (self.dither[1] - self.dither[0]) + self.dither[0]\\r\\n for candidate in range(np.size(self.population, 0)):\\r\\n if nfev > self.maxfun:\\r\\n warning_flag = True\\r\\n status_message = _status_message['maxfev']\\r\\n break\\r\\n\\r\\n trial = self._mutate(candidate)\\r\\n self._ensure_constraint(trial)\\r\\n parameters = self._scale_parameters(trial)\\r\\n\\r\\n energy = self.func(parameters, *self.args)\\r\\n nfev += 1\\r\\n\\r\\n if energy < self.population_energies[candidate]:\\r\\n self.population[candidate] = trial\\r\\n self.population_energies[candidate] = energy\\r\\n\\r\\n if energy < self.population_energies[0]:\\r\\n self.population_energies[0] = energy\\r\\n self.population[0] = trial\\r\\n\\r\\n # stop when the fractional s.d. of the population is less than tol\\r\\n # of the mean energy\\r\\n convergence = (np.std(self.population_energies) /\\r\\n np.abs(np.mean(self.population_energies) +\\r\\n _MACHEPS))\\r\\n\\r\\n if self.disp:\\r\\n print(\\\"differential_evolution step %d: f(x)= %g\\\"\\r\\n % (nit,\\r\\n self.population_energies[0]))\\r\\n\\r\\n if (self.callback and\\r\\n self.callback(self._scale_parameters(self.population[0]),\\r\\n convergence=self.tol / convergence) is True):\\r\\n\\r\\n warning_flag = True\\r\\n status_message = ('callback function requested stop early '\\r\\n 'by returning True')\\r\\n break\\r\\n\\r\\n if convergence < self.tol or warning_flag:\\r\\n break\\r\\n\\r\\n else:\\r\\n status_message = _status_message['maxiter']\\r\\n warning_flag = True\\r\\n\\r\\n DE_result = OptimizeResult(\\r\\n x=self.x,\\r\\n fun=self.population_energies[0],\\r\\n nfev=nfev,\\r\\n nit=nit,\\r\\n message=status_message,\\r\\n success=(warning_flag != True))\\r\\n\\r\\n if self.polish:\\r\\n result = minimize(self.func,\\r\\n np.copy(DE_result.x),\\r\\n method='L-BFGS-B',\\r\\n bounds=self.limits.T,\\r\\n args=self.args)\\r\\n\\r\\n nfev += result.nfev\\r\\n DE_result.nfev = nfev\\r\\n\\r\\n if result.fun < DE_result.fun:\\r\\n DE_result.fun = result.fun\\r\\n DE_result.x = result.x\\r\\n DE_result.jac = result.jac\\r\\n # to keep internal state consistent\\r\\n self.population_energies[0] = result.fun\\r\\n self.population[0] = self._unscale_parameters(result.x)\\r\\n\\r\\n return DE_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0117546e96810312ac163daf208b031\",\n \"score\": \"0.6755187\",\n \"text\": \"def search_optimal(self):\\n\\n for i in range(self.get_nsource()):\\n ith_bee_fitness = self.bee[i].get_fitness()\\n best_fitness = self.optimal.get_fitness()\\n if best_fitness < ith_bee_fitness:\\n self.suboptimal.greedy_choice(self.optimal)\\n self.optimal.greedy_choice(self.bee[i])\\n elif best_fitness == ith_bee_fitness:\\n pass # protect updating suboptimal in this case\\n elif self.suboptimal.get_fitness() < ith_bee_fitness:\\n self.suboptimal.greedy_choice(self.bee[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f474c1ee4cea7c9306c18d7443490471\",\n \"score\": \"0.6726404\",\n \"text\": \"def _getBestSolution(obj):\\n filename = _getOutputFilename(obj.folder_output + BEST_SOLUTION_FILENAME)\\n obj.bestSolution = _loadCSV(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6112d959c74a77caaeb6a8ef2f3e1704\",\n \"score\": \"0.6724941\",\n \"text\": \"def getstart_temp(self):\\n\\n assert(not self.dims is None)\\n lrange = self.lower\\n urange = self.upper\\n fmax = _double_min\\n fmin = _double_max\\n for _ in range(self.Ninit):\\n x0 = random.uniform(size=self.dims)*(urange-lrange) + lrange\\n fval = self.func(x0, *self.args)\\n self.feval += 1\\n if fval > fmax:\\n fmax = fval\\n if fval < fmin:\\n fmin = fval\\n bestEnergy = fval\\n bestSolution = array(x0)\\n\\n self.T0 = (fmax-fmin)*1.5\\n return bestSolution, bestEnergy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f818ca9f877589cbc25e77d1157ae1\",\n \"score\": \"0.6616518\",\n \"text\": \"def get_best():\\n\\n best_h = 3\\n best_not = 3\\n runs = 7\\n\\n np.save(\\\"H_START_S_BEST.npy\\\",0)\\n np.save(\\\"NOT_START_S_BEST.npy\\\",0)\\n np.save(\\\"H_START_S.npy\\\",0)\\n np.save(\\\"NOT_START_S.npy\\\",0)\\n\\n for i in range(runs):\\n \\n # Run Sim-H\\n sim_h.run_main_h()\\n # Run Sim-NOT\\n sim_not.run_main_not()\\n\\n hloss = run_waves(\\\"H\\\")\\n print(\\\"Loss of H Gate\\\")\\n print(hloss)\\n nloss = run_waves(\\\"N\\\")\\n print(\\\"Loss of NOT Gate\\\")\\n print(nloss)\\n\\n # If this is a better H value, update\\n if (hloss < best_h):\\n best_h = hloss\\n overwriteH()\\n\\n # If this is a better NOT value, update\\n if (nloss < best_not):\\n best_not = nloss\\n overwriteNOT()\\n\\n\\n h_waves_list = np.load(\\\"H_START_S_BEST.npy\\\")\\n not_waves_list = np.load(\\\"NOT_START_S_BEST.npy\\\")\\n\\n return (h_waves_list, not_waves_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90862e6480a983a07b72ffac8f4a63f1\",\n \"score\": \"0.6562416\",\n \"text\": \"def solve(self):\\n self.runtime = Timer() # Start the timer to measure the runtime\\n self.model.min_to_max() # Convert minimization problems to maximization problems\\n self.model.construct_payoff() # Construct a payoff table from the objective function values\\n self.model.find_obj_range() # Find the range of each objective function\\n self.model.convert_prob() # Convert the payoff table\\n\\n self._find_solutions() # Find all solutions to the optimization problem\\n self._process_solutions() # Identify the unique solutions\\n self._get_hv_indicator() # Compute the HV indicator\\n\\n # Save the results to an Excel file if requested\\n if self.opts.output_excel:\\n self._output_excel()\\n\\n # Compute the total runtime and print a summary of the results\\n self.runtime = round(self.runtime.get(), 2)\\n Helper.clear_line()\\n print(\\n f\\\"Solved {self.model.models_solved.value()} models for \\\"\\n f\\\"{self.num_unique_pareto_sols} unique Pareto solutions in \\\"\\n f\\\"{self.runtime} seconds\\\"\\n )\\n\\n # Log a summary of the results\\n self.logger.info(Helper.separator())\\n self.logger.info(f\\\"Runtime: {self.runtime} seconds\\\")\\n self.logger.info(f\\\"Models solved: {self.model.models_solved.value()}\\\")\\n self.logger.info(f\\\"Infeasibilities: {self.model.infeasibilities.value()}\\\")\\n self.logger.info(f\\\"Solutions: {self.num_sols}\\\")\\n self.logger.info(f\\\"Unique solutions: {self.num_unique_sols}\\\")\\n self.logger.info(f\\\"Unique Pareto solutions: {self.num_unique_pareto_sols}\\\")\\n self.logger.info(f\\\"Hypervolume indicator: {self.hv_indicator}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f57fe14d76194d4919d36fb73dcce81\",\n \"score\": \"0.6554548\",\n \"text\": \"def get_best_result(self):\\n # self.result_data\\n # raise NotImplementedError\\n param_names = ['r', 'z', 'phi', 'scale', 'align', 'cloud']\\n \\n\\n for chan in self.chan_list:\\n for i in self.index:\\n self.best_params[chan][i] = []\\n #loops over all the fit waveforms\\n # for model_num,single_wf_model in enumerate(self.wf_model):\\n model_num = i\\n single_wf_model = self.wf_model[i]\\n print(\\\"Calculating best values for {}\\\".format(model_num))\\n\\n for j in range(single_wf_model.num_params):\\n # loops over all the parameters\\n param_values = self.result_data[i][j]\\n\\n param_avg = np.mean(param_values)\\n param_std = np.std(param_values)\\n\\n self.best_params[model_num].append(param_avg)\\n\\n print(\\\"{name}: {avg:4.4f} +/- {std:4.4f}\\\".format(\\n name=param_names[j], \\n avg=param_avg, \\n std=param_std)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c22ab22848e4af7f6852a38c863bd374\",\n \"score\": \"0.6523312\",\n \"text\": \"def solve(self):\\n fit_partial = self.fit_partial\\n n_jobs = self.n_jobs\\n gen = self.gen\\n\\n # get results in parallel\\n all_res = Parallel(n_jobs=n_jobs)(\\n delayed(fit_partial)(\\n order=order,\\n seasonal_order=seasonal_order,\\n )\\n for order, seasonal_order in gen\\n )\\n\\n sorted_fits = _sort_and_filter_fits(all_res)\\n if self.trace and sorted_fits:\\n print(f\\\"\\\\nBest model: {str(sorted_fits[0])}\\\")\\n\\n return sorted_fits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"390db28702298605d2d7b3a6236568e8\",\n \"score\": \"0.6472718\",\n \"text\": \"def run(self):\\n self.time_start = time.time()\\n self.step = 0\\n self.step_idle = 0\\n\\n E = self.energy()\\n\\n prev_state = self.copy_state(self.state)\\n prev_energy = E\\n\\n self.best_state = self.copy_state(self.state)\\n self.best_energy = E\\n self.best_step = 0\\n self.energy_history = [E] * self.history_length\\n Ehmean = E\\n Ehvar = 0.\\n Nvar = float(max(self.history_length - 1, 1))\\n\\n if self.updates_every > 0:\\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\\n steps_since_update = 0\\n\\n while not self.terminate_search() and not self.user_exit:\\n self.move()\\n E = self.energy()\\n\\n if E >= prev_energy:\\n self.step_idle += 1\\n else:\\n self.step_idle = 0\\n\\n v = self.step % self.history_length\\n Ev = self.energy_history[v]\\n if E < Ev or E <= prev_energy:\\n # accept candidate state\\n prev_state = self.copy_state(self.state)\\n prev_energy = E\\n\\n if E < self.best_energy:\\n self.best_state = self.copy_state(self.state)\\n self.best_energy = E\\n self.best_step = self.step\\n else:\\n # restore previous state\\n self.state = self.copy_state(prev_state)\\n E = prev_energy\\n\\n if E < Ev:\\n # Update energy history\\n self.energy_history[v] = E\\n\\n # and its mean and variance\\n dE = E - Ev\\n Ehmean_old = Ehmean\\n\\n Ehmean += dE / self.history_length\\n Ehvar += dE * (E-Ehmean+Ev-Ehmean_old) / Nvar\\n\\n self.step += 1\\n steps_since_update += 1\\n\\n if steps_since_update == self.updates_every:\\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\\n steps_since_update = 0\\n\\n self.state = self.copy_state(self.best_state)\\n if self.save_state_on_exit:\\n self.save_state()\\n\\n self.time_end = time.time()\\n return self.best_state, self.best_energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ec8c15419ad8bc00193cb7e262615f0\",\n \"score\": \"0.646268\",\n \"text\": \"def optimal(self):\\n optimal_check = self.checker(True)\\n return optimal_check\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8707ac8ab5a432499cd686262931a91\",\n \"score\": \"0.6461675\",\n \"text\": \"def get_best_solution(self):\\n print \\\"getting best solution\\\"\\n greatest_number_of_words = 0\\n prelim_best_solutions = []\\n for psol in self.potential_solutions:\\n l = len(\\\" \\\".join(map(lambda x:x['title'],psol)))\\n if l == greatest_number_of_words:\\n prelim_best_solutions.append(psol)\\n elif l > greatest_number_of_words:\\n greatest_number_of_words = l\\n prelim_best_solutions = [psol]\\n best_solution = None\\n least_number_of_tracks = 999999\\n for bsol in prelim_best_solutions:\\n if len(bsol) < least_number_of_tracks:\\n least_number_of_tracks = len(bsol)\\n best_solution = bsol\\n return best_solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbafe1f015464da4a494c735c33ee07\",\n \"score\": \"0.6442411\",\n \"text\": \"def getSolution(self):\\n self.var_constr_dict = self.mapVarToConstraints()\\n\\n start = time.time()\\n solution = self.solver.getSolution(self)\\n self.runtime = time.time() - start\\n # solution is an assignment of problem, where the assignments for all variables are filled in. \\n return solution, self.getStatistics()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede2c84a0104dd19a0e4737190f698ad\",\n \"score\": \"0.63996756\",\n \"text\": \"def save_best_sol(self):\\n\\n self.save(result=self.best_solutions_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ba86d89bccfe98a869ed480491d257\",\n \"score\": \"0.6390985\",\n \"text\": \"def optimize(self):\\n rankedSol = self._rankSolutions()\\n bestSol = rankedSol[0]\\n # include best solution in next generation solutions\\n newSols = [bestSol]\\n\\n for s in rankedSol[1:]:\\n if np.random.uniform(0.0, 1.0) > 0.5:\\n a = self._computeA()\\n normA = np.linalg.norm(a)\\n if normA < 1.0:\\n newS = self._encircle(s, bestSol, a)\\n else:\\n # ##select random sol\\n randomSol = self._sols[np.random.randint(self._sols.shape[0])]\\n newS = self._search(s, randomSol, a)\\n else:\\n newS = self._attack(s, bestSol)\\n newSols.append(self._constrain_solution(newS))\\n\\n self._sols = np.stack(newSols)\\n self._a -= self._aStep\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c1eca33dce422738cb450fdee65c6e\",\n \"score\": \"0.63894033\",\n \"text\": \"def solver_optimize(self):\\n parts = self.feats_obj.feat_partitions\\n self.N_s = [None for i in parts]\\n self.opt_sol = [None for i in parts]\\n self.norm_z = [None for i in parts]\\n self.compute_zero_stats()\\n\\n for i, partition in enumerate(parts):\\n self.compute_data_stats(partition, i)\\n self.compute_zc(partition, i)\\n self.compute_theta(partition, i)\\n\\n # print('N_s is: ', self.N_s)\\n # print('Z_c is: ', self.norm_z)\\n # print('Theta is: ', self.opt_sol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4b156c2e290526811e8a8e8d75a345\",\n \"score\": \"0.6357096\",\n \"text\": \"def _solve(self):\\n #algorithm = self.config['algorithm'](self.config)\\n \\n self.output = self.algorithm.run(self.config['u_0'],self.config['TOL'],self.config['MAXIT'])\\n print('Iterations:' + str(self.output['iter']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e45c5f527db22d70313ad80ec8673d\",\n \"score\": \"0.6339497\",\n \"text\": \"def solve(self):\\n\\n while True:\\n self.iteration += 1\\n\\n logger.debug('iteration: n=%s', self.iteration)\\n\\n self._iterate()\\n\\n if self._converged():\\n break\\n\\n return self._find_strongest_solution()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149015545ba83e85aff88252b4aa13f7\",\n \"score\": \"0.63234216\",\n \"text\": \"def run(self, iterations, run_from_start=True):\\n if run_from_start:\\n self.run_init()\\n for _ in range(iterations):\\n self._build_ant_solutions()\\n self._update_iteration_best()\\n self._update_global_best()\\n self.global_pheromone_update(self.global_best)\\n self.iteration += 1\\n return self.global_best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2f056ef0ae1d598ecbc81e870cab69\",\n \"score\": \"0.62895966\",\n \"text\": \"def best_fitness(self):\\n return self.fitness[self.order[-1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0f291c79ddcfe9833db29d6e0a3b0f6\",\n \"score\": \"0.62888795\",\n \"text\": \"def _solve_impl(self):\\n\\n self.start_time = clock() # Used to show the total time took the process\\n bellman_updates = 0 # used to track the performance improvement.\\n converged = False\\n iteration = 0\\n\\n num_states = self.representation.num_states_total\\n\\n while self.has_time() and not converged:\\n iteration += 1\\n\\n # Store the weight vector for comparison\\n prev_weight = self.representation.weight.copy()\\n\\n # Sweep The State Space\\n for i in range(num_states):\\n\\n s = self.representation.stateID2state(i)\\n # Sweep through possible actions\\n if self.domain.is_terminal(s):\\n continue\\n for a in self.domain.possible_actions(s):\\n\\n self.bellman_backup(s, a, ns_samples=self.ns_samples)\\n bellman_updates += 1\\n\\n # Create Log\\n if bellman_updates % self.log_interval == 0:\\n performance_return, _, _, _ = self.performance_run()\\n self._log_updates(performance_return, bellman_updates)\\n\\n # check for convergence\\n weight_diff = l_norm(prev_weight - self.representation.weight)\\n converged = weight_diff < self.convergence_threshold\\n\\n # log the stats\\n (\\n perf_return,\\n perf_steps,\\n perf_term,\\n perf_disc_return,\\n ) = self.performance_run()\\n self.logger.info(\\n \\\"PI #%d [%s]: BellmanUpdates=%d, ||delta-weight_vec||=%0.4f, \\\"\\n \\\"Return=%0.4f, Steps=%d\\\"\\n % (\\n iteration,\\n hhmmss(deltaT(self.start_time)),\\n bellman_updates,\\n weight_diff,\\n perf_return,\\n perf_steps,\\n )\\n )\\n\\n # Show the domain and value function\\n if self._visualize_mode:\\n self.domain.show_learning(self.representation)\\n\\n # store stats\\n self.result[\\\"bellman_updates\\\"].append(bellman_updates)\\n self.result[\\\"return\\\"].append(perf_return)\\n self.result[\\\"planning_time\\\"].append(deltaT(self.start_time))\\n self.result[\\\"num_features\\\"].append(self.representation.features_num)\\n self.result[\\\"steps\\\"].append(perf_steps)\\n self.result[\\\"terminated\\\"].append(perf_term)\\n self.result[\\\"discounted_return\\\"].append(perf_disc_return)\\n self.result[\\\"iteration\\\"].append(iteration)\\n\\n if converged:\\n self.logger.info(\\\"Converged!\\\")\\n self.log_value()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2b1c534c039f242d2a7c458ea6fb951\",\n \"score\": \"0.6226838\",\n \"text\": \"def calculateBest():\\r\\n\\r\\n if var.score>var.best:\\r\\n setNewBest()\\r\\n return var.score\\r\\n else: return var.best\\r\\n #if the actual score is bigger than the best score\\r\\n #set the best score to the actual score\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7fe40017c2980b624d976cb8e5d1632\",\n \"score\": \"0.6216328\",\n \"text\": \"def search(self, config):\\n self.iteration = 0\\n while self.iteration < self.maxIterations:\\n self.GAStep(config)\\n self.iteration += 1\\n\\n print (\\\"Total iterations: \\\",self.iteration)\\n print (\\\"Best Solution: \\\", self.best.getFitness())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ba52f8f0040d777dcae8c696c03ef8\",\n \"score\": \"0.6213454\",\n \"text\": \"def _solution(self):\\n return self.__solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d78b5b7da1a48b218ae692e26ffa46\",\n \"score\": \"0.62130654\",\n \"text\": \"def solve(self):\\n \\n # Do the actual run to find the fit and trendline\\n self.fitModel = WarmingFitModel(self.dates, self.temps)\\n self.constants = self.fitModel.solve()\\n self.devs = self.fitModel.deviations()\\n \\n # Verify the optimality of solutions\\n xvariabilities = { \\\"min\\\" : [], \\\"max\\\" : [] }\\n if not self.DISABLE_VARIABILITY_CHECK:\\n for op in xvariabilities.keys():\\n for xsub in [0, 1]:\\n variabilityModel = WarmingVariabilityModel(self.dates, self.temps, xsub, op, self.constants, sum(list(map(abs, self.devs))))\\n xvariabilities[op].append(variabilityModel.solve())\\n pprint.pprint(xvariabilities)\\n \\n # Verify the chosen value for solar cycle length\\n cycleLengths = [9.7 + 0.2 * i for i in range(11)]\\n cycleDevs = []\\n if not self.DISABLE_CYCLE_LENGTH_CHECK:\\n for cycleLength in cycleLengths:\\n solarFitModel = WarmingFitModel(self.dates, self.temps, solarCycle=cycleLength)\\n cycleDevs.append(sum([abs(dev) for dev in solarFitModel.deviations()]))\\n pprint.pprint(cycleDevs)\\n \\n stdevs = [0 for x in self.constants]\\n if not self.DISABLE_CONFIDENCE_INTERVALS:\\n # Do a bunch more iterations to find a confidence interval for the original fit\\n ITER_COUNT = 50\\n random.seed()\\n xstar = []\\n for fuzzIter in range(ITER_COUNT):\\n fuzzedTemps = [self.temps[i] + random.choice(self.devs) for i in list(range(len(self.dates)))]\\n fuzzedModel = WarmingFitModel(self.dates, fuzzedTemps)\\n fuzzedConstants = fuzzedModel.solve()\\n print(\\\"Fuzzy constants: \\\" + str(fuzzedConstants))\\n xstar.append(fuzzedConstants)\\n \\n # Actually compute the confidence interval\\n if ITER_COUNT > 0:\\n cMeans = [sum(fuzzedX[xpos] for fuzzedX in xstar) / float(ITER_COUNT) for xpos in range(len(self.constants))]\\n for pos in range(len(self.constants)):\\n cDevs = [(xstar[i][pos] - cMeans[pos]) ** 2 for i in range(ITER_COUNT)]\\n cStdDev = math.sqrt(sum(cDevs) / float(len(cDevs)))\\n stdevs[pos] = cStdDev\\n \\n return {\\\"fit\\\" : [(self.constants[i], 2 * stdevs[i]) for i in range(len(self.constants))], \\\\\\n \\\"xvariabilities\\\" : xvariabilities, \\\\\\n \\\"cycle\\\" : (cycleLengths, cycleDevs) }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b1caf17582b8c1975b073d829c4e34\",\n \"score\": \"0.62079585\",\n \"text\": \"def find_solution(self):\\n start_time = timer.time()\\n result = []\\n constraints = []\\n \\n ##############################\\n # Task 2.4 : Addressing Failure\\n #reordering agent priority using heuristic costs\\n agent_max_cost = []\\n for agent in range(self.num_of_agents):\\n max_cost = -1\\n for loc, cost in self.heuristics[agent].items():\\n print(loc, cost)\\n if cost > max_cost:\\n max_cost = cost\\n agent_max_cost.append([cost, agent])\\n agent_max_cost.sort()\\n agent_max_cost.reverse()\\n \\n for _, i in agent_max_cost:\\n ##############################\\n\\n # original\\n # for i in range(self.num_of_agents): # Find path for each agent\\n \\n path = a_star(self.my_map, self.starts[i], self.goals[i], self.heuristics[i],\\n i, constraints)\\n if path is None:\\n raise BaseException('No solutions')\\n result.append(path)\\n \\n print('Agent[{}]'.format(i), 'path = ', path)\\n \\n ##############################\\n # Task 2: Add constraints here\\n # Useful variables:\\n # * path contains the solution path of the current (i'th) agent, e.g., [(1,1),(1,2),(1,3)]\\n # * self.num_of_agents has the number of total agents\\n # * constraints: array of constraints to consider for future A* searches\\n\\n # Task 2.1 : adding vertex constraints\\n # for node in path:\\n # constraint = dict()\\n # constraint['agent'] = i\\n # constraint['loc'] = [node[0], None]\\n # constraint['timestep'] = node[1]\\n # constraints.append(constraint)\\n \\n # build constraint from a path\\n for p in range(len(path)):\\n curr_loc = path[p]\\n next_loc = None\\n curr_timestep = p\\n \\n # vertex constraint\\n constraint = dict()\\n constraint['agent'] = i\\n constraint['loc'] = [curr_loc, next_loc]\\n constraint['timestep'] = curr_timestep\\n constraints.append(constraint)\\n \\n # edge constraint except start cell\\n if p > 0:\\n prev_loc = path[p - 1]\\n next_timestep = p\\n \\n constraint = dict()\\n constraint['agent'] = i\\n constraint['loc'] = [curr_loc, prev_loc]\\n constraint['timestep'] = next_timestep\\n constraints.append(constraint)\\n \\n ##############################\\n \\n # Task 2.3: additional constraints\\n # agent_0 reached its goal cell at timestep 3,\\n # but, agent_1 will reach to the goal cell at timestep 7\\n # then, agent_0 will be wait \\n \\n # but, we don't know the value 7\\n # so, just estimate this value with the prior knowledge\\n # for example, # of a valid cell\\n \\n ##############################\\n \\n last_loc = path[-1]\\n last_timestep = len(path) -1\\n \\n max_cells = 0\\n \\n for x in range(len(self.my_map)):\\n for y in range(len(self.my_map[0])):\\n if not self.my_map[x][y]:\\n max_cells += 1\\n \\n for timestep in range(last_timestep + 1, max_cells):\\n constraint = dict()\\n constraint['agent'] = i\\n constraint['loc'] = [last_loc, None]\\n constraint['timestep'] = timestep\\n constraints.append(constraint) \\n\\n self.CPU_time = timer.time() - start_time\\n\\n print(\\\"\\\\n Found a solution! \\\\n\\\")\\n print(\\\"CPU time (s): {:.2f}\\\".format(self.CPU_time))\\n print(\\\"Sum of costs: {}\\\".format(get_sum_of_cost(result)))\\n print(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65a27ab5b4a47ccc18024490760dc46\",\n \"score\": \"0.61903894\",\n \"text\": \"def result(self):\\n return self.best.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2e8b4ec0309484e14adeec64980705\",\n \"score\": \"0.6185776\",\n \"text\": \"def getBestSol(sqe, Res, c_ms):\\n #---docstring------------------------------------------------------------------\\n #---end-docstring--------------------------------------------------------------\\n res = dc(Res)\\n C_ms = dc(c_ms)\\n lsqSc = numpy.zeros(len(res),'d') + numpy.inf \\n lsqMu = numpy.zeros(len(res),'d') + numpy.inf \\n lsqSl = numpy.zeros(len(res),'d') + numpy.inf\\n for i in range(len(res)):\\n try:\\n zeroCut = sqe.zeroInd + 1\\n sqeCut = sqe.zeroInd+res[i][1].cutoffInd\\n slopeCut = sqe.slopeCut()\\n sqeLin = sqe.se[sqeCut:slopeCut]\\n mulSqeLin = nar(nar(res[i][0][1:]))[sqeCut:slopeCut]\\n eLin = sqe.e[sqeCut:slopeCut]\\n sqeSlope,inter = mfit.poly(eLin,sqeLin ,1)\\n mulSlope,inter = mfit.poly(eLin,mulSqeLin,1)\\n line = mfit.poly_of_x(eLin,mfit.poly(eLin,sqeLin ,1) )\\n lsqSl[i] = (sqeSlope-mulSlope)**2.0\\n lsqMu[i] = nar( (mulSqeLin - line )**2.0 /float(len(line)) )\\n lsqSc[i] = nar( nar( (sqe.sqe - nar(res[i][0]) )**2.0 ) )\\n except:\\n pass\\n \\n start = 0\\n while start 0 and self.all_costs[-1] < best_costs:\\n best_option = copy.deepcopy(self.batteries)\\n best_costs = self.all_costs[-1]\\n\\n if self.scheme == \\\"linear\\\":\\n self.temp = self.temp - self.cooling_rate\\n elif self.scheme == \\\"exp\\\":\\n self.temp *= 1 - self.cooling_rate\\n\\n self.batteries = best_option\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e51584fbf426cc78699e1fa846fb810\",\n \"score\": \"0.61729866\",\n \"text\": \"def post_optimization(pdb):\\n print('Optimizing...')\\n energies = optimize(pdb, os.path.abspath(os.getcwd()))\\n print('Energy before optimizing: %s' % str(energies[0][0]))\\n print('Energy after optimizing: %s' % str(energies[1][0]))\\n print('Model completed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cbbdf5b657c88ce44e3b8c8d148fa37\",\n \"score\": \"0.6171333\",\n \"text\": \"def guessBest(self):\\n #Get the true(past tries excluded) possible values for each value\\n search_field = {i:(self.possibleValues[i] - self.impossibleValues[i]) for i in self.possibleValues}\\n #Get the counts of times a number is a possible value @deprecated\\n #counts = {i:sum(i in search_field[coord_values] for coord_values in search_field if len(search_field[coord_values])) for i in range(10)} \\n #Get the coord with the least number of possible values\\n bestCoord = min(search_field, key=lambda k: len(search_field[k]) if len(search_field[k]) > 1 else 10)\\n #Apply the additional heuristic, sort the chosen coordinates possible values by how often those values could be other solutions @ deprecated\\n #vals = sorted(search_field[bestCoord], key = lambda k: counts[k], reverse=True)\\n vals = list(search_field[bestCoord])\\n #print \\\"Guessing\\\", bestCoord, vals[0]\\n #self.printData()\\n self.setValue(bestCoord, guess=True, guessVal = vals[0])\\n self.root = (bestCoord, self.getCoordValue(bestCoord))\\n self.possibleValues[bestCoord] = {self.getCoordValue(bestCoord)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379f6fcd7411622d26dc90b601fbb81e\",\n \"score\": \"0.61595905\",\n \"text\": \"def extra_convergence_criteria(self, results):\\n # type: (List[Result]) -> bool\\n self.iter_count += len(results)\\n\\n # Skip the setup period\\n if not results:\\n return False\\n\\n # Update the best local\\n crtl_elapsed_time = 0\\n self.best_unchange += len(results)\\n valid_result = 0\\n iter_best_result = None\\n for result in results:\\n latency = result.time\\n resource = result.size\\n crtl_elapsed_time = max(crtl_elapsed_time, result.elapsed)\\n if latency == float('inf'):\\n continue\\n elif resource >= 1.0:\\n continue\\n\\n valid_result += 1\\n if (not iter_best_result) or latency < iter_best_result.time:\\n iter_best_result = result\\n\\n self.elapsed_time += crtl_elapsed_time\\n\\n # Terminate this partition if we cannot get a valid\\n # result from the seed design point.\\n #if (not self.best_result and self.iter_count >= len(self.seeds)\\n # and valid_result == 0):\\n # return True\\n\\n # Update the history\\n if iter_best_result:\\n if ((not self.best_result) or\\n iter_best_result.time < self.best_result.time):\\n self.best_result = iter_best_result\\n self.best_unchange = -1\\n if self.best_result is not None:\\n self.log_best(self.iter_count, self.elapsed_time,\\n self.best_result.time)\\n\\n # Keep the best result of current iteration for computing\\n # the entropy at the next iteration.\\n self.prev_result = iter_best_result\\n\\n # Update the best global\\n count = 1\\n new_ranking = float('inf')\\n if os.path.exists('.who_is_the_best'):\\n with open('.who_is_the_best') as filep:\\n for line in filep:\\n latency = float(line.replace('\\\\n', ''))\\n if self.best_result:\\n if latency == float(self.best_result.time):\\n # Found itself\\n new_ranking = count\\n count += 1\\n new_ranking /= count\\n\\n # Add one thread as the manager requested\\n if os.path.exists('.add_threads'):\\n with open('.add_threads') as filep:\\n line = filep.readline()\\n addition_thread = int(line.replace('\\\\n', ''))\\n self.more_parallelism(addition_thread)\\n run_command('rm -f .add_threads')\\n\\n # Skip the warm up period\\n if new_ranking == float('inf') or count < 4:\\n return False\\n\\n # Skip the rest criteria to let partitions run as long as possible\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32bff97f4800c45fadf86ac73a30a350\",\n \"score\": \"0.61526996\",\n \"text\": \"def get_best_values(self):\\n return self.g_best_score, self.g_best_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d035d1802dc286f7a043d398d9b8e29d\",\n \"score\": \"0.6148896\",\n \"text\": \"def optimize():\\n stopwatch = StopWatch()\\n\\n ANNEAL_AREA = False\\n logger = LoggerUtils.configure_log(name='Optimization script', use_console=True, use_file=True)\\n\\n click.clear()\\n\\n metrics = GeneticAlgorithmConstants();\\n\\n # Generate initial population\\n population = Evolution.populate(initial=True)\\n\\n # Calculate fitness for each individual and sort them\\n ranked_pop = Evolution.adaptability(population)\\n \\n generation = 0\\n same_solution_count = 0\\n optimal = {\\\"genome\\\": None, \\\"fitness\\\": None, \\\"links\\\": None}\\n\\n logger.info('Starting first optimization loop.')\\n # The first optimization loop defines the number of nodes for each of the line instalation areas, \\n # while keeping squared areas number of nodes constant\\n\\n # Main loop for the first optimization\\n while(generation < metrics.MAX_GENERATIONS and same_solution_count != metrics.MAX_STAGNATED_OPTIMAL):\\n generation += 1\\n \\n # Perform the crossover \\n ranked_pop = Evolution.crossover(ranked_pop, metrics.CROSSOVER_PROBABILITY)\\n\\n # Mutate population\\n ranked_pop = Evolution.mutate(ranked_pop, metrics.MUTATION_PROB)\\n\\n # Next generation\\n population = [individual.get('genome') for individual in ranked_pop]\\n\\n # Calculate fitness for each individual and sort them\\n ranked_pop = Evolution.adaptability(population)\\n\\n # Update the optimal value and count improvements\\n if optimal.get('fitness') is None:\\n optimal = copy.deepcopy(ranked_pop[0])\\n elif ranked_pop[0].get('fitness') < optimal.get('fitness'):\\n optimal = copy.deepcopy(ranked_pop[0])\\n same_solution_count = 0\\n else:\\n same_solution_count += 1\\n\\n # Perform environment pressure\\n ranked_pop = Evolution.environment_pressure(ranked_pop)\\n \\n logger.info(''.join(['[Gen {}] '.format(generation), 'Optimal:{solution: ', str(optimal.get('genome')), ', fitness: ', str(optimal.get('fitness')), \\n ', min_links: ', str(optimal.get('links')), '}']))\\n \\n logger.info('Finished first optimization with %s generations. Total time: %s.' % (generation, stopwatch.read()))\\n logger.info('OPTIMAL FOR FIRST OPTIMIZATION: N1: {}, N2: {}, N3: {}, N4: {} '.format(optimal['genome'][0], \\n optimal['genome'][1], optimal['genome'][2], optimal['genome'][3]))\\n \\n # SECOND OPTIMIZATION ROUTINE:\\n # Voronoi : generate optimized points distribution in an area.\\n # L-BFGS-B : \\n # POSSIBILITY: Model the second optimization problem as max f(x) = min SUM distances(i,j) \\n\\n # GETTING READY FOR SECOND OPTIMIZATION\\n\\n n1, n2, n3, n4 = optimal['genome']\\n # network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\\n\\n area_nodes = n4\\n generation = 0\\n\\n logger.info('Starting second optimization.')\\n # The second optimization cares to find the min number of nodes within squared areas. At this part of the code\\n # it's assumed that the min number of nodes for the line areas are known due the first optimization loop.\\n\\n logger.info('Initial genome [N1 = {}, N2 = {}, N3 = {}, N4 = {}].'. format(n1, n2, n3, n4))\\n stopwatch_loop_2 = StopWatch()\\n\\n\\n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4) \\n lines = ['N1', 'N2', 'N3', 'N4']\\n nodeArray = [network.get('SINK')]\\n for line in lines:\\n nodeArray += list(network.get(line))\\n # Plotter.plot_node_list(nodeArray, title='First Optimization Result', xLabel='Grid Coordinate (m)', yLabel='Grid Coordinate (m)')\\n\\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'first.geojson')\\n\\n # Main loop for the second optimization\\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 3:\\n # First find the min number n of nodes of node at each side of the nxn grid\\n \\n generation += 1\\n area_nodes -= 1\\n\\n if area_nodes == 0:\\n break\\n\\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, area_nodes)\\n\\n logger.info('[Gen {}] Fitness for N4 = {}: {}'.format(generation, area_nodes, fitness))\\n\\n # When the loop ends, area_nodes have a number with invalid fitness for n4.\\n # So the correct min number is the result area_nodes + 1.\\n n4 = area_nodes + 1\\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\\n\\n logger.info('Finished second optimization with %s generations. Total time: %s.' % (generation, stopwatch_loop_2.read()))\\n logger.info('Optimal for second optimization: N1: {}, N2: {}, N3: {}, N4: {} '.format(n1, n2, n3, n4))\\n logger.info('Fitness for second optimization: {}'.format(fitness))\\n\\n # Retrieve network \\n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\\n\\n metrics = SelectiveAneelingConstants()\\n\\n networkArea = list(network.get('N4'))\\n current_optimal_network_area = networkArea \\n\\n if len(networkArea) >= 2 :\\n fitness = NetworkModel.getFitnessForNetwork(networkArea)\\n generation = 1\\n\\n # Define area boundaries\\n boundaries = dict()\\n boundaries['MIN_X'] = GlobalParameters.N4_DIM.top_left[0]\\n boundaries['MAX_X'] = GlobalParameters.N4_DIM.botom_right[0]\\n boundaries['MIN_Y'] = GlobalParameters.N4_DIM.botom_right[1]\\n boundaries['MAX_Y'] = GlobalParameters.N4_DIM.top_left[1]\\n\\n if ANNEAL_AREA: logger.info('Starting third optimization. Selective aneeling')\\n\\n # Finds the optimal number of nodes within area maximizing the distance between them\\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 2 and len(networkArea) >= 2 and ANNEAL_AREA:\\n\\n current_optimal_network_area = networkArea\\n networkArea.pop()\\n\\n # Configuring Annealer\\n annealer = DistanceAnnealing(networkArea, boundaries)\\n annealer.copy_strategy = metrics.COPY_STRATEGY\\n\\n # Calibrating Annealer\\n click.secho('Calibrating annealer', fg='yellow')\\n auto_schedule = annealer.auto(minutes=1)\\n annealer.set_schedule(auto_schedule)\\n click.secho('Annealer calibrated! Parameters: {}'.format(auto_schedule), fg='green')\\n\\n networkArea, energy = annealer.anneal()\\n \\n fitness = NetworkModel.getFitnessForNetwork(networkArea)\\n\\n logger.info(\\\"[GEN {}] Number of Links: {} Energy: {}, fitness: {}\\\".format(len(networkArea), generation, energy, fitness))\\n\\n generation += 1\\n \\n \\n logger.info('Finished second optimization. Nodes positions inside area defined. {} nodes installed.'.format(len(current_optimal_network_area)))\\n\\n # Saving the new area node disposition\\n network['N4'] = np.array(current_optimal_network_area)\\n\\n # Adding all nodes of the network in one array\\n\\n lines = ['N1', 'N2', 'N3', 'N4']\\n nodeArray = [network.get('SINK')]\\n\\n for line in lines:\\n nodeArray += list(network.get(line))\\n \\n Plotter.plot_node_list(nodeArray, add=True, annotate=False)\\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'second.geojson')\\n \\n logger.info('Annealing nodes. Current configuration have %s nodes.' %(len(nodeArray)))\\n annealer = PositionAnnealing(nodeArray)\\n annealer.copy_strategy = metrics.COPY_STRATEGY\\n\\n # click.secho('Calibrating annealer.', fg='yellow')\\n\\n # auto_schedule = annealer.auto(minutes=1)\\n # annealer.set_schedule(auto_schedule)\\n\\n # click.secho('Annealer calibrated!', fg='green')\\n # logger.info('Annealer parameters: {}'.format(auto_schedule))\\n\\n annealer.Tmax=1.5\\n annealer.Tmin=1e-10\\n\\n nodeArray, energy = annealer.anneal()\\n\\n network[line] = nodeArray\\n \\n click.secho('Finished Optimization', fg='green')\\n logger.info('Finished network optimization. Total elapsed time: {}'.format(stopwatch.read()))\\n logger.info('Writing result geoJSON at {}.'.format(OUTPUT_PATH))\\n\\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'result.geojson')\\n\\n Plotter.plot_node_list(nodeArray, title='Representação gráfica da Rede', xLabel='Longitude', yLabel='Latitude',\\n color='red', annotate=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6b2d1b0b101462372338089711edf7\",\n \"score\": \"0.61201286\",\n \"text\": \"def calc_energy(self, force=True):\\n if self.atoms_moved:\\n self.timer.switch_on(\\\"energy\\\")\\n if self.qeq:\\n self.timer.switch_to(\\\"qeq\\\")\\n self.qeq.preopt_q()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.virtual_atoms:\\n self.timer.switch_to(\\\"virt atoms set pos\\\")\\n self.virt_atom_set_pos()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.enforce_nlist_rebuild:\\n # if some operation (move atoms/molecules around) could break the neigborlist enforce its rebuild\\n self.dlp_nlst.nlst_notest = True\\n self.dlp_nlst.nlst_rebuild = True\\n ### nullify stress tensor\\n self.dlp_conf.stress[:]=0.0\\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\\n self.dlp.calc_energy_force()\\n ### ################################################### ###\\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\\n if self.QMMM:\\n self.timer.switch_to(\\\"qmmm\\\")\\n self.QMMM_eng = self.QMMM_interface(force)\\n self.timer.switch_to(\\\"energy\\\")\\n # for a QMMM run we report the energies every step\\n self.report_energies()\\n if self.virtual_atoms:\\n self.timer.switch_to(\\\"virt atoms dist force\\\")\\n self.virt_atom_distribute_force()\\n self.timer.switch_to(\\\"energy\\\")\\n self.atoms_moved = False\\n if self.enforce_nlist_rebuild:\\n self.dlp_nlst.nlst_notest = False\\n self.dlp_nlst.nlst_rebuild = False\\n self.enforce_nlist_rebuild = False\\n# if self.QMMM:\\n# self.timer.switch_to(\\\"qmmm\\\")\\n# self.QMMM_eng = self.QMMM_interface(force)\\n# self.timer.switch_to(\\\"energy\\\")\\n# # for a QMMM run we report the energies every step\\n# self.report_energies()\\n if self.extra_term:\\n self.timer.switch_to(\\\"extra term\\\")\\n self.extra_eng = self.extra_term()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.plumed:\\n self.timer.switch_to(\\\"metamd plumed\\\")\\n self.meta_eng = self.plumed_energy()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.constraint:\\n self.multiply_force(self.constraint_mask)\\n for s in self.extra_systems:\\n self.timer.switch_to(s.get_name())\\n s.calc_energy()\\n self.timer.switch_to(\\\"energy\\\")\\n self.timer.switch_off()\\n # return configuration energy in current units\\n energy = self.dlp.engcfg/self.dlp.engunit\\n if self.QMMM: energy += self.QMMM_eng\\n if self.extra_term: energy += self.extra_eng\\n if self.plumed: energy += self.meta_eng\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8abf4c4ebfe4d352c7ad6e941203b1e7\",\n \"score\": \"0.61065435\",\n \"text\": \"def get_solutions_fitness(self, solution):\\n value = self.get_solutions_value(solution)\\n return self.get_fitness(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d65270fe6d8d9e8590e16fe04479aff\",\n \"score\": \"0.6098882\",\n \"text\": \"def compute_solution(self,dataset):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50508a445861c4588a2e65c595bf6459\",\n \"score\": \"0.60829246\",\n \"text\": \"def getOwnBestFitness(self):\\r\\n\\t\\t\\r\\n\\t\\treturn self.ownBestFitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4add5b38f8e282631cc6b0b016fc55\",\n \"score\": \"0.6068796\",\n \"text\": \"def optimal(self):\\n return np.max(self.expected_rewards())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330e6fdf9357f731486615c89f7e2c32\",\n \"score\": \"0.6067864\",\n \"text\": \"def getBestFit(self):\\n self.envLock.acquire()\\n best = self.environment.getMostAdapted()\\n self.envLock.release()\\n return best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380cba45db1ed8aaf173abe49ca24a3a\",\n \"score\": \"0.6064149\",\n \"text\": \"def get_solution(self):\\n index = -1\\n max_f = -math.inf\\n\\n for i in range(self.N):\\n if self.train_v[i] > 1.2 and self.train_f[i] > max_f:\\n max_f = self.train_f[i]\\n index = i\\n return self.train_x[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4596652f65f223725709173384ec358d\",\n \"score\": \"0.6045643\",\n \"text\": \"def greedy():\\n\\tglobal table, weights, values, capacity, max_value, best_solution, unit_values\\n\\tunit_values = [((values[i]*1.0)/weights[i],i,weights[i],values[i]) for i in xrange(len(weights))]\\n\\tunit_values.sort()\\n\\tinv_sort_order = [(unit_values[i][1],i) for i in xrange(len(unit_values))]\\n\\tinv_sort_order.sort()\\n\\tsort_order = [x[1] for x in inv_sort_order]\\n\\t\\n\\tweight = 0\\n\\tmax_value = 0\\n\\tbest_solution = [0]*len(weights)\\n\\ti = len(weights) - 1\\n\\twhile(weight < capacity and i >= 0):\\n\\t\\tif weight + unit_values[i][2] <= capacity:\\n\\t\\t\\tbest_solution[i] = 1\\n\\t\\t\\tmax_value += unit_values[i][3]\\n\\t\\t\\tweight += unit_values[i][2]\\n\\t\\telse:\\n\\t\\t\\tbest_solution[i]=0\\n\\t\\ti -= 1\\n\\n\\tbest_solution = [best_solution[i] for i in sort_order]\\n\\t\\n\\treturn {'value':max_value, 'taken':best_solution, 'optimality':0}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af394f586b631d9fba6859afb2e6cc75\",\n \"score\": \"0.60455203\",\n \"text\": \"def solve(self):\\n convergence = False\\n val = 0\\n for i in range(self.maxiter):\\n grad = self.dR()\\n self.x = self.x - self.optStep()*grad\\n if np.sum(np.abs(grad)) < self.eps*(np.log(self.x.shape[0])):\\n convergence = True\\n break\\n\\n if not convergence:\\n print('WARNING: Did not converge. Try increasing maxiter or a smaller eps.') \\n\\n return(self.eig,self.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc50367fe11ae1b2a1e4fd8e6b9c5b37\",\n \"score\": \"0.60270655\",\n \"text\": \"def betterEvaluationFunction(currentGameState):\\n \\\"*** YOUR CODE HERE ***\\\"\\n #more food = worse score\\n #closer food = good score\\n #closer than 3 ghost = bad score\\n \\n foodGrid = currentGameState.getFood()\\n food = foodGrid.asList()\\n pos = currentGameState.getPacmanPosition()\\n ghosts = []\\n gdists = []\\n for i in range(1, currentGameState.getNumAgents()):\\n g = currentGameState.getGhostPosition(i)\\n ghosts.append(g)\\n gdists.append(manhattanDistance(pos, g))\\n \\n #fterm = len(food)\\n fterm = foodGrid.width*foodGrid.height-len(food)\\n #gterm = closest(pos, ghosts)[0]\\n gterm = 0\\n fdist, floc = closest(pos, food)\\n \\n # breadth first search\\n #problem = PositionSearchProblem(currentGameState, goal=floc, start=pos, warn=False)\\n #realDist = len(breadthFirstSearch(problem))\\n \\n dterm = 1/float(fdist+1)\\n \\n #coefficients\\n a = 10\\n b = 1\\n c = 15\\n\\n for d in gdists:\\n if d<3:\\n gterm-=50\\n if d<2:\\n gterm-=50\\n \\n total = a*fterm + b*gterm + c*dterm\\n \\n # break ties\\n #if right:\\n # total+=2\\n #if up:\\n # total+=1\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287e49f8017272b659afe2b7a22ade44\",\n \"score\": \"0.60227233\",\n \"text\": \"def solve(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80d1d5c90fceaa679fdec499b70a5175\",\n \"score\": \"0.60214704\",\n \"text\": \"def evaluate_best_agent(self):\\n\\n eval_eps = Config.EVALUATION_EPISODES\\n reward_sum = 0\\n log_list = []\\n\\n print(\\\"Evaluate The Best Network with Test data: episode \\\", self.best_episode, \\\" reward \\\", self.best_reward)\\n self.restore_best_network_to_train_network()\\n\\n for i in range(eval_eps):\\n [reward, dist, trainsize, pred] = self.evaluate(isValidation=False)\\n reward_sum = reward_sum + reward\\n log = {\\n \\\"episode\\\": None,\\n \\\"top_reward\\\": reward,\\n \\\"exp_rate\\\": None,\\n \\\"trainsize\\\": trainsize,\\n \\\"top_dist\\\": dist,\\n \\\"top_pred\\\": pred\\n }\\n log_list.append(log)\\n\\n AgentLogger.print_trianing_results(log)\\n\\n mean_reward = reward_sum/eval_eps\\n print(\\\"Mean: \\\", mean_reward)\\n AgentLogger.log_evaluation_results(log_list, self.logger, self.best_episode, self.best_reward, mean_reward)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2147cc31fc145610dbcba7b9c5f6d2ea\",\n \"score\": \"0.6020431\",\n \"text\": \"def find_best_object_and_behavior(self):\\n potential_event_deflection = np.inf\\n\\n if not (self.model.network_structure is None):\\n # check if network structure was passed and only consider\\n # candidate objects to which actor has link\\n possible_ids = [i for i,link\\n in enumerate(self.model.network_structure[self.unique_id])\\n if link == 1]\\n potential_objects = [ag for ag in self.model.schedule.agents\\n if ag.unique_id in possible_ids]\\n if self.model.action_on_group:\\n potential_objects += [self.model.group]\\n else:\\n potential_objects = [ag for ag in self.model.schedule.agents\\n if ag.unique_id != self.unique_id]\\n if self.model.action_on_group:\\n potential_objects += [self.model.group]\\n\\n if self.model.object_choice == \\\"random\\\":\\n potential_object = np.random.choice(potential_objects)\\n potential_action = compute_opt_behavior(self, potential_object)\\n potential_transients = compute_transients(\\n self.current_transients,\\n potential_action,\\n potential_object.current_transients,\\n self.model.abo_coefficients)\\n else:\\n np.random.shuffle(potential_objects)\\n\\n for obj in potential_objects:\\n opt_beh = compute_opt_behavior(self, obj)\\n transients = compute_transients(self.current_transients,\\n opt_beh, obj.current_transients,\\n self.model.abo_coefficients)\\n\\n if self.model.object_choice == \\\"min event-tension\\\":\\n fundamentals_stack = np.hstack((self.fundamentals, opt_beh,\\n obj.fundamentals))\\n event_deflection = np.linalg.norm(fundamentals_stack\\n - transients)**2\\n\\n elif self.model.object_choice == \\\"max deflection-reduction\\\":\\n actor_deflection_diff = (\\n np.linalg.norm(self.fundamentals - transients[:3])**2\\n - self.personal_deflection\\n )\\n beh_deflection = np.linalg.norm(opt_beh - transients[3:6])**2\\n # we include the actual deflection reduction of all agents\\n # in the object choice, not just the group object\\n if obj.unique_id == -1:\\n object_deflection_diff = (\\n np.linalg.norm(obj.fundamentals - transients[6:])**2\\n - obj.personal_deflection\\n )\\n for ag in [agent for agent in self.model.schedule.agents\\n if agent.unique_id != self.unique_id]:\\n transients_g = compute_transients(\\n self.current_transients, opt_beh,\\n ag.current_transients,\\n self.model.abo_coefficients\\n )\\n object_deflection_diff+= (\\n np.linalg.norm(ag.fundamentals - transients_g[6:])**2\\n - ag.personal_deflection\\n )\\n else:\\n object_deflection_diff = (\\n np.linalg.norm(obj.fundamentals - transients[6:])**2\\n - obj.personal_deflection\\n )\\n\\n event_deflection = ( actor_deflection_diff\\n + beh_deflection\\n + object_deflection_diff )\\n else:\\n print(\\\"unknown or no object selection criterion specified\\\")\\n\\n # check if deflection for this behavior is smaller\\n # than for other behaviors\\n if event_deflection < potential_event_deflection:\\n potential_event_deflection = event_deflection\\n potential_transients = transients\\n potential_object = obj\\n potential_action = opt_beh\\n\\n return (potential_object, potential_action, potential_transients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ccb88b554cf570c6a44f750a0c3ef0\",\n \"score\": \"0.60071445\",\n \"text\": \"def run(self):\\n self.logger.info(\\\"\\\\n\\\\n************************* Genetic Algorithm *************************\\\")\\n self.logger.info(f\\\"Objective: {self.objective}\\\")\\n self.logger.info(f\\\"============================ Execution with seed: {self._seed} ============================\\\")\\n self.logger.info(f\\\"{time.strftime('%d/%m/%Y %H:%M:%S')}\\\\n\\\\n\\\")\\n\\n random.seed(self._seed)\\n np.random.seed(self._seed)\\n\\n self._generate_initial_population()\\n self._population_statistic()\\n self._print_population_info(self.new_pop, 0)\\n\\n best_fitness = self.new_pop.bestFitness\\n count_patience = 0\\n\\n compare_op_obj = partial(ge) if self.objective == 'maximization' else partial(le)\\n compare_op_best = partial(gt) if self.objective == 'maximization' else partial(lt)\\n\\n for gen in range(1, self.nr_max_gen + 1):\\n self._generating_new_population()\\n self._population_statistic()\\n self._print_population_info(self.new_pop, gen)\\n\\n if compare_op_best(self.new_pop.bestFitness, best_fitness):\\n best_fitness = self.new_pop.bestFitness\\n count_patience = 0\\n else:\\n count_patience += 1\\n\\n if self.objective_fitness is not None:\\n if compare_op_obj(self.new_pop.bestFitness, self.objective_fitness):\\n self.logger.info(f\\\"Stop because found the objective fitness {self.objective_fitness}\\\")\\n break\\n\\n if self.patience > 0:\\n if count_patience >= self.patience:\\n self.logger.info(f\\\"Stop because patience limit\\\")\\n break\\n\\n self.logger.info(f\\\"Best solution found: {self._statistic[-1]['best_indiv']}\\\")\\n save_statistic(self.output_dir, self._statistic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7acdb6e0620fe4b000accc940aa89b\",\n \"score\": \"0.5998345\",\n \"text\": \"def get_energy(self):\\n\\n D, self.e, self.C = self.build_density(self.H)\\n\\n E_old = 0.0\\n F_old = None\\n\\n for iteration in range(self.max_iter):\\n\\n J = np.einsum(\\\"pqrs, rs -> pq\\\", self.g, D)\\n K = np.einsum(\\\"prqs, rs -> pq\\\", self.g, D)\\n\\n # F = H_pq + 2 * G_pqrs D_rs - G_prqs D_rs\\n F = self.H + 2.0 * J - K\\n\\n if self.damping == 'on' and iteration >= self.damp_start:\\n F = self.damp(F_old, F)\\n\\n F_old = F\\n\\n if self.diis == 'on' and iteration >= self.diis_start:\\n F = diis_helper.diis(F, D, self.diis_start,self.diis_vector)\\n\\n E_electric = np.sum((F + self.H) * D)\\n self.E = E_electric + self.mol.nuclear_repulsion_energy()\\n\\n E_diff = self.E - E_old\\n E_old = self.E\\n\\n # Build the AO gradient\\n grad = F @ D @ self.S - self.S @ D @ F\\n grad_rms = np.mean(grad ** 2) ** 0.5\\n\\n D, self.e, self.C = self.build_density(F)\\n\\n print(\\\"Iteration: %3d Energy: % 16.12f Energy difference: % 8.4f Gradient difference: % 8.4f\\\" % (iteration, self.E, E_diff,grad_rms))\\n\\n if E_diff < self.e_conv and grad_rms < self.d_conv:\\n break\\n\\n print(\\\"SCF has finished! \\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b24c4d70a946f3b9b496e46c1a8767e\",\n \"score\": \"0.5992288\",\n \"text\": \"def initPopulation(self):\\n for i in range(0, self.popSize):\\n individual = Individual(self.genSize, self.data)\\n individual.computeFitness()\\n self.population.append(individual)\\n\\n self.best = self.population[0].copy()\\n for ind_i in self.population:\\n if self.best.getFitness() > ind_i.getFitness():\\n self.best = ind_i.copy()\\n print (\\\"Best initial sol: \\\",self.best.getFitness())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ed1251824de1d9a976769dd87cfabf\",\n \"score\": \"0.59906185\",\n \"text\": \"def _find_solutions(self):\\n # Set progress message\\n self.model.progress.set_message(\\\"finding solutions\\\")\\n\\n # Generate grid of indices to search for solutions\\n grid_range = range(self.opts.gp)\\n indices = [tuple([n for n in grid_range]) for _ in self.model.iter_obj2]\\n self.cp = list(itertools.product(*indices))\\n self.cp = [i[::-1] for i in self.cp]\\n\\n # Pickle the model and initialize queue and process handlers\\n self.model.pickle()\\n self.queues = QueueHandler(self.cp, self.opts)\\n self.queues.split_work()\\n self.procs = ProcessHandler(self.opts, self.model, self.queues)\\n\\n # Start processes and wait for results\\n self.procs.start()\\n self.unprocessed_sols = []\\n while not self.procs.join():\\n self.unprocessed_sols.extend(self.queues.get_result())\\n # One last sweep to make sure all results have been collected\\n self.unprocessed_sols.extend(self.queues.get_result())\\n\\n # In case all worker processes were killed, not all work may have been done.\\n if self.procs.any_killed:\\n raise Exception(\\\"At least one worker exited prematurely, not all computations may have been done.\\\")\\n\\n # Clean the pickled model\\n self.model.clean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb69b984732c6d732aacba92def1e51\",\n \"score\": \"0.5982387\",\n \"text\": \"def solution_most_iterations_done(self) -> int:\\n return int(self.dss_obj.SolutionI(ctypes.c_int32(41), ctypes.c_int32(0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17959bc5f94063bea50630dbccfdc9af\",\n \"score\": \"0.59580135\",\n \"text\": \"def solve(self, problem):\\r\\n recommended_solns = []\\r\\n intermediate_budgets = []\\r\\n expended_budget = 0\\r\\n delta_max = self.factors[\\\"delta_max\\\"]\\r\\n gamma_0 = self.factors[\\\"gamma_0\\\"]\\r\\n delta_candidate = [gamma_0 * delta_max, delta_max, delta_max / gamma_0]\\r\\n #print(delta_candidate)\\r\\n\\r\\n # default values\\r\\n eta_1 = self.factors[\\\"eta_1\\\"]\\r\\n eta_2 = self.factors[\\\"eta_2\\\"]\\r\\n gamma_1 = self.factors[\\\"gamma_1\\\"]\\r\\n gamma_2 = self.factors[\\\"gamma_2\\\"]\\r\\n k = 0 # iteration number\\r\\n\\r\\n # Start with the initial solution\\r\\n new_x = problem.factors[\\\"initial_solution\\\"]\\r\\n new_solution = self.create_new_solution(new_x, problem)\\r\\n recommended_solns.append(new_solution)\\r\\n intermediate_budgets.append(expended_budget)\\r\\n\\r\\n # Parameter tuning run\\r\\n tp_final_ob_pt, k, delta, recommended_solns, intermediate_budgets, expended_budget, new_x = self.parameter_tuning(\\r\\n delta_candidate[0], problem)\\r\\n for i in range(1, 3):\\r\\n final_ob_pt, k_pt, delta_pt, recommended_solns_pt, intermediate_budgets_pt, expended_budget_pt, new_x_pt = self.parameter_tuning(\\r\\n delta_candidate[i], problem)\\r\\n expended_budget += expended_budget_pt\\r\\n if -1 * problem.minmax[0] * final_ob_pt < -1 * problem.minmax[0] * tp_final_ob_pt:\\r\\n k = k_pt\\r\\n delta = delta_pt\\r\\n recommended_solns = recommended_solns_pt\\r\\n intermediate_budgets = intermediate_budgets_pt\\r\\n new_x = new_x_pt\\r\\n\\r\\n intermediate_budgets = (\\r\\n intermediate_budgets + 2 * np.ones(len(intermediate_budgets)) * problem.factors[\\\"budget\\\"] * 0.01).tolist()\\r\\n intermediate_budgets[0] = 0\\r\\n\\r\\n while expended_budget < problem.factors[\\\"budget\\\"]:\\r\\n k += 1\\r\\n fval, Y, q, grad, Hessian, delta_k, expended_budget = self.model_construction(new_x, delta, k, problem,\\r\\n expended_budget)\\r\\n\\r\\n # Cauchy reduction\\r\\n if np.matmul(np.matmul(grad, Hessian), grad) <= 0:\\r\\n tau = 1\\r\\n else:\\r\\n tau = min(1, norm(grad) ** 3 / (delta * np.matmul(np.matmul(grad, Hessian), grad)))\\r\\n\\r\\n grad = np.reshape(grad, (1, problem.dim))[0]\\r\\n candidate_x = new_x - tau * delta * grad / norm(grad)\\r\\n\\r\\n for i in range(problem.dim):\\r\\n if candidate_x[i] < problem.lower_bounds[i]:\\r\\n candidate_x[i] = problem.lower_bounds[i] + 0.01\\r\\n elif candidate_x[i] > problem.upper_bounds[i]:\\r\\n candidate_x[i] = problem.upper_bounds[i] - 0.01\\r\\n\\r\\n candidate_solution = self.create_new_solution(tuple(candidate_x), problem)\\r\\n\\r\\n # adaptive sampling needed\\r\\n problem.simulate(candidate_solution, 1)\\r\\n expended_budget += 1\\r\\n sample_size = 1\\r\\n\\r\\n # Adaptive sampling\\r\\n while True:\\r\\n problem.simulate(candidate_solution, 1)\\r\\n expended_budget += 1\\r\\n sample_size += 1\\r\\n sig2 = candidate_solution.objectives_var\\r\\n if sample_size >= self.samplesize(k, sig2, delta_k):\\r\\n break\\r\\n\\r\\n # calculate success ratio\\r\\n fval_tilde = -1 * problem.minmax[0] * candidate_solution.objectives_mean\\r\\n\\r\\n # replace the candidate x if the interpolation set has lower objective function value\\r\\n if min(fval) < fval_tilde:\\r\\n minpos = fval.index(min(fval))\\r\\n fval_tilde = min(fval)\\r\\n candidate_x = Y[minpos][0]\\r\\n\\r\\n if (self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\\r\\n np.array(candidate_x) - np.array(new_x), q)) == 0:\\r\\n rho = 0\\r\\n else:\\r\\n rho = (fval[0] - fval_tilde) / (\\r\\n self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\\r\\n candidate_x - new_x, q));\\r\\n\\r\\n if rho >= eta_2: # very successful\\r\\n new_x = candidate_x\\r\\n delta_k = min(gamma_1 * delta_k, delta_max)\\r\\n recommended_solns.append(candidate_solution)\\r\\n intermediate_budgets.append(expended_budget)\\r\\n elif rho >= eta_1: # successful\\r\\n new_x = candidate_x\\r\\n delta_k = min(delta_k, delta_max)\\r\\n recommended_solns.append(candidate_solution)\\r\\n intermediate_budgets.append(expended_budget)\\r\\n else:\\r\\n delta_k = min(gamma_2 * delta_k, delta_max)\\r\\n\\r\\n return recommended_solns, intermediate_budgets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2df6e82235fe60278712b06fb8d042ae\",\n \"score\": \"0.5955973\",\n \"text\": \"def calculate(self):\\n # initialize transformation from canonic to energy\\n # optimal states (if necessary)\\n if self.W_mn is None:\\n self.initialize_orbitals(rattle=self.rattle)\\n\\n # optimize the non-unitary invariant part of the\\n # functional\\n self.unitary_optimization()\\n\\n return self.esic, self.ekin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad29ddb80d7682684f4cf035f8b96f23\",\n \"score\": \"0.59508735\",\n \"text\": \"def overall_fitness(self):\\n # Use a decyaing function to generate fitness\\n # Use two step decaying function\\n # First block gives importance to exploration and when as soon\\n # food has been found, the next block will focus on dropping\\n # the food on hub\\n self.individual[0].fitness = (1 - self.beta) * self.delayed_reward \\\\\\n + self.exploration_fitness() + self.carrying_fitness() \\\\\\n + self.food_collected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa0eaf976005e56d162f1b3f95a98d0\",\n \"score\": \"0.5949581\",\n \"text\": \"def solveAll(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4f00dadad3e1e7502978585f5eeb041\",\n \"score\": \"0.5946176\",\n \"text\": \"def optimzeGeneralParameters():\\n\\t#initialize qm energies dictionaries for each molecule\\n\\tglobal qm_energies\\n\\tqm_energies = {mol : {} for mol in molecules}\\n\\n\\t#set parameters for minimize function\\n\\tmaxiter = 5000\\n\\tpgtol = -1e-17\\n\\tftol = 1e-17\\n\\n\\t#get parameter bounds for minimization\\n\\tall_bnds = make_bounds_list(initialValuesList, True)\\n\\ta_bnds = make_bounds_list(initialValuesList, False)\\n\\n\\t#optimize A parameters for each energy component, store in final_params list\\n\\tparameterList = initialValuesList\\n\\tmetaPOInter_initial_list = remove_C_params(parameterList)\\n\\tglobal fitting_component\\n\\twhile fitting_component < 4:\\n\\n\\t\\t#make list of bounds for component to pass down to POInter. Only include B bound if fitting exchange\\n\\t\\tif fitting_component == 0:\\n\\t\\t\\tbnds = all_bnds\\n\\t\\telse:\\n\\t\\t\\tbnds = a_bnds\\n\\n\\t\\t#make list of parameters for component to pass down to POInter\\n\\t\\t#get only parameters for the component we are fitting\\n\\n\\t\\t#TODO: make so user can choose whether or not to fit B params\\n\\t\\t#TODO: make sure input B params are the same for every molecule\\n\\t\\t\\t#TODO: use same .exp file ()\\n\\t\\tmetaPOInter_input = get_component_parameters(metaPOInter_initial_list)\\n\\n\\t\\tres = minimize(metaPOInter,metaPOInter_input,method='L-BFGS-B',\\\\\\n\\t\\t\\t\\t\\t\\t\\tjac=True,\\\\\\n\\t\\t\\t\\t\\t\\t\\toptions={'disp':True,'gtol':pgtol,'ftol':ftol,'maxiter':maxiter},\\\\\\n\\t\\t\\t\\t\\t\\t\\tbounds = bnds)\\n\\n\\t\\tpopt = res.x\\n\\t\\tprint \\\"Optimized parameters for component \\\" + str(fitting_component) + \\\" : \\\" + str(popt)\\n\\n\\t\\tif not res.success:\\n\\t\\t\\tprint 'Warning! Optimizer did not terminate successfully, and quit with the following error message:'\\n\\t\\t\\tprint\\n\\t\\t\\tprint res.message\\n\\t\\t\\tprint\\n\\t\\t\\treturn\\n\\n\\t\\tglobal optimized_params\\n\\t\\toptimized_params[energyComponents[fitting_component]] = popt\\n\\n\\t\\t#reset new B params in POInter objects after fitting exchange component\\n\\t\\tif fitting_component == 0:\\n\\t\\t\\tupdated_B_params = get_updated_B_from_exchange(popt)\\n\\t\\t\\tfor mol in molecules.keys():\\n\\t\\t\\t\\ti = 0\\n\\t\\t\\t\\tfor atomtype in allAtomtypes:\\n\\t\\t\\t\\t\\tpointerModel = molecule_POInter_objects[mol]\\n\\t\\t\\t\\t\\tpointerModel.params[atomtype][0]['B'] = updated_B_params[i]\\n\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\n\\t\\tfitting_component += 1\\n\\n\\t#write output JSON file with optimized parameters\\n\\toptimized_params[\\\"Dispersion\\\"] = [1.0 for atomtype in allAtomtypes]\\n\\tfinal_params_list = write_output_params_to_list(optimized_params)\\n\\tmap_params(final_params_list, \\\"optimized_params\\\")\\n\\n\\t#calc dispersion lsq_error and store\\n\\tdisp_lsq_error = calc_disp_lsq_error()\\n\\tcomponent_lsq_error[energyComponents[fitting_component]] = disp_lsq_error\\n\\n\\t#write lsq_error output file\\n\\twith open(\\\"component_lsq_error.txt\\\", 'w') as f:\\n\\t\\tfor component in energyComponents:\\n\\t\\t\\tf.write(component + \\\":\\\" + '\\\\t')\\n\\t\\t\\tf.write(str(component_lsq_error[component]) + '\\\\n')\\n\\n\\tprint \\\"===========================================================================\\\"\\n\\tprint \\\" Optimized parameters successfully written to optimized_params.constraints \\\"\\n\\tprint \\\"===========================================================================\\\"\\n\\n\\treturn res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036faa1e19c7270a93947e53c76cf9c0\",\n \"score\": \"0.5943793\",\n \"text\": \"def get_solution(self):\\n\\n return self._funcs[\\\"get_solution\\\"](self.problem, self.variable_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469e51f3864b44e39addeb0ee1556f22\",\n \"score\": \"0.59420794\",\n \"text\": \"def get_analysis(self):\\n return self.best_fitness, self.avg_fitness, self.worst_fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e417ac9a10cd6b79cb7903657a121a\",\n \"score\": \"0.593701\",\n \"text\": \"def solutions(self):\\r\\n if self.results is None:\\r\\n warning('results')\\r\\n return \\r\\n if self.is_isoschizomer():\\r\\n iso = True\\r\\n else:\\r\\n iso = False\\r\\n if type(self.best) == list:\\r\\n print('Results obtained using the following group of enzymes:')\\r\\n for e in self.best:\\r\\n print(e, sep=',')\\r\\n print('Other possible solutions:')\\r\\n for el in self.results:\\r\\n if el != self.best:\\r\\n print(el)\\r\\n else:\\r\\n if iso:\\r\\n warning('enzyme3')\\r\\n print('Best enzyme:\\\\n', self.best)\\r\\n print('Other possible solutions:')\\r\\n b = self.results.index(self.best)\\r\\n print(self.results[0:b]+ self.results[b+1:])\\r\\n\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f362cc586c58c587ff2150bc663a74b\",\n \"score\": \"0.5930106\",\n \"text\": \"def optimize(self):\\n\\n\\t\\tstill_needed = []\\n\\t\\t# Verify the required information\\n\\t\\tif self.preference_input is None:\\n\\t\\t\\tstill_needed.append(\\\"Preferences\\\")\\n\\t\\tif self.LP_input is None:\\n\\t\\t\\tstill_needed.append(\\\"LP_input\\\")\\n\\t\\tif self.teacher_file is None:\\n\\t\\t\\tstill_needed.append(\\\"Teacher File (secondary)\\\")\\n\\n\\t\\ts = \\\"\\\"\\n\\t\\tif len(still_needed) > 0:\\n\\t\\t\\ts = str(still_needed)\\n\\t\\t\\tmessagebox.showerror(\\\"Error\\\", \\\"You are missing the following\\\\n\\\\n\\\" + s)\\n\\t\\t\\treturn \\n\\n\\t\\tGAP = self.slider.get()\\n\\t\\tprint(GAP)\\n\\t\\t\\n\\t\\tprint(\\\"All good\\\")\\n\\n\\t\\t# optimize_schedule(self.preference_input, self.LP_input, None,\\n\\t\\t# \\tself.teacher_file, GAP, self.requirements, self.save_location)\\n\\t\\t# The none is for Grades, as I just hard coded that in the opt code for now\\n\\n\\t\\t# Create optimization instance\\n\\t\\tO = Optimizer(self.preference_input, self.LP_input, None,\\n\\t\\t\\t\\tself.teacher_file, GAP, self.requirements, self.save_location)\\n\\t\\tO.optimize()\\n\\n\\n\\n\\n\\n\\t\\t# Things we need to think abou thow to deal with:\\n\\t\\t# \\tGrades\\n\\t\\t#\\tProximity\\n\\t\\t#\\t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2320927b5675caba0e0b584d30231a5\",\n \"score\": \"0.59246856\",\n \"text\": \"def solve(self):\\n self.DSSSolution.Solve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79aa869a66d8a075a889aaa4a27ba494\",\n \"score\": \"0.5923873\",\n \"text\": \"def APSP_1(self):\\n\\n\\t\\tt_start = time.time()\\n\\t\\tres = float('inf')\\n\\t\\tfor s in self.G.keys():\\n\\t\\t\\tprint('Working on node {} of total {} nodes... time elapsed: {:5.2f} minutes.'\\\\\\n\\t\\t\\t\\t.format(s, self.n, (time.time() - t_start)/60))\\n\\t\\t\\tdp = self.Bellman_Ford(s, self.G)\\n\\t\\t\\t\\n\\t\\t\\tif dp is None:\\n\\t\\t\\t\\tprint('\\\\nThere is a negative cost cycle. Exit the algorithm.')\\n\\t\\t\\t\\treturn None\\n\\t\\t\\tres = min(res, min(dp.values()))\\n\\n\\t\\treturn res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de4dcf4702ef09949cd1b9ec034b0e13\",\n \"score\": \"0.59213895\",\n \"text\": \"def run(self, max_iterations=100) -> Tuple[List[str], List[float]]:\\n\\n\\n for i in range(max_iterations):\\n # Generate Moveset\\n moves = self.generate_moveset(self.state)\\n\\n if len(moves) == 0:\\n return (self.state, self.costs)\\n\\n # Choose S i randomly from Moveset(S)\\n m = random.choice(moves)\\n\\n # Define dV=V(S i )-V(S)\\n v_s = self.graph.solution_cost(self.state)\\n v_m = self.graph.solution_cost(m)\\n dV = v_s - v_m\\n\\n # If dV>0 then S←S i else with probability p, S←S i\\n if dV > 0 or (random.random() <= self.generate_p(dV)):\\n self.state = m\\n self.costs.append(v_m)\\n self.decrease_temperature( i/max_iterations)\\n else:\\n self.costs.append(v_s)\\n # If downhill descent is minimal, terminate\\n if abs(dV/v_s) < SimulatedAnnealing.STOPPAGE_VALUE:\\n return (self.state, self.costs)\\n\\n return (self.state, self.costs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5913a3f71cbfc93d162a9dbdedf2bd1c\",\n \"score\": \"0.5912698\",\n \"text\": \"def startSearch(self):\\n current = self.initNode\\n bestScore = float(\\\"inf\\\")\\n for i in range(0,100):\\n current = self.initNode\\n while True:\\n neighbors = current.expand()\\n if not neighbors:\\n break\\n current = min(neighbors, key=attrgetter(\\\"h\\\"))\\n \\n # Random minimum\\n listOfMins = [x for x in neighbors if current.h == x.h]\\n index = random.randint(0, len(listOfMins)-1)\\n current = listOfMins[index]\\n \\n if self.successTest(current.state):\\n if current.g < bestScore:\\n bestScore = current.g\\n bestOne = current\\n break\\n return self._extractPlan(bestOne)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96eb04d01c5e9d4aff74f3f921f6ca9\",\n \"score\": \"0.5908016\",\n \"text\": \"def best(self):\\n if self._best is None:\\n raise BestNotFound()\\n return self._best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72e6d6de196d127dfef8df3d116ae88\",\n \"score\": \"0.590735\",\n \"text\": \"def employed_bees_phase(self):\\n for i in range(self.get_nsource()):\\n # nominate neighbour solution except\\n k = self.random_nominee_except(i)\\n # randomly select soln vector element xi to change\\n xi = rand.randint(0,self.get_dimension()-1)\\n phi = self.a * rand.uniform(-1,1)\\n # get current food source\\n eith_src = self.bee[i].get_source()\\n # generate a new solution with all vector element in current\\n # vector and change the randomly selected parameter only\\n enew_src = np.copy(eith_src)\\n ekth_src = self.bee[k].get_source()\\n # generate new candidate food source\\n enew_src[xi] = eith_src[xi] + phi * (eith_src[xi] - ekth_src[xi])\\n # update new soln in range of solution lower_limit\\n enew_src = self.band_filter(enew_src)\\n enew_cost = self.cost_of(enew_src)\\n # generate new bee with new_src\\n\\n enew_bee = food.Nectar(enew_src, enew_cost)\\n # greedy selection by current employed bee\\n self.bee[i].greedy_choice(enew_bee)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd0932f9ee1bdccd14136fe6af53b0f2\",\n \"score\": \"0.58952796\",\n \"text\": \"def report_best(self):\\n lowest_Err = min(self.Err)\\n index = self.Err.index(lowest_Err)\\n best_x = self.x[index]\\n formatted_x = '[' + ', '.join(['%.2f' % xi for xi in best_x]) + ']'\\n print 'best x: %s' % formatted_x\\n print 'lowest Err: %.3E' % lowest_Err\\n return best_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cce4fa387bf2542bc60e2516c287211\",\n \"score\": \"0.5893321\",\n \"text\": \"def computeOptimalPlays(self):\\n\\n # Base Cases\\n for i in range(self.size):\\n # If there is only one pot of gold left, take that pot\\n self.cache[(i, i)] = {'score': self.gold[i], 'choice': 'left'}\\n for i in range(self.size - 1):\\n # If there are two pots of gold left, take the larger one\\n self.cache[(i, i+1)] = {\\n 'score': max(self.gold[i], self.gold[i+1]),\\n 'choice': 'left' if self.gold[i] >= self.gold[i+1] else 'right'\\n }\\n\\n # Inductive Step\\n for length in range(2,self.size):\\n for j in range(self.size - length):\\n k = j + length\\n # Here, we decide whether to take from the left (the j side)\\n # or the right (the k side). If we take from the left, then\\n # (if) our opponent makes the optimal move, we'll be left with\\n # the minimum of two pots taken from the left or one pot taken\\n # from either side (because it is a zero-sum game).\\n # Similarly, if we take from the right, then our worst-case\\n # score is the minimum of the subgame situation where either\\n # two pots are taken from the right or one from either side.\\n # The subgame structure is what makes this a DP problem.\\n leftMove = self.gold[j]\\n rightMove = self.gold[k]\\n afterLeftMove = min(self.score(j+2,k), self.score(j+1,k-1))\\n afterRightMove = min(self.score(j+1,k-1), self.score(j,k-2))\\n\\n if leftMove + afterLeftMove >= rightMove + afterRightMove:\\n score = leftMove + afterLeftMove\\n choice = 'left'\\n else:\\n score = rightMove + afterRightMove\\n choice = 'right'\\n\\n self.cache[(j,k)] = {\\n 'score': score,\\n 'choice': choice\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e515c8f7954ca9a5e4c489dc52a14b0d\",\n \"score\": \"0.5886591\",\n \"text\": \"def best_ind(self):\\n random.seed(64)\\n NGEN = 10\\n MU = 10\\n pop = self.toolbox.population(n=MU)\\n hof = tools.ParetoFront()\\n stats = tools.Statistics(lambda ind: ind.fitness.values)\\n stats.register(\\\"avg\\\", numpy.mean, axis=0)\\n stats.register(\\\"std\\\", numpy.std, axis=0)\\n stats.register(\\\"min\\\", numpy.min, axis=0)\\n stats.register(\\\"max\\\", numpy.max, axis=0)\\n # algorithms.eaMuPlusLambda(pop, self.toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats, halloffame=hof)\\n algorithms.eaSimple(pop, self.toolbox, cxpb=0.6, mutpb=0.05, ngen=NGEN, halloffame=hof)\\n print(\\\"The best individual is :\\\", hof[-1])\\n # print(\\\"The best fitness is :\\\", eval_ind(self, hof[-1]))\\n return hof[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d1dea5cd1d94568fde8f01cc0d0532\",\n \"score\": \"0.5884986\",\n \"text\": \"def optimise(self, verbose=False, guess=None, \\n elitism=5, method='GA',cooling_rate=0.1, maxtime=60, maxstallgen=100,\\n nswap=3, popsize=50, mut=0.02, maxgens=50, dimension_bits=1):\\n # TODO: check length of init guess\\n from scipy.optimize import minimize\\n from scipy.optimize import basinhopping\\n self.models = []\\n def eval_func_in(x):\\n return self.eval_func(x, prior=[])\\n\\n N = self.N_reactions\\n\\n import inspyred\\n from random import Random\\n rand = Random()\\n if method == 'GA':\\n ea = inspyred.ec.GA(rand)\\n else:\\n ea = inspyred.ec.SA(rand)\\n self.scores = []\\n self.lower = 0\\n self.upper = 1\\n self.best_score = 1\\n\\n self.scores_all = []\\n def evaluator(candidates, args):\\n dimensions = args['dimensions']\\n dimension_bits = args['dimension_bits']\\n fitness = []\\n for cs in enumerate(candidates):\\n #if len(params) != dimensions:\\n #print(cs, dimensions, dimension_bits)\\n if method == 'GA':\\n params = self._binary_to_real(cs[1], dimensions, dimension_bits)\\n else:\\n params = cs[1]\\n score = eval_func_in(params)\\n #score = eval_func_in(cs)\\n fitness.append(score)\\n # TODO selection pressure could be added here \\n self.scores_all.extend(fitness)\\n self.scores.append(min(np.array(fitness)))\\n if self.scores[-1] < self.best_score:\\n self.best_score = self.scores[-1]\\n i = np.argmin(np.array(fitness))\\n self.best_individual = candidates[i]\\n start = False\\n print(self.scores[-1], self.best_score)\\n\\n return fitness\\n dimensions = N\\n #dimension_bits = 4 # preceision is 1/(2**n-1)\\n def generator(random, args):\\n # For the first generation only.\\n #print('HERE')\\n dimension_bits = args['dimension_bits']\\n #guess = args['guess'][:]\\n pop = [random.choice([0, 1]) \\n for _ in xrange(self.N_reactions * dimension_bits)]\\n #if guess is not None:\\n # pop[0] = guess\\n self.pop = pop\\n if hasattr(self, 'best_individual'):\\n pop = self.best_individual\\n return pop\\n\\n import inspyred.ec\\n import inspyred.ec.ec\\n ea.terminator = inspyred.ec.terminators.evaluation_termination\\n bounder = inspyred.ec.ec.Bounder(0,1)\\n\\n self.storage = []\\n\\n def my_observer(population, num_generations, num_evaluations, args):\\n #best = max(population)\\n #print('{0:6} -- {1} : '.format(num_generations, best.fitness))\\n\\n #print(population)\\n self.storage.append(population[0].candidate[:])\\n for pop in population:\\n self.models.append(pop.candidate[:])\\n ea.observer = my_observer\\n\\n\\n ea.selector = inspyred.ec.selectors.uniform_selection\\n #ea.selector = inspyred.ec.selectors.rank_selection\\n #ea.selector = inspyred.ec.selectors.fitness_proportionate_selection\\n #ea.selector = inspyred.ec.selectors.tournament_selection\\n\\n ea.replacer = inspyred.ec.replacers.truncation_replacement\\n #ea.replacer = inspyred.ec.replacers.plus_replacement\\n self.ea = ea\\n\\n final = ea.evolve(generator=generator, evaluator=evaluator, \\n maximize=False, bounder=bounder, temperature=100, cooling_rate=cooling_rate,\\n max_evaluations=popsize*maxgens,dimensions=len(self.model.reactions),\\n dimension_bits = dimension_bits, guess=guess, gaussian_mean=0.5, gaussian_std=0.5,\\n pop_size=popsize, mutation_rate=mut, num_elites=elitism)\\n\\n self.ea = ea\\n self.final = final\\n res = None\\n self.best_bitstring = max(self.ea.population).candidate\\n print self.best_bitstring\\n try:\\n self.best_bitstring = self._binary_to_real(self.best_bitstring,\\n len(self.model.reactions), dimension_bits)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a446de06c2fc8fde29effb2ef29f743\",\n \"score\": \"0.5871639\",\n \"text\": \"def potential_energy_distribution(self):\\n\\n if len(self.traces) is None:\\n raise ValueError(\\\"Can't predict final energy with just one trace!\\\")\\n\\n self.run_all_iterations()\\n return self.energy_model.predict_from_trace(self.observed_energies)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2080004ed77bb58b93101bbcdb5a9740\",\n \"score\": \"0.5868522\",\n \"text\": \"def result(self):\\n return (self.best_mu, self.best_reward,\\n self.curr_best_reward, self.sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d29e673ca70278dd187ed8c0e891f2\",\n \"score\": \"0.5866907\",\n \"text\": \"def get_optimal():\\n LOG.info(\\\"Retrieving Optimal with url : %s\\\", request.url)\\n recipe = request.get_json()\\n LOG.info(recipe)\\n LOG.info(str(recipe['name']))\\n current_time = int(time.time())\\n workload_name = 'optimal_'+str(current_time)\\n # if 'ts_from' in recipe:\\n # LOG.debug(recipe['ts_from'])\\n # LOG.debug(recipe['ts_to'])\\n # workload = Workload(str(recipe['name']), ts_from= recipe['ts_from'], ts_to= recipe['ts_to'])\\n # eng = Engine()\\n # eng.run('optimal', recipe['name'], recipe['ts_from'], recipe['ts_to'])\\n # else:\\n # workload = Workload(str(recipe['name']))\\n config = {}\\n if recipe.get('device_id'):\\n config['device_id'] = recipe['device_id'].strip(\\n ).lower().replace('-', '_')\\n if recipe.get('project'):\\n config['project'] = recipe['project']\\n if recipe.get('sort_order'):\\n config['sort_order'] = recipe['sort_order']\\n if recipe.get('telemetry_filter', None) is not None:\\n config['telemetry_filter'] = recipe['telemetry_filter']\\n else:\\n config['telemetry_filter'] = False\\n workload = Workload(workload_name, workload_config=config)\\n # storing initial recipe\\n # TODO: validate recipe format\\n recipe_bean = Recipe()\\n recipe_bean.from_json(recipe)\\n workload.add_recipe(\\n int(\\\"{}{}\\\".format(int(round(time.time())), '000000000')), recipe_bean)\\n pipe_exec = OptimalPipe()\\n node_type = 'machine'\\n try:\\n workload = pipe_exec.run(workload, node_type)\\n except KeyError:\\n return Response('Service not ready yet, please wait or restart landscape', status=202)\\n if workload.get_latest_graph() is None and config.get('device_id') is not None:\\n return Response('Device not found', status=404)\\n if workload.get_latest_graph() is None:\\n return Response('Landscape not ready yet?', status=202)\\n results = workload.get_metadata(OptimalFilter.__filter_name__)\\n # return Response(results.to_json(), mimetype=MIME)\\n # return Response(results.to_dict('results'), mimetype=MIME)\\n json_results = json.dumps(results.to_dict('results'))\\n return Response(json_results, mimetype=MIME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f76478ca755f6f62b9a4feabf72ea7b\",\n \"score\": \"0.58448356\",\n \"text\": \"def solve(self):\\n start_time = time.time()\\n maxdepth = 0\\n break_cond = False\\n\\n while(not break_cond):\\n if self.algo == \\\"bfs\\\":\\n # Pop the leftmost element if doing a bfs (Queue)\\n current_state = self.frontier.popleft()\\n elif self.algo == \\\"dfs\\\":\\n # Pop the rightmost element if doing a dfs (Stack)\\n current_state = self.frontier.pop()\\n else:\\n # Get element with highest priority if doing A* (Heap)\\n current_state = self.frontier.get()\\n self.frontier_set.remove(current_state)\\n if (self.isFinalState(current_state)):\\n soln = self.get_solution_moves(current_state)\\n end_time = time.time()\\n stats = {}\\n stats[\\\"nodes_expanded\\\"] = len(self.explored)\\n stats[\\\"search_depth\\\"] = current_state.depth\\n stats[\\\"max_search_depth\\\"] = maxdepth\\n stats[\\\"cost_of_path\\\"] = len(soln)\\n stats[\\\"time\\\"] = end_time - start_time\\n stats[\\\"path\\\"] = soln\\n return stats\\n neighbors = current_state.generate_possible_states()\\n if self.algo == \\\"dfs\\\":\\n neighbors.reverse()\\n\\n for neighbor in neighbors:\\n if neighbor not in self.explored and neighbor not in self.frontier_set:\\n if self.algo == \\\"bfs\\\" or self.algo == \\\"dfs\\\":\\n self.frontier.append(neighbor)\\n else:\\n self.frontier.put(neighbor)\\n self.frontier_set.add(neighbor)\\n if neighbor.depth > maxdepth:\\n maxdepth = neighbor.depth\\n self.explored.add(current_state)\\n if self.algo == \\\"bfs\\\" or self.algo == \\\"dfs\\\":\\n frontier_sz = len(self.frontier)\\n else:\\n frontier_sz = self.frontier.qsize()\\n logging.debug(\\\"Frontier size = \\\" +\\n str(frontier_sz) +\\n \\\"; Explored size = \\\" +\\n str(len(self.explored)))\\n if self.algo == \\\"bfs\\\" or self.algo == \\\"dfs\\\":\\n break_cond = len(self.frontier) == 0\\n else:\\n break_cond = self.frontier.empty()\\n logging.error(\\\"This is an unsolvable board!\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"468d623199cf946ade4500c72622bb3f\",\n \"score\": \"0.58404225\",\n \"text\": \"def doit(self):\\n self.reduceSolutions()\\n keyz = self.solutions.keys()\\n keyz.sort()\\n prodarr = self.mergeIdent(keyz)\\n keyz += self.chkForMore(keyz,prodarr)\\n localans = self.solvePell()\\n setx = set(keyz+localans)\\n ansvec = list(setx)\\n answer = 1+sum(ansvec)\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"370c27f8417ed8239b7d1a695857c183\",\n \"score\": \"0.5814705\",\n \"text\": \"def first_state(self):\\n new_sol = Solution(\\n self.all_ts[0][:1],\\n self.all_ys[0][:, :1],\\n self.all_models[:1],\\n self.all_inputs[:1],\\n None,\\n None,\\n \\\"final time\\\",\\n )\\n new_sol._all_inputs_casadi = self.all_inputs_casadi[:1]\\n new_sol._sub_solutions = self.sub_solutions[:1]\\n\\n new_sol.solve_time = 0\\n new_sol.integration_time = 0\\n new_sol.set_up_time = 0\\n\\n return new_sol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc528770f4cb159b4a99b6fd7ff1d8f\",\n \"score\": \"0.5813268\",\n \"text\": \"def run(self):\\n problem = self.testproblem.problem\\n results = TestResults(self.testproblem, self.config)\\n # results = Test\\n try:\\n start = time.time() # Time the solve\\n print(\\\"starting\\\",self.testproblem.id,\\\"with config\\\",self.config.id,\\\"at\\\",start)\\n problem.solve(solver = self.config.solver, verbose = self.config.verbose, **self.config.kwargs)\\n print(\\\"finished solve for\\\", self.testproblem.id, \\\"with config\\\", self.config.id)\\n if problem.solver_stats.solve_time is not None:\\n results.solve_time = problem.solver_stats.solve_time\\n else:\\n results.solve_time = time.time() - start\\n if problem.solver_stats.setup_time is not None:\\n results.setup_time = problem.solver_stats.setup_time\\n if problem.solver_stats.num_iters is not None:\\n results.num_iters = problem.solver_stats.num_iters\\n\\n results.status = problem.status\\n results.opt_val = problem.value\\n except Exception as e:\\n print(e)\\n # Configuration could not solve the given problem\\n results = TestResults(self.testproblem, self.config)\\n results.size_metrics = problem.size_metrics\\n print(\\\"failure solving\\\",self.testproblem.id,\\\"with config\\\",self.config.id)\\n return results\\n\\n # Record residual gross stats:\\n results.avg_abs_resid, results.max_resid = TestInstance.compute_residual_stats(problem)\\n print(\\\"computed stats for\\\", self.testproblem.id, \\\"with config\\\", self.config.id)\\n\\n # Record problem metrics:\\n results.size_metrics = problem.size_metrics\\n\\n print(\\\"finished\\\",self.testproblem.id,\\\"with config\\\",self.config.id,\\\"at\\\",time.time()-start)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a4ddcf70a8389e9ebf5384e8f000f5b\",\n \"score\": \"0.5812091\",\n \"text\": \"def optimize(self):\\n self._add_constraints()\\n self.Model.update()\\n self.Model.optimize()\\n if (\\n self.Model.Status == 2\\n ): # Status = 2 means optimal solution found and model is solved\\n self._solved = True\\n self._head_results = self._get_head_results()\\n print(\\\"OPTIMIZATION COMPLETE\\\")\\n else:\\n self._head_results = None\\n print(\\\"OPTIMIZATION NOT COMPLETE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d1185bafadfc3331c9f21166a9d22f2\",\n \"score\": \"0.5804594\",\n \"text\": \"def baseSearch(self, gameState):\\n\\t\\tactions = gameState.getLegalActions(self.index)\\n\\n\\t\\t# You can profile your evaluation time by uncommenting these lines\\n\\t\\t# start = time.time()\\n\\t\\tv = []\\n\\t\\tfor action in actions:\\n\\t\\t\\tif action == 'Stop':\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tsuccessor = gameState.generateSuccessor(self.index, action)\\n\\t\\t\\tv.append((self.evaluate(successor), action))\\n\\n\\t\\t# print 'eval time for agent %d: %.4f' % (self.index, time.time() - start)\\n\\n\\t\\t# print \\\"Values in baseSearch \\\", v\\n\\t\\tmaxValues = [i for i in v if i == max(v)]\\n\\t\\tmaxValue = random.choice(maxValues)\\n\\n\\t\\t# print maxValue\\n\\t\\tbestAction = maxValue[1]\\n\\t\\t# print bestAction\\n\\t\\tfoodLeft = len(self.getFood(gameState).asList())\\n\\t\\t# print foodLeft\\n\\t\\tif foodLeft <= 2:\\n\\t\\t\\t# print \\\"foodl <= 2\\\"\\n\\t\\t\\tbestDist = 9999\\n\\t\\t\\tfor action in actions:\\n\\t\\t\\t\\tif action == 'Stop':\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tsuccessor = gameState.generateSuccessor(self.index, action)\\n\\t\\t\\t\\tpos2 = successor.getAgentPosition(self.index)\\n\\t\\t\\t\\tdist = self.getMazeDistance(gameState.getAgentPosition(self.index), pos2)\\n\\t\\t\\t\\t# print \\\"Comparing {0} < {1}\\\".format(dist, bestDist)\\n\\t\\t\\t\\tif dist < bestDist:\\n\\t\\t\\t\\t\\t# print \\\"Change action to \\\",action\\n\\t\\t\\t\\t\\tbestAction = action\\n\\t\\t\\t\\t\\tbestDist = dist\\n\\t\\t\\t# print \\\"Base search return with \\\", bestAction\\n\\t\\t\\treturn bestAction\\n\\t\\t# print \\\"Base search return with \\\",bestAction\\n\\t\\treturn bestAction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e9eb1c81491450c937f6743bbc7fd60\",\n \"score\": \"0.5800815\",\n \"text\": \"def judge(self):\\r\\n temp = -1\\r\\n best_one = None\\r\\n for individual in self.individuals:\\r\\n distance = self.get_disctance(individual)\\r\\n if temp == -1 or distance < temp:\\r\\n temp = distance\\r\\n best_one = individual\\r\\n\\r\\n self.best_ones.append(best_one)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f58e89ad63958879fc3bcc91bf5057c\",\n \"score\": \"0.5797411\",\n \"text\": \"def optimal_eps(self):\\n Network = self.Network\\n W = self.get_W()\\n B = self._get_B(W)\\n N = Network.size()\\n eps = np.zeros([N,N])\\n for j in range(N):\\n eps += self._get_eps_j(j,W[j],B[j])\\n return eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d5185a0acaa718fea4eb3c5221e4da\",\n \"score\": \"0.5795139\",\n \"text\": \"def test_griewangk():\\n\\n print \\\"Testing Griewangk:\\\"\\n print \\\"Expected: x=[0.]*10 and f=0\\\"\\n from mystic.models import griewangk as costfunc\\n ndim = 10\\n lb = [-400.]*ndim\\n ub = [400.]*ndim\\n maxiter = 10000\\n seed = 123 # Re-seed for each solver to have them all start at same x0\\n \\n # DifferentialEvolutionSolver\\n print \\\"\\\\nUsing DifferentialEvolutionSolver:\\\"\\n npop = 50\\n random_seed(seed)\\n from mystic.solvers import DifferentialEvolutionSolver\\n from mystic.termination import ChangeOverGeneration as COG\\n from mystic.termination import CandidateRelativeTolerance as CRT\\n from mystic.termination import VTR\\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\\n esow = Monitor()\\n ssow = Monitor() \\n solver = DifferentialEvolutionSolver(ndim, npop)\\n solver.SetRandomInitialPoints(lb, ub)\\n solver.SetStrictRanges(lb, ub)\\n solver.SetEvaluationLimits(generations=maxiter)\\n solver.SetEvaluationMonitor(esow)\\n solver.SetGenerationMonitor(ssow)\\n solver.enable_signal_handler()\\n #term = COG(1e-10)\\n #term = CRT()\\n term = VTR(0.)\\n time1 = time.time() # Is this an ok way of timing?\\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\\\\n CrossProbability=0.3, ScalingFactor=1.0)\\n sol = solver.Solution()\\n time_elapsed = time.time() - time1\\n fx = solver.bestEnergy\\n print \\\"Solution: \\\", sol\\n print \\\"f value: \\\", fx\\n print \\\"Iterations: \\\", solver.generations\\n print \\\"Function evaluations: \\\", len(esow.x)\\n print \\\"Time elapsed: \\\", time_elapsed, \\\" seconds\\\"\\n assert almostEqual(fx, 0.0, tol=3e-3)\\n\\n # DifferentialEvolutionSolver2\\n print \\\"\\\\nUsing DifferentialEvolutionSolver2:\\\"\\n npop = 50\\n random_seed(seed)\\n from mystic.solvers import DifferentialEvolutionSolver2\\n from mystic.termination import ChangeOverGeneration as COG\\n from mystic.termination import CandidateRelativeTolerance as CRT\\n from mystic.termination import VTR\\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\\n esow = Monitor()\\n ssow = Monitor() \\n solver = DifferentialEvolutionSolver2(ndim, npop)\\n solver.SetRandomInitialPoints(lb, ub)\\n solver.SetStrictRanges(lb, ub)\\n solver.SetEvaluationLimits(generations=maxiter)\\n solver.SetEvaluationMonitor(esow)\\n solver.SetGenerationMonitor(ssow)\\n #term = COG(1e-10)\\n #term = CRT()\\n term = VTR(0.)\\n time1 = time.time() # Is this an ok way of timing?\\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\\\\n CrossProbability=0.3, ScalingFactor=1.0)\\n sol = solver.Solution()\\n time_elapsed = time.time() - time1\\n fx = solver.bestEnergy\\n print \\\"Solution: \\\", sol\\n print \\\"f value: \\\", fx\\n print \\\"Iterations: \\\", solver.generations\\n print \\\"Function evaluations: \\\", len(esow.x)\\n print \\\"Time elapsed: \\\", time_elapsed, \\\" seconds\\\"\\n assert almostEqual(fx, 0.0, tol=3e-3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6b76e94e2da8452d3f6eb07296ad32\",\n \"score\": \"0.57891214\",\n \"text\": \"def evolutionary_algorithm(self):\\n # Creating the Individual Using DEAP\\n creator.create(\\\"FitnessMax\\\", base.Fitness, weights=(1.0,))\\n creator.create(\\\"Individual\\\", list, fitness=creator.FitnessMax)\\n\\n # Creating ToolBox For The DEAP Framework\\n toolbox = base.Toolbox()\\n toolbox.register(\\\"attr_bool\\\", random.randint, 0, 1)\\n toolbox.register(\\\"individual\\\", tools.initRepeat, creator.Individual, toolbox.attr_bool, self.n_features)\\n toolbox.register(\\\"population\\\", tools.initRepeat, list, toolbox.individual)\\n toolbox.register(\\\"evaluate\\\", self.fitness_test)\\n toolbox.register(\\\"mate\\\", tools.cxOnePoint)\\n toolbox.register(\\\"mutate\\\", tools.mutFlipBit, indpb=0.05)\\n toolbox.register(\\\"select\\\", tools.selTournament, tournsize=3)\\n\\n # Initialize Parameters\\n pop = toolbox.population(n=self.n_pop)\\n hof = tools.HallOfFame(self.n_pop * self.n_gen)\\n stats = tools.Statistics(lambda ind: ind.fitness.values)\\n stats.register(\\\"avg\\\", np.mean)\\n stats.register(\\\"min\\\", np.min)\\n stats.register(\\\"max\\\", np.max)\\n\\n # Genetic Algorithm\\n pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=self.n_gen, stats=stats, halloffame=hof,verbose=True)\\n\\n # Return Fall Of Home\\n return hof\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763350b629661a962875e4097a558371\",\n \"score\": \"0.5786748\",\n \"text\": \"def best_model() :\\n file_path = os.path.join(os.path.os.getcwd(), cf.PERF_FILE)\\n with open(file_path, 'r') as perf_file:\\n prec = perf_file.readlines()[0].split(';')\\n\\n precisions = [t for t in prec if len(t)>0]\\n\\n models = [t.split(':')[0] for t in precisions]\\n models = sorted(models)\\n precisions = np.array([float(t.split(':')[1]) for t in precisions])\\n index_best_model = np.argmax(precisions)\\n #best_model = models[index_best_model]\\n\\n output_files, model_dict = get_output_files()\\n\\n chosen_model = model_dict[int(index_best_model)+1]\\n chosen_output_file = output_files[int(index_best_model)+1]\\n\\n print('Best model is ' + chosen_model + ' with precision ' + str(max(precisions)) + '%')\\n\\n return chosen_model, chosen_output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152015f9a8d342528e38fde1197c42a4\",\n \"score\": \"0.57838523\",\n \"text\": \"def get_solution(self):\\n if self.is_solution_available():\\n pass\\n raise errors.IllegalState()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af224aec567cf6f051e98f8188c816f\",\n \"score\": \"0.5780064\",\n \"text\": \"def run(self):\\n if self.current_control:\\n target_value = self.current_density\\n else:\\n target_value = self.average_cell_voltage * self.stack.n_cells\\n if not isinstance(target_value, (list, tuple, np.ndarray)):\\n target_value = [target_value]\\n cell_voltages = []\\n current_densities = []\\n local_data_list = []\\n global_data_list = []\\n for i, tar_value in enumerate(target_value):\\n current_errors = []\\n temp_errors = []\\n convergence_flags = []\\n self.timing['simulation'] = 0.0\\n self.timing['output'] = 0.0\\n simulation_start_time = timeit.default_timer()\\n counter = 0\\n while True:\\n if counter == 0:\\n if self.current_control:\\n self.stack.update(current_density=tar_value)\\n else:\\n self.stack.update(voltage=tar_value)\\n else:\\n self.stack.update()\\n if self.stack.break_program:\\n break\\n current_error, temp_error = self.calc_convergence_criteria()\\n current_errors.append(current_error)\\n temp_errors.append(temp_error)\\n if len(target_value) < 1:\\n print(counter)\\n counter += 1\\n if ((current_error < self.it_crit and temp_error < self.it_crit)\\n and counter > self.min_it) or counter > self.max_it:\\n break\\n if counter > self.max_it:\\n convergence_flag = False\\n else:\\n convergence_flag = True\\n convergence_flags.append(convergence_flag)\\n simulation_stop_time = timeit.default_timer()\\n simulation_time = simulation_stop_time - simulation_start_time\\n self.timing['simulation'] += simulation_time\\n output_start_time = timeit.default_timer()\\n\\n if not self.stack.break_program:\\n # voltage_loss = self.get_voltage_losses(self.stack)\\n cell_voltages.append(np.average([cell.v for cell in\\n self.stack.cells]))\\n current_densities.append(self.stack.i_cd_avg)\\n\\n case_name = 'Case'+str(i)\\n self.output.save(case_name, self.stack)\\n local_data_dict = self.output.get_data(self.stack)\\n local_data_dict['Iterations'] = \\\\\\n {'value': [list(range(counter))], 'units': '-'}\\n local_data_dict['Current Density Error'] = \\\\\\n {'value': [current_errors], 'units': '-',\\n 'xkey': 'Iterations'}\\n local_data_dict['Temperature Error'] = \\\\\\n {'value': [current_errors], 'units': '-',\\n 'xkey': 'Iterations'}\\n\\n local_data_list.append(local_data_dict)\\n if self.output.save_plot:\\n path = os.path.join(self.output.output_dir, case_name,\\n 'plots', 'Convergence.png')\\n self.output.create_figure(path, list(range(counter)),\\n [current_errors, temp_errors],\\n xlabels='Iteration',\\n ylabels='Error',\\n yscale='log',\\n legend=['Current Density',\\n 'Temperature'])\\n else:\\n target_value = target_value[0:-i]\\n break\\n\\n average_current_density = \\\\\\n np.average([np.average(cell.i_cd, weights=cell.active_area_dx)\\n for cell in self.stack.cells])\\n if self.stack.coolant_circuit is None:\\n cool_mass_flow = 0.0\\n else:\\n cool_mass_flow = self.stack.coolant_circuit.mass_flow_in\\n global_data = \\\\\\n {\\n 'Convergence':\\n {'value': convergence_flag, 'units': ' '},\\n 'Stack Voltage': {'value': self.stack.v_stack, 'units': 'V'},\\n 'Average Cell Voltage':\\n {'value': self.stack.v_stack / self.stack.n_cells,\\n 'units': 'V'},\\n 'Minimum Cell Voltage':\\n {'value': np.min(self.stack.v), 'units': 'V'},\\n 'Maximum Cell Voltage':\\n {'value': np.max(self.stack.v), 'units': 'V'},\\n 'Average Current Density':\\n {'value': average_current_density, 'units': 'A/m²'},\\n 'Stack Power Density':\\n {'value': self.stack.v_stack * average_current_density,\\n 'units': 'W/m²'},\\n 'Stack Power':\\n {'value': self.stack.v_stack * average_current_density\\n * self.stack.cells[0].active_area,\\n 'units': 'W'},\\n 'Cooling Mass Flow Rate:':\\n {'value': cool_mass_flow,\\n 'units': 'kg/s', 'format': '.4E'},\\n 'Cathode Mass Flow Rate:':\\n {'value': self.stack.fuel_circuits[0].mass_flow_in,\\n 'units': 'kg/s', 'format': '.4E'},\\n 'Anode Mass Flow Rate:':\\n {'value': self.stack.fuel_circuits[1].mass_flow_in,\\n 'units': 'kg/s', 'format': '.4E'},\\n }\\n global_data_list.append(global_data)\\n output_stop_time = timeit.default_timer()\\n self.timing['output'] += output_stop_time - output_start_time\\n self.output.print_global_data(self, global_data)\\n self.output.save_global_results(global_data)\\n\\n output_start_time = timeit.default_timer()\\n\\n # print polarization curve, if more than one current_density value\\n # was provided\\n if len(target_value) > 1:\\n self.output.write_data(current_densities, cell_voltages,\\n 'Current Density [A/m²]', 'Cell Voltage',\\n units='V', directory=self.output.output_dir,\\n # save_csv=True, save_plot=True,\\n write_mode='w')\\n\\n output_stop_time = timeit.default_timer()\\n self.timing['output'] += output_stop_time - output_start_time\\n\\n return global_data_list, local_data_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"182e8144cc9cf7df0a32def1d95bd1d8\",\n \"score\": \"0.5779991\",\n \"text\": \"def _eval_energy(self):\\n S = self.S\\n M = S.mera\\n SIlayer = self.SIlayer # = 2\\n SIlayer_p1 = SIlayer + 1\\n\\n for i in range(self.num_of_SIlayer):\\n #这里是反复地从 SIlayer ascending 到 SIlayer_p1, WITH tau changing\\n if i>0:\\n for o in self.ham_op_names:\\n S.__getattribute__(o)[SIlayer][0].data[:] = S.__getattribute__(o)[SIlayer_p1][0].data[:]\\n self.ascending_ham(M, S, SIlayer, tau=SIlayer + i)\\n #above only update H, no need update rho, as the later is scale-invar not changed in each layer\\n \\n if 1:\\n S.rho_2[SIlayer_p1][0].data[:] = S.rho_2[SIlayer][0].data[:]\\n if not S.only_NN:\\n S.rho_3[SIlayer_p1][0].data[:] = S.rho_3[SIlayer][0].data[:]\\n #in the end eval eng at the layer above the first SIlayer\\n S.eng_ham(SIlayer_p1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"682efc983522090afcf58edc4c64d068\",\n \"score\": \"0.5779628\",\n \"text\": \"def optimize(self):\\n self.open_output_files()\\n while (self.n_iter < self.n_maxiter and not self.is_converged):\\n self.n_iter += 1\\n self.choose_step_direction(self.opt_type)\\n self.line_search(-1.0 * self.step_dir)\\n self.update_energy()\\n self.update_gradient()\\n self.traj.append_step(self.mol)\\n self.update_criteria()\\n self.check_convergence()\\n self.print_status()\\n self.close_output_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec4666d8a6ab245f0cab4066f997667\",\n \"score\": \"0.57755536\",\n \"text\": \"def optimize_ga(self):\\n\\n converged = False\\n ngens = 1\\n generation = 1\\n difference = self.tol * 10000.0\\n self.solution_history = np.zeros(self.max_generation + 1)\\n self.solution_history[0] = np.min(self.parent_fitness)\\n\\n run_time = 0.0\\n start_time = time.time()\\n while converged is False and ngens < self.max_generation and \\\\\\n run_time < self.max_time:\\n self.crossover()\\n self.mutate()\\n # determine fitness of offspring\\n for i in range(self.population_size):\\n self.chromosome_2_variables(self.offspring_population[i])\\n self.offspring_fitness[i] = \\\\\\n self.objective_function(self.design_variables)\\n\\n # rank the total population from best to worst\\n total_fitness = np.append(self.parent_fitness,\\n self.offspring_fitness)\\n ranked_fitness = \\\\\\n np.argsort(total_fitness)[0:int(self.population_size)]\\n\\n total_population = \\\\\\n np.vstack([self.parent_population, self.offspring_population])\\n self.parent_population[:, :] = total_population[ranked_fitness, :]\\n self.parent_fitness[:] = total_fitness[ranked_fitness]\\n\\n # store solution history and wrap up generation\\n self.solution_history[generation] = np.min(self.parent_fitness)\\n\\n if generation > self.convergence_iters:\\n difference = \\\\\\n self.solution_history[generation - self.convergence_iters]\\\\\\n - self.solution_history[generation]\\n else:\\n difference = 1000\\n if abs(difference) <= self.tol:\\n converged = True\\n\\n # shuffle up the order of the population\\n shuffle_order = np.arange(1, self.population_size)\\n np.random.shuffle(shuffle_order)\\n shuffle_order = np.append([0], shuffle_order)\\n self.parent_population = self.parent_population[shuffle_order]\\n self.parent_fitness = self.parent_fitness[shuffle_order]\\n\\n generation += 1\\n ngens += 1\\n\\n run_time = time.time() - start_time\\n\\n # Assign final outputs\\n self.solution_history = self.solution_history[0:ngens]\\n self.optimized_function_value = np.min(self.parent_fitness)\\n self.chromosome_2_variables(\\n self.parent_population[np.argmin(self.parent_fitness)])\\n self.optimized_design_variables = self.design_variables\\n\\n logger.debug('The GA ran for this many generations: {}'\\n .format(ngens))\\n logger.debug('The GA ran for this many seconds: {:.3f}'\\n .format(run_time))\\n logger.debug('The optimized function value was: {:.3e}'\\n .format(self.optimized_function_value))\\n logger.debug('The optimal design variables were: {}'\\n .format(self.optimized_design_variables))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f62b19d875ff1dfa2e3d4bfd9a5172\",\n \"score\": \"0.57655716\",\n \"text\": \"def return_fitness(self, e):\\r\\n\\t\\treturn e.fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83fe6af1079e34a1c9740f124d26c109\",\n \"score\": \"0.576445\",\n \"text\": \"def solve():\\n return round(7 * (1 - choose(60, 20) / choose(70, 20)), 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595ecfd9facfe91a02000cddd7224112\",\n \"score\": \"0.57595515\",\n \"text\": \"def fittest(self):\\n return self.best_fit, self.best_fit_fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c1e9395f4e30e4b41f6aa74720d615\",\n \"score\": \"0.57595515\",\n \"text\": \"def test_best_improvement():\\n process_list = [1, 2, 4, 1, 3]\\n expected_solution = [3, 1, 4, 2, 1]\\n current_neighborhood = 0\\n\\n best_solution = best_improvement(process_list, current_neighborhood, 60)\\n assert best_solution == expected_solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1774df4c382a9a263ca22e025ab62161\",\n \"score\": \"0.5756853\",\n \"text\": \"def save_result(self, output_dir):\\n self.best_solver.save_tree(output_dir)\\n self.best_solver.save_best_solution(output_dir)\\n for i in range(self.best_solver.tube_num):\\n self.best_solver.visualize_full_search(output_dir, tube_num=i, with_solution=True)\\n\\n filename = output_dir / \\\"optimize_result.txt\\\"\\n # noinspection PyTypeChecker\\n optimize_result = open(filename, \\\"w\\\")\\n\\n optimize_result.write(f\\\"Optimizer Success: {self.success}\\\\n\\\")\\n optimize_result.write(f\\\"Goal Reached: {self.best_solver.found_solution}\\\\n\\\")\\n optimize_result.write(f\\\"Completion Time: {self.completion_time}\\\\n\\\")\\n optimize_result.write(f\\\"Number of Iterations: {self.num_iterations}\\\\n\\\")\\n optimize_result.write(f\\\"Number of Function Evals: {self.num_function_eval}\\\\n\\\")\\n q = \\\", \\\".join(str(i) for i in self.best_q)\\n optimize_result.write(f\\\"Optimized Q: {q}\\\\n\\\")\\n\\n optimize_result.close()\\n\\n filename = output_dir / \\\"optimize_process.txt\\\"\\n # noinspection PyTypeChecker\\n optimize_process = open(filename, \\\"w\\\")\\n\\n for i in range(len(self.optimize_process)):\\n q = \\\", \\\".join(str(j) for j in self.optimize_process[i].get('q'))\\n cost = self.optimize_process[i].get('cost')\\n optimize_process.write(f\\\"{q} | {cost}\\\\n\\\")\\n\\n optimize_process.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d155e5d49b180fe2d888a7e23955477\",\n \"score\": \"0.5756708\",\n \"text\": \"def betterEvaluationFunction(currentGameState):\\n \\\"*** YOUR CODE HERE ***\\\"\\n foods = currentGameState.getFood()\\n newGhostStates=currentGameState.getGhostStates()\\n newPos = currentGameState.getPacmanPosition()\\n print 'New position:', newPos\\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\\n\\n\\n\\n optimal_food = []\\n optimal_ghost = []\\n food = currentGameState.getFood().asList()\\n\\n for f in food:\\n optimal_food.append(manhattanDistance(newPos, f))\\n if len(optimal_food) == 0:\\n return 0\\n a = min(optimal_food)\\n\\n if a == 0:\\n a = currentGameState.getScore()\\n cap=[]\\n b = max(optimal_food)\\n f=currentGameState.getNumFood()\\n\\n # for g in currentGameState.getGhostPositions():\\n # optimal_ghost.append(manhattanDistance(newPos, g))\\n # if manhattanDistance(newPos, g) == 0:\\n # return -100000.0\\n # elif manhattanDistance(newPos, g) > 0.0 and manhattanDistance(newPos, g) <= 1.0:\\n # return -100000.0/6\\n\\n ghost_PM_dist = []\\n ghost_dist_nearest = 0\\n for ghost in newGhostStates:\\n Ghost_Pos = ghost.getPosition()\\n GP_dist = manhattanDistance(Ghost_Pos, newPos)\\n ghost_PM_dist.append(GP_dist)\\n\\n if len(ghost_PM_dist) > 0:\\n ghost_dist_nearest = min(ghost_PM_dist)\\n else:\\n ghost_dist_nearest = currentGameState.getScore()\\n # print ghost_dist_nearest\\n ghost_feature = 1 / (ghost_dist_nearest + 1)\\n score = 0\\n # c=currentGameState.getCapsules()\\n # print 'cap',c\\n # for i in c:\\n # cap.append(manhattanDistance(newPos,i))\\n # if len(optimal_ghost) > 0:\\n # ghost_dist_nearest = min(optimal_ghost)\\n # else:\\n # ghost_dist_nearest = currentGameState.getScore()\\n # # print ghost_dist_nearest\\n # ghost_feature = 1 / (ghost_dist_nearest + 1)\\n # score = 0\\n # score=0\\n # if currentGameState.isWin():\\n # score = 1000000\\n return currentGameState.getScore() + 1.0/(a) +100*score+ 2*(ghost_feature)\\n\\n util.raiseNotDefined()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c0ae96cd69e709a3affb6aa640c97d\",\n \"score\": \"0.5754821\",\n \"text\": \"def best_result(self):\\n if self.result is not None:\\n return self.result[0]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c226930b9f22f2d5ed885f9483f4c19\",\n \"score\": \"0.57431376\",\n \"text\": \"def evolve(self):\\n #Configure properly each island\\n self.__parametrize__()\\n\\n # Get evolution generator of each island\\n self._rc[:].execute(\\\"gen = gp_engine.sequentially_evolve()\\\", block=True)\\n\\n # Loop over all the sessions\\n i = 0\\n while True:\\n logging.info('Launch generation evolution')\\n\\n #Do generation computing\\n self._rc[:].execute(\\\"chosen = gen.next()\\\")\\n\\n #Print best individual\\n logging.info('Get best individuals')\\n self._rc[:].execute(\\\"best = gp_engine.get_best_individual()\\\")\\n bests = self._rc[:]['best']\\n\\n print \\\"Generation %d\\\" % i\\n print \\\"=\\\"*15\\n print \\\"Island\\\\t| fit\\\\t\\\"\\n print \\\"\\\\n\\\".join(\\\\\\n [ \\\"%d\\\\t%f\\\" % (j, bests[j][1]) \\\\\\n for j in range(len(bests))])\\n\\n print \\\"AVG\\\\t%f\\\" %np.mean( [bests[j][1] for j in range(len(bests))])\\n i = i + 1\\n\\n logging.info('Test if end of generation')\\n #Test if the process is finished\\n self._rc[:].execute('end=gp_engine.__evolver__.is_evolution_ended()')\\n\\n if np.any( self._rc[:]['end']):\\n break\\n\\n #Operate the migration\\n logging.info('Launch migration process')\\n self.__migration_operator__.manage_migration()\\n\\n #loop again\\n\\n print 'Evolution terminated'\\n return bests\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73f2751a1cb356fcb986044d948ed77\",\n \"score\": \"0.5730213\",\n \"text\": \"def is_better_then_global_best(self, solution):\\n return solution.personal_best.fitness_value < self.best_solution.fitness_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b31563d2c606a0c2b5ebe046d544277\",\n \"score\": \"0.5728114\",\n \"text\": \"def random_search():\\t\\t\\n\\tsim = simulator()\\t\\t\\t\\t\\t\\t\\t# simulator object, to be used during this algorithm\\n\\tnum_search = 20\\t\\t\\t\\t\\t\\t\\t\\t# number of robots evaluated\\n\\tnum_tetr_added = 14\\t\\t\\t\\t\\t\\t\\t# this is the size of our robots ( 15 tetrahedron total )\\n\\n\\trs_fit = np.zeros(num_search)\\t\\t\\t\\t# learning curve array\\n\\tbest_fit = 0\\t\\t\\t\\t\\t\\t\\t\\t# best fitness place holder\\n\\tbest_array = np.empty(8)\\t\\t\\t\\t\\t# top performing genome place holder\\n\\t\\n\\tfor i in range(num_search):\\t\\t\\t\\t\\t# do the random search\\t\\n\\t\\tprint('counter: ', i)\\n\\n\\t\\trobot = tetrahedron()\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# make a random robot\\n\\t\\trobot.set_tetr( np.array((0 , 0 , 0)) , np.array((0 , 0 , 0)))\\t\\t\\t# make the seed\\n\\t\\trobot.set_genome(num_tetr_added)\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# generate a random genome\\n\\t\\trobot.build_robot()\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# builds the robot \\n\\t\\t\\n\\t\\tmass_list = robot.mass_list\\t\\t\\t\\t# center the robot\\n\\t\\tcom = robot.get_com(mass_list)\\t\\t\\t# \\t\\t....\\n\\t\\trobot.center_object(com)\\t\\t\\t\\t# \\t\\t....\\n\\n\\t\\tfitness = calc_fitness( robot , sim)\\t# evaluate the robot's fitness\\n\\n\\t\\tif fitness > best_fit:\\t\\t\\t\\t\\t# if the fitness is better than current best\\n\\t\\t\\tbest_fit = fitness \\t\\t\\t\\t\\t# update placeholders\\n\\t\\t\\tbest_array = np.copy(robot.genome)\\t# \\t\\t....\\n\\n\\t\\trs_fit[i] = best_fit\\t\\t\\t\\t\\t# add the best fit to the learning curve\\n\\n\\tnp.savetxt(\\\"rs_robot.csv\\\", best_array, delimiter=\\\",\\\")\\n\\tnp.savetxt(\\\"rs_learning.csv\\\", rs_fit, delimiter=\\\",\\\")\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":189,"cells":{"query_id":{"kind":"string","value":"a232b5ed960453ab1d2673894b52333d"},"query":{"kind":"string","value":"submit a dos calculation and interpolate result if returns complete"},"positive_passages":{"kind":"list like","value":[{"docid":"5e0224e23e34529b3523d980d025abb2","score":"0.0","text":"def get_dos(self):\n\n label = 'KKR DOS calc.'\n dosdict = self.ctx.dos_params_dict\n description = 'dos calc: emin= {}, emax= {}, nepts= {}, tempr={}, kmesh={}'.format(dosdict['emin'], dosdict['emax'], dosdict['nepts'], dosdict['tempr'], dosdict['kmesh'])\n code = self.inputs.kkr\n remote = self.inputs.remote_data\n params = self.ctx.dos_kkrparams\n options = {\"max_wallclock_seconds\": self.ctx.max_wallclock_seconds,\n \"resources\": self.ctx.resources,\n \"queue_name\" : self.ctx.queue}#,\n if self.ctx.custom_scheduler_commands:\n options[\"custom_scheduler_commands\"] = self.ctx.custom_scheduler_commands\n inputs = get_inputs_kkr(code, remote, options, label, description, parameters=params, serial=(not self.ctx.use_mpi))\n\n # run the DOS calculation\n self.report('INFO: doing calculation')\n dosrun = self.submit(KkrCalculation, **inputs)\n\n # for restart workflow:\n self.ctx.last_calc = dosrun\n\n return ToContext(dosrun=dosrun)","title":""}],"string":"[\n {\n \"docid\": \"5e0224e23e34529b3523d980d025abb2\",\n \"score\": \"0.0\",\n \"text\": \"def get_dos(self):\\n\\n label = 'KKR DOS calc.'\\n dosdict = self.ctx.dos_params_dict\\n description = 'dos calc: emin= {}, emax= {}, nepts= {}, tempr={}, kmesh={}'.format(dosdict['emin'], dosdict['emax'], dosdict['nepts'], dosdict['tempr'], dosdict['kmesh'])\\n code = self.inputs.kkr\\n remote = self.inputs.remote_data\\n params = self.ctx.dos_kkrparams\\n options = {\\\"max_wallclock_seconds\\\": self.ctx.max_wallclock_seconds,\\n \\\"resources\\\": self.ctx.resources,\\n \\\"queue_name\\\" : self.ctx.queue}#,\\n if self.ctx.custom_scheduler_commands:\\n options[\\\"custom_scheduler_commands\\\"] = self.ctx.custom_scheduler_commands\\n inputs = get_inputs_kkr(code, remote, options, label, description, parameters=params, serial=(not self.ctx.use_mpi))\\n\\n # run the DOS calculation\\n self.report('INFO: doing calculation')\\n dosrun = self.submit(KkrCalculation, **inputs)\\n\\n # for restart workflow:\\n self.ctx.last_calc = dosrun\\n\\n return ToContext(dosrun=dosrun)\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"215366153e758085c8bc79a022ff8b6c","score":"0.6252689","text":"def calculate(self):\n item_number = self.fields.curselection()\n polynomial = self.json_data[\"functions\"][int(item_number[0])]\n result_floating = None\n result_interval = None\n if self.json_data[\"method\"] == \"regula_falsi\":\n try:\n result_floating = regula_falsi(polynomial[\"a\"],\n polynomial[\"b\"],\n function_from_list,\n polynomial[\"coeff\"]\n )\n except MyError as error:\n result_floating = error.value\n except ZeroDivisionError:\n result_floating = \"Unfortunately, division by 0 happened\"\n try:\n result_interval = regula_falsi_interval(polynomial[\"a\"],\n polynomial[\"b\"],\n function_from_list,\n polynomial[\"coeff\"]\n )\n result_interval = result_interval.format('%.20E')[9:]\n result_interval = result_interval[:-1]\n except MyError as error:\n result_interval = error.value\n elif self.json_data[\"method\"] == \"newton\":\n try:\n result_floating = newton(polynomial[\"x\"],\n polynomial[\"epsilon\"],\n polynomial[\"max_iterations\"],\n function_from_list,\n polynomial[\"coeff\"],\n polynomial[\"derivative_coeff\"]\n )\n except ZeroDivisionError:\n result_floating = \"Unfortunately, division by zero happened\"\n except MyError as error:\n result_floating = error.value\n try:\n result_interval = newton_interval(polynomial[\"x\"],\n polynomial[\"epsilon\"],\n polynomial[\"max_iterations\"],\n function_from_list,\n polynomial[\"coeff\"],\n polynomial[\"derivative_coeff\"]\n )\n result_interval = result_interval.format('%.20E')[9:]\n result_interval = result_interval[:-1]\n except MyError as error:\n result_interval = error.value\n else:\n result_interval = \"You propably\"\n result_floating = \"gave me wrong JSON\"\n self.show_result_floating.config(text=result_interval)\n self.show_result_interval.config(text=result_floating)","title":""},{"docid":"e0830761115955abace478bf63277abd","score":"0.6215401","text":"def calculate_results():\n\n print('Calculating results..')","title":""},{"docid":"d07116c7437a3ffc65ec7ced8caca88f","score":"0.6053697","text":"def _calculate(self):\n ...","title":""},{"docid":"977d47a74d67da7d19a1b22a8e36d483","score":"0.59952414","text":"def do_calculation():\n dT = config.getfloat(\"thermodynamics\", \"Tmin\")\n try:\n nmax = config.getint(\"thermodynamics\", \"nmax\")\n except ValueError:\n nmax = config.get(\"thermodynamics\", \"nmax\")\n if not nmax == \"automatic\":\n print(\"nmax has to be given as integer or \\\"automatic\\\"\") \n try:\n analytic_n0 = config.get(\"geometry\", \"analytic_n0\")\n except ValueError:\n print(\"analytic_n0 is not given correctly: \", analytic_n0)\n analytic_n0 = True\n if type(geom.r1) == list or type(geom.r2) == list:\n analytic_n0 = False\n Tmax = config.getfloat(\"thermodynamics\", \"Tmax\")\n f = thermodynamics.finiteT(dT, nmax, Tmax, geom, analytic_n0)\n quantity = config.get(\"thermodynamics\", \"quantity\")\n result = eval(\"f.%s\" %quantity)\n return result, f","title":""},{"docid":"f05dd3eeb2320b01c30e115c6928fb5f","score":"0.5965643","text":"def error_calculation():","title":""},{"docid":"69c8ad689e17b9cd1efc84e5ba7563bd","score":"0.59115","text":"def calc(self, *args):\n pass","title":""},{"docid":"387f4fedb43cb0287db06ecf37b13dc3","score":"0.5901406","text":"def calc():\r\n nonlocal func\r\n func = e.get()\r\n func = func.replace(\" \", \"\")\r\n func = toReversePolish(check(fixmult(findNeg(tokenizer(func)))))\r\n result.set(calculate(func, float(e2.get())))","title":""},{"docid":"b3ba20e0c2fecb5806fb08b0ca839ef9","score":"0.58904105","text":"def calculate(**kwargs):\n return None","title":""},{"docid":"29a6f09fde14e1f16e33b66e2b65226c","score":"0.58634573","text":"def _postCalculate(self,retList):\n\t\tpass","title":""},{"docid":"3eccf3447683a2dc735f5799fbff98c3","score":"0.58459675","text":"def calculate(self, *args, **kwargs) -> Any:","title":""},{"docid":"f37e62a56b03972b4b59b80389e4d31d","score":"0.5809612","text":"def compute(self):\n\n self.setInitialValue()\n self.partialResult = self.initialValue\n self.elementResult = None\n\n self.updateFunctionPort()\n\n self.set_output('Result', self.partialResult)","title":""},{"docid":"81d0a1c04bf6c68b3a921b158c6c03fb","score":"0.57289326","text":"def OnCalcDone(self, event):\n if event.data is None:\n self.log(\"No result from calculation!\")\n self.PSF_HDUlist = None\n else:\n self.PSF_HDUlist = event.data\n for w in ['Display PSF', 'Display profiles', 'Save PSF As...']:\n self.widgets[w].Enable(True)\n\n self._refresh_window()\n self.log(\"PSF calculation complete\")\n # In either event, the worker is done\n self.calcthread = None","title":""},{"docid":"3ae7ea9f7af562970e90b832e81e037a","score":"0.5720635","text":"def calculate(self):\n pass","title":""},{"docid":"fd18a5ecb864880f5b28ca9d88e14303","score":"0.56965977","text":"def on_evaluate(self, interp):\n return 0.000000","title":""},{"docid":"b8afeea6242d6a4492b1855f0c649af5","score":"0.5681769","text":"def Calculate():\r\n values = GetValues()\r\n values = SolveEqn(values)\r\n DisplayValues(values)","title":""},{"docid":"b38fe131032ab41802c0adfc8618129c","score":"0.5660559","text":"def _run_calculation(self):\n # collect input\n input_vs = OrderedDict()\n for intf in self.input_intfs:\n if intf.sibling_intf_allowed:\n input_vs.setdefault(intf.family_name, []).append(intf.get_calculated_value())\n else:\n input_vs[intf] = intf.get_calculated_value()\n\n # run actual function defined by user\n try:\n results = self.calculate(*input_vs.values())\n except Exception as e:\n # set all outputs NULL\n results = [NullValue(f\"calculation fail of {self}\")] * len(self.output_intfs)\n # record and print error status\n self._calculation_status = e\n self.print_status()\n raise\n else:\n # wrap singular result and match length\n results = [results] if not isinstance(results, (list, tuple)) else list(results)\n results += [NullValue(\"calculation didn't return enough values\")] * (len(self.output_intfs) - len(results))\n self._calculation_status = ''\n finally:\n # push calculation result down to outputs\n for result, intf in zip(results, self.output_intfs):\n intf.set_provoke_value(result)","title":""},{"docid":"e3918df179bdebeaa8326fa3d94277d7","score":"0.5617621","text":"def compute_value(self):\n result = 0.00000000\n if self.value1 and self.tasa:\n result = (self.value1 * 1) / self.tasa \n self.result = result\n if not self.flag1:\n self.id_record = self.env.context.get('active_id')\n self.act_model = self.env.context.get('active_model')\n self.flag1 = True\n return {\n 'view_mode': 'form',\n 'res_model': 'compute.crypto.value',\n 'res_id': self.id,\n 'view_id': False,\n 'type': 'ir.actions.act_window',\n 'target': 'new',\n 'context': {\"default_act_model\":self.act_model, \"default_result\": result, \"default_id_record\": self.id_record,},\n }\n else:\n raise UserError(\"You need set all values first.\")","title":""},{"docid":"e43cd1561f63a67897cd240b00229f59","score":"0.55982625","text":"def process_final(self, *args):\n return self._sum / self._count","title":""},{"docid":"aad11d6d48fea29f5ab6cfd496c2674f","score":"0.5591634","text":"def doCalculation()->JSON_RESPONSE: \n\n # Get JSON data from client\n first:int = int(request.json['first'])\n second:int = int(request.json['second'])\n oper:str = request.json['operation']\n\n rst:int = performCalc(first, second, oper)\n\n # Prepare the return structure\n result:dict[str,int] = {\"result\":rst}\n\n # Send JSON back to the client\n return json.dumps(result)","title":""},{"docid":"37453e9332d7d6b5fbee48eb2fe7abf2","score":"0.55907995","text":"def handle_calculate(self, value):\n #try:\n\n result = self.valid_number_check() * 1.6093\n self.root.ids.output_label.text = str(result)\n print(result)\n #except: ValueError","title":""},{"docid":"11725e0d4aa23fdfc0173c1231d04274","score":"0.5529947","text":"def _compute(self):","title":""},{"docid":"85e680e952960f28dbfe0efb0c4bc700","score":"0.5507777","text":"def calc(self):\n raise Exception(\"Workflow calculation not implemented yet\")","title":""},{"docid":"43ddd32b2c49f7c13ef977e81a00da9d","score":"0.54745024","text":"def calc_results():\n global num_solutions\n subt = 100*get_subt(0) + 10*get_subt(1) + get_subt(2)\n diff = 100*get_diff(0) + 10*get_diff(1) + get_diff(2)\n minu = 1000*get_minu(0) + 100*get_minu(1) + 10*get_minu(2) + get_minu(3)\n if (subt + diff == minu) and (minu >= 1000):\n print minu, \"-\", subt, \"==\", diff\n num_solutions += 1","title":""},{"docid":"4fb90820e644680bc6d5e502aead6fde","score":"0.5453361","text":"def calculate(self, structure, **kwargs):","title":""},{"docid":"70fffa82abf3ab29e63d6e09fafe4f39","score":"0.54355496","text":"def _postCalculate(self,retList,time):\n\t\tif len(retList) > 0: \n\t\t\tret = pandas.concat(retList,ignore_index=True)\n\t\t\tret = self.calculateThresholdPolygon(ret,time)\n\t\t\tret['percentEffected'] = 1.\n\t\telse: \n\t\t\tret = None\n\t\treturn ret","title":""},{"docid":"a94e16cfebaa89484f51c5df781ba26c","score":"0.5427222","text":"def __check_calc(self):\n # nothing done\n return","title":""},{"docid":"166673f4de6c078d348a3759ee698ae2","score":"0.54081076","text":"def calculate(self, var_list):\n # Run dummy calculation.\n index = var_list[1].split(' ')[0]\n submit = call('cp files/pw-scf_{}.out pw-scf.out'.format(index),\n shell=True)\n if submit != 0:\n raise AssertionError('Could not submit job.')\n energy = self.scrape_output(self.output_file)\n\n print('iteration {0} energy {1}'.format(index, energy))\n\n return energy","title":""},{"docid":"b8ed09a292242295d9bd0a9dc162cc28","score":"0.54075795","text":"def calificacion_final(par1, par2, par3):\n calfinal = par1 + par2 + par3\n promedio = calfinal / 3\n return promedio","title":""},{"docid":"d8124e8fa74cc5964d34246b27cb46bf","score":"0.5406774","text":"def run_interp():\n func = str(input('Enter string or trigonometric function: '))\n print('Value l')\n l_a = float_input(positive=True)\n print('Value u')\n u_b = float_input(positive=True)\n print('Value n')\n n_c = integer_input()\n print('Your intermediate results: {}'.format(interp(func, l_a, u_b, n_c)))","title":""},{"docid":"36a407754c15e9a2256c75e9d606681f","score":"0.53978","text":"def __calc(self):\n timezone = self.timezone # in hours, east is positive\n longitude= self.long # in decimal degrees, east is positive\n latitude = self.lat # in decimal degrees, north is positive\n\n time = self.time # percentage past midnight, i.e. noon is 0.5\n day = self.day # daynumber 1=1/1/1900\n \n Jday =day+2415018.5+time-timezone/24 # Julian day\n Jcent =(Jday-2451545)/36525 # Julian century\n\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\n Mobliq = 23+(26+((21.448-Jcent*(46.815+Jcent*(0.00059-Jcent*0.001813))))/60)/60\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent))+sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+sin(rad(3*Manom))*0.000289\n Struelong= Mlong+Seqcent\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\n \n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))-0.5*vary*vary*sin(4*rad(Mlong))-1.25*Eccent*Eccent*sin(2*rad(Manom)))\n\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))*cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\n\n self.solarnoon_t=(720-4*longitude-eqtime+timezone*60)/1440\n self.sunrise_t =self.solarnoon_t-hourangle*4/1440\n self.sunset_t =self.solarnoon_t+hourangle*4/1440","title":""},{"docid":"9393ef12dcb8b1ff273f5733e0539ddb","score":"0.53911626","text":"def compute(self) -> float:\n raise NotImplementedError","title":""},{"docid":"b254264ee7ae0c135148c1ce91717f2d","score":"0.5388854","text":"def Exemples_calculs():\n while True :\n (xA,yA)=input_coord(\"A\")\n while True :\n (xB,yB)=input_coord(\"B\")\n if xB!=xA or yB!=yA :\n break\n else :\n display(\"Il faut deux points distincts pour faire une droite ! Recommencez !\")\n\n intro=Latex(\"\"\"Nous allons calculer le coefficient directeur de la droite (AB) avec\n A( {} ; {} ) et B( {} ; {} )\"\"\".format(xA,yA,xB,yB))\n if xA==xB :\n\n calcul=Latex(\"\"\"Les droites parallèles à l'axe des ordonnées n'ont pas de coefficient directeur !\n Il faut donc que vos points n'aient pas la même abscisse :\"\"\")\n else :\n frac=fractions.Fraction((yB-yA),(xB-xA))\n if frac==int(frac):\n frac=str(int(frac))\n else :\n frac=\"\\dfrac{{{numerator}}}{{{denominator}}}\".format(numerator=frac.numerator,denominator=frac.denominator)\n calcul=Math(\"m= \\dfrac{{y_B-y_A}}{{x_B-x_A}}= \\dfrac{{{yB}-{yA}}}{{{xB}-{xA}}}= \\dfrac{{{yByA}}}{{{xBxA}}}={frac}\"\n .format(xA=xA,yA=yA,xB=xB,yB=yB,yByA=yB-yA,xBxA=xB-xA,frac=frac))\n Trace_droite(xA,yA,xB,yB)\n display(intro,calcul)\n s=input(\"Voulez-vous recommencer ?(o/n)\")\n if s.lower() in ['n','non','no'] :\n break\n else :\n clear_output()","title":""},{"docid":"5dd7780b29a79b6fd8d3b696267d256c","score":"0.53845376","text":"def processAlgorithm(self, parameters, context, feedback):\n\n # Load present raster\n present_raster = self.parameterAsRasterLayer(parameters, self.INPUT1, context)\n ds_present = gdal.Open(present_raster.dataProvider().dataSourceUri())\n arr_present = ds_present.GetRasterBand(1).ReadAsArray()\n # Clean negative values\n arr_present[arr_present<0] = 0\n\n # Load future raster\n future_raster = self.parameterAsRasterLayer(parameters, self.INPUT2, context)\n ds_future = gdal.Open(future_raster.dataProvider().dataSourceUri())\n arr_future = ds_future.GetRasterBand(1).ReadAsArray()\n # Clean negative values\n arr_future[arr_future<0] = 0\n\n print(arr_future)\n print(np.unique(arr_future))\n # Parameters\n V = self.parameterAsDouble(parameters, self.INPUT5, context)\n\n r = 0\n\n c = 3\n\n area_pixel = self.parameterAsInt(parameters, self.PIXEL_RES, context) * self.parameterAsInt(\n parameters, self.PIXEL_RES, context)\n # Years\n present = self.parameterAsInt(parameters, self.INPUT3, context)\n future = self.parameterAsInt(parameters, self.INPUT4, context)\n\n # Calculate coeff sequestration\n arr_diff = arr_future - arr_present\n arr_diff_norm = arr_diff / float((future - present))\n arr_years = np.array(range(0, future - present))\n coeff = sum(1 / ((1 + r / 100) ** arr_years * ((1 + c / 100) ** arr_years)))\n carbon_sequestration_value = V * arr_diff_norm * coeff\n carbon_sequestration_difference = arr_future - arr_present\n # Initialize and write on output raster\n path_output = self.parameterAsString(parameters, self.OUTPUT, context)\n file_output = path_output + '/SE_01_carbon_sequestration_delta_euro.tiff'\n driver = gdal.GetDriverByName(\"GTiff\")\n [cols, rows] = carbon_sequestration_value.shape\n carbon_sequestration_difference_area = np.sum(carbon_sequestration_difference) / (cols * rows * area_pixel)\n outdata = driver.Create(file_output, rows, cols, 1, gdal.GDT_Float64)\n outdata.SetGeoTransform(ds_present.GetGeoTransform())##sets same geotransform as input\n outdata.SetProjection(ds_present.GetProjection())##sets same projection as input\n outdata.GetRasterBand(1).WriteArray(carbon_sequestration_value)\n print(np.max(outdata.GetRasterBand(1).ReadAsArray()))\n outdata.FlushCache() ##saves to disk!!\n outdata = None\n band = None\n ds = None\n print(np.sum(arr_present))\n report_output = path_output + '/SE_sequestro_carbonio.txt'\n f = open(report_output, \"w+\")\n today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n f.write(\"Sommario dell'analisi del sequestro di carbonio\\n\")\n f.write(\"Data: \" + today +\"\\n\\n\\n\")\n f.write(\"Analisi stato di fatto\\n\\n\")\n f.write(\"Anno corrente: %i \\n\" % (present))\n f.write(\"Sequestro carbonio Stato attuale (ton Corg): %f \\n\" % (np.sum(arr_present)))\n f.write(\"Valore medio del carbonio sequestrato per unità di superficie - Stato attuale (ton Corg/ha): : %f \\n\" % (\n np.sum(arr_present) / (cols * rows * area_pixel) * 10000))\n f.write(\"Valore totale del sequestro di carbonio (€): %f \\n\\n\\n\" % ((np.sum(arr_present)*V*coeff)/ float((future - present))))\n f.write(\"Analisi stato di progetto\\n\\n\")\n f.write(\"Anno progetto: %i \\n\" % (future))\n f.write(\"Sequestro carbonio Stato di progetto (ton Corg): %f \\n\" % (np.sum(arr_future)))\n f.write(\"Valore medio del carbonio sequestrato per unità di superficie - Stato di progetto (ton Corg/ha): %f \\n\" % (\n np.sum(arr_future) / (cols * rows * area_pixel) * 10000))\n f.write(\"Valore totale del sequestro di carbonio (€): %f \\n\\n\\n\" % ((np.sum(arr_future)*V*coeff) / float((future - present))))\n f.write(\"Differenze tra stato di progetto e stato attuale\\n\\n\")\n f.write(\"Periodo di analisi: %i - %i\\n\" % (present, future))\n f.write(\"Differenza di sequestro carbonio (ton Corg): %f \\n\" % (np.sum(arr_diff)))\n f.write(\"Differenza carbonio sequestrato per unità di superficie (ton Corg/ha): %f \\n\" % (\n carbon_sequestration_difference_area * 10000))\n f.write(\"Differenza in termini economici del SE di sequestro di carbonio (stato di progetto – stato attuale) (€):%d \\n\" % (\n np.sum(carbon_sequestration_value)))\n return {self.OUTPUT: carbon_sequestration_value}\n\n \n # ----------------------------------------------------------------------------------- \n # Copyright (c) 2021 Città di Torino.\n # \n # This material is free software: you can redistribute it and/or modify\n # it under the terms of the GNU General Public License as published by\n # the Free Software Foundation, either version 2 of the License, or\n # (at your option) any later version.\n # \n # This program is distributed in the hope that it will be useful,\n # but WITHOUT ANY WARRANTY; without even the implied warranty of\n # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n # GNU General Public License for more details.\n # \n # You should have received a copy of the GNU General Public License\n # along with this program. If not, see http://www.gnu.org/licenses.\n # ----------------------------------------------------------------------------------- ","title":""},{"docid":"ddba8492bd02da00237d8e1464e5a126","score":"0.5371956","text":"def _run(self):\n self._solve()\n return self._ret","title":""},{"docid":"11080b93adcf2859b7f8662c9f7e6fff","score":"0.53654003","text":"def _compute(y, t, N, b, k):\n S, I, R = y\n #print(t)\n '''The Susceptible Equation'''\n dSdt = -b[int(t)] * S * I / N\n \n '''The Infected Equation = -The Susceptible Equation - The Recovered Equation'''\n dIdt = b[int(t)] * S * I / N - k * I\n \n '''The Recovered Equation'''\n dRdt = k * I\n return dSdt, dIdt, dRdt","title":""},{"docid":"fd122062e4d6256eaf6d4aa2bbf05994","score":"0.53594244","text":"def integral(self, time1, time2):","title":""},{"docid":"1fb010d6f5ea79ce4b09974b0bc0d8c9","score":"0.5352363","text":"def bulk_calc(self):\n self.calc_csa()\n self.calc_CCC()\n self.calc_impedence()\n self.calc_runVd()","title":""},{"docid":"7ca2393dd1b3cb44fb5ee38d774cc7de","score":"0.5340337","text":"def compute_values(self):\n pass","title":""},{"docid":"7ca2393dd1b3cb44fb5ee38d774cc7de","score":"0.5340337","text":"def compute_values(self):\n pass","title":""},{"docid":"c8e6b33eeb34f158ca8b2ddbd6257563","score":"0.5340099","text":"def run_calculation(self):\n\n if w.ignore_history_false.isChecked():\n\n fp = w.input_file_line_edit.text()\n\n w.df, w.cache = read_from_file(fp, raw_mt=True)\n # TODO: Check if the filter index is still necessary when matching string indices in match_target_mt below.\n filter_index = w.df.query('status == \"mt\"').index\n w.df = w.df.loc[filter_index]\n\n # Create boolean filter with all repetitions except for first occurrences set to True\n filter_index = w.df.query('stype == \"source\"').duplicated('text')\n w.df = w.df[-filter_index]\n\n else:\n w.df = new_translation(w.df, w.cache, w.sample_object)\n\n df_mt = match_target_mt(w.df)\n\n w.cache = pe_density(df_mt, w.cache)\n w.actionSave_as.setEnabled(True)\n w.textOutput.setText(str('Your Post-Edit Density score is {:.3f}\\n'.format(w.cache['ped'])))\n w.statistics()\n\n w.autosave()","title":""},{"docid":"db551929b95c9e61285499b44233dd40","score":"0.5333397","text":"def calc(self, x):\n pass","title":""},{"docid":"9ab45ce819ad5f27ffcf041865253bdf","score":"0.53273827","text":"def post(self, start_value, end_value):\n start_value, end_value = int(start_value), int(end_value)\n engine_name = get_engine_name()\n if os.path.exists(engine_name):\n self.__clean_hashes()\n\n if not os.path.exists(json_filename):\n create_minion_json()\n engine_db_name = 'sqlite:///' + engine_name\n try:\n p = Process(target=calculate_range, args=(start_value, end_value, engine_db_name, m_id))\n p.start()\n processes.append(p)\n except:\n print('Error: failed to run calculation on minion ' + str(m_id) + '.')\n return {'status': 'error', 'message': 'calculation failed'}\n update_status('busy')\n return {'status': 'busy', 'message': 'calculation started'}","title":""},{"docid":"81f4e7059b0fd4d4a86a03694951504c","score":"0.5327031","text":"def compute():\n message1[\"text\"] = \"\" #reset messages\n message2[\"text\"] = \"\"\n message4[\"text\"] = \"\"\n message5[\"text\"] = \"\" \n message6[\"text\"] = \"\" \n message7[\"text\"] = \"\" \n \n smux=str(smux_entry.get()) #return the smux value in the tkinter entry \n try:\n volts_min=float(volt_min_entry.get()) # min voltage \n volts_max=float(volt_max_entry.get()) # max voltage \n R=float(resistance_entry.get()) # protection resistance \n if abs(volts_max)>20 or abs(volts_min)>20:\n texte5=\"abs(volt) <=10\"\n message5[\"text\"] = texte5 \n print(texte5) \n else: \n nb=int(point_number_entry.get()) #measures nb\n if nb<1:\n texte4=\"nb>0\"\n message4[\"text\"] = texte4 \n print(texte4) \n else:\n # delay=float(delay_entry.get()) #not needed\n # print(smux,volts_min,volts_max,nb,delay) #used to debug\n try:\n complete_measure(volts_min,volts_max,nb,smux=smux,R=R,compliance=compliance)\n texte7=\"Measures done\"\n message7[\"text\"] = texte7 \n print(texte7) \n except Exception as ex:\n texte6=\"Error from measurement detected: %s\" % str(ex) #display the error name\n message6[\"text\"] = texte6 \n print(texte6) \n reset() \n except:\n texte2=\"floats\"\n message2[\"text\"] = texte2 \n print(texte2)","title":""},{"docid":"1594a7502a7cae5142f313f0066993f3","score":"0.53201216","text":"def func(progress_remaining: float) -> float:\n return initial_value","title":""},{"docid":"c176dcfdf4a68d52eaa145558ca732f5","score":"0.53149915","text":"def calc(self, pvalues, lamb):\n\t\traise NotImplementedError","title":""},{"docid":"2c01c7f7208d17f565623b9efa9f2829","score":"0.5313737","text":"def _evaluate_function(self, inputs, aliases):\n # TODO how to handle aliases????\n method = self._sub_name\n request = inputs.pop('request', None) # sometimes just the inputs will be used without the request, like in elasticity curves\n result = self._obj.evaluate(method, request, inputs)\n balance = result[0]\n meta = result[1]\n return balance, meta","title":""},{"docid":"0b7bf3ab90c18dae7b8553fba4cb5d3e","score":"0.53023535","text":"def calculate(self, mol: Molecule) -> float:","title":""},{"docid":"034d41aa8d8f3385ce7f1536f5f8b7b5","score":"0.52879363","text":"def __calc(self):\n timezone = self.timezone # in hours, east is positive\n longitude= self.long # in decimal degrees, east is positive\n latitude = self.lat # in decimal degrees, north is positive\n\n time = self.time # percentage past midnight, i.e. noon is 0.5\n day = self.day # daynumber 1=1/1/1900\n \n # NOAA Spreadsheet\n Jday = day+2415018.5+time-timezone/24. # Julian day\n Jcent = (Jday-2451545)/36525 # Julian century\n\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\n Mobliq = 23+(26+((21.448-Jcent*(46.815+ \\\n Jcent*(0.00059-Jcent*0.001813))))/60)/60\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent)) \\\n +sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+ \\\n sin(rad(3*Manom))*0.000289\n Struelong= Mlong+Seqcent\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\n \n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+ \\\n 4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))- \\\n 0.5*vary*vary*sin(4*rad(Mlong))- \\\n 1.25*Eccent*Eccent*sin(2*rad(Manom)))\n\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))* \\\n cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\n\n self.solarnoon_t = (720-4*longitude-eqtime+timezone*60)/1440.\n self.sunrise_t = self.solarnoon_t-hourangle*4/1440.\n self.sunset_t = self.solarnoon_t+hourangle*4/1440.","title":""},{"docid":"ee1d7a33c192af97967f2c8a2e3c713b","score":"0.5286321","text":"def execute_strategy(self):\n data = self.results.copy().dropna()\n data['position'] = np.sign(data['returns'].rolling(self.mom).mean())\n data['strategy'] = data['position'].shift(1) * data['returns']\n # determine when trades take place\n # trades = data['position'].diff().fillna(0) != 0\n # subtract transaction costs from return where trades take place\n # data['strategy'][trades] -= self.tc\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)","title":""},{"docid":"7b38b01232a3f553a7ee77b5f648b7ec","score":"0.5280257","text":"def calc(self):\n sorted_pop = self.sortpop()\n initial_route = self.population[sorted_pop[0][0]]\n distance = 1 / sorted_pop[0][1]\n progress = [ distance ]\n if callable(self.animate):\n self.plot = True\n individual = Individual(initial_route)\n stop_animation = Event()\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\n else:\n self.plot = False\n if self.prn:\n print(f\"Initial distance: {distance}\")\n try:\n if self.plot:\n for i in range(self.generations):\n population = self.next_gen()\n sorted_pop = self.sortpop()\n distance = 1 / sorted_pop[0][1]\n progress.append(distance)\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n route = population[sorted_pop[0][0]]\n individual.update(route)\n else:\n for i in range(self.generations):\n population = self.next_gen()\n distance = 1 / self.sortpop()[0][1]\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n \n \n except KeyboardInterrupt:\n pass\n try:\n stop_animation.set()\n except NameError:\n pass\n final_route_index = self.sortpop()[0][0]\n final_route = population[final_route_index]\n if self.prn:\n print(\"Final route:\", final_route)\n\n return initial_route, final_route, distance","title":""},{"docid":"7b38b01232a3f553a7ee77b5f648b7ec","score":"0.5280257","text":"def calc(self):\n sorted_pop = self.sortpop()\n initial_route = self.population[sorted_pop[0][0]]\n distance = 1 / sorted_pop[0][1]\n progress = [ distance ]\n if callable(self.animate):\n self.plot = True\n individual = Individual(initial_route)\n stop_animation = Event()\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\n else:\n self.plot = False\n if self.prn:\n print(f\"Initial distance: {distance}\")\n try:\n if self.plot:\n for i in range(self.generations):\n population = self.next_gen()\n sorted_pop = self.sortpop()\n distance = 1 / sorted_pop[0][1]\n progress.append(distance)\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n route = population[sorted_pop[0][0]]\n individual.update(route)\n else:\n for i in range(self.generations):\n population = self.next_gen()\n distance = 1 / self.sortpop()[0][1]\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n \n \n except KeyboardInterrupt:\n pass\n try:\n stop_animation.set()\n except NameError:\n pass\n final_route_index = self.sortpop()[0][0]\n final_route = population[final_route_index]\n if self.prn:\n print(\"Final route:\", final_route)\n\n return initial_route, final_route, distance","title":""},{"docid":"75cc49ea82b117f6b2d47af646f819d0","score":"0.527877","text":"def _evaluate(data, context):\n # print \"Received : \", data, \" Sequence Number : \", context[\"data_sequence_no\"]\n score = 0\n secondary_score = 0\n\n for k in range(100):\n time.sleep(random.randint(0,100)/1000.0)\n percent_complete = k*1.0/100 * 100\n update_progress(context, percent_complete, \"\")\n # print \"Context Response Channel ::: \", context['response_channel']\n # if k%20==0:\n # # print \"Update : \", percent_complete\n score += random.randint(1,100)*1.0/0.7 / 100\n secondary_score += random.randint(1,100)*1.0/0.7 / 100\n\n _result_object = {\n \"score\" : score,\n \"secondary_score\" : secondary_score,\n }\n return _result_object","title":""},{"docid":"e0178d47f659aae4e12fbcd69db2468c","score":"0.52750695","text":"def on_pushButton_calc_clicked(self):\r\n # TODO: not implemented yet\r\n if self.str1 == '':\r\n a = 0\r\n else:\r\n a = float(self.str1)\r\n if self.str2 == '':\r\n b = 0\r\n else:\r\n b = float(self.str2)\r\n res = 0\r\n if self.op == '+':\r\n res = a + b\r\n elif self.op == '-':\r\n res = a - b\r\n elif self.op == '*':\r\n res = a * b\r\n elif self.op == '/':\r\n if b == 0:\r\n res = 0\r\n else:\r\n res = a / b\r\n\r\n self.lineEdit_result.setText(str(res))\r\n self.flag = 2\r\n\r\n # raise NotImplementedError\r","title":""},{"docid":"320ea0ff97999dcee761fcc123dd114c","score":"0.52461845","text":"def calculate(self):\n self.correct_extinction()\n self.rdistance = calculate_radial_distance(self.position, self.center,\n self.distance)\n self.r23 = calculate_r23(self.fluxes)\n self.calculate_OH()\n self.SFR = calculate_sfr(self.distance, self.fluxes['halpha'])","title":""},{"docid":"23dd86b596bed8c6aac3dcd00301e5c7","score":"0.5244022","text":"def compute(self, today, assets, out, opens, closes):\n out[:] = (opens[-1] - closes[0]) / closes[0]","title":""},{"docid":"23dd86b596bed8c6aac3dcd00301e5c7","score":"0.5244022","text":"def compute(self, today, assets, out, opens, closes):\n out[:] = (opens[-1] - closes[0]) / closes[0]","title":""},{"docid":"a33836659fccc659301fd2466ce5d572","score":"0.5242563","text":"def run(self):\n self.evaluate()\n self.accumulate()\n self.summarize()","title":""},{"docid":"675188c454c830f80a91cf94327c7e3e","score":"0.52329695","text":"def process_final(self, *args):\n mean = self._sum / self._count\n mean = mean ** 2\n variance = (self._sum_sq / self._count) - mean\n if variance < 0:\n return 0\n else: \n return variance ** 0.5","title":""},{"docid":"598dc322c12f3f2638ec51cc2bcf894c","score":"0.52291757","text":"def func(progress_remaining: float):\n return progress_remaining * initial_value","title":""},{"docid":"14aa6575bdb29a5d01bfab980c63bd12","score":"0.5228993","text":"def linear_schedule(progress):\n return 1 - progress","title":""},{"docid":"c27142e62aa568661d8bbdf43d69f8b6","score":"0.5228111","text":"def computation(self, results, *parameters):\n\n return self.add_computation(computation(results, *parameters))","title":""},{"docid":"7768d4b0f72db48a4418dd627fdd9c73","score":"0.52274144","text":"def _compute(self):\n pass","title":""},{"docid":"6abf202e5afb80f54132416bc47cb3f1","score":"0.52269894","text":"def inspect_calculation(self):\n converged_mc = True\n self.ctx.restart_calc = self.ctx.calculation['retrieved']\n if converged_mc:\n self.report(\"Calculation converged, terminating the workflow\")\n self.ctx.done = True","title":""},{"docid":"28e2904de8eda761267f0b480736bd0d","score":"0.52208334","text":"def thread_start_calculating(self, distance_value):\n print(\"Breakpoint Distance value:{}\".format(distance_value))\n logger.info('Slot:thread_start_calculating :', \n extra={'sim_index': self._data_store.get_simulation_index()})\n\n if distance_value == 0:\n logger.info('Slot:thread_start_calculating SINGLE STEP NOT IMPLEMENTED:', \n extra={'sim_index': self._data_store.get_simulation_index()})\n #TODO - finish this breakpoint case\n self.simulationComputing = False\n\n elif distance_value == -1:\n logger.info('Slot:thread_start_calculating RUN TO COMPLETION :', \n extra={'sim_index': self._data_store.get_simulation_index()})\n # set the breakpoint to be a very large number to indicate run to completion\n self.breakpointDistance = 9999999\n self.simulationComputing = True\n else:\n # run to the distance value point in the track\n sim_index = self._data_store.get_simulation_index()\n if distance_value > self._data_store.get_distance_at_index(sim_index) :\n logger.info('Slot:thread_start_calculating RUN TO DISTANCE :', \n extra={'sim_index': sim_index})\n # requested breakpoint is further down the track\n self.breakpointDistance = distance_value\n # Start computing and acknowledge to MainWindow by sending a signal back \n self.simulationThreadSignal.emit(\"Calculating...\")\n # \"state\" variable indicating thread should be calculating\n self.simulationComputing = True\n else:\n logger.info('Slot:thread_start_calculating PAST REQUESTED DISTANCE :', \n extra={'sim_index': sim_index})\n # simulation has already past this point in the track, don't proceed\n self.simulationComputing = False","title":""},{"docid":"4213b442ff6408444f7a99b113d82637","score":"0.52148896","text":"def _calculate_avg():\n while (True):\n _avg_num(_q_uwb_a, q_to_a, \"to_a\", 8)\n _avg_num(_q_uwb_b, q_to_b, \"to_b\", 8)\n time.sleep(0.015)","title":""},{"docid":"6a06c78d7eb16247bf403e1389083f14","score":"0.52093","text":"def integrate(self, t):","title":""},{"docid":"4ee388bb9a884aa4cea1bef1a52b88fa","score":"0.5208004","text":"def __call__(self, *args, **kwargs) -> Any:\n return self.calculate(*args, **kwargs)","title":""},{"docid":"a7a19b61964963525e9d4ad7f5b8669f","score":"0.5207855","text":"def func(progress_remaining: float) -> float:\n return progress_remaining * initial_value","title":""},{"docid":"a7a19b61964963525e9d4ad7f5b8669f","score":"0.5207855","text":"def func(progress_remaining: float) -> float:\n return progress_remaining * initial_value","title":""},{"docid":"54047f7afc5fafe5f39587ee486749fd","score":"0.5203203","text":"def __cost_function(self, sense='single'):\n\n self.SnappyHexMeshrun()\n self.Optimisationrun()\n if sense==\"single\":\n t = self.RunUtilities()\n return p\n elif sense==\"multi\":\n t, p = self.RunUtilities(sense=sense)\n return t, p\n else:\n print(\"Invalid input for sense: \", sense)\n print(\"Available options are: 'single' or 'multi'\")\n return None","title":""},{"docid":"30447d2a14ea9b45e16c20607615711e","score":"0.5196032","text":"def calculate_derivative(param):","title":""},{"docid":"e1106def928c6e8ab53fb563e3bff385","score":"0.51902646","text":"def evaluate(self) -> None:\n\n self._generate_paths()\n self._compute_payoff_fn()\n self._is_evaluated = True","title":""},{"docid":"f1b28047d6734cbcd08da68de38b381b","score":"0.51892775","text":"def done(self, result):\n output_file = result['output']\n GLF = result['GLF']\n Qt.QApplication.restoreOverrideCursor()\n self.statusBar().showMessage('GLF = {:0.1f}%'.format(GLF*100))\n # Show a MessageBox.\n QtWidgets.QMessageBox.information(self, 'Ausgabe erzeugt',\n str(output_file),\n QtWidgets.QMessageBox.Ok)\n self.start_button.setEnabled(True)\n\n if self.plot_timeseries:\n try: # Create a line plot of the time series in a new window\n self.plot_window = PlotWindow()\n self.plot_window.update_lineplot(result['df_sum'])\n self.plot_window.show()\n except Exception as e:\n logger.exception(e)\n QtWidgets.QMessageBox.critical(self, 'Fehler', str(e),\n QtWidgets.QMessageBox.Ok)","title":""},{"docid":"daceaadc42197c7d5f2b3753e3192aee","score":"0.5189256","text":"def when_computation_done(self, out, param):\r\n tkMessageBox.showinfo('Computation is done', out)\r\n self.set_state('normal')\r\n\r\n if self.plugin_type == \"Chimera\":\r\n self.profile_wd = os.path.realpath(\r\n os.path.normcase(self.main_parameters[1] + \"/chimera/\"))\r\n\r\n tunnels_script = os.path.join(self.profile_wd, 'complex.py')\r\n paths_script = os.path.join(self.wd, 'paths.py')\r\n pores_script = os.path.join(self.wd, 'pores.py')\r\n autopores_script = os.path.join(self.wd, 'autopores.py')\r\n dom = parse(os.path.join(self.xml_wd, 'tunnels.xml'))\r\n\r\n for e in dom.getElementsByTagName('Exception'):\r\n tkMessageBox.showinfo('Exception', e.getAttribute('Text'))\r\n\r\n if self.plugin_type == \"PyMOL\":\r\n\r\n if param == \"pores\":\r\n self.group_objects(tunnels_script, 'Pores', \"MOLE_tunnels\")\r\n else:\r\n self.group_objects(tunnels_script, 'Tunnels', \"MOLE_pores\")\r\n\r\n self.group_objects(paths_script, 'Paths', 'MOLE_Paths')\r\n self.group_objects(pores_script, 'Pores', 'MOLE_Pores')\r\n self.group_objects(autopores_script, 'AutoPores', 'MOLE_AutoPores')\r\n plugin().show(\"cgo\", \"Interior*\")\r\n plugin().set_view(self.original_view)\r\n\r\n else:\r\n plugin.run(tunnels_script)\r\n\r\n self.set_state('normal')","title":""},{"docid":"8bc4859850d902bbbabda8a6f64c2238","score":"0.51880544","text":"def render(self, request):\n self._calculatePi(request).addCallback(self._gotResult, request)\n return server.NOT_DONE_YET","title":""},{"docid":"7174f03ecc0b1631771bcd650b043bf5","score":"0.5187847","text":"def _postCalculate(self,retList,time):\n\t\tmodList = []\n\t\tfor vals in retList: \n\t\t\tvals[\"percentEffected\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\n\t\t\tvals = self.calculateThresholdPolygon(vals,time)\n\t\t\tmodList.append(vals)\n\n\t\treturn pandas.concat(modList,ignore_index=True)","title":""},{"docid":"7174f03ecc0b1631771bcd650b043bf5","score":"0.5187847","text":"def _postCalculate(self,retList,time):\n\t\tmodList = []\n\t\tfor vals in retList: \n\t\t\tvals[\"percentEffected\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\n\t\t\tvals = self.calculateThresholdPolygon(vals,time)\n\t\t\tmodList.append(vals)\n\n\t\treturn pandas.concat(modList,ignore_index=True)","title":""},{"docid":"36b0c8b9bdb177f85aa5d45a556a0e22","score":"0.5185649","text":"def Calculate(self):\n pass","title":""},{"docid":"b214cdc4438aebfa3d63f75c8e78c910","score":"0.5171598","text":"def compute_pi_main(workers, accuracy, batchsize):\n n_total = 0\n s_total = 0\n tasks_queue = mp.Queue() # each task (or batch) is represented by the batchsize\n done_queue = mp.Queue() # result of each task is the number of successes in that batch\n processes = mp.Pool(workers, initializer=compute_pi_worker, initargs=(tasks_queue, done_queue))\n\n for _ in range(workers):\n tasks_queue.put(batchsize)\n\n while True:\n s = done_queue.get(block=True)\n s_total += s\n n_total += batchsize\n pi_est = (4.0 * s_total) / n_total\n error = abs(math.pi - pi_est)\n\n if error > accuracy:\n tasks_queue.put(batchsize)\n else:\n processes.terminate()\n processes.join()\n break\n\n print(\"Estimate:\", pi_est)\n print(\"Error:\", error)\n print(\"Steps:\", n_total)\n return pi_est, error, n_total","title":""},{"docid":"67da759295be1801c868c36af3dd3052","score":"0.5168196","text":"def calculate_float(self, default: int):\n self.total_float = self.latest_finish - self.earliest_finish\n\n if not self.successors:\n self.free_float = default - self.earliest_finish\n else:\n self.free_float = min(s.earliest_start for s in self.successors) - self.earliest_finish","title":""},{"docid":"ce99a8d17f1502a9ede8378469d864af","score":"0.5166104","text":"def calculate(s):","title":""},{"docid":"fff35d5e25b8885f6ecc9ff6d4200e5b","score":"0.5155409","text":"def handler(event, context):\n try:\n parsed_event = CalculatorEvent(**event)\n except ValidationError as exp:\n raise Exception(json.dumps(f\"[BadRequest] {exp}\"))\n\n if parsed_event.year not in crypto_prices[parsed_event.query]['old_prices']:\n raise Exception(json.dumps(f\"[BadRequest] Data for requested year not available\"))\n\n\n current_value = (parsed_event.amount/crypto_prices[parsed_event.query]['old_prices'][parsed_event.year]) * crypto_prices[parsed_event.query]['current_price']\n \n return round(current_value,2)","title":""},{"docid":"a28c312c92b4217aed7e3c0932a1bfe0","score":"0.5150363","text":"def execute_strategy(self):\n data = self.results.copy().dropna()\n data['position'] = np.where(data['SMA1'] > data['SMA2'], 1, 0)\n data['strategy'] = data['position'].shift(1) * data['returns']\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)","title":""},{"docid":"da3563d9154880d9234b4a9a1fead992","score":"0.5143086","text":"def evaluate_metric(self, assignments):\n sleep_seconds = 10\n print('{0} - Sleeping for {1} seconds to simulate expensive computation...'.format(threading.current_thread(), sleep_seconds))\n time.sleep(sleep_seconds)\n return sigopt.examples.franke_function(assignments['x1'], assignments['x2'])","title":""},{"docid":"0647838977b755e73ac53acb14e7ed14","score":"0.5141489","text":"def execute_strategy(self):\n data = self.results.copy().dropna()\n data['sma'] = data['returns'].rolling(self.sma).mean()\n data['distance'] = data['close'] - data['sma']\n # sell signals\n data['position'] = np.where(data['distance'] > self.threshold, -1, np.nan)\n # buy signals\n data['position'] = np.where(data['distance'] < -self.threshold, 1, data['position'])\n # cross of current price and SMA (zero distance)\n data['position'] = np.where(data['distance'] * data['distance'].shift(1) < 0, 0, data['position'])\n data['position'] = data['position'].ffill().fillna(0)\n data['strategy'] = data['position'].shift(1) * data['returns']\n # determine when trades take place\n # trades = data['position'].diff().fillna(0) != 0\n # subtract transaction costs from return where trades take place\n # data['strategy'][trades] -= self.tc\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)","title":""},{"docid":"ab977317ec5b5501427a8c5d5683e375","score":"0.51393765","text":"def calculate(self):\n super().calculate()\n\n\n tr_key = Constants.get_key(\"TR\")\n trn_key = Constants.get_key(\"TRn\")\n\n dm_plus_key = Constants.get_key(\"DMplus\")\n dm_plus_n_key = Constants.get_key(\"DMplusN\")\n\n dm_minus_key = Constants.get_key(\"DMminus\")\n dm_minus_n_key = Constants.get_key(\"DMminusN\")\n di_plus_n_key = Constants.get_key(\"DIplusN\")\n di_minus_n_key = Constants.get_key(\"DIminusN\")\n di_diff_key = Constants.get_key(\"DIdiff\")\n di_sum_key = Constants.get_key(\"DIsum\")\n dx_key = Constants.get_key(\"DX\")\n\n df_result = []\n\n for ticker in self.tickers:\n\n df_data_atr = self.df[[ticker]].copy()\n atr_ind = ATR(df=df_data_atr, n=14)\n\n df_data = self.df[ticker].copy()\n # the period parameter of ATR function does not matter because period does not influence TR calculation\n df_data[tr_key] = atr_ind.calculate()[ticker][[tr_key]]\n\n df_data[dm_plus_key] = \\\n np.where(\n (df_data[self.high_key] - df_data[self.high_key].shift(1)) >\n (df_data[self.low_key].shift(1) - df_data[self.low_key]),\n df_data[self.high_key] - df_data[self.high_key].shift(1),\n 0)\n\n df_data[dm_plus_key] = \\\n np.where(\n df_data[dm_plus_key] < 0,\n 0,\n df_data[dm_plus_key])\n\n df_data[dm_minus_key] = \\\n np.where((df_data[self.low_key].shift(1) - df_data[self.low_key]) >\n (df_data[self.high_key] - df_data[self.high_key].shift(1)),\n df_data[self.low_key].shift(1) - df_data[self.low_key],\n 0)\n\n df_data[dm_minus_key] = np.where(df_data[dm_minus_key] < 0, 0, df_data[dm_minus_key])\n\n TRn = []\n DMplusN = []\n DMminusN = []\n TR = df_data[tr_key].tolist()\n DMplus = df_data[dm_plus_key].tolist()\n DMminus = df_data[dm_minus_key].tolist()\n\n for i in range(len(df_data)):\n if i < self.n:\n TRn.append(np.NaN)\n DMplusN.append(np.NaN)\n DMminusN.append(np.NaN)\n elif i == self.n:\n TRn.append(df_data[tr_key].rolling(self.n).sum().tolist()[self.n])\n DMplusN.append(df_data[dm_plus_key].rolling(self.n).sum().tolist()[self.n])\n DMminusN.append(df_data[dm_minus_key].rolling(self.n).sum().tolist()[self.n])\n elif i > self.n:\n TRn.append(TRn[i - 1] - (TRn[i - 1] / 14) + TR[i])\n DMplusN.append(DMplusN[i - 1] - (DMplusN[i - 1] / 14) + DMplus[i])\n DMminusN.append(DMminusN[i - 1] - (DMminusN[i - 1] / 14) + DMminus[i])\n\n df_data[trn_key] = np.array(TRn)\n df_data[dm_plus_n_key] = np.array(DMplusN)\n df_data[dm_minus_n_key] = np.array(DMminusN)\n df_data[di_plus_n_key] = 100 * (df_data[dm_plus_n_key] / df_data[trn_key])\n df_data[di_minus_n_key] = 100 * (df_data[dm_minus_n_key] / df_data[trn_key])\n df_data[di_diff_key] = abs(df_data[di_plus_n_key] - df_data[di_minus_n_key])\n df_data[di_sum_key] = df_data[di_plus_n_key] + df_data[di_minus_n_key]\n df_data[dx_key] = 100 * (df_data[di_diff_key] / df_data[di_sum_key])\n ADX = []\n DX = df_data[dx_key].tolist()\n for j in range(len(df_data)):\n if j < 2 * self.n - 1:\n ADX.append(np.NaN)\n elif j == 2 * self.n - 1:\n ADX.append(df_data[dx_key][j - self.n + 1:j + 1].mean())\n elif j > 2 * self.n - 1:\n ADX.append(((self.n - 1) * ADX[j - 1] + DX[j]) / self.n)\n df_data[self.indicator_key] = np.array(ADX)\n\n df_result.append(df_data.loc[:, [self.indicator_key]])\n\n self.df = pd.concat(df_result, axis=1, keys=self.tickers)\n\n return self.df","title":""},{"docid":"eafd971da2d043a48e336831a0a68557","score":"0.5138379","text":"def process_queue_fringes(self):\n\n try:\n result = self.queue.get(0)\n\n if result == \"ABORT\":\n self.addlog(\"Mission aborted.\", self.warningcolor2)\n self.abortmission.set(0)\n self.totaltime = 0\n self.removeprogressbar()\n return\n\n # Show result of the task if needed\n self.entry_23.delete(0, END)\n self.entry_23.insert(0, '{0:.2f}'.format(result[0]))\n self.entry_24.delete(0, END)\n self.entry_24.insert(0, '{0:.2f}'.format(result[1]))\n\n self.setoffsets()\n\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list = [], [], [], []\n\n for i in range(1, self.layernumber + 1):\n self.layertype_list.append(getattr(self, \"layertypevar{}\".format(i)).get())\n self.entry_x_list.append(float(getattr(self, \"entry_x_{}\".format(i)).get()))\n self.entry_d_list.append(float(getattr(self, \"entry_d_{}\".format(i)).get()))\n self.checklayer_list.append(int(getattr(self, \"checklayer{}\".format(i)).get()))\n\n fitobject = FIT_FTIR(self.Temp, self.wavenumbers_cut, self.trans_cut, self.entry_d_0.get(),\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list,\n float(self.entry_21.get()), self.angle,\n float(self.entry_23.get()), float(self.entry_24.get()), self.subtype, 2,\n self.listbox, self.progress_var, self.wn_beingcalculated,\n self.FTIRplot, self.absorptionplot, self.canvas, self.blindcal, self.abortmission)\n self.peakvalues_fit = fitobject.returnpeakvalues()\n\n self.addlog('Fitting fringes complete. Total time: {:.1f}s. MSE={}'.format(self.totaltime, result[2]))\n\n self.totaltime = 0\n\n self.try_remove_fitline(self.fitline_trans)\n self.fitline_trans = self.plot_and_show(self.FTIRplot, self.fitline_trans, 0, self.wavenumbers_cut,\n self.peakvalues_fit, 'r', '', 'Transmission (%)', 0, 'upper right')\n\n self.removeprogressbar()\n\n except queue.Empty:\n self.totaltime += 0.1\n self.after(100, self.process_queue_fringes)","title":""},{"docid":"5820bfa6b2e06e705d2fad4ec1847994","score":"0.5132884","text":"def compute(\n self,\n blade_cost,\n blade_mass,\n hub_system_cost,\n hub_system_mass,\n nacelle_mass,\n nacelle_cost,\n tower_cost,\n tower_mass,\n blade_number=3,\n offshore=True,\n ):\n\n # Variables\n self.blade_cost = (\n blade_cost # Float(0.0, units='USD', iotype='in', desc='cost for a single wind turbine blade')\n )\n self.blade_mass = blade_mass # Float(0.0, units='kg', iotype='in', desc='mass for a single wind turbine blade')\n self.hub_system_cost = hub_system_cost # Float(0.0, units='USD', iotype='in', desc='hub system cost')\n self.hub_system_mass = hub_system_mass # Float(0.0, units='kg', iotype='in', desc='hub system mass')\n self.nacelle_mass = nacelle_mass # Float(0.0, units='kg', iotype='in', desc='nacelle mass')\n self.nacelle_cost = nacelle_cost # Float(0.0, units='USD', iotype='in', desc='nacelle cost')\n self.tower_cost = tower_cost # Float(0.0, units='USD', iotype='in', desc='cost for a tower')\n self.tower_mass = tower_mass # Float(0.0, units='kg', iotype='in', desc='mass for a turbine tower')\n\n # Parameters (and ignored inputs)\n self.blade_number = blade_number # Int(3, iotype='in', desc = 'number of rotor blades')\n self.offshore = offshore # Bool(False, iotype='in', desc= 'boolean for offshore')\n\n # high level output assignment\n self.rotor_mass = self.blade_mass * self.blade_number + self.hub_system_mass\n self.rotor_cost = self.blade_cost * self.blade_number + self.hub_system_cost\n self.turbine_mass = self.rotor_mass + self.nacelle_mass + self.tower_mass\n self.turbine_cost = self.rotor_cost + self.nacelle_cost + self.tower_cost\n\n if self.offshore:\n self.turbine_cost *= 1.1\n\n # derivatives\n self.d_mass_d_blade_mass = self.blade_number\n self.d_mass_d_hub_mass = 1.0\n self.d_mass_d_nacelle_mass = 1.0\n self.d_mass_d_tower_mass = 1.0\n\n if self.offshore:\n self.d_cost_d_blade_cost = 1.1 * self.blade_number\n self.d_cost_d_hub_cost = 1.1\n self.d_cost_d_nacelle_cost = 1.1\n self.d_cost_d_tower_cost = 1.1\n else:\n self.d_cost_d_blade_cost = self.blade_number\n self.d_cost_d_hub_cost = 1.0\n self.d_cost_d_nacelle_cost = 1.0\n self.d_cost_d_tower_cost = 1.0","title":""},{"docid":"a2818a75b1740c04b7c7c9442d420e04","score":"0.5128805","text":"def getval(self):\n \n if self.averagingAttemps > 1:\n result = 0.0\n readings = 0.0\n readingsc = 0.0\n\t readings_array = {}\n for i in range(0, self.averagingAttemps):\n reading = self.getReading()\n if (reading != 0 and reading != 1023):\n if self.averagingMethod == \"max\":\n if reading > readings:\n\t\t\t \"\"\"print(str(reading) + \" higher as \" + str(readings))\"\"\"\n readings = reading\n else:\n readings = readings + reading\n readingsc = readingsc + 1\n\t\t if reading in readings_array:\n\t\t readings_array[reading] = readings_array[reading] + 1\n\t\t else:\n\t\t\treadings_array[reading] = 1\n time.sleep(self.averagingTimeout)\n if readingsc == 0:\n result = self.getReading()\n else:\n\t\tif self.averagingMethod == \"most\":\n\t\t sorted_x = sorted(readings_array.items(), key=operator.itemgetter(1), reverse=True)\n\t\t result = sorted_x[0][0]\n elif self.averagingMethod == \"max\":\n result = readings\n else:\n result = readings / readingsc\n else:\n result = self.getReading()\n \n if result == 0:\n msg = \"Error: Check wiring for the \" + self.sensorname\n msg += \" measurement, no voltage detected on ADC input \"\n msg += str(self.adcpin)\n print(msg)\n return None\n if result == 1023:\n if self.sensorname == \"LDR\":\n # Carrying on with 1023 gives divide by zero error below\n result = 1022\n else:\n msg = \"Error: Check wiring for the \" + self.sensorname\n msg += \" measurement, full voltage detected on ADC input \"\n msg += str(self.adcpin)\n print(msg)\n return None\n\n\tif self.sensorname == \"WindDirection\":\n\t return result\n\n\t\"\"\"print(self.sensorname + \" \" + str(self.sensorvoltage))\"\"\"\n\n vout = float(result)/1023 * self.sensorvoltage\n if self.pulldown != None:\n resout = (self.pulldown * self.sensorvoltage) / vout - self.pulldown\n elif self.pullup != None:\n resout = self.pullup / ((self.sensorvoltage / vout) - 1)\n else:\n resout = vout * 1000\n return resout","title":""},{"docid":"621c70cbb28fa1e4a9c190a799b7b1e6","score":"0.51253504","text":"def compute(self): # pylint: disable=E0202\n pass","title":""},{"docid":"621c70cbb28fa1e4a9c190a799b7b1e6","score":"0.51253504","text":"def compute(self): # pylint: disable=E0202\n pass","title":""},{"docid":"01998f20124e1512ef226959005fc520","score":"0.512176","text":"def calculate_callback(self):\n\n # Create the interpreter\n from asteval import Interpreter\n aeval = Interpreter()\n\n # Grab the calculation from the text box which the user wants to do\n calculation = str(self.calculation_text.text())\n\n lhs = calculation.split('=')[0].strip()\n\n # Use the package asteval to do the calculation, we are going to\n # assume here that the lhs of the equals sign is going to be the output named variable\n\n try:\n if lhs in self.data_components:\n raise KeyError('{} is already in the data components, use a different variable on the left hand side.'.format(lhs))\n\n # Pull in the required data and run the calculation\n for dc in self.data_components:\n if dc in calculation:\n aeval.symtable[dc] = self.data[dc]\n aeval(calculation)\n\n # Pull out the output data and add to the proper drop-downs\n out_data = aeval.symtable[lhs]\n self.data.add_component(out_data, lhs)\n\n # Add the new data to the list of available data for arithemitic operations\n self.data_components.append(lhs)\n\n self.close()\n\n except KeyError as e:\n self.calculation_text.setStyleSheet(\"background-color: rgba(255, 0, 0, 128);\")\n\n # Display the error in the Qt popup\n if aeval.error_msg:\n self.error_label_text.setText('{}'.format(aeval.error_msg))\n else:\n self.error_label_text.setText('{}'.format(e))\n\n self.error_label_text.setStyleSheet(\"color: rgba(255, 0, 0, 128)\")","title":""},{"docid":"9ad61c57a35376b206230052921c1336","score":"0.512145","text":"def update(self,current_value):\n\n self.error = self.set_point - current_value\n\n self.P_value = self.Kp * self.error\n if (self.last_value >= current_value):\n change = self.error - self.last_error\n else:\n change = 0.0\n\n if self.error > 0.0:\n self.I_value = self.Integrator * self.Ki\n else:\n self.I_value = (self.Integrator * self.Ki)\n\n\n #self.D_value = self.Kd * ( self.error - self.Derivator)\n self.D_value = self.Kd * change\n self.Derivator = self.error\n\n self.Integrator = self.Integrator + self.error\n\n if self.Integrator > self.Integrator_max:\n self.Integrator = self.Integrator_max\n elif self.Integrator < self.Integrator_min:\n self.Integrator = self.Integrator_min\n\n self.last_error = self.error\n self.last_value = current_value\n\n PID = self.P_value + self.I_value + self.D_value\n\n self._z_data[\"data\"][\"P\"] = self.P_value\n self._z_data[\"data\"][\"I\"] = self.I_value\n self._z_data[\"data\"][\"D\"] = self.D_value\n self._z_data[\"data\"][\"E\"] = self.error\n self._z_data[\"data\"][\"SP\"] = self.set_point\n self._z_data[\"data\"][\"OUT\"] = PID\n\n if self._zmq:\n try:\n self._zmq.send_json(self._z_data, zmq.NOBLOCK)\n except zmq.error.Again:\n pass\n\n return PID","title":""},{"docid":"472ea9d79a812c81d9dbbb43bbe45dc9","score":"0.50997347","text":"def _calc_result(self):\n import mcmc\n\n g=zeros(2)\n gcov=zeros((2,2))\n gsens = zeros(2)\n\n if self.eta:\n eta1vals,eta2vals=self.trials[:,2], self.trials[:,3]\n g1vals,g2vals=self._get_gvals_from_eta(eta1vals,eta2vals)\n else:\n g1vals=self.trials[:,2]\n g2vals=self.trials[:,3]\n\n prior = self.gprior(g1vals,g2vals)\n dpri_by_g1 = self.gprior.dbyg1(g1vals,g2vals)\n dpri_by_g2 = self.gprior.dbyg2(g1vals,g2vals)\n\n psum = prior.sum()\n\n g0=None\n gcov0=None\n if self.when_prior=='after':\n # this will be eta not g if using that parametrization\n g0,gcov0 = mcmc.extract_stats(self.trials[:,2:4])\n\n pars, pcov = mcmc.extract_stats(self.trials,weights=prior)\n # we need to multiply each by the prior\n \"\"\"\n g[0] = (g1vals*prior).sum()/psum\n g[1] = (g2vals*prior).sum()/psum\n\n\n gcov[0,0] = (g1diff**2*prior).sum()/psum\n gcov[0,1] = (g1diff*g2diff*prior).sum()/psum\n gcov[1,0] = gcov[0,1]\n gcov[1,1] = (g2diff**2*prior).sum()/psum\n \"\"\"\n\n g[:] = pars[2:4]\n gcov[:,:] = pcov[2:4, 2:4]\n\n # now the sensitivity is \n # sum( (-g) L*dP/dg )\n # ----------------------\n # sum(L*P)\n #\n # the likelihood is already in the points\n\n g1diff = g[0]-g1vals\n g2diff = g[1]-g2vals\n gsens[0] = 1. - (g1diff*dpri_by_g1).sum()/psum\n gsens[1] = 1. - (g2diff*dpri_by_g2).sum()/psum\n else:\n # prior is already in the distribution of\n # points. This is simpler for most things but\n # for sensitivity we need a factor of (1/P)dP/de\n\n pars,pcov = mcmc.extract_stats(self.trials)\n #g, gcov = mcmc.extract_stats(self.trials[:,2:2+2])\n\n g[:] = pars[2:4]\n gcov[:,:] = pcov[2:4, 2:4]\n\n g1diff = g[0]-g1vals\n g2diff = g[1]-g2vals\n\n w,=where(prior > 0)\n if w.size == 0:\n raise ValueError(\"no prior values > 0!\")\n\n gsens[0]= 1.-(g1diff[w]*dpri_by_g1[w]/prior[w]).mean()\n gsens[1]= 1.-(g2diff[w]*dpri_by_g2[w]/prior[w]).mean()\n\n \n arates = self._emcee_sampler.acceptance_fraction\n arate = arates.mean()\n #print 'acceptance rate:',w.size/float(self.trials.size)\n\n # weighted s/n based on the most likely point\n s2n,loglike,chi2per,dof,prob=self._calculate_maxlike_stats()\n if self.eta:\n g0name='eta'\n else:\n g0name='g'\n\n Tmean=pars[4]\n Terr=sqrt(pcov[4,4])\n Ts2n=pars[4]/sqrt(pcov[4,4])\n #print 'T s/n:',Ts2n\n self._result={'model':self.model,\n 'g':g,\n 'gcov':gcov,\n 'gsens':gsens,\n g0name+'0':g0,\n g0name+'cov0':gcov0,\n 'pars':pars,\n 'pcov':pcov,\n 'Tmean':Tmean,\n 'Terr':Terr,\n 'Ts2n':Ts2n,\n 'arate':arate,\n 's2n_w':s2n,\n 'loglike':loglike,\n 'chi2per':chi2per,\n 'dof':dof,\n 'fit_prob':prob}\n #wlog(\"arate:\",self._result['arate'])","title":""},{"docid":"81e1d43e967bd2ad66fe32eeb4f512d8","score":"0.5096479","text":"def interpolate(self, double: float) -> float:\n ...","title":""},{"docid":"f9ccb6ccc97f671fb95181dc45fb44f8","score":"0.5088823","text":"def postsolve(self, lbfgs_result):\n raise NotImplementedError","title":""},{"docid":"7a7d46d8d9ffadc367fe21aad2f8c9bb","score":"0.50876844","text":"def result_relative(self, result1, result2):\n value1 = self.objective.get_mean(result1)\n value2 = self.objective.get_mean(result2)\n if value2 == 0:\n return float('inf') * value1\n return value1 / value2","title":""},{"docid":"0bc1f58636b2ddf43f909825f55e8d1e","score":"0.50867045","text":"def calcul_throughput(self, spectrumrangeairmass):\n \"\"\"\n data_mag = spectrumrangeairmass.data_mag.T\n data_mag -= np.log(self.data_calspec)\n spectrumrangeairmass.data_mag = data_mag.T\n\n data_order2 = spectrumrangeairmass.order2.T\n data_order2 /= self.data_calspec\n spectrumrangeairmass.order2 = data_order2.T\n #print(self.data_calspec)\n #print(self.data_calspec.T)\n #print(self.data_calspec.T @ self.data_calspec)\n MULT = np.zeros((len(self.data_calspec),len(self.data_calspec)))\n for i in range(len(self.data_calspec)):\n for j in range(len(self.data_calspec)):\n MULT[i][j], MULT[j][i] = self.data_calspec[i] * self.data_calspec[j], self.data_calspec[i] * self.data_calspec[j]\n\n spectrumrangeairmass.INVCOV *= MULT\n spectrumrangeairmass.cov /= MULT\n \"\"\"\n\n self.slope, self.ord, self.err_slope, self.err_ord = spectrumrangeairmass.bouguer_line()\n disp = np.loadtxt(self.rep_disp_name)\n Data_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1]/100, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n tel = np.loadtxt(self.rep_tel_name)\n Data_tel = sp.interpolate.interp1d(tel.T[0], tel.T[1], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err_tel = sp.interpolate.interp1d(tel.T[0], tel.T[2], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n \"\"\"\n data_disp_new_lambda = Data_disp(self.new_lambda)\n err_disp_new_lambda = Err_disp(self.new_lambda)\n data_tel_new_lambda = Data_tel(self.new_lambda)\n err_tel_new_lambda = Err_tel(self.new_lambda)\n \"\"\"\n self.data_bouguer = np.exp(self.ord)\n err_bouguer = self.err_ord * self.data_bouguer\n self.data = self.data_bouguer / self.data_calspec\n self.lambdas = self.new_lambda\n self.err = err_bouguer / self.data_calspec\n\n # self.data = filter_detect_lines(self.lambdas, self.data, self.plot_filt, self.save_filter)\n\n Data = sp.interpolate.interp1d(self.lambdas, self.data, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err = sp.interpolate.interp1d(self.lambdas, self.err, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n self.lambdas = self.new_lambda\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\n\n self.data_tel = Data_tel(self.lambdas)\n self.data_tel_err = Err_tel(self.lambdas)\n self.data_disp = Data_disp(self.lambdas)\n self.data_disp_err = Err_disp(self.lambdas)\n self.data = Data(self.lambdas)\n self.err = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n # self.data = sp.signal.savgol_filter(self.data, 111, 2)\n\n #spectrumrangeairmass.archi_mega_fit()\n if self.order2 and not self.mega_fit:\n self.slope2, self.ord2, self.err_slope2, self.err_ord2, self.A2, self.A2_err = spectrumrangeairmass.bouguer_line_order2()\n self.data_bouguer = np.exp(self.ord2)\n err_bouguer = self.err_ord2 * self.data_bouguer\n self.data_order2 = self.data_bouguer / self.data_calspec\n self.lambdas = self.new_lambda\n self.err_order2 = err_bouguer / self.data_calspec\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n self.lambdas = self.new_lambda\n self.data_order2 = Data(self.lambdas)\n self.err_order2 = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n # self.data_order2 = sp.signal.savgol_filter(self.data_order2, 111, 2)\n\n if self.mega_fit:\n self.ord2, self.err_order2 = spectrumrangeairmass.megafit_emcee()\n print(self.ord2)\n print(len(self.ord2))\n self.data_order2 = self.ord2 / self.data_calspec\n self.err_order2 = self.err_order2 / self.data_calspec\n self.lambdas = self.new_lambda\n\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.ord2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n self.lambdas = self.new_lambda\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\n self.data_order2 = Data(self.lambdas)\n self.err_order2 = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n self.data_order2[21:-21] = sp.signal.savgol_filter(self.data_order2[21:-21], 11, 2)","title":""},{"docid":"83954746e4599a297a386e46edd8832a","score":"0.5083051","text":"def solve(self, show_progress=True):\n dt = self.dt\n old_dt = self.dt\n\n if self.in_parallel:\n show = False\n else:\n show = show_progress\n bar = FloatPBar(self.t, self.tf, show=show)\n\n # Initial solution\n self.dump_output()\n self.barrier() # everybody waits for this to complete\n\n # initial solution damping time\n tdamp = self.tdamp\n\n # Compute the accelerations once for the predictor corrector\n # integrator to work correctly at the first time step.\n self.acceleration_eval.compute(self.t, dt)\n\n # solution output times\n output_at_times = numpy.array( self.output_at_times )\n\n while self.t < self.tf:\n\n # perform any pre step functions\n for func in self.pre_step_functions:\n func.eval(self)\n\n if self.rank == 0:\n logger.debug(\n \"Iteration=%d, time=%f, timestep=%f\" % \\\n (self.count, self.t, dt)\n )\n # perform the integration and update the time.\n #print 'Solver Iteration', self.count, dt, self.t, tdamp\n self.integrator.step(self.t, dt)\n\n # perform any post step functions\n for func in self.post_step_functions:\n func.eval(self)\n\n # update time and iteration counters if successfully\n # integrated\n self.t += dt\n self.count += 1\n\n # dump output if the iteration number is a multiple of the\n # printing frequency\n if self.count % self.pfreq == 0:\n self.dump_output()\n self.barrier()\n\n # dump output if forced\n if self.force_output:\n self.dump_output()\n self.barrier()\n\n self.force_output = False\n\n # re-set the time-step to the old time step before it\n # was adjusted\n dt = old_dt\n\n # update progress bar\n bar.update(self.t)\n\n # update the time for all arrays\n self.update_particle_time()\n\n # compute the new time step across all processors\n if self.adaptive_timestep:\n\n # locally stable time step\n dt = self.integrator.compute_time_step(\n self.dt, self.cfl)\n\n # damp the initial solution\n if self.t < tdamp:\n dt *= 0.5 * (numpy.sin(numpy.pi*(-0.5+self.t/tdamp)) + 1.0)\n\n # set the globally stable time step\n if self.in_parallel:\n dt = self.pm.update_time_steps(dt)\n\n # adjust dt to land on final time\n if self.t + dt > self.tf:\n dt = self.tf - self.t\n\n # adjust dt to land on specific output times\n if output_at_times.size > 0:\n tdiff = output_at_times - self.t\n condition = (tdiff > 0) & (tdiff < dt)\n\n if numpy.any( condition ):\n output_time = output_at_times[ numpy.where(condition) ]\n\n # save the old time-step and compute the new\n # time-step to fall on the specified output time\n # instant\n old_dt = dt\n dt = float( output_time - self.t )\n\n self.force_output = True\n\n # set the adjusted time-step for the solver\n self.dt = dt\n\n if self.execute_commands is not None:\n if self.count % self.command_interval == 0:\n self.execute_commands(self)\n\n # close the progress bar\n bar.finish()\n\n # final output save\n self.dump_output()","title":""},{"docid":"a0a145553849371012e1201d8c01f8d3","score":"0.50810856","text":"def evaluate(solutions, results, rollouts=100): # pipaek : rollout 100 -> 10 , originally 100\n index_min = np.argmin(results)\n best_guess = solutions[index_min]\n restimates = []\n\n for s_id in range(rollouts):\n print('p_queue.put(), s_id=%d' % s_id)\n p_queue.put((s_id, best_guess))\n print('>>>rollout_routine!!')\n rollout_routine() # pipaek : 여기서도 p_queue.put 하자마자 바로 처리..\n\n print(\">>>Evaluating...\")\n for _ in tqdm(range(rollouts)):\n #while r_queue.empty():\n # sleep(.1) # pipaek : multi-process가 아니므로\n if not r_queue.empty(): # pipaek : 20180718 r_queue.get()에서 stuck되어 있는 것을 방지하기 위해 체크!!\n #print('r_queue.get()')\n #restimates.append(r_queue.get()[1])\n r_s_id, r = r_queue.get()\n print('in evaluate r_queue.get() r_s_id=%d, r_queue remain=%d' % (r_s_id, r_queue.qsize()))\n restimates.append(r)\n else:\n print('r_queue.empty() -> break!!')\n break\n\n return best_guess, np.mean(restimates), np.std(restimates)","title":""},{"docid":"5ce81706280fd507142c3a9781b2eaa7","score":"0.5079482","text":"def test_calc_final_slope():\n calc_final_slope(gridv1.data)\n assert True","title":""}],"string":"[\n {\n \"docid\": \"215366153e758085c8bc79a022ff8b6c\",\n \"score\": \"0.6252689\",\n \"text\": \"def calculate(self):\\n item_number = self.fields.curselection()\\n polynomial = self.json_data[\\\"functions\\\"][int(item_number[0])]\\n result_floating = None\\n result_interval = None\\n if self.json_data[\\\"method\\\"] == \\\"regula_falsi\\\":\\n try:\\n result_floating = regula_falsi(polynomial[\\\"a\\\"],\\n polynomial[\\\"b\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"]\\n )\\n except MyError as error:\\n result_floating = error.value\\n except ZeroDivisionError:\\n result_floating = \\\"Unfortunately, division by 0 happened\\\"\\n try:\\n result_interval = regula_falsi_interval(polynomial[\\\"a\\\"],\\n polynomial[\\\"b\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"]\\n )\\n result_interval = result_interval.format('%.20E')[9:]\\n result_interval = result_interval[:-1]\\n except MyError as error:\\n result_interval = error.value\\n elif self.json_data[\\\"method\\\"] == \\\"newton\\\":\\n try:\\n result_floating = newton(polynomial[\\\"x\\\"],\\n polynomial[\\\"epsilon\\\"],\\n polynomial[\\\"max_iterations\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"],\\n polynomial[\\\"derivative_coeff\\\"]\\n )\\n except ZeroDivisionError:\\n result_floating = \\\"Unfortunately, division by zero happened\\\"\\n except MyError as error:\\n result_floating = error.value\\n try:\\n result_interval = newton_interval(polynomial[\\\"x\\\"],\\n polynomial[\\\"epsilon\\\"],\\n polynomial[\\\"max_iterations\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"],\\n polynomial[\\\"derivative_coeff\\\"]\\n )\\n result_interval = result_interval.format('%.20E')[9:]\\n result_interval = result_interval[:-1]\\n except MyError as error:\\n result_interval = error.value\\n else:\\n result_interval = \\\"You propably\\\"\\n result_floating = \\\"gave me wrong JSON\\\"\\n self.show_result_floating.config(text=result_interval)\\n self.show_result_interval.config(text=result_floating)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0830761115955abace478bf63277abd\",\n \"score\": \"0.6215401\",\n \"text\": \"def calculate_results():\\n\\n print('Calculating results..')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07116c7437a3ffc65ec7ced8caca88f\",\n \"score\": \"0.6053697\",\n \"text\": \"def _calculate(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977d47a74d67da7d19a1b22a8e36d483\",\n \"score\": \"0.59952414\",\n \"text\": \"def do_calculation():\\n dT = config.getfloat(\\\"thermodynamics\\\", \\\"Tmin\\\")\\n try:\\n nmax = config.getint(\\\"thermodynamics\\\", \\\"nmax\\\")\\n except ValueError:\\n nmax = config.get(\\\"thermodynamics\\\", \\\"nmax\\\")\\n if not nmax == \\\"automatic\\\":\\n print(\\\"nmax has to be given as integer or \\\\\\\"automatic\\\\\\\"\\\") \\n try:\\n analytic_n0 = config.get(\\\"geometry\\\", \\\"analytic_n0\\\")\\n except ValueError:\\n print(\\\"analytic_n0 is not given correctly: \\\", analytic_n0)\\n analytic_n0 = True\\n if type(geom.r1) == list or type(geom.r2) == list:\\n analytic_n0 = False\\n Tmax = config.getfloat(\\\"thermodynamics\\\", \\\"Tmax\\\")\\n f = thermodynamics.finiteT(dT, nmax, Tmax, geom, analytic_n0)\\n quantity = config.get(\\\"thermodynamics\\\", \\\"quantity\\\")\\n result = eval(\\\"f.%s\\\" %quantity)\\n return result, f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05dd3eeb2320b01c30e115c6928fb5f\",\n \"score\": \"0.5965643\",\n \"text\": \"def error_calculation():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c8ad689e17b9cd1efc84e5ba7563bd\",\n \"score\": \"0.59115\",\n \"text\": \"def calc(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"387f4fedb43cb0287db06ecf37b13dc3\",\n \"score\": \"0.5901406\",\n \"text\": \"def calc():\\r\\n nonlocal func\\r\\n func = e.get()\\r\\n func = func.replace(\\\" \\\", \\\"\\\")\\r\\n func = toReversePolish(check(fixmult(findNeg(tokenizer(func)))))\\r\\n result.set(calculate(func, float(e2.get())))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ba20e0c2fecb5806fb08b0ca839ef9\",\n \"score\": \"0.58904105\",\n \"text\": \"def calculate(**kwargs):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a6f09fde14e1f16e33b66e2b65226c\",\n \"score\": \"0.58634573\",\n \"text\": \"def _postCalculate(self,retList):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eccf3447683a2dc735f5799fbff98c3\",\n \"score\": \"0.58459675\",\n \"text\": \"def calculate(self, *args, **kwargs) -> Any:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37e62a56b03972b4b59b80389e4d31d\",\n \"score\": \"0.5809612\",\n \"text\": \"def compute(self):\\n\\n self.setInitialValue()\\n self.partialResult = self.initialValue\\n self.elementResult = None\\n\\n self.updateFunctionPort()\\n\\n self.set_output('Result', self.partialResult)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d0a1c04bf6c68b3a921b158c6c03fb\",\n \"score\": \"0.57289326\",\n \"text\": \"def OnCalcDone(self, event):\\n if event.data is None:\\n self.log(\\\"No result from calculation!\\\")\\n self.PSF_HDUlist = None\\n else:\\n self.PSF_HDUlist = event.data\\n for w in ['Display PSF', 'Display profiles', 'Save PSF As...']:\\n self.widgets[w].Enable(True)\\n\\n self._refresh_window()\\n self.log(\\\"PSF calculation complete\\\")\\n # In either event, the worker is done\\n self.calcthread = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae7ea9f7af562970e90b832e81e037a\",\n \"score\": \"0.5720635\",\n \"text\": \"def calculate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd18a5ecb864880f5b28ca9d88e14303\",\n \"score\": \"0.56965977\",\n \"text\": \"def on_evaluate(self, interp):\\n return 0.000000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8afeea6242d6a4492b1855f0c649af5\",\n \"score\": \"0.5681769\",\n \"text\": \"def Calculate():\\r\\n values = GetValues()\\r\\n values = SolveEqn(values)\\r\\n DisplayValues(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38fe131032ab41802c0adfc8618129c\",\n \"score\": \"0.5660559\",\n \"text\": \"def _run_calculation(self):\\n # collect input\\n input_vs = OrderedDict()\\n for intf in self.input_intfs:\\n if intf.sibling_intf_allowed:\\n input_vs.setdefault(intf.family_name, []).append(intf.get_calculated_value())\\n else:\\n input_vs[intf] = intf.get_calculated_value()\\n\\n # run actual function defined by user\\n try:\\n results = self.calculate(*input_vs.values())\\n except Exception as e:\\n # set all outputs NULL\\n results = [NullValue(f\\\"calculation fail of {self}\\\")] * len(self.output_intfs)\\n # record and print error status\\n self._calculation_status = e\\n self.print_status()\\n raise\\n else:\\n # wrap singular result and match length\\n results = [results] if not isinstance(results, (list, tuple)) else list(results)\\n results += [NullValue(\\\"calculation didn't return enough values\\\")] * (len(self.output_intfs) - len(results))\\n self._calculation_status = ''\\n finally:\\n # push calculation result down to outputs\\n for result, intf in zip(results, self.output_intfs):\\n intf.set_provoke_value(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3918df179bdebeaa8326fa3d94277d7\",\n \"score\": \"0.5617621\",\n \"text\": \"def compute_value(self):\\n result = 0.00000000\\n if self.value1 and self.tasa:\\n result = (self.value1 * 1) / self.tasa \\n self.result = result\\n if not self.flag1:\\n self.id_record = self.env.context.get('active_id')\\n self.act_model = self.env.context.get('active_model')\\n self.flag1 = True\\n return {\\n 'view_mode': 'form',\\n 'res_model': 'compute.crypto.value',\\n 'res_id': self.id,\\n 'view_id': False,\\n 'type': 'ir.actions.act_window',\\n 'target': 'new',\\n 'context': {\\\"default_act_model\\\":self.act_model, \\\"default_result\\\": result, \\\"default_id_record\\\": self.id_record,},\\n }\\n else:\\n raise UserError(\\\"You need set all values first.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43cd1561f63a67897cd240b00229f59\",\n \"score\": \"0.55982625\",\n \"text\": \"def process_final(self, *args):\\n return self._sum / self._count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad11d6d48fea29f5ab6cfd496c2674f\",\n \"score\": \"0.5591634\",\n \"text\": \"def doCalculation()->JSON_RESPONSE: \\n\\n # Get JSON data from client\\n first:int = int(request.json['first'])\\n second:int = int(request.json['second'])\\n oper:str = request.json['operation']\\n\\n rst:int = performCalc(first, second, oper)\\n\\n # Prepare the return structure\\n result:dict[str,int] = {\\\"result\\\":rst}\\n\\n # Send JSON back to the client\\n return json.dumps(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37453e9332d7d6b5fbee48eb2fe7abf2\",\n \"score\": \"0.55907995\",\n \"text\": \"def handle_calculate(self, value):\\n #try:\\n\\n result = self.valid_number_check() * 1.6093\\n self.root.ids.output_label.text = str(result)\\n print(result)\\n #except: ValueError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11725e0d4aa23fdfc0173c1231d04274\",\n \"score\": \"0.5529947\",\n \"text\": \"def _compute(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85e680e952960f28dbfe0efb0c4bc700\",\n \"score\": \"0.5507777\",\n \"text\": \"def calc(self):\\n raise Exception(\\\"Workflow calculation not implemented yet\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ddd32b2c49f7c13ef977e81a00da9d\",\n \"score\": \"0.54745024\",\n \"text\": \"def calc_results():\\n global num_solutions\\n subt = 100*get_subt(0) + 10*get_subt(1) + get_subt(2)\\n diff = 100*get_diff(0) + 10*get_diff(1) + get_diff(2)\\n minu = 1000*get_minu(0) + 100*get_minu(1) + 10*get_minu(2) + get_minu(3)\\n if (subt + diff == minu) and (minu >= 1000):\\n print minu, \\\"-\\\", subt, \\\"==\\\", diff\\n num_solutions += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb90820e644680bc6d5e502aead6fde\",\n \"score\": \"0.5453361\",\n \"text\": \"def calculate(self, structure, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70fffa82abf3ab29e63d6e09fafe4f39\",\n \"score\": \"0.54355496\",\n \"text\": \"def _postCalculate(self,retList,time):\\n\\t\\tif len(retList) > 0: \\n\\t\\t\\tret = pandas.concat(retList,ignore_index=True)\\n\\t\\t\\tret = self.calculateThresholdPolygon(ret,time)\\n\\t\\t\\tret['percentEffected'] = 1.\\n\\t\\telse: \\n\\t\\t\\tret = None\\n\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94e16cfebaa89484f51c5df781ba26c\",\n \"score\": \"0.5427222\",\n \"text\": \"def __check_calc(self):\\n # nothing done\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166673f4de6c078d348a3759ee698ae2\",\n \"score\": \"0.54081076\",\n \"text\": \"def calculate(self, var_list):\\n # Run dummy calculation.\\n index = var_list[1].split(' ')[0]\\n submit = call('cp files/pw-scf_{}.out pw-scf.out'.format(index),\\n shell=True)\\n if submit != 0:\\n raise AssertionError('Could not submit job.')\\n energy = self.scrape_output(self.output_file)\\n\\n print('iteration {0} energy {1}'.format(index, energy))\\n\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ed09a292242295d9bd0a9dc162cc28\",\n \"score\": \"0.54075795\",\n \"text\": \"def calificacion_final(par1, par2, par3):\\n calfinal = par1 + par2 + par3\\n promedio = calfinal / 3\\n return promedio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8124e8fa74cc5964d34246b27cb46bf\",\n \"score\": \"0.5406774\",\n \"text\": \"def run_interp():\\n func = str(input('Enter string or trigonometric function: '))\\n print('Value l')\\n l_a = float_input(positive=True)\\n print('Value u')\\n u_b = float_input(positive=True)\\n print('Value n')\\n n_c = integer_input()\\n print('Your intermediate results: {}'.format(interp(func, l_a, u_b, n_c)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a407754c15e9a2256c75e9d606681f\",\n \"score\": \"0.53978\",\n \"text\": \"def __calc(self):\\n timezone = self.timezone # in hours, east is positive\\n longitude= self.long # in decimal degrees, east is positive\\n latitude = self.lat # in decimal degrees, north is positive\\n\\n time = self.time # percentage past midnight, i.e. noon is 0.5\\n day = self.day # daynumber 1=1/1/1900\\n \\n Jday =day+2415018.5+time-timezone/24 # Julian day\\n Jcent =(Jday-2451545)/36525 # Julian century\\n\\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\\n Mobliq = 23+(26+((21.448-Jcent*(46.815+Jcent*(0.00059-Jcent*0.001813))))/60)/60\\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent))+sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+sin(rad(3*Manom))*0.000289\\n Struelong= Mlong+Seqcent\\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\\n \\n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))-0.5*vary*vary*sin(4*rad(Mlong))-1.25*Eccent*Eccent*sin(2*rad(Manom)))\\n\\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))*cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\\n\\n self.solarnoon_t=(720-4*longitude-eqtime+timezone*60)/1440\\n self.sunrise_t =self.solarnoon_t-hourangle*4/1440\\n self.sunset_t =self.solarnoon_t+hourangle*4/1440\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9393ef12dcb8b1ff273f5733e0539ddb\",\n \"score\": \"0.53911626\",\n \"text\": \"def compute(self) -> float:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b254264ee7ae0c135148c1ce91717f2d\",\n \"score\": \"0.5388854\",\n \"text\": \"def Exemples_calculs():\\n while True :\\n (xA,yA)=input_coord(\\\"A\\\")\\n while True :\\n (xB,yB)=input_coord(\\\"B\\\")\\n if xB!=xA or yB!=yA :\\n break\\n else :\\n display(\\\"Il faut deux points distincts pour faire une droite ! Recommencez !\\\")\\n\\n intro=Latex(\\\"\\\"\\\"Nous allons calculer le coefficient directeur de la droite (AB) avec\\n A( {} ; {} ) et B( {} ; {} )\\\"\\\"\\\".format(xA,yA,xB,yB))\\n if xA==xB :\\n\\n calcul=Latex(\\\"\\\"\\\"Les droites parallèles à l'axe des ordonnées n'ont pas de coefficient directeur !\\n Il faut donc que vos points n'aient pas la même abscisse :\\\"\\\"\\\")\\n else :\\n frac=fractions.Fraction((yB-yA),(xB-xA))\\n if frac==int(frac):\\n frac=str(int(frac))\\n else :\\n frac=\\\"\\\\dfrac{{{numerator}}}{{{denominator}}}\\\".format(numerator=frac.numerator,denominator=frac.denominator)\\n calcul=Math(\\\"m= \\\\dfrac{{y_B-y_A}}{{x_B-x_A}}= \\\\dfrac{{{yB}-{yA}}}{{{xB}-{xA}}}= \\\\dfrac{{{yByA}}}{{{xBxA}}}={frac}\\\"\\n .format(xA=xA,yA=yA,xB=xB,yB=yB,yByA=yB-yA,xBxA=xB-xA,frac=frac))\\n Trace_droite(xA,yA,xB,yB)\\n display(intro,calcul)\\n s=input(\\\"Voulez-vous recommencer ?(o/n)\\\")\\n if s.lower() in ['n','non','no'] :\\n break\\n else :\\n clear_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd7780b29a79b6fd8d3b696267d256c\",\n \"score\": \"0.53845376\",\n \"text\": \"def processAlgorithm(self, parameters, context, feedback):\\n\\n # Load present raster\\n present_raster = self.parameterAsRasterLayer(parameters, self.INPUT1, context)\\n ds_present = gdal.Open(present_raster.dataProvider().dataSourceUri())\\n arr_present = ds_present.GetRasterBand(1).ReadAsArray()\\n # Clean negative values\\n arr_present[arr_present<0] = 0\\n\\n # Load future raster\\n future_raster = self.parameterAsRasterLayer(parameters, self.INPUT2, context)\\n ds_future = gdal.Open(future_raster.dataProvider().dataSourceUri())\\n arr_future = ds_future.GetRasterBand(1).ReadAsArray()\\n # Clean negative values\\n arr_future[arr_future<0] = 0\\n\\n print(arr_future)\\n print(np.unique(arr_future))\\n # Parameters\\n V = self.parameterAsDouble(parameters, self.INPUT5, context)\\n\\n r = 0\\n\\n c = 3\\n\\n area_pixel = self.parameterAsInt(parameters, self.PIXEL_RES, context) * self.parameterAsInt(\\n parameters, self.PIXEL_RES, context)\\n # Years\\n present = self.parameterAsInt(parameters, self.INPUT3, context)\\n future = self.parameterAsInt(parameters, self.INPUT4, context)\\n\\n # Calculate coeff sequestration\\n arr_diff = arr_future - arr_present\\n arr_diff_norm = arr_diff / float((future - present))\\n arr_years = np.array(range(0, future - present))\\n coeff = sum(1 / ((1 + r / 100) ** arr_years * ((1 + c / 100) ** arr_years)))\\n carbon_sequestration_value = V * arr_diff_norm * coeff\\n carbon_sequestration_difference = arr_future - arr_present\\n # Initialize and write on output raster\\n path_output = self.parameterAsString(parameters, self.OUTPUT, context)\\n file_output = path_output + '/SE_01_carbon_sequestration_delta_euro.tiff'\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n [cols, rows] = carbon_sequestration_value.shape\\n carbon_sequestration_difference_area = np.sum(carbon_sequestration_difference) / (cols * rows * area_pixel)\\n outdata = driver.Create(file_output, rows, cols, 1, gdal.GDT_Float64)\\n outdata.SetGeoTransform(ds_present.GetGeoTransform())##sets same geotransform as input\\n outdata.SetProjection(ds_present.GetProjection())##sets same projection as input\\n outdata.GetRasterBand(1).WriteArray(carbon_sequestration_value)\\n print(np.max(outdata.GetRasterBand(1).ReadAsArray()))\\n outdata.FlushCache() ##saves to disk!!\\n outdata = None\\n band = None\\n ds = None\\n print(np.sum(arr_present))\\n report_output = path_output + '/SE_sequestro_carbonio.txt'\\n f = open(report_output, \\\"w+\\\")\\n today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\\n f.write(\\\"Sommario dell'analisi del sequestro di carbonio\\\\n\\\")\\n f.write(\\\"Data: \\\" + today +\\\"\\\\n\\\\n\\\\n\\\")\\n f.write(\\\"Analisi stato di fatto\\\\n\\\\n\\\")\\n f.write(\\\"Anno corrente: %i \\\\n\\\" % (present))\\n f.write(\\\"Sequestro carbonio Stato attuale (ton Corg): %f \\\\n\\\" % (np.sum(arr_present)))\\n f.write(\\\"Valore medio del carbonio sequestrato per unità di superficie - Stato attuale (ton Corg/ha): : %f \\\\n\\\" % (\\n np.sum(arr_present) / (cols * rows * area_pixel) * 10000))\\n f.write(\\\"Valore totale del sequestro di carbonio (€): %f \\\\n\\\\n\\\\n\\\" % ((np.sum(arr_present)*V*coeff)/ float((future - present))))\\n f.write(\\\"Analisi stato di progetto\\\\n\\\\n\\\")\\n f.write(\\\"Anno progetto: %i \\\\n\\\" % (future))\\n f.write(\\\"Sequestro carbonio Stato di progetto (ton Corg): %f \\\\n\\\" % (np.sum(arr_future)))\\n f.write(\\\"Valore medio del carbonio sequestrato per unità di superficie - Stato di progetto (ton Corg/ha): %f \\\\n\\\" % (\\n np.sum(arr_future) / (cols * rows * area_pixel) * 10000))\\n f.write(\\\"Valore totale del sequestro di carbonio (€): %f \\\\n\\\\n\\\\n\\\" % ((np.sum(arr_future)*V*coeff) / float((future - present))))\\n f.write(\\\"Differenze tra stato di progetto e stato attuale\\\\n\\\\n\\\")\\n f.write(\\\"Periodo di analisi: %i - %i\\\\n\\\" % (present, future))\\n f.write(\\\"Differenza di sequestro carbonio (ton Corg): %f \\\\n\\\" % (np.sum(arr_diff)))\\n f.write(\\\"Differenza carbonio sequestrato per unità di superficie (ton Corg/ha): %f \\\\n\\\" % (\\n carbon_sequestration_difference_area * 10000))\\n f.write(\\\"Differenza in termini economici del SE di sequestro di carbonio (stato di progetto – stato attuale) (€):%d \\\\n\\\" % (\\n np.sum(carbon_sequestration_value)))\\n return {self.OUTPUT: carbon_sequestration_value}\\n\\n \\n # ----------------------------------------------------------------------------------- \\n # Copyright (c) 2021 Città di Torino.\\n # \\n # This material is free software: you can redistribute it and/or modify\\n # it under the terms of the GNU General Public License as published by\\n # the Free Software Foundation, either version 2 of the License, or\\n # (at your option) any later version.\\n # \\n # This program is distributed in the hope that it will be useful,\\n # but WITHOUT ANY WARRANTY; without even the implied warranty of\\n # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\\n # GNU General Public License for more details.\\n # \\n # You should have received a copy of the GNU General Public License\\n # along with this program. If not, see http://www.gnu.org/licenses.\\n # ----------------------------------------------------------------------------------- \",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddba8492bd02da00237d8e1464e5a126\",\n \"score\": \"0.5371956\",\n \"text\": \"def _run(self):\\n self._solve()\\n return self._ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11080b93adcf2859b7f8662c9f7e6fff\",\n \"score\": \"0.53654003\",\n \"text\": \"def _compute(y, t, N, b, k):\\n S, I, R = y\\n #print(t)\\n '''The Susceptible Equation'''\\n dSdt = -b[int(t)] * S * I / N\\n \\n '''The Infected Equation = -The Susceptible Equation - The Recovered Equation'''\\n dIdt = b[int(t)] * S * I / N - k * I\\n \\n '''The Recovered Equation'''\\n dRdt = k * I\\n return dSdt, dIdt, dRdt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd122062e4d6256eaf6d4aa2bbf05994\",\n \"score\": \"0.53594244\",\n \"text\": \"def integral(self, time1, time2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb010d6f5ea79ce4b09974b0bc0d8c9\",\n \"score\": \"0.5352363\",\n \"text\": \"def bulk_calc(self):\\n self.calc_csa()\\n self.calc_CCC()\\n self.calc_impedence()\\n self.calc_runVd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca2393dd1b3cb44fb5ee38d774cc7de\",\n \"score\": \"0.5340337\",\n \"text\": \"def compute_values(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca2393dd1b3cb44fb5ee38d774cc7de\",\n \"score\": \"0.5340337\",\n \"text\": \"def compute_values(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e6b33eeb34f158ca8b2ddbd6257563\",\n \"score\": \"0.5340099\",\n \"text\": \"def run_calculation(self):\\n\\n if w.ignore_history_false.isChecked():\\n\\n fp = w.input_file_line_edit.text()\\n\\n w.df, w.cache = read_from_file(fp, raw_mt=True)\\n # TODO: Check if the filter index is still necessary when matching string indices in match_target_mt below.\\n filter_index = w.df.query('status == \\\"mt\\\"').index\\n w.df = w.df.loc[filter_index]\\n\\n # Create boolean filter with all repetitions except for first occurrences set to True\\n filter_index = w.df.query('stype == \\\"source\\\"').duplicated('text')\\n w.df = w.df[-filter_index]\\n\\n else:\\n w.df = new_translation(w.df, w.cache, w.sample_object)\\n\\n df_mt = match_target_mt(w.df)\\n\\n w.cache = pe_density(df_mt, w.cache)\\n w.actionSave_as.setEnabled(True)\\n w.textOutput.setText(str('Your Post-Edit Density score is {:.3f}\\\\n'.format(w.cache['ped'])))\\n w.statistics()\\n\\n w.autosave()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db551929b95c9e61285499b44233dd40\",\n \"score\": \"0.5333397\",\n \"text\": \"def calc(self, x):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab45ce819ad5f27ffcf041865253bdf\",\n \"score\": \"0.53273827\",\n \"text\": \"def post(self, start_value, end_value):\\n start_value, end_value = int(start_value), int(end_value)\\n engine_name = get_engine_name()\\n if os.path.exists(engine_name):\\n self.__clean_hashes()\\n\\n if not os.path.exists(json_filename):\\n create_minion_json()\\n engine_db_name = 'sqlite:///' + engine_name\\n try:\\n p = Process(target=calculate_range, args=(start_value, end_value, engine_db_name, m_id))\\n p.start()\\n processes.append(p)\\n except:\\n print('Error: failed to run calculation on minion ' + str(m_id) + '.')\\n return {'status': 'error', 'message': 'calculation failed'}\\n update_status('busy')\\n return {'status': 'busy', 'message': 'calculation started'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f4e7059b0fd4d4a86a03694951504c\",\n \"score\": \"0.5327031\",\n \"text\": \"def compute():\\n message1[\\\"text\\\"] = \\\"\\\" #reset messages\\n message2[\\\"text\\\"] = \\\"\\\"\\n message4[\\\"text\\\"] = \\\"\\\"\\n message5[\\\"text\\\"] = \\\"\\\" \\n message6[\\\"text\\\"] = \\\"\\\" \\n message7[\\\"text\\\"] = \\\"\\\" \\n \\n smux=str(smux_entry.get()) #return the smux value in the tkinter entry \\n try:\\n volts_min=float(volt_min_entry.get()) # min voltage \\n volts_max=float(volt_max_entry.get()) # max voltage \\n R=float(resistance_entry.get()) # protection resistance \\n if abs(volts_max)>20 or abs(volts_min)>20:\\n texte5=\\\"abs(volt) <=10\\\"\\n message5[\\\"text\\\"] = texte5 \\n print(texte5) \\n else: \\n nb=int(point_number_entry.get()) #measures nb\\n if nb<1:\\n texte4=\\\"nb>0\\\"\\n message4[\\\"text\\\"] = texte4 \\n print(texte4) \\n else:\\n # delay=float(delay_entry.get()) #not needed\\n # print(smux,volts_min,volts_max,nb,delay) #used to debug\\n try:\\n complete_measure(volts_min,volts_max,nb,smux=smux,R=R,compliance=compliance)\\n texte7=\\\"Measures done\\\"\\n message7[\\\"text\\\"] = texte7 \\n print(texte7) \\n except Exception as ex:\\n texte6=\\\"Error from measurement detected: %s\\\" % str(ex) #display the error name\\n message6[\\\"text\\\"] = texte6 \\n print(texte6) \\n reset() \\n except:\\n texte2=\\\"floats\\\"\\n message2[\\\"text\\\"] = texte2 \\n print(texte2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1594a7502a7cae5142f313f0066993f3\",\n \"score\": \"0.53201216\",\n \"text\": \"def func(progress_remaining: float) -> float:\\n return initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c176dcfdf4a68d52eaa145558ca732f5\",\n \"score\": \"0.53149915\",\n \"text\": \"def calc(self, pvalues, lamb):\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c01c7f7208d17f565623b9efa9f2829\",\n \"score\": \"0.5313737\",\n \"text\": \"def _evaluate_function(self, inputs, aliases):\\n # TODO how to handle aliases????\\n method = self._sub_name\\n request = inputs.pop('request', None) # sometimes just the inputs will be used without the request, like in elasticity curves\\n result = self._obj.evaluate(method, request, inputs)\\n balance = result[0]\\n meta = result[1]\\n return balance, meta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7bf3ab90c18dae7b8553fba4cb5d3e\",\n \"score\": \"0.53023535\",\n \"text\": \"def calculate(self, mol: Molecule) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034d41aa8d8f3385ce7f1536f5f8b7b5\",\n \"score\": \"0.52879363\",\n \"text\": \"def __calc(self):\\n timezone = self.timezone # in hours, east is positive\\n longitude= self.long # in decimal degrees, east is positive\\n latitude = self.lat # in decimal degrees, north is positive\\n\\n time = self.time # percentage past midnight, i.e. noon is 0.5\\n day = self.day # daynumber 1=1/1/1900\\n \\n # NOAA Spreadsheet\\n Jday = day+2415018.5+time-timezone/24. # Julian day\\n Jcent = (Jday-2451545)/36525 # Julian century\\n\\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\\n Mobliq = 23+(26+((21.448-Jcent*(46.815+ \\\\\\n Jcent*(0.00059-Jcent*0.001813))))/60)/60\\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent)) \\\\\\n +sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+ \\\\\\n sin(rad(3*Manom))*0.000289\\n Struelong= Mlong+Seqcent\\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\\n \\n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+ \\\\\\n 4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))- \\\\\\n 0.5*vary*vary*sin(4*rad(Mlong))- \\\\\\n 1.25*Eccent*Eccent*sin(2*rad(Manom)))\\n\\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))* \\\\\\n cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\\n\\n self.solarnoon_t = (720-4*longitude-eqtime+timezone*60)/1440.\\n self.sunrise_t = self.solarnoon_t-hourangle*4/1440.\\n self.sunset_t = self.solarnoon_t+hourangle*4/1440.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee1d7a33c192af97967f2c8a2e3c713b\",\n \"score\": \"0.5286321\",\n \"text\": \"def execute_strategy(self):\\n data = self.results.copy().dropna()\\n data['position'] = np.sign(data['returns'].rolling(self.mom).mean())\\n data['strategy'] = data['position'].shift(1) * data['returns']\\n # determine when trades take place\\n # trades = data['position'].diff().fillna(0) != 0\\n # subtract transaction costs from return where trades take place\\n # data['strategy'][trades] -= self.tc\\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\\n self.results = data\\n # absolute performance of indicator\\n aperf = data['cstrategy'].ix[-1]\\n # out/under performance of indicator\\n operf = aperf - data['creturns'].ix[-1]\\n return round(aperf, 2), round(operf, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b38b01232a3f553a7ee77b5f648b7ec\",\n \"score\": \"0.5280257\",\n \"text\": \"def calc(self):\\n sorted_pop = self.sortpop()\\n initial_route = self.population[sorted_pop[0][0]]\\n distance = 1 / sorted_pop[0][1]\\n progress = [ distance ]\\n if callable(self.animate):\\n self.plot = True\\n individual = Individual(initial_route)\\n stop_animation = Event()\\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\\n else:\\n self.plot = False\\n if self.prn:\\n print(f\\\"Initial distance: {distance}\\\")\\n try:\\n if self.plot:\\n for i in range(self.generations):\\n population = self.next_gen()\\n sorted_pop = self.sortpop()\\n distance = 1 / sorted_pop[0][1]\\n progress.append(distance)\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n route = population[sorted_pop[0][0]]\\n individual.update(route)\\n else:\\n for i in range(self.generations):\\n population = self.next_gen()\\n distance = 1 / self.sortpop()[0][1]\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n \\n \\n except KeyboardInterrupt:\\n pass\\n try:\\n stop_animation.set()\\n except NameError:\\n pass\\n final_route_index = self.sortpop()[0][0]\\n final_route = population[final_route_index]\\n if self.prn:\\n print(\\\"Final route:\\\", final_route)\\n\\n return initial_route, final_route, distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b38b01232a3f553a7ee77b5f648b7ec\",\n \"score\": \"0.5280257\",\n \"text\": \"def calc(self):\\n sorted_pop = self.sortpop()\\n initial_route = self.population[sorted_pop[0][0]]\\n distance = 1 / sorted_pop[0][1]\\n progress = [ distance ]\\n if callable(self.animate):\\n self.plot = True\\n individual = Individual(initial_route)\\n stop_animation = Event()\\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\\n else:\\n self.plot = False\\n if self.prn:\\n print(f\\\"Initial distance: {distance}\\\")\\n try:\\n if self.plot:\\n for i in range(self.generations):\\n population = self.next_gen()\\n sorted_pop = self.sortpop()\\n distance = 1 / sorted_pop[0][1]\\n progress.append(distance)\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n route = population[sorted_pop[0][0]]\\n individual.update(route)\\n else:\\n for i in range(self.generations):\\n population = self.next_gen()\\n distance = 1 / self.sortpop()[0][1]\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n \\n \\n except KeyboardInterrupt:\\n pass\\n try:\\n stop_animation.set()\\n except NameError:\\n pass\\n final_route_index = self.sortpop()[0][0]\\n final_route = population[final_route_index]\\n if self.prn:\\n print(\\\"Final route:\\\", final_route)\\n\\n return initial_route, final_route, distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75cc49ea82b117f6b2d47af646f819d0\",\n \"score\": \"0.527877\",\n \"text\": \"def _evaluate(data, context):\\n # print \\\"Received : \\\", data, \\\" Sequence Number : \\\", context[\\\"data_sequence_no\\\"]\\n score = 0\\n secondary_score = 0\\n\\n for k in range(100):\\n time.sleep(random.randint(0,100)/1000.0)\\n percent_complete = k*1.0/100 * 100\\n update_progress(context, percent_complete, \\\"\\\")\\n # print \\\"Context Response Channel ::: \\\", context['response_channel']\\n # if k%20==0:\\n # # print \\\"Update : \\\", percent_complete\\n score += random.randint(1,100)*1.0/0.7 / 100\\n secondary_score += random.randint(1,100)*1.0/0.7 / 100\\n\\n _result_object = {\\n \\\"score\\\" : score,\\n \\\"secondary_score\\\" : secondary_score,\\n }\\n return _result_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0178d47f659aae4e12fbcd69db2468c\",\n \"score\": \"0.52750695\",\n \"text\": \"def on_pushButton_calc_clicked(self):\\r\\n # TODO: not implemented yet\\r\\n if self.str1 == '':\\r\\n a = 0\\r\\n else:\\r\\n a = float(self.str1)\\r\\n if self.str2 == '':\\r\\n b = 0\\r\\n else:\\r\\n b = float(self.str2)\\r\\n res = 0\\r\\n if self.op == '+':\\r\\n res = a + b\\r\\n elif self.op == '-':\\r\\n res = a - b\\r\\n elif self.op == '*':\\r\\n res = a * b\\r\\n elif self.op == '/':\\r\\n if b == 0:\\r\\n res = 0\\r\\n else:\\r\\n res = a / b\\r\\n\\r\\n self.lineEdit_result.setText(str(res))\\r\\n self.flag = 2\\r\\n\\r\\n # raise NotImplementedError\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320ea0ff97999dcee761fcc123dd114c\",\n \"score\": \"0.52461845\",\n \"text\": \"def calculate(self):\\n self.correct_extinction()\\n self.rdistance = calculate_radial_distance(self.position, self.center,\\n self.distance)\\n self.r23 = calculate_r23(self.fluxes)\\n self.calculate_OH()\\n self.SFR = calculate_sfr(self.distance, self.fluxes['halpha'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dd86b596bed8c6aac3dcd00301e5c7\",\n \"score\": \"0.5244022\",\n \"text\": \"def compute(self, today, assets, out, opens, closes):\\n out[:] = (opens[-1] - closes[0]) / closes[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dd86b596bed8c6aac3dcd00301e5c7\",\n \"score\": \"0.5244022\",\n \"text\": \"def compute(self, today, assets, out, opens, closes):\\n out[:] = (opens[-1] - closes[0]) / closes[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33836659fccc659301fd2466ce5d572\",\n \"score\": \"0.5242563\",\n \"text\": \"def run(self):\\n self.evaluate()\\n self.accumulate()\\n self.summarize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675188c454c830f80a91cf94327c7e3e\",\n \"score\": \"0.52329695\",\n \"text\": \"def process_final(self, *args):\\n mean = self._sum / self._count\\n mean = mean ** 2\\n variance = (self._sum_sq / self._count) - mean\\n if variance < 0:\\n return 0\\n else: \\n return variance ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598dc322c12f3f2638ec51cc2bcf894c\",\n \"score\": \"0.52291757\",\n \"text\": \"def func(progress_remaining: float):\\n return progress_remaining * initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14aa6575bdb29a5d01bfab980c63bd12\",\n \"score\": \"0.5228993\",\n \"text\": \"def linear_schedule(progress):\\n return 1 - progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27142e62aa568661d8bbdf43d69f8b6\",\n \"score\": \"0.5228111\",\n \"text\": \"def computation(self, results, *parameters):\\n\\n return self.add_computation(computation(results, *parameters))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7768d4b0f72db48a4418dd627fdd9c73\",\n \"score\": \"0.52274144\",\n \"text\": \"def _compute(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abf202e5afb80f54132416bc47cb3f1\",\n \"score\": \"0.52269894\",\n \"text\": \"def inspect_calculation(self):\\n converged_mc = True\\n self.ctx.restart_calc = self.ctx.calculation['retrieved']\\n if converged_mc:\\n self.report(\\\"Calculation converged, terminating the workflow\\\")\\n self.ctx.done = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e2904de8eda761267f0b480736bd0d\",\n \"score\": \"0.52208334\",\n \"text\": \"def thread_start_calculating(self, distance_value):\\n print(\\\"Breakpoint Distance value:{}\\\".format(distance_value))\\n logger.info('Slot:thread_start_calculating :', \\n extra={'sim_index': self._data_store.get_simulation_index()})\\n\\n if distance_value == 0:\\n logger.info('Slot:thread_start_calculating SINGLE STEP NOT IMPLEMENTED:', \\n extra={'sim_index': self._data_store.get_simulation_index()})\\n #TODO - finish this breakpoint case\\n self.simulationComputing = False\\n\\n elif distance_value == -1:\\n logger.info('Slot:thread_start_calculating RUN TO COMPLETION :', \\n extra={'sim_index': self._data_store.get_simulation_index()})\\n # set the breakpoint to be a very large number to indicate run to completion\\n self.breakpointDistance = 9999999\\n self.simulationComputing = True\\n else:\\n # run to the distance value point in the track\\n sim_index = self._data_store.get_simulation_index()\\n if distance_value > self._data_store.get_distance_at_index(sim_index) :\\n logger.info('Slot:thread_start_calculating RUN TO DISTANCE :', \\n extra={'sim_index': sim_index})\\n # requested breakpoint is further down the track\\n self.breakpointDistance = distance_value\\n # Start computing and acknowledge to MainWindow by sending a signal back \\n self.simulationThreadSignal.emit(\\\"Calculating...\\\")\\n # \\\"state\\\" variable indicating thread should be calculating\\n self.simulationComputing = True\\n else:\\n logger.info('Slot:thread_start_calculating PAST REQUESTED DISTANCE :', \\n extra={'sim_index': sim_index})\\n # simulation has already past this point in the track, don't proceed\\n self.simulationComputing = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4213b442ff6408444f7a99b113d82637\",\n \"score\": \"0.52148896\",\n \"text\": \"def _calculate_avg():\\n while (True):\\n _avg_num(_q_uwb_a, q_to_a, \\\"to_a\\\", 8)\\n _avg_num(_q_uwb_b, q_to_b, \\\"to_b\\\", 8)\\n time.sleep(0.015)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a06c78d7eb16247bf403e1389083f14\",\n \"score\": \"0.52093\",\n \"text\": \"def integrate(self, t):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee388bb9a884aa4cea1bef1a52b88fa\",\n \"score\": \"0.5208004\",\n \"text\": \"def __call__(self, *args, **kwargs) -> Any:\\n return self.calculate(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a19b61964963525e9d4ad7f5b8669f\",\n \"score\": \"0.5207855\",\n \"text\": \"def func(progress_remaining: float) -> float:\\n return progress_remaining * initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a19b61964963525e9d4ad7f5b8669f\",\n \"score\": \"0.5207855\",\n \"text\": \"def func(progress_remaining: float) -> float:\\n return progress_remaining * initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54047f7afc5fafe5f39587ee486749fd\",\n \"score\": \"0.5203203\",\n \"text\": \"def __cost_function(self, sense='single'):\\n\\n self.SnappyHexMeshrun()\\n self.Optimisationrun()\\n if sense==\\\"single\\\":\\n t = self.RunUtilities()\\n return p\\n elif sense==\\\"multi\\\":\\n t, p = self.RunUtilities(sense=sense)\\n return t, p\\n else:\\n print(\\\"Invalid input for sense: \\\", sense)\\n print(\\\"Available options are: 'single' or 'multi'\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30447d2a14ea9b45e16c20607615711e\",\n \"score\": \"0.5196032\",\n \"text\": \"def calculate_derivative(param):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1106def928c6e8ab53fb563e3bff385\",\n \"score\": \"0.51902646\",\n \"text\": \"def evaluate(self) -> None:\\n\\n self._generate_paths()\\n self._compute_payoff_fn()\\n self._is_evaluated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b28047d6734cbcd08da68de38b381b\",\n \"score\": \"0.51892775\",\n \"text\": \"def done(self, result):\\n output_file = result['output']\\n GLF = result['GLF']\\n Qt.QApplication.restoreOverrideCursor()\\n self.statusBar().showMessage('GLF = {:0.1f}%'.format(GLF*100))\\n # Show a MessageBox.\\n QtWidgets.QMessageBox.information(self, 'Ausgabe erzeugt',\\n str(output_file),\\n QtWidgets.QMessageBox.Ok)\\n self.start_button.setEnabled(True)\\n\\n if self.plot_timeseries:\\n try: # Create a line plot of the time series in a new window\\n self.plot_window = PlotWindow()\\n self.plot_window.update_lineplot(result['df_sum'])\\n self.plot_window.show()\\n except Exception as e:\\n logger.exception(e)\\n QtWidgets.QMessageBox.critical(self, 'Fehler', str(e),\\n QtWidgets.QMessageBox.Ok)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daceaadc42197c7d5f2b3753e3192aee\",\n \"score\": \"0.5189256\",\n \"text\": \"def when_computation_done(self, out, param):\\r\\n tkMessageBox.showinfo('Computation is done', out)\\r\\n self.set_state('normal')\\r\\n\\r\\n if self.plugin_type == \\\"Chimera\\\":\\r\\n self.profile_wd = os.path.realpath(\\r\\n os.path.normcase(self.main_parameters[1] + \\\"/chimera/\\\"))\\r\\n\\r\\n tunnels_script = os.path.join(self.profile_wd, 'complex.py')\\r\\n paths_script = os.path.join(self.wd, 'paths.py')\\r\\n pores_script = os.path.join(self.wd, 'pores.py')\\r\\n autopores_script = os.path.join(self.wd, 'autopores.py')\\r\\n dom = parse(os.path.join(self.xml_wd, 'tunnels.xml'))\\r\\n\\r\\n for e in dom.getElementsByTagName('Exception'):\\r\\n tkMessageBox.showinfo('Exception', e.getAttribute('Text'))\\r\\n\\r\\n if self.plugin_type == \\\"PyMOL\\\":\\r\\n\\r\\n if param == \\\"pores\\\":\\r\\n self.group_objects(tunnels_script, 'Pores', \\\"MOLE_tunnels\\\")\\r\\n else:\\r\\n self.group_objects(tunnels_script, 'Tunnels', \\\"MOLE_pores\\\")\\r\\n\\r\\n self.group_objects(paths_script, 'Paths', 'MOLE_Paths')\\r\\n self.group_objects(pores_script, 'Pores', 'MOLE_Pores')\\r\\n self.group_objects(autopores_script, 'AutoPores', 'MOLE_AutoPores')\\r\\n plugin().show(\\\"cgo\\\", \\\"Interior*\\\")\\r\\n plugin().set_view(self.original_view)\\r\\n\\r\\n else:\\r\\n plugin.run(tunnels_script)\\r\\n\\r\\n self.set_state('normal')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc4859850d902bbbabda8a6f64c2238\",\n \"score\": \"0.51880544\",\n \"text\": \"def render(self, request):\\n self._calculatePi(request).addCallback(self._gotResult, request)\\n return server.NOT_DONE_YET\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7174f03ecc0b1631771bcd650b043bf5\",\n \"score\": \"0.5187847\",\n \"text\": \"def _postCalculate(self,retList,time):\\n\\t\\tmodList = []\\n\\t\\tfor vals in retList: \\n\\t\\t\\tvals[\\\"percentEffected\\\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\\n\\t\\t\\tvals = self.calculateThresholdPolygon(vals,time)\\n\\t\\t\\tmodList.append(vals)\\n\\n\\t\\treturn pandas.concat(modList,ignore_index=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7174f03ecc0b1631771bcd650b043bf5\",\n \"score\": \"0.5187847\",\n \"text\": \"def _postCalculate(self,retList,time):\\n\\t\\tmodList = []\\n\\t\\tfor vals in retList: \\n\\t\\t\\tvals[\\\"percentEffected\\\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\\n\\t\\t\\tvals = self.calculateThresholdPolygon(vals,time)\\n\\t\\t\\tmodList.append(vals)\\n\\n\\t\\treturn pandas.concat(modList,ignore_index=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b0c8b9bdb177f85aa5d45a556a0e22\",\n \"score\": \"0.5185649\",\n \"text\": \"def Calculate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b214cdc4438aebfa3d63f75c8e78c910\",\n \"score\": \"0.5171598\",\n \"text\": \"def compute_pi_main(workers, accuracy, batchsize):\\n n_total = 0\\n s_total = 0\\n tasks_queue = mp.Queue() # each task (or batch) is represented by the batchsize\\n done_queue = mp.Queue() # result of each task is the number of successes in that batch\\n processes = mp.Pool(workers, initializer=compute_pi_worker, initargs=(tasks_queue, done_queue))\\n\\n for _ in range(workers):\\n tasks_queue.put(batchsize)\\n\\n while True:\\n s = done_queue.get(block=True)\\n s_total += s\\n n_total += batchsize\\n pi_est = (4.0 * s_total) / n_total\\n error = abs(math.pi - pi_est)\\n\\n if error > accuracy:\\n tasks_queue.put(batchsize)\\n else:\\n processes.terminate()\\n processes.join()\\n break\\n\\n print(\\\"Estimate:\\\", pi_est)\\n print(\\\"Error:\\\", error)\\n print(\\\"Steps:\\\", n_total)\\n return pi_est, error, n_total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67da759295be1801c868c36af3dd3052\",\n \"score\": \"0.5168196\",\n \"text\": \"def calculate_float(self, default: int):\\n self.total_float = self.latest_finish - self.earliest_finish\\n\\n if not self.successors:\\n self.free_float = default - self.earliest_finish\\n else:\\n self.free_float = min(s.earliest_start for s in self.successors) - self.earliest_finish\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce99a8d17f1502a9ede8378469d864af\",\n \"score\": \"0.5166104\",\n \"text\": \"def calculate(s):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff35d5e25b8885f6ecc9ff6d4200e5b\",\n \"score\": \"0.5155409\",\n \"text\": \"def handler(event, context):\\n try:\\n parsed_event = CalculatorEvent(**event)\\n except ValidationError as exp:\\n raise Exception(json.dumps(f\\\"[BadRequest] {exp}\\\"))\\n\\n if parsed_event.year not in crypto_prices[parsed_event.query]['old_prices']:\\n raise Exception(json.dumps(f\\\"[BadRequest] Data for requested year not available\\\"))\\n\\n\\n current_value = (parsed_event.amount/crypto_prices[parsed_event.query]['old_prices'][parsed_event.year]) * crypto_prices[parsed_event.query]['current_price']\\n \\n return round(current_value,2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a28c312c92b4217aed7e3c0932a1bfe0\",\n \"score\": \"0.5150363\",\n \"text\": \"def execute_strategy(self):\\n data = self.results.copy().dropna()\\n data['position'] = np.where(data['SMA1'] > data['SMA2'], 1, 0)\\n data['strategy'] = data['position'].shift(1) * data['returns']\\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\\n self.results = data\\n # absolute performance of indicator\\n aperf = data['cstrategy'].ix[-1]\\n # out/under performance of indicator\\n operf = aperf - data['creturns'].ix[-1]\\n return round(aperf, 2), round(operf, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3563d9154880d9234b4a9a1fead992\",\n \"score\": \"0.5143086\",\n \"text\": \"def evaluate_metric(self, assignments):\\n sleep_seconds = 10\\n print('{0} - Sleeping for {1} seconds to simulate expensive computation...'.format(threading.current_thread(), sleep_seconds))\\n time.sleep(sleep_seconds)\\n return sigopt.examples.franke_function(assignments['x1'], assignments['x2'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0647838977b755e73ac53acb14e7ed14\",\n \"score\": \"0.5141489\",\n \"text\": \"def execute_strategy(self):\\n data = self.results.copy().dropna()\\n data['sma'] = data['returns'].rolling(self.sma).mean()\\n data['distance'] = data['close'] - data['sma']\\n # sell signals\\n data['position'] = np.where(data['distance'] > self.threshold, -1, np.nan)\\n # buy signals\\n data['position'] = np.where(data['distance'] < -self.threshold, 1, data['position'])\\n # cross of current price and SMA (zero distance)\\n data['position'] = np.where(data['distance'] * data['distance'].shift(1) < 0, 0, data['position'])\\n data['position'] = data['position'].ffill().fillna(0)\\n data['strategy'] = data['position'].shift(1) * data['returns']\\n # determine when trades take place\\n # trades = data['position'].diff().fillna(0) != 0\\n # subtract transaction costs from return where trades take place\\n # data['strategy'][trades] -= self.tc\\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\\n self.results = data\\n # absolute performance of indicator\\n aperf = data['cstrategy'].ix[-1]\\n # out/under performance of indicator\\n operf = aperf - data['creturns'].ix[-1]\\n return round(aperf, 2), round(operf, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab977317ec5b5501427a8c5d5683e375\",\n \"score\": \"0.51393765\",\n \"text\": \"def calculate(self):\\n super().calculate()\\n\\n\\n tr_key = Constants.get_key(\\\"TR\\\")\\n trn_key = Constants.get_key(\\\"TRn\\\")\\n\\n dm_plus_key = Constants.get_key(\\\"DMplus\\\")\\n dm_plus_n_key = Constants.get_key(\\\"DMplusN\\\")\\n\\n dm_minus_key = Constants.get_key(\\\"DMminus\\\")\\n dm_minus_n_key = Constants.get_key(\\\"DMminusN\\\")\\n di_plus_n_key = Constants.get_key(\\\"DIplusN\\\")\\n di_minus_n_key = Constants.get_key(\\\"DIminusN\\\")\\n di_diff_key = Constants.get_key(\\\"DIdiff\\\")\\n di_sum_key = Constants.get_key(\\\"DIsum\\\")\\n dx_key = Constants.get_key(\\\"DX\\\")\\n\\n df_result = []\\n\\n for ticker in self.tickers:\\n\\n df_data_atr = self.df[[ticker]].copy()\\n atr_ind = ATR(df=df_data_atr, n=14)\\n\\n df_data = self.df[ticker].copy()\\n # the period parameter of ATR function does not matter because period does not influence TR calculation\\n df_data[tr_key] = atr_ind.calculate()[ticker][[tr_key]]\\n\\n df_data[dm_plus_key] = \\\\\\n np.where(\\n (df_data[self.high_key] - df_data[self.high_key].shift(1)) >\\n (df_data[self.low_key].shift(1) - df_data[self.low_key]),\\n df_data[self.high_key] - df_data[self.high_key].shift(1),\\n 0)\\n\\n df_data[dm_plus_key] = \\\\\\n np.where(\\n df_data[dm_plus_key] < 0,\\n 0,\\n df_data[dm_plus_key])\\n\\n df_data[dm_minus_key] = \\\\\\n np.where((df_data[self.low_key].shift(1) - df_data[self.low_key]) >\\n (df_data[self.high_key] - df_data[self.high_key].shift(1)),\\n df_data[self.low_key].shift(1) - df_data[self.low_key],\\n 0)\\n\\n df_data[dm_minus_key] = np.where(df_data[dm_minus_key] < 0, 0, df_data[dm_minus_key])\\n\\n TRn = []\\n DMplusN = []\\n DMminusN = []\\n TR = df_data[tr_key].tolist()\\n DMplus = df_data[dm_plus_key].tolist()\\n DMminus = df_data[dm_minus_key].tolist()\\n\\n for i in range(len(df_data)):\\n if i < self.n:\\n TRn.append(np.NaN)\\n DMplusN.append(np.NaN)\\n DMminusN.append(np.NaN)\\n elif i == self.n:\\n TRn.append(df_data[tr_key].rolling(self.n).sum().tolist()[self.n])\\n DMplusN.append(df_data[dm_plus_key].rolling(self.n).sum().tolist()[self.n])\\n DMminusN.append(df_data[dm_minus_key].rolling(self.n).sum().tolist()[self.n])\\n elif i > self.n:\\n TRn.append(TRn[i - 1] - (TRn[i - 1] / 14) + TR[i])\\n DMplusN.append(DMplusN[i - 1] - (DMplusN[i - 1] / 14) + DMplus[i])\\n DMminusN.append(DMminusN[i - 1] - (DMminusN[i - 1] / 14) + DMminus[i])\\n\\n df_data[trn_key] = np.array(TRn)\\n df_data[dm_plus_n_key] = np.array(DMplusN)\\n df_data[dm_minus_n_key] = np.array(DMminusN)\\n df_data[di_plus_n_key] = 100 * (df_data[dm_plus_n_key] / df_data[trn_key])\\n df_data[di_minus_n_key] = 100 * (df_data[dm_minus_n_key] / df_data[trn_key])\\n df_data[di_diff_key] = abs(df_data[di_plus_n_key] - df_data[di_minus_n_key])\\n df_data[di_sum_key] = df_data[di_plus_n_key] + df_data[di_minus_n_key]\\n df_data[dx_key] = 100 * (df_data[di_diff_key] / df_data[di_sum_key])\\n ADX = []\\n DX = df_data[dx_key].tolist()\\n for j in range(len(df_data)):\\n if j < 2 * self.n - 1:\\n ADX.append(np.NaN)\\n elif j == 2 * self.n - 1:\\n ADX.append(df_data[dx_key][j - self.n + 1:j + 1].mean())\\n elif j > 2 * self.n - 1:\\n ADX.append(((self.n - 1) * ADX[j - 1] + DX[j]) / self.n)\\n df_data[self.indicator_key] = np.array(ADX)\\n\\n df_result.append(df_data.loc[:, [self.indicator_key]])\\n\\n self.df = pd.concat(df_result, axis=1, keys=self.tickers)\\n\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eafd971da2d043a48e336831a0a68557\",\n \"score\": \"0.5138379\",\n \"text\": \"def process_queue_fringes(self):\\n\\n try:\\n result = self.queue.get(0)\\n\\n if result == \\\"ABORT\\\":\\n self.addlog(\\\"Mission aborted.\\\", self.warningcolor2)\\n self.abortmission.set(0)\\n self.totaltime = 0\\n self.removeprogressbar()\\n return\\n\\n # Show result of the task if needed\\n self.entry_23.delete(0, END)\\n self.entry_23.insert(0, '{0:.2f}'.format(result[0]))\\n self.entry_24.delete(0, END)\\n self.entry_24.insert(0, '{0:.2f}'.format(result[1]))\\n\\n self.setoffsets()\\n\\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list = [], [], [], []\\n\\n for i in range(1, self.layernumber + 1):\\n self.layertype_list.append(getattr(self, \\\"layertypevar{}\\\".format(i)).get())\\n self.entry_x_list.append(float(getattr(self, \\\"entry_x_{}\\\".format(i)).get()))\\n self.entry_d_list.append(float(getattr(self, \\\"entry_d_{}\\\".format(i)).get()))\\n self.checklayer_list.append(int(getattr(self, \\\"checklayer{}\\\".format(i)).get()))\\n\\n fitobject = FIT_FTIR(self.Temp, self.wavenumbers_cut, self.trans_cut, self.entry_d_0.get(),\\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list,\\n float(self.entry_21.get()), self.angle,\\n float(self.entry_23.get()), float(self.entry_24.get()), self.subtype, 2,\\n self.listbox, self.progress_var, self.wn_beingcalculated,\\n self.FTIRplot, self.absorptionplot, self.canvas, self.blindcal, self.abortmission)\\n self.peakvalues_fit = fitobject.returnpeakvalues()\\n\\n self.addlog('Fitting fringes complete. Total time: {:.1f}s. MSE={}'.format(self.totaltime, result[2]))\\n\\n self.totaltime = 0\\n\\n self.try_remove_fitline(self.fitline_trans)\\n self.fitline_trans = self.plot_and_show(self.FTIRplot, self.fitline_trans, 0, self.wavenumbers_cut,\\n self.peakvalues_fit, 'r', '', 'Transmission (%)', 0, 'upper right')\\n\\n self.removeprogressbar()\\n\\n except queue.Empty:\\n self.totaltime += 0.1\\n self.after(100, self.process_queue_fringes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5820bfa6b2e06e705d2fad4ec1847994\",\n \"score\": \"0.5132884\",\n \"text\": \"def compute(\\n self,\\n blade_cost,\\n blade_mass,\\n hub_system_cost,\\n hub_system_mass,\\n nacelle_mass,\\n nacelle_cost,\\n tower_cost,\\n tower_mass,\\n blade_number=3,\\n offshore=True,\\n ):\\n\\n # Variables\\n self.blade_cost = (\\n blade_cost # Float(0.0, units='USD', iotype='in', desc='cost for a single wind turbine blade')\\n )\\n self.blade_mass = blade_mass # Float(0.0, units='kg', iotype='in', desc='mass for a single wind turbine blade')\\n self.hub_system_cost = hub_system_cost # Float(0.0, units='USD', iotype='in', desc='hub system cost')\\n self.hub_system_mass = hub_system_mass # Float(0.0, units='kg', iotype='in', desc='hub system mass')\\n self.nacelle_mass = nacelle_mass # Float(0.0, units='kg', iotype='in', desc='nacelle mass')\\n self.nacelle_cost = nacelle_cost # Float(0.0, units='USD', iotype='in', desc='nacelle cost')\\n self.tower_cost = tower_cost # Float(0.0, units='USD', iotype='in', desc='cost for a tower')\\n self.tower_mass = tower_mass # Float(0.0, units='kg', iotype='in', desc='mass for a turbine tower')\\n\\n # Parameters (and ignored inputs)\\n self.blade_number = blade_number # Int(3, iotype='in', desc = 'number of rotor blades')\\n self.offshore = offshore # Bool(False, iotype='in', desc= 'boolean for offshore')\\n\\n # high level output assignment\\n self.rotor_mass = self.blade_mass * self.blade_number + self.hub_system_mass\\n self.rotor_cost = self.blade_cost * self.blade_number + self.hub_system_cost\\n self.turbine_mass = self.rotor_mass + self.nacelle_mass + self.tower_mass\\n self.turbine_cost = self.rotor_cost + self.nacelle_cost + self.tower_cost\\n\\n if self.offshore:\\n self.turbine_cost *= 1.1\\n\\n # derivatives\\n self.d_mass_d_blade_mass = self.blade_number\\n self.d_mass_d_hub_mass = 1.0\\n self.d_mass_d_nacelle_mass = 1.0\\n self.d_mass_d_tower_mass = 1.0\\n\\n if self.offshore:\\n self.d_cost_d_blade_cost = 1.1 * self.blade_number\\n self.d_cost_d_hub_cost = 1.1\\n self.d_cost_d_nacelle_cost = 1.1\\n self.d_cost_d_tower_cost = 1.1\\n else:\\n self.d_cost_d_blade_cost = self.blade_number\\n self.d_cost_d_hub_cost = 1.0\\n self.d_cost_d_nacelle_cost = 1.0\\n self.d_cost_d_tower_cost = 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2818a75b1740c04b7c7c9442d420e04\",\n \"score\": \"0.5128805\",\n \"text\": \"def getval(self):\\n \\n if self.averagingAttemps > 1:\\n result = 0.0\\n readings = 0.0\\n readingsc = 0.0\\n\\t readings_array = {}\\n for i in range(0, self.averagingAttemps):\\n reading = self.getReading()\\n if (reading != 0 and reading != 1023):\\n if self.averagingMethod == \\\"max\\\":\\n if reading > readings:\\n\\t\\t\\t \\\"\\\"\\\"print(str(reading) + \\\" higher as \\\" + str(readings))\\\"\\\"\\\"\\n readings = reading\\n else:\\n readings = readings + reading\\n readingsc = readingsc + 1\\n\\t\\t if reading in readings_array:\\n\\t\\t readings_array[reading] = readings_array[reading] + 1\\n\\t\\t else:\\n\\t\\t\\treadings_array[reading] = 1\\n time.sleep(self.averagingTimeout)\\n if readingsc == 0:\\n result = self.getReading()\\n else:\\n\\t\\tif self.averagingMethod == \\\"most\\\":\\n\\t\\t sorted_x = sorted(readings_array.items(), key=operator.itemgetter(1), reverse=True)\\n\\t\\t result = sorted_x[0][0]\\n elif self.averagingMethod == \\\"max\\\":\\n result = readings\\n else:\\n result = readings / readingsc\\n else:\\n result = self.getReading()\\n \\n if result == 0:\\n msg = \\\"Error: Check wiring for the \\\" + self.sensorname\\n msg += \\\" measurement, no voltage detected on ADC input \\\"\\n msg += str(self.adcpin)\\n print(msg)\\n return None\\n if result == 1023:\\n if self.sensorname == \\\"LDR\\\":\\n # Carrying on with 1023 gives divide by zero error below\\n result = 1022\\n else:\\n msg = \\\"Error: Check wiring for the \\\" + self.sensorname\\n msg += \\\" measurement, full voltage detected on ADC input \\\"\\n msg += str(self.adcpin)\\n print(msg)\\n return None\\n\\n\\tif self.sensorname == \\\"WindDirection\\\":\\n\\t return result\\n\\n\\t\\\"\\\"\\\"print(self.sensorname + \\\" \\\" + str(self.sensorvoltage))\\\"\\\"\\\"\\n\\n vout = float(result)/1023 * self.sensorvoltage\\n if self.pulldown != None:\\n resout = (self.pulldown * self.sensorvoltage) / vout - self.pulldown\\n elif self.pullup != None:\\n resout = self.pullup / ((self.sensorvoltage / vout) - 1)\\n else:\\n resout = vout * 1000\\n return resout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"621c70cbb28fa1e4a9c190a799b7b1e6\",\n \"score\": \"0.51253504\",\n \"text\": \"def compute(self): # pylint: disable=E0202\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"621c70cbb28fa1e4a9c190a799b7b1e6\",\n \"score\": \"0.51253504\",\n \"text\": \"def compute(self): # pylint: disable=E0202\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01998f20124e1512ef226959005fc520\",\n \"score\": \"0.512176\",\n \"text\": \"def calculate_callback(self):\\n\\n # Create the interpreter\\n from asteval import Interpreter\\n aeval = Interpreter()\\n\\n # Grab the calculation from the text box which the user wants to do\\n calculation = str(self.calculation_text.text())\\n\\n lhs = calculation.split('=')[0].strip()\\n\\n # Use the package asteval to do the calculation, we are going to\\n # assume here that the lhs of the equals sign is going to be the output named variable\\n\\n try:\\n if lhs in self.data_components:\\n raise KeyError('{} is already in the data components, use a different variable on the left hand side.'.format(lhs))\\n\\n # Pull in the required data and run the calculation\\n for dc in self.data_components:\\n if dc in calculation:\\n aeval.symtable[dc] = self.data[dc]\\n aeval(calculation)\\n\\n # Pull out the output data and add to the proper drop-downs\\n out_data = aeval.symtable[lhs]\\n self.data.add_component(out_data, lhs)\\n\\n # Add the new data to the list of available data for arithemitic operations\\n self.data_components.append(lhs)\\n\\n self.close()\\n\\n except KeyError as e:\\n self.calculation_text.setStyleSheet(\\\"background-color: rgba(255, 0, 0, 128);\\\")\\n\\n # Display the error in the Qt popup\\n if aeval.error_msg:\\n self.error_label_text.setText('{}'.format(aeval.error_msg))\\n else:\\n self.error_label_text.setText('{}'.format(e))\\n\\n self.error_label_text.setStyleSheet(\\\"color: rgba(255, 0, 0, 128)\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad61c57a35376b206230052921c1336\",\n \"score\": \"0.512145\",\n \"text\": \"def update(self,current_value):\\n\\n self.error = self.set_point - current_value\\n\\n self.P_value = self.Kp * self.error\\n if (self.last_value >= current_value):\\n change = self.error - self.last_error\\n else:\\n change = 0.0\\n\\n if self.error > 0.0:\\n self.I_value = self.Integrator * self.Ki\\n else:\\n self.I_value = (self.Integrator * self.Ki)\\n\\n\\n #self.D_value = self.Kd * ( self.error - self.Derivator)\\n self.D_value = self.Kd * change\\n self.Derivator = self.error\\n\\n self.Integrator = self.Integrator + self.error\\n\\n if self.Integrator > self.Integrator_max:\\n self.Integrator = self.Integrator_max\\n elif self.Integrator < self.Integrator_min:\\n self.Integrator = self.Integrator_min\\n\\n self.last_error = self.error\\n self.last_value = current_value\\n\\n PID = self.P_value + self.I_value + self.D_value\\n\\n self._z_data[\\\"data\\\"][\\\"P\\\"] = self.P_value\\n self._z_data[\\\"data\\\"][\\\"I\\\"] = self.I_value\\n self._z_data[\\\"data\\\"][\\\"D\\\"] = self.D_value\\n self._z_data[\\\"data\\\"][\\\"E\\\"] = self.error\\n self._z_data[\\\"data\\\"][\\\"SP\\\"] = self.set_point\\n self._z_data[\\\"data\\\"][\\\"OUT\\\"] = PID\\n\\n if self._zmq:\\n try:\\n self._zmq.send_json(self._z_data, zmq.NOBLOCK)\\n except zmq.error.Again:\\n pass\\n\\n return PID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"472ea9d79a812c81d9dbbb43bbe45dc9\",\n \"score\": \"0.50997347\",\n \"text\": \"def _calc_result(self):\\n import mcmc\\n\\n g=zeros(2)\\n gcov=zeros((2,2))\\n gsens = zeros(2)\\n\\n if self.eta:\\n eta1vals,eta2vals=self.trials[:,2], self.trials[:,3]\\n g1vals,g2vals=self._get_gvals_from_eta(eta1vals,eta2vals)\\n else:\\n g1vals=self.trials[:,2]\\n g2vals=self.trials[:,3]\\n\\n prior = self.gprior(g1vals,g2vals)\\n dpri_by_g1 = self.gprior.dbyg1(g1vals,g2vals)\\n dpri_by_g2 = self.gprior.dbyg2(g1vals,g2vals)\\n\\n psum = prior.sum()\\n\\n g0=None\\n gcov0=None\\n if self.when_prior=='after':\\n # this will be eta not g if using that parametrization\\n g0,gcov0 = mcmc.extract_stats(self.trials[:,2:4])\\n\\n pars, pcov = mcmc.extract_stats(self.trials,weights=prior)\\n # we need to multiply each by the prior\\n \\\"\\\"\\\"\\n g[0] = (g1vals*prior).sum()/psum\\n g[1] = (g2vals*prior).sum()/psum\\n\\n\\n gcov[0,0] = (g1diff**2*prior).sum()/psum\\n gcov[0,1] = (g1diff*g2diff*prior).sum()/psum\\n gcov[1,0] = gcov[0,1]\\n gcov[1,1] = (g2diff**2*prior).sum()/psum\\n \\\"\\\"\\\"\\n\\n g[:] = pars[2:4]\\n gcov[:,:] = pcov[2:4, 2:4]\\n\\n # now the sensitivity is \\n # sum( (-g) L*dP/dg )\\n # ----------------------\\n # sum(L*P)\\n #\\n # the likelihood is already in the points\\n\\n g1diff = g[0]-g1vals\\n g2diff = g[1]-g2vals\\n gsens[0] = 1. - (g1diff*dpri_by_g1).sum()/psum\\n gsens[1] = 1. - (g2diff*dpri_by_g2).sum()/psum\\n else:\\n # prior is already in the distribution of\\n # points. This is simpler for most things but\\n # for sensitivity we need a factor of (1/P)dP/de\\n\\n pars,pcov = mcmc.extract_stats(self.trials)\\n #g, gcov = mcmc.extract_stats(self.trials[:,2:2+2])\\n\\n g[:] = pars[2:4]\\n gcov[:,:] = pcov[2:4, 2:4]\\n\\n g1diff = g[0]-g1vals\\n g2diff = g[1]-g2vals\\n\\n w,=where(prior > 0)\\n if w.size == 0:\\n raise ValueError(\\\"no prior values > 0!\\\")\\n\\n gsens[0]= 1.-(g1diff[w]*dpri_by_g1[w]/prior[w]).mean()\\n gsens[1]= 1.-(g2diff[w]*dpri_by_g2[w]/prior[w]).mean()\\n\\n \\n arates = self._emcee_sampler.acceptance_fraction\\n arate = arates.mean()\\n #print 'acceptance rate:',w.size/float(self.trials.size)\\n\\n # weighted s/n based on the most likely point\\n s2n,loglike,chi2per,dof,prob=self._calculate_maxlike_stats()\\n if self.eta:\\n g0name='eta'\\n else:\\n g0name='g'\\n\\n Tmean=pars[4]\\n Terr=sqrt(pcov[4,4])\\n Ts2n=pars[4]/sqrt(pcov[4,4])\\n #print 'T s/n:',Ts2n\\n self._result={'model':self.model,\\n 'g':g,\\n 'gcov':gcov,\\n 'gsens':gsens,\\n g0name+'0':g0,\\n g0name+'cov0':gcov0,\\n 'pars':pars,\\n 'pcov':pcov,\\n 'Tmean':Tmean,\\n 'Terr':Terr,\\n 'Ts2n':Ts2n,\\n 'arate':arate,\\n 's2n_w':s2n,\\n 'loglike':loglike,\\n 'chi2per':chi2per,\\n 'dof':dof,\\n 'fit_prob':prob}\\n #wlog(\\\"arate:\\\",self._result['arate'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e1d43e967bd2ad66fe32eeb4f512d8\",\n \"score\": \"0.5096479\",\n \"text\": \"def interpolate(self, double: float) -> float:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ccb6ccc97f671fb95181dc45fb44f8\",\n \"score\": \"0.5088823\",\n \"text\": \"def postsolve(self, lbfgs_result):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7d46d8d9ffadc367fe21aad2f8c9bb\",\n \"score\": \"0.50876844\",\n \"text\": \"def result_relative(self, result1, result2):\\n value1 = self.objective.get_mean(result1)\\n value2 = self.objective.get_mean(result2)\\n if value2 == 0:\\n return float('inf') * value1\\n return value1 / value2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bc1f58636b2ddf43f909825f55e8d1e\",\n \"score\": \"0.50867045\",\n \"text\": \"def calcul_throughput(self, spectrumrangeairmass):\\n \\\"\\\"\\\"\\n data_mag = spectrumrangeairmass.data_mag.T\\n data_mag -= np.log(self.data_calspec)\\n spectrumrangeairmass.data_mag = data_mag.T\\n\\n data_order2 = spectrumrangeairmass.order2.T\\n data_order2 /= self.data_calspec\\n spectrumrangeairmass.order2 = data_order2.T\\n #print(self.data_calspec)\\n #print(self.data_calspec.T)\\n #print(self.data_calspec.T @ self.data_calspec)\\n MULT = np.zeros((len(self.data_calspec),len(self.data_calspec)))\\n for i in range(len(self.data_calspec)):\\n for j in range(len(self.data_calspec)):\\n MULT[i][j], MULT[j][i] = self.data_calspec[i] * self.data_calspec[j], self.data_calspec[i] * self.data_calspec[j]\\n\\n spectrumrangeairmass.INVCOV *= MULT\\n spectrumrangeairmass.cov /= MULT\\n \\\"\\\"\\\"\\n\\n self.slope, self.ord, self.err_slope, self.err_ord = spectrumrangeairmass.bouguer_line()\\n disp = np.loadtxt(self.rep_disp_name)\\n Data_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1], kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1]/100, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n tel = np.loadtxt(self.rep_tel_name)\\n Data_tel = sp.interpolate.interp1d(tel.T[0], tel.T[1], kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err_tel = sp.interpolate.interp1d(tel.T[0], tel.T[2], kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n \\\"\\\"\\\"\\n data_disp_new_lambda = Data_disp(self.new_lambda)\\n err_disp_new_lambda = Err_disp(self.new_lambda)\\n data_tel_new_lambda = Data_tel(self.new_lambda)\\n err_tel_new_lambda = Err_tel(self.new_lambda)\\n \\\"\\\"\\\"\\n self.data_bouguer = np.exp(self.ord)\\n err_bouguer = self.err_ord * self.data_bouguer\\n self.data = self.data_bouguer / self.data_calspec\\n self.lambdas = self.new_lambda\\n self.err = err_bouguer / self.data_calspec\\n\\n # self.data = filter_detect_lines(self.lambdas, self.data, self.plot_filt, self.save_filter)\\n\\n Data = sp.interpolate.interp1d(self.lambdas, self.data, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err = sp.interpolate.interp1d(self.lambdas, self.err, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n self.lambdas = self.new_lambda\\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\\n\\n self.data_tel = Data_tel(self.lambdas)\\n self.data_tel_err = Err_tel(self.lambdas)\\n self.data_disp = Data_disp(self.lambdas)\\n self.data_disp_err = Err_disp(self.lambdas)\\n self.data = Data(self.lambdas)\\n self.err = Err(self.lambdas)\\n self.data_bouguer = Data_bouguer(self.lambdas)\\n # self.data = sp.signal.savgol_filter(self.data, 111, 2)\\n\\n #spectrumrangeairmass.archi_mega_fit()\\n if self.order2 and not self.mega_fit:\\n self.slope2, self.ord2, self.err_slope2, self.err_ord2, self.A2, self.A2_err = spectrumrangeairmass.bouguer_line_order2()\\n self.data_bouguer = np.exp(self.ord2)\\n err_bouguer = self.err_ord2 * self.data_bouguer\\n self.data_order2 = self.data_bouguer / self.data_calspec\\n self.lambdas = self.new_lambda\\n self.err_order2 = err_bouguer / self.data_calspec\\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n self.lambdas = self.new_lambda\\n self.data_order2 = Data(self.lambdas)\\n self.err_order2 = Err(self.lambdas)\\n self.data_bouguer = Data_bouguer(self.lambdas)\\n # self.data_order2 = sp.signal.savgol_filter(self.data_order2, 111, 2)\\n\\n if self.mega_fit:\\n self.ord2, self.err_order2 = spectrumrangeairmass.megafit_emcee()\\n print(self.ord2)\\n print(len(self.ord2))\\n self.data_order2 = self.ord2 / self.data_calspec\\n self.err_order2 = self.err_order2 / self.data_calspec\\n self.lambdas = self.new_lambda\\n\\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n\\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.ord2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n\\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n\\n self.lambdas = self.new_lambda\\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\\n self.data_order2 = Data(self.lambdas)\\n self.err_order2 = Err(self.lambdas)\\n self.data_bouguer = Data_bouguer(self.lambdas)\\n self.data_order2[21:-21] = sp.signal.savgol_filter(self.data_order2[21:-21], 11, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83954746e4599a297a386e46edd8832a\",\n \"score\": \"0.5083051\",\n \"text\": \"def solve(self, show_progress=True):\\n dt = self.dt\\n old_dt = self.dt\\n\\n if self.in_parallel:\\n show = False\\n else:\\n show = show_progress\\n bar = FloatPBar(self.t, self.tf, show=show)\\n\\n # Initial solution\\n self.dump_output()\\n self.barrier() # everybody waits for this to complete\\n\\n # initial solution damping time\\n tdamp = self.tdamp\\n\\n # Compute the accelerations once for the predictor corrector\\n # integrator to work correctly at the first time step.\\n self.acceleration_eval.compute(self.t, dt)\\n\\n # solution output times\\n output_at_times = numpy.array( self.output_at_times )\\n\\n while self.t < self.tf:\\n\\n # perform any pre step functions\\n for func in self.pre_step_functions:\\n func.eval(self)\\n\\n if self.rank == 0:\\n logger.debug(\\n \\\"Iteration=%d, time=%f, timestep=%f\\\" % \\\\\\n (self.count, self.t, dt)\\n )\\n # perform the integration and update the time.\\n #print 'Solver Iteration', self.count, dt, self.t, tdamp\\n self.integrator.step(self.t, dt)\\n\\n # perform any post step functions\\n for func in self.post_step_functions:\\n func.eval(self)\\n\\n # update time and iteration counters if successfully\\n # integrated\\n self.t += dt\\n self.count += 1\\n\\n # dump output if the iteration number is a multiple of the\\n # printing frequency\\n if self.count % self.pfreq == 0:\\n self.dump_output()\\n self.barrier()\\n\\n # dump output if forced\\n if self.force_output:\\n self.dump_output()\\n self.barrier()\\n\\n self.force_output = False\\n\\n # re-set the time-step to the old time step before it\\n # was adjusted\\n dt = old_dt\\n\\n # update progress bar\\n bar.update(self.t)\\n\\n # update the time for all arrays\\n self.update_particle_time()\\n\\n # compute the new time step across all processors\\n if self.adaptive_timestep:\\n\\n # locally stable time step\\n dt = self.integrator.compute_time_step(\\n self.dt, self.cfl)\\n\\n # damp the initial solution\\n if self.t < tdamp:\\n dt *= 0.5 * (numpy.sin(numpy.pi*(-0.5+self.t/tdamp)) + 1.0)\\n\\n # set the globally stable time step\\n if self.in_parallel:\\n dt = self.pm.update_time_steps(dt)\\n\\n # adjust dt to land on final time\\n if self.t + dt > self.tf:\\n dt = self.tf - self.t\\n\\n # adjust dt to land on specific output times\\n if output_at_times.size > 0:\\n tdiff = output_at_times - self.t\\n condition = (tdiff > 0) & (tdiff < dt)\\n\\n if numpy.any( condition ):\\n output_time = output_at_times[ numpy.where(condition) ]\\n\\n # save the old time-step and compute the new\\n # time-step to fall on the specified output time\\n # instant\\n old_dt = dt\\n dt = float( output_time - self.t )\\n\\n self.force_output = True\\n\\n # set the adjusted time-step for the solver\\n self.dt = dt\\n\\n if self.execute_commands is not None:\\n if self.count % self.command_interval == 0:\\n self.execute_commands(self)\\n\\n # close the progress bar\\n bar.finish()\\n\\n # final output save\\n self.dump_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a145553849371012e1201d8c01f8d3\",\n \"score\": \"0.50810856\",\n \"text\": \"def evaluate(solutions, results, rollouts=100): # pipaek : rollout 100 -> 10 , originally 100\\n index_min = np.argmin(results)\\n best_guess = solutions[index_min]\\n restimates = []\\n\\n for s_id in range(rollouts):\\n print('p_queue.put(), s_id=%d' % s_id)\\n p_queue.put((s_id, best_guess))\\n print('>>>rollout_routine!!')\\n rollout_routine() # pipaek : 여기서도 p_queue.put 하자마자 바로 처리..\\n\\n print(\\\">>>Evaluating...\\\")\\n for _ in tqdm(range(rollouts)):\\n #while r_queue.empty():\\n # sleep(.1) # pipaek : multi-process가 아니므로\\n if not r_queue.empty(): # pipaek : 20180718 r_queue.get()에서 stuck되어 있는 것을 방지하기 위해 체크!!\\n #print('r_queue.get()')\\n #restimates.append(r_queue.get()[1])\\n r_s_id, r = r_queue.get()\\n print('in evaluate r_queue.get() r_s_id=%d, r_queue remain=%d' % (r_s_id, r_queue.qsize()))\\n restimates.append(r)\\n else:\\n print('r_queue.empty() -> break!!')\\n break\\n\\n return best_guess, np.mean(restimates), np.std(restimates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce81706280fd507142c3a9781b2eaa7\",\n \"score\": \"0.5079482\",\n \"text\": \"def test_calc_final_slope():\\n calc_final_slope(gridv1.data)\\n assert True\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":190,"cells":{"query_id":{"kind":"string","value":"3510ac42e4ef7ae99ff81d34dfc62eaa"},"query":{"kind":"string","value":"Get List of field names when a container name is given. Container names if repeated in the snapshot metadata, will always contain the same internal metadata, so the parent of the container doesn't matter. container_field_name The container of whose List of field names are required List of field names"},"positive_passages":{"kind":"list like","value":[{"docid":"19a19c2a810e3f9fd8aa2036cfc9c0ba","score":"0.8049855","text":"def data_field_container_field_name_list_get(self, container_field_name):\n def dfs_helper(dict_to_find_in, container_field_name, ret_list):\n if dict_to_find_in is None:\n return\n if container_field_name in dict_to_find_in:\n for field_name, field in list(dict_to_find_in[container_field_name].container_dict.items()):\n ret_list.append(field.name)\n return\n else:\n for field_name, field in list(dict_to_find_in.items()):\n return dfs_helper(field.container_dict, container_field_name, ret_list)\n ret_list = []\n dfs_helper(self.data_dict, container_field_name, ret_list)\n return ret_list","title":""}],"string":"[\n {\n \"docid\": \"19a19c2a810e3f9fd8aa2036cfc9c0ba\",\n \"score\": \"0.8049855\",\n \"text\": \"def data_field_container_field_name_list_get(self, container_field_name):\\n def dfs_helper(dict_to_find_in, container_field_name, ret_list):\\n if dict_to_find_in is None:\\n return\\n if container_field_name in dict_to_find_in:\\n for field_name, field in list(dict_to_find_in[container_field_name].container_dict.items()):\\n ret_list.append(field.name)\\n return\\n else:\\n for field_name, field in list(dict_to_find_in.items()):\\n return dfs_helper(field.container_dict, container_field_name, ret_list)\\n ret_list = []\\n dfs_helper(self.data_dict, container_field_name, ret_list)\\n return ret_list\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"30c975d7087e5406555f5a29ddfc5d2f","score":"0.61451197","text":"def _get_list(container, name):\n\n try:\n return container[name]\n except KeyError:\n return []","title":""},{"docid":"f10c4c60092e3146611d5e905294f06d","score":"0.5811185","text":"def _field_names_(self):\n ret = []\n\n def _merge(f, rename):\n if not f:\n return\n\n if not rename:\n ret.extend([n for n, _ in f])\n else:\n for n, _ in f:\n new_n = rename.get(n, None)\n ret.append(new_n) if new_n else ret.append(n)\n\n for b in self.__class__.__mro__:\n _merge(\n getattr(b, '__swagger_fields__', None),\n getattr(b, '__swagger_rename__', None)\n )\n\n return ret","title":""},{"docid":"437d5d3800f0ce0776ef979ed27f1d87","score":"0.5712169","text":"def fields_container(self):\n return self._fields_container","title":""},{"docid":"437d5d3800f0ce0776ef979ed27f1d87","score":"0.5712169","text":"def fields_container(self):\n return self._fields_container","title":""},{"docid":"437d5d3800f0ce0776ef979ed27f1d87","score":"0.5712169","text":"def fields_container(self):\n return self._fields_container","title":""},{"docid":"b1441683baa86cccd7332f66af646866","score":"0.5601177","text":"def get_container_entries(self, container, prefix):\n try:\n headers = self._headers()\n swift_objects = self.conn.get_container(container,\n prefix=prefix,\n full_listing=True,\n headers=headers)[1]\n except socket.error as err:\n raise exception.SwiftConnectionFailed(reason=err)\n swift_object_names = [swift_obj['name'] for swift_obj in swift_objects]\n return swift_object_names","title":""},{"docid":"4d8c066c5cc911b046911ca8ae57b598","score":"0.5588793","text":"def GetContainerFields(request, layer):\n icon = request.get('icon')\n icon = util.GetInstance(model.Resource, icon, layer, required=False)\n if icon and icon.type != 'icon':\n raise util.BadRequest('Invalid (non-icon) resource specified.')\n\n folder = request.get('folder')\n folder = util.GetInstance(model.Folder, folder, layer, required=False)\n folder_index = request.get('folder_index', None)\n if folder_index is not None: folder_index = int(folder_index)\n\n region = request.get('region')\n region = util.GetInstance(model.Region, region, layer, required=False)\n\n return {\n 'icon': icon,\n 'folder': folder,\n 'folder_index': folder_index,\n 'region': region,\n 'name': request.get('name', None),\n 'description': request.get('description', None),\n 'item_type': request.get('item_type', None),\n 'custom_kml': request.get('custom_kml', None)\n }","title":""},{"docid":"9876c3a1dd2807b45e9acca543a94186","score":"0.551232","text":"def list_containers(self):\r\n if self._container_names is None:\r\n visited = set([id(self)])\r\n if hasattr(self, '_parent'): # fix for weird unpickling bug\r\n visited.add(id(self.parent))\r\n names = []\r\n for n, v in self.__dict__.items():\r\n if is_instance(v, Container) and id(v) not in visited:\r\n visited.add(id(v))\r\n names.append(n)\r\n self._container_names = names\r\n return self._container_names","title":""},{"docid":"b619bd1f3f17cba660356f07ab0102bd","score":"0.55013055","text":"def field_names(self) -> List[str]:\n return [x.name for x in self.fields.all()]","title":""},{"docid":"49731fc3f52b6436791ae50e39483390","score":"0.5474507","text":"def get_list_of_fields_for_node(node_name):\n return _value_to_node_fields[node_name]","title":""},{"docid":"1f462a5031e0d1a0da6991690b93c2ef","score":"0.5405363","text":"def flatten_container(self, container):\n for names in ARG_MAP.values():\n if names[TransformationTypes.CHRONOS.value]['name'] and \\\n '.' in names[TransformationTypes.CHRONOS.value]['name']:\n chronos_dotted_name = names[TransformationTypes.CHRONOS.value]['name']\n parts = chronos_dotted_name.split('.')\n\n if parts[-2] == 'parameters':\n # Special lookup for docker parameters\n common_type = names[TransformationTypes.CHRONOS.value].get('type')\n result = self._lookup_parameter(container, parts[-1], common_type)\n if result:\n container[chronos_dotted_name] = result\n else:\n result = lookup_nested_dict(container, *parts)\n if result:\n container[chronos_dotted_name] = result\n return container","title":""},{"docid":"8d7120d4d10f2d84545c761e0ae1cb6e","score":"0.53995526","text":"def data_field_name_list_get(self, action_name=None):\n ret_field_name_list = []\n if action_name is not None:\n ret_field_name_list=[field_name_ for field_name_, data_ in \n list(self.action_dict[self.action_dict_allname[action_name]].data_dict.items())]\n for data_name_, data_ in list(self.data_dict.items()):\n ret_field_name_list.append(data_name_)\n return ret_field_name_list","title":""},{"docid":"613b688dbcd887d59bea4ed78d7433a4","score":"0.5380753","text":"def get_field_names(self):\n\n field_names = set()\n\n # Add field names from each component.\n for component in self.components:\n field_names.update(component.get_field_names())\n\n return list(field_names)","title":""},{"docid":"5390ba244ba96352a1d6c9f1db4879ab","score":"0.53681356","text":"def get_field_names(self, format_string):\n return self.name_pattern.findall(format_string)","title":""},{"docid":"d862b976f871cbcee5188ed6f6cb4e95","score":"0.536005","text":"def get_container_list(self, source):\n storage_client = None\n container = None\n\n if 'fileGroup' in source:\n # Input data stored in auto-storage\n storage_client = self.resolve_storage_account()\n container = _get_container_name(source['fileGroup'])\n elif 'containerUrl' in source:\n uri = urlsplit(source['containerUrl'])\n if not uri.query:\n raise ValueError('Invalid container url.')\n storage_account_name = uri.netloc.split('.')[0]\n sas_token = uri.query\n storage_client = BlockBlobService(account_name=storage_account_name,\n sas_token=sas_token)\n container = uri.pathname.split('/')[1]\n else:\n raise ValueError('Unknown source.')\n\n return self.list_container_contents(source, container, storage_client)","title":""},{"docid":"a0dfb7fb0ea38f44ad645e2780ee0cca","score":"0.535109","text":"def getAllFields(layer):\n fields = layer.fields()\n fieldLists = []\n for field in fields:\n fieldLists.append(field.name())\n return fieldLists","title":""},{"docid":"7d0822d0c9a73ae8026a352e77623b96","score":"0.5347515","text":"def get_field_names(self):\n raise NotImplementedError","title":""},{"docid":"cb33f81c7565c3cc3e972a6b30431442","score":"0.52762234","text":"def get_field_names(self):\n\n field_names = set(sch_field_id_to_name.values())\n for component in self.components:\n field_names.update(component.get_field_names())\n return list(field_names)","title":""},{"docid":"70dc8549c75e78f1846fe87ad0c2d70a","score":"0.5217515","text":"def getFieldNames(self):\n return self.fieldNames","title":""},{"docid":"5ee8470c4b9687c8fe862ec39f66c2a4","score":"0.5198622","text":"def get_field_names(\n project_type,\n table_name,\n):\n # sql = \"SELECT * FROM [{}]\".format(table_name)\n sql = \"PRAGMA table_info( [{}] );\".format(table_name)\n\n data = run_query(sql, project_type)\n if data:\n return [x.get(\"name\") for x in data]\n else:\n return []","title":""},{"docid":"b674a72881c343ca3e65f83343c954a6","score":"0.51946247","text":"def data_field_name_list_get(self, *unused):\n return [data_name_ for data_name_, data_ in list(self.data_dict.items())]","title":""},{"docid":"b2434dc23b4245c69caa615e0fd07d5d","score":"0.5171875","text":"def get_containers_names(client, prefix=DOCK_CONTAINER_NAME_PREFIX_BTC):\n return [container.name for container in client.containers.list(\"all\") if prefix in container.name]","title":""},{"docid":"7b77ae64070e8055affd29d40113c872","score":"0.5169079","text":"def _get_list_field_names(self):\n\n all_fields = ()\n if not self.list_fields:\n all_fields = self._get_default_list_fields()\n else:\n all_fields = list(self.list_fields)\n if self._is_list_pk_required():\n self._inject_primary_key(all_fields)\n\n all_fields = self._extract_field_names(all_fields, allow_string=True)\n if self.list_indexed is True:\n index_name = self.list_index_name\n if index_name in (None, ''):\n index_name = config_services.get('api', 'schema', 'index_name')\n\n all_fields = list(all_fields)\n all_fields.insert(0, index_name)\n all_fields = tuple(all_fields)\n\n duplicates = misc_utils.get_duplicates(all_fields)\n if duplicates:\n raise DuplicateListFieldNamesError('There are some duplicate field names '\n 'in \"list_fields\" of [{admin}] class. '\n 'this will make the find result incorrect. '\n 'please remove duplicate fields or set '\n 'unique labels for them: {duplicates}'\n .format(admin=self, duplicates=duplicates))\n\n return all_fields","title":""},{"docid":"c48e94f111a0111011a30f30aff4c88a","score":"0.5166085","text":"def _field_names(self):\n names = []\n self._read_token('{')\n if self._is_field_name():\n names.append(self._read_token().value)\n while self._next_token().kind == ',':\n self._read_token(',')\n if self._is_field_name():\n name == self._read_token().value\n if name in names:\n raise Exception(\"Repeated field name: %s\" % name)\n names.append(name)\n else:\n raise Exception(\"field name expected, found %s\" %\n self._next_token().value)\n self._read_token('}')\n return names","title":""},{"docid":"55843e0a9f4a7fe56c2087de43caa3c4","score":"0.5160062","text":"def field_names(self):\r\n return self._names","title":""},{"docid":"25d221ff14221b5be396cb2fd14b695f","score":"0.51470417","text":"def get_field_names(self):\n\n field_names = set()\n for f in self.fields:\n try:\n field_names.add(unquote(f[\"name\"]))\n except KeyError:\n pass\n field_names.discard(\"\")\n return field_names","title":""},{"docid":"2c14e57dbd84db2ec879f1e1adf15d5f","score":"0.5139172","text":"def list(self) -> list:\n labels = []\n for k, v in self._labels.items():\n labels.append(\"{}={}\".format(k, v))\n\n #TODO: return names\n containers = self.client.containers.list(filters={\"label\": labels})\n return list(map(lambda s: s.name, containers))","title":""},{"docid":"b6c2df00f269215d05e90947cd79bb3f","score":"0.51312125","text":"def export_field_names(self) -> List[str]:\n return [f.name for f in self.fields.all()]","title":""},{"docid":"b7aae64056ae801052747746a5423abd","score":"0.5128673","text":"def fields(self):\r\n try:\r\n self._field_name_cache\r\n except AttributeError:\r\n self._fill_fields_cache()\r\n return self._field_name_cache","title":""},{"docid":"e7d6dc8c87f29a148679a18694ce2684","score":"0.51274216","text":"def get_field_names(self):\n\n return {f[\"name\"] for f in self.fields}","title":""},{"docid":"3a797639a31c6fabc6d35cc0ef6e6484","score":"0.5123486","text":"def get_versioned_field_names(self, version_number):\n return []","title":""},{"docid":"7b77852ac9831875ec06d5fe55dde409","score":"0.5094264","text":"def get_field_names(self):\n field_names = []\n for field in self.data_fields.all():\n field_names.append(field.name)\n return field_names","title":""},{"docid":"aa3d8bbaa16f7920fd4f9fcc1da8b28c","score":"0.50611734","text":"def get_column_names_list(record_info_in: sdk.RecordInfo) -> List[str]:\n return [field.name for field in record_info_in]","title":""},{"docid":"704297f96eac44cd01a88ca465d5b805","score":"0.5060997","text":"def field_names(self):\n return frozenset(self._children.keys())","title":""},{"docid":"f64563f859bbb1a7450773db6425e8bd","score":"0.5054078","text":"def list(self, container_name: str) -> None:\n try:\n res = requests.get(self.url + 'list', json={'container_name': container_name})\n if res.status_code == 200:\n self.logger.info(f\"Listing files of container {container_name}\")\n return json.loads(res.content)\n else:\n return None\n except ConnectionError as ce:\n raise ce","title":""},{"docid":"d3dfb9b4a47eed06de1d9963222288d2","score":"0.5046736","text":"def get_field_names(self):\n return self.fields.values()","title":""},{"docid":"f23b83640bca611ba5973965e550f962","score":"0.50379735","text":"def get_container_name(self, container_id):\n if not os.path.isdir(self.containersdir):\n return []\n link_list = []\n for fname in os.listdir(self.containersdir):\n container = self.containersdir + \"/\" + fname\n if os.path.islink(container):\n real_container = os.readlink(container)\n if os.path.basename(real_container) == container_id:\n link_list.append(fname)\n return link_list","title":""},{"docid":"4636b4e0dcd5bfaf02de8b0ee0c445e3","score":"0.5035241","text":"def fieldsNames_get(self,recordset):\r\n\t\tif recordset.upper().startswith('SELECT'):\r\n\t\t\trs=self.__open_recordset(self.conn, recordset, maxrecords=1)\r\n\t\telse:\r\n\t\t\trs=self.__open_recordset(self.conn, '['+recordset+']', maxrecords=1)\r\n\r\n\t\tif rs.Fields.Count>0:\r\n\t\t\tfields=[rs.Fields.Item(item).Name for item in range(rs.Fields.Count)]\r\n\t\telse:\r\n\t\t\tfields=[]\r\n\t\treturn fields","title":""},{"docid":"50f25ecf9257d7170423361e610a667f","score":"0.50169486","text":"def get_fields(self, field_name):\r\n if field_name not in self.fields:\r\n raise OGRException('invalid field name: %s' % field_name)\r\n return [feat.get(field_name) for feat in self]","title":""},{"docid":"7d34c0f7d6e4e777f87562f000824e1f","score":"0.4993697","text":"def get_fieldnames_from_cursor(cursor: Cursor) -> List[str]:\n return [i[0] for i in cursor.description]","title":""},{"docid":"4d3d15cdef20f82411536b5fcbf3cacc","score":"0.49833426","text":"def fields(self):\r\n return [force_text(capi.get_field_name(capi.get_field_defn(self._ldefn, i)),\r\n self._ds.encoding, strings_only=True)\r\n for i in xrange(self.num_fields)]","title":""},{"docid":"cc2836b21ba3be6d217c40e9a0be9a63","score":"0.49688077","text":"def _extract_field_names(self, fields, allow_string=True):\n\n names = []\n for item in fields:\n if self._is_valid_field(item):\n names.append(item.key)\n elif allow_string is True and isinstance(item, str) \\\n and self._is_valid_method(item):\n names.append(item)\n else:\n message = 'Provided field [{field}] is not a valid value. ' \\\n 'it must be a column attribute{sign} ' \\\n 'expression level hybrid property{end}'\n\n if allow_string is True:\n message = message.format(\n field=str(item), sign=',',\n end=' or a string representing a method name of [{admin}] class.')\n else:\n message = message.format(field=str(item), sign=' or',\n end='.')\n\n raise InvalidListFieldError(message.format(admin=self))\n\n return tuple(names)","title":""},{"docid":"f24c7585e363e44d0434d30c329dbd1e","score":"0.49683803","text":"def field_list(cls) -> List[str]:\n return list(attr.fields_dict(cls).keys())","title":""},{"docid":"4f6299e85d77d37267f451d0f8648195","score":"0.495515","text":"def _to_fields(d: DictAny) -> List[str]:\n cs: List[DictAny] = d[\"meta\"][\"view\"][\"columns\"]\n\n # written this way to get type annotations to work\n def f(x: DictAny) -> str:\n sx: str = x['name']\n return sx\n\n return list(map(f, cs))","title":""},{"docid":"574c75a4ad74382c72ae9e5f91447525","score":"0.49546686","text":"def container_image_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"container_image_names\")","title":""},{"docid":"8a079a0ce17f9ad49ef4125b8aff75ab","score":"0.49508816","text":"def stored_field_names(self):\r\n \r\n bn = self._by_name\r\n return [name for name in self._names if bn[name].stored]","title":""},{"docid":"f72b6899c69e686da1f2003d0a10b4cc","score":"0.49477538","text":"def key_field_name_list_get(self):\n return list(self.key_dict.keys())","title":""},{"docid":"e9f44857f2954aef14200b01f3bdce23","score":"0.49375585","text":"def get_names(collection):\n return [element.name for element in collection]","title":""},{"docid":"f1ea66213ccf40c7c8ec94541b1078fb","score":"0.49365887","text":"def fields(self):\n return [self._fields[name] for name in self.fieldNames]","title":""},{"docid":"8638226e6ca75fe8bb4d4eb091341832","score":"0.49363112","text":"def field_names(self, record_type=\"\"):\n return [f[\"name\"] for f in self.record_types[record_type][\"fields\"]]","title":""},{"docid":"06107a2cdf60711c2ff205daad75e3a5","score":"0.49352884","text":"def fieldnames(self):\n return self._fieldnames","title":""},{"docid":"0fc69c4893af6b3663f692a42e03452b","score":"0.49313736","text":"def get_canonical_container_name(container):\n return sorted(container['Names'], key=lambda name: len(name))[0][1:]","title":""},{"docid":"5ca31ef9e8cbb6928657eed4cbea6060","score":"0.48984218","text":"def containers(self, module_name):\n\t\tbase, name = split_module_name(module_name)\n\n\t\t# the printer modules does not have the same name scheme\n\t\tif module_name == 'shares/print':\n\t\t\tbase = 'printers'\n\n\t\tself._read_directories()\n\n\t\tif self.directory is None:\n\t\t\treturn []\n\t\treturn map(lambda x: {'id': x, 'label': ldap_dn2path(x)}, self.directory.info.get(base, []))","title":""},{"docid":"bec166b06411e731bed78a486883042f","score":"0.48848704","text":"def get_field_list(self):\n return list(self.table_format_dict)","title":""},{"docid":"1918484db22ccaff5f158a9c670452d1","score":"0.48847437","text":"def fields(self):\r\n try:\r\n return self._fields[self.tname]\r\n except KeyError:\r\n # introspect the field names from the database\r\n # by relying on the DB API 2.0\r\n # NB: we cannot trust the ordering in the Django model\r\n curs = connections[self.alias].cursor()\r\n curs.execute('select * from %s where 1=0' % self.tname)\r\n names = self._fields[self.tname] = [\r\n r[0] for r in curs.description if r[0] != 'id']\r\n return names","title":""},{"docid":"4e066b142abbdaa5a587ebabadc21915","score":"0.48470128","text":"def get_field_names(self):\n key_fields = [ d[0] for d in self.fpkey.fields ]\n data_fields = [ d[0] for d in self.fpdata.fields ]\n return key_fields + data_fields","title":""},{"docid":"c44ff81bc789d7aee7edef6b560353f0","score":"0.48232073","text":"def get_field_names(table):\n return [field['name'] for field in get_db().execute(f'SELECT name FROM {FIELDS + get_table_id(table)}').fetchall()]","title":""},{"docid":"899305a9f751ae70d7a33b5600f4746a","score":"0.47798315","text":"def list_fields(self):\n _require_metadata(gnats.MINIMAL_METADATA)\n return list(self.ordered_fields)","title":""},{"docid":"da861e81945237457eaed0a3ff7571d6","score":"0.47780028","text":"def get_field_names(self):\n return sorted(self.column_name_describer.keys())","title":""},{"docid":"92c4c389c1b8081ba8e278620cc39858","score":"0.47687584","text":"def fields(cls) -> List[str]:\n fields = attr.fields(cls)\n field_types = [\n (f.name, innermost_type(f.type)) for f in fields if f.name != \"gid\"\n ]\n field_names = [\n [f\"{name}.{f}\" for f in typ.fields()]\n if issubclass(typ, _HasFields)\n else [name]\n for (name, typ) in field_types\n ]\n return [name for names in field_names for name in names]","title":""},{"docid":"72667bc449108aded84cbcafc14597fe","score":"0.47650254","text":"def _list_field_names(self, field_type):\n fields = self._properties.get('indexedField', {})\n return fields.get(field_type)","title":""},{"docid":"b09a9db0023271ff3cce904fceae0e09","score":"0.47543573","text":"def fields(self):\n return self.field_list","title":""},{"docid":"f3a8bf83e5d8353fb269facfffe1d9f9","score":"0.4749665","text":"def fields(self) -> t.List[str]:\n return []","title":""},{"docid":"06b4eb3cb3fd1ecb3f1e60574510dad1","score":"0.47387376","text":"def list_collection_fields(self, environment_id, collection_id, **kwargs):\n\n if environment_id is None:\n raise ValueError('environment_id must be provided')\n if collection_id is None:\n raise ValueError('collection_id must be provided')\n\n headers = {}\n if 'headers' in kwargs:\n headers.update(kwargs.get('headers'))\n sdk_headers = get_sdk_headers('discovery', 'V1',\n 'list_collection_fields')\n headers.update(sdk_headers)\n\n params = {'version': self.version}\n\n url = '/v1/environments/{0}/collections/{1}/fields'.format(\n *self._encode_path_vars(environment_id, collection_id))\n response = self.request(\n method='GET',\n url=url,\n headers=headers,\n params=params,\n accept_json=True)\n return response","title":""},{"docid":"c323dd4c958bbb31f175a4ea32a98404","score":"0.4738311","text":"def fields(self):\n\n if hasattr(self, '__parameters__'):\n return self.__parameters__\n else:\n return [f'f{i}' for i in self.keys()]","title":""},{"docid":"3975d8dac6769204bb26cdba3ace7719","score":"0.47211117","text":"def get_network_container_hostnames(name):\n for network in get_networks():\n if network[\"Name\"] == name:\n return [get_container_hostname(container) for container in network[\"Containers\"]]","title":""},{"docid":"499acedfa5db0e6d0724a7a7620e9479","score":"0.47182256","text":"def get_parameter_names(diagnostic_fields: List[List[str]]) -> List[List[str]]:\n parameter_names = []\n for condition in diagnostic_fields:\n if isinstance(condition, list):\n parameter_names.append(get_parameter_names(condition))\n elif is_variable(condition):\n parameter_names.append(condition)\n return parameter_names","title":""},{"docid":"e40fba1fc77f5cea111df8942e072ae9","score":"0.47160146","text":"def _string_helper(self, field_name): # pylint: disable=g-ambiguous-str-annotation\n result = [\"{} {}\".format(str(self.node), str(field_name))]\n for k, v in self._children.items():\n recursive = v._string_helper(k) # pylint: disable=protected-access\n result.extend([\" {}\".format(x) for x in recursive])\n return result","title":""},{"docid":"2d12959f5bd0d6a8af770d946d3c9bcb","score":"0.47127327","text":"def _get_dates_from_fields(self):\n\n if not self._fields_container:\n return []\n\n dates = []\n # Widgets are arranged in the reverse order of their addition.\n for field in reversed(self._fields_container.children):\n try:\n date = datetime.datetime.strptime(field.text, \"%d/%m/%Y\").date()\n except ValueError:\n date = None\n dates.append(date)\n\n return dates","title":""},{"docid":"1eca8a0bdc4cc6aa80b7a640dd671a0c","score":"0.47007495","text":"def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\n from .planner_container_type import PlannerContainerType\n\n from .planner_container_type import PlannerContainerType\n\n fields: Dict[str, Callable[[Any], None]] = {\n \"containerId\": lambda n : setattr(self, 'container_id', n.get_str_value()),\n \"@odata.type\": lambda n : setattr(self, 'odata_type', n.get_str_value()),\n \"type\": lambda n : setattr(self, 'type', n.get_enum_value(PlannerContainerType)),\n \"url\": lambda n : setattr(self, 'url', n.get_str_value()),\n }\n return fields","title":""},{"docid":"340c5011c1971fe81abd7d8ab3651ba7","score":"0.4667802","text":"def fieldnames(self):\n fieldnames = {} # sets are unordered\n for row in self.rows:\n for field in row:\n fieldnames[field] = True\n return list(fieldnames.keys())","title":""},{"docid":"6ca6b7bfdfdbc0d80d69b191611a1481","score":"0.46639836","text":"def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:","title":""},{"docid":"13a3989ca8336b4ac5811f4c12b9894c","score":"0.46563387","text":"def get_containers_list(self, dir_only=True):\n containers_list = []\n if not os.path.isdir(self.containersdir):\n return []\n for fname in os.listdir(self.containersdir):\n container_dir = self.containersdir + '/' + fname\n if os.path.isdir(container_dir):\n try:\n filep = open(container_dir + \"/imagerepo.name\", 'r')\n except (IOError, OSError):\n reponame = \"\"\n else:\n reponame = filep.read()\n filep.close()\n if dir_only:\n containers_list.append(container_dir)\n elif not os.path.islink(container_dir):\n names = self.get_container_name(fname)\n if not names:\n names = \"\"\n containers_list.append((fname, reponame, str(names)))\n return containers_list","title":""},{"docid":"f5f79f4914c26655b3a3e72b6b5129e7","score":"0.46369","text":"def get_split_names(self):\n try:\n return [split.name for split in self.get_all()]\n except Exception:\n _LOGGER.error('Error getting split names from storage')\n _LOGGER.debug('Error: ', exc_info=True)\n return None","title":""},{"docid":"da11509e1da3a03ab4800f720bc7a9de","score":"0.4634459","text":"def name_get(self):\n result = []\n for document in self:\n if document.version:\n name = '[%s] %s'%(document.version, document.name)\n else: \n name = document.name\n \n result.append((document.id, name))\n \n return result","title":""},{"docid":"49c2e57520127645e0075b6b0029a0b4","score":"0.46329495","text":"def _get_field_names(reader: IO) -> Sequence[str]:\n csv_reader = csv.DictReader(reader)\n next(csv_reader)\n reader.seek(0)\n return csv_reader.fieldnames or []","title":""},{"docid":"3660cc3d64c393055d4ac42e3b877e8f","score":"0.4630384","text":"def get_fields(ms_name):\n logger.debug(\"Getting antenna names\")\n\n subname = \"::\".join((ms_name, \"FIELD\"))\n field_subtab = list(xm.xds_from_table(subname))\n field_subtab = field_subtab[0]\n field_names = field_subtab.NAME\n\n logger.debug(f\"Fields found: {', '.join(field_names.values)}\")\n return field_names","title":""},{"docid":"7e2bf9854325734e655c6fe8dad3c739","score":"0.46236715","text":"def dependencies(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:","title":""},{"docid":"b8ca2acf5377bdb047a8b663c874b75c","score":"0.46212012","text":"def _get_user_field_names(self):\n field_names = []\n internal_fields = (\"left\", \"right\", \"tree_id\", \"level\")\n for field in self._meta.fields:\n if (field.name not in internal_fields) and (not isinstance(field, AutoField)) and (not field.primary_key):\n field_names.append(field.name)\n return field_names","title":""},{"docid":"0ab96239137f7598c22ea2ed46b559b2","score":"0.46098047","text":"def list_fields(table):\r\n if not table:\r\n return []\r\n cols = table.__table__.columns if hasattr(table, '__table__') else table.columns\r\n return [f.name for f in cols]","title":""},{"docid":"5cb28afef9eb9d2da7ce3083fc7e4c48","score":"0.46097603","text":"def field_names(cls):\n return [field.name for field in cls()]","title":""},{"docid":"0e676c72e977e1d12726b12347010650","score":"0.46061674","text":"def _GetToListFields(self, get_fields=None):\n if get_fields:\n list_fields = set()\n for field in get_fields:\n list_fields.add('items/' + field)\n return list_fields","title":""},{"docid":"adf73010cff289c4920cac47f74bb3a2","score":"0.4604988","text":"def container_name(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"containerName\"),\n )","title":""},{"docid":"e1fe3030e55145e0e75c85e0b470c3a5","score":"0.46005386","text":"def __init__(self, field_list, field_container, *args, **kwargs):\r\n super(DynamicForm, self).__init__(*args, **kwargs)\r\n\r\n self.field_structure = []\r\n\r\n for field_name in field_list:\r\n field_data = getattr(field_container, field_name)[1]\r\n\r\n field_object = getattr(forms.fields, field_data['type'])(**field_data['kwargs'])\r\n self.fields[field_name] = field_object\r\n\r\n field_data['field_name'] = field_name\r\n self.field_structure.append(field_data)","title":""},{"docid":"dc90e0b264c2830b135d06667b3634bc","score":"0.45973927","text":"def fld_lst(type):\n lst = []\n if isinstance(type, dict):\n return list(type.keys())\n elif isinstance(type, list):\n for i in range(1, len(type) + 1):\n lst.append(\"element \" + str(i))\n return lst","title":""},{"docid":"1d47eaac5c428fd666c6b79881ef1b66","score":"0.45942882","text":"def get_signature_fields(interview_metadata_dict):\r\n signature_fields = []\r\n for field_dict in (interview_metadata_dict.get('built_in_fields_used',[]) + interview_metadata_dict.get('fields',[])):\r\n field = map_names(field_dict.get('variable',''))\r\n if field.endswith('.signature'):\r\n signature_fields.append(field)\r\n return signature_fields","title":""},{"docid":"6cd0478bf7c2cea9e76455ff40a8f67f","score":"0.4587273","text":"def flatten_container_info(self, inner_ip):\n def iter_container(tg):\n for g in tg:\n for d in g['data']['containerStatuses']:\n c = {'name': d['name'],\n 'image': d['image'],\n 'status': d['status'].lower(),\n 'container_id': d['containerID']}\n yield c\n taskgroups = self.get_unit_info(inner_ip, fields='data,namespace').get('data') or []\n containers = sorted([i for i in iter_container(taskgroups)],\n key=lambda x: constants.DockerStatusOrdering.get(\n x['status'], constants.DockerStatusDefaultOrder))\n return containers","title":""},{"docid":"e1dee9b7b5498c9877ec8ba5dbdf9fc9","score":"0.45822173","text":"def get_field_name_order(field_option_list):\n\n field_name_list = [None] * len(field_option_list)\n while not all(field_name_list):\n for idx, field_option in enumerate(field_option_list):\n if len(field_option) == 1:\n found_name = field_option.pop()\n field_name_list[idx] = found_name\n for field_set in field_option_list:\n field_set.discard(found_name)\n break\n\n return field_name_list","title":""},{"docid":"335a0e40733d20c568f2211893f67242","score":"0.4575407","text":"def get_all_field_names(self):\r\n try:\r\n cache = self._name_map\r\n except AttributeError:\r\n cache = self.init_name_map()\r\n names = sorted(cache.keys())\r\n # Internal-only names end with \"+\" (symmetrical m2m related names being\r\n # the main example). Trim them.\r\n return [val for val in names if not val.endswith('+')]","title":""},{"docid":"e70dd224a19862e060ab3b2ef1410db9","score":"0.4568496","text":"def test_schema_sql_field_list(self):\n sql_fields = BQTestSchema.get_sql_field_names()\n self.assertEqual('descr, timestamp, nested', sql_fields)\n\n sql_fields = BQTestSchema.get_sql_field_names(exclude_fields=['timestamp'])\n self.assertEqual('descr, nested', sql_fields)","title":""},{"docid":"212711c27e5a6607162d499c7949a51a","score":"0.45502537","text":"def get_fields(self, field_name: str) -> Optional[PropertyValues]:\n for p in self.properties:\n if p.name == field_name:\n return p.values\n return None","title":""},{"docid":"95b9ae9153f14cf90dae683977ade3e8","score":"0.45465776","text":"def list_containers():","title":""},{"docid":"6d778c5d87a68079760c520f0f65fa8c","score":"0.45192477","text":"def list_names(self):\n return self.child_names","title":""},{"docid":"9ff30b92abf478bba51988c5f5f550f4","score":"0.45170188","text":"def get_list_fields(self):\n return self.fields","title":""},{"docid":"7386c2c08d3ec1ca914444f07634040d","score":"0.45169434","text":"def get_field_ids_by_name(self, name_regex):\n d = {k: v for k, v in self._get_id_to_name_dict().items() if name_regex.match(v) is not None}\n return d","title":""},{"docid":"417fcc89d4af8adfbb5d16facc1a5543","score":"0.45124117","text":"def getContainerName(ctx, containerFlavor):\n return getattr(ctx.cf.ocp.containers, containerFlavor).name","title":""},{"docid":"ce030cf1a66b95ea5ba9759c1b2a34f5","score":"0.45090052","text":"def _string_helper(self, field_name: path.Step) -> Sequence[str]:\n result = [\"{} {}\".format(str(self.node), str(field_name))]\n for k, v in self._children.items():\n recursive = v._string_helper(k) # pylint: disable=protected-access\n result.extend([\" {}\".format(x) for x in recursive])\n return result","title":""},{"docid":"0762fbc266f3c0da9136fea60d69961c","score":"0.44986236","text":"def _list_containers(self):\n response = self._session.get(\"http+unix://%2Fvar%2Frun%2Fdocker.sock/containers/json?all=1\")\n if not response.ok:\n raise Exception(\"Couldn't get Containers list. Check if your docker daemon alive or not\")\n\n strio = self.stringIO(response.text)\n json_response = self.json.load(strio)\n lst_containers = [str(container[\"Names\"])[3:-2] for container in json_response]\n return lst_containers","title":""},{"docid":"85b097ce3af3f1fcd437792f757f3e58","score":"0.44954124","text":"def get_list(self):\n\n if self.mode == 'history':\n container_list = self.dopq.history\n elif self.mode == 'enqueued':\n container_list = self.dopq.container_list\n else:\n raise ValueError('invalid mode of operation: {}'.format(self.mode))\n\n if len(container_list) > self.max_containers:\n container_list = container_list[:self.max_containers]\n\n return container_list","title":""},{"docid":"9a2e65a55f6d8a915330cf30b44d59e3","score":"0.44941473","text":"def getFields(context, field_names=None):\n pairs = []\n if context.Schema(): # is archetype\n schema = context.Schema()\n fields = schema.fields()\n if field_names:\n for field_name in field_names:\n val = None\n pairs.append(schema[field_name].getName())\n val = schema[field_name].get(context)\n if isinstance(val, tuple) or isinstance(val, list):\n val = iterToTags(val)\n elif str(val).find('\\n') != -1:\n val = newlinesToTags(val)\n pairs.append(val)\n else:\n for field in fields:\n val = None\n pairs.append(field.getName())\n val = str(field.get(context))\n if val.find('\\n') != -1:\n val = newlinesToTags(val)\n pairs.append(val)\n return pairs","title":""}],"string":"[\n {\n \"docid\": \"30c975d7087e5406555f5a29ddfc5d2f\",\n \"score\": \"0.61451197\",\n \"text\": \"def _get_list(container, name):\\n\\n try:\\n return container[name]\\n except KeyError:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10c4c60092e3146611d5e905294f06d\",\n \"score\": \"0.5811185\",\n \"text\": \"def _field_names_(self):\\n ret = []\\n\\n def _merge(f, rename):\\n if not f:\\n return\\n\\n if not rename:\\n ret.extend([n for n, _ in f])\\n else:\\n for n, _ in f:\\n new_n = rename.get(n, None)\\n ret.append(new_n) if new_n else ret.append(n)\\n\\n for b in self.__class__.__mro__:\\n _merge(\\n getattr(b, '__swagger_fields__', None),\\n getattr(b, '__swagger_rename__', None)\\n )\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d5d3800f0ce0776ef979ed27f1d87\",\n \"score\": \"0.5712169\",\n \"text\": \"def fields_container(self):\\n return self._fields_container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d5d3800f0ce0776ef979ed27f1d87\",\n \"score\": \"0.5712169\",\n \"text\": \"def fields_container(self):\\n return self._fields_container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d5d3800f0ce0776ef979ed27f1d87\",\n \"score\": \"0.5712169\",\n \"text\": \"def fields_container(self):\\n return self._fields_container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1441683baa86cccd7332f66af646866\",\n \"score\": \"0.5601177\",\n \"text\": \"def get_container_entries(self, container, prefix):\\n try:\\n headers = self._headers()\\n swift_objects = self.conn.get_container(container,\\n prefix=prefix,\\n full_listing=True,\\n headers=headers)[1]\\n except socket.error as err:\\n raise exception.SwiftConnectionFailed(reason=err)\\n swift_object_names = [swift_obj['name'] for swift_obj in swift_objects]\\n return swift_object_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d8c066c5cc911b046911ca8ae57b598\",\n \"score\": \"0.5588793\",\n \"text\": \"def GetContainerFields(request, layer):\\n icon = request.get('icon')\\n icon = util.GetInstance(model.Resource, icon, layer, required=False)\\n if icon and icon.type != 'icon':\\n raise util.BadRequest('Invalid (non-icon) resource specified.')\\n\\n folder = request.get('folder')\\n folder = util.GetInstance(model.Folder, folder, layer, required=False)\\n folder_index = request.get('folder_index', None)\\n if folder_index is not None: folder_index = int(folder_index)\\n\\n region = request.get('region')\\n region = util.GetInstance(model.Region, region, layer, required=False)\\n\\n return {\\n 'icon': icon,\\n 'folder': folder,\\n 'folder_index': folder_index,\\n 'region': region,\\n 'name': request.get('name', None),\\n 'description': request.get('description', None),\\n 'item_type': request.get('item_type', None),\\n 'custom_kml': request.get('custom_kml', None)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9876c3a1dd2807b45e9acca543a94186\",\n \"score\": \"0.551232\",\n \"text\": \"def list_containers(self):\\r\\n if self._container_names is None:\\r\\n visited = set([id(self)])\\r\\n if hasattr(self, '_parent'): # fix for weird unpickling bug\\r\\n visited.add(id(self.parent))\\r\\n names = []\\r\\n for n, v in self.__dict__.items():\\r\\n if is_instance(v, Container) and id(v) not in visited:\\r\\n visited.add(id(v))\\r\\n names.append(n)\\r\\n self._container_names = names\\r\\n return self._container_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b619bd1f3f17cba660356f07ab0102bd\",\n \"score\": \"0.55013055\",\n \"text\": \"def field_names(self) -> List[str]:\\n return [x.name for x in self.fields.all()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49731fc3f52b6436791ae50e39483390\",\n \"score\": \"0.5474507\",\n \"text\": \"def get_list_of_fields_for_node(node_name):\\n return _value_to_node_fields[node_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f462a5031e0d1a0da6991690b93c2ef\",\n \"score\": \"0.5405363\",\n \"text\": \"def flatten_container(self, container):\\n for names in ARG_MAP.values():\\n if names[TransformationTypes.CHRONOS.value]['name'] and \\\\\\n '.' in names[TransformationTypes.CHRONOS.value]['name']:\\n chronos_dotted_name = names[TransformationTypes.CHRONOS.value]['name']\\n parts = chronos_dotted_name.split('.')\\n\\n if parts[-2] == 'parameters':\\n # Special lookup for docker parameters\\n common_type = names[TransformationTypes.CHRONOS.value].get('type')\\n result = self._lookup_parameter(container, parts[-1], common_type)\\n if result:\\n container[chronos_dotted_name] = result\\n else:\\n result = lookup_nested_dict(container, *parts)\\n if result:\\n container[chronos_dotted_name] = result\\n return container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7120d4d10f2d84545c761e0ae1cb6e\",\n \"score\": \"0.53995526\",\n \"text\": \"def data_field_name_list_get(self, action_name=None):\\n ret_field_name_list = []\\n if action_name is not None:\\n ret_field_name_list=[field_name_ for field_name_, data_ in \\n list(self.action_dict[self.action_dict_allname[action_name]].data_dict.items())]\\n for data_name_, data_ in list(self.data_dict.items()):\\n ret_field_name_list.append(data_name_)\\n return ret_field_name_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"613b688dbcd887d59bea4ed78d7433a4\",\n \"score\": \"0.5380753\",\n \"text\": \"def get_field_names(self):\\n\\n field_names = set()\\n\\n # Add field names from each component.\\n for component in self.components:\\n field_names.update(component.get_field_names())\\n\\n return list(field_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5390ba244ba96352a1d6c9f1db4879ab\",\n \"score\": \"0.53681356\",\n \"text\": \"def get_field_names(self, format_string):\\n return self.name_pattern.findall(format_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d862b976f871cbcee5188ed6f6cb4e95\",\n \"score\": \"0.536005\",\n \"text\": \"def get_container_list(self, source):\\n storage_client = None\\n container = None\\n\\n if 'fileGroup' in source:\\n # Input data stored in auto-storage\\n storage_client = self.resolve_storage_account()\\n container = _get_container_name(source['fileGroup'])\\n elif 'containerUrl' in source:\\n uri = urlsplit(source['containerUrl'])\\n if not uri.query:\\n raise ValueError('Invalid container url.')\\n storage_account_name = uri.netloc.split('.')[0]\\n sas_token = uri.query\\n storage_client = BlockBlobService(account_name=storage_account_name,\\n sas_token=sas_token)\\n container = uri.pathname.split('/')[1]\\n else:\\n raise ValueError('Unknown source.')\\n\\n return self.list_container_contents(source, container, storage_client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0dfb7fb0ea38f44ad645e2780ee0cca\",\n \"score\": \"0.535109\",\n \"text\": \"def getAllFields(layer):\\n fields = layer.fields()\\n fieldLists = []\\n for field in fields:\\n fieldLists.append(field.name())\\n return fieldLists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d0822d0c9a73ae8026a352e77623b96\",\n \"score\": \"0.5347515\",\n \"text\": \"def get_field_names(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb33f81c7565c3cc3e972a6b30431442\",\n \"score\": \"0.52762234\",\n \"text\": \"def get_field_names(self):\\n\\n field_names = set(sch_field_id_to_name.values())\\n for component in self.components:\\n field_names.update(component.get_field_names())\\n return list(field_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70dc8549c75e78f1846fe87ad0c2d70a\",\n \"score\": \"0.5217515\",\n \"text\": \"def getFieldNames(self):\\n return self.fieldNames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee8470c4b9687c8fe862ec39f66c2a4\",\n \"score\": \"0.5198622\",\n \"text\": \"def get_field_names(\\n project_type,\\n table_name,\\n):\\n # sql = \\\"SELECT * FROM [{}]\\\".format(table_name)\\n sql = \\\"PRAGMA table_info( [{}] );\\\".format(table_name)\\n\\n data = run_query(sql, project_type)\\n if data:\\n return [x.get(\\\"name\\\") for x in data]\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b674a72881c343ca3e65f83343c954a6\",\n \"score\": \"0.51946247\",\n \"text\": \"def data_field_name_list_get(self, *unused):\\n return [data_name_ for data_name_, data_ in list(self.data_dict.items())]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2434dc23b4245c69caa615e0fd07d5d\",\n \"score\": \"0.5171875\",\n \"text\": \"def get_containers_names(client, prefix=DOCK_CONTAINER_NAME_PREFIX_BTC):\\n return [container.name for container in client.containers.list(\\\"all\\\") if prefix in container.name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b77ae64070e8055affd29d40113c872\",\n \"score\": \"0.5169079\",\n \"text\": \"def _get_list_field_names(self):\\n\\n all_fields = ()\\n if not self.list_fields:\\n all_fields = self._get_default_list_fields()\\n else:\\n all_fields = list(self.list_fields)\\n if self._is_list_pk_required():\\n self._inject_primary_key(all_fields)\\n\\n all_fields = self._extract_field_names(all_fields, allow_string=True)\\n if self.list_indexed is True:\\n index_name = self.list_index_name\\n if index_name in (None, ''):\\n index_name = config_services.get('api', 'schema', 'index_name')\\n\\n all_fields = list(all_fields)\\n all_fields.insert(0, index_name)\\n all_fields = tuple(all_fields)\\n\\n duplicates = misc_utils.get_duplicates(all_fields)\\n if duplicates:\\n raise DuplicateListFieldNamesError('There are some duplicate field names '\\n 'in \\\"list_fields\\\" of [{admin}] class. '\\n 'this will make the find result incorrect. '\\n 'please remove duplicate fields or set '\\n 'unique labels for them: {duplicates}'\\n .format(admin=self, duplicates=duplicates))\\n\\n return all_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48e94f111a0111011a30f30aff4c88a\",\n \"score\": \"0.5166085\",\n \"text\": \"def _field_names(self):\\n names = []\\n self._read_token('{')\\n if self._is_field_name():\\n names.append(self._read_token().value)\\n while self._next_token().kind == ',':\\n self._read_token(',')\\n if self._is_field_name():\\n name == self._read_token().value\\n if name in names:\\n raise Exception(\\\"Repeated field name: %s\\\" % name)\\n names.append(name)\\n else:\\n raise Exception(\\\"field name expected, found %s\\\" %\\n self._next_token().value)\\n self._read_token('}')\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55843e0a9f4a7fe56c2087de43caa3c4\",\n \"score\": \"0.5160062\",\n \"text\": \"def field_names(self):\\r\\n return self._names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d221ff14221b5be396cb2fd14b695f\",\n \"score\": \"0.51470417\",\n \"text\": \"def get_field_names(self):\\n\\n field_names = set()\\n for f in self.fields:\\n try:\\n field_names.add(unquote(f[\\\"name\\\"]))\\n except KeyError:\\n pass\\n field_names.discard(\\\"\\\")\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c14e57dbd84db2ec879f1e1adf15d5f\",\n \"score\": \"0.5139172\",\n \"text\": \"def list(self) -> list:\\n labels = []\\n for k, v in self._labels.items():\\n labels.append(\\\"{}={}\\\".format(k, v))\\n\\n #TODO: return names\\n containers = self.client.containers.list(filters={\\\"label\\\": labels})\\n return list(map(lambda s: s.name, containers))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c2df00f269215d05e90947cd79bb3f\",\n \"score\": \"0.51312125\",\n \"text\": \"def export_field_names(self) -> List[str]:\\n return [f.name for f in self.fields.all()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7aae64056ae801052747746a5423abd\",\n \"score\": \"0.5128673\",\n \"text\": \"def fields(self):\\r\\n try:\\r\\n self._field_name_cache\\r\\n except AttributeError:\\r\\n self._fill_fields_cache()\\r\\n return self._field_name_cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d6dc8c87f29a148679a18694ce2684\",\n \"score\": \"0.51274216\",\n \"text\": \"def get_field_names(self):\\n\\n return {f[\\\"name\\\"] for f in self.fields}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a797639a31c6fabc6d35cc0ef6e6484\",\n \"score\": \"0.5123486\",\n \"text\": \"def get_versioned_field_names(self, version_number):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b77852ac9831875ec06d5fe55dde409\",\n \"score\": \"0.5094264\",\n \"text\": \"def get_field_names(self):\\n field_names = []\\n for field in self.data_fields.all():\\n field_names.append(field.name)\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa3d8bbaa16f7920fd4f9fcc1da8b28c\",\n \"score\": \"0.50611734\",\n \"text\": \"def get_column_names_list(record_info_in: sdk.RecordInfo) -> List[str]:\\n return [field.name for field in record_info_in]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"704297f96eac44cd01a88ca465d5b805\",\n \"score\": \"0.5060997\",\n \"text\": \"def field_names(self):\\n return frozenset(self._children.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64563f859bbb1a7450773db6425e8bd\",\n \"score\": \"0.5054078\",\n \"text\": \"def list(self, container_name: str) -> None:\\n try:\\n res = requests.get(self.url + 'list', json={'container_name': container_name})\\n if res.status_code == 200:\\n self.logger.info(f\\\"Listing files of container {container_name}\\\")\\n return json.loads(res.content)\\n else:\\n return None\\n except ConnectionError as ce:\\n raise ce\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3dfb9b4a47eed06de1d9963222288d2\",\n \"score\": \"0.5046736\",\n \"text\": \"def get_field_names(self):\\n return self.fields.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23b83640bca611ba5973965e550f962\",\n \"score\": \"0.50379735\",\n \"text\": \"def get_container_name(self, container_id):\\n if not os.path.isdir(self.containersdir):\\n return []\\n link_list = []\\n for fname in os.listdir(self.containersdir):\\n container = self.containersdir + \\\"/\\\" + fname\\n if os.path.islink(container):\\n real_container = os.readlink(container)\\n if os.path.basename(real_container) == container_id:\\n link_list.append(fname)\\n return link_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4636b4e0dcd5bfaf02de8b0ee0c445e3\",\n \"score\": \"0.5035241\",\n \"text\": \"def fieldsNames_get(self,recordset):\\r\\n\\t\\tif recordset.upper().startswith('SELECT'):\\r\\n\\t\\t\\trs=self.__open_recordset(self.conn, recordset, maxrecords=1)\\r\\n\\t\\telse:\\r\\n\\t\\t\\trs=self.__open_recordset(self.conn, '['+recordset+']', maxrecords=1)\\r\\n\\r\\n\\t\\tif rs.Fields.Count>0:\\r\\n\\t\\t\\tfields=[rs.Fields.Item(item).Name for item in range(rs.Fields.Count)]\\r\\n\\t\\telse:\\r\\n\\t\\t\\tfields=[]\\r\\n\\t\\treturn fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f25ecf9257d7170423361e610a667f\",\n \"score\": \"0.50169486\",\n \"text\": \"def get_fields(self, field_name):\\r\\n if field_name not in self.fields:\\r\\n raise OGRException('invalid field name: %s' % field_name)\\r\\n return [feat.get(field_name) for feat in self]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d34c0f7d6e4e777f87562f000824e1f\",\n \"score\": \"0.4993697\",\n \"text\": \"def get_fieldnames_from_cursor(cursor: Cursor) -> List[str]:\\n return [i[0] for i in cursor.description]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3d15cdef20f82411536b5fcbf3cacc\",\n \"score\": \"0.49833426\",\n \"text\": \"def fields(self):\\r\\n return [force_text(capi.get_field_name(capi.get_field_defn(self._ldefn, i)),\\r\\n self._ds.encoding, strings_only=True)\\r\\n for i in xrange(self.num_fields)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2836b21ba3be6d217c40e9a0be9a63\",\n \"score\": \"0.49688077\",\n \"text\": \"def _extract_field_names(self, fields, allow_string=True):\\n\\n names = []\\n for item in fields:\\n if self._is_valid_field(item):\\n names.append(item.key)\\n elif allow_string is True and isinstance(item, str) \\\\\\n and self._is_valid_method(item):\\n names.append(item)\\n else:\\n message = 'Provided field [{field}] is not a valid value. ' \\\\\\n 'it must be a column attribute{sign} ' \\\\\\n 'expression level hybrid property{end}'\\n\\n if allow_string is True:\\n message = message.format(\\n field=str(item), sign=',',\\n end=' or a string representing a method name of [{admin}] class.')\\n else:\\n message = message.format(field=str(item), sign=' or',\\n end='.')\\n\\n raise InvalidListFieldError(message.format(admin=self))\\n\\n return tuple(names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24c7585e363e44d0434d30c329dbd1e\",\n \"score\": \"0.49683803\",\n \"text\": \"def field_list(cls) -> List[str]:\\n return list(attr.fields_dict(cls).keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f6299e85d77d37267f451d0f8648195\",\n \"score\": \"0.495515\",\n \"text\": \"def _to_fields(d: DictAny) -> List[str]:\\n cs: List[DictAny] = d[\\\"meta\\\"][\\\"view\\\"][\\\"columns\\\"]\\n\\n # written this way to get type annotations to work\\n def f(x: DictAny) -> str:\\n sx: str = x['name']\\n return sx\\n\\n return list(map(f, cs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574c75a4ad74382c72ae9e5f91447525\",\n \"score\": \"0.49546686\",\n \"text\": \"def container_image_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"container_image_names\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a079a0ce17f9ad49ef4125b8aff75ab\",\n \"score\": \"0.49508816\",\n \"text\": \"def stored_field_names(self):\\r\\n \\r\\n bn = self._by_name\\r\\n return [name for name in self._names if bn[name].stored]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f72b6899c69e686da1f2003d0a10b4cc\",\n \"score\": \"0.49477538\",\n \"text\": \"def key_field_name_list_get(self):\\n return list(self.key_dict.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f44857f2954aef14200b01f3bdce23\",\n \"score\": \"0.49375585\",\n \"text\": \"def get_names(collection):\\n return [element.name for element in collection]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1ea66213ccf40c7c8ec94541b1078fb\",\n \"score\": \"0.49365887\",\n \"text\": \"def fields(self):\\n return [self._fields[name] for name in self.fieldNames]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8638226e6ca75fe8bb4d4eb091341832\",\n \"score\": \"0.49363112\",\n \"text\": \"def field_names(self, record_type=\\\"\\\"):\\n return [f[\\\"name\\\"] for f in self.record_types[record_type][\\\"fields\\\"]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06107a2cdf60711c2ff205daad75e3a5\",\n \"score\": \"0.49352884\",\n \"text\": \"def fieldnames(self):\\n return self._fieldnames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fc69c4893af6b3663f692a42e03452b\",\n \"score\": \"0.49313736\",\n \"text\": \"def get_canonical_container_name(container):\\n return sorted(container['Names'], key=lambda name: len(name))[0][1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca31ef9e8cbb6928657eed4cbea6060\",\n \"score\": \"0.48984218\",\n \"text\": \"def containers(self, module_name):\\n\\t\\tbase, name = split_module_name(module_name)\\n\\n\\t\\t# the printer modules does not have the same name scheme\\n\\t\\tif module_name == 'shares/print':\\n\\t\\t\\tbase = 'printers'\\n\\n\\t\\tself._read_directories()\\n\\n\\t\\tif self.directory is None:\\n\\t\\t\\treturn []\\n\\t\\treturn map(lambda x: {'id': x, 'label': ldap_dn2path(x)}, self.directory.info.get(base, []))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec166b06411e731bed78a486883042f\",\n \"score\": \"0.48848704\",\n \"text\": \"def get_field_list(self):\\n return list(self.table_format_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1918484db22ccaff5f158a9c670452d1\",\n \"score\": \"0.48847437\",\n \"text\": \"def fields(self):\\r\\n try:\\r\\n return self._fields[self.tname]\\r\\n except KeyError:\\r\\n # introspect the field names from the database\\r\\n # by relying on the DB API 2.0\\r\\n # NB: we cannot trust the ordering in the Django model\\r\\n curs = connections[self.alias].cursor()\\r\\n curs.execute('select * from %s where 1=0' % self.tname)\\r\\n names = self._fields[self.tname] = [\\r\\n r[0] for r in curs.description if r[0] != 'id']\\r\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e066b142abbdaa5a587ebabadc21915\",\n \"score\": \"0.48470128\",\n \"text\": \"def get_field_names(self):\\n key_fields = [ d[0] for d in self.fpkey.fields ]\\n data_fields = [ d[0] for d in self.fpdata.fields ]\\n return key_fields + data_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44ff81bc789d7aee7edef6b560353f0\",\n \"score\": \"0.48232073\",\n \"text\": \"def get_field_names(table):\\n return [field['name'] for field in get_db().execute(f'SELECT name FROM {FIELDS + get_table_id(table)}').fetchall()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"899305a9f751ae70d7a33b5600f4746a\",\n \"score\": \"0.47798315\",\n \"text\": \"def list_fields(self):\\n _require_metadata(gnats.MINIMAL_METADATA)\\n return list(self.ordered_fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da861e81945237457eaed0a3ff7571d6\",\n \"score\": \"0.47780028\",\n \"text\": \"def get_field_names(self):\\n return sorted(self.column_name_describer.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c4c389c1b8081ba8e278620cc39858\",\n \"score\": \"0.47687584\",\n \"text\": \"def fields(cls) -> List[str]:\\n fields = attr.fields(cls)\\n field_types = [\\n (f.name, innermost_type(f.type)) for f in fields if f.name != \\\"gid\\\"\\n ]\\n field_names = [\\n [f\\\"{name}.{f}\\\" for f in typ.fields()]\\n if issubclass(typ, _HasFields)\\n else [name]\\n for (name, typ) in field_types\\n ]\\n return [name for names in field_names for name in names]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72667bc449108aded84cbcafc14597fe\",\n \"score\": \"0.47650254\",\n \"text\": \"def _list_field_names(self, field_type):\\n fields = self._properties.get('indexedField', {})\\n return fields.get(field_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b09a9db0023271ff3cce904fceae0e09\",\n \"score\": \"0.47543573\",\n \"text\": \"def fields(self):\\n return self.field_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a8bf83e5d8353fb269facfffe1d9f9\",\n \"score\": \"0.4749665\",\n \"text\": \"def fields(self) -> t.List[str]:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b4eb3cb3fd1ecb3f1e60574510dad1\",\n \"score\": \"0.47387376\",\n \"text\": \"def list_collection_fields(self, environment_id, collection_id, **kwargs):\\n\\n if environment_id is None:\\n raise ValueError('environment_id must be provided')\\n if collection_id is None:\\n raise ValueError('collection_id must be provided')\\n\\n headers = {}\\n if 'headers' in kwargs:\\n headers.update(kwargs.get('headers'))\\n sdk_headers = get_sdk_headers('discovery', 'V1',\\n 'list_collection_fields')\\n headers.update(sdk_headers)\\n\\n params = {'version': self.version}\\n\\n url = '/v1/environments/{0}/collections/{1}/fields'.format(\\n *self._encode_path_vars(environment_id, collection_id))\\n response = self.request(\\n method='GET',\\n url=url,\\n headers=headers,\\n params=params,\\n accept_json=True)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c323dd4c958bbb31f175a4ea32a98404\",\n \"score\": \"0.4738311\",\n \"text\": \"def fields(self):\\n\\n if hasattr(self, '__parameters__'):\\n return self.__parameters__\\n else:\\n return [f'f{i}' for i in self.keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3975d8dac6769204bb26cdba3ace7719\",\n \"score\": \"0.47211117\",\n \"text\": \"def get_network_container_hostnames(name):\\n for network in get_networks():\\n if network[\\\"Name\\\"] == name:\\n return [get_container_hostname(container) for container in network[\\\"Containers\\\"]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"499acedfa5db0e6d0724a7a7620e9479\",\n \"score\": \"0.47182256\",\n \"text\": \"def get_parameter_names(diagnostic_fields: List[List[str]]) -> List[List[str]]:\\n parameter_names = []\\n for condition in diagnostic_fields:\\n if isinstance(condition, list):\\n parameter_names.append(get_parameter_names(condition))\\n elif is_variable(condition):\\n parameter_names.append(condition)\\n return parameter_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40fba1fc77f5cea111df8942e072ae9\",\n \"score\": \"0.47160146\",\n \"text\": \"def _string_helper(self, field_name): # pylint: disable=g-ambiguous-str-annotation\\n result = [\\\"{} {}\\\".format(str(self.node), str(field_name))]\\n for k, v in self._children.items():\\n recursive = v._string_helper(k) # pylint: disable=protected-access\\n result.extend([\\\" {}\\\".format(x) for x in recursive])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d12959f5bd0d6a8af770d946d3c9bcb\",\n \"score\": \"0.47127327\",\n \"text\": \"def _get_dates_from_fields(self):\\n\\n if not self._fields_container:\\n return []\\n\\n dates = []\\n # Widgets are arranged in the reverse order of their addition.\\n for field in reversed(self._fields_container.children):\\n try:\\n date = datetime.datetime.strptime(field.text, \\\"%d/%m/%Y\\\").date()\\n except ValueError:\\n date = None\\n dates.append(date)\\n\\n return dates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eca8a0bdc4cc6aa80b7a640dd671a0c\",\n \"score\": \"0.47007495\",\n \"text\": \"def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\\n from .planner_container_type import PlannerContainerType\\n\\n from .planner_container_type import PlannerContainerType\\n\\n fields: Dict[str, Callable[[Any], None]] = {\\n \\\"containerId\\\": lambda n : setattr(self, 'container_id', n.get_str_value()),\\n \\\"@odata.type\\\": lambda n : setattr(self, 'odata_type', n.get_str_value()),\\n \\\"type\\\": lambda n : setattr(self, 'type', n.get_enum_value(PlannerContainerType)),\\n \\\"url\\\": lambda n : setattr(self, 'url', n.get_str_value()),\\n }\\n return fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340c5011c1971fe81abd7d8ab3651ba7\",\n \"score\": \"0.4667802\",\n \"text\": \"def fieldnames(self):\\n fieldnames = {} # sets are unordered\\n for row in self.rows:\\n for field in row:\\n fieldnames[field] = True\\n return list(fieldnames.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ca6b7bfdfdbc0d80d69b191611a1481\",\n \"score\": \"0.46639836\",\n \"text\": \"def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a3989ca8336b4ac5811f4c12b9894c\",\n \"score\": \"0.46563387\",\n \"text\": \"def get_containers_list(self, dir_only=True):\\n containers_list = []\\n if not os.path.isdir(self.containersdir):\\n return []\\n for fname in os.listdir(self.containersdir):\\n container_dir = self.containersdir + '/' + fname\\n if os.path.isdir(container_dir):\\n try:\\n filep = open(container_dir + \\\"/imagerepo.name\\\", 'r')\\n except (IOError, OSError):\\n reponame = \\\"\\\"\\n else:\\n reponame = filep.read()\\n filep.close()\\n if dir_only:\\n containers_list.append(container_dir)\\n elif not os.path.islink(container_dir):\\n names = self.get_container_name(fname)\\n if not names:\\n names = \\\"\\\"\\n containers_list.append((fname, reponame, str(names)))\\n return containers_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f79f4914c26655b3a3e72b6b5129e7\",\n \"score\": \"0.46369\",\n \"text\": \"def get_split_names(self):\\n try:\\n return [split.name for split in self.get_all()]\\n except Exception:\\n _LOGGER.error('Error getting split names from storage')\\n _LOGGER.debug('Error: ', exc_info=True)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da11509e1da3a03ab4800f720bc7a9de\",\n \"score\": \"0.4634459\",\n \"text\": \"def name_get(self):\\n result = []\\n for document in self:\\n if document.version:\\n name = '[%s] %s'%(document.version, document.name)\\n else: \\n name = document.name\\n \\n result.append((document.id, name))\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49c2e57520127645e0075b6b0029a0b4\",\n \"score\": \"0.46329495\",\n \"text\": \"def _get_field_names(reader: IO) -> Sequence[str]:\\n csv_reader = csv.DictReader(reader)\\n next(csv_reader)\\n reader.seek(0)\\n return csv_reader.fieldnames or []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3660cc3d64c393055d4ac42e3b877e8f\",\n \"score\": \"0.4630384\",\n \"text\": \"def get_fields(ms_name):\\n logger.debug(\\\"Getting antenna names\\\")\\n\\n subname = \\\"::\\\".join((ms_name, \\\"FIELD\\\"))\\n field_subtab = list(xm.xds_from_table(subname))\\n field_subtab = field_subtab[0]\\n field_names = field_subtab.NAME\\n\\n logger.debug(f\\\"Fields found: {', '.join(field_names.values)}\\\")\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2bf9854325734e655c6fe8dad3c739\",\n \"score\": \"0.46236715\",\n \"text\": \"def dependencies(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ca2acf5377bdb047a8b663c874b75c\",\n \"score\": \"0.46212012\",\n \"text\": \"def _get_user_field_names(self):\\n field_names = []\\n internal_fields = (\\\"left\\\", \\\"right\\\", \\\"tree_id\\\", \\\"level\\\")\\n for field in self._meta.fields:\\n if (field.name not in internal_fields) and (not isinstance(field, AutoField)) and (not field.primary_key):\\n field_names.append(field.name)\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab96239137f7598c22ea2ed46b559b2\",\n \"score\": \"0.46098047\",\n \"text\": \"def list_fields(table):\\r\\n if not table:\\r\\n return []\\r\\n cols = table.__table__.columns if hasattr(table, '__table__') else table.columns\\r\\n return [f.name for f in cols]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb28afef9eb9d2da7ce3083fc7e4c48\",\n \"score\": \"0.46097603\",\n \"text\": \"def field_names(cls):\\n return [field.name for field in cls()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e676c72e977e1d12726b12347010650\",\n \"score\": \"0.46061674\",\n \"text\": \"def _GetToListFields(self, get_fields=None):\\n if get_fields:\\n list_fields = set()\\n for field in get_fields:\\n list_fields.add('items/' + field)\\n return list_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf73010cff289c4920cac47f74bb3a2\",\n \"score\": \"0.4604988\",\n \"text\": \"def container_name(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"containerName\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1fe3030e55145e0e75c85e0b470c3a5\",\n \"score\": \"0.46005386\",\n \"text\": \"def __init__(self, field_list, field_container, *args, **kwargs):\\r\\n super(DynamicForm, self).__init__(*args, **kwargs)\\r\\n\\r\\n self.field_structure = []\\r\\n\\r\\n for field_name in field_list:\\r\\n field_data = getattr(field_container, field_name)[1]\\r\\n\\r\\n field_object = getattr(forms.fields, field_data['type'])(**field_data['kwargs'])\\r\\n self.fields[field_name] = field_object\\r\\n\\r\\n field_data['field_name'] = field_name\\r\\n self.field_structure.append(field_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc90e0b264c2830b135d06667b3634bc\",\n \"score\": \"0.45973927\",\n \"text\": \"def fld_lst(type):\\n lst = []\\n if isinstance(type, dict):\\n return list(type.keys())\\n elif isinstance(type, list):\\n for i in range(1, len(type) + 1):\\n lst.append(\\\"element \\\" + str(i))\\n return lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d47eaac5c428fd666c6b79881ef1b66\",\n \"score\": \"0.45942882\",\n \"text\": \"def get_signature_fields(interview_metadata_dict):\\r\\n signature_fields = []\\r\\n for field_dict in (interview_metadata_dict.get('built_in_fields_used',[]) + interview_metadata_dict.get('fields',[])):\\r\\n field = map_names(field_dict.get('variable',''))\\r\\n if field.endswith('.signature'):\\r\\n signature_fields.append(field)\\r\\n return signature_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd0478bf7c2cea9e76455ff40a8f67f\",\n \"score\": \"0.4587273\",\n \"text\": \"def flatten_container_info(self, inner_ip):\\n def iter_container(tg):\\n for g in tg:\\n for d in g['data']['containerStatuses']:\\n c = {'name': d['name'],\\n 'image': d['image'],\\n 'status': d['status'].lower(),\\n 'container_id': d['containerID']}\\n yield c\\n taskgroups = self.get_unit_info(inner_ip, fields='data,namespace').get('data') or []\\n containers = sorted([i for i in iter_container(taskgroups)],\\n key=lambda x: constants.DockerStatusOrdering.get(\\n x['status'], constants.DockerStatusDefaultOrder))\\n return containers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1dee9b7b5498c9877ec8ba5dbdf9fc9\",\n \"score\": \"0.45822173\",\n \"text\": \"def get_field_name_order(field_option_list):\\n\\n field_name_list = [None] * len(field_option_list)\\n while not all(field_name_list):\\n for idx, field_option in enumerate(field_option_list):\\n if len(field_option) == 1:\\n found_name = field_option.pop()\\n field_name_list[idx] = found_name\\n for field_set in field_option_list:\\n field_set.discard(found_name)\\n break\\n\\n return field_name_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335a0e40733d20c568f2211893f67242\",\n \"score\": \"0.4575407\",\n \"text\": \"def get_all_field_names(self):\\r\\n try:\\r\\n cache = self._name_map\\r\\n except AttributeError:\\r\\n cache = self.init_name_map()\\r\\n names = sorted(cache.keys())\\r\\n # Internal-only names end with \\\"+\\\" (symmetrical m2m related names being\\r\\n # the main example). Trim them.\\r\\n return [val for val in names if not val.endswith('+')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70dd224a19862e060ab3b2ef1410db9\",\n \"score\": \"0.4568496\",\n \"text\": \"def test_schema_sql_field_list(self):\\n sql_fields = BQTestSchema.get_sql_field_names()\\n self.assertEqual('descr, timestamp, nested', sql_fields)\\n\\n sql_fields = BQTestSchema.get_sql_field_names(exclude_fields=['timestamp'])\\n self.assertEqual('descr, nested', sql_fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"212711c27e5a6607162d499c7949a51a\",\n \"score\": \"0.45502537\",\n \"text\": \"def get_fields(self, field_name: str) -> Optional[PropertyValues]:\\n for p in self.properties:\\n if p.name == field_name:\\n return p.values\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95b9ae9153f14cf90dae683977ade3e8\",\n \"score\": \"0.45465776\",\n \"text\": \"def list_containers():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d778c5d87a68079760c520f0f65fa8c\",\n \"score\": \"0.45192477\",\n \"text\": \"def list_names(self):\\n return self.child_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ff30b92abf478bba51988c5f5f550f4\",\n \"score\": \"0.45170188\",\n \"text\": \"def get_list_fields(self):\\n return self.fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7386c2c08d3ec1ca914444f07634040d\",\n \"score\": \"0.45169434\",\n \"text\": \"def get_field_ids_by_name(self, name_regex):\\n d = {k: v for k, v in self._get_id_to_name_dict().items() if name_regex.match(v) is not None}\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"417fcc89d4af8adfbb5d16facc1a5543\",\n \"score\": \"0.45124117\",\n \"text\": \"def getContainerName(ctx, containerFlavor):\\n return getattr(ctx.cf.ocp.containers, containerFlavor).name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce030cf1a66b95ea5ba9759c1b2a34f5\",\n \"score\": \"0.45090052\",\n \"text\": \"def _string_helper(self, field_name: path.Step) -> Sequence[str]:\\n result = [\\\"{} {}\\\".format(str(self.node), str(field_name))]\\n for k, v in self._children.items():\\n recursive = v._string_helper(k) # pylint: disable=protected-access\\n result.extend([\\\" {}\\\".format(x) for x in recursive])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0762fbc266f3c0da9136fea60d69961c\",\n \"score\": \"0.44986236\",\n \"text\": \"def _list_containers(self):\\n response = self._session.get(\\\"http+unix://%2Fvar%2Frun%2Fdocker.sock/containers/json?all=1\\\")\\n if not response.ok:\\n raise Exception(\\\"Couldn't get Containers list. Check if your docker daemon alive or not\\\")\\n\\n strio = self.stringIO(response.text)\\n json_response = self.json.load(strio)\\n lst_containers = [str(container[\\\"Names\\\"])[3:-2] for container in json_response]\\n return lst_containers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85b097ce3af3f1fcd437792f757f3e58\",\n \"score\": \"0.44954124\",\n \"text\": \"def get_list(self):\\n\\n if self.mode == 'history':\\n container_list = self.dopq.history\\n elif self.mode == 'enqueued':\\n container_list = self.dopq.container_list\\n else:\\n raise ValueError('invalid mode of operation: {}'.format(self.mode))\\n\\n if len(container_list) > self.max_containers:\\n container_list = container_list[:self.max_containers]\\n\\n return container_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a2e65a55f6d8a915330cf30b44d59e3\",\n \"score\": \"0.44941473\",\n \"text\": \"def getFields(context, field_names=None):\\n pairs = []\\n if context.Schema(): # is archetype\\n schema = context.Schema()\\n fields = schema.fields()\\n if field_names:\\n for field_name in field_names:\\n val = None\\n pairs.append(schema[field_name].getName())\\n val = schema[field_name].get(context)\\n if isinstance(val, tuple) or isinstance(val, list):\\n val = iterToTags(val)\\n elif str(val).find('\\\\n') != -1:\\n val = newlinesToTags(val)\\n pairs.append(val)\\n else:\\n for field in fields:\\n val = None\\n pairs.append(field.getName())\\n val = str(field.get(context))\\n if val.find('\\\\n') != -1:\\n val = newlinesToTags(val)\\n pairs.append(val)\\n return pairs\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":191,"cells":{"query_id":{"kind":"string","value":"3d86c1af870937aabf2ff3595bf01c6e"},"query":{"kind":"string","value":"Return package metadata information."},"positive_passages":{"kind":"list like","value":[{"docid":"5c54becc64e16a019dde1941f01c5892","score":"0.0","text":"def get_classifiers():\n return [\n 'Private :: Do Not Upload',\n 'Development Status :: 4 - Beta',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3.7',\n 'Intended Audience :: System Administrators',\n 'License :: OSI Approved :: The MIT License (MIT)',\n ]","title":""}],"string":"[\n {\n \"docid\": \"5c54becc64e16a019dde1941f01c5892\",\n \"score\": \"0.0\",\n \"text\": \"def get_classifiers():\\n return [\\n 'Private :: Do Not Upload',\\n 'Development Status :: 4 - Beta',\\n 'Operating System :: POSIX :: Linux',\\n 'Programming Language :: Python',\\n 'Programming Language :: Python :: 3.7',\\n 'Intended Audience :: System Administrators',\\n 'License :: OSI Approved :: The MIT License (MIT)',\\n ]\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"ba35cfa5e926725340a889cc98973d81","score":"0.8002718","text":"def get_package_metadata(self, id=None, name=None, version=None, architecture=None):\n url = self._package_url(id, name, version, architecture)\n return self._get_request(url)","title":""},{"docid":"dfc47df194b466847bdda6c4beb22510","score":"0.76922953","text":"def get_metadata(distribution_name='imgclas'):\n\n pkg = pkg_resources.get_distribution(distribution_name)\n meta = {\n 'Name': None,\n 'Version': None,\n 'Summary': None,\n 'Home-page': None,\n 'Author': None,\n 'Author-email': None,\n 'License': None,\n }\n\n for line in pkg.get_metadata_lines(\"PKG-INFO\"):\n for par in meta:\n if line.startswith(par):\n _, value = line.split(\": \", 1)\n meta[par] = value\n\n # Update information with Docker info (provided as 'CONTAINER_*' env variables)\n r = re.compile(\"^CONTAINER_(.*?)$\")\n container_vars = list(filter(r.match, list(os.environ)))\n for var in container_vars:\n meta[var.capitalize()] = os.getenv(var)\n\n return meta","title":""},{"docid":"0ac9aa610a51c65a9debacae3f4fb6f0","score":"0.76084256","text":"def getPackageInfo( self ):\n pkgData = {}\n\n pkgInfo = get_distribution('keggimporter').get_metadata('PKG-INFO')\n\n rawData = pkgInfo.split(\"\\n\")\n rawData.pop()\n\n reData = re.compile('^(.*?):\\s(.*)')\n for data in rawData:\n records = reData.search( data ) \n\n key = records.group(1)\n value = records.group(2)\n\n pkgData.update( { key: value } ) \n\n return pkgData","title":""},{"docid":"68cf248a7e87a034c3b46db7dce258b3","score":"0.7495511","text":"def package_info(self) -> None:\n self.package_info_cmake()\n self.package_info_env()","title":""},{"docid":"1007f0110f55beb346aa74b6bcd4880a","score":"0.7289923","text":"def package_info(self, pref):\n return self.for_package_file(pref, CONANINFO)","title":""},{"docid":"6a7e3aa84b21d2936df412f0ad5b7aac","score":"0.7280466","text":"def get_metadata(self):\n pass","title":""},{"docid":"ff36622adedfe06276943c2a74d2017f","score":"0.72115207","text":"def sdist_metadata(self):\n if self.is_wheel:\n raise TypeError(\"Requirement is not a source distribution!\")\n return self.pip_requirement.pkg_info()","title":""},{"docid":"c1254a764867fcda711b4061940be3a8","score":"0.71683055","text":"def get_metadata(self):\n return self.metadata","title":""},{"docid":"dd9932c4b48b7eba32c0650cbbd0c014","score":"0.7132883","text":"def metadata(self):\n return self.metadata_at_version(self.max_version)","title":""},{"docid":"fe57db91318a21e3e84b585ebaeea23f","score":"0.71302515","text":"def getMetadata(self):\n return self._metadata","title":""},{"docid":"a0102f653530de87a0e764b126b64a1b","score":"0.7083335","text":"def get_metadata(self):\n return self.metadata","title":""},{"docid":"313d484fd58e8569b09855fa18f677d7","score":"0.7056691","text":"def metadata(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"0b7711f833b4a1fc8e95e6f563e74c1c","score":"0.7032857","text":"def metadata(self) -> Dict[str, Any]:\n pass","title":""},{"docid":"0b7711f833b4a1fc8e95e6f563e74c1c","score":"0.7032857","text":"def metadata(self) -> Dict[str, Any]:\n pass","title":""},{"docid":"1eaf5eed246b8d49b257ac5a678b0695","score":"0.7015979","text":"def get_metadata(session, name):\n resp = session.get(\"https://pypi.python.org/pypi/{}/json\".format(name))\n if resp.status_code == 200:\n return resp.json()\n return {}","title":""},{"docid":"d938306b829603f77ccce0d944cb1c1c","score":"0.6989091","text":"def get_metadata(self):\n return self._metadata","title":""},{"docid":"6bab4d88f7bf555ff963f51d1281f055","score":"0.6965754","text":"def get_metadata(self):\n return self.config.metadata","title":""},{"docid":"58434e76703ecfd4c6a3b9449382b19c","score":"0.6961348","text":"def get_metadata(self):\n return self.client._perform_json(\"GET\", \"/projects/%s/metadata\" % self.project_key)","title":""},{"docid":"7b3059f61caf561f1c7c9e54676c6e2f","score":"0.6959908","text":"def _get_package_metadata(\n self, submitted_metadata, file_name, file_size, hashes, urls, contents\n ):\n\n def _get_filename_from_urls(submitted_metadata, urls):\n file_name = \"\"\n if not urls:\n logging.warning(f\"No URLs provided for: {submitted_metadata}\")\n for url in urls:\n _file_name = os.path.basename(url)\n if not file_name:\n file_name = _file_name\n else:\n if file_name != _file_name:\n logging.warning(\n f\"Received multiple URLs with different file names; will use the first URL (file name '{file_name}'): {submitted_metadata}\"\n )\n return file_name\n\n file_name_from_url = _get_filename_from_urls(submitted_metadata, urls)\n if not file_name:\n file_name = file_name_from_url\n\n now = str(datetime.utcnow())\n metadata = {\n \"type\": \"package\",\n \"package\": {\n \"version\": \"0.1\",\n \"file_name\": file_name,\n \"created_time\": now,\n \"updated_time\": now,\n \"size\": file_size,\n \"hashes\": hashes,\n \"contents\": contents or None,\n },\n \"_upload_status\": \"uploaded\",\n }\n return metadata","title":""},{"docid":"3b265be06c1a51c782d13c4a662992f0","score":"0.6948777","text":"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"3b265be06c1a51c782d13c4a662992f0","score":"0.6948777","text":"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"93bf09352497e0598c30511875afee00","score":"0.69407064","text":"def get_package_metadata(\n self,\n package_name: str,\n package_version: str,\n repository_name: Optional[str] = None,\n ) -> PythonPackagePayload:\n if repository_name:\n repo_config = self._config.get_repository_configuration(repository_name)\n else:\n repo_config = self._config.default_repository_configuration\n\n repo = self._repo_factory.get_repository(repo_config)\n\n metadata = repo.show(package_name, package_version)\n\n return PythonPackagePayload(\n repository_base_url=repo_config.base_url,\n package_name=package_name,\n package_version=package_version,\n package_metadata=metadata,\n )","title":""},{"docid":"b06810d8c09ac0aa1fc53ae4bea9e99f","score":"0.6907203","text":"def metadata(self) -> Optional[Any]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"b06810d8c09ac0aa1fc53ae4bea9e99f","score":"0.6907203","text":"def metadata(self) -> Optional[Any]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"4f55338024553b3bff3d71189a7c55bb","score":"0.6883791","text":"def get_metadata():\n message = {\n 'dasher_version': __version__,\n 'repo': 'https://github.com/lsst-sqre/ltd-dasher'\n }\n return jsonify(message), 200","title":""},{"docid":"8a5ded6e33a74f60e3b56eff81dbdf82","score":"0.6876241","text":"def metadata(self):\n pass","title":""},{"docid":"8a5ded6e33a74f60e3b56eff81dbdf82","score":"0.6876241","text":"def metadata(self):\n pass","title":""},{"docid":"123ac441393e251d1eb9282dc67a2e23","score":"0.68461096","text":"def getPackageInfo(self, pid):\n info = self.core.files.getPackageInfo(pid)\n if not info:\n raise PackageDoesNotExists(pid)\n return info","title":""},{"docid":"fb584ab32b0d36795f375e3605fd35fe","score":"0.6830067","text":"def metadata():\n return MetadataConfig","title":""},{"docid":"6eed88201e21210965c066fb1d60dfb2","score":"0.6826499","text":"def get_meta_information():\n raise NotImplementedError()","title":""},{"docid":"1b4dabdee30365ee52fd7bf2830f6ef5","score":"0.6817423","text":"def global_metadata():\n from ctapipe import __version__ as ctapipe_version\n from ctapipe_io_lst import __version__ as ctapipe_io_lst_version\n from .. import __version__ as lstchain_version\n\n metadata = MetaData()\n metadata.LSTCHAIN_VERSION = lstchain_version\n metadata.CTAPIPE_VERSION = ctapipe_version\n metadata.CTAPIPE_IO_LST_VERSION = ctapipe_io_lst_version\n metadata.CONTACT = \"LST Consortium\"\n\n return metadata","title":""},{"docid":"6916c01dffbfac6104f7b478a6ac5b10","score":"0.68164563","text":"def get_metadata(name):","title":""},{"docid":"6916c01dffbfac6104f7b478a6ac5b10","score":"0.68164563","text":"def get_metadata(name):","title":""},{"docid":"781fe4aa2c8086a4b8b0685f5fb5801c","score":"0.6803053","text":"def metadata(self):\n raise NotImplementedError()","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"9d0e426fd621e83779148306521a2154","score":"0.6772988","text":"def info(self):\n return self._driver.GetMetadata()","title":""},{"docid":"036fa730fe1c1a9fc4e36edc04999122","score":"0.6763207","text":"def metadata_dictionary(self) -> Dict[str, MetadataValue]:\n meta = self.metadata\n data: Dict[str, MetadataValue] = {\n # identify release\n \"name\": self.safe_name,\n \"version\": meta.version,\n # file content\n \"filetype\": self.filetype,\n \"pyversion\": self.python_version,\n # additional meta-data\n \"metadata_version\": meta.metadata_version,\n \"summary\": meta.summary,\n \"home_page\": meta.home_page,\n \"author\": meta.author,\n \"author_email\": meta.author_email,\n \"maintainer\": meta.maintainer,\n \"maintainer_email\": meta.maintainer_email,\n \"license\": meta.license,\n \"description\": meta.description,\n \"keywords\": meta.keywords,\n \"platform\": meta.platforms,\n \"classifiers\": meta.classifiers,\n \"download_url\": meta.download_url,\n \"supported_platform\": meta.supported_platforms,\n \"comment\": self.comment,\n \"sha256_digest\": self.sha2_digest,\n # PEP 314\n \"provides\": meta.provides,\n \"requires\": meta.requires,\n \"obsoletes\": meta.obsoletes,\n # Metadata 1.2\n \"project_urls\": meta.project_urls,\n \"provides_dist\": meta.provides_dist,\n \"obsoletes_dist\": meta.obsoletes_dist,\n \"requires_dist\": meta.requires_dist,\n \"requires_external\": meta.requires_external,\n \"requires_python\": meta.requires_python,\n # Metadata 2.1\n \"provides_extras\": meta.provides_extras,\n \"description_content_type\": meta.description_content_type,\n # Metadata 2.2\n \"dynamic\": meta.dynamic,\n }\n\n if self.gpg_signature is not None:\n data[\"gpg_signature\"] = self.gpg_signature\n\n # FIPS disables MD5 and Blake2, making the digest values None. Some package\n # repositories don't allow null values, so this only sends non-null values.\n # See also: https://github.com/pypa/twine/issues/775\n if self.md5_digest:\n data[\"md5_digest\"] = self.md5_digest\n\n if self.blake2_256_digest:\n data[\"blake2_256_digest\"] = self.blake2_256_digest\n\n return data","title":""},{"docid":"832edcf06b78d2aa52937b3a0b67659f","score":"0.6761121","text":"def get_package_metadata(f: typing.BinaryIO,\n workspace_pkg_meta: dict = {},\n ) -> PackageMetadata:\n cargo_toml = tomllib.load(f)\n\n if \"package\" not in cargo_toml and \"workspace\" in cargo_toml:\n raise WorkspaceCargoTomlError(\n \"Specified directory seems to be a workspace root, please run \"\n \"pycargoebuild on one of its members instead: \"\n f\"{' '.join(cargo_toml['workspace']['members'])}\")\n\n pkg_meta = cargo_toml[\"package\"]\n _get_meta_key = functools.partial(get_meta_key,\n pkg_meta=pkg_meta,\n workspace_pkg_meta=workspace_pkg_meta)\n\n pkg_license = _get_meta_key(\"license\")\n if pkg_license is not None:\n pkg_license = cargo_to_spdx(pkg_license)\n\n pkg_version = _get_meta_key(\"version\")\n if pkg_version is None:\n raise ValueError(f\"No version found in {f.name}\")\n\n return PackageMetadata(\n name=pkg_meta[\"name\"],\n version=pkg_version,\n license=pkg_license,\n license_file=_get_meta_key(\"license-file\"),\n description=_get_meta_key(\"description\"),\n homepage=_get_meta_key(\"homepage\"))","title":""},{"docid":"43cce22e0e8fd0700f5089403ce7ec38","score":"0.6746312","text":"def get_metadata(package_name, cache_dir=None, max_age=ONE_DAY):\n url = '{base_url}/{package_name}/json'.format(\n base_url=PYPI_SERVER, package_name=package_name)\n if cache_dir:\n metadata = get_cached_metadata(package_name, cache_dir, max_age)\n if metadata is not None:\n if metadata == {}:\n headers = email.message_from_string('\\n\\n')\n raise urllib.error.HTTPError(url, 404, 'Not Found (cached)',\n headers, StringIO())\n return metadata\n try:\n metadata = get_json(url)\n except urllib.error.HTTPError as e:\n if e.code == 404 and cache_dir:\n put_cached_metadata(package_name, cache_dir, {})\n raise\n if cache_dir:\n put_cached_metadata(package_name, cache_dir, metadata)\n return metadata","title":""},{"docid":"2e27056fe7bc95a31b5f9354a91d3f28","score":"0.67442584","text":"def rknn_package_info():\n import pkg_resources\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit', None)\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit2', toolkit)\n if toolkit is None:\n return dict(name=None, version=None)\n else:\n return dict(name=toolkit.project_name, version=toolkit.version)","title":""},{"docid":"d6fdacbbdaef58841093f2e9cc230265","score":"0.6739671","text":"def extract_interesting_information(metadata):\n info = metadata['info']\n return dict(version=info['version'],\n supports=extract_py_versions(info['classifiers']),\n sdist_url=extract_sdist_url(metadata))","title":""},{"docid":"35c8411e074b5b92479bf09665f7f714","score":"0.6736538","text":"def get_metadata(self):\n api_call = self.api + \"/api/catalog/\" + self.catalog_id\n\n response = get_response(api_call)\n return json.load(response)","title":""},{"docid":"b2df54058c20ded92130188b46ab1d32","score":"0.6729516","text":"def get_metadata(self):\n raise NotImplementedError(self.get_metadata)","title":""},{"docid":"12e0cddb41202eac9708968055360b8c","score":"0.67191714","text":"def getMetadata(self):\n return self.jonesmeta","title":""},{"docid":"722f168872a75b677f9d83722ddfc9c5","score":"0.6719154","text":"def Metadata(self):\n return self.get('Metadata')","title":""},{"docid":"30f9c03c99fda600dde75c1608981b66","score":"0.6705416","text":"def metadata(self) -> Optional[str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"72c543ce036eabbc7b3f9792c5adb0a0","score":"0.6690269","text":"def getMetaDataDict(self):\n return self._meta_data","title":""},{"docid":"dd768878be679a81d51219a42f09179b","score":"0.6674757","text":"def info(self):\n file = apt.apt_pkg.Config.FindFile(\"Dir::Cache::pkgcache\")\n return dict(\n timestamp = os.path.getmtime(file),\n values = [\n dict(name = \"installedsize\", desc = \"installed size\"),\n dict(name = \"packagesize\", desc = \"package size\")\n ])","title":""},{"docid":"59e0d35b76afcb6e4168e5e731d9a749","score":"0.6673888","text":"def metadata(self):\n return self.file_source.metadata()","title":""},{"docid":"c1d7e03021f66cb913849eec069dc2dd","score":"0.6672617","text":"def get_metadata(config):\n version = 'python-%s' % __version__\n ip_address, hostname = _get_hostname_and_ip(config)\n return SdkMetadata(version, hostname, ip_address)","title":""},{"docid":"0a7e2f3469eaa3b2a0e39f631b778e93","score":"0.6670984","text":"def generate_metadata(self):\n if len(self.files) > 0:\n this_netcdf = self.files[0].file_location\n ds, df_summ = self.files[0].process_netcdf(netcdf=this_netcdf)\n self.license = ds.attrs['license']\n self.title = ds.attrs['title']\n self.creator = ds.attrs['creator_name']\n self.creator_email = ds.attrs['creator_email']\n self.institution = ds.attrs['institution']\n self.aknowledgements = ds.attrs['acknowledgement']\n self.feature_type = ds.attrs['featureType']\n self.summary = ds.attrs['summary']\n self.conventions = ds.attrs['Conventions']\n self.naming_authority = ds.attrs['naming_authority']\n if self.date is None:\n from datetime import datetime\n self.date = datetime.fromordinal(\n datetime.toordinal(datetime(\n year=self.year, month=1, day=1)\n ) + self.doy - 1\n )\n self.month = self.date.month\n self.day = self.date.day\n self.parse_files()\n self.save()\n slack.chat.post_message(\n '#mpalatower',\n self.slack(),\n username='Mpala Tower',\n icon_emoji=':package:')\n return self\n else:\n return \"No files found for %d, day of year %d\" \\\n % (self.year, self.doy)","title":""},{"docid":"3836962bcb2d08f66f30d5971b5b5b98","score":"0.66429526","text":"def metadata(self):\r\n if self._metadata is None:\r\n from ._metadata import MetadataManager\r\n self._metadata = MetadataManager(gis=self._gis)\r\n return self._metadata","title":""},{"docid":"e07437fa75e2f2fde49582ffd07d79dd","score":"0.6639232","text":"def metadata(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"e07437fa75e2f2fde49582ffd07d79dd","score":"0.6639232","text":"def metadata(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"5532678da1f9f0b68eae561323b14148","score":"0.6638328","text":"def cpp_info(self):\n return self.cpp.package","title":""},{"docid":"10e8e8ac44bc646a901bda3f07df0fe8","score":"0.6635488","text":"def metadata(self):\n return self.properties.get(\"metadata\", None)","title":""},{"docid":"110972e40e3fea17a4d2bbe71077718d","score":"0.6622566","text":"def get_metadata(self):\n return {\"file_url\": self.file_name, \"file_date\": self.file_date}","title":""},{"docid":"83c70623b8a045c06ec71d9adc6c883c","score":"0.6593214","text":"def metadata(self) -> typing.Optional[OrderedDict]:\n return self._metadata","title":""},{"docid":"d3860fd5b4d8c15e8482c5b4b6ddb3d0","score":"0.6559086","text":"def metadata(self) -> \"Metadata\":\n return self._metadata","title":""},{"docid":"f4776f7ac12a82ee05fdc51eff7d0c73","score":"0.65374756","text":"def metadata(self):\n if self._metadata is None:\n from ._metadata import MetadataManager\n self._metadata = MetadataManager(gis=self._gis)\n return self._metadata","title":""},{"docid":"a1000d0703702469bdef8f3b250c6913","score":"0.6504106","text":"def metadata(self):\n\n json_response = self.my_http_request('GET', '/metadata')\n diction = json.loads(json_response)\n return diction[\"name\"], diction[\"thumbnail\"], diction[\"vector\"], diction[\"video\"], diction[\"size\"]","title":""},{"docid":"44c567e74857cc487f0122e205712e91","score":"0.64919186","text":"def metadata(self, name):\n return self.get_member(str(name)).metadata","title":""},{"docid":"1a26f464e9a2d88110da87941ec84150","score":"0.6489349","text":"def info(self):\n return (self._title, self._version, self._descr)","title":""},{"docid":"98be9a01b954a98ad4ef20a3c5d45cfc","score":"0.64881223","text":"def metadata(self):\n return self.db.metadata","title":""},{"docid":"abe75cd8bae6a9e4cf34f9a16c5012e2","score":"0.64858735","text":"def get_package_info(self, pref, remote):\n pref = self._resolve_latest_pref(pref, remote)\n return self._call_remote(remote, \"get_package_info\", pref), pref","title":""},{"docid":"66cd491c59f84623372a3ae377715738","score":"0.64818406","text":"def get_metadata_for(name):","title":""},{"docid":"27a90178be0933965bdf6417d2077104","score":"0.6446554","text":"def get_metadata(root: str) -> Metadata:\n name = dataset_name(root)\n dataset = load_dataset(name)\n return Metadata(coordinates=dataset.coordinates, labels=dataset.labels)","title":""},{"docid":"b974e3130720f51fd4a3dbafb54ea2d4","score":"0.64415383","text":"def metadata(self):\n return {}","title":""},{"docid":"f895baa107fe0e546f9c66feb46f420b","score":"0.6436223","text":"def metadata(self) -> Metadata:\n return self._metadata","title":""},{"docid":"f2c709a4f61c7e97e9b2ce9798ce4d92","score":"0.6422315","text":"def get_metadata_from_pack(pack_name):\n\n with open(find_pack_path(pack_name)[0], \"r\") as pack_metadata:\n pack_meta_file_content = json.loads(pack_metadata.read())\n\n return pack_meta_file_content","title":""},{"docid":"e709a77b8134766aa38b74043bdf665a","score":"0.6422189","text":"def dist_metainfo_dict(dist):\n\n distmetadata = dist.get_metadata(\"PKG-INFO\")\n ret = Parser(policy=default).parsestr(distmetadata)\n return ret","title":""},{"docid":"93f34a7e0c0001967fe8ecc29f3bb44e","score":"0.64209324","text":"def get_metadata():\n bd = config_drive()\n if bd:\n return bd.get_metadata()\n return {}","title":""},{"docid":"1f333089fc8b35d099aa62ca67751ad1","score":"0.6407901","text":"def metadata(self) -> Optional[ArbitraryMetadataMapping]:\n return self._metadata","title":""},{"docid":"77c29f8b92f2acfa2ef5cd264ae92d55","score":"0.6402572","text":"def about_dict(repo_root, package):\n package_init = os.path.join(repo_root, package, \"__init__.py\")\n spec = iutil.spec_from_file_location(\"package\", package_init)\n package = iutil.module_from_spec(spec)\n spec.loader.exec_module(package)\n\n return package.__dict__","title":""},{"docid":"831de987a235ee68af5a2e06d32fa661","score":"0.638527","text":"def get_metadata(self):\n return self.variable_metadata","title":""},{"docid":"4ea2558bad54c5de2e4ccfca695fe8d8","score":"0.6373588","text":"def metadata(zclient, module):\n url_path = \"settings/modules/\" + module\n return zclient._get(url_path, {})","title":""},{"docid":"f23fe4b1d36ac241574346230763ca8a","score":"0.6357783","text":"def package(self):\n\t\treturn self.get('package')","title":""},{"docid":"4c178dca1d0a34176f11fc3658cb8e5e","score":"0.6349725","text":"def get_metadata(self):\n raw_metadata = self.redis.get(self.key)\n\n if raw_metadata is None:\n self.logger.debug(\"No metadata found for: {} {}\".format(self.name, self.version))\n return None\n\n metadata = loads(raw_metadata)\n\n if Version.FILENAME not in metadata:\n self.logger.debug(\"Incomplete metadata for: {} {}\".format(self.name, self.version))\n return None\n\n return metadata","title":""},{"docid":"88a26f1bdf7d478cbc3a27b55f7fc19e","score":"0.6346251","text":"def metadata(self):\r\n meta = {k: getattr(self, k) for k in self.valid_metadata}\r\n meta[\"filename\"] = self.filename\r\n meta[\"time_points\"] = tuple(self.time_points)\r\n meta.update(self.compression_params)\r\n\r\n # Ordered dictionary by keys is easiest to inspect\r\n return OrderedDict(sorted(meta.items(), key=lambda t: t[0]))","title":""},{"docid":"b2ae1ae01f0267fab58f9391c9a5572f","score":"0.63295174","text":"def assert_basic_meta_data(self, package_path):\n _LOGGER.debug(\"Begin asserting metadata for %s\", package_path)\n\n raw_found_metadata = self._pkg_inspector.get_meta_data(package_path)\n _LOGGER.debug(\"Found raw metadata: %s\", str(raw_found_metadata))\n\n try:\n self._pkg_inspector.validate_meta_data(raw_found_metadata)\n except AssertionError as error:\n pytest.fail(\"Package at {} failed metadata validation: {}\".format(package_path, error))\n else:\n expected_metadata = {\n key: self._expected_values[key] for key in ['project_name', 'package_version']\n }\n found_metadata = {\n 'project_name': raw_found_metadata.project_name,\n 'package_version': raw_found_metadata.version,\n }\n testfixtures.compare(expected_metadata, found_metadata)\n\n _LOGGER.debug(\"Finished asserting metadata for %s\", package_path)","title":""},{"docid":"fc7a534f45634c86dcdda48cafe3c5d8","score":"0.63201094","text":"def fetch_metadata():\n\tproducts_releases = fetch_products_and_releases()\n\t# print(pretty(products_releases))\n\tproducts_releases_versions, projects_tarballs = fetch_projects_versions_and_tarballs(products_releases)\n\t# print(pretty(products_releases_versions))\n\tprojects_tarballs = fetch_unreferenced_projects(projects_tarballs)\n\tprojects_tarballs = fetch_unreferenced_versions(projects_tarballs)\n\n\tprint(pretty(projects_tarballs))\n\tprint(pretty(products_releases_versions))\n\n\twith open('projects_tarballs.json', 'w') as outfile:\n\t json.dump(projects_tarballs, outfile, sort_keys=True,indent=2)\n\n\twith open('products_releases_versions.json', 'w') as outfile:\n\t json.dump(products_releases_versions, outfile,sort_keys=True,indent=2)","title":""},{"docid":"0accc00d15254fd933a8c397c21bc547","score":"0.63126266","text":"def default_metadata(pkg_info):\n if pkg_info.is_dependency:\n return dict(_default_dep_metadata)\n\n if pkg_info.name in _shipped_metadata:\n metadata = dict(_default_pkg_metadata)\n metadata.update(_shipped_metadata.get(pkg_info.name))\n\n if not pkg_info.has_possible_overrides(simple=False):\n metadata[\"version\"] = \"Sublime %s\" % sublime.version()\n\n if pkg_info.name == \"User\":\n metadata[\"version\"] = \"Unversioned\"\n\n if pkg_info.name not in _closed_default_packages:\n metadata[\"url\"] = \"https://github.com/sublimehq/Packages\"\n\n return metadata\n\n return dict(_default_pkg_metadata)","title":""},{"docid":"b9e11f4ceb1002b1dcddfab323ddc71e","score":"0.63126","text":"def metadata():\n\treturn {\n\t\t\"name\": \"Data Type\",\n\t\t\"description\": \"Defines a type of plug-in that defines a data type, so that different components are certain they can interact with each other via this data type.\",\n\t\t\"version\": 1,\n\t\t\"dependencies\": {},\n\n\t\t\"type\": { #This is a \"plug-in type\" plug-in.\n\t\t\t\"type_name\": \"data\",\n\t\t\t\"api\": datatype.data,\n\t\t\t\"validate_metadata\": validate_metadata\n\t\t}\n\t}","title":""},{"docid":"dfa3f03046a1025b0aed0a8109d953a2","score":"0.6308763","text":"def parse_package_meta(package_path, package, require=True):\n # TODO: Allow passing in full model path and only require one argument\n # instead of path and package name. This lets us avoid passing in an awkward\n # empty string in spacy.load() if user supplies full model path.\n location = package_path / package / 'meta.json'\n if location.is_file():\n return read_json(location)\n elif require:\n raise IOError(\"Could not read meta.json from %s\" % location)\n else:\n return None","title":""},{"docid":"1f3fb9e0a972db552788b8b81692123d","score":"0.6305958","text":"def get_metadata(data):\n return _read_spss(data, metadata_only = True)[1]","title":""},{"docid":"1b3e3b4f39add93e562073fc235b1d56","score":"0.6303926","text":"def _read_metadata_element(self):\n if self.manifest is not None:\n metadata = self.manifest.xpath('/pkg:package/pkg:metadata', namespaces=ns)\n\n if metadata is not None:\n return metadata[0]","title":""},{"docid":"e4cd6c4d38b31a60b45e5d4378a61fe3","score":"0.6296391","text":"def info(repo=False):\n if repo:\n return False\n return (\"pkg_info\", \"-aoQ\")","title":""},{"docid":"7bafe305358beaf2759e470f28f97a32","score":"0.62835026","text":"def export_getCompatibleMetadata(self,metaDict):\n return fcDB.dmeta.getCompatibleMetadata(metaDict, self.getRemoteCredentials())","title":""},{"docid":"ada907d6b390f9e7b4abcd8cf9b26dbc","score":"0.6273457","text":"def metadata(self) -> Optional['outputs.ServiceTemplateMetadata']:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"28e2471c6dedce5d119dd1e3efba9cba","score":"0.62575465","text":"def get_info(self):\n return self.common.get_info()","title":""},{"docid":"6805d9231865395d1d2830bd4261f43a","score":"0.6234828","text":"def metadata(self):\n information = {'competition': self.competition,\n 'wordnet_version': self.info['wn_version'],\n 'answers_downloaded_from': self.info[\n 'answers_downloaded_from'],\n 'date_of_downloading': self.info[\n 'answers_downloaded_at'],\n 'paper': self.info['paper'],\n 'bibtex': self.info['bibtex']}\n\n # df = pd.DataFrame.from_dict({'categories': list(information.keys()),\n # 'values': list(information.values())})\n\n # display(df)\n wordnet_utils.print_dict(information)","title":""},{"docid":"6db141588facaab15029599d2fca2f1d","score":"0.62202054","text":"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"6db141588facaab15029599d2fca2f1d","score":"0.62202054","text":"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"50621d403d46ad868502580266c8cb08","score":"0.6218983","text":"def GetMetadata(self, rpc, request, response):\n raise NotImplementedError","title":""},{"docid":"50b9ac5cfee9a834121015d353b2ce21","score":"0.62061596","text":"def meta(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\n return pulumi.get(self, \"meta\")","title":""},{"docid":"9f3f733cf94e951aecf44bec44a9f109","score":"0.6199301","text":"def GetMetadata(self):\n basic_data = {\n 'spark_service': self.SERVICE_NAME,\n 'spark_svc_cloud': self.CLOUD,\n 'spark_cluster_id': self.cluster_id,\n 'spark_cluster_zone': getattr(self, 'zone', None) or 'unknown'\n }\n # TODO grab this information for user_managed clusters.\n if not self.user_managed:\n basic_data.update({'num_workers': str(self.spec.worker_group.vm_count),\n 'worker_machine_type':\n str(self.spec.worker_group.vm_spec.machine_type)})\n return basic_data","title":""},{"docid":"531b6d11b6df849a0e3b204584ffbed0","score":"0.6188202","text":"def getInternalMetadata(self, **kwargs):\n return {'omeinfo': self._omeinfo}","title":""},{"docid":"980b08b8218793f4694888b53954bdb7","score":"0.61798024","text":"def getInfo():","title":""}],"string":"[\n {\n \"docid\": \"ba35cfa5e926725340a889cc98973d81\",\n \"score\": \"0.8002718\",\n \"text\": \"def get_package_metadata(self, id=None, name=None, version=None, architecture=None):\\n url = self._package_url(id, name, version, architecture)\\n return self._get_request(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc47df194b466847bdda6c4beb22510\",\n \"score\": \"0.76922953\",\n \"text\": \"def get_metadata(distribution_name='imgclas'):\\n\\n pkg = pkg_resources.get_distribution(distribution_name)\\n meta = {\\n 'Name': None,\\n 'Version': None,\\n 'Summary': None,\\n 'Home-page': None,\\n 'Author': None,\\n 'Author-email': None,\\n 'License': None,\\n }\\n\\n for line in pkg.get_metadata_lines(\\\"PKG-INFO\\\"):\\n for par in meta:\\n if line.startswith(par):\\n _, value = line.split(\\\": \\\", 1)\\n meta[par] = value\\n\\n # Update information with Docker info (provided as 'CONTAINER_*' env variables)\\n r = re.compile(\\\"^CONTAINER_(.*?)$\\\")\\n container_vars = list(filter(r.match, list(os.environ)))\\n for var in container_vars:\\n meta[var.capitalize()] = os.getenv(var)\\n\\n return meta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac9aa610a51c65a9debacae3f4fb6f0\",\n \"score\": \"0.76084256\",\n \"text\": \"def getPackageInfo( self ):\\n pkgData = {}\\n\\n pkgInfo = get_distribution('keggimporter').get_metadata('PKG-INFO')\\n\\n rawData = pkgInfo.split(\\\"\\\\n\\\")\\n rawData.pop()\\n\\n reData = re.compile('^(.*?):\\\\s(.*)')\\n for data in rawData:\\n records = reData.search( data ) \\n\\n key = records.group(1)\\n value = records.group(2)\\n\\n pkgData.update( { key: value } ) \\n\\n return pkgData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68cf248a7e87a034c3b46db7dce258b3\",\n \"score\": \"0.7495511\",\n \"text\": \"def package_info(self) -> None:\\n self.package_info_cmake()\\n self.package_info_env()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1007f0110f55beb346aa74b6bcd4880a\",\n \"score\": \"0.7289923\",\n \"text\": \"def package_info(self, pref):\\n return self.for_package_file(pref, CONANINFO)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7e3aa84b21d2936df412f0ad5b7aac\",\n \"score\": \"0.7280466\",\n \"text\": \"def get_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff36622adedfe06276943c2a74d2017f\",\n \"score\": \"0.72115207\",\n \"text\": \"def sdist_metadata(self):\\n if self.is_wheel:\\n raise TypeError(\\\"Requirement is not a source distribution!\\\")\\n return self.pip_requirement.pkg_info()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1254a764867fcda711b4061940be3a8\",\n \"score\": \"0.71683055\",\n \"text\": \"def get_metadata(self):\\n return self.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9932c4b48b7eba32c0650cbbd0c014\",\n \"score\": \"0.7132883\",\n \"text\": \"def metadata(self):\\n return self.metadata_at_version(self.max_version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe57db91318a21e3e84b585ebaeea23f\",\n \"score\": \"0.71302515\",\n \"text\": \"def getMetadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0102f653530de87a0e764b126b64a1b\",\n \"score\": \"0.7083335\",\n \"text\": \"def get_metadata(self):\\n return self.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313d484fd58e8569b09855fa18f677d7\",\n \"score\": \"0.7056691\",\n \"text\": \"def metadata(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7711f833b4a1fc8e95e6f563e74c1c\",\n \"score\": \"0.7032857\",\n \"text\": \"def metadata(self) -> Dict[str, Any]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7711f833b4a1fc8e95e6f563e74c1c\",\n \"score\": \"0.7032857\",\n \"text\": \"def metadata(self) -> Dict[str, Any]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eaf5eed246b8d49b257ac5a678b0695\",\n \"score\": \"0.7015979\",\n \"text\": \"def get_metadata(session, name):\\n resp = session.get(\\\"https://pypi.python.org/pypi/{}/json\\\".format(name))\\n if resp.status_code == 200:\\n return resp.json()\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938306b829603f77ccce0d944cb1c1c\",\n \"score\": \"0.6989091\",\n \"text\": \"def get_metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bab4d88f7bf555ff963f51d1281f055\",\n \"score\": \"0.6965754\",\n \"text\": \"def get_metadata(self):\\n return self.config.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58434e76703ecfd4c6a3b9449382b19c\",\n \"score\": \"0.6961348\",\n \"text\": \"def get_metadata(self):\\n return self.client._perform_json(\\\"GET\\\", \\\"/projects/%s/metadata\\\" % self.project_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3059f61caf561f1c7c9e54676c6e2f\",\n \"score\": \"0.6959908\",\n \"text\": \"def _get_package_metadata(\\n self, submitted_metadata, file_name, file_size, hashes, urls, contents\\n ):\\n\\n def _get_filename_from_urls(submitted_metadata, urls):\\n file_name = \\\"\\\"\\n if not urls:\\n logging.warning(f\\\"No URLs provided for: {submitted_metadata}\\\")\\n for url in urls:\\n _file_name = os.path.basename(url)\\n if not file_name:\\n file_name = _file_name\\n else:\\n if file_name != _file_name:\\n logging.warning(\\n f\\\"Received multiple URLs with different file names; will use the first URL (file name '{file_name}'): {submitted_metadata}\\\"\\n )\\n return file_name\\n\\n file_name_from_url = _get_filename_from_urls(submitted_metadata, urls)\\n if not file_name:\\n file_name = file_name_from_url\\n\\n now = str(datetime.utcnow())\\n metadata = {\\n \\\"type\\\": \\\"package\\\",\\n \\\"package\\\": {\\n \\\"version\\\": \\\"0.1\\\",\\n \\\"file_name\\\": file_name,\\n \\\"created_time\\\": now,\\n \\\"updated_time\\\": now,\\n \\\"size\\\": file_size,\\n \\\"hashes\\\": hashes,\\n \\\"contents\\\": contents or None,\\n },\\n \\\"_upload_status\\\": \\\"uploaded\\\",\\n }\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b265be06c1a51c782d13c4a662992f0\",\n \"score\": \"0.6948777\",\n \"text\": \"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b265be06c1a51c782d13c4a662992f0\",\n \"score\": \"0.6948777\",\n \"text\": \"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93bf09352497e0598c30511875afee00\",\n \"score\": \"0.69407064\",\n \"text\": \"def get_package_metadata(\\n self,\\n package_name: str,\\n package_version: str,\\n repository_name: Optional[str] = None,\\n ) -> PythonPackagePayload:\\n if repository_name:\\n repo_config = self._config.get_repository_configuration(repository_name)\\n else:\\n repo_config = self._config.default_repository_configuration\\n\\n repo = self._repo_factory.get_repository(repo_config)\\n\\n metadata = repo.show(package_name, package_version)\\n\\n return PythonPackagePayload(\\n repository_base_url=repo_config.base_url,\\n package_name=package_name,\\n package_version=package_version,\\n package_metadata=metadata,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06810d8c09ac0aa1fc53ae4bea9e99f\",\n \"score\": \"0.6907203\",\n \"text\": \"def metadata(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06810d8c09ac0aa1fc53ae4bea9e99f\",\n \"score\": \"0.6907203\",\n \"text\": \"def metadata(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f55338024553b3bff3d71189a7c55bb\",\n \"score\": \"0.6883791\",\n \"text\": \"def get_metadata():\\n message = {\\n 'dasher_version': __version__,\\n 'repo': 'https://github.com/lsst-sqre/ltd-dasher'\\n }\\n return jsonify(message), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5ded6e33a74f60e3b56eff81dbdf82\",\n \"score\": \"0.6876241\",\n \"text\": \"def metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5ded6e33a74f60e3b56eff81dbdf82\",\n \"score\": \"0.6876241\",\n \"text\": \"def metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123ac441393e251d1eb9282dc67a2e23\",\n \"score\": \"0.68461096\",\n \"text\": \"def getPackageInfo(self, pid):\\n info = self.core.files.getPackageInfo(pid)\\n if not info:\\n raise PackageDoesNotExists(pid)\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb584ab32b0d36795f375e3605fd35fe\",\n \"score\": \"0.6830067\",\n \"text\": \"def metadata():\\n return MetadataConfig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eed88201e21210965c066fb1d60dfb2\",\n \"score\": \"0.6826499\",\n \"text\": \"def get_meta_information():\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4dabdee30365ee52fd7bf2830f6ef5\",\n \"score\": \"0.6817423\",\n \"text\": \"def global_metadata():\\n from ctapipe import __version__ as ctapipe_version\\n from ctapipe_io_lst import __version__ as ctapipe_io_lst_version\\n from .. import __version__ as lstchain_version\\n\\n metadata = MetaData()\\n metadata.LSTCHAIN_VERSION = lstchain_version\\n metadata.CTAPIPE_VERSION = ctapipe_version\\n metadata.CTAPIPE_IO_LST_VERSION = ctapipe_io_lst_version\\n metadata.CONTACT = \\\"LST Consortium\\\"\\n\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6916c01dffbfac6104f7b478a6ac5b10\",\n \"score\": \"0.68164563\",\n \"text\": \"def get_metadata(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6916c01dffbfac6104f7b478a6ac5b10\",\n \"score\": \"0.68164563\",\n \"text\": \"def get_metadata(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781fe4aa2c8086a4b8b0685f5fb5801c\",\n \"score\": \"0.6803053\",\n \"text\": \"def metadata(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0e426fd621e83779148306521a2154\",\n \"score\": \"0.6772988\",\n \"text\": \"def info(self):\\n return self._driver.GetMetadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036fa730fe1c1a9fc4e36edc04999122\",\n \"score\": \"0.6763207\",\n \"text\": \"def metadata_dictionary(self) -> Dict[str, MetadataValue]:\\n meta = self.metadata\\n data: Dict[str, MetadataValue] = {\\n # identify release\\n \\\"name\\\": self.safe_name,\\n \\\"version\\\": meta.version,\\n # file content\\n \\\"filetype\\\": self.filetype,\\n \\\"pyversion\\\": self.python_version,\\n # additional meta-data\\n \\\"metadata_version\\\": meta.metadata_version,\\n \\\"summary\\\": meta.summary,\\n \\\"home_page\\\": meta.home_page,\\n \\\"author\\\": meta.author,\\n \\\"author_email\\\": meta.author_email,\\n \\\"maintainer\\\": meta.maintainer,\\n \\\"maintainer_email\\\": meta.maintainer_email,\\n \\\"license\\\": meta.license,\\n \\\"description\\\": meta.description,\\n \\\"keywords\\\": meta.keywords,\\n \\\"platform\\\": meta.platforms,\\n \\\"classifiers\\\": meta.classifiers,\\n \\\"download_url\\\": meta.download_url,\\n \\\"supported_platform\\\": meta.supported_platforms,\\n \\\"comment\\\": self.comment,\\n \\\"sha256_digest\\\": self.sha2_digest,\\n # PEP 314\\n \\\"provides\\\": meta.provides,\\n \\\"requires\\\": meta.requires,\\n \\\"obsoletes\\\": meta.obsoletes,\\n # Metadata 1.2\\n \\\"project_urls\\\": meta.project_urls,\\n \\\"provides_dist\\\": meta.provides_dist,\\n \\\"obsoletes_dist\\\": meta.obsoletes_dist,\\n \\\"requires_dist\\\": meta.requires_dist,\\n \\\"requires_external\\\": meta.requires_external,\\n \\\"requires_python\\\": meta.requires_python,\\n # Metadata 2.1\\n \\\"provides_extras\\\": meta.provides_extras,\\n \\\"description_content_type\\\": meta.description_content_type,\\n # Metadata 2.2\\n \\\"dynamic\\\": meta.dynamic,\\n }\\n\\n if self.gpg_signature is not None:\\n data[\\\"gpg_signature\\\"] = self.gpg_signature\\n\\n # FIPS disables MD5 and Blake2, making the digest values None. Some package\\n # repositories don't allow null values, so this only sends non-null values.\\n # See also: https://github.com/pypa/twine/issues/775\\n if self.md5_digest:\\n data[\\\"md5_digest\\\"] = self.md5_digest\\n\\n if self.blake2_256_digest:\\n data[\\\"blake2_256_digest\\\"] = self.blake2_256_digest\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832edcf06b78d2aa52937b3a0b67659f\",\n \"score\": \"0.6761121\",\n \"text\": \"def get_package_metadata(f: typing.BinaryIO,\\n workspace_pkg_meta: dict = {},\\n ) -> PackageMetadata:\\n cargo_toml = tomllib.load(f)\\n\\n if \\\"package\\\" not in cargo_toml and \\\"workspace\\\" in cargo_toml:\\n raise WorkspaceCargoTomlError(\\n \\\"Specified directory seems to be a workspace root, please run \\\"\\n \\\"pycargoebuild on one of its members instead: \\\"\\n f\\\"{' '.join(cargo_toml['workspace']['members'])}\\\")\\n\\n pkg_meta = cargo_toml[\\\"package\\\"]\\n _get_meta_key = functools.partial(get_meta_key,\\n pkg_meta=pkg_meta,\\n workspace_pkg_meta=workspace_pkg_meta)\\n\\n pkg_license = _get_meta_key(\\\"license\\\")\\n if pkg_license is not None:\\n pkg_license = cargo_to_spdx(pkg_license)\\n\\n pkg_version = _get_meta_key(\\\"version\\\")\\n if pkg_version is None:\\n raise ValueError(f\\\"No version found in {f.name}\\\")\\n\\n return PackageMetadata(\\n name=pkg_meta[\\\"name\\\"],\\n version=pkg_version,\\n license=pkg_license,\\n license_file=_get_meta_key(\\\"license-file\\\"),\\n description=_get_meta_key(\\\"description\\\"),\\n homepage=_get_meta_key(\\\"homepage\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43cce22e0e8fd0700f5089403ce7ec38\",\n \"score\": \"0.6746312\",\n \"text\": \"def get_metadata(package_name, cache_dir=None, max_age=ONE_DAY):\\n url = '{base_url}/{package_name}/json'.format(\\n base_url=PYPI_SERVER, package_name=package_name)\\n if cache_dir:\\n metadata = get_cached_metadata(package_name, cache_dir, max_age)\\n if metadata is not None:\\n if metadata == {}:\\n headers = email.message_from_string('\\\\n\\\\n')\\n raise urllib.error.HTTPError(url, 404, 'Not Found (cached)',\\n headers, StringIO())\\n return metadata\\n try:\\n metadata = get_json(url)\\n except urllib.error.HTTPError as e:\\n if e.code == 404 and cache_dir:\\n put_cached_metadata(package_name, cache_dir, {})\\n raise\\n if cache_dir:\\n put_cached_metadata(package_name, cache_dir, metadata)\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e27056fe7bc95a31b5f9354a91d3f28\",\n \"score\": \"0.67442584\",\n \"text\": \"def rknn_package_info():\\n import pkg_resources\\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit', None)\\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit2', toolkit)\\n if toolkit is None:\\n return dict(name=None, version=None)\\n else:\\n return dict(name=toolkit.project_name, version=toolkit.version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6fdacbbdaef58841093f2e9cc230265\",\n \"score\": \"0.6739671\",\n \"text\": \"def extract_interesting_information(metadata):\\n info = metadata['info']\\n return dict(version=info['version'],\\n supports=extract_py_versions(info['classifiers']),\\n sdist_url=extract_sdist_url(metadata))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c8411e074b5b92479bf09665f7f714\",\n \"score\": \"0.6736538\",\n \"text\": \"def get_metadata(self):\\n api_call = self.api + \\\"/api/catalog/\\\" + self.catalog_id\\n\\n response = get_response(api_call)\\n return json.load(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2df54058c20ded92130188b46ab1d32\",\n \"score\": \"0.6729516\",\n \"text\": \"def get_metadata(self):\\n raise NotImplementedError(self.get_metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e0cddb41202eac9708968055360b8c\",\n \"score\": \"0.67191714\",\n \"text\": \"def getMetadata(self):\\n return self.jonesmeta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722f168872a75b677f9d83722ddfc9c5\",\n \"score\": \"0.6719154\",\n \"text\": \"def Metadata(self):\\n return self.get('Metadata')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f9c03c99fda600dde75c1608981b66\",\n \"score\": \"0.6705416\",\n \"text\": \"def metadata(self) -> Optional[str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c543ce036eabbc7b3f9792c5adb0a0\",\n \"score\": \"0.6690269\",\n \"text\": \"def getMetaDataDict(self):\\n return self._meta_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd768878be679a81d51219a42f09179b\",\n \"score\": \"0.6674757\",\n \"text\": \"def info(self):\\n file = apt.apt_pkg.Config.FindFile(\\\"Dir::Cache::pkgcache\\\")\\n return dict(\\n timestamp = os.path.getmtime(file),\\n values = [\\n dict(name = \\\"installedsize\\\", desc = \\\"installed size\\\"),\\n dict(name = \\\"packagesize\\\", desc = \\\"package size\\\")\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e0d35b76afcb6e4168e5e731d9a749\",\n \"score\": \"0.6673888\",\n \"text\": \"def metadata(self):\\n return self.file_source.metadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d7e03021f66cb913849eec069dc2dd\",\n \"score\": \"0.6672617\",\n \"text\": \"def get_metadata(config):\\n version = 'python-%s' % __version__\\n ip_address, hostname = _get_hostname_and_ip(config)\\n return SdkMetadata(version, hostname, ip_address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7e2f3469eaa3b2a0e39f631b778e93\",\n \"score\": \"0.6670984\",\n \"text\": \"def generate_metadata(self):\\n if len(self.files) > 0:\\n this_netcdf = self.files[0].file_location\\n ds, df_summ = self.files[0].process_netcdf(netcdf=this_netcdf)\\n self.license = ds.attrs['license']\\n self.title = ds.attrs['title']\\n self.creator = ds.attrs['creator_name']\\n self.creator_email = ds.attrs['creator_email']\\n self.institution = ds.attrs['institution']\\n self.aknowledgements = ds.attrs['acknowledgement']\\n self.feature_type = ds.attrs['featureType']\\n self.summary = ds.attrs['summary']\\n self.conventions = ds.attrs['Conventions']\\n self.naming_authority = ds.attrs['naming_authority']\\n if self.date is None:\\n from datetime import datetime\\n self.date = datetime.fromordinal(\\n datetime.toordinal(datetime(\\n year=self.year, month=1, day=1)\\n ) + self.doy - 1\\n )\\n self.month = self.date.month\\n self.day = self.date.day\\n self.parse_files()\\n self.save()\\n slack.chat.post_message(\\n '#mpalatower',\\n self.slack(),\\n username='Mpala Tower',\\n icon_emoji=':package:')\\n return self\\n else:\\n return \\\"No files found for %d, day of year %d\\\" \\\\\\n % (self.year, self.doy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3836962bcb2d08f66f30d5971b5b5b98\",\n \"score\": \"0.66429526\",\n \"text\": \"def metadata(self):\\r\\n if self._metadata is None:\\r\\n from ._metadata import MetadataManager\\r\\n self._metadata = MetadataManager(gis=self._gis)\\r\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07437fa75e2f2fde49582ffd07d79dd\",\n \"score\": \"0.6639232\",\n \"text\": \"def metadata(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07437fa75e2f2fde49582ffd07d79dd\",\n \"score\": \"0.6639232\",\n \"text\": \"def metadata(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5532678da1f9f0b68eae561323b14148\",\n \"score\": \"0.6638328\",\n \"text\": \"def cpp_info(self):\\n return self.cpp.package\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e8e8ac44bc646a901bda3f07df0fe8\",\n \"score\": \"0.6635488\",\n \"text\": \"def metadata(self):\\n return self.properties.get(\\\"metadata\\\", None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110972e40e3fea17a4d2bbe71077718d\",\n \"score\": \"0.6622566\",\n \"text\": \"def get_metadata(self):\\n return {\\\"file_url\\\": self.file_name, \\\"file_date\\\": self.file_date}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c70623b8a045c06ec71d9adc6c883c\",\n \"score\": \"0.6593214\",\n \"text\": \"def metadata(self) -> typing.Optional[OrderedDict]:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3860fd5b4d8c15e8482c5b4b6ddb3d0\",\n \"score\": \"0.6559086\",\n \"text\": \"def metadata(self) -> \\\"Metadata\\\":\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4776f7ac12a82ee05fdc51eff7d0c73\",\n \"score\": \"0.65374756\",\n \"text\": \"def metadata(self):\\n if self._metadata is None:\\n from ._metadata import MetadataManager\\n self._metadata = MetadataManager(gis=self._gis)\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1000d0703702469bdef8f3b250c6913\",\n \"score\": \"0.6504106\",\n \"text\": \"def metadata(self):\\n\\n json_response = self.my_http_request('GET', '/metadata')\\n diction = json.loads(json_response)\\n return diction[\\\"name\\\"], diction[\\\"thumbnail\\\"], diction[\\\"vector\\\"], diction[\\\"video\\\"], diction[\\\"size\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c567e74857cc487f0122e205712e91\",\n \"score\": \"0.64919186\",\n \"text\": \"def metadata(self, name):\\n return self.get_member(str(name)).metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a26f464e9a2d88110da87941ec84150\",\n \"score\": \"0.6489349\",\n \"text\": \"def info(self):\\n return (self._title, self._version, self._descr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98be9a01b954a98ad4ef20a3c5d45cfc\",\n \"score\": \"0.64881223\",\n \"text\": \"def metadata(self):\\n return self.db.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe75cd8bae6a9e4cf34f9a16c5012e2\",\n \"score\": \"0.64858735\",\n \"text\": \"def get_package_info(self, pref, remote):\\n pref = self._resolve_latest_pref(pref, remote)\\n return self._call_remote(remote, \\\"get_package_info\\\", pref), pref\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66cd491c59f84623372a3ae377715738\",\n \"score\": \"0.64818406\",\n \"text\": \"def get_metadata_for(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27a90178be0933965bdf6417d2077104\",\n \"score\": \"0.6446554\",\n \"text\": \"def get_metadata(root: str) -> Metadata:\\n name = dataset_name(root)\\n dataset = load_dataset(name)\\n return Metadata(coordinates=dataset.coordinates, labels=dataset.labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b974e3130720f51fd4a3dbafb54ea2d4\",\n \"score\": \"0.64415383\",\n \"text\": \"def metadata(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f895baa107fe0e546f9c66feb46f420b\",\n \"score\": \"0.6436223\",\n \"text\": \"def metadata(self) -> Metadata:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c709a4f61c7e97e9b2ce9798ce4d92\",\n \"score\": \"0.6422315\",\n \"text\": \"def get_metadata_from_pack(pack_name):\\n\\n with open(find_pack_path(pack_name)[0], \\\"r\\\") as pack_metadata:\\n pack_meta_file_content = json.loads(pack_metadata.read())\\n\\n return pack_meta_file_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e709a77b8134766aa38b74043bdf665a\",\n \"score\": \"0.6422189\",\n \"text\": \"def dist_metainfo_dict(dist):\\n\\n distmetadata = dist.get_metadata(\\\"PKG-INFO\\\")\\n ret = Parser(policy=default).parsestr(distmetadata)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f34a7e0c0001967fe8ecc29f3bb44e\",\n \"score\": \"0.64209324\",\n \"text\": \"def get_metadata():\\n bd = config_drive()\\n if bd:\\n return bd.get_metadata()\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f333089fc8b35d099aa62ca67751ad1\",\n \"score\": \"0.6407901\",\n \"text\": \"def metadata(self) -> Optional[ArbitraryMetadataMapping]:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c29f8b92f2acfa2ef5cd264ae92d55\",\n \"score\": \"0.6402572\",\n \"text\": \"def about_dict(repo_root, package):\\n package_init = os.path.join(repo_root, package, \\\"__init__.py\\\")\\n spec = iutil.spec_from_file_location(\\\"package\\\", package_init)\\n package = iutil.module_from_spec(spec)\\n spec.loader.exec_module(package)\\n\\n return package.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831de987a235ee68af5a2e06d32fa661\",\n \"score\": \"0.638527\",\n \"text\": \"def get_metadata(self):\\n return self.variable_metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ea2558bad54c5de2e4ccfca695fe8d8\",\n \"score\": \"0.6373588\",\n \"text\": \"def metadata(zclient, module):\\n url_path = \\\"settings/modules/\\\" + module\\n return zclient._get(url_path, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23fe4b1d36ac241574346230763ca8a\",\n \"score\": \"0.6357783\",\n \"text\": \"def package(self):\\n\\t\\treturn self.get('package')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c178dca1d0a34176f11fc3658cb8e5e\",\n \"score\": \"0.6349725\",\n \"text\": \"def get_metadata(self):\\n raw_metadata = self.redis.get(self.key)\\n\\n if raw_metadata is None:\\n self.logger.debug(\\\"No metadata found for: {} {}\\\".format(self.name, self.version))\\n return None\\n\\n metadata = loads(raw_metadata)\\n\\n if Version.FILENAME not in metadata:\\n self.logger.debug(\\\"Incomplete metadata for: {} {}\\\".format(self.name, self.version))\\n return None\\n\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a26f1bdf7d478cbc3a27b55f7fc19e\",\n \"score\": \"0.6346251\",\n \"text\": \"def metadata(self):\\r\\n meta = {k: getattr(self, k) for k in self.valid_metadata}\\r\\n meta[\\\"filename\\\"] = self.filename\\r\\n meta[\\\"time_points\\\"] = tuple(self.time_points)\\r\\n meta.update(self.compression_params)\\r\\n\\r\\n # Ordered dictionary by keys is easiest to inspect\\r\\n return OrderedDict(sorted(meta.items(), key=lambda t: t[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ae1ae01f0267fab58f9391c9a5572f\",\n \"score\": \"0.63295174\",\n \"text\": \"def assert_basic_meta_data(self, package_path):\\n _LOGGER.debug(\\\"Begin asserting metadata for %s\\\", package_path)\\n\\n raw_found_metadata = self._pkg_inspector.get_meta_data(package_path)\\n _LOGGER.debug(\\\"Found raw metadata: %s\\\", str(raw_found_metadata))\\n\\n try:\\n self._pkg_inspector.validate_meta_data(raw_found_metadata)\\n except AssertionError as error:\\n pytest.fail(\\\"Package at {} failed metadata validation: {}\\\".format(package_path, error))\\n else:\\n expected_metadata = {\\n key: self._expected_values[key] for key in ['project_name', 'package_version']\\n }\\n found_metadata = {\\n 'project_name': raw_found_metadata.project_name,\\n 'package_version': raw_found_metadata.version,\\n }\\n testfixtures.compare(expected_metadata, found_metadata)\\n\\n _LOGGER.debug(\\\"Finished asserting metadata for %s\\\", package_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7a534f45634c86dcdda48cafe3c5d8\",\n \"score\": \"0.63201094\",\n \"text\": \"def fetch_metadata():\\n\\tproducts_releases = fetch_products_and_releases()\\n\\t# print(pretty(products_releases))\\n\\tproducts_releases_versions, projects_tarballs = fetch_projects_versions_and_tarballs(products_releases)\\n\\t# print(pretty(products_releases_versions))\\n\\tprojects_tarballs = fetch_unreferenced_projects(projects_tarballs)\\n\\tprojects_tarballs = fetch_unreferenced_versions(projects_tarballs)\\n\\n\\tprint(pretty(projects_tarballs))\\n\\tprint(pretty(products_releases_versions))\\n\\n\\twith open('projects_tarballs.json', 'w') as outfile:\\n\\t json.dump(projects_tarballs, outfile, sort_keys=True,indent=2)\\n\\n\\twith open('products_releases_versions.json', 'w') as outfile:\\n\\t json.dump(products_releases_versions, outfile,sort_keys=True,indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0accc00d15254fd933a8c397c21bc547\",\n \"score\": \"0.63126266\",\n \"text\": \"def default_metadata(pkg_info):\\n if pkg_info.is_dependency:\\n return dict(_default_dep_metadata)\\n\\n if pkg_info.name in _shipped_metadata:\\n metadata = dict(_default_pkg_metadata)\\n metadata.update(_shipped_metadata.get(pkg_info.name))\\n\\n if not pkg_info.has_possible_overrides(simple=False):\\n metadata[\\\"version\\\"] = \\\"Sublime %s\\\" % sublime.version()\\n\\n if pkg_info.name == \\\"User\\\":\\n metadata[\\\"version\\\"] = \\\"Unversioned\\\"\\n\\n if pkg_info.name not in _closed_default_packages:\\n metadata[\\\"url\\\"] = \\\"https://github.com/sublimehq/Packages\\\"\\n\\n return metadata\\n\\n return dict(_default_pkg_metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e11f4ceb1002b1dcddfab323ddc71e\",\n \"score\": \"0.63126\",\n \"text\": \"def metadata():\\n\\treturn {\\n\\t\\t\\\"name\\\": \\\"Data Type\\\",\\n\\t\\t\\\"description\\\": \\\"Defines a type of plug-in that defines a data type, so that different components are certain they can interact with each other via this data type.\\\",\\n\\t\\t\\\"version\\\": 1,\\n\\t\\t\\\"dependencies\\\": {},\\n\\n\\t\\t\\\"type\\\": { #This is a \\\"plug-in type\\\" plug-in.\\n\\t\\t\\t\\\"type_name\\\": \\\"data\\\",\\n\\t\\t\\t\\\"api\\\": datatype.data,\\n\\t\\t\\t\\\"validate_metadata\\\": validate_metadata\\n\\t\\t}\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfa3f03046a1025b0aed0a8109d953a2\",\n \"score\": \"0.6308763\",\n \"text\": \"def parse_package_meta(package_path, package, require=True):\\n # TODO: Allow passing in full model path and only require one argument\\n # instead of path and package name. This lets us avoid passing in an awkward\\n # empty string in spacy.load() if user supplies full model path.\\n location = package_path / package / 'meta.json'\\n if location.is_file():\\n return read_json(location)\\n elif require:\\n raise IOError(\\\"Could not read meta.json from %s\\\" % location)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3fb9e0a972db552788b8b81692123d\",\n \"score\": \"0.6305958\",\n \"text\": \"def get_metadata(data):\\n return _read_spss(data, metadata_only = True)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3e3b4f39add93e562073fc235b1d56\",\n \"score\": \"0.6303926\",\n \"text\": \"def _read_metadata_element(self):\\n if self.manifest is not None:\\n metadata = self.manifest.xpath('/pkg:package/pkg:metadata', namespaces=ns)\\n\\n if metadata is not None:\\n return metadata[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4cd6c4d38b31a60b45e5d4378a61fe3\",\n \"score\": \"0.6296391\",\n \"text\": \"def info(repo=False):\\n if repo:\\n return False\\n return (\\\"pkg_info\\\", \\\"-aoQ\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bafe305358beaf2759e470f28f97a32\",\n \"score\": \"0.62835026\",\n \"text\": \"def export_getCompatibleMetadata(self,metaDict):\\n return fcDB.dmeta.getCompatibleMetadata(metaDict, self.getRemoteCredentials())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ada907d6b390f9e7b4abcd8cf9b26dbc\",\n \"score\": \"0.6273457\",\n \"text\": \"def metadata(self) -> Optional['outputs.ServiceTemplateMetadata']:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e2471c6dedce5d119dd1e3efba9cba\",\n \"score\": \"0.62575465\",\n \"text\": \"def get_info(self):\\n return self.common.get_info()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6805d9231865395d1d2830bd4261f43a\",\n \"score\": \"0.6234828\",\n \"text\": \"def metadata(self):\\n information = {'competition': self.competition,\\n 'wordnet_version': self.info['wn_version'],\\n 'answers_downloaded_from': self.info[\\n 'answers_downloaded_from'],\\n 'date_of_downloading': self.info[\\n 'answers_downloaded_at'],\\n 'paper': self.info['paper'],\\n 'bibtex': self.info['bibtex']}\\n\\n # df = pd.DataFrame.from_dict({'categories': list(information.keys()),\\n # 'values': list(information.values())})\\n\\n # display(df)\\n wordnet_utils.print_dict(information)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db141588facaab15029599d2fca2f1d\",\n \"score\": \"0.62202054\",\n \"text\": \"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db141588facaab15029599d2fca2f1d\",\n \"score\": \"0.62202054\",\n \"text\": \"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50621d403d46ad868502580266c8cb08\",\n \"score\": \"0.6218983\",\n \"text\": \"def GetMetadata(self, rpc, request, response):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b9ac5cfee9a834121015d353b2ce21\",\n \"score\": \"0.62061596\",\n \"text\": \"def meta(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\\n return pulumi.get(self, \\\"meta\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f3f733cf94e951aecf44bec44a9f109\",\n \"score\": \"0.6199301\",\n \"text\": \"def GetMetadata(self):\\n basic_data = {\\n 'spark_service': self.SERVICE_NAME,\\n 'spark_svc_cloud': self.CLOUD,\\n 'spark_cluster_id': self.cluster_id,\\n 'spark_cluster_zone': getattr(self, 'zone', None) or 'unknown'\\n }\\n # TODO grab this information for user_managed clusters.\\n if not self.user_managed:\\n basic_data.update({'num_workers': str(self.spec.worker_group.vm_count),\\n 'worker_machine_type':\\n str(self.spec.worker_group.vm_spec.machine_type)})\\n return basic_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"531b6d11b6df849a0e3b204584ffbed0\",\n \"score\": \"0.6188202\",\n \"text\": \"def getInternalMetadata(self, **kwargs):\\n return {'omeinfo': self._omeinfo}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980b08b8218793f4694888b53954bdb7\",\n \"score\": \"0.61798024\",\n \"text\": \"def getInfo():\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":192,"cells":{"query_id":{"kind":"string","value":"4370a2db89aa376572fa763677d8f568"},"query":{"kind":"string","value":"Add the hooks to the bot."},"positive_passages":{"kind":"list like","value":[{"docid":"f6a523a74b100be16e1871ae1e1ba3f3","score":"0.579434","text":"def setup(bot: commands.Bot):\n bot.before_invoke(before_invoke)\n bot.after_invoke(after_invoke)","title":""}],"string":"[\n {\n \"docid\": \"f6a523a74b100be16e1871ae1e1ba3f3\",\n \"score\": \"0.579434\",\n \"text\": \"def setup(bot: commands.Bot):\\n bot.before_invoke(before_invoke)\\n bot.after_invoke(after_invoke)\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"42d07c0d20fbaab7a3ce210e4325d89b","score":"0.7362344","text":"def hook_events(self):\n for cmd in self.commands:\n self.core.add_command(cmd, self)\n for event in self.hooks:\n self.core.add_callback(event, self)\n for cmd in self.rawhooks:\n self.core.add_raw(cmd, self)","title":""},{"docid":"5450bb7786ed53fe294cc35583a4b091","score":"0.7053781","text":"def set_game_hooks(hooks: GameHooks) -> None:\n global _game_hooks\n _game_hooks = hooks","title":""},{"docid":"1d717911c38f804986b4721041fd10b5","score":"0.69996136","text":"def register_hooks(self, hooks):\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)","title":""},{"docid":"1d717911c38f804986b4721041fd10b5","score":"0.69996136","text":"def register_hooks(self, hooks):\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)","title":""},{"docid":"8a249d756642ea01dd9ad4457bff3712","score":"0.6859483","text":"def enable_hooks(self) -> None:\n self.hooks_enabled = True","title":""},{"docid":"11a1126a6ab2f0b85fc66e57b16e028e","score":"0.6821246","text":"def putInHooks ():\n\n # Need to modify Leo's Kernel first\n # Overwrite existing Leo methods.\n g.funcToMethod(replaceTargetFileIfDifferent,leoAtFile.atFile)\n g.funcToMethod(massageComment,leoImport.leoImportCommands)\n\n # Add new methods used by this plugin to various classes.\n g.funcToMethod(openForRead,leoAtFile.atFile)\n g.funcToMethod(openForWrite,leoAtFile.atFile)\n g.funcToMethod(gotoLineNumberOpen,leoCommands.Commands)\n g.funcToMethod(applyLineNumberMappingIfAny, leoCommands.Commands)","title":""},{"docid":"88d6493b3885f6278f8212ed6423e014","score":"0.67838776","text":"def prepare_hooks(self, hooks):\r\n for event in hooks:\r\n self.register_hook(event, hooks[event])","title":""},{"docid":"88d6493b3885f6278f8212ed6423e014","score":"0.67838776","text":"def prepare_hooks(self, hooks):\r\n for event in hooks:\r\n self.register_hook(event, hooks[event])","title":""},{"docid":"cc16bf846e0425e5913d8b44001ce900","score":"0.6763753","text":"def configure_hooks_feature(self):\n hooks_folder = getattr(config, \"HOOKS_FOLDER\", \"./hooks\")\n if not os.path.exists(hooks_folder):\n self.logger.info(\"Hooks folder '%s' doesn't exist, creating it\" % hooks_folder)\n os.makedirs(hooks_folder)\n self.hook_files = glob.glob(os.path.join(hooks_folder, \"*.py\"))","title":""},{"docid":"8e754d61588c30b277f496a6f5fe68f2","score":"0.6674685","text":"def _init_hooks(self):\n self._hooks = Hooks()\n self._hooks.ready_to_run = self._init_hexrays_hooks\n self._hooks.hook()","title":""},{"docid":"8e754d61588c30b277f496a6f5fe68f2","score":"0.6674685","text":"def _init_hooks(self):\n self._hooks = Hooks()\n self._hooks.ready_to_run = self._init_hexrays_hooks\n self._hooks.hook()","title":""},{"docid":"0489a542c08e02331af5086e2981ff82","score":"0.66713774","text":"def hook(self):\n\n\t\tself.db.begin()\n\n\t\tret = self.db.get_hook()\n\t\texisting = [dict(_)['name'] for _ in ret]\n\n\t\t# Add hook modules\n\t\tif len(self.args.hook):\n\t\t\tfor h in self.args.hook:\n\t\t\t\th = h.strip()\n\t\t\t\tif h in existing:\n\t\t\t\t\tprint(\"Hook module '%s' already in hook list\" % h)\n\t\t\t\telse:\n\t\t\t\t\tself.db.add_hook(h)\n\t\t\t\t\tself.db.commit()\n\t\t\t\t\tprint(\"Added: %s\" % h)\n\n\t\t# List all hooks\n\t\telse:\n\t\t\tif len(existing):\n\t\t\t\tprint(\"Existing hooks:\")\n\t\t\t\texisting = sorted(existing)\n\n\t\t\t\tfor h in existing:\n\t\t\t\t\tprint(\"\\t%s\" % h)\n\n\t\t\telse:\n\t\t\t\tprint(\"No existing hooks\")","title":""},{"docid":"b303d72b01b79b91713af6bf520ec161","score":"0.66632324","text":"def go_hook_all():\n global hooks\n priority = ''\n version = weechat.info_get('version_number', '') or 0\n # use high priority for hook to prevent conflict with other plugins/scripts\n # (WeeChat >= 0.3.4 only)\n if int(version) >= 0x00030400:\n priority = '2000|'\n for hook, value in HOOK_COMMAND_RUN.items():\n if hook not in hooks:\n hooks[hook] = weechat.hook_command_run(\n '%s%s' % (priority, value[0]),\n value[1], '')\n if 'modifier' not in hooks:\n hooks['modifier'] = weechat.hook_modifier(\n 'input_text_display_with_cursor', 'go_input_modifier', '')","title":""},{"docid":"b14f7b51831e081ee793e34d442312a4","score":"0.6640559","text":"def register_hooks(self, hooks: List[Hook]):\n if not hasattr(self, '_hooks'):\n self._hooks: List[Hook] = []\n for hook in hooks:\n hook.register()\n self._hooks.append(hook)","title":""},{"docid":"c29c6b427d21004ac609bfb2d25391cc","score":"0.6605009","text":"def hook_all():\n global hook_command_run, hooks\n priority = \"\"\n version = weechat.info_get(\"version_number\", \"\") or 0\n # use high priority for hook to prevent conflict with other plugins/scripts\n # (WeeChat >= 0.3.4 only)\n if int(version) >= 0x00030400:\n priority = \"2000|\"\n for hook, value in hook_command_run.items():\n if hook not in hooks:\n hooks[hook] = weechat.hook_command_run(\"%s%s\" % (priority, value[0]),\n value[1], \"\")\n if \"modifier\" not in hooks:\n hooks[\"modifier\"] = weechat.hook_modifier(\n \"input_text_display_with_cursor\", \"input_modifier\", \"\")","title":""},{"docid":"28d97868f2cac67cd0e8cc6006ca1d2c","score":"0.65615565","text":"def add_hook(self, hook):\n if hook not in [type(me) for me in self.hooks]:\n self.__hooks += [hook()]","title":""},{"docid":"0cb8b3979e2bf906d4dc8356ad14ebb7","score":"0.6503424","text":"def _setup_hooks(self): # pragma no cover\n pass","title":""},{"docid":"fa0b1cadc33681909a5166075eac3202","score":"0.6501873","text":"def addhook(self, name, uname, function):\n self.server.addhook(name, \"%s:%s\" % (self.index, uname), function)\n self.hooks.append((name, \"%s:%s\" % (self.index, uname)))","title":""},{"docid":"c5c7e9824dc521cf94e916cb68430537","score":"0.64930224","text":"def hooks_initializer(initializer):\n # Register default build hooks\n initializer.register_hook(hooks.add_versions_chooser)\n initializer.register_hook(hooks.add_warning)","title":""},{"docid":"4344c7329eb0f2cf2e62da3a45bb6d00","score":"0.6403244","text":"def _register_required_hooks(self):\n\n packaging_services.register_hook(PackagingHook())","title":""},{"docid":"1f736d2001be5931c63ce218fa4d8bb9","score":"0.6390347","text":"def register_hooks(self):\n\n self.on_pre_GET += on_list\n self.on_delete_item += on_delete\n self.on_insert += init_document_with_acl\n self.on_update += on_update","title":""},{"docid":"fb89b8f5d17bf4cb987b08cf0ec8efdc","score":"0.63517725","text":"def pytest_addhooks(pluginmanager) -> None:\n from . import newhooks\n\n pluginmanager.add_hookspecs(newhooks)","title":""},{"docid":"08edef69582cf0ea9d879b20c97d29c6","score":"0.63485664","text":"def init_hooks():\n raise NotImplementedError()","title":""},{"docid":"988c259581ed00239a5e4b0bb9dfd80f","score":"0.6284471","text":"def run_hooks(self, config):\n for hook in self.setup_hooks:\n hook(config)","title":""},{"docid":"ddfac105d4a5917c847228d02b558d0c","score":"0.6270114","text":"def register_hook(self, hook):\n self._hooks.append(hook)","title":""},{"docid":"63d9dee33b36016b6d95e7ff7079755f","score":"0.6270062","text":"def pytest_addhooks(pluginmanager):\n from seekret.apitest.pytest_plugin import newhooks\n pluginmanager.add_hookspecs(newhooks)","title":""},{"docid":"8b98798e5110a7ae6e8ac8286d29d7c3","score":"0.6247726","text":"def add_events(self):\n self.bot.event(self.on_ready)","title":""},{"docid":"fed26d9ec1952a689b322a0a269bff07","score":"0.6242092","text":"def _addAEHooks():\n # Realflow uses the AEshapeHooks global variable as a convention for sharing AEshapeTemplate overrides,\n # so we will too, unless a more popular convention is found.\n pm.melGlobals.initVar('string[]', 'AEshapeHooks')\n hooks = list(pm.melGlobals['AEshapeHooks'])\n import mtoa.ui.ae.templates\n procName = utils.pyToMelProc(mtoa.ui.ae.templates.loadArnoldTemplate, [('string', 'nodeName')], useName=True)\n hooks.append(procName)\n pm.melGlobals['AEshapeHooks'] = hooks","title":""},{"docid":"afbcf0e45ef1bcc1330e7d298d50d166","score":"0.62211084","text":"def add_hook(self, command, method):\n self.hooks[command] = method","title":""},{"docid":"b24452a621b7bae3da58941d871f16db","score":"0.6214256","text":"def install_hooks(self, pipe):\n assert len(self.hooks) == 0\n text_encoder_targets = self._get_target_modules(pipe.text_encoder, \"lora_te\", [\"CLIPAttention\", \"CLIPMLP\"])\n unet_targets = self._get_target_modules(pipe.unet, \"lora_unet\", [\"Transformer2DModel\", \"Attention\"])\n for name, target_module in text_encoder_targets + unet_targets:\n hook = LoRAHook()\n hook.install(target_module)\n self.hooks[name] = hook\n # print(name)\n\n self.device = pipe.device\n self.dtype = pipe.unet.dtype","title":""},{"docid":"f5be4d905626ef49be5a636ab0ea8ee5","score":"0.6170191","text":"def game_hooks() -> GameHooks:\n return _game_hooks","title":""},{"docid":"1484d616169295d164f73b32366494ad","score":"0.6154442","text":"def run(self, hooks):\n if settings['disable_hooks']:\n return\n\n queue = []\n for value in hooks.values():\n value = seedlib.apply_template(value, self.values)\n queue.append(value)\n \n for command in queue:\n print ('info: running hook \"%s\"' % command)\n seedlib.run_pipe(command)","title":""},{"docid":"482eb32d67492d3848f3b2001ca25120","score":"0.61518055","text":"def setup(bot):\n bot.add_cog(Events(bot))","title":""},{"docid":"e40ea95a597c036314c0ac0b5a4bb00d","score":"0.6124972","text":"def startHook(self):\n # Empty ","title":""},{"docid":"e5b43f0224a2a54a6a30b1fdb6d5b8f1","score":"0.6113805","text":"def set_bottle_hooks(self, app, shutdown, rollback):\n if hasattr(app, 'hook'):\n app.hook('after_request')(shutdown)","title":""},{"docid":"925a2a709e21bed37d6e591bc6029891","score":"0.611031","text":"def install_hooks(config):\n config.on_create.extend([copy_environ, hide_cookie])\n config.on_create.insert(0, update_report)","title":""},{"docid":"f6c60de55b36b553993bc477dfb188a7","score":"0.607913","text":"def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n parse_source=parse_source,\n )\n return hook","title":""},{"docid":"b44b466b97d7a6660a00d78c539037c5","score":"0.6075598","text":"def add_hook(self, hook):\n hook_addr = hook.identifier\n \n if self.hooks.has_key(hook_addr):\n self.hooks[hook_addr].append(hook)\n logging.debug(\"Adding another hook for addr 0x%08x (name: %s)\" % \\\n (hook_addr,\n hook.name))\n else:\n self.hooks[hook_addr] = [hook]","title":""},{"docid":"a8255d4c578bc301629d6436a109124a","score":"0.60621554","text":"async def initialize_code_hook_functions(self):\n for ability in await self.data_svc.locate('abilities'):\n for executor in ability.executors:\n if executor.code and executor.language:\n executor.HOOKS[executor.language] = self.generate_ability_execution_method","title":""},{"docid":"8b80ce9f5a3f87e9bb9c1642bdcfb4a3","score":"0.60540694","text":"def setup(bot):\n bot.add_cog(EventLoops(bot))","title":""},{"docid":"0c45dce46acba5ba5710436ac8ac26b6","score":"0.6000275","text":"def setup(bot):\n bot.add_cog(Logs(bot))","title":""},{"docid":"36d5b30cf031b019fc2c3b8fed1a6dee","score":"0.5998982","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Tags(bot))","title":""},{"docid":"1944791d46eca2a6bbb32d2297613235","score":"0.59902436","text":"def assign_standard_hooks(obj, get, conf):\n hook_names = ['step_hook', 'post_process_hook',\n 'post_process_hook_final', 'pre_process_hook']\n for hook_name in hook_names:\n setattr(obj, hook_name, conf.get_function(get(hook_name, None)))","title":""},{"docid":"c773fd5a7366f71b8bc48cf16391dd3a","score":"0.59853834","text":"def _register_hook(self, hook_name, func):\r\n self.hooks.setdefault(hook_name, []).append(func)","title":""},{"docid":"a5a3262430aa75a2dc4230898591f488","score":"0.59784955","text":"def add_hook(self, level, location, hook, position=-1):\n\n if location not in self.levels[level].hooks:\n self.levels[level].hooks[location] = []\n\n if position == -1:\n position = len(self.levels[level].hooks[location])\n\n self.levels[level].hooks[location].insert(position, hook)","title":""},{"docid":"cc99595a1169c0fa58e110dd5336a50f","score":"0.59615684","text":"def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n source_init=source_init,\n transform_source=transform_source,\n )\n return hook","title":""},{"docid":"cc99595a1169c0fa58e110dd5336a50f","score":"0.59615684","text":"def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n source_init=source_init,\n transform_source=transform_source,\n )\n return hook","title":""},{"docid":"42af3093cb3fc5de769600b2d9bcde98","score":"0.5953135","text":"def run_hooks(hooks, data):\n for hook_key, hook_class in hooks.items():\n hook = hook_class(data)\n log.info('Running {} hook'.format(hook_key))\n hook.run()","title":""},{"docid":"b0a8b923b37eba8f732792eb53cb6b71","score":"0.5936176","text":"async def run_init_hooks(self) -> None:\n self.logger.info(\"Running init hooks\")\n for hook in self.init_hooks:\n self.logger.info(f\"Running init hook for plugin {hook.plugin}\")\n await hook.func() if inspect.iscoroutinefunction(hook.func) else hook.func()","title":""},{"docid":"d65c171195ea46e096e3c1c55ae1c291","score":"0.5925443","text":"def on(self, hook):\n\n def decorator(handler):\n self.registry[hook].append(handler)\n\n return handler\n\n return decorator","title":""},{"docid":"792e20dbb3796793cf5123be2589cedc","score":"0.59254354","text":"def register_hook(self, func):\r\n\r\n if func not in self.hooks:\r\n self.hooks.append(func)","title":""},{"docid":"992e660201db15b027d78bbf73909ecd","score":"0.5917185","text":"def _before_application_run(self):\n\n for hook in self._get_hooks():\n hook.before_application_run()","title":""},{"docid":"4098a25602a66288543c0be8a1720c0a","score":"0.59140986","text":"def setup(bot):\n bot.add_cog(Dictionary())","title":""},{"docid":"128feab0e9e690fd164055f3dc6224ce","score":"0.5912825","text":"def _after_application_loaded(self):\n\n for hook in self._get_hooks():\n hook.after_application_loaded()","title":""},{"docid":"7a3b2b4a6f0402b41ae4eff4cf4fc845","score":"0.5884704","text":"def on_start(self):\n self.add_bot(ScoutManager(bot_player=self))\n self.add_bot(BuildManager(bot_player=self))\n self.add_bot(GatherManager(bot_player=self))\n self.add_bot(DefenseManager(bot_player=self))\n self.init_request_board()","title":""},{"docid":"2a582bbb4bf0a67821652519bd94f5ec","score":"0.588175","text":"def add_hook(cls, hook_type, hook_func):\r\n if hook_type not in cls._hooks_map:\r\n cls._hooks_map[hook_type] = []\r\n\r\n cls._hooks_map[hook_type].append(hook_func)","title":""},{"docid":"200dd6ea332f6486cfb958496dd7eafe","score":"0.5878965","text":"async def setup(bot) -> None:\n await bot.add_cog(HotReload(bot))","title":""},{"docid":"61cbfc3f4a2d622fbd0030e22de81d12","score":"0.58777094","text":"def setup(bot: Bot):\n bot.add_cog(EventoryCog(bot))\n log.info(\"loaded Eventory extension!\")","title":""},{"docid":"269b00d45f4e69768306379771ce086d","score":"0.5848767","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Utilities(bot))","title":""},{"docid":"69a5ab4cf1d8372d05b8687ae7d4fdc2","score":"0.58468145","text":"def setup(bot):\n bot.add_cog(Tickets(bot))","title":""},{"docid":"bae9f0da4d2e05b9bdea26aa011ab59a","score":"0.58353835","text":"def setup(bot: Bot) -> None:\n bot.add_cog(LemonStuff(bot))\n log.info(\"Cog loaded: LemonStuff\")","title":""},{"docid":"9ca07bcea77b5998babc1f8f475dda35","score":"0.5832757","text":"def hooks(self):\n return self.__hooks","title":""},{"docid":"b6d22ce1884a7faa025d158e62aa578e","score":"0.5821258","text":"def hook_dispatch(self) -> None:\n if sys.argv[2].strip().lower() == \"add\":\n self.add_hook(\n hook_state = sys.argv[3],\n cmd = \" \".join(sys.argv[4:]),\n )\n\n elif sys.argv[2].strip().lower() == \"del\":\n self.del_hook(\n hook_state = sys.argv[3],\n )","title":""},{"docid":"e666c8c599a3a0a56b13e57e58b45c50","score":"0.58076733","text":"def _add_trachook(self, req, hookfile, current):\n base_url = self.config.get('trac', 'base_url')\n # try to append hook\n try:\n fp = open(hookfile, 'a')\n fp.writelines(os.linesep)\n if 'REPOS=\"$1\"' not in current:\n fp.writelines('REPOS=\"$1\"' + os.linesep)\n if 'REV=\"$2\"' not in current:\n fp.writelines('REV=\"$2\"' + os.linesep)\n if 'LOG=' not in current:\n fp.writelines('LOG=`/usr/bin/svnlook log -r $REV $REPOS`' \\\n + os.linesep)\n if 'AUTHOR=' not in current:\n fp.writelines('AUTHOR=`/usr/bin/svnlook author -r $REV ' + \\\n '$REPOS`' + os.linesep)\n if 'TRAC_ENV=' not in current:\n fp.writelines('TRAC_ENV=\"'+self.env.path+'\"' + os.linesep)\n if 'TRAC_URL=' not in current:\n fp.writelines('TRAC_URL=\"'+base_url+'\"' + os.linesep)\n fp.writelines('/usr/bin/python /usr/share/trac/contrib/' + \\\n 'trac-post-commit-hook \\\\' + os.linesep)\n fp.writelines('-p \"$TRAC_ENV\" \\\\' + os.linesep)\n fp.writelines('-r \"$REV\" \\\\' + os.linesep)\n fp.writelines('-u \"$AUTHOR\" \\\\' + os.linesep)\n fp.writelines('-m \"$LOG\" \\\\' + os.linesep)\n fp.writelines('-s \"$TRAC_URL\"' + os.linesep)\n fp.writelines(os.linesep)\n fp.close()\n except Exception:\n raise TracError(\"Can't write repository hook %s\" % hookfile)","title":""},{"docid":"b1a651ece69c23f871ae87944408d516","score":"0.5799101","text":"def run_hooks(cls, hook_type, *args, **kwargs):\r\n hook_funcs = cls._hooks_map.get(hook_type) or []\r\n for hook_func in hook_funcs:\r\n hook_func(*args, **kwargs)","title":""},{"docid":"4fcf31c68e866cf847c0a5373217c2b3","score":"0.5785056","text":"def register_default_hooks(self, *hooks: Hook, as_group: bool = True):\n self.hooks.extend(hooks)\n\n if as_group and self.group is not None:\n for g in get_group_by_name(self.group):\n if g is not self:\n g.register_hooks(*hooks, as_group=False)","title":""},{"docid":"396772b6a60c6762461d747ec1f4b8dc","score":"0.5783806","text":"async def setup(bot) -> None:\n await bot.add_cog(Gambling(bot))","title":""},{"docid":"f50c868844e933e05acf6e0224d81642","score":"0.57818186","text":"def install_custom_hook(self, hook_name):\n self._install_custom_hook(hook_name)","title":""},{"docid":"e677a9f8d0ec6ba94293c47993d275df","score":"0.5769153","text":"def add_hook(self,\n hook_state: str,\n cmd: str,\n ) -> None:\n state = hook_state.strip().lower()\n\n if state not in self.valid_hook_states:\n print(f\"Invalid hook state: {hook_state}. Valid states are: {self.valid_hook_states}\")\n return\n\n # At this point we simply overwrite any existing command\n # and store it to the data file\n data = self.load()\n data[\"hooks\"][state] = cmd\n self.save(data)\n\n # If cmd is None this function as been called from within del_hook()\n if cmd is None:\n print(f\"Deleted hook command for state {state}\")\n else:\n print(f\"Added hook command for state {state}: {cmd}\")","title":""},{"docid":"b8c18e6f43da52ca2d550b82ddba2845","score":"0.5764374","text":"def install_api_hooks(dbg, dll_api_hooks):\n # _pt_log(\"remain system dlls to install api hook: %d\" % (len(dll_api_hooks)))\n\n new_sys_dll = dbg.system_dlls[-1]\n dll_name = new_sys_dll.name.lower()\n\n for (dll_name, api_ctrl_list) in dll_api_hooks.items():\n if dbg.check_has_system_dll(dll_name):\n for api_ctrl in api_ctrl_list:\n dbg_set_api_bp(dbg, api_ctrl)\n del dll_api_hooks[dll_name]","title":""},{"docid":"e63584e75c449d989e5334871a61d146","score":"0.574739","text":"def setup(bot):\n bot.add_cog(Messages(bot))","title":""},{"docid":"ba6688fa7d65bc9590cd3596cc6eead6","score":"0.5747233","text":"def InstallHook(self):\r\n pass","title":""},{"docid":"e17e625fb36c2298dbac9f5647e71522","score":"0.5714983","text":"def setup(bot: Bot) -> None:\n bot.add_cog(MessageLogCog(bot))","title":""},{"docid":"efb0f516bd6a1e7fe91485da5245ead6","score":"0.5709663","text":"def registerhooks(entry=[], exit=[], *args, **kwargs):\n def wrap(f):\n _kwargs = dict(kwargs)\n _kwargs.update({'entry': entry, 'exit': exit})\n def wrapped_f(*args, **kwargs):\n return f(*args, **kwargs)\n wrapped_f.__hooks = _kwargs\n return wrapped_f\n return wrap","title":""},{"docid":"044a2ad7b8e51eec0d32c615365ec6bc","score":"0.570686","text":"def setup(bot):\n bt.INFO('Loading Misc.py')\n bot.add_cog(Misc(bot))","title":""},{"docid":"71fa5b3abe2be9c9c55ea45b9c82dd43","score":"0.56982493","text":"async def setup(self) -> None:\n if self.playing_title is not None:\n await self.change_presence(\n activity=discord.Game(name=self.playing_title),\n )\n\n self.tree.clear_commands(guild=None)\n for guild in self.guilds:\n self.tree.clear_commands(guild=guild)\n\n commands = registered_app_commands()\n for command in commands:\n self.tree.add_command(command)\n logger.info(f'registered {len(commands)} app commands')\n\n listeners = registered_listeners()\n for listener in listeners:\n self.add_listener(listener.func, listener.event)\n logger.info(f'registered {len(listeners)} listeners')\n\n if self._cmd_group_exts is not None:\n for ext in self._cmd_group_exts:\n self.tree.add_command(ext)\n await ext.post_init(self)\n logger.info(f'registered {ext.name} command group')\n\n await self.tree.sync()\n for guild in self.guilds:\n await self.tree.sync(guild=guild)","title":""},{"docid":"fccb4dede5aa12489b6365d7dac5f30b","score":"0.5684656","text":"def setup(bot):\n bot.add_cog(LoadHelp(bot))","title":""},{"docid":"59ace20514f392a89f07428e9134c5bc","score":"0.5670448","text":"def list_hooks(self) -> None:\n data = self.load()\n hooks = data.get(\"hooks\", {})\n\n print(\"Current hook command configuration:\")\n for state in self.valid_hook_states:\n cmd = hooks.get(state, None)\n print(f\" {state:6s} {cmd}\")","title":""},{"docid":"c13e1b3cbfa89944d2a510fb35f6ed58","score":"0.5668042","text":"def on_bot_startup(bot):\n pass","title":""},{"docid":"5a8ba5a4c77b793ba734b50ced6e7aaf","score":"0.56633395","text":"def register(name):\r\n global SUPPORTED_HOOKS\r\n if not name in SUPPORTED_HOOKS:\r\n SUPPORTED_HOOKS.append(name)","title":""},{"docid":"5ad29e41de5a20f7deb3ed694f518194","score":"0.5659891","text":"def add_permissions_hook(self, hook: commands.CheckPredicate) -> None:\n self._permissions_hooks.append(hook)","title":""},{"docid":"85d8761c3de9dfeea1cd5f99b7ec825c","score":"0.56558925","text":"def register_hook(self, event, hook):\r\n\r\n if isinstance(hook, collections.Callable):\r\n self.hooks[event].append(hook)\r\n elif hasattr(hook, '__iter__'):\r\n self.hooks[event].extend(h for h in hook if isinstance(h, collections.Callable))","title":""},{"docid":"3258ecf3f6e2c20cf1fd7f7cecca9cc6","score":"0.5652265","text":"def get_hooks():\n tensor_to_log = {'loss': loss}\n # tensor_to_log = {}\n logging_tensor_hook = tf.train.LoggingTensorHook(\n tensors=tensor_to_log,\n every_n_iter=2\n )\n return [logging_tensor_hook]","title":""},{"docid":"55c7449a2267ac9fd64c1310ad51e382","score":"0.56491655","text":"def apply_hooks():\n for line in fileinput.input():\n std_input = line.split(\" \")\n oldrev, newrev, refname = [item.strip() for item in std_input]\n # Check for zero commit, check branch deletions\n # also, avoid new package additions\n if ZERO_COMMIT in (oldrev, newrev):\n continue\n # insert new hooks here\n package_start_build(oldrev, newrev, refname)\n # Pass in 'length' as an optional argument\n # default 'length' of feed is 499\n write_rss_feed(oldrev, newrev, refname, length=499)\n return","title":""},{"docid":"f70c1c6821cc9de1cb8cab705ac6ee85","score":"0.5649146","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Internal(bot))","title":""},{"docid":"0f6a737d4f0384ad0b93c99010ebf635","score":"0.5637375","text":"def hook(self, server=None):\n\n if server is not None:\n logging.debug('Adding hook route to web server')\n server.add_route(\n Wrapper(callable=self.get_hook(),\n route=self.context.get('server.hook', '/hook')))\n\n if (self.context.get('server.binding') is not None\n and self.context.get('server.url') is not None):\n\n self.space.register(\n hook_url=self.context.get('server.url')\n + self.context.get('server.hook', '/hook'))","title":""},{"docid":"1b50dc58fd7d07d03898d7d1205095a7","score":"0.56359833","text":"def setup(bot):\n\n bot.add_cog(Eval(bot))","title":""},{"docid":"b683c47d5bdae345391754b51c5bac6f","score":"0.5632748","text":"def add_callback(self, hook, function, pre=False):\n if hook not in self._hooks:\n raise Exception('This program has no hook named \"%s\"' % hook)\n hook_def = self._hook_defs.get(hook, None)\n\n if (hook_def is None or not isinstance(hook_def, FunctionChain)):\n raise TypeError(\"Cannot add callback to hook '%s'; not a \"\n \"FunctionChain. (%s)\" % (hook, type(hook_def)))\n\n if pre:\n hook_def.insert(0, function)\n else:\n hook_def.append(function)\n\n # TODO: remove or resurrect\n #self._install_dep_callbacks(function)\n self._need_build = True","title":""},{"docid":"d4499e1842dac2feaba1f51e52a73f48","score":"0.56314707","text":"def run_hooks():\n\n hookdir = '/usr/lib/oem-config/post-install'\n\n if os.path.isdir(hookdir):\n # Exclude hooks containing '.', so that *.dpkg-* et al are avoided.\n hooks = filter(lambda entry: '.' not in entry, os.listdir(hookdir))\n child_env = dict(os.environ)\n child_env['DEBIAN_FRONTEND'] = 'noninteractive'\n if 'DEBIAN_HAS_FRONTEND' in child_env:\n del child_env['DEBIAN_HAS_FRONTEND']\n for hookentry in hooks:\n hook = os.path.join(hookdir, hookentry)\n if os.access(hook, os.X_OK):\n # Errors are ignored at present, although this may change.\n subprocess.call([hook], env=child_env)","title":""},{"docid":"1b864916beac1e7a3522c10bae5c0f74","score":"0.56313306","text":"def run_hook(db, hook_name, **kwargs):\n\n\t# Get all hooks in the DB\n\thooks = db.get_hook()\n\thooks = [dict(_)['name'] for _ in hooks]\n\n\t# Iteratue through the hooks\n\tfor hook in hooks:\n\t\t# Try importing the hook module and finding any functions that have used the ydl.hook decorate\n\t\t# which means it has a _hooks attribute indicating which hooks it accepts\n\t\ttry:\n\t\t\tz = importlib.import_module(hook)\n\t\t\tfor x in dir(z):\n\t\t\t\to = getattr(z, x)\n\t\t\t\tif hasattr(o, '_hooks') and hook_name in o._hooks:\n\t\t\t\t\to(hook_name, db, **kwargs)\n\t\t\t\tdel o\n\t\t\tdel z\n\t\texcept Exception as e:\n\t\t\ttraceback.print_exc()\n\t\t\t# Move along","title":""},{"docid":"2cfa6dc8d943c488df97857dd75522ee","score":"0.5624743","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Reminder(bot))\n log.info(\"Cog loaded: reminder\")","title":""},{"docid":"e7df076bc40f988cd86f24cb3e5db89e","score":"0.56241655","text":"def setup(bot: Bot) -> None:\n bot.add_cog(SpookyEightBall())","title":""},{"docid":"53fa260f6f97a677c52fabbf44b86b38","score":"0.5622936","text":"def add_commands(self, *args):\n for command in args:\n self._script += command + '\\n'","title":""},{"docid":"7496c40c7d9e4b36c3df9cb62d2c5ad4","score":"0.55818033","text":"def run(self, hook, *args, **kwargs):\n\n assert hook in self._callbacks, f\"hook '{hook}' not found in callbacks {self._callbacks}\"\n\n for logger in self._callbacks[hook]:\n logger['callback'](*args, **kwargs)","title":""},{"docid":"9499c2213ce0272e0a32ffd4439f8e08","score":"0.5580782","text":"def _init_airflow_core_hooks(self):\n core_dummy_hooks = {\n \"generic\": \"Generic\",\n \"email\": \"Email\",\n \"mesos_framework-id\": \"Mesos Framework ID\",\n }\n for key, display in core_dummy_hooks.items():\n self._hooks_lazy_dict[key] = HookInfo(\n hook_class_name=None,\n connection_id_attribute_name=None,\n package_name=None,\n hook_name=display,\n connection_type=None,\n connection_testable=False,\n )\n for cls in [FSHook, PackageIndexHook]:\n package_name = cls.__module__\n hook_class_name = f\"{cls.__module__}.{cls.__name__}\"\n hook_info = self._import_hook(\n connection_type=None,\n provider_info=None,\n hook_class_name=hook_class_name,\n package_name=package_name,\n )\n self._hook_provider_dict[hook_info.connection_type] = HookClassProvider(\n hook_class_name=hook_class_name, package_name=package_name\n )\n self._hooks_lazy_dict[hook_info.connection_type] = hook_info","title":""},{"docid":"4fd2b1eb01258336636e0bd8adc0178b","score":"0.5579576","text":"def _trigger_hooks(self, hook_dict, hook_name, **kwargs):\n for attached_callback in hook_dict[hook_name].attached_callbacks:\n try:\n if attached_callback.callback(**kwargs):\n if attached_callback.action == MMHookAct.LOAD_MODULE:\n # load the module!\n self.logger.debug('some hook returned true, '\n 'loading module {}'\n .format(attached_callback.argument))\n # module must accept same kwargs, this is mandatory\n # with this discovery event\n try:\n cb_arg = attached_callback.argument\n module_name = cb_arg.get_module_desc().arg_name\n self.load_module(module_name,\n **kwargs)\n except Exception as ex:\n self.logger.error('loading of module of class '\n '\"{}\" failed with: {}'\n .format(cb_arg.__name__,\n str(ex)))\n elif attached_callback.action == MMHookAct.UNLOAD_MODULE:\n # unload the attached module\n self.logger.debug('a hook required module '\n '{} to be unloaded'\n .format(attached_callback.argument))\n self.unload_module(attached_callback.argument)\n except Exception as ex:\n self.logger.error('failed to call function '\n '{} attached to \"{}\" with: {}'\n .format(attached_callback,\n hook_name,\n str(ex)))","title":""},{"docid":"b19e4deca1d85266e102d958ffa41ff0","score":"0.55784094","text":"def setup(bot):\n bot.add_cog(ReminderCog(bot))","title":""},{"docid":"8e2d79bb76fc224f12b869daf1739030","score":"0.55750185","text":"def runHook(self):\n self.run()","title":""},{"docid":"a78408bd292126b7640c238bfb0c8ec8","score":"0.55676615","text":"async def setup(bot: Bot) -> None:\n await bot.add_cog(Patreon(bot))","title":""},{"docid":"05f45412830a1b3a2b2c9b7bdb0d1cbb","score":"0.556712","text":"def hooks(self) -> MutableMapping[str, HookInfo | None]:\n self.initialize_providers_hooks()\n # When we return hooks here it will only be used to retrieve hook information\n return self._hooks_lazy_dict","title":""}],"string":"[\n {\n \"docid\": \"42d07c0d20fbaab7a3ce210e4325d89b\",\n \"score\": \"0.7362344\",\n \"text\": \"def hook_events(self):\\n for cmd in self.commands:\\n self.core.add_command(cmd, self)\\n for event in self.hooks:\\n self.core.add_callback(event, self)\\n for cmd in self.rawhooks:\\n self.core.add_raw(cmd, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5450bb7786ed53fe294cc35583a4b091\",\n \"score\": \"0.7053781\",\n \"text\": \"def set_game_hooks(hooks: GameHooks) -> None:\\n global _game_hooks\\n _game_hooks = hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d717911c38f804986b4721041fd10b5\",\n \"score\": \"0.69996136\",\n \"text\": \"def register_hooks(self, hooks):\\n hooks = [h for h in hooks if h is not None]\\n for h in hooks:\\n assert isinstance(h, HookBase)\\n # To avoid circular reference, hooks and trainer cannot own each other.\\n # This normally does not matter, but will cause memory leak if the\\n # involved objects contain __del__:\\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\\n h.trainer = weakref.proxy(self)\\n self._hooks.extend(hooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d717911c38f804986b4721041fd10b5\",\n \"score\": \"0.69996136\",\n \"text\": \"def register_hooks(self, hooks):\\n hooks = [h for h in hooks if h is not None]\\n for h in hooks:\\n assert isinstance(h, HookBase)\\n # To avoid circular reference, hooks and trainer cannot own each other.\\n # This normally does not matter, but will cause memory leak if the\\n # involved objects contain __del__:\\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\\n h.trainer = weakref.proxy(self)\\n self._hooks.extend(hooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a249d756642ea01dd9ad4457bff3712\",\n \"score\": \"0.6859483\",\n \"text\": \"def enable_hooks(self) -> None:\\n self.hooks_enabled = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a1126a6ab2f0b85fc66e57b16e028e\",\n \"score\": \"0.6821246\",\n \"text\": \"def putInHooks ():\\n\\n # Need to modify Leo's Kernel first\\n # Overwrite existing Leo methods.\\n g.funcToMethod(replaceTargetFileIfDifferent,leoAtFile.atFile)\\n g.funcToMethod(massageComment,leoImport.leoImportCommands)\\n\\n # Add new methods used by this plugin to various classes.\\n g.funcToMethod(openForRead,leoAtFile.atFile)\\n g.funcToMethod(openForWrite,leoAtFile.atFile)\\n g.funcToMethod(gotoLineNumberOpen,leoCommands.Commands)\\n g.funcToMethod(applyLineNumberMappingIfAny, leoCommands.Commands)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d6493b3885f6278f8212ed6423e014\",\n \"score\": \"0.67838776\",\n \"text\": \"def prepare_hooks(self, hooks):\\r\\n for event in hooks:\\r\\n self.register_hook(event, hooks[event])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d6493b3885f6278f8212ed6423e014\",\n \"score\": \"0.67838776\",\n \"text\": \"def prepare_hooks(self, hooks):\\r\\n for event in hooks:\\r\\n self.register_hook(event, hooks[event])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc16bf846e0425e5913d8b44001ce900\",\n \"score\": \"0.6763753\",\n \"text\": \"def configure_hooks_feature(self):\\n hooks_folder = getattr(config, \\\"HOOKS_FOLDER\\\", \\\"./hooks\\\")\\n if not os.path.exists(hooks_folder):\\n self.logger.info(\\\"Hooks folder '%s' doesn't exist, creating it\\\" % hooks_folder)\\n os.makedirs(hooks_folder)\\n self.hook_files = glob.glob(os.path.join(hooks_folder, \\\"*.py\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e754d61588c30b277f496a6f5fe68f2\",\n \"score\": \"0.6674685\",\n \"text\": \"def _init_hooks(self):\\n self._hooks = Hooks()\\n self._hooks.ready_to_run = self._init_hexrays_hooks\\n self._hooks.hook()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e754d61588c30b277f496a6f5fe68f2\",\n \"score\": \"0.6674685\",\n \"text\": \"def _init_hooks(self):\\n self._hooks = Hooks()\\n self._hooks.ready_to_run = self._init_hexrays_hooks\\n self._hooks.hook()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0489a542c08e02331af5086e2981ff82\",\n \"score\": \"0.66713774\",\n \"text\": \"def hook(self):\\n\\n\\t\\tself.db.begin()\\n\\n\\t\\tret = self.db.get_hook()\\n\\t\\texisting = [dict(_)['name'] for _ in ret]\\n\\n\\t\\t# Add hook modules\\n\\t\\tif len(self.args.hook):\\n\\t\\t\\tfor h in self.args.hook:\\n\\t\\t\\t\\th = h.strip()\\n\\t\\t\\t\\tif h in existing:\\n\\t\\t\\t\\t\\tprint(\\\"Hook module '%s' already in hook list\\\" % h)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.db.add_hook(h)\\n\\t\\t\\t\\t\\tself.db.commit()\\n\\t\\t\\t\\t\\tprint(\\\"Added: %s\\\" % h)\\n\\n\\t\\t# List all hooks\\n\\t\\telse:\\n\\t\\t\\tif len(existing):\\n\\t\\t\\t\\tprint(\\\"Existing hooks:\\\")\\n\\t\\t\\t\\texisting = sorted(existing)\\n\\n\\t\\t\\t\\tfor h in existing:\\n\\t\\t\\t\\t\\tprint(\\\"\\\\t%s\\\" % h)\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"No existing hooks\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b303d72b01b79b91713af6bf520ec161\",\n \"score\": \"0.66632324\",\n \"text\": \"def go_hook_all():\\n global hooks\\n priority = ''\\n version = weechat.info_get('version_number', '') or 0\\n # use high priority for hook to prevent conflict with other plugins/scripts\\n # (WeeChat >= 0.3.4 only)\\n if int(version) >= 0x00030400:\\n priority = '2000|'\\n for hook, value in HOOK_COMMAND_RUN.items():\\n if hook not in hooks:\\n hooks[hook] = weechat.hook_command_run(\\n '%s%s' % (priority, value[0]),\\n value[1], '')\\n if 'modifier' not in hooks:\\n hooks['modifier'] = weechat.hook_modifier(\\n 'input_text_display_with_cursor', 'go_input_modifier', '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14f7b51831e081ee793e34d442312a4\",\n \"score\": \"0.6640559\",\n \"text\": \"def register_hooks(self, hooks: List[Hook]):\\n if not hasattr(self, '_hooks'):\\n self._hooks: List[Hook] = []\\n for hook in hooks:\\n hook.register()\\n self._hooks.append(hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29c6b427d21004ac609bfb2d25391cc\",\n \"score\": \"0.6605009\",\n \"text\": \"def hook_all():\\n global hook_command_run, hooks\\n priority = \\\"\\\"\\n version = weechat.info_get(\\\"version_number\\\", \\\"\\\") or 0\\n # use high priority for hook to prevent conflict with other plugins/scripts\\n # (WeeChat >= 0.3.4 only)\\n if int(version) >= 0x00030400:\\n priority = \\\"2000|\\\"\\n for hook, value in hook_command_run.items():\\n if hook not in hooks:\\n hooks[hook] = weechat.hook_command_run(\\\"%s%s\\\" % (priority, value[0]),\\n value[1], \\\"\\\")\\n if \\\"modifier\\\" not in hooks:\\n hooks[\\\"modifier\\\"] = weechat.hook_modifier(\\n \\\"input_text_display_with_cursor\\\", \\\"input_modifier\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d97868f2cac67cd0e8cc6006ca1d2c\",\n \"score\": \"0.65615565\",\n \"text\": \"def add_hook(self, hook):\\n if hook not in [type(me) for me in self.hooks]:\\n self.__hooks += [hook()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb8b3979e2bf906d4dc8356ad14ebb7\",\n \"score\": \"0.6503424\",\n \"text\": \"def _setup_hooks(self): # pragma no cover\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0b1cadc33681909a5166075eac3202\",\n \"score\": \"0.6501873\",\n \"text\": \"def addhook(self, name, uname, function):\\n self.server.addhook(name, \\\"%s:%s\\\" % (self.index, uname), function)\\n self.hooks.append((name, \\\"%s:%s\\\" % (self.index, uname)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c7e9824dc521cf94e916cb68430537\",\n \"score\": \"0.64930224\",\n \"text\": \"def hooks_initializer(initializer):\\n # Register default build hooks\\n initializer.register_hook(hooks.add_versions_chooser)\\n initializer.register_hook(hooks.add_warning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4344c7329eb0f2cf2e62da3a45bb6d00\",\n \"score\": \"0.6403244\",\n \"text\": \"def _register_required_hooks(self):\\n\\n packaging_services.register_hook(PackagingHook())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f736d2001be5931c63ce218fa4d8bb9\",\n \"score\": \"0.6390347\",\n \"text\": \"def register_hooks(self):\\n\\n self.on_pre_GET += on_list\\n self.on_delete_item += on_delete\\n self.on_insert += init_document_with_acl\\n self.on_update += on_update\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb89b8f5d17bf4cb987b08cf0ec8efdc\",\n \"score\": \"0.63517725\",\n \"text\": \"def pytest_addhooks(pluginmanager) -> None:\\n from . import newhooks\\n\\n pluginmanager.add_hookspecs(newhooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08edef69582cf0ea9d879b20c97d29c6\",\n \"score\": \"0.63485664\",\n \"text\": \"def init_hooks():\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"988c259581ed00239a5e4b0bb9dfd80f\",\n \"score\": \"0.6284471\",\n \"text\": \"def run_hooks(self, config):\\n for hook in self.setup_hooks:\\n hook(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddfac105d4a5917c847228d02b558d0c\",\n \"score\": \"0.6270114\",\n \"text\": \"def register_hook(self, hook):\\n self._hooks.append(hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63d9dee33b36016b6d95e7ff7079755f\",\n \"score\": \"0.6270062\",\n \"text\": \"def pytest_addhooks(pluginmanager):\\n from seekret.apitest.pytest_plugin import newhooks\\n pluginmanager.add_hookspecs(newhooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b98798e5110a7ae6e8ac8286d29d7c3\",\n \"score\": \"0.6247726\",\n \"text\": \"def add_events(self):\\n self.bot.event(self.on_ready)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fed26d9ec1952a689b322a0a269bff07\",\n \"score\": \"0.6242092\",\n \"text\": \"def _addAEHooks():\\n # Realflow uses the AEshapeHooks global variable as a convention for sharing AEshapeTemplate overrides,\\n # so we will too, unless a more popular convention is found.\\n pm.melGlobals.initVar('string[]', 'AEshapeHooks')\\n hooks = list(pm.melGlobals['AEshapeHooks'])\\n import mtoa.ui.ae.templates\\n procName = utils.pyToMelProc(mtoa.ui.ae.templates.loadArnoldTemplate, [('string', 'nodeName')], useName=True)\\n hooks.append(procName)\\n pm.melGlobals['AEshapeHooks'] = hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afbcf0e45ef1bcc1330e7d298d50d166\",\n \"score\": \"0.62211084\",\n \"text\": \"def add_hook(self, command, method):\\n self.hooks[command] = method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24452a621b7bae3da58941d871f16db\",\n \"score\": \"0.6214256\",\n \"text\": \"def install_hooks(self, pipe):\\n assert len(self.hooks) == 0\\n text_encoder_targets = self._get_target_modules(pipe.text_encoder, \\\"lora_te\\\", [\\\"CLIPAttention\\\", \\\"CLIPMLP\\\"])\\n unet_targets = self._get_target_modules(pipe.unet, \\\"lora_unet\\\", [\\\"Transformer2DModel\\\", \\\"Attention\\\"])\\n for name, target_module in text_encoder_targets + unet_targets:\\n hook = LoRAHook()\\n hook.install(target_module)\\n self.hooks[name] = hook\\n # print(name)\\n\\n self.device = pipe.device\\n self.dtype = pipe.unet.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5be4d905626ef49be5a636ab0ea8ee5\",\n \"score\": \"0.6170191\",\n \"text\": \"def game_hooks() -> GameHooks:\\n return _game_hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1484d616169295d164f73b32366494ad\",\n \"score\": \"0.6154442\",\n \"text\": \"def run(self, hooks):\\n if settings['disable_hooks']:\\n return\\n\\n queue = []\\n for value in hooks.values():\\n value = seedlib.apply_template(value, self.values)\\n queue.append(value)\\n \\n for command in queue:\\n print ('info: running hook \\\"%s\\\"' % command)\\n seedlib.run_pipe(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"482eb32d67492d3848f3b2001ca25120\",\n \"score\": \"0.61518055\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Events(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40ea95a597c036314c0ac0b5a4bb00d\",\n \"score\": \"0.6124972\",\n \"text\": \"def startHook(self):\\n # Empty \",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b43f0224a2a54a6a30b1fdb6d5b8f1\",\n \"score\": \"0.6113805\",\n \"text\": \"def set_bottle_hooks(self, app, shutdown, rollback):\\n if hasattr(app, 'hook'):\\n app.hook('after_request')(shutdown)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"925a2a709e21bed37d6e591bc6029891\",\n \"score\": \"0.611031\",\n \"text\": \"def install_hooks(config):\\n config.on_create.extend([copy_environ, hide_cookie])\\n config.on_create.insert(0, update_report)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c60de55b36b553993bc477dfb188a7\",\n \"score\": \"0.607913\",\n \"text\": \"def add_hook(**_kwargs):\\n hook = import_hook.create_hook(\\n hook_name=__name__,\\n parse_source=parse_source,\\n )\\n return hook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44b466b97d7a6660a00d78c539037c5\",\n \"score\": \"0.6075598\",\n \"text\": \"def add_hook(self, hook):\\n hook_addr = hook.identifier\\n \\n if self.hooks.has_key(hook_addr):\\n self.hooks[hook_addr].append(hook)\\n logging.debug(\\\"Adding another hook for addr 0x%08x (name: %s)\\\" % \\\\\\n (hook_addr,\\n hook.name))\\n else:\\n self.hooks[hook_addr] = [hook]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8255d4c578bc301629d6436a109124a\",\n \"score\": \"0.60621554\",\n \"text\": \"async def initialize_code_hook_functions(self):\\n for ability in await self.data_svc.locate('abilities'):\\n for executor in ability.executors:\\n if executor.code and executor.language:\\n executor.HOOKS[executor.language] = self.generate_ability_execution_method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b80ce9f5a3f87e9bb9c1642bdcfb4a3\",\n \"score\": \"0.60540694\",\n \"text\": \"def setup(bot):\\n bot.add_cog(EventLoops(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c45dce46acba5ba5710436ac8ac26b6\",\n \"score\": \"0.6000275\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Logs(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d5b30cf031b019fc2c3b8fed1a6dee\",\n \"score\": \"0.5998982\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Tags(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1944791d46eca2a6bbb32d2297613235\",\n \"score\": \"0.59902436\",\n \"text\": \"def assign_standard_hooks(obj, get, conf):\\n hook_names = ['step_hook', 'post_process_hook',\\n 'post_process_hook_final', 'pre_process_hook']\\n for hook_name in hook_names:\\n setattr(obj, hook_name, conf.get_function(get(hook_name, None)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c773fd5a7366f71b8bc48cf16391dd3a\",\n \"score\": \"0.59853834\",\n \"text\": \"def _register_hook(self, hook_name, func):\\r\\n self.hooks.setdefault(hook_name, []).append(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a3262430aa75a2dc4230898591f488\",\n \"score\": \"0.59784955\",\n \"text\": \"def add_hook(self, level, location, hook, position=-1):\\n\\n if location not in self.levels[level].hooks:\\n self.levels[level].hooks[location] = []\\n\\n if position == -1:\\n position = len(self.levels[level].hooks[location])\\n\\n self.levels[level].hooks[location].insert(position, hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc99595a1169c0fa58e110dd5336a50f\",\n \"score\": \"0.59615684\",\n \"text\": \"def add_hook(**_kwargs):\\n hook = import_hook.create_hook(\\n hook_name=__name__,\\n source_init=source_init,\\n transform_source=transform_source,\\n )\\n return hook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc99595a1169c0fa58e110dd5336a50f\",\n \"score\": \"0.59615684\",\n \"text\": \"def add_hook(**_kwargs):\\n hook = import_hook.create_hook(\\n hook_name=__name__,\\n source_init=source_init,\\n transform_source=transform_source,\\n )\\n return hook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42af3093cb3fc5de769600b2d9bcde98\",\n \"score\": \"0.5953135\",\n \"text\": \"def run_hooks(hooks, data):\\n for hook_key, hook_class in hooks.items():\\n hook = hook_class(data)\\n log.info('Running {} hook'.format(hook_key))\\n hook.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a8b923b37eba8f732792eb53cb6b71\",\n \"score\": \"0.5936176\",\n \"text\": \"async def run_init_hooks(self) -> None:\\n self.logger.info(\\\"Running init hooks\\\")\\n for hook in self.init_hooks:\\n self.logger.info(f\\\"Running init hook for plugin {hook.plugin}\\\")\\n await hook.func() if inspect.iscoroutinefunction(hook.func) else hook.func()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65c171195ea46e096e3c1c55ae1c291\",\n \"score\": \"0.5925443\",\n \"text\": \"def on(self, hook):\\n\\n def decorator(handler):\\n self.registry[hook].append(handler)\\n\\n return handler\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792e20dbb3796793cf5123be2589cedc\",\n \"score\": \"0.59254354\",\n \"text\": \"def register_hook(self, func):\\r\\n\\r\\n if func not in self.hooks:\\r\\n self.hooks.append(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992e660201db15b027d78bbf73909ecd\",\n \"score\": \"0.5917185\",\n \"text\": \"def _before_application_run(self):\\n\\n for hook in self._get_hooks():\\n hook.before_application_run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4098a25602a66288543c0be8a1720c0a\",\n \"score\": \"0.59140986\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Dictionary())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128feab0e9e690fd164055f3dc6224ce\",\n \"score\": \"0.5912825\",\n \"text\": \"def _after_application_loaded(self):\\n\\n for hook in self._get_hooks():\\n hook.after_application_loaded()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3b2b4a6f0402b41ae4eff4cf4fc845\",\n \"score\": \"0.5884704\",\n \"text\": \"def on_start(self):\\n self.add_bot(ScoutManager(bot_player=self))\\n self.add_bot(BuildManager(bot_player=self))\\n self.add_bot(GatherManager(bot_player=self))\\n self.add_bot(DefenseManager(bot_player=self))\\n self.init_request_board()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a582bbb4bf0a67821652519bd94f5ec\",\n \"score\": \"0.588175\",\n \"text\": \"def add_hook(cls, hook_type, hook_func):\\r\\n if hook_type not in cls._hooks_map:\\r\\n cls._hooks_map[hook_type] = []\\r\\n\\r\\n cls._hooks_map[hook_type].append(hook_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"200dd6ea332f6486cfb958496dd7eafe\",\n \"score\": \"0.5878965\",\n \"text\": \"async def setup(bot) -> None:\\n await bot.add_cog(HotReload(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cbfc3f4a2d622fbd0030e22de81d12\",\n \"score\": \"0.58777094\",\n \"text\": \"def setup(bot: Bot):\\n bot.add_cog(EventoryCog(bot))\\n log.info(\\\"loaded Eventory extension!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269b00d45f4e69768306379771ce086d\",\n \"score\": \"0.5848767\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Utilities(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a5ab4cf1d8372d05b8687ae7d4fdc2\",\n \"score\": \"0.58468145\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Tickets(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae9f0da4d2e05b9bdea26aa011ab59a\",\n \"score\": \"0.58353835\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(LemonStuff(bot))\\n log.info(\\\"Cog loaded: LemonStuff\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ca07bcea77b5998babc1f8f475dda35\",\n \"score\": \"0.5832757\",\n \"text\": \"def hooks(self):\\n return self.__hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d22ce1884a7faa025d158e62aa578e\",\n \"score\": \"0.5821258\",\n \"text\": \"def hook_dispatch(self) -> None:\\n if sys.argv[2].strip().lower() == \\\"add\\\":\\n self.add_hook(\\n hook_state = sys.argv[3],\\n cmd = \\\" \\\".join(sys.argv[4:]),\\n )\\n\\n elif sys.argv[2].strip().lower() == \\\"del\\\":\\n self.del_hook(\\n hook_state = sys.argv[3],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e666c8c599a3a0a56b13e57e58b45c50\",\n \"score\": \"0.58076733\",\n \"text\": \"def _add_trachook(self, req, hookfile, current):\\n base_url = self.config.get('trac', 'base_url')\\n # try to append hook\\n try:\\n fp = open(hookfile, 'a')\\n fp.writelines(os.linesep)\\n if 'REPOS=\\\"$1\\\"' not in current:\\n fp.writelines('REPOS=\\\"$1\\\"' + os.linesep)\\n if 'REV=\\\"$2\\\"' not in current:\\n fp.writelines('REV=\\\"$2\\\"' + os.linesep)\\n if 'LOG=' not in current:\\n fp.writelines('LOG=`/usr/bin/svnlook log -r $REV $REPOS`' \\\\\\n + os.linesep)\\n if 'AUTHOR=' not in current:\\n fp.writelines('AUTHOR=`/usr/bin/svnlook author -r $REV ' + \\\\\\n '$REPOS`' + os.linesep)\\n if 'TRAC_ENV=' not in current:\\n fp.writelines('TRAC_ENV=\\\"'+self.env.path+'\\\"' + os.linesep)\\n if 'TRAC_URL=' not in current:\\n fp.writelines('TRAC_URL=\\\"'+base_url+'\\\"' + os.linesep)\\n fp.writelines('/usr/bin/python /usr/share/trac/contrib/' + \\\\\\n 'trac-post-commit-hook \\\\\\\\' + os.linesep)\\n fp.writelines('-p \\\"$TRAC_ENV\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-r \\\"$REV\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-u \\\"$AUTHOR\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-m \\\"$LOG\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-s \\\"$TRAC_URL\\\"' + os.linesep)\\n fp.writelines(os.linesep)\\n fp.close()\\n except Exception:\\n raise TracError(\\\"Can't write repository hook %s\\\" % hookfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a651ece69c23f871ae87944408d516\",\n \"score\": \"0.5799101\",\n \"text\": \"def run_hooks(cls, hook_type, *args, **kwargs):\\r\\n hook_funcs = cls._hooks_map.get(hook_type) or []\\r\\n for hook_func in hook_funcs:\\r\\n hook_func(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fcf31c68e866cf847c0a5373217c2b3\",\n \"score\": \"0.5785056\",\n \"text\": \"def register_default_hooks(self, *hooks: Hook, as_group: bool = True):\\n self.hooks.extend(hooks)\\n\\n if as_group and self.group is not None:\\n for g in get_group_by_name(self.group):\\n if g is not self:\\n g.register_hooks(*hooks, as_group=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"396772b6a60c6762461d747ec1f4b8dc\",\n \"score\": \"0.5783806\",\n \"text\": \"async def setup(bot) -> None:\\n await bot.add_cog(Gambling(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f50c868844e933e05acf6e0224d81642\",\n \"score\": \"0.57818186\",\n \"text\": \"def install_custom_hook(self, hook_name):\\n self._install_custom_hook(hook_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e677a9f8d0ec6ba94293c47993d275df\",\n \"score\": \"0.5769153\",\n \"text\": \"def add_hook(self,\\n hook_state: str,\\n cmd: str,\\n ) -> None:\\n state = hook_state.strip().lower()\\n\\n if state not in self.valid_hook_states:\\n print(f\\\"Invalid hook state: {hook_state}. Valid states are: {self.valid_hook_states}\\\")\\n return\\n\\n # At this point we simply overwrite any existing command\\n # and store it to the data file\\n data = self.load()\\n data[\\\"hooks\\\"][state] = cmd\\n self.save(data)\\n\\n # If cmd is None this function as been called from within del_hook()\\n if cmd is None:\\n print(f\\\"Deleted hook command for state {state}\\\")\\n else:\\n print(f\\\"Added hook command for state {state}: {cmd}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c18e6f43da52ca2d550b82ddba2845\",\n \"score\": \"0.5764374\",\n \"text\": \"def install_api_hooks(dbg, dll_api_hooks):\\n # _pt_log(\\\"remain system dlls to install api hook: %d\\\" % (len(dll_api_hooks)))\\n\\n new_sys_dll = dbg.system_dlls[-1]\\n dll_name = new_sys_dll.name.lower()\\n\\n for (dll_name, api_ctrl_list) in dll_api_hooks.items():\\n if dbg.check_has_system_dll(dll_name):\\n for api_ctrl in api_ctrl_list:\\n dbg_set_api_bp(dbg, api_ctrl)\\n del dll_api_hooks[dll_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63584e75c449d989e5334871a61d146\",\n \"score\": \"0.574739\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Messages(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba6688fa7d65bc9590cd3596cc6eead6\",\n \"score\": \"0.5747233\",\n \"text\": \"def InstallHook(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17e625fb36c2298dbac9f5647e71522\",\n \"score\": \"0.5714983\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(MessageLogCog(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb0f516bd6a1e7fe91485da5245ead6\",\n \"score\": \"0.5709663\",\n \"text\": \"def registerhooks(entry=[], exit=[], *args, **kwargs):\\n def wrap(f):\\n _kwargs = dict(kwargs)\\n _kwargs.update({'entry': entry, 'exit': exit})\\n def wrapped_f(*args, **kwargs):\\n return f(*args, **kwargs)\\n wrapped_f.__hooks = _kwargs\\n return wrapped_f\\n return wrap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044a2ad7b8e51eec0d32c615365ec6bc\",\n \"score\": \"0.570686\",\n \"text\": \"def setup(bot):\\n bt.INFO('Loading Misc.py')\\n bot.add_cog(Misc(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71fa5b3abe2be9c9c55ea45b9c82dd43\",\n \"score\": \"0.56982493\",\n \"text\": \"async def setup(self) -> None:\\n if self.playing_title is not None:\\n await self.change_presence(\\n activity=discord.Game(name=self.playing_title),\\n )\\n\\n self.tree.clear_commands(guild=None)\\n for guild in self.guilds:\\n self.tree.clear_commands(guild=guild)\\n\\n commands = registered_app_commands()\\n for command in commands:\\n self.tree.add_command(command)\\n logger.info(f'registered {len(commands)} app commands')\\n\\n listeners = registered_listeners()\\n for listener in listeners:\\n self.add_listener(listener.func, listener.event)\\n logger.info(f'registered {len(listeners)} listeners')\\n\\n if self._cmd_group_exts is not None:\\n for ext in self._cmd_group_exts:\\n self.tree.add_command(ext)\\n await ext.post_init(self)\\n logger.info(f'registered {ext.name} command group')\\n\\n await self.tree.sync()\\n for guild in self.guilds:\\n await self.tree.sync(guild=guild)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fccb4dede5aa12489b6365d7dac5f30b\",\n \"score\": \"0.5684656\",\n \"text\": \"def setup(bot):\\n bot.add_cog(LoadHelp(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ace20514f392a89f07428e9134c5bc\",\n \"score\": \"0.5670448\",\n \"text\": \"def list_hooks(self) -> None:\\n data = self.load()\\n hooks = data.get(\\\"hooks\\\", {})\\n\\n print(\\\"Current hook command configuration:\\\")\\n for state in self.valid_hook_states:\\n cmd = hooks.get(state, None)\\n print(f\\\" {state:6s} {cmd}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13e1b3cbfa89944d2a510fb35f6ed58\",\n \"score\": \"0.5668042\",\n \"text\": \"def on_bot_startup(bot):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8ba5a4c77b793ba734b50ced6e7aaf\",\n \"score\": \"0.56633395\",\n \"text\": \"def register(name):\\r\\n global SUPPORTED_HOOKS\\r\\n if not name in SUPPORTED_HOOKS:\\r\\n SUPPORTED_HOOKS.append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad29e41de5a20f7deb3ed694f518194\",\n \"score\": \"0.5659891\",\n \"text\": \"def add_permissions_hook(self, hook: commands.CheckPredicate) -> None:\\n self._permissions_hooks.append(hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d8761c3de9dfeea1cd5f99b7ec825c\",\n \"score\": \"0.56558925\",\n \"text\": \"def register_hook(self, event, hook):\\r\\n\\r\\n if isinstance(hook, collections.Callable):\\r\\n self.hooks[event].append(hook)\\r\\n elif hasattr(hook, '__iter__'):\\r\\n self.hooks[event].extend(h for h in hook if isinstance(h, collections.Callable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3258ecf3f6e2c20cf1fd7f7cecca9cc6\",\n \"score\": \"0.5652265\",\n \"text\": \"def get_hooks():\\n tensor_to_log = {'loss': loss}\\n # tensor_to_log = {}\\n logging_tensor_hook = tf.train.LoggingTensorHook(\\n tensors=tensor_to_log,\\n every_n_iter=2\\n )\\n return [logging_tensor_hook]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c7449a2267ac9fd64c1310ad51e382\",\n \"score\": \"0.56491655\",\n \"text\": \"def apply_hooks():\\n for line in fileinput.input():\\n std_input = line.split(\\\" \\\")\\n oldrev, newrev, refname = [item.strip() for item in std_input]\\n # Check for zero commit, check branch deletions\\n # also, avoid new package additions\\n if ZERO_COMMIT in (oldrev, newrev):\\n continue\\n # insert new hooks here\\n package_start_build(oldrev, newrev, refname)\\n # Pass in 'length' as an optional argument\\n # default 'length' of feed is 499\\n write_rss_feed(oldrev, newrev, refname, length=499)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70c1c6821cc9de1cb8cab705ac6ee85\",\n \"score\": \"0.5649146\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Internal(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6a737d4f0384ad0b93c99010ebf635\",\n \"score\": \"0.5637375\",\n \"text\": \"def hook(self, server=None):\\n\\n if server is not None:\\n logging.debug('Adding hook route to web server')\\n server.add_route(\\n Wrapper(callable=self.get_hook(),\\n route=self.context.get('server.hook', '/hook')))\\n\\n if (self.context.get('server.binding') is not None\\n and self.context.get('server.url') is not None):\\n\\n self.space.register(\\n hook_url=self.context.get('server.url')\\n + self.context.get('server.hook', '/hook'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b50dc58fd7d07d03898d7d1205095a7\",\n \"score\": \"0.56359833\",\n \"text\": \"def setup(bot):\\n\\n bot.add_cog(Eval(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b683c47d5bdae345391754b51c5bac6f\",\n \"score\": \"0.5632748\",\n \"text\": \"def add_callback(self, hook, function, pre=False):\\n if hook not in self._hooks:\\n raise Exception('This program has no hook named \\\"%s\\\"' % hook)\\n hook_def = self._hook_defs.get(hook, None)\\n\\n if (hook_def is None or not isinstance(hook_def, FunctionChain)):\\n raise TypeError(\\\"Cannot add callback to hook '%s'; not a \\\"\\n \\\"FunctionChain. (%s)\\\" % (hook, type(hook_def)))\\n\\n if pre:\\n hook_def.insert(0, function)\\n else:\\n hook_def.append(function)\\n\\n # TODO: remove or resurrect\\n #self._install_dep_callbacks(function)\\n self._need_build = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4499e1842dac2feaba1f51e52a73f48\",\n \"score\": \"0.56314707\",\n \"text\": \"def run_hooks():\\n\\n hookdir = '/usr/lib/oem-config/post-install'\\n\\n if os.path.isdir(hookdir):\\n # Exclude hooks containing '.', so that *.dpkg-* et al are avoided.\\n hooks = filter(lambda entry: '.' not in entry, os.listdir(hookdir))\\n child_env = dict(os.environ)\\n child_env['DEBIAN_FRONTEND'] = 'noninteractive'\\n if 'DEBIAN_HAS_FRONTEND' in child_env:\\n del child_env['DEBIAN_HAS_FRONTEND']\\n for hookentry in hooks:\\n hook = os.path.join(hookdir, hookentry)\\n if os.access(hook, os.X_OK):\\n # Errors are ignored at present, although this may change.\\n subprocess.call([hook], env=child_env)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b864916beac1e7a3522c10bae5c0f74\",\n \"score\": \"0.56313306\",\n \"text\": \"def run_hook(db, hook_name, **kwargs):\\n\\n\\t# Get all hooks in the DB\\n\\thooks = db.get_hook()\\n\\thooks = [dict(_)['name'] for _ in hooks]\\n\\n\\t# Iteratue through the hooks\\n\\tfor hook in hooks:\\n\\t\\t# Try importing the hook module and finding any functions that have used the ydl.hook decorate\\n\\t\\t# which means it has a _hooks attribute indicating which hooks it accepts\\n\\t\\ttry:\\n\\t\\t\\tz = importlib.import_module(hook)\\n\\t\\t\\tfor x in dir(z):\\n\\t\\t\\t\\to = getattr(z, x)\\n\\t\\t\\t\\tif hasattr(o, '_hooks') and hook_name in o._hooks:\\n\\t\\t\\t\\t\\to(hook_name, db, **kwargs)\\n\\t\\t\\t\\tdel o\\n\\t\\t\\tdel z\\n\\t\\texcept Exception as e:\\n\\t\\t\\ttraceback.print_exc()\\n\\t\\t\\t# Move along\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cfa6dc8d943c488df97857dd75522ee\",\n \"score\": \"0.5624743\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Reminder(bot))\\n log.info(\\\"Cog loaded: reminder\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7df076bc40f988cd86f24cb3e5db89e\",\n \"score\": \"0.56241655\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(SpookyEightBall())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53fa260f6f97a677c52fabbf44b86b38\",\n \"score\": \"0.5622936\",\n \"text\": \"def add_commands(self, *args):\\n for command in args:\\n self._script += command + '\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7496c40c7d9e4b36c3df9cb62d2c5ad4\",\n \"score\": \"0.55818033\",\n \"text\": \"def run(self, hook, *args, **kwargs):\\n\\n assert hook in self._callbacks, f\\\"hook '{hook}' not found in callbacks {self._callbacks}\\\"\\n\\n for logger in self._callbacks[hook]:\\n logger['callback'](*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9499c2213ce0272e0a32ffd4439f8e08\",\n \"score\": \"0.5580782\",\n \"text\": \"def _init_airflow_core_hooks(self):\\n core_dummy_hooks = {\\n \\\"generic\\\": \\\"Generic\\\",\\n \\\"email\\\": \\\"Email\\\",\\n \\\"mesos_framework-id\\\": \\\"Mesos Framework ID\\\",\\n }\\n for key, display in core_dummy_hooks.items():\\n self._hooks_lazy_dict[key] = HookInfo(\\n hook_class_name=None,\\n connection_id_attribute_name=None,\\n package_name=None,\\n hook_name=display,\\n connection_type=None,\\n connection_testable=False,\\n )\\n for cls in [FSHook, PackageIndexHook]:\\n package_name = cls.__module__\\n hook_class_name = f\\\"{cls.__module__}.{cls.__name__}\\\"\\n hook_info = self._import_hook(\\n connection_type=None,\\n provider_info=None,\\n hook_class_name=hook_class_name,\\n package_name=package_name,\\n )\\n self._hook_provider_dict[hook_info.connection_type] = HookClassProvider(\\n hook_class_name=hook_class_name, package_name=package_name\\n )\\n self._hooks_lazy_dict[hook_info.connection_type] = hook_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fd2b1eb01258336636e0bd8adc0178b\",\n \"score\": \"0.5579576\",\n \"text\": \"def _trigger_hooks(self, hook_dict, hook_name, **kwargs):\\n for attached_callback in hook_dict[hook_name].attached_callbacks:\\n try:\\n if attached_callback.callback(**kwargs):\\n if attached_callback.action == MMHookAct.LOAD_MODULE:\\n # load the module!\\n self.logger.debug('some hook returned true, '\\n 'loading module {}'\\n .format(attached_callback.argument))\\n # module must accept same kwargs, this is mandatory\\n # with this discovery event\\n try:\\n cb_arg = attached_callback.argument\\n module_name = cb_arg.get_module_desc().arg_name\\n self.load_module(module_name,\\n **kwargs)\\n except Exception as ex:\\n self.logger.error('loading of module of class '\\n '\\\"{}\\\" failed with: {}'\\n .format(cb_arg.__name__,\\n str(ex)))\\n elif attached_callback.action == MMHookAct.UNLOAD_MODULE:\\n # unload the attached module\\n self.logger.debug('a hook required module '\\n '{} to be unloaded'\\n .format(attached_callback.argument))\\n self.unload_module(attached_callback.argument)\\n except Exception as ex:\\n self.logger.error('failed to call function '\\n '{} attached to \\\"{}\\\" with: {}'\\n .format(attached_callback,\\n hook_name,\\n str(ex)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19e4deca1d85266e102d958ffa41ff0\",\n \"score\": \"0.55784094\",\n \"text\": \"def setup(bot):\\n bot.add_cog(ReminderCog(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e2d79bb76fc224f12b869daf1739030\",\n \"score\": \"0.55750185\",\n \"text\": \"def runHook(self):\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a78408bd292126b7640c238bfb0c8ec8\",\n \"score\": \"0.55676615\",\n \"text\": \"async def setup(bot: Bot) -> None:\\n await bot.add_cog(Patreon(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05f45412830a1b3a2b2c9b7bdb0d1cbb\",\n \"score\": \"0.556712\",\n \"text\": \"def hooks(self) -> MutableMapping[str, HookInfo | None]:\\n self.initialize_providers_hooks()\\n # When we return hooks here it will only be used to retrieve hook information\\n return self._hooks_lazy_dict\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":193,"cells":{"query_id":{"kind":"string","value":"0678ed29cb4ed04785fd661416546558"},"query":{"kind":"string","value":"Check is a path is a broken symlink."},"positive_passages":{"kind":"list like","value":[{"docid":"202729acc69cd2b9313649493f56ef9f","score":"0.870235","text":"def is_symlink_broken(path):\n if os.path.islink(path):\n return not os.path.exists(os.readlink(path))\n else:\n raise TypeError('path={!r} is not a symbolic link'.format(path))","title":""}],"string":"[\n {\n \"docid\": \"202729acc69cd2b9313649493f56ef9f\",\n \"score\": \"0.870235\",\n \"text\": \"def is_symlink_broken(path):\\n if os.path.islink(path):\\n return not os.path.exists(os.readlink(path))\\n else:\\n raise TypeError('path={!r} is not a symbolic link'.format(path))\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"71564a56fc163c8e7d254c575b623e30","score":"0.7800918","text":"def checklink(path):\n # mktemp is not racy because symlink creation will fail if the\n # file already exists\n name = tempfile.mktemp(dir=path)\n try:\n os.symlink(\".\", name)\n os.unlink(name)\n return True\n except (OSError, AttributeError):\n return False","title":""},{"docid":"1301b1cb5da838eaea975a866ca6bb1f","score":"0.7800738","text":"def test_is_symlink(path: Path) -> None:\n assert (path / \"link\").is_symlink()","title":""},{"docid":"b1c304a94d85d6653cded13e445a0b19","score":"0.7560013","text":"def is_symlink(self):\n try:\n return S_ISLNK(self.lstat().st_mode)\n except OSError as e:\n if e.errno not in (ENOENT, ENOTDIR):\n raise\n # Path doesn't exist\n return False","title":""},{"docid":"45e1ac62ad49004dab7dc483330692cb","score":"0.75018257","text":"def is_symlink(self):\n return False","title":""},{"docid":"896891d9194d8ad8e3c7b93d9f4735d6","score":"0.7391739","text":"def test_symlink_on_unknown_file(self):\n root = URI(self.baseurl)\n notexisting_path = root / 'ma' / 'moo'\n tee_path = root / 'helloworld'\n notexisting_path.symlink(tee_path)\n\n self.assertTrue(tee_path.islink())\n self.assertEqual(tee_path.readlink(), notexisting_path)\n self.assertTrue(not notexisting_path.exists())","title":""},{"docid":"9543b5f7076123e979ef23a52736cca5","score":"0.7297981","text":"def test_symlink_missing_src(file, source):\n target = source.parent / \"symlink.lnk\"\n missing_source = source.parent / \"missing.txt\"\n try:\n file.symlink(str(missing_source), str(target))\n assert salt.utils.path.islink(str(target))\n finally:\n target.unlink()","title":""},{"docid":"faaf23de79bf29e8868d7acf350b1324","score":"0.72364473","text":"def islink(path):\r\n return False","title":""},{"docid":"117fd290d3a937d8daa06de391ab888b","score":"0.7161739","text":"def islink(self, path):\n path = ftputil.tool.as_unicode(path)\n try:\n lstat_result = self._host.lstat(\n path, _exception_for_missing_path=False)\n except ftputil.error.RootDirError:\n return False\n else:\n if lstat_result is None:\n # Non-existent path\n return False\n else:\n return stat.S_ISLNK(lstat_result.st_mode)","title":""},{"docid":"7e466325a1eb6f6c43507803db038e87","score":"0.7131687","text":"def islink(path):\r\n try:\r\n st = os.lstat(path)\r\n except (os.error, AttributeError):\r\n return False\r\n return stat.S_ISLNK(st.st_mode)","title":""},{"docid":"e1f68b0566a7c14c2badd11901266889","score":"0.71089053","text":"def is_symlink(self):\n if os.name == \"posix\": return os.path.islink(self.path)\n if os.name == \"nt\":\n import win32api\n import win32con\n num = win32con.FILE_ATTRIBUTE_REPARSE_POINT\n return bool(win32api.GetFileAttributes(self.path) & num)","title":""},{"docid":"dac7484c1ede678b11d426cfd0dff2ef","score":"0.71058583","text":"def islink(path):\n try:\n st = os.lstat(path)\n except (OSError, AttributeError):\n return False\n return stat.S_ISLNK(st.st_mode)","title":""},{"docid":"dac7484c1ede678b11d426cfd0dff2ef","score":"0.71058583","text":"def islink(path):\n try:\n st = os.lstat(path)\n except (OSError, AttributeError):\n return False\n return stat.S_ISLNK(st.st_mode)","title":""},{"docid":"4c1aec9e1a0da95eb624cbc8cdd68e79","score":"0.70764434","text":"def test_symlink_exists_different(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n with pytest.raises(CommandExecutionError) as exc:\n file.symlink(str(source), str(target))\n assert \"Found existing symlink:\" in exc.value.message\n finally:\n target.unlink()","title":""},{"docid":"8816e12f05b8fce70a4dc0a21726d931","score":"0.70211464","text":"def test_symlink_exists_different_force(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n file.symlink(str(source), str(target), force=True)\n assert salt.utils.path.readlink(str(target)) == str(source)\n finally:\n target.unlink()","title":""},{"docid":"bb95c61abb8f5354e7e9d4d715e4ef7a","score":"0.6982841","text":"def test_readlink(path: Path) -> None:\n assert PurePath(\"file\") == (path / \"link\").readlink()","title":""},{"docid":"f6c1e77dc34b5c401947222e0bbf911a","score":"0.6982368","text":"def _is_link(self, path):\n return os.path.islink(os.path.expanduser(path))","title":""},{"docid":"8627bffa0d7e759f5d4710ef759afa77","score":"0.69228035","text":"def is_symlink(self):\n if self._is_symlink is None:\n self._is_symlink = os.path.islink(self.storage_url.object_name)\n return self._is_symlink","title":""},{"docid":"fe21b7cecc2c9973a253737963706133","score":"0.68756634","text":"def test_constrain_symlinks_to_filesystem() -> None:\n path = root(\n {\n \"file\": \"text\",\n \"dir\": {\"link\": PurePath(\"..\", \"file\")},\n },\n innerpath=PurePath(\"dir\"),\n )\n # XXX Should we rewrite the error message to hide the 'dir/' prefix?\n with pytest.raises(FileNotFoundError, match=\"file not found: dir/file\"):\n assert (path / \"link\").read_text()","title":""},{"docid":"923a01647ae572518a7cb698b01d2789","score":"0.6867287","text":"def test_symlink_exists_file(file, source):\n with pytest.helpers.temp_file(\"symlink.txt\", contents=\"Source content\") as target:\n with pytest.raises(CommandExecutionError) as exc:\n file.symlink(str(source), str(target))\n assert \"Existing path is not a symlink:\" in exc.value.message","title":""},{"docid":"41bff0c324b7c71a826535c2a5479b5c","score":"0.6856679","text":"def test_symlink(file, source):\n target = source.parent / \"symlink.lnk\"\n try:\n file.symlink(str(source), str(target))\n assert salt.utils.path.islink(str(target))\n finally:\n target.unlink()","title":""},{"docid":"1d7a46dd208c245d602979b6f8c24590","score":"0.68321455","text":"def test_symlink_exists_same(file, source):\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(source)\n try:\n before_time = os.stat(str(target)).st_mtime\n ret = file.symlink(str(source), str(target))\n after_time = os.stat(str(target)).st_mtime\n assert before_time == after_time\n assert ret is True\n finally:\n target.unlink()","title":""},{"docid":"73c3309bca6d764f2d429a94c01e2867","score":"0.68014234","text":"def is_symlink(\n linkfile: T.Union[str, pathlib.Path],\n src: T.Union[None, str, pathlib.Path] = None,\n) -> bool:\n linkfile = pathlib.Path(linkfile)\n if not linkfile.is_symlink():\n return False\n\n if src is None:\n return True\n\n return linkfile.resolve() == pathlib.Path(src).resolve()","title":""},{"docid":"6a5673d6915d6847708663b71cf2eab7","score":"0.67836535","text":"def check_if_relative_link(link):\n\n if not link.startswith(\"http\"):\n if not link.startswith(\"/\"):\n return True\n return False","title":""},{"docid":"1067f2b5ebb747c4d55bca1975463a6a","score":"0.6782696","text":"def _valid_link(self, link, fail_on_disk_check=True):\n\n if not link in self._links:\n raise Exception('link [{0}] not found in timeline!'.format(link))\n\n if not os.path.islink(self._links[link]['path']):\n msg = 'link [{0}] not found!'.format(self._links[link]['path'])\n if fail_on_disk_check:\n raise Exception(msg)\n self.logger.warning(msg)\n return False\n\n if not os.path.exists(self._links[link]['path']):\n msg = 'link [{0}] is broken!'.format(self._links[link]['path'])\n if fail_on_disk_check:\n raise Exception(msg)\n self.logger.warning(msg)\n return False\n\n return True","title":""},{"docid":"968ba0654b4a586ad577e6ad741ee0ed","score":"0.6718646","text":"def is_symlink(self):\n return self.type == self.Type.SYMLINK","title":""},{"docid":"f7331b2df6b96881496979b7a913c461","score":"0.6693001","text":"def is_symlink(self):\n if (\n not self.custom_fields\n or resource_util.SYMLINK_METADATA_KEY not in self.custom_fields\n ):\n return False\n return (\n self.custom_fields[resource_util.SYMLINK_METADATA_KEY].lower() == 'true'\n )","title":""},{"docid":"81d65ef687eac4cb308d24311371b04b","score":"0.66920704","text":"def islink(self, path: AnyStr) -> bool:\n return self.filesystem.islink(path)","title":""},{"docid":"0a1435142fcb2dce5b3f51f29d763b77","score":"0.66706246","text":"def islink (self) :\n try :\n return stat.S_ISLNK (self.mode)\n except OSError :\n return 0","title":""},{"docid":"318e97be28a1d07940b9a69de3c8b1f6","score":"0.6630757","text":"def copy_if_symlink_fails(\n src: Union[str, Path],\n dst: Union[str, Path],\n backend_args: Optional[dict] = None,\n) -> bool:\n backend = get_file_backend(\n src, backend_args=backend_args, enable_singleton=True)\n return backend.copy_if_symlink_fails(src, dst)","title":""},{"docid":"8c3050fd54816a14c87c867d2e9a3748","score":"0.6584976","text":"def test_through_symlink(self) -> None:\n link1 = os.path.join(self.tmp_dir, \"link1\")\n os.symlink(self.mount, link1)\n\n info1 = json.loads(self.eden.run_cmd(\"info\", link1))\n self.assertEqual(self.mount, info1[\"mount\"])\n\n # Create a non-normalized symlink pointing to the parent directory\n # of the mount\n link2 = os.path.join(self.tmp_dir, \"mounts_link\")\n os.symlink(self.mount + \"//..\", link2)\n mount_through_link2 = os.path.join(link2, self.repo_name)\n\n info2 = json.loads(self.eden.run_cmd(\"info\", mount_through_link2))\n self.assertEqual(self.mount, info2[\"mount\"])","title":""},{"docid":"fc10782d2de274eef803bde25fbaf972","score":"0.65710294","text":"def test_symlink_exists_different_atomic(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n file.symlink(str(source), str(target), atomic=True)\n assert salt.utils.path.readlink(str(target)) == str(source)\n finally:\n target.unlink()","title":""},{"docid":"741e573a30028ce18a81c363e3d5a5e9","score":"0.6550941","text":"def islink(s):\n\n try:\n import Carbon.File\n return Carbon.File.ResolveAliasFile(s, 0)[2]\n except:\n return False","title":""},{"docid":"3b04b3104b1dd15775e4fb15a939ca49","score":"0.6517931","text":"def islink(pathname):\n from os import listdir\n from os.path import isdir, isfile\n import os.path\n pathname = pathname.rstrip(\"/\")\n if os.path.islink(pathname): return True\n elif isfile(pathname) and ishidden(pathname): return True\n elif isdir(pathname):\n entries = listdir(pathname)\n for entry in entries:\n if not islink(pathname+\"/\"+entry): return False\n return True\n else: return False","title":""},{"docid":"2c17a63c3f070a9ace82b381e0997881","score":"0.6510741","text":"def check_file(filename,path):\n chksumX = chksum_file(\"%s/%s\" % (path,filename))\n (pathY,chksumY) = find_file(filename,path,chksumX)\n if chksumX == chksumY:\n\thardlink(filename,path,pathY)","title":""},{"docid":"6924dff27d54d6cab5d242f412cc20a7","score":"0.6508725","text":"def silent_remove_symlink(path): # TODO: test\n result = {'error': ''}\n if symlink_exists(path):\n try:\n os.unlink(path)\n except OSError as e:\n result['error'] = str(e)\n return result","title":""},{"docid":"595ab9271ea617ec1ff274f308897593","score":"0.64662755","text":"def has_valid_link(dotfile, dotfiles_dir):\n homefolder_path = get_homefolder_path(dotfile, dotfiles_dir)\n if os.path.islink(homefolder_path) and os.readlink(homefolder_path) == dotfile:\n return True\n\n return False","title":""},{"docid":"b8b6fb9618f82b8429612697328e30ae","score":"0.6455614","text":"def is_valid_file(abs_path):\n if os.path.exists(abs_path) and os.path.isfile(abs_path) \\\n and not os.path.islink(abs_path):\n return True\n\n else: return False","title":""},{"docid":"8a6353f544288001a6c15f9a0558223e","score":"0.6414611","text":"def ismount_raw(path):\r\n try:\r\n s1 = os.lstat(path)\r\n except os.error as err:\r\n if err.errno == errno.ENOENT:\r\n # It doesn't exist -- so not a mount point :-)\r\n return False\r\n raise\r\n\r\n if stat.S_ISLNK(s1.st_mode):\r\n # A symlink can never be a mount point\r\n return False\r\n\r\n s2 = os.lstat(os.path.join(path, '..'))\r\n dev1 = s1.st_dev\r\n dev2 = s2.st_dev\r\n if dev1 != dev2:\r\n # path/.. on a different device as path\r\n return True\r\n\r\n ino1 = s1.st_ino\r\n ino2 = s2.st_ino\r\n if ino1 == ino2:\r\n # path/.. is the same i-node as path\r\n return True\r\n\r\n return False","title":""},{"docid":"8d851ba210920164362039bc52ea42fd","score":"0.6394149","text":"def is_linked(self) -> bool:\n return self.parent_path.is_symlink() \\\n and (not self.child_path.is_symlink()) \\\n and self.child_path.samefile(self.parent_path)","title":""},{"docid":"6b635c87ed41938fbf0245d78d827c98","score":"0.63874704","text":"def symlinks_supported():\n with tempfile.TemporaryDirectory() as temp_dir:\n original_path = os.path.join(temp_dir, 'original')\n symlink_path = os.path.join(temp_dir, 'symlink')\n os.makedirs(original_path)\n try:\n os.symlink(original_path, symlink_path)\n supported = True\n except (OSError, NotImplementedError):\n supported = False\n return supported","title":""},{"docid":"89bd6c13a93b82e506573bc794a24693","score":"0.6330474","text":"def check_path(data_pointer, log, msg):\n if not os.path.exists(data_pointer):\n log.debug(msg)\n return False\n else:\n return data_pointer","title":""},{"docid":"13fb5713db8da8497f3a87fe901a9745","score":"0.6315982","text":"def test_symlink_target_relative_path(file, source):\n target = \"..{}symlink.lnk\".format(os.path.sep)\n with pytest.raises(SaltInvocationError) as exc:\n file.symlink(str(source), str(target))\n assert \"Link path must be absolute\" in exc.value.message","title":""},{"docid":"0f87c41104cb5341d0b2c9ad58a4970e","score":"0.6286066","text":"def _resolve_symlinks_and_relative_paths(path):\n # type: pathlib.Path -> pathlib.Path\n try:\n path = path.resolve()\n except WindowsError: # TODO: check for more specific error\n pass\n except OSError as e:\n if e.errno == 2:\n pass\n else:\n raise\n\n return path","title":""},{"docid":"d75a5874ba22708116a98d08093a24ae","score":"0.6252006","text":"def check_path(path):\n if(os.path.exists(path) == False):\n print_error_and_exit(\"Cannot find %s\" % path)","title":""},{"docid":"3d007389a31ebd7fb16f28910d95422f","score":"0.62242776","text":"def symlink(self, target, source):\n if self.debug == True:\n logging.debug(\"in symlink\")\n raise FuseOSError(ENOENT)","title":""},{"docid":"90387b542107e8c28fba24a7fd822dad","score":"0.62149453","text":"def path_ok(match_tuple: MatchTuple) -> bool:\n relative_path = match_tuple.link.split(\"#\")[0]\n full_path = os.path.join(os.path.dirname(str(match_tuple.source)), relative_path)\n return os.path.exists(full_path)","title":""},{"docid":"c41d676430289b18beaa7f4c28d60dc6","score":"0.6214474","text":"def test_clear_broken_symlink(self):\n nonexistent_file_path = os.path.join(settings.STATIC_ROOT, 'nonexistent.txt')\n broken_symlink_path = os.path.join(settings.STATIC_ROOT, 'symlink.txt')\n os.symlink(nonexistent_file_path, broken_symlink_path)\n self.run_collectstatic(clear=True)\n self.assertFalse(os.path.lexists(broken_symlink_path))","title":""},{"docid":"7db9930863d4dcb3dd9b4a03dbd9b6d5","score":"0.6211496","text":"def test_symlinks():\n\n config_file = \"study/profit_symlink.yaml\"\n config = BaseConfig.from_file(config_file)\n base_file = \"./study/run_000/mockup.in\"\n link_file = \"./study/run_000/some_subdir/symlink_link.txt\"\n try:\n run(f\"profit run {config_file}\", shell=True, timeout=TIMEOUT)\n with open(link_file, \"r\") as link:\n with open(base_file, \"r\") as base:\n link_data = link.read()\n base_data = base.read()\n assert link_data == base_data and not link_data.startswith(\"{\")\n finally:\n clean(config)\n run(f\"profit clean --all {config_file}\", shell=True, timeout=CLEAN_TIMEOUT)","title":""},{"docid":"de9e9a27b97a6b79b9f1382fe5c6addc","score":"0.6205026","text":"def do_ismount(path):\n try:\n s1 = os.lstat(path)\n except os.error as err:\n if err.errno == errno.ENOENT:\n # It doesn't exist -- so not a mount point :-)\n return False\n else:\n raise GlusterFileSystemOSError(\n err.errno, '%s, os.lstat(\"%s\")' % (err.strerror, path))\n\n if stat.S_ISLNK(s1.st_mode):\n # A symlink can never be a mount point\n return False\n\n try:\n s2 = os.lstat(os.path.join(path, '..'))\n except os.error as err:\n raise GlusterFileSystemOSError(\n err.errno, '%s, os.lstat(\"%s\")' % (err.strerror,\n os.path.join(path, '..')))\n\n dev1 = s1.st_dev\n dev2 = s2.st_dev\n if dev1 != dev2:\n # path/.. on a different device as path\n return True\n\n ino1 = s1.st_ino\n ino2 = s2.st_ino\n if ino1 == ino2:\n # path/.. is the same i-node as path\n return True\n\n return False","title":""},{"docid":"d97181c311338ad47abd8855991b9b9a","score":"0.6189933","text":"def test_broken_symlink(self):\n path = os.path.join(settings.STATIC_ROOT, 'test.txt')\n os.unlink(path)\n self.run_collectstatic()\n self.assertTrue(os.path.islink(path))","title":""},{"docid":"a4525da4084481db3ff2dc0510d16515","score":"0.6175588","text":"def lexists(self, path: AnyStr) -> bool:\n return self.filesystem.exists(path, check_link=True)","title":""},{"docid":"e869be7867b9731154d811f96ce03792","score":"0.6169737","text":"def check_if_link_in_use(filepath, link):\n\n if not \"previous\" in link:\n if not in_use_links.get(link):\n new_file_name = remove_extra_from_path(filepath)\n\n if new_file_name.startswith(\"\\\\\"):\n new_file_name = new_file_name.replace(\"\\\\\", \"/\")\n\n if new_file_name != link:\n in_use_links[link] = True","title":""},{"docid":"e3a6e146797833743267c59619036c22","score":"0.61650556","text":"def ismount(path):\n try:\n s1 = os.lstat(path)\n except OSError:\n # It doesn't exist -- so not a mount point. :-)\n return False\n else:\n # A symlink can never be a mount point\n if stat.S_ISLNK(s1.st_mode):\n return False\n\n if isinstance(path, bytes):\n parent = join(path, b'..')\n else:\n parent = join(path, '..')\n try:\n s2 = os.lstat(parent)\n except OSError:\n return False\n\n dev1 = s1.st_dev\n dev2 = s2.st_dev\n if dev1 != dev2:\n return True # path/.. on a different device as path\n ino1 = s1.st_ino\n ino2 = s2.st_ino\n if ino1 == ino2:\n return True # path/.. is the same i-node as path\n return False","title":""},{"docid":"2e371c24edcbab6afb4eab8ea6a95def","score":"0.61448544","text":"def check_path(path):\r\n if os.path.exists(path):\r\n return True\r\n return False","title":""},{"docid":"6bfbb920868b15f86a5bd492a0366588","score":"0.6144068","text":"def test_check_permissions_and_broken_symlinks(self, filesystem_world_writable_whitelist, filesystem_ignore_patterns):\n stack = [] # Used for storing directories to parse\n starting_dir = \"/\"\n failed = False\n\n # Helper functions\n is_dir = lambda x: stat.S_ISDIR( x.st_mode)\n is_lnk = lambda x: stat.S_ISLNK(x.st_mode)\n is_sock = lambda x: stat.S_ISSOCK(x.st_mode)\n is_chr = lambda x: stat.S_ISCHR(x.st_mode)\n is_blk = lambda x: stat.S_ISBLK(x.st_mode)\n is_reg = lambda x: stat.S_ISREG(x.st_mode)\n is_world_writable = lambda x: stat.S_IWOTH & x.st_mode\n\n # Start it up\n stack.append(starting_dir)\n while len(stack) > 0:\n # Get actual directory\n directory = stack.pop()\n # Parse through it\n for entry in os.listdir(directory):\n # Stat it\n path = \"%s/%s\" % (directory, entry)\n if path.startswith(\"//\"): # Strip beginning // because of root dir\n path = path[1:]\n\n # Skip this file/dir, has the wrong pattern\n if any([fnmatch.fnmatch(path, p) for p in filesystem_ignore_patterns]):\n sys.stdout.write(\"[ignoring] %s\\n\" % (path,))\n continue\n\n info = os.lstat(path)\n if is_lnk(info):\n # Check if it's broken or not\n if not Test.Shell.exists_in_path(os.readlink(path), os.path.abspath(directory)):\n sys.stderr.write(\"[broken-symlink] %s -> %s\\n\" % (path, os.readlink(path)))\n failed = True\n else:\n if is_dir(info):\n stack.append(path)\n\n # FIXME - determine whether file is truly writable (parent dir\n # may not be readable)\n # Check its permissions, if wrong, append it\n if is_world_writable(info):\n # If the path isn't on the whitelist ... we found a failure\n if not any([fnmatch.fnmatch(path, p) for p in filesystem_world_writable_whitelist]):\n sys.stderr.write(\"[world-writable] %s %s\\n\" % (self.fmt_mode_str(info.st_mode), path))\n failed = True\n if failed:\n Test.Fail(msg=\"Test failed\")","title":""},{"docid":"8ffe407236011d5ddfea62710e51f2d8","score":"0.6135178","text":"def is_device_link_fail(self):\n return self._tag == 'device_link_fail'","title":""},{"docid":"3d9a697a78f12fdb87286c0b5c3558e3","score":"0.6132719","text":"def test_symlink_file5(self):\n src = os.path.join(self.rootdir,\"non-existing-file\") \n target = os.path.join(self.targetdir,os.path.basename(src))\n with self.assertRaises(transfer.TransferError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"771b570903a84e5ab0839ea57aff428e","score":"0.6085972","text":"def silent_symlink(src, dst):\n try:\n os.symlink(src, dst)\n except OSError:\n pass","title":""},{"docid":"b346d5a2d9646b6ff0a8844a820c0b6e","score":"0.60828763","text":"def readlink(path):\n if not path:\n return path\n\n if path[0] == \"~\":\n path = os.path.expanduser(path)\n\n return run_bash_cmd([\"readlink\", \"-f\", path]) or path","title":""},{"docid":"bdc8a241fd5bbbd50d166cdb6622d9a4","score":"0.6063484","text":"def _is_valid_path(path):\r\n try:\r\n urlresolvers.resolve(path)\r\n return True\r\n except urlresolvers.Resolver404:\r\n return False","title":""},{"docid":"a8bae97837e4ff75dd6ca0ac4d853a0f","score":"0.6037345","text":"def exists(path, followlinks=True):\n if path is None:\n raise Tools.exceptions.Value(\"Path is not passed in system.fs.exists\")\n found = False\n try:\n st = os.lstat(path)\n found = True\n except (OSError, AttributeError):\n pass\n if found and followlinks and stat.S_ISLNK(st.st_mode):\n if MyEnv.debug:\n Tools.log(\"path %s exists\" % str(path.encode(\"utf-8\")))\n linkpath = os.readlink(path)\n if linkpath[0] != \"/\":\n linkpath = os.path.join(Tools.path_parent(path), linkpath)\n return Tools.exists(linkpath)\n if found:\n return True\n # Tools.log('path %s does not exist' % str(path.encode(\"utf-8\")))\n return False","title":""},{"docid":"dd3c3cb6566686327f09cc836e98684d","score":"0.60363925","text":"def check_path(p):\n if not os.path.exists(p):\n msg = 'Error: Path \"{}\" does not exist.'.format(p)\n print_help(msg)","title":""},{"docid":"b6931c31cb4ea78fdd2dcb5957104c1b","score":"0.6035318","text":"def check_path(path):\n if (not os.path.isfile(path)):\n raise Exception('The path specified does not point to a file')","title":""},{"docid":"bfdbda55742febd9b0ba06e3c8969c13","score":"0.6016378","text":"def symlinks_test(dirname):\n\n links = []\n\n for (path, dirs, files) in os.walk(dirname):\n if path.find('/.git') > -1:\n continue\n\n # search for directory links\n for d in dirs:\n dname = os.path.join(path, d)\n if os.path.islink(dname):\n links.append(os.path.relpath(dname, dirname)+'/')\n\n # search for file links\n for f in files:\n fname = os.path.join(path, f)\n if os.path.islink(fname):\n links.append(os.path.relpath(fname, dirname))\n\n if links:\n return (FAIL, '\\n'.join(links))\n else:\n return (PASS, \"None\")","title":""},{"docid":"a97ceeb3e2a1ef2d4f8add3a4d41507d","score":"0.60025454","text":"def test_force_symlink_overwrite(tmpdir):\n src = join(str(tmpdir), \"src\")\n dst = join(str(tmpdir), \"dst\")\n\n with open(src, \"w\") as f:\n f.write(\"Correct.\")\n\n with open(dst, \"w\") as f:\n f.write(\"Wrong.\")\n\n utils.force_symlink(src, dst)\n assert os.path.islink(dst)\n\n with open(dst, \"r\") as f:\n assert \"Correct\" in f.read()","title":""},{"docid":"5fbf806aa189307c157cd564dbcd1a8d","score":"0.59952956","text":"def fix_all_git_symlinked(topdir):\n # Determine whether or not symlinks need fixing (they don’t if installing\n # from a .tar.gz file)\n with io.open(topdir + r'\\nikola\\data\\symlink-test-link.txt', 'r', encoding='utf-8-sig') as f:\n text = f.read()\n if text.startswith(\"NIKOLA_SYMLINKS=OK\"):\n return -1\n with io.open(topdir + r'\\nikola\\data\\symlinked.txt', 'r', encoding='utf-8-sig') as f:\n text = f.read()\n # expect each line a relpath from git or zip root,\n # smoke test relpaths are relative to git root\n if text.startswith('.'):\n raise Exception(r'Bad data in \\nikola\\data\\symlinked.txt')\n relnames = text.split('\\n')\n relnames = [name.strip().replace('/', '\\\\') for name in relnames]\n relnames = [name for name in relnames if name]\n\n failures = 0\n for name in relnames:\n # build dst path and do some basic validation\n dst = os.path.join(topdir, name)\n # don't access files outside topdir\n if not is_file_into_dir(dst, topdir):\n continue\n if os.path.isdir(dst):\n # assume the file was de-symlinked\n continue\n\n # build src path and do some basic validation\n with io.open(os.path.join(topdir, dst), 'r', encoding='utf-8-sig') as f:\n text = f.read()\n dst_dir = os.path.dirname(dst)\n try:\n src = os.path.normpath(os.path.join(dst_dir, text))\n if not os.path.exists(src):\n # assume the file was de-symlinked before\n continue\n # don't access files outside topdir\n if not is_file_into_dir(src, topdir):\n continue\n except Exception:\n # assume the file was de-symlinked before\n continue\n\n # copy src to dst\n try:\n if os.path.isdir(src):\n os.unlink(dst)\n shutil.copytree(src, dst)\n else:\n shutil.copy2(src, dst)\n except Exception:\n failures += 1\n print(\"*** copy failed for\")\n print(\"\\t src:\", src)\n print(\"\\t dst:\", dst)\n\n return failures","title":""},{"docid":"727d2ea9be0ae6be316b42e299d3ad3b","score":"0.599236","text":"def test_hardlink(self):\n # Prevent errors from being caught\n self.tar.errorlevel = 1\n\n self.tar.extract(\"0-REGTYPE\", dirname())\n try:\n # Extract 1-LNKTYPE which is a hardlink to 0-REGTYPE\n self.tar.extract(\"1-LNKTYPE\", dirname())\n except EnvironmentError, e:\n import errno\n if e.errno == errno.ENOENT:\n self.fail(\"hardlink not extracted properly\")","title":""},{"docid":"e9745941b98c710c4c4ec6d21f02e9e1","score":"0.59648645","text":"def test_symlink_folder7(self):\n src = self.rootdir\n target = os.path.join(self.targetdir,os.path.basename(src))\n with mock.patch.object(\n transfer.os,\n 'symlink',\n side_effect=OSError(\"Mocked error\")):\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"01c8991464103ff5c306a31023d7fce3","score":"0.5945459","text":"def can_symlink_to(self, outfile_name, symlink_candidate):\n if symlink_candidate == outfile_name:\n # Symlinking to ourself is *not* ok.\n return False\n\n if symlink_candidate in self._db.transaction_keys():\n # Don't try to symlink if our target file is being updated\n # itself; we can't tell whether we're 'equivalent' to it\n # or not if it's currently being modified!\n return False\n\n symlink_mtime_map = self._db.get(symlink_candidate)\n if symlink_mtime_map is None:\n # We don't have up-to-date-ness information on the candidate.\n return False\n\n # outfile_name is currently in a transaction (changed_files()\n # returned false), so we have to get its mtime info that way.\n outfile_mtime_map = self._db.get_transaction(outfile_name)\n\n # Get a copy of mtime_maps that do not include the output file.\n pruned_outfile_mtime_map = outfile_mtime_map.copy()\n pruned_outfile_mtime_map.pop(outfile_name)\n pruned_symlink_mtime_map = symlink_mtime_map.copy()\n pruned_symlink_mtime_map.pop(symlink_candidate)\n\n if (frozenset(pruned_outfile_mtime_map.keys()) !=\n frozenset(pruned_symlink_mtime_map.keys())):\n # This means symlink_candidate has different deps that we\n # do (common case).\n return False\n\n for (k, v) in pruned_outfile_mtime_map.iteritems():\n # This means symlink_candidate has the same deps as us,\n # but those deps aren't up to date.\n # This holds because infile_map has the *current* mtimes of\n # the input files, and if the (db-based) values of\n # symlink_candidate don't match, that means it's out of date.\n symlink_v = pruned_symlink_mtime_map.get(k, (None, None, None))\n if not file_info_equal(v, symlink_v):\n return False\n\n if not file_info_equal(get_file_info(symlink_candidate),\n symlink_mtime_map[symlink_candidate]):\n # This finishes off the 'other_outfile is up-to-date' check.\n # We tested all the input files are up-to-date above, now we\n # need to test the output file too. Note we never bother to\n # do the crc check here; we assume that as a generated file,\n # other_outfile will not suffer from the\n # 'git-changed-my-mtime' problem.\n return False\n\n return True","title":""},{"docid":"1e9b2fc97f39ea65d0aff639dfc352fb","score":"0.5944636","text":"def verify_path(self, path):\n if Path(path).exists():\n return True\n return False","title":""},{"docid":"28f897359724e00998034cf686012bf2","score":"0.59438217","text":"def check_path(path):\n assert isinstance(path, basestring), \"Expected string or unicode, found %s.\" % type(path)\n try:\n open(path, \"r\")\n except IOError:\n raise IOError('File does not exist: %s' % path)\n return path","title":""},{"docid":"1d2dcd5fddfe2a01bb5a15519baa67b1","score":"0.59393996","text":"def test_symlink_folder8(self):\n src = self.rootdir\n target = self.targetdir\n with mock.patch.object(\n transfer.os.path,\n 'exists',\n side_effect=Exception(\"Mocked error\")):\n with self.assertRaises(Exception):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"a04cd212041a2c05af0b7a22f90a585d","score":"0.5926641","text":"def is_wsl2_path(path: p.PurePath) -> bool:\n return re.match(r\"^/mnt/[a-z]/\", path.as_posix()) is not None","title":""},{"docid":"72eaf06e2133216050eaec901507e2aa","score":"0.59235525","text":"def S_ISLNK(mode):\n return S_IFMT(mode) == S_IFLNK","title":""},{"docid":"2b55905af587ce30f9e7fd3e568d64b0","score":"0.59146184","text":"def is_malformed_path(self):\n return self._tag == 'malformed_path'","title":""},{"docid":"b32a7f3b0b52be5499eccabc3f8c8143","score":"0.59071994","text":"def is_link_ok(link: str) -> bool:\n return not any([re.search(exc, link) for exc in excludes])","title":""},{"docid":"e1dfb6e5ef886289956a028ef3bd3d34","score":"0.5889858","text":"def is_safe_path(basedir, path, follow_symlinks=True):\n if basedir is None:\n return True\n if follow_symlinks:\n return os.path.realpath(path).startswith(basedir.encode('utf-8'))\n return os.path.abspath(path).startswith(basedir.encode('utf-8'))","title":""},{"docid":"d5ffdb8f73ffd102df61f9778afce65d","score":"0.5889097","text":"def valid_permlink(permlink, allow_empty=False):\n if not permlink:\n assert allow_empty, 'permlink cannot be blank'\n return \"\"\n assert isinstance(permlink, str), 'permlink must be string'\n assert len(permlink) <= 256, \"invalid permlink length\"\n return permlink","title":""},{"docid":"d5ffdb8f73ffd102df61f9778afce65d","score":"0.5889097","text":"def valid_permlink(permlink, allow_empty=False):\n if not permlink:\n assert allow_empty, 'permlink cannot be blank'\n return \"\"\n assert isinstance(permlink, str), 'permlink must be string'\n assert len(permlink) <= 256, \"invalid permlink length\"\n return permlink","title":""},{"docid":"7413b59212368171f2e2be09dfcd62d8","score":"0.58867145","text":"def check_links(params):\n\n\tfor s in _file_links:\n\t\t#print \"check:\", s, cfg.out_dir\n\t\tout_file = os.path.join(cfg.out_dir, s)\n\t\tif not os.path.exists(out_file):\n\t \t\twarning(\"%s: invalid link to '%s'\" % (_file_links[s], s))","title":""},{"docid":"e62c55844b430153e153df0db262b290","score":"0.588417","text":"def _resolve_link(path):\r\n paths_seen = []\r\n while islink(path):\r\n if path in paths_seen:\r\n # Already seen this path, so we must have a symlink loop\r\n return None\r\n paths_seen.append(path)\r\n # Resolve where the link points to\r\n resolved = os.readlink(path)\r\n if not isabs(resolved):\r\n dir = dirname(path)\r\n path = normpath(join(dir, resolved))\r\n else:\r\n path = normpath(resolved)\r\n return path","title":""},{"docid":"c8b85174b6188a7a19df4eff61790b94","score":"0.58817095","text":"def hardlink(source, link_name):\n try:\n os.link(source, link_name)\n except FileExistsError:\n # It's possible that the user created a different file with the same name as the\n # one we're trying to download. Thus we need to check the if the inode is different\n # and raise an error in this case.\n source_stat = os.stat(source)\n dest_stat = os.stat(link_name)\n # Check device first because different drives can have the same inode number\n if source_stat.st_dev != dest_stat.st_dev or source_stat.st_ino != dest_stat.st_ino:\n raise","title":""},{"docid":"962858e7a2194d210b9dfedd9b981e87","score":"0.5866979","text":"def _force_symlink(\n root: Path, target: Union[str, PurePath], link_to: Union[str, Path]\n) -> None:\n current_symlink = root.joinpath(target)\n try:\n current_symlink.unlink()\n except OSError:\n pass\n try:\n current_symlink.symlink_to(link_to)\n except Exception:\n pass","title":""},{"docid":"adf4744cb862d5219816f94cf0ef9f77","score":"0.58613175","text":"def _process_symlink(self, target, path): #aka target_path, dotfile_path\n\n if not os.path.lexists(target):\n self._error = True\n print('\\tmissing target: {}'.format(target))\n return ('DOTFILE NOT FOUND', None)\n\n if not os.path.lexists(path):\n self._changed = True\n if self.args.test:\n return ('missing', None)\n else:\n create_dotfile_link(target, path)\n return ('missing', 'synced')\n\n existing_realpath = os.path.realpath(path)\n if existing_realpath != os.path.realpath(target):\n if self.args.debug:\n print('\\tCurrent target path: {}'.format(existing_realpath))\n status = 'unmanaged' if os.path.lexists(existing_realpath) else 'broken'\n\n if self.args.verbose:\n pass # XXX: TODO: ...\n\n if status != 'broken' and not self.args.force:\n return (status, 'skipping')\n self._changed = True\n if self.args.test:\n return (status, None)\n\n #import pdb; pdb.set_trace()\n remove_any(path)\n create_dotfile_link(target, path)\n return (status, 'removed; synced')\n\n return ('ok', None)","title":""},{"docid":"0f124a12bcf057e1e114461e076db3da","score":"0.58529246","text":"def _checkLink(link):\n try:\n link = link.attrs['href']\n except KeyError:\n return -1\n if link[0] == '/':\n link = '://'.join(list(urllib.parse.urlparse(self.address)[0:2])) + link\n elif re.match(r'^mailto|^#', link):\n return None\n good = ['200', '206', '301', '302']\n try:\n status, response = self.http.request(link)\n if status['status'] not in good:\n self.badlinks.append((status['status'], link))\n else:\n self.goodlinks.append(link)\n except httplib2.ServerNotFoundError:\n self.other.append((\"httplib2.ServerNotFoundError\", link))\n except httplib2.ssl.SSLError:\n self.other.append((\"httplib2.ssl.SSLError\", link))\n except ConnectionResetError:\n self.other.append((\"ConnectionResetError\", link))\n except UnicodeError:\n self.other.append((\"UnicodeError\", link))","title":""},{"docid":"af98442fe78a61c211571253d3a076e5","score":"0.58304495","text":"def internal_link_checker(filepath, html_str):\n\n # Flag to determine if internal link is broken\n internal_link_error = False\n\n # \n\n problems = []\n relative_links = []\n\n # find all links\n for prefix in [\"href\", \"src\"]:\n\n links = re.findall(\n f\"{prefix}\\s?=\\s?[\\\"']([{allowed_in_link}]+)[\\\"']\", html_str)\n\n # check if link has a dest\n for link in links:\n\n # Check if link is relative path\n is_relative = check_if_relative_link(link)\n\n # Add to relative links list if relative\n if is_relative:\n if not link in relative_links:\n relative_links.append(link)\n\n # Get correct path\n link = get_correct_link(link)\n\n # Check if link is in use\n check_if_link_in_use(filepath, link)\n\n if link in links_list:\n if links_list[link]:\n if link not in problems:\n problems.append(link) \n else:\n if internal_link_test(link):\n problems.append(link)\n links_list[link] = True\n\n if not internal_link_error:\n internal_link_error = True\n else:\n links_list[link] = False\n\n return problems, relative_links, internal_link_error","title":""},{"docid":"8a8c415ec1777ae102d937832bd39339","score":"0.5820183","text":"def symlink (self, src, dst):\n if self.system ('/bin/ln -sf %s %s'%(src, dst)):\n raise OSError, 'can\\'t symlink [%s] as [%s]' % (src, dst)","title":""},{"docid":"bf5255caff8fa0c01af5e9a0e3b0a7b8","score":"0.58178663","text":"def test_symlink_file6(self):\n src = self.rootdir\n target = os.path.join(self.targetdir,\"target-file\")\n open(target,'w').close()\n with mock.patch.object(\n transfer.os,\n 'unlink',\n side_effect=OSError(\"Mocked error\")):\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"9b019bad4cd351d9787fe08c2bc3f228","score":"0.57966596","text":"def symlink(self, req, link, parent, name):\n self.reply_err(req, EROFS)","title":""},{"docid":"b9684816cb8583b1d44b66d7cd04ca40","score":"0.5782404","text":"def test_force_link(tmpdir):\n src = join(str(tmpdir), \"src\")\n dst = join(str(tmpdir), \"dst\")\n\n with open(src, \"w\") as f:\n f.write(\"Not empty.\")\n\n utils.force_link(src, dst)\n assert os.path.isfile(dst)\n assert not os.path.islink(dst)","title":""},{"docid":"6419b2fcb183ad206a852387f3bc8d86","score":"0.57806474","text":"def test_scan_files(self):\n self.assertFalse(fdf.valid_path('some thing wrong'))\n result = fdf.scan_files(join(getcwd(), 'test'))\n self.assertNotIn(join(getcwd(), 'test/symlink/symlink'), result)","title":""},{"docid":"e2fcb55acc8c1275ebfe5f75e72876a0","score":"0.57789207","text":"def test_non_html_links_are_path_sensitive(self):\n validator = self._create_validator(\n \"\"\"Use [this CSV](data.csv) for the exercise.\"\"\")\n self.assertFalse(validator._validate_links())","title":""},{"docid":"169daa1a343f03131a9d00db91a8b596","score":"0.5773865","text":"def test_symlink_folder4(self):\n src = os.path.join(self.rootdir) \n target = os.path.join(self.targetdir)\n with mock.patch('taca.utils.transfer.os.path') as mockobj:\n mockobj.ismount.return_value = False\n mockobj.isfile.return_value = False\n mockobj.islink.return_value = False\n mockobj.isdir.return_value = False\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"7a3cd10319fcfb6990ceba408de225a7","score":"0.5773567","text":"def is_file_link(self, link):\n return link.startswith(\"###file\") or link.startswith('/repository') or \\\n (link.startswith('../') and '/files/' in link)","title":""},{"docid":"78c2fa09a69a0c70db6567fbff768f2d","score":"0.57712406","text":"def provide_link(dest, src):\n if not os.path.exists(dest):\n # strip trailing /'s just in case it exists\n os.symlink(src, dest.rstrip(\"/\"))","title":""},{"docid":"f5141a7e126b929ce6f8259580dbeb5c","score":"0.5764898","text":"def filepath_validator(path):\n if isinstance(path, str):\n path = Path(path)\n return path.exists() and path.isfile()","title":""},{"docid":"308f791745e59acf2880a0ec1831b152","score":"0.5761583","text":"def test_symlink_lock(self):\n\n # Get an image object and tests its manual lock mechanism.\n api_obj = self.get_img_api_obj()\n img = api_obj.img\n\n # Verify a lock file is created.\n img.lock()\n lfpath = os.path.join(img.imgdir, \"lock\")\n self.assertTrue(os.path.exists(lfpath))\n img.unlock()\n os.remove(lfpath)\n\n # Make lock file a symlink by pointing it to a random file .\n tmp_file = os.path.join(img.imgdir, \"test_symlink\")\n fo = open(tmp_file, 'wb+')\n fo.close()\n os.symlink(tmp_file, lfpath)\n\n # Verify that both pkg install and refresh generate an error\n # if the lock file is a symlink.\n self.pkg(\"install foo\", su_wrap=True, exit=1)\n self.assertTrue(\"contains a symlink\" in self.errout)\n\n self.pkg(\"refresh --full\", su_wrap=True, exit=1)\n self.assertTrue(\"contains a symlink\" in self.errout)","title":""},{"docid":"18658f8e04412069e3a03d80b360b5b3","score":"0.57601094","text":"def check_path_existence(fpath: str) -> bool:\n return os.path.exists(fpath)","title":""},{"docid":"31411b5398f85370a62fa13f46f581a5","score":"0.5740772","text":"def validate_path(self, path):\n if not os.path.exists(path):\n raise IOError('Path {} does not exist'.format(path))\n return True","title":""},{"docid":"042985e4ccc6c626626e50eb20ef3c8a","score":"0.5739684","text":"def ismount(path):\r\n try:\r\n s1 = os.lstat(path)\r\n s2 = os.lstat(join(path, '..'))\r\n except os.error:\r\n return False # It doesn't exist -- so not a mount point :-)\r\n dev1 = s1.st_dev\r\n dev2 = s2.st_dev\r\n if dev1 != dev2:\r\n return True # path/.. on a different device as path\r\n ino1 = s1.st_ino\r\n ino2 = s2.st_ino\r\n if ino1 == ino2:\r\n return True # path/.. is the same i-node as path\r\n return False","title":""}],"string":"[\n {\n \"docid\": \"71564a56fc163c8e7d254c575b623e30\",\n \"score\": \"0.7800918\",\n \"text\": \"def checklink(path):\\n # mktemp is not racy because symlink creation will fail if the\\n # file already exists\\n name = tempfile.mktemp(dir=path)\\n try:\\n os.symlink(\\\".\\\", name)\\n os.unlink(name)\\n return True\\n except (OSError, AttributeError):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1301b1cb5da838eaea975a866ca6bb1f\",\n \"score\": \"0.7800738\",\n \"text\": \"def test_is_symlink(path: Path) -> None:\\n assert (path / \\\"link\\\").is_symlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c304a94d85d6653cded13e445a0b19\",\n \"score\": \"0.7560013\",\n \"text\": \"def is_symlink(self):\\n try:\\n return S_ISLNK(self.lstat().st_mode)\\n except OSError as e:\\n if e.errno not in (ENOENT, ENOTDIR):\\n raise\\n # Path doesn't exist\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e1ac62ad49004dab7dc483330692cb\",\n \"score\": \"0.75018257\",\n \"text\": \"def is_symlink(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"896891d9194d8ad8e3c7b93d9f4735d6\",\n \"score\": \"0.7391739\",\n \"text\": \"def test_symlink_on_unknown_file(self):\\n root = URI(self.baseurl)\\n notexisting_path = root / 'ma' / 'moo'\\n tee_path = root / 'helloworld'\\n notexisting_path.symlink(tee_path)\\n\\n self.assertTrue(tee_path.islink())\\n self.assertEqual(tee_path.readlink(), notexisting_path)\\n self.assertTrue(not notexisting_path.exists())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9543b5f7076123e979ef23a52736cca5\",\n \"score\": \"0.7297981\",\n \"text\": \"def test_symlink_missing_src(file, source):\\n target = source.parent / \\\"symlink.lnk\\\"\\n missing_source = source.parent / \\\"missing.txt\\\"\\n try:\\n file.symlink(str(missing_source), str(target))\\n assert salt.utils.path.islink(str(target))\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faaf23de79bf29e8868d7acf350b1324\",\n \"score\": \"0.72364473\",\n \"text\": \"def islink(path):\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117fd290d3a937d8daa06de391ab888b\",\n \"score\": \"0.7161739\",\n \"text\": \"def islink(self, path):\\n path = ftputil.tool.as_unicode(path)\\n try:\\n lstat_result = self._host.lstat(\\n path, _exception_for_missing_path=False)\\n except ftputil.error.RootDirError:\\n return False\\n else:\\n if lstat_result is None:\\n # Non-existent path\\n return False\\n else:\\n return stat.S_ISLNK(lstat_result.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e466325a1eb6f6c43507803db038e87\",\n \"score\": \"0.7131687\",\n \"text\": \"def islink(path):\\r\\n try:\\r\\n st = os.lstat(path)\\r\\n except (os.error, AttributeError):\\r\\n return False\\r\\n return stat.S_ISLNK(st.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f68b0566a7c14c2badd11901266889\",\n \"score\": \"0.71089053\",\n \"text\": \"def is_symlink(self):\\n if os.name == \\\"posix\\\": return os.path.islink(self.path)\\n if os.name == \\\"nt\\\":\\n import win32api\\n import win32con\\n num = win32con.FILE_ATTRIBUTE_REPARSE_POINT\\n return bool(win32api.GetFileAttributes(self.path) & num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac7484c1ede678b11d426cfd0dff2ef\",\n \"score\": \"0.71058583\",\n \"text\": \"def islink(path):\\n try:\\n st = os.lstat(path)\\n except (OSError, AttributeError):\\n return False\\n return stat.S_ISLNK(st.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac7484c1ede678b11d426cfd0dff2ef\",\n \"score\": \"0.71058583\",\n \"text\": \"def islink(path):\\n try:\\n st = os.lstat(path)\\n except (OSError, AttributeError):\\n return False\\n return stat.S_ISLNK(st.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1aec9e1a0da95eb624cbc8cdd68e79\",\n \"score\": \"0.70764434\",\n \"text\": \"def test_symlink_exists_different(file, source):\\n dif_source = source.parent / \\\"dif_source.txt\\\"\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(dif_source)\\n try:\\n with pytest.raises(CommandExecutionError) as exc:\\n file.symlink(str(source), str(target))\\n assert \\\"Found existing symlink:\\\" in exc.value.message\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8816e12f05b8fce70a4dc0a21726d931\",\n \"score\": \"0.70211464\",\n \"text\": \"def test_symlink_exists_different_force(file, source):\\n dif_source = source.parent / \\\"dif_source.txt\\\"\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(dif_source)\\n try:\\n file.symlink(str(source), str(target), force=True)\\n assert salt.utils.path.readlink(str(target)) == str(source)\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb95c61abb8f5354e7e9d4d715e4ef7a\",\n \"score\": \"0.6982841\",\n \"text\": \"def test_readlink(path: Path) -> None:\\n assert PurePath(\\\"file\\\") == (path / \\\"link\\\").readlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c1e77dc34b5c401947222e0bbf911a\",\n \"score\": \"0.6982368\",\n \"text\": \"def _is_link(self, path):\\n return os.path.islink(os.path.expanduser(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8627bffa0d7e759f5d4710ef759afa77\",\n \"score\": \"0.69228035\",\n \"text\": \"def is_symlink(self):\\n if self._is_symlink is None:\\n self._is_symlink = os.path.islink(self.storage_url.object_name)\\n return self._is_symlink\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe21b7cecc2c9973a253737963706133\",\n \"score\": \"0.68756634\",\n \"text\": \"def test_constrain_symlinks_to_filesystem() -> None:\\n path = root(\\n {\\n \\\"file\\\": \\\"text\\\",\\n \\\"dir\\\": {\\\"link\\\": PurePath(\\\"..\\\", \\\"file\\\")},\\n },\\n innerpath=PurePath(\\\"dir\\\"),\\n )\\n # XXX Should we rewrite the error message to hide the 'dir/' prefix?\\n with pytest.raises(FileNotFoundError, match=\\\"file not found: dir/file\\\"):\\n assert (path / \\\"link\\\").read_text()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923a01647ae572518a7cb698b01d2789\",\n \"score\": \"0.6867287\",\n \"text\": \"def test_symlink_exists_file(file, source):\\n with pytest.helpers.temp_file(\\\"symlink.txt\\\", contents=\\\"Source content\\\") as target:\\n with pytest.raises(CommandExecutionError) as exc:\\n file.symlink(str(source), str(target))\\n assert \\\"Existing path is not a symlink:\\\" in exc.value.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41bff0c324b7c71a826535c2a5479b5c\",\n \"score\": \"0.6856679\",\n \"text\": \"def test_symlink(file, source):\\n target = source.parent / \\\"symlink.lnk\\\"\\n try:\\n file.symlink(str(source), str(target))\\n assert salt.utils.path.islink(str(target))\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7a46dd208c245d602979b6f8c24590\",\n \"score\": \"0.68321455\",\n \"text\": \"def test_symlink_exists_same(file, source):\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(source)\\n try:\\n before_time = os.stat(str(target)).st_mtime\\n ret = file.symlink(str(source), str(target))\\n after_time = os.stat(str(target)).st_mtime\\n assert before_time == after_time\\n assert ret is True\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c3309bca6d764f2d429a94c01e2867\",\n \"score\": \"0.68014234\",\n \"text\": \"def is_symlink(\\n linkfile: T.Union[str, pathlib.Path],\\n src: T.Union[None, str, pathlib.Path] = None,\\n) -> bool:\\n linkfile = pathlib.Path(linkfile)\\n if not linkfile.is_symlink():\\n return False\\n\\n if src is None:\\n return True\\n\\n return linkfile.resolve() == pathlib.Path(src).resolve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5673d6915d6847708663b71cf2eab7\",\n \"score\": \"0.67836535\",\n \"text\": \"def check_if_relative_link(link):\\n\\n if not link.startswith(\\\"http\\\"):\\n if not link.startswith(\\\"/\\\"):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1067f2b5ebb747c4d55bca1975463a6a\",\n \"score\": \"0.6782696\",\n \"text\": \"def _valid_link(self, link, fail_on_disk_check=True):\\n\\n if not link in self._links:\\n raise Exception('link [{0}] not found in timeline!'.format(link))\\n\\n if not os.path.islink(self._links[link]['path']):\\n msg = 'link [{0}] not found!'.format(self._links[link]['path'])\\n if fail_on_disk_check:\\n raise Exception(msg)\\n self.logger.warning(msg)\\n return False\\n\\n if not os.path.exists(self._links[link]['path']):\\n msg = 'link [{0}] is broken!'.format(self._links[link]['path'])\\n if fail_on_disk_check:\\n raise Exception(msg)\\n self.logger.warning(msg)\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968ba0654b4a586ad577e6ad741ee0ed\",\n \"score\": \"0.6718646\",\n \"text\": \"def is_symlink(self):\\n return self.type == self.Type.SYMLINK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7331b2df6b96881496979b7a913c461\",\n \"score\": \"0.6693001\",\n \"text\": \"def is_symlink(self):\\n if (\\n not self.custom_fields\\n or resource_util.SYMLINK_METADATA_KEY not in self.custom_fields\\n ):\\n return False\\n return (\\n self.custom_fields[resource_util.SYMLINK_METADATA_KEY].lower() == 'true'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d65ef687eac4cb308d24311371b04b\",\n \"score\": \"0.66920704\",\n \"text\": \"def islink(self, path: AnyStr) -> bool:\\n return self.filesystem.islink(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1435142fcb2dce5b3f51f29d763b77\",\n \"score\": \"0.66706246\",\n \"text\": \"def islink (self) :\\n try :\\n return stat.S_ISLNK (self.mode)\\n except OSError :\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318e97be28a1d07940b9a69de3c8b1f6\",\n \"score\": \"0.6630757\",\n \"text\": \"def copy_if_symlink_fails(\\n src: Union[str, Path],\\n dst: Union[str, Path],\\n backend_args: Optional[dict] = None,\\n) -> bool:\\n backend = get_file_backend(\\n src, backend_args=backend_args, enable_singleton=True)\\n return backend.copy_if_symlink_fails(src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3050fd54816a14c87c867d2e9a3748\",\n \"score\": \"0.6584976\",\n \"text\": \"def test_through_symlink(self) -> None:\\n link1 = os.path.join(self.tmp_dir, \\\"link1\\\")\\n os.symlink(self.mount, link1)\\n\\n info1 = json.loads(self.eden.run_cmd(\\\"info\\\", link1))\\n self.assertEqual(self.mount, info1[\\\"mount\\\"])\\n\\n # Create a non-normalized symlink pointing to the parent directory\\n # of the mount\\n link2 = os.path.join(self.tmp_dir, \\\"mounts_link\\\")\\n os.symlink(self.mount + \\\"//..\\\", link2)\\n mount_through_link2 = os.path.join(link2, self.repo_name)\\n\\n info2 = json.loads(self.eden.run_cmd(\\\"info\\\", mount_through_link2))\\n self.assertEqual(self.mount, info2[\\\"mount\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc10782d2de274eef803bde25fbaf972\",\n \"score\": \"0.65710294\",\n \"text\": \"def test_symlink_exists_different_atomic(file, source):\\n dif_source = source.parent / \\\"dif_source.txt\\\"\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(dif_source)\\n try:\\n file.symlink(str(source), str(target), atomic=True)\\n assert salt.utils.path.readlink(str(target)) == str(source)\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"741e573a30028ce18a81c363e3d5a5e9\",\n \"score\": \"0.6550941\",\n \"text\": \"def islink(s):\\n\\n try:\\n import Carbon.File\\n return Carbon.File.ResolveAliasFile(s, 0)[2]\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b04b3104b1dd15775e4fb15a939ca49\",\n \"score\": \"0.6517931\",\n \"text\": \"def islink(pathname):\\n from os import listdir\\n from os.path import isdir, isfile\\n import os.path\\n pathname = pathname.rstrip(\\\"/\\\")\\n if os.path.islink(pathname): return True\\n elif isfile(pathname) and ishidden(pathname): return True\\n elif isdir(pathname):\\n entries = listdir(pathname)\\n for entry in entries:\\n if not islink(pathname+\\\"/\\\"+entry): return False\\n return True\\n else: return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c17a63c3f070a9ace82b381e0997881\",\n \"score\": \"0.6510741\",\n \"text\": \"def check_file(filename,path):\\n chksumX = chksum_file(\\\"%s/%s\\\" % (path,filename))\\n (pathY,chksumY) = find_file(filename,path,chksumX)\\n if chksumX == chksumY:\\n\\thardlink(filename,path,pathY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6924dff27d54d6cab5d242f412cc20a7\",\n \"score\": \"0.6508725\",\n \"text\": \"def silent_remove_symlink(path): # TODO: test\\n result = {'error': ''}\\n if symlink_exists(path):\\n try:\\n os.unlink(path)\\n except OSError as e:\\n result['error'] = str(e)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595ab9271ea617ec1ff274f308897593\",\n \"score\": \"0.64662755\",\n \"text\": \"def has_valid_link(dotfile, dotfiles_dir):\\n homefolder_path = get_homefolder_path(dotfile, dotfiles_dir)\\n if os.path.islink(homefolder_path) and os.readlink(homefolder_path) == dotfile:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b6fb9618f82b8429612697328e30ae\",\n \"score\": \"0.6455614\",\n \"text\": \"def is_valid_file(abs_path):\\n if os.path.exists(abs_path) and os.path.isfile(abs_path) \\\\\\n and not os.path.islink(abs_path):\\n return True\\n\\n else: return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6353f544288001a6c15f9a0558223e\",\n \"score\": \"0.6414611\",\n \"text\": \"def ismount_raw(path):\\r\\n try:\\r\\n s1 = os.lstat(path)\\r\\n except os.error as err:\\r\\n if err.errno == errno.ENOENT:\\r\\n # It doesn't exist -- so not a mount point :-)\\r\\n return False\\r\\n raise\\r\\n\\r\\n if stat.S_ISLNK(s1.st_mode):\\r\\n # A symlink can never be a mount point\\r\\n return False\\r\\n\\r\\n s2 = os.lstat(os.path.join(path, '..'))\\r\\n dev1 = s1.st_dev\\r\\n dev2 = s2.st_dev\\r\\n if dev1 != dev2:\\r\\n # path/.. on a different device as path\\r\\n return True\\r\\n\\r\\n ino1 = s1.st_ino\\r\\n ino2 = s2.st_ino\\r\\n if ino1 == ino2:\\r\\n # path/.. is the same i-node as path\\r\\n return True\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d851ba210920164362039bc52ea42fd\",\n \"score\": \"0.6394149\",\n \"text\": \"def is_linked(self) -> bool:\\n return self.parent_path.is_symlink() \\\\\\n and (not self.child_path.is_symlink()) \\\\\\n and self.child_path.samefile(self.parent_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b635c87ed41938fbf0245d78d827c98\",\n \"score\": \"0.63874704\",\n \"text\": \"def symlinks_supported():\\n with tempfile.TemporaryDirectory() as temp_dir:\\n original_path = os.path.join(temp_dir, 'original')\\n symlink_path = os.path.join(temp_dir, 'symlink')\\n os.makedirs(original_path)\\n try:\\n os.symlink(original_path, symlink_path)\\n supported = True\\n except (OSError, NotImplementedError):\\n supported = False\\n return supported\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bd6c13a93b82e506573bc794a24693\",\n \"score\": \"0.6330474\",\n \"text\": \"def check_path(data_pointer, log, msg):\\n if not os.path.exists(data_pointer):\\n log.debug(msg)\\n return False\\n else:\\n return data_pointer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13fb5713db8da8497f3a87fe901a9745\",\n \"score\": \"0.6315982\",\n \"text\": \"def test_symlink_target_relative_path(file, source):\\n target = \\\"..{}symlink.lnk\\\".format(os.path.sep)\\n with pytest.raises(SaltInvocationError) as exc:\\n file.symlink(str(source), str(target))\\n assert \\\"Link path must be absolute\\\" in exc.value.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f87c41104cb5341d0b2c9ad58a4970e\",\n \"score\": \"0.6286066\",\n \"text\": \"def _resolve_symlinks_and_relative_paths(path):\\n # type: pathlib.Path -> pathlib.Path\\n try:\\n path = path.resolve()\\n except WindowsError: # TODO: check for more specific error\\n pass\\n except OSError as e:\\n if e.errno == 2:\\n pass\\n else:\\n raise\\n\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75a5874ba22708116a98d08093a24ae\",\n \"score\": \"0.6252006\",\n \"text\": \"def check_path(path):\\n if(os.path.exists(path) == False):\\n print_error_and_exit(\\\"Cannot find %s\\\" % path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d007389a31ebd7fb16f28910d95422f\",\n \"score\": \"0.62242776\",\n \"text\": \"def symlink(self, target, source):\\n if self.debug == True:\\n logging.debug(\\\"in symlink\\\")\\n raise FuseOSError(ENOENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90387b542107e8c28fba24a7fd822dad\",\n \"score\": \"0.62149453\",\n \"text\": \"def path_ok(match_tuple: MatchTuple) -> bool:\\n relative_path = match_tuple.link.split(\\\"#\\\")[0]\\n full_path = os.path.join(os.path.dirname(str(match_tuple.source)), relative_path)\\n return os.path.exists(full_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41d676430289b18beaa7f4c28d60dc6\",\n \"score\": \"0.6214474\",\n \"text\": \"def test_clear_broken_symlink(self):\\n nonexistent_file_path = os.path.join(settings.STATIC_ROOT, 'nonexistent.txt')\\n broken_symlink_path = os.path.join(settings.STATIC_ROOT, 'symlink.txt')\\n os.symlink(nonexistent_file_path, broken_symlink_path)\\n self.run_collectstatic(clear=True)\\n self.assertFalse(os.path.lexists(broken_symlink_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db9930863d4dcb3dd9b4a03dbd9b6d5\",\n \"score\": \"0.6211496\",\n \"text\": \"def test_symlinks():\\n\\n config_file = \\\"study/profit_symlink.yaml\\\"\\n config = BaseConfig.from_file(config_file)\\n base_file = \\\"./study/run_000/mockup.in\\\"\\n link_file = \\\"./study/run_000/some_subdir/symlink_link.txt\\\"\\n try:\\n run(f\\\"profit run {config_file}\\\", shell=True, timeout=TIMEOUT)\\n with open(link_file, \\\"r\\\") as link:\\n with open(base_file, \\\"r\\\") as base:\\n link_data = link.read()\\n base_data = base.read()\\n assert link_data == base_data and not link_data.startswith(\\\"{\\\")\\n finally:\\n clean(config)\\n run(f\\\"profit clean --all {config_file}\\\", shell=True, timeout=CLEAN_TIMEOUT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9e9a27b97a6b79b9f1382fe5c6addc\",\n \"score\": \"0.6205026\",\n \"text\": \"def do_ismount(path):\\n try:\\n s1 = os.lstat(path)\\n except os.error as err:\\n if err.errno == errno.ENOENT:\\n # It doesn't exist -- so not a mount point :-)\\n return False\\n else:\\n raise GlusterFileSystemOSError(\\n err.errno, '%s, os.lstat(\\\"%s\\\")' % (err.strerror, path))\\n\\n if stat.S_ISLNK(s1.st_mode):\\n # A symlink can never be a mount point\\n return False\\n\\n try:\\n s2 = os.lstat(os.path.join(path, '..'))\\n except os.error as err:\\n raise GlusterFileSystemOSError(\\n err.errno, '%s, os.lstat(\\\"%s\\\")' % (err.strerror,\\n os.path.join(path, '..')))\\n\\n dev1 = s1.st_dev\\n dev2 = s2.st_dev\\n if dev1 != dev2:\\n # path/.. on a different device as path\\n return True\\n\\n ino1 = s1.st_ino\\n ino2 = s2.st_ino\\n if ino1 == ino2:\\n # path/.. is the same i-node as path\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97181c311338ad47abd8855991b9b9a\",\n \"score\": \"0.6189933\",\n \"text\": \"def test_broken_symlink(self):\\n path = os.path.join(settings.STATIC_ROOT, 'test.txt')\\n os.unlink(path)\\n self.run_collectstatic()\\n self.assertTrue(os.path.islink(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4525da4084481db3ff2dc0510d16515\",\n \"score\": \"0.6175588\",\n \"text\": \"def lexists(self, path: AnyStr) -> bool:\\n return self.filesystem.exists(path, check_link=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e869be7867b9731154d811f96ce03792\",\n \"score\": \"0.6169737\",\n \"text\": \"def check_if_link_in_use(filepath, link):\\n\\n if not \\\"previous\\\" in link:\\n if not in_use_links.get(link):\\n new_file_name = remove_extra_from_path(filepath)\\n\\n if new_file_name.startswith(\\\"\\\\\\\\\\\"):\\n new_file_name = new_file_name.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n\\n if new_file_name != link:\\n in_use_links[link] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a6e146797833743267c59619036c22\",\n \"score\": \"0.61650556\",\n \"text\": \"def ismount(path):\\n try:\\n s1 = os.lstat(path)\\n except OSError:\\n # It doesn't exist -- so not a mount point. :-)\\n return False\\n else:\\n # A symlink can never be a mount point\\n if stat.S_ISLNK(s1.st_mode):\\n return False\\n\\n if isinstance(path, bytes):\\n parent = join(path, b'..')\\n else:\\n parent = join(path, '..')\\n try:\\n s2 = os.lstat(parent)\\n except OSError:\\n return False\\n\\n dev1 = s1.st_dev\\n dev2 = s2.st_dev\\n if dev1 != dev2:\\n return True # path/.. on a different device as path\\n ino1 = s1.st_ino\\n ino2 = s2.st_ino\\n if ino1 == ino2:\\n return True # path/.. is the same i-node as path\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e371c24edcbab6afb4eab8ea6a95def\",\n \"score\": \"0.61448544\",\n \"text\": \"def check_path(path):\\r\\n if os.path.exists(path):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bfbb920868b15f86a5bd492a0366588\",\n \"score\": \"0.6144068\",\n \"text\": \"def test_check_permissions_and_broken_symlinks(self, filesystem_world_writable_whitelist, filesystem_ignore_patterns):\\n stack = [] # Used for storing directories to parse\\n starting_dir = \\\"/\\\"\\n failed = False\\n\\n # Helper functions\\n is_dir = lambda x: stat.S_ISDIR( x.st_mode)\\n is_lnk = lambda x: stat.S_ISLNK(x.st_mode)\\n is_sock = lambda x: stat.S_ISSOCK(x.st_mode)\\n is_chr = lambda x: stat.S_ISCHR(x.st_mode)\\n is_blk = lambda x: stat.S_ISBLK(x.st_mode)\\n is_reg = lambda x: stat.S_ISREG(x.st_mode)\\n is_world_writable = lambda x: stat.S_IWOTH & x.st_mode\\n\\n # Start it up\\n stack.append(starting_dir)\\n while len(stack) > 0:\\n # Get actual directory\\n directory = stack.pop()\\n # Parse through it\\n for entry in os.listdir(directory):\\n # Stat it\\n path = \\\"%s/%s\\\" % (directory, entry)\\n if path.startswith(\\\"//\\\"): # Strip beginning // because of root dir\\n path = path[1:]\\n\\n # Skip this file/dir, has the wrong pattern\\n if any([fnmatch.fnmatch(path, p) for p in filesystem_ignore_patterns]):\\n sys.stdout.write(\\\"[ignoring] %s\\\\n\\\" % (path,))\\n continue\\n\\n info = os.lstat(path)\\n if is_lnk(info):\\n # Check if it's broken or not\\n if not Test.Shell.exists_in_path(os.readlink(path), os.path.abspath(directory)):\\n sys.stderr.write(\\\"[broken-symlink] %s -> %s\\\\n\\\" % (path, os.readlink(path)))\\n failed = True\\n else:\\n if is_dir(info):\\n stack.append(path)\\n\\n # FIXME - determine whether file is truly writable (parent dir\\n # may not be readable)\\n # Check its permissions, if wrong, append it\\n if is_world_writable(info):\\n # If the path isn't on the whitelist ... we found a failure\\n if not any([fnmatch.fnmatch(path, p) for p in filesystem_world_writable_whitelist]):\\n sys.stderr.write(\\\"[world-writable] %s %s\\\\n\\\" % (self.fmt_mode_str(info.st_mode), path))\\n failed = True\\n if failed:\\n Test.Fail(msg=\\\"Test failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ffe407236011d5ddfea62710e51f2d8\",\n \"score\": \"0.6135178\",\n \"text\": \"def is_device_link_fail(self):\\n return self._tag == 'device_link_fail'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d9a697a78f12fdb87286c0b5c3558e3\",\n \"score\": \"0.6132719\",\n \"text\": \"def test_symlink_file5(self):\\n src = os.path.join(self.rootdir,\\\"non-existing-file\\\") \\n target = os.path.join(self.targetdir,os.path.basename(src))\\n with self.assertRaises(transfer.TransferError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"771b570903a84e5ab0839ea57aff428e\",\n \"score\": \"0.6085972\",\n \"text\": \"def silent_symlink(src, dst):\\n try:\\n os.symlink(src, dst)\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b346d5a2d9646b6ff0a8844a820c0b6e\",\n \"score\": \"0.60828763\",\n \"text\": \"def readlink(path):\\n if not path:\\n return path\\n\\n if path[0] == \\\"~\\\":\\n path = os.path.expanduser(path)\\n\\n return run_bash_cmd([\\\"readlink\\\", \\\"-f\\\", path]) or path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc8a241fd5bbbd50d166cdb6622d9a4\",\n \"score\": \"0.6063484\",\n \"text\": \"def _is_valid_path(path):\\r\\n try:\\r\\n urlresolvers.resolve(path)\\r\\n return True\\r\\n except urlresolvers.Resolver404:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8bae97837e4ff75dd6ca0ac4d853a0f\",\n \"score\": \"0.6037345\",\n \"text\": \"def exists(path, followlinks=True):\\n if path is None:\\n raise Tools.exceptions.Value(\\\"Path is not passed in system.fs.exists\\\")\\n found = False\\n try:\\n st = os.lstat(path)\\n found = True\\n except (OSError, AttributeError):\\n pass\\n if found and followlinks and stat.S_ISLNK(st.st_mode):\\n if MyEnv.debug:\\n Tools.log(\\\"path %s exists\\\" % str(path.encode(\\\"utf-8\\\")))\\n linkpath = os.readlink(path)\\n if linkpath[0] != \\\"/\\\":\\n linkpath = os.path.join(Tools.path_parent(path), linkpath)\\n return Tools.exists(linkpath)\\n if found:\\n return True\\n # Tools.log('path %s does not exist' % str(path.encode(\\\"utf-8\\\")))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd3c3cb6566686327f09cc836e98684d\",\n \"score\": \"0.60363925\",\n \"text\": \"def check_path(p):\\n if not os.path.exists(p):\\n msg = 'Error: Path \\\"{}\\\" does not exist.'.format(p)\\n print_help(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6931c31cb4ea78fdd2dcb5957104c1b\",\n \"score\": \"0.6035318\",\n \"text\": \"def check_path(path):\\n if (not os.path.isfile(path)):\\n raise Exception('The path specified does not point to a file')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfdbda55742febd9b0ba06e3c8969c13\",\n \"score\": \"0.6016378\",\n \"text\": \"def symlinks_test(dirname):\\n\\n links = []\\n\\n for (path, dirs, files) in os.walk(dirname):\\n if path.find('/.git') > -1:\\n continue\\n\\n # search for directory links\\n for d in dirs:\\n dname = os.path.join(path, d)\\n if os.path.islink(dname):\\n links.append(os.path.relpath(dname, dirname)+'/')\\n\\n # search for file links\\n for f in files:\\n fname = os.path.join(path, f)\\n if os.path.islink(fname):\\n links.append(os.path.relpath(fname, dirname))\\n\\n if links:\\n return (FAIL, '\\\\n'.join(links))\\n else:\\n return (PASS, \\\"None\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a97ceeb3e2a1ef2d4f8add3a4d41507d\",\n \"score\": \"0.60025454\",\n \"text\": \"def test_force_symlink_overwrite(tmpdir):\\n src = join(str(tmpdir), \\\"src\\\")\\n dst = join(str(tmpdir), \\\"dst\\\")\\n\\n with open(src, \\\"w\\\") as f:\\n f.write(\\\"Correct.\\\")\\n\\n with open(dst, \\\"w\\\") as f:\\n f.write(\\\"Wrong.\\\")\\n\\n utils.force_symlink(src, dst)\\n assert os.path.islink(dst)\\n\\n with open(dst, \\\"r\\\") as f:\\n assert \\\"Correct\\\" in f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fbf806aa189307c157cd564dbcd1a8d\",\n \"score\": \"0.59952956\",\n \"text\": \"def fix_all_git_symlinked(topdir):\\n # Determine whether or not symlinks need fixing (they don’t if installing\\n # from a .tar.gz file)\\n with io.open(topdir + r'\\\\nikola\\\\data\\\\symlink-test-link.txt', 'r', encoding='utf-8-sig') as f:\\n text = f.read()\\n if text.startswith(\\\"NIKOLA_SYMLINKS=OK\\\"):\\n return -1\\n with io.open(topdir + r'\\\\nikola\\\\data\\\\symlinked.txt', 'r', encoding='utf-8-sig') as f:\\n text = f.read()\\n # expect each line a relpath from git or zip root,\\n # smoke test relpaths are relative to git root\\n if text.startswith('.'):\\n raise Exception(r'Bad data in \\\\nikola\\\\data\\\\symlinked.txt')\\n relnames = text.split('\\\\n')\\n relnames = [name.strip().replace('/', '\\\\\\\\') for name in relnames]\\n relnames = [name for name in relnames if name]\\n\\n failures = 0\\n for name in relnames:\\n # build dst path and do some basic validation\\n dst = os.path.join(topdir, name)\\n # don't access files outside topdir\\n if not is_file_into_dir(dst, topdir):\\n continue\\n if os.path.isdir(dst):\\n # assume the file was de-symlinked\\n continue\\n\\n # build src path and do some basic validation\\n with io.open(os.path.join(topdir, dst), 'r', encoding='utf-8-sig') as f:\\n text = f.read()\\n dst_dir = os.path.dirname(dst)\\n try:\\n src = os.path.normpath(os.path.join(dst_dir, text))\\n if not os.path.exists(src):\\n # assume the file was de-symlinked before\\n continue\\n # don't access files outside topdir\\n if not is_file_into_dir(src, topdir):\\n continue\\n except Exception:\\n # assume the file was de-symlinked before\\n continue\\n\\n # copy src to dst\\n try:\\n if os.path.isdir(src):\\n os.unlink(dst)\\n shutil.copytree(src, dst)\\n else:\\n shutil.copy2(src, dst)\\n except Exception:\\n failures += 1\\n print(\\\"*** copy failed for\\\")\\n print(\\\"\\\\t src:\\\", src)\\n print(\\\"\\\\t dst:\\\", dst)\\n\\n return failures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"727d2ea9be0ae6be316b42e299d3ad3b\",\n \"score\": \"0.599236\",\n \"text\": \"def test_hardlink(self):\\n # Prevent errors from being caught\\n self.tar.errorlevel = 1\\n\\n self.tar.extract(\\\"0-REGTYPE\\\", dirname())\\n try:\\n # Extract 1-LNKTYPE which is a hardlink to 0-REGTYPE\\n self.tar.extract(\\\"1-LNKTYPE\\\", dirname())\\n except EnvironmentError, e:\\n import errno\\n if e.errno == errno.ENOENT:\\n self.fail(\\\"hardlink not extracted properly\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9745941b98c710c4c4ec6d21f02e9e1\",\n \"score\": \"0.59648645\",\n \"text\": \"def test_symlink_folder7(self):\\n src = self.rootdir\\n target = os.path.join(self.targetdir,os.path.basename(src))\\n with mock.patch.object(\\n transfer.os,\\n 'symlink',\\n side_effect=OSError(\\\"Mocked error\\\")):\\n with self.assertRaises(transfer.SymlinkError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c8991464103ff5c306a31023d7fce3\",\n \"score\": \"0.5945459\",\n \"text\": \"def can_symlink_to(self, outfile_name, symlink_candidate):\\n if symlink_candidate == outfile_name:\\n # Symlinking to ourself is *not* ok.\\n return False\\n\\n if symlink_candidate in self._db.transaction_keys():\\n # Don't try to symlink if our target file is being updated\\n # itself; we can't tell whether we're 'equivalent' to it\\n # or not if it's currently being modified!\\n return False\\n\\n symlink_mtime_map = self._db.get(symlink_candidate)\\n if symlink_mtime_map is None:\\n # We don't have up-to-date-ness information on the candidate.\\n return False\\n\\n # outfile_name is currently in a transaction (changed_files()\\n # returned false), so we have to get its mtime info that way.\\n outfile_mtime_map = self._db.get_transaction(outfile_name)\\n\\n # Get a copy of mtime_maps that do not include the output file.\\n pruned_outfile_mtime_map = outfile_mtime_map.copy()\\n pruned_outfile_mtime_map.pop(outfile_name)\\n pruned_symlink_mtime_map = symlink_mtime_map.copy()\\n pruned_symlink_mtime_map.pop(symlink_candidate)\\n\\n if (frozenset(pruned_outfile_mtime_map.keys()) !=\\n frozenset(pruned_symlink_mtime_map.keys())):\\n # This means symlink_candidate has different deps that we\\n # do (common case).\\n return False\\n\\n for (k, v) in pruned_outfile_mtime_map.iteritems():\\n # This means symlink_candidate has the same deps as us,\\n # but those deps aren't up to date.\\n # This holds because infile_map has the *current* mtimes of\\n # the input files, and if the (db-based) values of\\n # symlink_candidate don't match, that means it's out of date.\\n symlink_v = pruned_symlink_mtime_map.get(k, (None, None, None))\\n if not file_info_equal(v, symlink_v):\\n return False\\n\\n if not file_info_equal(get_file_info(symlink_candidate),\\n symlink_mtime_map[symlink_candidate]):\\n # This finishes off the 'other_outfile is up-to-date' check.\\n # We tested all the input files are up-to-date above, now we\\n # need to test the output file too. Note we never bother to\\n # do the crc check here; we assume that as a generated file,\\n # other_outfile will not suffer from the\\n # 'git-changed-my-mtime' problem.\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9b2fc97f39ea65d0aff639dfc352fb\",\n \"score\": \"0.5944636\",\n \"text\": \"def verify_path(self, path):\\n if Path(path).exists():\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28f897359724e00998034cf686012bf2\",\n \"score\": \"0.59438217\",\n \"text\": \"def check_path(path):\\n assert isinstance(path, basestring), \\\"Expected string or unicode, found %s.\\\" % type(path)\\n try:\\n open(path, \\\"r\\\")\\n except IOError:\\n raise IOError('File does not exist: %s' % path)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2dcd5fddfe2a01bb5a15519baa67b1\",\n \"score\": \"0.59393996\",\n \"text\": \"def test_symlink_folder8(self):\\n src = self.rootdir\\n target = self.targetdir\\n with mock.patch.object(\\n transfer.os.path,\\n 'exists',\\n side_effect=Exception(\\\"Mocked error\\\")):\\n with self.assertRaises(Exception):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a04cd212041a2c05af0b7a22f90a585d\",\n \"score\": \"0.5926641\",\n \"text\": \"def is_wsl2_path(path: p.PurePath) -> bool:\\n return re.match(r\\\"^/mnt/[a-z]/\\\", path.as_posix()) is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72eaf06e2133216050eaec901507e2aa\",\n \"score\": \"0.59235525\",\n \"text\": \"def S_ISLNK(mode):\\n return S_IFMT(mode) == S_IFLNK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b55905af587ce30f9e7fd3e568d64b0\",\n \"score\": \"0.59146184\",\n \"text\": \"def is_malformed_path(self):\\n return self._tag == 'malformed_path'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b32a7f3b0b52be5499eccabc3f8c8143\",\n \"score\": \"0.59071994\",\n \"text\": \"def is_link_ok(link: str) -> bool:\\n return not any([re.search(exc, link) for exc in excludes])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1dfb6e5ef886289956a028ef3bd3d34\",\n \"score\": \"0.5889858\",\n \"text\": \"def is_safe_path(basedir, path, follow_symlinks=True):\\n if basedir is None:\\n return True\\n if follow_symlinks:\\n return os.path.realpath(path).startswith(basedir.encode('utf-8'))\\n return os.path.abspath(path).startswith(basedir.encode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ffdb8f73ffd102df61f9778afce65d\",\n \"score\": \"0.5889097\",\n \"text\": \"def valid_permlink(permlink, allow_empty=False):\\n if not permlink:\\n assert allow_empty, 'permlink cannot be blank'\\n return \\\"\\\"\\n assert isinstance(permlink, str), 'permlink must be string'\\n assert len(permlink) <= 256, \\\"invalid permlink length\\\"\\n return permlink\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ffdb8f73ffd102df61f9778afce65d\",\n \"score\": \"0.5889097\",\n \"text\": \"def valid_permlink(permlink, allow_empty=False):\\n if not permlink:\\n assert allow_empty, 'permlink cannot be blank'\\n return \\\"\\\"\\n assert isinstance(permlink, str), 'permlink must be string'\\n assert len(permlink) <= 256, \\\"invalid permlink length\\\"\\n return permlink\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7413b59212368171f2e2be09dfcd62d8\",\n \"score\": \"0.58867145\",\n \"text\": \"def check_links(params):\\n\\n\\tfor s in _file_links:\\n\\t\\t#print \\\"check:\\\", s, cfg.out_dir\\n\\t\\tout_file = os.path.join(cfg.out_dir, s)\\n\\t\\tif not os.path.exists(out_file):\\n\\t \\t\\twarning(\\\"%s: invalid link to '%s'\\\" % (_file_links[s], s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e62c55844b430153e153df0db262b290\",\n \"score\": \"0.588417\",\n \"text\": \"def _resolve_link(path):\\r\\n paths_seen = []\\r\\n while islink(path):\\r\\n if path in paths_seen:\\r\\n # Already seen this path, so we must have a symlink loop\\r\\n return None\\r\\n paths_seen.append(path)\\r\\n # Resolve where the link points to\\r\\n resolved = os.readlink(path)\\r\\n if not isabs(resolved):\\r\\n dir = dirname(path)\\r\\n path = normpath(join(dir, resolved))\\r\\n else:\\r\\n path = normpath(resolved)\\r\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b85174b6188a7a19df4eff61790b94\",\n \"score\": \"0.58817095\",\n \"text\": \"def hardlink(source, link_name):\\n try:\\n os.link(source, link_name)\\n except FileExistsError:\\n # It's possible that the user created a different file with the same name as the\\n # one we're trying to download. Thus we need to check the if the inode is different\\n # and raise an error in this case.\\n source_stat = os.stat(source)\\n dest_stat = os.stat(link_name)\\n # Check device first because different drives can have the same inode number\\n if source_stat.st_dev != dest_stat.st_dev or source_stat.st_ino != dest_stat.st_ino:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962858e7a2194d210b9dfedd9b981e87\",\n \"score\": \"0.5866979\",\n \"text\": \"def _force_symlink(\\n root: Path, target: Union[str, PurePath], link_to: Union[str, Path]\\n) -> None:\\n current_symlink = root.joinpath(target)\\n try:\\n current_symlink.unlink()\\n except OSError:\\n pass\\n try:\\n current_symlink.symlink_to(link_to)\\n except Exception:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf4744cb862d5219816f94cf0ef9f77\",\n \"score\": \"0.58613175\",\n \"text\": \"def _process_symlink(self, target, path): #aka target_path, dotfile_path\\n\\n if not os.path.lexists(target):\\n self._error = True\\n print('\\\\tmissing target: {}'.format(target))\\n return ('DOTFILE NOT FOUND', None)\\n\\n if not os.path.lexists(path):\\n self._changed = True\\n if self.args.test:\\n return ('missing', None)\\n else:\\n create_dotfile_link(target, path)\\n return ('missing', 'synced')\\n\\n existing_realpath = os.path.realpath(path)\\n if existing_realpath != os.path.realpath(target):\\n if self.args.debug:\\n print('\\\\tCurrent target path: {}'.format(existing_realpath))\\n status = 'unmanaged' if os.path.lexists(existing_realpath) else 'broken'\\n\\n if self.args.verbose:\\n pass # XXX: TODO: ...\\n\\n if status != 'broken' and not self.args.force:\\n return (status, 'skipping')\\n self._changed = True\\n if self.args.test:\\n return (status, None)\\n\\n #import pdb; pdb.set_trace()\\n remove_any(path)\\n create_dotfile_link(target, path)\\n return (status, 'removed; synced')\\n\\n return ('ok', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f124a12bcf057e1e114461e076db3da\",\n \"score\": \"0.58529246\",\n \"text\": \"def _checkLink(link):\\n try:\\n link = link.attrs['href']\\n except KeyError:\\n return -1\\n if link[0] == '/':\\n link = '://'.join(list(urllib.parse.urlparse(self.address)[0:2])) + link\\n elif re.match(r'^mailto|^#', link):\\n return None\\n good = ['200', '206', '301', '302']\\n try:\\n status, response = self.http.request(link)\\n if status['status'] not in good:\\n self.badlinks.append((status['status'], link))\\n else:\\n self.goodlinks.append(link)\\n except httplib2.ServerNotFoundError:\\n self.other.append((\\\"httplib2.ServerNotFoundError\\\", link))\\n except httplib2.ssl.SSLError:\\n self.other.append((\\\"httplib2.ssl.SSLError\\\", link))\\n except ConnectionResetError:\\n self.other.append((\\\"ConnectionResetError\\\", link))\\n except UnicodeError:\\n self.other.append((\\\"UnicodeError\\\", link))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af98442fe78a61c211571253d3a076e5\",\n \"score\": \"0.58304495\",\n \"text\": \"def internal_link_checker(filepath, html_str):\\n\\n # Flag to determine if internal link is broken\\n internal_link_error = False\\n\\n # \\n\\n problems = []\\n relative_links = []\\n\\n # find all links\\n for prefix in [\\\"href\\\", \\\"src\\\"]:\\n\\n links = re.findall(\\n f\\\"{prefix}\\\\s?=\\\\s?[\\\\\\\"']([{allowed_in_link}]+)[\\\\\\\"']\\\", html_str)\\n\\n # check if link has a dest\\n for link in links:\\n\\n # Check if link is relative path\\n is_relative = check_if_relative_link(link)\\n\\n # Add to relative links list if relative\\n if is_relative:\\n if not link in relative_links:\\n relative_links.append(link)\\n\\n # Get correct path\\n link = get_correct_link(link)\\n\\n # Check if link is in use\\n check_if_link_in_use(filepath, link)\\n\\n if link in links_list:\\n if links_list[link]:\\n if link not in problems:\\n problems.append(link) \\n else:\\n if internal_link_test(link):\\n problems.append(link)\\n links_list[link] = True\\n\\n if not internal_link_error:\\n internal_link_error = True\\n else:\\n links_list[link] = False\\n\\n return problems, relative_links, internal_link_error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a8c415ec1777ae102d937832bd39339\",\n \"score\": \"0.5820183\",\n \"text\": \"def symlink (self, src, dst):\\n if self.system ('/bin/ln -sf %s %s'%(src, dst)):\\n raise OSError, 'can\\\\'t symlink [%s] as [%s]' % (src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf5255caff8fa0c01af5e9a0e3b0a7b8\",\n \"score\": \"0.58178663\",\n \"text\": \"def test_symlink_file6(self):\\n src = self.rootdir\\n target = os.path.join(self.targetdir,\\\"target-file\\\")\\n open(target,'w').close()\\n with mock.patch.object(\\n transfer.os,\\n 'unlink',\\n side_effect=OSError(\\\"Mocked error\\\")):\\n with self.assertRaises(transfer.SymlinkError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b019bad4cd351d9787fe08c2bc3f228\",\n \"score\": \"0.57966596\",\n \"text\": \"def symlink(self, req, link, parent, name):\\n self.reply_err(req, EROFS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9684816cb8583b1d44b66d7cd04ca40\",\n \"score\": \"0.5782404\",\n \"text\": \"def test_force_link(tmpdir):\\n src = join(str(tmpdir), \\\"src\\\")\\n dst = join(str(tmpdir), \\\"dst\\\")\\n\\n with open(src, \\\"w\\\") as f:\\n f.write(\\\"Not empty.\\\")\\n\\n utils.force_link(src, dst)\\n assert os.path.isfile(dst)\\n assert not os.path.islink(dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6419b2fcb183ad206a852387f3bc8d86\",\n \"score\": \"0.57806474\",\n \"text\": \"def test_scan_files(self):\\n self.assertFalse(fdf.valid_path('some thing wrong'))\\n result = fdf.scan_files(join(getcwd(), 'test'))\\n self.assertNotIn(join(getcwd(), 'test/symlink/symlink'), result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2fcb55acc8c1275ebfe5f75e72876a0\",\n \"score\": \"0.57789207\",\n \"text\": \"def test_non_html_links_are_path_sensitive(self):\\n validator = self._create_validator(\\n \\\"\\\"\\\"Use [this CSV](data.csv) for the exercise.\\\"\\\"\\\")\\n self.assertFalse(validator._validate_links())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"169daa1a343f03131a9d00db91a8b596\",\n \"score\": \"0.5773865\",\n \"text\": \"def test_symlink_folder4(self):\\n src = os.path.join(self.rootdir) \\n target = os.path.join(self.targetdir)\\n with mock.patch('taca.utils.transfer.os.path') as mockobj:\\n mockobj.ismount.return_value = False\\n mockobj.isfile.return_value = False\\n mockobj.islink.return_value = False\\n mockobj.isdir.return_value = False\\n with self.assertRaises(transfer.SymlinkError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3cd10319fcfb6990ceba408de225a7\",\n \"score\": \"0.5773567\",\n \"text\": \"def is_file_link(self, link):\\n return link.startswith(\\\"###file\\\") or link.startswith('/repository') or \\\\\\n (link.startswith('../') and '/files/' in link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c2fa09a69a0c70db6567fbff768f2d\",\n \"score\": \"0.57712406\",\n \"text\": \"def provide_link(dest, src):\\n if not os.path.exists(dest):\\n # strip trailing /'s just in case it exists\\n os.symlink(src, dest.rstrip(\\\"/\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5141a7e126b929ce6f8259580dbeb5c\",\n \"score\": \"0.5764898\",\n \"text\": \"def filepath_validator(path):\\n if isinstance(path, str):\\n path = Path(path)\\n return path.exists() and path.isfile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308f791745e59acf2880a0ec1831b152\",\n \"score\": \"0.5761583\",\n \"text\": \"def test_symlink_lock(self):\\n\\n # Get an image object and tests its manual lock mechanism.\\n api_obj = self.get_img_api_obj()\\n img = api_obj.img\\n\\n # Verify a lock file is created.\\n img.lock()\\n lfpath = os.path.join(img.imgdir, \\\"lock\\\")\\n self.assertTrue(os.path.exists(lfpath))\\n img.unlock()\\n os.remove(lfpath)\\n\\n # Make lock file a symlink by pointing it to a random file .\\n tmp_file = os.path.join(img.imgdir, \\\"test_symlink\\\")\\n fo = open(tmp_file, 'wb+')\\n fo.close()\\n os.symlink(tmp_file, lfpath)\\n\\n # Verify that both pkg install and refresh generate an error\\n # if the lock file is a symlink.\\n self.pkg(\\\"install foo\\\", su_wrap=True, exit=1)\\n self.assertTrue(\\\"contains a symlink\\\" in self.errout)\\n\\n self.pkg(\\\"refresh --full\\\", su_wrap=True, exit=1)\\n self.assertTrue(\\\"contains a symlink\\\" in self.errout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18658f8e04412069e3a03d80b360b5b3\",\n \"score\": \"0.57601094\",\n \"text\": \"def check_path_existence(fpath: str) -> bool:\\n return os.path.exists(fpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31411b5398f85370a62fa13f46f581a5\",\n \"score\": \"0.5740772\",\n \"text\": \"def validate_path(self, path):\\n if not os.path.exists(path):\\n raise IOError('Path {} does not exist'.format(path))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"042985e4ccc6c626626e50eb20ef3c8a\",\n \"score\": \"0.5739684\",\n \"text\": \"def ismount(path):\\r\\n try:\\r\\n s1 = os.lstat(path)\\r\\n s2 = os.lstat(join(path, '..'))\\r\\n except os.error:\\r\\n return False # It doesn't exist -- so not a mount point :-)\\r\\n dev1 = s1.st_dev\\r\\n dev2 = s2.st_dev\\r\\n if dev1 != dev2:\\r\\n return True # path/.. on a different device as path\\r\\n ino1 = s1.st_ino\\r\\n ino2 = s2.st_ino\\r\\n if ino1 == ino2:\\r\\n return True # path/.. is the same i-node as path\\r\\n return False\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":194,"cells":{"query_id":{"kind":"string","value":"54c87c218016afc006e423f7687dcaee"},"query":{"kind":"string","value":"Returns a boolean indicating whether this type is an 'interface' type that is implemented in Gecko. At the moment, this returns true for all interface types that are not types from the TypedArray spec."},"positive_passages":{"kind":"list like","value":[{"docid":"50186e06c1db50f28ee3b3f7d75a25e9","score":"0.700691","text":"def isGeckoInterface(self):\n return self.isInterface() and not self.isSpiderMonkeyInterface()","title":""}],"string":"[\n {\n \"docid\": \"50186e06c1db50f28ee3b3f7d75a25e9\",\n \"score\": \"0.700691\",\n \"text\": \"def isGeckoInterface(self):\\n return self.isInterface() and not self.isSpiderMonkeyInterface()\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"3fd0af121674f7ce21b6fec67acc48c9","score":"0.75389546","text":"def isSpiderMonkeyInterface(self):\n return self.isInterface() and (self.isArrayBuffer() or \\\n self.isArrayBufferView() or \\\n self.isTypedArray())","title":""},{"docid":"f22ccea57db2c6f27555c3c7019e94e2","score":"0.74693924","text":"def is_interface(self):\n\n return True","title":""},{"docid":"f22ccea57db2c6f27555c3c7019e94e2","score":"0.74693924","text":"def is_interface(self):\n\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"b10dfb26a4192cdf8ba42575e9069538","score":"0.6441149","text":"def has_interface(self, name):\n if not self.all_interfaces:\n self.find_interfaces()\n return name in self.all_interfaces","title":""},{"docid":"45b6ef24706b35f4b5afbd0e1d5f38d2","score":"0.5977207","text":"def obj_has_interface(obj, *ifaces):\r\n for iface in ifaces:\r\n if iface.providedBy(obj):\r\n return True\r\n return False","title":""},{"docid":"533ee492d61d1c5bc1503a56ef9018b8","score":"0.5907879","text":"def is_implemented(self):\n try:\n self.coxeter_diagram()\n return True\n except Exception:\n return False","title":""},{"docid":"a118f27e95bb4e2efdd0e2efeaa97b85","score":"0.5775072","text":"def InterfaceType(self):\n return self._get_attribute('interfaceType')","title":""},{"docid":"c6611878a3ca087c3654d231659f4be6","score":"0.5677758","text":"def hasImplementation(self):\n return False","title":""},{"docid":"56bb5520d6d5bbc20c414ff8f77589ec","score":"0.5661154","text":"def is_interface_available(interface_name):\n\n try:\n interface = NXCLI('show run interface %s' %interface_name)\n return True\n except:\n return False","title":""},{"docid":"de489f3501d8d96a1747af4e3fa8365a","score":"0.56517386","text":"def is_available(self, hub, interface_name):\n \n result = False\n\n names_dict = self._names[hub.interface_type]\n if interface_name in names_dict: result = True\n\n return result","title":""},{"docid":"6e398ccc621d69c564f8f847747c78c4","score":"0.5499881","text":"def is_ipython() -> bool:\n\n try:\n __IPYTHON__ # type: ignore[name-defined] # pylint: disable=pointless-statement\n return True\n except NameError:\n return False","title":""},{"docid":"2b1f1e38910f5021d929657b36f4107b","score":"0.5479125","text":"def interface_type(self) -> Optional[str]:\n return pulumi.get(self, \"interface_type\")","title":""},{"docid":"76782b80ec61eb9faf8d19425d74ec52","score":"0.5415978","text":"def has_web_interface(self) -> bool:\n for port in self._open_ports:\n if port in WEB_PORTS:\n return True\n return False","title":""},{"docid":"68a8c3def7d14b06861b675d1393a489","score":"0.5412653","text":"def _is_ipython() -> bool:\n try:\n import IPython # pylint: disable=g-import-not-at-top\n ipy = IPython.get_ipython()\n if ipy is None:\n return False\n except ImportError:\n return False\n\n return True","title":""},{"docid":"9b8e18a44978fd4eb1e209c2c287ec51","score":"0.54086965","text":"def Interfaces(self):\n return self._get_attribute('interfaces')","title":""},{"docid":"a3fea24ba206c82a91dda73264705530","score":"0.5395206","text":"def interface(self) -> \"RegisterArray.Interface\":\n\n return self._interface","title":""},{"docid":"3e9d2e76a21ccc26f741935f1d57e547","score":"0.538823","text":"def _fallback_hasinterface(self, conn, ident, obj, typename):\r\n # It's very difficult to get an arbitrary `typename` here.\r\n dot = typename.rfind('.')\r\n module_name = typename[:dot]\r\n class_name = typename[dot+1:]\r\n try:\r\n module = __import__(module_name)\r\n except ImportError: #pragma no cover\r\n return False\r\n module = sys.modules[module_name]\r\n try:\r\n cls = getattr(module, class_name)\r\n except AttributeError: #pragma no cover\r\n return False\r\n return obj_has_interface(obj, cls)","title":""},{"docid":"cac6848b7d97d0e550c1e19e3a5b2931","score":"0.53677785","text":"def has_name(self, hub, interface_name):\n \n result = False\n \n if not self.is_available(hub, interface_name): return result\n\n interface_cls_name = self.get_cls_name(hub, interface_name) \n if interface_cls_name in hub.get_interface_map(): result = True\n \n return result","title":""},{"docid":"14c87943a24ff4af02ded3128e5f9cfb","score":"0.53670704","text":"def validate_interface(self, interface):\n url = 'http://0.0.0.0:8181/api/kytos/topology/v3/interfaces'\n headers = {'Content-Type': 'application/json'}\n current_interfaces = requests.get(url, headers=headers).json()\n\n return interface in [current_interfaces['interfaces'][key]['id'] for key in current_interfaces['interfaces']]","title":""},{"docid":"2b8c14d1778a807ad3477e673e5f52ae","score":"0.5335405","text":"def is_using_ipython():\n try:\n __IPYTHON__ # pylint: disable=undefined-variable, pointless-statement\n return True\n except NameError:\n return False","title":""},{"docid":"877ebdf13c62ad5cc4e9d351404c0b5a","score":"0.53146756","text":"def connected(self):\n return self._interface is not None","title":""},{"docid":"3b65645c6a7e2b0dde1897ad823f21a9","score":"0.53040075","text":"def interface(self) -> \"Register.Interface\":\n\n return self._interface","title":""},{"docid":"f26c30c251276dcf6c03b41f175a6d71","score":"0.52766734","text":"def is_enumerated_type(self):\n return self.enumerated_values is not None","title":""},{"docid":"8b0df933c507721b9942727b22ad436a","score":"0.5256926","text":"def implements(self, calculation_type: \"CalculationType\") -> bool:","title":""},{"docid":"f2518bc585dd4a9de207fd4517c48cbc","score":"0.5226839","text":"def _is_intf_deconfigured(self, interface):\n _logger.debug('%s\\n%s', where_am_i(), pformat(interface, indent=4))\n for deconf in self.vnic_info.get('deconfig', ()):\n if deconf in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\n return True\n return False","title":""},{"docid":"f5f354b72d13a9226af14d6c81f81035","score":"0.5220707","text":"def interface_profile_has_metrics(profile):\n for m in profile.metrics:\n if m.is_active and m.metric_type.name in (\n \"Interface | Load | In\",\n \"Interface | Load | Out\"\n ):\n return True\n return False","title":""},{"docid":"fda1a9bf8244ba8f27d6fdf5af8308a1","score":"0.5209826","text":"def has_alibi_witness (self):\n if not self.alibi:\n return False\n\n if self.alibi.witness == -1:\n return False\n\n return True","title":""},{"docid":"6517bec04735935d4ee0ff5369e567b2","score":"0.51612246","text":"def interfaces(self):\n return self._view_by_kind(Database._Kind.INTERFACE)","title":""},{"docid":"9361244c8e0054f3dfbc19e65431e661","score":"0.515429","text":"def is_notebook():\n try:\n from IPython import get_ipython\n\n if \"IPKernelApp\" not in get_ipython().config: # pragma: no cover\n return False\n except ImportError:\n return False\n except AttributeError:\n return False\n return True","title":""},{"docid":"59bb25ccb55103b904b985f4bc55107c","score":"0.5145271","text":"def isinstance(self, obj: Any) -> bool:\n raise NotImplementedError()","title":""},{"docid":"003fb219083b8ab7fd5ffbadc5bf29be","score":"0.5139457","text":"def using_ipython() -> bool:\n return hasattr(builtins, '__IPYTHON__')","title":""},{"docid":"538031a7ad259ac07679a53e8f09cde4","score":"0.51392484","text":"def interface_exists(interface):\n return os.path.exists(\"/sys/class/net/%s\" % interface)","title":""},{"docid":"aee1f9d0829c76e4f029811214eaaff8","score":"0.51390105","text":"def test_implemented_by_subinterface(self):\n self.assertTrue(IFoo.implemented_by(IFooBar))","title":""},{"docid":"8c36c1320b613f243935dd850a8b2ded","score":"0.513061","text":"def implements_attribute_manager(obj):\n return all([hasattr(obj, n) for n in\n ['keys', '__getitem__', '__contains__', '__iter__']])","title":""},{"docid":"b62457039b5b99728ebd277fe83408b8","score":"0.5127941","text":"def interfaces(self):\n return self._interfaces","title":""},{"docid":"b62457039b5b99728ebd277fe83408b8","score":"0.5127941","text":"def interfaces(self):\n return self._interfaces","title":""},{"docid":"7ab9332932fc0556505ed054e4534e36","score":"0.5107286","text":"def implements_dataset(obj):\n return all([hasattr(obj, n) for n in\n ['attrs', 'keys', 'items',\n '__getitem__', '__contains__', '__iter__']])","title":""},{"docid":"08bc9a476b5488ef4062c42d2385795a","score":"0.50944895","text":"def is_type(self):\n return True","title":""},{"docid":"ab35b1572f9c98cfe710abd8db4719b5","score":"0.50910896","text":"def interface_exists(interface):\n return os.path.exists(\"/sys/class/net/\" + interface)","title":""},{"docid":"3abfdcdaa155732f67dec65ff27eb22a","score":"0.5085412","text":"def _currently_in_ipython():\n try:\n __IPYTHON__ # pylint: disable=undefined-variable\n return True\n except NameError:\n return False","title":""},{"docid":"2c118e3dfce2fd85243a6f79eb006fb1","score":"0.50795716","text":"def _was_registered(m, interface):\n \n for item in m.call_args_list:\n if item[0][-1] == interface:\n return True\n \n return False","title":""},{"docid":"d88ac3ffa427897baf9ab47042c839d8","score":"0.5077435","text":"def interface(self):\n return self.__interface","title":""},{"docid":"4ec85e70ccdb9bd315392ad5fd59f147","score":"0.50658995","text":"def in_ipython():\n try:\n shell = get_ipython().__class__.__name__\n if shell == \"ZMQInteractiveShell\":\n return True # Jupyter notebook or qtconsole\n elif shell == \"TerminalInteractiveShell\":\n return False # Terminal running IPython\n else:\n return False # Other type (?)\n except NameError:\n return False # Probably standard Python interpreter","title":""},{"docid":"cefc0b8f212634a96a065179fee635a8","score":"0.50610983","text":"def is_abstract(obj: typing.Type) -> bool:\n try:\n return ABC in obj.__bases__\n except AttributeError:\n return False","title":""},{"docid":"d9eca26713378f7517471cf61b8928f5","score":"0.50561816","text":"def GetInterfaces(self):\n return self._interfaces","title":""},{"docid":"289f245efa808a9a32ad5962d51e5c8d","score":"0.5047994","text":"def get_interface_class() -> Interface:\n interface = Office.get_interface() # gets str name of interface class\n if not interface:\n raise ValueError('Should be run as macro using XLWings or PyUno.'\n 'Neither could be detected.')\n return getattr(Office, interface)","title":""},{"docid":"52ebfab792e76b181e3ad009385342d2","score":"0.5029029","text":"def is_this_run_in_ipython():\n try:\n __IPYTHON__\n return True\n except NameError:\n return False","title":""},{"docid":"e5e8dbc9fc4e755ad5723910adda3862","score":"0.50271434","text":"def interface(self):\n return self._interface","title":""},{"docid":"e5e8dbc9fc4e755ad5723910adda3862","score":"0.50271434","text":"def interface(self):\n return self._interface","title":""},{"docid":"565ddff6ab951dd08895c4b3ac15d578","score":"0.501947","text":"def is_mpsse_interface(self, interface: int) -> bool:\n if not self.has_mpsse:\n return False\n if self.device_version == 0x0800 and interface > 2:\n return False\n if self.device_version == 0x3600 and interface > 2:\n return False\n return True","title":""},{"docid":"018e794216fecc1b79414a808c496be8","score":"0.49981865","text":"def _is_intf_excluded(self, interface):\n _logger.debug('%s\\n%s', where_am_i(), pformat(interface, indent=4))\n for excl in self.vnic_info.get('exclude', ()):\n if excl in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\n return True\n return False","title":""},{"docid":"dc45a0530a6ce88744047b14cb31fae3","score":"0.4976998","text":"def get_interface() -> str or None:\n # test for Python Uno\n try:\n XSCRIPTCONTEXT # if this variable exists, PyUno is being used.\n except NameError:\n pass\n else:\n return 'Uno'\n\n if xw is not None:\n return 'XW'\n\n # otherwise, return None / False","title":""},{"docid":"5f5dd9da15c84c05dd10c6e4f2bf8554","score":"0.4964717","text":"def _in_ipython():\n\n try:\n # See https://stackoverflow.com/questions/15411967\n shell = get_ipython().__class__.__name__\n return bool(shell == \"TerminalInteractiveShell\")\n except NameError:\n return False","title":""},{"docid":"4ce46daaa6dd2bbf0905ee3bc8dc17a1","score":"0.49643916","text":"def interface_mode(self):\n return self._interface_mode.value if self._interface_mode else None","title":""},{"docid":"7dd29c00eb8a74ef6f6863fa100b1b37","score":"0.49566942","text":"def isnotebook(self):\n try:\n shell = get_ipython().__class__.__name__\n if (shell == 'ZMQInteractiveShell' or shell == 'TerminalInteractiveShell'):\n return True # Jupyter notebook or IPython\n else:\n return False # Other type (?)\n except NameError:\n return False","title":""},{"docid":"0494fee01a6c84336625999ec71e9e88","score":"0.49517235","text":"def exposed_interfaces(self):\n raise exceptions.NotImplementedError()","title":""},{"docid":"9370580bbbb45adf1a90aee162216377","score":"0.49513036","text":"def __eq__(self, other):\n if not isinstance(other, Interface):\n return False\n\n return self.to_dict() == other.to_dict()","title":""},{"docid":"3e95ddf88ba69823cfcf93bffd8f262b","score":"0.49503726","text":"def interface(self):\n return self._if.interface()","title":""},{"docid":"2642419c8ab4cb9c915374e4d5b11405","score":"0.49493083","text":"def has_dax_datatypes(intf):\n xnat_datatypes = intf.inspect.datatypes()\n for dax_datatype in DAX_SETTINGS.get_xsitype_include():\n if dax_datatype not in xnat_datatypes:\n return False\n return True","title":""},{"docid":"c2485db54dc47373ad9a3a24581a01ce","score":"0.49441043","text":"def is_integer(self) -> bool:\n return isinstance(self, IntegerTyp)","title":""},{"docid":"ce814aee006c85f85177fbe1b80a91d5","score":"0.49327436","text":"def getInterface(self):\n return self.__interface","title":""},{"docid":"eb9bafa5ebb16cf0c63b3a9320dde068","score":"0.49253488","text":"def initial_interface_checks(iface_name):\n if not interface.is_valid_interface(iface_name):\n print 'Error: %s is not a valid interface.' % iface_name\n return False\n\n return True","title":""},{"docid":"e54278cd2bd316da7523d00f43e4cbd5","score":"0.4919012","text":"def test_interface_glue(self):\r\n iface = self.create(items.InterfaceItem, UML.Interface)\r\n impl = self.create(items.ImplementationItem)\r\n\r\n glued = self.allow(impl, impl.head, iface)\r\n self.assertTrue(glued)","title":""},{"docid":"56aeeefcd366178ea0ba238a08926672","score":"0.49182153","text":"def verify_ospf_interface_type(device, interface, interface_type, \r\n max_time=60, check_interval=10):\r\n timeout = Timeout(max_time, check_interval)\r\n while timeout.iterate():\r\n out = None\r\n try:\r\n out = device.parse('show ospf interface extensive')\r\n except SchemaEmptyParserError:\r\n timeout.sleep()\r\n continue\r\n for ospf_interface in out.q.get_values('ospf-interface'):\r\n intf = ospf_interface.get('interface-name', None)\r\n intf_type = ospf_interface.get('interface-type', None)\r\n if intf == interface and intf_type == interface_type:\r\n return True\r\n timeout.sleep()\r\n return False","title":""},{"docid":"d4d5ffb882554298c0be64514283fa9f","score":"0.48915172","text":"def isframe(object):\n return isinstance(object, types.FrameType)","title":""},{"docid":"d72ec5d1dcac59b3959fbe6302505c3c","score":"0.48835492","text":"def is_image(self):\n if self.axes is None:\n return True\n return len(self.axes) == 0","title":""},{"docid":"607beb0c4acf51ab0e4a475909c1bf4f","score":"0.48734248","text":"def enterInterfaceDeclaration(self, ctx:JavaParserLabeled.InterfaceDeclarationContext):\n \n self.__current_class_or_interface = ctx.IDENTIFIER().getText()\n self.__classes.append(ctx.IDENTIFIER().getText())\n self.__methods_of_classes[self.__current_class_or_interface] = []\n self.__class_interface[ctx.IDENTIFIER().getText()] = True\n\n if ctx.EXTENDS() is not None and ctx.typeList().getText() in self.__classes:\n self.__parent_and_child_classes[ctx.IDENTIFIER().getText()] = ctx.typeList().getText()","title":""},{"docid":"d32a13afe4db6cc3251dcfeb92b8725f","score":"0.48706833","text":"def ProtocolInterface(self):\n return self._get_attribute('protocolInterface')","title":""},{"docid":"fbe526509239c5808b11866ccc137425","score":"0.48549366","text":"def is_known_type(cls, ioc_type: str) -> bool:\n return ioc_type in IoCType.__members__ and ioc_type != \"unknown\"","title":""},{"docid":"b3c71b64bcf1f098b63928f0c2fbc4bc","score":"0.48411354","text":"def is_simulator(self) -> bool:\n return isinstance(self._backend, Simulator)","title":""},{"docid":"e18c61d197fa747c4617a6582d110ba0","score":"0.48401403","text":"def is_elemental(self):\n return self._is_elemental","title":""},{"docid":"f61697810a3675c5564cf631af3d2d91","score":"0.48228928","text":"def interfaces():\n pass","title":""},{"docid":"913c32a9fad13671240a3accbca77d37","score":"0.48065004","text":"def SupportsPlugins(cls):\n return cls._plugin_classes is not None","title":""},{"docid":"a1ed8f865cc32f16b7caa6a7baaebbcb","score":"0.4798193","text":"def __ne__(self, other):\n if not isinstance(other, Interface):\n return True\n\n return self.to_dict() != other.to_dict()","title":""},{"docid":"8f3f206bf554e28eac92333360608cdf","score":"0.47953272","text":"def in_ipynb():\n try:\n if \"terminal\" in str(type(get_ipython())):\n return False\n else:\n return True\n except NameError:\n return False","title":""},{"docid":"89148e8b9fea47ebc8679ffdb438721b","score":"0.47926775","text":"def get_interfaces(self):\n return self._interfaces","title":""},{"docid":"e5c5decf9179fb87c752b8e5fdac1663","score":"0.47859254","text":"def is_supported(self) -> bool:\n pass","title":""},{"docid":"67585469ad50e8769ef5e4346dcb255d","score":"0.47641656","text":"def isnotebook():\r\n try:\r\n shell = get_ipython().__class__.__name__\r\n if shell == 'ZMQInteractiveShell':\r\n return True # Jupyter notebook or qtconsole\r\n elif shell == 'TerminalInteractiveShell':\r\n return False # Terminal running IPython\r\n else:\r\n return False # Other type (?)\r\n except NameError:\r\n return False # Probably standard Python interpreter\r","title":""},{"docid":"25a503865f67cf1555d23e56fe98cf76","score":"0.47600353","text":"def is_simulator(self) -> bool:\n return isinstance(self._backend, OT3Simulator)","title":""},{"docid":"8a5c5cd0af7c197859e68fa667250759","score":"0.47472557","text":"def test_interface(self):\n flags = self.config_flags.NAT_IS_INSIDE\n self.vapi.nat64_add_del_interface(\n is_add=1, flags=flags, sw_if_index=self.pg0.sw_if_index\n )\n self.vapi.nat64_add_del_interface(\n is_add=1, flags=0, sw_if_index=self.pg1.sw_if_index\n )\n\n interfaces = self.vapi.nat64_interface_dump()\n self.assertEqual(len(interfaces), 2)\n pg0_found = False\n pg1_found = False\n for intf in interfaces:\n if intf.sw_if_index == self.pg0.sw_if_index:\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_INSIDE)\n pg0_found = True\n elif intf.sw_if_index == self.pg1.sw_if_index:\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_OUTSIDE)\n pg1_found = True\n self.assertTrue(pg0_found)\n self.assertTrue(pg1_found)\n\n features = self.vapi.cli(\"show interface features pg0\")\n self.assertIn(\"nat64-in2out\", features)\n features = self.vapi.cli(\"show interface features pg1\")\n self.assertIn(\"nat64-out2in\", features)\n\n self.vapi.nat64_add_del_interface(\n is_add=0, flags=flags, sw_if_index=self.pg0.sw_if_index\n )\n self.vapi.nat64_add_del_interface(\n is_add=0, flags=flags, sw_if_index=self.pg1.sw_if_index\n )\n\n interfaces = self.vapi.nat64_interface_dump()\n self.assertEqual(len(interfaces), 0)","title":""},{"docid":"28b4220da7617062876632280a83bcb1","score":"0.47413203","text":"def has_nat(self):\n return self.typeof in self","title":""},{"docid":"843d7ba37f9c30d53aeab8fdf39117e2","score":"0.47389","text":"def check_if_interface_exists(device_name, interface_type, interface_number):\n with ncs.maapi.single_write_trans('admin', 'python', groups=['ncsadmin']) as t:\n root = ncs.maagic.get_root(t)\n device = root.devices.device[device_name]\n print type(device.interface[interface_type])\n if interface_number in device.interface[interface_type]:\n print(\"Interface is on the device!\")\n else:\n print(\"Interface is not on the device!\")","title":""},{"docid":"252c601cfbd7392ad20a08385be7058d","score":"0.47366798","text":"def has_donor_cbginterface(self,cbg):\n if hasattr(cbg,\"_CBGinterface3p\") and cbg._CBGinterface3p:\n return True\n else:\n return False","title":""},{"docid":"b79bc03e384ef302826e112ba6ed98c1","score":"0.47240645","text":"def test_subclass_provides_interface(self):\n class IFooBarSubclass(self.FooBarHasFooBarBaz):\n\n def provides_interface(self, interface):\n return interface.implemented_by(IFooBaz)\n\n obj = IFooBarSubclass()\n with self.assertRaises(TypeError):\n IFooBar(obj)\n foobaz = IFooBaz(obj)\n foobaz.baz()\n self.assertEqual(obj.foo, 3)","title":""},{"docid":"d6eef8e9daccf5af22fc3e60aa0db7da","score":"0.4722961","text":"def any_extension_types(self) -> bool:\n return False # any(block.is_extension for block in self.blocks)","title":""},{"docid":"5c6e122479958a19399bf44fd93c3b6c","score":"0.47229505","text":"def is_interface_connected_to_source(self):\n source_ip = self.get_tree_id()[0]\n for network in self.get_all_interface_networks():\n if ipaddress.ip_address(source_ip) in ipaddress.ip_network(network):\n return True\n return False","title":""},{"docid":"bd25262cd7cec55d00e342d756b3109e","score":"0.47206524","text":"def isa(self, other):\r\n\r\n m = self\r\n while m and m is not other:\r\n m = m.inherits\r\n return bool(m)","title":""},{"docid":"f3547b097146dd27e26dae98c79f66fe","score":"0.47147197","text":"def declare_interface(ea):\n kind = (get_struct_val(ea, 'go_type0.kind') & 0x1f)\n if kind != TYPEKIND_VALS['kindInterface'][0]:\n msg = 'type at 0x%s not an interface' % ea\n LOG.error(msg)\n raise Exception(msg)\n\n module_idx = resolve_moduledata_idx(ea)\n type_size = get_ida_struc_size('go_type0')\n tflag = (get_struct_val(ea, 'go_type0.tflag') & 0x7)\n\n iface_name = 'anonymous'\n if tflag & TYPE_TFLAGS['named']:\n iface_name = normalize_name(get_type_name(ea), ea,\n prepend_type_info=False)\n if tflag & TYPE_TFLAGS['extraStar']:\n iface_name = iface_name[1:]\n LOG.info('declare_interface: declaring iface %s at 0x%x',\n iface_name, ea)\n\n iface_ea = ea + type_size\n iface_mhdr_ea = iface_ea + PTRSIZE\n mhdr_ea = get_struct_val(iface_mhdr_ea, 'go_slice.array')\n mhdr_len = get_struct_val(iface_mhdr_ea, 'go_slice.len')\n iface_methods = []\n for i in range(0, mhdr_len):\n mhdr_entry_ea = mhdr_ea + (i * PTRSIZE)\n imethod_struc_name = 'go_type%d_metadata_imethod' % module_idx\n imethod_struc_size = get_ida_struc_size(imethod_struc_name)\n name_offset = nameoff(\n get_struct_val(mhdr_entry_ea,\n '%s.name' % imethod_struc_name),\n module_idx)\n imethod_name = get_raw_name_str(name_offset)\n LOG.info('found interface method %s.%s()',\n iface_name, imethod_name)\n ida_bytes.del_items(mhdr_entry_ea, imethod_struc_size)\n ida_bytes.create_struct(mhdr_entry_ea,\n imethod_struc_size,\n ida_struct.get_struc_id(imethod_struc_name))\n iface_methods.append((\n normalize_name(imethod_name, None,\n prepend_type_info=False),\n PTRSIZE,\n FF_PTR|FF_DATA|off_flag(),\n OFF_CURRENT_SEGMENT, None, None\n ))\n\n itab_fields = [\n ('inter', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\n OFF_CURRENT_SEGMENT, None, None),\n ('_type', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\n OFF_CURRENT_SEGMENT, None, None),\n ('hash', 4, FF_DWORD|FF_DATA, None, None, None),\n ('_unused', 4, FF_DWORD|FF_DATA, None, None, None)\n ] + iface_methods\n LOG.debug('Creating new itab for %s', iface_name)\n create_and_populate_struct('go_itab__%s' % iface_name,\n itab_fields)\n\n LOG.debug('Creating new iface for %s', iface_name)\n interface_tinfo = ida_typeinf.tinfo_t()\n ida_typeinf.parse_decl(interface_tinfo,\n idaapi.cvar.idati,\n 'go_itab__%s *;' % iface_name,\n ida_typeinf.PT_TYP)\n iface_fields = [\n ('tab', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\n None, None),\n ('data', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\n None, None)\n ]\n iface_tinfos = [interface_tinfo, None]\n iface_name = 'go_iface__%s' % iface_name\n create_and_populate_struct(iface_name,\n iface_fields, None,\n iface_tinfos)\n return iface_name","title":""},{"docid":"c6ee064dbb5293432396b13c5e23c308","score":"0.47141168","text":"def inherited_interface(self):\n raise exceptions.NotImplementedError()","title":""},{"docid":"0d6f031d8e91e7018033fd8f3d7e0118","score":"0.47140905","text":"def is_array(obj):\n return hasattr(obj, 'dtype')","title":""},{"docid":"d25fcdb091462da09938c70988e017d7","score":"0.47132087","text":"def isnotebook():\n try:\n shell = get_ipython().__class__.__name__\n if shell == 'ZMQInteractiveShell':\n return True # Jupyter notebook or qtconsole\n elif shell == 'TerminalInteractiveShell':\n return False # Terminal running IPython\n else:\n return False # Other type (?)\n except NameError:\n return False","title":""},{"docid":"9b3021caa56b8e96d786105815561d54","score":"0.47128734","text":"def are_supported(config: configuration.NamespaceConfig,\n installer: Optional[interfaces.Installer]) -> bool:\n for enh in enabled_enhancements(config):\n if not isinstance(installer, enh[\"class\"]):\n return False\n return True","title":""}],"string":"[\n {\n \"docid\": \"3fd0af121674f7ce21b6fec67acc48c9\",\n \"score\": \"0.75389546\",\n \"text\": \"def isSpiderMonkeyInterface(self):\\n return self.isInterface() and (self.isArrayBuffer() or \\\\\\n self.isArrayBufferView() or \\\\\\n self.isTypedArray())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22ccea57db2c6f27555c3c7019e94e2\",\n \"score\": \"0.74693924\",\n \"text\": \"def is_interface(self):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22ccea57db2c6f27555c3c7019e94e2\",\n \"score\": \"0.74693924\",\n \"text\": \"def is_interface(self):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b10dfb26a4192cdf8ba42575e9069538\",\n \"score\": \"0.6441149\",\n \"text\": \"def has_interface(self, name):\\n if not self.all_interfaces:\\n self.find_interfaces()\\n return name in self.all_interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b6ef24706b35f4b5afbd0e1d5f38d2\",\n \"score\": \"0.5977207\",\n \"text\": \"def obj_has_interface(obj, *ifaces):\\r\\n for iface in ifaces:\\r\\n if iface.providedBy(obj):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533ee492d61d1c5bc1503a56ef9018b8\",\n \"score\": \"0.5907879\",\n \"text\": \"def is_implemented(self):\\n try:\\n self.coxeter_diagram()\\n return True\\n except Exception:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a118f27e95bb4e2efdd0e2efeaa97b85\",\n \"score\": \"0.5775072\",\n \"text\": \"def InterfaceType(self):\\n return self._get_attribute('interfaceType')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6611878a3ca087c3654d231659f4be6\",\n \"score\": \"0.5677758\",\n \"text\": \"def hasImplementation(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56bb5520d6d5bbc20c414ff8f77589ec\",\n \"score\": \"0.5661154\",\n \"text\": \"def is_interface_available(interface_name):\\n\\n try:\\n interface = NXCLI('show run interface %s' %interface_name)\\n return True\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de489f3501d8d96a1747af4e3fa8365a\",\n \"score\": \"0.56517386\",\n \"text\": \"def is_available(self, hub, interface_name):\\n \\n result = False\\n\\n names_dict = self._names[hub.interface_type]\\n if interface_name in names_dict: result = True\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e398ccc621d69c564f8f847747c78c4\",\n \"score\": \"0.5499881\",\n \"text\": \"def is_ipython() -> bool:\\n\\n try:\\n __IPYTHON__ # type: ignore[name-defined] # pylint: disable=pointless-statement\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b1f1e38910f5021d929657b36f4107b\",\n \"score\": \"0.5479125\",\n \"text\": \"def interface_type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"interface_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76782b80ec61eb9faf8d19425d74ec52\",\n \"score\": \"0.5415978\",\n \"text\": \"def has_web_interface(self) -> bool:\\n for port in self._open_ports:\\n if port in WEB_PORTS:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68a8c3def7d14b06861b675d1393a489\",\n \"score\": \"0.5412653\",\n \"text\": \"def _is_ipython() -> bool:\\n try:\\n import IPython # pylint: disable=g-import-not-at-top\\n ipy = IPython.get_ipython()\\n if ipy is None:\\n return False\\n except ImportError:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b8e18a44978fd4eb1e209c2c287ec51\",\n \"score\": \"0.54086965\",\n \"text\": \"def Interfaces(self):\\n return self._get_attribute('interfaces')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3fea24ba206c82a91dda73264705530\",\n \"score\": \"0.5395206\",\n \"text\": \"def interface(self) -> \\\"RegisterArray.Interface\\\":\\n\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9d2e76a21ccc26f741935f1d57e547\",\n \"score\": \"0.538823\",\n \"text\": \"def _fallback_hasinterface(self, conn, ident, obj, typename):\\r\\n # It's very difficult to get an arbitrary `typename` here.\\r\\n dot = typename.rfind('.')\\r\\n module_name = typename[:dot]\\r\\n class_name = typename[dot+1:]\\r\\n try:\\r\\n module = __import__(module_name)\\r\\n except ImportError: #pragma no cover\\r\\n return False\\r\\n module = sys.modules[module_name]\\r\\n try:\\r\\n cls = getattr(module, class_name)\\r\\n except AttributeError: #pragma no cover\\r\\n return False\\r\\n return obj_has_interface(obj, cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cac6848b7d97d0e550c1e19e3a5b2931\",\n \"score\": \"0.53677785\",\n \"text\": \"def has_name(self, hub, interface_name):\\n \\n result = False\\n \\n if not self.is_available(hub, interface_name): return result\\n\\n interface_cls_name = self.get_cls_name(hub, interface_name) \\n if interface_cls_name in hub.get_interface_map(): result = True\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c87943a24ff4af02ded3128e5f9cfb\",\n \"score\": \"0.53670704\",\n \"text\": \"def validate_interface(self, interface):\\n url = 'http://0.0.0.0:8181/api/kytos/topology/v3/interfaces'\\n headers = {'Content-Type': 'application/json'}\\n current_interfaces = requests.get(url, headers=headers).json()\\n\\n return interface in [current_interfaces['interfaces'][key]['id'] for key in current_interfaces['interfaces']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8c14d1778a807ad3477e673e5f52ae\",\n \"score\": \"0.5335405\",\n \"text\": \"def is_using_ipython():\\n try:\\n __IPYTHON__ # pylint: disable=undefined-variable, pointless-statement\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877ebdf13c62ad5cc4e9d351404c0b5a\",\n \"score\": \"0.53146756\",\n \"text\": \"def connected(self):\\n return self._interface is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b65645c6a7e2b0dde1897ad823f21a9\",\n \"score\": \"0.53040075\",\n \"text\": \"def interface(self) -> \\\"Register.Interface\\\":\\n\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f26c30c251276dcf6c03b41f175a6d71\",\n \"score\": \"0.52766734\",\n \"text\": \"def is_enumerated_type(self):\\n return self.enumerated_values is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0df933c507721b9942727b22ad436a\",\n \"score\": \"0.5256926\",\n \"text\": \"def implements(self, calculation_type: \\\"CalculationType\\\") -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2518bc585dd4a9de207fd4517c48cbc\",\n \"score\": \"0.5226839\",\n \"text\": \"def _is_intf_deconfigured(self, interface):\\n _logger.debug('%s\\\\n%s', where_am_i(), pformat(interface, indent=4))\\n for deconf in self.vnic_info.get('deconfig', ()):\\n if deconf in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f354b72d13a9226af14d6c81f81035\",\n \"score\": \"0.5220707\",\n \"text\": \"def interface_profile_has_metrics(profile):\\n for m in profile.metrics:\\n if m.is_active and m.metric_type.name in (\\n \\\"Interface | Load | In\\\",\\n \\\"Interface | Load | Out\\\"\\n ):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fda1a9bf8244ba8f27d6fdf5af8308a1\",\n \"score\": \"0.5209826\",\n \"text\": \"def has_alibi_witness (self):\\n if not self.alibi:\\n return False\\n\\n if self.alibi.witness == -1:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6517bec04735935d4ee0ff5369e567b2\",\n \"score\": \"0.51612246\",\n \"text\": \"def interfaces(self):\\n return self._view_by_kind(Database._Kind.INTERFACE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9361244c8e0054f3dfbc19e65431e661\",\n \"score\": \"0.515429\",\n \"text\": \"def is_notebook():\\n try:\\n from IPython import get_ipython\\n\\n if \\\"IPKernelApp\\\" not in get_ipython().config: # pragma: no cover\\n return False\\n except ImportError:\\n return False\\n except AttributeError:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59bb25ccb55103b904b985f4bc55107c\",\n \"score\": \"0.5145271\",\n \"text\": \"def isinstance(self, obj: Any) -> bool:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"003fb219083b8ab7fd5ffbadc5bf29be\",\n \"score\": \"0.5139457\",\n \"text\": \"def using_ipython() -> bool:\\n return hasattr(builtins, '__IPYTHON__')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538031a7ad259ac07679a53e8f09cde4\",\n \"score\": \"0.51392484\",\n \"text\": \"def interface_exists(interface):\\n return os.path.exists(\\\"/sys/class/net/%s\\\" % interface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aee1f9d0829c76e4f029811214eaaff8\",\n \"score\": \"0.51390105\",\n \"text\": \"def test_implemented_by_subinterface(self):\\n self.assertTrue(IFoo.implemented_by(IFooBar))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c36c1320b613f243935dd850a8b2ded\",\n \"score\": \"0.513061\",\n \"text\": \"def implements_attribute_manager(obj):\\n return all([hasattr(obj, n) for n in\\n ['keys', '__getitem__', '__contains__', '__iter__']])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62457039b5b99728ebd277fe83408b8\",\n \"score\": \"0.5127941\",\n \"text\": \"def interfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62457039b5b99728ebd277fe83408b8\",\n \"score\": \"0.5127941\",\n \"text\": \"def interfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ab9332932fc0556505ed054e4534e36\",\n \"score\": \"0.5107286\",\n \"text\": \"def implements_dataset(obj):\\n return all([hasattr(obj, n) for n in\\n ['attrs', 'keys', 'items',\\n '__getitem__', '__contains__', '__iter__']])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bc9a476b5488ef4062c42d2385795a\",\n \"score\": \"0.50944895\",\n \"text\": \"def is_type(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab35b1572f9c98cfe710abd8db4719b5\",\n \"score\": \"0.50910896\",\n \"text\": \"def interface_exists(interface):\\n return os.path.exists(\\\"/sys/class/net/\\\" + interface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3abfdcdaa155732f67dec65ff27eb22a\",\n \"score\": \"0.5085412\",\n \"text\": \"def _currently_in_ipython():\\n try:\\n __IPYTHON__ # pylint: disable=undefined-variable\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c118e3dfce2fd85243a6f79eb006fb1\",\n \"score\": \"0.50795716\",\n \"text\": \"def _was_registered(m, interface):\\n \\n for item in m.call_args_list:\\n if item[0][-1] == interface:\\n return True\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d88ac3ffa427897baf9ab47042c839d8\",\n \"score\": \"0.5077435\",\n \"text\": \"def interface(self):\\n return self.__interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec85e70ccdb9bd315392ad5fd59f147\",\n \"score\": \"0.50658995\",\n \"text\": \"def in_ipython():\\n try:\\n shell = get_ipython().__class__.__name__\\n if shell == \\\"ZMQInteractiveShell\\\":\\n return True # Jupyter notebook or qtconsole\\n elif shell == \\\"TerminalInteractiveShell\\\":\\n return False # Terminal running IPython\\n else:\\n return False # Other type (?)\\n except NameError:\\n return False # Probably standard Python interpreter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cefc0b8f212634a96a065179fee635a8\",\n \"score\": \"0.50610983\",\n \"text\": \"def is_abstract(obj: typing.Type) -> bool:\\n try:\\n return ABC in obj.__bases__\\n except AttributeError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9eca26713378f7517471cf61b8928f5\",\n \"score\": \"0.50561816\",\n \"text\": \"def GetInterfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289f245efa808a9a32ad5962d51e5c8d\",\n \"score\": \"0.5047994\",\n \"text\": \"def get_interface_class() -> Interface:\\n interface = Office.get_interface() # gets str name of interface class\\n if not interface:\\n raise ValueError('Should be run as macro using XLWings or PyUno.'\\n 'Neither could be detected.')\\n return getattr(Office, interface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ebfab792e76b181e3ad009385342d2\",\n \"score\": \"0.5029029\",\n \"text\": \"def is_this_run_in_ipython():\\n try:\\n __IPYTHON__\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e8dbc9fc4e755ad5723910adda3862\",\n \"score\": \"0.50271434\",\n \"text\": \"def interface(self):\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e8dbc9fc4e755ad5723910adda3862\",\n \"score\": \"0.50271434\",\n \"text\": \"def interface(self):\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565ddff6ab951dd08895c4b3ac15d578\",\n \"score\": \"0.501947\",\n \"text\": \"def is_mpsse_interface(self, interface: int) -> bool:\\n if not self.has_mpsse:\\n return False\\n if self.device_version == 0x0800 and interface > 2:\\n return False\\n if self.device_version == 0x3600 and interface > 2:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018e794216fecc1b79414a808c496be8\",\n \"score\": \"0.49981865\",\n \"text\": \"def _is_intf_excluded(self, interface):\\n _logger.debug('%s\\\\n%s', where_am_i(), pformat(interface, indent=4))\\n for excl in self.vnic_info.get('exclude', ()):\\n if excl in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc45a0530a6ce88744047b14cb31fae3\",\n \"score\": \"0.4976998\",\n \"text\": \"def get_interface() -> str or None:\\n # test for Python Uno\\n try:\\n XSCRIPTCONTEXT # if this variable exists, PyUno is being used.\\n except NameError:\\n pass\\n else:\\n return 'Uno'\\n\\n if xw is not None:\\n return 'XW'\\n\\n # otherwise, return None / False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f5dd9da15c84c05dd10c6e4f2bf8554\",\n \"score\": \"0.4964717\",\n \"text\": \"def _in_ipython():\\n\\n try:\\n # See https://stackoverflow.com/questions/15411967\\n shell = get_ipython().__class__.__name__\\n return bool(shell == \\\"TerminalInteractiveShell\\\")\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce46daaa6dd2bbf0905ee3bc8dc17a1\",\n \"score\": \"0.49643916\",\n \"text\": \"def interface_mode(self):\\n return self._interface_mode.value if self._interface_mode else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd29c00eb8a74ef6f6863fa100b1b37\",\n \"score\": \"0.49566942\",\n \"text\": \"def isnotebook(self):\\n try:\\n shell = get_ipython().__class__.__name__\\n if (shell == 'ZMQInteractiveShell' or shell == 'TerminalInteractiveShell'):\\n return True # Jupyter notebook or IPython\\n else:\\n return False # Other type (?)\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0494fee01a6c84336625999ec71e9e88\",\n \"score\": \"0.49517235\",\n \"text\": \"def exposed_interfaces(self):\\n raise exceptions.NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9370580bbbb45adf1a90aee162216377\",\n \"score\": \"0.49513036\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, Interface):\\n return False\\n\\n return self.to_dict() == other.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e95ddf88ba69823cfcf93bffd8f262b\",\n \"score\": \"0.49503726\",\n \"text\": \"def interface(self):\\n return self._if.interface()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2642419c8ab4cb9c915374e4d5b11405\",\n \"score\": \"0.49493083\",\n \"text\": \"def has_dax_datatypes(intf):\\n xnat_datatypes = intf.inspect.datatypes()\\n for dax_datatype in DAX_SETTINGS.get_xsitype_include():\\n if dax_datatype not in xnat_datatypes:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2485db54dc47373ad9a3a24581a01ce\",\n \"score\": \"0.49441043\",\n \"text\": \"def is_integer(self) -> bool:\\n return isinstance(self, IntegerTyp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce814aee006c85f85177fbe1b80a91d5\",\n \"score\": \"0.49327436\",\n \"text\": \"def getInterface(self):\\n return self.__interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb9bafa5ebb16cf0c63b3a9320dde068\",\n \"score\": \"0.49253488\",\n \"text\": \"def initial_interface_checks(iface_name):\\n if not interface.is_valid_interface(iface_name):\\n print 'Error: %s is not a valid interface.' % iface_name\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54278cd2bd316da7523d00f43e4cbd5\",\n \"score\": \"0.4919012\",\n \"text\": \"def test_interface_glue(self):\\r\\n iface = self.create(items.InterfaceItem, UML.Interface)\\r\\n impl = self.create(items.ImplementationItem)\\r\\n\\r\\n glued = self.allow(impl, impl.head, iface)\\r\\n self.assertTrue(glued)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56aeeefcd366178ea0ba238a08926672\",\n \"score\": \"0.49182153\",\n \"text\": \"def verify_ospf_interface_type(device, interface, interface_type, \\r\\n max_time=60, check_interval=10):\\r\\n timeout = Timeout(max_time, check_interval)\\r\\n while timeout.iterate():\\r\\n out = None\\r\\n try:\\r\\n out = device.parse('show ospf interface extensive')\\r\\n except SchemaEmptyParserError:\\r\\n timeout.sleep()\\r\\n continue\\r\\n for ospf_interface in out.q.get_values('ospf-interface'):\\r\\n intf = ospf_interface.get('interface-name', None)\\r\\n intf_type = ospf_interface.get('interface-type', None)\\r\\n if intf == interface and intf_type == interface_type:\\r\\n return True\\r\\n timeout.sleep()\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d5ffb882554298c0be64514283fa9f\",\n \"score\": \"0.48915172\",\n \"text\": \"def isframe(object):\\n return isinstance(object, types.FrameType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72ec5d1dcac59b3959fbe6302505c3c\",\n \"score\": \"0.48835492\",\n \"text\": \"def is_image(self):\\n if self.axes is None:\\n return True\\n return len(self.axes) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607beb0c4acf51ab0e4a475909c1bf4f\",\n \"score\": \"0.48734248\",\n \"text\": \"def enterInterfaceDeclaration(self, ctx:JavaParserLabeled.InterfaceDeclarationContext):\\n \\n self.__current_class_or_interface = ctx.IDENTIFIER().getText()\\n self.__classes.append(ctx.IDENTIFIER().getText())\\n self.__methods_of_classes[self.__current_class_or_interface] = []\\n self.__class_interface[ctx.IDENTIFIER().getText()] = True\\n\\n if ctx.EXTENDS() is not None and ctx.typeList().getText() in self.__classes:\\n self.__parent_and_child_classes[ctx.IDENTIFIER().getText()] = ctx.typeList().getText()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32a13afe4db6cc3251dcfeb92b8725f\",\n \"score\": \"0.48706833\",\n \"text\": \"def ProtocolInterface(self):\\n return self._get_attribute('protocolInterface')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbe526509239c5808b11866ccc137425\",\n \"score\": \"0.48549366\",\n \"text\": \"def is_known_type(cls, ioc_type: str) -> bool:\\n return ioc_type in IoCType.__members__ and ioc_type != \\\"unknown\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c71b64bcf1f098b63928f0c2fbc4bc\",\n \"score\": \"0.48411354\",\n \"text\": \"def is_simulator(self) -> bool:\\n return isinstance(self._backend, Simulator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18c61d197fa747c4617a6582d110ba0\",\n \"score\": \"0.48401403\",\n \"text\": \"def is_elemental(self):\\n return self._is_elemental\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61697810a3675c5564cf631af3d2d91\",\n \"score\": \"0.48228928\",\n \"text\": \"def interfaces():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913c32a9fad13671240a3accbca77d37\",\n \"score\": \"0.48065004\",\n \"text\": \"def SupportsPlugins(cls):\\n return cls._plugin_classes is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ed8f865cc32f16b7caa6a7baaebbcb\",\n \"score\": \"0.4798193\",\n \"text\": \"def __ne__(self, other):\\n if not isinstance(other, Interface):\\n return True\\n\\n return self.to_dict() != other.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3f206bf554e28eac92333360608cdf\",\n \"score\": \"0.47953272\",\n \"text\": \"def in_ipynb():\\n try:\\n if \\\"terminal\\\" in str(type(get_ipython())):\\n return False\\n else:\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89148e8b9fea47ebc8679ffdb438721b\",\n \"score\": \"0.47926775\",\n \"text\": \"def get_interfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c5decf9179fb87c752b8e5fdac1663\",\n \"score\": \"0.47859254\",\n \"text\": \"def is_supported(self) -> bool:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67585469ad50e8769ef5e4346dcb255d\",\n \"score\": \"0.47641656\",\n \"text\": \"def isnotebook():\\r\\n try:\\r\\n shell = get_ipython().__class__.__name__\\r\\n if shell == 'ZMQInteractiveShell':\\r\\n return True # Jupyter notebook or qtconsole\\r\\n elif shell == 'TerminalInteractiveShell':\\r\\n return False # Terminal running IPython\\r\\n else:\\r\\n return False # Other type (?)\\r\\n except NameError:\\r\\n return False # Probably standard Python interpreter\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a503865f67cf1555d23e56fe98cf76\",\n \"score\": \"0.47600353\",\n \"text\": \"def is_simulator(self) -> bool:\\n return isinstance(self._backend, OT3Simulator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5c5cd0af7c197859e68fa667250759\",\n \"score\": \"0.47472557\",\n \"text\": \"def test_interface(self):\\n flags = self.config_flags.NAT_IS_INSIDE\\n self.vapi.nat64_add_del_interface(\\n is_add=1, flags=flags, sw_if_index=self.pg0.sw_if_index\\n )\\n self.vapi.nat64_add_del_interface(\\n is_add=1, flags=0, sw_if_index=self.pg1.sw_if_index\\n )\\n\\n interfaces = self.vapi.nat64_interface_dump()\\n self.assertEqual(len(interfaces), 2)\\n pg0_found = False\\n pg1_found = False\\n for intf in interfaces:\\n if intf.sw_if_index == self.pg0.sw_if_index:\\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_INSIDE)\\n pg0_found = True\\n elif intf.sw_if_index == self.pg1.sw_if_index:\\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_OUTSIDE)\\n pg1_found = True\\n self.assertTrue(pg0_found)\\n self.assertTrue(pg1_found)\\n\\n features = self.vapi.cli(\\\"show interface features pg0\\\")\\n self.assertIn(\\\"nat64-in2out\\\", features)\\n features = self.vapi.cli(\\\"show interface features pg1\\\")\\n self.assertIn(\\\"nat64-out2in\\\", features)\\n\\n self.vapi.nat64_add_del_interface(\\n is_add=0, flags=flags, sw_if_index=self.pg0.sw_if_index\\n )\\n self.vapi.nat64_add_del_interface(\\n is_add=0, flags=flags, sw_if_index=self.pg1.sw_if_index\\n )\\n\\n interfaces = self.vapi.nat64_interface_dump()\\n self.assertEqual(len(interfaces), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b4220da7617062876632280a83bcb1\",\n \"score\": \"0.47413203\",\n \"text\": \"def has_nat(self):\\n return self.typeof in self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"843d7ba37f9c30d53aeab8fdf39117e2\",\n \"score\": \"0.47389\",\n \"text\": \"def check_if_interface_exists(device_name, interface_type, interface_number):\\n with ncs.maapi.single_write_trans('admin', 'python', groups=['ncsadmin']) as t:\\n root = ncs.maagic.get_root(t)\\n device = root.devices.device[device_name]\\n print type(device.interface[interface_type])\\n if interface_number in device.interface[interface_type]:\\n print(\\\"Interface is on the device!\\\")\\n else:\\n print(\\\"Interface is not on the device!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252c601cfbd7392ad20a08385be7058d\",\n \"score\": \"0.47366798\",\n \"text\": \"def has_donor_cbginterface(self,cbg):\\n if hasattr(cbg,\\\"_CBGinterface3p\\\") and cbg._CBGinterface3p:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79bc03e384ef302826e112ba6ed98c1\",\n \"score\": \"0.47240645\",\n \"text\": \"def test_subclass_provides_interface(self):\\n class IFooBarSubclass(self.FooBarHasFooBarBaz):\\n\\n def provides_interface(self, interface):\\n return interface.implemented_by(IFooBaz)\\n\\n obj = IFooBarSubclass()\\n with self.assertRaises(TypeError):\\n IFooBar(obj)\\n foobaz = IFooBaz(obj)\\n foobaz.baz()\\n self.assertEqual(obj.foo, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6eef8e9daccf5af22fc3e60aa0db7da\",\n \"score\": \"0.4722961\",\n \"text\": \"def any_extension_types(self) -> bool:\\n return False # any(block.is_extension for block in self.blocks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6e122479958a19399bf44fd93c3b6c\",\n \"score\": \"0.47229505\",\n \"text\": \"def is_interface_connected_to_source(self):\\n source_ip = self.get_tree_id()[0]\\n for network in self.get_all_interface_networks():\\n if ipaddress.ip_address(source_ip) in ipaddress.ip_network(network):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd25262cd7cec55d00e342d756b3109e\",\n \"score\": \"0.47206524\",\n \"text\": \"def isa(self, other):\\r\\n\\r\\n m = self\\r\\n while m and m is not other:\\r\\n m = m.inherits\\r\\n return bool(m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3547b097146dd27e26dae98c79f66fe\",\n \"score\": \"0.47147197\",\n \"text\": \"def declare_interface(ea):\\n kind = (get_struct_val(ea, 'go_type0.kind') & 0x1f)\\n if kind != TYPEKIND_VALS['kindInterface'][0]:\\n msg = 'type at 0x%s not an interface' % ea\\n LOG.error(msg)\\n raise Exception(msg)\\n\\n module_idx = resolve_moduledata_idx(ea)\\n type_size = get_ida_struc_size('go_type0')\\n tflag = (get_struct_val(ea, 'go_type0.tflag') & 0x7)\\n\\n iface_name = 'anonymous'\\n if tflag & TYPE_TFLAGS['named']:\\n iface_name = normalize_name(get_type_name(ea), ea,\\n prepend_type_info=False)\\n if tflag & TYPE_TFLAGS['extraStar']:\\n iface_name = iface_name[1:]\\n LOG.info('declare_interface: declaring iface %s at 0x%x',\\n iface_name, ea)\\n\\n iface_ea = ea + type_size\\n iface_mhdr_ea = iface_ea + PTRSIZE\\n mhdr_ea = get_struct_val(iface_mhdr_ea, 'go_slice.array')\\n mhdr_len = get_struct_val(iface_mhdr_ea, 'go_slice.len')\\n iface_methods = []\\n for i in range(0, mhdr_len):\\n mhdr_entry_ea = mhdr_ea + (i * PTRSIZE)\\n imethod_struc_name = 'go_type%d_metadata_imethod' % module_idx\\n imethod_struc_size = get_ida_struc_size(imethod_struc_name)\\n name_offset = nameoff(\\n get_struct_val(mhdr_entry_ea,\\n '%s.name' % imethod_struc_name),\\n module_idx)\\n imethod_name = get_raw_name_str(name_offset)\\n LOG.info('found interface method %s.%s()',\\n iface_name, imethod_name)\\n ida_bytes.del_items(mhdr_entry_ea, imethod_struc_size)\\n ida_bytes.create_struct(mhdr_entry_ea,\\n imethod_struc_size,\\n ida_struct.get_struc_id(imethod_struc_name))\\n iface_methods.append((\\n normalize_name(imethod_name, None,\\n prepend_type_info=False),\\n PTRSIZE,\\n FF_PTR|FF_DATA|off_flag(),\\n OFF_CURRENT_SEGMENT, None, None\\n ))\\n\\n itab_fields = [\\n ('inter', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\\n OFF_CURRENT_SEGMENT, None, None),\\n ('_type', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\\n OFF_CURRENT_SEGMENT, None, None),\\n ('hash', 4, FF_DWORD|FF_DATA, None, None, None),\\n ('_unused', 4, FF_DWORD|FF_DATA, None, None, None)\\n ] + iface_methods\\n LOG.debug('Creating new itab for %s', iface_name)\\n create_and_populate_struct('go_itab__%s' % iface_name,\\n itab_fields)\\n\\n LOG.debug('Creating new iface for %s', iface_name)\\n interface_tinfo = ida_typeinf.tinfo_t()\\n ida_typeinf.parse_decl(interface_tinfo,\\n idaapi.cvar.idati,\\n 'go_itab__%s *;' % iface_name,\\n ida_typeinf.PT_TYP)\\n iface_fields = [\\n ('tab', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\\n None, None),\\n ('data', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\\n None, None)\\n ]\\n iface_tinfos = [interface_tinfo, None]\\n iface_name = 'go_iface__%s' % iface_name\\n create_and_populate_struct(iface_name,\\n iface_fields, None,\\n iface_tinfos)\\n return iface_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ee064dbb5293432396b13c5e23c308\",\n \"score\": \"0.47141168\",\n \"text\": \"def inherited_interface(self):\\n raise exceptions.NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d6f031d8e91e7018033fd8f3d7e0118\",\n \"score\": \"0.47140905\",\n \"text\": \"def is_array(obj):\\n return hasattr(obj, 'dtype')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25fcdb091462da09938c70988e017d7\",\n \"score\": \"0.47132087\",\n \"text\": \"def isnotebook():\\n try:\\n shell = get_ipython().__class__.__name__\\n if shell == 'ZMQInteractiveShell':\\n return True # Jupyter notebook or qtconsole\\n elif shell == 'TerminalInteractiveShell':\\n return False # Terminal running IPython\\n else:\\n return False # Other type (?)\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3021caa56b8e96d786105815561d54\",\n \"score\": \"0.47128734\",\n \"text\": \"def are_supported(config: configuration.NamespaceConfig,\\n installer: Optional[interfaces.Installer]) -> bool:\\n for enh in enabled_enhancements(config):\\n if not isinstance(installer, enh[\\\"class\\\"]):\\n return False\\n return True\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":195,"cells":{"query_id":{"kind":"string","value":"260a4b8f6a111255d751b34ee4804b99"},"query":{"kind":"string","value":"Return an annual probability given a return period $$AEP = 1 \\exp{1/ARI}$$"},"positive_passages":{"kind":"list like","value":[{"docid":"76ed3955f395457f23e1a92d5ee923b0","score":"0.78453267","text":"def probability(ari):\n aep = 1.0 - np.exp(-1.0/ari)\n return aep","title":""}],"string":"[\n {\n \"docid\": \"76ed3955f395457f23e1a92d5ee923b0\",\n \"score\": \"0.78453267\",\n \"text\": \"def probability(ari):\\n aep = 1.0 - np.exp(-1.0/ari)\\n return aep\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"a969b34f946ea4cab04180a858e705ce","score":"0.66628295","text":"def probability(e, en, t):\n if t == 0:\n return 0\n else:\n return exp((e-en)/t)","title":""},{"docid":"6d171e23a9d819ff00ddad221521a2ea","score":"0.64231294","text":"def annuity(tot_sum: float, required_return: float, period: int) -> float:\n v = 1 / (1 + required_return)\n a_t = (1 - v ** period) / required_return\n return tot_sum / a_t","title":""},{"docid":"e54c4f992753aed19aae3a0fbf9e69b6","score":"0.63084","text":"def _prob(self, rate):\n return 1 - m.exp(-rate * self.timestep_len)","title":""},{"docid":"07232e7572226eacc7c8ad4d764030eb","score":"0.6257997","text":"def annualized_return(df,periodicity=252):\n # convert return series to numpy array (in case Pandas series is provided)\n df = np.asarray(df)\n # how many years of returns data is provided in df\n difference_in_years = len(df)/periodicity\n # starting net asset value / NAV (assumed to be 1) and cumulative returns (r) over time period provided in returns data\n start_NAV=1.0\n r = np.nancumprod(df+start_NAV)\n # end NAV based on final cumulative return\n end_NAV=r[-1]\n # determine annualized return\n AnnualReturn = end_NAV**(1 / difference_in_years) - 1\n return AnnualReturn","title":""},{"docid":"154922c23546a6280747801269452348","score":"0.60345423","text":"def annualized_return(returns, start_date, end_date):\n total_return = ((1 + returns).cumprod())[-1]\n t = (end_date - start_date).days / 365.25\n ar = (total_return ** (1 / t)) - 1\n return ar","title":""},{"docid":"66b4443c3b33f1455b678df725351107","score":"0.60135186","text":"def probability(self, v, a):\n return self.effective_energy(v, a).exp()","title":""},{"docid":"d93567a5d1ddf14025996e3afb62cbb8","score":"0.59987706","text":"def ev_log_p(eta):\n return ev_log_h(eta) + ev_t(eta) @ eta - a(eta)","title":""},{"docid":"cc13a260398e24edc6261513dec9992a","score":"0.5968521","text":"def annualized_returns(ser):\n return ser.mean() * DAYS_IN_YEAR","title":""},{"docid":"41c09486a9eefe1f4ecd2286d6e97b57","score":"0.5949281","text":"def get_annualized_percent_change_per_year(diff, years):\n return math.exp(math.log(diff) / years) - 1","title":""},{"docid":"eccfd5ead284dde79de143510d846be1","score":"0.59371513","text":"def probability(self):\n product=self.features.dot(self.w)\n #print(product)\n expr=np.exp(-product)\n \n return 1/(1+expr)","title":""},{"docid":"17ad259a90ab4b35af7af1020d840805","score":"0.5879564","text":"def avg_rate(p):\n return (p[-1][0]-p[0][0])/(p[-1][1]-p[0][1])","title":""},{"docid":"e88279a38ba012acd7789ced66e89ea0","score":"0.58563554","text":"def getfreq(pianonum):\n \n exponential = 2 ** ((pianonum - 49) / 12)\n return exponential * 440","title":""},{"docid":"d08118845d5b36ce5bd753f63e3d3b10","score":"0.58353317","text":"def annualise(num):\n anld = float(num) * 12 * 100\n return anld","title":""},{"docid":"017db4fe9f5b64e04d26169fa1596b1a","score":"0.5829257","text":"def Probability(o):\n return o / (o + 1)","title":""},{"docid":"8d6e15058f1871d5a04c9c8ec9906aed","score":"0.5813514","text":"def ioniz_prob(ergs):\n prob = np.exp(0.05*ergs - 8.0)\n return prob","title":""},{"docid":"f28da29699709c641d11b78e12aa222a","score":"0.576968","text":"def dbl_logistic_model ( p, x ):\n return p[0] + p[1]* ( 1./(1+np.exp(p[2]*(365.*x-p[3]))) + \\\n 1./(1+np.exp(p[4]*(365.*x-p[5]))) - 1 )","title":""},{"docid":"8983ec782f85690e786fb0777161d298","score":"0.57667994","text":"def probability(self, observation, next_state, action):\n if self.sample(next_state, action) == observation:\n return 1.0 - self.epsilon\n else:\n return self.epsilon","title":""},{"docid":"cf2ed7994e322867aa727db1a84dc57f","score":"0.5764005","text":"def _annualizationFactor(lifetime, r=4.0):\n if r==0: return lifetime\n return (1-(1+(r/100.0))**-lifetime)/(r/100.0)","title":""},{"docid":"7998a972bd9d384b08964c1ef376da06","score":"0.576322","text":"def _eta(data):\r\n # if len(data) <= 1: return 0\r\n ldata = len(list(data))\r\n if ldata <= 1: return 0\r\n _exp = exp(1)\r\n counts = Counter()\r\n for d in data:\r\n counts[d] += 1\r\n # probs = [float(c) / len(data) for c in counts.values()]\r\n probs = [float(c) / ldata for c in counts.values()]\r\n probs = [p for p in probs if p > 0.]\r\n ent = 0\r\n for p in probs:\r\n if p > 0.:\r\n ent -= p * log(p, _exp)\r\n return ent","title":""},{"docid":"1bb053fe634110591f254c113c674cd4","score":"0.5735856","text":"def payback_period(self, proforma):\r\n capex = abs(proforma.loc['CAPEX Year', :].sum())\r\n\r\n first_opt_year = min(self.opt_years)\r\n yearlynetbenefit = proforma.loc[pd.Period(year=first_opt_year, freq='y'), :].sum()\r\n\r\n return capex/yearlynetbenefit","title":""},{"docid":"ba1bd10268b1aa5df2ab5497a287b4f8","score":"0.5728151","text":"def prob(self, Pn):\n if self.norm==\"Scargle\": return exp(-Pn)\n if self.norm==\"HorneBaliunas\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\n if self.norm==\"Cumming\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)","title":""},{"docid":"ba1bd10268b1aa5df2ab5497a287b4f8","score":"0.5728151","text":"def prob(self, Pn):\n if self.norm==\"Scargle\": return exp(-Pn)\n if self.norm==\"HorneBaliunas\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\n if self.norm==\"Cumming\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)","title":""},{"docid":"36452380e66393425596e4644ba5f147","score":"0.57024795","text":"def airdensity(p,t):\n Re = 287.04 \n rho = p / ( Re * t ) \n return rho","title":""},{"docid":"cede57d7caad42c6c13cfe71a4d66b9f","score":"0.56989205","text":"def amplitude_calc(x, p, beta=1, alpha=0):\n return (1+alpha**2)/beta/2 * x**2 + alpha*x*p + beta/2*p**2","title":""},{"docid":"baea0c4c34222f3ca14644480b1365ab","score":"0.5695328","text":"def find_aps(r,kappain):\n return -2.*np.log(r) - kappain**2./(np.exp(1.)*r**2.)","title":""},{"docid":"2e175a0a8d31194ee97285b667f9f17c","score":"0.5669334","text":"def P(self):\n\t\tP = 2*pi * sqrt( (self.a()*AU)**3. / (G*(self.mCent+self.mass)*mSun) )/day\n\t\treturn P","title":""},{"docid":"f3b5dd9bb01f02b96740daacd3610301","score":"0.5666913","text":"def P(self):\n\t\tP = 2*pi * sqrt( (self.a*AU)**3. / (G*(self.M+self.m)*mSun) )/(24*3600.)\n\t\treturn P","title":""},{"docid":"2031ab84851620cb2d48a6d0123baee0","score":"0.5654314","text":"def _calculate_aprior_probabilities(aposterior_probabilities: np.array) -> np.array:\n return aposterior_probabilities.mean(axis=0)","title":""},{"docid":"6c64cde34a2700c76d76fc56c57511d9","score":"0.5640836","text":"def get_a(x):\n\n return np.exp(x)","title":""},{"docid":"b5526b4851786a92173c9ec8c0b34e99","score":"0.56370974","text":"def p(self, x: np.array) -> float:\n p = - x[0]**2 / 200.0 - 0.5 * (x[1] - self.b * x[0]**2 +\n 100.0 * self.b)**2\n p_sum = - 0.5 * np.sum(x[2:-1]**2)\n p = p + p_sum\n return np.exp(p)","title":""},{"docid":"7512fe5873ab477ff6210d31de27da47","score":"0.5598786","text":"def aire(R):\n return pi * (R ** 2)","title":""},{"docid":"e4a3541d9cae87df9cc65e10c53d9c51","score":"0.5589556","text":"def eta_ideal(self):\n return 1. - (self.engine_in.pr_ovr ** ((1 - self.engine_in.ambient.kappa_air ) /\n self.engine_in.ambient.kappa_air))","title":""},{"docid":"e1d890d991df68637d2ed572b546f57f","score":"0.5578697","text":"def adiabatic_lapse_rate_from_t(SA, t, p):\n\n return -gibbs(n0, n1, n1, SA, t, p) / (gibbs(n0, n2, n0, SA, t, p))","title":""},{"docid":"441f4390671151a658ae641b017047d6","score":"0.556664","text":"def ppmv2pa(x, p):\n return x * p / (1e6 + x)","title":""},{"docid":"d27ca1e003b53ece5ab9f1f0e8f2ce93","score":"0.55485916","text":"def f_pseudo_periodique(beta, omega, A, B, t):\n return math.exp(-beta*t)*(A*math.cos(omega*t)+B*math.sin(omega*t))","title":""},{"docid":"06cd4e2088c2c1d43960776b6e1a579e","score":"0.5546343","text":"def annualized_return(self) -> float:\n return self.__annualized_return","title":""},{"docid":"fe98470dccf2e8ced11da6af1252fce1","score":"0.5527254","text":"def getProbability(self):\n return self.p","title":""},{"docid":"daeb75883fd9d23198eaf7361e39c953","score":"0.5521143","text":"def p_alpha(p,p0,th,s):\n a11,a12,a13,a14,a15,a16,a21,a22,a23,a24,a25,a26,b11,b12,b13,b21,b22,b23=eosben07_const()\n #\n b1i=1/(b11+b12*th+b13*s)\n a1=(a11+th*(a12+a14*th+a15*s)+s*(a13+a16*s))*b1i\n a2=(a21+th*(a22+a24*th+a25*s)+s*(a23+a26*s))*b1i\n b2=(b21+b22*th+b23*s)*b1i\n #\n r=b2*(p-p0)+(a2-a1*b2)*ma.log((a1+p)/(a1+p0))\n #\n return r","title":""},{"docid":"5e3d6309e5ae8993e438d8b810b110c3","score":"0.55092055","text":"def proton_distribution_probability(product, substrate, precursor):\n global BASIC_AA\n p = substrate[1]\n q = substrate[2]\n p1 = product[1]\n p2 = p - p1\n q1 = product[2]\n q2 = q - q1\n p0 = precursor[1]\n nu = len(substrate[0])\n nu1 = len(product[0])\n #nu = sum(aa in BASIC_AA for aa in substrate[0])\n #nu1 = sum(aa in BASIC_AA for aa in product[0])\n nu2 = nu - nu1\n # Distribution over initial charging on product sequence:\n # Hypergeometric with population nu, successes nu1, trials p0\n XA1 = hypergeom_pmf_iterator(N=nu, k=nu1, n=p0)\n XA1_0 = max(0, p0-nu2)\n PTR = iterate_over_ptr_probabilities(p1=p1, q1=q1, p=p, q=q, p0=p0)\n PTR_0 = p1+q1+1\n kmin = max(XA1_0, PTR_0)\n kmax = min(nu1, p0-p2-q2+1)\n # Moving past the \"null\" summands\n for i in range(max(0, kmin - PTR_0 - 1)):\n PTR.next()\n for i in range(max(0, kmin - XA1_0 - 1)):\n XA1.next()\n pr = 0.\n for i in range(kmin, kmax+1):\n A, B = PTR.next()\n C = XA1.next()\n pr += (float(A)*float(nu2)/nu + float(nu1)/nu)*B*C\n #pr = sum((float(A)*float(nu2)/nu + float(nu1)/nu)*B*C for i, (A, B), C in enumerate(zip(PTR, XA1)) if i + kmin < kmax)\n return pr","title":""},{"docid":"d1a724016faa87efe811cac38a976021","score":"0.55043864","text":"def avg_amp(self, params):\n amps = params[:self.no_ants*2:2]\n if self.logamp:\n amps = np.exp(amps)\n return np.mean(amps) - 1","title":""},{"docid":"66fe6cf3be98ef6d29b947bf9083ca27","score":"0.54913193","text":"def compute_period(a, M, m):\n return np.sqrt(4 * np.pi **2 / (takahe.constants.G * (M+m) * takahe.constants.SOLAR_MASS) * (a * takahe.constants.SOLAR_RADIUS)**3) / (60 * 60 * 24)","title":""},{"docid":"e5a5607d44f9bb2f5301283876b8f61d","score":"0.5488452","text":"def prob(self, at, log = False):","title":""},{"docid":"7ca10d9bd1a5946e2d0dff1c986e69c1","score":"0.54698384","text":"def exp_decay(p,x):\n a, b, c = p\n\n return a + b * np.exp(-x / c)","title":""},{"docid":"8f906cdb8b7cc43327c26bd266d0974a","score":"0.5467352","text":"def get_annual_eps_value(eps_data: dict):\n years = list(set([int(k.split('-')[0]) for (k, v) in eps_data.items()]))\n max_year = max(years)\n three_years_ago = max_year - 3\n filter_years = sorted([str(x) for x in years if max_year >= x >= three_years_ago], reverse=True)\n values = list()\n for year in filter_years:\n values.append(round(sum([v for (k, v) in eps_data.items() if year in k]), 2))\n return dict(zip(filter_years, values))","title":""},{"docid":"71d8b77d14e51ab8e133db3f50a7fe87","score":"0.5463908","text":"def prob(self, Pn):\n return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)","title":""},{"docid":"5878d8693907bf81216b28f335fdc419","score":"0.5459113","text":"def rate(e):\n d = np.abs(e - e[-1])\n p = []\n for i in range(len(d)-1):\n p.append(np.log(d[i+1])/np.log(d[i]))\n return p","title":""},{"docid":"7be049e111a0f8923aebf37b90cef533","score":"0.5458327","text":"def paretovariate_impl(alpha):\n # Jain, pg. 495\n u = 1.0 - _random()\n return 1.0 / u ** (1.0/alpha)","title":""},{"docid":"6aa974bfaae301fa79ac0fbeabb3bb21","score":"0.54536045","text":"def EP(d):\n return 4*d*(5/6)**d + 1 - (5/6)**d","title":""},{"docid":"46e76d91ba872e7607db1518181831cf","score":"0.54457945","text":"def calc_alpha(epsilon):\n\n if (epsilon == 0):\n return np.inf\n\n\n return 0.5 * np.log( (1. - epsilon)/epsilon )","title":""},{"docid":"0c4c2f4d23858b4b4c3cc0ad0d940f8d","score":"0.5436166","text":"def beamfrac(FWHM, length, angle):\n height_of_sample = length * np.sin(np.radians(angle))\n beam_sd = FWHM / 2 / np.sqrt(2 * np.log(2))\n probability = 2.0 * (\n stats.norm.cdf(height_of_sample / 2.0 / beam_sd) - 0.5\n )\n return probability","title":""},{"docid":"d0f6ccb38864d16aea5f119c73c0b0fb","score":"0.54260474","text":"def theta(self, *args, eps=0.001):\n # Fix the random generator seed for MonteCarlo pricing\n st = np.random.get_state()\n price1 = self.price(*args)\n np.random.set_state(st)\n self.term -= eps\n price2 = self.price(*args)\n self.term += eps\n return (price2 - price1) / (365 * eps)","title":""},{"docid":"bad795ccd1af697171146833dd55aaa9","score":"0.5425807","text":"def _calculate_PAYE(period_income, period):\r\n predicted_annual_income = period * period_income\r\n \r\n income_tax = _calculate_income_tax(predicted_annual_income)\r\n acc_levy = _calculate_ACC_levy(predicted_annual_income)\r\n \r\n total_tax_paid = income_tax + acc_levy\r\n \r\n return round(total_tax_paid / period, 2)","title":""},{"docid":"e00a47c7e5892e10021e3d495160bb4d","score":"0.5419779","text":"def e_var(partition):\n P = np.log(partition)\n S = len(partition)\n X = 0\n for x in P:\n X += (x - np.mean(P))**2/S\n evar = 1 - 2/math.pi*np.arctan(X) \n return(evar)","title":""},{"docid":"55297a2ff4086c2bf1acf53482780e23","score":"0.5414374","text":"def expected_earnings(mao):\r\n return (.5 - (mao - ((mao ** 2) / 2)))","title":""},{"docid":"244d6a5bac71aaacecf03ad1089d25dc","score":"0.5397943","text":"def calculate_probability(x, mean, stdev):\n exponent = np.exp(-1.0 * ((x-mean)**2 / (2*(stdev**2))))\n return (1/(np.sqrt(2.0 * np.pi) * stdev)) * exponent","title":""},{"docid":"2dac13767a930f12ba0d31b9db65a986","score":"0.5397827","text":"def empirical_prob(self, *items, delta = 0):\n assert (len(items) in [1, 2])\n if (len(items) == 1):\n denominator = self.total_counts + delta * self.num_vocab ** 2\n else:\n denominator = self.total_edges + delta * self.num_possible_pairs\n return self.empirical_freq(*items, delta = delta) / denominator","title":""},{"docid":"647fe4d57e4a1de86bbc9cd34a971c9c","score":"0.5396929","text":"def soilmoisture( self, evaporative_fraction ):\n\t\t\tresult = (exp((evaporative_fraction-1.2836)/0.4213))/0.511\n\t\t\treturn result","title":""},{"docid":"0a486069b20a599edb89c810c19a2d7b","score":"0.53961486","text":"def gaussian_moment(p):\n if p % 2 == 0:\n return factorial2(p-1)\n else:\n return 0","title":""},{"docid":"49ab846f51909ca0b71e08b8f97013e3","score":"0.53892523","text":"def logit2probability(log_odds):\n # convert log odds to odds\n odds = math.exp(log_odds)\n # return probability\n return odds / float(1 + odds)","title":""},{"docid":"8170543ac5ff1d2bba1fd16b24f8d220","score":"0.5387339","text":"def decay(t, p):\n return p[0] * np.exp(-t / p[1]) + p[2]","title":""},{"docid":"40958abdc2ac4eefc3a72a6573351082","score":"0.53814656","text":"def _approx_ap(airf,pres):\n pvap = pres * (1-airf)/(airf*_EPSW + 1-airf)\n v = numpy.log(pvap/_PTPE)/_BVI\n x = maths4.lamb2(v,_AVI/_BVI)\n temp = _TTP/x\n dhum = (pres-pvap + _EPSW*pvap) / (_RDRY*temp)\n return temp, dhum","title":""},{"docid":"cba8b19e85284fbfe2a59ea77809b5ed","score":"0.53794813","text":"def startprob(self):\n return np.exp(self._log_startprob)","title":""},{"docid":"008acf3c978605d22a56d6110134eaba","score":"0.5377723","text":"def get_exp_probabilities(self, fluxes, inst_params):\n prob = 1 - np.exp(-fluxes / inst_params[\"scale\"])\n\n return prob","title":""},{"docid":"3c14c31873659ea1950017763c7e4e2b","score":"0.5364507","text":"def get_ev(a: np.ndarray) -> Param:\n\n return a / np.sum(a),","title":""},{"docid":"54881653f7ce52de6c69bcc2f8abd6f2","score":"0.53623134","text":"def topp(v):\n return np.exp(v) / np.sum(np.exp(v))","title":""},{"docid":"eb044c957e5b8785a3ac8b0bf516416f","score":"0.5361167","text":"def probability(self, next_state, state, action):\n if self.sample(state, action) == next_state:\n return 1.0 - self.epsilon\n else:\n return self.epsilon","title":""},{"docid":"d9c6ca6447b8cce60ebe738b1842c361","score":"0.5350514","text":"def test_getAngularDistribution_PWave(self):\n egrid = self.RRR1PRes.generateEnergyGrid()\n eStep = 1\n J = 5.5 # for this test data\n S = 4.5 # for this test data\n from numericalFunctions import angularMomentumCoupling as nf_amc\n self.assertAlmostEqual(\n nf_amc.z_coefficient(2, int(2 * J), 2, int(2 * J), int(2 * S), 4) / nf_amc.z_coefficient(2, int(2 * J), 2,\n int(2 * J),\n int(2 * S), 0),\n math.sqrt(0.33090909)) # nf_amc functions use the 2J trick\n for E in egrid[::eStep]:\n B = self.RRR1PRes.getAngularDistribution(E)\n self.assertAllWithinXPercent([x / B[0] for x in B], [1.0, 0.0, 0.33090909])","title":""},{"docid":"54012fa6ca93fc00bc7a9e27ee1fe249","score":"0.5335799","text":"def offer_prob(year, month, sex, race, referral, precomputed_emp_dist=None):\n if precomputed_emp_dist is not None:\n emp = precomputed_emp_dist[year][month - 1][race.name][sex.name]\n else:\n emp = employment_dist(year, month, sex, race)\n\n # quick-and-dirty mapping to race keys\n if not isinstance(race, str):\n if race.name == 'aian':\n race = 'nativeamerican'\n elif race.name == 'white':\n # b/c white and hispanic are merged together\n # TODO find dataset that separates these?\n race = random.choice(['hispanic', 'white'])\n elif race.name in ['chinese', 'japanese', 'api']:\n race = 'asian'\n elif race.name == 'black':\n race = 'black'\n\n # mean of two random races. this is a sloppy guess,\n # since the actual evidence may not work this way\n elif race.name in ['other', 'two', 'three_plus']:\n races = list(p_offer['offered']['race'].keys())\n op1 = _offer_prob(sex, random.choice(races), referral, emp)\n op2 = _offer_prob(sex, random.choice(races), referral, emp)\n return (op1 + op2)/2\n\n return _offer_prob(sex, race, referral, emp)","title":""},{"docid":"f7550b5825aea3cfb202d2a09abfeabe","score":"0.53341776","text":"def ampsAtPeriod(period):\n checkPeriod(period, \"ampsAtPeriod::period\")\n return startingSequence[period-1][1]","title":""},{"docid":"8680f547542f4aa8920e1df44de4b6b9","score":"0.5333341","text":"def AIC(self):\n RSS = self.getRMSE()\n k = len(self.variables)\n n = len(self.exp_data)\n AIC = 2 * k + n * (log(2 * pi * RSS/n) + 1)\n return AIC","title":""},{"docid":"879fc398a4991981587ecdfa58f50ded","score":"0.5328824","text":"def prediction_interval(self, xp=None, yp=None, alpha=0.05):\n if not (xp is None) ^ (yp is None):\n raise ValueError('Only one of [xp, yp] must be specified.')\n\n if xp is not None:\n return self._y_pi(xp, alpha=alpha)\n if yp is not None:\n return self._x_pi(yp, alpha=alpha)","title":""},{"docid":"243d4ce62d5695a4a1cde42e12efba40","score":"0.5327332","text":"def alpha_wrt_t_exact(SA, t, p):\n\n return gibbs(n0, n1, n1, SA, t, p) / gibbs(n0, n0, n1, SA, t, p)","title":""},{"docid":"dbaeb65c4918cd305cc1d2de1739b203","score":"0.53269297","text":"def probability():\n f1 = AffineTransform(0, 0, 0, 0.16, 0, 0)\n f2 = AffineTransform(0.85, 0.04, -0.04, 0.85, 0, 1.60)\n f3 = AffineTransform(0.20, -0.26, 0.23, 0.22, 0, 1.60)\n f4 = AffineTransform(-0.15, 0.28, 0.26, 0.24, 0, 0.44)\n functions = [f1, f2, f3, f4]\n p1 = 0.01\n p2 = 0.85\n p3 = 0.07\n p4 = 0.07\n p_cumulative = np.array([[0, p1],\n [1, p1+p2],\n [2, p1+p2+p3],\n [3, p1+p2+p3+p4]])\n r = np.random.random()\n for j, p in p_cumulative:\n if r < p:\n return functions[int(j)]","title":""},{"docid":"7a47e37d6559b69bf1b357b7e8a0d017","score":"0.5326326","text":"def exponential_var(p,x):\n \n \n plt.subplot(311)\n plt.semilogy(x, exp_decay(np.array([p[0[0]],p[1[3]],p[2[3]]])), x)\n plt.savefig('KEG_exponential_var.pdf', bbox_inches='tight', dpi=300)","title":""},{"docid":"90e6a4c6840c0695d1f98535c676f6f3","score":"0.5323668","text":"def date_of_annual_after(title, eff_date):\n publication_date = date(eff_date.year, publication_month(title), 1)\n if eff_date <= publication_date:\n return publication_date\n else:\n return publication_date.replace(year=eff_date.year + 1)","title":""},{"docid":"2b026ba3820417246be18a6149607298","score":"0.53191286","text":"def cronbach_alpha(data):\n n_items = data.shape[0]\n\n valid_mask = data != INVALID_RESPONSE\n \n item_variance = np.var(data, axis=1, ddof=1, where=valid_mask).sum()\n people_variance = (n_items * n_items\n * np.mean(data, axis=0, where=valid_mask).var(ddof=1))\n \n return (n_items / (n_items - 1) \n * (1 - item_variance / people_variance))","title":""},{"docid":"7e73ca74ce81005f48c4c98ed9cd9001","score":"0.53163207","text":"def calc_extreme_value(params,distribution,nyear,datafraction=1.0):\n probability=1.0/(nyear*365.0*datafraction)\n \n x=np.arange(0,10000,10)\n pdf=distribution.pdf(x,*params)\n pos=np.where((pdf>=probability) & (pdf>0))[0]\n \n if len(pos)==0:return 0\n pos=pos[-1]\n\n x=np.arange(x[pos]-10,x[pos]+10,0.1)\n pdf=distribution.pdf(x,*params)\n pos=np.where((pdf<=probability) &(pdf>0))[0]\n\n if x[pos[0]]<0:return 0\n return x[pos[0]]","title":""},{"docid":"a87e4e5986c8905599dc2a722d7a32cd","score":"0.5314459","text":"def period(r):\n m = 0\n d = 1\n a = a0 = floor(r)\n p = []\n\n while a != 2*a0:\n m = round(d*a - m)\n d = round((r*r - m*m)/d)\n a = round(floor((a0 + m)/d))\n p.append(a)\n return (a0, p)","title":""},{"docid":"2330a22f92cb5bc3db086c0c7218a8cb","score":"0.5306773","text":"def calculateAnom(var):\n ### Import modules\n import numpy as np\n \n mean = np.nanmean(var,axis=0)\n anom = var - mean\n \n print('Completed: Calculate anomalies for polar cap!')\n return anom","title":""},{"docid":"c6a555f2566874f2dfe5d796711f788f","score":"0.53059053","text":"def exponential_annealing(init_value, min_value, decay_ratio):\n result = init_value\n\n while True:\n result = max(min_value, result * decay_ratio)\n yield result","title":""},{"docid":"ccaab3a5cf7e0e80c93134e6ef44d7e9","score":"0.53049403","text":"def per(soup):\r\n\r\n period = soup.find(id=\"planet_period_0\").text\r\n\r\n period = float(period.split(\" \")[0])\r\n \r\n return period","title":""},{"docid":"80192f78f06ba42dced0be35570c30c2","score":"0.5300492","text":"def calculate_probability(self, value, mean, std_dev):\n # Calculates the exponent.\n exponent = math.exp(-(math.pow(value-mean,2)/(2*math.pow(std_dev,2))))\n # Returns the probability.\n return (1 / (math.sqrt(2*math.pi) * std_dev)) * exponent","title":""},{"docid":"d683d54fb547bb0d5ffb95227395954c","score":"0.52974075","text":"def getFrequency(self):\n if self.period: return 1.0/self.period\n return 0","title":""},{"docid":"8af254d526217ba25a3ddee9e94c8a83","score":"0.52971166","text":"def annual_eval(out_dir, pts):\n dgp_value = np.genfromtxt(f\"{out_dir}dgp_{pts}.out\", delimiter=\" \", usecols=3)\n fdgpe = np.zeros(3) # DGP > 0.35, 0.4, 0.45\n for i in range(len(dgp_value)):\n for j in range(3):\n if dgp_value[i] > (0.35 + j * 0.05):\n fdgpe[j] = fdgpe[j] + 1\n for j in range(3):\n fdgpe[j] = fdgpe[j] * 5.43 / 1304\n np.savetxt(f\"{out_dir}fDGPe_{pts}.out\", fdgpe, delimiter=\" \", fmt=\"%1.3f\")","title":""},{"docid":"db93ccc3207dc8aa7a31f25624b10f80","score":"0.52957386","text":"def zolotarev_pdf_exponentiated(self, x, alpha):\n denominator = pow(sinc(alpha * x), alpha) \\\n * pow(sinc((1. - alpha) * x), (1. - alpha))\n numerator = sinc(x)\n return numerator / denominator","title":""},{"docid":"5133c303c323d063c9da40af348b07b2","score":"0.5292389","text":"def bar2psia(a):\n return (a + 1.013) * 14.50377","title":""},{"docid":"433b64525c26c03fe98d5aacf62f0afd","score":"0.5291426","text":"def p_from_am(sma, ms):\n a = sma * u.au.to(u.m)\n ms = ms * u.Msun.to(u.kg)\n G = c.G.value\n p = np.sqrt((4 * np.pi ** 2 * a ** 3) / (G * ms))\n return p * u.second.to(u.day)","title":""},{"docid":"b7ea2e4a7c9d0289f55632f817962b64","score":"0.52898544","text":"def damping_ratio(self):\n return self._eta","title":""},{"docid":"60f281fb55f9ec9866f8938ebd39e617","score":"0.5287896","text":"def mean(p):\n\n v = 0.0\n for i, pi in zip(_I, p):\n v += i*pi\n\n return v","title":""},{"docid":"8d5dacdd4a7189d99c9572027701b844","score":"0.52864736","text":"def get_probsrsa_vectorized(number, exp_rate):\r\n return (np.power(exp_rate, number))*(np.exp(-exp_rate))/(scipy.special.factorial(number))","title":""},{"docid":"5b25b7c54d9e8dc6830baa497956688d","score":"0.52847904","text":"def arc_length(Eps, alpha):\n return Eps * alpha","title":""},{"docid":"b5fc16f5816f57878f83370c9daf880f","score":"0.5284486","text":"def int_eta0(self,p,T,muB,muQ,muS,**kwargs):\n Ep = np.sqrt(p**2.+self.M(T,muB,muQ,muS,**kwargs)**2.) # pole energy\n ffq = self.fq(Ep,T,muB,muQ,muS) # distribution function\n return 1./(15.*T)*self.dg*(4.*pi*p**2.)/((2.*pi)**3)*(p**4.)/(Ep**2.)*ffq*(1-self.BEFD*ffq)/(2*self.w(T,muB,muQ,muS,**kwargs))","title":""},{"docid":"5f95cf88f90172d89ed05f46bff261c0","score":"0.52806705","text":"def period(x, y, ofac=4, hifac):\n n = len(x)\n (wi, wpi, wpr, wr, px, py) = ([0]*n, [0]*n, [0]*n, [0]*n, [0]*n)\n nout = int(.5*ofac*hifac*n)\n if np < nout:\n px.np.ndarray.resize(nout)\n py.np.ndarray.resize(nout)\n ave = np.avearge(y)\n var = np.var(y)\n if var == 0:\n print(\"Zero variacne in period\")\n return\n xmax = xmin = x[0]\n for j in range(0, n):\n if x[j] > xmax:\n xmax = x[j]\n if x[j] < xmin:\n xmin = x[j]\n xdif = xmax - xmin\n xave = .5*(xmax+xmin)\n pymax =0\n pnow = 1/(xdif*ofac) # starting frequency\n for j in range(0, n): # initialize values for the trigonometric recurrences at each data point\n arg = 2*np.pi*(x[j]-ave)*pnow\n wpr[j] = -2*np.sqrt(np.sin(.5*arg))\n wpi[j] = np.sin(arg)\n wr[j] = np.cos(arg)\n wi[j] = wpi[j]\n for i in range(0, nout):\n px[i] = pnow\n sumsh = sumc = 0\n for j in range(0,n):\n c = wr[j]\n s = wi[j]\n sumsh += s*c\n sumc += (c-s)*(c+s)\n wtau = .5*np.arctan(p2*sumsh, sumc])\n swtau = np.sin(wtau)\n cwtau = np.cos(wtau)\n sums = sumc= sumsy = sumcy = 0\n for j in range(0, n): # loop over the data again to get the periiodogram value\n s = wi[j]\n c = wr[j]\n ss = s * cwtau - c*swtau\n cc = c*cwtau + s*swtau\n sums += ss*ss\n sumc += cc*cc\n yy = y[j] - ave\n sumsy += yy*ss\n sumcy += yy*cc\n wtemp = wr[j]\n wr[j] = (wtemp*wpr[j] - wr[j] * wpi[j]) + wr[j] # trigonometric recurrences\n wi[j] = (wi[j] * wpr[j] + wtemp * wpi[j]) + wi[j]\n py[i] = .5*(sumcy * sumcy/sumc + sumsy * sumsy/sums)/var\n if py[i] >= pymax:\n jmax = i\n pymax = py[jmax]\n pnow += 1/(ofac*xdif)\n expy = np.exp(-pymax)\n effm = 2*nout/ofac\n prob = effm * expy\n if prob > .01:\n prob = 1 - np.power(1 - expy, effm)\n return px, py, prob","title":""},{"docid":"73fa5608009670c1ebc32a92411e499a","score":"0.5279629","text":"def _probability_of_title_with_length(self, length):\n sigma = 6\n return np.exp(-1 * ((length - self.avg_length) ** 2) / (2 * (sigma ** 2))) / (math.sqrt(2 * np.pi) * sigma)","title":""},{"docid":"f4d435eac6074cd3164079f5ca5870ba","score":"0.5273136","text":"def frac_position(eta,eta_distribution):\n pass","title":""},{"docid":"a337c988c1ebbf9f39e6dda243a101f1","score":"0.52646464","text":"def apo(price, period_slow = 26, period_fast = 12, ma = \"sma\"):\n if ma.lower() not in [\"sma\", \"ema\"]:\n raise ValueError(\"param 'ma' needs to be 'ema' or 'sma'\")\n\n return apo_calc(price, period_slow, period_fast, ma.lower())","title":""},{"docid":"7dea0661caea45ea6b8f392fa9985659","score":"0.5260097","text":"def rand_exp(rate):\n \n return -log(random()) / rate","title":""},{"docid":"c0621abd8797937864d627e54d54a7c8","score":"0.5259038","text":"def discounted_payback_period(self, proforma):\r\n payback_period = self.payback_period(proforma) # This is simply (capex/yearlynetbenefit)\r\n\r\n if not self.npv_discount_rate:\r\n dr = self.npv_discount_rate*.01\r\n discounted_pp = np.log(1/(1-(dr*payback_period)))/np.log(1+dr)\r\n else:\r\n # if discount_rate = 0, then the equation simplifies to np.log(1)/np.log(1)-- which is undefined, so we return nan\r\n discounted_pp = 'nan'\r\n\r\n return discounted_pp","title":""},{"docid":"6c08eac2e3e543e690b820889ca1868d","score":"0.5257021","text":"def ann_return1_year(self) -> float:\n return self.__ann_return1_year","title":""}],"string":"[\n {\n \"docid\": \"a969b34f946ea4cab04180a858e705ce\",\n \"score\": \"0.66628295\",\n \"text\": \"def probability(e, en, t):\\n if t == 0:\\n return 0\\n else:\\n return exp((e-en)/t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d171e23a9d819ff00ddad221521a2ea\",\n \"score\": \"0.64231294\",\n \"text\": \"def annuity(tot_sum: float, required_return: float, period: int) -> float:\\n v = 1 / (1 + required_return)\\n a_t = (1 - v ** period) / required_return\\n return tot_sum / a_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54c4f992753aed19aae3a0fbf9e69b6\",\n \"score\": \"0.63084\",\n \"text\": \"def _prob(self, rate):\\n return 1 - m.exp(-rate * self.timestep_len)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07232e7572226eacc7c8ad4d764030eb\",\n \"score\": \"0.6257997\",\n \"text\": \"def annualized_return(df,periodicity=252):\\n # convert return series to numpy array (in case Pandas series is provided)\\n df = np.asarray(df)\\n # how many years of returns data is provided in df\\n difference_in_years = len(df)/periodicity\\n # starting net asset value / NAV (assumed to be 1) and cumulative returns (r) over time period provided in returns data\\n start_NAV=1.0\\n r = np.nancumprod(df+start_NAV)\\n # end NAV based on final cumulative return\\n end_NAV=r[-1]\\n # determine annualized return\\n AnnualReturn = end_NAV**(1 / difference_in_years) - 1\\n return AnnualReturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154922c23546a6280747801269452348\",\n \"score\": \"0.60345423\",\n \"text\": \"def annualized_return(returns, start_date, end_date):\\n total_return = ((1 + returns).cumprod())[-1]\\n t = (end_date - start_date).days / 365.25\\n ar = (total_return ** (1 / t)) - 1\\n return ar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b4443c3b33f1455b678df725351107\",\n \"score\": \"0.60135186\",\n \"text\": \"def probability(self, v, a):\\n return self.effective_energy(v, a).exp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d93567a5d1ddf14025996e3afb62cbb8\",\n \"score\": \"0.59987706\",\n \"text\": \"def ev_log_p(eta):\\n return ev_log_h(eta) + ev_t(eta) @ eta - a(eta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc13a260398e24edc6261513dec9992a\",\n \"score\": \"0.5968521\",\n \"text\": \"def annualized_returns(ser):\\n return ser.mean() * DAYS_IN_YEAR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c09486a9eefe1f4ecd2286d6e97b57\",\n \"score\": \"0.5949281\",\n \"text\": \"def get_annualized_percent_change_per_year(diff, years):\\n return math.exp(math.log(diff) / years) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eccfd5ead284dde79de143510d846be1\",\n \"score\": \"0.59371513\",\n \"text\": \"def probability(self):\\n product=self.features.dot(self.w)\\n #print(product)\\n expr=np.exp(-product)\\n \\n return 1/(1+expr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ad259a90ab4b35af7af1020d840805\",\n \"score\": \"0.5879564\",\n \"text\": \"def avg_rate(p):\\n return (p[-1][0]-p[0][0])/(p[-1][1]-p[0][1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e88279a38ba012acd7789ced66e89ea0\",\n \"score\": \"0.58563554\",\n \"text\": \"def getfreq(pianonum):\\n \\n exponential = 2 ** ((pianonum - 49) / 12)\\n return exponential * 440\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08118845d5b36ce5bd753f63e3d3b10\",\n \"score\": \"0.58353317\",\n \"text\": \"def annualise(num):\\n anld = float(num) * 12 * 100\\n return anld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017db4fe9f5b64e04d26169fa1596b1a\",\n \"score\": \"0.5829257\",\n \"text\": \"def Probability(o):\\n return o / (o + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6e15058f1871d5a04c9c8ec9906aed\",\n \"score\": \"0.5813514\",\n \"text\": \"def ioniz_prob(ergs):\\n prob = np.exp(0.05*ergs - 8.0)\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28da29699709c641d11b78e12aa222a\",\n \"score\": \"0.576968\",\n \"text\": \"def dbl_logistic_model ( p, x ):\\n return p[0] + p[1]* ( 1./(1+np.exp(p[2]*(365.*x-p[3]))) + \\\\\\n 1./(1+np.exp(p[4]*(365.*x-p[5]))) - 1 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8983ec782f85690e786fb0777161d298\",\n \"score\": \"0.57667994\",\n \"text\": \"def probability(self, observation, next_state, action):\\n if self.sample(next_state, action) == observation:\\n return 1.0 - self.epsilon\\n else:\\n return self.epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2ed7994e322867aa727db1a84dc57f\",\n \"score\": \"0.5764005\",\n \"text\": \"def _annualizationFactor(lifetime, r=4.0):\\n if r==0: return lifetime\\n return (1-(1+(r/100.0))**-lifetime)/(r/100.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7998a972bd9d384b08964c1ef376da06\",\n \"score\": \"0.576322\",\n \"text\": \"def _eta(data):\\r\\n # if len(data) <= 1: return 0\\r\\n ldata = len(list(data))\\r\\n if ldata <= 1: return 0\\r\\n _exp = exp(1)\\r\\n counts = Counter()\\r\\n for d in data:\\r\\n counts[d] += 1\\r\\n # probs = [float(c) / len(data) for c in counts.values()]\\r\\n probs = [float(c) / ldata for c in counts.values()]\\r\\n probs = [p for p in probs if p > 0.]\\r\\n ent = 0\\r\\n for p in probs:\\r\\n if p > 0.:\\r\\n ent -= p * log(p, _exp)\\r\\n return ent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bb053fe634110591f254c113c674cd4\",\n \"score\": \"0.5735856\",\n \"text\": \"def payback_period(self, proforma):\\r\\n capex = abs(proforma.loc['CAPEX Year', :].sum())\\r\\n\\r\\n first_opt_year = min(self.opt_years)\\r\\n yearlynetbenefit = proforma.loc[pd.Period(year=first_opt_year, freq='y'), :].sum()\\r\\n\\r\\n return capex/yearlynetbenefit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1bd10268b1aa5df2ab5497a287b4f8\",\n \"score\": \"0.5728151\",\n \"text\": \"def prob(self, Pn):\\n if self.norm==\\\"Scargle\\\": return exp(-Pn)\\n if self.norm==\\\"HorneBaliunas\\\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\\n if self.norm==\\\"Cumming\\\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1bd10268b1aa5df2ab5497a287b4f8\",\n \"score\": \"0.5728151\",\n \"text\": \"def prob(self, Pn):\\n if self.norm==\\\"Scargle\\\": return exp(-Pn)\\n if self.norm==\\\"HorneBaliunas\\\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\\n if self.norm==\\\"Cumming\\\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36452380e66393425596e4644ba5f147\",\n \"score\": \"0.57024795\",\n \"text\": \"def airdensity(p,t):\\n Re = 287.04 \\n rho = p / ( Re * t ) \\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cede57d7caad42c6c13cfe71a4d66b9f\",\n \"score\": \"0.56989205\",\n \"text\": \"def amplitude_calc(x, p, beta=1, alpha=0):\\n return (1+alpha**2)/beta/2 * x**2 + alpha*x*p + beta/2*p**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baea0c4c34222f3ca14644480b1365ab\",\n \"score\": \"0.5695328\",\n \"text\": \"def find_aps(r,kappain):\\n return -2.*np.log(r) - kappain**2./(np.exp(1.)*r**2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e175a0a8d31194ee97285b667f9f17c\",\n \"score\": \"0.5669334\",\n \"text\": \"def P(self):\\n\\t\\tP = 2*pi * sqrt( (self.a()*AU)**3. / (G*(self.mCent+self.mass)*mSun) )/day\\n\\t\\treturn P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b5dd9bb01f02b96740daacd3610301\",\n \"score\": \"0.5666913\",\n \"text\": \"def P(self):\\n\\t\\tP = 2*pi * sqrt( (self.a*AU)**3. / (G*(self.M+self.m)*mSun) )/(24*3600.)\\n\\t\\treturn P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2031ab84851620cb2d48a6d0123baee0\",\n \"score\": \"0.5654314\",\n \"text\": \"def _calculate_aprior_probabilities(aposterior_probabilities: np.array) -> np.array:\\n return aposterior_probabilities.mean(axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c64cde34a2700c76d76fc56c57511d9\",\n \"score\": \"0.5640836\",\n \"text\": \"def get_a(x):\\n\\n return np.exp(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5526b4851786a92173c9ec8c0b34e99\",\n \"score\": \"0.56370974\",\n \"text\": \"def p(self, x: np.array) -> float:\\n p = - x[0]**2 / 200.0 - 0.5 * (x[1] - self.b * x[0]**2 +\\n 100.0 * self.b)**2\\n p_sum = - 0.5 * np.sum(x[2:-1]**2)\\n p = p + p_sum\\n return np.exp(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7512fe5873ab477ff6210d31de27da47\",\n \"score\": \"0.5598786\",\n \"text\": \"def aire(R):\\n return pi * (R ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a3541d9cae87df9cc65e10c53d9c51\",\n \"score\": \"0.5589556\",\n \"text\": \"def eta_ideal(self):\\n return 1. - (self.engine_in.pr_ovr ** ((1 - self.engine_in.ambient.kappa_air ) /\\n self.engine_in.ambient.kappa_air))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d890d991df68637d2ed572b546f57f\",\n \"score\": \"0.5578697\",\n \"text\": \"def adiabatic_lapse_rate_from_t(SA, t, p):\\n\\n return -gibbs(n0, n1, n1, SA, t, p) / (gibbs(n0, n2, n0, SA, t, p))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441f4390671151a658ae641b017047d6\",\n \"score\": \"0.556664\",\n \"text\": \"def ppmv2pa(x, p):\\n return x * p / (1e6 + x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27ca1e003b53ece5ab9f1f0e8f2ce93\",\n \"score\": \"0.55485916\",\n \"text\": \"def f_pseudo_periodique(beta, omega, A, B, t):\\n return math.exp(-beta*t)*(A*math.cos(omega*t)+B*math.sin(omega*t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06cd4e2088c2c1d43960776b6e1a579e\",\n \"score\": \"0.5546343\",\n \"text\": \"def annualized_return(self) -> float:\\n return self.__annualized_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe98470dccf2e8ced11da6af1252fce1\",\n \"score\": \"0.5527254\",\n \"text\": \"def getProbability(self):\\n return self.p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daeb75883fd9d23198eaf7361e39c953\",\n \"score\": \"0.5521143\",\n \"text\": \"def p_alpha(p,p0,th,s):\\n a11,a12,a13,a14,a15,a16,a21,a22,a23,a24,a25,a26,b11,b12,b13,b21,b22,b23=eosben07_const()\\n #\\n b1i=1/(b11+b12*th+b13*s)\\n a1=(a11+th*(a12+a14*th+a15*s)+s*(a13+a16*s))*b1i\\n a2=(a21+th*(a22+a24*th+a25*s)+s*(a23+a26*s))*b1i\\n b2=(b21+b22*th+b23*s)*b1i\\n #\\n r=b2*(p-p0)+(a2-a1*b2)*ma.log((a1+p)/(a1+p0))\\n #\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3d6309e5ae8993e438d8b810b110c3\",\n \"score\": \"0.55092055\",\n \"text\": \"def proton_distribution_probability(product, substrate, precursor):\\n global BASIC_AA\\n p = substrate[1]\\n q = substrate[2]\\n p1 = product[1]\\n p2 = p - p1\\n q1 = product[2]\\n q2 = q - q1\\n p0 = precursor[1]\\n nu = len(substrate[0])\\n nu1 = len(product[0])\\n #nu = sum(aa in BASIC_AA for aa in substrate[0])\\n #nu1 = sum(aa in BASIC_AA for aa in product[0])\\n nu2 = nu - nu1\\n # Distribution over initial charging on product sequence:\\n # Hypergeometric with population nu, successes nu1, trials p0\\n XA1 = hypergeom_pmf_iterator(N=nu, k=nu1, n=p0)\\n XA1_0 = max(0, p0-nu2)\\n PTR = iterate_over_ptr_probabilities(p1=p1, q1=q1, p=p, q=q, p0=p0)\\n PTR_0 = p1+q1+1\\n kmin = max(XA1_0, PTR_0)\\n kmax = min(nu1, p0-p2-q2+1)\\n # Moving past the \\\"null\\\" summands\\n for i in range(max(0, kmin - PTR_0 - 1)):\\n PTR.next()\\n for i in range(max(0, kmin - XA1_0 - 1)):\\n XA1.next()\\n pr = 0.\\n for i in range(kmin, kmax+1):\\n A, B = PTR.next()\\n C = XA1.next()\\n pr += (float(A)*float(nu2)/nu + float(nu1)/nu)*B*C\\n #pr = sum((float(A)*float(nu2)/nu + float(nu1)/nu)*B*C for i, (A, B), C in enumerate(zip(PTR, XA1)) if i + kmin < kmax)\\n return pr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a724016faa87efe811cac38a976021\",\n \"score\": \"0.55043864\",\n \"text\": \"def avg_amp(self, params):\\n amps = params[:self.no_ants*2:2]\\n if self.logamp:\\n amps = np.exp(amps)\\n return np.mean(amps) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fe6cf3be98ef6d29b947bf9083ca27\",\n \"score\": \"0.54913193\",\n \"text\": \"def compute_period(a, M, m):\\n return np.sqrt(4 * np.pi **2 / (takahe.constants.G * (M+m) * takahe.constants.SOLAR_MASS) * (a * takahe.constants.SOLAR_RADIUS)**3) / (60 * 60 * 24)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a5607d44f9bb2f5301283876b8f61d\",\n \"score\": \"0.5488452\",\n \"text\": \"def prob(self, at, log = False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca10d9bd1a5946e2d0dff1c986e69c1\",\n \"score\": \"0.54698384\",\n \"text\": \"def exp_decay(p,x):\\n a, b, c = p\\n\\n return a + b * np.exp(-x / c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f906cdb8b7cc43327c26bd266d0974a\",\n \"score\": \"0.5467352\",\n \"text\": \"def get_annual_eps_value(eps_data: dict):\\n years = list(set([int(k.split('-')[0]) for (k, v) in eps_data.items()]))\\n max_year = max(years)\\n three_years_ago = max_year - 3\\n filter_years = sorted([str(x) for x in years if max_year >= x >= three_years_ago], reverse=True)\\n values = list()\\n for year in filter_years:\\n values.append(round(sum([v for (k, v) in eps_data.items() if year in k]), 2))\\n return dict(zip(filter_years, values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d8b77d14e51ab8e133db3f50a7fe87\",\n \"score\": \"0.5463908\",\n \"text\": \"def prob(self, Pn):\\n return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5878d8693907bf81216b28f335fdc419\",\n \"score\": \"0.5459113\",\n \"text\": \"def rate(e):\\n d = np.abs(e - e[-1])\\n p = []\\n for i in range(len(d)-1):\\n p.append(np.log(d[i+1])/np.log(d[i]))\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be049e111a0f8923aebf37b90cef533\",\n \"score\": \"0.5458327\",\n \"text\": \"def paretovariate_impl(alpha):\\n # Jain, pg. 495\\n u = 1.0 - _random()\\n return 1.0 / u ** (1.0/alpha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa974bfaae301fa79ac0fbeabb3bb21\",\n \"score\": \"0.54536045\",\n \"text\": \"def EP(d):\\n return 4*d*(5/6)**d + 1 - (5/6)**d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e76d91ba872e7607db1518181831cf\",\n \"score\": \"0.54457945\",\n \"text\": \"def calc_alpha(epsilon):\\n\\n if (epsilon == 0):\\n return np.inf\\n\\n\\n return 0.5 * np.log( (1. - epsilon)/epsilon )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4c2f4d23858b4b4c3cc0ad0d940f8d\",\n \"score\": \"0.5436166\",\n \"text\": \"def beamfrac(FWHM, length, angle):\\n height_of_sample = length * np.sin(np.radians(angle))\\n beam_sd = FWHM / 2 / np.sqrt(2 * np.log(2))\\n probability = 2.0 * (\\n stats.norm.cdf(height_of_sample / 2.0 / beam_sd) - 0.5\\n )\\n return probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f6ccb38864d16aea5f119c73c0b0fb\",\n \"score\": \"0.54260474\",\n \"text\": \"def theta(self, *args, eps=0.001):\\n # Fix the random generator seed for MonteCarlo pricing\\n st = np.random.get_state()\\n price1 = self.price(*args)\\n np.random.set_state(st)\\n self.term -= eps\\n price2 = self.price(*args)\\n self.term += eps\\n return (price2 - price1) / (365 * eps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad795ccd1af697171146833dd55aaa9\",\n \"score\": \"0.5425807\",\n \"text\": \"def _calculate_PAYE(period_income, period):\\r\\n predicted_annual_income = period * period_income\\r\\n \\r\\n income_tax = _calculate_income_tax(predicted_annual_income)\\r\\n acc_levy = _calculate_ACC_levy(predicted_annual_income)\\r\\n \\r\\n total_tax_paid = income_tax + acc_levy\\r\\n \\r\\n return round(total_tax_paid / period, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e00a47c7e5892e10021e3d495160bb4d\",\n \"score\": \"0.5419779\",\n \"text\": \"def e_var(partition):\\n P = np.log(partition)\\n S = len(partition)\\n X = 0\\n for x in P:\\n X += (x - np.mean(P))**2/S\\n evar = 1 - 2/math.pi*np.arctan(X) \\n return(evar)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55297a2ff4086c2bf1acf53482780e23\",\n \"score\": \"0.5414374\",\n \"text\": \"def expected_earnings(mao):\\r\\n return (.5 - (mao - ((mao ** 2) / 2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244d6a5bac71aaacecf03ad1089d25dc\",\n \"score\": \"0.5397943\",\n \"text\": \"def calculate_probability(x, mean, stdev):\\n exponent = np.exp(-1.0 * ((x-mean)**2 / (2*(stdev**2))))\\n return (1/(np.sqrt(2.0 * np.pi) * stdev)) * exponent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dac13767a930f12ba0d31b9db65a986\",\n \"score\": \"0.5397827\",\n \"text\": \"def empirical_prob(self, *items, delta = 0):\\n assert (len(items) in [1, 2])\\n if (len(items) == 1):\\n denominator = self.total_counts + delta * self.num_vocab ** 2\\n else:\\n denominator = self.total_edges + delta * self.num_possible_pairs\\n return self.empirical_freq(*items, delta = delta) / denominator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"647fe4d57e4a1de86bbc9cd34a971c9c\",\n \"score\": \"0.5396929\",\n \"text\": \"def soilmoisture( self, evaporative_fraction ):\\n\\t\\t\\tresult = (exp((evaporative_fraction-1.2836)/0.4213))/0.511\\n\\t\\t\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a486069b20a599edb89c810c19a2d7b\",\n \"score\": \"0.53961486\",\n \"text\": \"def gaussian_moment(p):\\n if p % 2 == 0:\\n return factorial2(p-1)\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ab846f51909ca0b71e08b8f97013e3\",\n \"score\": \"0.53892523\",\n \"text\": \"def logit2probability(log_odds):\\n # convert log odds to odds\\n odds = math.exp(log_odds)\\n # return probability\\n return odds / float(1 + odds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8170543ac5ff1d2bba1fd16b24f8d220\",\n \"score\": \"0.5387339\",\n \"text\": \"def decay(t, p):\\n return p[0] * np.exp(-t / p[1]) + p[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40958abdc2ac4eefc3a72a6573351082\",\n \"score\": \"0.53814656\",\n \"text\": \"def _approx_ap(airf,pres):\\n pvap = pres * (1-airf)/(airf*_EPSW + 1-airf)\\n v = numpy.log(pvap/_PTPE)/_BVI\\n x = maths4.lamb2(v,_AVI/_BVI)\\n temp = _TTP/x\\n dhum = (pres-pvap + _EPSW*pvap) / (_RDRY*temp)\\n return temp, dhum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba8b19e85284fbfe2a59ea77809b5ed\",\n \"score\": \"0.53794813\",\n \"text\": \"def startprob(self):\\n return np.exp(self._log_startprob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"008acf3c978605d22a56d6110134eaba\",\n \"score\": \"0.5377723\",\n \"text\": \"def get_exp_probabilities(self, fluxes, inst_params):\\n prob = 1 - np.exp(-fluxes / inst_params[\\\"scale\\\"])\\n\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c14c31873659ea1950017763c7e4e2b\",\n \"score\": \"0.5364507\",\n \"text\": \"def get_ev(a: np.ndarray) -> Param:\\n\\n return a / np.sum(a),\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54881653f7ce52de6c69bcc2f8abd6f2\",\n \"score\": \"0.53623134\",\n \"text\": \"def topp(v):\\n return np.exp(v) / np.sum(np.exp(v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb044c957e5b8785a3ac8b0bf516416f\",\n \"score\": \"0.5361167\",\n \"text\": \"def probability(self, next_state, state, action):\\n if self.sample(state, action) == next_state:\\n return 1.0 - self.epsilon\\n else:\\n return self.epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c6ca6447b8cce60ebe738b1842c361\",\n \"score\": \"0.5350514\",\n \"text\": \"def test_getAngularDistribution_PWave(self):\\n egrid = self.RRR1PRes.generateEnergyGrid()\\n eStep = 1\\n J = 5.5 # for this test data\\n S = 4.5 # for this test data\\n from numericalFunctions import angularMomentumCoupling as nf_amc\\n self.assertAlmostEqual(\\n nf_amc.z_coefficient(2, int(2 * J), 2, int(2 * J), int(2 * S), 4) / nf_amc.z_coefficient(2, int(2 * J), 2,\\n int(2 * J),\\n int(2 * S), 0),\\n math.sqrt(0.33090909)) # nf_amc functions use the 2J trick\\n for E in egrid[::eStep]:\\n B = self.RRR1PRes.getAngularDistribution(E)\\n self.assertAllWithinXPercent([x / B[0] for x in B], [1.0, 0.0, 0.33090909])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54012fa6ca93fc00bc7a9e27ee1fe249\",\n \"score\": \"0.5335799\",\n \"text\": \"def offer_prob(year, month, sex, race, referral, precomputed_emp_dist=None):\\n if precomputed_emp_dist is not None:\\n emp = precomputed_emp_dist[year][month - 1][race.name][sex.name]\\n else:\\n emp = employment_dist(year, month, sex, race)\\n\\n # quick-and-dirty mapping to race keys\\n if not isinstance(race, str):\\n if race.name == 'aian':\\n race = 'nativeamerican'\\n elif race.name == 'white':\\n # b/c white and hispanic are merged together\\n # TODO find dataset that separates these?\\n race = random.choice(['hispanic', 'white'])\\n elif race.name in ['chinese', 'japanese', 'api']:\\n race = 'asian'\\n elif race.name == 'black':\\n race = 'black'\\n\\n # mean of two random races. this is a sloppy guess,\\n # since the actual evidence may not work this way\\n elif race.name in ['other', 'two', 'three_plus']:\\n races = list(p_offer['offered']['race'].keys())\\n op1 = _offer_prob(sex, random.choice(races), referral, emp)\\n op2 = _offer_prob(sex, random.choice(races), referral, emp)\\n return (op1 + op2)/2\\n\\n return _offer_prob(sex, race, referral, emp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7550b5825aea3cfb202d2a09abfeabe\",\n \"score\": \"0.53341776\",\n \"text\": \"def ampsAtPeriod(period):\\n checkPeriod(period, \\\"ampsAtPeriod::period\\\")\\n return startingSequence[period-1][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8680f547542f4aa8920e1df44de4b6b9\",\n \"score\": \"0.5333341\",\n \"text\": \"def AIC(self):\\n RSS = self.getRMSE()\\n k = len(self.variables)\\n n = len(self.exp_data)\\n AIC = 2 * k + n * (log(2 * pi * RSS/n) + 1)\\n return AIC\",\n \"title\": \"\"\n },\n {\n \"docid\": \"879fc398a4991981587ecdfa58f50ded\",\n \"score\": \"0.5328824\",\n \"text\": \"def prediction_interval(self, xp=None, yp=None, alpha=0.05):\\n if not (xp is None) ^ (yp is None):\\n raise ValueError('Only one of [xp, yp] must be specified.')\\n\\n if xp is not None:\\n return self._y_pi(xp, alpha=alpha)\\n if yp is not None:\\n return self._x_pi(yp, alpha=alpha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243d4ce62d5695a4a1cde42e12efba40\",\n \"score\": \"0.5327332\",\n \"text\": \"def alpha_wrt_t_exact(SA, t, p):\\n\\n return gibbs(n0, n1, n1, SA, t, p) / gibbs(n0, n0, n1, SA, t, p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbaeb65c4918cd305cc1d2de1739b203\",\n \"score\": \"0.53269297\",\n \"text\": \"def probability():\\n f1 = AffineTransform(0, 0, 0, 0.16, 0, 0)\\n f2 = AffineTransform(0.85, 0.04, -0.04, 0.85, 0, 1.60)\\n f3 = AffineTransform(0.20, -0.26, 0.23, 0.22, 0, 1.60)\\n f4 = AffineTransform(-0.15, 0.28, 0.26, 0.24, 0, 0.44)\\n functions = [f1, f2, f3, f4]\\n p1 = 0.01\\n p2 = 0.85\\n p3 = 0.07\\n p4 = 0.07\\n p_cumulative = np.array([[0, p1],\\n [1, p1+p2],\\n [2, p1+p2+p3],\\n [3, p1+p2+p3+p4]])\\n r = np.random.random()\\n for j, p in p_cumulative:\\n if r < p:\\n return functions[int(j)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a47e37d6559b69bf1b357b7e8a0d017\",\n \"score\": \"0.5326326\",\n \"text\": \"def exponential_var(p,x):\\n \\n \\n plt.subplot(311)\\n plt.semilogy(x, exp_decay(np.array([p[0[0]],p[1[3]],p[2[3]]])), x)\\n plt.savefig('KEG_exponential_var.pdf', bbox_inches='tight', dpi=300)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e6a4c6840c0695d1f98535c676f6f3\",\n \"score\": \"0.5323668\",\n \"text\": \"def date_of_annual_after(title, eff_date):\\n publication_date = date(eff_date.year, publication_month(title), 1)\\n if eff_date <= publication_date:\\n return publication_date\\n else:\\n return publication_date.replace(year=eff_date.year + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b026ba3820417246be18a6149607298\",\n \"score\": \"0.53191286\",\n \"text\": \"def cronbach_alpha(data):\\n n_items = data.shape[0]\\n\\n valid_mask = data != INVALID_RESPONSE\\n \\n item_variance = np.var(data, axis=1, ddof=1, where=valid_mask).sum()\\n people_variance = (n_items * n_items\\n * np.mean(data, axis=0, where=valid_mask).var(ddof=1))\\n \\n return (n_items / (n_items - 1) \\n * (1 - item_variance / people_variance))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e73ca74ce81005f48c4c98ed9cd9001\",\n \"score\": \"0.53163207\",\n \"text\": \"def calc_extreme_value(params,distribution,nyear,datafraction=1.0):\\n probability=1.0/(nyear*365.0*datafraction)\\n \\n x=np.arange(0,10000,10)\\n pdf=distribution.pdf(x,*params)\\n pos=np.where((pdf>=probability) & (pdf>0))[0]\\n \\n if len(pos)==0:return 0\\n pos=pos[-1]\\n\\n x=np.arange(x[pos]-10,x[pos]+10,0.1)\\n pdf=distribution.pdf(x,*params)\\n pos=np.where((pdf<=probability) &(pdf>0))[0]\\n\\n if x[pos[0]]<0:return 0\\n return x[pos[0]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a87e4e5986c8905599dc2a722d7a32cd\",\n \"score\": \"0.5314459\",\n \"text\": \"def period(r):\\n m = 0\\n d = 1\\n a = a0 = floor(r)\\n p = []\\n\\n while a != 2*a0:\\n m = round(d*a - m)\\n d = round((r*r - m*m)/d)\\n a = round(floor((a0 + m)/d))\\n p.append(a)\\n return (a0, p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2330a22f92cb5bc3db086c0c7218a8cb\",\n \"score\": \"0.5306773\",\n \"text\": \"def calculateAnom(var):\\n ### Import modules\\n import numpy as np\\n \\n mean = np.nanmean(var,axis=0)\\n anom = var - mean\\n \\n print('Completed: Calculate anomalies for polar cap!')\\n return anom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a555f2566874f2dfe5d796711f788f\",\n \"score\": \"0.53059053\",\n \"text\": \"def exponential_annealing(init_value, min_value, decay_ratio):\\n result = init_value\\n\\n while True:\\n result = max(min_value, result * decay_ratio)\\n yield result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccaab3a5cf7e0e80c93134e6ef44d7e9\",\n \"score\": \"0.53049403\",\n \"text\": \"def per(soup):\\r\\n\\r\\n period = soup.find(id=\\\"planet_period_0\\\").text\\r\\n\\r\\n period = float(period.split(\\\" \\\")[0])\\r\\n \\r\\n return period\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80192f78f06ba42dced0be35570c30c2\",\n \"score\": \"0.5300492\",\n \"text\": \"def calculate_probability(self, value, mean, std_dev):\\n # Calculates the exponent.\\n exponent = math.exp(-(math.pow(value-mean,2)/(2*math.pow(std_dev,2))))\\n # Returns the probability.\\n return (1 / (math.sqrt(2*math.pi) * std_dev)) * exponent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d683d54fb547bb0d5ffb95227395954c\",\n \"score\": \"0.52974075\",\n \"text\": \"def getFrequency(self):\\n if self.period: return 1.0/self.period\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8af254d526217ba25a3ddee9e94c8a83\",\n \"score\": \"0.52971166\",\n \"text\": \"def annual_eval(out_dir, pts):\\n dgp_value = np.genfromtxt(f\\\"{out_dir}dgp_{pts}.out\\\", delimiter=\\\" \\\", usecols=3)\\n fdgpe = np.zeros(3) # DGP > 0.35, 0.4, 0.45\\n for i in range(len(dgp_value)):\\n for j in range(3):\\n if dgp_value[i] > (0.35 + j * 0.05):\\n fdgpe[j] = fdgpe[j] + 1\\n for j in range(3):\\n fdgpe[j] = fdgpe[j] * 5.43 / 1304\\n np.savetxt(f\\\"{out_dir}fDGPe_{pts}.out\\\", fdgpe, delimiter=\\\" \\\", fmt=\\\"%1.3f\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db93ccc3207dc8aa7a31f25624b10f80\",\n \"score\": \"0.52957386\",\n \"text\": \"def zolotarev_pdf_exponentiated(self, x, alpha):\\n denominator = pow(sinc(alpha * x), alpha) \\\\\\n * pow(sinc((1. - alpha) * x), (1. - alpha))\\n numerator = sinc(x)\\n return numerator / denominator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5133c303c323d063c9da40af348b07b2\",\n \"score\": \"0.5292389\",\n \"text\": \"def bar2psia(a):\\n return (a + 1.013) * 14.50377\",\n \"title\": \"\"\n },\n {\n \"docid\": \"433b64525c26c03fe98d5aacf62f0afd\",\n \"score\": \"0.5291426\",\n \"text\": \"def p_from_am(sma, ms):\\n a = sma * u.au.to(u.m)\\n ms = ms * u.Msun.to(u.kg)\\n G = c.G.value\\n p = np.sqrt((4 * np.pi ** 2 * a ** 3) / (G * ms))\\n return p * u.second.to(u.day)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ea2e4a7c9d0289f55632f817962b64\",\n \"score\": \"0.52898544\",\n \"text\": \"def damping_ratio(self):\\n return self._eta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60f281fb55f9ec9866f8938ebd39e617\",\n \"score\": \"0.5287896\",\n \"text\": \"def mean(p):\\n\\n v = 0.0\\n for i, pi in zip(_I, p):\\n v += i*pi\\n\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d5dacdd4a7189d99c9572027701b844\",\n \"score\": \"0.52864736\",\n \"text\": \"def get_probsrsa_vectorized(number, exp_rate):\\r\\n return (np.power(exp_rate, number))*(np.exp(-exp_rate))/(scipy.special.factorial(number))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b25b7c54d9e8dc6830baa497956688d\",\n \"score\": \"0.52847904\",\n \"text\": \"def arc_length(Eps, alpha):\\n return Eps * alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5fc16f5816f57878f83370c9daf880f\",\n \"score\": \"0.5284486\",\n \"text\": \"def int_eta0(self,p,T,muB,muQ,muS,**kwargs):\\n Ep = np.sqrt(p**2.+self.M(T,muB,muQ,muS,**kwargs)**2.) # pole energy\\n ffq = self.fq(Ep,T,muB,muQ,muS) # distribution function\\n return 1./(15.*T)*self.dg*(4.*pi*p**2.)/((2.*pi)**3)*(p**4.)/(Ep**2.)*ffq*(1-self.BEFD*ffq)/(2*self.w(T,muB,muQ,muS,**kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f95cf88f90172d89ed05f46bff261c0\",\n \"score\": \"0.52806705\",\n \"text\": \"def period(x, y, ofac=4, hifac):\\n n = len(x)\\n (wi, wpi, wpr, wr, px, py) = ([0]*n, [0]*n, [0]*n, [0]*n, [0]*n)\\n nout = int(.5*ofac*hifac*n)\\n if np < nout:\\n px.np.ndarray.resize(nout)\\n py.np.ndarray.resize(nout)\\n ave = np.avearge(y)\\n var = np.var(y)\\n if var == 0:\\n print(\\\"Zero variacne in period\\\")\\n return\\n xmax = xmin = x[0]\\n for j in range(0, n):\\n if x[j] > xmax:\\n xmax = x[j]\\n if x[j] < xmin:\\n xmin = x[j]\\n xdif = xmax - xmin\\n xave = .5*(xmax+xmin)\\n pymax =0\\n pnow = 1/(xdif*ofac) # starting frequency\\n for j in range(0, n): # initialize values for the trigonometric recurrences at each data point\\n arg = 2*np.pi*(x[j]-ave)*pnow\\n wpr[j] = -2*np.sqrt(np.sin(.5*arg))\\n wpi[j] = np.sin(arg)\\n wr[j] = np.cos(arg)\\n wi[j] = wpi[j]\\n for i in range(0, nout):\\n px[i] = pnow\\n sumsh = sumc = 0\\n for j in range(0,n):\\n c = wr[j]\\n s = wi[j]\\n sumsh += s*c\\n sumc += (c-s)*(c+s)\\n wtau = .5*np.arctan(p2*sumsh, sumc])\\n swtau = np.sin(wtau)\\n cwtau = np.cos(wtau)\\n sums = sumc= sumsy = sumcy = 0\\n for j in range(0, n): # loop over the data again to get the periiodogram value\\n s = wi[j]\\n c = wr[j]\\n ss = s * cwtau - c*swtau\\n cc = c*cwtau + s*swtau\\n sums += ss*ss\\n sumc += cc*cc\\n yy = y[j] - ave\\n sumsy += yy*ss\\n sumcy += yy*cc\\n wtemp = wr[j]\\n wr[j] = (wtemp*wpr[j] - wr[j] * wpi[j]) + wr[j] # trigonometric recurrences\\n wi[j] = (wi[j] * wpr[j] + wtemp * wpi[j]) + wi[j]\\n py[i] = .5*(sumcy * sumcy/sumc + sumsy * sumsy/sums)/var\\n if py[i] >= pymax:\\n jmax = i\\n pymax = py[jmax]\\n pnow += 1/(ofac*xdif)\\n expy = np.exp(-pymax)\\n effm = 2*nout/ofac\\n prob = effm * expy\\n if prob > .01:\\n prob = 1 - np.power(1 - expy, effm)\\n return px, py, prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73fa5608009670c1ebc32a92411e499a\",\n \"score\": \"0.5279629\",\n \"text\": \"def _probability_of_title_with_length(self, length):\\n sigma = 6\\n return np.exp(-1 * ((length - self.avg_length) ** 2) / (2 * (sigma ** 2))) / (math.sqrt(2 * np.pi) * sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d435eac6074cd3164079f5ca5870ba\",\n \"score\": \"0.5273136\",\n \"text\": \"def frac_position(eta,eta_distribution):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a337c988c1ebbf9f39e6dda243a101f1\",\n \"score\": \"0.52646464\",\n \"text\": \"def apo(price, period_slow = 26, period_fast = 12, ma = \\\"sma\\\"):\\n if ma.lower() not in [\\\"sma\\\", \\\"ema\\\"]:\\n raise ValueError(\\\"param 'ma' needs to be 'ema' or 'sma'\\\")\\n\\n return apo_calc(price, period_slow, period_fast, ma.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dea0661caea45ea6b8f392fa9985659\",\n \"score\": \"0.5260097\",\n \"text\": \"def rand_exp(rate):\\n \\n return -log(random()) / rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0621abd8797937864d627e54d54a7c8\",\n \"score\": \"0.5259038\",\n \"text\": \"def discounted_payback_period(self, proforma):\\r\\n payback_period = self.payback_period(proforma) # This is simply (capex/yearlynetbenefit)\\r\\n\\r\\n if not self.npv_discount_rate:\\r\\n dr = self.npv_discount_rate*.01\\r\\n discounted_pp = np.log(1/(1-(dr*payback_period)))/np.log(1+dr)\\r\\n else:\\r\\n # if discount_rate = 0, then the equation simplifies to np.log(1)/np.log(1)-- which is undefined, so we return nan\\r\\n discounted_pp = 'nan'\\r\\n\\r\\n return discounted_pp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c08eac2e3e543e690b820889ca1868d\",\n \"score\": \"0.5257021\",\n \"text\": \"def ann_return1_year(self) -> float:\\n return self.__ann_return1_year\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":196,"cells":{"query_id":{"kind":"string","value":"a2553f674f9dde5c403aedc77dda2d72"},"query":{"kind":"string","value":"Gets the metadata for the copyright."},"positive_passages":{"kind":"list like","value":[{"docid":"add6e0375766c1ae82f03796afa040ba","score":"0.8633044","text":"def get_copyright_metadata(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"add6e0375766c1ae82f03796afa040ba\",\n \"score\": \"0.8633044\",\n \"text\": \"def get_copyright_metadata(self):\\n pass\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"24f84680cf596560ecdac39cce9e64bc","score":"0.8069426","text":"def get_copyright_registration_metadata(self):\n pass","title":""},{"docid":"9b439f8061f824cb732d55672fc3eaff","score":"0.7513539","text":"def copyright():\n # pull and print the meta-data\n return print(meta.header)","title":""},{"docid":"9b439f8061f824cb732d55672fc3eaff","score":"0.7513539","text":"def copyright():\n # pull and print the meta-data\n return print(meta.header)","title":""},{"docid":"9ce9dfaa695a5f317033c21935fab989","score":"0.72402924","text":"def copyright(self):\n return self._copyright","title":""},{"docid":"69c61a3fb10cf9c01b25ed094a4ef720","score":"0.7208793","text":"def copyright(self) -> str:\n return pulumi.get(self, \"copyright\")","title":""},{"docid":"69c61a3fb10cf9c01b25ed094a4ef720","score":"0.7208793","text":"def copyright(self) -> str:\n return pulumi.get(self, \"copyright\")","title":""},{"docid":"69acb209ae4f2b9afe7528869611d6af","score":"0.702879","text":"def get_copyright(self):\n return self._payload.get_copyright()","title":""},{"docid":"f60a01cb4b03c7e41fdce6f74baa2074","score":"0.7024526","text":"def copyright():\n return print(meta.header)","title":""},{"docid":"dc89c32813693811b9f0d3f971f18ea0","score":"0.698063","text":"def _copyright(self):\n return self.copyright","title":""},{"docid":"2160cef48267207c30a1b6b4184ac02f","score":"0.69552654","text":"def getCopyright(self):\n return self.copyright","title":""},{"docid":"6d8b1679f609c6d0124c424dcbf59974","score":"0.69459635","text":"def get_metadata(self, citation):\n pass","title":""},{"docid":"bad2ebb93b92f073ae4061987b3d7f07","score":"0.6943443","text":"def GetCopyright(self):","title":""},{"docid":"ec88284d34c75931c56892c679c7a4bb","score":"0.6834667","text":"def feed_copyright(self):\n return _siteinfo().copyright","title":""},{"docid":"671c3475d5dbaafeb2cd46f5c7684610","score":"0.6762198","text":"def meta_copyright(cls, text, time=0.0):\n return cls._textmeta(mm.kCopyRight, text, time)","title":""},{"docid":"97a01f19b6c844cfe4b5f92954152630","score":"0.6670829","text":"def item_copyright(self):\n return _siteinfo().copyright","title":""},{"docid":"9c6a1486503376ff18f1a8044bc275f8","score":"0.66507226","text":"def get_metadata(self, soup):\n metadata = {}\n meta = soup.find_all('meta')\n for m in meta:\n if m.has_attr('name') and m.get('name').startswith('dcterms'):\n metadata[m.get('name')] = m.get('content')\n return metadata","title":""},{"docid":"83dbd6adadea9e223f97c4bc546b48d0","score":"0.6647738","text":"def copyright():\n # generate the message\n return print(meta.copyright)","title":""},{"docid":"d59e15d78633fd7875488fb65753797d","score":"0.6624673","text":"def metadata(self):\n # identifiers\n dump = self.download()\n if dump['totalItems'] == 0:\n return 0\n\n google_books_data = dump['items'][0]['volumeInfo']\n authors = \"\"\n for author in google_books_data['authors']:\n authors = author + \",\" + authors\n authors = authors[0:-1]\n # i=0\n # for val in meta_data.keys():\n # t = google_books_data[kickstr[i]]\n # if val == 'cover':\n # t = google_books_data['imageLinks']['thumbnail']\n # elif val == 'published_date':\n # t=\n # meta_data[val] = t\n # i+=1\n meta_data = {\n 'google_url' : \"\",\n 'description' : \"\",\n 'tags' : \"\",\n 'cover' : \"\",\n 'published_date': \"\",\n 'publisher' : \"\",\n 'authors' : \"\",\n 'page_count' : \"\",\n 'title' : \"\"\n }\n try:\n meta_data['google_url'] = google_books_data['canonicalVolumeLink']\n except:\n meta_data['google_url'] = None\n try:\n meta_data['description'] = google_books_data['description']\n except:\n meta_data['description'] = None\n try:\n meta_data['tags'] = google_books_data['categories']\n except:\n meta_data['tags'] = None\n try:\n meta_data['cover'] = google_books_data['imageLinks']['thumbnail']\n except:\n meta_data['cover'] = None\n try:\n meta_data['published_date'] = parser.parse(google_books_data['publishedDate'])\n except:\n meta_data['published_date'] = None\n try:\n meta_data['publisher'] = google_books_data['publisher']\n except:\n meta_data['publisher'] = None\n try:\n meta_data['authors'] = authors\n except:\n meta_data['authors'] = None\n try:\n meta_data['page_count'] = google_books_data['pageCount']\n except:\n meta_data['page_count'] = None\n try:\n meta_data['title'] = google_books_data['title']\n except:\n meta_data['title'] = \"Title\"\n print(meta_data)\n print(google_books_data)\n return meta_data","title":""},{"docid":"a4f7eb2f8c6a2c093a771176339fc58d","score":"0.6615866","text":"def get_metadata(self):\n data = {\n \"license_code\": self.license_code,\n \"version\": self.version,\n \"title_english\": self.title_english,\n }\n if self.jurisdiction_code:\n data[\"jurisdiction\"] = self.jurisdiction_code\n\n data[\"permits_derivative_works\"] = self.permits_derivative_works\n data[\"permits_reproduction\"] = self.permits_reproduction\n data[\"permits_distribution\"] = self.permits_distribution\n data[\"permits_sharing\"] = self.permits_sharing\n data[\"requires_share_alike\"] = self.requires_share_alike\n data[\"requires_notice\"] = self.requires_notice\n data[\"requires_attribution\"] = self.requires_attribution\n data[\"requires_source_code\"] = self.requires_source_code\n data[\"prohibits_commercial_use\"] = self.prohibits_commercial_use\n data[\n \"prohibits_high_income_nation_use\"\n ] = self.prohibits_high_income_nation_use\n\n data[\"translations\"] = {}\n for lc in self.legal_codes.order_by(\"language_code\"):\n language_code = lc.language_code\n with active_translation(lc.get_translation_object()):\n data[\"translations\"][language_code] = {\n \"license\": lc.license_url,\n \"deed\": lc.deed_url,\n \"title\": gettext(self.title_english),\n }\n\n return data","title":""},{"docid":"930ea34840c9e82253cc2b16322d01cf","score":"0.65950376","text":"def copyright(self):\n\n return self.tagsToFind[self.COPYRIGHT_TAG].strip('\\x20\\x00')","title":""},{"docid":"7be0226f9ced814809bed0f04b879888","score":"0.6556654","text":"def copyright(self, format=\"json\"):\n\n endpoint = \"copyright\"\n\n return self._make_request(endpoint, {\"format\": format})","title":""},{"docid":"6d0fe9427617aca6967e17a2b63681dc","score":"0.6530905","text":"def copyright_str(self) -> str:\n with self.path.open() as f:\n match = re.search(self.copyright_re, f.read(1000))\n return match.group() if match is not None else ''","title":""},{"docid":"f11dac91b5ae360193af2698e24cbfa6","score":"0.64764947","text":"def get_metadata(self):\n return self.content[\"metadata\"]","title":""},{"docid":"5a02524fe6ba0d458c650a2c566f23b7","score":"0.63898975","text":"def getMetadata():","title":""},{"docid":"ddf409025c90dbd123a7ac7132e8c2ce","score":"0.633487","text":"def get_copyright_registration(self):\n return self._payload.get_copyright_registration()","title":""},{"docid":"5cc7229c6196b75042c80dd2fb36e5ab","score":"0.6308308","text":"def copyrights(self) -> List[CopyrightObject]:\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]","title":""},{"docid":"5cc7229c6196b75042c80dd2fb36e5ab","score":"0.6308308","text":"def copyrights(self) -> List[CopyrightObject]:\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]","title":""},{"docid":"5cc7229c6196b75042c80dd2fb36e5ab","score":"0.6308308","text":"def copyrights(self) -> List[CopyrightObject]:\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]","title":""},{"docid":"bb0d25b59c21e2cbc2ce757c420006cc","score":"0.6302299","text":"def get_metadata():\n here = os.path.abspath(os.path.dirname(__file__))\n init_path = os.path.join(here, 'src', 'serde_ext', '__init__.py')\n readme_path = os.path.join(here, 'README.rst')\n\n with io.open(init_path, encoding='utf-8') as f:\n about_text = f.read()\n\n metadata = {\n key: re.search(r'__' + key + r'__ = [\"\\'](.*?)[\"\\']', about_text).group(1)\n for key in ('title', 'version', 'url', 'author', 'author_email', 'license', 'description')\n }\n metadata['name'] = metadata.pop('title')\n\n with io.open(readme_path, encoding='utf-8') as f:\n metadata['long_description'] = f.read()\n\n return metadata","title":""},{"docid":"78d4d24772433b15772260ee08b53245","score":"0.62911433","text":"def get_metadata(html, text, filename):\n blog, post = filename.split(\"_\")\n url = \"http://\" + blog + \".hypotheses.org/\" + str(post)\n title = get_title(html, filename)\n author = get_author(html)\n date = get_date(html)\n licence = get_licence(html)\n language = check_language(text)\n category = get_category(text, title)\n numwords = get_numwords(text)\n metadata = [filename, language, author, numwords, category, date, licence, blog, post, url, title]\n return metadata","title":""},{"docid":"70d5607b649ee929aa19bf87e13a4a9d","score":"0.6261161","text":"def get_metadata(self, **kwargs):\n return self.get_entry(\n METADATA_URI, desired_class=gdata.docs.data.Metadata, **kwargs)","title":""},{"docid":"e92c69e3e660e418673713ba774eaf56","score":"0.62316877","text":"def get_license():\n data_license = {}\n data_license[\"name\"] = \"cc-by-4.0\"\n data_license[\"url\"] = \"https://creativecommons.org/licenses/by/4.0/\"\n data_license[\n \"description\"] = \"The content can be shared and adapted but you must\\\n give appropriate credit and cannot restrict access to others.\"\n return data_license","title":""},{"docid":"db49b6089ece2e54a555d92c706d8067","score":"0.6197543","text":"def get_metadata(self):\n api_call = self._get_url(\"catalog\")\n\n response = get_response(api_call)\n return json.load(response)","title":""},{"docid":"6916c01dffbfac6104f7b478a6ac5b10","score":"0.619042","text":"def get_metadata(name):","title":""},{"docid":"661b5544077ce492c145dac1c9cebc98","score":"0.6173343","text":"def getMetadata(content):","title":""},{"docid":"b4dea74a7d52e3b49307d8736c3d2022","score":"0.616945","text":"def meta(data):\n return data[1]['metadata']","title":""},{"docid":"2a09a6ed670672426b8d110a4b474f1b","score":"0.6166778","text":"def _GetMetadata(self, version):\n raw_json = None\n version_base = os.path.join(self.gs_base, version)\n with self.misc_cache.Lookup(\n (self.board, version, constants.METADATA_JSON)) as ref:\n if ref.Exists(lock=True):\n raw_json = osutils.ReadFile(ref.path)\n else:\n raw_json = self.gs_ctx.Cat(\n os.path.join(version_base, constants.METADATA_JSON)).output\n ref.AssignText(raw_json)\n\n return json.loads(raw_json)","title":""},{"docid":"f3aab84491c21d1127d87bfaa6edee3d","score":"0.6166168","text":"def metadata(self):\r\n url = \"{url}/info/metadata\".format(url=self._url)\r\n params = {\"f\": \"json\"}\r\n return self._con.get(url, params, token=self._token)","title":""},{"docid":"75b4e254ee8240f4ac8da787b98023aa","score":"0.61653906","text":"def get_metadata(self, identifier):","title":""},{"docid":"ecd4d1e1ec210a4543fda79dfb14e941","score":"0.61573976","text":"def get_dataObject_modac_meta(data_object_path):\n #data_object_path = encode_path(data_object_path)\n auth = authenticate_modac()\n\n get_response = requests.get(data_object_path, auth = auth)\n if get_response.status_code != 200:\n print(\"Error downloading from modac.cancer.gov\", data_object_path)\n raise Exception(\"Response code: {0}, Response message: {1}\".format(get_response.status_code, get_response.text))\n\n metadata_dic = json.loads(get_response.text)\n self_metadata = metadata_dic['metadataEntries']['selfMetadataEntries']['systemMetadataEntries']\n self_dic = {}\n for pair in self_metadata:\n self_dic[pair['attribute']] = pair['value'] \n\n return self_dic","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"4c1a5d4d20aca43089c913959ba5b359","score":"0.6152495","text":"def get_metadata(self) -> Mapping[int, Any]:\n return self._call(CredentialManagement.CMD.GET_CREDS_METADATA)","title":""},{"docid":"2791defc294d703d6056962a5e568286","score":"0.61457705","text":"def extract_metadata(self) -> DocumentMetadata:\n raise NotImplementedError","title":""},{"docid":"b6717d0b3afb11db4705a85098f55754","score":"0.6125887","text":"def get_metadata():\n try:\n metadata = cache.getDataset(os.environ.get('GCS_BUCKET'),\n os.environ.get('METADATA_FILENAME'))\n except Exception as err:\n logging.error(err)\n return 'Internal server error: {}'.format(err), 500\n\n def generate_response(data: bytes):\n next_row = b'['\n for row in data.splitlines():\n yield next_row\n next_row = row + b','\n yield next_row.rstrip(b',') + b']'\n headers = Headers()\n headers.add('Content-Disposition', 'attachment',\n filename=os.environ.get('METADATA_FILENAME'))\n headers.add('Vary', 'Accept-Encoding')\n return Response(generate_response(metadata), mimetype='application/json',\n headers=headers)","title":""},{"docid":"6d19e00e8e2c4b71131d2c0a0c3d7852","score":"0.61157686","text":"def __md(self):\n p_l = self.__basepl('metadata')\n p_l['content'] = 'metadata'\n return RCRequest(self.url, p_l, 'metadata').execute()[0]","title":""},{"docid":"6677f88ea288b5f1cd71ffa7b6c7309d","score":"0.61138695","text":"def metadata(self):\n self._parse_metadata_dates()\n if self._single_result:\n return self._metadata[0]\n else:\n return self._metadata","title":""},{"docid":"f7f1b36e9ad5f893583292d517e8d016","score":"0.6051656","text":"def copyright(self, plexus, **kwds):\n # show the copyright note\n plexus.info.log(hello.meta.copyright)\n # all done\n return","title":""},{"docid":"d73e78de7590fd2b5f905a784502060e","score":"0.60199094","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"fe079f4baf7615ee412f97917cef5c70","score":"0.6006893","text":"def get_published_metadata(self):\n pass","title":""},{"docid":"a0102f653530de87a0e764b126b64a1b","score":"0.6002597","text":"def get_metadata(self):\n return self.metadata","title":""},{"docid":"538f7cb4a8353bd46997b84091fa8913","score":"0.6001879","text":"def _get_metadata(cfg, relpath, doc_type):\n return {\n 'document_id': _get_document_id(cfg, relpath, doc_type),\n 'compilation_date': cfg['timestamp']['date'],\n 'timebox_id': cfg['timestamp']['timebox_id'],\n 'configuration_id': cfg['defined_baseline']['short_hexsha']}","title":""},{"docid":"9c321faf1c3d2465ee5a469c968dda67","score":"0.598438","text":"def get_metadata(self, params=None):\n metadata_url = \"{CDN_URL}/{handle}/metadata\".format(\n CDN_URL=CDN_URL, handle=self.handle\n )\n response = utils.make_call(metadata_url, 'get',\n params=params,\n security=self.security)\n return response.json()","title":""},{"docid":"2c4f2a4feca8651f337f99957dac17b2","score":"0.59841037","text":"def get_metadata(self, region, locale):\n resource = \"/hearthstone/metadata\"\n query_params = {\"locale\": locale}\n return super().get_resource(resource, region, query_params)","title":""},{"docid":"db16e2f10afadc62d177dad977a75d96","score":"0.5975165","text":"def get_title_metadata(self):\n pass","title":""},{"docid":"6a5061a1edd34767f69b736b9d627df0","score":"0.59744596","text":"def metadata(self) -> str:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"6a5061a1edd34767f69b736b9d627df0","score":"0.59744596","text":"def metadata(self) -> str:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"6a5061a1edd34767f69b736b9d627df0","score":"0.59744596","text":"def metadata(self) -> str:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"c04a536b7e84c43786e374e02689c96b","score":"0.59680593","text":"def metadata(self):\n return metadata(self.data)","title":""},{"docid":"bd6cfe5148d4e25ae2c6fd4ea9638c39","score":"0.59559137","text":"def metadata(self) -> 'outputs.MetadataResponse':\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"bd6cfe5148d4e25ae2c6fd4ea9638c39","score":"0.59559137","text":"def metadata(self) -> 'outputs.MetadataResponse':\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"8cbc65d3d9618dbbba2ce5834dbce34a","score":"0.59523225","text":"def get_assets_metadata(self):\n return # osid.Metadata","title":""},{"docid":"2489839cae2ffcb513a9cb6d44d5a3ca","score":"0.59516597","text":"def get_metadata(self, key, version=None):\n rsp = urllib2.urlopen(self.commit_uri(key, version) + \"/meta\")\n return json.loads(rsp.read())","title":""},{"docid":"be9138810d5a6abda32fd82152f8f6bd","score":"0.59148645","text":"def get_metadata_fields(corpus_name):\n if corpus_name == 'sample_novels':\n return ['author', 'date', 'title', 'country_publication', 'author_gender', 'filename', 'notes']\n else:\n return common.METADATA_LIST","title":""},{"docid":"59e0d35b76afcb6e4168e5e731d9a749","score":"0.591214","text":"def metadata(self):\n return self.file_source.metadata()","title":""},{"docid":"d938306b829603f77ccce0d944cb1c1c","score":"0.59070027","text":"def get_metadata(self):\n return self._metadata","title":""},{"docid":"d938306b829603f77ccce0d944cb1c1c","score":"0.59070027","text":"def get_metadata(self):\n return self._metadata","title":""},{"docid":"05a395d61ee79ebb591a6d627cd4eb5d","score":"0.590246","text":"def metadata(self):\r\n return self._md","title":""},{"docid":"33e6f7385b46d30f36840fa3e286b657","score":"0.5901217","text":"def metadata(self):\n\n if self._metadata is None:\n # metadata_url = \"https://raw.githubusercontent.com/{owner}/{repo_name}/master/metadata.yaml\".format(owner=self.repo_owner,\n # repo_name=self.repo_name)\n\n # self._metadata = metadata.pandata.Pandata(metadata_url)\n\n self._metadata = metadata.pandata.Pandata(datafile=None)\n md_hash = self.gh_repo.contents(\"metadata.yaml\")\n # if the file exists, load the content\n if md_hash is not None:\n self._metadata.metadata = yaml.safe_load(md_hash.decoded)\n return self._metadata\n else:\n return self._metadata","title":""},{"docid":"f7a66a1b2c1db8d2055399b9710bf827","score":"0.5895827","text":"def get_metadata(self):\n metadata_path = \"{}/{}/{}\".format(self.url,\n self.metadata_dir,\n self.metadata_file)\n metadata_sig_path = \"{}/{}/{}.sig\".format(self.url.rstrip('/'),\n self.metadata_dir,\n self.metadata_file)\n # load metadata\n req = requests.get(metadata_path)\n if req.status_code is 200:\n raw_metadata = req.content\n else:\n raise RepositoryError(metadata_path, (\"status code not 200: \"\n \"{}\".format(req.status_code)))\n\n if self.gpg_verify:\n self.verify_data_signature(metadata_sig_path, metadata_path,\n raw_metadata)\n\n return self.parse_metadata(raw_metadata)","title":""},{"docid":"a13eec55499ba96229f465afe282ce4d","score":"0.5892936","text":"def process_license(license):\n\n ind = license.find('Attribution')\n ind = license.find('creativecommons.org/licenses/')\n if ind == -1:\n zlicense = { \"description\": {\"en\": license},\n \"title\": {\"en\": \"Custom license\"} }\n else:\n zlicense = {\"id\": \"-\".join([\"cc\", license[ind:].split(\"/\")[2],\"4.0\"])}\n return zlicense","title":""},{"docid":"93943df688f99276f31135ff8816b065","score":"0.58883893","text":"def get_share_metadata(self, share, microversion=None):\n metadata_raw = self.manila(\n 'metadata-show %s' % share, microversion=microversion)\n metadata = output_parser.details(metadata_raw)\n return metadata","title":""},{"docid":"d10ca39cdcff87db3ff6582c6a897a61","score":"0.5878597","text":"def license():\n # print it\n return print(meta.license)","title":""},{"docid":"471733d816771050fa7bfb8e10b643fc","score":"0.58772045","text":"def get_socrata_metadata(credentials, nadac_parameters):\n client = setup_socrata_client(credentials, nadac_parameters)\n metadata = client.get_metadata(nadac_parameters['DATA_LOCATION'],\n content_type='json')\n\n check_build_filepath('raw_data')\n save_to_disk('raw_data/price_metadata.json', metadata)\n client.close()","title":""},{"docid":"db72656e8108d61bfdd93f1a3161f692","score":"0.58748287","text":"def get_infos(self):\n infos = super().get_infos()\n infos[\"licenses\"].append(chime5_license)\n return infos","title":""},{"docid":"c7f3c442696494bee65b316832b76d3d","score":"0.58630574","text":"def get_metadata(self,mp3):\n metadata = None\n if self.mp3items.has_key(mp3):\n metadata = self.mp3items[mp3]['metadata']\n \n return metadata","title":""},{"docid":"e1ae2264504cb454f98acedbc9a9e486","score":"0.5856848","text":"def _extractMetadata(self):\n\n try:\n dcMetadata = dicom.read_file(self.path, stop_before_pixels=True)\n if dcMetadata:\n self.metadata = dict()\n for item in dcMetadata:\n # pass forces rawDataElement to DataElement conversion\n pass\n if not item.tag.is_private:\n name = str(item.name).replace('.', '')\n value = str(item.value)\n self.metadata[name] = value\n self.metadata['Info Extracted'] = True\n else:\n self.metadata = None\n\n except dicom.filereader.InvalidDicomError:\n self.metadata = None","title":""},{"docid":"36e33dd772743148f1dae807f728d1ef","score":"0.5852637","text":"def extract_metadata(self):\n self.contacts_list = self.metadata['chanlabels']\n try:\n # comment out\n self.modality = self.metadata['modality']\n except Exception as e:\n self.metadata['modality'] = 'ieeg'\n self.modality = self.metadata['modality']\n print(\"Loading result object. Error in extracting metadata: \", e)\n\n # convert channel labels into a Contacts data struct\n if self.reference == 'bipolar' or self.modality == 'scalp':\n self.contacts = Contacts(self.contacts_list, require_matching=False)\n else:\n self.contacts = Contacts(self.contacts_list)","title":""},{"docid":"72a2c0e421539a33a7afbeec62a64b64","score":"0.5843592","text":"def set_img_copyright(self, uri): \n \n tags = [\"MWG:Copyright\"]\n key = \"copyright\"\n self.set_meta_prop(tags, uri, key)","title":""},{"docid":"7dc2755324d92788991852a56f33beb0","score":"0.58300406","text":"def get_metadata():\n res = requests.get(URL).json()\n return res[\"response\"][\"docs\"]","title":""},{"docid":"87ba37b3dbaa8401e45f44686382e2fd","score":"0.5820466","text":"def get_metadata(publisher, package):\n try:\n metadata = MetaDataDB.query.filter_by(name=package, publisher=publisher).first().descriptor\n return jsonify({\"data\": metadata, \"status\": \"OK\"}), 200\n except Exception as e:\n return jsonify({'status': 'KO', 'message': e.message}), 500","title":""},{"docid":"7fb812cf1b4c681f3e913eb98d7cfed2","score":"0.58103585","text":"def metadata(self):\n if self.__last_get_response is None:\n if self.__initial_metadata is not None:\n return self.__initial_metadata\n self.get()\n return self.__last_get_response.content","title":""},{"docid":"3abfbd4d70ab9fab15ccb068927f8b4c","score":"0.579478","text":"def getMetadata(self, private_key, client_id):\n return self.proxy.samp.hub.getMetadata(private_key, client_id)","title":""},{"docid":"b39a28ca457275565c55db1f7331e0be","score":"0.57906055","text":"def get_courses_metadata(self):\n return # osid.Metadata","title":""},{"docid":"e1cd31ac6dcb6d8e9adf6c1218e1daf7","score":"0.578709","text":"def create_metadata(title, comment='', author=None):\n author = 'https://github.com/narimiran' if author is None else author\n return dict(artist=author, title=title, comment=comment)","title":""},{"docid":"80f03cdcdbcf6f0f4de1c9d9204f4851","score":"0.5785481","text":"def metadata(self):\n metadata_service = self.find_service_by_type(ServiceTypes.METADATA)\n return metadata_service.values['metadata']","title":""},{"docid":"ae30f97bc9dda8b92cc34dc15d1adf6c","score":"0.57851726","text":"def get_license_data(self):\n return licenses.get_license_by_url(self.license or \"\")","title":""},{"docid":"afeddd9616b57cd176036fd0071ee294","score":"0.5769014","text":"def metadata(forge):\n forge.metadata()","title":""},{"docid":"9a751d8085f977491b2730caf4593e12","score":"0.5764288","text":"def get_source_metadata(self):\n pass","title":""},{"docid":"e936e6fd083d940e003160caa3de0391","score":"0.576013","text":"def files_license_info(self) -> Sequence[str]:\n return pulumi.get(self, \"files_license_info\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"4ac9d5037085069356d9b5b257996854","score":"0.5740702","text":"def getMetadata(self):\n return self._metadata","title":""},{"docid":"b06810d8c09ac0aa1fc53ae4bea9e99f","score":"0.57405835","text":"def metadata(self) -> Optional[Any]:\n return pulumi.get(self, \"metadata\")","title":""}],"string":"[\n {\n \"docid\": \"24f84680cf596560ecdac39cce9e64bc\",\n \"score\": \"0.8069426\",\n \"text\": \"def get_copyright_registration_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b439f8061f824cb732d55672fc3eaff\",\n \"score\": \"0.7513539\",\n \"text\": \"def copyright():\\n # pull and print the meta-data\\n return print(meta.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b439f8061f824cb732d55672fc3eaff\",\n \"score\": \"0.7513539\",\n \"text\": \"def copyright():\\n # pull and print the meta-data\\n return print(meta.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce9dfaa695a5f317033c21935fab989\",\n \"score\": \"0.72402924\",\n \"text\": \"def copyright(self):\\n return self._copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c61a3fb10cf9c01b25ed094a4ef720\",\n \"score\": \"0.7208793\",\n \"text\": \"def copyright(self) -> str:\\n return pulumi.get(self, \\\"copyright\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c61a3fb10cf9c01b25ed094a4ef720\",\n \"score\": \"0.7208793\",\n \"text\": \"def copyright(self) -> str:\\n return pulumi.get(self, \\\"copyright\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69acb209ae4f2b9afe7528869611d6af\",\n \"score\": \"0.702879\",\n \"text\": \"def get_copyright(self):\\n return self._payload.get_copyright()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60a01cb4b03c7e41fdce6f74baa2074\",\n \"score\": \"0.7024526\",\n \"text\": \"def copyright():\\n return print(meta.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc89c32813693811b9f0d3f971f18ea0\",\n \"score\": \"0.698063\",\n \"text\": \"def _copyright(self):\\n return self.copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2160cef48267207c30a1b6b4184ac02f\",\n \"score\": \"0.69552654\",\n \"text\": \"def getCopyright(self):\\n return self.copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8b1679f609c6d0124c424dcbf59974\",\n \"score\": \"0.69459635\",\n \"text\": \"def get_metadata(self, citation):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad2ebb93b92f073ae4061987b3d7f07\",\n \"score\": \"0.6943443\",\n \"text\": \"def GetCopyright(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec88284d34c75931c56892c679c7a4bb\",\n \"score\": \"0.6834667\",\n \"text\": \"def feed_copyright(self):\\n return _siteinfo().copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671c3475d5dbaafeb2cd46f5c7684610\",\n \"score\": \"0.6762198\",\n \"text\": \"def meta_copyright(cls, text, time=0.0):\\n return cls._textmeta(mm.kCopyRight, text, time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97a01f19b6c844cfe4b5f92954152630\",\n \"score\": \"0.6670829\",\n \"text\": \"def item_copyright(self):\\n return _siteinfo().copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6a1486503376ff18f1a8044bc275f8\",\n \"score\": \"0.66507226\",\n \"text\": \"def get_metadata(self, soup):\\n metadata = {}\\n meta = soup.find_all('meta')\\n for m in meta:\\n if m.has_attr('name') and m.get('name').startswith('dcterms'):\\n metadata[m.get('name')] = m.get('content')\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83dbd6adadea9e223f97c4bc546b48d0\",\n \"score\": \"0.6647738\",\n \"text\": \"def copyright():\\n # generate the message\\n return print(meta.copyright)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59e15d78633fd7875488fb65753797d\",\n \"score\": \"0.6624673\",\n \"text\": \"def metadata(self):\\n # identifiers\\n dump = self.download()\\n if dump['totalItems'] == 0:\\n return 0\\n\\n google_books_data = dump['items'][0]['volumeInfo']\\n authors = \\\"\\\"\\n for author in google_books_data['authors']:\\n authors = author + \\\",\\\" + authors\\n authors = authors[0:-1]\\n # i=0\\n # for val in meta_data.keys():\\n # t = google_books_data[kickstr[i]]\\n # if val == 'cover':\\n # t = google_books_data['imageLinks']['thumbnail']\\n # elif val == 'published_date':\\n # t=\\n # meta_data[val] = t\\n # i+=1\\n meta_data = {\\n 'google_url' : \\\"\\\",\\n 'description' : \\\"\\\",\\n 'tags' : \\\"\\\",\\n 'cover' : \\\"\\\",\\n 'published_date': \\\"\\\",\\n 'publisher' : \\\"\\\",\\n 'authors' : \\\"\\\",\\n 'page_count' : \\\"\\\",\\n 'title' : \\\"\\\"\\n }\\n try:\\n meta_data['google_url'] = google_books_data['canonicalVolumeLink']\\n except:\\n meta_data['google_url'] = None\\n try:\\n meta_data['description'] = google_books_data['description']\\n except:\\n meta_data['description'] = None\\n try:\\n meta_data['tags'] = google_books_data['categories']\\n except:\\n meta_data['tags'] = None\\n try:\\n meta_data['cover'] = google_books_data['imageLinks']['thumbnail']\\n except:\\n meta_data['cover'] = None\\n try:\\n meta_data['published_date'] = parser.parse(google_books_data['publishedDate'])\\n except:\\n meta_data['published_date'] = None\\n try:\\n meta_data['publisher'] = google_books_data['publisher']\\n except:\\n meta_data['publisher'] = None\\n try:\\n meta_data['authors'] = authors\\n except:\\n meta_data['authors'] = None\\n try:\\n meta_data['page_count'] = google_books_data['pageCount']\\n except:\\n meta_data['page_count'] = None\\n try:\\n meta_data['title'] = google_books_data['title']\\n except:\\n meta_data['title'] = \\\"Title\\\"\\n print(meta_data)\\n print(google_books_data)\\n return meta_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f7eb2f8c6a2c093a771176339fc58d\",\n \"score\": \"0.6615866\",\n \"text\": \"def get_metadata(self):\\n data = {\\n \\\"license_code\\\": self.license_code,\\n \\\"version\\\": self.version,\\n \\\"title_english\\\": self.title_english,\\n }\\n if self.jurisdiction_code:\\n data[\\\"jurisdiction\\\"] = self.jurisdiction_code\\n\\n data[\\\"permits_derivative_works\\\"] = self.permits_derivative_works\\n data[\\\"permits_reproduction\\\"] = self.permits_reproduction\\n data[\\\"permits_distribution\\\"] = self.permits_distribution\\n data[\\\"permits_sharing\\\"] = self.permits_sharing\\n data[\\\"requires_share_alike\\\"] = self.requires_share_alike\\n data[\\\"requires_notice\\\"] = self.requires_notice\\n data[\\\"requires_attribution\\\"] = self.requires_attribution\\n data[\\\"requires_source_code\\\"] = self.requires_source_code\\n data[\\\"prohibits_commercial_use\\\"] = self.prohibits_commercial_use\\n data[\\n \\\"prohibits_high_income_nation_use\\\"\\n ] = self.prohibits_high_income_nation_use\\n\\n data[\\\"translations\\\"] = {}\\n for lc in self.legal_codes.order_by(\\\"language_code\\\"):\\n language_code = lc.language_code\\n with active_translation(lc.get_translation_object()):\\n data[\\\"translations\\\"][language_code] = {\\n \\\"license\\\": lc.license_url,\\n \\\"deed\\\": lc.deed_url,\\n \\\"title\\\": gettext(self.title_english),\\n }\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930ea34840c9e82253cc2b16322d01cf\",\n \"score\": \"0.65950376\",\n \"text\": \"def copyright(self):\\n\\n return self.tagsToFind[self.COPYRIGHT_TAG].strip('\\\\x20\\\\x00')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be0226f9ced814809bed0f04b879888\",\n \"score\": \"0.6556654\",\n \"text\": \"def copyright(self, format=\\\"json\\\"):\\n\\n endpoint = \\\"copyright\\\"\\n\\n return self._make_request(endpoint, {\\\"format\\\": format})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0fe9427617aca6967e17a2b63681dc\",\n \"score\": \"0.6530905\",\n \"text\": \"def copyright_str(self) -> str:\\n with self.path.open() as f:\\n match = re.search(self.copyright_re, f.read(1000))\\n return match.group() if match is not None else ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f11dac91b5ae360193af2698e24cbfa6\",\n \"score\": \"0.64764947\",\n \"text\": \"def get_metadata(self):\\n return self.content[\\\"metadata\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a02524fe6ba0d458c650a2c566f23b7\",\n \"score\": \"0.63898975\",\n \"text\": \"def getMetadata():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf409025c90dbd123a7ac7132e8c2ce\",\n \"score\": \"0.633487\",\n \"text\": \"def get_copyright_registration(self):\\n return self._payload.get_copyright_registration()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc7229c6196b75042c80dd2fb36e5ab\",\n \"score\": \"0.6308308\",\n \"text\": \"def copyrights(self) -> List[CopyrightObject]:\\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc7229c6196b75042c80dd2fb36e5ab\",\n \"score\": \"0.6308308\",\n \"text\": \"def copyrights(self) -> List[CopyrightObject]:\\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc7229c6196b75042c80dd2fb36e5ab\",\n \"score\": \"0.6308308\",\n \"text\": \"def copyrights(self) -> List[CopyrightObject]:\\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb0d25b59c21e2cbc2ce757c420006cc\",\n \"score\": \"0.6302299\",\n \"text\": \"def get_metadata():\\n here = os.path.abspath(os.path.dirname(__file__))\\n init_path = os.path.join(here, 'src', 'serde_ext', '__init__.py')\\n readme_path = os.path.join(here, 'README.rst')\\n\\n with io.open(init_path, encoding='utf-8') as f:\\n about_text = f.read()\\n\\n metadata = {\\n key: re.search(r'__' + key + r'__ = [\\\"\\\\'](.*?)[\\\"\\\\']', about_text).group(1)\\n for key in ('title', 'version', 'url', 'author', 'author_email', 'license', 'description')\\n }\\n metadata['name'] = metadata.pop('title')\\n\\n with io.open(readme_path, encoding='utf-8') as f:\\n metadata['long_description'] = f.read()\\n\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d4d24772433b15772260ee08b53245\",\n \"score\": \"0.62911433\",\n \"text\": \"def get_metadata(html, text, filename):\\n blog, post = filename.split(\\\"_\\\")\\n url = \\\"http://\\\" + blog + \\\".hypotheses.org/\\\" + str(post)\\n title = get_title(html, filename)\\n author = get_author(html)\\n date = get_date(html)\\n licence = get_licence(html)\\n language = check_language(text)\\n category = get_category(text, title)\\n numwords = get_numwords(text)\\n metadata = [filename, language, author, numwords, category, date, licence, blog, post, url, title]\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d5607b649ee929aa19bf87e13a4a9d\",\n \"score\": \"0.6261161\",\n \"text\": \"def get_metadata(self, **kwargs):\\n return self.get_entry(\\n METADATA_URI, desired_class=gdata.docs.data.Metadata, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92c69e3e660e418673713ba774eaf56\",\n \"score\": \"0.62316877\",\n \"text\": \"def get_license():\\n data_license = {}\\n data_license[\\\"name\\\"] = \\\"cc-by-4.0\\\"\\n data_license[\\\"url\\\"] = \\\"https://creativecommons.org/licenses/by/4.0/\\\"\\n data_license[\\n \\\"description\\\"] = \\\"The content can be shared and adapted but you must\\\\\\n give appropriate credit and cannot restrict access to others.\\\"\\n return data_license\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db49b6089ece2e54a555d92c706d8067\",\n \"score\": \"0.6197543\",\n \"text\": \"def get_metadata(self):\\n api_call = self._get_url(\\\"catalog\\\")\\n\\n response = get_response(api_call)\\n return json.load(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6916c01dffbfac6104f7b478a6ac5b10\",\n \"score\": \"0.619042\",\n \"text\": \"def get_metadata(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"661b5544077ce492c145dac1c9cebc98\",\n \"score\": \"0.6173343\",\n \"text\": \"def getMetadata(content):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4dea74a7d52e3b49307d8736c3d2022\",\n \"score\": \"0.616945\",\n \"text\": \"def meta(data):\\n return data[1]['metadata']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a09a6ed670672426b8d110a4b474f1b\",\n \"score\": \"0.6166778\",\n \"text\": \"def _GetMetadata(self, version):\\n raw_json = None\\n version_base = os.path.join(self.gs_base, version)\\n with self.misc_cache.Lookup(\\n (self.board, version, constants.METADATA_JSON)) as ref:\\n if ref.Exists(lock=True):\\n raw_json = osutils.ReadFile(ref.path)\\n else:\\n raw_json = self.gs_ctx.Cat(\\n os.path.join(version_base, constants.METADATA_JSON)).output\\n ref.AssignText(raw_json)\\n\\n return json.loads(raw_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3aab84491c21d1127d87bfaa6edee3d\",\n \"score\": \"0.6166168\",\n \"text\": \"def metadata(self):\\r\\n url = \\\"{url}/info/metadata\\\".format(url=self._url)\\r\\n params = {\\\"f\\\": \\\"json\\\"}\\r\\n return self._con.get(url, params, token=self._token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b4e254ee8240f4ac8da787b98023aa\",\n \"score\": \"0.61653906\",\n \"text\": \"def get_metadata(self, identifier):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd4d1e1ec210a4543fda79dfb14e941\",\n \"score\": \"0.61573976\",\n \"text\": \"def get_dataObject_modac_meta(data_object_path):\\n #data_object_path = encode_path(data_object_path)\\n auth = authenticate_modac()\\n\\n get_response = requests.get(data_object_path, auth = auth)\\n if get_response.status_code != 200:\\n print(\\\"Error downloading from modac.cancer.gov\\\", data_object_path)\\n raise Exception(\\\"Response code: {0}, Response message: {1}\\\".format(get_response.status_code, get_response.text))\\n\\n metadata_dic = json.loads(get_response.text)\\n self_metadata = metadata_dic['metadataEntries']['selfMetadataEntries']['systemMetadataEntries']\\n self_dic = {}\\n for pair in self_metadata:\\n self_dic[pair['attribute']] = pair['value'] \\n\\n return self_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1a5d4d20aca43089c913959ba5b359\",\n \"score\": \"0.6152495\",\n \"text\": \"def get_metadata(self) -> Mapping[int, Any]:\\n return self._call(CredentialManagement.CMD.GET_CREDS_METADATA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2791defc294d703d6056962a5e568286\",\n \"score\": \"0.61457705\",\n \"text\": \"def extract_metadata(self) -> DocumentMetadata:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6717d0b3afb11db4705a85098f55754\",\n \"score\": \"0.6125887\",\n \"text\": \"def get_metadata():\\n try:\\n metadata = cache.getDataset(os.environ.get('GCS_BUCKET'),\\n os.environ.get('METADATA_FILENAME'))\\n except Exception as err:\\n logging.error(err)\\n return 'Internal server error: {}'.format(err), 500\\n\\n def generate_response(data: bytes):\\n next_row = b'['\\n for row in data.splitlines():\\n yield next_row\\n next_row = row + b','\\n yield next_row.rstrip(b',') + b']'\\n headers = Headers()\\n headers.add('Content-Disposition', 'attachment',\\n filename=os.environ.get('METADATA_FILENAME'))\\n headers.add('Vary', 'Accept-Encoding')\\n return Response(generate_response(metadata), mimetype='application/json',\\n headers=headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d19e00e8e2c4b71131d2c0a0c3d7852\",\n \"score\": \"0.61157686\",\n \"text\": \"def __md(self):\\n p_l = self.__basepl('metadata')\\n p_l['content'] = 'metadata'\\n return RCRequest(self.url, p_l, 'metadata').execute()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6677f88ea288b5f1cd71ffa7b6c7309d\",\n \"score\": \"0.61138695\",\n \"text\": \"def metadata(self):\\n self._parse_metadata_dates()\\n if self._single_result:\\n return self._metadata[0]\\n else:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f1b36e9ad5f893583292d517e8d016\",\n \"score\": \"0.6051656\",\n \"text\": \"def copyright(self, plexus, **kwds):\\n # show the copyright note\\n plexus.info.log(hello.meta.copyright)\\n # all done\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73e78de7590fd2b5f905a784502060e\",\n \"score\": \"0.60199094\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe079f4baf7615ee412f97917cef5c70\",\n \"score\": \"0.6006893\",\n \"text\": \"def get_published_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0102f653530de87a0e764b126b64a1b\",\n \"score\": \"0.6002597\",\n \"text\": \"def get_metadata(self):\\n return self.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538f7cb4a8353bd46997b84091fa8913\",\n \"score\": \"0.6001879\",\n \"text\": \"def _get_metadata(cfg, relpath, doc_type):\\n return {\\n 'document_id': _get_document_id(cfg, relpath, doc_type),\\n 'compilation_date': cfg['timestamp']['date'],\\n 'timebox_id': cfg['timestamp']['timebox_id'],\\n 'configuration_id': cfg['defined_baseline']['short_hexsha']}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c321faf1c3d2465ee5a469c968dda67\",\n \"score\": \"0.598438\",\n \"text\": \"def get_metadata(self, params=None):\\n metadata_url = \\\"{CDN_URL}/{handle}/metadata\\\".format(\\n CDN_URL=CDN_URL, handle=self.handle\\n )\\n response = utils.make_call(metadata_url, 'get',\\n params=params,\\n security=self.security)\\n return response.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c4f2a4feca8651f337f99957dac17b2\",\n \"score\": \"0.59841037\",\n \"text\": \"def get_metadata(self, region, locale):\\n resource = \\\"/hearthstone/metadata\\\"\\n query_params = {\\\"locale\\\": locale}\\n return super().get_resource(resource, region, query_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db16e2f10afadc62d177dad977a75d96\",\n \"score\": \"0.5975165\",\n \"text\": \"def get_title_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5061a1edd34767f69b736b9d627df0\",\n \"score\": \"0.59744596\",\n \"text\": \"def metadata(self) -> str:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5061a1edd34767f69b736b9d627df0\",\n \"score\": \"0.59744596\",\n \"text\": \"def metadata(self) -> str:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5061a1edd34767f69b736b9d627df0\",\n \"score\": \"0.59744596\",\n \"text\": \"def metadata(self) -> str:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04a536b7e84c43786e374e02689c96b\",\n \"score\": \"0.59680593\",\n \"text\": \"def metadata(self):\\n return metadata(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6cfe5148d4e25ae2c6fd4ea9638c39\",\n \"score\": \"0.59559137\",\n \"text\": \"def metadata(self) -> 'outputs.MetadataResponse':\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6cfe5148d4e25ae2c6fd4ea9638c39\",\n \"score\": \"0.59559137\",\n \"text\": \"def metadata(self) -> 'outputs.MetadataResponse':\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbc65d3d9618dbbba2ce5834dbce34a\",\n \"score\": \"0.59523225\",\n \"text\": \"def get_assets_metadata(self):\\n return # osid.Metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2489839cae2ffcb513a9cb6d44d5a3ca\",\n \"score\": \"0.59516597\",\n \"text\": \"def get_metadata(self, key, version=None):\\n rsp = urllib2.urlopen(self.commit_uri(key, version) + \\\"/meta\\\")\\n return json.loads(rsp.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9138810d5a6abda32fd82152f8f6bd\",\n \"score\": \"0.59148645\",\n \"text\": \"def get_metadata_fields(corpus_name):\\n if corpus_name == 'sample_novels':\\n return ['author', 'date', 'title', 'country_publication', 'author_gender', 'filename', 'notes']\\n else:\\n return common.METADATA_LIST\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e0d35b76afcb6e4168e5e731d9a749\",\n \"score\": \"0.591214\",\n \"text\": \"def metadata(self):\\n return self.file_source.metadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938306b829603f77ccce0d944cb1c1c\",\n \"score\": \"0.59070027\",\n \"text\": \"def get_metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938306b829603f77ccce0d944cb1c1c\",\n \"score\": \"0.59070027\",\n \"text\": \"def get_metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05a395d61ee79ebb591a6d627cd4eb5d\",\n \"score\": \"0.590246\",\n \"text\": \"def metadata(self):\\r\\n return self._md\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e6f7385b46d30f36840fa3e286b657\",\n \"score\": \"0.5901217\",\n \"text\": \"def metadata(self):\\n\\n if self._metadata is None:\\n # metadata_url = \\\"https://raw.githubusercontent.com/{owner}/{repo_name}/master/metadata.yaml\\\".format(owner=self.repo_owner,\\n # repo_name=self.repo_name)\\n\\n # self._metadata = metadata.pandata.Pandata(metadata_url)\\n\\n self._metadata = metadata.pandata.Pandata(datafile=None)\\n md_hash = self.gh_repo.contents(\\\"metadata.yaml\\\")\\n # if the file exists, load the content\\n if md_hash is not None:\\n self._metadata.metadata = yaml.safe_load(md_hash.decoded)\\n return self._metadata\\n else:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a66a1b2c1db8d2055399b9710bf827\",\n \"score\": \"0.5895827\",\n \"text\": \"def get_metadata(self):\\n metadata_path = \\\"{}/{}/{}\\\".format(self.url,\\n self.metadata_dir,\\n self.metadata_file)\\n metadata_sig_path = \\\"{}/{}/{}.sig\\\".format(self.url.rstrip('/'),\\n self.metadata_dir,\\n self.metadata_file)\\n # load metadata\\n req = requests.get(metadata_path)\\n if req.status_code is 200:\\n raw_metadata = req.content\\n else:\\n raise RepositoryError(metadata_path, (\\\"status code not 200: \\\"\\n \\\"{}\\\".format(req.status_code)))\\n\\n if self.gpg_verify:\\n self.verify_data_signature(metadata_sig_path, metadata_path,\\n raw_metadata)\\n\\n return self.parse_metadata(raw_metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a13eec55499ba96229f465afe282ce4d\",\n \"score\": \"0.5892936\",\n \"text\": \"def process_license(license):\\n\\n ind = license.find('Attribution')\\n ind = license.find('creativecommons.org/licenses/')\\n if ind == -1:\\n zlicense = { \\\"description\\\": {\\\"en\\\": license},\\n \\\"title\\\": {\\\"en\\\": \\\"Custom license\\\"} }\\n else:\\n zlicense = {\\\"id\\\": \\\"-\\\".join([\\\"cc\\\", license[ind:].split(\\\"/\\\")[2],\\\"4.0\\\"])}\\n return zlicense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93943df688f99276f31135ff8816b065\",\n \"score\": \"0.58883893\",\n \"text\": \"def get_share_metadata(self, share, microversion=None):\\n metadata_raw = self.manila(\\n 'metadata-show %s' % share, microversion=microversion)\\n metadata = output_parser.details(metadata_raw)\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10ca39cdcff87db3ff6582c6a897a61\",\n \"score\": \"0.5878597\",\n \"text\": \"def license():\\n # print it\\n return print(meta.license)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"471733d816771050fa7bfb8e10b643fc\",\n \"score\": \"0.58772045\",\n \"text\": \"def get_socrata_metadata(credentials, nadac_parameters):\\n client = setup_socrata_client(credentials, nadac_parameters)\\n metadata = client.get_metadata(nadac_parameters['DATA_LOCATION'],\\n content_type='json')\\n\\n check_build_filepath('raw_data')\\n save_to_disk('raw_data/price_metadata.json', metadata)\\n client.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db72656e8108d61bfdd93f1a3161f692\",\n \"score\": \"0.58748287\",\n \"text\": \"def get_infos(self):\\n infos = super().get_infos()\\n infos[\\\"licenses\\\"].append(chime5_license)\\n return infos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7f3c442696494bee65b316832b76d3d\",\n \"score\": \"0.58630574\",\n \"text\": \"def get_metadata(self,mp3):\\n metadata = None\\n if self.mp3items.has_key(mp3):\\n metadata = self.mp3items[mp3]['metadata']\\n \\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ae2264504cb454f98acedbc9a9e486\",\n \"score\": \"0.5856848\",\n \"text\": \"def _extractMetadata(self):\\n\\n try:\\n dcMetadata = dicom.read_file(self.path, stop_before_pixels=True)\\n if dcMetadata:\\n self.metadata = dict()\\n for item in dcMetadata:\\n # pass forces rawDataElement to DataElement conversion\\n pass\\n if not item.tag.is_private:\\n name = str(item.name).replace('.', '')\\n value = str(item.value)\\n self.metadata[name] = value\\n self.metadata['Info Extracted'] = True\\n else:\\n self.metadata = None\\n\\n except dicom.filereader.InvalidDicomError:\\n self.metadata = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e33dd772743148f1dae807f728d1ef\",\n \"score\": \"0.5852637\",\n \"text\": \"def extract_metadata(self):\\n self.contacts_list = self.metadata['chanlabels']\\n try:\\n # comment out\\n self.modality = self.metadata['modality']\\n except Exception as e:\\n self.metadata['modality'] = 'ieeg'\\n self.modality = self.metadata['modality']\\n print(\\\"Loading result object. Error in extracting metadata: \\\", e)\\n\\n # convert channel labels into a Contacts data struct\\n if self.reference == 'bipolar' or self.modality == 'scalp':\\n self.contacts = Contacts(self.contacts_list, require_matching=False)\\n else:\\n self.contacts = Contacts(self.contacts_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a2c0e421539a33a7afbeec62a64b64\",\n \"score\": \"0.5843592\",\n \"text\": \"def set_img_copyright(self, uri): \\n \\n tags = [\\\"MWG:Copyright\\\"]\\n key = \\\"copyright\\\"\\n self.set_meta_prop(tags, uri, key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dc2755324d92788991852a56f33beb0\",\n \"score\": \"0.58300406\",\n \"text\": \"def get_metadata():\\n res = requests.get(URL).json()\\n return res[\\\"response\\\"][\\\"docs\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ba37b3dbaa8401e45f44686382e2fd\",\n \"score\": \"0.5820466\",\n \"text\": \"def get_metadata(publisher, package):\\n try:\\n metadata = MetaDataDB.query.filter_by(name=package, publisher=publisher).first().descriptor\\n return jsonify({\\\"data\\\": metadata, \\\"status\\\": \\\"OK\\\"}), 200\\n except Exception as e:\\n return jsonify({'status': 'KO', 'message': e.message}), 500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb812cf1b4c681f3e913eb98d7cfed2\",\n \"score\": \"0.58103585\",\n \"text\": \"def metadata(self):\\n if self.__last_get_response is None:\\n if self.__initial_metadata is not None:\\n return self.__initial_metadata\\n self.get()\\n return self.__last_get_response.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3abfbd4d70ab9fab15ccb068927f8b4c\",\n \"score\": \"0.579478\",\n \"text\": \"def getMetadata(self, private_key, client_id):\\n return self.proxy.samp.hub.getMetadata(private_key, client_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b39a28ca457275565c55db1f7331e0be\",\n \"score\": \"0.57906055\",\n \"text\": \"def get_courses_metadata(self):\\n return # osid.Metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1cd31ac6dcb6d8e9adf6c1218e1daf7\",\n \"score\": \"0.578709\",\n \"text\": \"def create_metadata(title, comment='', author=None):\\n author = 'https://github.com/narimiran' if author is None else author\\n return dict(artist=author, title=title, comment=comment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80f03cdcdbcf6f0f4de1c9d9204f4851\",\n \"score\": \"0.5785481\",\n \"text\": \"def metadata(self):\\n metadata_service = self.find_service_by_type(ServiceTypes.METADATA)\\n return metadata_service.values['metadata']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae30f97bc9dda8b92cc34dc15d1adf6c\",\n \"score\": \"0.57851726\",\n \"text\": \"def get_license_data(self):\\n return licenses.get_license_by_url(self.license or \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afeddd9616b57cd176036fd0071ee294\",\n \"score\": \"0.5769014\",\n \"text\": \"def metadata(forge):\\n forge.metadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a751d8085f977491b2730caf4593e12\",\n \"score\": \"0.5764288\",\n \"text\": \"def get_source_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e936e6fd083d940e003160caa3de0391\",\n \"score\": \"0.576013\",\n \"text\": \"def files_license_info(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"files_license_info\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ac9d5037085069356d9b5b257996854\",\n \"score\": \"0.5740702\",\n \"text\": \"def getMetadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06810d8c09ac0aa1fc53ae4bea9e99f\",\n \"score\": \"0.57405835\",\n \"text\": \"def metadata(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":197,"cells":{"query_id":{"kind":"string","value":"f5f36a0534f4ea94adb8aad46271138a"},"query":{"kind":"string","value":"Can this RecurringCost be enacted"},"positive_passages":{"kind":"list like","value":[{"docid":"757ffa3fd3a6bd9841075b459360f26d","score":"0.49190748","text":"def is_enactable(self, as_of):\n return \\\n not self.disabled and \\\n not self.archived and \\\n not self._is_finished(as_of) and \\\n self._is_ready(as_of) and \\\n not self._is_billing_complete()","title":""}],"string":"[\n {\n \"docid\": \"757ffa3fd3a6bd9841075b459360f26d\",\n \"score\": \"0.49190748\",\n \"text\": \"def is_enactable(self, as_of):\\n return \\\\\\n not self.disabled and \\\\\\n not self.archived and \\\\\\n not self._is_finished(as_of) and \\\\\\n self._is_ready(as_of) and \\\\\\n not self._is_billing_complete()\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"4c04ec54a498200fddebe61bfad145c7","score":"0.6974328","text":"def enact(self, billing_cycle, disable_if_done=True):\n as_of = billing_cycle.date_range.lower\n if not self.is_enactable(as_of):\n raise CannotEnactUnenactableRecurringCostError(\n \"RecurringCost {} is unenactable.\".format(self.uuid)\n )\n\n if self.has_enacted(billing_cycle):\n raise RecurringCostAlreadyEnactedForBillingCycle(\n 'RecurringCost cost {} already enacted for {}'.format(self, billing_cycle)\n )\n\n with db_transaction.atomic():\n recurred_cost = RecurredCost(\n recurring_cost=self,\n billing_cycle=billing_cycle,\n )\n recurred_cost.make_transaction()\n recurred_cost.save()\n\n if disable_if_done:\n self.disable_if_done(billing_cycle)","title":""},{"docid":"b7445a2491181d4ec5c6d4f004d143bf","score":"0.6717582","text":"def has_enacted(self, billing_cycle):\n return RecurredCost.objects.filter(\n recurring_cost=self,\n billing_cycle=billing_cycle,\n ).exists()","title":""},{"docid":"a365ef9fd5b0a002ccf6bc7bdc0e6573","score":"0.62935936","text":"def make_eligible(self):\n pass","title":""},{"docid":"d8dff91131c125b350fd2012d9790602","score":"0.5993728","text":"def charge_recurring(self, grace_period=None):\n pass","title":""},{"docid":"9039e5f6aa07f9bcc62aac117fff3c64","score":"0.5735424","text":"def confirmed(self, cr, uid, ids, context=None):\n\tallow_archive_line_obj = self.pool.get('services.contracts.allowances.lines')\n for record in self.browse(cr, uid, ids, context=context):\n\t\tif not record.allowances_lines_before :\n \traise osv.except_osv(_('Partner Lines !'), _('Sorry no partner Lines!'))\n\n\t \tlines_ids = [line.id for line in record.allowances_lines_after]\n \tallow_archive_line_obj.unlink(cr,uid,lines_ids,context=context)\n\n\t\tfor lines in record.allowances_lines_before:\n\t\t\tif lines.percentage_rating < 0 or lines.percentage_rating > 100 :\n \t\traise osv.except_osv(_('Rate Error !'), _('Sorry you insert wrong rate ... rate is between (0,100)!'))\n \t\tamount_after_rate_id = allow_archive_line_obj.create(cr, uid, {\n \t\t\t\t'cost_of_rent':lines.cost_of_rent,\n \t\t\t\t'amount_untaxed':round (lines.amount_untaxed*lines.percentage_rating/100,2),\n \t\t\t\t'amount_tax':round(lines.amount_tax*lines.percentage_rating/100,2),\n \t\t\t\t'amount_total':round(lines.amount_total*lines.percentage_rating/100,2),\n \t\t\t\t'deduct_days':lines.deduct_days,\n \t\t\t\t'deduct_amount':lines.deduct_amount,\n \t\t\t\t'contract_id':lines.contract_id.id,\n\t\t\t\t\t'env_allow_id_after_rate':record.id,\n\t\t\t\t\t'type': 'after',\n 'category_id':lines.category_id.id,\n\t\t\t\t\t'percentage_rating':lines.percentage_rating,\n\n })\n\t\t\n \n self.write(cr, uid, ids, {'state':'confirmed'})\n return True","title":""},{"docid":"e06878f1f73ebe95cde824d74b51178a","score":"0.57182837","text":"def credit_deliverer():\n return True","title":""},{"docid":"072e3adb9a2b8b733955c2d0470f5cfd","score":"0.57024777","text":"def disable_costs():\n RecurringCost.objects.all().disable_if_done()","title":""},{"docid":"729f7ea408343845dae0e20a444735a6","score":"0.5669726","text":"def awaiting_payment(self):","title":""},{"docid":"46b89439cb1fb4ae0622c0559693f5b3","score":"0.56223905","text":"def test_add_recurring_schedule(self):\n pass","title":""},{"docid":"43c2e807bcff1aea5874de05585dc1b9","score":"0.55890375","text":"def test_renew_user_pending_cancel(self):\n self.braintree_customer.active = True\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.subscription_id = \"ValidSubscriptionID\"\n\n result = SubscriptionManager.renew(self.braintree_customer)\n self.assertEqual(\"ValidSubscriptionID\",result)\n self.assertFalse(self.braintree_customer.pending_cancel)","title":""},{"docid":"c4473da0917973c1c85d7b653ba0f05d","score":"0.5505976","text":"def _check_cost(self, cr, uid, ids, context=None):\n for enrich in self.browse(cr, uid, ids, context=context):\n if enrich.amount <= 0:\n raise osv.except_osv(_('ValidateError'), _('The Cost Must Be Greater Than Zero!'))\n return True","title":""},{"docid":"2e2212377ef5be3f290b7c7bf4d129b1","score":"0.5493613","text":"def test_subscribe_already_subscribed(self):\n self.braintree_customer.active = True\n self.braintree_customer.save()\n with self.assertRaises(BraintreeError):\n SubscriptionManager.subscribe(self.braintree_customer)\n\n # Check state not altered\n self.assertTrue(self.braintree_customer.active)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertIsNone(self.braintree_customer.expiry_date)","title":""},{"docid":"0eff043c23370b9890345712648bb3b8","score":"0.54642403","text":"def test_renew_attempt_on_active_subscription(self):\n self.braintree_customer.pending_cancel = False\n self.braintree_customer.active = True\n\n with self.assertRaises(BraintreeError):\n SubscriptionManager.renew(self.braintree_customer)","title":""},{"docid":"2878f031423ce10692b6087cd1179530","score":"0.5447805","text":"def make_transaction(self):\n if self.pk:\n raise CannotRecreateTransactionOnRecurredCost(\n 'The transaction for this recurred cost has already been created. You cannot create it again.'\n )\n\n amount = self.recurring_cost.get_amount(self.billing_cycle)\n\n # It is quite possible that there will be nothing to bill, in which\n # case we cannot create a transaction with no legs, nor can we create\n # legs with zero values. Therefore we don't create any transaction.\n if not amount:\n return None\n\n self.transaction = Transaction.objects.create(\n description='Created by recurring cost',\n date=self.billing_cycle.date_range.lower\n )\n\n # Use the SplitManager's custom queryset's split() method to get the\n # amount to be billed for each split\n splits = self.recurring_cost.splits.all().split(amount)\n\n # Create the transaction leg for the outbound funds\n # (normally to an expense account)\n self.transaction.legs.add(Leg.objects.create(\n transaction=self.transaction,\n amount=Money(amount, self.recurring_cost.currency),\n account=self.recurring_cost.to_account,\n ))\n\n for split, split_amount in splits:\n # Create the transaction legs for the inbound funds\n # (from housemate accounts)\n if split_amount:\n self.transaction.legs.add(Leg.objects.create(\n transaction=self.transaction,\n amount=Money(split_amount * -1, self.recurring_cost.currency),\n account=split.from_account,\n ))\n\n return self.transaction","title":""},{"docid":"ba47d041e96bc2b902f7e951534eb141","score":"0.5434562","text":"def _cost(self, action):\n raise NotImplementedError","title":""},{"docid":"0e3085b8493d861cc1b7d398c776e29a","score":"0.54062164","text":"def is_recurring(self):\n return self.__is_recurring","title":""},{"docid":"c624f7dcb4ff52e7b73c58f67c9e6318","score":"0.5402047","text":"def test_request_cancel_already_pending_cancel(self):\n self.braintree_customer.subscription_id = \"1234\"\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.save()\n\n self.assertFalse(SubscriptionManager.request_cancel(self.braintree_customer))\n self.assertTrue(self.braintree_customer.pending_cancel)","title":""},{"docid":"33fbd06b632f15c7255f0e18c5897445","score":"0.53812355","text":"def is_invited_pending_activation(self):\n if self.registration_method == self.INVITED \\\n and self.is_pending_activation():\n return True\n else:\n return False","title":""},{"docid":"ceb71fff2f7605e3d2c71500830dcbfa","score":"0.53572863","text":"def give_raise(self):\r\n self.hourly_pay = 12.00","title":""},{"docid":"a1633c21b982abbaba97996b7c87877d","score":"0.5347127","text":"def charge(self, other):\n if self.flag:\n self.credit += other\n return \"{} Tomans has been added to your card credit and now the credit of your card is {} Tomans\".format(\n other, self.credit)\n else:\n return \"Sorry, your card has expired.\"","title":""},{"docid":"8a0bca7e754388a34640f20c91afa68b","score":"0.534086","text":"def payment_approval(self, house_cost: (int, float)):\n if self.money_available >= house_cost: # Person has enough available money to make a deal with Realtor\n self.money_available -= house_cost\n print(f'Payment from {self.name} was approved')\n return True\n print(f'{self.name} doesn\\'t have enough money to buy this house')\n return False","title":""},{"docid":"a34fc2ccd08cf094015a4a7a71287468","score":"0.53131855","text":"def test_request_cancel_active_subscription(self):\n self.braintree_customer.subscription_id = \"1234\"\n self.braintree_customer.pending_cancel = False\n self.braintree_customer.save()\n self.assertTrue(SubscriptionManager.request_cancel(self.braintree_customer))\n self.assertTrue(self.braintree_customer.pending_cancel)","title":""},{"docid":"3e8c9b966e3614e15ccd1ab4a2563ef9","score":"0.5306632","text":"def test_overlap(self):\r\n t = Expense(name = \"fake lunch\",\r\n amount = 1.,\r\n on = (WeeklyRecurring(FR,\r\n fromdt = self.fromdt,\r\n todt = self.todt),\r\n DailyRecurring(fromdt = self.fromdt, \r\n todt = self.todt)))\r\n\r\n self.m.addTransaction(t)\r\n self.assertEqual(self.m.totalSaved(self.fromdt, self.todt), -365.)","title":""},{"docid":"fa3965aee40434d2d49b4ec667e75657","score":"0.5294787","text":"def reports_editable(self):\n end_plus_time = self.datetime_end + datetime.timedelta(days=CCR_DELTA)\n return timezone.now() < end_plus_time","title":""},{"docid":"bc118ce9f8f76044d0d8341c976cc671","score":"0.5265665","text":"def make_payment(self, cost):\n self.process_coins()\n if self.money_received >= cost:\n change = round(self.money_received - cost, 2)\n print(f\"Here is {self.CURRENCY}{change} in change.\")\n self.profit += cost\n self.money_received = 0\n return True\n else:\n print(\"Sorry that's not enough money. Money refunded.\")\n self.money_received = 0\n return False","title":""},{"docid":"58a11d04b563ecf30411d522d7a9987d","score":"0.5261785","text":"def test_create_sugar_cpn_exp_task(self):\n offset_days = 2\n coupon = Coupon.objects.get(id=416)\n coupon.expiration_date = (datetime.date.today() + \n datetime.timedelta(days=offset_days))\n coupon.save()\n self.assertEqual(create_sugar_coupon_expire_task(MockSugar(), \n offset_days=offset_days), None)","title":""},{"docid":"27b0305fb2f568ecd0568b90978b95e3","score":"0.52602494","text":"async def resign(self, ctx):\n currency = await bank.get_currency_name(ctx.guild)\n await self.config.user(ctx.author).gameRole.set(\"User\")\n await ctx.send(\n f\"{ctx.author} has spent 10,000 {currency}- to resign from their current job.\"\n )","title":""},{"docid":"8491f0b61b51e158a8d7c69100f4d319","score":"0.5213568","text":"def test_subscription_transaction_declined_suspend(self):\n\n # We'll create a plan that starts here\n start_date = dt.date(2019, 1, 1)\n\n # And the trial date ends here too\n trial_end_date = dt.date(2019, 1, 1)\n\n # The customer will use some metered features here\n metered_usage_on = dt.date(2019, 1, 10)\n\n # Docs will be generated to bill here.\n prev_billing_date = dt.date(2019, 1, 3)\n\n # So, the customer grace period ends here.\n # First billing interval: dt.date(2019, 2, 1)\n # \n billing_grace_exp = dt.date(2019, 2, 3)\n\n # The next billing check should discover that the subscription\n # is unpaid.\n # Billing due date is: dt.date(2019, 2, 6)\n # With the grace period: dt.date(2019, 2, 9)\n # \n billing_post_grace_check = dt.date(2019, 2, 10)\n\n # Create a customer\n #\n customer = CustomerFactory.create(sales_tax_percent=Decimal('0.00'),\n payment_due_days=3)\n PaymentMethodFactory.create(\n payment_processor=triggered_processor, customer=customer,\n canceled=False,\n verified=True,\n )\n\n # Create a metered feature\n #\n mf_price = Decimal('2.5')\n metered_feature = MeteredFeatureFactory(\n included_units_during_trial=Decimal('0.00'),\n price_per_unit=mf_price)\n currency = 'USD'\n\n # Crate a plan with metered features. Generate the invoice after\n # the 5 day trial period, the plan will be billed every 30 days.\n # \n generate_after = 5\n plan = PlanFactory.create(interval=Plan.INTERVALS.DAY,\n interval_count=30,\n generate_after=generate_after,\n enabled=True,\n amount=Decimal('20.00'),\n trial_period_days=1,\n metered_features=[metered_feature],\n currency=currency)\n\n # Subscribe the customer\n #\n subscription = SubscriptionFactory.create(\n plan=plan, start_date=start_date, customer=customer)\n subscription.activate()\n subscription.save()\n\n # Log some metered usage\n consumed_1 = Decimal('5.00')\n consumed_2 = Decimal('5.00')\n mf_log = MeteredFeatureUnitsLogFactory.create(\n subscription=subscription,\n metered_feature=metered_feature,\n start_date=metered_usage_on,\n end_date=subscription.trial_end,\n consumed_units=consumed_1)\n\n # Generate the docs\n call_command('generate_docs',\n billing_date=prev_billing_date,\n stdout=self.output)\n\n proforma = Proforma.objects.first()\n\n assert proforma.proforma_entries.count() != 0\n assert Subscription.objects.all().count() == 1\n assert Invoice.objects.all().count() == 0\n assert Proforma.objects.all()[0].total > Decimal('0.00')\n\n # Consume more units\n mf_log.consumed_units += consumed_2\n mf_log.save()\n\n call_command('generate_docs',\n billing_date=billing_grace_exp,\n stdout=self.output)\n\n assert Proforma.objects.all().count() != 0\n assert Invoice.objects.all().count() == 0\n\n for pf in Proforma.objects.all():\n # # Issue the proforma to generate transactions\n # proforma = Proforma.objects.all()[1]\n pf.issue()\n pf.save()\n\n self.assertEqual(pf.state, Proforma.STATES.ISSUED)\n # Fail the transaction\n for tx in pf.transactions:\n # tx = proforma.transactions[0]\n tx.fail()\n tx.save()\n self.assertEqual(tx.state, Transaction.States.Failed)\n\n assert Transaction.objects.all().count() != 0\n\n call_command('check_subscriptions',\n billing_date=billing_post_grace_check,\n stdout=self.output)\n\n subscr = Subscription.objects.first()\n\n # Scan for subscriptions with unpaid documents\n logging.debug(\"subscr %s\" % subscr)\n self.assertEqual(subscr.state, Subscription.STATES.CANCELED)","title":""},{"docid":"5417e376b07167245209d7488eceae5c","score":"0.5208941","text":"def test_get_active_coupon(self):\n coupon = COUPON_FACTORY.create_coupon()\n slot = SLOT_FACTORY.create_slot(coupon=coupon)\n future_date = datetime.date.today() + datetime.timedelta(weeks=1)\n coupon.expiration_date = future_date\n coupon.save()\n self.assertEqual(slot.get_active_coupon(), coupon)","title":""},{"docid":"257ba26e9929795ad5f5632d4cddacf1","score":"0.52071846","text":"def test_create_sugar_cc_exp_task(self):\n current_date = datetime.datetime.now()\n order = Order.objects.create(billing_record_id=114)\n credit_card = CreditCard.objects.get(id=500)\n credit_card.exp_month = int(current_date.strftime(\"%m\"))\n credit_card.exp_year = int(current_date.strftime(\"%y\"))\n credit_card.encrypt_cc('4111111111111111')\n credit_card.clean()\n credit_card.save()\n Payment.objects.create(order=order, credit_card=credit_card,\n amount='50.00', method='C', status='p')\n self.assertEqual(create_sugar_cc_expire_task(MockSugar(), \n test_mode=True), None)","title":""},{"docid":"eb78960a18fbea5f420521a73cc9d91e","score":"0.5201274","text":"def save(self, *args, **kwargs):\n if not self.pk:\n self.start_time_rent = datetime.date.today()\n self.end_time_rent = self.start_time_rent + datetime.timedelta(days=7)\n self.reservation.isrented = True\n self.reservation.save()\n return super(Rental, self).save(*args, **kwargs)","title":""},{"docid":"f499e3c7140c536233b59aa5018b00e0","score":"0.5198995","text":"def life_insurance_to_recive_total(self):\n pass","title":""},{"docid":"93e3ba91aae2b3005b29c825e9df6617","score":"0.51950544","text":"def pay_costs(self):\r\n cost = self.cost\r\n if cost:\r\n self.owner.player.char_ob.pay_money(cost)\r\n self.owner.player.msg(\"You pay %s coins for the event.\" % cost)","title":""},{"docid":"4a554eae4772018c64970255353249ec","score":"0.5190392","text":"def accepting(self):\n quote = self.get_quotes('labor')[0]\n quantity = max(0, quote.price - 14)\n self.create('labor', quantity)\n self.accept_quote_partial(quote, quantity)","title":""},{"docid":"da7b38cc190f388ab6d0549317e33158","score":"0.51902777","text":"def recap(self, request, pk=None):\n retreat = self.get_object()\n # This is a hard-coded limitation to allow anonymous users to call\n # the function.\n time_limit = retreat.end_time - timedelta(days=1)\n if timezone.now() < time_limit:\n response_data = {\n 'detail': \"Retreat ends in more than 1 day.\"\n }\n return Response(response_data, status=status.HTTP_200_OK)\n\n # Notify a user for every reserved seat\n emails = []\n for reservation in retreat.reservations.filter(\n is_active=True,\n post_event_send=False):\n send_post_retreat_email(reservation.user, retreat)\n reservation.post_event_send = True\n reservation.save()\n emails.append(reservation.user.email)\n\n response_data = {\n 'stop': True,\n 'emails': emails\n }\n return Response(response_data, status=status.HTTP_200_OK)","title":""},{"docid":"40e1634797064f5c392aeeff54879a26","score":"0.5186644","text":"def check_costs(self):\r\n if self.cost > self.owner.player.char_ob.currency:\r\n self.add_error(\r\n \"celebration_tier\",\r\n \"You cannot afford to pay the cost of %s.\" % self.cost,\r\n )","title":""},{"docid":"d10a00c4c4117a81dd34a039b5016bec","score":"0.51843166","text":"def test_no_credit_change_for_same_subscription(self):\n credits = 20\n\n current_plan = Subscription.get_plan_by_id('pro')\n new_plan = Subscription.get_plan_by_id('pro')\n\n may_29_2015 = datetime.datetime(2015, 5, 29, 0, 0, 0)\n may_29_2015 = pytz.utc.localize(may_29_2015)\n\n credits = add_subscription_credits(credits, current_plan, new_plan,\n may_29_2015)\n\n assert credits == 20","title":""},{"docid":"f6cfe667dbc270d1f0777d11ac37720c","score":"0.51762897","text":"def test_remove_recurring_schedule(self):\n pass","title":""},{"docid":"ffd0405d5d1dcce4d6e9af01483ebde3","score":"0.5167032","text":"def confirmed(self):","title":""},{"docid":"eb1bb3838c74ae2e2fc33261a8bcad5d","score":"0.51638985","text":"def test_retire_rate_plan(self):\n pass","title":""},{"docid":"acd5fc643f60a16857bf0dbbe97c12f5","score":"0.5128787","text":"def add_to_excess(self) -> None:\n if self.msg.value <= 0:\n revert(\"No amount added to excess\")\n self._treasury_balance.set(self.icx.get_balance(self.address))\n self.FundReceived(self.msg.sender, self.msg.value, f\"{self.msg.value} added to excess\")","title":""},{"docid":"e170852adf1c091d578eada0184631b9","score":"0.51178837","text":"async def admin_credit(self, ctx, target: discord.Member, sum: int = 100):\n if is_registered(target.id):\n \n inventories = get_file(\"inventories\")\n inventories[str(target.id)][\"balance\"] += sum\n update_file(\"inventories\", inventories)\n\n embed = discord.Embed(color=admin_color)\n embed.set_author(name=\"🛠️ Admin\")\n embed.add_field(name=\"💰 Credit\",\n value=f\"{ctx.author.mention}, {target.mention} a été crédité de `{sum}` PO (pièces d'or)\")\n embed = set_footer(embed, ctx)\n await ctx.send(embed=embed)","title":""},{"docid":"41e253144f1d44f70f72725a3cdfd381","score":"0.5104723","text":"def test_plan_autorenew_at_expire(self):\n up = baker.make(\"UserPlan\", expire=date(2020, 1, 1))\n self.assertEqual(up.plan_autorenew_at(), date(2020, 1, 1))","title":""},{"docid":"6ba629d8534d92877f8ce279d15aafa1","score":"0.5101618","text":"def trigger_expiration_notices(at_time=None, nb_days=15, dry_run=False):\n\n def _handle_organization_notices(organization):\n if organization.processor_card_key:\n card = organization.retrieve_card()\n try:\n exp_month, exp_year = card['exp_date'].split('/')\n exp_date = datetime(year=int(exp_year),\n month=int(exp_month), day=1, tzinfo=at_time.tzinfo)\n if lower >= exp_date:\n LOGGER.info(\"payment method expires soon for %s\",\n organization)\n if not dry_run:\n signals.card_expires_soon.send(\n sender=__name__, organization=organization,\n nb_days=nb_days)\n except (KeyError, ValueError):\n # exp info is missing or the format is incorrect\n pass\n else:\n LOGGER.info(\"%s doesn't have a payment method attached\",\n organization)\n if not dry_run:\n signals.payment_method_absent.send(sender=__name__,\n organization=organization)\n\n at_time = datetime_or_now(at_time)\n lower = at_time + relativedelta(days=nb_days)\n upper = at_time + relativedelta(days=nb_days + 1)\n LOGGER.info(\n \"trigger notifications for subscription expiring within [%s,%s[ ...\",\n lower, upper)\n prev_organization = None\n subscription = None\n for subscription in Subscription.objects.valid_for(ends_at__gte=lower,\n ends_at__lt=upper).order_by('organization'):\n org = subscription.organization\n plan = subscription.plan\n\n try:\n if subscription.auto_renew:\n if plan.renewal_type == plan.AUTO_RENEW:\n if org.id != prev_organization:\n _handle_organization_notices(org)\n\n prev_organization = org.id\n else:\n if plan.renewal_type == plan.ONE_TIME:\n LOGGER.info(\"trigger upgrade soon for %s\", subscription)\n if not dry_run:\n signals.subscription_upgrade.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n elif plan.renewal_type == plan.REPEAT:\n LOGGER.info(\"trigger expires soon for %s\", subscription)\n if not dry_run:\n signals.expires_soon.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n except Exception as err: #pylint:disable=broad-except\n # We use `Exception` because the email server might be\n # unavailable but ConnectionRefusedError is not a subclass\n # of RuntimeError.\n LOGGER.exception(\"error: %s\", err)\n\n # flushing the last organization\n if subscription and subscription.organization.id != prev_organization:\n if subscription.auto_renew:\n if subscription.plan.renewal_type == subscription.plan.AUTO_RENEW:\n _handle_organization_notices(subscription.organization)","title":""},{"docid":"0c66ea0657cc0bf9e28af5aaeb6647c4","score":"0.5100977","text":"def redeem(self, instance, customer, save=True):\n start = timezone.now().date()\n end = start + relativedelta(months=self.duration)\n discount = Discount(instance=instance,\n coupon=self,\n start=start,\n end=end,\n customer=customer)\n discount.full_clean()\n if save:\n discount.save()\n return discount","title":""},{"docid":"be729754fa4102aa1c8b61477d0cdb82","score":"0.50970876","text":"def customer_wants_condiments(self):\n return True","title":""},{"docid":"58052ca7885a95388e8a943ad79fc9ee","score":"0.50891936","text":"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 11, 10, tzinfo=dt_timezone.utc\n )\n self.ticketed_event.ticket_cost = 0\n self.ticketed_event.save()\n self.assertFalse(self.unpaid.cancelled)\n self.assertFalse(self.paid.cancelled)\n\n management.call_command('cancel_unpaid_ticket_bookings')\n # emails are sent to user per cancelled booking and studio once for all\n # cancelled bookings\n self.unpaid.refresh_from_db()\n self.paid.refresh_from_db()\n self.assertEqual(len(mail.outbox), 0)\n self.assertFalse(self.unpaid.cancelled)\n self.assertFalse(self.paid.cancelled)","title":""},{"docid":"c02baa7b4f7d3c8e78826cc17010a89a","score":"0.5078882","text":"def register_withdraw(self, withdraw_intent): \n if withdraw_intent > 0:\n self.teo.register_withdraw(self, withdraw_intent)","title":""},{"docid":"87a75db34bf124af171ddcacdf0705d9","score":"0.5062819","text":"def reckon(self):","title":""},{"docid":"3ef1115a68c9a4958c538af0517b7e7f","score":"0.5059867","text":"def reimport_eligible(self) -> bool:\n return pulumi.get(self, \"reimport_eligible\")","title":""},{"docid":"cabd4c0d5435e6616abe559534230b62","score":"0.5054001","text":"def test_add_taxation_strategy_to_rate_plan(self):\n pass","title":""},{"docid":"e3d7067817879f0fa2eb4466e3f35086","score":"0.50444573","text":"def can_act(self) -> bool:\n return self.cooldown < 1","title":""},{"docid":"e3d7067817879f0fa2eb4466e3f35086","score":"0.50444573","text":"def can_act(self) -> bool:\n return self.cooldown < 1","title":""},{"docid":"57a314d8c47829fe7aea4b854dab54cb","score":"0.5037591","text":"def restock(self):\n self.money = 9999","title":""},{"docid":"5b99cdf09d4409ff6cacc373e96ffd86","score":"0.5028068","text":"def may_send_escalation(self, source_type: str, escalation_reminder_cadence: timedelta) -> bool:\n with self.session.begin() as session:\n last_escalated = (\n session.query(EventRecord.escalated_at)\n .filter(EventRecord.source_type == source_type)\n .order_by(EventRecord.escalated_at.desc())\n .limit(1)\n .one_or_none()\n )\n\n if not last_escalated[0]:\n return True\n\n return last_escalated[0] <= datetime.utcnow() - escalation_reminder_cadence","title":""},{"docid":"846e2e0abaea6b50e2948f1d6060a39c","score":"0.50160515","text":"def filter(self, event, *args, **kwargs):\n return event[\"expiration_datetime\"] <= datetime.now()","title":""},{"docid":"201aa010ced8cf71f548ecf6d393575b","score":"0.5011571","text":"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 10, 10, tzinfo=dt_timezone.utc\n )\n self.event.cost = 0\n self.event.save()\n self.assertEqual(\n self.unpaid.status, 'OPEN', self.unpaid.status\n )\n self.assertEqual(\n self.paid.status, 'OPEN', self.paid.status\n )\n management.call_command('cancel_unpaid_bookings')\n # emails are sent to user per cancelled booking and studio once for all\n # cancelled bookings\n unpaid_booking = Booking.objects.get(id=self.unpaid.id)\n paid_booking = Booking.objects.get(id=self.paid.id)\n self.assertEqual(len(mail.outbox), 0)\n self.assertEqual(\n unpaid_booking.status, 'OPEN', unpaid_booking.status\n )\n self.assertEqual(\n paid_booking.status, 'OPEN', paid_booking.status\n )\n\n # auto_cancelled set to True only on cancelled bookings\n self.assertFalse(unpaid_booking.auto_cancelled)\n self.assertFalse(paid_booking.auto_cancelled)","title":""},{"docid":"820d1f3307e066ab24821548eb04bf4c","score":"0.50115657","text":"def isOnSaleSoon(ticket):\n return ticket.start_time > timezone.now()","title":""},{"docid":"99f1843e58d485cff565bd73b3ba806c","score":"0.500794","text":"def _claim_iscore(self) -> None:\n iscore_details_dict = self._system.queryIScore(self.address)\n if iscore_details_dict['estimatedICX'] != 0:\n amount = iscore_details_dict[\"estimatedICX\"]\n self._system.claimIScore()\n self._daily_reward.set(amount)\n self._rate.set(self.getRate())\n self._total_lifetime_reward.set(self.getLifetimeReward() + amount)\n self._distributing.set(True)","title":""},{"docid":"800b7a21c89f88bfe6327f7bb4361f22","score":"0.5006686","text":"def should_pay_attention(self):\n return random.randint(1,100) > self.wander","title":""},{"docid":"d3c1c2ca303c52e34683b990d4edc7da","score":"0.50048196","text":"def test_authorize_pending_payment(self):\n pass","title":""},{"docid":"e9eeb30737a0056e7591f58ee9369f45","score":"0.5003403","text":"def acknowledge_visitor(visitor):\n visitor.acknowledged = True\n visitor.acknowledged_time = datetime.now()\n visitor.save()","title":""},{"docid":"a4fcf332d5161239a6433b289ae158ed","score":"0.50032485","text":"def resent(self):\n return 'Resent-Date' in self","title":""},{"docid":"42ef7fe9adf9b416d5c880d173273e84","score":"0.49881572","text":"def pre_approve(self, cr, uid, ids, context={}):\n \tfor voucher in self.browse(cr, uid, ids, context=context):\n \t if not voucher.department_id.analytic_account_id:\n \t raise osv.except_osv(_('Configration Check!'), _(\"Please add cost center for your department!\"))\n \t periods = self.pool.get('account.period').search(cr, uid, [('date_start','<=',voucher.date),('date_stop','>=',voucher.date),('company_id','=',voucher.company_id.id)], context=context)\n\n\n res=0.0\n if voucher.purpose:\n if not voucher.purpose.account_id: raise osv.except_osv(_('Warning!'), _('Please configure account for this purpose!')) \n voucher_line = {\n \t\t'voucher_id': voucher.id,\n \t\t'partner_id': voucher.partner_id.id,\n \t\t'untax_amount': voucher.amount,\n \t\t'amount': voucher.amount,\n 'name': voucher.narration,\n \t\t'type': 'dr',\n \t\t'account_analytic_id': voucher.department_id.analytic_account_id and voucher.department_id.analytic_account_id.id,\n 'account_id': voucher.purpose.account_id.id,\n \t }\n new_amount = res and res or voucher.amount \n voucher_line.update({'amount':new_amount,'untax_amount':new_amount})\n \t if voucher.line_ids :\n for line in voucher.line_ids:\n \t\t self.pool.get('account.voucher.line').write(cr, uid, line.id, {\n \t\t'voucher_id': voucher.id,\n \t\t'partner_id': voucher.partner_id.id,\n \t\t'untax_amount': res or line.amount,\n \t\t'amount': line.amount,\n 'name': voucher.narration,\n \t\t'type': 'dr',\n \t\t'account_analytic_id': line.account_analytic_id and line.account_analytic_id.id or voucher.department_id.analytic_account_id.id,\n 'account_id': voucher.purpose.account_id.id or line.account_id.id,\n \t }, context=context)\n \t else:\n\n \t\t new_voucher_line = self.pool.get('account.voucher.line').create(cr, uid, voucher_line, context=context)\n context.update({'purchase':True})\n self.create_budget_confirmation(cr, uid, [voucher.id], context)\n \tself.write(cr, uid, ids,{'state': 'preapprove','type':'purchase','ratification':True}, context=context)\n #cxt = context.copy()\n #cxt.update({'type':'ratification'})\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\n self.write(cr, uid, ids, {'state': 'approved'}, context=context)\n\n \t'''self.write(cr, uid, ids, {'state': 'preapprove'})\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\n self.write(cr, uid, ids, {'state': 'approve','type':'purchase','ratification':True}, context=context)'''\n return True","title":""},{"docid":"9bcdcd6a69d116b1f4b370a2e026f1a4","score":"0.49817756","text":"def test_update_rate_plan(self):\n pass","title":""},{"docid":"4acf948659222da3835bbf26fc3a9017","score":"0.49740785","text":"def test_carryover(self):\n carryover = Money(100)\n available = {Decimal(0.5): carryover}\n self.forecast(available)\n self.assertEqual(\n self.forecast.carryover,\n carryover)","title":""},{"docid":"3c2504305e30cfc1efa4189640cffd33","score":"0.4970885","text":"def recurring(self):\n return self.filter(total_billing_cycles__isnull=True)","title":""},{"docid":"29d0e95adc90beb142a910e5cbf8e54c","score":"0.4959831","text":"def activate_card(**kwargs):\n for card in ClientClubCard.objects.filter(\n date_begin__isnull=True, is_paid_activate=False):\n # period for activate\n days = card.club_card.period_activation\n if card.date_start + timedelta(days - 1) < date.today():\n card.activate()","title":""},{"docid":"12d51add250d35f7917a5213649c725c","score":"0.49592566","text":"def aspiration_criteria_satisfied(self,index):\n\t\tnewEnergy = self.currentEnergy + self._diffEi[index]#self.count_energy(state)\n\t\t# newTax = self._taxes[index]#self.count_tax(state=state)\n\t\ttmpState = copy(self.currentState)\n\t\ttmpState[index] = 1 - tmpState[index]\n\t\tnewTax = self.count_tax(state=tmpState)\n\t\toutput(\"Checking aspiration criteria: oldEnergy={}, current tax = {}; new energy = {}, new tax = {}\"\\\n\t\t\t\t\t \t\t\t\t.format(self.currentEnergy,self.currentTax,newEnergy,newTax),isDebug=True,tabsNum=1)\n\t\tif newEnergy < self.localMinimumEnergy or newTax < self.localMinimumTax:\n\t\t\treturn True\n\t\treturn False","title":""},{"docid":"b5cc8f4771dd58297a6e3b452182daef","score":"0.4951388","text":"def agree_to_tos(self, regr):\n return self.update_registration(\n regr.update(\n body=regr.body.update(\n agreement=regr.terms_of_service)))","title":""},{"docid":"75ab2bff6b751ae042892e7659f71902","score":"0.49507055","text":"def test_deny_pending_payment(self):\n pass","title":""},{"docid":"3edd57c4e43f7b72baf285f6459805fb","score":"0.49433905","text":"def test_4(self):\n c1 = Store.Customer(\"harold\", \"qcf\", False)\n self.assertFalse(c1.is_premium_member(), \"IS premium member\")","title":""},{"docid":"e9b6b7553ae5fcebc14a9b407f801a55","score":"0.4942659","text":"def test_rate_cost_type_valid(self):\n self.ocp_data[\"rates\"][0][\"tiered_rates\"] = [\n {\n \"unit\": \"USD\",\n \"value\": 0.22,\n \"usage\": {\"usage_start\": None, \"usage_end\": None},\n \"cost_type\": \"Infrastructure\",\n }\n ]\n\n with tenant_context(self.tenant):\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\n if serializer.is_valid(raise_exception=True):\n serializer.save()\n\n self.ocp_data[\"rates\"][0][\"tiered_rates\"] = [\n {\n \"unit\": \"USD\",\n \"value\": 0.22,\n \"usage\": {\"usage_start\": None, \"usage_end\": None},\n \"cost_type\": \"Supplementary\",\n }\n ]\n\n with tenant_context(self.tenant):\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\n if serializer.is_valid(raise_exception=True):\n serializer.save()","title":""},{"docid":"89995bce1c766439b816fae14b3a212a","score":"0.49375176","text":"def test_cancel_on_braintree_success(self):\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.active = True\n self.braintree_customer.subscription_id = self.cancellable_id\n\n SubscriptionManager.cancel_on_braintree(self.braintree_customer)\n\n self.assertEqual(\"\",self.braintree_customer.subscription_id)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertFalse(self.braintree_customer.active)","title":""},{"docid":"ceb541e8c03f9188a6ad804fc06af67f","score":"0.49262586","text":"def test_create_new_order_VIES_fault(self):\n rup = baker.make(\n \"RecurringUserPlan\",\n user_plan__user__billinginfo__country=\"CZ\",\n user_plan__user__billinginfo__tax_number=\"CZ0123\",\n amount=10,\n tax=11,\n )\n with no_connection():\n order = rup.create_renew_order()\n self.assertEqual(order.tax, 11)","title":""},{"docid":"b1d65a3322432bdf78bb8926583c4261","score":"0.49252683","text":"def test_charge_correct_for_regular_after_close_1_day(self):\n rental = create_test_rental(\n book=self.book3,\n customer=self.user1,\n date_borrowed=\"2019-05-24 00:00:00.400952+00:00\",\n )\n close_rental_url = reverse(\"close_rental\", kwargs={\"pk\": rental.pk})\n\n data = {\"date_returned\": \"2019-05-25 13:46:57.249145+03:00\"}\n response = self.client.put(close_rental_url, data=data, format=\"json\")\n\n self.assertEqual(response.data[\"amount_charged\"], \"2.00\")\n self.assertEqual(response.data[\"rental_status\"], \"Closed\")\n self.assertEqual(response.data[\"currency\"], CURRENCY)","title":""},{"docid":"01d505b0decf38a647955a28599ddb50","score":"0.4904093","text":"def modify_expiration(self, new_expiration):\n if self.is_cancellable:\n if self.expiration!=new_expiration:\n log.info(\"bo#%s: modify expiration of pending order\" % self.ticket)\n dt = get_datetime()\n new_expiration = dt_td_to_dt(new_expiration, dt)\n not_implemented_error(\"Can't modify modify expiration of pending order\")\n else:\n return","title":""},{"docid":"8dbfc818a7ffbd6d8f09400f973bae44","score":"0.49022606","text":"def contribute(k):\n global amount\n global _alarm\n amount = amount + k\n # remove the alarm\n if amount >= initial_amount * 0.3:\n _alarm = False","title":""},{"docid":"d2a8259def53576441e2c3cbf731cff5","score":"0.48961106","text":"def test_3(self):\n c1 = Store.Customer(\"harold\", \"qcf\", True)\n self.assertTrue(c1.is_premium_member(), \"not premium member\")","title":""},{"docid":"ed609928d752e18a160ececd58722048","score":"0.48943877","text":"def done(self,cr,uid,ids,context=None):\n for record in self.browse(cr, uid, ids, context=context):\n search_result = self.pool.get('payment.enrich').browse(cr, uid,record.enrich_id.id)\n if record.cost < 1 :\n raise osv.except_osv(_('Invalid Action Error'), _('The Entered Cost Is Wrong!'))\n if record.cost > search_result.residual_amount :\n raise osv.except_osv(_('Invalid Action Error'), _('Your Residual Balance Is Less Than Your Cost!'))\n if context:\n if 'owner' in context and 'model_id' in context:\n owner = context['owner']\n owner = int(owner)\n model_id = context['model_id']\n if str(model_id) == 'fleet.vehicle.log.contract':\n model_obj = self.pool.get(model_id)\n model = model_obj.browse(cr, uid, owner, context=context)\n model.write({'state':'open'})\n return self.write(cr, uid, ids, {'state':'done'},context=context)","title":""},{"docid":"69a162a79de7a75785f9e6b2d9f07588","score":"0.48916712","text":"def use(self):\n if self.flag:\n if self.credit < self.price_of_trip:\n return \"Your credit is not enough, please increase your credit\"\n else:\n self.credit -= self.price_of_trip\n return \"Done\"\n else:\n return \"Sorry, your card has expired.\"","title":""},{"docid":"8f42aed0cc21bdb3278ce55b720546c9","score":"0.48887736","text":"def save(self, *args, **kwargs): #pylint: disable=W0221\n created = not self.uuid\n if created:\n existing_balance = self.account.calculated_balance\n else:\n all_other_active_transactions = [x for x in self.account.transactions.all() if x != self and x.active]\n existing_balance = sum(x.amount for x in all_other_active_transactions)\n\n if not self.active:\n pass\n elif (existing_balance + self.amount) < 0:\n raise AccountBalanceError(\n 'Balance of account {} would be brought below 0'.format(self.account)\n )\n\n instance = super().save(*args, **kwargs)\n self.account.update_balance()\n return instance","title":""},{"docid":"b363d7bccb6a832fb10e43ce7a25224e","score":"0.48865578","text":"def test_check_cost():","title":""},{"docid":"5414a8c5839816743bf0cbd8ac9e56b2","score":"0.4879177","text":"def test_subscribe_no_previous_subscription(self):\n self.braintree_customer.payment_method_token = 'valid_payment_token'\n SubscriptionManager.subscribe(self.braintree_customer)\n\n # fetch customer again\n self.braintree_customer = BraintreeUser.objects.get(user=self.user)\n self.assertTrue(self.braintree_customer.active)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertEqual(self.braintree_customer.expiry_date, timezone.make_aware(datetime.datetime(2017,7,25,0,0),pytz.utc))","title":""},{"docid":"8ca9f8a29d701d9d48f8e45102180e20","score":"0.48787516","text":"def use(self):\n if self.credit < self.price_of_trip:\n print(\"Your credit is not enough, please increase your credit\")\n else:\n self.credit -= self.price_of_trip\n print(\"Done\")","title":""},{"docid":"9cd45ad080c1c56c8cbfc9207b5b5102","score":"0.48765975","text":"def user_added_credit(self):\n return (self.user.Credit > 0)","title":""},{"docid":"062c77ab3749342bb1564bcd2adac432","score":"0.48746642","text":"def can_accept_credit(self, value):\n return value >= 0","title":""},{"docid":"8122483a63fd15da82f48566f50c3229","score":"0.48686194","text":"def can_reschedule(self) -> bool:\n return pulumi.get(self, \"can_reschedule\")","title":""},{"docid":"16f6380e73fd49f8b86458e7fc9e0b33","score":"0.48665464","text":"def evaluate_cost(self, msg):\n raise NotImplementedError()","title":""},{"docid":"6f89aace960b6791c1f3e1ff7d144a06","score":"0.4865596","text":"def event_charge(order_type):\r\n if order_type == \"Promo event\":\r\n return 400\r\n\r\n else:\r\n return 0\r\n # promo event adds $400\r","title":""},{"docid":"47d5396dbeb716ea766bf4f1d486f78b","score":"0.48641136","text":"def accept_venue_transferring(venue_transferring_id, notice_id):\n result = False\n try:\n venue_transferring = VenueAccountTransferring.objects.get(pk=venue_transferring_id)\n except VenueAccountTransferring.DoesNotExist:\n venue_transferring = None\n\n if venue_transferring and venue_transferring.target:\n venue_account = venue_transferring.venue_account\n from_user = venue_transferring.venue_account.account.user\n target = venue_transferring.target.user\n\n venue_account.account = venue_transferring.target\n venue_account.save()\n\n venue_account.venue.user = venue_transferring.target.user\n venue_account.venue.save()\n\n Event.events.filter(venue_account_owner_id=venue_account.id).update(owner=venue_transferring.target.user.id)\n\n venue_transferring.delete()\n try:\n notice = Notice.objects.get(pk=notice_id)\n except Notice.DoesNotExist:\n notice = None\n\n if notice:\n notice_data = json.loads(notice.log)\n notice_data['state'] = 'Accepted'\n notice.log = json.dumps(notice_data)\n notice.read = True\n notice.save()\n\n target_name = target.username\n target_link = reverse('userena_profile_detail', kwargs={'username': target.username})\n notice_service.create_notice('venue_transferring_accepting', from_user, {\n 'subject': 'Cityfusion: transferring of your venue has been accepted.',\n 'user': from_user,\n 'venue_account': venue_account,\n 'target_name': target_name,\n 'target_link': target_link,\n }, {\n 'venue_name': venue_account.venue.name,\n 'venue_link': reverse('public_venue_account', kwargs={'slug': venue_account.slug}),\n 'target_name': target_name,\n 'target_link': target_link,\n 'date': datetime.datetime.now().strftime('%A, %b. %d, %I:%M %p'),\n })\n\n result = True\n return result","title":""},{"docid":"49498e9ce2c5bce44185577c91b23796","score":"0.48574036","text":"def test_calendar_query_expanded_recurring(self):\n raise SkipTest(\"test unimplemented\")","title":""},{"docid":"c6324c28d862c00ffdc2ae0dc5e70e6b","score":"0.48518384","text":"def canAct(self) -> bool:\n return self.cooldown < 1","title":""},{"docid":"f45779ffbce56bfee3e2be30e2f8f871","score":"0.48489207","text":"def can_act(self, **kwargs):\n return True","title":""},{"docid":"93a9c89b27742aba82795030c9f364b2","score":"0.48466724","text":"def allowed(self, user, amount):\n return True","title":""},{"docid":"03ed1b16924abba4e92036f3818dbe89","score":"0.48446918","text":"def accept_transfer(self):\n self.is_accepted = True\n self.date_time_accepted = models.DateTimeField(auto_now=True)","title":""},{"docid":"1840fd1a32ea102c2e1a3f9b81b39e3c","score":"0.4842639","text":"def reservation_mark_entrance(user: User, reservation: Reservation):\n owns_restaurant = reservation.restaurant.operator == user\n if owns_restaurant and reservation.status is ReservationState.ACCEPTED and reservation.reservation_time <= datetime.datetime.now():\n #Might want to add user notification\n reservation.entrance_time = datetime.datetime.now()\n reservation.status = ReservationState.SEATED\n db.session.commit()\n return True\n\n return False","title":""},{"docid":"feb54b24ff87cfae41c9d6f70d27ac2f","score":"0.48392415","text":"def is_pending_approval(self):\n if self.registration_method == self.REQUESTED \\\n and self.is_pending_activation():\n return True\n else:\n return False","title":""},{"docid":"19cfca091b1463122f1d72df8cd26df0","score":"0.48392242","text":"def save(self, *args, **kwargs):\n self.expire_date = self.sent_date + timedelta(days=self.period)\n super(Referral, self).save(*args, **kwargs)","title":""},{"docid":"a0cc51ab46ff66425dd3db7cba10015c","score":"0.48387","text":"def test_no_credit_change_for_subscription_downgrade(self):\n credits = 20\n\n current_plan = Subscription.get_plan_by_id('pro')\n new_plan = Subscription.get_plan_by_id('standard')\n\n credits = add_subscription_credits(credits, current_plan, new_plan, None)\n\n assert credits == 20","title":""}],"string":"[\n {\n \"docid\": \"4c04ec54a498200fddebe61bfad145c7\",\n \"score\": \"0.6974328\",\n \"text\": \"def enact(self, billing_cycle, disable_if_done=True):\\n as_of = billing_cycle.date_range.lower\\n if not self.is_enactable(as_of):\\n raise CannotEnactUnenactableRecurringCostError(\\n \\\"RecurringCost {} is unenactable.\\\".format(self.uuid)\\n )\\n\\n if self.has_enacted(billing_cycle):\\n raise RecurringCostAlreadyEnactedForBillingCycle(\\n 'RecurringCost cost {} already enacted for {}'.format(self, billing_cycle)\\n )\\n\\n with db_transaction.atomic():\\n recurred_cost = RecurredCost(\\n recurring_cost=self,\\n billing_cycle=billing_cycle,\\n )\\n recurred_cost.make_transaction()\\n recurred_cost.save()\\n\\n if disable_if_done:\\n self.disable_if_done(billing_cycle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7445a2491181d4ec5c6d4f004d143bf\",\n \"score\": \"0.6717582\",\n \"text\": \"def has_enacted(self, billing_cycle):\\n return RecurredCost.objects.filter(\\n recurring_cost=self,\\n billing_cycle=billing_cycle,\\n ).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a365ef9fd5b0a002ccf6bc7bdc0e6573\",\n \"score\": \"0.62935936\",\n \"text\": \"def make_eligible(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8dff91131c125b350fd2012d9790602\",\n \"score\": \"0.5993728\",\n \"text\": \"def charge_recurring(self, grace_period=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9039e5f6aa07f9bcc62aac117fff3c64\",\n \"score\": \"0.5735424\",\n \"text\": \"def confirmed(self, cr, uid, ids, context=None):\\n\\tallow_archive_line_obj = self.pool.get('services.contracts.allowances.lines')\\n for record in self.browse(cr, uid, ids, context=context):\\n\\t\\tif not record.allowances_lines_before :\\n \\traise osv.except_osv(_('Partner Lines !'), _('Sorry no partner Lines!'))\\n\\n\\t \\tlines_ids = [line.id for line in record.allowances_lines_after]\\n \\tallow_archive_line_obj.unlink(cr,uid,lines_ids,context=context)\\n\\n\\t\\tfor lines in record.allowances_lines_before:\\n\\t\\t\\tif lines.percentage_rating < 0 or lines.percentage_rating > 100 :\\n \\t\\traise osv.except_osv(_('Rate Error !'), _('Sorry you insert wrong rate ... rate is between (0,100)!'))\\n \\t\\tamount_after_rate_id = allow_archive_line_obj.create(cr, uid, {\\n \\t\\t\\t\\t'cost_of_rent':lines.cost_of_rent,\\n \\t\\t\\t\\t'amount_untaxed':round (lines.amount_untaxed*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'amount_tax':round(lines.amount_tax*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'amount_total':round(lines.amount_total*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'deduct_days':lines.deduct_days,\\n \\t\\t\\t\\t'deduct_amount':lines.deduct_amount,\\n \\t\\t\\t\\t'contract_id':lines.contract_id.id,\\n\\t\\t\\t\\t\\t'env_allow_id_after_rate':record.id,\\n\\t\\t\\t\\t\\t'type': 'after',\\n 'category_id':lines.category_id.id,\\n\\t\\t\\t\\t\\t'percentage_rating':lines.percentage_rating,\\n\\n })\\n\\t\\t\\n \\n self.write(cr, uid, ids, {'state':'confirmed'})\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e06878f1f73ebe95cde824d74b51178a\",\n \"score\": \"0.57182837\",\n \"text\": \"def credit_deliverer():\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"072e3adb9a2b8b733955c2d0470f5cfd\",\n \"score\": \"0.57024777\",\n \"text\": \"def disable_costs():\\n RecurringCost.objects.all().disable_if_done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729f7ea408343845dae0e20a444735a6\",\n \"score\": \"0.5669726\",\n \"text\": \"def awaiting_payment(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b89439cb1fb4ae0622c0559693f5b3\",\n \"score\": \"0.56223905\",\n \"text\": \"def test_add_recurring_schedule(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c2e807bcff1aea5874de05585dc1b9\",\n \"score\": \"0.55890375\",\n \"text\": \"def test_renew_user_pending_cancel(self):\\n self.braintree_customer.active = True\\n self.braintree_customer.pending_cancel = True\\n self.braintree_customer.subscription_id = \\\"ValidSubscriptionID\\\"\\n\\n result = SubscriptionManager.renew(self.braintree_customer)\\n self.assertEqual(\\\"ValidSubscriptionID\\\",result)\\n self.assertFalse(self.braintree_customer.pending_cancel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4473da0917973c1c85d7b653ba0f05d\",\n \"score\": \"0.5505976\",\n \"text\": \"def _check_cost(self, cr, uid, ids, context=None):\\n for enrich in self.browse(cr, uid, ids, context=context):\\n if enrich.amount <= 0:\\n raise osv.except_osv(_('ValidateError'), _('The Cost Must Be Greater Than Zero!'))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2212377ef5be3f290b7c7bf4d129b1\",\n \"score\": \"0.5493613\",\n \"text\": \"def test_subscribe_already_subscribed(self):\\n self.braintree_customer.active = True\\n self.braintree_customer.save()\\n with self.assertRaises(BraintreeError):\\n SubscriptionManager.subscribe(self.braintree_customer)\\n\\n # Check state not altered\\n self.assertTrue(self.braintree_customer.active)\\n self.assertFalse(self.braintree_customer.pending_cancel)\\n self.assertIsNone(self.braintree_customer.expiry_date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eff043c23370b9890345712648bb3b8\",\n \"score\": \"0.54642403\",\n \"text\": \"def test_renew_attempt_on_active_subscription(self):\\n self.braintree_customer.pending_cancel = False\\n self.braintree_customer.active = True\\n\\n with self.assertRaises(BraintreeError):\\n SubscriptionManager.renew(self.braintree_customer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2878f031423ce10692b6087cd1179530\",\n \"score\": \"0.5447805\",\n \"text\": \"def make_transaction(self):\\n if self.pk:\\n raise CannotRecreateTransactionOnRecurredCost(\\n 'The transaction for this recurred cost has already been created. You cannot create it again.'\\n )\\n\\n amount = self.recurring_cost.get_amount(self.billing_cycle)\\n\\n # It is quite possible that there will be nothing to bill, in which\\n # case we cannot create a transaction with no legs, nor can we create\\n # legs with zero values. Therefore we don't create any transaction.\\n if not amount:\\n return None\\n\\n self.transaction = Transaction.objects.create(\\n description='Created by recurring cost',\\n date=self.billing_cycle.date_range.lower\\n )\\n\\n # Use the SplitManager's custom queryset's split() method to get the\\n # amount to be billed for each split\\n splits = self.recurring_cost.splits.all().split(amount)\\n\\n # Create the transaction leg for the outbound funds\\n # (normally to an expense account)\\n self.transaction.legs.add(Leg.objects.create(\\n transaction=self.transaction,\\n amount=Money(amount, self.recurring_cost.currency),\\n account=self.recurring_cost.to_account,\\n ))\\n\\n for split, split_amount in splits:\\n # Create the transaction legs for the inbound funds\\n # (from housemate accounts)\\n if split_amount:\\n self.transaction.legs.add(Leg.objects.create(\\n transaction=self.transaction,\\n amount=Money(split_amount * -1, self.recurring_cost.currency),\\n account=split.from_account,\\n ))\\n\\n return self.transaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba47d041e96bc2b902f7e951534eb141\",\n \"score\": \"0.5434562\",\n \"text\": \"def _cost(self, action):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e3085b8493d861cc1b7d398c776e29a\",\n \"score\": \"0.54062164\",\n \"text\": \"def is_recurring(self):\\n return self.__is_recurring\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c624f7dcb4ff52e7b73c58f67c9e6318\",\n \"score\": \"0.5402047\",\n \"text\": \"def test_request_cancel_already_pending_cancel(self):\\n self.braintree_customer.subscription_id = \\\"1234\\\"\\n self.braintree_customer.pending_cancel = True\\n self.braintree_customer.save()\\n\\n self.assertFalse(SubscriptionManager.request_cancel(self.braintree_customer))\\n self.assertTrue(self.braintree_customer.pending_cancel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33fbd06b632f15c7255f0e18c5897445\",\n \"score\": \"0.53812355\",\n \"text\": \"def is_invited_pending_activation(self):\\n if self.registration_method == self.INVITED \\\\\\n and self.is_pending_activation():\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb71fff2f7605e3d2c71500830dcbfa\",\n \"score\": \"0.53572863\",\n \"text\": \"def give_raise(self):\\r\\n self.hourly_pay = 12.00\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1633c21b982abbaba97996b7c87877d\",\n \"score\": \"0.5347127\",\n \"text\": \"def charge(self, other):\\n if self.flag:\\n self.credit += other\\n return \\\"{} Tomans has been added to your card credit and now the credit of your card is {} Tomans\\\".format(\\n other, self.credit)\\n else:\\n return \\\"Sorry, your card has expired.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a0bca7e754388a34640f20c91afa68b\",\n \"score\": \"0.534086\",\n \"text\": \"def payment_approval(self, house_cost: (int, float)):\\n if self.money_available >= house_cost: # Person has enough available money to make a deal with Realtor\\n self.money_available -= house_cost\\n print(f'Payment from {self.name} was approved')\\n return True\\n print(f'{self.name} doesn\\\\'t have enough money to buy this house')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34fc2ccd08cf094015a4a7a71287468\",\n \"score\": \"0.53131855\",\n \"text\": \"def test_request_cancel_active_subscription(self):\\n self.braintree_customer.subscription_id = \\\"1234\\\"\\n self.braintree_customer.pending_cancel = False\\n self.braintree_customer.save()\\n self.assertTrue(SubscriptionManager.request_cancel(self.braintree_customer))\\n self.assertTrue(self.braintree_customer.pending_cancel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e8c9b966e3614e15ccd1ab4a2563ef9\",\n \"score\": \"0.5306632\",\n \"text\": \"def test_overlap(self):\\r\\n t = Expense(name = \\\"fake lunch\\\",\\r\\n amount = 1.,\\r\\n on = (WeeklyRecurring(FR,\\r\\n fromdt = self.fromdt,\\r\\n todt = self.todt),\\r\\n DailyRecurring(fromdt = self.fromdt, \\r\\n todt = self.todt)))\\r\\n\\r\\n self.m.addTransaction(t)\\r\\n self.assertEqual(self.m.totalSaved(self.fromdt, self.todt), -365.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3965aee40434d2d49b4ec667e75657\",\n \"score\": \"0.5294787\",\n \"text\": \"def reports_editable(self):\\n end_plus_time = self.datetime_end + datetime.timedelta(days=CCR_DELTA)\\n return timezone.now() < end_plus_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc118ce9f8f76044d0d8341c976cc671\",\n \"score\": \"0.5265665\",\n \"text\": \"def make_payment(self, cost):\\n self.process_coins()\\n if self.money_received >= cost:\\n change = round(self.money_received - cost, 2)\\n print(f\\\"Here is {self.CURRENCY}{change} in change.\\\")\\n self.profit += cost\\n self.money_received = 0\\n return True\\n else:\\n print(\\\"Sorry that's not enough money. Money refunded.\\\")\\n self.money_received = 0\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a11d04b563ecf30411d522d7a9987d\",\n \"score\": \"0.5261785\",\n \"text\": \"def test_create_sugar_cpn_exp_task(self):\\n offset_days = 2\\n coupon = Coupon.objects.get(id=416)\\n coupon.expiration_date = (datetime.date.today() + \\n datetime.timedelta(days=offset_days))\\n coupon.save()\\n self.assertEqual(create_sugar_coupon_expire_task(MockSugar(), \\n offset_days=offset_days), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27b0305fb2f568ecd0568b90978b95e3\",\n \"score\": \"0.52602494\",\n \"text\": \"async def resign(self, ctx):\\n currency = await bank.get_currency_name(ctx.guild)\\n await self.config.user(ctx.author).gameRole.set(\\\"User\\\")\\n await ctx.send(\\n f\\\"{ctx.author} has spent 10,000 {currency}- to resign from their current job.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8491f0b61b51e158a8d7c69100f4d319\",\n \"score\": \"0.5213568\",\n \"text\": \"def test_subscription_transaction_declined_suspend(self):\\n\\n # We'll create a plan that starts here\\n start_date = dt.date(2019, 1, 1)\\n\\n # And the trial date ends here too\\n trial_end_date = dt.date(2019, 1, 1)\\n\\n # The customer will use some metered features here\\n metered_usage_on = dt.date(2019, 1, 10)\\n\\n # Docs will be generated to bill here.\\n prev_billing_date = dt.date(2019, 1, 3)\\n\\n # So, the customer grace period ends here.\\n # First billing interval: dt.date(2019, 2, 1)\\n # \\n billing_grace_exp = dt.date(2019, 2, 3)\\n\\n # The next billing check should discover that the subscription\\n # is unpaid.\\n # Billing due date is: dt.date(2019, 2, 6)\\n # With the grace period: dt.date(2019, 2, 9)\\n # \\n billing_post_grace_check = dt.date(2019, 2, 10)\\n\\n # Create a customer\\n #\\n customer = CustomerFactory.create(sales_tax_percent=Decimal('0.00'),\\n payment_due_days=3)\\n PaymentMethodFactory.create(\\n payment_processor=triggered_processor, customer=customer,\\n canceled=False,\\n verified=True,\\n )\\n\\n # Create a metered feature\\n #\\n mf_price = Decimal('2.5')\\n metered_feature = MeteredFeatureFactory(\\n included_units_during_trial=Decimal('0.00'),\\n price_per_unit=mf_price)\\n currency = 'USD'\\n\\n # Crate a plan with metered features. Generate the invoice after\\n # the 5 day trial period, the plan will be billed every 30 days.\\n # \\n generate_after = 5\\n plan = PlanFactory.create(interval=Plan.INTERVALS.DAY,\\n interval_count=30,\\n generate_after=generate_after,\\n enabled=True,\\n amount=Decimal('20.00'),\\n trial_period_days=1,\\n metered_features=[metered_feature],\\n currency=currency)\\n\\n # Subscribe the customer\\n #\\n subscription = SubscriptionFactory.create(\\n plan=plan, start_date=start_date, customer=customer)\\n subscription.activate()\\n subscription.save()\\n\\n # Log some metered usage\\n consumed_1 = Decimal('5.00')\\n consumed_2 = Decimal('5.00')\\n mf_log = MeteredFeatureUnitsLogFactory.create(\\n subscription=subscription,\\n metered_feature=metered_feature,\\n start_date=metered_usage_on,\\n end_date=subscription.trial_end,\\n consumed_units=consumed_1)\\n\\n # Generate the docs\\n call_command('generate_docs',\\n billing_date=prev_billing_date,\\n stdout=self.output)\\n\\n proforma = Proforma.objects.first()\\n\\n assert proforma.proforma_entries.count() != 0\\n assert Subscription.objects.all().count() == 1\\n assert Invoice.objects.all().count() == 0\\n assert Proforma.objects.all()[0].total > Decimal('0.00')\\n\\n # Consume more units\\n mf_log.consumed_units += consumed_2\\n mf_log.save()\\n\\n call_command('generate_docs',\\n billing_date=billing_grace_exp,\\n stdout=self.output)\\n\\n assert Proforma.objects.all().count() != 0\\n assert Invoice.objects.all().count() == 0\\n\\n for pf in Proforma.objects.all():\\n # # Issue the proforma to generate transactions\\n # proforma = Proforma.objects.all()[1]\\n pf.issue()\\n pf.save()\\n\\n self.assertEqual(pf.state, Proforma.STATES.ISSUED)\\n # Fail the transaction\\n for tx in pf.transactions:\\n # tx = proforma.transactions[0]\\n tx.fail()\\n tx.save()\\n self.assertEqual(tx.state, Transaction.States.Failed)\\n\\n assert Transaction.objects.all().count() != 0\\n\\n call_command('check_subscriptions',\\n billing_date=billing_post_grace_check,\\n stdout=self.output)\\n\\n subscr = Subscription.objects.first()\\n\\n # Scan for subscriptions with unpaid documents\\n logging.debug(\\\"subscr %s\\\" % subscr)\\n self.assertEqual(subscr.state, Subscription.STATES.CANCELED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5417e376b07167245209d7488eceae5c\",\n \"score\": \"0.5208941\",\n \"text\": \"def test_get_active_coupon(self):\\n coupon = COUPON_FACTORY.create_coupon()\\n slot = SLOT_FACTORY.create_slot(coupon=coupon)\\n future_date = datetime.date.today() + datetime.timedelta(weeks=1)\\n coupon.expiration_date = future_date\\n coupon.save()\\n self.assertEqual(slot.get_active_coupon(), coupon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"257ba26e9929795ad5f5632d4cddacf1\",\n \"score\": \"0.52071846\",\n \"text\": \"def test_create_sugar_cc_exp_task(self):\\n current_date = datetime.datetime.now()\\n order = Order.objects.create(billing_record_id=114)\\n credit_card = CreditCard.objects.get(id=500)\\n credit_card.exp_month = int(current_date.strftime(\\\"%m\\\"))\\n credit_card.exp_year = int(current_date.strftime(\\\"%y\\\"))\\n credit_card.encrypt_cc('4111111111111111')\\n credit_card.clean()\\n credit_card.save()\\n Payment.objects.create(order=order, credit_card=credit_card,\\n amount='50.00', method='C', status='p')\\n self.assertEqual(create_sugar_cc_expire_task(MockSugar(), \\n test_mode=True), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb78960a18fbea5f420521a73cc9d91e\",\n \"score\": \"0.5201274\",\n \"text\": \"def save(self, *args, **kwargs):\\n if not self.pk:\\n self.start_time_rent = datetime.date.today()\\n self.end_time_rent = self.start_time_rent + datetime.timedelta(days=7)\\n self.reservation.isrented = True\\n self.reservation.save()\\n return super(Rental, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f499e3c7140c536233b59aa5018b00e0\",\n \"score\": \"0.5198995\",\n \"text\": \"def life_insurance_to_recive_total(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e3ba91aae2b3005b29c825e9df6617\",\n \"score\": \"0.51950544\",\n \"text\": \"def pay_costs(self):\\r\\n cost = self.cost\\r\\n if cost:\\r\\n self.owner.player.char_ob.pay_money(cost)\\r\\n self.owner.player.msg(\\\"You pay %s coins for the event.\\\" % cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a554eae4772018c64970255353249ec\",\n \"score\": \"0.5190392\",\n \"text\": \"def accepting(self):\\n quote = self.get_quotes('labor')[0]\\n quantity = max(0, quote.price - 14)\\n self.create('labor', quantity)\\n self.accept_quote_partial(quote, quantity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7b38cc190f388ab6d0549317e33158\",\n \"score\": \"0.51902777\",\n \"text\": \"def recap(self, request, pk=None):\\n retreat = self.get_object()\\n # This is a hard-coded limitation to allow anonymous users to call\\n # the function.\\n time_limit = retreat.end_time - timedelta(days=1)\\n if timezone.now() < time_limit:\\n response_data = {\\n 'detail': \\\"Retreat ends in more than 1 day.\\\"\\n }\\n return Response(response_data, status=status.HTTP_200_OK)\\n\\n # Notify a user for every reserved seat\\n emails = []\\n for reservation in retreat.reservations.filter(\\n is_active=True,\\n post_event_send=False):\\n send_post_retreat_email(reservation.user, retreat)\\n reservation.post_event_send = True\\n reservation.save()\\n emails.append(reservation.user.email)\\n\\n response_data = {\\n 'stop': True,\\n 'emails': emails\\n }\\n return Response(response_data, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e1634797064f5c392aeeff54879a26\",\n \"score\": \"0.5186644\",\n \"text\": \"def check_costs(self):\\r\\n if self.cost > self.owner.player.char_ob.currency:\\r\\n self.add_error(\\r\\n \\\"celebration_tier\\\",\\r\\n \\\"You cannot afford to pay the cost of %s.\\\" % self.cost,\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10a00c4c4117a81dd34a039b5016bec\",\n \"score\": \"0.51843166\",\n \"text\": \"def test_no_credit_change_for_same_subscription(self):\\n credits = 20\\n\\n current_plan = Subscription.get_plan_by_id('pro')\\n new_plan = Subscription.get_plan_by_id('pro')\\n\\n may_29_2015 = datetime.datetime(2015, 5, 29, 0, 0, 0)\\n may_29_2015 = pytz.utc.localize(may_29_2015)\\n\\n credits = add_subscription_credits(credits, current_plan, new_plan,\\n may_29_2015)\\n\\n assert credits == 20\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cfe667dbc270d1f0777d11ac37720c\",\n \"score\": \"0.51762897\",\n \"text\": \"def test_remove_recurring_schedule(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd0405d5d1dcce4d6e9af01483ebde3\",\n \"score\": \"0.5167032\",\n \"text\": \"def confirmed(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1bb3838c74ae2e2fc33261a8bcad5d\",\n \"score\": \"0.51638985\",\n \"text\": \"def test_retire_rate_plan(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd5fc643f60a16857bf0dbbe97c12f5\",\n \"score\": \"0.5128787\",\n \"text\": \"def add_to_excess(self) -> None:\\n if self.msg.value <= 0:\\n revert(\\\"No amount added to excess\\\")\\n self._treasury_balance.set(self.icx.get_balance(self.address))\\n self.FundReceived(self.msg.sender, self.msg.value, f\\\"{self.msg.value} added to excess\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e170852adf1c091d578eada0184631b9\",\n \"score\": \"0.51178837\",\n \"text\": \"async def admin_credit(self, ctx, target: discord.Member, sum: int = 100):\\n if is_registered(target.id):\\n \\n inventories = get_file(\\\"inventories\\\")\\n inventories[str(target.id)][\\\"balance\\\"] += sum\\n update_file(\\\"inventories\\\", inventories)\\n\\n embed = discord.Embed(color=admin_color)\\n embed.set_author(name=\\\"🛠️ Admin\\\")\\n embed.add_field(name=\\\"💰 Credit\\\",\\n value=f\\\"{ctx.author.mention}, {target.mention} a été crédité de `{sum}` PO (pièces d'or)\\\")\\n embed = set_footer(embed, ctx)\\n await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e253144f1d44f70f72725a3cdfd381\",\n \"score\": \"0.5104723\",\n \"text\": \"def test_plan_autorenew_at_expire(self):\\n up = baker.make(\\\"UserPlan\\\", expire=date(2020, 1, 1))\\n self.assertEqual(up.plan_autorenew_at(), date(2020, 1, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba629d8534d92877f8ce279d15aafa1\",\n \"score\": \"0.5101618\",\n \"text\": \"def trigger_expiration_notices(at_time=None, nb_days=15, dry_run=False):\\n\\n def _handle_organization_notices(organization):\\n if organization.processor_card_key:\\n card = organization.retrieve_card()\\n try:\\n exp_month, exp_year = card['exp_date'].split('/')\\n exp_date = datetime(year=int(exp_year),\\n month=int(exp_month), day=1, tzinfo=at_time.tzinfo)\\n if lower >= exp_date:\\n LOGGER.info(\\\"payment method expires soon for %s\\\",\\n organization)\\n if not dry_run:\\n signals.card_expires_soon.send(\\n sender=__name__, organization=organization,\\n nb_days=nb_days)\\n except (KeyError, ValueError):\\n # exp info is missing or the format is incorrect\\n pass\\n else:\\n LOGGER.info(\\\"%s doesn't have a payment method attached\\\",\\n organization)\\n if not dry_run:\\n signals.payment_method_absent.send(sender=__name__,\\n organization=organization)\\n\\n at_time = datetime_or_now(at_time)\\n lower = at_time + relativedelta(days=nb_days)\\n upper = at_time + relativedelta(days=nb_days + 1)\\n LOGGER.info(\\n \\\"trigger notifications for subscription expiring within [%s,%s[ ...\\\",\\n lower, upper)\\n prev_organization = None\\n subscription = None\\n for subscription in Subscription.objects.valid_for(ends_at__gte=lower,\\n ends_at__lt=upper).order_by('organization'):\\n org = subscription.organization\\n plan = subscription.plan\\n\\n try:\\n if subscription.auto_renew:\\n if plan.renewal_type == plan.AUTO_RENEW:\\n if org.id != prev_organization:\\n _handle_organization_notices(org)\\n\\n prev_organization = org.id\\n else:\\n if plan.renewal_type == plan.ONE_TIME:\\n LOGGER.info(\\\"trigger upgrade soon for %s\\\", subscription)\\n if not dry_run:\\n signals.subscription_upgrade.send(sender=__name__,\\n subscription=subscription, nb_days=nb_days)\\n\\n elif plan.renewal_type == plan.REPEAT:\\n LOGGER.info(\\\"trigger expires soon for %s\\\", subscription)\\n if not dry_run:\\n signals.expires_soon.send(sender=__name__,\\n subscription=subscription, nb_days=nb_days)\\n\\n except Exception as err: #pylint:disable=broad-except\\n # We use `Exception` because the email server might be\\n # unavailable but ConnectionRefusedError is not a subclass\\n # of RuntimeError.\\n LOGGER.exception(\\\"error: %s\\\", err)\\n\\n # flushing the last organization\\n if subscription and subscription.organization.id != prev_organization:\\n if subscription.auto_renew:\\n if subscription.plan.renewal_type == subscription.plan.AUTO_RENEW:\\n _handle_organization_notices(subscription.organization)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c66ea0657cc0bf9e28af5aaeb6647c4\",\n \"score\": \"0.5100977\",\n \"text\": \"def redeem(self, instance, customer, save=True):\\n start = timezone.now().date()\\n end = start + relativedelta(months=self.duration)\\n discount = Discount(instance=instance,\\n coupon=self,\\n start=start,\\n end=end,\\n customer=customer)\\n discount.full_clean()\\n if save:\\n discount.save()\\n return discount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be729754fa4102aa1c8b61477d0cdb82\",\n \"score\": \"0.50970876\",\n \"text\": \"def customer_wants_condiments(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58052ca7885a95388e8a943ad79fc9ee\",\n \"score\": \"0.50891936\",\n \"text\": \"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\\n mock_tz.now.return_value = datetime(\\n 2015, 2, 11, 10, tzinfo=dt_timezone.utc\\n )\\n self.ticketed_event.ticket_cost = 0\\n self.ticketed_event.save()\\n self.assertFalse(self.unpaid.cancelled)\\n self.assertFalse(self.paid.cancelled)\\n\\n management.call_command('cancel_unpaid_ticket_bookings')\\n # emails are sent to user per cancelled booking and studio once for all\\n # cancelled bookings\\n self.unpaid.refresh_from_db()\\n self.paid.refresh_from_db()\\n self.assertEqual(len(mail.outbox), 0)\\n self.assertFalse(self.unpaid.cancelled)\\n self.assertFalse(self.paid.cancelled)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c02baa7b4f7d3c8e78826cc17010a89a\",\n \"score\": \"0.5078882\",\n \"text\": \"def register_withdraw(self, withdraw_intent): \\n if withdraw_intent > 0:\\n self.teo.register_withdraw(self, withdraw_intent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a75db34bf124af171ddcacdf0705d9\",\n \"score\": \"0.5062819\",\n \"text\": \"def reckon(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef1115a68c9a4958c538af0517b7e7f\",\n \"score\": \"0.5059867\",\n \"text\": \"def reimport_eligible(self) -> bool:\\n return pulumi.get(self, \\\"reimport_eligible\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabd4c0d5435e6616abe559534230b62\",\n \"score\": \"0.5054001\",\n \"text\": \"def test_add_taxation_strategy_to_rate_plan(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d7067817879f0fa2eb4466e3f35086\",\n \"score\": \"0.50444573\",\n \"text\": \"def can_act(self) -> bool:\\n return self.cooldown < 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d7067817879f0fa2eb4466e3f35086\",\n \"score\": \"0.50444573\",\n \"text\": \"def can_act(self) -> bool:\\n return self.cooldown < 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a314d8c47829fe7aea4b854dab54cb\",\n \"score\": \"0.5037591\",\n \"text\": \"def restock(self):\\n self.money = 9999\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b99cdf09d4409ff6cacc373e96ffd86\",\n \"score\": \"0.5028068\",\n \"text\": \"def may_send_escalation(self, source_type: str, escalation_reminder_cadence: timedelta) -> bool:\\n with self.session.begin() as session:\\n last_escalated = (\\n session.query(EventRecord.escalated_at)\\n .filter(EventRecord.source_type == source_type)\\n .order_by(EventRecord.escalated_at.desc())\\n .limit(1)\\n .one_or_none()\\n )\\n\\n if not last_escalated[0]:\\n return True\\n\\n return last_escalated[0] <= datetime.utcnow() - escalation_reminder_cadence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846e2e0abaea6b50e2948f1d6060a39c\",\n \"score\": \"0.50160515\",\n \"text\": \"def filter(self, event, *args, **kwargs):\\n return event[\\\"expiration_datetime\\\"] <= datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"201aa010ced8cf71f548ecf6d393575b\",\n \"score\": \"0.5011571\",\n \"text\": \"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\\n mock_tz.now.return_value = datetime(\\n 2015, 2, 10, 10, tzinfo=dt_timezone.utc\\n )\\n self.event.cost = 0\\n self.event.save()\\n self.assertEqual(\\n self.unpaid.status, 'OPEN', self.unpaid.status\\n )\\n self.assertEqual(\\n self.paid.status, 'OPEN', self.paid.status\\n )\\n management.call_command('cancel_unpaid_bookings')\\n # emails are sent to user per cancelled booking and studio once for all\\n # cancelled bookings\\n unpaid_booking = Booking.objects.get(id=self.unpaid.id)\\n paid_booking = Booking.objects.get(id=self.paid.id)\\n self.assertEqual(len(mail.outbox), 0)\\n self.assertEqual(\\n unpaid_booking.status, 'OPEN', unpaid_booking.status\\n )\\n self.assertEqual(\\n paid_booking.status, 'OPEN', paid_booking.status\\n )\\n\\n # auto_cancelled set to True only on cancelled bookings\\n self.assertFalse(unpaid_booking.auto_cancelled)\\n self.assertFalse(paid_booking.auto_cancelled)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"820d1f3307e066ab24821548eb04bf4c\",\n \"score\": \"0.50115657\",\n \"text\": \"def isOnSaleSoon(ticket):\\n return ticket.start_time > timezone.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f1843e58d485cff565bd73b3ba806c\",\n \"score\": \"0.500794\",\n \"text\": \"def _claim_iscore(self) -> None:\\n iscore_details_dict = self._system.queryIScore(self.address)\\n if iscore_details_dict['estimatedICX'] != 0:\\n amount = iscore_details_dict[\\\"estimatedICX\\\"]\\n self._system.claimIScore()\\n self._daily_reward.set(amount)\\n self._rate.set(self.getRate())\\n self._total_lifetime_reward.set(self.getLifetimeReward() + amount)\\n self._distributing.set(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800b7a21c89f88bfe6327f7bb4361f22\",\n \"score\": \"0.5006686\",\n \"text\": \"def should_pay_attention(self):\\n return random.randint(1,100) > self.wander\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c1c2ca303c52e34683b990d4edc7da\",\n \"score\": \"0.50048196\",\n \"text\": \"def test_authorize_pending_payment(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9eeb30737a0056e7591f58ee9369f45\",\n \"score\": \"0.5003403\",\n \"text\": \"def acknowledge_visitor(visitor):\\n visitor.acknowledged = True\\n visitor.acknowledged_time = datetime.now()\\n visitor.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4fcf332d5161239a6433b289ae158ed\",\n \"score\": \"0.50032485\",\n \"text\": \"def resent(self):\\n return 'Resent-Date' in self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ef7fe9adf9b416d5c880d173273e84\",\n \"score\": \"0.49881572\",\n \"text\": \"def pre_approve(self, cr, uid, ids, context={}):\\n \\tfor voucher in self.browse(cr, uid, ids, context=context):\\n \\t if not voucher.department_id.analytic_account_id:\\n \\t raise osv.except_osv(_('Configration Check!'), _(\\\"Please add cost center for your department!\\\"))\\n \\t periods = self.pool.get('account.period').search(cr, uid, [('date_start','<=',voucher.date),('date_stop','>=',voucher.date),('company_id','=',voucher.company_id.id)], context=context)\\n\\n\\n res=0.0\\n if voucher.purpose:\\n if not voucher.purpose.account_id: raise osv.except_osv(_('Warning!'), _('Please configure account for this purpose!')) \\n voucher_line = {\\n \\t\\t'voucher_id': voucher.id,\\n \\t\\t'partner_id': voucher.partner_id.id,\\n \\t\\t'untax_amount': voucher.amount,\\n \\t\\t'amount': voucher.amount,\\n 'name': voucher.narration,\\n \\t\\t'type': 'dr',\\n \\t\\t'account_analytic_id': voucher.department_id.analytic_account_id and voucher.department_id.analytic_account_id.id,\\n 'account_id': voucher.purpose.account_id.id,\\n \\t }\\n new_amount = res and res or voucher.amount \\n voucher_line.update({'amount':new_amount,'untax_amount':new_amount})\\n \\t if voucher.line_ids :\\n for line in voucher.line_ids:\\n \\t\\t self.pool.get('account.voucher.line').write(cr, uid, line.id, {\\n \\t\\t'voucher_id': voucher.id,\\n \\t\\t'partner_id': voucher.partner_id.id,\\n \\t\\t'untax_amount': res or line.amount,\\n \\t\\t'amount': line.amount,\\n 'name': voucher.narration,\\n \\t\\t'type': 'dr',\\n \\t\\t'account_analytic_id': line.account_analytic_id and line.account_analytic_id.id or voucher.department_id.analytic_account_id.id,\\n 'account_id': voucher.purpose.account_id.id or line.account_id.id,\\n \\t }, context=context)\\n \\t else:\\n\\n \\t\\t new_voucher_line = self.pool.get('account.voucher.line').create(cr, uid, voucher_line, context=context)\\n context.update({'purchase':True})\\n self.create_budget_confirmation(cr, uid, [voucher.id], context)\\n \\tself.write(cr, uid, ids,{'state': 'preapprove','type':'purchase','ratification':True}, context=context)\\n #cxt = context.copy()\\n #cxt.update({'type':'ratification'})\\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\\n self.write(cr, uid, ids, {'state': 'approved'}, context=context)\\n\\n \\t'''self.write(cr, uid, ids, {'state': 'preapprove'})\\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\\n self.write(cr, uid, ids, {'state': 'approve','type':'purchase','ratification':True}, context=context)'''\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcdcd6a69d116b1f4b370a2e026f1a4\",\n \"score\": \"0.49817756\",\n \"text\": \"def test_update_rate_plan(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acf948659222da3835bbf26fc3a9017\",\n \"score\": \"0.49740785\",\n \"text\": \"def test_carryover(self):\\n carryover = Money(100)\\n available = {Decimal(0.5): carryover}\\n self.forecast(available)\\n self.assertEqual(\\n self.forecast.carryover,\\n carryover)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2504305e30cfc1efa4189640cffd33\",\n \"score\": \"0.4970885\",\n \"text\": \"def recurring(self):\\n return self.filter(total_billing_cycles__isnull=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d0e95adc90beb142a910e5cbf8e54c\",\n \"score\": \"0.4959831\",\n \"text\": \"def activate_card(**kwargs):\\n for card in ClientClubCard.objects.filter(\\n date_begin__isnull=True, is_paid_activate=False):\\n # period for activate\\n days = card.club_card.period_activation\\n if card.date_start + timedelta(days - 1) < date.today():\\n card.activate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12d51add250d35f7917a5213649c725c\",\n \"score\": \"0.49592566\",\n \"text\": \"def aspiration_criteria_satisfied(self,index):\\n\\t\\tnewEnergy = self.currentEnergy + self._diffEi[index]#self.count_energy(state)\\n\\t\\t# newTax = self._taxes[index]#self.count_tax(state=state)\\n\\t\\ttmpState = copy(self.currentState)\\n\\t\\ttmpState[index] = 1 - tmpState[index]\\n\\t\\tnewTax = self.count_tax(state=tmpState)\\n\\t\\toutput(\\\"Checking aspiration criteria: oldEnergy={}, current tax = {}; new energy = {}, new tax = {}\\\"\\\\\\n\\t\\t\\t\\t\\t \\t\\t\\t\\t.format(self.currentEnergy,self.currentTax,newEnergy,newTax),isDebug=True,tabsNum=1)\\n\\t\\tif newEnergy < self.localMinimumEnergy or newTax < self.localMinimumTax:\\n\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cc8f4771dd58297a6e3b452182daef\",\n \"score\": \"0.4951388\",\n \"text\": \"def agree_to_tos(self, regr):\\n return self.update_registration(\\n regr.update(\\n body=regr.body.update(\\n agreement=regr.terms_of_service)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ab2bff6b751ae042892e7659f71902\",\n \"score\": \"0.49507055\",\n \"text\": \"def test_deny_pending_payment(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3edd57c4e43f7b72baf285f6459805fb\",\n \"score\": \"0.49433905\",\n \"text\": \"def test_4(self):\\n c1 = Store.Customer(\\\"harold\\\", \\\"qcf\\\", False)\\n self.assertFalse(c1.is_premium_member(), \\\"IS premium member\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b6b7553ae5fcebc14a9b407f801a55\",\n \"score\": \"0.4942659\",\n \"text\": \"def test_rate_cost_type_valid(self):\\n self.ocp_data[\\\"rates\\\"][0][\\\"tiered_rates\\\"] = [\\n {\\n \\\"unit\\\": \\\"USD\\\",\\n \\\"value\\\": 0.22,\\n \\\"usage\\\": {\\\"usage_start\\\": None, \\\"usage_end\\\": None},\\n \\\"cost_type\\\": \\\"Infrastructure\\\",\\n }\\n ]\\n\\n with tenant_context(self.tenant):\\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\\n if serializer.is_valid(raise_exception=True):\\n serializer.save()\\n\\n self.ocp_data[\\\"rates\\\"][0][\\\"tiered_rates\\\"] = [\\n {\\n \\\"unit\\\": \\\"USD\\\",\\n \\\"value\\\": 0.22,\\n \\\"usage\\\": {\\\"usage_start\\\": None, \\\"usage_end\\\": None},\\n \\\"cost_type\\\": \\\"Supplementary\\\",\\n }\\n ]\\n\\n with tenant_context(self.tenant):\\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\\n if serializer.is_valid(raise_exception=True):\\n serializer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89995bce1c766439b816fae14b3a212a\",\n \"score\": \"0.49375176\",\n \"text\": \"def test_cancel_on_braintree_success(self):\\n self.braintree_customer.pending_cancel = True\\n self.braintree_customer.active = True\\n self.braintree_customer.subscription_id = self.cancellable_id\\n\\n SubscriptionManager.cancel_on_braintree(self.braintree_customer)\\n\\n self.assertEqual(\\\"\\\",self.braintree_customer.subscription_id)\\n self.assertFalse(self.braintree_customer.pending_cancel)\\n self.assertFalse(self.braintree_customer.active)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb541e8c03f9188a6ad804fc06af67f\",\n \"score\": \"0.49262586\",\n \"text\": \"def test_create_new_order_VIES_fault(self):\\n rup = baker.make(\\n \\\"RecurringUserPlan\\\",\\n user_plan__user__billinginfo__country=\\\"CZ\\\",\\n user_plan__user__billinginfo__tax_number=\\\"CZ0123\\\",\\n amount=10,\\n tax=11,\\n )\\n with no_connection():\\n order = rup.create_renew_order()\\n self.assertEqual(order.tax, 11)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d65a3322432bdf78bb8926583c4261\",\n \"score\": \"0.49252683\",\n \"text\": \"def test_charge_correct_for_regular_after_close_1_day(self):\\n rental = create_test_rental(\\n book=self.book3,\\n customer=self.user1,\\n date_borrowed=\\\"2019-05-24 00:00:00.400952+00:00\\\",\\n )\\n close_rental_url = reverse(\\\"close_rental\\\", kwargs={\\\"pk\\\": rental.pk})\\n\\n data = {\\\"date_returned\\\": \\\"2019-05-25 13:46:57.249145+03:00\\\"}\\n response = self.client.put(close_rental_url, data=data, format=\\\"json\\\")\\n\\n self.assertEqual(response.data[\\\"amount_charged\\\"], \\\"2.00\\\")\\n self.assertEqual(response.data[\\\"rental_status\\\"], \\\"Closed\\\")\\n self.assertEqual(response.data[\\\"currency\\\"], CURRENCY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01d505b0decf38a647955a28599ddb50\",\n \"score\": \"0.4904093\",\n \"text\": \"def modify_expiration(self, new_expiration):\\n if self.is_cancellable:\\n if self.expiration!=new_expiration:\\n log.info(\\\"bo#%s: modify expiration of pending order\\\" % self.ticket)\\n dt = get_datetime()\\n new_expiration = dt_td_to_dt(new_expiration, dt)\\n not_implemented_error(\\\"Can't modify modify expiration of pending order\\\")\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dbfc818a7ffbd6d8f09400f973bae44\",\n \"score\": \"0.49022606\",\n \"text\": \"def contribute(k):\\n global amount\\n global _alarm\\n amount = amount + k\\n # remove the alarm\\n if amount >= initial_amount * 0.3:\\n _alarm = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2a8259def53576441e2c3cbf731cff5\",\n \"score\": \"0.48961106\",\n \"text\": \"def test_3(self):\\n c1 = Store.Customer(\\\"harold\\\", \\\"qcf\\\", True)\\n self.assertTrue(c1.is_premium_member(), \\\"not premium member\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed609928d752e18a160ececd58722048\",\n \"score\": \"0.48943877\",\n \"text\": \"def done(self,cr,uid,ids,context=None):\\n for record in self.browse(cr, uid, ids, context=context):\\n search_result = self.pool.get('payment.enrich').browse(cr, uid,record.enrich_id.id)\\n if record.cost < 1 :\\n raise osv.except_osv(_('Invalid Action Error'), _('The Entered Cost Is Wrong!'))\\n if record.cost > search_result.residual_amount :\\n raise osv.except_osv(_('Invalid Action Error'), _('Your Residual Balance Is Less Than Your Cost!'))\\n if context:\\n if 'owner' in context and 'model_id' in context:\\n owner = context['owner']\\n owner = int(owner)\\n model_id = context['model_id']\\n if str(model_id) == 'fleet.vehicle.log.contract':\\n model_obj = self.pool.get(model_id)\\n model = model_obj.browse(cr, uid, owner, context=context)\\n model.write({'state':'open'})\\n return self.write(cr, uid, ids, {'state':'done'},context=context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a162a79de7a75785f9e6b2d9f07588\",\n \"score\": \"0.48916712\",\n \"text\": \"def use(self):\\n if self.flag:\\n if self.credit < self.price_of_trip:\\n return \\\"Your credit is not enough, please increase your credit\\\"\\n else:\\n self.credit -= self.price_of_trip\\n return \\\"Done\\\"\\n else:\\n return \\\"Sorry, your card has expired.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f42aed0cc21bdb3278ce55b720546c9\",\n \"score\": \"0.48887736\",\n \"text\": \"def save(self, *args, **kwargs): #pylint: disable=W0221\\n created = not self.uuid\\n if created:\\n existing_balance = self.account.calculated_balance\\n else:\\n all_other_active_transactions = [x for x in self.account.transactions.all() if x != self and x.active]\\n existing_balance = sum(x.amount for x in all_other_active_transactions)\\n\\n if not self.active:\\n pass\\n elif (existing_balance + self.amount) < 0:\\n raise AccountBalanceError(\\n 'Balance of account {} would be brought below 0'.format(self.account)\\n )\\n\\n instance = super().save(*args, **kwargs)\\n self.account.update_balance()\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b363d7bccb6a832fb10e43ce7a25224e\",\n \"score\": \"0.48865578\",\n \"text\": \"def test_check_cost():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5414a8c5839816743bf0cbd8ac9e56b2\",\n \"score\": \"0.4879177\",\n \"text\": \"def test_subscribe_no_previous_subscription(self):\\n self.braintree_customer.payment_method_token = 'valid_payment_token'\\n SubscriptionManager.subscribe(self.braintree_customer)\\n\\n # fetch customer again\\n self.braintree_customer = BraintreeUser.objects.get(user=self.user)\\n self.assertTrue(self.braintree_customer.active)\\n self.assertFalse(self.braintree_customer.pending_cancel)\\n self.assertEqual(self.braintree_customer.expiry_date, timezone.make_aware(datetime.datetime(2017,7,25,0,0),pytz.utc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca9f8a29d701d9d48f8e45102180e20\",\n \"score\": \"0.48787516\",\n \"text\": \"def use(self):\\n if self.credit < self.price_of_trip:\\n print(\\\"Your credit is not enough, please increase your credit\\\")\\n else:\\n self.credit -= self.price_of_trip\\n print(\\\"Done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd45ad080c1c56c8cbfc9207b5b5102\",\n \"score\": \"0.48765975\",\n \"text\": \"def user_added_credit(self):\\n return (self.user.Credit > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062c77ab3749342bb1564bcd2adac432\",\n \"score\": \"0.48746642\",\n \"text\": \"def can_accept_credit(self, value):\\n return value >= 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8122483a63fd15da82f48566f50c3229\",\n \"score\": \"0.48686194\",\n \"text\": \"def can_reschedule(self) -> bool:\\n return pulumi.get(self, \\\"can_reschedule\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f6380e73fd49f8b86458e7fc9e0b33\",\n \"score\": \"0.48665464\",\n \"text\": \"def evaluate_cost(self, msg):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f89aace960b6791c1f3e1ff7d144a06\",\n \"score\": \"0.4865596\",\n \"text\": \"def event_charge(order_type):\\r\\n if order_type == \\\"Promo event\\\":\\r\\n return 400\\r\\n\\r\\n else:\\r\\n return 0\\r\\n # promo event adds $400\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d5396dbeb716ea766bf4f1d486f78b\",\n \"score\": \"0.48641136\",\n \"text\": \"def accept_venue_transferring(venue_transferring_id, notice_id):\\n result = False\\n try:\\n venue_transferring = VenueAccountTransferring.objects.get(pk=venue_transferring_id)\\n except VenueAccountTransferring.DoesNotExist:\\n venue_transferring = None\\n\\n if venue_transferring and venue_transferring.target:\\n venue_account = venue_transferring.venue_account\\n from_user = venue_transferring.venue_account.account.user\\n target = venue_transferring.target.user\\n\\n venue_account.account = venue_transferring.target\\n venue_account.save()\\n\\n venue_account.venue.user = venue_transferring.target.user\\n venue_account.venue.save()\\n\\n Event.events.filter(venue_account_owner_id=venue_account.id).update(owner=venue_transferring.target.user.id)\\n\\n venue_transferring.delete()\\n try:\\n notice = Notice.objects.get(pk=notice_id)\\n except Notice.DoesNotExist:\\n notice = None\\n\\n if notice:\\n notice_data = json.loads(notice.log)\\n notice_data['state'] = 'Accepted'\\n notice.log = json.dumps(notice_data)\\n notice.read = True\\n notice.save()\\n\\n target_name = target.username\\n target_link = reverse('userena_profile_detail', kwargs={'username': target.username})\\n notice_service.create_notice('venue_transferring_accepting', from_user, {\\n 'subject': 'Cityfusion: transferring of your venue has been accepted.',\\n 'user': from_user,\\n 'venue_account': venue_account,\\n 'target_name': target_name,\\n 'target_link': target_link,\\n }, {\\n 'venue_name': venue_account.venue.name,\\n 'venue_link': reverse('public_venue_account', kwargs={'slug': venue_account.slug}),\\n 'target_name': target_name,\\n 'target_link': target_link,\\n 'date': datetime.datetime.now().strftime('%A, %b. %d, %I:%M %p'),\\n })\\n\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49498e9ce2c5bce44185577c91b23796\",\n \"score\": \"0.48574036\",\n \"text\": \"def test_calendar_query_expanded_recurring(self):\\n raise SkipTest(\\\"test unimplemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6324c28d862c00ffdc2ae0dc5e70e6b\",\n \"score\": \"0.48518384\",\n \"text\": \"def canAct(self) -> bool:\\n return self.cooldown < 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45779ffbce56bfee3e2be30e2f8f871\",\n \"score\": \"0.48489207\",\n \"text\": \"def can_act(self, **kwargs):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a9c89b27742aba82795030c9f364b2\",\n \"score\": \"0.48466724\",\n \"text\": \"def allowed(self, user, amount):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ed1b16924abba4e92036f3818dbe89\",\n \"score\": \"0.48446918\",\n \"text\": \"def accept_transfer(self):\\n self.is_accepted = True\\n self.date_time_accepted = models.DateTimeField(auto_now=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1840fd1a32ea102c2e1a3f9b81b39e3c\",\n \"score\": \"0.4842639\",\n \"text\": \"def reservation_mark_entrance(user: User, reservation: Reservation):\\n owns_restaurant = reservation.restaurant.operator == user\\n if owns_restaurant and reservation.status is ReservationState.ACCEPTED and reservation.reservation_time <= datetime.datetime.now():\\n #Might want to add user notification\\n reservation.entrance_time = datetime.datetime.now()\\n reservation.status = ReservationState.SEATED\\n db.session.commit()\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb54b24ff87cfae41c9d6f70d27ac2f\",\n \"score\": \"0.48392415\",\n \"text\": \"def is_pending_approval(self):\\n if self.registration_method == self.REQUESTED \\\\\\n and self.is_pending_activation():\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19cfca091b1463122f1d72df8cd26df0\",\n \"score\": \"0.48392242\",\n \"text\": \"def save(self, *args, **kwargs):\\n self.expire_date = self.sent_date + timedelta(days=self.period)\\n super(Referral, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0cc51ab46ff66425dd3db7cba10015c\",\n \"score\": \"0.48387\",\n \"text\": \"def test_no_credit_change_for_subscription_downgrade(self):\\n credits = 20\\n\\n current_plan = Subscription.get_plan_by_id('pro')\\n new_plan = Subscription.get_plan_by_id('standard')\\n\\n credits = add_subscription_credits(credits, current_plan, new_plan, None)\\n\\n assert credits == 20\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":198,"cells":{"query_id":{"kind":"string","value":"ff858a38afa1ebd09c9cc6d02fb8e698"},"query":{"kind":"string","value":"r\"\"\" Computes the reduced density matrix representation of the state. May be numerical or symbolic."},"positive_passages":{"kind":"list like","value":[{"docid":"5571e02f616db6f7dcabfb10903524cd","score":"0.6066919","text":"def reduced_dm(self, modes, **kwargs):\n if modes == list(range(self.num_modes)):\n # reduced state is full state\n return self.dm(**kwargs)\n\n if isinstance(modes, int):\n modes = [modes]\n if modes != sorted(modes):\n raise ValueError(\"The specified modes cannot be duplicated.\")\n if len(modes) > self.num_modes:\n raise ValueError(\n \"The number of specified modes cannot \" \"be larger than the number of subsystems.\"\n )\n\n reduced = self.dm(**kwargs)\n reduced = reduced_density_matrix(reduced, modes, False, batched=self.batched)\n\n s = tf.identity(reduced, name=\"reduced_density_matrix\")\n return s","title":""}],"string":"[\n {\n \"docid\": \"5571e02f616db6f7dcabfb10903524cd\",\n \"score\": \"0.6066919\",\n \"text\": \"def reduced_dm(self, modes, **kwargs):\\n if modes == list(range(self.num_modes)):\\n # reduced state is full state\\n return self.dm(**kwargs)\\n\\n if isinstance(modes, int):\\n modes = [modes]\\n if modes != sorted(modes):\\n raise ValueError(\\\"The specified modes cannot be duplicated.\\\")\\n if len(modes) > self.num_modes:\\n raise ValueError(\\n \\\"The number of specified modes cannot \\\" \\\"be larger than the number of subsystems.\\\"\\n )\\n\\n reduced = self.dm(**kwargs)\\n reduced = reduced_density_matrix(reduced, modes, False, batched=self.batched)\\n\\n s = tf.identity(reduced, name=\\\"reduced_density_matrix\\\")\\n return s\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"364f54a74c07f748ea0080ffc36d9b1d","score":"0.6417854","text":"def subsystem_reduced_density_matrix(self, bit_labels):\n bit_labels_inds = [i-1 for i in bit_labels]\n other_labels_inds = list(set(range(self.system_size)) - set(bit_labels_inds))\n state_reshape = np.reshape(self.state, tuple([2]*self.system_size))\n state_reshape = np.transpose(state_reshape, bit_labels_inds + other_labels_inds)\n state_reshape = np.reshape(state_reshape, (2**len(bit_labels_inds), 2**len(other_labels_inds)))\n state_reshape = np.asmatrix(state_reshape)\n rho = state_reshape*state_reshape.H\n return rho","title":""},{"docid":"f06c3a022b5422d6e7db9221dbeeb3a6","score":"0.6406758","text":"def purity(state):\n mat = np.reshape(state, (2, 2))\n reduced_rho = np.dot(mat, np.conj(mat.T))\n return np.trace(np.dot(reduced_rho, reduced_rho))","title":""},{"docid":"06ee8f9e55a76e8517b4a14e76094cf4","score":"0.6384309","text":"def reduced_density_matrix(self, left_bits, right_bits, which='left'):\n reshaped_state = np.reshape(self.state, (2**left_bits,\n 2**right_bits)) \n reshaped_state = np.asmatrix(reshaped_state)\n if which == 'left':\n rho = reshaped_state*reshaped_state.H\n return rho","title":""},{"docid":"85162c067d7fa099823f5501dad6eedc","score":"0.57764155","text":"def _derivatives(self, state):\n ad_state = ad.adnumber(state)\n state_deriv = self.force_model(0, ad_state)\n a_matrix = jacobian(state_deriv, ad_state)\n # remove ad number from all state values before returning\n state_deriv = [state.real for state in state_deriv]\n\n return state_deriv, a_matrix","title":""},{"docid":"79cf5a3c466bbd9f31a3da7ef43a8265","score":"0.5732339","text":"def get_state_realspace_density(self, b, k, s, dim=None):\n\n\t\tself.check_c_projectors()\n\t\tif dim is not None:\n\t\t\tself.update_dim(np.array(dim)//2)\n\t\treturn self._get_realspace_state_density(b, k, s)","title":""},{"docid":"e4bb90809375f1c7d4c2865bd2ccec94","score":"0.558464","text":"def get_density(self):\n return nx.density(self.G)","title":""},{"docid":"05d106b220b1ee6e4f25244506b6018c","score":"0.55698353","text":"def quantum_density(self):\n d12 = self._D12.sum()\n\n rho = np.array(\n [[self._D11.sum(), d12], [d12.conjugate(), self._D22.sum()]]\n )\n\n rho *= self.dqdp\n\n # For the following consistency checks, get eigenvalues\n p = np.linalg.eigvalsh(rho)\n\n assert np.allclose(p[p < 0], 0), \"Quantum density matrix must be non-negative\"\n\n assert np.allclose(p.sum(), 1), \"Trace of quantum density matrix must be one\"\n\n return rho","title":""},{"docid":"1c698a987e20025f57dbbd45c3e3d824","score":"0.5514214","text":"def NE2001_dens():\n pass","title":""},{"docid":"954fe278d25fb5a651c45cd128595f60","score":"0.5507463","text":"def _density(self):\n prof_copy = self.profile.copy()\n ptype = prof_copy.pop('type', \"NFW\")\n return density_factory(ptype, **prof_copy)","title":""},{"docid":"1dc786523a52221b43a05731b29580a9","score":"0.5466165","text":"def stateDensity(self, Eps):\n return self._stateDensity(Eps)\n '''\n # Alternative calculaltion without interpolation\n if not isinstance(Eps, (collections.Sequence, np.ndarray)):\n return 16.*np.pi**2 * quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0]\n return 16.*np.pi**2 *np.array([quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0] for Eps in Eps])\n '''","title":""},{"docid":"1e7e001fdb35f9e2b0b2b37b56ddebaa","score":"0.54642904","text":"def __call__(self, state: tf.Tensor, is_density_matrix: bool = False\n ) -> tf.Tensor:\n if self._nqubits is None:\n if is_density_matrix:\n self.nqubits = len(tuple(state.shape)) // 2\n else:\n self.nqubits = len(tuple(state.shape))\n\n if self.is_controlled_by:\n return self._controlled_by_call(state, is_density_matrix)\n\n if is_density_matrix:\n state = self.einsum(self.calculation_cache.right, state,\n tf.math.conj(self.matrix))\n state = self.einsum(self.calculation_cache.left, state, self.matrix)\n return state\n\n return self.einsum(self.calculation_cache.vector, state, self.matrix)","title":""},{"docid":"73ac4e6dcdd8dfcf436fc9926b6cf18a","score":"0.54595035","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)\n self.mode.fourier = None; self.mode.energies = None\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"f6cec97371d2389325ad11ed52f664f0","score":"0.54263574","text":"def density(self, state: c_void_p, temperature: float) -> float:\n return self.api.glycolDensity(state, self.instance, temperature)","title":""},{"docid":"b5a522d307701bceef7662e3d7225273","score":"0.53983563","text":"def mass_density(self):\n return self._get_data('density') * pq.g / pq.cm**3","title":""},{"docid":"efcf24d459728bfefcbcfc4a9558562f","score":"0.53568625","text":"def calcStatePopulationDensity(self) -> None:\n\t\t# selecting rows that correspond with the state\n\t\tsumPopArea = self.stateCensusTractGDF['DP0010001'].sum(axis=0)\n\t\tsumLandArea = self.stateCensusTractGDF['ALAND10'].sum(axis=0)\n\t\t# sums_population_area= self.state.sum(axis=0)\n\t\t# pop_density_calclulated= sums_population_area.DP001001/sums_population_area.LAND_AREA\n\t\tpopDensityCalculated = sumPopArea / sumLandArea\n\t\t# adding new col\n\t\t# self.nationalCensusTractsGDF['Population Density']=None\n\t\tself.nationalCensusTractsGDF['statePopDensity'] = popDensityCalculated","title":""},{"docid":"fc4ed56fbc646f9391728c57ddf0c7cb","score":"0.53507805","text":"def W_red(W):\n assert issparse(W), \"Matrix needs to be sparse\"\n D_inv = degree_matrix(W, squared=False, indegree=False) # Assumes undireced graph, thus in-degree = out-degree.\n D_inv.data[:] = np.power(D_inv.data, -0.5) # D^(-1/2)\n return D_inv.dot(W).dot(D_inv)","title":""},{"docid":"24ad33e776fd43339cbc1a24518cc4b1","score":"0.5348118","text":"def saturated_density(self, state: c_void_p, temperature: float, quality: float) -> float:\n return self.api.refrigerantSaturatedDensity(state, self.instance, temperature, quality)","title":""},{"docid":"166bdca3b0bc2ee9a22ccd5d071e23ae","score":"0.5335536","text":"def cc_dense(self):\n cc_dense = self.iweights[[\"pid\", \"STATE\", \"iweight_state\"]].copy()\n # Use PID to match initial weights with weighted targets\n cc_dense = cc_dense.merge(self.puf_2017_filter, on=\"pid\")\n for var in self.targ_list:\n state_var = var + \"_targ\"\n cc_dense[state_var] = cc_dense[var] * cc_dense[\"iweight_state\"]\n cc_dense = cc_dense.drop(labels=self.var_list, axis=1)\n cc_dense = cc_dense.drop(labels=[\"s006\", \"AGI_STUB\"], axis=1)\n # j is an index for x, the variable we will solve for\n cc_dense[\"j\"] = np.arange(len(cc_dense)) + 1\n return cc_dense","title":""},{"docid":"89e3b690833477628c973f2500d5ee5c","score":"0.5307281","text":"def density(self):\n return DensityModel(a=14.1 * units.gram /\n units.cm**3, model='constant')","title":""},{"docid":"614c386f70be9342154c0d4d615222e9","score":"0.530172","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"614c386f70be9342154c0d4d615222e9","score":"0.530172","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"614c386f70be9342154c0d4d615222e9","score":"0.530172","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"e30eb831acf5f41270a6137d9ea4fed5","score":"0.5297523","text":"def reduced_state(self, wires):\n if len(wires) == self.num_wires:\n # reduced state is full state\n return self._state\n\n # translate to wire labels used by device\n device_wires = self.map_wires(wires)\n\n # reduce rho down to specified subsystems\n ind = np.concatenate([device_wires.toarray(), device_wires.toarray() + self.num_wires])\n rows = ind.reshape(-1, 1)\n cols = ind.reshape(1, -1)\n\n return self._state[0][rows, cols], self._state[1][ind]","title":""},{"docid":"956672841395e0ef4f499aa106f010f1","score":"0.52795523","text":"def density_matrix(psi):\n return np.dot(psi[:, np.newaxis], psi[np.newaxis, :])","title":""},{"docid":"825591400c59d579f1da6146edd1c41e","score":"0.52540666","text":"def compute_density(self):\n return density(self.coef_)","title":""},{"docid":"825591400c59d579f1da6146edd1c41e","score":"0.52540666","text":"def compute_density(self):\n return density(self.coef_)","title":""},{"docid":"84dfdc84274e9b5827a102a50f6fb572","score":"0.52432853","text":"def _discrete_state(self):\n return np.array(int(round(self.S * self.N)))","title":""},{"docid":"72b2749f4888f4104463f042150bf700","score":"0.5217364","text":"def DO(self):\n # statevector.py and operators.py have circular import\n from . import operators\n return operators.DensityOperator(self ^ self.conj(), self.basis)","title":""},{"docid":"fe31db5ab488dc0f4a5f72a53c34cf52","score":"0.520201","text":"def deq_mm(ssa):\n return 1e3 * 6 / (917 * np.asarray(ssa))","title":""},{"docid":"03b2c137509c41dff51bf722abe37482","score":"0.5191413","text":"def reactive_density(\n assignments, sinks, source=None, all_reactive=False, verbose=False):\n if not all_reactive:\n pathways = reactive_pathways(assignments, sinks, verbose=verbose)\n else:\n pathways = assignments\n\n state_counts = np.array(\n np.bincount(np.concatenate(pathways)))\n densities = state_counts / np.sum(state_counts)\n\n return densities","title":""},{"docid":"c10bea322cdeeb893df7b384935f3487","score":"0.5177796","text":"def __getDressedMatrixElements(self,UNmat):\n UNmatdimension = len(UNmat.toarray())\n #print(UNmat.toarray())\n n0 = self.n\n j0 = self.j\n l0 = self.l\n m0 = self.m1\n state_main = [self.nn,self.ll,self.jj,self.m2]\n d0 = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\n Omega_array = []\n Omg0 = self.Omega0 \n for i in range(UNmatdimension):\n d = 0 \n if (self.basisStates[i][:4] == state_main) or (self.basisStates[i][4:] == state_main):\n d = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\n Omega_array = np.append(Omega_array,0.5*Omg0*d/d0)\n row = np.zeros(UNmatdimension) + 1\n col = np.arange(UNmatdimension)\n mat = csr_matrix((Omega_array, (row, col)), shape=(2, UNmatdimension))\n UNmat = vstack([mat,UNmat])\n row = np.arange(UNmatdimension+2)\n row = np.concatenate((np.array([1]), row))\n col = np.zeros(UNmatdimension+2) + 1\n col = np.concatenate((np.array([0]), col))\n Omega_array = np.concatenate((np.array([Omg0*0.5,Omg0*0.5,self.Delta0]), Omega_array))\n mat = csr_matrix((Omega_array, (row, col)), shape=(UNmatdimension+2, 2))\n UNmat = hstack([mat,UNmat])\n UNmat = csr_matrix(UNmat)\n #print(UNmat.toarray())\n return UNmat","title":""},{"docid":"c93bea98cc7212c6e4a8d5a3c7305310","score":"0.51725036","text":"def dm(self, **kwargs):\n if self.is_pure:\n if self.batched:\n batch_index = indices[0]\n free_indices = indices[1:]\n else:\n batch_index = \"\"\n free_indices = indices\n ket = self.data\n left_str = [batch_index] + [free_indices[i] for i in range(0, 2 * self.num_modes, 2)]\n right_str = [batch_index] + [free_indices[i] for i in range(1, 2 * self.num_modes, 2)]\n out_str = [batch_index] + [free_indices[: 2 * self.num_modes]]\n einstr = \"\".join(left_str + [\",\"] + right_str + [\"->\"] + out_str)\n s = tf.einsum(einstr, ket, tf.math.conj(ket))\n else:\n s = tf.identity(self.data, name=\"density_matrix\")\n\n return s","title":""},{"docid":"9c48980a00fc054b50cebc53e52fd805","score":"0.515427","text":"def to_valid_density_matrix(\n density_matrix_rep: Union[int, np.ndarray],\n num_qubits: Optional[int] = None,\n *, # Force keyword arguments\n qid_shape: Optional[Tuple[int, ...]] = None,\n dtype: Type[np.number] = np.complex64,\n atol: float = 1e-7) -> np.ndarray:\n qid_shape = _qid_shape_from_args(num_qubits, qid_shape)\n if (isinstance(density_matrix_rep, np.ndarray) and\n density_matrix_rep.ndim == 2):\n if density_matrix_rep.shape != (np.prod(qid_shape, dtype=int),) * 2:\n raise ValueError(\n 'Density matrix was not square and of size 2 ** num_qubit, '\n 'instead was {}'.format(density_matrix_rep.shape))\n if not np.allclose(density_matrix_rep,\n np.transpose(np.conj(density_matrix_rep)),\n atol=atol):\n raise ValueError('The density matrix is not hermitian.')\n if not np.isclose(np.trace(density_matrix_rep), 1.0, atol=atol):\n raise ValueError(\n 'Density matrix did not have trace 1 but instead {}'.format(\n np.trace(density_matrix_rep)))\n if density_matrix_rep.dtype != dtype:\n raise ValueError(\n 'Density matrix had dtype {} but expected {}'.format(\n density_matrix_rep.dtype, dtype))\n if not np.all(np.linalg.eigvalsh(density_matrix_rep) > -atol):\n raise ValueError('The density matrix is not positive semidefinite.')\n return density_matrix_rep\n\n state_vector = to_valid_state_vector(density_matrix_rep,\n len(qid_shape),\n qid_shape=qid_shape,\n dtype=dtype)\n return np.outer(state_vector, np.conj(state_vector))","title":""},{"docid":"63f5bd7522ce30ae82d10f2d485eea53","score":"0.5153912","text":"def density(self):\n return(self.mass() / ((4.0/3.0)*scipy.pi*(self.radius()**3)))","title":""},{"docid":"9ce4558b5432a7aad8b52addf8a14f14","score":"0.51530224","text":"def density_matrix_from_state_vector(\n state_vector: Sequence,\n indices: Optional[Iterable[int]] = None,\n qid_shape: Optional[Tuple[int, ...]] = None,\n) -> np.ndarray:\n shape = validate_qid_shape(state_vector, qid_shape)\n n_qubits = len(shape)\n\n if indices is None:\n return np.outer(state_vector, np.conj(state_vector))\n\n indices = list(indices)\n validate_indices(n_qubits, indices)\n\n state_vector = np.asarray(state_vector).reshape(shape)\n\n sum_inds = np.array(range(n_qubits))\n sum_inds[indices] += n_qubits\n\n rho = np.einsum(state_vector, list(range(n_qubits)), np.conj(state_vector),\n sum_inds.tolist(), indices + sum_inds[indices].tolist())\n new_shape = np.prod([shape[i] for i in indices], dtype=int)\n\n return rho.reshape((new_shape, new_shape))","title":""},{"docid":"6a3b6f070d782e552643309a0e086854","score":"0.51415163","text":"def density_model(self) -> BaseModel1D:\n return self._dens_model","title":""},{"docid":"134a3fd7dc38484a5d225c4c8a41acc5","score":"0.5096904","text":"def spd_decomposition(S):\n\tP = force_symmetric_to_symmetric_positive_definite(S)\n\tM = symmetric_positive_definite_decomposition(P)\n\treturn M","title":""},{"docid":"0771801c8c7fff1b3ad82e4b2a1da3ff","score":"0.5084259","text":"def diagonalize(reduced_density_matrix):\n try:\n eigenvals, eigenvecs = np.linalg.eigh(reduced_density_matrix)\t\n except LinAlgError:\n\traise DMRGException(\"Error diagonalizing the reduced DM\")\n\n return (eigenvals, eigenvecs)","title":""},{"docid":"7ef40d865d68440d621ba2b4df6efb3b","score":"0.50785637","text":"def deco_symmatrix ( m ) :\n\n if m in LinAlg.decorated_matrices : return m \n\n LinAlg.deco_matrix ( m )\n \n R = m.rep_type\n \n m.__str__ = LinAlg.MS_STR\n m.__repr__ = LinAlg.MS_STR\n m.table = LinAlg.MS_STR\n\n m.correlations = LinAlg.MS_CORR\n \n m.Sim = LinAlg.SIM\n m.sim = LinAlg.SIM\n m.SimT = LinAlg.SIMT\n m.simT = LinAlg.SIMT\n \n m.eigenValues = LinAlg.MS_EIGENVALUES \n m.eigenVectors = LinAlg.MS_EIGENVECTORS\n m.eigen_values = LinAlg.MS_EIGENVALUES \n m.eigen_vectors = LinAlg.MS_EIGENVECTORS\n\n m.__reduce__ = LinAlg.MS_REDUCE \n m.rep_size = classgetter ( lambda cls : cls.rep_type.kSize ) \n \n return m","title":""},{"docid":"d02996822e121457dbadc79e77833684","score":"0.50629157","text":"def normalized(self):\n # swap cols and rows and row reduce\n A = sp.Matrix(self.mat).transpose().rref()[0]\n # ensure all entries are integral by multiplying by the LCD\n A *= reduce(lcm, map(lambda x: x.as_numer_denom()[1], # get denominators\n sp.utilities.iterables.flatten(A)))\n # see https://groups.google.com/forum/#!topic/sympy/e8hcF4QAldc\n A = np.array(sp.lambdify((), A, 'numpy')()) # convert to numpy array\n A = A[np.any(A,1),:].transpose() # remove 0 rows, swap rows and columns\n return HashableMatrix(np.matrix(A))","title":""},{"docid":"e9d2948fdd0278c0960eac13394ff777","score":"0.50607157","text":"def test_dephasing(self):\n\n # Check for identity on |11> state\n test_density_matrix = (dephasing_channel(self.density_matrix, 1, 1))\n self.assertAlmostEqual(norm(self.density_matrix - test_density_matrix),\n 0.0)\n\n test_density_matrix = (dephasing_channel(self.density_matrix,\n 1,\n 1,\n transpose=True))\n\n correct_matrix = np.array([[0., 0., 0., 0.], [0., 0., 0., 0.],\n [0., 0., 0.5, -0.5], [0., 0., -0.5, 1.]])\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)\n\n # Check for correct action on cat state\n # With probability = 0\n test_density_matrix = (dephasing_channel(self.cat_matrix, 0, 1))\n self.assertAlmostEqual(norm(self.cat_matrix - test_density_matrix), 0.0)\n # With probability = 1\n\n correct_matrix = np.array([[0.50, 0.25, 0.00, 0.00],\n [0.25, 0.25, 0.00, -0.25],\n [0.00, 0.00, 0.00, 0.00],\n [0.00, -0.25, 0.00, 0.50]])\n test_density_matrix = (dephasing_channel(self.cat_matrix, 1, 1))\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)","title":""},{"docid":"3fc538d60bed88cd11599c3c6959f4b5","score":"0.5043117","text":"def density(self):\r\n graph = self.__graph_dict.copy()\r\n vertexes = len(graph.keys())\r\n edges = len(self.edges())\r\n\r\n return 2.0 * edges / (vertexes *(vertexes - 1))","title":""},{"docid":"e5f9f9e4020c8f8330863b342a455e0c","score":"0.50127727","text":"def density(compound, state, value_index=0):\n table = density_table(compound)\n state_idx = state_index(state)\n try:\n return list(table.loc[(table['state'] == state_idx), 'value'])[value_index]\n except IndexError:\n print('Invalid state or compound')","title":""},{"docid":"3f9417401b3ef118e225d0562a146075","score":"0.50119233","text":"def dd(self, mu: int, nu: int):\n\t\tif self.evaluated[mu,nu]: return self.Ric[mu,nu]\n\t\tRic = 0\n\t\tfor rho in range(self.dim):\n\t\t\tRic += self.R.uddd(rho,mu,rho,nu)\n\t\tself.Ric[mu,nu] = Ric.simplify()\n\t\tself.evaluated[mu,nu] = True\n\t\treturn self.Ric[mu,nu]","title":""},{"docid":"ef79b4384b1a00538764f5c98445ce4d","score":"0.50119007","text":"def density(self):\n return sum(np.array(mass(self.symbols))) / self.volume","title":""},{"docid":"b87cc4a601fab0d64ed28e35a4405a96","score":"0.50103897","text":"def density(self, state: c_void_p, barometric_pressure: float, dry_bulb_temp: float, humidity_ratio: float) -> float:\n return self.api.psyRhoFnPbTdbW(state, barometric_pressure, dry_bulb_temp, humidity_ratio)","title":""},{"docid":"fe0da1191fb5e7d644d2e8a32e1b3aed","score":"0.5010093","text":"def classical_density(self):\n rho = self._D11 + self._D22\n\n # Check for the normalization condition\n assert np.allclose(rho.sum() * self.dqdp, 1), \"Classical density must be normalized.\"\n\n # Check for the positivity of the density\n assert np.allclose(rho[rho < 0], 0), \"Classical density mus be nonnegative\"\n\n assert not np.isnan(rho).any(), \"Classical density contains NaNs.\"\n\n # Make sure all values are real\n assert isinstance(rho[0, 0], np.float), \"Classical density must be real\"\n\n return rho","title":""},{"docid":"2fe6a799ba948be43589b718b1442f2a","score":"0.5002014","text":"def get_reversible_rate_matrix(nstates):\n M = np.exp(np.random.randn(nstates, nstates))\n pre_Q = 0.5 * (M + M.T)\n v = sample_distn(nstates)\n v_sqrt = np.sqrt(v)\n pre_Q = (pre_Q.T / v_sqrt).T * v_sqrt\n Q = pre_Q - np.diag(np.sum(pre_Q, axis=1))\n assert_reversibility(Q, v)\n return Q, v","title":""},{"docid":"080916809178ad11c99fcca08377dc79","score":"0.49931666","text":"def density(self):\n return self._density","title":""},{"docid":"ea7bff0064c282b34ff4c47214f0f4ed","score":"0.49819955","text":"def evaluate_density_vec(self, x):\r\n return self._evaluate_scalar_quantity(x[:,0], x[:,1], x[:,2], MNnModel.mn_density)","title":""},{"docid":"8fe41ebade879197650c9af2761a6dfc","score":"0.4976917","text":"def get_density(self,wavefunction): \n if wavefunction is None:\n return self.density\n \n density=np.zeros(self.shape,dtype=np.float)\n for k in range(self.NK):\n for n in range(self.nbands):\n for m in range(self.nbands):\n density+=2*self.wk[k]*self.f[k,n]*np.abs(wavefunction[k,m,n]*self.ukn[k,m])**2\n return density","title":""},{"docid":"5d65c01ac6a9758392a6b3882f17e447","score":"0.49748877","text":"def get_density(self, x):\n \n return self.get_logdensity(x).exp()","title":""},{"docid":"c272faaf51fe765b66950a3fcb109cce","score":"0.4974089","text":"def generate_reduced_sim_mat(self):\n self.reduced_sim_mat = np.zeros((self.k, self.k), dtype='float32')\n\n for r in range(2, self.k + 1):\n for s in (range(1, r) if not self.drop_edges_between_irregular_pairs else self.regularity_list[r - 2]):\n if self.is_weighted:\n cl_pair = WeightedClassesPair(self.sim_mat, self.adj_mat, self.classes, r, s, self.epsilon)\n else:\n cl_pair = ClassesPair(self.adj_mat, self.classes, r, s, self.epsilon)\n self.reduced_sim_mat[r - 1, s - 1] = self.reduced_sim_mat[s - 1, r - 1] = cl_pair.bip_density","title":""},{"docid":"30bb90332888e55a02603a36f21e30ef","score":"0.4962091","text":"def initialize_density(x,dx,normalization=1):\n rho=exp(-x**2)\n A=simps(rho,x)\n return normalization/A*rho","title":""},{"docid":"c9ef96029d241edd7a4d21334f51ce44","score":"0.4958901","text":"def density(self, r):\n pass","title":""},{"docid":"1ebd7cc640cd6a72cce91981a2d5ea69","score":"0.49578607","text":"def mass_matrix(self):\r\n if not self._fr or not self._frstar:\r\n raise ValueError('Need to compute Fr, Fr* first.')\r\n return Matrix([self._k_d, self._k_dnh])","title":""},{"docid":"5af34f7f073abed1d0aba606c4e3d4c5","score":"0.49560684","text":"def _matrix(self):\n n, d = self._x.shape\n if n < d:\n return np.dot(self._x, self._x.T) / float(n - 1)\n return np.dot(self._x.T, self._x) / float(n - 1)","title":""},{"docid":"b72faf3dd76737395397e71108fe09c0","score":"0.49559936","text":"def depurify(self, eps):\n probs = np.random.random(self.num_rows)\n probs = probs/np.sum(probs)\n arr = (self.arr + eps*np.diag(probs)) / (self.trace() + eps)\n return DenMat(self.num_rows, self.row_shape, arr)","title":""},{"docid":"a5db345c5c728b77651f7354563b650c","score":"0.49555916","text":"def irreducible(self):\r\n\t\treturn farq((self.num/MCD(self.den,self.num)),(self.den/MCD(self.den,self.num)))","title":""},{"docid":"0acd9a2c48d8dba88077ac1ef6e038df","score":"0.4938685","text":"def compute_diagonalizing_gates(\n basis_state, wires\n ): # pylint: disable=arguments-differ,unused-argument\n return []","title":""},{"docid":"22cbc58a6fc66c8903026332ba3d82ec","score":"0.49319252","text":"def distribution_matrix(mat):\n return np.diag(stationary(mat))","title":""},{"docid":"b9ae00ca62821f82771a40d1b404a03f","score":"0.49295992","text":"def normalize_adj(adj):\n # adj: dense adj\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1)) # D\n d_inv_sqrt = np.power(rowsum, -0.5).flatten() # D^-0.5\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D^-0.5\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() # D^-0.5AD^0.5","title":""},{"docid":"a94ce23a0f9d4ffeea5706804d3a5d4d","score":"0.49190745","text":"def densityFactorDry(weather):\n temperature = extractTemperature(weather, useKelvin=True)\n waterVaporPressure = humidityToPressure(weather)\n airPressure = weather.getAirPressure()*units.pascal\n dryPressure = airPressure - waterVaporPressure\n eqn = dryPressure.to_value(cds.mbar)*(57.90E-8 - 9.3250E-4/temperature.to_value(units.Kelvin)\n + 0.25844/temperature.to_value(units.Kelvin)**2.)\n densityFactor = (1. + eqn)*dryPressure.to_value(cds.mbar)/temperature.to_value(units.Kelvin)\n return densityFactor","title":""},{"docid":"8fd36f9555dc547e0a2e2a0b67384f63","score":"0.49190095","text":"def strain_displacement_matrix(self):\n L = np.zeros((3,4)) # matrix used for mapping shape function derivatives into strains\n L[0,0] = 1; L[1,3] = 1; L[2,1:3] = 1;\n xi = 0; eta = 0; # evaluation point\n _,_,dNmat = self.mesh.eval_shape_func(xi,eta) # differentiated shape functions evaluated in (xi,eta)\n Jac = np.array([[0.5,0],[0,0.5]]) # mapping back to global coordinates\n detJ = np.linalg.det(Jac)\n invJ = 1/detJ*np.array([[Jac[1,1], -Jac[0,1]], [-Jac[1,0], Jac[0,0]]])\n G = np.zeros((4,4)) # mapping matrix between rectangular element space coordinates and global coordinates\n G[:2,:2] = invJ; G[-2:,-2:] = invJ\n B0 = np.matmul(np.matmul(L,G),dNmat) # strain displacement matrix\n\n return B0","title":""},{"docid":"7acb1f9b47bb5ec27070f96bdb361bc2","score":"0.491127","text":"def evaluate_density(self, x, y, z):\r\n return self._evaluate_scalar_quantity(x, y, z, MNnModel.mn_density)","title":""},{"docid":"9e7e01cd867d1edacc06d2d11684496c","score":"0.48791188","text":"def _densityfunction(\n r: float,\n core_wavenumber: floatcomplex,\n shell_wavenumber: floatcomplex,\n core_width: float,\n shell_width: float,\n) -> float:\n return (\n abs(\n _wavefunction(r, core_wavenumber, shell_wavenumber, core_width, shell_width)\n )\n ** 2\n )","title":""},{"docid":"7ef32ca4bb9d73ed6bf0304dda863126","score":"0.4866826","text":"def cost_down(states):\n return np.linalg.norm(states)","title":""},{"docid":"7873db806f156abc30c0f468865452ee","score":"0.48629946","text":"def getDensity(self):\n return self.__density","title":""},{"docid":"9936c5f50045750f35b6449fe5ae7d59","score":"0.4831887","text":"def _d_sigma(self):\n try:\n dd = self.d_derivs\n except ValueError:\n return None\n den = self.density\n dv = np.matrix(np.zeros((len(den.deriv_params))))\n for i, pname in enumerate(den.deriv_params):\n dv[0, i] = dd[pname].angularIntegral(self.psi_max)[0]\n return np.sqrt((dv * self.density.covar * dv.T)[0, 0])","title":""},{"docid":"0743541340bf045e3787e5333449e532","score":"0.48263004","text":"def renormalise(self):\n\t\trn = self.compute_partition_function_fast()\n\t\tfor prod in self.productions:\n\t\t\tif rn[prod[0]] == 0:\n\t\t\t\tself.parameters[prod] = 0\n\t\t\telse:\n\t\t\t\tif len(prod) == 3:\n\t\t\t\t\ta,b,c = prod\n\t\t\t\t\tself.parameters[prod] *= (rn[b] * rn[c])/rn[a] \n\t\t\t\telse:\n\t\t\t\t\tself.parameters[prod] *= 1.0/rn[prod[0]]\n\t\tself.trim_zeros()\n\t\tself.set_log_parameters()\n\t\treturn self","title":""},{"docid":"0e562d84de910866b7f2e9ce09c62f04","score":"0.48175946","text":"def densFunc(z):\t\n\t\treturn rho00 + 0.5 * (rho01-rho00) * (1 + np.tanh((z-ze)/we))","title":""},{"docid":"12b2038a3047cfa21d8adad453bad6a9","score":"0.4816128","text":"def build_distribution_normalization_matrix(self):\n vector = np.array((self.distribution_matrix * self.xtable_matrix).sum(axis=1)).T\n mask = vector > 0\n vector[mask] = 1 / vector[mask]\n return diags(vector, [0], format=\"csr\", dtype=np.float32)","title":""},{"docid":"a495785d1aad265edf5f3d89225b4319","score":"0.4811534","text":"def d2matrix(dx):\n N, Nngb, dim = dx.shape\n N_derivs = 2*dim + comb(dim, 2, exact=True)\n A = np.empty((N, Nngb, N_derivs), dtype=np.float64) \n for k in range(N):\n for i in range(Nngb):\n for j in range(N_derivs):\n if j < dim:\n A[k,i,j] = dx[k,i,j] \n elif j < 2*dim:\n A[k,i,j] = dx[k,i,j-dim] * dx[k,i,j-dim] / 2\n else:\n A[k,i,j] = dx[k,i,(j+1)%dim] * dx[k,i,(j+2)%dim] # this does the cross-terms, e.g. xy, xz, yz\n return A","title":""},{"docid":"170bba8658ede3ab821a07eb00ffc8e7","score":"0.48088142","text":"def d(self):\n\n # Calculate and return the local displacement vector\n return matmul(self.T, self.D())","title":""},{"docid":"b836ed4e795f661348350e710cb2bb65","score":"0.48069096","text":"def density(self, q, t=0.0):\n q = self._remove_units_prepare_shape(q)\n orig_shape, q = self._get_c_valid_arr(q)\n t = self._validate_prepare_time(t, q)\n ret_unit = self.units[\"mass\"] / self.units[\"length\"] ** 3\n return (self._density(q, t=t).T * ret_unit).to(self.units[\"mass density\"])","title":""},{"docid":"8cc5445f9fee2382cf934dd683ea59bf","score":"0.48019257","text":"def calculate_density(self):\n wavelet_density = len(self.phi_matrices[0].nonzero()[0])/(self.graph.number_of_nodes()**2)\n wavelet_density = str(round(100*wavelet_density, 2))\n inverse_wavelet_density = len(self.phi_matrices[1].nonzero()[0])/(self.graph.number_of_nodes()**2)\n inverse_wavelet_density = str(round(100*inverse_wavelet_density, 2))\n print(\"Density of wavelets: \"+wavelet_density+\"%.\")\n print(\"Density of inverse wavelets: \"+inverse_wavelet_density+\"%.\\n\")","title":""},{"docid":"a22dc3d06834558bc2cdfe95c4af1565","score":"0.48018953","text":"def DesignMatrix(states):\n N = np.size(states, 0)\n size3 = (N, L ** 2)\n X = np.zeros(size3)\n\n for i in range(0, N):\n X[i] = np.outer(states[i, :], states[i, :]).reshape(1, -1) # .ravel()\n return X","title":""},{"docid":"92a80ae3e4b8a7df27f667e195552e8e","score":"0.4795498","text":"def _normalize_adj_diag_enhance_dense(adj, diag_lambda:float, improved=False):\n fill_value = 1 if not improved else 2\n \n adj = add_self_loop_dense(adj, fill_value=fill_value)\n degree = torch.sum(adj, dim=-1)\n degree_inv = 1 / degree\n degree_inv[degree_inv == float(\"inf\")] = 0\n adj = adj * degree_inv.reshape(-1, 1)\n\n adj += (diag_lambda * adj.diag()).diag()\n\n return adj","title":""},{"docid":"b0377a23d2b9b5ab5ed62edfd4e05488","score":"0.4789015","text":"def _dcorss_dm(self):\n\n dcdm = np.empty((self.num_mog_comp, self.num_latent_proc, self.num_inducing))\n for j in range(self.num_latent_proc):\n dcdm[:, j, :] = -cho_solve((self.chol[j, :, :], True), self.MoG.m[:, j, :].T).T * self.MoG.pi[:, np.newaxis]\n return dcdm","title":""},{"docid":"eb66346d8ffa513bf4e6829ee29cc39d","score":"0.478672","text":"def FssInvDiag(self, recompute = True):\r\n raise NotImplementedError","title":""},{"docid":"bc864306e26f90c471f48a40ac16a0ec","score":"0.4782814","text":"def rh_density(self, init_dens, gamma, mach):\n\n dens_ratio = ((gamma+1)*mach**2)/(2+(gamma-1)*mach**2)\n\n final_dens = dens_ratio*init_dens\n\n return final_dens","title":""},{"docid":"039502c9e7c7e8d4ad0fd52010a922ed","score":"0.47798538","text":"def evalulate_function(self, disk_density: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) -> torch.Tensor:\n rmat, zmat = torch.meshgrid(self.rmid, self.zmid)\n return disk_density(rmat, zmat)","title":""},{"docid":"9564f033e5d7d266c571edf867268ed7","score":"0.4779669","text":"def _dcross_ds(self):\n\n dc_ds = np.empty((self.num_mog_comp, self.num_latent_proc, self.MoG.get_sjk_size()))\n for j in range(self.num_latent_proc):\n dc_ds[:, j] = -1. / 2 * np.array(\n [self.MoG.dAinvS_dS(self.chol[j, :, :], k, j) * self.MoG.pi[k] for k in range(self.num_mog_comp)])\n return dc_ds","title":""},{"docid":"43d44e17c8cab7dfd74d9dfe4bbc03ce","score":"0.47766522","text":"def compute_dci_d(importances):\n\n # normalize across the state_variable dimension, so we get the importance of\n # each state variable for a slot\n slot_normalized_importances = importances / importances.sum(axis=0)\n sni = slot_normalized_importances\n\n # each entropy computed is the entropy for a slot over all the state variables\n slot_entropy = entropy(sni, base=importances.shape[0])\n dci_ds = 1 - slot_entropy\n dci_d = dci_ds.mean()\n\n return dci_d","title":""},{"docid":"63108ffb70e437a436de9e0fe5091598","score":"0.47744197","text":"def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)","title":""},{"docid":"63108ffb70e437a436de9e0fe5091598","score":"0.47744197","text":"def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)","title":""},{"docid":"63108ffb70e437a436de9e0fe5091598","score":"0.47744197","text":"def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)","title":""},{"docid":"a8697247f45a6c2c24dd1e3ea7b3ffc0","score":"0.47690842","text":"def stiffness_matrix_assembly(self,rho,sparse=True):\n rho = rho.flatten(order='F')\n if sparse is False:\n self.stiffness_matrix = np.zeros((self.mesh.ndof,self.mesh.ndof))\n mat_idx = np.indices((8,8))\n I = mat_idx[0].flatten()\n J = mat_idx[1].flatten()\n # assemble stiffness matrix\n for ele in range(len(self.mesh.edofMat)):\n edof = self.mesh.edofMat[ele]\n self.stiffness_matrix[edof[I],edof[J]] += (self.Emin+(rho[ele])**self.penal*(self.Emax-self.Emin))*self.KE[I,J]\n elif sparse is True:\n iK = np.kron(self.mesh.edofMat,np.ones((8,1))).flatten()\n jK = np.kron(self.mesh.edofMat,np.ones((1,8))).flatten()\n sK=((self.KE.flatten()[np.newaxis]).T*(self.Emin+(rho)**self.penal*(self.Emax-self.Emin))).flatten(order='F')\n self.stiffness_matrix = sp.coo_matrix((sK,(iK,jK)),shape=(self.mesh.ndof,self.mesh.ndof)).tocsc()\n\n return self.stiffness_matrix","title":""},{"docid":"af13d60edf934fd69ff211074ce62cb7","score":"0.47683695","text":"def simplify(self):\r\n fact = greatest_common_divisor(self.__numerator, self.__denominator)\r\n self.__numerator = self.__numerator//fact\r\n self.__denominator = self.__denominator//fact","title":""},{"docid":"2e023b8c609745368bc2f84375d6a06c","score":"0.47601324","text":"def simplify_state(state: SDFGState, remove_views: bool = False) -> MultiDiGraph:\n\n sdfg = state.parent\n\n # Copy the whole state\n G = MultiDiGraph()\n for n in state.nodes():\n G.add_node(n)\n for n in state.nodes():\n for e in state.all_edges(n):\n G.add_edge(e.src, e.dst)\n # Collapse all mappings and their scopes into one node\n scope_children = state.scope_children()\n for n in scope_children[None]:\n if isinstance(n, nodes.EntryNode):\n G.add_edges_from([(n, x) for (y, x) in G.out_edges(state.exit_node(n))])\n G.remove_nodes_from(scope_children[n])\n # Remove all nodes that are not AccessNodes or have incoming\n # wcr edges and connect their predecessors and successors\n for n in state.nodes():\n if n in G.nodes():\n if (not isinstance(n, nodes.AccessNode) or (remove_views and isinstance(sdfg.arrays[n.data], data.View))):\n for p in G.predecessors(n):\n for c in G.successors(n):\n G.add_edge(p, c)\n G.remove_node(n)\n else:\n for e in state.all_edges(n):\n if e.data.wcr is not None:\n for p in G.predecessors(n):\n for s in G.successors(n):\n G.add_edge(p, s)\n G.remove_node(n)\n break\n\n return G","title":""},{"docid":"5852be77521a54263f39fb8e44ef4dce","score":"0.47589034","text":"def ddx(n):\n return sp.spdiags((np.ones((n + 1, 1)) * [-1, 1]).T, [0, 1], n, n + 1, format=\"csr\")","title":""},{"docid":"1305563ab5d730662b91cba0c765426e","score":"0.4755412","text":"def compute(self):\n m, n = self.D.shape\n\n # Allocate memory.\n # We need +2 because we use indices starting from 1\n # and to deal with edge cases in the backward recursion.\n self.R_ = np.zeros((m+2, n+2), dtype=np.float64)\n\n _soft_dtw(self.D, self.R_, gamma=self.gamma)\n\n return self.R_[m, n]","title":""},{"docid":"2ff928f269c5ac8d7b3d2ae000cd42a5","score":"0.47552603","text":"def density(self, r):\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**3, 0.)","title":""},{"docid":"779305611eb50458e49f536acc482e7f","score":"0.47546223","text":"def to_dense(self):\n if self.size is None:\n size = max(self.d.keys()) + 1\n else:\n size = self.size\n dense = np.zeros(size)\n for k,v in self.d.items():\n dense[k] = v\n return dense","title":""},{"docid":"1db22c46cf4d3e4a22ca0797871bab30","score":"0.4754565","text":"def density(self, r):\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**2, 0.)","title":""},{"docid":"be8eb30b81846b14a296dec1b1e7d2ea","score":"0.47464868","text":"def reduced_formula(self):\n if self.structure is not None:\n return self.composition.reduced_formula\n return \"Unkonwn\"","title":""},{"docid":"efbd2abf113aed04471eebea96b78baf","score":"0.47410783","text":"def s_d(self) -> float:\n return self.s0 + 1 / (self.w * self.k_x) * self.v_t","title":""},{"docid":"f15bc534a6a908e854b492720e967cf5","score":"0.47379398","text":"def diagonalize_del_dm_in_sbasis_in_degenerate_spaces(self):\n classes = ut.get_equiv_classes(list(self.dm0_eigen_sys[0]))\n umat = np.eye(self.del_dm.num_rows, dtype=complex)\n # eq = equivalence\n for eq_class in classes:\n dim = len(eq_class)\n if dim > 1:\n if self.verbose:\n print(\"non-trivial eq class\", eq_class)\n eq_class = sorted(eq_class)\n arr = np.empty((dim, dim), dtype=complex)\n for k1, kk1 in enumerate(eq_class):\n for k2, kk2 in enumerate(eq_class):\n arr[k1, k2] = self.del_dm_in_sbasis[kk1, kk2]\n norm_arr = np.linalg.norm(arr)\n # print('norm arr', norm_arr)\n if norm_arr > 1e-4:\n _, evec_cols = np.linalg.eigh(arr)\n assert ut.is_unitary_arr(evec_cols)\n # print('bbbbbbbb', np.around(np.dot(np.dot(\n # evec_cols.conj().T,\n # arr), evec_cols).real,4))\n for k1, kk1 in enumerate(eq_class):\n for k2, kk2 in enumerate(eq_class):\n umat[kk1, kk2] = evec_cols[k1, k2]\n assert ut.is_unitary_arr(umat)\n # print('ccccccccnorm of rotated_del_dm', np.linalg.norm(\n # self.del_dm_in_sbasis.arr))\n rotated_del_dm = np.dot(\n np.dot(umat.conj().T, self.del_dm_in_sbasis.arr), umat)\n # print('norm of rotated_del_dm', np.linalg.norm(rotated_del_dm))\n self.del_dm_in_sbasis.arr = rotated_del_dm","title":""},{"docid":"b666a78a3c4f8253b314a53a530136dd","score":"0.4735516","text":"def pgradient(self):\n d = {}\n\n # Det coeff\n curr_val = self.value()\n nonzero = curr_val[0] != 0.0\n\n # dets[spin][ (phase,log), configuration, determinant]\n dets = (\n self._dets[0][:, :, self._det_map[0]],\n self._dets[1][:, :, self._det_map[1]],\n )\n\n d[\"det_coeff\"] = gpu.cp.zeros(dets[0].shape[1:], dtype=dets[0].dtype)\n d[\"det_coeff\"][nonzero, :] = (\n dets[0][0, nonzero, :]\n * dets[1][0, nonzero, :]\n * gpu.cp.exp(\n dets[0][1, nonzero, :]\n + dets[1][1, nonzero, :]\n - gpu.cp.array(curr_val[1][nonzero, np.newaxis])\n )\n / gpu.cp.array(curr_val[0][nonzero, np.newaxis])\n )\n\n for s, parm in zip([0, 1], [\"mo_coeff_alpha\", \"mo_coeff_beta\"]):\n ao = self._aovals[\n :, :, s * self._nelec[0] : self._nelec[s] + s * self._nelec[0], :\n ]\n\n split, aos = self.orbitals.pgradient(ao, s)\n mos = gpu.cp.split(gpu.cp.arange(split[-1]), gpu.asnumpy(split).astype(int))\n # Compute dj Diu/Diu\n nao = aos[0].shape[-1]\n nconf = aos[0].shape[0]\n nmo = int(split[-1])\n deriv = gpu.cp.zeros(\n (len(self._det_occup[s]), nconf, nao, nmo), dtype=curr_val[0].dtype\n )\n for det, occ in enumerate(self._det_occup[s]):\n for ao, mo in zip(aos, mos):\n for i in mo:\n if i in occ:\n col = occ.index(i)\n deriv[det, :, :, i] = self._testcol(det, col, s, ao)\n\n # now we reduce over determinants\n d[parm] = gpu.cp.zeros(deriv.shape[1:], dtype=curr_val[0].dtype)\n for di, coeff in enumerate(self.parameters[\"det_coeff\"]):\n whichdet = self._det_map[s][di]\n d[parm] += (\n deriv[whichdet]\n * coeff\n * d[\"det_coeff\"][:, di, np.newaxis, np.newaxis]\n )\n\n for k, v in d.items():\n d[k] = gpu.asnumpy(v)\n\n for k in list(d.keys()):\n if np.prod(d[k].shape) == 0:\n del d[k]\n return d","title":""},{"docid":"5b62944f2f5f14daf76514400d4af117","score":"0.4733377","text":"def __repr__(self):\n stats = self.population.getStats(self.truncFrac)\n outStr = (\"\"\"DeMin state:\n Generation: %s\n best trial solution's cost value: %s\n best trial solution: %s\\n\n worst trial solution's cost value: %s\n worst trial solution: %s\\n\n mean cost value: %s\n standard deviation of cost values: %s \n fractional tolerance: %s\n chi-square: %s\n ndf: %s\n np_trunc: %s\\n\n Parameter mean values:\\n%s\\n\n Covariance Matrix for np_trunc members:\\n%s\n \"\"\" % (self.population.generation,\n stats[0],\n self.population[self.population.ibest].x,\n stats[1],\n self.population[self.population.iworst].x,\n stats[2],\n stats[3],\n stats[4],\n stats[5],\n stats[6],\n self.population.getNpTrunc(self.truncFrac),\n stats[7],\n self.population.getCovMatRepr(stats[8])))\n return outStr","title":""}],"string":"[\n {\n \"docid\": \"364f54a74c07f748ea0080ffc36d9b1d\",\n \"score\": \"0.6417854\",\n \"text\": \"def subsystem_reduced_density_matrix(self, bit_labels):\\n bit_labels_inds = [i-1 for i in bit_labels]\\n other_labels_inds = list(set(range(self.system_size)) - set(bit_labels_inds))\\n state_reshape = np.reshape(self.state, tuple([2]*self.system_size))\\n state_reshape = np.transpose(state_reshape, bit_labels_inds + other_labels_inds)\\n state_reshape = np.reshape(state_reshape, (2**len(bit_labels_inds), 2**len(other_labels_inds)))\\n state_reshape = np.asmatrix(state_reshape)\\n rho = state_reshape*state_reshape.H\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06c3a022b5422d6e7db9221dbeeb3a6\",\n \"score\": \"0.6406758\",\n \"text\": \"def purity(state):\\n mat = np.reshape(state, (2, 2))\\n reduced_rho = np.dot(mat, np.conj(mat.T))\\n return np.trace(np.dot(reduced_rho, reduced_rho))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ee8f9e55a76e8517b4a14e76094cf4\",\n \"score\": \"0.6384309\",\n \"text\": \"def reduced_density_matrix(self, left_bits, right_bits, which='left'):\\n reshaped_state = np.reshape(self.state, (2**left_bits,\\n 2**right_bits)) \\n reshaped_state = np.asmatrix(reshaped_state)\\n if which == 'left':\\n rho = reshaped_state*reshaped_state.H\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85162c067d7fa099823f5501dad6eedc\",\n \"score\": \"0.57764155\",\n \"text\": \"def _derivatives(self, state):\\n ad_state = ad.adnumber(state)\\n state_deriv = self.force_model(0, ad_state)\\n a_matrix = jacobian(state_deriv, ad_state)\\n # remove ad number from all state values before returning\\n state_deriv = [state.real for state in state_deriv]\\n\\n return state_deriv, a_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79cf5a3c466bbd9f31a3da7ef43a8265\",\n \"score\": \"0.5732339\",\n \"text\": \"def get_state_realspace_density(self, b, k, s, dim=None):\\n\\n\\t\\tself.check_c_projectors()\\n\\t\\tif dim is not None:\\n\\t\\t\\tself.update_dim(np.array(dim)//2)\\n\\t\\treturn self._get_realspace_state_density(b, k, s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bb90809375f1c7d4c2865bd2ccec94\",\n \"score\": \"0.558464\",\n \"text\": \"def get_density(self):\\n return nx.density(self.G)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d106b220b1ee6e4f25244506b6018c\",\n \"score\": \"0.55698353\",\n \"text\": \"def quantum_density(self):\\n d12 = self._D12.sum()\\n\\n rho = np.array(\\n [[self._D11.sum(), d12], [d12.conjugate(), self._D22.sum()]]\\n )\\n\\n rho *= self.dqdp\\n\\n # For the following consistency checks, get eigenvalues\\n p = np.linalg.eigvalsh(rho)\\n\\n assert np.allclose(p[p < 0], 0), \\\"Quantum density matrix must be non-negative\\\"\\n\\n assert np.allclose(p.sum(), 1), \\\"Trace of quantum density matrix must be one\\\"\\n\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c698a987e20025f57dbbd45c3e3d824\",\n \"score\": \"0.5514214\",\n \"text\": \"def NE2001_dens():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954fe278d25fb5a651c45cd128595f60\",\n \"score\": \"0.5507463\",\n \"text\": \"def _density(self):\\n prof_copy = self.profile.copy()\\n ptype = prof_copy.pop('type', \\\"NFW\\\")\\n return density_factory(ptype, **prof_copy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc786523a52221b43a05731b29580a9\",\n \"score\": \"0.5466165\",\n \"text\": \"def stateDensity(self, Eps):\\n return self._stateDensity(Eps)\\n '''\\n # Alternative calculaltion without interpolation\\n if not isinstance(Eps, (collections.Sequence, np.ndarray)):\\n return 16.*np.pi**2 * quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0]\\n return 16.*np.pi**2 *np.array([quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0] for Eps in Eps])\\n '''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7e001fdb35f9e2b0b2b37b56ddebaa\",\n \"score\": \"0.54642904\",\n \"text\": \"def __call__(self, state: tf.Tensor, is_density_matrix: bool = False\\n ) -> tf.Tensor:\\n if self._nqubits is None:\\n if is_density_matrix:\\n self.nqubits = len(tuple(state.shape)) // 2\\n else:\\n self.nqubits = len(tuple(state.shape))\\n\\n if self.is_controlled_by:\\n return self._controlled_by_call(state, is_density_matrix)\\n\\n if is_density_matrix:\\n state = self.einsum(self.calculation_cache.right, state,\\n tf.math.conj(self.matrix))\\n state = self.einsum(self.calculation_cache.left, state, self.matrix)\\n return state\\n\\n return self.einsum(self.calculation_cache.vector, state, self.matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73ac4e6dcdd8dfcf436fc9926b6cf18a\",\n \"score\": \"0.54595035\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\\n self.mode.fourier = None; self.mode.energies = None\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cec97371d2389325ad11ed52f664f0\",\n \"score\": \"0.54263574\",\n \"text\": \"def density(self, state: c_void_p, temperature: float) -> float:\\n return self.api.glycolDensity(state, self.instance, temperature)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a522d307701bceef7662e3d7225273\",\n \"score\": \"0.53983563\",\n \"text\": \"def mass_density(self):\\n return self._get_data('density') * pq.g / pq.cm**3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efcf24d459728bfefcbcfc4a9558562f\",\n \"score\": \"0.53568625\",\n \"text\": \"def calcStatePopulationDensity(self) -> None:\\n\\t\\t# selecting rows that correspond with the state\\n\\t\\tsumPopArea = self.stateCensusTractGDF['DP0010001'].sum(axis=0)\\n\\t\\tsumLandArea = self.stateCensusTractGDF['ALAND10'].sum(axis=0)\\n\\t\\t# sums_population_area= self.state.sum(axis=0)\\n\\t\\t# pop_density_calclulated= sums_population_area.DP001001/sums_population_area.LAND_AREA\\n\\t\\tpopDensityCalculated = sumPopArea / sumLandArea\\n\\t\\t# adding new col\\n\\t\\t# self.nationalCensusTractsGDF['Population Density']=None\\n\\t\\tself.nationalCensusTractsGDF['statePopDensity'] = popDensityCalculated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4ed56fbc646f9391728c57ddf0c7cb\",\n \"score\": \"0.53507805\",\n \"text\": \"def W_red(W):\\n assert issparse(W), \\\"Matrix needs to be sparse\\\"\\n D_inv = degree_matrix(W, squared=False, indegree=False) # Assumes undireced graph, thus in-degree = out-degree.\\n D_inv.data[:] = np.power(D_inv.data, -0.5) # D^(-1/2)\\n return D_inv.dot(W).dot(D_inv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ad33e776fd43339cbc1a24518cc4b1\",\n \"score\": \"0.5348118\",\n \"text\": \"def saturated_density(self, state: c_void_p, temperature: float, quality: float) -> float:\\n return self.api.refrigerantSaturatedDensity(state, self.instance, temperature, quality)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166bdca3b0bc2ee9a22ccd5d071e23ae\",\n \"score\": \"0.5335536\",\n \"text\": \"def cc_dense(self):\\n cc_dense = self.iweights[[\\\"pid\\\", \\\"STATE\\\", \\\"iweight_state\\\"]].copy()\\n # Use PID to match initial weights with weighted targets\\n cc_dense = cc_dense.merge(self.puf_2017_filter, on=\\\"pid\\\")\\n for var in self.targ_list:\\n state_var = var + \\\"_targ\\\"\\n cc_dense[state_var] = cc_dense[var] * cc_dense[\\\"iweight_state\\\"]\\n cc_dense = cc_dense.drop(labels=self.var_list, axis=1)\\n cc_dense = cc_dense.drop(labels=[\\\"s006\\\", \\\"AGI_STUB\\\"], axis=1)\\n # j is an index for x, the variable we will solve for\\n cc_dense[\\\"j\\\"] = np.arange(len(cc_dense)) + 1\\n return cc_dense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e3b690833477628c973f2500d5ee5c\",\n \"score\": \"0.5307281\",\n \"text\": \"def density(self):\\n return DensityModel(a=14.1 * units.gram /\\n units.cm**3, model='constant')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614c386f70be9342154c0d4d615222e9\",\n \"score\": \"0.530172\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614c386f70be9342154c0d4d615222e9\",\n \"score\": \"0.530172\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614c386f70be9342154c0d4d615222e9\",\n \"score\": \"0.530172\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30eb831acf5f41270a6137d9ea4fed5\",\n \"score\": \"0.5297523\",\n \"text\": \"def reduced_state(self, wires):\\n if len(wires) == self.num_wires:\\n # reduced state is full state\\n return self._state\\n\\n # translate to wire labels used by device\\n device_wires = self.map_wires(wires)\\n\\n # reduce rho down to specified subsystems\\n ind = np.concatenate([device_wires.toarray(), device_wires.toarray() + self.num_wires])\\n rows = ind.reshape(-1, 1)\\n cols = ind.reshape(1, -1)\\n\\n return self._state[0][rows, cols], self._state[1][ind]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956672841395e0ef4f499aa106f010f1\",\n \"score\": \"0.52795523\",\n \"text\": \"def density_matrix(psi):\\n return np.dot(psi[:, np.newaxis], psi[np.newaxis, :])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825591400c59d579f1da6146edd1c41e\",\n \"score\": \"0.52540666\",\n \"text\": \"def compute_density(self):\\n return density(self.coef_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825591400c59d579f1da6146edd1c41e\",\n \"score\": \"0.52540666\",\n \"text\": \"def compute_density(self):\\n return density(self.coef_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84dfdc84274e9b5827a102a50f6fb572\",\n \"score\": \"0.52432853\",\n \"text\": \"def _discrete_state(self):\\n return np.array(int(round(self.S * self.N)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72b2749f4888f4104463f042150bf700\",\n \"score\": \"0.5217364\",\n \"text\": \"def DO(self):\\n # statevector.py and operators.py have circular import\\n from . import operators\\n return operators.DensityOperator(self ^ self.conj(), self.basis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe31db5ab488dc0f4a5f72a53c34cf52\",\n \"score\": \"0.520201\",\n \"text\": \"def deq_mm(ssa):\\n return 1e3 * 6 / (917 * np.asarray(ssa))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b2c137509c41dff51bf722abe37482\",\n \"score\": \"0.5191413\",\n \"text\": \"def reactive_density(\\n assignments, sinks, source=None, all_reactive=False, verbose=False):\\n if not all_reactive:\\n pathways = reactive_pathways(assignments, sinks, verbose=verbose)\\n else:\\n pathways = assignments\\n\\n state_counts = np.array(\\n np.bincount(np.concatenate(pathways)))\\n densities = state_counts / np.sum(state_counts)\\n\\n return densities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10bea322cdeeb893df7b384935f3487\",\n \"score\": \"0.5177796\",\n \"text\": \"def __getDressedMatrixElements(self,UNmat):\\n UNmatdimension = len(UNmat.toarray())\\n #print(UNmat.toarray())\\n n0 = self.n\\n j0 = self.j\\n l0 = self.l\\n m0 = self.m1\\n state_main = [self.nn,self.ll,self.jj,self.m2]\\n d0 = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\\n Omega_array = []\\n Omg0 = self.Omega0 \\n for i in range(UNmatdimension):\\n d = 0 \\n if (self.basisStates[i][:4] == state_main) or (self.basisStates[i][4:] == state_main):\\n d = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\\n Omega_array = np.append(Omega_array,0.5*Omg0*d/d0)\\n row = np.zeros(UNmatdimension) + 1\\n col = np.arange(UNmatdimension)\\n mat = csr_matrix((Omega_array, (row, col)), shape=(2, UNmatdimension))\\n UNmat = vstack([mat,UNmat])\\n row = np.arange(UNmatdimension+2)\\n row = np.concatenate((np.array([1]), row))\\n col = np.zeros(UNmatdimension+2) + 1\\n col = np.concatenate((np.array([0]), col))\\n Omega_array = np.concatenate((np.array([Omg0*0.5,Omg0*0.5,self.Delta0]), Omega_array))\\n mat = csr_matrix((Omega_array, (row, col)), shape=(UNmatdimension+2, 2))\\n UNmat = hstack([mat,UNmat])\\n UNmat = csr_matrix(UNmat)\\n #print(UNmat.toarray())\\n return UNmat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93bea98cc7212c6e4a8d5a3c7305310\",\n \"score\": \"0.51725036\",\n \"text\": \"def dm(self, **kwargs):\\n if self.is_pure:\\n if self.batched:\\n batch_index = indices[0]\\n free_indices = indices[1:]\\n else:\\n batch_index = \\\"\\\"\\n free_indices = indices\\n ket = self.data\\n left_str = [batch_index] + [free_indices[i] for i in range(0, 2 * self.num_modes, 2)]\\n right_str = [batch_index] + [free_indices[i] for i in range(1, 2 * self.num_modes, 2)]\\n out_str = [batch_index] + [free_indices[: 2 * self.num_modes]]\\n einstr = \\\"\\\".join(left_str + [\\\",\\\"] + right_str + [\\\"->\\\"] + out_str)\\n s = tf.einsum(einstr, ket, tf.math.conj(ket))\\n else:\\n s = tf.identity(self.data, name=\\\"density_matrix\\\")\\n\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c48980a00fc054b50cebc53e52fd805\",\n \"score\": \"0.515427\",\n \"text\": \"def to_valid_density_matrix(\\n density_matrix_rep: Union[int, np.ndarray],\\n num_qubits: Optional[int] = None,\\n *, # Force keyword arguments\\n qid_shape: Optional[Tuple[int, ...]] = None,\\n dtype: Type[np.number] = np.complex64,\\n atol: float = 1e-7) -> np.ndarray:\\n qid_shape = _qid_shape_from_args(num_qubits, qid_shape)\\n if (isinstance(density_matrix_rep, np.ndarray) and\\n density_matrix_rep.ndim == 2):\\n if density_matrix_rep.shape != (np.prod(qid_shape, dtype=int),) * 2:\\n raise ValueError(\\n 'Density matrix was not square and of size 2 ** num_qubit, '\\n 'instead was {}'.format(density_matrix_rep.shape))\\n if not np.allclose(density_matrix_rep,\\n np.transpose(np.conj(density_matrix_rep)),\\n atol=atol):\\n raise ValueError('The density matrix is not hermitian.')\\n if not np.isclose(np.trace(density_matrix_rep), 1.0, atol=atol):\\n raise ValueError(\\n 'Density matrix did not have trace 1 but instead {}'.format(\\n np.trace(density_matrix_rep)))\\n if density_matrix_rep.dtype != dtype:\\n raise ValueError(\\n 'Density matrix had dtype {} but expected {}'.format(\\n density_matrix_rep.dtype, dtype))\\n if not np.all(np.linalg.eigvalsh(density_matrix_rep) > -atol):\\n raise ValueError('The density matrix is not positive semidefinite.')\\n return density_matrix_rep\\n\\n state_vector = to_valid_state_vector(density_matrix_rep,\\n len(qid_shape),\\n qid_shape=qid_shape,\\n dtype=dtype)\\n return np.outer(state_vector, np.conj(state_vector))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f5bd7522ce30ae82d10f2d485eea53\",\n \"score\": \"0.5153912\",\n \"text\": \"def density(self):\\n return(self.mass() / ((4.0/3.0)*scipy.pi*(self.radius()**3)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce4558b5432a7aad8b52addf8a14f14\",\n \"score\": \"0.51530224\",\n \"text\": \"def density_matrix_from_state_vector(\\n state_vector: Sequence,\\n indices: Optional[Iterable[int]] = None,\\n qid_shape: Optional[Tuple[int, ...]] = None,\\n) -> np.ndarray:\\n shape = validate_qid_shape(state_vector, qid_shape)\\n n_qubits = len(shape)\\n\\n if indices is None:\\n return np.outer(state_vector, np.conj(state_vector))\\n\\n indices = list(indices)\\n validate_indices(n_qubits, indices)\\n\\n state_vector = np.asarray(state_vector).reshape(shape)\\n\\n sum_inds = np.array(range(n_qubits))\\n sum_inds[indices] += n_qubits\\n\\n rho = np.einsum(state_vector, list(range(n_qubits)), np.conj(state_vector),\\n sum_inds.tolist(), indices + sum_inds[indices].tolist())\\n new_shape = np.prod([shape[i] for i in indices], dtype=int)\\n\\n return rho.reshape((new_shape, new_shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3b6f070d782e552643309a0e086854\",\n \"score\": \"0.51415163\",\n \"text\": \"def density_model(self) -> BaseModel1D:\\n return self._dens_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134a3fd7dc38484a5d225c4c8a41acc5\",\n \"score\": \"0.5096904\",\n \"text\": \"def spd_decomposition(S):\\n\\tP = force_symmetric_to_symmetric_positive_definite(S)\\n\\tM = symmetric_positive_definite_decomposition(P)\\n\\treturn M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0771801c8c7fff1b3ad82e4b2a1da3ff\",\n \"score\": \"0.5084259\",\n \"text\": \"def diagonalize(reduced_density_matrix):\\n try:\\n eigenvals, eigenvecs = np.linalg.eigh(reduced_density_matrix)\\t\\n except LinAlgError:\\n\\traise DMRGException(\\\"Error diagonalizing the reduced DM\\\")\\n\\n return (eigenvals, eigenvecs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef40d865d68440d621ba2b4df6efb3b\",\n \"score\": \"0.50785637\",\n \"text\": \"def deco_symmatrix ( m ) :\\n\\n if m in LinAlg.decorated_matrices : return m \\n\\n LinAlg.deco_matrix ( m )\\n \\n R = m.rep_type\\n \\n m.__str__ = LinAlg.MS_STR\\n m.__repr__ = LinAlg.MS_STR\\n m.table = LinAlg.MS_STR\\n\\n m.correlations = LinAlg.MS_CORR\\n \\n m.Sim = LinAlg.SIM\\n m.sim = LinAlg.SIM\\n m.SimT = LinAlg.SIMT\\n m.simT = LinAlg.SIMT\\n \\n m.eigenValues = LinAlg.MS_EIGENVALUES \\n m.eigenVectors = LinAlg.MS_EIGENVECTORS\\n m.eigen_values = LinAlg.MS_EIGENVALUES \\n m.eigen_vectors = LinAlg.MS_EIGENVECTORS\\n\\n m.__reduce__ = LinAlg.MS_REDUCE \\n m.rep_size = classgetter ( lambda cls : cls.rep_type.kSize ) \\n \\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02996822e121457dbadc79e77833684\",\n \"score\": \"0.50629157\",\n \"text\": \"def normalized(self):\\n # swap cols and rows and row reduce\\n A = sp.Matrix(self.mat).transpose().rref()[0]\\n # ensure all entries are integral by multiplying by the LCD\\n A *= reduce(lcm, map(lambda x: x.as_numer_denom()[1], # get denominators\\n sp.utilities.iterables.flatten(A)))\\n # see https://groups.google.com/forum/#!topic/sympy/e8hcF4QAldc\\n A = np.array(sp.lambdify((), A, 'numpy')()) # convert to numpy array\\n A = A[np.any(A,1),:].transpose() # remove 0 rows, swap rows and columns\\n return HashableMatrix(np.matrix(A))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d2948fdd0278c0960eac13394ff777\",\n \"score\": \"0.50607157\",\n \"text\": \"def test_dephasing(self):\\n\\n # Check for identity on |11> state\\n test_density_matrix = (dephasing_channel(self.density_matrix, 1, 1))\\n self.assertAlmostEqual(norm(self.density_matrix - test_density_matrix),\\n 0.0)\\n\\n test_density_matrix = (dephasing_channel(self.density_matrix,\\n 1,\\n 1,\\n transpose=True))\\n\\n correct_matrix = np.array([[0., 0., 0., 0.], [0., 0., 0., 0.],\\n [0., 0., 0.5, -0.5], [0., 0., -0.5, 1.]])\\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)\\n\\n # Check for correct action on cat state\\n # With probability = 0\\n test_density_matrix = (dephasing_channel(self.cat_matrix, 0, 1))\\n self.assertAlmostEqual(norm(self.cat_matrix - test_density_matrix), 0.0)\\n # With probability = 1\\n\\n correct_matrix = np.array([[0.50, 0.25, 0.00, 0.00],\\n [0.25, 0.25, 0.00, -0.25],\\n [0.00, 0.00, 0.00, 0.00],\\n [0.00, -0.25, 0.00, 0.50]])\\n test_density_matrix = (dephasing_channel(self.cat_matrix, 1, 1))\\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc538d60bed88cd11599c3c6959f4b5\",\n \"score\": \"0.5043117\",\n \"text\": \"def density(self):\\r\\n graph = self.__graph_dict.copy()\\r\\n vertexes = len(graph.keys())\\r\\n edges = len(self.edges())\\r\\n\\r\\n return 2.0 * edges / (vertexes *(vertexes - 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f9f9e4020c8f8330863b342a455e0c\",\n \"score\": \"0.50127727\",\n \"text\": \"def density(compound, state, value_index=0):\\n table = density_table(compound)\\n state_idx = state_index(state)\\n try:\\n return list(table.loc[(table['state'] == state_idx), 'value'])[value_index]\\n except IndexError:\\n print('Invalid state or compound')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9417401b3ef118e225d0562a146075\",\n \"score\": \"0.50119233\",\n \"text\": \"def dd(self, mu: int, nu: int):\\n\\t\\tif self.evaluated[mu,nu]: return self.Ric[mu,nu]\\n\\t\\tRic = 0\\n\\t\\tfor rho in range(self.dim):\\n\\t\\t\\tRic += self.R.uddd(rho,mu,rho,nu)\\n\\t\\tself.Ric[mu,nu] = Ric.simplify()\\n\\t\\tself.evaluated[mu,nu] = True\\n\\t\\treturn self.Ric[mu,nu]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef79b4384b1a00538764f5c98445ce4d\",\n \"score\": \"0.50119007\",\n \"text\": \"def density(self):\\n return sum(np.array(mass(self.symbols))) / self.volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b87cc4a601fab0d64ed28e35a4405a96\",\n \"score\": \"0.50103897\",\n \"text\": \"def density(self, state: c_void_p, barometric_pressure: float, dry_bulb_temp: float, humidity_ratio: float) -> float:\\n return self.api.psyRhoFnPbTdbW(state, barometric_pressure, dry_bulb_temp, humidity_ratio)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe0da1191fb5e7d644d2e8a32e1b3aed\",\n \"score\": \"0.5010093\",\n \"text\": \"def classical_density(self):\\n rho = self._D11 + self._D22\\n\\n # Check for the normalization condition\\n assert np.allclose(rho.sum() * self.dqdp, 1), \\\"Classical density must be normalized.\\\"\\n\\n # Check for the positivity of the density\\n assert np.allclose(rho[rho < 0], 0), \\\"Classical density mus be nonnegative\\\"\\n\\n assert not np.isnan(rho).any(), \\\"Classical density contains NaNs.\\\"\\n\\n # Make sure all values are real\\n assert isinstance(rho[0, 0], np.float), \\\"Classical density must be real\\\"\\n\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe6a799ba948be43589b718b1442f2a\",\n \"score\": \"0.5002014\",\n \"text\": \"def get_reversible_rate_matrix(nstates):\\n M = np.exp(np.random.randn(nstates, nstates))\\n pre_Q = 0.5 * (M + M.T)\\n v = sample_distn(nstates)\\n v_sqrt = np.sqrt(v)\\n pre_Q = (pre_Q.T / v_sqrt).T * v_sqrt\\n Q = pre_Q - np.diag(np.sum(pre_Q, axis=1))\\n assert_reversibility(Q, v)\\n return Q, v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080916809178ad11c99fcca08377dc79\",\n \"score\": \"0.49931666\",\n \"text\": \"def density(self):\\n return self._density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea7bff0064c282b34ff4c47214f0f4ed\",\n \"score\": \"0.49819955\",\n \"text\": \"def evaluate_density_vec(self, x):\\r\\n return self._evaluate_scalar_quantity(x[:,0], x[:,1], x[:,2], MNnModel.mn_density)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe41ebade879197650c9af2761a6dfc\",\n \"score\": \"0.4976917\",\n \"text\": \"def get_density(self,wavefunction): \\n if wavefunction is None:\\n return self.density\\n \\n density=np.zeros(self.shape,dtype=np.float)\\n for k in range(self.NK):\\n for n in range(self.nbands):\\n for m in range(self.nbands):\\n density+=2*self.wk[k]*self.f[k,n]*np.abs(wavefunction[k,m,n]*self.ukn[k,m])**2\\n return density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d65c01ac6a9758392a6b3882f17e447\",\n \"score\": \"0.49748877\",\n \"text\": \"def get_density(self, x):\\n \\n return self.get_logdensity(x).exp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c272faaf51fe765b66950a3fcb109cce\",\n \"score\": \"0.4974089\",\n \"text\": \"def generate_reduced_sim_mat(self):\\n self.reduced_sim_mat = np.zeros((self.k, self.k), dtype='float32')\\n\\n for r in range(2, self.k + 1):\\n for s in (range(1, r) if not self.drop_edges_between_irregular_pairs else self.regularity_list[r - 2]):\\n if self.is_weighted:\\n cl_pair = WeightedClassesPair(self.sim_mat, self.adj_mat, self.classes, r, s, self.epsilon)\\n else:\\n cl_pair = ClassesPair(self.adj_mat, self.classes, r, s, self.epsilon)\\n self.reduced_sim_mat[r - 1, s - 1] = self.reduced_sim_mat[s - 1, r - 1] = cl_pair.bip_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30bb90332888e55a02603a36f21e30ef\",\n \"score\": \"0.4962091\",\n \"text\": \"def initialize_density(x,dx,normalization=1):\\n rho=exp(-x**2)\\n A=simps(rho,x)\\n return normalization/A*rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ef96029d241edd7a4d21334f51ce44\",\n \"score\": \"0.4958901\",\n \"text\": \"def density(self, r):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ebd7cc640cd6a72cce91981a2d5ea69\",\n \"score\": \"0.49578607\",\n \"text\": \"def mass_matrix(self):\\r\\n if not self._fr or not self._frstar:\\r\\n raise ValueError('Need to compute Fr, Fr* first.')\\r\\n return Matrix([self._k_d, self._k_dnh])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af34f7f073abed1d0aba606c4e3d4c5\",\n \"score\": \"0.49560684\",\n \"text\": \"def _matrix(self):\\n n, d = self._x.shape\\n if n < d:\\n return np.dot(self._x, self._x.T) / float(n - 1)\\n return np.dot(self._x.T, self._x) / float(n - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72faf3dd76737395397e71108fe09c0\",\n \"score\": \"0.49559936\",\n \"text\": \"def depurify(self, eps):\\n probs = np.random.random(self.num_rows)\\n probs = probs/np.sum(probs)\\n arr = (self.arr + eps*np.diag(probs)) / (self.trace() + eps)\\n return DenMat(self.num_rows, self.row_shape, arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5db345c5c728b77651f7354563b650c\",\n \"score\": \"0.49555916\",\n \"text\": \"def irreducible(self):\\r\\n\\t\\treturn farq((self.num/MCD(self.den,self.num)),(self.den/MCD(self.den,self.num)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0acd9a2c48d8dba88077ac1ef6e038df\",\n \"score\": \"0.4938685\",\n \"text\": \"def compute_diagonalizing_gates(\\n basis_state, wires\\n ): # pylint: disable=arguments-differ,unused-argument\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22cbc58a6fc66c8903026332ba3d82ec\",\n \"score\": \"0.49319252\",\n \"text\": \"def distribution_matrix(mat):\\n return np.diag(stationary(mat))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ae00ca62821f82771a40d1b404a03f\",\n \"score\": \"0.49295992\",\n \"text\": \"def normalize_adj(adj):\\n # adj: dense adj\\n adj = sp.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1)) # D\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten() # D^-0.5\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D^-0.5\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() # D^-0.5AD^0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94ce23a0f9d4ffeea5706804d3a5d4d\",\n \"score\": \"0.49190745\",\n \"text\": \"def densityFactorDry(weather):\\n temperature = extractTemperature(weather, useKelvin=True)\\n waterVaporPressure = humidityToPressure(weather)\\n airPressure = weather.getAirPressure()*units.pascal\\n dryPressure = airPressure - waterVaporPressure\\n eqn = dryPressure.to_value(cds.mbar)*(57.90E-8 - 9.3250E-4/temperature.to_value(units.Kelvin)\\n + 0.25844/temperature.to_value(units.Kelvin)**2.)\\n densityFactor = (1. + eqn)*dryPressure.to_value(cds.mbar)/temperature.to_value(units.Kelvin)\\n return densityFactor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd36f9555dc547e0a2e2a0b67384f63\",\n \"score\": \"0.49190095\",\n \"text\": \"def strain_displacement_matrix(self):\\n L = np.zeros((3,4)) # matrix used for mapping shape function derivatives into strains\\n L[0,0] = 1; L[1,3] = 1; L[2,1:3] = 1;\\n xi = 0; eta = 0; # evaluation point\\n _,_,dNmat = self.mesh.eval_shape_func(xi,eta) # differentiated shape functions evaluated in (xi,eta)\\n Jac = np.array([[0.5,0],[0,0.5]]) # mapping back to global coordinates\\n detJ = np.linalg.det(Jac)\\n invJ = 1/detJ*np.array([[Jac[1,1], -Jac[0,1]], [-Jac[1,0], Jac[0,0]]])\\n G = np.zeros((4,4)) # mapping matrix between rectangular element space coordinates and global coordinates\\n G[:2,:2] = invJ; G[-2:,-2:] = invJ\\n B0 = np.matmul(np.matmul(L,G),dNmat) # strain displacement matrix\\n\\n return B0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7acb1f9b47bb5ec27070f96bdb361bc2\",\n \"score\": \"0.491127\",\n \"text\": \"def evaluate_density(self, x, y, z):\\r\\n return self._evaluate_scalar_quantity(x, y, z, MNnModel.mn_density)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7e01cd867d1edacc06d2d11684496c\",\n \"score\": \"0.48791188\",\n \"text\": \"def _densityfunction(\\n r: float,\\n core_wavenumber: floatcomplex,\\n shell_wavenumber: floatcomplex,\\n core_width: float,\\n shell_width: float,\\n) -> float:\\n return (\\n abs(\\n _wavefunction(r, core_wavenumber, shell_wavenumber, core_width, shell_width)\\n )\\n ** 2\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef32ca4bb9d73ed6bf0304dda863126\",\n \"score\": \"0.4866826\",\n \"text\": \"def cost_down(states):\\n return np.linalg.norm(states)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7873db806f156abc30c0f468865452ee\",\n \"score\": \"0.48629946\",\n \"text\": \"def getDensity(self):\\n return self.__density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9936c5f50045750f35b6449fe5ae7d59\",\n \"score\": \"0.4831887\",\n \"text\": \"def _d_sigma(self):\\n try:\\n dd = self.d_derivs\\n except ValueError:\\n return None\\n den = self.density\\n dv = np.matrix(np.zeros((len(den.deriv_params))))\\n for i, pname in enumerate(den.deriv_params):\\n dv[0, i] = dd[pname].angularIntegral(self.psi_max)[0]\\n return np.sqrt((dv * self.density.covar * dv.T)[0, 0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0743541340bf045e3787e5333449e532\",\n \"score\": \"0.48263004\",\n \"text\": \"def renormalise(self):\\n\\t\\trn = self.compute_partition_function_fast()\\n\\t\\tfor prod in self.productions:\\n\\t\\t\\tif rn[prod[0]] == 0:\\n\\t\\t\\t\\tself.parameters[prod] = 0\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif len(prod) == 3:\\n\\t\\t\\t\\t\\ta,b,c = prod\\n\\t\\t\\t\\t\\tself.parameters[prod] *= (rn[b] * rn[c])/rn[a] \\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.parameters[prod] *= 1.0/rn[prod[0]]\\n\\t\\tself.trim_zeros()\\n\\t\\tself.set_log_parameters()\\n\\t\\treturn self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e562d84de910866b7f2e9ce09c62f04\",\n \"score\": \"0.48175946\",\n \"text\": \"def densFunc(z):\\t\\n\\t\\treturn rho00 + 0.5 * (rho01-rho00) * (1 + np.tanh((z-ze)/we))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b2038a3047cfa21d8adad453bad6a9\",\n \"score\": \"0.4816128\",\n \"text\": \"def build_distribution_normalization_matrix(self):\\n vector = np.array((self.distribution_matrix * self.xtable_matrix).sum(axis=1)).T\\n mask = vector > 0\\n vector[mask] = 1 / vector[mask]\\n return diags(vector, [0], format=\\\"csr\\\", dtype=np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a495785d1aad265edf5f3d89225b4319\",\n \"score\": \"0.4811534\",\n \"text\": \"def d2matrix(dx):\\n N, Nngb, dim = dx.shape\\n N_derivs = 2*dim + comb(dim, 2, exact=True)\\n A = np.empty((N, Nngb, N_derivs), dtype=np.float64) \\n for k in range(N):\\n for i in range(Nngb):\\n for j in range(N_derivs):\\n if j < dim:\\n A[k,i,j] = dx[k,i,j] \\n elif j < 2*dim:\\n A[k,i,j] = dx[k,i,j-dim] * dx[k,i,j-dim] / 2\\n else:\\n A[k,i,j] = dx[k,i,(j+1)%dim] * dx[k,i,(j+2)%dim] # this does the cross-terms, e.g. xy, xz, yz\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170bba8658ede3ab821a07eb00ffc8e7\",\n \"score\": \"0.48088142\",\n \"text\": \"def d(self):\\n\\n # Calculate and return the local displacement vector\\n return matmul(self.T, self.D())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b836ed4e795f661348350e710cb2bb65\",\n \"score\": \"0.48069096\",\n \"text\": \"def density(self, q, t=0.0):\\n q = self._remove_units_prepare_shape(q)\\n orig_shape, q = self._get_c_valid_arr(q)\\n t = self._validate_prepare_time(t, q)\\n ret_unit = self.units[\\\"mass\\\"] / self.units[\\\"length\\\"] ** 3\\n return (self._density(q, t=t).T * ret_unit).to(self.units[\\\"mass density\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cc5445f9fee2382cf934dd683ea59bf\",\n \"score\": \"0.48019257\",\n \"text\": \"def calculate_density(self):\\n wavelet_density = len(self.phi_matrices[0].nonzero()[0])/(self.graph.number_of_nodes()**2)\\n wavelet_density = str(round(100*wavelet_density, 2))\\n inverse_wavelet_density = len(self.phi_matrices[1].nonzero()[0])/(self.graph.number_of_nodes()**2)\\n inverse_wavelet_density = str(round(100*inverse_wavelet_density, 2))\\n print(\\\"Density of wavelets: \\\"+wavelet_density+\\\"%.\\\")\\n print(\\\"Density of inverse wavelets: \\\"+inverse_wavelet_density+\\\"%.\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22dc3d06834558bc2cdfe95c4af1565\",\n \"score\": \"0.48018953\",\n \"text\": \"def DesignMatrix(states):\\n N = np.size(states, 0)\\n size3 = (N, L ** 2)\\n X = np.zeros(size3)\\n\\n for i in range(0, N):\\n X[i] = np.outer(states[i, :], states[i, :]).reshape(1, -1) # .ravel()\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a80ae3e4b8a7df27f667e195552e8e\",\n \"score\": \"0.4795498\",\n \"text\": \"def _normalize_adj_diag_enhance_dense(adj, diag_lambda:float, improved=False):\\n fill_value = 1 if not improved else 2\\n \\n adj = add_self_loop_dense(adj, fill_value=fill_value)\\n degree = torch.sum(adj, dim=-1)\\n degree_inv = 1 / degree\\n degree_inv[degree_inv == float(\\\"inf\\\")] = 0\\n adj = adj * degree_inv.reshape(-1, 1)\\n\\n adj += (diag_lambda * adj.diag()).diag()\\n\\n return adj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0377a23d2b9b5ab5ed62edfd4e05488\",\n \"score\": \"0.4789015\",\n \"text\": \"def _dcorss_dm(self):\\n\\n dcdm = np.empty((self.num_mog_comp, self.num_latent_proc, self.num_inducing))\\n for j in range(self.num_latent_proc):\\n dcdm[:, j, :] = -cho_solve((self.chol[j, :, :], True), self.MoG.m[:, j, :].T).T * self.MoG.pi[:, np.newaxis]\\n return dcdm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb66346d8ffa513bf4e6829ee29cc39d\",\n \"score\": \"0.478672\",\n \"text\": \"def FssInvDiag(self, recompute = True):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc864306e26f90c471f48a40ac16a0ec\",\n \"score\": \"0.4782814\",\n \"text\": \"def rh_density(self, init_dens, gamma, mach):\\n\\n dens_ratio = ((gamma+1)*mach**2)/(2+(gamma-1)*mach**2)\\n\\n final_dens = dens_ratio*init_dens\\n\\n return final_dens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039502c9e7c7e8d4ad0fd52010a922ed\",\n \"score\": \"0.47798538\",\n \"text\": \"def evalulate_function(self, disk_density: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) -> torch.Tensor:\\n rmat, zmat = torch.meshgrid(self.rmid, self.zmid)\\n return disk_density(rmat, zmat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9564f033e5d7d266c571edf867268ed7\",\n \"score\": \"0.4779669\",\n \"text\": \"def _dcross_ds(self):\\n\\n dc_ds = np.empty((self.num_mog_comp, self.num_latent_proc, self.MoG.get_sjk_size()))\\n for j in range(self.num_latent_proc):\\n dc_ds[:, j] = -1. / 2 * np.array(\\n [self.MoG.dAinvS_dS(self.chol[j, :, :], k, j) * self.MoG.pi[k] for k in range(self.num_mog_comp)])\\n return dc_ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d44e17c8cab7dfd74d9dfe4bbc03ce\",\n \"score\": \"0.47766522\",\n \"text\": \"def compute_dci_d(importances):\\n\\n # normalize across the state_variable dimension, so we get the importance of\\n # each state variable for a slot\\n slot_normalized_importances = importances / importances.sum(axis=0)\\n sni = slot_normalized_importances\\n\\n # each entropy computed is the entropy for a slot over all the state variables\\n slot_entropy = entropy(sni, base=importances.shape[0])\\n dci_ds = 1 - slot_entropy\\n dci_d = dci_ds.mean()\\n\\n return dci_d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63108ffb70e437a436de9e0fe5091598\",\n \"score\": \"0.47744197\",\n \"text\": \"def current_density(self, xyz):\\n return self.sigma * self.electric_field(xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63108ffb70e437a436de9e0fe5091598\",\n \"score\": \"0.47744197\",\n \"text\": \"def current_density(self, xyz):\\n return self.sigma * self.electric_field(xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63108ffb70e437a436de9e0fe5091598\",\n \"score\": \"0.47744197\",\n \"text\": \"def current_density(self, xyz):\\n return self.sigma * self.electric_field(xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8697247f45a6c2c24dd1e3ea7b3ffc0\",\n \"score\": \"0.47690842\",\n \"text\": \"def stiffness_matrix_assembly(self,rho,sparse=True):\\n rho = rho.flatten(order='F')\\n if sparse is False:\\n self.stiffness_matrix = np.zeros((self.mesh.ndof,self.mesh.ndof))\\n mat_idx = np.indices((8,8))\\n I = mat_idx[0].flatten()\\n J = mat_idx[1].flatten()\\n # assemble stiffness matrix\\n for ele in range(len(self.mesh.edofMat)):\\n edof = self.mesh.edofMat[ele]\\n self.stiffness_matrix[edof[I],edof[J]] += (self.Emin+(rho[ele])**self.penal*(self.Emax-self.Emin))*self.KE[I,J]\\n elif sparse is True:\\n iK = np.kron(self.mesh.edofMat,np.ones((8,1))).flatten()\\n jK = np.kron(self.mesh.edofMat,np.ones((1,8))).flatten()\\n sK=((self.KE.flatten()[np.newaxis]).T*(self.Emin+(rho)**self.penal*(self.Emax-self.Emin))).flatten(order='F')\\n self.stiffness_matrix = sp.coo_matrix((sK,(iK,jK)),shape=(self.mesh.ndof,self.mesh.ndof)).tocsc()\\n\\n return self.stiffness_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af13d60edf934fd69ff211074ce62cb7\",\n \"score\": \"0.47683695\",\n \"text\": \"def simplify(self):\\r\\n fact = greatest_common_divisor(self.__numerator, self.__denominator)\\r\\n self.__numerator = self.__numerator//fact\\r\\n self.__denominator = self.__denominator//fact\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e023b8c609745368bc2f84375d6a06c\",\n \"score\": \"0.47601324\",\n \"text\": \"def simplify_state(state: SDFGState, remove_views: bool = False) -> MultiDiGraph:\\n\\n sdfg = state.parent\\n\\n # Copy the whole state\\n G = MultiDiGraph()\\n for n in state.nodes():\\n G.add_node(n)\\n for n in state.nodes():\\n for e in state.all_edges(n):\\n G.add_edge(e.src, e.dst)\\n # Collapse all mappings and their scopes into one node\\n scope_children = state.scope_children()\\n for n in scope_children[None]:\\n if isinstance(n, nodes.EntryNode):\\n G.add_edges_from([(n, x) for (y, x) in G.out_edges(state.exit_node(n))])\\n G.remove_nodes_from(scope_children[n])\\n # Remove all nodes that are not AccessNodes or have incoming\\n # wcr edges and connect their predecessors and successors\\n for n in state.nodes():\\n if n in G.nodes():\\n if (not isinstance(n, nodes.AccessNode) or (remove_views and isinstance(sdfg.arrays[n.data], data.View))):\\n for p in G.predecessors(n):\\n for c in G.successors(n):\\n G.add_edge(p, c)\\n G.remove_node(n)\\n else:\\n for e in state.all_edges(n):\\n if e.data.wcr is not None:\\n for p in G.predecessors(n):\\n for s in G.successors(n):\\n G.add_edge(p, s)\\n G.remove_node(n)\\n break\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5852be77521a54263f39fb8e44ef4dce\",\n \"score\": \"0.47589034\",\n \"text\": \"def ddx(n):\\n return sp.spdiags((np.ones((n + 1, 1)) * [-1, 1]).T, [0, 1], n, n + 1, format=\\\"csr\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1305563ab5d730662b91cba0c765426e\",\n \"score\": \"0.4755412\",\n \"text\": \"def compute(self):\\n m, n = self.D.shape\\n\\n # Allocate memory.\\n # We need +2 because we use indices starting from 1\\n # and to deal with edge cases in the backward recursion.\\n self.R_ = np.zeros((m+2, n+2), dtype=np.float64)\\n\\n _soft_dtw(self.D, self.R_, gamma=self.gamma)\\n\\n return self.R_[m, n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ff928f269c5ac8d7b3d2ae000cd42a5\",\n \"score\": \"0.47552603\",\n \"text\": \"def density(self, r):\\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**3, 0.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"779305611eb50458e49f536acc482e7f\",\n \"score\": \"0.47546223\",\n \"text\": \"def to_dense(self):\\n if self.size is None:\\n size = max(self.d.keys()) + 1\\n else:\\n size = self.size\\n dense = np.zeros(size)\\n for k,v in self.d.items():\\n dense[k] = v\\n return dense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db22c46cf4d3e4a22ca0797871bab30\",\n \"score\": \"0.4754565\",\n \"text\": \"def density(self, r):\\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**2, 0.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8eb30b81846b14a296dec1b1e7d2ea\",\n \"score\": \"0.47464868\",\n \"text\": \"def reduced_formula(self):\\n if self.structure is not None:\\n return self.composition.reduced_formula\\n return \\\"Unkonwn\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efbd2abf113aed04471eebea96b78baf\",\n \"score\": \"0.47410783\",\n \"text\": \"def s_d(self) -> float:\\n return self.s0 + 1 / (self.w * self.k_x) * self.v_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15bc534a6a908e854b492720e967cf5\",\n \"score\": \"0.47379398\",\n \"text\": \"def diagonalize_del_dm_in_sbasis_in_degenerate_spaces(self):\\n classes = ut.get_equiv_classes(list(self.dm0_eigen_sys[0]))\\n umat = np.eye(self.del_dm.num_rows, dtype=complex)\\n # eq = equivalence\\n for eq_class in classes:\\n dim = len(eq_class)\\n if dim > 1:\\n if self.verbose:\\n print(\\\"non-trivial eq class\\\", eq_class)\\n eq_class = sorted(eq_class)\\n arr = np.empty((dim, dim), dtype=complex)\\n for k1, kk1 in enumerate(eq_class):\\n for k2, kk2 in enumerate(eq_class):\\n arr[k1, k2] = self.del_dm_in_sbasis[kk1, kk2]\\n norm_arr = np.linalg.norm(arr)\\n # print('norm arr', norm_arr)\\n if norm_arr > 1e-4:\\n _, evec_cols = np.linalg.eigh(arr)\\n assert ut.is_unitary_arr(evec_cols)\\n # print('bbbbbbbb', np.around(np.dot(np.dot(\\n # evec_cols.conj().T,\\n # arr), evec_cols).real,4))\\n for k1, kk1 in enumerate(eq_class):\\n for k2, kk2 in enumerate(eq_class):\\n umat[kk1, kk2] = evec_cols[k1, k2]\\n assert ut.is_unitary_arr(umat)\\n # print('ccccccccnorm of rotated_del_dm', np.linalg.norm(\\n # self.del_dm_in_sbasis.arr))\\n rotated_del_dm = np.dot(\\n np.dot(umat.conj().T, self.del_dm_in_sbasis.arr), umat)\\n # print('norm of rotated_del_dm', np.linalg.norm(rotated_del_dm))\\n self.del_dm_in_sbasis.arr = rotated_del_dm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b666a78a3c4f8253b314a53a530136dd\",\n \"score\": \"0.4735516\",\n \"text\": \"def pgradient(self):\\n d = {}\\n\\n # Det coeff\\n curr_val = self.value()\\n nonzero = curr_val[0] != 0.0\\n\\n # dets[spin][ (phase,log), configuration, determinant]\\n dets = (\\n self._dets[0][:, :, self._det_map[0]],\\n self._dets[1][:, :, self._det_map[1]],\\n )\\n\\n d[\\\"det_coeff\\\"] = gpu.cp.zeros(dets[0].shape[1:], dtype=dets[0].dtype)\\n d[\\\"det_coeff\\\"][nonzero, :] = (\\n dets[0][0, nonzero, :]\\n * dets[1][0, nonzero, :]\\n * gpu.cp.exp(\\n dets[0][1, nonzero, :]\\n + dets[1][1, nonzero, :]\\n - gpu.cp.array(curr_val[1][nonzero, np.newaxis])\\n )\\n / gpu.cp.array(curr_val[0][nonzero, np.newaxis])\\n )\\n\\n for s, parm in zip([0, 1], [\\\"mo_coeff_alpha\\\", \\\"mo_coeff_beta\\\"]):\\n ao = self._aovals[\\n :, :, s * self._nelec[0] : self._nelec[s] + s * self._nelec[0], :\\n ]\\n\\n split, aos = self.orbitals.pgradient(ao, s)\\n mos = gpu.cp.split(gpu.cp.arange(split[-1]), gpu.asnumpy(split).astype(int))\\n # Compute dj Diu/Diu\\n nao = aos[0].shape[-1]\\n nconf = aos[0].shape[0]\\n nmo = int(split[-1])\\n deriv = gpu.cp.zeros(\\n (len(self._det_occup[s]), nconf, nao, nmo), dtype=curr_val[0].dtype\\n )\\n for det, occ in enumerate(self._det_occup[s]):\\n for ao, mo in zip(aos, mos):\\n for i in mo:\\n if i in occ:\\n col = occ.index(i)\\n deriv[det, :, :, i] = self._testcol(det, col, s, ao)\\n\\n # now we reduce over determinants\\n d[parm] = gpu.cp.zeros(deriv.shape[1:], dtype=curr_val[0].dtype)\\n for di, coeff in enumerate(self.parameters[\\\"det_coeff\\\"]):\\n whichdet = self._det_map[s][di]\\n d[parm] += (\\n deriv[whichdet]\\n * coeff\\n * d[\\\"det_coeff\\\"][:, di, np.newaxis, np.newaxis]\\n )\\n\\n for k, v in d.items():\\n d[k] = gpu.asnumpy(v)\\n\\n for k in list(d.keys()):\\n if np.prod(d[k].shape) == 0:\\n del d[k]\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b62944f2f5f14daf76514400d4af117\",\n \"score\": \"0.4733377\",\n \"text\": \"def __repr__(self):\\n stats = self.population.getStats(self.truncFrac)\\n outStr = (\\\"\\\"\\\"DeMin state:\\n Generation: %s\\n best trial solution's cost value: %s\\n best trial solution: %s\\\\n\\n worst trial solution's cost value: %s\\n worst trial solution: %s\\\\n\\n mean cost value: %s\\n standard deviation of cost values: %s \\n fractional tolerance: %s\\n chi-square: %s\\n ndf: %s\\n np_trunc: %s\\\\n\\n Parameter mean values:\\\\n%s\\\\n\\n Covariance Matrix for np_trunc members:\\\\n%s\\n \\\"\\\"\\\" % (self.population.generation,\\n stats[0],\\n self.population[self.population.ibest].x,\\n stats[1],\\n self.population[self.population.iworst].x,\\n stats[2],\\n stats[3],\\n stats[4],\\n stats[5],\\n stats[6],\\n self.population.getNpTrunc(self.truncFrac),\\n stats[7],\\n self.population.getCovMatRepr(stats[8])))\\n return outStr\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":199,"cells":{"query_id":{"kind":"string","value":"8c117fa933aa0261b2f095be44a1acc1"},"query":{"kind":"string","value":"Datatype of data in the Column."},"positive_passages":{"kind":"list like","value":[{"docid":"05b88be13604b1f61d82f087e7106449","score":"0.0","text":"def type(self) -> str:\n return pulumi.get(self, \"type\")","title":""}],"string":"[\n {\n \"docid\": \"05b88be13604b1f61d82f087e7106449\",\n \"score\": \"0.0\",\n \"text\": \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"5b8088fbe0ac252d28bc703bdde1e7c3","score":"0.8037336","text":"def datatype(self):\n return self._datatype","title":""},{"docid":"48de7eb74ceee9fe56fd65453b09294b","score":"0.7955723","text":"def dtype(self):\n return self._datatype","title":""},{"docid":"eb12f10ae87cbd514e47d2c2795b615f","score":"0.78110397","text":"def data_type(self):\n return self.__data_type","title":""},{"docid":"6d4ed81bb5320d6c2423bb3d69df574e","score":"0.7808144","text":"def data_type(self):\n return self._data_type","title":""},{"docid":"40c7e089bbf793d39ec281cc356feabe","score":"0.778775","text":"def data_type(self):\n\n return self._data_type","title":""},{"docid":"52e16ba8c58e3994b0c6693904280082","score":"0.75562376","text":"def getDataType(self):\n return self.data_type","title":""},{"docid":"fe38f5416da9523ff5363ef844ee1eee","score":"0.7527811","text":"def dtype(self):\n return self.data.dtype","title":""},{"docid":"fe38f5416da9523ff5363ef844ee1eee","score":"0.7527811","text":"def dtype(self):\n return self.data.dtype","title":""},{"docid":"fe38f5416da9523ff5363ef844ee1eee","score":"0.7527811","text":"def dtype(self):\n return self.data.dtype","title":""},{"docid":"3e025b354e1a7c9e829d89648c7c1087","score":"0.7440406","text":"def dtype(self):\n return self._data.dtype","title":""},{"docid":"3d7efb8bff7b892e5d43fe3f26645ce4","score":"0.74388254","text":"def get_data_type(self):\n return self._data_type","title":""},{"docid":"35c0a3df1a58449164ab6d9e7873efef","score":"0.7437267","text":"def GetColumnType(self, col):","title":""},{"docid":"35c0a3df1a58449164ab6d9e7873efef","score":"0.7437267","text":"def GetColumnType(self, col):","title":""},{"docid":"b0e1c4ffe8d888a052cd055327d933a5","score":"0.74280405","text":"def type(self):\n return self.data_type","title":""},{"docid":"66e145ddf40c8ad79cf7f5ccd449da7c","score":"0.74093187","text":"def _derive_data_type(column: Dict[str, Any]) -> str:\n data_type = column['data_type']\n\n if data_type in ['bigint',\n 'int',\n 'smallint',\n 'tinyint',\n 'bit',\n 'money',\n 'smallmoney',\n 'float',\n 'real',\n 'date',\n 'datetime',\n 'datetime2',\n 'datetimeoffset',\n 'smalldatetime',\n 'time',\n 'text',\n 'ntext',\n 'binary',\n 'image',\n 'xml',\n 'geography',\n 'geometry',\n 'sysname'\n ]:\n return data_type\n\n if data_type in ['decimal', 'numeric']:\n return 'decimal({0:d},{1:d})'.format(column['precision'], column['scale'])\n\n if data_type == 'char':\n return 'char({0:d})'.format(column['max_length'])\n\n if data_type == 'varchar':\n if column['max_length'] == -1:\n return 'varchar(max)'\n\n return 'varchar({0:d})'.format(column['max_length'])\n\n if data_type == 'nchar':\n return 'nchar({0:d})'.format(int(column['max_length'] / 2))\n\n if data_type == 'nvarchar':\n if column['max_length'] == -1:\n return 'nvarchar(max)'\n\n return 'nvarchar({0:d})'.format(int(column['max_length'] / 2))\n\n if data_type == 'varbinary':\n return 'varbinary({0:d})'.format(column['max_length'])\n\n raise Exception(\"Unexpected data type '{0}'\".format(data_type))","title":""},{"docid":"a879494c135a9817e6cda2d6c14a3a40","score":"0.7405552","text":"def type(self):\n return self.dtype().value","title":""},{"docid":"a1c3a4c4cfae33d72a8a32d47fd78116","score":"0.7386811","text":"def dtype(self):\r\n return self._dtype","title":""},{"docid":"73d5c050d0850073e083a35754827e37","score":"0.73845667","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"e01c3da06324ecb23284e4c7ccb2cf30","score":"0.7310465","text":"def get_data_type(self) -> np.dtype:\n return self.type","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"2908046b45c31bc75a4a2a8806286407","score":"0.72685397","text":"def get_dtype(self):\n pass","title":""},{"docid":"d574b68629284c0f77e3c5f614641524","score":"0.7255222","text":"def getDataType(self):\n\n return 'Scalar'","title":""},{"docid":"8759282617828147bf72a15054f23221","score":"0.72273684","text":"def dtype(self):\n return self.__dtype","title":""},{"docid":"af54a1addb8847db50a38cb5f6a5ec52","score":"0.7204325","text":"def dtype(self):\n if self.view_attr is None and self.schema.nattr > 1:\n raise NotImplementedError(\"Multi-attribute does not have single dtype!\")\n return self.schema.attr(0).dtype","title":""},{"docid":"ffd9c01758dc76f6ddf927924726b4c1","score":"0.71553457","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"ffd9c01758dc76f6ddf927924726b4c1","score":"0.71553457","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"a846b510367f7c1ed56f736981e17466","score":"0.7122086","text":"def getDataType(self) -> ghidra.program.model.data.DataType:\n ...","title":""},{"docid":"8fb6045337f96701627acbda5c3ae2ae","score":"0.71182996","text":"def dtype(self):\n return self._impl.dtype","title":""},{"docid":"115f152d1e6edb1fa8e49d3d0c8a01b5","score":"0.71133626","text":"def dtype(self):\n\n return self.description._v_dtype","title":""},{"docid":"95ea02a66e8954707b382f68edbb70e0","score":"0.7089385","text":"def get_dtype(self):\n return self.dtype","title":""},{"docid":"2d9d34d1426fa35423718cbbb253ae45","score":"0.7053333","text":"def export_datatype(self):\n raise NotImplementedError","title":""},{"docid":"64f92513c029247743fde4da8da4c601","score":"0.70321596","text":"def data_type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"cdce282b1c2cb466e62f311a8e837196","score":"0.69717824","text":"def data_type(self):\n # datetime and date to int\n date_cols = ['visit_date', 'prev_visit_date', 'prev_item_move_date',\n 'last_edit_date', 'creation_date']\n for col in date_cols:\n # convert multiple time formats into single string format\n self.df[col] = pd.to_datetime(self.df[col]).dt.strftime('%Y-%m-%d %H:%M:%S')\n # make time features specific data type in order to distinguish from other numberic values\n self.df['{}_int'.format(col)] = self.df.apply(self._date_to_int, col=col, axis=1).astype(np.float32)\n # convert string format back into datetime\n self.df[col] = pd.to_datetime(self.df[col])\n # objects\n obj_cols = ['ship_id', 'address1', 'customer_id', 'sales_rep_id', 'item_id', 'old_item_id',\n 'item_UPC', 'old_item_UPC', 'ship_list_pk', 'sales_rep_id_2', 'list_header_id']\n for col in obj_cols:\n self.df[col] = self.df[col].astype(object)","title":""},{"docid":"970d2776f6fcf0c468381762f0026068","score":"0.69492733","text":"def dtype(x):\n\t return x.dtype.name","title":""},{"docid":"a4d35de6d937f89f506dd8465fa7e55f","score":"0.69273543","text":"def dtype(self):\n return self._dats[0].dtype","title":""},{"docid":"cc13474be25c43ba2a4f847adfad3c1e","score":"0.69220364","text":"def data_type(self) -> Optional[str]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"cc13474be25c43ba2a4f847adfad3c1e","score":"0.69220364","text":"def data_type(self) -> Optional[str]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"2e581d5c45d038177e9e16ff2d3f0f65","score":"0.69217163","text":"def getDtype(self):\n return self.dtype_str","title":""},{"docid":"4b20ec86c0865de94c347bbe146ffe34","score":"0.6917254","text":"def get_data_type(self, df, col):\n if col not in df.columns:\n raise KeyError(f'Column \"{col:s}\" not in input dataframe.')\n dt = dict(df.dtypes)[col]\n # spark conversions to numpy or python equivalent\n if dt == \"string\":\n dt = \"str\"\n elif dt in [\"timestamp\", \"date\"]:\n dt = np.datetime64\n elif dt == \"boolean\":\n dt = bool\n elif dt == \"bigint\":\n dt = np.int64\n elif dt.startswith(\"decimal(\"):\n return np.dtype(float, metadata={\"decimal\": True})\n\n return np.dtype(dt)","title":""},{"docid":"6e6a4dbea08f1e018c1f3e584fd04e63","score":"0.6893777","text":"def dtype(self) -> torch.dtype:\n pass # pragma: no cover","title":""},{"docid":"6e11f987f48a548c782a79d62674f408","score":"0.6882582","text":"def ctype(self):\n return self.data.ctype","title":""},{"docid":"5d70b338904a1e355f32e2c3c8c9575d","score":"0.68728685","text":"def data_type(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"f17bd447341f0fed24483c839c5da49f","score":"0.6854025","text":"def as_type(self):\n\t\ttypes = {\n\t\t\t'float': self.numeric_f,\n\t\t\t'bool': self.binary_class_f,\n\t\t\t'str': self.multi_class_f + self.id_f,\n\t\t\t'datetime64[ns]': self.datetime_f\n\t\t}\n\n\t\tfor i in types.keys():\n\t\t\tif len(types[i]) > 0:\n\t\t\t\tif i == 'bool':\n\t\t\t\t\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i).astype('float')\n\t\t\t\telse:\n\t\t\t\t\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i)","title":""},{"docid":"2240a99a83a8ce6962bb7e2d8dfc4adb","score":"0.68359935","text":"def transform_datatype(self, datatype):\n return datatype","title":""},{"docid":"698bd5a61e5e91af828f4d065d7a5ca1","score":"0.68257904","text":"def dtype(self):\n return self._snode._dtype","title":""},{"docid":"8e59b8ca93a41098a598cdf8ae49cfdb","score":"0.6806045","text":"def _get_column_type(self) -> None:\n rows = self._dl.get_all_table_columns()\n for row in rows:\n key = '@{0}.{1}.{2}%type@'.format(row['schema_name'], row['table_name'], row['column_name'])\n key = key.lower()\n\n value = self._derive_data_type(row)\n\n self._replace_pairs[key] = value\n\n self._io.text('Selected {0} column types for substitution'.format(len(rows)))","title":""},{"docid":"34c9b7ff2489d05df3d53ede2b72a675","score":"0.6774951","text":"def table_column_type(self, i):\n with self.free_cstring(_lib.TableColumnType(self.table, i)) as typ:\n return typ","title":""},{"docid":"4efb21d1ed016e4286e0138e960e1427","score":"0.6770946","text":"def dtype(self):\n return self.tensor.dtype","title":""},{"docid":"c44f8e10a39e055c04ffe566290dff5b","score":"0.67646646","text":"def ctype(self):\n return as_cstr(self.dtype)","title":""},{"docid":"3de070e4c310a5a07ad0e41eb0e5af3a","score":"0.6740433","text":"def form_datatype(self):\n dt = self.dataype_name()\n nD = str(len(self.nda.shape))\n et = get_lh5_element_type(self)\n return dt + '<' + nD + '>{' + et + '}'","title":""},{"docid":"7ab6124b611861e8f7261b35fba0b7ba","score":"0.67115027","text":"def get_type(self):\n return self.header.data_type","title":""},{"docid":"30eb89c3b7c6e89211db4d8a2626c2ae","score":"0.6709729","text":"def dtype(self):\n return self._get_component(\"array\").dtype","title":""},{"docid":"fbc6a7de0d3fa23da85fef18a59483ac","score":"0.66972804","text":"def _get_predefined_datatype(self):\n return None","title":""},{"docid":"d72ee64ed3cb0350c1e14d545c185955","score":"0.669686","text":"def convert_datatype(coldatatype):\n\n datatype = SqlType.text()\n def_value = ''\n \n if 'datetime' in coldatatype.lower():\n datatype = SqlType.timestamp()\n elif 'str' in coldatatype.lower():\n datatype = SqlType.text()\n elif 'boolean' in coldatatype.lower():\n datatype = SqlType.bool()\n elif 'int' in coldatatype.lower():\n datatype = SqlType.int()\n def_value = 0\n elif 'float' in coldatatype.lower():\n datatype = SqlType.double()\n def_value = 0\n elif 'period' in coldatatype.lower():\n datatype = SqlType.interval()\n elif 'object' in coldatatype.lower():\n datatype = SqlType.text()\n else:\n datatype = SqlType.text()\n \n return (datatype,def_value)","title":""},{"docid":"7d21875c687169cd72cd948c52e38935","score":"0.66671395","text":"def dtypes_of(data):\n return data.dtype","title":""},{"docid":"1e6961728ac2e377ee3f8d845ee1a929","score":"0.6664429","text":"def data_type(self) -> pulumi.Input['InstructionDataType']:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"2eb54b93b32955b17f8f324a6815ba54","score":"0.66327494","text":"def dtype(self):\n\n hdr = self.header()\n\n # Get numpy data type\n dtype = {'char': np.byte,\n 'short': np.short,\n 'int': np.intc,\n 'long': np.int_,\n 'ushort': np.ushort,\n 'uint': np.uintc,\n 'float': np.single,\n 'vec3f': np.single,\n 'vec9f': np.single,\n 'double': np.double}[hdr['type']]\n dtype = np.dtype(dtype)\n\n # Get python byte order string\n endian = {'little_endian': '<',\n 'big_endian': '>'}[hdr['systeme']]\n\n # Return data type with correct order\n return dtype.newbyteorder(endian)","title":""},{"docid":"d3ff223c19791fe116cb325d66c1bf29","score":"0.6632438","text":"def dtype(self) -> 'np.dtype':\n pass","title":""},{"docid":"81f1542bddac468ee8c73316b63c956b","score":"0.66217273","text":"def value_type(self):\n return self._value_type","title":""},{"docid":"ca8e1547158b09092fcee6c52435901b","score":"0.6602762","text":"def get_data_type(cls) -> str:\n raise NotImplementedError","title":""},{"docid":"afccd0b6a180a8c7c07bab235818ef65","score":"0.65588313","text":"def dtype(self) -> np.dtype:\n return self.__dtype","title":""},{"docid":"54fd9c1f535c68e9c4072ec355200553","score":"0.65281224","text":"def detect_datatype(data):\n if type(data) == type(np.empty(0)):\n return DataType.NUMPY\n elif type(data) == type(pd.DataFrame({'A': []})):\n return DataType.DATAFRAME","title":""},{"docid":"abbb449013006308c392dbf38126154f","score":"0.6517055","text":"def dtype(self):\n\n return self.descr._v_dtypes[self.name].base # Get rid of shape info","title":""},{"docid":"ce26c164a3ccce72b91a16f49eb2d172","score":"0.65145123","text":"def dtypes(self) -> List[str]:\n pass","title":""},{"docid":"ad950edaae613c0833cc9ef9121bb241","score":"0.65138894","text":"def dtype(self):\n return type(self._get_sequence_iloc_(0)) if len(self) > 0 else None","title":""},{"docid":"4dabbabfbf6955d71241653343428b38","score":"0.6507724","text":"def getSizeDataType(self) -> ghidra.program.model.data.DataType:\n ...","title":""},{"docid":"cb0db954e693796b7be6c46f5f716fce","score":"0.65049326","text":"def _determine_column_type(data_types):\n # Allow None which will be converted to NaN.\n if all(\n issubclass(t, (_numbers.Number, type(None))) and not issubclass(t, bool)\n for t in data_types\n ):\n return 'number'\n return 'string'","title":""},{"docid":"491f6c839537d99f47524e1b16fea22e","score":"0.6501615","text":"def type_value(ctype: Any, value: Any):\n\n # Boolean\n if ctype == platform.xlrd.XL_CELL_BOOLEAN:\n return bool(value)\n\n # Excel numbers are only float\n # Float with no decimals can be cast into int\n if ctype == platform.xlrd.XL_CELL_NUMBER and value == value // 1:\n return int(value)\n\n # Datetime\n if ctype == platform.xlrd.XL_CELL_DATE:\n return platform.xlrd.xldate.xldate_as_datetime(value, book.datemode)\n\n return value","title":""},{"docid":"eff8705d409d3f0d737ed36858dddd70","score":"0.6487331","text":"def get_field_type(self, data_type, row):\n return DatabaseIntrospection.data_types_reverse[row[7]]","title":""},{"docid":"d6e1e39e63e4d422f5e3267c1fd821fd","score":"0.64852417","text":"def _get_dtype(self) -> np.dtype:\n return self._dtype","title":""},{"docid":"06216a5ce712b85ec41a8045033e184f","score":"0.64840126","text":"def get_data_type(self, column, table):\n\t\ttry:\n\t\t\ttype_override = self.type_override[column[\"column_type\"]]\n\t\t\toverride_to = type_override[\"override_to\"]\n\t\t\toverride_tables = type_override[\"override_tables\"]\n\t\t\t\n\t\t\tif override_tables[0] == '*' or table in override_tables:\n\t\t\t\tcolumn_type = override_to\n\t\t\telse:\n\t\t\t\tcolumn_type = self.type_dictionary[column[\"data_type\"]]\n\t\texcept:\n\t\t\tcolumn_type = self.type_dictionary[column[\"data_type\"]]\n\t\treturn column_type","title":""},{"docid":"e43c2ffb38d4ac01066a916166a7693b","score":"0.64505047","text":"def data_types(self) -> dict:\n return self._data_types","title":""},{"docid":"963156aaa9eba32adec44835ab8da0ba","score":"0.6433906","text":"def evaluateColumnTypeV2(valColumn):\r\n res = \"numerical\"\r\n\r\n # get a column without missing val\r\n l = [val for val in valColumn if val != \"\"]\r\n\r\n if (len(l) == 0):\r\n res = \"empty\"\r\n else:\r\n try:\r\n for val in l:\r\n float(val)\r\n\r\n res = \"numerical\"\r\n pass\r\n except ValueError:\r\n res = \"categorical\"\r\n pass\r\n \r\n return res","title":""},{"docid":"6463851be879cbcc32965429551efc01","score":"0.64146054","text":"def test_datatype(self):\n self.assertTrue(lib.INTEGER.kind_of(lib.NUMBER))\n self.assertTrue(lib.FLOAT.kind_of(lib.NUMBER))\n self.assertTrue(lib.INTEGER.kind_of(env.ANY))","title":""},{"docid":"af0d2ae1fbf4adc2260cf4b13988c771","score":"0.64124054","text":"def data_type(a):\n\ta_type =type(a)\n\tif a_type == str:\n\t\tif len(a) == 0:\n\t\t\treturn 'no value'\n\t\telse:\n\t\t\treturn len(a)\n\t\t\n\telif a_type ==bool:\n\t\treturn a\n\telif a_type ==int:\n\n\t\tif a == 100:\n\t\t\treturn 'equal to 100'\n\t\telif a < 100:\n\t\t\treturn 'less than 100'\n\n\t\telse:\n\t\t\treturn 'more than 100'\n\n\telif a_type ==list:\n\t\ttry:\n\t\t\tif a[2]:\n\t\t\t\treturn a[2]\n\t\texcept(IndexError):\n\t\t\treturn None\n\t\telse:\n\t\t\treturn 'no value'","title":""},{"docid":"49436abf6ea1757f0b1750bdd28bcbc4","score":"0.63909143","text":"def column_data(self, column_name):\n for col in self.raw_frame.columns:\n if col.name == column_name:\n if col.dtype == fpb.INTEGER:\n return col.ints\n elif col.dtype == fpb.FLOAT:\n return col.floats\n elif col.dtype == fpb.STRING:\n return col.strings\n elif col.dtype == fpb.TIME:\n return col.times\n elif col.dtype == fpb.BOOLEAN:\n return col.bools\n else:\n raise ReadError('{} - unsupported type - {}'.format(column_name, col.dtype))\n\n raise ReadError('No column named \"{}\"'.format(column_name))","title":""},{"docid":"6d8ac16c1589d50950dd97a4040f2144","score":"0.63876945","text":"def column_types(self):\n return self._column_types","title":""},{"docid":"822ed2a73282d41b745f1ac6b8c604d7","score":"0.6382394","text":"def dtype(self) -> StringDtype: # type: ignore[override]\n return self._dtype","title":""},{"docid":"aba956cd8d21abd0d782256aba8e033a","score":"0.63798404","text":"def cell_type(self, row, col):\n if col == 0: return 'heading'\n else: return 'data'","title":""},{"docid":"ccedb8e1a1ae08ee787daf348c6814b6","score":"0.6374998","text":"def dtype(self) -> torch.dtype:\n return self.mvn.loc.dtype","title":""},{"docid":"94113e48de1e1265adaed5b57226040f","score":"0.6372429","text":"def generate_col_data(self, col: dict) -> Any:\n if self.null_column_value(col):\n return None\n\n elif col.get(\"enum\"):\n return self.fake_enum(col.get(\"enum\"))\n\n elif col[\"type\"] in [\"int\", \"long\"]:\n return self.fake_int(col)\n\n elif col[\"type\"] in [\"double\", \"float\"]:\n return self.fake_double(col)\n\n elif col[\"type\"] in [\"date\", \"datetime\"]:\n return self.fake_datetime(col)\n\n elif col[\"type\"] == \"boolean\":\n return self.fake_bool()\n\n elif col[\"type\"] == \"character\":\n return self.fake_character(special_type=self.special_cols.get(col[\"name\"]))\n else:\n n = col[\"name\"]\n t = col[\"type\"]\n raise TypeError(f\"Column: {n} has unsupported type: {t}\")","title":""},{"docid":"20b0117e6714bcff7701ddac17787f5e","score":"0.6360994","text":"def return_type(self) -> pyspark.sql.connect.proto.types_pb2.DataType:","title":""},{"docid":"0618311691836a44481f571981a2ebb3","score":"0.63605905","text":"def getDtype(self):\n return self.params.get(\"dtype\", str)","title":""},{"docid":"1bd90187a38fff80a194a06ed3175960","score":"0.63502675","text":"def dtype(self):\n return self._sparseMap.dtype","title":""},{"docid":"c3e8c8137c68f40a9b1a3039362769d5","score":"0.6344556","text":"def validate_datatype(self, datatype):\n pass","title":""},{"docid":"0a5f42bc17246b9f9362049fe4a978ec","score":"0.63274246","text":"def dtype(self, cid):\n\n # grab a small piece of data\n ind = tuple([slice(0, 1)] * self.ndim)\n arr = self.get_data(cid, view=ind)\n return arr.dtype","title":""},{"docid":"658ed7647a1702558e7c7d63c4b3b069","score":"0.63121635","text":"def dtype(self) -> torch.dtype:\n return self.test_mean.dtype","title":""},{"docid":"dff00028133ea3dbd1628442aca30d65","score":"0.63052446","text":"def convert_column_type(self,column,type=type(1)):\n self.Data_Frame[column]=self.Data_Frame[column].astype(type)","title":""},{"docid":"d49e169d9add8732b4a23006a99fb576","score":"0.63043797","text":"def GetTypeOfCol(self, *args):\n return _gdal.RasterAttributeTable_GetTypeOfCol(self, *args)","title":""},{"docid":"4c97ff9a9f5608d6e124c9fe865478e4","score":"0.63002914","text":"def get_data_type(self):\n if self.dtype == 0:\n return(\"Pumping rate\")\n elif self.dtype == 1:\n return(\"Drawdown vs time\")\n elif self.dtype == 2:\n return(\"First drawdown derivative\")\n elif self.dtype == 3:\n return(\"First drawdown derivative\")\n elif self.dtype == 4:\n return(\"Drawdown vs distance\")","title":""},{"docid":"5c4d3ad159d19f5bc6e660839c39499f","score":"0.62953544","text":"def get_datatype(self):\n return \"SET\"","title":""},{"docid":"1647d03568832267144127b0bb767492","score":"0.62765014","text":"def dtype(self):\n bitpix = self._obj._orig_bitpix\n if self._obj._orig_bscale == 1 and self._obj._orig_bzero == 0:\n dtype = fits.BITPIX2DTYPE[bitpix]\n else:\n # this method from astropy will return the dtype if the data\n # needs to be converted to unsigned int or scaled to float\n dtype = self._obj._dtype_for_bitpix()\n\n if dtype is None:\n if bitpix < 0:\n dtype = np.dtype('float{}'.format(abs(bitpix)))\n if (self._obj.header['EXTNAME'] == 'DQ' or self._obj._uint and\n self._obj._orig_bscale == 1 and bitpix == 8):\n dtype = np.uint16\n return dtype","title":""},{"docid":"921a4592765aa88fca4a8f065c3ef7ed","score":"0.62666196","text":"def _get_column_type(data, column_index):\n data_types = set()\n for row in data:\n cell = row[column_index]\n t = type(_get_value(cell))\n is_known_type = (\n cell is None or issubclass(t, _numbers.Number) or issubclass(t, str)\n )\n if not is_known_type:\n t = str\n data_types.add(t)\n return _determine_column_type(data_types)","title":""},{"docid":"487a3388fa1a65d48de03b5828ea56b3","score":"0.62574834","text":"def db_type(self):\n return 'varchar(100)'","title":""},{"docid":"cece28ed05ef7b4c4b3b2971a00c4bb5","score":"0.6256657","text":"def dtype(self):\n if hasattr(self, \"_dtype\"):\n return self._dtype\n\n self._dtype = self.get_frame(0).dtype\n return self._dtype","title":""},{"docid":"b5097c8f62ad072974a1356fb844be87","score":"0.6234462","text":"def current_datatype(self):\n app.notify(subtitle=f\"Current C lang datatype: {self.datatype}\")","title":""},{"docid":"5f57a9754a423eaa3d67e11234e443c1","score":"0.62313956","text":"def get_column_type(self,column_name):\n #import pdb;pdb.set_trace()\n out = None\n cols = self._get_table_info()\n \n for col in cols:\n if col[1] == column_name:\n out = col['type'].upper()\n break\n \n if out == None:\n raise KeyError(\"That column name is not in this table\")\n \n return out","title":""}],"string":"[\n {\n \"docid\": \"5b8088fbe0ac252d28bc703bdde1e7c3\",\n \"score\": \"0.8037336\",\n \"text\": \"def datatype(self):\\n return self._datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48de7eb74ceee9fe56fd65453b09294b\",\n \"score\": \"0.7955723\",\n \"text\": \"def dtype(self):\\n return self._datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb12f10ae87cbd514e47d2c2795b615f\",\n \"score\": \"0.78110397\",\n \"text\": \"def data_type(self):\\n return self.__data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4ed81bb5320d6c2423bb3d69df574e\",\n \"score\": \"0.7808144\",\n \"text\": \"def data_type(self):\\n return self._data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c7e089bbf793d39ec281cc356feabe\",\n \"score\": \"0.778775\",\n \"text\": \"def data_type(self):\\n\\n return self._data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e16ba8c58e3994b0c6693904280082\",\n \"score\": \"0.75562376\",\n \"text\": \"def getDataType(self):\\n return self.data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe38f5416da9523ff5363ef844ee1eee\",\n \"score\": \"0.7527811\",\n \"text\": \"def dtype(self):\\n return self.data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe38f5416da9523ff5363ef844ee1eee\",\n \"score\": \"0.7527811\",\n \"text\": \"def dtype(self):\\n return self.data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe38f5416da9523ff5363ef844ee1eee\",\n \"score\": \"0.7527811\",\n \"text\": \"def dtype(self):\\n return self.data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e025b354e1a7c9e829d89648c7c1087\",\n \"score\": \"0.7440406\",\n \"text\": \"def dtype(self):\\n return self._data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7efb8bff7b892e5d43fe3f26645ce4\",\n \"score\": \"0.74388254\",\n \"text\": \"def get_data_type(self):\\n return self._data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c0a3df1a58449164ab6d9e7873efef\",\n \"score\": \"0.7437267\",\n \"text\": \"def GetColumnType(self, col):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c0a3df1a58449164ab6d9e7873efef\",\n \"score\": \"0.7437267\",\n \"text\": \"def GetColumnType(self, col):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0e1c4ffe8d888a052cd055327d933a5\",\n \"score\": \"0.74280405\",\n \"text\": \"def type(self):\\n return self.data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e145ddf40c8ad79cf7f5ccd449da7c\",\n \"score\": \"0.74093187\",\n \"text\": \"def _derive_data_type(column: Dict[str, Any]) -> str:\\n data_type = column['data_type']\\n\\n if data_type in ['bigint',\\n 'int',\\n 'smallint',\\n 'tinyint',\\n 'bit',\\n 'money',\\n 'smallmoney',\\n 'float',\\n 'real',\\n 'date',\\n 'datetime',\\n 'datetime2',\\n 'datetimeoffset',\\n 'smalldatetime',\\n 'time',\\n 'text',\\n 'ntext',\\n 'binary',\\n 'image',\\n 'xml',\\n 'geography',\\n 'geometry',\\n 'sysname'\\n ]:\\n return data_type\\n\\n if data_type in ['decimal', 'numeric']:\\n return 'decimal({0:d},{1:d})'.format(column['precision'], column['scale'])\\n\\n if data_type == 'char':\\n return 'char({0:d})'.format(column['max_length'])\\n\\n if data_type == 'varchar':\\n if column['max_length'] == -1:\\n return 'varchar(max)'\\n\\n return 'varchar({0:d})'.format(column['max_length'])\\n\\n if data_type == 'nchar':\\n return 'nchar({0:d})'.format(int(column['max_length'] / 2))\\n\\n if data_type == 'nvarchar':\\n if column['max_length'] == -1:\\n return 'nvarchar(max)'\\n\\n return 'nvarchar({0:d})'.format(int(column['max_length'] / 2))\\n\\n if data_type == 'varbinary':\\n return 'varbinary({0:d})'.format(column['max_length'])\\n\\n raise Exception(\\\"Unexpected data type '{0}'\\\".format(data_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a879494c135a9817e6cda2d6c14a3a40\",\n \"score\": \"0.7405552\",\n \"text\": \"def type(self):\\n return self.dtype().value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c3a4c4cfae33d72a8a32d47fd78116\",\n \"score\": \"0.7386811\",\n \"text\": \"def dtype(self):\\r\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d5c050d0850073e083a35754827e37\",\n \"score\": \"0.73845667\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01c3da06324ecb23284e4c7ccb2cf30\",\n \"score\": \"0.7310465\",\n \"text\": \"def get_data_type(self) -> np.dtype:\\n return self.type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2908046b45c31bc75a4a2a8806286407\",\n \"score\": \"0.72685397\",\n \"text\": \"def get_dtype(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d574b68629284c0f77e3c5f614641524\",\n \"score\": \"0.7255222\",\n \"text\": \"def getDataType(self):\\n\\n return 'Scalar'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8759282617828147bf72a15054f23221\",\n \"score\": \"0.72273684\",\n \"text\": \"def dtype(self):\\n return self.__dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af54a1addb8847db50a38cb5f6a5ec52\",\n \"score\": \"0.7204325\",\n \"text\": \"def dtype(self):\\n if self.view_attr is None and self.schema.nattr > 1:\\n raise NotImplementedError(\\\"Multi-attribute does not have single dtype!\\\")\\n return self.schema.attr(0).dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd9c01758dc76f6ddf927924726b4c1\",\n \"score\": \"0.71553457\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd9c01758dc76f6ddf927924726b4c1\",\n \"score\": \"0.71553457\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a846b510367f7c1ed56f736981e17466\",\n \"score\": \"0.7122086\",\n \"text\": \"def getDataType(self) -> ghidra.program.model.data.DataType:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb6045337f96701627acbda5c3ae2ae\",\n \"score\": \"0.71182996\",\n \"text\": \"def dtype(self):\\n return self._impl.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"115f152d1e6edb1fa8e49d3d0c8a01b5\",\n \"score\": \"0.71133626\",\n \"text\": \"def dtype(self):\\n\\n return self.description._v_dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ea02a66e8954707b382f68edbb70e0\",\n \"score\": \"0.7089385\",\n \"text\": \"def get_dtype(self):\\n return self.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9d34d1426fa35423718cbbb253ae45\",\n \"score\": \"0.7053333\",\n \"text\": \"def export_datatype(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f92513c029247743fde4da8da4c601\",\n \"score\": \"0.70321596\",\n \"text\": \"def data_type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdce282b1c2cb466e62f311a8e837196\",\n \"score\": \"0.69717824\",\n \"text\": \"def data_type(self):\\n # datetime and date to int\\n date_cols = ['visit_date', 'prev_visit_date', 'prev_item_move_date',\\n 'last_edit_date', 'creation_date']\\n for col in date_cols:\\n # convert multiple time formats into single string format\\n self.df[col] = pd.to_datetime(self.df[col]).dt.strftime('%Y-%m-%d %H:%M:%S')\\n # make time features specific data type in order to distinguish from other numberic values\\n self.df['{}_int'.format(col)] = self.df.apply(self._date_to_int, col=col, axis=1).astype(np.float32)\\n # convert string format back into datetime\\n self.df[col] = pd.to_datetime(self.df[col])\\n # objects\\n obj_cols = ['ship_id', 'address1', 'customer_id', 'sales_rep_id', 'item_id', 'old_item_id',\\n 'item_UPC', 'old_item_UPC', 'ship_list_pk', 'sales_rep_id_2', 'list_header_id']\\n for col in obj_cols:\\n self.df[col] = self.df[col].astype(object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970d2776f6fcf0c468381762f0026068\",\n \"score\": \"0.69492733\",\n \"text\": \"def dtype(x):\\n\\t return x.dtype.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d35de6d937f89f506dd8465fa7e55f\",\n \"score\": \"0.69273543\",\n \"text\": \"def dtype(self):\\n return self._dats[0].dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc13474be25c43ba2a4f847adfad3c1e\",\n \"score\": \"0.69220364\",\n \"text\": \"def data_type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc13474be25c43ba2a4f847adfad3c1e\",\n \"score\": \"0.69220364\",\n \"text\": \"def data_type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e581d5c45d038177e9e16ff2d3f0f65\",\n \"score\": \"0.69217163\",\n \"text\": \"def getDtype(self):\\n return self.dtype_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b20ec86c0865de94c347bbe146ffe34\",\n \"score\": \"0.6917254\",\n \"text\": \"def get_data_type(self, df, col):\\n if col not in df.columns:\\n raise KeyError(f'Column \\\"{col:s}\\\" not in input dataframe.')\\n dt = dict(df.dtypes)[col]\\n # spark conversions to numpy or python equivalent\\n if dt == \\\"string\\\":\\n dt = \\\"str\\\"\\n elif dt in [\\\"timestamp\\\", \\\"date\\\"]:\\n dt = np.datetime64\\n elif dt == \\\"boolean\\\":\\n dt = bool\\n elif dt == \\\"bigint\\\":\\n dt = np.int64\\n elif dt.startswith(\\\"decimal(\\\"):\\n return np.dtype(float, metadata={\\\"decimal\\\": True})\\n\\n return np.dtype(dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6a4dbea08f1e018c1f3e584fd04e63\",\n \"score\": \"0.6893777\",\n \"text\": \"def dtype(self) -> torch.dtype:\\n pass # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e11f987f48a548c782a79d62674f408\",\n \"score\": \"0.6882582\",\n \"text\": \"def ctype(self):\\n return self.data.ctype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d70b338904a1e355f32e2c3c8c9575d\",\n \"score\": \"0.68728685\",\n \"text\": \"def data_type(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17bd447341f0fed24483c839c5da49f\",\n \"score\": \"0.6854025\",\n \"text\": \"def as_type(self):\\n\\t\\ttypes = {\\n\\t\\t\\t'float': self.numeric_f,\\n\\t\\t\\t'bool': self.binary_class_f,\\n\\t\\t\\t'str': self.multi_class_f + self.id_f,\\n\\t\\t\\t'datetime64[ns]': self.datetime_f\\n\\t\\t}\\n\\n\\t\\tfor i in types.keys():\\n\\t\\t\\tif len(types[i]) > 0:\\n\\t\\t\\t\\tif i == 'bool':\\n\\t\\t\\t\\t\\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i).astype('float')\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2240a99a83a8ce6962bb7e2d8dfc4adb\",\n \"score\": \"0.68359935\",\n \"text\": \"def transform_datatype(self, datatype):\\n return datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"698bd5a61e5e91af828f4d065d7a5ca1\",\n \"score\": \"0.68257904\",\n \"text\": \"def dtype(self):\\n return self._snode._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e59b8ca93a41098a598cdf8ae49cfdb\",\n \"score\": \"0.6806045\",\n \"text\": \"def _get_column_type(self) -> None:\\n rows = self._dl.get_all_table_columns()\\n for row in rows:\\n key = '@{0}.{1}.{2}%type@'.format(row['schema_name'], row['table_name'], row['column_name'])\\n key = key.lower()\\n\\n value = self._derive_data_type(row)\\n\\n self._replace_pairs[key] = value\\n\\n self._io.text('Selected {0} column types for substitution'.format(len(rows)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c9b7ff2489d05df3d53ede2b72a675\",\n \"score\": \"0.6774951\",\n \"text\": \"def table_column_type(self, i):\\n with self.free_cstring(_lib.TableColumnType(self.table, i)) as typ:\\n return typ\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efb21d1ed016e4286e0138e960e1427\",\n \"score\": \"0.6770946\",\n \"text\": \"def dtype(self):\\n return self.tensor.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44f8e10a39e055c04ffe566290dff5b\",\n \"score\": \"0.67646646\",\n \"text\": \"def ctype(self):\\n return as_cstr(self.dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de070e4c310a5a07ad0e41eb0e5af3a\",\n \"score\": \"0.6740433\",\n \"text\": \"def form_datatype(self):\\n dt = self.dataype_name()\\n nD = str(len(self.nda.shape))\\n et = get_lh5_element_type(self)\\n return dt + '<' + nD + '>{' + et + '}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ab6124b611861e8f7261b35fba0b7ba\",\n \"score\": \"0.67115027\",\n \"text\": \"def get_type(self):\\n return self.header.data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30eb89c3b7c6e89211db4d8a2626c2ae\",\n \"score\": \"0.6709729\",\n \"text\": \"def dtype(self):\\n return self._get_component(\\\"array\\\").dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc6a7de0d3fa23da85fef18a59483ac\",\n \"score\": \"0.66972804\",\n \"text\": \"def _get_predefined_datatype(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72ee64ed3cb0350c1e14d545c185955\",\n \"score\": \"0.669686\",\n \"text\": \"def convert_datatype(coldatatype):\\n\\n datatype = SqlType.text()\\n def_value = ''\\n \\n if 'datetime' in coldatatype.lower():\\n datatype = SqlType.timestamp()\\n elif 'str' in coldatatype.lower():\\n datatype = SqlType.text()\\n elif 'boolean' in coldatatype.lower():\\n datatype = SqlType.bool()\\n elif 'int' in coldatatype.lower():\\n datatype = SqlType.int()\\n def_value = 0\\n elif 'float' in coldatatype.lower():\\n datatype = SqlType.double()\\n def_value = 0\\n elif 'period' in coldatatype.lower():\\n datatype = SqlType.interval()\\n elif 'object' in coldatatype.lower():\\n datatype = SqlType.text()\\n else:\\n datatype = SqlType.text()\\n \\n return (datatype,def_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d21875c687169cd72cd948c52e38935\",\n \"score\": \"0.66671395\",\n \"text\": \"def dtypes_of(data):\\n return data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6961728ac2e377ee3f8d845ee1a929\",\n \"score\": \"0.6664429\",\n \"text\": \"def data_type(self) -> pulumi.Input['InstructionDataType']:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eb54b93b32955b17f8f324a6815ba54\",\n \"score\": \"0.66327494\",\n \"text\": \"def dtype(self):\\n\\n hdr = self.header()\\n\\n # Get numpy data type\\n dtype = {'char': np.byte,\\n 'short': np.short,\\n 'int': np.intc,\\n 'long': np.int_,\\n 'ushort': np.ushort,\\n 'uint': np.uintc,\\n 'float': np.single,\\n 'vec3f': np.single,\\n 'vec9f': np.single,\\n 'double': np.double}[hdr['type']]\\n dtype = np.dtype(dtype)\\n\\n # Get python byte order string\\n endian = {'little_endian': '<',\\n 'big_endian': '>'}[hdr['systeme']]\\n\\n # Return data type with correct order\\n return dtype.newbyteorder(endian)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ff223c19791fe116cb325d66c1bf29\",\n \"score\": \"0.6632438\",\n \"text\": \"def dtype(self) -> 'np.dtype':\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f1542bddac468ee8c73316b63c956b\",\n \"score\": \"0.66217273\",\n \"text\": \"def value_type(self):\\n return self._value_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca8e1547158b09092fcee6c52435901b\",\n \"score\": \"0.6602762\",\n \"text\": \"def get_data_type(cls) -> str:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afccd0b6a180a8c7c07bab235818ef65\",\n \"score\": \"0.65588313\",\n \"text\": \"def dtype(self) -> np.dtype:\\n return self.__dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54fd9c1f535c68e9c4072ec355200553\",\n \"score\": \"0.65281224\",\n \"text\": \"def detect_datatype(data):\\n if type(data) == type(np.empty(0)):\\n return DataType.NUMPY\\n elif type(data) == type(pd.DataFrame({'A': []})):\\n return DataType.DATAFRAME\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abbb449013006308c392dbf38126154f\",\n \"score\": \"0.6517055\",\n \"text\": \"def dtype(self):\\n\\n return self.descr._v_dtypes[self.name].base # Get rid of shape info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce26c164a3ccce72b91a16f49eb2d172\",\n \"score\": \"0.65145123\",\n \"text\": \"def dtypes(self) -> List[str]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad950edaae613c0833cc9ef9121bb241\",\n \"score\": \"0.65138894\",\n \"text\": \"def dtype(self):\\n return type(self._get_sequence_iloc_(0)) if len(self) > 0 else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dabbabfbf6955d71241653343428b38\",\n \"score\": \"0.6507724\",\n \"text\": \"def getSizeDataType(self) -> ghidra.program.model.data.DataType:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0db954e693796b7be6c46f5f716fce\",\n \"score\": \"0.65049326\",\n \"text\": \"def _determine_column_type(data_types):\\n # Allow None which will be converted to NaN.\\n if all(\\n issubclass(t, (_numbers.Number, type(None))) and not issubclass(t, bool)\\n for t in data_types\\n ):\\n return 'number'\\n return 'string'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"491f6c839537d99f47524e1b16fea22e\",\n \"score\": \"0.6501615\",\n \"text\": \"def type_value(ctype: Any, value: Any):\\n\\n # Boolean\\n if ctype == platform.xlrd.XL_CELL_BOOLEAN:\\n return bool(value)\\n\\n # Excel numbers are only float\\n # Float with no decimals can be cast into int\\n if ctype == platform.xlrd.XL_CELL_NUMBER and value == value // 1:\\n return int(value)\\n\\n # Datetime\\n if ctype == platform.xlrd.XL_CELL_DATE:\\n return platform.xlrd.xldate.xldate_as_datetime(value, book.datemode)\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff8705d409d3f0d737ed36858dddd70\",\n \"score\": \"0.6487331\",\n \"text\": \"def get_field_type(self, data_type, row):\\n return DatabaseIntrospection.data_types_reverse[row[7]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e1e39e63e4d422f5e3267c1fd821fd\",\n \"score\": \"0.64852417\",\n \"text\": \"def _get_dtype(self) -> np.dtype:\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06216a5ce712b85ec41a8045033e184f\",\n \"score\": \"0.64840126\",\n \"text\": \"def get_data_type(self, column, table):\\n\\t\\ttry:\\n\\t\\t\\ttype_override = self.type_override[column[\\\"column_type\\\"]]\\n\\t\\t\\toverride_to = type_override[\\\"override_to\\\"]\\n\\t\\t\\toverride_tables = type_override[\\\"override_tables\\\"]\\n\\t\\t\\t\\n\\t\\t\\tif override_tables[0] == '*' or table in override_tables:\\n\\t\\t\\t\\tcolumn_type = override_to\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcolumn_type = self.type_dictionary[column[\\\"data_type\\\"]]\\n\\t\\texcept:\\n\\t\\t\\tcolumn_type = self.type_dictionary[column[\\\"data_type\\\"]]\\n\\t\\treturn column_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43c2ffb38d4ac01066a916166a7693b\",\n \"score\": \"0.64505047\",\n \"text\": \"def data_types(self) -> dict:\\n return self._data_types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963156aaa9eba32adec44835ab8da0ba\",\n \"score\": \"0.6433906\",\n \"text\": \"def evaluateColumnTypeV2(valColumn):\\r\\n res = \\\"numerical\\\"\\r\\n\\r\\n # get a column without missing val\\r\\n l = [val for val in valColumn if val != \\\"\\\"]\\r\\n\\r\\n if (len(l) == 0):\\r\\n res = \\\"empty\\\"\\r\\n else:\\r\\n try:\\r\\n for val in l:\\r\\n float(val)\\r\\n\\r\\n res = \\\"numerical\\\"\\r\\n pass\\r\\n except ValueError:\\r\\n res = \\\"categorical\\\"\\r\\n pass\\r\\n \\r\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6463851be879cbcc32965429551efc01\",\n \"score\": \"0.64146054\",\n \"text\": \"def test_datatype(self):\\n self.assertTrue(lib.INTEGER.kind_of(lib.NUMBER))\\n self.assertTrue(lib.FLOAT.kind_of(lib.NUMBER))\\n self.assertTrue(lib.INTEGER.kind_of(env.ANY))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0d2ae1fbf4adc2260cf4b13988c771\",\n \"score\": \"0.64124054\",\n \"text\": \"def data_type(a):\\n\\ta_type =type(a)\\n\\tif a_type == str:\\n\\t\\tif len(a) == 0:\\n\\t\\t\\treturn 'no value'\\n\\t\\telse:\\n\\t\\t\\treturn len(a)\\n\\t\\t\\n\\telif a_type ==bool:\\n\\t\\treturn a\\n\\telif a_type ==int:\\n\\n\\t\\tif a == 100:\\n\\t\\t\\treturn 'equal to 100'\\n\\t\\telif a < 100:\\n\\t\\t\\treturn 'less than 100'\\n\\n\\t\\telse:\\n\\t\\t\\treturn 'more than 100'\\n\\n\\telif a_type ==list:\\n\\t\\ttry:\\n\\t\\t\\tif a[2]:\\n\\t\\t\\t\\treturn a[2]\\n\\t\\texcept(IndexError):\\n\\t\\t\\treturn None\\n\\t\\telse:\\n\\t\\t\\treturn 'no value'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49436abf6ea1757f0b1750bdd28bcbc4\",\n \"score\": \"0.63909143\",\n \"text\": \"def column_data(self, column_name):\\n for col in self.raw_frame.columns:\\n if col.name == column_name:\\n if col.dtype == fpb.INTEGER:\\n return col.ints\\n elif col.dtype == fpb.FLOAT:\\n return col.floats\\n elif col.dtype == fpb.STRING:\\n return col.strings\\n elif col.dtype == fpb.TIME:\\n return col.times\\n elif col.dtype == fpb.BOOLEAN:\\n return col.bools\\n else:\\n raise ReadError('{} - unsupported type - {}'.format(column_name, col.dtype))\\n\\n raise ReadError('No column named \\\"{}\\\"'.format(column_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8ac16c1589d50950dd97a4040f2144\",\n \"score\": \"0.63876945\",\n \"text\": \"def column_types(self):\\n return self._column_types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"822ed2a73282d41b745f1ac6b8c604d7\",\n \"score\": \"0.6382394\",\n \"text\": \"def dtype(self) -> StringDtype: # type: ignore[override]\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba956cd8d21abd0d782256aba8e033a\",\n \"score\": \"0.63798404\",\n \"text\": \"def cell_type(self, row, col):\\n if col == 0: return 'heading'\\n else: return 'data'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccedb8e1a1ae08ee787daf348c6814b6\",\n \"score\": \"0.6374998\",\n \"text\": \"def dtype(self) -> torch.dtype:\\n return self.mvn.loc.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94113e48de1e1265adaed5b57226040f\",\n \"score\": \"0.6372429\",\n \"text\": \"def generate_col_data(self, col: dict) -> Any:\\n if self.null_column_value(col):\\n return None\\n\\n elif col.get(\\\"enum\\\"):\\n return self.fake_enum(col.get(\\\"enum\\\"))\\n\\n elif col[\\\"type\\\"] in [\\\"int\\\", \\\"long\\\"]:\\n return self.fake_int(col)\\n\\n elif col[\\\"type\\\"] in [\\\"double\\\", \\\"float\\\"]:\\n return self.fake_double(col)\\n\\n elif col[\\\"type\\\"] in [\\\"date\\\", \\\"datetime\\\"]:\\n return self.fake_datetime(col)\\n\\n elif col[\\\"type\\\"] == \\\"boolean\\\":\\n return self.fake_bool()\\n\\n elif col[\\\"type\\\"] == \\\"character\\\":\\n return self.fake_character(special_type=self.special_cols.get(col[\\\"name\\\"]))\\n else:\\n n = col[\\\"name\\\"]\\n t = col[\\\"type\\\"]\\n raise TypeError(f\\\"Column: {n} has unsupported type: {t}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b0117e6714bcff7701ddac17787f5e\",\n \"score\": \"0.6360994\",\n \"text\": \"def return_type(self) -> pyspark.sql.connect.proto.types_pb2.DataType:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0618311691836a44481f571981a2ebb3\",\n \"score\": \"0.63605905\",\n \"text\": \"def getDtype(self):\\n return self.params.get(\\\"dtype\\\", str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd90187a38fff80a194a06ed3175960\",\n \"score\": \"0.63502675\",\n \"text\": \"def dtype(self):\\n return self._sparseMap.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e8c8137c68f40a9b1a3039362769d5\",\n \"score\": \"0.6344556\",\n \"text\": \"def validate_datatype(self, datatype):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5f42bc17246b9f9362049fe4a978ec\",\n \"score\": \"0.63274246\",\n \"text\": \"def dtype(self, cid):\\n\\n # grab a small piece of data\\n ind = tuple([slice(0, 1)] * self.ndim)\\n arr = self.get_data(cid, view=ind)\\n return arr.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"658ed7647a1702558e7c7d63c4b3b069\",\n \"score\": \"0.63121635\",\n \"text\": \"def dtype(self) -> torch.dtype:\\n return self.test_mean.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dff00028133ea3dbd1628442aca30d65\",\n \"score\": \"0.63052446\",\n \"text\": \"def convert_column_type(self,column,type=type(1)):\\n self.Data_Frame[column]=self.Data_Frame[column].astype(type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49e169d9add8732b4a23006a99fb576\",\n \"score\": \"0.63043797\",\n \"text\": \"def GetTypeOfCol(self, *args):\\n return _gdal.RasterAttributeTable_GetTypeOfCol(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c97ff9a9f5608d6e124c9fe865478e4\",\n \"score\": \"0.63002914\",\n \"text\": \"def get_data_type(self):\\n if self.dtype == 0:\\n return(\\\"Pumping rate\\\")\\n elif self.dtype == 1:\\n return(\\\"Drawdown vs time\\\")\\n elif self.dtype == 2:\\n return(\\\"First drawdown derivative\\\")\\n elif self.dtype == 3:\\n return(\\\"First drawdown derivative\\\")\\n elif self.dtype == 4:\\n return(\\\"Drawdown vs distance\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c4d3ad159d19f5bc6e660839c39499f\",\n \"score\": \"0.62953544\",\n \"text\": \"def get_datatype(self):\\n return \\\"SET\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1647d03568832267144127b0bb767492\",\n \"score\": \"0.62765014\",\n \"text\": \"def dtype(self):\\n bitpix = self._obj._orig_bitpix\\n if self._obj._orig_bscale == 1 and self._obj._orig_bzero == 0:\\n dtype = fits.BITPIX2DTYPE[bitpix]\\n else:\\n # this method from astropy will return the dtype if the data\\n # needs to be converted to unsigned int or scaled to float\\n dtype = self._obj._dtype_for_bitpix()\\n\\n if dtype is None:\\n if bitpix < 0:\\n dtype = np.dtype('float{}'.format(abs(bitpix)))\\n if (self._obj.header['EXTNAME'] == 'DQ' or self._obj._uint and\\n self._obj._orig_bscale == 1 and bitpix == 8):\\n dtype = np.uint16\\n return dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921a4592765aa88fca4a8f065c3ef7ed\",\n \"score\": \"0.62666196\",\n \"text\": \"def _get_column_type(data, column_index):\\n data_types = set()\\n for row in data:\\n cell = row[column_index]\\n t = type(_get_value(cell))\\n is_known_type = (\\n cell is None or issubclass(t, _numbers.Number) or issubclass(t, str)\\n )\\n if not is_known_type:\\n t = str\\n data_types.add(t)\\n return _determine_column_type(data_types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487a3388fa1a65d48de03b5828ea56b3\",\n \"score\": \"0.62574834\",\n \"text\": \"def db_type(self):\\n return 'varchar(100)'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cece28ed05ef7b4c4b3b2971a00c4bb5\",\n \"score\": \"0.6256657\",\n \"text\": \"def dtype(self):\\n if hasattr(self, \\\"_dtype\\\"):\\n return self._dtype\\n\\n self._dtype = self.get_frame(0).dtype\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5097c8f62ad072974a1356fb844be87\",\n \"score\": \"0.6234462\",\n \"text\": \"def current_datatype(self):\\n app.notify(subtitle=f\\\"Current C lang datatype: {self.datatype}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f57a9754a423eaa3d67e11234e443c1\",\n \"score\": \"0.62313956\",\n \"text\": \"def get_column_type(self,column_name):\\n #import pdb;pdb.set_trace()\\n out = None\\n cols = self._get_table_info()\\n \\n for col in cols:\\n if col[1] == column_name:\\n out = col['type'].upper()\\n break\\n \\n if out == None:\\n raise KeyError(\\\"That column name is not in this table\\\")\\n \\n return out\",\n \"title\": \"\"\n }\n]"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":1,"numItemsPerPage":100,"numTotalItems":994482,"offset":100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc0ODk2ODkzNSwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc0ODk3MjUzNSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.4xYsAXW1pmTDRjzEvEArHjIBbRTP8sV4qwesTiGr3uhCRCMu2VJq2HRNkP6SaobXOrYG2V62t2fe9B1HKdAJDQ","displayUrls":true},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
Test aall for gt Array code i. General test odd length array without SIMD.
[
{
"docid": "3f2746b88d212b25fc12f6bb1dabfa96",
"score": "0.0",
"text": "def test_aall_basic_gt_b3(self):\n\t\t# One test value near the end of the array is equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = 1052688062\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\t\ttestdata[-2] = testval\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
}
]
[
{
"docid": "2f27d5e3b57c09c8c86baa613f177d15",
"score": "0.69521815",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "2f27d5e3b57c09c8c86baa613f177d15",
"score": "0.69520867",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "42b4b8f5355fee5edf9f835bdb458f36",
"score": "0.6926313",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "42b4b8f5355fee5edf9f835bdb458f36",
"score": "0.6926313",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "e79c9551c469fa8d93c8dcbac28392f4",
"score": "0.691178",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "e79c9551c469fa8d93c8dcbac28392f4",
"score": "0.691178",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "e703a22d0223e1f909eb8d106eab3222",
"score": "0.6899496",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25700\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "e703a22d0223e1f909eb8d106eab3222",
"score": "0.6899496",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25700\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "469e7772a2687bd518b5f06cc0eda177",
"score": "0.68452233",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "469e7772a2687bd518b5f06cc0eda177",
"score": "0.68452233",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "9eb73ba52ba65245bf8ab98b38d9b63a",
"score": "0.68434054",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "9eb73ba52ba65245bf8ab98b38d9b63a",
"score": "0.68434054",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "5410fd4b32d3e615c4af471a09f648a8",
"score": "0.679382",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "5410fd4b32d3e615c4af471a09f648a8",
"score": "0.679382",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "42aa13ddf4f5463e0cf9ef8c8cfba317",
"score": "0.67773706",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "42aa13ddf4f5463e0cf9ef8c8cfba317",
"score": "0.67773706",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4137c4a37ef8f9cd6765f2f2baad330f",
"score": "0.67706996",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4137c4a37ef8f9cd6765f2f2baad330f",
"score": "0.67706996",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "0c7d9a3233b22db1eaa630d959d7fe90",
"score": "0.67703956",
"text": "def test_aall_neginf_gt_b1(self):\n\t\t# All data and test values match.\n\t\ttestval = -math.inf\n\t\tarrayval = 100.0\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "0c7d9a3233b22db1eaa630d959d7fe90",
"score": "0.67703956",
"text": "def test_aall_neginf_gt_b1(self):\n\t\t# All data and test values match.\n\t\ttestval = -math.inf\n\t\tarrayval = 100.0\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "052f2fb1a112d374075ddf56c1abf2a5",
"score": "0.67596835",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "052f2fb1a112d374075ddf56c1abf2a5",
"score": "0.67596835",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "35ba0c6c228f77e292f4f8ca950c5813",
"score": "0.67506313",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "35ba0c6c228f77e292f4f8ca950c5813",
"score": "0.67506313",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "948d9b3584e83567151712d00a0b74e9",
"score": "0.67409384",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 24931\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "948d9b3584e83567151712d00a0b74e9",
"score": "0.67409384",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 24931\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4efde85f7b5e5b5f21ac8a7fb245e2e1",
"score": "0.6737059",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = testval\n\t\ttestdata = array.array('l', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4efde85f7b5e5b5f21ac8a7fb245e2e1",
"score": "0.6737059",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = testval\n\t\ttestdata = array.array('l', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "59180a764d8e468e2d69184066b630ba",
"score": "0.67289627",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25700\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "59180a764d8e468e2d69184066b630ba",
"score": "0.67289627",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25700\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "d0ea55eda65d88db741603f54c05b4e8",
"score": "0.67261827",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "d0ea55eda65d88db741603f54c05b4e8",
"score": "0.67261827",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4627e4ef558879796d856050e5b52c2a",
"score": "0.6721413",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -25850\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4627e4ef558879796d856050e5b52c2a",
"score": "0.6721413",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -25850\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "532a1d801ad0d83f418f7677eca49178",
"score": "0.67177767",
"text": "def test_aall_neginf_gt_b1(self):\n\t\t# All data and test values match.\n\t\ttestval = -math.inf\n\t\tarrayval = 100.0\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "532a1d801ad0d83f418f7677eca49178",
"score": "0.67177767",
"text": "def test_aall_neginf_gt_b1(self):\n\t\t# All data and test values match.\n\t\ttestval = -math.inf\n\t\tarrayval = 100.0\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "a8a81818c7b635f0b36f7fa438df018b",
"score": "0.6707076",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "a8a81818c7b635f0b36f7fa438df018b",
"score": "0.6707076",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "911b8e4d5e0e1f758388ea80ef13913f",
"score": "0.6694107",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "911b8e4d5e0e1f758388ea80ef13913f",
"score": "0.6694107",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "94f5b1eec837033708b22bccfdee1880",
"score": "0.6687951",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -1694498815\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "94f5b1eec837033708b22bccfdee1880",
"score": "0.6687951",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -1694498815\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "8c7643fb182f35014d77e097004e9ad6",
"score": "0.66854566",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 3508498382646718642\n\t\tarrayval = testval\n\t\ttestdata = array.array('Q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "8c7643fb182f35014d77e097004e9ad6",
"score": "0.6684972",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 3508498382646718642\n\t\tarrayval = testval\n\t\ttestdata = array.array('Q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "168de8d3ffe3f61759b8491862a63dc0",
"score": "0.6676877",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "168de8d3ffe3f61759b8491862a63dc0",
"score": "0.6676877",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "cdd6c978e56e934ebeaf6859fe13f8dd",
"score": "0.6675503",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "cdd6c978e56e934ebeaf6859fe13f8dd",
"score": "0.6675503",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "f9145b6ffb9a97812c10ff87df521431",
"score": "0.6666108",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 7262497666814784100\n\t\tarrayval = testval\n\t\ttestdata = array.array('q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "f9145b6ffb9a97812c10ff87df521431",
"score": "0.6666108",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 7262497666814784100\n\t\tarrayval = testval\n\t\ttestdata = array.array('q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "756279dbd7cc290aab32b7501faf8394",
"score": "0.6665809",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -1694498815\n\t\tarrayval = testval\n\t\ttestdata = array.array('l', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "756279dbd7cc290aab32b7501faf8394",
"score": "0.6665809",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -1694498815\n\t\tarrayval = testval\n\t\ttestdata = array.array('l', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "cf823eebd8d1dda966ede8a871d33d6c",
"score": "0.6664166",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "cf823eebd8d1dda966ede8a871d33d6c",
"score": "0.6664166",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25000\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "d0fd9cdfdd336df2319c085c39ca9ef8",
"score": "0.66631246",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "d0fd9cdfdd336df2319c085c39ca9ef8",
"score": "0.6662836",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "2e5606bb549a80b7ba01b5ab454a3a7f",
"score": "0.6655907",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -25850.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "2e5606bb549a80b7ba01b5ab454a3a7f",
"score": "0.6655907",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -25850.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "51c513031f8f135ca9905ca1d495f4e7",
"score": "0.6654366",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "51c513031f8f135ca9905ca1d495f4e7",
"score": "0.6654366",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('h', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "8a9ef419a9b94b7f48c646b418754010",
"score": "0.66505176",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -1694498815.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "8a9ef419a9b94b7f48c646b418754010",
"score": "0.66505176",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -1694498815.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "52d22a4ef1db667227945c3c38f824eb",
"score": "0.664853",
"text": "def test_aall_basic_gt_b1(self):\n\t\t# All data values are greater than the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = 1633771876\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertTrue(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "52d22a4ef1db667227945c3c38f824eb",
"score": "0.6647227",
"text": "def test_aall_basic_gt_b1(self):\n\t\t# All data values are greater than the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = 1633771876\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertTrue(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "a2aeb13da2200df944cc8a053471bf6a",
"score": "0.66453093",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 7234017283807667300\n\t\tarrayval = testval\n\t\ttestdata = array.array('Q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "a2aeb13da2200df944cc8a053471bf6a",
"score": "0.66438323",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 7234017283807667300\n\t\tarrayval = testval\n\t\ttestdata = array.array('Q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "9c4160ab7db5a4e750ae331bfec83148",
"score": "0.66354287",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "9c4160ab7db5a4e750ae331bfec83148",
"score": "0.66354287",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "ced67b6f261058dc6019631c61c34575",
"score": "0.66295767",
"text": "def test_aall_basic_gt_b1(self):\n\t\t# All data values are greater than the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = 1633771876\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertTrue(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "ced67b6f261058dc6019631c61c34575",
"score": "0.66295767",
"text": "def test_aall_basic_gt_b1(self):\n\t\t# All data values are greater than the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = 1633771876\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertTrue(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "07433de0be88584e95ced6f50f713c1d",
"score": "0.6627973",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25700\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "07433de0be88584e95ced6f50f713c1d",
"score": "0.6627973",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 25700\n\t\tarrayval = testval\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "726a8dac64b93dbc623d9c1907946a69",
"score": "0.6627895",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "726a8dac64b93dbc623d9c1907946a69",
"score": "0.6627895",
"text": "def test_aall_neginf_gt_b2(self):\n\t\t# All data and test values fail.\n\t\ttestval = 100.0\n\t\tarrayval = -math.inf\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "04558121311ab507675ff843d1f7beed",
"score": "0.6622324",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "04558121311ab507675ff843d1f7beed",
"score": "0.6622324",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "f27a29bc39b0b22bb7f8678802bac30d",
"score": "0.6618444",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "f27a29bc39b0b22bb7f8678802bac30d",
"score": "0.6618444",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "3d43d5a64f9dde1c5d82fd7f753a1652",
"score": "0.66143984",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "3d43d5a64f9dde1c5d82fd7f753a1652",
"score": "0.66143984",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "63d55276074c2347c936e37be028b02f",
"score": "0.66035485",
"text": "def test_aall_basic_gt_b1(self):\n\t\t# All data values are greater than the test value.\n\t\ttestval = 25700\n\t\tarrayval = 37265\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertTrue(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "63d55276074c2347c936e37be028b02f",
"score": "0.66035485",
"text": "def test_aall_basic_gt_b1(self):\n\t\t# All data values are greater than the test value.\n\t\ttestval = 25700\n\t\tarrayval = 37265\n\t\ttestdata = array.array('H', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertTrue(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertTrue(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "579d066fc6f968dcf8cf46bc7dd4ec3f",
"score": "0.66025686",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "579d066fc6f968dcf8cf46bc7dd4ec3f",
"score": "0.66025686",
"text": "def test_aall_basic_lt_d2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x < testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('<', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4224f56309d64233f0a91ccf4c2960ac",
"score": "0.6600486",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "4224f56309d64233f0a91ccf4c2960ac",
"score": "0.6600486",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1684300900\n\t\tarrayval = testval\n\t\ttestdata = array.array('L', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "620f93b704e5dfe89116dd61875d03c3",
"score": "0.65989643",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -7393337902558193305\n\t\tarrayval = testval\n\t\ttestdata = array.array('q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "620f93b704e5dfe89116dd61875d03c3",
"score": "0.65989643",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = -7393337902558193305\n\t\tarrayval = testval\n\t\ttestdata = array.array('q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "161a1736ce7843d231858389210aa3f1",
"score": "0.6597275",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('l', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "161a1736ce7843d231858389210aa3f1",
"score": "0.6597275",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('l', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "baebec4c5d05392938fc135cdf08228c",
"score": "0.65938777",
"text": "def test_aall_basic_gt_b3(self):\n\t\t# One test value near the end of the array is equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = 1633771876\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\t\ttestdata[-2] = testval\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "1a2be001203b0e909ca7015aa36aaeea",
"score": "0.6593876",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "1a2be001203b0e909ca7015aa36aaeea",
"score": "0.6593876",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = testval\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval )\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "520ce3129c6d8f131dde4d2a8e4588f7",
"score": "0.65933424",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "520ce3129c6d8f131dde4d2a8e4588f7",
"score": "0.65933424",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('d', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "baebec4c5d05392938fc135cdf08228c",
"score": "0.6592927",
"text": "def test_aall_basic_gt_b3(self):\n\t\t# One test value near the end of the array is equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = 1633771876\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\t\ttestdata[-2] = testval\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "b6bc945764f327274af7bd0e5976f809",
"score": "0.659044",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('Q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "b6bc945764f327274af7bd0e5976f809",
"score": "0.6589354",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('Q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "e976c5de4570f907f28cfc8b7050ede7",
"score": "0.65815765",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "e976c5de4570f907f28cfc8b7050ede7",
"score": "0.65815765",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100.0\n\t\tarrayval = testval\n\t\ttestdata = array.array('f', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
},
{
"docid": "eeb2a8a936d7f309bb13b05768e6fdb4",
"score": "0.65782756",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 100\n\t\tarrayval = testval\n\t\ttestdata = array.array('q', [arrayval] * self.arraylength)\n\n\t\t# Verify test compatibility.\n\t\texpected = all([(x > testval) for x in testdata])\n\t\tself.assertFalse(expected)\n\n\t\t# The actual test.\n\t\tresult = arrayfunc.aall('>', testdata, testval , nosimd=True)\n\t\tself.assertFalse(result)\n\t\tself.assertEqual(result, expected)",
"title": ""
}
]
66319ebc81fc61a9b8194f91f5fbe55b
Port format from javascript to python version of Overcooked
[
{
"docid": "9327d0b064b33d66aafa8ae053ee0aa1",
"score": "0.0",
"text": "def json_joint_action_to_python_action(json_joint_action):\n if type(json_joint_action) is str:\n json_joint_action = eval(json_joint_action)\n return tuple(json_action_to_python_action(a) for a in json_joint_action)",
"title": ""
}
]
[
{
"docid": "68efa6b52c41765c3886efd016195294",
"score": "0.7036426",
"text": "def send_message(self, sms_gateway_id, from_number, to_number, sms_content, my_model_name='', my_record_id=0, media=None, queued_sms_message=None, media_filename=False):\n sms_account = self.env['sms.account'].browse(sms_gateway_id)\n\n #format the from number before sending\n format_from = from_number\n if \" \" in format_from: format_from.replace(\" \", \"\")\n\n #format the to number before sending\n format_to = to_number\n if \" \" in format_to: format_to.replace(\" \", \"\")\n\n #send the sms message\n params = {\n 'userkey': sms_account.zenviva_userkey,\n 'passkey': sms_account.zenviva_passkey,\n 'to': format_to,\n 'message': sms_content,\n }\n\n response = requests.post(\n sms_account.zenviva_api_url,\n data=params\n )\n\n #Analyse the response string and determine if it sent successfully other wise return a human readable error message\n human_read_error = \"\"\n sms_gateway_message_id = \"\"\n delivary_state = \"failed\"\n if response:\n json_response = json.loads(response.text)\n if json_response[\"status\"] == \"1\":\n delivary_state = \"successful\"\n elif json_response[\"status\"] == \"0\":\n delivary_state = \"failed\"\n human_read_error = json_response[\"text\"]\n else:\n delivary_state = \"failed\"\n human_read_error = \"Kode kesalahan tidak diketahui\"\n\n #send a response back saying how the sending went\n my_sms_response = sms_response()\n my_sms_response.delivary_state = delivary_state\n my_sms_response.response_string = response.text\n my_sms_response.human_read_error = human_read_error\n my_sms_response.message_id = sms_gateway_message_id\n return my_sms_response",
"title": ""
}
]
[
{
"docid": "99a9ead6ee65b8d6a10b557b8b247a88",
"score": "0.7578458",
"text": "def _send(self, message):\r\n charset='UTF-8'\r\n params = {\r\n 'action' : 'sendsms',\r\n 'user' : self.get_username(),\r\n 'password' : self.get_password(),\r\n 'from' : message.from_phone,\r\n 'to' : \",\".join(message.to),\r\n 'text' : message.body,\r\n 'clientcharset' : charset,\r\n 'detectcharset' : 1,\r\n 'maxsplit': int(math.ceil(len(message.body) / 160))\r\n }\r\n \r\n req = urllib2.Request(SMSGLOBAL_API_URL_SENDSMS, urllib.urlencode(params))\r\n result_page = urllib2.urlopen(req).read()\r\n results = self._parse_response(result_page)\r\n \r\n if results is None:\r\n if not self.fail_silently:\r\n raise Exception(\"Error determining response: [\" + result_page + \"]\")\r\n return False\r\n \r\n code, sendqmsgid, msgid = results\r\n \r\n if code != '0':\r\n if not self.fail_silently:\r\n raise Exception(\"Error sending sms: [%s], extracted results(code, sendqmsgid, msgid): [%s]\" % (result_page, results))\r\n return False\r\n else:\r\n logger.info('SENT to: %s; sender: %s; code: %s; sendqmsgid: %s; msgid: %s; message: %s' % (\r\n message.to,\r\n message.from_phone,\r\n code, \r\n sendqmsgid, \r\n msgid,\r\n message.body\r\n ))\r\n return True",
"title": ""
},
{
"docid": "0e2f82952ee813822d7490c73e4644e4",
"score": "0.71073484",
"text": "def send_sms(self, sms_text):\n client = Client(TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN)\n message = client.messages.create(body=sms_text, from_=TWILIO_PH_NO, to=MY_PH_NO)\n print(f'SMS Status: {message.status}')",
"title": ""
},
{
"docid": "e8e19f80bb8b1baa2c7c5e6d48d4903a",
"score": "0.7080253",
"text": "def send_sms_transactional(request):\n try:\n input_json = request\n headers_var = {\n 'content-type': \"application/json\",\n 'authkey': \"334097AuBU02YGOYB5efae2b6P1\"\n }\n payload = dict(zip(['sender', 'route', 'country', 'sms'],\n [\"genericbackend\", \"4\", input_json['country_code'],\n [dict(zip([\"message\", \"to\"], [input_json['text'], [input_json['phone_number']]]))]]))\n result = requests.post(\"https://api.msg91.com/api/v2/sendsms\", headers=headers_var, json=payload)\n # print(result.content)\n output_json = dict(zip(['Status', 'Message', 'Payload'],\n [\"Success\", \"OTP SMS has been sent to given phone number\", result.content]))\n return output_json\n\n except Exception as ex:\n output_json = dict(zip([\"Status\", \"Message\", \"Payload\"],\n [\"Failure\", f\"Unable to send SMS via MSG91. Exception encountered: {ex}\", None]))\n return output_json",
"title": ""
},
{
"docid": "531d812c989856c9e679ad87d12f4c06",
"score": "0.7064733",
"text": "def send_sms(self, toNumber, message):\r\n\t\tself._gv.send_sms(toNumber, message)",
"title": ""
},
{
"docid": "c5ca4825c66440add9ac10d930f7646e",
"score": "0.69973546",
"text": "def send():",
"title": ""
},
{
"docid": "74990ef0f79f7a8dd414330fd50805e5",
"score": "0.69689703",
"text": "def send(self, fail_silently=False):\r\n if not self.to:\r\n # Don't bother creating the connection if there's nobody to send to\r\n return 0\r\n res = self.get_connection(fail_silently).send_messages([self])\r\n sms_post_send.send(sender=self, to=self.to, from_phone=self.from_phone, body=self.body)\r\n return res",
"title": ""
},
{
"docid": "f7a6b71a76e919d15fec2373ed83a69b",
"score": "0.69555724",
"text": "def test_send_sms_message(self):\n deviceid = 1234\n body = 'Hello world!'\n resp = self.client.sms.send_message(deviceid, body)\n self.assertTrue(resp.get('success'))",
"title": ""
},
{
"docid": "98c3b634fca89fd2cdc7b60596b9b8e9",
"score": "0.6949564",
"text": "def send_sms(request):\n try:\n input_json = request\n # initializing invalid input types to promotional sms\n if 'sms_type' not in input_json or input_json['sms_type'] not in [\"transactional\", \"promotional\"]:\n input_json['sms_type'] = \"promotional\"\n if input_json['sms_type'] == \"promotional\":\n output_json = send_sms_promotional(input_json)\n return output_json\n output_json = send_sms_transactional(input_json)\n return output_json\n except Exception as ex:\n output_json = dict(zip([\"Status\", \"Message\", \"Payload\"],\n [\"Failure\", f\"Unable to send SMS. Exception encountered: {ex}\", None]))\n return output_json",
"title": ""
},
{
"docid": "05dd3744e475f54ce857c9b9e021b1ba",
"score": "0.6907953",
"text": "def send_sms(self, msg ):\n load_dotenv(find_dotenv())\n account_sid = os.environ.get(\"TWILIO_ACCOUNT_SID\")\n my_phone = os.environ.get(\"MY_PHONE\")\n auth_token = os.environ.get(\"AUTH_TOKEN\")\n\n client = Client(account_sid,auth_token)\n client.messages.create(\n to=my_phone,\n from_=\"+18082154128\",\n body=msg\n )\n call = client.calls.create(\n to=my_phone,\n from_=\"+18082154128\",\n twiml=\"<Response><Say>PS5 is in stock!</Say></Response>\",\n )",
"title": ""
},
{
"docid": "956b96f08d15dba7701f08d2b866bc0d",
"score": "0.68351704",
"text": "def send(self, msg):\n pass",
"title": ""
},
{
"docid": "be5c404be8a220bc4b4cb4279a3fb036",
"score": "0.6794282",
"text": "def SendSMS(product,\n sms_from,\n mobiles,\n content):\n socket.setdefaulttimeout(DEFAULT_SOCKET_TIMEOUT)\n return ServerProxy(\"http://nanny.netease.com:8080\").accept_msg(\n product,\n sms_from,\n mobiles,\n content)",
"title": ""
},
{
"docid": "c6da9d4f74293b34c2d2337c572684c5",
"score": "0.6761882",
"text": "def send(msg, delay=True, *args, **kwargs):\n context = {\n 'phone_number': urllib.quote(msg.phone_number),\n 'sender_id': urllib.quote(kwargs.get(\"sender_id\", DEFAULT_SENDER_ID)),\n }\n encoding_param = \"\"\n try:\n text = msg.text.encode(\"iso-8859-1\")\n context[\"message\"] = clean_outgoing_sms_text(text)\n except UnicodeEncodeError:\n context[\"message\"] = msg.text.encode(\"utf-16-be\").encode(\"hex\")\n encoding_param = \"&encoding=ucs\"\n url = \"%s?%s%s\" % (settings.SMS_GATEWAY_URL, settings.SMS_GATEWAY_PARAMS % context, encoding_param)\n # just opening the url is enough to send the message\n # TODO, check response\n resp = urllib2.urlopen(url).read()\n msg.save()\n\n create_billable_for_sms(msg, API_ID, delay=delay, response=resp)\n\n return resp",
"title": ""
},
{
"docid": "0f796c012c3c26ecf182579972999b61",
"score": "0.67497635",
"text": "def SendTextToPhone(self, number, message):\n try:\n if(self.gm862.serialConnect.isOpen()):\n print(\"Serial port is open GSM\")\n phoneNumberCmd = \"AT+CMGS=\"+number+\"\\r\"\n self.gm862.serialConnect.write(phoneNumberCmd.encode())\n self.gm862.serialConnect.flush()\n response = self.gm862.serialConnect.read(1000)\n self.gm862.serialConnect.flush()\n print(response.strip()) \n \"\"\"Send SMS. Set phone number, wait for > then send message in IRA format \n ending with ctrl-z (0x1a) or 0x1b to cancel message send\n wait for > error 331 is network service, cmgs<message ref number> is valid sent result\n dont always get the >... just send anyway\"\"\" \n self.gm862.serialConnect.write(self.gsm_encode(message) + b\"\\x1A\")\n self.gm862.serialConnect.flush()\n print(self.gm862.serialConnect.read(250).strip())\n self.gm862.serialConnect.flush()\n time.sleep(1)\n else:\n print(\"Serial port is not open\")\n except:\n print(\"Error with serial port: \", sys.exc_info()[0])",
"title": ""
},
{
"docid": "4bf008b416fa8a8e7ca8f4f04e32fbe6",
"score": "0.6718336",
"text": "def test_sms(callto=\"5102297683\",callfrom=\"5102503825\"):\n text = \"How doth the little crocodile improve his shining tail\"\n sms(callto,callfrom,text)",
"title": ""
},
{
"docid": "638a1aa35ef16bd528a7ddb8b042ab53",
"score": "0.669619",
"text": "def sendMessage(self, message):\n self.server.sendSMS(self.conversation[\"address\"], message)",
"title": ""
},
{
"docid": "1943457a000d832d4c67eea2c0e3d29f",
"score": "0.6658529",
"text": "def _send(self, message):\r\n\r\n params = {\r\n 'V': SMSPUBLI_API_VERSION, \r\n 'UN': SMSPUBLI_USERNAME, \r\n 'PWD': SMSPUBLI_PASSWORD,\r\n 'R': SMSPUBLI_ROUTE, \r\n 'SA': message.from_phone,\r\n 'DA': ','.join(message.to),\r\n 'M': message.body.encode('latin-1'),\r\n 'DC': SMSPUBLI_DC,\r\n 'DR': SMSPUBLI_DR, \r\n 'UR': message.from_phone\r\n }\r\n if SMSPUBLI_ALLOW_LONG_SMS:\r\n params['LM'] = '1'\r\n\r\n response = requests.post(SMSPUBLI_API_URL, params)\r\n if response.status_code != 200:\r\n if not self.fail_silently:\r\n raise\r\n else:\r\n return False\r\n\r\n response_msg, response_code = response.content.split(':')\r\n if response_msg == 'OK':\r\n try:\r\n if \",\" in response_code:\r\n codes = map(int, response_code.split(\",\"))\r\n else:\r\n codes = [int(response_code)]\r\n\r\n for code in codes:\r\n if code == -5:\r\n #: TODO send error signal (no $$)\r\n pass\r\n elif code == -3:\r\n #: TODO send error signal (incorrect num)\r\n pass\r\n\r\n return True\r\n\r\n except (ValueError, TypeError):\r\n if not self.fail_silently:\r\n raise\r\n return False\r\n \r\n return False",
"title": ""
},
{
"docid": "4608389b4e57c8e26cab8fd6f5f5c296",
"score": "0.6646789",
"text": "def send_mt_nosign(self, id, msgid, mobile, content):\r\n url = 'http://120.197.89.173:8081/openapi/router'\r\n secret = '494e58f3a9808daea3bef94078563109'\r\n #NOTE: system_para\r\n appKey = 'j1baerwhjp'\r\n sessionId = ''\r\n method = 'sms.service.send'\r\n v = '1.0'\r\n format = 'json'\r\n locale = ''\r\n sign = '' #upper()\r\n system_para_dict = dict(appKey=appKey,\r\n method=method,\r\n v=v,\r\n format=format,\r\n locale=locale,\r\n sessionId=sessionId,\r\n sign=sign)\r\n\r\n system_para_list = []\r\n business_para_list = []\r\n system_para_list = [\"appKey\" + appKey, \"method\" + method, \"v\" + v, \"format\" + format]\r\n if locale:\r\n system_para_list.append(\"locale\"+locale)\r\n if sessionId:\r\n system_para_list.append(\"sessionId\"+sessionId)\r\n\r\n #NOTE: business_para, 4 items.\r\n phoneNumbers = mobile\r\n Content = content \r\n EntCode = '106571205329'\r\n ReportId = msgid\r\n\r\n isImmediately = True #lower\r\n\r\n business_para_list = [\"phoneNumbers\"+phoneNumbers, \"Content\"+Content, \"EntCode\"+EntCode, \"ReportId\"+str(ReportId), \"isImmediately\"+str(isImmediately)]\r\n business_para_dict = dict(phoneNumbers=phoneNumbers,\r\n Content=Content,\r\n EntCode=EntCode,\r\n ReportId=ReportId,\r\n isImmediately=isImmediately)\r\n\r\n parameters_list = self.get_parameters_list(system_para_list, business_para_list)\r\n sign = self.get_sign(secret, parameters_list)\r\n system_para_dict['sign'] = sign\r\n request_url = self.get_request_url(url, system_para_dict, business_para_dict)\r\n\r\n h = httplib2.Http(timeout=20) # second\r\n response, content = h.request(request_url)\r\n return response, content",
"title": ""
},
{
"docid": "e6ce8e21e873063c29feb418f3df0400",
"score": "0.6644227",
"text": "def dispatch_message(self):\n print(self.to)\n code = self.create_code()\n\n self.send_sms(self.to, code)\n\n print('~CODE', code)\n return {'message': _('SMS message with MFA code has been sent.')}",
"title": ""
},
{
"docid": "b13665b8574ac0d33a9714d98fbcc352",
"score": "0.661255",
"text": "def incoming_twil_sms():\n sms_from = request.form['From']\n print(f\"from: {sms_from} type: {type(sms_from)}\")\n sms_to = request.form['To']\n sms_txt = request.form['Body']\n address = sms.email_lookup(sms_to) # Twilio numbers send a +\n\n if address:\n return str(sms.send_email(to_email=address, txt_from=sms_from, txt_body=sms_txt, txt_to=sms_to))\n else:\n return \"invalid number\"",
"title": ""
},
{
"docid": "5e63e8e22e24a35b0919fb7e1289a46e",
"score": "0.65959954",
"text": "def sendall(self, msg):\n self.write(msg)",
"title": ""
},
{
"docid": "8ecd191f2249562baac6d1a7eee5382a",
"score": "0.6595028",
"text": "def do_send_sms_spryng(num, text):\n cleaned_num = cleaned_number(num)\n if not is_valid_phone_number(cleaned_num):\n return False\n\n if not text:\n return False\n\n if len(text) > app.config[\"MAX_SMS_LENGTH\"]:\n return False\n\n url = app.config[\"SPRYNG_API_URL\"]\n token = app.config[\"SPRYNG_API_BEARER_TOKEN\"]\n spryng_headers = {\n \"Accept\": \"application/json\",\n \"Authorization\": f\"Bearer {token}\",\n }\n\n spryng_request = {\n \"body\": text,\n \"encoding\": \"auto\",\n \"originator\": app.config[\"SPRYNG_MSG_ORIGINATOR\"],\n \"recipients\": [num],\n \"route\": app.config[\"SPRYNG_API_ROUTE\"],\n # \"scheduled_at\": \"now\" #optional, default: \"now\"\n }\n print(\"spryng request\", url, spryng_request, spryng_headers)\n response = requests.post(url, headers=spryng_headers, json=spryng_request)\n rj = response.json()\n print(\"spryng response\", response, \"json-content:\", rj)\n\n if not response.ok:\n return False\n\n #success = rj[\"status\"] in [\"scheduled\", \"pending\", \"delivered\"]\n # could also be \"failed\"\n success = True\n #assume success, FIXME!\n\n return success",
"title": ""
},
{
"docid": "8f183dac2a4c5ecdf2147882052a8bc5",
"score": "0.65809655",
"text": "def test_send(twilio_client_class):\n sms = twilio.TwilioSMSBackend()\n twilio_client_class.assert_called_with('account_sid', 'token')\n\n sms.send('1', '2', 'body')\n sms.client.sms.messages.create.assert_called_with(\n to='1', from_='2', body='body')",
"title": ""
},
{
"docid": "f22e139237cefe79d9413798195a7a64",
"score": "0.65795803",
"text": "def SendSMS(phone, pattern, message):\n # *************************************************\n \"\"\"!!!Формирование текста СМС и номеров получателей!!!\"\"\"\n ph = re.sub(r'([-\\s\\+\\(\\)]*)', \"\", phone)\n assert (ph)[:2] == '79', 'phone should start with 79'\n try:\n ph = int(ph)\n except ValueError as e:\n raise ValueError(\"invalid phone number {0}\".format(phone))\n assert isinstance(pattern, int) and pattern >= 0 and pattern < len(settings.SMS_PATTERNS), \"invalid pattern\"\n\n # кому отправлять СМС\n # phone = 7...\n\n # текст СМС\n # newPass = \"Qwer1234\" # для записи переменных данных\n # msg = \"ваш новый пароль к системе мониторинга \" + newPass\n # msg = \"тестовое СМС для системы мониторинга\"\n # *************************************************\n\n if re.search(r'\\{\\d\\}', settings.SMS_PATTERNS[pattern]):\n msg = settings.SMS_PATTERNS[pattern].format(message)\n else:\n msg = settings.SMS_PATTERNS[pattern]\n\n # тело запроса\n sms = {\n \"from\": \"PZMONITOR\",\n \"to\": ph,\n \"message\": msg\n }\n\n # заголовок\n head = {\n \"Authorization\": \"Basic %s\" % settings.SMS_PASS_PHRASE\n }\n\n # отправка запроса\n r = requests.post(settings.SMS_API_URL, headers=head, json=sms)\n\n # статус и отчет о выполнении, не обязательно\n print(r.status_code)\n print(r.text)\n\n return r.status_code",
"title": ""
},
{
"docid": "6ba7dde28e535a58f591d3703cbfd157",
"score": "0.65672004",
"text": "def send_sms(message):\n if not SEND_SMS:\n return False\n\n client = Client(twilio_settings.account_sid, twilio_settings.auth_token)\n\n message = client.api.account.messages.create(\n to=twilio_settings.phone_to,\n from_=twilio_settings.phone_from,\n body=message)\n\n return True",
"title": ""
},
{
"docid": "22a372ee308621dffdb06fc00cbfd3b4",
"score": "0.65611416",
"text": "def send_message():\n\n # Get content from form\n student_id = request.form.get('phone_dropdown_id')\n text_message_content = request.form.get('message_content')\n student = crud.get_student_phone(student_id)\n student_num = student.student_phone\n\n # If testing, don't send the text\n if os.environ.get('TESTING'):\n return jsonify({'message_content': text_message_content})\n\n account_sid = os.environ.get('ACCOUNT_SID')\n auth_token = os.environ.get('AUTH_TOKEN')\n client = Client(account_sid, auth_token)\n\n client.messages.create(\n body=text_message_content,\n to=str(\"1\" + student_num),\n from_=os.environ[\"TWILIO_PHONE\"]\n )\n\n return jsonify({'message_content': text_message_content})",
"title": ""
},
{
"docid": "abb5441a413e07b473f717792f356588",
"score": "0.6556712",
"text": "def send(self):\n pass",
"title": ""
},
{
"docid": "a0b053f1b6a17c5806181af8b7dfc3ec",
"score": "0.6539937",
"text": "def thread_send(self, body, recipients):\r\n try:\r\n for recipient in recipients: \r\n sms = {}\r\n sms['from'] = self.conf['SENDER_NAME']\r\n sms['to'] = recipient\r\n sms['text'] = body\r\n logger.debug(str(sms))\r\n http_response = self.provider.send_message(sms)\r\n response = http_response['messages'][0]\r\n if response['status'] == '0':\r\n logger.info('SMS sent to %s' % (recipient))\r\n else:\r\n logger.error('SMS Fail to %s Error: %s' % (recipient, response['error-text']))\r\n \r\n # log nexmo balance\r\n balance = '%.1f' % (float(response['remaining-balance']))\r\n logger.info('Remaining balance is %s' % (balance))\r\n except Exception as e:\r\n logger.exception('Failed sending SMS')\r\n \r\n # Email the balance value to admin\r\n try: EmailNexmoBalance(response['remaining-balance'])\r\n except: pass",
"title": ""
},
{
"docid": "5018cd7a7f835a6f2189b10e79a8de19",
"score": "0.6533249",
"text": "def send_message(phone, num, serial_set):\n if num == 1:\n sms_message = '{} card Activated'.format(num) + ' ' + 'serial number: {}'.format(serial_set[0])\n else:\n sms_message = '{} cards Activated'.format(num) + ' ' + 'range: from {} to {}'.format(serial_set[0], serial_set[-1])\n\n phone_number = [phone]\n\n try:\n response = sms.send(sms_message, phone_number)\n print(response)\n except ConnectionError:\n return 'Network Error'",
"title": ""
},
{
"docid": "3c9fb3451d7cca2a46f9cb8323fc2b48",
"score": "0.6512432",
"text": "def sms(self, sms):\n\n self._sms = sms",
"title": ""
},
{
"docid": "909ca706b601058b03ac71158466a77b",
"score": "0.6511297",
"text": "def test_sms_sending(self):\n reader = ListReader(SAMPLE_FRIENDS)\n sender = PrintSMSSender()\n\n with unittest.mock.patch.object(PrintSMSSender, \"send_sms\") as send_sms:\n utils.send_messages(datetime.date(2020, 11, 7), reader, sender)\n send_sms.assert_not_called()\n\n utils.send_messages(datetime.date(2000, 9, 11), reader, sender)\n f, msg = SAMPLE_FRIENDS[1], SAMPLE_MESSAGES[1]\n send_sms.assert_called_with(f, f.extra_data[\"phone\"], msg)\n\n with unittest.mock.patch.object(PrintSMSSender, \"send_sms\") as send_sms:\n with self.assertRaises(Exception):\n utils.send_messages(datetime.date(2000, 10, 8), reader, sender)\n send_sms.assert_not_called()\n\n with unittest.mock.patch.object(PrintSender, \"send_email\") as send_email:\n utils.send_messages(datetime.date(1900, 9, 11), reader, sender)\n send_email.assert_not_called()",
"title": ""
},
{
"docid": "ccc77f0e1ee5bd0a86fd111e8c33b8e3",
"score": "0.64992535",
"text": "def send_message(self, message):\n pass",
"title": ""
},
{
"docid": "ed5db0f53adce5073aa44af9e7daa8a7",
"score": "0.6490204",
"text": "def send_sms():\n\n # Extract the form values:\n to_number = request.form['to_number']\n message = request.form['message']\n\n # Send the SMS message:\n result = nexmo_client.send_message({\n 'from': NEXMO_NUMBER,\n 'to': to_number,\n 'text': message,\n })\n\n # Set a message for the user to see on the next view:\n err = extract_error(result)\n if err is not None:\n flash(\"There was a problem sending your message: \" + err, 'error')\n else:\n flash(\"You just sent a message to \" + to_number)\n\n # Redirect the user back to the form:\n return redirect(url_for('index'))",
"title": ""
},
{
"docid": "6f6cf280f0b1a0717ff642ec89332dd7",
"score": "0.6488142",
"text": "def send_mobileconfirmation_sms(code):\n log_info('###send_mobileconfirmation_sms###')\n user = User.query.filter_by(id=current_user.id).first()\n user.mobileConfirmationCode = code\n user.mobileConfirmationCodeDT = datetime.now()\n user.mobileConfirmed = False\n user.mobileConfirmedDT = None\n db.session.commit()\n sms_message = render_template('administration/sms_templates/sms_mobile_confirmation.html', verification_code=code)\n smsfrom = 'Ganimides'\n log_variable('message', sms_message)\n #result = send_sms(user.mobile,smsfrom,sms_message)\n subject = \"please confirm your mobile\"\n result = send_email(user.email, subject, sms_message)\n log_variable('result',result)\n return(result)",
"title": ""
},
{
"docid": "7c2e8f0217c1b48e53424c6ae899d42c",
"score": "0.6480138",
"text": "def incoming_anv_sms():\n sms_from = request.args.get('from', '')\n sms_to = request.args.get('to', '')\n sms_txt = request.args.get('message', '')\n address = sms.email_lookup(sms_to) # Anveo numbers do not send the +\n\n if address:\n return str(sms.send_email(to_email=address, txt_from=sms_from, txt_body=sms_txt, txt_to=sms_to))\n else:\n return \"invalid number\"",
"title": ""
},
{
"docid": "d32e41512c022a7bc770b542c0bc2ab4",
"score": "0.64628315",
"text": "def send_msg(self, msg):\n\n # implemented in sub classes",
"title": ""
},
{
"docid": "d96367d47a0620be03c0d22de7814d35",
"score": "0.6438378",
"text": "def sendMsg(self ):\n encrypted_msg = 'dgjzZcuwYVvgiMtBlzoa8RS7edxfMniMPR2naJakzDo6jfQKGGbzEee6ENKT4qW8o95BhdaLX1yonQuqKImGAJv9fdeyZEvjlfzrT5S4g3I='\n if self._m_user_id >= MIN_FROM_ID and self._m_user_id < MAX_FROM_ID:\n to_user_id = self._m_user_id + 5000 - 1\n log.debug(\"In sendMsg, from {} -> {}\".format(self._m_user_id, to_user_id))\n pdu_msg = ClientConnReq._MsgData(self._m_user_id, to_user_id, encrypted_msg )\n self._socket.send(pdu_msg)\n elif self._m_user_id >= MIN_TO_ID and self._m_user_id < MAX_TO_ID:\n pass",
"title": ""
},
{
"docid": "5afc795b9fcdeaf3c508babb2bfb796a",
"score": "0.64379925",
"text": "def send_sms(body, from_phone, to, flash=False, fail_silently=False,\r\n auth_user=None, auth_password=None, connection=None):\r\n from sendsms.message import SmsMessage\r\n connection = connection or get_connection(\r\n username = auth_user, \r\n password = auth_password,\r\n fail_silently = fail_silently\r\n )\r\n return SmsMessage(body=body, from_phone=from_phone, to=to, \\\r\n flash=flash, connection=connection).send()",
"title": ""
},
{
"docid": "0923c0f91f34b6d32bb1f87c63f11240",
"score": "0.6432759",
"text": "def send_messages(self, messages):\r\n count = 0\r\n for message in messages:\r\n message_body = unicodedata.normalize('NFKD', unicode(message.body)).encode('ascii', 'ignore')\r\n for tel_number in message.to:\r\n try:\r\n self.client.send(tel_number, message_body, getattr(settings, 'SMS_SLUZBA_API_USE_POST', True))\r\n except Exception:\r\n if self.fail_silently:\r\n log.exception('Error while sending sms via sms.sluzba.cz backend API.')\r\n else:\r\n raise\r\n else:\r\n count += 1\r\n\r\n return count",
"title": ""
},
{
"docid": "ee2d1d7f41e6f8c5f643f6ded16147ac",
"score": "0.6400599",
"text": "def post_send_message(self, msg):\r\n pass",
"title": ""
},
{
"docid": "bacc054dce12f26c682bae445c748f16",
"score": "0.6359422",
"text": "def test_send_message(self):\n pass",
"title": ""
},
{
"docid": "9bd7e0b394350ce160e63c734b2d682f",
"score": "0.63553065",
"text": "def send_sms_promotional(request):\n client = boto3.client(\"sns\", aws_access_key_id=AWS_ACCESS_KEY_ID,\n aws_secret_access_key=AWS_SECRET_ACCESS_KEY,\n region_name=AWS_REGION)\n # client.setSMSAttributes(\n # {\n # attributes : {\n # DefaultSMSType : \"Transactional\"\n # }\n # },\n # function(error){\n # if(error){\n # console.log(error);\n # }\n # }\n # );\n # input_jsons = dict(mobile_numbers=[\n # '+917406135629',\n # '+919741601203',\n # '+917795123525',\n # '+918939099619',\n # '+919606230339',\n # '+918909308092',\n # '+919741292046',\n # '+919686064664'\n # ], message=\"Hello folks, welcome to www.genericbackend.com\")\n phone_number = request['phone_number']\n input_json = dict(mobile_numbers=[phone_number], message=f\"Hello folks, welcome to www.genericbackend.com, \"\n f\"The One Time Password (OTP) for registering to \"\n f\"genericbackend.com is {request['text']}\")\n some_list_of_contacts = input_json['mobile_numbers']\n\n # Add SMS Subscribers\n for number in some_list_of_contacts:\n client.subscribe(\n TopicArn=AWS_ARN,\n Protocol='sms',\n Endpoint=number # <-- number who'll receive an SMS message.\n )\n response = client.publish(\n TopicArn=AWS_ARN,\n Message=input_json[\"message\"],\n MessageAttributes={'string': {'DataType': 'String', 'StringValue': 'String', },\n 'AWS.SNS.SMS.SenderID': {'DataType': 'String', 'StringValue': \"abk\"}}\n )\n if response[\"ResponseMetadata\"][\"HTTPStatusCode\"] == 200:\n output_json = {\"Status\": \"Success\", \"Message\": \"SMS has been sent\"}\n\n else:\n output_json = {\"Status\": \"Failure\", \"Message\": \"Something Went Wrong\"}\n return output_json",
"title": ""
},
{
"docid": "0df87e7afe58015e8346192777bb1e51",
"score": "0.6274136",
"text": "def send_msg(self, msg):\n return self.sendall(Message(msg))",
"title": ""
},
{
"docid": "2bb57875ddf03c493cd820e3affc3c50",
"score": "0.6248984",
"text": "def send_test_message(numObj):\n to_number = numObj.phone_number\n \n from_number= settings.TWILIO_TEST_NUMBER\n account_sid= settings.TWILIO_TEST_SID\n auth_token = settings.TWILIO_TEST_TOKEN\n \n out = send_text(\"Test.\", to_number, from_number, account_sid, auth_token)\n return(out)",
"title": ""
},
{
"docid": "6725f9be61d53b4d3c7af748bff2f51d",
"score": "0.62479943",
"text": "def at_msg_send(self, text=None, to_obj=None, **kwargs):\r\n pass",
"title": ""
},
{
"docid": "b8543f44dc92e0ade20b282382f36873",
"score": "0.62401277",
"text": "def message_sent(self, message):\n pass",
"title": ""
},
{
"docid": "1079ea0aa65f149904bd5e24f9c9306b",
"score": "0.6227844",
"text": "def send_messages(self):\n\n messages = SendReminder.objects.filter(status='queued')[:50]\n self.info('found {0} reminder(s) to send'.format(messages.count()))\n for message in messages:\n connection = message.recipient.default_connection\n template = u'{reminder} {content}'.format(\n reminder=self.reminder,\n content=message.message or \"\"\n )\n if len(template) > 160:\n # Truncate at 160 characters but keeping whole words\n template = template[:160]\n words = template.split(' ')[:-1]\n template = u' '.join(words) + u'...'\n msg = OutgoingMessage(connection=connection, template=template)\n success = True\n try:\n self.router.outgoing(msg)\n except Exception, e:\n self.exception(e)\n success = False\n if success and msg.sent:\n self.debug('message sent successfully')\n message.status = 'sent'\n message.date_sent = datetime.datetime.now()\n else:\n self.debug('message failed to send')\n message.status = 'error'\n message.save()",
"title": ""
},
{
"docid": "c4580b24534477452f02e60bbfa75285",
"score": "0.62187266",
"text": "def to_send(self):\n pass",
"title": ""
},
{
"docid": "2580fe3f20a692c1d91505033f6cef2b",
"score": "0.62180763",
"text": "def do_POST(self):\r\n\r\n # Get a timestamp for the request\r\n timestamp = time.strftime('%d/%m/%y %H:%M:%S')\r\n\r\n # Redirect any POST request that does not match the current URL\r\n if not self.path == '/send_message':\r\n self.send_response(303)\r\n self.send_header('Location', '/sms_sender.html')\r\n self.end_headers()\r\n return\r\n\r\n # Get the length of the POST content\r\n content_length = int(self.headers.getheader('content-length'))\r\n\r\n # If there is POST content then process it\r\n if content_length:\r\n\r\n # Get the form data\r\n post_data = cgi.parse_qs(self.rfile.read(content_length))\r\n try:\r\n recipients = post_data['recipients'][0]\r\n message = post_data['message'][0]\r\n except KeyError, IndexError:\r\n self.serve_message(400, 'Error', 'The message request was missing either recipient or message data.')\r\n return\r\n\r\n # Check if message class is defined, if not default to '1'\r\n # Class 0 = Message is displayed but not stored on phone\r\n # Class 1 = Store the message on the phone\r\n # Class 2 = Store the message on the SIM card\r\n try:\r\n msg_class = post_data['class'][0]\r\n except KeyError, IndexError:\r\n msg_class = '1'\r\n\r\n # Validate the recipient data\r\n for char in recipients:\r\n if char not in ';+0123456789 \\t':\r\n self.serve_message(400, 'Error', 'The recipient data contains an invalid character (\"%s\").' % char)\r\n return\r\n\r\n # Validate the SMS class data\r\n try:\r\n msg_class = int(msg_class)\r\n except ValueError:\r\n self.serve_message(400, 'Error', 'The given SMS class is invalid.')\r\n\r\n if not 0 <= msg_class <= 2:\r\n self.serve_message(400, 'Error', 'The SMS class can only be either 0, 1 or 2.')\r\n return\r\n\r\n # Get a list of recipients from the recipient string\r\n recipient_list = [x.strip() for x in recipients.split(';') if x]\r\n\r\n for recipient in recipient_list:\r\n self.server.message_received({'timestamp': timestamp,\r\n 'recipient': recipient,\r\n 'class': msg_class,\r\n 'message': message,\r\n 'sender_ip': self.client_address[0]})\r\n\r\n self.serve_message(200, 'Message(s) Queued', 'Your message(s) have been added to the queue to be sent.')",
"title": ""
},
{
"docid": "c457691f1bbcc47dc359933a458f8136",
"score": "0.6215273",
"text": "def create_sms(self, request, queryset):\n for verification_obj in queryset:\n self.send_code(request, verification_obj, PhoneVerification.VERIFICATION_METHOD_CHOICES.sms)",
"title": ""
},
{
"docid": "e94fb647386dfc5205659c5f487c35b1",
"score": "0.62148833",
"text": "def send_sms_message(recipient, message, mtype='text'):\n params = {\n 'api_key': settings.NEXMO_USERNAME,\n 'api_secret': settings.NEXMO_PASSWORD,\n 'type': mtype,\n 'from': getSourceNumber(),\n 'to': recipient,\n 'text': message.encode('utf-8'),\n }\n sms = NexmoMessage(params)\n response = sms.send_request()\n return response",
"title": ""
},
{
"docid": "701b175f4e726ee4b5f14e9c7bd91e34",
"score": "0.62095726",
"text": "def send_messages(self, messages):\n pass",
"title": ""
},
{
"docid": "f3984407ee01ea9ffd54b608a0d7f981",
"score": "0.62094337",
"text": "def send_verification(self, number, otp):\n message = self._generate_message(otp)\n\n self.backend.send_sms(number, message)",
"title": ""
},
{
"docid": "cb6d455a04dd347dbb10181e9c665702",
"score": "0.6183902",
"text": "def incoming_sms():\n global data\n # Get the message the user sent our Twilio number\n body = request.values.get('Body', None).lower()\n # Get the number the request was sent from\n from_number = request.form['From'].lower()\n\n response = game_logic.determine_response(data, from_number, body)\n # Text back the response\n if response is not None:\n resp = MessagingResponse()\n resp.message(response)\n return str(resp)\n return \"Tbd\"",
"title": ""
},
{
"docid": "1cc5eb12a8d6afe8f07ad19debecc2e0",
"score": "0.61696696",
"text": "def send_test(to):",
"title": ""
},
{
"docid": "45993d4dc767abcc3ad56a5edd4bbe84",
"score": "0.61664146",
"text": "def send_messages(self, sms_messages):\r\n if not sms_messages:\r\n return\r\n \r\n num_sent = 0\r\n for message in sms_messages:\r\n if self._send(message):\r\n num_sent += 1\r\n return num_sent",
"title": ""
},
{
"docid": "da339a3f032f8da1e1427eb78c9ecca3",
"score": "0.6164981",
"text": "def sms_reply():\r\n # Fetch the message\r\n msg = request.form.get('Body')\r\n phone_no = request.form.get('From')\r\n\r\n print(phone_no , \" => \" , msg.lower() )\r\n client = Client(account_sid, auth_token)\r\n \r\n \r\n if msg.lower() == \"hi cubestop\" :\r\n \r\n media_url = ('https://he-s3.s3.amazonaws.com/media/uploads/0ca15d3.png')\r\n \r\n msgBody = \" Hi Sagar , Welcome to cubestop 1-stop Destination for all your need \\n \\n Visit : https://www.cubestop.com \"\r\n resp = MessagingResponse()\r\n mymsg = resp.message(msgBody)\r\n mymsg.media(media_url)\r\n\r\n return str(resp)\r\n elif msg.lower() == \"find nearest restaurant\":\r\n \r\n print( \"String Case matched !!\") \r\n \r\n msgBody = \" Plz Click on the link to share your Location 📍 with Cube Stop . \\n \\n Visit : http://751648d76e73.ngrok.io/share \\n \\n OR \\n \\n Share your thorugh whatsapp itself !! 😊 \"\r\n resp = MessagingResponse()\r\n mymsg = resp.message(msgBody)\r\n\r\n return str(resp)\r\n\r\n elif msg.lower() == \"done\":\r\n \r\n print( \"String Case matched = Done!!\") \r\n \r\n media_url = ('https://i.pinimg.com/736x/6a/fc/63/6afc63bf06c99262ad7efa4683f956b1.jpg')\r\n \r\n msgBody = \" Hi Sagar , \\n \\n We found the nearest cubestop to you . \\n \\n Click below link to Navigate : https://goo.gl/maps/vVwTX6p95fkpJFNp6 \"\r\n resp = MessagingResponse()\r\n mymsg = resp.message(msgBody)\r\n mymsg.media(media_url)\r\n\r\n return str(resp)\r\n\r\n elif msg.lower() == \"great service\":\r\n \r\n print( \"String Case matched = great service !!\") \r\n \r\n happy_url = ('https://www.futurenetzero.com/wp-content/uploads/2014/06/Smiley-face-emoticon-575.jpg')\r\n \r\n msgBody = \" Thanks for the FeedBack . \\n \\n We are happy to Help you . \\n \\n If you would like to recommed someone to visit cubestop just tell us their number and you will get a Free Service next Time !! \"\r\n resp = MessagingResponse()\r\n mymsg = resp.message(msgBody)\r\n mymsg.media(happy_url)\r\n\r\n return str(resp)\r\n\r\n\r\n else:\r\n reply = fetch_reply(msg, phone_no)\r\n resp = MessagingResponse()\r\n # Create reply\r\n resp.message(reply)\r\n return str(resp)",
"title": ""
},
{
"docid": "9c90bfd352d3aa81063676cf1fb7acc5",
"score": "0.61614615",
"text": "def incoming_sms():\n number = request.values.get('From', None)\n body = request.values.get('Body', None)\n print(body)\n # Start our TwiML response\n resp = MessagingResponse()\n if body is None:\n resp.message(\"Invalid: Enter your name, class, and session# separated by spaces as shown (one student at a time). Examples:\\nAvi Patel grade1 session1\\nRavi Rao PreK session1\\nMira Singh KG session2\")\n return str(resp)\n body = body.lower()\n body = body.strip()\n body_arr = body.split()\n class_name = \"\"\n name = \"\"\n if len(body_arr) == 4:\n first_name = body_arr[0]\n last_name = body_arr[1]\n name = first_name + \" \" + last_name\n class_name = body_arr[2] + body_arr[3]\n elif len(body_arr) == 6:\n first_name = body_arr[0]\n last_name = body_arr[1]\n name = first_name + \" \" + last_name\n class_name = body_arr[2] + body_arr[3] + body_arr[4] + body_arr[5]\n else:\n resp.message(\"Invalid: Enter your name, class, and session# separated by spaces as shown (one student at a time). Examples:\\nAvi Patel grade1 session1\\nRavi Rao PreK session1\\nMira Singh KG session2\")\n return str(resp)\n\n # forward_message(class_name, number, name)\n\n return str(resp)",
"title": ""
},
{
"docid": "9c94bb9a4949edeff0ab6549d6c786b9",
"score": "0.6160334",
"text": "def send(self, msg):\n self.sendLine(msg)",
"title": ""
},
{
"docid": "9a8163d55682c78ce4d0da075a3491ef",
"score": "0.615045",
"text": "def __send(self, tosend, source):\n for line in tosend.splitlines(True):\n if self.delay:\n debug('ReplyHandler.send: delay answer for %d ms', self.delay)\n sleep(self.delay / 1000.0)\n debug('ReplyHandler.send(%s): %r', source, line)\n self.request.sendall(line.encode('utf-8'))",
"title": ""
},
{
"docid": "2119bd67cadd1aabe93e25bd038dd1d8",
"score": "0.61460364",
"text": "def sms_reply():\n body = request.values.get('Body', None)\n app.logger.debug(\"sms received: {}\".format(body))\n Qqueue.enqueue(body)\n Qqueue.save()\n \n resp = MessagingResponse()\n resp.message(\"Your message has been received.\")\n\n return str(resp)",
"title": ""
},
{
"docid": "071ffa79d52ead6598c625a3e24cfc89",
"score": "0.613295",
"text": "def send(self, txrecords):\n # txrecords is a list of TransmissionLog IDs\n txrecords = TransmissionLog.objects.filter(pk__in=txrecords)\n message = txrecords[0].message\n notification_id = txrecords[0].notification_id\n self.LAST_MESSAGE = message\n self.LAST_NOTIFICATION_ID = notification_id\n \n # allows for testing of failed gateway send\n send_ok = False if message == 'FAIL MESSAGE' else True\n \n for tx in txrecords:\n tx.enqueued = False \n tx.send_ok = send_ok\n tx.gateway_response = self.GATEWAY_MID\n tx.save()\n \n if not send_ok: \n raise squawk.GatewayFailError(\"Send failed\", notification_id = notification_id)",
"title": ""
},
{
"docid": "0881c91542efb47932c4489e26b14a1b",
"score": "0.611443",
"text": "def send(data):\r\n\t\tpass",
"title": ""
},
{
"docid": "e34792ef807449002a12889c43053d36",
"score": "0.6102822",
"text": "def _emit(self):\n print \"emitting \", self.conf['id']\n if self.conf['repeat'] in _done_action_types:\n self.eventObj.update({\"action\": \"Done\"})\n else:\n self.eventObj.update({\"action\": _correct_out_time(datetime.now()).strftime(_format)})\n\n event = {\n \"type\": \"send_sms\",\n \"data\": self.eventObj\n }\n\n if self.conf['repeat'] == 'Fortnightly':\n print \"Additional emit condition\"\n self.trigger = DateTrigger(datetime.now() + timedelta(days=14))\n self._schedule(create_cancel=False)\n\n if self.conf['repeat'] not in ['Once', 'Immediately']:\n self._schedule_data_download()\n\n dispatcher.send(signal=SIG, event=event, sender=self)",
"title": ""
},
{
"docid": "ab56f0f2ab6acc9449c70822b72a8522",
"score": "0.61000264",
"text": "def send_message(self,to,message):\n # we make sure to put a newline on the end so the client receives the\n # message on its own line\n\t\tself._attempt_send(to,message+\"\\n\\r\")",
"title": ""
},
{
"docid": "9b049ac7ee3e13f290e4aa70cea3b582",
"score": "0.6096604",
"text": "def send_sms(self, recipient, text, max_messages = 255): \n pdus = gsmpdu.get_outbound_pdus(text, recipient)\n if len(pdus) > max_messages:\n raise ValueError(\n 'Max_message is %d and text requires %d messages' %\n (max_messages, len(pdus))\n )\n\n for pdu in pdus:\n self._send_pdu(pdu)\n return True",
"title": ""
},
{
"docid": "40900a6d7053e90c442c367ce1c45b73",
"score": "0.6090905",
"text": "def send_sms(self, text, **kwargs):\n\n params = {\n 'user': self._user,\n 'pass': self._password,\n 'msg': text\n }\n\n response = requests.get(self.BASE_URL, params=params, **kwargs)\n return FreeResponse(response)",
"title": ""
},
{
"docid": "17529dd3ba6d2249176130ef20c8b8d1",
"score": "0.60863715",
"text": "def send(self, msg):\n from ututi.lib.messaging import EmailMessage, SMSMessage\n if isinstance(msg, EmailMessage):\n email = self.email\n if email.confirmed or msg.force:\n msg.send(email.email)\n else:\n log.info(\"Could not send message to unconfirmed email %(email)s\" % dict(email=email.email))\n elif isinstance(msg, SMSMessage):\n if self.phone_number is not None and (self.phone_confirmed or msg.force):\n msg.recipient=self\n msg.send(self.phone_number)\n else:\n log.info(\"Could not send message to uncofirmed phone number %(num)s\" % dict(num=self.phone_number))",
"title": ""
},
{
"docid": "cabf33e382a6d7362fa1f83076c4a7c6",
"score": "0.60832",
"text": "def fire_sms_event(sms):\n data = {\n ATTR_HOST: modem_data.host,\n ATTR_SMS_ID: sms.id,\n ATTR_FROM: sms.sender,\n ATTR_MESSAGE: sms.message,\n }\n hass.bus.async_fire(EVENT_SMS, data)",
"title": ""
},
{
"docid": "6d64f45e99c85e05da8c665ce6d0315e",
"score": "0.6079566",
"text": "def send(self, msg):\n return self.client.send(msg)",
"title": ""
},
{
"docid": "910aaab9f50e67d0bd5a88c0dfbf14e5",
"score": "0.6074344",
"text": "def smsCanSend(self, uid, callback):\n j = Json().put(u\"uid\", uid)\n self.callMethodRetInteger(u\"sms.canSend\", j.getJavaScriptObject(), callback)",
"title": ""
},
{
"docid": "61b1e03cb23e1a72b56b2ed212176dad",
"score": "0.60611385",
"text": "def on_send(self, text):\n pass",
"title": ""
},
{
"docid": "06db7117db6e02363f48619c6c4bd98b",
"score": "0.60578454",
"text": "def _send(self):\n # Set data as appropriate\n if self.conversation:\n linecount = 1\n for line in reversed(self.conversation):\n linecount += 1\n self.data['vText' + str(linecount)] = line\n if linecount == 8:\n break\n\n # Generate the token\n enc_data = urlencode(self.data)\n digest_txt = enc_data[9:35]\n token = hashlib.md5(digest_txt.encode('utf-8')).hexdigest()\n self.data['icognocheck'] = token\n\n # POST the data to Cleverbot and return\n return self.session.post(self.SERVICE_URL,\n data=self.data,\n headers=self.headers)",
"title": ""
},
{
"docid": "6ff6a1414f0e447719071aad331f9280",
"score": "0.6055317",
"text": "def _send_text(self) -> None:\n text_message = self._dq.pop_left()\n\n # ------------------\n # In development: send a text\n # ------------------\n # # send text and store the returned MessageInstance\n # twilio_message = send_text(text_message)\n #\n # LOGGER.info(\"text sent: {}\".format(text_message))\n # LOGGER.info(\"sid: {}\".format(twilio_message.sid))\n\n LOGGER.info(text_message)\n\n self.broker.put(ReceiptPacket(text_message.text, datetime.datetime.now(), text_message.id))",
"title": ""
},
{
"docid": "3c3f4eb5b0d600973eaec598d8108a22",
"score": "0.603847",
"text": "def send(self,data):\n self.s.sendall(data.encode())",
"title": ""
},
{
"docid": "eb161df4d4a489774277f948e8ed8527",
"score": "0.602129",
"text": "def test_send(self):\n msg_flag = self.instance.send(self.msg_short)\n assert(msg_flag)\n msg_flag, msg_recv = self.driver.recv(self.timeout)\n assert(msg_flag)\n nt.assert_equal(msg_recv, self.msg_short)",
"title": ""
},
{
"docid": "0484a99543f615a96817efe51596699e",
"score": "0.6010134",
"text": "def send_message(phone_no='', message_body=''):\n client = Client(TWILIO_DETAILS['account_sid'], TWILIO_DETAILS['auth_token'])\n\n # Phone number given to you by Twilio\n # all message will come from this number\n message_body = '\\n' + message_body\n client.messages.create(to=phone_no, from_=TWILIO_DETAILS['number'], body=message_body)",
"title": ""
},
{
"docid": "29b3664fc3f659491b98ba4946f6adef",
"score": "0.6007218",
"text": "def _msg_send(self, adapter: Bus, logger: SizedRotatingLogger) -> None:\n while not self.send_queue.empty():\n message: Message = self.send_queue.get()\n adapter.send(message)\n message.is_rx = False # to have it correctly in the logger\n logger(message)",
"title": ""
},
{
"docid": "4e51a2d648c81cc552cb94b7fc239d96",
"score": "0.6005658",
"text": "def send_text(self, num, message, test=False):\n if test:\n print(\"Sending a text message to number '{0:s}'. The message reads as follows\\n{1:s}\".format(num,message))\n else:\n self.client.messages.create(body=message, from_=self.number, to=num)",
"title": ""
},
{
"docid": "dd5d81bc54a1af0d9121829301fbd6a7",
"score": "0.5995788",
"text": "def send_sms(contact, content):\n account_sid = os.environ[\"TWILIO_SID\"]\n auth_token = os.environ[\"TWILIO_TOKEN\"]\n twilio_number = os.environ[\"TWILIO_NUMBER\"]\n client = TwilioRestClient(account_sid, auth_token)\n client.messages.create(\n to='+1' + contact.phone,\n from_=twilio_number,\n body=content\n )",
"title": ""
},
{
"docid": "74534312db9c275958120ec4596d1f76",
"score": "0.5988096",
"text": "def _send(self, *args, **kwargs):",
"title": ""
},
{
"docid": "7f47feb8caa36df12f45843697c7e4b2",
"score": "0.59877837",
"text": "def test_send_csr_message(self):\n deviceid = 54321\n data = 'Hello, Hologram!'\n resp = self.client.csr.send_message(deviceid, data)\n self.assertTrue(resp.get('success'))",
"title": ""
},
{
"docid": "985e1d5103f35aa5cb3607d7709d6a05",
"score": "0.59871304",
"text": "def send_messages(self, messages):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "338bffdfd2ba1ba2a4a3e8a65b109cef",
"score": "0.5980607",
"text": "def send(self, msg):\n print(\"[OUT]\"+str(msg))\n self.socket.sendall(bytes((str(msg)+'\\0').encode(\"utf-8\")))",
"title": ""
},
{
"docid": "577001f493484872d17271fb732d6b28",
"score": "0.5979784",
"text": "def sendMessage(self, message, sender):\n info = {\"msg\":message, \"sender\":sender}\n msg = json.dumps(info).encode(\"utf-8\")\n try: \n self.conn.send(msg)\n except:\n print(\"That user is not getting massage\")",
"title": ""
},
{
"docid": "566e94616e3a067af475f18969cc1b0b",
"score": "0.59787863",
"text": "def sms_reply():\n\n # Use this data in your application logic\n for key in request.form.keys():\n print(key)\n time.sleep(1)\n print(request.form[key])\n print(\"\")\n for key in request.values.keys():\n print(key)\n time.sleep(1)\n print(request.values[key])\n print(\"\")\n \n\n # Start our TwiML response\n resp = MessagingResponse()\n\n # Add a message\n resp.message(\"Did this work?\")\n\n return str(resp)",
"title": ""
},
{
"docid": "92689dec76541305a138fcce46104456",
"score": "0.59781194",
"text": "def send(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "64f9542b2c45ba2f40bd8d97d2cea221",
"score": "0.59726256",
"text": "def send_to_calculate():\n data_to_send = request.get_json()\n app.logger.debug(str(data_to_send))\n mq = rabbitmqclient.RabbitMQ()\n mq.new_task(str(data_to_send))\n\n return str(data_to_send), \"sent\\n\"",
"title": ""
},
{
"docid": "266f95fea86b971417605660ee9cf8b4",
"score": "0.5972053",
"text": "def send(self, buf):",
"title": ""
},
{
"docid": "d8fbc1a6ddf1938f0785f19dac5346f9",
"score": "0.59629893",
"text": "def send_sms(self, recipient, msg):\n\n # User may not have configured twilio - don't initialize it until it's\n # first used\n if self.twilio_client is None:\n self.logger.info(\"Initializing Twilio\")\n\n if cfg.TWILIO_ACCOUNT == '' or cfg.TWILIO_TOKEN == '':\n self.logger.error(\"Twilio account or token not specified - unable to send SMS!\")\n else:\n self.twilio_client = TwilioRestClient(cfg.TWILIO_ACCOUNT, cfg.TWILIO_TOKEN)\n\n if self.twilio_client != None:\n self.logger.info(\"Sending SMS to %s: %s\", recipient, msg)\n try:\n self.twilio_client.sms.messages.create(\n to=recipient,\n from_=cfg.TWILIO_PHONE_NUMBER,\n body=truncate(msg, 140))\n except TwilioRestException as ex:\n self.logger.error(\"Unable to send SMS: %s\", ex)\n except httplib2.ServerNotFoundError as ex:\n self.logger.error(\"Unable to send SMS - internet connectivity issues: %s\", ex)\n except:\n self.logger.error(\"Exception sending SMS: %s\", sys.exc_info()[0])",
"title": ""
},
{
"docid": "178fbbf817040b651a100a44d77bd410",
"score": "0.5962635",
"text": "def onSend(self):\r\n #resolve unicode encoding for text parts;\r\n bodytextEncoding, attachesEncodings = self.resolveUnicodeEncodings()\r\n\r\n # get components from GUI\r\n fieldvalues = [entry.get() for entry in self.hdrFields]\r\n From, To, Cc, Subj = fieldvalues[:4]\r\n extraHdrs = [('Cc', Cc), ('X-Mailer', appname + '(Python)')]\r\n extraHdrs += list(zip(self.userHdrs, fieldvalues[4:]))\r\n bodytext = self.editor.getAllText()\r\n\r\n # split multiple reciepent lists on ',', fix empty fields\r\n Tos = self.splitAddresses(To)\r\n for (ix, (name, value)) in enumerate(extraHdrs):\r\n if value:\r\n if value == '?':\r\n extraHdrs[ix] = (name, '')\r\n elif name.lower() in ['cc', 'bcc']:\r\n extraHdrs[ix] = (name, self.splitAddresses(value))\r\n\r\n # withdraw to disallow send duriing send\r\n self.withdraw()\r\n self.getPassword()\r\n popup = popuputil.BusyBoxNowait(appname, 'Sending message')\r\n sendingBusy.incr()\r\n threadtools.startThread(\r\n action = self.sendMessage,\r\n args = (From, Tos, Subj, extraHdrs, bodytext, self.attaches, saveMailSeparator, bodytextEncoding, attachesEncodings),\r\n context = (popup,),\r\n onExit = self.onSendExit,\r\n onFail = self.onSendFail\r\n )",
"title": ""
},
{
"docid": "ca30487e5e0cbf28c1418ceb7aed3027",
"score": "0.5957321",
"text": "def send_cmd(self):\n pass",
"title": ""
},
{
"docid": "a6e6d02e71d431fc6dda9117fd90138b",
"score": "0.59546053",
"text": "def send(self, stanza):\n pass",
"title": ""
},
{
"docid": "70c32b793e8e733866597cd44d63c79e",
"score": "0.5948438",
"text": "def send(self, message: str) -> None:\n\n pass",
"title": ""
},
{
"docid": "157150c0b365d466388936184c9ebe8d",
"score": "0.5946702",
"text": "async def send_message(self) -> bool:\r\n\r\n TELEGRAM_SEND_MESSAGE_URL = await telegram_sender(self.token_bot,\r\n self.chat_id,\r\n self.outgoing_message_text)\r\n\r\n res = await requests.post(TELEGRAM_SEND_MESSAGE_URL)\r\n return True if res.status_code == 200 else False",
"title": ""
},
{
"docid": "cfe90333a5b8b0f02a71f798fc5e8ed9",
"score": "0.5941839",
"text": "def sms_reply():\n # Fetch the message\n msg = request.form.get('Body')\n remitente = request.form.get('From')\n\n # todo los siguientes dos no me funcionan\n fecha = request.form.get('date_created')\n msgID = request.form.get('sid')\n\n\n # Create reply\n resp = MessagingResponse()\n respuesta = \"Hola {}, como estas! Tu mensage es: {} y lo mandaste el: {}. El id del msg es: {}\".format(remitente, msg, fecha, msgID)\n resp.message(respuesta)\n\n return str(resp)",
"title": ""
},
{
"docid": "3cde0f3ede5ee0679497aa506f5749d1",
"score": "0.5931106",
"text": "def test_send_dm(self):\n self.mock_sc.chat_postMessage = mock.MagicMock(return_value=OK_RESP)\n\n self.bot.send_dm(\"Hahahaha\", \"UD8UCTN05\")\n self.mock_sc.chat_postMessage.assert_called_with(\n text=\"Hahahaha\",\n channel=\"UD8UCTN05\",\n as_user=True\n )",
"title": ""
},
{
"docid": "1e145a5329098d27307ed985b42436f9",
"score": "0.592634",
"text": "def send(self, message_obj):\n\n tx_buff = self._get_tx_buffer() # info = addr.\n if tx_buff is None:\n print(\"No transmit buffer available to send\")\n return False\n\n #print(\"\\t\\tTX Buffer:\", tx_buff)\n\n return self._write_message(tx_buff, message_obj)",
"title": ""
},
{
"docid": "500fe964b63642f9ad665993353248b4",
"score": "0.5925051",
"text": "def _send(self, message):\n response = requests.post(\n self.send_url,\n message,\n headers={'Content-Type': 'text/plain'})\n return response.status_code in (200, 204)",
"title": ""
},
{
"docid": "500fe964b63642f9ad665993353248b4",
"score": "0.5925051",
"text": "def _send(self, message):\n response = requests.post(\n self.send_url,\n message,\n headers={'Content-Type': 'text/plain'})\n return response.status_code in (200, 204)",
"title": ""
},
{
"docid": "facd911aafd6bd230ea2025e6655cf74",
"score": "0.5918287",
"text": "def at_message_send(self, message, to_object):\r\n pass",
"title": ""
}
]
4af6ee9402e059ed8fad043e8af712a8
Safely JSONencode an object. To protect against XSS attacks, HTML special characters (, &) and unicode newlines are replaced by escaped unicode characters. Django does not escape these characters by default. Output of this method is not marked as HTML safe. If you use it inside an HTML
[
{
"docid": "d1642861764950e40025644edb7a72f9",
"score": "0.75244474",
"text": "def json_encode(obj):\r\n return to_unicode(_json_encode(obj))",
"title": ""
},
{
"docid": "9e1e4e8590df6c0ca5534c6946317323",
"score": "0.71535826",
"text": "def _safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n # obj is unicode\n return unicode(obj).encode('unicode_escape')",
"title": ""
},
{
"docid": "6f98d7e94d15549d20e34366d240bfe1",
"score": "0.69944006",
"text": "def safe_str(obj):\r\n try:\r\n return str(obj)\r\n except UnicodeEncodeError:\r\n # obj is unicode\r\n return unicode(obj).encode('unicode_escape')",
"title": ""
},
{
"docid": "e23dcf94f90b0e796dbcef975b02882e",
"score": "0.69850713",
"text": "def safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n # obj is unicode\n return unicode(obj).encode('unicode_escape')",
"title": ""
},
{
"docid": "e23dcf94f90b0e796dbcef975b02882e",
"score": "0.69850713",
"text": "def safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n # obj is unicode\n return unicode(obj).encode('unicode_escape')",
"title": ""
},
{
"docid": "f3f0ecf55da803f762229a78d9f3f9d9",
"score": "0.6926217",
"text": "def json(value):\n uncleaned = jsonlib.dumps(value)\n clean = bleach.clean(uncleaned)\n # Small replacements\n clean = clean.replace('&', '&')\n clean = clean.replace('<', '<')\n return mark_safe(clean)",
"title": ""
},
{
"docid": "e0209d01a93a032004569dfa716f150d",
"score": "0.68114585",
"text": "def encode(obj):\n if _CJSON:\n return cjson.encode(obj)\n else:\n return json.dumps(obj)",
"title": ""
},
{
"docid": "22de1c514336766f06840e9ca284efeb",
"score": "0.679083",
"text": "def json_filter(value):\n return mark_safe(json.dumps(value, cls=SafeJSONEncoder))",
"title": ""
},
{
"docid": "f74d9d4356f3e6b3729cf0ee4a2ddbd8",
"score": "0.6723732",
"text": "def render(self, obj: object) -> str:\n return json.dumps(\n asdict(obj, dict_factory=self.dict_factory),\n cls=self.encoder,\n indent=self.indent,\n )",
"title": ""
},
{
"docid": "0e0d6e23db4799e69a2f69a4610a9e73",
"score": "0.6714277",
"text": "def safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n return obj",
"title": ""
},
{
"docid": "381eeb79dd2b3d08c4a039438c2fa10b",
"score": "0.6686007",
"text": "def safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n return unicode(obj).encode('UTF-8')",
"title": ""
},
{
"docid": "592dea91afdcc6fd9d55157fe0456c0d",
"score": "0.6655969",
"text": "def json(value):\n uncleaned = jsonlib.dumps(value, iterable_as_array=True)\n clean = html.parser.unescape(bleach.clean(uncleaned))\n return mark_safe(clean)",
"title": ""
},
{
"docid": "0f4b281c7c2e02ab3bc23b6f39feb431",
"score": "0.6623755",
"text": "def json_encode(value):\r\n # JSON permits but does not require forward slashes to be escaped.\r\n # This is useful when json data is emitted in a <script> tag\r\n # in HTML, as it prevents </script> tags from prematurely terminating\r\n # the javscript. Some json libraries do this escaping by default,\r\n # although python's standard library does not, so we do it here.\r\n # http://stackoverflow.com/questions/1580647/json-why-are-forward-slashes-escaped\r\n return json.dumps(value).replace(\"</\", \"<\\\\/\")",
"title": ""
},
{
"docid": "0f4b281c7c2e02ab3bc23b6f39feb431",
"score": "0.6623755",
"text": "def json_encode(value):\r\n # JSON permits but does not require forward slashes to be escaped.\r\n # This is useful when json data is emitted in a <script> tag\r\n # in HTML, as it prevents </script> tags from prematurely terminating\r\n # the javscript. Some json libraries do this escaping by default,\r\n # although python's standard library does not, so we do it here.\r\n # http://stackoverflow.com/questions/1580647/json-why-are-forward-slashes-escaped\r\n return json.dumps(value).replace(\"</\", \"<\\\\/\")",
"title": ""
},
{
"docid": "be350e29925c76c83dd3262d9a09fab3",
"score": "0.6599834",
"text": "def escapeJSONForScriptTag(self, s):\n return s.replace(\"<\",\"\\\\u003C\").replace(\">\",\"\\\\u003E\").replace(\"&\",\"\\\\u0026\")",
"title": ""
},
{
"docid": "c851914bf0ac6622538d2aaa211d9a1e",
"score": "0.6589596",
"text": "def json_encode(value):\r\n # JSON permits but does not require forward slashes to be escaped.\r\n # This is useful when json data is emitted in a <script> tag\r\n # in HTML, as it prevents </script> tags from prematurely terminating\r\n # the javscript. Some json libraries do this escaping by default,\r\n # although python's standard library does not, so we do it here.\r\n # http://stackoverflow.com/questions/1580647/json-why-are-forward-slashes-escaped\r\n return _json_encode(recursive_unicode(value), default=_defaultjson).replace(\"</\", \"<\\\\/\")",
"title": ""
},
{
"docid": "22642d0a2bd0f18b82a58c34e8b98c47",
"score": "0.65451306",
"text": "def _html_print_obj(self, obj_json, str_list: list, indent):\n if isinstance(obj_json, dict):\n self._html_print_dict(obj_json, str_list, indent)\n elif isinstance(obj_json, list):\n self._html_print_array(obj_json, str_list, indent)\n elif isinstance(obj_json, str):\n str_list.append('"')\n str_list.append(html.escape(obj_json))\n str_list.append('"')\n elif isinstance(obj_json, int):\n str_list.append(str(obj_json))\n elif isinstance(obj_json, bool):\n str_list.append(str(obj_json))\n elif obj_json is None:\n str_list.append('null')",
"title": ""
},
{
"docid": "79c8d9ea1a77a0816813d4bb596e914c",
"score": "0.65404683",
"text": "def do_forceescape(value):\r\n if hasattr(value, '__html__'):\r\n value = value.__html__()\r\n return escape(unicode(value))",
"title": ""
},
{
"docid": "d7972b4c978a21a270c5ac078e7282c1",
"score": "0.6522463",
"text": "def as_json(self, obj=None):\n json = simplejson.dumps(obj, indent=True, sort_keys=True)\n json = re.sub(\"<\",\"<\",json)\n return \"<h3>Debug JSON</h3><pre class='usage'>%s</pre>\" % json",
"title": ""
},
{
"docid": "62e46cca090f3a45962f1bbd8696e68a",
"score": "0.6481337",
"text": "def safe_unicode(obj, *args):\n try:\n return str(obj, *args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return str(ascii_text)",
"title": ""
},
{
"docid": "99ace45a8c74fdada1bdf2150bb9199a",
"score": "0.64710265",
"text": "def escapejs_filter(value):\n return escapejson(value)",
"title": ""
},
{
"docid": "9ae8a54bbf37836e6ec63660309c21a3",
"score": "0.6467364",
"text": "def jsonify(obj):\n return json.dumps(raw_json(obj))",
"title": ""
},
{
"docid": "a5b6945312fbaab42c609e532a689a10",
"score": "0.643901",
"text": "def json_format(obj):\n\n return json.dumps(obj, cls=BytesBase64Encoder)",
"title": ""
},
{
"docid": "f6ec63e85aa9c1aec019802155ddc423",
"score": "0.6437541",
"text": "def safestr(obj, encoding='utf-8'):\n if isinstance(obj, unicode):\n return obj.encode(encoding)\n elif isinstance(obj, str):\n return obj\n elif hasattr(obj, 'next'): # iterator\n return itertools.imap(safestr, obj)\n else:\n return str(obj)",
"title": ""
},
{
"docid": "69016a68688b0141769175f5d2ed61a8",
"score": "0.6325441",
"text": "def safe_bytestring(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n # obj is unicode\n return str(obj).encode('unicode_escape')",
"title": ""
},
{
"docid": "6dde9f7de7e2d61ac0b111e2d3e6d57e",
"score": "0.62997544",
"text": "def json_encode(data, *args, **kwargs):\n\n def _any(data):\n ret = None\n # Opps, we used to check if it is of type list, but that fails\n # i.e. in the case of django.newforms.utils.ErrorList, which extends\n # the type \"list\". Oh man, that was a dumb mistake!\n if hasattr(data, 'canonical'):\n ret = _any(data.canonical())\n elif isinstance(data, list):\n ret = _list(data)\n elif isinstance(data, set):\n ret = _list(list(data))\n # Same as for lists above.\n elif isinstance(data, dict):\n ret = _dict(data)\n # elif isinstance(data, CallableBool):\n # ret = bool(data)\n elif isinstance(data, (Decimal, ObjectId)):\n # json.dumps() cant handle Decimal\n ret = str(data)\n elif isinstance(data, models.query.QuerySet):\n # Actually its the same as a list ...\n ret = _list(data)\n elif isinstance(data, MongoQuerySet):\n # Actually its the same as a list ...\n ret = _list(data)\n elif isinstance(data, models.Model):\n ret = _model(data)\n # here we need to encode the string as unicode (otherwise we get utf-16 in the json-response)\n elif isinstance(data, bytes):\n ret = data.decode('utf-8', 'ignore')\n elif isinstance(data, str):\n ret = smart_str(data)\n elif isinstance(data, Exception):\n ret = str(data)\n # see http://code.djangoproject.com/ticket/5868\n elif isinstance(data, Promise):\n ret = force_text(data)\n elif isinstance(data, datetime.datetime) or isinstance(data, datetime.date):\n ret = str(data)\n elif hasattr(data, 'to_json'):\n ret = data.to_json()\n else:\n ret = data\n return ret\n\n def _model(data):\n ret = {}\n # If we only have a model, we only want to encode the fields.\n for f in data._meta.fields:\n ret[f.attname] = _any(getattr(data, f.attname))\n # And additionally encode arbitrary properties that had been added.\n fields = dir(data.__class__) + list(ret.keys())\n add_ons = [k for k in dir(data) if k not in fields]\n for k in add_ons:\n ret[k] = _any(getattr(data, k))\n return ret\n\n def _list(data):\n ret = []\n for v in data:\n ret.append(_any(v))\n return ret\n\n def _dict(data):\n ret = {}\n for k, v in list(data.items()):\n ret[str(k)] = _any(v)\n return ret\n\n if hasattr(data, 'to_json'):\n data = data.to_json()\n ret = _any(data)\n return json.dumps(ret)",
"title": ""
},
{
"docid": "3115a7d7a67da04720f06f042b49116b",
"score": "0.6270912",
"text": "def encode_json_for_js(data, indent=None):\n return json.dumps(data, indent=indent, cls=JSONEncoderForHTML)",
"title": ""
},
{
"docid": "02b6b7ce038b533f8091edcfd03dd92f",
"score": "0.6268698",
"text": "def _safe_unicode(obj, * args):\n try:\n return unicode(obj, * args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return unicode(ascii_text)",
"title": ""
},
{
"docid": "0a00c03389cc793264cb4aba8f152ef6",
"score": "0.62523603",
"text": "def render(value):\n return json.dumps(value)",
"title": ""
},
{
"docid": "3bcb96ce0d7efabfdf9c8818fb32dc1d",
"score": "0.62409",
"text": "def escape(inp):\n def conv(obj):\n if isinstance(obj, list):\n rv = as_unicode('[' + ','.join(conv(o) for o in obj) + ']')\n elif isinstance(obj, dict):\n rv = as_unicode('{' + ','.join([\n \"%s:%s\" % (conv(key), conv(value))\n for key, value in obj.iteritems()]) + '}')\n else:\n rv = as_unicode('\"%s\"') % as_unicode(obj).replace('\"', '\\\\\"')\n return rv\n return conv(inp)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
},
{
"docid": "c3f109cd29474b74d36d85d6d3129711",
"score": "0.6209368",
"text": "def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)",
"title": ""
}
]
504cd070164a98c21131c2e0fc61571e
Exclude certain well types from aggregate or individual metrics.
[
{
"docid": "f528baa413011893daa3efb05e640201",
"score": "0.0",
"text": "def build_exclude_query(query, exclusions, joined_entities):\n if EXCLUDE_LOW_EVENTS in exclusions:\n if WellMetric not in joined_entities:\n query = query.join(WellMetric).filter(WellMetric.accepted_event_count > 1000)\n\n if EXCLUDE_NO_CALL in exclusions:\n # assume a channel has been selected\n if WellChannelMetric not in joined_entities:\n query = query.join(WellChannelMetric)\n query = query.filter(WellChannelMetric.concentration > 0)\n\n return query",
"title": ""
}
]
[
{
"docid": "47d34d37d13fe7831c767a526b208400",
"score": "0.59837747",
"text": "def remove_measurements(self):\n def ismeasurement(gate):\n return isinstance(gate, qiskit.circuit.measure.Measure)\n self.data = [data for data in self.data if not ismeasurement(data[0])]\n return",
"title": ""
},
{
"docid": "94a9c3cc5d23db80274ad01423aea675",
"score": "0.5879248",
"text": "def reject_all_outliers(train):\n train = reject_outliers(train,\"Income in EUR\")\n train = reject_outliers(train,\"Age\")\n train = reject_outliers(train,\"Body Height [cm]\")\n train = reject_outliers(train,\"Size of City\")\n train = reject_outliers(train,\"Year of Record\")\n return train",
"title": ""
},
{
"docid": "d134970068c35ac46f4b9b52b4985072",
"score": "0.5871861",
"text": "def exclude_filters(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['MetricStreamExcludeFilterArgs']]]]:\n return pulumi.get(self, \"exclude_filters\")",
"title": ""
},
{
"docid": "d134970068c35ac46f4b9b52b4985072",
"score": "0.5871861",
"text": "def exclude_filters(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['MetricStreamExcludeFilterArgs']]]]:\n return pulumi.get(self, \"exclude_filters\")",
"title": ""
},
{
"docid": "379572575bca438e3c772e3cc27b95d6",
"score": "0.5850303",
"text": "def _filter_metrics(self, _type: Type[TMetric]) -> List[Type[TMetric]]:\n return [metric for metric in self.metrics if issubclass(metric, _type)]",
"title": ""
},
{
"docid": "a65fdd64b7d8a63618c8199e662069da",
"score": "0.57918996",
"text": "def test_exclude_functionality(self):\n exclude_opts = list(OCPExcludeSerializer._opfields)\n exclude_opts.remove(\"infrastructures\") # Tested separately\n exclude_opts.remove(\"category\")\n for exclude_opt in exclude_opts:\n for view in [OCPCostView, OCPCpuView, OCPMemoryView, OCPVolumeView]:\n with self.subTest(exclude_opt):\n overall_url = f\"?group_by[{exclude_opt}]=*\"\n query_params = self.mocked_query_params(overall_url, view)\n handler = OCPReportQueryHandler(query_params)\n overall_output = handler.execute_query()\n overall_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n opt_value = None\n for date_dict in overall_output.get(\"data\", [{}]):\n for element in date_dict.get(f\"{exclude_opt}s\"):\n if f\"No-{exclude_opt}\" != element.get(exclude_opt):\n opt_value = element.get(exclude_opt)\n break\n if opt_value:\n break\n # Grab filtered value\n filtered_url = f\"?group_by[{exclude_opt}]=*&filter[{exclude_opt}]={opt_value}\"\n query_params = self.mocked_query_params(filtered_url, view)\n handler = OCPReportQueryHandler(query_params)\n handler.execute_query()\n filtered_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n expected_total = overall_total - filtered_total\n # Test exclude\n exclude_url = f\"?group_by[{exclude_opt}]=*&exclude[{exclude_opt}]={opt_value}\"\n query_params = self.mocked_query_params(exclude_url, view)\n handler = OCPReportQueryHandler(query_params)\n self.assertIsNotNone(handler.query_exclusions)\n excluded_output = handler.execute_query()\n excluded_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n excluded_data = excluded_output.get(\"data\")\n # Check to make sure the value is not in the return\n for date_dict in excluded_data:\n grouping_list = date_dict.get(f\"{exclude_opt}s\", [])\n self.assertIsNotNone(grouping_list)\n for group_dict in grouping_list:\n if f\"No-{exclude_opt}\" != opt_value:\n self.assertNotEqual(opt_value, group_dict.get(exclude_opt))\n self.assertAlmostEqual(expected_total, excluded_total, 6)\n self.assertNotEqual(overall_total, excluded_total)",
"title": ""
},
{
"docid": "b3f39c45164b0d3542a2a6acc73ab0ce",
"score": "0.57276094",
"text": "def exclude_filters(self) -> pulumi.Output[Optional[Sequence['outputs.MetricStreamExcludeFilter']]]:\n return pulumi.get(self, \"exclude_filters\")",
"title": ""
},
{
"docid": "b1ca3718cfd88bb3b1ec856295eaf4c0",
"score": "0.57185674",
"text": "def test_multi_exclude_functionality(self):\n exclude_opts = list(OCPExcludeSerializer._opfields)\n exclude_opts.remove(\"infrastructures\")\n exclude_opts.remove(\"category\")\n for ex_opt in exclude_opts:\n base_url = f\"?group_by[{ex_opt}]=*&filter[time_scope_units]=month&filter[resolution]=monthly&filter[time_scope_value]=-1\" # noqa: E501\n for view in [OCPVolumeView, OCPCostView, OCPCpuView, OCPMemoryView]:\n query_params = self.mocked_query_params(base_url, view)\n handler = OCPReportQueryHandler(query_params)\n overall_output = handler.execute_query()\n opt_dict = overall_output.get(\"data\", [{}])[0]\n opt_list = opt_dict.get(f\"{ex_opt}s\")\n exclude_one = None\n exclude_two = None\n for exclude_option in opt_list:\n if \"No-\" not in exclude_option.get(ex_opt):\n if not exclude_one:\n exclude_one = exclude_option.get(ex_opt)\n elif not exclude_two:\n exclude_two = exclude_option.get(ex_opt)\n else:\n continue\n if not exclude_one or not exclude_two:\n continue\n url = base_url + f\"&exclude[or:{ex_opt}]={exclude_one}&exclude[or:{ex_opt}]={exclude_two}\"\n with self.subTest(url=url, view=view, ex_opt=ex_opt):\n query_params = self.mocked_query_params(url, view)\n handler = OCPReportQueryHandler(query_params)\n self.assertIsNotNone(handler.query_exclusions)\n excluded_output = handler.execute_query()\n excluded_data = excluded_output.get(\"data\")\n self.assertIsNotNone(excluded_data)\n for date_dict in excluded_data:\n grouping_list = date_dict.get(f\"{ex_opt}s\", [])\n self.assertIsNotNone(grouping_list)\n for group_dict in grouping_list:\n self.assertNotIn(group_dict.get(ex_opt), [exclude_one, exclude_two])",
"title": ""
},
{
"docid": "44b324c312223092332665263d610d6c",
"score": "0.55464876",
"text": "def test_multi_exclude_functionality(self):\n exclude_opts = list(OCPAllExcludeSerializer._opfields)\n exclude_opts.remove(\"source_type\")\n exclude_opts.remove(\"account\")\n exclude_opts.remove(\"az\")\n exclude_opts.remove(\"instance_type\")\n exclude_opts.remove(\"storage_type\")\n for ex_opt in exclude_opts:\n base_url = f\"?group_by[{ex_opt}]=*&filter[time_scope_units]=month&filter[resolution]=monthly&filter[time_scope_value]=-1\" # noqa: E501\n for view in [OCPAllCostView, OCPAllStorageView, OCPAllInstanceTypeView]:\n query_params = self.mocked_query_params(base_url, view)\n handler = OCPAllReportQueryHandler(query_params)\n overall_output = handler.execute_query()\n opt_dict = overall_output.get(\"data\", [{}])[0]\n opt_list = opt_dict.get(f\"{ex_opt}s\")\n exclude_one = None\n exclude_two = None\n for exclude_option in opt_list:\n if \"No-\" not in exclude_option.get(ex_opt):\n if not exclude_one:\n exclude_one = exclude_option.get(ex_opt)\n elif not exclude_two:\n exclude_two = exclude_option.get(ex_opt)\n else:\n continue\n if not exclude_one or not exclude_two:\n continue\n url = base_url + f\"&exclude[or:{ex_opt}]={exclude_one}&exclude[or:{ex_opt}]={exclude_two}\"\n with self.subTest(url=url, view=view, ex_opt=ex_opt):\n query_params = self.mocked_query_params(url, view)\n handler = OCPAllReportQueryHandler(query_params)\n self.assertIsNotNone(handler.query_exclusions)\n excluded_output = handler.execute_query()\n excluded_data = excluded_output.get(\"data\")\n self.assertIsNotNone(excluded_data)\n for date_dict in excluded_data:\n grouping_list = date_dict.get(f\"{ex_opt}s\", [])\n self.assertIsNotNone(grouping_list)\n for group_dict in grouping_list:\n self.assertNotIn(group_dict.get(ex_opt), [exclude_one, exclude_two])",
"title": ""
},
{
"docid": "cb68c8e45ea7b6597924525d7b4d5df5",
"score": "0.55350363",
"text": "def exclude(self, *args, **kwargs):\n # return self._filter_or_exclude(True, *args, **kwargs)\n raise NotImplementedError",
"title": ""
},
{
"docid": "7fba54342465d0780172e87554087c73",
"score": "0.5490191",
"text": "def only_missing_etypes(self):\n have = [enroll[\"etype\"] for enroll in self.data[\"ids\"]]\n return [x for x in self.etypes if x not in have]",
"title": ""
},
{
"docid": "917623b1de7dbbb9784cb71950f4a2bf",
"score": "0.54244316",
"text": "def _remove_empty_obstype_fields(self):\n remove_obstype = [] # List with observation types, which should be removed from Dataset.\n remove_obstype_sys = {} # Dictionary with obstypes for each GNSS, should be removed from meta['obstypes'].\n for obstype, obs in self.data[\"obs\"].items():\n if not obs or np.all(np.array(obs) == 0.0):\n remove_obstype.append(obstype)\n systems = set(self.data[\"text\"][\"system\"])\n for sys in systems:\n\n # Filter observations depending on GNSS\n idx = np.array(self.data[\"text\"][\"system\"]) == sys\n\n if np.all(np.array(obs)[idx] == 0.0):\n remove_obstype_sys.setdefault(sys, list()).append(obstype)\n\n log.debug(\n f\"The following observation types are removed, because no observations were found: \"\n f\"{' '.join(sorted(remove_obstype))}\"\n )\n\n # Remove empty observation type data fields\n for obstype in remove_obstype:\n for sys in list(self.meta[\"obstypes\"]):\n if obstype in self.meta[\"obstypes\"][sys]:\n self.meta[\"obstypes\"][sys].remove(obstype)\n\n del self.data[\"obs\"][obstype]\n del self.data[\"cycle_slip\"][obstype]\n del self.data[\"signal_strength\"][obstype]\n\n # Remove empty observation types for a given GNSS from meta['obstypes'] and other meta variables\n for sys, obstypes in remove_obstype_sys.items():\n for obstype in obstypes:\n if obstype in self.meta[\"obstypes\"][sys]:\n self.meta[\"obstypes\"][sys].remove(obstype)",
"title": ""
},
{
"docid": "ff73ac8cf4ce41e58a8f43a1215d139c",
"score": "0.5364947",
"text": "def ignore_member(cls, version, m_name, m_type):\n # This will probably need more granularity as more extensions are added\n if (type_maps.class_is_extension(cls, version) and (\n m_name == \"experimenter\" or\n m_name == \"subtype\")):\n return True\n return loxi_utils.skip_member_name(m_name) or m_type not in scalar_types",
"title": ""
},
{
"docid": "89d3e60ca0ff279c45e87b2e732e87c8",
"score": "0.5358879",
"text": "def drop_all_metrics(self):\n super(HybridAccessor, self).drop_all_metrics()\n self._metadata_accessor.drop_all_metrics()\n self._data_accessor.drop_all_metrics()",
"title": ""
},
{
"docid": "82eb5cbe19a5ed34ec1d02f15dadaf66",
"score": "0.53534263",
"text": "def test_exclude_by_users(self):\n df = copy.copy(self.test_df.iloc[: self.limit])\n target = \"K_VRH\"\n exclude = [\"ElementProperty\"]\n\n ep = ElementProperty.from_preset(\"matminer\")\n ep_feats = ep.feature_labels()\n\n # Test to make sure excluded does not show up\n af = AutoFeaturizer(exclude=exclude, preset=\"express\")\n af.fit(df, target)\n df = af.fit_transform(df, target)\n\n self.assertTrue(af.auto_featurizer)\n self.assertIn(\"ElementProperty\", af.exclude)\n self.assertFalse(any([f in df.columns for f in ep_feats]))",
"title": ""
},
{
"docid": "8b239a990d7b68fe62e10da5431ba383",
"score": "0.53428864",
"text": "def exclude(self, *args, **kwargs):\r\n return self._filter_or_exclude(True, *args, **kwargs)",
"title": ""
},
{
"docid": "0fc85be72122226abc44cd2ca6708b91",
"score": "0.53275746",
"text": "def test_get_fields_nosubtypes(self):\n fields = GrondwaterFilter.get_fields(include_subtypes=False)\n for field in fields:\n assert field not in ('datum', 'tijdstip', 'peil_mtaw',\n 'betrouwbaarheid', 'methode')",
"title": ""
},
{
"docid": "47bc09250187907ae6ee825ca2bc34cc",
"score": "0.5313815",
"text": "def graph_exclude_data_dff(self):\n # Data dffs and wmask dffs are only for writing so are not useful for evaluating read delay.\n for inst in self.data_dff_insts:\n self.graph_inst_exclude.add(inst)\n if self.write_size:\n for inst in self.wmask_dff_insts:\n self.graph_inst_exclude.add(inst)\n if self.num_spare_cols:\n for inst in self.spare_wen_dff_insts:\n self.graph_inst_exclude.add(inst)",
"title": ""
},
{
"docid": "2f2a8f844ece3b84e830ca72454d39bc",
"score": "0.5290781",
"text": "def unfiltered():",
"title": ""
},
{
"docid": "f5886e18f689dcc254872f9acd02704d",
"score": "0.52487826",
"text": "def meaningful(self):\n return self.exclude(value=0, correction=0)",
"title": ""
},
{
"docid": "f670e7fa1d17f5e47c713a3faccf11da",
"score": "0.5230749",
"text": "def apply_excludes(self):\n pass",
"title": ""
},
{
"docid": "e5403c106f92832ffa0b5a6f6e284171",
"score": "0.5222039",
"text": "def test_exclude_infastructures(self):\n # It works on cost endpoint, but not the other views:\n for view in [OCPVolumeView, OCPCostView, OCPCpuView, OCPMemoryView]:\n with self.subTest(view=view):\n # Grab overall value\n overall_url = \"?\"\n query_params = self.mocked_query_params(overall_url, view)\n handler = OCPReportQueryHandler(query_params)\n handler.execute_query()\n ocp_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n ocp_raw = handler.query_sum.get(\"cost\").get(\"raw\", {}).get(\"value\")\n # Grab azure filtered value\n azure_url = \"?filter[infrastructures]=azure\"\n query_params = self.mocked_query_params(azure_url, view)\n handler = OCPReportQueryHandler(query_params)\n handler.execute_query()\n azure_filtered_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n # Grab gcp filtered value\n gcp_url = \"?filter[infrastructures]=gcp\"\n query_params = self.mocked_query_params(gcp_url, view)\n handler = OCPReportQueryHandler(query_params)\n handler.execute_query()\n gcp_filtered_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n # Test exclude\n # we subtract the ocp_raw cost here because we only want cost associated to\n # an infrastructure here, or atleast that is my understanding.\n expected_total = (ocp_total + azure_filtered_total + gcp_filtered_total) - ocp_raw\n exclude_url = \"?exclude[infrastructures]=aws\"\n query_params = self.mocked_query_params(exclude_url, view)\n handler = OCPReportQueryHandler(query_params)\n self.assertIsNotNone(handler.query_exclusions)\n handler.execute_query()\n excluded_result = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n self.assertAlmostEqual(expected_total, excluded_result, 6)",
"title": ""
},
{
"docid": "9b161b41c5b4d36df4135b2ea7a52990",
"score": "0.5198749",
"text": "def test_parse_exclude_params_type_fail(self):\n types = [\"bad1\", \"aws_tags\"]\n for tag_type in types:\n exclude_params = {\"type\": None}\n exclude_params[\"type\"] = tag_type\n serializer = OCPExcludeSerializer(data=exclude_params)\n self.assertFalse(serializer.is_valid())",
"title": ""
},
{
"docid": "2fa155b7dfbba013ce1b7cbc466b93a7",
"score": "0.51906395",
"text": "def take_out_wells_with_no_tops(self):\n #### THIS FUNCTION ASSUMES SOME STRUCTURES THAT MIGHT NOT EXIST IN YOUR PROJECT\n #### YOU MAY HAVE TO DO THIS A DIFFERENT WAY\n print(\n \"THIS FUNCTION ASSUMES SOME STRUCTURES THAT MIGHT NOT EXIST IN YOUR PROJECT. It should work find with Mannville default data\"\n )\n #### produces dataframe with no picks that have a value of zero\n noZeroPicks = self.input.picks_df[self.input.picks_df.Pick != 0]\n #### produces dataframe that doesn't have any picks with a quality of negative one, meaning not to be trusted or present\n noNullPicks = noZeroPicks[noZeroPicks.Quality != -1]\n self.picks_df_noNullPicks = noNullPicks",
"title": ""
},
{
"docid": "d8dffa50adc1bfb5b26eb2aa45e05d73",
"score": "0.5142001",
"text": "def test_exclude_tags(self):\n query_params = self.mocked_query_params(\"?\", OCPAllTagView)\n handler = OCPAllTagQueryHandler(query_params)\n tags = handler.get_tags()\n group_tag = None\n check_no_option = False\n exclude_vals = []\n for tag_dict in tags:\n if len(tag_dict.get(\"values\")) > len(exclude_vals):\n group_tag = tag_dict.get(\"key\")\n exclude_vals = tag_dict.get(\"values\")\n self.assertNotEqual(len(exclude_vals), 0)\n url = f\"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=daily&group_by[tag:{group_tag}]=*\" # noqa: E501\n query_params = self.mocked_query_params(url, OCPAllCostView)\n handler = OCPAllReportQueryHandler(query_params)\n data = handler.execute_query().get(\"data\")\n if f\"No-{group_tag}\" in str(data):\n check_no_option = True\n previous_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n for exclude_value in exclude_vals:\n url += f\"&exclude[tag:{group_tag}]={exclude_value}\"\n query_params = self.mocked_query_params(url, OCPAllCostView)\n handler = OCPAllReportQueryHandler(query_params)\n data = handler.execute_query()\n if check_no_option:\n self.assertIn(f\"No-{group_tag}\", str(data))\n current_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n self.assertLess(current_total, previous_total)\n previous_total = current_total",
"title": ""
},
{
"docid": "6b4d65bc87dcb7a81eccff95eaa52b62",
"score": "0.51360476",
"text": "def filter_types(self, types): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "ddf26f3fd63dfb1ddfa74b0474cc3113",
"score": "0.5111908",
"text": "def excludes(self, package):\n raise NotImplementedError",
"title": ""
},
{
"docid": "975ed9a3430bb4989e102c8a7208e670",
"score": "0.50944704",
"text": "def derive_exclude(self):\n exclude = []\n if self.exclude:\n exclude += self.exclude\n\n return exclude",
"title": ""
},
{
"docid": "ef1daad688a23b418c50023facc8648f",
"score": "0.50816983",
"text": "def no_filter(x):\n return True",
"title": ""
},
{
"docid": "3da6e21dcf30e9357d3c41d962f4c54e",
"score": "0.50760067",
"text": "def not_included(self):",
"title": ""
},
{
"docid": "9f0041cd965b311fc90b311c411b0fbe",
"score": "0.50666183",
"text": "def _remove_experts(self):\n self.experts = [ex for ex in self.experts if np.mean(\n ex.weight) >= self.theta]",
"title": ""
},
{
"docid": "40e79996b50bc5c000b8626ebafe0eaf",
"score": "0.5066329",
"text": "def exclude_buildingblocks(self, bb_to_ex):\n\n for bb in bb_to_ex:\n if bb in sequence.interprete_dict.keys():\n element = sequence.interprete(bb)\n if element in sequence.AA:\n self.exclude_aminoacids(element)\n elif element in sequence.B:\n self.exclude_branching(element)\n elif element in sequence.CT:\n self.exclude_C_terminal(element)\n elif element in sequence.NT:\n self.exclude_N_capping(element) \n else:\n print(\"can't exclude \", bb)\n else:\n print(\"can't exclude \", bb)",
"title": ""
},
{
"docid": "d153b29db1561e895c025656966326e2",
"score": "0.50648767",
"text": "def test_recorddao_not_type_list(self):\n get_several = list(self.record_dao.get_all_of_type(types=not_([\"bar\", \"spamrec\",\n \"eggrec\", \"run\"]),\n ids_only=True))\n self.assertEqual(len(get_several), 3)\n six.assertCountEqual(self, get_several,\n [\"spam3\", \"spam3ish\", \"shared_curve_set_and_matching_scalar_data\"])",
"title": ""
},
{
"docid": "d41a04552205a46ffa5f13e50a824dc7",
"score": "0.5063712",
"text": "def remove_genomewide_negatives(self):\n # TODO(dk) use an attributes tag\n new_files = [\n h5_file for h5_file in self.data_files\n if \"genomewide-negatives\" not in h5_file]\n new_dataloader = H5DataLoader(\n self.data_dir, data_files=new_files, fasta=self.fasta)\n \n return new_dataloader",
"title": ""
},
{
"docid": "bc2dcf2d207331a56edc08ee8a21949a",
"score": "0.50565696",
"text": "def remove_fit_type(self):\n\n self.spectrum_oned.remove_fit_type()",
"title": ""
},
{
"docid": "48a0c338a39cd5cb33a87a5cbac94568",
"score": "0.5042094",
"text": "def test_parse_metrics_suppress_error(self):\n message = \"k:1|nope\"\n results = Collector(None)._parse_metrics(message)\n\n assert 0 == len(results)",
"title": ""
},
{
"docid": "2b16d74896d8f5eda7d0c57f0e10179f",
"score": "0.50405216",
"text": "def test_exclude_tags(self):\n query_params = self.mocked_query_params(\"?\", OCPTagView)\n handler = OCPTagQueryHandler(query_params)\n tags = handler.get_tags()\n group_tag = None\n check_no_option = False\n exclude_vals = []\n for tag_dict in tags:\n if len(tag_dict.get(\"values\")) > len(exclude_vals):\n group_tag = tag_dict.get(\"key\")\n exclude_vals = tag_dict.get(\"values\")\n url = f\"?filter[time_scope_units]=month&filter[time_scope_value]=-1&filter[resolution]=daily&group_by[tag:{group_tag}]=*\" # noqa: E501\n query_params = self.mocked_query_params(url, OCPCostView)\n handler = OCPReportQueryHandler(query_params)\n data = handler.execute_query().get(\"data\")\n if f\"No-{group_tag}\" in str(data):\n check_no_option = True\n returned_values = []\n for date in data:\n date_list = date.get(f\"{group_tag}s\")\n for date_dict in date_list:\n tag_value = date_dict.get(group_tag)\n if tag_value not in returned_values:\n returned_values.append(tag_value)\n exclude_vals = [value for value in exclude_vals if value in returned_values]\n previous_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n for exclude_value in exclude_vals:\n url += f\"&exclude[tag:{group_tag}]={exclude_value}\"\n query_params = self.mocked_query_params(url, OCPCostView)\n handler = OCPReportQueryHandler(query_params)\n data = handler.execute_query()\n if check_no_option:\n self.assertIn(f\"No-{group_tag}\", str(data))\n current_total = handler.query_sum.get(\"cost\", {}).get(\"total\", {}).get(\"value\")\n self.assertLess(current_total, previous_total)\n previous_total = current_total",
"title": ""
},
{
"docid": "36487b954aa7bc5c160488b24c5644fd",
"score": "0.5039861",
"text": "def filter_annotations(img_all_annotations, used_classes):\n\n img_filtered_annotations = {}\n\n # Filter the type of the objects.\n relevant_annotation_indices = [\n i for i, x in enumerate(img_all_annotations['type']) if x in used_classes\n ]\n\n for key in img_all_annotations.keys():\n img_filtered_annotations[key] = (\n img_all_annotations[key][relevant_annotation_indices])\n\n if 'dontcare' in used_classes:\n dont_care_indices = [i for i,\n x in enumerate(img_filtered_annotations['type'])\n if x == 'dontcare']\n\n # bounding box format [y_min, x_min, y_max, x_max]\n all_boxes = np.stack([img_filtered_annotations['2d_bbox_top'],\n img_filtered_annotations['2d_bbox_left'],\n img_filtered_annotations['2d_bbox_bottom'],\n img_filtered_annotations['2d_bbox_right']],\n axis=1)\n\n ious = iou(boxes1=all_boxes,\n boxes2=all_boxes[dont_care_indices])\n\n # Remove all bounding boxes that overlap with a dontcare region.\n if ious.size > 0:\n boxes_to_remove = np.amax(ious, axis=1) > 0.0\n for key in img_all_annotations.keys():\n img_filtered_annotations[key] = (\n img_filtered_annotations[key][np.logical_not(boxes_to_remove)])\n\n return img_filtered_annotations",
"title": ""
},
{
"docid": "f85bf42e5617a2af95e647dacf9ea2f6",
"score": "0.5035401",
"text": "def dontfeedpet(self):\n self.mypet.fless()",
"title": ""
},
{
"docid": "76af6ff32de5ddec3d926ad6a6112f20",
"score": "0.50229496",
"text": "def excludeFromDump(self):\n pass",
"title": ""
},
{
"docid": "1dbf4b95263c7aa8c048eac30394af80",
"score": "0.5022236",
"text": "def consume_metric_or_not(vim_type, metric_type):\n return metric_type in METRICS_WHITE_LIST.get(vim_type, [])",
"title": ""
},
{
"docid": "22d8516d64f741c55d0d8f468e0f9294",
"score": "0.50193727",
"text": "def __filter(self, inset, gtype):\n \n if gtype != 'all':\n if verbose:\n print(\"Filter type {0} in collections\".format(gtype))\n remove_list = []\n for item in inset:\n do_transact = True\n while(do_transact):\n game = self.game(name=item)\n \n if game is None:\n continue\n \n do_transact = False\n \n isexpansion = game.expansion\n \n if ((gtype == 'expansion' and not isexpansion) or\n (gtype == 'base' and isexpansion)):\n remove_list.append(item)\n\n [inset.discard(item) for item in remove_list]\n \n return inset",
"title": ""
},
{
"docid": "9bd41eb0f69f659fd6ab820e977862b9",
"score": "0.5014455",
"text": "def dontcleanpet(self):\n self.mypet.cless()",
"title": ""
},
{
"docid": "2d55fa3ea75356e69856f41d66fff71c",
"score": "0.5000527",
"text": "def filter_excludes(data, loci):\n ## Get all the samples to exclude. The 'or' conditional is a hack to account\n ## for the fact that paramsdict may either be an empty string or a list of\n ## names to exclude. List and \"\" don't append right, so if you do this 'or'\n ## and either of the lists is empty you'll get [\"\", \"1B_0\"]\n excludes = (data.paramsdict[\"excludes\"] or [\"\"]) \\\n + (data.paramsdict[\"outgroups\"] or [\"\"])\n LOGGER.info(\"Excluding these individuals - {}\".format(excludes))\n\n count = 0\n for i, loc in enumerate(loci):\n\n ## Get the count of excludes for logging/stats\n count += len(filter(lambda x: x[0] in excludes, loc))\n \n ## Actually filter the little buggers\n loci[i] = filter(lambda x: x[0] not in excludes, loc)\n\n LOGGER.info(\"Filterered exclude/outgroup sequences - {}\".format(count))\n return loci",
"title": ""
},
{
"docid": "1b183754cb485727b549de8559a7744c",
"score": "0.49942797",
"text": "def sanitize(self):\n for attribute in ['occupations', 'energies', 'coefficients']:\n array = getattr(self, attribute)\n selection = np.where(np.isnan(array))\n array[selection] = 0.0\n\n selection = np.where(self.types == '-')\n self.types[selection] = 's'",
"title": ""
},
{
"docid": "efe6c49e89365b7112714933f4ed408c",
"score": "0.49897572",
"text": "def test_parse_exclude_params_type_success(self):\n types = [\"pod\", \"storage\"]\n for tag_type in types:\n exclude_params = {\"type\": None}\n exclude_params[\"type\"] = tag_type\n serializer = OCPExcludeSerializer(data=exclude_params)\n self.assertTrue(serializer.is_valid())",
"title": ""
},
{
"docid": "ae994cd7ca76b56644a4e91070eeb513",
"score": "0.49832016",
"text": "async def test_nr_of_tests_without_tests(self):\n json = dict(component=dict(measures=[]))\n response = await self.collect(self.metric, get_request_json_return_value=json)\n self.assert_measurement(\n response, value=None, total=None, parse_error=\"KeyError\", landing_url=self.tests_landing_url)",
"title": ""
},
{
"docid": "ecf078381afbd2db4a897b871e919cdc",
"score": "0.49816883",
"text": "def filter_from_warnings(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "364ddd9a8adc75c3726122bb32b52833",
"score": "0.4960375",
"text": "def _disable_all_quantizers(self):\n for quantsim_wrapper in self._module_to_quantsim_wrapper_dict.values():\n for quantizer in quantsim_wrapper.input_quantizers + quantsim_wrapper.output_quantizers:\n quantizer.enabled = False\n quantizer.use_symmetric_encodings = False\n for param_quantizer in quantsim_wrapper.param_quantizers.values():\n param_quantizer.enabled = False\n param_quantizer.use_symmetric_encodings = False",
"title": ""
},
{
"docid": "01130c22a7408c646bc71806ee45158e",
"score": "0.4932181",
"text": "def ignore( self, other ):\r\n if isinstance( other, Suppress ):\r\n if other not in self.ignoreExprs:\r\n self.ignoreExprs.append( other )\r\n else:\r\n self.ignoreExprs.append( Suppress( other ) )\r\n return self",
"title": ""
},
{
"docid": "6218311b95fad0f44c61d073e7ad93ab",
"score": "0.49310634",
"text": "def test_ignore_excluded_fields(self):\n class ExcludedFieldSerializer(serializers.ModelSerializer):\n class Meta:\n model = UniquenessTogetherModel\n fields = ('id', 'race_name',)\n serializer = ExcludedFieldSerializer()\n expected = dedent(\"\"\"\n ExcludedFieldSerializer():\n id = IntegerField(label='ID', read_only=True)\n race_name = CharField(max_length=100)\n \"\"\")\n assert repr(serializer) == expected",
"title": ""
},
{
"docid": "018cf149195a76bbc1f6b3d389082e97",
"score": "0.4927664",
"text": "def remove_unwanted_tags(self):\n keep_tags = [\"artist\", \"albumartist\", \"title\", \"tracknumber\", \"album\", \"discnumber\", \"date\", \"genre\"]\n for k in (k for k in self.etags.keys() if k not in keep_tags):\n self.remove_tag(k)",
"title": ""
},
{
"docid": "738ba8a9f8306842a3aedf065677a885",
"score": "0.49266225",
"text": "def getIgnored():",
"title": ""
},
{
"docid": "acb435a6e4b40049371e55db4ffde4b4",
"score": "0.4919252",
"text": "def _infer_discard_events(pfx_event):\n\n inferences = []\n\n if pfx_event.has_tag(\"recurring-pfx-event\"):\n inferences.append(\n Inference(\n inference_id=\"hide-recurring-pfx-event\",\n explanation=\"recurring event; hide event\",\n suspicion_level=-1,\n confidence=100,\n labels=[LABEL_HIDE]\n )\n )\n\n if pfx_event.has_tag(\"short-prefix\"):\n inferences.append(\n Inference(\n inference_id=\"hide-short-prefix\",\n explanation=\"prefix is too large (less specific than /8); hide event\",\n suspicion_level=-1,\n confidence=100,\n labels=[LABEL_HIDE]\n )\n )\n\n # if pfx_event.has_tag(\"subpfx-moas\") or pfx_event.has_tag(\"superpfx-moas\"):\n # inferences.append(\n # Inference(\n # inference_id=\"hide-moas-caused-submoas\",\n # explanation=\"a moas event that also causes a submoas; hide\",\n # suspicion_level=-1,\n # confidence=100,\n # labels=[LABEL_HIDE]\n # )\n # )\n\n if pfx_event.has_tag(\"submoas-covered-by-moas-subpfx\") or \\\n pfx_event.has_tag(\"submoas-covered-by-moas-superpfx\"):\n inferences.append(\n Inference(\n inference_id=\"hide-submoas-covered-by-moas\",\n explanation=\"This is a MOAS in the first place\",\n suspicion_level=-1,\n confidence=100,\n labels=[LABEL_HIDE]\n )\n )\n\n if pfx_event.has_tag(\"no-newcomer\") and pfx_event.has_tag(\"less-origins\"):\n inferences.append(\n Inference(\n inference_id=\"hide-shrinking-event\",\n explanation=\"a shrinking moas event; hide event\",\n suspicion_level=-1,\n confidence=100,\n labels=[LABEL_HIDE]\n )\n )\n\n return inferences",
"title": ""
},
{
"docid": "380613d84ca95e36c612d8ac06463e10",
"score": "0.49097455",
"text": "def ignore( self, other ):\r\n if isinstance( other, Suppress ):\r\n if other not in self.ignoreExprs:\r\n self.ignoreExprs.append( other.copy() )\r\n else:\r\n self.ignoreExprs.append( Suppress( other.copy() ) )\r\n return self",
"title": ""
},
{
"docid": "0381601b43f2b3d894c458c1faf43d3b",
"score": "0.4898034",
"text": "def ExcludeVariants(self, type_name, variant_to_exclude):\n # Find the list of operand with type `type_name`.\n relevant_operands = filter(lambda operand: operand.type_name == type_name,\n self)\n for operand in relevant_operands:\n # Remove the intersection of the existing variants and variants we do not\n # want.\n for variant in set(operand.variants) & set(variant_to_exclude):\n operand.variants.remove(variant)",
"title": ""
},
{
"docid": "9cbf291c2ef5c49ca1aca1603e334b6f",
"score": "0.489669",
"text": "def __neg__(self):\n other = None\n return ibmdbpy.aggregation.aggregate_idadf(self, \"neg\", other)",
"title": ""
},
{
"docid": "17b841f93afe04699f9d4184ea5c6363",
"score": "0.48855612",
"text": "def find_cols_to_exclude(self, df):\n lst = []\n for col in df.columns:\n if (\n \"address\" in str(col)\n or \"first_name\" in str(col)\n or \"last_name\" in str(col)\n or \"username\" in str(col)\n or \"_id\" in str(col)\n or \"date\" in str(col)\n or \"time\" in str(col)\n ):\n lst.append({col: \"Considering excluding because potential PII column.\"})\n elif df[col].isnull().sum() / float(df.shape[0]) >= 0.5:\n lst.append(\n {\n col: \"Considering excluding because {}% of column is null.\".format(\n (df[col].isnull().sum() / float(df.shape[0]) * 100.0)\n )\n }\n )\n elif len(df[col].unique()) <= 1:\n lst.append(\n {\n col: \"Considering excluding because column includes only one value.\"\n }\n )\n elif df[col].dtype == \"datetime64[ns]\":\n lst.append(\n {col: \"Considering excluding because column is a timestamp.\"}\n )\n elif df[col].dtype not in [\"object\", \"bool\"]:\n if df[col].var() < 0.00001:\n lst.append(\n {\n col: \"Considering excluding because column variance is low ({})\".format(\n df[col].var()\n )\n }\n )\n elif df[col].dtype in [\"object\", \"bool\"]:\n if len(df[col].unique()) > 200:\n lst.append(\n {\n col: \"Considering excluding because object column has large number of unique values ({})\".format(\n len(df[col].unique())\n )\n }\n )\n\n [print(x) for x in lst]\n\n return lst",
"title": ""
},
{
"docid": "3ec6d47b81deb22388d3a57727f214c5",
"score": "0.48781773",
"text": "def test_extra_info_rows_without_metric_source(self):\n project = domain.Project()\n violation_metric = metric.ViolationSuppressions(subject=self.__subject, project=project)\n result = violation_metric.extra_info_rows()\n\n self.assertEqual([], result)",
"title": ""
},
{
"docid": "f183774aba8ad98ad596c57653ee4c17",
"score": "0.4875181",
"text": "def _not_included(self):",
"title": ""
},
{
"docid": "814492973e0ececca690417d69dce2e2",
"score": "0.48491505",
"text": "def ignore( self, other ):\r\n if isinstance(other, basestring):\r\n other = Suppress(other)\r\n\r\n if isinstance( other, Suppress ):\r\n if other not in self.ignoreExprs:\r\n self.ignoreExprs.append(other)\r\n else:\r\n self.ignoreExprs.append( Suppress( other.copy() ) )\r\n return self",
"title": ""
},
{
"docid": "73ee36b5b65cc273d266647b4bb78f20",
"score": "0.48484337",
"text": "def everything_except(\n excluded_types: type | tuple[type, ...],\n) -> SearchStrategy[Any]:\n if not isinstance(excluded_types, tuple):\n excluded_types = tuple([excluded_types])\n\n checked_types = []\n for excluded_type in excluded_types:\n try:\n isinstance(0, excluded_type)\n checked_types.append(excluded_type)\n except TypeError:\n continue\n\n if len(checked_types) == 0:\n return from_type(type).flatmap(from_type)\n\n return (\n from_type(type)\n .flatmap(from_type)\n .filter(lambda x: not isinstance(x, tuple(checked_types)))\n )",
"title": ""
},
{
"docid": "3272b6b12fce94918a1fa402046c047b",
"score": "0.48466238",
"text": "def without_overlaps(self, other_tier):\n span_groups = self.group_spans_by_containing_span(other_tier,\n allow_partial_containment=True)\n result = []\n for span, group in span_groups:\n if len(group) == 0:\n result.append(span)\n return AnnoTier(result)",
"title": ""
},
{
"docid": "ec3e7f89eea56d6975340fcf19fedcce",
"score": "0.48459032",
"text": "def filter_empty_scores(self):\n for tune in self.tunes:\n if len(tune.score) == 0:\n print(f\"No input data for {tune.title}\")\n self.tunes.remove(tune)",
"title": ""
},
{
"docid": "8b5378059d46cf2bc79c80ca9ab68250",
"score": "0.4840246",
"text": "def no_gates(self):\n combined = self.trivials\n if self.gated: combined += self.gated\n return enn.FieldType(self.gspace, combined.representations)",
"title": ""
},
{
"docid": "430b634ce8ef3a42fe670cfc93dd8cd1",
"score": "0.48338276",
"text": "def _remove_empty_systems(self):\n for sys in list(self.meta[\"obstypes\"].keys()):\n if sys not in self.data[\"text\"][\"system\"]:\n log.debug(f\"No observation given for GNSS {sys!r}. GNSS {sys!r} is removed from Dataset.\")\n del self.meta[\"obstypes\"][sys]",
"title": ""
},
{
"docid": "fab0e0592808bfb721c178224ba9d6dc",
"score": "0.4831567",
"text": "def test_blacklist(self, insertions, genes):\n\n annotator = WindowAnnotator.from_window_size(\n genes, window_size=20000, blacklist={'ENSMUSG00000026510'})\n annotated = list(annotator.annotate(insertions))\n\n assert 'gene_name' not in annotated[0].metadata",
"title": ""
},
{
"docid": "2c03c999258f84b00b94d990b0dc4ed5",
"score": "0.48266947",
"text": "def ignoreChecks(self):\r\n \r\n return self._ignoreChecks",
"title": ""
},
{
"docid": "57f2ebfc6a54467e8e860eceba885fa0",
"score": "0.48249123",
"text": "def uncollect_if_test_sampler(**kwargs):\n clf_dataset_name = kwargs['rf_classifier'].name.split(\"_\")[0] # \"adult_data\" -> \"adult\"\n exp_dataset_name = kwargs['explainer'].name.split(\"_\")[1] # \"at_adult_explainer\" -> \"adult\"\n dataset_name = kwargs['dataset'].name.split(\"_\")[0] # \"adult_data\" -> \"adult\"\n\n conditions = [\n len({clf_dataset_name, exp_dataset_name, dataset_name}) != 1,\n ]\n\n return any(conditions)",
"title": ""
},
{
"docid": "46975340c13acaedcb75a371af5c5802",
"score": "0.4824228",
"text": "def Exclude_Outliers_Checkbox(self):\n if self.exOutliersCheckbox.isChecked():\n self.exclude1T1Checkbox.setChecked(False)\n\n if self.gui_dict[\"gui_function\"][\"hydrationEdits\"]:\n self.Hydration_Calculator()\n else:\n pass",
"title": ""
},
{
"docid": "83f20213edd5c233432f8ce1c5af8c8d",
"score": "0.48221034",
"text": "def nan_filter(self):\r\n\r\n for att_name in self.required_catalog_atts:\r\n # treat sky coordinates differently form the other arrays\r\n if att_name == \"coords\":\r\n inds = (\r\n ~np.isnan(self.coords.data.lon)\r\n & ~np.isnan(self.coords.data.lat)\r\n & ~np.isnan(self.coords.data.distance)\r\n )\r\n else:\r\n # all other attributes should be ndarrays or quantity ndarrays\r\n # in either case, they should have a dtype\r\n att = getattr(self, att_name)\r\n if np.issubdtype(att.dtype, np.number):\r\n inds = ~np.isnan(att)\r\n elif np.issubdtype(att.dtype, str):\r\n inds = att != \"\"\r\n elif att.dtype == bool:\r\n pass\r\n else:\r\n warnings.warn(\r\n f\"Cannot filter attribute {att_name} of type {att.dtype}\"\r\n )\r\n\r\n # only need to do something if there are any False in inds:\r\n if not (np.all(inds)):\r\n self.revise_lists(np.where(inds)[0])",
"title": ""
},
{
"docid": "17a208b3eaa7def530d6a77f05b5d213",
"score": "0.47985053",
"text": "def test_criteria_not_split_if_no_type(self):\n criteria = {'summary': 'test', Key.REMAINING_TIME: '2',\n 'id': 'not in (1, 2, 3)'}\n self.assert_none(self.manager._split_ticket_type(criteria))",
"title": ""
},
{
"docid": "c88b95d2763a792aad481c9c0cd66570",
"score": "0.47911814",
"text": "def keep_nutri_g_only(self):\n prod_list = self.result_list\n temp = [x for x in prod_list if 'nutrition_grades' in x.keys()]\n self.result_list = temp",
"title": ""
},
{
"docid": "7eca033d9b5036bbfcec64cd93da0b58",
"score": "0.47862098",
"text": "def removeWeight(self, weight):\n if weight in self.weights:\n self.weights.remove(weight)\n\n for s in self.sampleList:\n if not s.isData and not s.isQCD and not s.isDiscovery:\n if weight in s.weights:\n s.removeWeight(weight)\n\n for syst in self.systDict.values():\n if syst.type == \"weight\":\n if weight in syst.high:\n syst.high.remove(weight)\n if weight in syst.low:\n syst.low.remove(weight)\n return",
"title": ""
},
{
"docid": "f3a7a9a97d5253b5ba570a231375089c",
"score": "0.47845492",
"text": "def prune_samplers(self,leverage_sum):\n keys_to_del = []\n for r_id in self.sampler.keys():\n u = self.sampler[r_id]['uniform_key']\n lev_score = self.sampler[r_id]['leverage']\n lev_ratio = lev_score / (lev_score + leverage_sum)\n \n # If the uniform weight is too large then remove r_id from the sampler.\n # There is probably a better way to do this to avoid the auxiliary list\n if u > lev_ratio:\n keys_to_del.append(r_id)\n for k in keys_to_del:\n del self.sampler[k]",
"title": ""
},
{
"docid": "92ab194e7e84250a7669ba1bfc13a0f3",
"score": "0.47751665",
"text": "def apply_preference_excludes(self, qs):\n if not self.show_gay_females:\n qs = qs.exclude(gender='F', sexual_preference=2)\n if not self.show_straight_males:\n qs = qs.exclude(gender='M', sexual_preference=1)\n if not self.show_straight_females:\n qs = qs.exclude(gender='F', sexual_preference=1)\n if not self.show_gay_males:\n qs = qs.exclude(gender='M', sexual_preference=2)\n if not self.show_bisexual_females:\n qs = qs.exclude(gender='F', sexual_preference=3)\n if not self.show_bisexual_males:\n qs = qs.exclude(gender='M', sexual_preference=3)\n\n return qs",
"title": ""
},
{
"docid": "16aa201142d205e480aa2c797be485f3",
"score": "0.47750944",
"text": "def clearExcluded():\n excluded.clear()",
"title": ""
},
{
"docid": "d230a20f677ce92e7a45a50d72123c5c",
"score": "0.4773189",
"text": "def _exclude_modules_from_quantization(model: torch.nn.Module, sim: QuantizationSimModel,\n modules_to_ignore: List[torch.nn.Module]):\n name_to_quant_wrapper_dict = {}\n for name, module in sim.model.named_modules():\n name_to_quant_wrapper_dict[name] = module\n\n module_to_name_dict = {}\n for name, module in model.named_modules():\n module_to_name_dict[module] = name\n\n quant_wrappers_to_ignore = []\n for module in modules_to_ignore:\n name = module_to_name_dict[module]\n quant_wrapper = name_to_quant_wrapper_dict[name]\n quant_wrappers_to_ignore.append(quant_wrapper)\n\n sim.exclude_layers_from_quantization(quant_wrappers_to_ignore)",
"title": ""
},
{
"docid": "a7928aaac5977bb88aa3186764268a84",
"score": "0.47711077",
"text": "def exclude(self, codes, axis='x'):\n self._missingfy(codes, axis=axis, keep_base=False, inplace=True)\n return self",
"title": ""
},
{
"docid": "42144ca224e886884da3d371eca41d3f",
"score": "0.47650152",
"text": "def ignored_item(item):\n return item['name'].upper() == 'SPARE' or 'x' in item['fmt']",
"title": ""
},
{
"docid": "87f6fe07435563e586fb6be6800c5f01",
"score": "0.47649622",
"text": "def test_no_listing_exclusions(self):\n self.validate_class_hierarchy(TestedExclusionTypes.NoExclusions)\n self.validate_namespace_listings(TestedExclusionTypes.NoExclusions)\n self.validate_file_listings()\n self.checkAllFilesIncluded()",
"title": ""
},
{
"docid": "3f6e5124bcb6d72f2ff66f6638f7e2e3",
"score": "0.47648185",
"text": "def exclude_tapebumps(tbd, data, extra):\n import numpy\n # I'm sure it doesn't matter, but add an extra 0.5 pixel to the slop because the tape bump\n # values are in terms of which pixels to include as part of the tape bump. So the edges\n # are an extra 0.5 pixel outside of that.\n extra += 0.5\n x = data['X_IMAGE']\n y = data['Y_IMAGE']\n masks = [(y>tb[0]-extra) & (x>tb[1]-extra) & (y<tb[2]+extra) & (x<tb[3]+extra) for tb in tbd]\n mask = numpy.any(masks, axis=0)\n if sum(mask) > 0:\n print ' masking %d stars for being in or near a bump'%sum(mask)\n print ' tapebumps = ',tbd\n print ' excluded x,y = ',zip(x[mask],y[mask])\n return data[numpy.logical_not(mask)]",
"title": ""
},
{
"docid": "179c0dacd412aaeaa35cc09419ebed2b",
"score": "0.4762217",
"text": "def test_counts_exclude_tag_without_decisions_needed(self):\n extra_tag = factories.TagFactory.create()\n extra_tagged_trait = factories.TaggedTraitFactory.create()\n study_response = factories.StudyResponseFactory.create(status=models.StudyResponse.STATUS_DISAGREE)\n response = self.client.get(self.get_url())\n context = response.context\n self.assertIn('grouped_study_tag_counts', context)\n counts = context['grouped_study_tag_counts']\n self.assertEqual(len(counts), 1) # One study.\n study1 = counts[0]\n self.assertEqual(study1[1][0]['tt_total'], 1)\n self.assertEqual(study1[1][0]['tt_decision_required_count'], 1)\n self.assertEqual(len(study1[1]), 1) # Only one tag.",
"title": ""
},
{
"docid": "2fccea1839cd72c65a9d473f734b2cee",
"score": "0.4748887",
"text": "def test_trace_blacklist():\n trace_factory.get_trace().url_patterns_to_ignore = set(('test.net', 'test2.net'))\n assert epsagon.http_filters.is_payload_collection_blacklisted('http://www.test.net')\n assert epsagon.http_filters.is_payload_collection_blacklisted('http://www.bla.test.net')\n assert not epsagon.http_filters.is_payload_collection_blacklisted('http://www.test.new.net')\n trace_factory.get_trace().url_patterns_to_ignore = set()\n assert not epsagon.http_filters.is_payload_collection_blacklisted('http://www.test.net')\n assert not epsagon.http_filters.is_payload_collection_blacklisted('http://www.bla.test.net')",
"title": ""
},
{
"docid": "ae55291cb864b31c270404c86ad44d77",
"score": "0.47418103",
"text": "def test_filtered_warning(warning_fixture):\n warnings.filterwarnings(\"ignore\", category=SuspiciousUsageWarning)\n look_out = network.Bus()\n with warnings.catch_warnings(record=True) as w:\n network.Sink(outputs={look_out: \"A typo!\"})\n assert len(w) == 0",
"title": ""
},
{
"docid": "556bc0a3d9cc4714e3a681c0635459c1",
"score": "0.47374457",
"text": "def clean_panel(self):\n if 'softmax' in self.args.MAB_algo and not self.explanation_type:\n expl_tuples = set()\n forbidden_groupBy = list()\n forbidden_aggregation = list()\n for expl, attributes_list in self.explanations_to_apply.items():\n if 'groupBy' in expl:\n if 'remove' in expl:\n forbidden_groupBy = attributes_list\n self.panel.groupBy = [att for att in self.panel.groupBy if att not in attributes_list]\n if 'softmax' in self.args.MAB_algo and not self.explanation_type:\n expl_tuples.add(('-', 'groupBy'))\n else:\n self.panel.groupBy += attributes_list\n if 'softmax' in self.args.MAB_algo and not self.explanation_type:\n expl_tuples.add(('+', 'groupBy'))\n elif 'aggregation' in expl:\n if 'remove' in expl:\n forbidden_aggregation = attributes_list\n for att in attributes_list:\n del self.panel.aggregates[att]\n if 'softmax' in self.args.MAB_algo and not self.explanation_type:\n expl_tuples.add(('-', 'aggregation'))\n else:\n for agg_att in self.explanations_to_apply['aggregation_to_add']:\n functions = choose_functions(agg_att)\n # if 'sum' in functions and len(functions) > 1:\n # functions.remove('sum')\n self.panel.aggregates[agg_att] = functions\n if 'softmax' in self.args.MAB_algo and not self.explanation_type:\n expl_tuples.add(('+', 'aggregation'))\n else:\n for agg_att, functions in attributes_list.items():\n for func in functions:\n if func in self.panel.aggregates[agg_att]:\n self.panel.aggregates[agg_att].remove(func)\n if 'softmax' in self.args.MAB_algo and not self.explanation_type:\n expl_tuples.add(('-', func))\n else:\n self.panel.aggregates[agg_att].append(func)\n if 'softmax' in self.args.MAB_algo and not self.explanation_type:\n expl_tuples.add(('+', func))\n self.panel.attributes_to_vector()\n self.ranking.calculate_ranking('groupBy', self.panel, self.diversity, forbidden_groupBy)\n self.ranking.calculate_ranking('aggregation', self.panel, self.diversity, forbidden_aggregation)\n if 'softmax' in self.args.MAB_algo:\n if self.explanation_type: # if MAB iteration\n self.applied_explanations = [self.explanation_type]\n self.explanation_type = None\n else: # if user_explanation iteration\n self.applied_explanations = [expl for expl in self.explanations if (expl.remove_or_add, expl.dimension) in expl_tuples]\n \n return forbidden_groupBy, forbidden_aggregation",
"title": ""
},
{
"docid": "e4fcc8f7d847e3f4936693b218c265d1",
"score": "0.472435",
"text": "def rule_nonsoundness_from_specialization_nonsoundness(\r\n general: InferenceRule, specialization: InferenceRule, model: Model) \\\r\n -> Model:\r\n assert specialization.is_specialization_of(general)\r\n assert not evaluate_inference(specialization, model)\r\n # Task 4.9\r",
"title": ""
},
{
"docid": "acee3152e400f897b585b9e280bc7e8b",
"score": "0.47158518",
"text": "def _remove_empty_images(self):\n non_empty_images = set()\n for obj in self.obj_annotations.values():\n non_empty_images.add(obj[\"image_id\"])\n self.samples = [sample for sample in self.samples if sample in non_empty_images]",
"title": ""
},
{
"docid": "07fa013c756a9ea29a8cdc21a7a88baf",
"score": "0.47128284",
"text": "def filter_out_vulnerability(self, vuln):\n return vuln",
"title": ""
},
{
"docid": "335e6289d2ab513e8835877618400e82",
"score": "0.47122744",
"text": "def __filter_lfhu__(self):\n self.__filter_lfh__()\n self.__filter_unknown__()",
"title": ""
},
{
"docid": "5c10c11c2a4e2c104d5b66c885bd3fc9",
"score": "0.47089565",
"text": "def ignore(self, other):\n if isinstance(other, basestring):\n other = Suppress(other)\n\n if isinstance(other, Suppress):\n if other not in self.ignoreExprs:\n self.ignoreExprs.append(other)\n else:\n self.ignoreExprs.append(Suppress(other.copy()))\n return self",
"title": ""
},
{
"docid": "a398686b3283ba34dcb26a99ea1fa361",
"score": "0.47047096",
"text": "def remove_grains_not_in_map(self):\n _,not_in_map,_ = self.compute_grains_map_table_intersection()\n self.remove_grains_from_table(not_in_map)\n return",
"title": ""
},
{
"docid": "7a8242277795fe00f0242837bfd7c956",
"score": "0.47033793",
"text": "def is_area_ignored(data):\r\n x, y, screen = data[\"x\"], data[\"y\"], data[\"display\"]\r\n if screen in self.rois and not any(in_area((x, y), a) for a in self.rois[screen]) \\\r\n or screen in self.rods and any(in_area((x, y), a) for a in self.rods[screen]):\r\n return True\r\n return False",
"title": ""
},
{
"docid": "eafe966ad1acdca539fff5de5905236b",
"score": "0.47027546",
"text": "def test_miss_shot_type():\n with session_scope() as session:\n all_shot_types = session.query(distinct(Miss.shot_type)).all()\n all_shot_types = [shot_type for (shot_type,) in all_shot_types]\n for shot_type in all_shot_types:\n # null is an acceptable shot type, too\n if shot_type is None:\n continue\n assert shot_type in VALID_SHOT_TYPES",
"title": ""
},
{
"docid": "f0fe44b23ad4a353c3ad0c4206567150",
"score": "0.47023416",
"text": "def _collect_db_metrics(self, whitelist):\n database_type = self._cfg['databaseConfiguration']['databaseType']\n if database_type == 'influxdb':\n pass\n elif database_type == 'vertica':\n self._add_vertica_metrics(whitelist)\n else:\n log.warn('Failed finding database type in %s', self.CONFIG_FILE)",
"title": ""
},
{
"docid": "b0725a63addc8c08d6ed9712e26ecd3c",
"score": "0.469623",
"text": "def testNotUgly(self):\n for nonugly in self.nonUglys:\n result = UglyNumbers.is_ugly(nonugly)\n self.assertFalse(result)",
"title": ""
},
{
"docid": "26228e9a289f2f9e0e01f2c2e7e232d7",
"score": "0.46953133",
"text": "def test_subfilter_invalid_ignore(db_session):\n query_builder = ModelResourceQueryBuilder()\n query = db_session.query(Album)\n subfilters = {\n \"tracks\": SubfilterInfo(\n filters={\"track_id\": {\"$bad\": 5}}\n )\n }\n query = query_builder.apply_subquery_loads(\n query=query,\n resource=AlbumResource(\n session=db_session),\n subfilters=subfilters,\n embeds=[],\n dialect_override=False,\n strict=False\n )\n result = query.all()\n assert len(result) > 0",
"title": ""
},
{
"docid": "f03f7712dfe296d204b1f5e98db9abff",
"score": "0.4692336",
"text": "def removeWeight(self, weight):\n if weight in self.weights:\n self.weights.remove(weight)\n for syst in self.systDict.values():\n if syst.type == \"weight\":\n if weight in syst.high:\n syst.high.remove(weight)\n if weight in syst.low:\n syst.low.remove(weight)\n return",
"title": ""
},
{
"docid": "5350acd980e546f17a3d85ddea805222",
"score": "0.46903497",
"text": "def test_no_blacklist(self, data):\n # pylint: disable=no-value-for-parameter\n data.draw(data.draw(dbus_signature_strategy()))",
"title": ""
},
{
"docid": "544966776092db7b3f1fc11143b1efb0",
"score": "0.46798372",
"text": "def summary_only():",
"title": ""
},
{
"docid": "2dbb7080650fbc4337940cc8bbe36130",
"score": "0.46749455",
"text": "def noncreatures(self):\n if self.api_type == 'scryfall':\n return self.where(type_line='creature', invert=True)\n else:\n return self.where(type=['creature'], invert=True)",
"title": ""
}
]
bd8e878260d0ab711a53aff554de6151
Selects a piece when it is clicked.
[
{
"docid": "2075c1dd5a97e285e31d5632515370a7",
"score": "0.61129594",
"text": "def handleMouseRelease(self, event):\n \n # if the piece is already selected when it is clicked, it deselects it\n # and changes its border color back\n if self._selected:\n self.changeColorBack()\n self._board.deselect()\n self._selected = False\n return\n \n # returns if there is already a piece selected\n if self._board.isSelected():\n return\n \n # if the piece is active, it activates it and tells the board that there\n # is a piece selected\n if self._active:\n self._board.select()\n self._selected = True\n self._back.setBorderColor('black')\n self._board.report(self)\n else:\n return",
"title": ""
}
]
[
{
"docid": "219190222d15d19ce3881bf63f04aa43",
"score": "0.7438641",
"text": "def click(self, event) -> None:\n colsize = rowsize = 64\n\n # The canvas is 512 by 512.\n # But our board is 8 by 8\n # This converts our clicked position to what it is on the board.\n # (int floors the result of the division (quotient, I know))\n current_row = int(event.x / colsize)\n current_col = int(event.y / rowsize)\n\n print(\"You clicked {}, {}\".format(current_col, current_row))\n \n clicked_position = [current_col, current_row]\n piece = self.selected_piece\n piece_position = self.selected_piece.pos\n \n # If we already have a piece selected.\n if piece is not self.board.nothing:\n # move the piece\n self.move(piece_position, clicked_position)\n # deselect\n self.selected_piece = self.board.nothing\n # highlight nothing\n self.highlighted = []\n # refresh the board\n self.refresh()\n # draw the pieces (as they may have moved)\n self.draw_pieces()\n # If we have no selected piece.\n else:\n # select the piece and highlight the appropriate squares\n self.highlight(clicked_position)\n # refresh the board\n self.refresh()\n # no need to redraw pieces as they can't have moved.",
"title": ""
},
{
"docid": "02b6df11cd6cd06eae889fa03523d07b",
"score": "0.7349623",
"text": "def select_piece(self):\n color = self.board.turn_color\n for x, rows in enumerate(self.buttons):\n for y, button in enumerate(rows):\n piece = self.board.find_piece(Position(x, y))\n if piece.color == color and \\\n piece.moves != [] and \\\n piece.moves != None:\n func = partial(self.show_moves, piece)\n button.configure(\n command=func\n )",
"title": ""
},
{
"docid": "da6bad7f46a2feeb8e675c41104b0df8",
"score": "0.71844727",
"text": "def Select(self, row, col):\n piece = self.board.GetPiece(row, col)\n \n # If the selected piece is clicked again, it is deselected.\n if self.selectedPiece and self.selectedPiece == piece:\n self.selectedPiece.ToggleSelect()\n self.selectedPiece = None\n # Reset the valid moves because nothing is selected.\n self.validMoves = {}\n else:\n # If nothing is selected, then try to select the clicked piece.\n if not self.selectedPiece:\n # Can not select a piece at an empty location or the opposite player's\n # piece.\n if not piece or piece.color != self.turn:\n return\n\n # Selecting the clicked piece and calculating its valid moves.\n self.selectedPiece = piece\n self.selectedPiece.ToggleSelect()\n self.validMoves = self.board.ValidMoves(self.selectedPiece)\n # A piece is already selected. Try to move the selected piece to the location\n # which is currently clicked.\n else:\n # Can only move to an empty location and that too when it is one of the\n # valid moves.\n if not piece:\n if (row, col) in self.validMoves:\n self._move(row, col)\n self.selectedPiece.ToggleSelect()\n self.ChangePlayer() \n\n # If the same player's piece is selected again, then deselect the previous\n # piece and select this one.\n elif piece.color == self.turn:\n self.selectedPiece.ToggleSelect()\n self.selectedPiece = piece\n self.selectedPiece.ToggleSelect()\n self.validMoves = self.board.ValidMoves(self.selectedPiece)",
"title": ""
},
{
"docid": "f0805d8eb3237d8b0c18eab3741ea17b",
"score": "0.6904481",
"text": "def clicked_selector(self,event):\n for plant in self.selector.plants:\n if plant.rect.collidepoint(event.pos) and plant.ready:\n if self.energy >= plant.cost:\n self.selector.select_plant(plant)\n self.plant_cursor = plant.image.copy()\n return True\n self.selector.selected = None",
"title": ""
},
{
"docid": "ff4d275c1abc6d19c41eefb7d9204911",
"score": "0.68031174",
"text": "def select(group, x, y):\n for tower in group.get(): # get one tower each loop\n if tower.is_clicked(x, y): # detect if tower.is_clicked\n tower.get_selected(True)\n else:\n tower.get_selected(False)",
"title": ""
},
{
"docid": "823c727c2cece0bb51e9257b9cf60e8c",
"score": "0.6801079",
"text": "def selected_piece(self, piece):\n\n\t\treturn Checkerpiece(piece.color, piece.x, piece.y)",
"title": ""
},
{
"docid": "fa3d98875ea62c72c8c49f0f36b8fe79",
"score": "0.67442137",
"text": "def select_element(self):\n logging.debug(\"element selected\")\n if len(self._contents) > 0:\n self.to_background()\n self._contents[self.pointer][1]()\n self.to_foreground()\n if self.path_chosen:\n self.deactivate()\n else:\n self.to_foreground()",
"title": ""
},
{
"docid": "f8abe2256810d4235e1bb73e694ba651",
"score": "0.661268",
"text": "def _start_selecting(self, event):\n self._selecting = True\n canvas = self._canvas\n x = canvas.canvasx(event.x)\n y = canvas.canvasy(event.y)\n self._sstart = (x, y)\n if not self._sobject:\n self._sobject = canvas.create_rectangle(\n self._sstart[0], self._sstart[1], x, y,\n dash=(3,5), outline='#0000ff'\n )\n canvas.itemconfigure(self._sobject, state=tk.NORMAL)",
"title": ""
},
{
"docid": "dc51ab438da67792625f8871cc4865ec",
"score": "0.66075176",
"text": "def select_point(self, x):",
"title": ""
},
{
"docid": "4a88502654d2c88f19cefb6f28dc94d5",
"score": "0.65724677",
"text": "def select_tile(self, event):\n height = len(self.tiles) // 3\n # Obtain coordinates of the selected tile\n tile_x = int(event.x / 64)\n tile_y = int(self.canvas.yview()[0] * height + event.y / 64)\n\n # Ensure selection is within bounds\n if tile_x < 0 or tile_y < 0:\n return\n elif tile_x > 3 or tile_y > height:\n return\n\n # Ensure selection is a valid tile\n if tile_y * 3 + tile_x >= len(self.tiles):\n return\n\n # Obtain the selected tile\n self.selected_index.set(tile_y * 3 + tile_x)\n\n # Redraw the selection box\n self.canvas.delete(\"selection_box\")\n self.canvas.create_rectangle((tile_x * 64, tile_y * 64, tile_x * 64 + 64, tile_y * 64 + 64),\n fill=\"blue\",\n outline=\"blue\",\n width=2,\n stipple=\"gray50\",\n tag=\"selection_box\")",
"title": ""
},
{
"docid": "411ec01b0f78d4d2815f6740ddb0d563",
"score": "0.6495575",
"text": "def on_mouse_press(self, x, y, button, key_modifiers):\r\n if self.display_state == GAME_STARTED:\r\n\r\n pieces = arcade.get_sprites_at_point((x,y), self.board.pieces)\r\n if len(pieces) > 0 and pieces[0].type == self.turn:\r\n #draws lines\r\n self.board.get_valid_moves(pieces[0])\r\n self.state = SELECTED\r\n return\r\n \r\n if self.state == SELECTED:\r\n #piece selected by user\r\n tiles = arcade.get_sprites_at_point((x,y), self.tile_list)\r\n if len(tiles) > 0:\r\n #returns a boolean\r\n init_x = self.board.selected_piece.pos_x\r\n init_y = self.board.selected_piece.pos_y\r\n valid_move = self.board.move_piece(init_x, init_y, tiles[0].pos_x, tiles[0].pos_y, self.turn)\r\n if valid_move:\r\n self.state = not SELECTED\r\n self.winner = self.board.check_end_state(self.turn)\r\n self.turn *= -1\r\n\r\n #disable controls\r\n if self.winner != NONE:\r\n self.turn = NONE\r\n elif self.game_type == HUMAN_V_AI and self.turn == WHITE:\r\n self.handle_ai_move(init_x, init_y, tiles[0].pos_x, tiles[0].pos_y, self.turn)\r\n else:\r\n self.error_message = \"INVALID MOVE\"\r\n \r\n return\r\n elif self.display_state == SELECT_SIZE:\r\n click = arcade.get_sprites_at_point((x,y), self.select_size)\r\n if len(click) > 0:\r\n if click[0].center_y == SCREEN_HEIGHT * 0.75:\r\n self.start_game(6)\r\n else:\r\n self.start_game(8)\r\n elif self.display_state == SELECT_TYPE:\r\n click = arcade.get_sprites_at_point((x,y), self.select_size)\r\n if len(click) > 0:\r\n if click[0].center_y == SCREEN_HEIGHT * 0.75:\r\n self.game_type = HUMAN_V_HUMAN\r\n else:\r\n self.game_type = HUMAN_V_AI\r\n self.display_state = SELECT_SIZE",
"title": ""
},
{
"docid": "b1d96aa54fdc77aca5d4fff61ea5f142",
"score": "0.6465317",
"text": "def _click(self, event=None):\n parent_name = event.widget.winfo_parent().split('.')\n\n self.sel_ind = None\n self.selected_row = None\n\n counts = 0\n _children = self.list_canvas.winfo_children()\n if self.selected_w is not None:\n try:\n self.selected_w.configure(relief='', borderwidth=0)\n _shade(self.selected_w.winfo_children())\n self.selected_w = None\n except Exception:\n pass\n\n for win_ in _children:\n w_name = win_.winfo_name()\n if parent_name[len(parent_name)-1] == w_name:\n self._select(win_)\n self.sel_ind = counts\n\n else:\n if counts == len(_children):\n self.selected_w = None\n counts = counts + 1",
"title": ""
},
{
"docid": "b9fbdbf5a8befc725f8ec1581792defd",
"score": "0.64467895",
"text": "def TargetFromSelection(self):",
"title": ""
},
{
"docid": "d9cc220de1c25c9253ad81f4fc44e890",
"score": "0.644065",
"text": "def get_piece_click(self, event):\n if self.DEBUG_PRINT_FUNCTIONS:\n pass;\n print \"got_piece_click\"\n if self.piece != None:\n self.c.itemconfig(self.piece_square, outline=\"green\", width=1)\n try:\n self.piece_square, self.piece = self.c.find_overlapping(event.x, event.y, event.x, event.y)\n print \"self.piece_square, self.piece: \", self.c.find_overlapping(event.x, event.y, event.x, event.y)\n except ValueError:\n return\n self.got_piece = 1\n\n if self.check_piece(): # positive numbers are failure, for check_piece\n self.piece_square = None\n self.piece = None\n self.got_piece = 0\n else:\n self.c.itemconfig(self.piece_square, outline=\"blue\", width=3)",
"title": ""
},
{
"docid": "5f141e99244d7a0463fbb76054c5da08",
"score": "0.6436179",
"text": "def select_tile(self, event):\n # Obtain coordinates of the selected tile\n tile_x = int(event.x / 64)\n tile_y = int(self.selection_canvas.yview()[0] * self.selection_height + event.y / 64)\n\n # Ensure selection is within bounds\n if tile_x < 0 or tile_y < 0:\n return\n elif tile_x > 16 or tile_y > self.selection_height:\n return\n\n # Ensure selection is a valid tile\n if tile_y * 16 + tile_x >= len(self.selection_translator):\n self.selected_tile.set(-1)\n else:\n # Obtain the selected tile\n self.selected_tile.set(self.selection_translator[tile_y * 16 + tile_x])\n\n # Redraw the selection box\n self.selection_canvas.delete(\"selection_box\")\n self.selection_canvas.create_rectangle((tile_x * 64, tile_y * 64, tile_x * 64 + 64, tile_y * 64 + 64),\n fill=\"orange\",\n outline=\"orange\",\n width=2,\n stipple=\"gray50\",\n tag=\"selection_box\")\n\n # Update the currently open group pane\n if self.selected_group.get() != \"Select Group\":\n self.group_panes[self.selected_group.get()].set_tile(self.selected_tile.get())",
"title": ""
},
{
"docid": "92a77133ddb1050993c187dd30e7f27c",
"score": "0.64117575",
"text": "def selectCell(self, event):\n x, y = event.x, event.y\n rows, cols = self.addPieceMenu.rows, self.addPieceMenu.cols\n mw, sc = self.addPieceMenu.marginWidth, self.addPieceMenu.cellSize\n if mw < x < mw + cols * sc and mw < y < mw + rows * sc:\n j = int(x - self.addPieceMenu.marginWidth) / \\\n self.addPieceMenu.cellSize\n i = int(y - self.addPieceMenu.marginWidth) / \\\n self.addPieceMenu.cellSize\n self.addPieceMenu.itemconfigure(self.addPieceMenu.cells[i][j], \\\n fill = self.addPieceMenu.emptyColor if \\\n self.addPieceMenu.shape[i][j] else self.addPieceMenu.color)\n self.addPieceMenu.shape[i][j] = not self.addPieceMenu.shape[i][j]",
"title": ""
},
{
"docid": "376aa8f0a645738b7753a01c9667b0ee",
"score": "0.6405179",
"text": "def clickOn(self, selectElt):\n selectElt.super().click()",
"title": ""
},
{
"docid": "2a1063e8980a117c7af1cf338314bc72",
"score": "0.63924015",
"text": "def selectVertex(self, selectable):\n if selectable == self.primarySelected: # Clears all vertices if original is reclicked\n self.unselectPrimary()\n\n elif self.primarySelected is not None: # Checks if original already clicked\n\n if self.secondarySelected is None: # What to do if there is no secondarySelected\n self.selectSecondary(selectable)\n\n elif selectable == self.secondarySelected: # If already primarySelected, unselect\n self.unselectSecondary()\n\n else: # If not primarySelected, unselect\n self.unselectSecondary()\n self.selectSecondary(selectable)\n\n else:\n self.primarySelected = selectable\n selectable.setColor(selectable.selectedColor)\n\n pygame.display.update()",
"title": ""
},
{
"docid": "70eaf9095010df57b83ff7fb05153e2d",
"score": "0.6370214",
"text": "def selected(self, sel):\r\n pass",
"title": ""
},
{
"docid": "5659d54d14953c1e374a68702a4f695f",
"score": "0.6364428",
"text": "def report(self, piece):\n self._selectedpiece = piece",
"title": ""
},
{
"docid": "a6c9fba08d3cc37b2eef2d52cd0a120c",
"score": "0.63300836",
"text": "def clicked(self) -> None:\r\n self.selected = True\r\n self.color1 = COLOURS['yellow']\r\n for neighbour in self.neighbours:\r\n neighbour.selected = False\r\n neighbour.color1 = THECOLORS['white']",
"title": ""
},
{
"docid": "3052f210deda2fef3e81cc92de42b645",
"score": "0.6323697",
"text": "def get_user_click(self):\n x, y = pygame.mouse.get_pos()\n # Determine if click is:\n # On bottom menu\n if y > 600:\n pass\n # On right side menu\n elif x > 600:\n pass\n # If on board:\n else:\n # Convert coordinates into space\n selected_space = self.convert_coordinates_to_space(x, y)\n # If piece is not already selected:\n if not self.curr_selected_piece:\n\n # Validate and set curr_selected_piece to this piece\n if self.is_piece_of_curr_player(selected_space):\n self.new_piece_selected(selected_space)\n\n # Else if piece already selected:\n else:\n # Determine if selected space is in possible moves\n\n # If space is current selected space\n if selected_space == self.curr_selected_piece.position:\n self.deselect_piece()\n\n # Else if space in possible moves:\n elif selected_space in self.curr_poss_moves:\n #### Check if piece is a king!!! ###\n # Check if selected space is king and in poss_castle_move\n if self.curr_selected_piece.name == 'King' and selected_space in self.chess_board.get_castle_moves_for_curr_player():\n # Castle that king\n self.add_move(self.curr_selected_piece.position, selected_space)\n self.chess_board.castle_king(self.curr_selected_piece, selected_space)\n\n else:\n # Move selected piece to this spot\n self.add_move(self.curr_selected_piece.position, selected_space)\n self.move_piece(self.curr_selected_piece, selected_space)\n\n if self.curr_selected_piece.name == 'Pawn' and selected_space[1] == 0 or selected_space[1] == 7:\n self.chess_board.board[selected_space[0]][selected_space[1]] = None\n self.chess_board.board[selected_space[0]][selected_space[1]] = Queen(self.chess_board.curr_player, selected_space)\n\n # Deselect current piece and remove poss moves\n self.deselect_piece()\n # Change current player\n self.change_curr_player()\n\n # Check for checkmate and get new list of all possible moves\n self.all_poss_moves = self.get_all_poss_moves()\n checkmate = True\n for piece_pos in self.all_poss_moves:\n if len(self.all_poss_moves[piece_pos]) != 0:\n checkmate = False\n if checkmate:\n self.draw_window()\n self.message_display('Checkmate!', (400, 300))\n winner = 'White' if self.chess_board.curr_player == 'b' else 'Black'\n self.message_display('%s wins!' % winner, (400, 400))\n pygame.display.update()\n time.sleep(2)\n quit()\n\n # Else if another piece of curr player:\n elif selected_space in [piece.position for piece in self.chess_board.get_curr_player_pieces()]:\n # Make that piece current selected piece\n self.new_piece_selected(selected_space)\n\n # Else (random non-selectable space):\n else:\n # Deselect current move\n self.deselect_piece()",
"title": ""
},
{
"docid": "4cca87d6618a54c69462b0527c93274e",
"score": "0.63132703",
"text": "def click(self):\r\n self.clicked = True\r\n self.setBorder(YELLOW, 3)\r\n self.board.possibleMoves(self.number)",
"title": ""
},
{
"docid": "a797c5d8b16318dc3f1439d9b308ab52",
"score": "0.6306219",
"text": "def __click_cell(self, event):\r\n x, y = event.x, event.y\r\n # The click must be inside the grid\r\n if margin < x < width - margin and margin < y < height - margin:\r\n self.canvas.focus_set()\r\n # Computes line and columns in fuction of the click\r\n row, col = int((y - margin) / side), int((x - margin) / side)\r\n # If it is already clicked, we deselect it\r\n if self.original[row, col] is None:\r\n self.row, self.col = row, col\r\n # Highlights the selected case\r\n self.__selection_click()",
"title": ""
},
{
"docid": "4e5a835b62ed8b37ea90566c7ced8c18",
"score": "0.63021004",
"text": "def select_move(self, piece: Piece, position):\n self.reset_buttons()\n self.board.recalculate(piece, position, self.ask_promotion_type)\n self.board.delete_self_check()\n self.board.turn_counter += 1\n self.board.turn_color = int(not self.board.turn_color)\n self.draw()\n if self.board.check == True:\n self.board.check_mate = \\\n self.board.ischeckmate(self.board.turn_color)\n if self.board.check_mate == True:\n self.test_frame.configure(\n text='Check mate!'\n )\n else:\n self.test_frame.configure(text='Check!')\n else:\n message = Gui.turn_color_dict[self.board.turn_color] + \\\n ', it\\'s your turn'\n self.test_frame.configure(text=message)\n self.select_piece()",
"title": ""
},
{
"docid": "6595462cae7b5c772cc68ce53c5065e0",
"score": "0.63006",
"text": "def MouseClicked(self, item):",
"title": ""
},
{
"docid": "f32ff8057806554b123e594d8b924cd1",
"score": "0.62851626",
"text": "def click_event(self, event):\n self.start_x, self.start_y = self.event_to_coords(event)\n self.selected_image = event.widget.find_withtag('current')[0]",
"title": ""
},
{
"docid": "28400b805c2fbaa82f1a4c090de3b532",
"score": "0.62847763",
"text": "def select(self):\n self.selected = True",
"title": ""
},
{
"docid": "83ecd59a64cba17b236450f0336bdeab",
"score": "0.6266126",
"text": "def OnLeftClick(self, x, y, keys=0, attachment=0):\n shape = self.GetShape()\n print(shape.__class__, shape.GetClassName(), shape.a)\n canvas = shape.GetCanvas()\n dc = wx.ClientDC(canvas)\n canvas.PrepareDC(dc)\n\n if shape.Selected():\n shape.Select(False, dc)\n canvas.Redraw(dc)\n else:\n redraw = False\n shapeList = canvas.GetDiagram().GetShapeList()\n toUnselect = []\n for s in shapeList:\n if s.Selected():\n toUnselect.append(s)\n\n shape.Select(True, dc)\n\n if toUnselect:\n for s in toUnselect:\n s.Select(False, dc)\n canvas.Redraw(dc)",
"title": ""
},
{
"docid": "70ddfce6097904384b3370e51aabcd89",
"score": "0.6263467",
"text": "def mouseClicked(self, e):",
"title": ""
},
{
"docid": "e25af26163835ad4461c055ad3b4f839",
"score": "0.6262412",
"text": "def highlight_selected(self, coordinates):\n\n\t\tself.pieces[coordinates] = Checkerpiece(\"yellow\", *coordinates)",
"title": ""
},
{
"docid": "30253a3ceaee7e3c77624931d8963159",
"score": "0.62505174",
"text": "def selectLine(lineSelected,lineToSelect):",
"title": ""
},
{
"docid": "9b0d355e89bbea5a6947ee5cb480398b",
"score": "0.62486845",
"text": "def selection_clicked(self, event):\n # return if in not in selection mode \n if self._active != 'SELECT': return\n\n # If we're already in the middle of a selection, pressing another\n # button works to \"cancel\" \n if self._ids_selection != []:\n for selection_id in self._ids_selection:\n self.canvas.mpl_disconnect(selection_id)\n self.release(event)\n self.draw()\n self._selectionStart = None\n self._button_pressed = None\n self._ids_selection = []\n return\n\n if event.button == 1:\n self._button_pressed = 1\n elif event.button == 3:\n self._button_pressed = 3\n else:\n self._button_pressed = None\n return\n\n self._selectionStart = []\n \n x, y = event.x, event.y\n for a in self.canvas.figure.get_axes():\n if (x is not None and y is not None and a.in_axes(event)):\n self._selectionStart.append((x, y, a))\n\n if self.centeredSelection:\n id1 = self.canvas.mpl_connect('motion_notify_event', \n self.selection_move_centered)\n else:\n id1 = self.canvas.mpl_connect('motion_notify_event', \n self.selection_move)\n\n self._ids_selection = [id1]\n\n self.press(event)",
"title": ""
},
{
"docid": "2c47939f2974804247ad6fc3319574e4",
"score": "0.62416637",
"text": "def SetSelection(self, selection):",
"title": ""
},
{
"docid": "2c47939f2974804247ad6fc3319574e4",
"score": "0.62416637",
"text": "def SetSelection(self, selection):",
"title": ""
},
{
"docid": "e60d5de21e55aecfb7c8eb2e95b110fb",
"score": "0.6236269",
"text": "def select(self): \n if self.point in range(len(self.playlist)):\n if self.playlist[self.point] in self.selectedSongs: \n try:\n self.selected = self.selectedSongs.index(self.playlist[self.point])\n self.Ui.AudioTrack.topLevelItem(self.selected).setSelected(True)\n except:\n self.selected = None\n if not self.fetch:\n try:\n self.Playlist_Window.Playlist.topLevelItem(self.point).setSelected(True)\n except:\n pass",
"title": ""
},
{
"docid": "f5c8db2f1e0c190762a90cb494846dd0",
"score": "0.62336105",
"text": "def __selection_click(self):\r\n # Cleares the previously selected cell\r\n self.canvas.delete(\"cursor\")\r\n if self.row >= 0 and self.col >= 0:\r\n x0 = margin + self.col * side + 1\r\n y0 = margin + self.row * side + 1\r\n x1 = margin + (self.col + 1) * side - 1\r\n y1 = margin + (self.row + 1) * side - 1\r\n # Creates the rectangle\r\n self.canvas.create_rectangle(x0, y0, x1, y1, outline=\"#00bfff\", tags=\"cursor\")",
"title": ""
},
{
"docid": "675d9152fa97870cab797137812ede8f",
"score": "0.6225665",
"text": "def get_piece_click(self, event):\n if self.DEBUG_PRINT_FUNCTIONS:\n pass; print \"got_piece_click\"\n if self.piece != None:\n self.c.itemconfig(self.piece_square, outline=\"black\", width=1)\n try:\n self.piece_square, self.piece=self.c.find_overlapping(event.x, event.y, event.x, event.y)\n except ValueError:\n return 0\n self.got_piece=1\n \n if self.check_piece(): #positive numbers are failure, for check_piece\n self.piece_square=None\n self.piece=None\n self.got_piece=0\n else:\n self.c.itemconfig(self.piece_square, outline=\"blue\", width=3)",
"title": ""
},
{
"docid": "2775250d736502e8e784d4f739f38f6d",
"score": "0.62237567",
"text": "def play(self):\n\n\t\t# Variables for the piece selected and the turn counter.\n\n\t\tself.selected = None\n\t\tself.turn = 0\n\n\t\t# Infinite loop.\n\n\t\twhile True:\n\n\t\t\t# Calculate whose turn it is.\n\n\t\t\tif self.turn % 2 == 0:\n\t\t\t\tcolor = \"white\"\n\t\t\telse:\n\t\t\t\tcolor = \"red\"\n\n\t\t\t# Handle events.\n\n\t\t\tfor event in pygame.event.get():\n\n\t\t\t\t# If the user is pressing ESCAPE, exit the game.\n\n\t\t\t\tif event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE:\n\t\t\t\t\tsys.exit(0)\n\n\t\t\t\t# If the user is clicking their mouse... (this is where you start coding!)\n\n\t\t\t\telif event.type == pygame.MOUSEBUTTONDOWN:\n\n\t\t\t\t\t########## Your code starts here. ##########\n\t\t\t\t\t# Remember to consult the manual for help! #\n\t\t\t\t\t############################################\n\n\t\t\t\t\t# We need to find out which tile the user clicked... we need to get the coordinates.\n\n\t\t\t\t\tcoordinates = self.get_coordinates(event)\n\n\t\t\t\t\t# If the player selected their own non-highlighted piece...\n\t\t\t\t\t# (Remember to give valid inputs, see the manual!)\n\n\t\t\t\t\tif self.player_selected_their_own_piece(color, coordinates):\n\n\t\t\t\t\t\t# The current piece is the piece at the coordinate.\n\n\t\t\t\t\t\tpiece = self.pieces[coordinates]\n\n\t\t\t\t\t\t# Set self.selected to a copy of the selected piece.\n\n\t\t\t\t\t\tself.selected = self.selected_piece(piece)\n\n\t\t\t\t\t\t# Highlight the selected piece.\n\t\t\t\t\t\t# (Remember to give valid inputs, see the manual!)\n\n\t\t\t\t\t\tself.highlight_selected(coordinates)\n\n\t\t\t\t\t# If the player selected their own highted piece...\n\t\t\t\t\t# (Remember to give valid inputs, see the manual!)\n\n\t\t\t\t\telif self.player_selected_their_own_highlighted_piece(color, coordinates):\n\n\t\t\t\t\t\t# Unhighlight the piece.\n\n\t\t\t\t\t\tself.unhighlight_selected(coordinates)\n\n\t\t\t\t\t# Otherwise if a piece is highlighted and the player selects another tile...\n\n\t\t\t\t\telif self.piece_highlighted_and_player_selects_another_tile(color, coordinates):\n\n\t\t\t\t\t\t# If a piece move is successful...\n\n\t\t\t\t\t\tif self.move_highlighted_piece_to_was_successful(coordinates) == True:\n\n\t\t\t\t\t\t\t# Increment turns by 1.\n\n\t\t\t\t\t\t\tself.turn += 1\n\n\t\t\t\t\t################ Your code ends here. ######################\n\t\t\t\t\t# Good job! You're all done. You should run your code now. #\n\t\t\t\t\t############################################################\n\n\t\t\t# Show the display.\n\n\t\t\tself.show_display()",
"title": ""
},
{
"docid": "41685a58706ec94da62059cf1102a821",
"score": "0.6205957",
"text": "def getClick(event, x, y, _, __):\n global _selectedPoints\n global _currentImage\n\n if event == cv2.EVENT_LBUTTONDOWN:\n _selectedPoints.append((float(x), float(y)))\n cv2.circle(_currentImage, (x, y), 5, (0, 255, 0))\n cv2.imshow(_getPointsWindowName, _currentImage)",
"title": ""
},
{
"docid": "a1a91afc8eb93a3c70cc61ff60385b22",
"score": "0.61956036",
"text": "def select(self, color):\n\n self.selected = color\n\n if color: self.pan_to_color(color)\n\n self.queue_draw()\n self.emit('select-color', color)",
"title": ""
},
{
"docid": "976eea6e39c5bb9a3a8631cbc1aa4d91",
"score": "0.61948246",
"text": "def item_clicked(self, index):\n self.item_index = index\n self.select_item()",
"title": ""
},
{
"docid": "d5d2e0231ed477658de819f859aa2dc8",
"score": "0.61879176",
"text": "def select( self ):\n assert self.selected is False\n self.selected = True\n\n # call selection hook\n self.handle_select()",
"title": ""
},
{
"docid": "8ec0514812969a276b2bb7afe78b5542",
"score": "0.6182614",
"text": "def select(self):\n self._selected = True",
"title": ""
},
{
"docid": "1e0955e81085f9ffa92b41eba2db587e",
"score": "0.617391",
"text": "def select_item(self, keys):\n self.pos_list = self.make_select_item_pos_list()\n\n pos = self.pos_list[self.index]\n self.rect.x = pos[0] - 60\n self.rect.y = pos[1] + 20\n\n self.check_input(keys)",
"title": ""
},
{
"docid": "2b739e6ca246d798ea1a2ec3c02e4172",
"score": "0.6171045",
"text": "def test_selection_select(cvi):\n cvi.canvas.add(cvi.item)\n selection = Selection(cvi.item, cvi.view)\n assert cvi.item not in cvi.view.selected_items\n selection.select()\n assert cvi.item in cvi.view.selected_items\n assert cvi.item is cvi.view.focused_item\n selection.unselect()\n assert cvi.item not in cvi.view.selected_items\n assert None is cvi.view.focused_item",
"title": ""
},
{
"docid": "e556f1cf2b5b97569e2647e5cb2e2082",
"score": "0.6160461",
"text": "def highlight_piece(self, row, col, piece):\r\n square_id = self.get_square_id(row, col)\r\n self.highlight_square(row, col, colour=\"#646F40\")\r\n self.selected_piece = piece\r\n self.highlighted.append((row, col))\r\n for row_, col_ in self.get_possible_squares(row, col):\r\n piece, colour = self.square_contains_piece(row_, col_)\r\n # Move Square\r\n if colour is None:\r\n self.highlight_move_square(row_, col_, colour=\"#646F40\")\r\n # Piece Square\r\n else:\r\n self.highlight_piece_square(row_, col_, colour=\"#646F40\")\r\n self.highlighted.append((row_, col_))",
"title": ""
},
{
"docid": "337da9cab0e14bb173fef685749bcaa2",
"score": "0.61459255",
"text": "def select_p(self, p):\n c = self.c\n w = self.main_window\n ap = self.p_to_ap(p)\n # Be careful during startup.\n if not (w and w.tree):\n return\n if 'select' in g.app.debug:\n tag = 'py.app.select_p'\n print('%30s: %4s %s %s' % (tag, len(p.b), p.gnx, p.h))\n w.tree.set_ap(ap)\n # #1142: This code was not the problem.\n body = c.frame.body.wrapper\n w.body.set_text(body.s)\n w.body.set_insert_point(body.ins, body.sel)",
"title": ""
},
{
"docid": "2950e984766af620208b7c665d193efe",
"score": "0.61266476",
"text": "def select_action(self, event) :\n widget = event.widget\n selection = widget.curselection()\n if selection :\n index = int(selection[0])\n values = dict(zip(('index', 'offset', 'title', 'size'), widget.get(index)))\n self.notify('detail', **values)",
"title": ""
},
{
"docid": "7e498f91c843470ceb16fef451fadec0",
"score": "0.6112048",
"text": "def OnClickRelease( self, event ):\n node = HotMapNavigator.findNodeAtPosition(self.hot_map, event.GetPosition())\n self.SetSelected( node, event.GetPosition() )",
"title": ""
},
{
"docid": "d219ac84558718ae2379d01291a23df1",
"score": "0.6111472",
"text": "def select_action(self, event) :\n widget = event.widget\n selection = widget.selection()\n if selection :\n item = widget.item(selection[0])\n self.notify('detail', values=item['values'])",
"title": ""
},
{
"docid": "ecfc8894987d0385d325705f0185cd68",
"score": "0.60980904",
"text": "def mouseSelect(self,event=None,deselect=0):\n #print 'In select'\n self.x1=self.canvas.canvasx(event.x)\n self.y1=self.canvas.canvasy(event.y)\n self.clearSelBox()\n items = self.canvas.find_overlapping(self.x0,self.y0,self.x1,self.y1)\n #print items\n self.select(items,deselect)",
"title": ""
},
{
"docid": "85721ca662e22f59b72213ddf5d1baa9",
"score": "0.6096528",
"text": "def check_click(self, x_value, y_value):\r\n for num in range(self.connection.my_player.low_range,\r\n self.connection.my_player.low_range + 4):\r\n # e.g for \"red\" - range(0, 4), for \"green\" - range(4, 8)\r\n piece = self.connection.my_player. \\\r\n my_pieces[num - self.connection.my_player.low_range]\r\n # Gets index 0-3 to use my_pieces.\r\n pos = piece.get_position()\r\n if piece.movable:\r\n if piece.image.get_width() == 64:\r\n # If you clicked a piece, move them (if you rolled)\r\n if pos is not None and piece.image.get_rect(\r\n topleft=(coOrds[pos][0] - 7,\r\n coOrds[pos][1] - 25)).collidepoint(x_value, y_value):\r\n self.click_piece(num)\r\n break\r\n # If you clicked a piece in home and you rolled 6, move\r\n # them out.\r\n elif piece.image.get_rect(\r\n topleft=(self.board.home_coords[num])). \\\r\n collidepoint(x_value, y_value) and self.connection.my_player \\\r\n .roll == 6:\r\n self.click_piece(num)\r\n break\r\n else:\r\n # If you clicked a piece, move them (if you rolled)\r\n if piece.image.get_rect(topleft=(\r\n coOrds[pos][0], coOrds[pos][1])).collidepoint(x_value, y_value):\r\n self.click_piece(num)\r\n break",
"title": ""
},
{
"docid": "08050758b16df8940dc2881626fd93cd",
"score": "0.60928094",
"text": "def click_piece(self, num):\r\n self.board.move_piece(num, self.connection.my_player.roll)\r\n self.connection.send_movement(num, self.connection.my_player.roll)\r\n self.connection.end_roll()",
"title": ""
},
{
"docid": "299d50c416a19d3b1be975f0cbf3ee82",
"score": "0.6089458",
"text": "def on_mouse_press(self, x, y, button, modifiers):\n #Only left clicks are valid\n if (button == arcade.MOUSE_BUTTON_RIGHT):\n return\n #Get the square coordinates\n self.square_selected = squareClicked(x , y)\n #If square_coordinates are not valid end this function\n if (self.square_selected == None):\n #Diselect the grid square and dehighlight the squares \n update_grid_textures(self.square_selected, self.my_grid_sprites, self.my_textures) \n self.square_selected_possible_vales = None\n return\n\n #Check if the square is not empty \n y , x = self.square_selected\n if (self.my_grid[y][x] != 0):\n #Do not save the selected square\n self.square_selected = None\n #Diselect the grid square and dehighlight the squares \n update_grid_textures(self.square_selected, self.my_grid_sprites, self.my_textures) \n self.square_selected_possible_vales = None\n return \n\n #Highlight the selected square and its corresponding row and column \n update_grid_textures(self.square_selected, self.my_grid_sprites, self.my_textures)\n\n #Safe the possible values \n self.square_selected_possible_vales = getPossibleValesInSquare(self.square_selected, self.my_grid)",
"title": ""
},
{
"docid": "3001ffff822188378086ecdaccd4503f",
"score": "0.607852",
"text": "def SelectItem(self, item, select=True):",
"title": ""
},
{
"docid": "17a5ac30599ea9a8c97cc3b0e420e819",
"score": "0.60760474",
"text": "def click_button_select(self):\n self.click_element(self.BUTTON_SELECT)",
"title": ""
},
{
"docid": "ec2ab88c6d7855e21093b2762de2660b",
"score": "0.6067318",
"text": "def _keep_selecting(self, event):\n canvas = self._canvas\n x = canvas.canvasx(event.x)\n y = canvas.canvasy(event.y)\n canvas.coords(self._sobject,\n self._sstart[0], self._sstart[1], x, y)",
"title": ""
},
{
"docid": "7e41940cc7af327efe5851f513be267a",
"score": "0.6065461",
"text": "def select(self, position, selected):\n self.items[position]['selected'] = selected",
"title": ""
},
{
"docid": "88da34d8735771bd7a568e9ed280fa60",
"score": "0.6055804",
"text": "def mouse_click(self, event):\n x, y = event.x, event.y\n if PADDING < x < FRAME_WIDTH - PADDING and PADDING < y < FRAME_HEIGHT - PADDING:\n self.canvas.focus_set()\n row, col = (y - PADDING) / CELL_WIDTH, (x - PADDING) / CELL_WIDTH\n\n if DEBUG:\n print 'cell clicked: (%s, %s)' % (row, col)\n\n if not (0 <= row <= 8 and 0 <= col <= 8):\n return\n\n if (row, col) == (self.row, self.col):\n self.row = self.col = -1\n elif self.sudoku.puzzle[row * 9 + col] == '0':\n self.row, self.col = row, col\n else:\n self.row = self.col = -1\n\n self.canvas.delete('select')\n if self.row >= 0 and self.col >= 0:\n self.canvas.create_rectangle(\n PADDING + self.col * CELL_WIDTH + 1,\n PADDING + self.row * CELL_WIDTH + 1,\n PADDING + (self.col + 1) * CELL_WIDTH - 1,\n PADDING + (self.row + 1) * CELL_WIDTH - 1,\n outline='blue', tags='select'\n )",
"title": ""
},
{
"docid": "b7cb85f8b91af28dd4b8d7bf9072c72c",
"score": "0.60555977",
"text": "def click(self, ev):\n \n if ev.type == 'touchstart':\n if len(ev.targetTouches)>1:\n return\n \n # New mouse / finger position\n self.X0, self.Y0 = self.mouse_pos(ev)\n\n if self.tool == 'pen':\n ctx.lineWidth = self.line_width\n ctx.strokeStyle = self.color\n\n elif self.tool == 'select':\n self.store = ctx.getImageData(0, 0, zone.width, zone.height)\n\n elif self.tool == 'rubber':\n ctx.fillStyle = self.bgcolor\n\n self.drawing = True",
"title": ""
},
{
"docid": "da89e314e7fcf2c527eb1a60c6884c80",
"score": "0.60477",
"text": "def modify_selected(self, view, tile_x, tile_y, limited=False):\n if not self.render_mode:\n self.common_draw(view, tile_x, tile_y, self._modify_selected, limited)\n else:\n self.common_draw(view, tile_x, tile_y, self._modify_render_offset, limited)",
"title": ""
},
{
"docid": "c41c6c33e23be4878f03494fdaff5bc4",
"score": "0.6040477",
"text": "def on_mouse_press(self, x, y, button):\n\n if (x,y) in self:\n x = x // ( 6*self._scale)\n y = y // (13*self._scale)\n s = (int(self._scroll)+self.rows)*self.cols\n\n start = y*self.cols + x\n self._selection = start+s, start+s\n self._program[\"selection\"] = start, start",
"title": ""
},
{
"docid": "d3de4888536fb7d6768ee7daa9c36be5",
"score": "0.603018",
"text": "def selectShapesButtonPressed(self):\n print 'selectShapesButtonPressed'",
"title": ""
},
{
"docid": "ac8f9152636d1700f5d9e05f14f6a6ec",
"score": "0.6029377",
"text": "def action(self,objet,truc,idpt):\n if truc.type == gtk.gdk.BUTTON_PRESS :\n if idpt == (self.actu+1): #Action to execute if the selected point is the following of previous one\n xd,yd,xa,ya=self.POINT[(idpt-1)].x,self.POINT[(idpt-1)].y,self.POINT[idpt].x,self.POINT[idpt].y\n item = self.ROOT.add(gnomecanvas.CanvasLine,\n points=(xd,yd,xa,ya),\n fill_color='black',\n width_units=1.5)\n\n\n if idpt == 2: # Always raise the first point\n self.POINT[self.MAX].raise_to_top()\n self.TEXT[self.MAX].raise_to_top()\n\n self.POINT[idpt].hide()\n self.TEXT[idpt].hide()\n if idpt == self.MAX : #Action to execute if all points have been selected in good way\n gcompris.set_background(self.ROOT, self.data[self.gcomprisBoard.sublevel][0][2])\n self.gamewon = 1\n self.timeout = gobject.timeout_add(1500, self.lauch_bonus) # The level is complete -> Bonus display\n\n else : #Action to execute if the selected point isn't the last one of this level\n #self.POINT[(idpt+1)].set(fill_color='blue') #Set color in blue to next point. Too easy ???\n self.actu=self.actu+1 #self.actu update to set it at actual value of selected point",
"title": ""
},
{
"docid": "0cbed21829eb25eb3c9ae5d1315fc7e7",
"score": "0.6023838",
"text": "def clickEvent(self):\n self.activated.emit(self.text())",
"title": ""
},
{
"docid": "c2ba8e738b43335dd41e11b4c4314030",
"score": "0.60225546",
"text": "def select_tower(self, x, y):\n\t\tbox_size = self.map_height // len(self.map)\n\t\ti, j = x // box_size, y // box_size\n\t\tif self.map[j][i] == 2:\n\t\t\tx, y = i * box_size + box_size // 2, j * box_size + box_size // 2 \n\t\t\ttower = None\n\t\t\tfor t in self.towers:\n\t\t\t\tif t.x == x and t.y == y:\n\t\t\t\t\ttower = t\n\t\t\t\t\tbreak\n\t\t\t# If it's a click on the currently selected tower\n\t\t\tif self.tower_selected == tower:\n\t\t\t\tself.tower_selected = None\n\t\t\telse:\n\t\t\t\tself.tower_selected = tower\n\t\t\t# Un-selecting an enemy when a tower is selected\n\t\t\tself.enemy_selected = None\n\n\t\telse:\n\t\t\tself.tower_selected = None\n\t\t\tself.enemy_selected = None",
"title": ""
},
{
"docid": "4f4253e9d62931e6eef759166537db98",
"score": "0.6020156",
"text": "def on_click(self, kind, name):\n if kind == \"device\":\n self.select_devices([name])\n self.emit(Qt.SIGNAL(\"graphicItemSelected(QString)\"), name)\n elif kind == \"section\":\n self.zoom_to_section(name)",
"title": ""
},
{
"docid": "a757e0481663559db13ebeb3c3aacd18",
"score": "0.6015027",
"text": "def click_board(self, event):\n\n if GameOps.is_checkmate:\n return\n field_idx = self.coord_to_field(event)\n if field_idx is None:\n return\n field_name = self.index2label(field_idx)\n board_idx = self.label2index_game(field_name)\n\n self.layout['field_idx'] = field_idx\n piece = self.board_state[board_idx]\n piece_color = piece.color if piece else 0\n move_count = GameOps.move_count\n\n if self.click_idx == 0: # select piece\n self.first_board_click(piece, field_idx, piece_color, move_count)\n else:\n self.second_board_click(piece, field_idx, piece_color, field_name, move_count)\n return",
"title": ""
},
{
"docid": "8224dc38d7c9aa838f27e71ed6c0463c",
"score": "0.60142624",
"text": "def multsel(self):\n def toggle_selector(event):\n if toggle_selector.RS.active:\n toggle_selector.RS.set_active(False)\n if not toggle_selector.RS.active:\n toggle_selector.RS.set_active(True)\n\n self.state='SMP'\n toggle_selector.RS = RectangleSelector(self.ax, self.point_select_callback,\n drawtype='box', useblit=True,\n button=[1,3], # don't use middle button\n minspanx=5, minspany=5,\n spancoords='pixels')\n self.selector = toggle_selector.RS\n self.update_state()",
"title": ""
},
{
"docid": "1e74d3929ed4a3d719ec7fa1a59bcba4",
"score": "0.6012243",
"text": "def select(node, **_):\n uistate.update_button_state(node)",
"title": ""
},
{
"docid": "815da5944d077ef1004c950f0dfa303a",
"score": "0.60108274",
"text": "def SetSelected( self, node, point=None, propagate=True ):\n if node == self.selectedNode:\n return\n self.selectedNode = node\n self.UpdateDrawing()\n if node:\n wx.PostEvent( self, SquareSelectionEvent( node=node, point=point, map=self ) )",
"title": ""
},
{
"docid": "05667e9e21cdb084a61c502a4e23d3f1",
"score": "0.60057104",
"text": "def _handle_click(self, pipe):\n if self._panel_selection is not None:\n self._panel_selection(pipe)",
"title": ""
},
{
"docid": "cdfb43cd60c80ab3a762c138527460c6",
"score": "0.60041386",
"text": "def click(self, event):\r\n self.clicked.append((event.x, event.y))\r\n if len(self.clicked) > numEvents:\r\n del self.clicked[0]\r\n self.visit()",
"title": ""
},
{
"docid": "d36f5c9e3246778264700809a67589a1",
"score": "0.600275",
"text": "def clickedBy(self, clicker):\n GG.model.room_item.GGRoomItem.clickedBy(self, clicker)\n if GG.utils.checkNeighbour(clicker.getPosition(), self.getPosition()):\n clicker.setSelectedItem(self)",
"title": ""
},
{
"docid": "5ce6607656185e3a40d22f7915e9326d",
"score": "0.60002637",
"text": "def SetMainSelection(self, selection):",
"title": ""
},
{
"docid": "27f82af9e09321222d642471e7ef05e1",
"score": "0.59927964",
"text": "def select_shape(self, point, multiple_selection_mode):\n for shape in reversed(self.shapes):\n if not shape.containsPoint(point):\n continue\n if multiple_selection_mode:\n if shape not in self.selectedShapes:\n self.selectedShapes.append(shape)\n self.selectionChanged.emit()\n else:\n self.selectedShapes = [shape]\n self.selectionChanged.emit()\n return\n # If clicked on nothing, deselect all.\n self.deSelectShape()",
"title": ""
},
{
"docid": "6f01adb3db6ff8138f3587edbed8b403",
"score": "0.59911543",
"text": "def click(self):\n self.press()\n self.release()",
"title": ""
},
{
"docid": "d338296543d6a8b5834348607c95fb44",
"score": "0.59845114",
"text": "def click(self):\n self.patch.send('mouse %i %i 1 0' % (self.x + 1, self.y + 1))\n self.patch.send('mouseup %i %i 1 0' % (self.x + 1, self.y + 1))",
"title": ""
},
{
"docid": "a3beab537b06a4a8f31c34571202d005",
"score": "0.59838724",
"text": "def new_piece_selected(self, new_space):\n self.curr_selected_piece = self.chess_board.get_piece_at(new_space)\n self.curr_poss_moves = self.get_curr_poss_moves()",
"title": ""
},
{
"docid": "d9f770b5f30d870d4474863273d99e0a",
"score": "0.5976082",
"text": "def _start_select(self, event):\n if self.component.active_tool in (None, self):\n self.component.active_tool = self\n else:\n self._enabled = False\n self._screen_start = (event.x, event.y)\n self._screen_end = None\n self.event_state = \"selecting\"\n event.window.set_pointer(self.pointer)\n event.window.set_mouse_owner(self, event.net_transform())\n self.selecting_mouse_move(event)\n return",
"title": ""
},
{
"docid": "b925f50b0f1421bcf86fe8539c9d8467",
"score": "0.59681755",
"text": "def _pressed(self, evt):\n x, y, widget = evt.x, evt.y, evt.widget\n item = widget.identify_row(y)\n column = widget.identify_column(x)\n if not column or item not in self._items:\n return\n item_values = widget.item(item)['values']\n if not len(item_values):\n return\n text = item_values[(int(column[1]) - 1)]\n if not text:\n return\n bbox = widget.bbox(item, column)\n if not bbox:\n return\n text = '%02d' % text\n self._selection = (text, item, column)\n self._show_selection(text, bbox)",
"title": ""
},
{
"docid": "31c6b10c848fcb483d8e0b734bf4b38d",
"score": "0.5957512",
"text": "def point_select_callback(self,eclick, erelease):\n #eclick and erelease are the press and release events'\n self.update_state()\n if not (self.shift_is_held or self.ctrl_is_held):\n self.selectpt=[]\n self.selectseg=[]\n x1, y1 = eclick.xdata, eclick.ydata\n x2, y2 = erelease.xdata, erelease.ydata\n\n # print(x1,x2,y1,y2)\n if x1>x2:\n x1,x2=x2,x1\n if y1>y2:\n y1,y2=y2,y1\n if not (np.allclose(x1,x2) and np.allclose(y1,y2)) :\n try:\n selectpt,selectseg = self.L.get_zone([x1,x2,y1,y2])\n\n if not self.ctrl_is_held:\n self.selectpt.extend(selectpt)\n self.selectseg.extend(selectseg)\n self.selectseg=filter(lambda x: self.L.Gs.node[x]['connect'][0] in self.selectpt\n and self.L.Gs.node[x]['connect'][1] in self.selectpt,\n self.selectseg)\n\n self.selectpt=np.unique(self.selectpt).tolist()\n self.selectseg=np.unique(self.selectseg).tolist()\n else:\n [self.selectpt.pop(self.selectpt.index(x)) for x in selectpt if x in self.selectpt]\n [self.selectseg.pop(self.selectseg.index(x)) for x in selectseg if x in self.selectseg]\n except:\n if len(self.selectpt) == 0:\n self.modeIni()\n self.update_state()\n # print('empty selection')\n\n self.plotselptseg(self.selectpt)\n self.selected='pt'\n if len(self.selectpt) == 1:\n self.nsel=self.selectpt[0]\n self.state='SP1'\n self.update_state()\n else :\n self.modeIni()\n self.update_state()",
"title": ""
},
{
"docid": "f0c25d8401217bb6f86cca077c3217db",
"score": "0.59525204",
"text": "def selectionne(event):\n pass",
"title": ""
},
{
"docid": "5016b6eb898ba24eb7919432282cd4f1",
"score": "0.5935918",
"text": "def on_mouse_clicked(self):\n pass",
"title": ""
},
{
"docid": "1807db0545ce7ef4d5302b18aac460e8",
"score": "0.59345615",
"text": "def left_click_actor(self, act):\n if act.selected:\n self.unselect_actor(act)\n else:\n self.select_actor(act)",
"title": ""
},
{
"docid": "526a607fe8b5fcd985988ad600a3a659",
"score": "0.5930846",
"text": "def onClick(self: object, event: object):\n r = event.x // (self.options[\"piece_size\"] + self.options[\"border\"]) + 1\n c = event.y // (self.options[\"piece_size\"] + self.options[\"border\"]) + 1\n try:\n self.game.move(r, c)\n self.redrawCanvas()\n self.status()\n except:\n pass",
"title": ""
},
{
"docid": "0b49263e5b9f14bc225dcd2794653b4d",
"score": "0.5930005",
"text": "def child_clicked(self, pos):\n raise NotImplementedError",
"title": ""
},
{
"docid": "29b9077a1cc3662f113a85a40b9ad3ea",
"score": "0.59286916",
"text": "def pointSelect(self, event):\n\n if event.selection == self.sel_point:\n # same point(s) selected again, turn point(s) off\n self.pyslip.DeleteLayer(self.sel_point_layer)\n self.sel_point_layer = None\n self.sel_point = None\n elif event.selection:\n # some other point(s) selected, delete previous selection, if any\n if self.sel_point_layer:\n self.pyslip.DeleteLayer(self.sel_point_layer)\n\n # remember selection (need copy as highlight modifies attributes)\n self.sel_point = copy.deepcopy(event.selection)\n\n # choose different highlight colour for different type of selection\n selcolour = '#00ffff'\n if event.type == pySlipQt.PySlipQt.EVT_PYSLIPQT_SELECT: # TODO better visibility (like pySlip)\n selcolour = '#0000ff'\n\n # get selected points into form for display layer\n # delete 'colour' and 'radius' attributes as we want different values\n highlight = []\n for (x, y, d) in event.selection:\n del d['colour'] # AddLayer...() ensures keys exist\n del d['radius']\n highlight.append((x, y, d))\n\n # layer with highlight of selected poijnts\n self.sel_point_layer = \\\n self.pyslip.AddPointLayer(highlight, map_rel=True,\n colour=selcolour,\n radius=5, visible=True,\n show_levels=MRPointShowLevels,\n name='<sel_pt_layer>')\n\n # make sure highlight layer is BELOW selected layer\n self.pyslip.PlaceLayerBelowLayer(self.sel_point_layer,\n self.point_layer)\n # else: we ignore an empty selection\n\n return True",
"title": ""
},
{
"docid": "465d5e9d939d40f72e2602f8329a27f1",
"score": "0.5926622",
"text": "def tile_select(self, event):\n self.click +=1\n if self.click > 5:\n self.click = 0\n #boolean value of true/false compared to KenKen_board_data file\n self.tile_guess.set(self.tile_ident[self.click])\n try:\n #exception handling to take care of \" \" value comparison\n user_guess = eval(self.tile_guess.get())\n tile_val = tile_value(self.tile_number, self.board_num)\n except:\n user_guess = (self.tile_guess.get())\n tile_val = tile_value(self.tile_number, self.board_num)\n if user_guess == tile_val: #self documenting...\n self.right_answer = True\n else:\n self.right_answer = False",
"title": ""
},
{
"docid": "1bed338a836772849f5abe1046ec6815",
"score": "0.5912959",
"text": "def click(self,locator):\r\n self.do_command(\"click\", [locator,])",
"title": ""
},
{
"docid": "1bed338a836772849f5abe1046ec6815",
"score": "0.5912959",
"text": "def click(self,locator):\r\n self.do_command(\"click\", [locator,])",
"title": ""
},
{
"docid": "15247cc35aae7839f8366b2e903825f9",
"score": "0.59109485",
"text": "def _setSelected(self, xBoard, yBoard): \n self._selected = (xBoard, yBoard)",
"title": ""
},
{
"docid": "caab3c822788bb7c9259c69a6ae61bff",
"score": "0.59015024",
"text": "def give_click(self, button, mouse_button):",
"title": ""
},
{
"docid": "63db2e4fef4fd369e097cae7a7054d75",
"score": "0.5893464",
"text": "def piece_on(self, pos):",
"title": ""
},
{
"docid": "6a246d372325f0b41da4ce53f4e97e22",
"score": "0.58927417",
"text": "def MouseClickBegin(self, item):",
"title": ""
},
{
"docid": "2a47a1c457d9c193e3867596b134199f",
"score": "0.58899444",
"text": "def on_click(self, e):\n # Don't react when in move, attack or game over mode.\n if (self.mode == Modes.Moving or\n self.mode == Modes.GameOver):\n return\n \n # make sure we have focus and that it was the left mouse button\n if (e.type == pygame.MOUSEBUTTONUP\n and e.button == 1\n and pygame.mouse.get_focused()):\n \n # If this is in the map, we're dealing with units or tiles\n if self.map.rect.collidepoint(e.pos):\n # Get the tile's position\n to_tile_pos = self.map.tile_coords(e.pos)\n\n # get the unit at the mouseclick\n unit = self.get_unit_at_screen_pos(e.pos)\n \n if unit:\n # clicking the same unit again deselects it and, if\n # necessary, resets select mode\n if unit == self.sel_unit:\n self.change_mode(Modes.Select)\n self.sel_unit = None\n\n # select a new unit\n elif (self.mode == Modes.Select and\n unit.team == self.cur_team):\n self.sel_unit = unit\n SoundManager.play(SELECT_SOUND)\n \n # Attack\n elif (self.mode == Modes.ChooseAttack and\n self.sel_unit and\n to_tile_pos in self._attackable_tiles):\n # Attack the selected tile\n self.sel_unit_attack(to_tile_pos)\n else:\n # No unit there, so a tile was clicked\n if (self.mode == Modes.ChooseMove and\n self.sel_unit and\n to_tile_pos in self._movable_tiles):\n \n # Move to the selected tile\n self.sel_unit_move(to_tile_pos)\n \n # Otherwise, the user is interacting with the GUI panel\n else:\n # Check which button was pressed\n for button in self.buttons:\n # If the button is enabled and has a click function, call\n # the function\n if ((not button.condition or button.condition()) and\n self.get_button_rect(button).collidepoint(e.pos)):\n button.onClick()\n \n # Play the button sound\n SoundManager.play(BUTTON_SOUND)",
"title": ""
},
{
"docid": "a451b014af833025aa2e9925358955aa",
"score": "0.58857626",
"text": "def clicked_at(self, *pos):\n logging.debug('Enter GridWorldGUI.clicked_at')\n #todo: change this if patches become turtles (unlikely)\n patch = self.subject.patch_at(pos)\n self.handle_click1(subject=patch, location=pos)",
"title": ""
},
{
"docid": "2dafe7024714daa1b1aa127a92c3670e",
"score": "0.58836776",
"text": "def toggle_selected(self, event):\n if self._selected.get() == 1:\n self._selected.set(0)\n else:\n self._selected.set(1)\n self.set_image(self._last_roll, self._selected.get())",
"title": ""
},
{
"docid": "c19e1ad1dcc38a2a77a98b3ec8a59d36",
"score": "0.5882185",
"text": "def on_mouse_press(self, x, y, button):\n pass",
"title": ""
}
]
[
{
"docid": "fa75ac79feca99b06e944e975eadf784",
"score": "0.7750035",
"text": "def makelist(List):\n clist=\"\"\n for element in List:\n clist=clist+element+\":\"\n return clist[:-1]",
"title": ""
}
]
[
{
"docid": "11d1cb31209e79b9f40e262fba2d7268",
"score": "0.651048",
"text": "def splitingFunction(splitList):\n newlist = []\n for item in splitList:\n split = item.split(':')[0]\n newlist.append(split)\n return newlist",
"title": ""
},
{
"docid": "8669db494fbd1b91d74366e9904f2d6d",
"score": "0.64027977",
"text": "def _format_list(value: List) -> List:\n return value",
"title": ""
},
{
"docid": "70e0731c9915787f0698509afcaab7cc",
"score": "0.6296165",
"text": "def format_list(list):\n \n print \",\".join(list)",
"title": ""
},
{
"docid": "909b9fce2e56a9f87aa827a480236640",
"score": "0.6231475",
"text": "def __split_list_settings(value, separator):\n\n stripped = (a.strip() for a in value.split(separator))\n return list(a for a in stripped if len(a))",
"title": ""
},
{
"docid": "24570844110a59e2c6734df8ff7411a5",
"score": "0.6216427",
"text": "def list_str(l):\r\n return \"[\" + \", \".join([p.__str__() for p in l]) + \"]\"",
"title": ""
},
{
"docid": "00b160b8af2c8d5448bc98bc3996e378",
"score": "0.6210667",
"text": "def make_list(value, sep=','):\n return value.split(sep) if isinstance(value, str) else value",
"title": ""
},
{
"docid": "f9d0116c26cea4971b36c9b47b95419d",
"score": "0.6199183",
"text": "def format_list(seq):\n if hasattr(seq, 'tolist'):\n seq = seq.tolist()\n elif hasattr(seq, '__len__'):\n seq = list(seq)\n return ', '.join(list(map(str, seq)))",
"title": ""
},
{
"docid": "168edac6f34c37a0372a1a73ea103f07",
"score": "0.6197347",
"text": "def itemlist(item, sep, suppress_trailing=True):\n return attach(\n item\n + ZeroOrMore(sep + item)\n + Optional(sep.suppress() if suppress_trailing else sep),\n add_list_spacing,\n )",
"title": ""
},
{
"docid": "ee0b3e7be65f6a43dfbc9ed5b479196e",
"score": "0.6171717",
"text": "def listString(lister, delim):\n retstr = \"\"\n for i in range(len(lister)): retstr = retstr + lister[i] + delim\n\n retstr = retstr.rstrip(\";\")\n\n return retstr",
"title": ""
},
{
"docid": "fecbbab6f10ab629916a98370705210b",
"score": "0.6153299",
"text": "def catlist(alist, sep=\"\"):\n if len(alist) == 0:\n return \"\"\n if len(alist) == 1:\n return alist[0]\n f = alist[0]\n for p in alist[1:]:\n f = f + sep + p\n return f",
"title": ""
},
{
"docid": "52c88dc7f14cfae7b9e2688ee2f09430",
"score": "0.60904855",
"text": "def list_str(inlist):\n return '\\n'.join(inlist)",
"title": ""
},
{
"docid": "25e767ffe5fbeff068bf50477721d2af",
"score": "0.60802215",
"text": "def yamllist(items) :\n return \"\\n\".join([ \"- %s\" % x for x in items])",
"title": ""
},
{
"docid": "179ff0f632aefe976b7935fd27f6064f",
"score": "0.6025009",
"text": "def split_prefix(lst):\n return '_'.join(lst.split('_')[:-1])",
"title": ""
},
{
"docid": "6fc9467edf7140ce16ff33c030393397",
"score": "0.5995243",
"text": "def turn_list_to_str(list_values, sep = \";\"):\n\n output_str = \"\"\n for value in list_values[:-1]:\n output_str = output_str + str(value) + sep\n output_str = output_str + str(list_values[-1])\n return output_str",
"title": ""
},
{
"docid": "d05bc283681000c364a4bb80bf072077",
"score": "0.5950333",
"text": "def quote_list(the_list):\n return [\"'%s'\" % element for element in the_list]",
"title": ""
},
{
"docid": "051d2eed7cb08dfeeab3745a71a39fca",
"score": "0.5926013",
"text": "def list2str(list):\n return \"[\" + \", \".join([str(x) for x in list]) + \"]\"",
"title": ""
},
{
"docid": "7556476c31d3d94878391bea185c1f47",
"score": "0.5916555",
"text": "def list_to_string(input_list, separator):\n \n output = input_list[0]\n for item in input_list[1:]:\n output=string_concatenator(output, item, separator)\n return output",
"title": ""
},
{
"docid": "99c6b855d040f82a7b26567a1d28109c",
"score": "0.5899982",
"text": "def delist(l):\n if type(l).__name__ == 'list':\n return '_'.join([delist(x) for x in l])\n else:\n return str(l)",
"title": ""
},
{
"docid": "d80dfeec4662dd1b52c5aff194ec4378",
"score": "0.5859391",
"text": "def encodeList(items):\n line = []\n for item in items:\n item = item.strip()\n if not item:\n continue\n line.append(item)\n \n line = '\\t'.join(line)\n return line",
"title": ""
},
{
"docid": "7d7823b69d78d0661cb6f144f3ef4a25",
"score": "0.58574474",
"text": "def listToString(l):\n s = l[1] + ' ' + l[2] + ' ' + l[3]\n return s",
"title": ""
},
{
"docid": "9b47a889343e0c084e14e720912dcc79",
"score": "0.5846651",
"text": "def parse_list(msg):\n out_msg = []\n sub_msg = []\n esc = False\n in_quote = None\n for c in msg:\n if esc:\n sub_msg.append(c)\n esc = False\n else:\n if c == '\\\\':\n esc = True\n elif in_quote and c == in_quote:\n in_quote = None\n elif in_quote:\n sub_msg.append(c)\n elif c in ('\"', \"'\"):\n in_quote = c\n elif c == ',':\n m = ''.join(sub_msg).strip()\n if m != '':\n out_msg.append(m)\n sub_msg = []\n else:\n sub_msg.append(c)\n if len(sub_msg) != 0:\n m = ''.join(sub_msg).strip()\n if m != '':\n out_msg.append(m)\n return out_msg",
"title": ""
},
{
"docid": "70eb652bef41412fddf34185d9e4c8f9",
"score": "0.58362347",
"text": "def stitch_list(l):\n l = [str(i) for i in l] # Convert all elements to string\n l = ''.join(l) # Stitch them together\n return l",
"title": ""
},
{
"docid": "ea822332065d911ea3856700ce6239a7",
"score": "0.5829238",
"text": "def list2str(items, sep):\n rstr = \"\"\n for item in items:\n rstr = rstr + str(item) + sep\n return rstr[:-1]",
"title": ""
},
{
"docid": "41902442cfd8d6fb4befb5b2d49e6f87",
"score": "0.58138245",
"text": "def formatList(L):\r\n LFormatted = []\r\n for i in L:\r\n if i != []:\r\n LFormatted.append(int(str(i)[1:-1]))\r\n return LFormatted",
"title": ""
},
{
"docid": "047c749f97e575e30a043a46d6888fa1",
"score": "0.57916385",
"text": "def FormatList(items, formatter, level=0):\n return ', '.join(LimitedEnumerate(items, formatter, level=level))",
"title": ""
},
{
"docid": "085be826d14c778623fcf34a2d65150e",
"score": "0.57910395",
"text": "def _as_list(list_str, delimiter=','):\n return [str.strip(item).rstrip() for item in list_str.split(delimiter)]",
"title": ""
},
{
"docid": "c1447bce24354ff2e19f2cbce737fcc1",
"score": "0.57853526",
"text": "def list2string(string_list: list, separator='\\n') -> str:\n if len(string_list) > 0:\n return ''.join([f'{item}{separator}' for item in string_list])[\n :-len(separator)]\n return ''",
"title": ""
},
{
"docid": "84f030e7a2b50d1a4ef1be752ded931f",
"score": "0.5737608",
"text": "def sqllist(lst):\r\n if isinstance(lst, basestring): \r\n return lst\r\n else:\r\n return ', '.join(lst)",
"title": ""
},
{
"docid": "8847d85ec78a2a497626e023e6e5c05e",
"score": "0.57288104",
"text": "def _fixup_cc_list(cc_value):\r\n cclist = []\r\n for cc in re.split(r'[;,\\s]+', cc_value):\r\n if cc and cc not in cclist:\r\n cclist.append(cc)\r\n return ', '.join(cclist)",
"title": ""
},
{
"docid": "f8a3f0c5cce42badbd79b3b074341daa",
"score": "0.5728101",
"text": "def convert_list_to_string(org_list, seperator=''):\n return seperator.join(org_list)",
"title": ""
},
{
"docid": "01aa8d208f56745bb3e95d75819016dd",
"score": "0.57270056",
"text": "def list_to_dict_key(keylist):\r\n\treturn \":\".join(k for k in keylist)",
"title": ""
},
{
"docid": "113d9834e0bcda3a62dfb1c2fec9cf13",
"score": "0.57222646",
"text": "def __call__(self, value):\r\n if isinstance(value, basestring):\r\n return value.split(self.sep)\r\n else:\r\n try:\r\n return list(value)\r\n except Exception, exc:\r\n raise InvalidWidgetArgument(error=exc,\r\n title=_('List conversion error'))",
"title": ""
},
{
"docid": "ba763d224e06bd31feb54f4aac881b04",
"score": "0.5711922",
"text": "def charlist(in_list):\n out_list = []\n for i in in_list:\n assert type(i) in [str]\n if i == '':\n out_list.append(i)\n else:\n out_list += list(i)\n return out_list",
"title": ""
},
{
"docid": "6939b0ac6d9bce326b1ed065c9b389d8",
"score": "0.56960565",
"text": "def strip_list(list):\n return [item.strip() for item in list]",
"title": ""
},
{
"docid": "aebb6cd101083a2eb233c3b5b5914a51",
"score": "0.5689092",
"text": "def hex_list(L: List):\n return [hex(elt) for elt in L]",
"title": ""
},
{
"docid": "740a9f6e26a2e10ed6621e39a67da6ef",
"score": "0.5677563",
"text": "def indentList(lst, indent):\n\traise NotImplementedError(\"This function is deprecated\")\n\tjoiner = \" \"*indent*INDENT_AMOUNT\n\treturn joiner.join(lst)",
"title": ""
},
{
"docid": "c58e15bc8c0871b9dd2c397cedf0c0b1",
"score": "0.5677465",
"text": "def add_list_spacing(tokens):\n out = []\n for i, tok in enumerate(tokens):\n out.append(tok)\n if i % 2 == 1 and i < len(tokens) - 1:\n out.append(\" \")\n return \"\".join(out)",
"title": ""
},
{
"docid": "48a5113b905dbcde6c6279f47fb315d9",
"score": "0.567648",
"text": "def strip_list(l):\n return ([s.strip() for s in l])",
"title": ""
},
{
"docid": "b8e426c43473a309fe8931404826e523",
"score": "0.5674332",
"text": "def allToString(list):\n newList = []\n s = set()\n for e in list:\n if type(e)==types.ListType or type(e)==type(s):\n newList.append(\",\".join(e))\n else:\n newList.append(str(e))\n return newList",
"title": ""
},
{
"docid": "54a571c2d5f39b44eb706cec4ca34cd2",
"score": "0.56741405",
"text": "def rstripList(self, theList):\n # 2010/08/27: fix bug 618482.\n s = ''.join(theList).rstrip()\n return s.split('\\n')",
"title": ""
},
{
"docid": "6cb2f452e30716f155b7579715ac9ece",
"score": "0.56689364",
"text": "def convList2Array(list):\n\tarray = '{'\n\tfor item in list:\n\t\tarray += \"'%s',\" % pgdb.escape_string(item)\n\t# trim ending ','\n\tif len(array) > 1:\n\t\tarray = array[0:-1]\n\tarray += '}'\n\treturn array",
"title": ""
},
{
"docid": "723753fa2d1dd9caf8c41f3a217765b3",
"score": "0.56625366",
"text": "def PassTransferListToString(ll):\n\tstring = \"\"\n\tfor i in ll:\n\t\tstring += str(i)\n\t\tstring += \",\"\n\treturn string",
"title": ""
},
{
"docid": "df66b3c3acad00d3271b7c0d1c8e09a9",
"score": "0.56552744",
"text": "def parse_list(list_bytes):\n return [id.strip() for id in list_bytes.decode('utf8').split(_u('\\n'))\n if id.strip()]",
"title": ""
},
{
"docid": "12cc97a628a1640536e6350641905487",
"score": "0.56525207",
"text": "def fromListToString(listForToString):\n string = \" \".join(listForToString)\n return string",
"title": ""
},
{
"docid": "f0a673aedfff290e75ccacca49fe0f52",
"score": "0.5652254",
"text": "def format_list_to_commastring(ls):\n \n string = ''\n for item in ls:\n string += str(item).strip() + ','\n string = string.strip(',')\n \n return string",
"title": ""
},
{
"docid": "3e73c884127a5fca71cbbd83b5b1a12d",
"score": "0.56455827",
"text": "def list_2_string(in_list):\n return ','.join(in_list) # This is much faster but can't be used for list of lists",
"title": ""
},
{
"docid": "c47d11e681b8b108d9c9087fddcd7385",
"score": "0.5643744",
"text": "def formatList(theList, fmt = '%s\\n'):\n result = []\n for element in theList:\n# NTdebug(\"Doing element: \" +`element`)\n result.append(fmt%element.format())\n return ''.join(result)",
"title": ""
},
{
"docid": "24a3926182d32e0f190ec355a99f3fca",
"score": "0.56411743",
"text": "def list_str_to_list(list_str, separator=','):\n list_str = list_str.replace('[', '').replace(']', '')\n return list_str.split(separator)",
"title": ""
},
{
"docid": "fa0c3d1211ac0a1de66bd82b36a44d1b",
"score": "0.5628604",
"text": "def _list_format(data, per_line, style=WFX_FIELD_FMT):\n template = style * per_line\n leftover = len(data) % per_line\n # Template for last line.\n last_template = style * leftover\n\n pretty_list = [\n template % tuple(data[i : i + per_line])\n for i in range(0, len(data) - leftover, per_line)\n ]\n if leftover:\n return pretty_list + [last_template % tuple(data[-1 * leftover :])]\n return pretty_list",
"title": ""
},
{
"docid": "74fc83e1e5b77ac697fbe536777e68a3",
"score": "0.56123173",
"text": "def add_colons_to_mac(self, mac_addr):\n s = list()\n for i in range(12 / 2): # mac_addr should always be 12 chars, we work in groups of 2 chars\n s.append(mac_addr[i * 2:i * 2 + 2])\n r = \":\".join(s)\n return r",
"title": ""
},
{
"docid": "3d14b28922301fd6902541149245d805",
"score": "0.56039095",
"text": "def _format_list(param_list: Iterable[Any]) -> str:\n fmt_list = [f'\"{item}\"' for item in param_list]\n return \",\".join(fmt_list)",
"title": ""
},
{
"docid": "75d6c3c9b101545eb61736c8bd0aef45",
"score": "0.5591184",
"text": "def get_comma_sep_string_from_list(items):\n\n if not items:\n return ''\n\n if len(items) == 1:\n return items[0]\n\n return '%s and %s' % (', '.join(items[:-1]), items[-1])",
"title": ""
},
{
"docid": "053630d5b15a9a76bffcc2055a5a35f3",
"score": "0.558819",
"text": "def PassTransferStringToList(ss):\n\tll = []\n\tcurr = 0\n\tprev = 0\n\tfor i in ss:\n\t\tif i == ',':\n\t\t\tsub = ss[prev:curr]\n\t\t\tll.append(sub)\n\t\t\tprev = curr+1\n\t\tcurr += 1\n\treturn ll",
"title": ""
},
{
"docid": "bdb311b8d2ab9a7e121a7a06a3a60bb2",
"score": "0.5581604",
"text": "def transform_list(string_list=\"\"):\n chars_to_remove = [\"-\"]\n string_list = \"\".join(\n i for i in string_list if i not in chars_to_remove).strip()\n return \"<li>{}</li>\\n\".format(string_list)",
"title": ""
},
{
"docid": "4a327ac1124fafbbcd3da7ad2756573c",
"score": "0.55776584",
"text": "def ll_to_string(inputlist):\n inputlist = [list(map(str, i)) for i in inputlist]\n newlist = []\n for i in inputlist:\n newlist.append(' '.join(i).rstrip())\n string = '\\n'.join(newlist)\n return string",
"title": ""
},
{
"docid": "3016f29899e05612710975c39c57a727",
"score": "0.55764604",
"text": "def combined_to_list(self, path):\n with open(path, 'r') as raw:\n text = raw.readlines()\n res = []\n k = []\n for line in text:\n if ':' not in line:\n res.append(k + line.strip('\\n').split(','))\n else:\n k = [line.strip(':\\n')]\n return res",
"title": ""
},
{
"docid": "2b9b90435eb2c877aa4caf9255bab13b",
"score": "0.5573941",
"text": "def flatlist(group=None):\n return \" \".join(sorted(list_jails(group)))",
"title": ""
},
{
"docid": "27b7b81070b4520972adacec4425bbf4",
"score": "0.5559492",
"text": "def bash_array(lst):\n\n contents = ' '.join(str(x) for x in lst)\n return '({:s})'.format(contents)",
"title": ""
},
{
"docid": "3001fe18cc999b4a0e05b5e52d70d936",
"score": "0.5557065",
"text": "def listsplit(liststr=''):\n ret = []\n for e in liststr.split(','):\n ret.append(e.strip())\n return ret",
"title": ""
},
{
"docid": "be16f8ea1554e1508d0a94f9564ea308",
"score": "0.55556095",
"text": "def grouplist(mylist,seperator=\"/\"):\n\tmygroups={}\n\tfor x in mylist:\n\t\txs=x.split(seperator)\n\t\tif xs[0]==\".\":\n\t\t\txs=xs[1:]\n\t\tif xs[0] not in mygroups:\n\t\t\tmygroups[xs[0]]=[seperator.join(xs[1:])]\n\t\telse:\n\t\t\tmygroups[xs[0]]+=[seperator.join(xs[1:])]\n\treturn mygroups",
"title": ""
},
{
"docid": "f91b9f1026b34dabc609e7737e33a887",
"score": "0.552983",
"text": "def _truncate_list(self, x: Union[List[str], List[int]]) -> Union[List[str], List[int]]:\n # Save two slots for the first [CLS] token and the last [SEP] token.\n return x[: self._max_seq_length - 2]",
"title": ""
},
{
"docid": "14921434dd2670f4cc49537f61c08226",
"score": "0.5525684",
"text": "def list_stringer(input_list): #input a list\r\n\toutput_list=[]\r\n\tfor item in input_list:\r\n\t\toutput_list.append(str(item))\r\n\treturn ' '.join (output_list) #output a string\r",
"title": ""
},
{
"docid": "8fed9fc50e72ae6008f4bb242f38f740",
"score": "0.55247545",
"text": "def convert_line(l, c, id=0):\n return [f'{c}'] + [f'{i + 1}:{n}' for i, n in enumerate(l)]",
"title": ""
},
{
"docid": "5425bb7e247a359640143ccb267db7d5",
"score": "0.55200064",
"text": "def unlist_values(to_list):\r\n return [''.join(x) for x in to_list]",
"title": ""
},
{
"docid": "260e446f414d74c5d278a70e3608ad65",
"score": "0.55179256",
"text": "def list_join(L, x):\n if isinstance(x, string_types):\n x = (x, RESERVED_TOKEN)\n if len(L) == 0:\n return [], []\n out, out_types = copy.deepcopy(L[0][0]), copy.deepcopy(L[0][1])\n for v, t in L[1:]:\n if x:\n out += [x[0]]\n out_types += [x[1]]\n out += v\n out_types += t\n return out, out_types",
"title": ""
},
{
"docid": "e8ea5080455b3deb85d3b0aa93a41e2f",
"score": "0.55121875",
"text": "def Stringefy(intlist,delim):\n\n\tstring = str(intlist)[1:-1].replace(', ',delim)\n\treturn string",
"title": ""
},
{
"docid": "e914d4f69d20c8a66ef6fef59d5948e0",
"score": "0.5505",
"text": "def prepend(list, str):\n #add a {} at the end of the str\n str += '{0}'\n #recreate the list starting with the str\n list = [str.format(i) for i in list]\n return(list)",
"title": ""
},
{
"docid": "01fcea0f4443bf0ffa26846e958ea2ca",
"score": "0.550127",
"text": "def _prettify_list(items):\n assert isinstance(items, list)\n\n keys_list = 'Available Keys:'\n for item in items:\n keys_list += '\\n - {0}'.format(item)\n return keys_list",
"title": ""
},
{
"docid": "9a4519ce4b1f22204be87978e44d7b03",
"score": "0.5499873",
"text": "def get_delimited_string_from_list(_list, delimiter=', ', wrap_values_with_char=None, wrap_strings_with_char=None):\n\n if wrap_values_with_char is not None:\n return delimiter.join('{wrapper}{val}{wrapper}'.format(\n val=v,\n wrapper=wrap_values_with_char\n ) for v in _list)\n elif wrap_strings_with_char is not None:\n return delimiter.join(str(v) if not isinstance(v, str) else '{wrapper}{val}{wrapper}'.format(\n val=v,\n wrapper=wrap_strings_with_char\n ) for v in _list)\n else:\n return delimiter.join(str(v) for v in _list)",
"title": ""
},
{
"docid": "ebb5e0cd97f93466a5d7dcd9dc4f4591",
"score": "0.5498491",
"text": "def parse_list_header(value):\n result = []\n for item in _parse_list_header(value):\n if item[:1] == item[-1:] == '\"':\n item = item[1:-1]\n result.append(item)\n return result",
"title": ""
},
{
"docid": "62b0cfd7fb7472e9eaee5b9eb9e46ec6",
"score": "0.54980606",
"text": "def print_list(lst):\n for item in lst:\n print(' {}'.format(item))",
"title": ""
},
{
"docid": "09a291a11d53fc0efad96f9ee48895bc",
"score": "0.5496579",
"text": "def render_list(inlist):\n\n # Return empty string to avoid returning unnecessary newlines\n if not inlist:\n return ''\n\n return '\\n{}\\n\\n'.format('\\n'.join([str(x) for x in inlist]))",
"title": ""
},
{
"docid": "3a30719c5386a25871b7776eddaa837a",
"score": "0.5491656",
"text": "def listPrinter(lst):\r\n print(\"[\")\r\n for l in range(len(lst)):\r\n print(\" \" + str(lst[l]) + \",\" * (l != (len(lst) -1)))\r\n print(\"]\\n\")",
"title": ""
},
{
"docid": "647af4d08c50593d82d7c1282685e8bf",
"score": "0.5481166",
"text": "def parse_list_header(value):\r\n result = []\r\n for item in _parse_list_header(value):\r\n if item[:1] == item[-1:] == '\"':\r\n item = unquote_header_value(item[1:-1])\r\n result.append(item)\r\n return result",
"title": ""
},
{
"docid": "64874ae84be731fbe8982c4b302baf6d",
"score": "0.54795486",
"text": "def print_list(l):\n print('[' + ', '.join([x.__str__() for x in l]) + ']')",
"title": ""
},
{
"docid": "0fb706846bc7069da3f389198a20362f",
"score": "0.54726285",
"text": "def convert_dict(lista):\n dic = {}\n lista = lista[0].split(\",\")\n for item in lista:\n dic[item.split(':',1)[0]] = item.split(':',1)[1]\n return dic",
"title": ""
},
{
"docid": "837caf610280689fb0cee3a6ff08fe41",
"score": "0.54676163",
"text": "def _get_formatted_server_list(self, server_list):\n\n # dns/ntp server info from excel is of the format\n # 'xxx.xxx.xxx.xxx, (aaa.bbb.ccc.com)'\n # The function returns a list of comma separated dns ip addresses\n servers = []\n for data in server_list:\n if \"(\" not in data:\n servers.append(data)\n formatted_server_list = \",\".join(servers)\n return formatted_server_list",
"title": ""
},
{
"docid": "700462b3f1a9dc50851905a4b9242e79",
"score": "0.5460897",
"text": "def parse_list(cls, list_string):\n\n return [cls.parse_string(s) for s in list_string]",
"title": ""
},
{
"docid": "6da61c5499d748a445fab9d088a11562",
"score": "0.5453253",
"text": "def collapse_list(the_list):\n return [s for i in the_list for s in i]",
"title": ""
},
{
"docid": "5c8027c0d1ba5756bb954361cfe8a9bb",
"score": "0.5444541",
"text": "def get_list_comma_sep_string(input_string):\n final_list = input_string.split(',')\n for i in range(0, len(final_list)):\n final_list[i] = final_list[i].strip()\n return final_list",
"title": ""
},
{
"docid": "2c3cba30db49498a162a0a44589d496a",
"score": "0.54422456",
"text": "def listToParString(l):\n\treturn ' '.join([str(x) for x in l])",
"title": ""
},
{
"docid": "5ba6dd08df6f123d023dfaae7c2b757e",
"score": "0.54410183",
"text": "def makeList(stringList):\n if isinstance(stringList, list):\n return stringList\n if isinstance(stringList, basestring):\n toks = stringList.lstrip(' [').rstrip(' ]').split(',')\n if toks == ['']:\n return []\n return[str(tok.strip(' \\'\"')) for tok in toks]\n raise WMWorkloadToolsException",
"title": ""
},
{
"docid": "afc80c4c4b52698eb33e316a531abc79",
"score": "0.54396296",
"text": "def get_prep_value(self, value):\n super(ListField, self).get_prep_value(value)\n\n return ','.join(value)",
"title": ""
},
{
"docid": "8e6c8e2c31cfd073de998ba6029e2703",
"score": "0.5438427",
"text": "def get_path_from_list(to_parse: typing.List[str], path_prefix: str) -> str:\n ret = path_prefix\n\n for i in to_parse:\n ret += f\"/{i}\"\n\n return ret.lstrip(\"/\").rstrip(\"/\")",
"title": ""
},
{
"docid": "9599a9048fd8cc0bb9ea3574e22f5aa1",
"score": "0.5430547",
"text": "def to_list(str_rep_list: str) -> list:\n in_parts = str_rep_list.strip(\"[\").strip(\"]\").split(',')\n real_list = [part.strip(' ').strip(\"'\").strip('\"') for part in in_parts if part != '' and part != ' ']\n return real_list",
"title": ""
},
{
"docid": "f51a82fc648432f8c20b2fe995fe9cb6",
"score": "0.5430519",
"text": "def read_colon_list(tok: BaseTokenizer, had_colon: bool = False) -> Tuple[List[str], Token]:\n strings = []\n ready_for_string = had_colon # Did we have a colon before?\n token = Token.EOF\n for token, tok_value in tok:\n if token is Token.STRING:\n if not ready_for_string:\n raise tok.error('Too many strings ({!r})!', tok_value)\n strings.append(tok_value)\n ready_for_string = False\n elif token is Token.COLON:\n if ready_for_string:\n # ': :' means to have an empty string there.\n strings.append('')\n ready_for_string = True\n elif token is Token.PLUS:\n if ready_for_string or not strings:\n raise tok.error('\"+\" without a string before it!')\n strings[-1] += tok.expect(Token.STRING)\n elif ready_for_string and token is Token.NEWLINE:\n continue # skip over this in particular..\n else:\n if ready_for_string:\n raise tok.error(token)\n return strings, token\n raise tok.error(token)",
"title": ""
},
{
"docid": "61c5da8d264408b429a4243879ae37cb",
"score": "0.54226595",
"text": "def reformatStripList(stripList):\n String = ''\n for channel in stripList:\n String = String + str(channel) + ' '\n # Remove the last space.\n String = String[:-1]\n return String",
"title": ""
},
{
"docid": "4d1a2f79c64e1d492a841416f794feb0",
"score": "0.5421868",
"text": "def _list_to_name(self, name_list):\n if name_list[1] != \"\":\n return \"{}, {}, {}\".format(*name_list)\n else:\n return \"{}, {}\".format(name_list[0], name_list[2])",
"title": ""
},
{
"docid": "66e5001210d87e18c0101db116aa4146",
"score": "0.54189306",
"text": "def parse_http_list(s):\n res = []\n part = u''\n\n escape = quote = False\n for cur in s:\n if escape:\n part += cur\n escape = False\n continue\n if quote:\n if cur == u'\\\\':\n escape = True\n continue\n elif cur == u'\"':\n quote = False\n part += cur\n continue\n\n if cur == u',':\n res.append(part)\n part = u''\n continue\n\n if cur == u'\"':\n quote = True\n\n part += cur\n\n # append last part\n if part:\n res.append(part)\n\n return [part.strip() for part in res]",
"title": ""
},
{
"docid": "4bdc69c46b53a5e9015a1c4e49c5ec4c",
"score": "0.5393936",
"text": "def decodeList(line):\n items = []\n for item in line.split('\\t'):\n item = item.strip()\n if not item:\n continue\n items.append(item)\n return items",
"title": ""
},
{
"docid": "6ceacdda65c93baf94a0a5005165c504",
"score": "0.5382561",
"text": "def cut_list(l, length=100):\n if not isinstance(l, list):\n raise ValueError(\"A list is expected, got: %s\" % type(l))\n\n res = '['\n\n for idx, item in enumerate(l):\n s = str(item)\n\n new_len = len(res) + len(s)\n\n is_str = isinstance(item, str)\n\n if is_str:\n new_len += 2\n\n if new_len >= length:\n res += \"'%s...\" % s[:length - new_len] if is_str else \"%s...\" % s\n\n break\n else:\n res += \"'%s'\" % s if is_str else s\n res += ', ' if idx < len(l) - 1 else ']'\n\n return res",
"title": ""
},
{
"docid": "b5c3ddecd4e45a43b098b76b76a40c0a",
"score": "0.5379951",
"text": "def put_list_chars( self, chars ):\n\t\tself.pl.PL_put_list_chars( self.pl_term, chars )",
"title": ""
},
{
"docid": "24dd5316ae2424c928899bcd95a5f62b",
"score": "0.5378834",
"text": "def list_arg(raw_value):\n return str(raw_value).split(',')",
"title": ""
},
{
"docid": "ff4c0dc014b8944f67764a8f8e4a97ac",
"score": "0.53742325",
"text": "def prepend_name_to_itemlist(name, item_lst):\n return [(name + item[0], item[1]) for item in item_lst]",
"title": ""
},
{
"docid": "fd15d0f891ef42d821550a86ea30b1af",
"score": "0.53685784",
"text": "def abbrev_nodenames(node_list: List[str], prefix: str = None) -> List[str]:\n newlist = [s.split(\".\", 1)[0] for s in node_list]\n return newlist",
"title": ""
},
{
"docid": "c7ffede8795a8e75abf3edafbfbabe04",
"score": "0.53648967",
"text": "def reformat_bibserlist(bibserstr):\n return ' '.join(bibserstr.translate(BIBSERLIST_UTRANS).split())",
"title": ""
},
{
"docid": "fcb869f4deb67e4489437949bad785a9",
"score": "0.53521484",
"text": "def list_to_string(input_list):\n output_string = ''\n for index, item in enumerate(input_list):\n output_string += str(item)\n if index < len(input_list) - 2:\n output_string += ', '\n elif index == len(input_list) - 2:\n output_string += ' and '\n return output_string",
"title": ""
},
{
"docid": "c33754de1f004b8dc1f6324c9740fbdc",
"score": "0.53441066",
"text": "def human_task_name_list(list):\n ret = []\n for name in list:\n ret.append(BaseTaskLoader.human_task_name(name))\n return ', '.join(ret)",
"title": ""
},
{
"docid": "4b4019973d343f343992a4e35fe1a6a2",
"score": "0.534016",
"text": "def list_ingredients():\n return ', '.join(str(items) for items in ingredients_list)",
"title": ""
},
{
"docid": "9942b3aeb18592b86a97d61d6dd203f8",
"score": "0.53350043",
"text": "def _ListTypeFormatString(value):\n\n if isinstance(value, tuple):\n return '({0})'\n if isinstance(value, set):\n return '{{{0}}}'\n return '[{0}]'",
"title": ""
}
]
988e2a7752f8000ebc216e3b5fe90fff
This is the creator. It can be passed the the min/max X and Y values for the plane, and a transformation function (f). There are default values if the parameters are not passed to the creator. The creator then generates a 2D plane filled with the X & Y complex number coordinates of the specified plane transformed by the function f(). Note that the default function f() for computing the values in the plane is the identity function, so the values at the coordinate location are the coordinates themselves. Note also that the number of points in each axis is always forced to be fixed value.
[
{
"docid": "ad9e077f02647ddb17277f388d0ccd4c",
"score": "0.64841443",
"text": "def __init__(self, newXmin=-5., newXmax=5., newXlen=1001, newYmin=-5., newYmax=5., newYlen=1001, f=lambda x: x, maxLoop=100):\n self.xmin = newXmin\n self.xmax = newXmax\n self.ymin = newYmin\n self.ymax = newYmax\n self.xlen = newXlen\n self.ylen = newYlen\n # must sub 1 to get the correct actual step size, otherwise last element does not equal x or y max\n self.xstep = (self.xmax - self.xmin)/(self.xlen - 1)\n self.ystep = (self.ymax - self.ymin)/(self.ylen - 1)\n self.f = f\n self.max = maxLoop\n # call refresh() to generate the the plane and its contents\n self.refresh()",
"title": ""
}
]
[
{
"docid": "5e86faa6a75ba40509e0990332270853",
"score": "0.6098736",
"text": "def make_plane():\n\n source = vtk.vtkPlaneSource()\n source.SetOrigin(-10.0, -10.0, 0.0)\n source.SetPoint2(-10.0, 10.0, 0.0)\n source.SetPoint1(10.0, -10.0, 0.0)\n source.SetXResolution(20)\n source.SetYResolution(20)\n source.Update()\n\n transform = vtk.vtkTransform()\n transform.Translate(0.0, 0.0, 0.0)\n transform.RotateX(-90.0)\n transform_filter = vtk.vtkTransformPolyDataFilter()\n transform_filter.SetInputConnection(source.GetOutputPort())\n transform_filter.SetTransform(transform)\n transform_filter.Update()\n\n # We have a m x n array of quadrilaterals arranged as a regular tiling in a\n # plane. So pass it through a triangle filter since the curvature filter only\n # operates on polys.\n tri = vtk.vtkTriangleFilter()\n tri.SetInputConnection(transform_filter.GetOutputPort())\n\n # Pass it though a CleanPolyDataFilter and merge any points which\n # are coincident, or very close\n cleaner = vtk.vtkCleanPolyData()\n cleaner.SetInputConnection(tri.GetOutputPort())\n cleaner.SetTolerance(0.005)\n cleaner.Update()\n\n return cleaner.GetOutput()",
"title": ""
},
{
"docid": "bb3ce14f17bfc0196625fbf286186d0b",
"score": "0.6033272",
"text": "def __init__(self, newXmin=-5., newXmax=5., newXlen=11, newYmin=-5., newYmax=5., newYlen=11, c=(-1.037 + 0.17j), maxLoop=100):\n # set the function and re-compute the plane's values\n f = julia(c, maxLoop)\n self.c = c\n ComplexPlaneNP.__init__(self, newXmin, newXmax, newXlen, newYmin, newYmax, newYlen, f, maxLoop)",
"title": ""
},
{
"docid": "772e3836487337435021adf49f7f5c7d",
"score": "0.5945625",
"text": "def plane(self):\n return Plane(self.position, self.position.Vz, self.position.Vy)",
"title": ""
},
{
"docid": "c3c87c278b8e2d4513d1c98cb5fca3e5",
"score": "0.5920536",
"text": "def plane_grid(x_axis_lims, y_axis_lims, rotation_axis, rotation_angle, element_size):\n\n stub = \"\"\"\n Point(1) = {ax1_lim1, ax2_lim1, 0, cl};\n Point(2) = {ax1_lim2, ax2_lim1, 0, cl};\n Point(3) = {ax1_lim2, ax2_lim2, 0, cl};\n Point(4) = {ax1_lim1, ax2_lim2, 0, cl};\n Line(1) = {1, 2};\n Line(2) = {2, 3};\n Line(3) = {3, 4};\n Line(4) = {4, 1};\n Line Loop(1) = {1, 2, 3, 4};\n Plane Surface(2) = {1};\n Rotate {{rot_ax1, rot_ax2, rot_ax3}, {0, 0, 0}, rot_ang_rad} { Surface{2}; }\n Mesh.Algorithm = 2;\n \"\"\"\n import sys\n\n if sys.version_info.major >= 3 and sys.version_info.minor >= 6:\n return\n else:\n geometry = (\n \"ax1_lim1 = \"\n + str(x_axis_lims[0])\n + \";\\n\"\n + \"ax1_lim2 = \"\n + str(x_axis_lims[1])\n + \";\\n\"\n + \"ax2_lim1 = \"\n + str(y_axis_lims[0])\n + \";\\n\"\n + \"ax2_lim2 = \"\n + str(y_axis_lims[1])\n + \";\\n\"\n + \"rot_ax1 = \"\n + str(rotation_axis[0])\n + \";\\n\"\n + \"rot_ax2 = \"\n + str(rotation_axis[1])\n + \";\\n\"\n + \"rot_ax3 = \"\n + str(rotation_axis[2])\n + \";\\n\"\n + \"rot_ang_rad = \"\n + rotation_angle\n + \";\\n\"\n + \"cl = \"\n + str(element_size)\n + \";\\n\"\n + stub\n )\n return generate_grid_from_geo_string(geometry)",
"title": ""
},
{
"docid": "64a6b3cb8566a976fea33a5cf10ae9da",
"score": "0.58104485",
"text": "def __init__(self, newXmin=-5., newXmax=5., newXlen=11, newYmin=-5., newYmax=5., newYlen=11, c=(-1.037 + 0.17j), maxLoop=100):\n # set the function and re-compute the plane's values\n f = juliaNV(c, maxLoop)\n self.c = c\n ComplexPlaneNP.__init__(self, newXmin, newXmax, newXlen, newYmin, newYmax, newYlen, f, maxLoop)",
"title": ""
},
{
"docid": "699ef8eab5b4d8b155a71bea9c65dcc6",
"score": "0.57951874",
"text": "def plane(self, obj, *args, **kwargs):\n assert obj.type is Fol, \"Only Fol type instance could be plotted as plane.\"\n if \"zorder\" not in kwargs:\n kwargs[\"zorder\"] = 5\n animate = kwargs.pop(\"animate\", False)\n if isinstance(obj, Group):\n x = []\n y = []\n for azi, inc in obj.dd.T:\n xx, yy = self._cone(\n p2v(azi, inc),\n l2v(azi, inc),\n limit=89.9999,\n res=int(cosd(inc) * 179 + 2),\n )\n x = np.hstack((x, xx, np.nan))\n y = np.hstack((y, yy, np.nan))\n x = x[:-1]\n y = y[:-1]\n else:\n azi, inc = obj.dd\n x, y = self._cone(\n p2v(azi, inc),\n l2v(azi, inc),\n limit=89.9999,\n res=int(cosd(inc) * 179 + 2),\n )\n h = self.fig.axes[self.active].plot(x, y, *args, **kwargs)\n if animate:\n self.artists.append(tuple(h))\n self.draw()",
"title": ""
},
{
"docid": "415a2aa689ea5c123cba2e29b419086f",
"score": "0.57545996",
"text": "def generate_surface(cls, func, *,\n u_param=(-1., 1., .1), v_param=(-1., 1., .1)):\n def make_axis(param):\n return np.linspace(param[0], param[1],\n int((param[1] - param[0]) / param[2]) + 1)\n\n u_axis = make_axis(u_param)\n v_axis = make_axis(v_param)\n u, v = np.meshgrid(u_axis, v_axis)\n surface = func(u, v)\n return cls(surface, param=np.array([u, v]))",
"title": ""
},
{
"docid": "558fb53f9075cfaf2223918c8c78b5d6",
"score": "0.56716377",
"text": "def from_plane(cls, plane):\n raise NotImplementedError",
"title": ""
},
{
"docid": "c520c94ceb10ef83d7e39bf092a193c9",
"score": "0.56545216",
"text": "def fit_plane(self, x_in, y_in, z_in):\n points = array([x_in, y_in, z_in])\n center = points.mean(axis=1)\n points[0,:] -= center[0]\n points[1,:] -= center[1]\n points[2,:] -= center[2]\n covariance = cov(points)\n \n eval, evec = linalg.eig(covariance)\n ax_id = argmin(eval)\n plane_normal = evec[:, ax_id]\n plane_d = dot(center.T, plane_normal)\n \n # print \"center: %s\" % center\n # print \"cov: %s\" % covariance\n # print \"eval: %s\" % eval\n # print \"evec: %s\" % evec\n # print \"axe: %s\" % ax_id\n # print \"normal: %s\" % plane_normal\n # print \"plane_d: %s\" % plane_d\n \n return array([plane_normal[0], plane_normal[1], plane_normal[2], plane_d])",
"title": ""
},
{
"docid": "264422431472dd332162191815b9935e",
"score": "0.55873793",
"text": "def bl_make_plane(cls, name):\n verts=[]\n verts.append((1.0, -1.0, 0.0))\n verts.append((-1.0, -1.0, 0.0))\n verts.append((1.0, 1.0, 0.0))\n verts.append((-1.0, 1.0, 0.0))\n\n edges=[]\n edges.append((0,1))\n edges.append((2,3))\n edges.append((3,1))\n edges.append((0,2))\n\n faces=[]\n faces.append((2,3,1,0))\n \n meshplane = bpy.data.meshes.new(name)\n meshplane.from_pydata(verts, edges, faces)\n meshplane.name = name\n obj, base = SlicePlane.add_obj(meshplane, bpy.context)\n\n obj.location = Vector((0.,0.,0.))\n\n return obj # or is base the correct one?",
"title": ""
},
{
"docid": "e2d1552c8173ff9f2e7adbe9838fa012",
"score": "0.55644786",
"text": "def create_grid_points(resolution, plane_axes, plane_offset, bounding_box, mode):\n\n from ..utils.generic import bold_ul_text\n\n ax1_min, ax1_max, ax2_min, ax2_max = bounding_box\n if mode.lower() == \"numpy\":\n plot_grid = _np.mgrid[\n ax1_min : ax1_max : resolution[0] * 1j,\n ax2_min : ax2_max : resolution[1] * 1j,\n ]\n points_tmp = [_np.ones(plot_grid[0].size) * plane_offset] * 3\n points_tmp[plane_axes[0]] = plot_grid[0].ravel()\n points_tmp[plane_axes[1]] = plot_grid[1].ravel()\n points = _np.vstack((points_tmp,))\n plane = None\n\n elif mode.lower() == \"gmsh\":\n if 2 not in plane_axes:\n axis1_lims = bounding_box[0:2]\n axis2_lims = bounding_box[2:]\n rotation_axis = [0, 0, 1]\n rotation_angle = \"2*Pi\"\n elif 1 not in plane_axes:\n axis1_lims = bounding_box[0:2]\n axis2_lims = bounding_box[2:]\n rotation_axis = [1, 0, 0]\n rotation_angle = \"Pi/2\"\n elif 0 not in plane_axes:\n axis1_lims = bounding_box[2:]\n axis2_lims = bounding_box[0:2]\n rotation_axis = [0, 1, 0]\n rotation_angle = \"-Pi/2\"\n else:\n raise ValueError(\"Plane axis not correctly defined.\")\n\n elem_len = _np.min(\n [\n (axis1_lims[1] - axis1_lims[0]) / resolution[0],\n (axis2_lims[1] - axis2_lims[0]) / resolution[1],\n ]\n )\n\n plane = plane_grid(\n axis1_lims, axis2_lims, rotation_axis, rotation_angle, elem_len\n )\n points = plane.leaf_view.vertices\n else:\n raise TypeError(\n \"The correct values for the argument\"\n + bold_ul_text(\"mode\")\n + \"are numpy or gmsh.\"\n )\n\n return points, plane",
"title": ""
},
{
"docid": "6e4f70656e3cf67b8c6882bdb3970186",
"score": "0.5541129",
"text": "def ApplyCoordinateTransformation(self,f):\n npoints = self.ugrid.GetNumberOfPoints ()\n \n for i in range (npoints):\n (x,y,z) = self.ugrid.GetPoint (i)\n newX = f(arr([x,y,z]),t=0)\n self.ugrid.GetPoints ().SetPoint (i, newX[0], newX[1], newX[2])",
"title": ""
},
{
"docid": "86ab59a7a139e8a8dfd2783551df91f6",
"score": "0.55118614",
"text": "def topo_plane_paramEval(self, param):\n # Create an empty numpy array with the same number as pixels as the real data.\n self.topo_plane_fit_data = np.zeros((self.y_res, self.x_res))\n for y in range(0, self.y_res): # Iterate over the y-axis pixels.\n for x in range(0, self.x_res): # Iterate over the x-axis pixels.\n self.topo_plane_fit_data[y, x] = param[0]*x + param[1]*y + param[2] # Generate plane value.\n return self.topo_plane_fit_data # Return entire array.",
"title": ""
},
{
"docid": "c4c12b0bdc7f9cbf61045190be3d3c55",
"score": "0.5451705",
"text": "def create_plane (self, image_orientation):\n\n if self.plane_name in bpy.data.objects:\n return bpy.data.objects[self.plane_name]\n \n \"\"\"Create a mesh and add it to Blender\"\"\"\n if (len(self.img_names) == 0):\n raise ValueError(\"No images! Something is wrong\")\n \n idx = len(self.img_names) - 1\n img_name = self.img_names[idx//2]\n try:\n img = bpy.data.images[img_name]\n except KeyError:\n print(\"Couldn't find image \" + img_name + \"!\")\n return\n \n self.widthp,self.heightp = img.size\n \n \"\"\" Make a plane mesh and add to blender \"\"\"\n plane = SlicePlane.bl_make_plane(self.plane_name)\n\n bpy.ops.object.transform_apply(rotation=True,scale=True)\n if (self.orientation == Orientation('AXIAL')):\n self.widthf = self.widthp*self.spacing[0]\n self.heightf = self.heightp*self.spacing[1]\n plane.dimensions = self.widthf, self.heightf, 0.0\n\n plane.rotation_mode = 'YXZ'\n plane.rotation_euler = [0.,pi,0.]\n if image_orientation[0] == 'R':\n plane.rotation_euler[1] = 0\n if image_orientation[1] == 'P':\n plane.rotation_euler[2] = pi\n if image_orientation[2] == 'S':\n self.reverse = True\n plane.rotation_euler[1] -= pi\n elif (self.orientation == Orientation('SAGITTAL')):\n self.widthf = self.widthp*self.spacing[1]\n self.heightf = self.heightp*self.spacing[2]\n plane.dimensions = self.widthf, self.heightf, 0.0\n\n plane.rotation_mode = 'XYZ'\n plane.rotation_euler = [pi/2,0.,-pi/2]\n if image_orientation[0] == 'R':\n self.reverse = True\n if image_orientation[1] == 'P':\n plane.rotation_euler[2] = pi/2\n self.reverse = not self.reverse\n if image_orientation[2] == 'S':\n plane.rotation_euler[0] = -pi/2\n self.reverse = not self.reverse\n elif (self.orientation == Orientation('CORONAL')):\n self.widthf = self.widthp*self.spacing[0]\n self.heightf = self.heightp*self.spacing[2]\n plane.dimensions = self.widthf, self.heightf, 0.0\n\n plane.rotation_mode = 'XYZ'\n plane.rotation_euler = [pi/2,0.,0.]\n if image_orientation[0] == 'L':\n plane.rotation_euler[2] = pi\n if image_orientation[1] == 'P':\n plane.rotation_euler[2] -= pi\n self.reverse = True\n if image_orientation[2] == 'S':\n plane.rotation_euler[0] = -pi/2\n plane.rotation_euler[2] -= pi\n else:\n raise ValueError('orientation must be in Orientation')\n\n img = self.get_image_from_location (plane.location)\n tex = SlicePlane.create_image_texture (img)\n mat = self.create_material_for_texture (tex)\n\n plane.data.materials.append(mat)\n plane.data.uv_textures.new()\n plane.data.uv_textures[0].data[0].image = \\\n mat.texture_slots[0].texture.image\n\n plane.location = Vector (self.plane_centre)\n\n self.register_callback()\n\n return plane",
"title": ""
},
{
"docid": "1faa279fe9417e3cf9f6ba715e9efd49",
"score": "0.5444708",
"text": "def makeplane(self, estimator=np.nanmean):\n # THIS IS A HACK!!! isinstance(a function, function) must be a thing...\n FUNCTION = type(np.max)\n\n # estimator is NOT duck-typed\n if type(estimator) is FUNCTION:\n self.plane = estimator(self.Cube.cube,axis=0)\n elif isinstance(estimator, six.string_types):\n if estimator == 'max':\n self.plane = self.Cube.cube.max(axis=0)\n elif estimator == 'int':\n dx = np.abs(self.Cube.xarr[1:] - self.Cube.xarr[:-1])\n dx = np.concatenate([dx,[dx[-1]]])\n self.plane = (self.Cube.cube * dx[:,np.newaxis,np.newaxis]).sum(axis=0)\n elif estimator[-5:] == \".fits\":\n self.plane = pyfits.getdata(estimator)\n elif type(estimator) is slice:\n self.plane = self.Cube.cube[estimator,:,:]\n elif type(estimator) is int:\n if hasattr(self.Cube,'parcube'):\n self.plane = self.Cube.parcube[estimator,:,:]\n\n if self.plane is None:\n raise ValueError(\"Invalid estimator %s\" % (str(estimator)))\n\n if np.sum(np.isfinite(self.plane)) == 0:\n raise ValueError(\"Map is all NaNs or infs. Check your estimator or your input cube.\")",
"title": ""
},
{
"docid": "94b4da0965081fdc43d8f05362f4bb65",
"score": "0.5435796",
"text": "def createPlane(self):\n\n plane_dict = {}\n\n #Takes in input for plane insignia and puts it under \"planeInsignia\" key in plane_dict\n plane_dict[\"planeInsignia\"] = InputHandler().planeInsignia(\"Input plane insignia (e.g. TF-XXX): \")\n\n #Creates a list of airplane types in the list\n airplane_data_list = IOAPI().opener(self.dataFiles[\"AIRCRAFT_TYPE_FILE\"])\n airplaneType_list = []\n for a_line_dict in airplane_data_list:\n airplaneType_list.append(a_line_dict[\"planeTypeId\"])\n #Turns the list into a list of dictionaries\n plane_list = []\n for x in airplaneType_list:\n planeType_dict = {}\n planeType_dict[\"Plane Type IDs: \"] = x\n plane_list.append(planeType_dict)\n DisplayScreen().printOptions(plane_list, header = \"\")\n #Input for plane Type ID\n plane_dict[\"planeTypeId\"] = InputHandler().multipleNumChoices(plane_list, \"Choose Plane Type ID: \")\n plane_dict[\"manufacturer\"] = InputHandler().strNoCheck(\"Input plane manufacturer: \")\n plane_dict[\"capacity\"] = InputHandler().digit(\"Input plane seating capacity: \")\n #Input confirmation\n DisplayScreen().printList([plane_dict], header = \"\")\n confirmation_bool = InputHandler().yesOrNoConfirmation(\"Is this information correct? (y/n)\")\n if confirmation_bool:\n #Appending the input info to aircraft file\n IOAPI().appender(self.dataFiles[\"AIRCRAFT_FILE\"], plane_dict)",
"title": ""
},
{
"docid": "f892133cf55ec9528c0f346cdbb6b53b",
"score": "0.5417904",
"text": "def project_on_to_plane(self, plane_normal):\n return Vector()",
"title": ""
},
{
"docid": "95426ca54f018bacbccaf0009f7ad20e",
"score": "0.53655446",
"text": "def Plane(pos=(0, 0, 0), normal=(0, 0, 1), sx=1, sy=None, c=\"g\", alpha=1):\n if sy is None:\n sy = sx\n ps = vtk.vtkPlaneSource()\n ps.SetResolution(1, 1)\n tri = vtk.vtkTriangleFilter()\n tri.SetInputConnection(ps.GetOutputPort())\n tri.Update()\n poly = tri.GetOutput()\n axis = np.array(normal) / np.linalg.norm(normal)\n theta = np.arccos(axis[2])\n phi = np.arctan2(axis[1], axis[0])\n t = vtk.vtkTransform()\n t.PostMultiply()\n t.Scale(sx, sy, 1)\n t.RotateY(np.rad2deg(theta))\n t.RotateZ(np.rad2deg(phi))\n tf = vtk.vtkTransformPolyDataFilter()\n tf.SetInputData(poly)\n tf.SetTransform(t)\n tf.Update()\n pd = tf.GetOutput()\n actor = Actor(pd, c, alpha)\n actor.SetPosition(pos)\n settings.collectable_actors.append(actor)\n return actor",
"title": ""
},
{
"docid": "3da68bdf46f80e4f092d7ad2eccf0a7c",
"score": "0.5341773",
"text": "def project_point_on_to_plane(self, plane_base, plane_normal):\n return Vector()",
"title": ""
},
{
"docid": "194eb131c45d2cdc5a791a263f2863e0",
"score": "0.5318441",
"text": "def create_planes(func: vtk.vtkSampleFunction,\n number_of_planes: int) -> vtk.vtkActor:\n actor = vtk.vtkActor()\n append = vtk.vtkAppendFilter()\n\n dimensions = func.GetSampleDimensions()\n slice_increment = (dimensions[2] - 1) // (number_of_planes + 1)\n slice_num = -4\n for i in range(0, number_of_planes):\n extract = vtk.vtkExtractVOI()\n extract.SetInputConnection(func.GetOutputPort())\n extract.SetVOI(0, dimensions[0] - 1,\n 0, dimensions[1] - 1,\n slice_num + slice_increment,\n slice_num + slice_increment)\n append.AddInputConnection(extract.GetOutputPort())\n slice_num += slice_increment\n append.Update()\n\n planes_mapper = vtk.vtkDataSetMapper()\n planes_mapper.SetInputConnection(append.GetOutputPort())\n planes_mapper.SetScalarRange(0, 7)\n\n actor.SetMapper(planes_mapper)\n actor.GetProperty().SetAmbient(1.)\n\n return actor",
"title": ""
},
{
"docid": "4fe012752b7d81033e284fae3d7ac1ce",
"score": "0.5316544",
"text": "def set_plane(output, plane, scalar):\n\n\n parameters = {\n \"dst\":output,\n \"plane\":int(plane),\n \"value\":float(scalar)\n }\n\n execute(__file__, 'set_plane_' + str(len(output.shape)) + 'd_x.cl', 'set_plane_' + str(len(output.shape)) + 'd', output.shape, parameters);",
"title": ""
},
{
"docid": "db9f51a0ae4313e8d55d0c8991d6aa36",
"score": "0.53159255",
"text": "def mirror_by_plane(self, plane):\n return Vector()",
"title": ""
},
{
"docid": "1aeae27aa589545d752eac2a6e77040c",
"score": "0.53090656",
"text": "def plot_data_plane_init(self):\n plot_overrides = {'PLOT_DATA_PLANE_INPUT_TEMPLATE': 'template',\n 'PLOT_DATA_PLANE_OUTPUT_TEMPLATE': 'template',\n 'PLOT_DATA_PLANE_FIELD_NAME': 'field_name',\n 'PLOT_DATA_PLANE_CONVERT_TO_IMAGE': True,\n }\n\n if not self.c_dict['BACKGROUND_MAP']:\n plot_overrides['PLOT_DATA_PLANE_FIELD_EXTRA'] = (\n \"map_data={ source=[];}\"\n )\n\n pdp_wrapper = PlotDataPlaneWrapper(self.config,\n config_overrides=plot_overrides)\n return pdp_wrapper",
"title": ""
},
{
"docid": "e227bb27cae7ad1a97a2c5921891e3ac",
"score": "0.52886415",
"text": "def project_point_on_to_plane(cls, point, plane_base, plane_normal):\n return Vector()",
"title": ""
},
{
"docid": "b021b19d60ab56a42cd74d411f6f0538",
"score": "0.52709067",
"text": "def vector_mirror_by_plane(cls, a, plane):\n return Vector()",
"title": ""
},
{
"docid": "dacb90c01ba6c87447728f4b0183600f",
"score": "0.5269757",
"text": "def zplane(b,a,filename=None):\n plt.figure(3)\n # get a figure/plot\n ax = plt.subplot(111)\n\n # create the unit circle\n uc = patches.Circle((0,0), radius=1, fill=False,\n color='black', ls='dashed')\n ax.add_patch(uc)\n\n # The coefficients are less than 1, normalize the coeficients\n if np.max(b) > 1:\n kn = np.max(b)\n b = b/float(kn)\n else:\n kn = 1\n\n if np.max(a) > 1:\n kd = np.max(a)\n a = a/float(kd)\n else:\n kd = 1\n \n # Get the poles and zeros\n p = np.roots(a)\n z = np.roots(b)\n k = kn/float(kd)\n \n # Plot the zeros and set marker properties \n t1 = plt.plot(z.real, z.imag, 'go', ms=10)\n plt.setp( t1, markersize=10.0, markeredgewidth=1.0,\n markeredgecolor='k', markerfacecolor='g')\n\n # Plot the poles and set marker properties\n t2 = plt.plot(p.real, p.imag, 'rx', ms=10)\n plt.setp( t2, markersize=12.0, markeredgewidth=3.0,\n markeredgecolor='r', markerfacecolor='r')\n\n ax.spines['left'].set_position('center')\n ax.spines['bottom'].set_position('center')\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n\n # set the ticks\n r = 1.5; plt.axis('scaled'); plt.axis([-r, r, -r, r])\n ticks = [-1, -.5, .5, 1]; plt.xticks(ticks); plt.yticks(ticks)\n\n if filename is None:\n plt.show()\n else:\n plt.savefig(filename)\n \n\n return z, p, k",
"title": ""
},
{
"docid": "4d82d57f950859ef55fd56cd369f5318",
"score": "0.52664226",
"text": "def show_plane(orig, n, scale=1.0, **kwargs):\n b1 = orthogonal_vector(n)\n b1 /= la.norm(b1)\n b2 = np.cross(b1, n)\n b2 /= la.norm(b2)\n verts = [orig + scale*(-b1 - b2),\n orig + scale*(b1 - b2),\n orig + scale*(b1 + b2),\n orig + scale*(-b1 + b2)]\n faces = [(0, 1, 2), (0, 2, 3)]\n trimesh3d(np.array(verts), faces, **kwargs)",
"title": ""
},
{
"docid": "b1acd6daa53de952df986f618fd125d0",
"score": "0.5262819",
"text": "def project_vector_on_to_plane(cls, v, plane_normal):\n return Vector()",
"title": ""
},
{
"docid": "1591250981236eb6146620c6def62f6c",
"score": "0.5252082",
"text": "def plane_axes(plane):\n assert isinstance(plane, gen_cuts.Plane3D)\n p_normal = normal(plane)\n # Make sure Z is not fixed\n assert not (p_normal[0] == 0 and p_normal[1] == 0)\n\n # Get a sideways vector on the plane\n up = np.array([0, 0, 1])\n side_on_plane = normalize(np.cross(up, p_normal))\n\n # And an up vector on the plane\n up_on_plane = normalize(np.cross(side_on_plane, p_normal))\n if up_on_plane[2] < 0:\n up_on_plane = -up_on_plane\n\n # Take Ax + By + Cz + D = 0\n # Substitute Z = 0 and obtain Ax + By = -D\n # We get one of two solutions:\n # x = - (D + By) / A\n # y = - (D + Ax) / B\n # We want the more numerically stable solution, so we'll pick the first\n # if abs(A) > abs(B), otherwise we'll take the second. In these cases we\n # we'll set the other coordinate (x or y) to zero.\n if abs(plane.A) > abs(plane.B):\n x = -plane.D / plane.A\n y = 0\n z = 0\n else:\n x = 0\n y = -plane.D / plane.B\n z = 0\n\n return PlaneAxes(side=side_on_plane, up=up_on_plane,\n origin=np.array([x, y, z]))",
"title": ""
},
{
"docid": "82b986081d4f7fc5397a6696c1741442",
"score": "0.52338004",
"text": "def plane(normal=UP, point=ORIGIN):\n normal = _normalize(normal)\n def f(p):\n return np.dot(point - p, normal)\n return f",
"title": ""
},
{
"docid": "b259760952d92a860b48ee0f37057213",
"score": "0.52290386",
"text": "def refresh(self):\n planeArray = np.zeros([self.xlen,self.ylen])\n for xpos in range(self.xlen):\n for ypos in range(self.ylen):\n # compute the value at each of the coordinate points in the plane\n planeArray[(self.ylen-ypos-1),xpos] = self.f( (xpos*self.xstep+self.xmin) + (ypos*self.ystep+self.ymin)*1j )\n ylabels = [str(self.ymax-ypos*self.ystep) for ypos in range(self.ylen)]\n xlabels = [str(xpos*self.xstep+self.xmin) for xpos in range(self.xlen)]\n self.plane = pd.DataFrame(planeArray, index=ylabels, columns=xlabels)",
"title": ""
},
{
"docid": "904b027019e0718dcdeef8381d9f81c4",
"score": "0.5209764",
"text": "def _fit_plane(vertices):\n\n # The center is always the mean of the four corners.\n c = 0.25 * (vertices[0] + vertices[1] + vertices[2] + vertices[3])\n\n # Compute covariance matrix.\n C = matrix.rec(\n elems=tuple(vertices[0] - c) \\\n + tuple(vertices[1] - c) \\\n + tuple(vertices[2] - c) \\\n + tuple(vertices[3] - c),\n n=(4, 3))\n cov = C.transpose_multiply(C)\n\n # Obtain eigenvalues of covariance matrix. XXX Ouch, this is ugly.\n from tntbx import svd\n cov_flex = flex.double(flex.grid(3, 3))\n cov_flex += flex.double((cov(0, 0), cov(0, 1), cov(0, 2),\n cov(1, 0), cov(1, 1), cov(1, 2),\n cov(2, 0), cov(2, 1), cov(2, 2)))\n svd = svd(cov_flex)\n\n # Get least-squares residual (smallest eigenvalue) and plane normal\n # (eigenvector corresponding to smallest eigenvalue) from SVD.\n rss = svd.s()[2, 2]\n normal = matrix.col((svd.v()[0, 2], svd.v()[1, 2], svd.v()[2, 2]))\n\n \"\"\"\n rss2 = normal.dot(vertices[0] - c)**2 \\\n + normal.dot(vertices[1] - c)**2 \\\n + normal.dot(vertices[2] - c)**2 \\\n + normal.dot(vertices[3] - c)**2\n assert math.fabs(rss - rss2) < 1e-5\n \"\"\"\n\n # Project each vertex into the plane. Normal must have unit length.\n vertices_lsq = [(vertices[0] - c) - normal.dot(vertices[0] - c) * normal + c,\n (vertices[1] - c) - normal.dot(vertices[1] - c) * normal + c,\n (vertices[2] - c) - normal.dot(vertices[2] - c) * normal + c,\n (vertices[3] - c) - normal.dot(vertices[3] - c) * normal + c]\n\n return (vertices_lsq, rss)",
"title": ""
},
{
"docid": "b372576a686ed0a886ecce27b4153d93",
"score": "0.520438",
"text": "def parabola():\r\n # method 1: using the algebraic function\r\n fun = lambda x, y: x*x+y*y\r\n x1 = np.arange(-2, 2.5, 0.02)\r\n y1 = np.arange(-2, 2.5, 0.2)\r\n p_fun = new.mesh(xyfun=fun, x=x1, y=y1, name='parabola_fun', coll_name='surface')\r\n p_fun.loc += mathutils.Vector((4.0, -4.0, 0.0))\r\n\r\n def fun2mat(xyfun, tx=np.array([]), ty=np.array([])):\r\n \"\"\"\r\n This functionality is already in the bpn module.\r\n It is here only to illustrate the versatility of new.mesh creation\r\n \"\"\"\r\n assert isinstance(xyfun, types.FunctionType)\r\n assert xyfun.__code__.co_argcount == 2 # function has two input arguments\r\n if tx is fun2mat.__defaults__[0]: # default ranges\r\n tx = np.arange(-2, 2, 0.1)\r\n if ty is fun2mat.__defaults__[1]:\r\n ty = np.arange(-2, 2, 0.1)\r\n return np.array([[xyfun(xv, yv) for yv in ty] for xv in tx])\r\n\r\n # method 2: MATLAB-style surf, using a 2d matrix Z\r\n z1 = fun2mat(fun, x1, y1)\r\n p_xyz = new.mesh(x=x1, y=y1, z=z1, name='parabola_xyz', coll_name='surface')\r\n p_xyz.loc += mathutils.Vector((-4.0, -4.0, 0.0))\r\n\r\n def mat2mesh(tz, tx=np.array([]), ty=np.array([])):\r\n \"\"\"\r\n z is a 2-D numpy array or a 2D list\r\n returns:\r\n v list of vertices\r\n f list of faces\r\n This is here only for demonstration. It is already in bpn module.\r\n \"\"\"\r\n if tx is mat2mesh.__defaults__[0]:\r\n tx = np.arange(0, np.shape(tz)[0])\r\n if ty is mat2mesh.__defaults__[1]:\r\n ty = np.arange(0, np.shape(tz)[1])\r\n\r\n nX = len(tx)\r\n nY = len(ty)\r\n\r\n assert len(tx) == np.shape(tz)[0]\r\n assert len(ty) == np.shape(tz)[1]\r\n \r\n v = [(xv, yv, tz[ix][iy]) for iy, yv in enumerate(ty) for ix, xv in enumerate(tx)]\r\n f = [(iy*nX+ix, iy*nX+ix+1, (iy+1)*nX+(ix+1), (iy+1)*nX+ix) for iy in np.arange(0, nY-1) for ix in np.arange(0, nX-1)]\r\n return v, f\r\n\r\n # method 3: by specifying the vertices and faces - this is here mainly for testing\r\n v1, f1 = mat2mesh(z1, tx=x1, ty=y1)\r\n p_vf = new.mesh(v=v1, f=f1, name='parabola_vf', coll_name='surface')\r\n p_vf.loc += mathutils.Vector((0, 4.0, 0.0))",
"title": ""
},
{
"docid": "1eec3c5d621e0be8c5ff4c38792c86a1",
"score": "0.5197117",
"text": "def plane(xi, points, values):\n\n alpha = np.linalg.solve(points[1:] - points[0], values[1:] - values[0])\n return (np.stack(xi, axis=-1) - points[0]).dot(alpha) + values[0]",
"title": ""
},
{
"docid": "b6b027bf8d6944170c955a82c2eb6e66",
"score": "0.51860917",
"text": "def from_coords(origin, target):\n\t\txo,yo,zo = origin\n\t\txt,yt,zt = target\n\n\t\tn = Vector(xt-xo, yt-yo, zt-zo).unit()\n\t\treturn Plane.from_normal(n, n.i*xo + n.j*yo + n.k*zo)",
"title": ""
},
{
"docid": "21fd03067d0920c474b2951861b37250",
"score": "0.5146844",
"text": "def plot_line_in_complex_plane(t: np.ndarray,\n xf: Callable, yf: Callable,\n function: Callable, title: str,\n dlim: List[int] = [],\n show_identity=False) -> None:\n x = xf(t)\n y = yf(t)\n z = function(x + 1.0j*y)\n if show_identity is True:\n a, = plt.plot(x, y, color=\"black\", linewidth=0.75)\n b, = plt.plot(np.real(z), np.imag(z), color=\"aqua\", linewidth=0.75)\n plt.legend((a, b), ['z', 'f(z)'])\n b = plt.plot(np.real(z), np.imag(z), linewidth=0.75)\n # These set the aspect ratio to the correct scale,\n # as well as adding a title and labels.\n plt.gca().set_aspect('equal', adjustable='box')\n plt.title(title)\n plt.xlabel(\"x\")\n plt.ylabel(\"iy\")\n plt.grid(True)\n\n # The following lines of code remove the axis label.\n # Uncomment these and comment the labels above to remove labels.\n # plt.xticks([])\n # plt.yticks([])\n\n if (len(dlim) == 4):\n plt.xlim(dlim[0], dlim[1])\n plt.ylim(dlim[2], dlim[3])\n plt.savefig(title + '.png', dpi=400)\n plt.show()\n plt.close()",
"title": ""
},
{
"docid": "9c8ae24a7ecf92b8334ee2203f48089d",
"score": "0.5146186",
"text": "def planeMACl(self):\n return Plane(Point(-self.cMACyPos, 0, 0), Vector(1, 0, 0),\n hidden=True)",
"title": ""
},
{
"docid": "9c8ae24a7ecf92b8334ee2203f48089d",
"score": "0.5146186",
"text": "def planeMACl(self):\n return Plane(Point(-self.cMACyPos, 0, 0), Vector(1, 0, 0),\n hidden=True)",
"title": ""
},
{
"docid": "64366ac9cee7594d0fc003cda52faebb",
"score": "0.5127683",
"text": "def get_plane_ben(M,v, axis_str, x_range, y_range, z_range):\n x0, y0, _= M\n a, b, _= v\n\n if axis_str == \"x\":\n Y = y_range\n X = x0*np.ones(2)\n Z = np.array(list(z_range)+list(z_range)).reshape((2,2))\n elif axis_str == \"y\":\n X = x_range\n Y = y0*np.ones(2)\n Z = np.array([z_range[0]]*2+[z_range[1]]*2).reshape((2,2))\n elif axis_str == \"xy\":\n X = x_range\n Y = y0+b*(X-x0)/a\n Z = np.array([z_range[0]]*2+[z_range[1]]*2).reshape((2,2))\n else:\n pass\n return X, Y, Z",
"title": ""
},
{
"docid": "22f8c02f4597c06caa3283b0e53813c2",
"score": "0.50950354",
"text": "def planeMACr(self):\n return Plane(Point(self.cMACyPos, 0, 0), Vector(1, 0, 0),\n hidden=True)",
"title": ""
},
{
"docid": "22f8c02f4597c06caa3283b0e53813c2",
"score": "0.50950354",
"text": "def planeMACr(self):\n return Plane(Point(self.cMACyPos, 0, 0), Vector(1, 0, 0),\n hidden=True)",
"title": ""
},
{
"docid": "85cbf21e87584a9ba11457e6847bebfa",
"score": "0.50760114",
"text": "def Grid(\n pos=(0, 0, 0),\n normal=(0, 0, 1),\n sx=1,\n sy=1,\n c=\"g\",\n alpha=1,\n lw=1,\n resx=10,\n resy=10,\n):\n ps = vtk.vtkPlaneSource()\n ps.SetResolution(resx, resy)\n ps.Update()\n poly0 = ps.GetOutput()\n t0 = vtk.vtkTransform()\n t0.Scale(sx, sy, 1)\n tf0 = vtk.vtkTransformPolyDataFilter()\n tf0.SetInputData(poly0)\n tf0.SetTransform(t0)\n tf0.Update()\n poly = tf0.GetOutput()\n axis = np.array(normal) / np.linalg.norm(normal)\n theta = np.arccos(axis[2])\n phi = np.arctan2(axis[1], axis[0])\n t = vtk.vtkTransform()\n t.PostMultiply()\n t.RotateY(np.rad2deg(theta))\n t.RotateZ(np.rad2deg(phi))\n tf = vtk.vtkTransformPolyDataFilter()\n tf.SetInputData(poly)\n tf.SetTransform(t)\n tf.Update()\n pd = tf.GetOutput()\n actor = Actor(pd, c, alpha)\n actor.GetProperty().SetRepresentationToWireframe()\n actor.GetProperty().SetLineWidth(lw)\n actor.SetPosition(pos)\n settings.collectable_actors.append(actor)\n return actor",
"title": ""
},
{
"docid": "315dbe3a3fdcc1ebbe2805b28d82e159",
"score": "0.50717515",
"text": "def refresh(self):\n rx = np.linspace( self.xmin, self.xmax, self.xlen )\n ry = np.linspace( self.ymin, self.ymax, self.ylen )\n x, y = np.meshgrid( rx, ry )\n planeArray = x + y*1j\n self.f( planeArray )\n ylabels = [str(self.ymax-ypos*self.ystep) for ypos in range(self.ylen)]\n xlabels = [str(xpos*self.xstep+self.xmin) for xpos in range(self.xlen)]\n self.plane = pd.DataFrame(planeArray, index=ylabels, columns=xlabels)",
"title": ""
},
{
"docid": "6358aa9e98a71fc7ef6f2e1e45e99d10",
"score": "0.50683236",
"text": "def apply(self, f):\n ### Use pandas' applymap method to modify all plane points using f\n self.plane = self.plane.applymap(f)\n ### Store the applied function for future reference\n self.fs.append(f)",
"title": ""
},
{
"docid": "ae5d45f0bafcd51d7b7cfa6c2983f704",
"score": "0.5022259",
"text": "def Paraboloid(pos=(0, 0, 0), r=1, height=1, axis=(0, 0, 1), c=\"cyan\", alpha=1, res=50):\n quadric = vtk.vtkQuadric()\n quadric.SetCoefficients(1, 1, 0, 0, 0, 0, 0, 0, height / 4, 0)\n # F(x,y,z) = a0*x^2 + a1*y^2 + a2*z^2\n # + a3*x*y + a4*y*z + a5*x*z\n # + a6*x + a7*y + a8*z +a9\n sample = vtk.vtkSampleFunction()\n sample.SetSampleDimensions(res, res, res)\n sample.SetImplicitFunction(quadric)\n\n contours = vtk.vtkContourFilter()\n contours.SetInputConnection(sample.GetOutputPort())\n contours.GenerateValues(1, 0.01, 0.01)\n contours.Update()\n\n axis = np.array(axis) / np.linalg.norm(axis)\n theta = np.arccos(axis[2])\n phi = np.arctan2(axis[1], axis[0])\n t = vtk.vtkTransform()\n t.PostMultiply()\n t.RotateY(np.rad2deg(theta))\n t.RotateZ(np.rad2deg(phi))\n t.Scale(r, r, r)\n tf = vtk.vtkTransformPolyDataFilter()\n tf.SetInputData(contours.GetOutput())\n tf.SetTransform(t)\n tf.Update()\n pd = tf.GetOutput()\n\n actor = Actor(pd, c, alpha).flipNormals()\n actor.GetProperty().SetInterpolationToPhong()\n actor.mapper.ScalarVisibilityOff()\n actor.SetPosition(pos)\n settings.collectable_actors.append(actor)\n return actor",
"title": ""
},
{
"docid": "c405a9777b03aa4d9bd0b8902176322d",
"score": "0.49944082",
"text": "def from_plane(plane):\n\t\treturn plane.normal.copy()",
"title": ""
},
{
"docid": "38f78bf4082587ba0e0ad9644b53e1c4",
"score": "0.49903047",
"text": "def project_points_to_plane(self, x_in, y_in, z_in, plane_coeffs):\n # define the origin (on the plane) as the point pointed by the coeff vector\n # d * (a, b, c)\n origin = (plane_coeffs[3]) * plane_coeffs[0:3]\n # print \"origin: %s\" % origin\n \n # for each point project and obtain its x, y coords\n v_x = x_in - origin[0]\n v_y = y_in - origin[1]\n v_z = z_in - origin[2]\n \n dist = v_x * plane_coeffs[0] + v_y * plane_coeffs[1] + v_z * plane_coeffs[2]\n # print \"dist: %s\" % dist\n \n proj_x = x_in - dist * plane_coeffs[0]\n proj_y = y_in - dist * plane_coeffs[1]\n proj_z = z_in - dist * plane_coeffs[2]\n \n return proj_x, proj_y, proj_z, origin",
"title": ""
},
{
"docid": "5696a13c59169942d2d3b4acadb947b5",
"score": "0.49795002",
"text": "def __init__ (self, orientation, origin, plane_centre, image_names, spacing, image_orientation):\n\n self.x_vtx_offset = -1\n self.use_transparency = False\n self.orientation = orientation\n self.reverse = False # Reverse image indices\n self.origin = origin\n self.plane_centre = plane_centre\n\n self.img_names = image_names\n self.spacing = spacing\n \n self.loop_name = \"loop\" + str(self.orientation)[:3]\n self.plane_name = \"plane\"+str(self.orientation)[:3]\n self.is_updated = False\n \n plane = self.create_plane(image_orientation)\n self.update_image(plane)",
"title": ""
},
{
"docid": "93277d8c67a58b60e57c715ffe95ee20",
"score": "0.49560016",
"text": "def plane2PointNormal( plane ):\n (a,b,c,d) = plane\n return asarray((-d*a,-d*b,-d*c),'f'), asarray((a,b,c),'f')",
"title": ""
},
{
"docid": "19ff8aa031d772653af9a07dff1beac6",
"score": "0.49497247",
"text": "def add_plane(self,\n position,\n color,\n plane=None,\n normal=(0.0, 0.0, 1.0),\n type='circle',\n width=4,\n height=4,\n opacity=1.0):\n if type == 'square':\n geometry = PlaneGeometry(width=width,\n height=height,\n widthSegments=10,\n heightSegments=10)\n else:\n geometry = CircleGeometry(radius=width / 2, segments=48)\n\n material = MeshStandardMaterial(color=color,\n roughness=0.3,\n metalness=0.0,\n side='DoubleSide',\n transparent=True,\n opacity=opacity)\n\n mesh = Mesh(geometry=geometry, material=material, position=position)\n\n if plane == 'xy' or plane == 'yx':\n normal = (0.0, 0.0, 1.0)\n elif plane == 'xz' or plane == 'zx':\n normal = (0.0, 1.0, 0.0)\n elif plane == 'yz' or plane == 'zy':\n normal = (1.0, 0.0, 0.0)\n\n # If the plane is not rotated skip the rotation step\n if normal[2] != 1.0 or normal[2] != -1.0:\n R = self.__plane_rotation_matrix(normal)\n mesh.setRotationFromMatrix(R)\n self.scene.add(mesh)",
"title": ""
},
{
"docid": "cc473b5b5888979b794537a46f48a01c",
"score": "0.49165383",
"text": "def zplane(b, a, axis_lim=1, axes= None, circlecolor='black', figsize=(8,8), filename=None):\n\n # get a figure/plot \n if axes != None:\n ax = axes\n else:\n ax = plt.subplot(111)\n \n plt.rcParams[\"figure.figsize\"] = figsize\n \n # create the unit circle\n uc = patches.Circle((0,0), radius=1, fill=False,\n color=circlecolor, ls='dashed')\n ax.add_patch(uc)\n\n # The coefficients are less than 1, normalize the coeficients\n if np.max(b) > 1:\n kn = np.max(b)\n b = b/float(kn)\n else:\n kn = 1\n\n if np.max(a) > 1:\n kd = np.max(a)\n a = a/float(kd)\n else:\n kd = 1\n \n # Get the poles and zeros\n p = np.roots(a)\n z = np.roots(b)\n k = kn/float(kd)\n \n # Plot the zeros and set marker properties \n t1 = plt.plot(z.real, z.imag, 'go', ms=10)\n plt.setp( t1, markersize=10.0, markeredgewidth=1.0,\n markeredgecolor='k', markerfacecolor='g')\n\n # Plot the poles and set marker properties\n t2 = plt.plot(p.real, p.imag, 'rx', ms=10)\n plt.setp( t2, markersize=12.0, markeredgewidth=3.0,\n markeredgecolor='r', markerfacecolor='r')\n\n ax.spines['left'].set_position('center')\n ax.spines['bottom'].set_position('center')\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n\n # set the ticks\n r = axis_lim; r_axis = r + 0.5; plt.axis('scaled'); plt.axis([-r_axis, r_axis, -r_axis, r_axis])\n ticks = np.arange(-r, r + 0.5, 0.5); plt.xticks(ticks); plt.yticks(ticks)\n \n if filename is None:\n plt.show()\n else:\n plt.savefig(filename)\n \n\n return z, p, k",
"title": ""
},
{
"docid": "5cac940537cc3e1b76bfbe66e3e6913a",
"score": "0.48944932",
"text": "def __init__(\n self,\n backend=None,\n num_points=150,\n *,\n plane=None,\n length_scale_units=u.km,\n ):\n # Initialize the backend, number of points and length scale\n self._backend = backend or orbit_plotter_backends.Matplotlib2D(\n ax=None, use_dark_theme=False\n )\n self._num_points = num_points\n self._length_scale_units = length_scale_units\n\n # Initialize the attractor\n self._attractor = None\n self._attractor_radius = np.inf * length_scale_units\n\n # Initialize the plane and frame used by the plotter\n self._plane = plane or Planes.EARTH_EQUATOR\n self._frame = None\n\n # Initialize the list containing all the plotted trajectories\n self._trajectories = [] # type: List[Trajectory]",
"title": ""
},
{
"docid": "ade8680ddfa565aa7faa3992a0f93a24",
"score": "0.4871801",
"text": "def dodecahedron_planes(draw, r, tet_orig, scale = 300, shift = np.array([1000,1000,0])):\n phi = (1+np.sqrt(5)) / 2\n cind = -1\n for pm1 in [-1,1]:\n coeff1 = np.array([1, pm1 * phi, 0])\n for i1 in range(3):\n for pm2 in [-1,1]:\n cind += 1\n coeff = np.array([coeff1[ (i1+jj)%3] for jj in range(3)])\n penta = np.array([i for i in tet_orig if (np.dot(i, coeff ) + pm2*phi*phi == 0)])\n penta = np.dot(penta, r)\n hull = ConvexHull([i[:2] for i in penta]).vertices\n sqr1 = penta * scale + shift[:3]\n poly = [(sqr1[i][0],sqr1[i][1]) for i in hull]\n smat = sum(penta)\n face_angle = np.dot(smat/np.sqrt(sum(smat**2)), np.array([0,0.01,0.99]))\n forward_face = np.dot(sum(penta), np.array([0,0,1])) > -1e-3\n angles.append(face_angle)\n rgba = colorFromAngle2(face_angle,h=134,s=144,maxx=0.95)\n if forward_face: #Meaning the plane is facing forward.\n draw.polygon(poly, rgba)\n ## Uncomment if you want to draw the edges.\n #for i in range(len(poly)):\n # vv1 = poly[i]\n # vv2 = poly[(i+1)%5]\n # if forward_face:\n # draw.line((vv1[0],vv1[1],vv2[0],vv2[1]), fill = (0,255,0,255), width = 3)\n # else:\n # draw.line((vv1[0],vv1[1],vv2[0],vv2[1]), fill = (0,255,0,255), width = 3)",
"title": ""
},
{
"docid": "55f26ede5cead737428c3998c6d07b5d",
"score": "0.48582056",
"text": "def fromJSON( self, filename ):\n # open the file for reading\n from pprint import pprint\n\n # open and read in the json file, with help from: http://stackoverflow.com/questions/2835559/parsing-values-from-a-json-file-in-python\n with open( filename ) as jsonfile: \n data = json.load(jsonfile)\n\n # parse the parameter values\n self.xmin = data[\"JuliaPlaneParameters\"][\"xmin\"]\n self.xmax = data[\"JuliaPlaneParameters\"][\"xmax\"]\n self.xlen = int( data[\"JuliaPlaneParameters\"][\"xlen\"] )\n self.ymin = data[\"JuliaPlaneParameters\"][\"ymin\"]\n self.ymax = data[\"JuliaPlaneParameters\"][\"ymax\"]\n self.ylen = int( data[\"JuliaPlaneParameters\"][\"ylen\"] )\n self.c = data[\"JuliaPlaneParameters\"][\"creal\"] + data[\"JuliaPlaneParameters\"][\"cimaginary\"]*1j\n self.xstep = (self.xmax - self.xmin)/(self.xlen - 1)\n self.ystep = (self.ymax - self.ymin)/(self.ylen - 1)\n self.set_f( self.c ) # set the tranformation function. Note that this automatically calls refresh()\n\n # uncomment this line to see the re-constituted plane\n# print( self.plane )\n\n # we're done, clean up the file\n jsonfile.close()",
"title": ""
},
{
"docid": "5c56afa87723d55aa6774c9b143ebc05",
"score": "0.4843919",
"text": "def make_plane_from_point_and_normal(cls, point, normal):\n return Plane()",
"title": ""
},
{
"docid": "5e176beb547117f1b9f40ad70bf9ec57",
"score": "0.48368517",
"text": "def sample_from_planes(self,\n plane_features: torch.Tensor,\n coordinates: torch.Tensor,\n interp_mode: str = 'bilinear',\n box_warp: float = None) -> torch.Tensor:\n N, n_planes, C, H, W = plane_features.shape\n _, M, _ = coordinates.shape\n plane_features = plane_features.view(N * n_planes, C, H, W)\n\n coordinates = (2 / box_warp) * coordinates\n # NOTE: do not support change projection_mode for specific renderer,\n # use self.projection_mode\n projected_coordinates = self.project_onto_planes(coordinates)\n projected_coordinates = projected_coordinates[:, None, ...]\n\n output_features = torch.nn.functional.grid_sample(\n plane_features,\n projected_coordinates.float(),\n mode=interp_mode,\n padding_mode='zeros',\n align_corners=False)\n output_features = output_features.permute(0, 3, 2, 1).reshape(\n N, n_planes, M, C)\n return output_features",
"title": ""
},
{
"docid": "ddac1ed8573f0f722aa41a9748cfa2f1",
"score": "0.48304066",
"text": "def PolygonPlane(face, points):\n\n if len(face) < 3:\n return (0.0, 0.0, 1.0) # arbitrary, we really have no idea\n else:\n coords = [points.pos[i] for i in face]\n return Normal(coords)",
"title": ""
},
{
"docid": "0a09aed63ebb851d0fb5d53c93552f31",
"score": "0.48082134",
"text": "def project_onto_planes(self, coordinates: torch.Tensor) -> torch.Tensor:\n N, _, _ = coordinates.shape\n xy_coord = coordinates[:, :, (0, 1)] # (bz, N_points, 3)\n xz_coord = coordinates[:, :, (0, 2)] # (bz, N_points, 3)\n if self.projection_mode.upper() == 'OFFICIAL':\n yz_coord = coordinates[:, :, (2, 0)] # actually zx_coord\n else:\n yz_coord = coordinates[:, :, (2, 1)]\n coord_proejcted = torch.cat([xy_coord, xz_coord, yz_coord], dim=0)\n # create a index list to release the following remapping:\n # [xy, xy, ..., xz, xz, ..., yz, yz, ...] -> [xy, xz, yz, ...]\n index = []\n for n in range(N):\n index += [n, N + n, N * 2 + n]\n return coord_proejcted[index, ...]",
"title": ""
},
{
"docid": "7f8e545a6ee68923e48f380cf526e2dd",
"score": "0.47924134",
"text": "def plot_f_xy(self, f, mesh_domain, **kwargs):\n super().plot_surface(\n *mesh_domain, map_f_xy(f, mesh_domain),\n **kwargs)",
"title": ""
},
{
"docid": "b1ce0ff4d46fa9cd4ce0d6de4912726e",
"score": "0.47908765",
"text": "def move(self, point):\n\t\tx,y,z = point\n\t\ta,b,c = self.normal.tuple()\n\t\td = a*x + b*y + c*z\n\t\treturn Plane(a, b, c, d)",
"title": ""
},
{
"docid": "d4a3a8f9c76f883295b5a014de094791",
"score": "0.47838867",
"text": "def transformation_between_planes(newplane, oldplane):\n newaxis = np.array(newplane[:3])\n Rnew = rotation_matrix_from_axes(newaxis, Z_AXIS)[:3,:3]\n newpoint = np.dot(Rnew, np.array([0,0,newplane[3]]))\n oldaxis = np.array(oldplane[:3])\n Rold = rotation_matrix_from_axes(oldaxis, Z_AXIS)[:3,:3]\n oldpoint = np.dot(Rold, np.array([0,0,oldplane[3]]))\n T = rotation_matrix_from_axes(newaxis, oldaxis)\n T[:3,3] = -newpoint + np.dot(T[:3,:3], oldpoint)\n return T",
"title": ""
},
{
"docid": "ab78af1367979184dbef31c4625dd135",
"score": "0.4781972",
"text": "def SelectPlane(self, p_float=..., p_float=..., p_float=..., *args, **kwargs):\n ...",
"title": ""
},
{
"docid": "4687a2711d5c93b044f22aae839632cb",
"score": "0.47760838",
"text": "def translatepointnewcoordinatesystem(point, plane ): \n vector = rg.Point3d.Subtract(point, plane.Origin) \n \n\n xcomp = componentofbindira(plane.XAxis, vector)\n ycomp = componentofbindira(plane.YAxis, vector) \n zcomp = componentofbindira(plane.ZAxis, vector) \n \n newPoint = rg.Point3d(xcomp, ycomp, zcomp) \n \n return newPoint",
"title": ""
},
{
"docid": "060b75768ad33268bdb24e635aa3c05a",
"score": "0.47714168",
"text": "def intersect_with_plane(self, origin=(0, 0, 0), normal=(1, 0, 0)):\n plane = vtk.vtkPlane()\n plane.SetOrigin(origin)\n plane.SetNormal(normal)\n\n cutter = vtk.vtkPolyDataPlaneCutter()\n cutter.SetInputData(self.polydata())\n cutter.SetPlane(plane)\n cutter.InterpolateAttributesOn()\n cutter.ComputeNormalsOff()\n cutter.Update()\n\n msh = Mesh(cutter.GetOutput(), \"k\", 1).lighting(\"off\")\n msh.GetProperty().SetLineWidth(3)\n msh.name = \"PlaneIntersection\"\n\n msh.pipeline = OperationNode(\n \"intersect_with_plan\", parents=[self],\n comment=f\"#pts {msh.inputdata().GetNumberOfPoints()}\"\n )\n return msh",
"title": ""
},
{
"docid": "f9dccfcd2291fa67d33bff88ca7280fe",
"score": "0.4762412",
"text": "def __implementation_distance_to_plane(self,\n plane=None,\n pt_1=None, pt_2=None, pt_3=None):\n if not plane is None:\n plane_a = plane.a\n plane_b = plane.b\n plane_c = plane.c\n normal = Vector(x=plane_a, y=plane_b, z=plane_c)\n # TODO - remove hardcode\n if abs(self.parallel_vector.product_with(normal)) > 0.001:\n return 0\n return self.origin.distance_to_plane(plane)\n elif not None in (pt_1, pt_2, pt_3):\n x = ((pt_2.y - pt_1.y) * (pt_3.z - pt_1.z) -\n (pt_3.y - pt_1.y) * (pt_2.z - pt_1.z))\n y = -((pt_2.x - pt_1.x) * (pt_3.z - pt_1.z) -\n (pt_3.x - pt_1.x) * (pt_2.z - pt_1.z))\n z = ((pt_2.x - pt_1.x) * (pt_3.y - pt_1.y) -\n (pt_3.x - pt_1.x) * (pt_2.y - pt_1.y))\n normal = Vector(x=x, y=y, z=z)\n # TODO - remove hardcode\n if self.parallel_vector.product_with(normal) > 0.001:\n return 0\n elements = [pt_1.x - self.origin.x,\n pt_1.y - self.origin.y,\n pt_1.z - self.origin.z,\n pt_2.x - self.origin.x,\n pt_2.y - self.origin.y,\n pt_2.z - self.origin.z,\n pt_3.x - self.origin.x,\n pt_3.y - self.origin.y,\n pt_3.z - self.origin.z]\n tetrahedron_volume = Matrix3(elements=elements).det() / 6\n vec12 = Vector(pt_1=pt_1, pt_2=pt_2)\n vec13 = Vector(pt_1=pt_1, pt_2=pt_3)\n cos_angle_1 = (vec12.product_with(vec13) /\n vec12.length() /\n vec13.length())\n sin_angle_1 = (1 - cos_angle_1**2)**0.5\n triangle_area = vec12.length() * vec13.length() * sin_angle_1 / 2\n return 3 * tetrahedron_volume / triangle_area\n else:\n print('error in line.__implementation_distance_to_plane:',\n 'incorrect arguments')\n return None",
"title": ""
},
{
"docid": "c9722354f937865a87c5c440058ccae2",
"score": "0.4755353",
"text": "def toJSON( self, filename ):\n # open the file for writing\n with open( filename, 'w') as jsonfile:\n # output the parameters needed to recreate the plane\n data = { \"JuliaPlaneParameters\": { \"xmin\":self.xmin, \"xmax\":self.xmax, \"xlen\":self.xlen, \"ymin\":self.ymin, \"ymax\":self.ymax, \"ylen\":self.ylen, \"creal\":self.c.real, \"cimaginary\":self.c.imag }}\n\n # handle the output of the plane contents\n mat = self.plane.as_matrix()\n for row in range( 0, self.xlen ):\n print( mat[ row ] )\n l = mat[ row ].tolist()\n data[ \"JuliaPlaneContents\" + str( row )] = { self.plane.index[ row ]: l }\n\n # write the accummulated dictionary to the JSON file\n json.dump(data, jsonfile, indent=4, sort_keys=True, separators=(',', ':'))\n\n # we're done, clean up the file\n jsonfile.close()",
"title": ""
},
{
"docid": "1e11b9bc07cc2238a565c59eae0c15b8",
"score": "0.47398037",
"text": "def compute_polygon_plane(self, polygon_id):\n return Plane()",
"title": ""
},
{
"docid": "b38ed80ffe7a681340a70a2055065686",
"score": "0.4739005",
"text": "def GetPlanePositions(self):\n ...",
"title": ""
},
{
"docid": "5306db6f63ae1597368fd7f4b219a7ca",
"score": "0.47357008",
"text": "def show_Gingl_plane():\n point1 = np.array([29, 0, 50])\n normal1 = np.array([3.7504, 326.8702, -1])\n \n # a plane is a*x+b*y+c*z+d=0\n # [a,b,c] is the normal. Thus, we have to calculate\n # d and we're set\n# d1 = -np.sum(point1*normal1)# dot product \n d1 = -63.2415 # TODO: wtf?\n \n # create x,y\n xx, yy = np.meshgrid(np.linspace(31, 24.5,100), np.linspace(-0.02,0.02, 100)) #ys should be from -.03 to .03\n \n # calculate corresponding z\n z1 = (-normal1[0]*xx - normal1[1]*yy - d1)*1./normal1[2] \n# \n# z1 = normal1[0]*xx \n# z1 = (-normal1[0]*xx - normal1[1]*yy - d1)*1./normal1[2]\n \n ##make mesh transparent\n theCM = cm.get_cmap()\n theCM._init()\n alphas = np.abs(np.linspace(-1.0, 1.0, theCM.N))\n theCM._lut[:-3,-1] = alphas \n \n # plot the surface\n plt3d = plt.figure().gca(projection='3d')\n plt3d.plot_surface(xx, yy, z1, cmap=theCM)\n \n \n plt.show()",
"title": ""
},
{
"docid": "cb034a950c2cba36661f522ee1fc64cb",
"score": "0.47291055",
"text": "def generate_ordered_puzzle_plane(row, column):\n plane = []\n size = row * column\n empty_element_index = [0, 0]\n\n # Generating ordered numbers list\n numbers = []\n for n in range(0, size):\n numbers.append(n)\n\n # Getting a index of an empty_element\n start = 0\n begin = row\n\n # Creating a 2D plane from 1D\n while begin <= row * column:\n plane.append(numbers[start:begin])\n start += row\n begin += row\n\n return PuzzlePlane(plane, empty_element_index)",
"title": ""
},
{
"docid": "70fd69f0ce22862de51ef1e045934b50",
"score": "0.47160617",
"text": "def __init__(self, f):\n _complex_fem.ComplexGridFunction_swiginit(self, _complex_fem.new_ComplexGridFunction(f))",
"title": ""
},
{
"docid": "37b3557867496196901eccabfc976dfb",
"score": "0.4709728",
"text": "def SetPlanePositions(self, p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...",
"title": ""
},
{
"docid": "9fc46ba5c3e654824eb355a537565686",
"score": "0.46980014",
"text": "def _generate_uniform_planes(self):\n return np.random.randn(self.hash_size, self.input_dim)",
"title": ""
},
{
"docid": "2be0a15718d9248ea7de27121ba363a6",
"score": "0.46928856",
"text": "def create_transformation(self) -> Transformation:\n raise NotImplementedError",
"title": ""
},
{
"docid": "a8cf51bf6f5749c525405ee086fd1851",
"score": "0.46720093",
"text": "def draw_plane_cgo(obj, name,apex1,apex2,apex3,apex4,color=(0.5,0.5,0.5)):\r\n\r\n # Convert args to floating point numbers\r\n x1,y1,z1 = map(float,apex1)\r\n x2,y2,z2 = map(float,apex2)\r\n x3,y3,z3 = map(float,apex3)\r\n x4,y4,z4 = map(float,apex4)\r\n if type(color) == type(''):\r\n color = map(float,color.replace('(','').replace(')','').split(','))\r\n\r\n # Compute the normal vector for the triangle\r\n normal1 = compute_normal(x1, y1, z1, x2, y2, z2, x3, y3, z3)\r\n normal2 = compute_normal(x1, y1, z1, x3, y3, z3, x4, y4, z4)\r\n normal3 = compute_normal(x2, y2, z2, x3, y3, z3, x4, y4, z4)\r\n\r\n # Create the CGO objects\r\n # Uncomment ALPHA line below to draw semi-transparent hull\r\n obj.extend([\r\n BEGIN, TRIANGLE_STRIP,\r\n COLOR, color[0], color[1], color[2],\r\n# ALPHA, 0.5,\r\n NORMAL, normal1[0], normal1[1], normal1[2],\r\n VERTEX, x1, y1, z1,\r\n VERTEX, x2, y2, z2,\r\n VERTEX, x3, y3, z3,\r\n VERTEX, x4, y4, z4,\r\n\r\n END\r\n ])",
"title": ""
},
{
"docid": "6257fc02c45fb8dc9a4630b2b565498c",
"score": "0.46716306",
"text": "def __init__(self,nx=(2,2,2),ox=(0.,0.,0.),size=((0.0,1.0,1.0),(0.0,1.0,1.0)),linecolor=black,linewidth=None,planecolor=white,alpha=0.5,lines=True,planes=True):\n Actor.__init__(self)\n self.linecolor = saneColor(linecolor)\n self.planecolor = saneColor(planecolor)\n self.linewidth = linewidth\n self.alpha = alpha\n self.trans = True\n self.lines = lines\n self.planes = planes\n self.nx = asarray(nx)\n ox = asarray(ox)\n sz = asarray(size)\n self.x0,self.x1 = ox-sz[0], ox+sz[1]",
"title": ""
},
{
"docid": "d1864fac37b6ddeb331ce784ba14f9e5",
"score": "0.46699846",
"text": "def constrain_location_to_plane(self, location):\n return Vector()",
"title": ""
},
{
"docid": "87fd6b2ba0318dd4b5839b3e13f0e874",
"score": "0.46659094",
"text": "def render_surface(\n x: np.ndarray,\n y: np.ndarray,\n z_fcn: [Callable[[np.ndarray], np.ndarray], nn.Module],\n x_label: str,\n y_label: str,\n z_label: str,\n data_format='numpy',\n fig: plt.Figure = None,\n cmap: mpl.cm.ScalarMappable = None,\n title: str = None,\n) -> plt.Figure:\n if cmap is None:\n cmap = mpl.rcParams['image.cmap']\n\n if fig is None:\n fig = plt.figure()\n ax = Axes3D(fig)\n\n # Create mesh grid matrices from x and y vectors\n xx, yy = np.meshgrid(x, y)\n\n # Check which version to use based on the output of the function\n if data_format == 'numpy':\n # Operate on ndarrays\n zz = np.array([z_fcn(np.stack((x, y), axis=0)) for x, y in zip(xx, yy)])\n\n elif data_format == 'torch':\n # Operate on Tensors\n xx_tensor = to.from_numpy(xx)\n yy_tensor = to.from_numpy(yy)\n\n if hasattr(z_fcn, '_fcn'):\n # Passed function was wrapped (e.g. by functools)\n check_fcn = z_fcn._fcn\n else:\n check_fcn = z_fcn\n\n if isinstance(check_fcn, nn.Module):\n # Adapt for batch-first behavior of NN-based policies\n zz = to.stack([z_fcn(to.stack((x, y), dim=1).view(-1, 1, 2).to(to.get_default_dtype()))\n for x, y in zip(xx_tensor, yy_tensor)])\n else:\n zz = to.stack([z_fcn(to.stack((x, y), dim=1).transpose(0, 1).to(to.get_default_dtype()))\n for x, y in zip(xx_tensor, yy_tensor)])\n zz = zz.squeeze().detach().numpy()\n\n else:\n raise pyrado.ValueErr(given=data_format, eq_constraint=\"'numpy' or 'torch'\")\n\n # Generate the plot\n ax.plot_surface(xx, yy, zz, cmap=cmap)\n\n # Add labels\n ax.set_xlabel(x_label)\n ax.set_ylabel(y_label)\n ax.set_zlabel(z_label)\n if title is not None:\n ax.set_title(title)\n return fig",
"title": ""
},
{
"docid": "7fb049ec3cdd0dccd49aa67f91b5cbba",
"score": "0.46588483",
"text": "def from_axes(axes):\n\t\tif len(axes) != 2:\n\t\t\traise ValueError(\"too many axes\")\n\n\t\tif \"x\" in axes and \"y\" in axes:\n\t\t\treturn Plane.from_normal(Vector.from_axis(\"z\"),0)\n\t\tif \"x\" in axes and \"z\" in axes:\n\t\t\treturn Plane.from_normal(Vector.from_axis(\"y\"),0)\t\n\t\tif \"y\" in axes and \"z\" in axes:\n\t\t\treturn Plane.from_normal(Vector.from_axis(\"x\"),0)\n\n\t\traise ValueError(\"invalid axes\")",
"title": ""
},
{
"docid": "a0d1f314966e0aaf4e7288bbbbff5423",
"score": "0.46554577",
"text": "def plane(self):\n return self._plane",
"title": ""
},
{
"docid": "2e50899d4afa04ae80a8c66ff25de54d",
"score": "0.46470854",
"text": "def create_nurbs_plane(name='plane', width=1.0, length=1.0, patches_u=1, patches_v=1, **kwargs):\n\n raise NotImplementedError()",
"title": ""
},
{
"docid": "e70465a765d3314c1c52b0fb42d52497",
"score": "0.46440476",
"text": "def __init__(self):\n self.__board = [[0] * 10 for x in range(10)]\n self.__plane_id = 0\n self.__dict_planes = {}",
"title": ""
},
{
"docid": "2f6e1147065f1c045a716d9e91ce2aaa",
"score": "0.4643647",
"text": "def main():\n\n (a1,a2,a3,surface_normal,ns) = read_input()\n\n surface_normal = reduced_norm(surface_normal)\n\n #Here we find the atoms in the plane that passes through the\n #origin that is normal to the vector surface_normal\n atoms_in_plane = []\n \n #loop over possible lattice vector combinations.\n for i in range(-10,10):\n for j in range(-10,10):\n for m in range(-10,10):\n #find the multiples of the lattice vectors\n b1 = scalar_prod(i,a1)\n b2 = scalar_prod(j,a2)\n b3 = scalar_prod(m,a3)\n #add up the lattice vectors to determine the loctaion\n #of the atom\n atom = [b1[0]+b2[0]+b3[0],b1[1]+b2[1]+b3[1],b1[2]+b2[2]+b3[2]]\n #if the vector from the orgin to the point is in the\n #plane then it's dot product with the normal vector\n #will be zero and we want to add it to th list of\n #atoms in th plane.\n if (dot_prod(atom,surface_normal)==0):\n atoms_in_plane.append(atom)\n\n #After that we find out how many atoms were in that plane, which\n #is useful for the plotting later and then start building the\n #other planes, the plane variable tracks how many planes there are\n #and the distance between the current plane and the origin, the\n #different planes should always be equadistant. The shift array\n #keeps the location of an atom in each plane, which is then\n #treated as the new origin when finding the rest of the atoms in\n #that plane.\n atoms_per_plane = [len(atoms_in_plane)]\n planes = [0]\n shift = [[0,0,0]]\n \n #ns is the number of planes we want, this if there was only one\n #were done.\n if(ns > 1):\n #keep finding new planes and new atoms until we've found each\n #desired plane.\n \n while(len(planes) < ns):\n #logical, returns true once a new plane is found.\n next_plane = False\n\n #temporary variable useful for the loops\n t = 0\n #nn is used to limit the number of lattice vector\n #combinations used so that we don't have to generate an\n #arbitrarily large list. Instead if we need more lattice\n #points we'll increment nn.\n nn = 2\n\n #here we actually find the next plane in the sequenc. This\n #is done by going through the possible lattice vector\n #combinations and find the magnitude of their projection\n #onto the normal vector. If that value isn't in the list\n #of planes then we add it to the planes array, add the\n #lattice site to the shift array, then change next_plane\n #to True.\n while(next_plane == False):\n possible_sites = list(product(range(nn),repeat=3))\n \n while((next_plane == False) and (t < len(possible_sites))):\n v = possible_sites[t]\n #p2 is the lattice point in concideration\n p2 = [v[0]*a1[0]+v[1]*a2[0]+v[2]*a3[0],v[0]*a1[1]+v[1]*a2[1]+v[2]*a3[1],v[0]*a1[2]+v[1]*a2[2]+v[2]*a3[2]]\n #dtp is the magnitude of the projection of the\n #vector from the origin to p2 along the normal\n #vector.\n dtp = distance_between_planes([0,0,0],p2,surface_normal)\n #If dpt isn't zero or in planes then we found a new plane.\n if((dtp != 0) and (dtp not in planes)):\n next_plane = True\n planes.append(dtp)\n shift.append(p2)\n t += 1\n else:\n t += 1\n \n if(next_plane == False):\n #If we've been to all the points and haven't found\n #a new plane then we need to increase the\n #multiples of the lattice vectors we consider.\n nn +=1\n \n #For every plane we've found we now need to find the atoms in\n #that plane.\n for p in range(len(planes)):\n #We don't want to do the plane through the origin, we\n #alread found all the atoms in it.\n if (planes[p] != 0):\n atoms_in_nextplane = []\n #loop over possible lattice vector combinations.\n for i in range(-10,10):\n for j in range(-10,10):\n for k in range(-10,10):\n #p2 is the point that the normal vector\n #will pass through, ie the new origin for\n #this plane\n p2 = shift[p]\n #find the multiples of the lattice vectors\n b1 = scalar_prod(i,a1)\n b2 = scalar_prod(j,a2)\n b3 = scalar_prod(k,a3)\n #add up the lattice vectors to determine\n #the loctaion of the atom\n atom = [b1[0]+b2[0]+b3[0],b1[1]+b2[1]+b3[1],b1[2]+b2[2]+b3[2]]\n #now shift the atom by p2 to put it in the\n #appropriate coordinates for the plane\n #passing through p2\n shifted_atom = [atom[0]-p2[0],atom[1]-p2[1],atom[2]-p2[2]]\n #if the vector from the orgin to the point\n #is in the plane then it's dot product\n #with the normal vector will be zero and\n #we want to add it to th list of atoms in\n #th plane.\n if (dot_prod(shifted_atom,surface_normal)==0):\n atoms_in_nextplane.append(atom)\n\n #Add the atoms in the plane to the complete list of\n #atoms.\n for i in atoms_in_nextplane:\n atoms_in_plane.append(i)\n atoms_per_plane.append(len(atoms_in_nextplane))\n\n #Now we need to change our x,y coordinates so that we can go from\n #a 3D space to a 2D surface plot for atomic positions.\n if (surface_normal != [1,0,0]):\n new_y = cross_prod([1,0,0],surface_normal)\n else:\n new_y = cross_prod([0,0,1],surface_normal)\n \n new_x = cross_prod(new_y,surface_normal)\n\n #make the new x and y axis into unit vectors to get the\n #dimmensions right.\n new_z = find_unit_vector(surface_normal)\n new_y = find_unit_vector(new_y)\n new_x = find_unit_vector(new_x)\n\n atoms = []\n \n # we need to project our atomic positions onto the new 2D plane.\n for i in atoms_in_plane:\n # if (i!=[0,0,0]):\n # proj_x = scalar_prod(dot_prod(i,new_x)/mt.sqrt(dot_prod(i,i)),i)\n # proj_y = scalar_prod(dot_prod(i,new_y)/mt.sqrt(dot_prod(i,i)),i)\n # atoms.append([proj_x,proj_y])\n # else:\n # atoms.append(i)\n atoms.append([dot_prod(new_x,i),dot_prod(new_y,i)])\n\n #variables for the plot. which color changes so that each plane\n #will have a different color of atom so that we can tell which\n #atom are in the same plane.\n at_atom = 0\n which_color = 0\n \n #Now we plot each plane order\n for i in atoms_per_plane:\n for j in range(i):\n x = which_color/float(ns)\n y_mag = mt.sqrt(dot_prod(new_y,new_y))\n x_mag = mt.sqrt(dot_prod(new_x,new_x))\n plt.axis('equal')\n plt.ylim((-2.5*y_mag,2.5*y_mag))\n plt.xlim((-2.5*x_mag,2.5*x_mag))\n plt.plot(atoms[at_atom][0],atoms[at_atom][1], marker = 'o', color = cm.gist_ncar(x),ms = 40,ls = '')\n at_atom += 1\n which_color += 1\n\n plt.show()\n return()",
"title": ""
},
{
"docid": "f02d0e202acb3a7fb05e2a0c21b2da2c",
"score": "0.4642036",
"text": "def colorplane(x, y, width, height, *a):\n if len(a) == 2:\n # Top and bottom colors.\n clr1, clr2, clr3, clr4 = a[0], a[0], a[1], a[1]\n elif len(a) == 4:\n # Top left, top right, bottom right, bottom left.\n clr1, clr2, clr3, clr4 = a[0], a[1], a[2], a[3]\n elif len(a) == 3:\n # Top left, top right, bottom.\n clr1, clr2, clr3, clr4 = a[0], a[1], a[2], a[2]\n elif len(a) == 0:\n # Black top, white bottom.\n clr1 = clr2 = (0,0,0,1)\n clr3 = clr4 = (1,1,1,1)\n glPushMatrix()\n glTranslatef(x, y, 0)\n glScalef(width, height, 1)\n glBegin(GL_QUADS)\n glColor4f(clr1[0], clr1[1], clr1[2], clr1[3] * _alpha); glVertex2f(-0.0, 1.0)\n glColor4f(clr2[0], clr2[1], clr2[2], clr2[3] * _alpha); glVertex2f( 1.0, 1.0)\n glColor4f(clr3[0], clr3[1], clr3[2], clr3[3] * _alpha); glVertex2f( 1.0, -0.0)\n glColor4f(clr4[0], clr4[1], clr4[2], clr4[3] * _alpha); glVertex2f(-0.0, -0.0)\n glEnd()\n glPopMatrix()",
"title": ""
},
{
"docid": "d45debfd0f99c255ce0f787bf310604a",
"score": "0.46357605",
"text": "def generate_planes(segment_list, internal_points_list):\n i=1\n list = []\n\n while(i<len(segment_list)):\n \n list.append(TEXTURE(\"texture/tetto.jpg\")(OFFSET([0.1,0.1,0.1])(MKPOL([[segment_list[i][0],segment_list[i][1],internal_points_list[i-1],internal_points_list[i] ],[[1,2,3,4]],[1]]))))\n\n i=i+1\n\n list.append(TEXTURE(\"texture/tetto.jpg\")(OFFSET([0.1,0.1,0.1])(MKPOL([[segment_list[0][0],segment_list[0][1],internal_points_list[0],internal_points_list[len(internal_points_list)-1] ],[[1,2,3,4]],[1]]))))\n \n return STRUCT(list)",
"title": ""
},
{
"docid": "52f88e3232cc515e99144806086a0b75",
"score": "0.4634318",
"text": "def __init__(self, horizontal_n, vertical_m, function, value, segment_endpoints=None, callback=None):\n #self.epsilon = epsilon\n n = self.n = horizontal_n\n m = self.m = vertical_m\n self.corner = np.array([n, m], dtype=np.int)\n self.f = function\n self.z = value\n if segment_endpoints is None:\n self.search_grid()\n else:\n self.end_points = np.array(segment_endpoints, dtype=np.int)\n # cache of (x,y) --> f(x,y)\n #self.location_values = {}\n # ((x1,y1), (x2,y2)) --> (x0,y0), interpolated contour location.\n self.interpolated_contour_pairs = {}\n # set of ((x1,y1), (x2,y2)) for low-point/high-point pairs on horizon.\n self.new_contour_pairs = set()\n self.callback = callback\n # [(closed, point_sequence), ...] for contours found\n self.contours = []\n self.triangle_triples = set()",
"title": ""
},
{
"docid": "a693273c47ca6d9bf1dea75f33fee57c",
"score": "0.46181926",
"text": "def draw_debug_plane(cls, world_context_object, plane_coordinates, location, size, plane_color=[0.000000, 0.000000, 0.000000, 0.000000], duration=0.000000):\n return None",
"title": ""
},
{
"docid": "a74f0fbe0f03eb74c71f0c72c9d1949a",
"score": "0.46111906",
"text": "def random_point(self, plane=True):\r\n vertices = self.vertices(plane=plane)\r\n u_min = min([p[0] for p in vertices])\r\n u_max = max([p[0] for p in vertices])\r\n v_min = min([p[1] for p in vertices])\r\n v_max = max([p[1] for p in vertices])\r\n if plane:\r\n return uniform(u_min, u_max), uniform(v_min, v_max)\r\n else:\r\n if self.ellipsoidal_shape() == 'cap':\r\n # Need to adjust extremes.\r\n PI = self.ellipsoid.pi()\r\n u_max = PI\r\n if v_min > 0:\r\n v_max = PI/2\r\n else:\r\n v_min = -PI/2 \r\n # Sample longitude and latitude within extremes, but reject if\r\n # they don't lie in the cell.\r\n # Rejection can happen for polar cells, because they are not \r\n # rectangular.\r\n while True:\r\n lam, phi = self.ellipsoid.random_point(u_min, u_max, \r\n v_min, v_max) \r\n if self.contains((lam, phi), plane=False):\r\n # Success\r\n return lam, phi",
"title": ""
},
{
"docid": "eee0239cce1a0d162083ff340db4e08e",
"score": "0.4584949",
"text": "def plane_by_points(points):\n a, b, c = np.array(points)\n ab, ac = b - a, c - a\n n = vdot(ab, ac)\n return np.hstack([n, -np.dot(n, points[0])])",
"title": ""
},
{
"docid": "0c49c291e7fdf0a83edfff8d8d0c55ed",
"score": "0.4579398",
"text": "def cp(xmin = -1.5, xmax = 1.5, xres = 2**10,\n ymin = -1.5, ymax = 1.5, yres = 2**10):\n X = linspace(xmin,xmax,xres)\n Y = linspace(ymin,ymax,yres)\n x, y = meshgrid(X,Y)\n z = x + 1j*y\n return(z)",
"title": ""
},
{
"docid": "6675f2dc59321e0122b24e783f91e6ce",
"score": "0.45785245",
"text": "def plot_plane_curve(vertices, *args, **kwargs):\n x = vertices[:, 0]\n y = vertices[:, 1]\n fig = kwargs.pop(\"fig\", None)\n unique = (fig is None)\n if fig is None:\n plt.figure()\n plt.plot(x, y, *args, **kwargs)\n if unique:\n plt.show()",
"title": ""
},
{
"docid": "2670928651685a82e5a92c304d46a178",
"score": "0.4575455",
"text": "def plot_points_2d(f, points, ax=None, **kwargs):\n kwargs.setdefault('s', 100)\n kwargs.setdefault('c', TUM_COLORS['accent_orange'])\n kwargs.setdefault('depthshade', False)\n\n if ax is None:\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n\n ax.scatter(\n points[:, 0], points[:, 1], f(points),\n **kwargs)\n\n return ax",
"title": ""
},
{
"docid": "4339867d948c688d829d3853009ff2b0",
"score": "0.45744097",
"text": "def from_normal(normal, d=0):\n\t\treturn Plane(*normal.tuple(), d)",
"title": ""
},
{
"docid": "825ddb365099e2d25762d1c20245c35e",
"score": "0.45603502",
"text": "def add_clipping_plane(plane, d_u, d_v, views=pythoncom.Empty):\r\n return _base._rsf.add_clipping_plane(plane, d_u, d_v, views)",
"title": ""
},
{
"docid": "a01b0ae3e79ab497ee0a1acce29289a7",
"score": "0.4556325",
"text": "def icosahedron_planes(draw, r, scale = 300, shift = np.array([1000,1000,0])):\n cind = -1\n phi = (1 + np.sqrt(5)) / 2.0\n mat_orig = np.array([\n [1, phi, 0],\n [0, 1, phi],\n [phi, 0, 1]\n ])\n for ii in [1, -1]:\n for jj in [1, -1]:\n for kk in [1, -1]:\n cind += 1\n mat = np.copy(mat_orig)\n mat[0,0] = mat[0,0] * ii\n mat[1,0] = mat[1,0] * ii\n mat[2,0] = mat[2,0] * ii\n mat[0,1] = mat[0,1] * jj\n mat[1,1] = mat[1,1] * jj\n mat[2,1] = mat[2,1] * jj\n mat[0,2] = mat[0,2] * kk\n mat[1,2] = mat[1,2] * kk\n mat[2,2] = mat[2,2] * kk\n mat1 = np.dot(mat, r) * scale + shift[:3]\n smat = sum(mat1)\n forward_face = np.dot(smat, np.array([0,0,1])) > -1e-3\n face_angle = np.dot(smat/np.sqrt(sum(smat**2)), np.array([0,0.01,0.99]))\n if forward_face:\n poly = [(mat1[i][0],mat1[i][1]) for i in range(len(mat1))]\n rgba = colorFromAngle2(face_angle,h=153,s=120,maxx=0.25)\n draw.polygon(poly, rgba)\n #uncomment if you want to plot edges\n #for line in range(len(mat1)):\n # draw.line((mat1[line][0],mat1[line][1],mat1[(line+1)%3][0],mat1[(line+1)%3][1]), fill = (0,255,0,255), width = 5)\n #else:\n # for line in range(len(mat1)):\n # draw.line((mat1[line][0],mat1[line][1],mat1[(line+1)%3][0],mat1[(line+1)%3][1]), fill = (0,255,0,255), width = 3)\n for ii in range(3):\n for kk in [1, -1]:\n for ll in [1, -1]:\n cind += 1\n mat = np.copy(mat_orig)\n mat[0, ii] = kk * mat[0, ii]\n mat[1, ii] = kk * mat[1, ii]\n mat[2, ii] = kk * mat[2, ii]\n mat[0, (ii+2)%3] = ll * mat[0, (ii+2)%3]\n mat[1, (ii+2)%3] = ll * mat[1, (ii+2)%3]\n mat[2, (ii+2)%3] = ll * mat[2, (ii+2)%3]\n for jj in range(3):\n mat[ii,jj] = mat[(ii+1)%3,jj]\n mat[ii, (ii+1)%3] = -1 * mat[ii, (ii+1)%3]\n mat1 = np.dot(mat, r) * scale + shift[:3]\n smat = sum(mat1)\n forward_face = np.dot(smat, np.array([0,0,1])) > -1e-3\n face_angle = np.dot(smat/np.sqrt(sum(smat**2)), np.array([0,0.01,0.99]))\n if forward_face:\n poly = [(mat1[i][0],mat1[i][1]) for i in range(len(mat1))]\n rgba = colorFromAngle2(face_angle,h=153,s=120,maxx=0.25)\n draw.polygon(poly, rgba)\n # Uncomment if you want to plot edges.\n #for line in range(len(mat1)):\n # draw.line((mat1[line][0],mat1[line][1],mat1[(line+1)%3][0],mat1[(line+1)%3][1]), fill = (0,255,0,255), width = 5)\n #else:\n # for line in range(len(mat1)):\n # draw.line((mat1[line][0],mat1[line][1],mat1[(line+1)%3][0],mat1[(line+1)%3][1]), fill = (0,255,0,255), width = 3)",
"title": ""
},
{
"docid": "8df54f2b0f42f1006802de03a171268e",
"score": "0.45546117",
"text": "def run_plane(model, slow, npts, dt, baz=0, wvtype='P',\n obs=False, dp=100., c=1.5, rhof=1027):\n\n # Pass variables to Fortran conf\n model2for(model)\n wave2for(dt, slow, baz)\n\n # Run the ``plane`` module depending on land or OBS case.\n if obs:\n\n # If OBS, then further pass OBS-related paramters to Fortran conf\n obs2for(dp, c, rhof)\n\n # Get the Fourier transform of seismograms for ``obs``case\n yx, yy, yz = pw_f.plane_obs(\n npts, model.nlay, np.array(wvtype, dtype='c'))\n\n else:\n\n # Get the Fourier transform of seismograms for ``land`` case\n yx, yy, yz = pw_f.plane_land(\n npts, model.nlay, np.array(wvtype, dtype='c'))\n\n # Transfer displacement seismograms to an ``obspy`` ``Stream`` object.\n trxyz = get_trxyz(yx, yy, yz, npts, dt, slow, baz, wvtype)\n\n return trxyz",
"title": ""
},
{
"docid": "dac2fb45283b4ec32dc0f2a422403f12",
"score": "0.4551788",
"text": "def calculate_cross_plane(\n self, x_loc, x_resolution=200, y_resolution=200, x_bounds=None, y_bounds=None\n ):\n # Get the points of data in a dataframe\n df = get_plane_from_flow_data(self.flow_data, normal_vector=\"x\", x3_value=x_loc)\n\n # Compute and return the cutplane\n return CutPlane(df)",
"title": ""
},
{
"docid": "d6b98ee5a2ed7a87933b27f09a834f8e",
"score": "0.45500967",
"text": "def generate_points(self, batch=None):\n num_x, num_y, _ = self.get_dims()\n logger = logging.getLogger(__name__)\n logger.info(\"Surface for domain contains %d points (%d x %d).\",\n num_x * num_y, num_x, num_y)\n\n if batch:\n x_start, x_end = batch.x_range\n y_start, y_end = batch.y_range\n logger.info(\"Surface for batch contains %d points (%d x %d).\",\n (x_end - x_start) * (y_end - y_start), (x_end - x_start), (y_end - y_start))\n else:\n x_start = 0\n x_end = num_x\n y_start = 0\n y_end = num_y\n\n if self.x_resolution:\n x1 = numpy.linspace(0.0, self.x_resolution * (num_x - 1), num_x)[x_start:x_end]\n else:\n x1 = self.x_coordinates[x_start:x_end]\n if self.y_resolution:\n y1 = numpy.linspace(0.0, self.y_resolution * (num_y - 1), num_y)[y_start:y_end]\n else:\n y1 = self.y_coordinates[y_start:y_end]\n x, y = numpy.meshgrid(x1, y1, indexing=\"ij\")\n z = numpy.zeros(x.shape)\n\n xyz_geo = numpy.stack((x, y, z), axis=2)\n xyz_model = numpy.zeros(xyz_geo.shape)\n az_rad = self.model_metadata.y_azimuth * math.pi / 180.0\n xyz_model[:, :, 0] = self.model_metadata.origin_x + xyz_geo[:, :, 0] * \\\n math.cos(az_rad) + xyz_geo[:, :, 1] * math.sin(az_rad)\n xyz_model[:, :, 1] = self.model_metadata.origin_y - xyz_geo[:, :, 0] * \\\n math.sin(az_rad) + xyz_geo[:, :, 1] * math.cos(az_rad)\n return xyz_model",
"title": ""
},
{
"docid": "8ee3df9e38ce79489738c42a2ccc96dd",
"score": "0.45434946",
"text": "def generate_random_puzzle_plane(row, column):\n empty_element = 0\n plane = []\n size = row * column\n\n # Generating ordered numbers list\n numbers = []\n for n in range(0, size):\n numbers.append(n)\n\n random.shuffle(numbers)\n\n # Getting a index of an empty_element\n empty_element_index = numbers.index(empty_element)\n empty_element_index = [int(empty_element_index / row), int(empty_element_index % row)]\n\n start = 0\n begin = row\n\n # Creating a 2D plane from 1D\n while begin <= row * column:\n plane.append(numbers[start:begin])\n start += row\n begin += row\n\n return PuzzlePlane(plane, empty_element_index)",
"title": ""
},
{
"docid": "a7ee252a17121167261a49fa3bae9531",
"score": "0.454343",
"text": "def __init__(self, origin, x_vector, y_vector, scaling=(1,1)):\n\n\t\t# unwrap tuples into numpy array\n\t\tself.origin = Point(*origin)\n\t\tself.x_vector = x_vector.copy()\n\t\tself.y_vector = y_vector.copy()\n\t\tself.scaling = scaling\n\n\t\tself.update()",
"title": ""
}
]
0432d19c960ac28feb72115c29bf9549
This generator outputs unique lines from a file line by line.
[
{
"docid": "8af6f1e81a26b94d3e539cfb7442eae7",
"score": "0.7510291",
"text": "def read_file_line_by_line(file_name):\n with open(file_name) as f:\n unique_strings = []\n while True:\n line = f.readline()\n if line not in unique_strings:\n unique_strings.append(line)\n yield line\n elif not line:\n break",
"title": ""
}
]
[
{
"docid": "8d23355987653c742f5f54eaca177c26",
"score": "0.6710934",
"text": "def gen_lines(files):\n for file in files:\n with open(file, 'r') as fin:\n for line in fin.readlines():\n yield line",
"title": ""
},
{
"docid": "7ace98676562ef4bdfae958f481201d2",
"score": "0.6681335",
"text": "def genline(self):\n for line in self.lines:\n yield line",
"title": ""
},
{
"docid": "ec05eea60aad1c509000cd2767cf23b0",
"score": "0.6613316",
"text": "def read_log_gen(f):\r\n\twhile True:\r\n\t\tline = f.readline()\r\n\t\tif not line:\r\n\t\t\tbreak\r\n\t\tyield line.rstrip('\\n').strip()",
"title": ""
},
{
"docid": "b1ebdd35a78b0692794800d847ed5c21",
"score": "0.64156103",
"text": "def lines_from_file(path):\n with open(path) as f:\n for line in f:\n yield line.rstrip()",
"title": ""
},
{
"docid": "d4b68b0cf60ad12dfcf173a0d7f417f8",
"score": "0.63318044",
"text": "def lines_from_file(filename):\n with open(filename) as file_obj:\n for line in file_obj:\n yield line",
"title": ""
},
{
"docid": "09f13db297697095324f12c8c6b96c81",
"score": "0.6304959",
"text": "def _read_lines(cls, file_path):\n with open(file_path, 'r', encoding='utf-8-sig') as f:\n for line in f:\n # Strip whitespaces.\n line = line.strip()\n # Don't return blank lines and comments.\n if line != '' and line[0] != '#':\n yield line",
"title": ""
},
{
"docid": "af2fa596cd5bad2c45a58a4f93135793",
"score": "0.62737507",
"text": "def file_id_iter(fname, mode='r'):\n with open(fname, mode) as f:\n for line in f:\n yield line.strip()",
"title": ""
},
{
"docid": "256643a9e18818013f85695b045a7d4f",
"score": "0.62097704",
"text": "def read_line(path): \r\n for line in open(path):\r\n yield line.strip()",
"title": ""
},
{
"docid": "6ca94e65b68f7c75e947b7f0b58b5925",
"score": "0.6099867",
"text": "def lines(self):\n\t\twith open(self.path, \"r\") as fh:\n\t\t\tself.reading = True\n\t\t\tfor line in fh:\n\t\t\t\tself.in_lines.append(line)\n\t\t\t\tyield line\n\n\t\t\tself.reading = False",
"title": ""
},
{
"docid": "284eff33496ec4df607f9190dc12d47e",
"score": "0.6099574",
"text": "def block_generator(filename):\n buffer = []\n with open(filename) as f:\n for line in f:\n line = line.strip()\n if not line:\n yield buffer\n buffer = []\n else:\n buffer.append(line)",
"title": ""
},
{
"docid": "763c3dc1e77e5a569e684836a826c7ed",
"score": "0.6080848",
"text": "def read_log(file):\n\n while True:\n line = file.readline()\n if not line:\n break\n yield line",
"title": ""
},
{
"docid": "bb90468d32bdf7a82f68812259ea6350",
"score": "0.60781175",
"text": "def read_lines(file_path: str) -> Generator[str, None, None]:\n with open(file_path, 'r') as fp:\n for line in fp.readlines():\n yield line.strip()",
"title": ""
},
{
"docid": "be996d5b69fd856fdc815a1261ded39b",
"score": "0.59861994",
"text": "def keys_generator(fname):\n with open(fname, \"r\") as f:\n for line in f:\n fields = line.replace(\"\\n\", \"\").split(\"\\t\")\n yield int(fields[0]), fields[1]",
"title": ""
},
{
"docid": "d9430615b9c7045b96b89b6bee4bb6a3",
"score": "0.598367",
"text": "def lines(filename):\n\tfn_open = gzip.open if filename.endswith('.gz') else open\n\n\twith fn_open(filename) as fh:\n\t\tfor line in fh:\n\t\t\tif line.startswith('#'):\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tyield parse(line)",
"title": ""
},
{
"docid": "470d6d700b10a34a18798596e978c6e3",
"score": "0.5962142",
"text": "def _read_new_lines(self):\n file = open(self.file_path, \"r\")\n\n while True:\n line = file.readline()\n\n if not line:\n continue\n\n yield line",
"title": ""
},
{
"docid": "b63ecc5f4510a4b4b71f743c0435419b",
"score": "0.5960312",
"text": "def read(filename, num_lines):\n first_line = None\n with open(filename, 'r') as f:\n if not first_line:\n first_line = f.readline()\n for chunk in grouper(num_lines, f, ''):\n yield chunk",
"title": ""
},
{
"docid": "3e44d547f08bd917b316e887f1de01f7",
"score": "0.5942329",
"text": "def lines(filename):\n fn_open = gzip.open if filename.endswith('.gz') else open\n\n with fn_open(filename) as fh:\n for line in fh:\n if line.startswith('#'):\n continue\n else:\n yield parse(line) #see parse(line) below converts each line to a dict.",
"title": ""
},
{
"docid": "b4dec7e32194d7dc83105c5182794e07",
"score": "0.58823484",
"text": "def _fetch(self, file):\n file.seek(0, 2)\n while self.running:\n line = file.readline()\n if not line:\n time.sleep(self.frequency)\n continue\n yield line.rstrip('\\n')",
"title": ""
},
{
"docid": "080294c6929c362ae2270720f4cda199",
"score": "0.5873971",
"text": "def read_file(filename):\n f = open(filename, 'r', encoding='utf-8', errors='ignore')\n generator = (line.strip() for line in f if line != '---------------\\n')\n return generator",
"title": ""
},
{
"docid": "35c9abf153a0c215704e12414c5f3dfa",
"score": "0.58555937",
"text": "def makeFASTQGenerator(filename):\n\twith open(filename, 'rU') as f:\n\t\tfor i in range(5):\n\t\t\tfifth = f.readline()\n\t\tif fifth == \"\\n\":\n\t\t\tf.seek(0)\n\t\t\tfor chunk in grouper(f.readlines(), 5):\n\t\t\t\tchunky = [chunk[0].strip(\"\\n\"),chunk[1].strip(\"\\n\"),chunk[3].strip(\"\\n\")]\n\t\t\t\tyield chunky\n\t\telse:\n\t\t\tf.seek(0)\n\t\t\tfor chunk in grouper(f.readlines(), 4):\n\t\t\t\tchunky = [chunk[0].strip(\"\\n\"),chunk[1].strip(\"\\n\"),chunk[3].strip(\"\\n\")]\n\t\t\t\tyield chunky",
"title": ""
},
{
"docid": "762d66ee2f493db92998e00adeb33f88",
"score": "0.5836806",
"text": "def data_generator(filename):\n def data_gen():\n with open(filename) as file:\n for line in file:\n yield tuple(k.strip() for k in line.split(',')) \n\n return data_gen",
"title": ""
},
{
"docid": "aaeb765a6e9e8e326d766fdb54d4c64b",
"score": "0.5749873",
"text": "def read_from_file(self):\n\n file_input = open('input.txt', 'r')\n list_of_lines = file_input.readlines()\n\n for i in self.__parse_line(list_of_lines):\n yield i",
"title": ""
},
{
"docid": "c1b05697a591cc1359b626d4596ca354",
"score": "0.57417935",
"text": "def generateChunks(f, pa):\n # logger.info('Generator invoked with pattern %s for file: %s' % (pa, f))\n with open(f, 'r') as fi:\n reslist = []\n lineNumber = 1\n previousLineNumber = 1\n for line in fi:\n if pa in line:\n yield {'chunk': ''.join(reslist),\n 'lineNumber': previousLineNumber}\n reslist = []\n previousLineNumber = lineNumber\n reslist.append(line)\n lineNumber += 1\n yield {'chunk': ''.join(reslist), 'lineNumber': previousLineNumber}",
"title": ""
},
{
"docid": "e061ccf77bc89019a33afe2c7c293773",
"score": "0.57125324",
"text": "def readLogLines(self, file_handler):\r\n while True:\r\n line = file_handler.readline(4096)\r\n if not line:\r\n break\r\n yield line",
"title": ""
},
{
"docid": "a786f87c8f463accfaf31a5e77c71bd5",
"score": "0.57091033",
"text": "def _SourceLines(filename, line_num, context=3):\n import linecache\n for line_num in range(line_num - context, line_num + context + 1):\n yield line_num, linecache.getline(filename, line_num)",
"title": ""
},
{
"docid": "db173e2cfc274bf8998834a8f33c1c8f",
"score": "0.5704928",
"text": "def read_map_uniq(fh, sep='\\t'):\n for line in fh:\n key, found, value = line.partition(sep)\n if found and sep not in value:\n yield key, value.rstrip()",
"title": ""
},
{
"docid": "e3d138169707252807696b4d4aa9f2b8",
"score": "0.5682923",
"text": "def get_questions(file):\r\n with open(file, 'r') as fp:\r\n for lines in fp:\r\n newline = lines.strip()\r\n yield newline",
"title": ""
},
{
"docid": "c46d20b3e152e9af3900d4b5a2c7cf69",
"score": "0.56772584",
"text": "def _reader(path: str) -> Iterator[str]:\n with open(path, \"r\") as source:\n for line in source:\n yield line.rstrip()",
"title": ""
},
{
"docid": "6b8e0b25a5c9a8e1482ebb61d9fd4462",
"score": "0.56771106",
"text": "def line_file2set(in_file):\n with open(in_file, 'r') as f:\n return set(i.strip('\\n\\r') for i in f)",
"title": ""
},
{
"docid": "c7c1e22ea8279fe1ed802b8f886d2abe",
"score": "0.5663412",
"text": "def read_lines(filename):\n with open(filename, 'r') as f:\n lines = f.readlines();\n lines = filter(lambda line: not line.startswith('#'), lines)\n lines = map(lambda line: line.strip(), lines)\n return set(lines)",
"title": ""
},
{
"docid": "24ddc8e3ca66f5ae01624ced49cdcddd",
"score": "0.5655174",
"text": "def readlines(self):\n # avoid using file as iterator, since it implies buffering\n while True:\n l = self.input.readline()\n if not l:\n break\n \n # remove trailing newline\n if l[-1] == '\\n':\n l = l[0:-1]\n\n logger.debug(\"< \" + l)\n yield l",
"title": ""
},
{
"docid": "24171db3b8778c2569a4a663167a9016",
"score": "0.5654681",
"text": "def entries(self) -> Iterator[HashEntry]:\n while True:\n line = self._file_obj.readline()\n if len(line) == 0:\n break\n yield HashEntry.from_str(line, self.line_split, self.hash_type)",
"title": ""
},
{
"docid": "f0812f6abbf793e2dbfcb025f841ec94",
"score": "0.56400084",
"text": "def textfile_generator(textfile, linebreak=True, encoding=None):\n for t in textfile:\n if len(t) > 0:\n if encoding is None:\n yield t.strip() + ('\\n' if linebreak else '')\n else:\n yield t.decode(encoding).strip() + ('\\n' if linebreak else '')",
"title": ""
},
{
"docid": "ac6e25d8882cbb20b20231213f6d0851",
"score": "0.56201625",
"text": "def readDuplications( infile ):\n\n duplications = []\n first = True\n for line in infile:\n if first:\n first = False\n continue\n\n duplication = Duplication()\n duplication.readFromString( line )\n \n duplications.append( duplication )\n\n return duplications",
"title": ""
},
{
"docid": "57463040e3ce2ef3a73a7a9cb50b9b29",
"score": "0.5611486",
"text": "def edges_generator( file_name ) :\n\twith gzip.open( file_name ) as f :\n\t\treader = csv.reader( f )\n\t\t# Ignore the header\n\t\treader.next( )\n\t\tfor edges in reader :\n\t\t\tnodes = [ int( node ) for node in edges ]\n\t\t\tyield nodes",
"title": ""
},
{
"docid": "c9e857d10209340df5a15a4e359362a5",
"score": "0.561072",
"text": "def bloom_filter_set():\n with open('Proper.txt', 'r') as f:\n for line in f:\n yield line.strip()",
"title": ""
},
{
"docid": "66eee951d6fbca056e1488d79d2cf9eb",
"score": "0.55970323",
"text": "def random_line(afile):\r\n line = next(afile)\r\n for num, aline in enumerate(afile):\r\n if random.randrange(num + 2):\r\n continue\r\n line = aline\r\n return line",
"title": ""
},
{
"docid": "f7423389f7c1f9e2c339a870efa0b634",
"score": "0.559054",
"text": "def _LogLines(self):\n if (not self._temp_log_file_path or\n not os.path.isfile(self._temp_log_file_path)):\n yield '(N/A)'\n return\n with open(self._temp_log_file_path) as f:\n for line in f:\n yield line",
"title": ""
},
{
"docid": "63e6da5b95d6a20aee21ebf6e48d3db4",
"score": "0.55895424",
"text": "def yield_line_from_files(\n *, source_files: typing.Iterable[str]\n) -> typing.Iterator[str]:\n for source_file in source_files:\n print(f\"---- Loading lines from {source_file}...\")\n with gzip.open(source_file, mode=\"rt\", encoding=\"utf8\") as source:\n yield from source\n print(f\"---- All lines read from {source_file}\")",
"title": ""
},
{
"docid": "6a7aaba14cc186962d50a45a3dbb9547",
"score": "0.55712813",
"text": "def gen_tokens(self):\n for line in open(self.path, 'rb'):\n for tok in line.strip().lower().split():\n yield self.pre + tok",
"title": ""
},
{
"docid": "f56d22d9009ac88183d9d8c8e090f00a",
"score": "0.55665076",
"text": "def bloom_filter_set():\n with open('/Users/harshinder/Downloads/homework2-master/Proper.txt','r') as f:\n for line in f:\n yield line.strip()",
"title": ""
},
{
"docid": "40cbc9bb4009a0be231d6960d89dc431",
"score": "0.5555802",
"text": "def dataFromFile(fname):\n\tfile_iter = open(fname, 'rU')\n \tfor line in file_iter:\n\t\tline = line.strip().rstrip(',')\t\t\t\t# Remove trailing comma\n\t\trecord = frozenset(line.split(','))\n\t\tyield record",
"title": ""
},
{
"docid": "413facdaf28d8c5d8428a0ea7d97bbb9",
"score": "0.5553472",
"text": "async def chunk_stories_from_file(file: str, batch_size: int = 100) -> Tuple[List[str], List[int]]:\n line_count = 1\n lines = []\n story_nums = []\n async with AIOFile(file, mode=\"rb\") as f:\n async for line in LineReader(f):\n line = line.decode('utf-8', errors=\"ignore\")\n line = line.replace(\"<newline>\", \"\")\n lines.append(line)\n story_nums.append(line_count)\n line_count += 1\n if len(lines) == batch_size:\n yield lines, story_nums\n lines = []\n story_nums = []\n\n yield lines, story_nums",
"title": ""
},
{
"docid": "423ed894ad94b27936fc1952c2358bd3",
"score": "0.55305266",
"text": "def read_map_output(file):\n for line in file:\n yield line.strip().split(\"\\t\")",
"title": ""
},
{
"docid": "423ed894ad94b27936fc1952c2358bd3",
"score": "0.55305266",
"text": "def read_map_output(file):\n for line in file:\n yield line.strip().split(\"\\t\")",
"title": ""
},
{
"docid": "48ad078cd580cf5162f8bec731177da2",
"score": "0.55272305",
"text": "def extract_urls(input_file_path):\n with open(input_file_path) as input_file:\n for line in input_file:\n yield line.strip()",
"title": ""
},
{
"docid": "5ead3bb4acffccdaf5547fd61186e22d",
"score": "0.55080724",
"text": "def line_iterator(readable_file, size=None):\n # type: (IO[bytes], Optional[int]) -> Iterator[bytes]\n read = readable_file.read\n line = []\n byte = b\"1\"\n if size is None or size < 0:\n while byte:\n byte = read(1)\n line.append(byte)\n if byte in b\"\\n\":\n yield b\"\".join(line)\n del line[:]\n\n else:\n while byte and size:\n byte = read(1)\n size -= len(byte)\n line.append(byte)\n if byte in b\"\\n\" or not size:\n yield b\"\".join(line)\n del line[:]",
"title": ""
},
{
"docid": "d03cf6238506ca2a1b30562b2993e1f2",
"score": "0.55058074",
"text": "def generate(self):\r\n self.structure = []\r\n with open(self.file, \"r\") as file:\r\n for line in file:\r\n row = [square for square in line if square != '\\n']\r\n self.structure.append(row)",
"title": ""
},
{
"docid": "bc1d619280f9bb3aec44c1724579c05c",
"score": "0.549715",
"text": "def simple_corpus_iterator(corpus_file):\r\n l = corpus_file.readline()\r\n while l:\r\n line = l.strip()\r\n if line: # Nonempty line\r\n yield line\r\n else: # Empty line\r\n yield None \r\n l = corpus_file.readline()",
"title": ""
},
{
"docid": "d35bf9ecc08a740779723e6022a3504f",
"score": "0.5494441",
"text": "def gen_readlog(filename):\n log = gzip.open(filename, mode='rt', encoding='utf-8') if filename.endswith(\".gz\") \\\n else open(filename, mode='r', encoding='utf-8')\n for line in log:\n yield line.strip()\n log.close()",
"title": ""
},
{
"docid": "dea9c364c66a3fdc1c95b22b5f9555d6",
"score": "0.54815996",
"text": "def read_emails_from_file(file, n=10000):\n with open(file, 'rb') as file_in:\n while True:\n next_n_lines = list(islice(file_in, n))\n if not next_n_lines:\n break\n yield next_n_lines",
"title": ""
},
{
"docid": "ba7f5be38657b46b10bb252b0b34c855",
"score": "0.547566",
"text": "def _getSourceLines(self):\n filename = self.frame[1]\n lineNumber = self.frame[2]\n for snipLineNumber in range(lineNumber - 1, lineNumber + 2):\n yield (snipLineNumber,\n linecache.getline(filename, snipLineNumber).rstrip())",
"title": ""
},
{
"docid": "11526d3c0e01b9056e179bffe8940343",
"score": "0.5464331",
"text": "def fasta_iter(fasta_name):\n \"first open the file outside \"\n fh = open(fasta_name)\n\n # ditch the boolean (x[0]) and just keep the header or sequence since\n # we know they alternate.\n faiter = (x[1] for x in groupby(fh, lambda line: line[0] == \">\"))\n\n for header in faiter:\n # drop the \">\"\n headerStr = header.__next__()[1:].strip()\n\n # join all sequence lines to one.\n seq = \"\".join(s.strip() for s in faiter.__next__())\n\n yield (headerStr, seq)\n\n # --------------\n def add_hash(x,y):\n return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]",
"title": ""
},
{
"docid": "ef535ae269e8d4034b1f08c67b4ffcaa",
"score": "0.5463162",
"text": "def read_map_output(file):\n for line in file:\n yield line.strip().split(\"\\t\", 1)",
"title": ""
},
{
"docid": "485e0090e0dc1608e0cb589f3ceb1b2b",
"score": "0.5452605",
"text": "def FastaIterator(fh):\n def readTotitle(fh):\n \"\"\"returns a tuple ([lines before the next title line], next tile line)\n \"\"\"\n preLines = []\n while True:\n l = fh.readline().strip()\n if l.startswith('>'):\n return (preLines,l)\n elif l == '':\n return preLines,None\n else:\n preLines.append(l)\n\n\n if type(fh) in StringTypes:\n fh = file(fh)\n \n preLines,nextTitleLine =readTotitle(fh)\n\n while nextTitleLine != None:\n title = nextTitleLine[1:].rstrip()\n preLines,nextTitleLine=readTotitle(fh)\n yield (title,''.join(map(lambda x: x.rstrip(),preLines)))",
"title": ""
},
{
"docid": "485e0090e0dc1608e0cb589f3ceb1b2b",
"score": "0.5452605",
"text": "def FastaIterator(fh):\n def readTotitle(fh):\n \"\"\"returns a tuple ([lines before the next title line], next tile line)\n \"\"\"\n preLines = []\n while True:\n l = fh.readline().strip()\n if l.startswith('>'):\n return (preLines,l)\n elif l == '':\n return preLines,None\n else:\n preLines.append(l)\n\n\n if type(fh) in StringTypes:\n fh = file(fh)\n \n preLines,nextTitleLine =readTotitle(fh)\n\n while nextTitleLine != None:\n title = nextTitleLine[1:].rstrip()\n preLines,nextTitleLine=readTotitle(fh)\n yield (title,''.join(map(lambda x: x.rstrip(),preLines)))",
"title": ""
},
{
"docid": "2ee004162c51ae5d0cbe6d9395b86cdc",
"score": "0.5443738",
"text": "def _generate_examples(self, filename):\n\t\twith open(filename) as file_in:\n\t\t\tlines = np.array(file_in.readlines())\n\t\tlines = sp.char.rstrip(lines)\n\t\tfor line in lines:\n\t\t\tx, y = self._line_consumer(line)\n\t\t\tself._max_x_length = max(len(x), self._max_x_length)\n\t\t\tif self._max_y_length > -1:\n\t\t\t\tself._max_y_length = max(len(y), self._max_y_length)\n\t\t\tyield x, y",
"title": ""
},
{
"docid": "0f47ebc69d586bd99d8fece41a35da49",
"score": "0.5438642",
"text": "def read_file(filename):\n\ttry:\n\t\tf = open(filename)\n\t\twhile True:\n\t\t\tline = f.readline()\n\t\t\tif not line:\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tyield line\n\tfinally:\n\t\tf.close()",
"title": ""
},
{
"docid": "6e2d243e41f201ff205821ab608c892b",
"score": "0.54300267",
"text": "def files_to_lines(files):\n for filename in files:\n with open(filename) as file:\n data = file.read()\n data = data.splitlines()\n for lines in data:\n yield lines.strip()",
"title": ""
},
{
"docid": "7a9e4615ee37fe16eb36b990826bb2db",
"score": "0.54276913",
"text": "def simple_iterfasta(filehandle):\n \n # Skip to first header\n for probeline in filehandle:\n if probeline.startswith('>'):\n break\n else: # nobreak\n raise ValueError('No headers in this file.')\n \n header = probeline.strip('>\\n')\n buffer = list()\n \n # Iterate over lines\n for line in map(str.rstrip, filehandle):\n if line.startswith('>'): \n yield header, ''.join(buffer)\n buffer.clear()\n header = line[1:]\n \n else:\n buffer.append(line)\n \n yield header, ''.join(buffer)",
"title": ""
},
{
"docid": "2dcd86890bd8c3b743bac85a7dd997ac",
"score": "0.5410768",
"text": "def get_lines(path: str) -> Iterator[str]:\r\n try:\r\n fp: IO = open(path, 'r')\r\n except FileNotFoundError:\r\n raise FileNotFoundError(f\"Cant open {path}\")\r\n else:\r\n with fp:\r\n for line in fp:\r\n line = line.strip('\\n')\r\n while line.endswith('\\\\'):\r\n line = line[:-1] + fp.readline().strip('\\n')\r\n\r\n # if \"#\" in line:\r\n # if line.startswith('#'):\r\n # del line\r\n # else:\r\n # line = line.split('#', 1)[0].strip('\\n')\r\n # yield line\r\n # else:\r\n # yield line\r\n\r\n if not line.startswith('#'):\r\n yield line.split('#')[0]",
"title": ""
},
{
"docid": "d2e5a49911937d7b0a1af9ad3aa4fdda",
"score": "0.53985715",
"text": "def read_file(file_path):\n file_object = codecs.open(file_path, mode='r', encoding='utf-8')\n lines = file_object.readlines()\n random.shuffle(lines)\n file_object.close()\n return lines",
"title": ""
},
{
"docid": "7f719bd7300efbb564dbaea1dc127e68",
"score": "0.53823835",
"text": "def gen_set(file_name,*start_end):\n if 'gz' in file_name:\n iflines = gzip.open(file_name,'rb').readlines()\n else:\n iflines = open(file_name,'r').xreadlines()\n file_set = set([])\n x = iter(start_end)\n start_end_pairs = [(item, x.next()) for item in x]\n for line in iflines:\n keys = []\n for s,e in start_end_pairs:\n keys.append(line[s-1:e].strip())\n file_set.add('|'.join(keys))\n return file_set",
"title": ""
},
{
"docid": "ac93e547bd587e088dc2630aa63f76c4",
"score": "0.53783214",
"text": "def read_mapper_output(file, separator='\\t'):\n\n # Go through each line\n for line in file:\n yield line",
"title": ""
},
{
"docid": "9e42182f031217498b70144e1569db6c",
"score": "0.5373325",
"text": "def lines(file_name: str, known_encoding='utf-8') -> Iterator[str]:\n\tif file_name.endswith('.gz') or file_name.endswith('.gzip'):\n\t\twith io.TextIOWrapper(gzip.open(file_name, 'r'), encoding=known_encoding) as f:\n\t\t\tfor line in f: yield line.rstrip('\\n\\r')\n\telse:\n\t\twith open(file_name, 'r') as f:\n\t\t\tfor line in f: yield line.rstrip('\\n\\r')",
"title": ""
},
{
"docid": "d43f28f412538efd9e55a83a82b96bb4",
"score": "0.5355514",
"text": "def yield_lines(file_path, make_lower=False, threshold_len=0):\n for line in io.open(file_path, mode='r', encoding='utf-8'):\n line = line.strip()\n if make_lower:\n line = line.lower()\n if len(line) > threshold_len:\n yield line",
"title": ""
},
{
"docid": "c4ba5da653275484ca5bca2d544d6cb7",
"score": "0.5344083",
"text": "def read_lines(data_file):\n with codecs.open(data_file,'r',encoding='utf-8') as f:\n for line in f.readlines():\n yield line",
"title": ""
},
{
"docid": "e90d0e0bc620a97a00a68383e8fcebec",
"score": "0.5343919",
"text": "def generate_from_file(file_path: str):\n try:\n with open(file_path, 'r') as file:\n return [generate_from_template(line) for line in file]\n except FileNotFoundError:\n logging.error(f\"File '{file_path}' doesn\\'t exist\")",
"title": ""
},
{
"docid": "1377be9536c998a6ffa8faca2d25aea5",
"score": "0.53409183",
"text": "def _creategenerator(self):\n\t\tf=self._fileprefix()\n\t\tif f != self.filePrefix:\n\t\t\tself.filePrefix=f\n\n\t\t\tself.fileGenerator=itertools.imap( \n\t\t\t\t\t\t\tlambda n : self.filePrefix+\"_\"+str(n), \n\t\t\t\t\t\t\titertools.count(1)\n\t\t\t\t\t\t)",
"title": ""
},
{
"docid": "fcb557780b899561cb945bb55e45b067",
"score": "0.5340206",
"text": "def yield_records( input_lines ):\n buffer = \"\"\n for line in input_lines:\n if \"\" == line:\n yield buffer[1:]\n buffer=\"\"\n else:\n buffer += (\" \" + line)\n if buffer != \"\":\n yield buffer[1:]",
"title": ""
},
{
"docid": "2f238706573e06bf4bf42e8cb631c7a5",
"score": "0.5333908",
"text": "def create_data(filename, k, n, wc_prop):\n print(\"Reading in file...\\n\")\n f = open(filename)\n seqs = []\n for line in f.readlines()[:k]:\n seq = []\n for character in line[:n]:\n if random.random() > wc_prop:\n seq.append(int(character))\n else:\n seq.append('*')\n seqs.append(seq)\n return seqs",
"title": ""
},
{
"docid": "f16425205b06c8f50eafcfc3bc167888",
"score": "0.53305376",
"text": "def _striplines(m):\n while True:\n v = m.readline().decode('utf-8')\n if v:\n yield v.strip()\n else:\n break",
"title": ""
},
{
"docid": "d596a49b3bd5747e7b2830911dae5e7e",
"score": "0.5330089",
"text": "def __iter__(self):\n while True:\n al = self.file.readline()\n cl = self.file.readline()\n ll = self.file.readline()\n if al == '' or cl == '' or ll == '':\n if al == '' and (cl != '' or ll != ''):\n print \"CORRUPT LOGFILE!\"\n break\n\n action = tuple(map(float, al.strip().split()[1:]))\n cams = cl.strip().split()[2:]\n las = map(float, ll.strip().split()[1:])\n rs = las[::3]\n ps = las[1::3]\n ts = las[2::3]\n yield LogEntry(action, cams, zip(rs, ps, ts))",
"title": ""
},
{
"docid": "1191b3e18b79af93f1089ef5003d91aa",
"score": "0.5326357",
"text": "def grep_file(self, filename):\n\n try:\n with open(filename) as file:\n for linenum, line in enumerate(file, 1): # count line numbers from 1\n line = line.rstrip('\\n') # remove trailing newline\n for line in self.grep_line(line):\n yield GrepSuccess(filename, line, linenum)\n except IOError as e:\n yield GrepError(filename, e.strerror)",
"title": ""
},
{
"docid": "3903fded9610f4243d28d125579545be",
"score": "0.53260267",
"text": "def source_token_lines(self):\n if os.path.exists(self.filename):\n with open_source_file(self.filename) as f:\n for line in f:\n yield [('txt', line.rstrip('\\n'))]\n else:\n for line in self._iter_source_tokens():\n yield [('txt', line)]",
"title": ""
},
{
"docid": "2d3260055646f2742b3bac814954029d",
"score": "0.5323318",
"text": "def listGenerator(filename):\n filevar = []\n with open(filename) as f:\n filevar = f.read().split('\\n')\n return(filevar)",
"title": ""
},
{
"docid": "df557dd76b8e201c4dc6e775c4796172",
"score": "0.531289",
"text": "def read_streamed_file(file, chunk_size=10):\n counter = 0\n ret_lines = []\n with open(file) as f:\n while True:\n counter += 1\n line = f.readline()\n if line:\n ret_lines.append((counter, line))\n if (counter/chunk_size).is_integer() or not line:\n yield ret_lines\n ret_lines = []\n if not line:\n break",
"title": ""
},
{
"docid": "f178464a4b4758309fba185bbca644ec",
"score": "0.5301929",
"text": "def gen_files():\n with open(tempfile) as f:\n for line in f.read().splitlines():\n if line.split(\",\")[1] == \"True\":\n if line.split(\",\")[0].split(\"/\")[1] in IGNORE:\n continue\n yield (line.split(\",\")[0].split(\"/\"))",
"title": ""
},
{
"docid": "865816046aabfb053d5acdbf90041477",
"score": "0.529185",
"text": "def dataFromFile(fname):\n # file_iter = codecs.open(fname, 'rU','gbk',errors='ignore') # 张apriori\n file_iter = open(fname, 'rU') # apriori\n for line in file_iter:\n line = line.strip().rstrip(',') # Remove trailing comma\n record = frozenset(line.split(','))\n yield record",
"title": ""
},
{
"docid": "6b837c2945d056dce4c6887e6307d044",
"score": "0.5291131",
"text": "def read_mapper_output(file, separator='\\t'):\n\tfor line in file:\n\t\tyield line.rstrip().split(separator, 1)",
"title": ""
},
{
"docid": "558671318c2be151962e2924d96779be",
"score": "0.52895737",
"text": "def _read_file_by_lines(filename):\n with open(filename, \"r\") as f:\n return f.read().splitlines()",
"title": ""
},
{
"docid": "0757b9ca20d263cd6e3ea154e97acba6",
"score": "0.5285663",
"text": "def LineGenerator(self):\r\n\t\t# self.gennum+=1\r\n\t\tself.gennum = random.randint(1, 4)\r\n\t\tline = [0, 0, 0, 0]\r\n\t\ta = random.randint(1, 4)\r\n\t\tif self.gennum % 3 == 0:\r\n\t\t\tline[a - 1] = 2\r\n\t\t\tif self.Trained == True:\r\n\t\t\t\tself.bombnum += 1\r\n\t\telse:\r\n\t\t\tline[a - 1] = 1\r\n\t\t\tif self.Trained == True:\r\n\t\t\t\tself.coinnum += 1\r\n\t\treturn line",
"title": ""
},
{
"docid": "84c3d81f06f4a4b50a709fa79e7e3dd0",
"score": "0.5285531",
"text": "def trigram_bow_generator(filepath):\n \n for post in LineSentence(filepath):\n yield trigram_dictionary.doc2bow(post)",
"title": ""
},
{
"docid": "8ebce5cad0a0fe27d5b87106dede8f9e",
"score": "0.5277852",
"text": "def read_fasta(fasta_file):\n\n try :\n input = (gzip.open if fasta_file.endswith('.gz') else open)(fasta_file)\n except IOError:\n print \"[Error] Cannot find fasta file : %s !\" % fasta_file\n exit(-1)\n sanitize = re.compile(r'[^ACTGN]')\n sanitize_seq_id = re.compile(r'[^A-Za-z0-9]')\n\n chrom_seq = []\n chrom_id = None\n seen_ids = set()\n\n for line in input:\n if line[0] == '>':\n if chrom_id is not None:\n yield chrom_id, ''.join(chrom_seq)\n\n chrom_id = sanitize_seq_id.sub('_', line.split()[0][1:])\n\n if chrom_id in seen_ids:\n error('BS Seeker found identical sequence ids (id: %s) in the fasta file: %s. Please, make sure that all sequence ids are unique and contain only alphanumeric characters: A-Za-z0-9_' % (chrom_id, fasta_file))\n seen_ids.add(chrom_id)\n\n chrom_seq = []\n\n else:\n chrom_seq.append(sanitize.sub('N', line.strip().upper()))\n\n yield chrom_id, ''.join(chrom_seq)\n\n input.close()",
"title": ""
},
{
"docid": "ffcfbd867a212394466b6982bf04de20",
"score": "0.5275022",
"text": "def gen_list(file_name,*start_end):\n if 'gz' in file_name:\n iflines = gzip.open(file_name,'rb').readlines()\n else:\n iflines = open(file_name,'r').xreadlines()\n x = iter(start_end)\n start_end_pairs = [(item, x.next()) for item in x]\n for line in iflines:\n keys = []\n for s,e in start_end_pairs:\n keys.append(line[s-1:e].strip())\n yield '|'.join(keys)",
"title": ""
},
{
"docid": "35b30ee2299b06434b1f7415fcbb1c0e",
"score": "0.5272629",
"text": "def trigram_bow_generator(filepath):\n \n for review in LineSentence(filepath):\n yield trigram_dictionary.doc2bow(review)",
"title": ""
},
{
"docid": "2bf835f0f5789dcc8e0433a94bf34c97",
"score": "0.5248813",
"text": "def process_file(self, path):\n with open(path) as file_handler:\n while True:\n yield next(csv.reader(file_handler, delimiter=\",\"))",
"title": ""
},
{
"docid": "c3db5c251f04ee36e73d3fa8178818c4",
"score": "0.524564",
"text": "def read_mapper_output(file, separator):\n for line in file:\n yield line.rstrip().split(separator, 2)",
"title": ""
},
{
"docid": "0268361e109c7361d608a3ceba2d9a29",
"score": "0.52434385",
"text": "def get_entries(log_file):\r\n with open(log_file, 'rt') as f:\r\n for i,line in enumerate(f):\r\n entry = decode_log_line(line)\r\n if entry:\r\n yield [log_file,i] + entry",
"title": ""
},
{
"docid": "ca834d788c985c6f6bd39bc20040eef7",
"score": "0.5236938",
"text": "def rows(self):\n with self.input().open('r') as fobj:\n for line in fobj:\n yield line.strip('\\n').split('\\t')",
"title": ""
},
{
"docid": "87a0a97f249e8797b5517911a91f6f43",
"score": "0.5236725",
"text": "def line_review(filename):\n \n with codecs.open(filename, encoding='utf_8') as f:\n for review in f:\n yield review.replace('\\\\n', '\\n')",
"title": ""
},
{
"docid": "3a4d1859fac538ad0dc379589a2b8007",
"score": "0.5229629",
"text": "def process_fasta(infile):\r\n f = open(infile, \"r\")\r\n \r\n name, seq = None, []\r\n for line in f:\r\n if line.startswith(\">\"):\r\n # when we get to the next sequence, yield the one before it\r\n # seq is a list, so we joing everything into one string\r\n if name: \r\n yield (name, ''.join(seq))\r\n name, seq = line.strip(), [] # parsing starts here\r\n # sequence spanning multiple lines\r\n else:\r\n seq.append(line.strip())\r\n # handles the last sequence in the file\r\n if name: \r\n yield (name, ''.join(seq))\r\n \r\n f.close()",
"title": ""
},
{
"docid": "a7d9509ba56ca55b2897284ffa8edd25",
"score": "0.52281916",
"text": "def iterate_lines(self):\n for i in self.agp_lines:\n yield i",
"title": ""
},
{
"docid": "e2f3edb2747b43b2bc0e965bddc35312",
"score": "0.52089405",
"text": "def parse_flatfile(filehandle: IO) -> Iterator[Entry]:\n acs, pids = [], []\n for line in filehandle:\n if line.startswith(\"AC\"):\n tokens = line[5:].strip(\";\\n\").split(\"; \")\n acs += tokens\n elif line.startswith(\"DR EMBL\"):\n tokens = line[5:].strip(\";\\n\").split(\"; \")\n pid = tokens[2]\n if pid != \"-\":\n pids.append(pid)\n elif line == \"//\\n\":\n yield Entry(acs, pids)\n acs, pids = [], []",
"title": ""
},
{
"docid": "eb7846dee551e9e8b55ddff5b784b863",
"score": "0.5208255",
"text": "def lines(self, filePath):\n return list(self.linerator(filePath))",
"title": ""
},
{
"docid": "d0a4d76ef49ac546ff22f22c4588cefc",
"score": "0.52014",
"text": "def take(lines, n, header):\n while True:\n count = 0\n result = []\n while count < n:\n try:\n line = next(lines)\n except StopIteration:\n if result:\n yield result\n return\n else:\n if line != header:\n result.append(line)\n count += 1\n if count == n:\n yield result\n break",
"title": ""
},
{
"docid": "d4846a4a03801b35fa36667daf8d4240",
"score": "0.52007395",
"text": "def _universal_newlines(fp):\n # if file was opened with universal newline support we don't need to convert\n if 'U' in getattr(fp, 'mode', ''):\n for line in fp:\n yield line\n else:\n for line in fp:\n line = line.replace(b'\\r\\n', b'\\n').replace(b'\\r', b'\\n')\n for piece in line.split(b'\\n'):\n yield piece",
"title": ""
},
{
"docid": "30366ea8cf46964d044a5c139112a06c",
"score": "0.51954883",
"text": "def get_next_line(self):\n # patch to skip first few lines\n num_of_lines_to_ignore = 4\n for line in self.lines:\n if num_of_lines_to_ignore:\n num_of_lines_to_ignore -= 1\n continue\n if line == \"\\r\\n\" or line[0]==\"-\": # ignore empy lines and irrelevant lines\n continue\n items = self.parse_line(line)\n yield items",
"title": ""
},
{
"docid": "cc9108a9b572257bdc1c42c3fd03a732",
"score": "0.51861453",
"text": "def read(file):\n if not file:\n for line in sys.stdin: yield line\n else:\n with open(file, 'r') as f: \n for line in f: yield line",
"title": ""
},
{
"docid": "62a32db4af506b34055245bcd38c21c9",
"score": "0.51813376",
"text": "def unique_config_sections(self, config_file):\r\n # taken from https://github.com/allanzelener/YAD2K/blob/master/yad2k.py\r\n section_counters = defaultdict(int)\r\n output_stream = io.StringIO()\r\n with open(config_file) as fin:\r\n for line in fin:\r\n if line.startswith('['):\r\n section = line.strip().strip('[]')\r\n _section = section + '_' + str(section_counters[section])\r\n section_counters[section] += 1\r\n line = line.replace(section, _section)\r\n output_stream.write(line)\r\n output_stream.seek(0)\r\n return output_stream",
"title": ""
}
]
217c6404ce15f75ab8975641f863146e
Discretization of scattering energies.
[
{
"docid": "0d2a37ec01b25e7aa9ff57dfa9c9c6a7",
"score": "0.53659004",
"text": "def discrete_energies(E, dE, N=1024, WE=8.):\n # Discrete energies that contains the \"elastic\" points:\n # nu0=nu2 and nu0=nu3.\n NdE = np.ceil(N / 2 / (dE / 2 + WE) * dE / 2)\n Lq = N / 2 / NdE * dE / 2 if np.abs(dE) > 0 else WE\n qs = np.linspace(-Lq, Lq, N, endpoint=False) + E / 2\n\n return qs",
"title": ""
}
]
[
{
"docid": "336d994e96b34208653edd65317db622",
"score": "0.5948378",
"text": "def _spatial_dispersal(self, iteration: int, n_iterations: int) -> None:\n\n coef = ((n_iterations - iteration) ** self.e) / (\n (n_iterations + c.EPSILON) ** self.e\n )\n\n self.sigma = coef * (self.init_sigma - self.final_sigma) + self.final_sigma",
"title": ""
},
{
"docid": "5fe34fa70cd049621f2fe32df0315bb1",
"score": "0.5892061",
"text": "def calculate_energy_slope(self):\n \n# e = self.elev_c + self.depth + self.U**2 / (2. * g)\n# # e = self.depth + self.U**2 / (2. * g)\n# \n# self.domain.set_quantity('energy', e, location='centroids')\n \n# quant = self.domain.quantities['energy']\n# quant.compute_gradients()\n# \n# self.S = num.sqrt((quant.x_gradient/ self.dx)**2 + (quant.y_gradient/ self.dx)**2)\n# \n# \n quant = self.domain.quantities['elevation']\n quant.compute_gradients()\n \n self.S = num.sqrt((quant.x_gradient/ self.dx)**2 + (quant.y_gradient/ self.dx)**2)\n self.S[self.S > num.mean(self.S)/2] = num.mean(self.S)/2\n\n # self.S = num.sqrt((quant.x_gradient/ self.dx)**2 + (quant.y_gradient/ self.dx)**2)\n \n# self.S[self.S <= 0.000001] = 0.000001\n# self.S[self.S > 0.01] = 0.01\n \n# self.domain.set_quantity('ee', self.S, location='centroids')\n # self.domain.set_quantity('vel', self.U, location='centroids')\n \n return self.S",
"title": ""
},
{
"docid": "368ba7157b0a8c9ea0bf84e48722dfa9",
"score": "0.57776797",
"text": "def sediment_flux(self):\n \n normal_vels = num.zeros((self.num_cells,3))\n \n normal_vels[self.ind,:] = ((self.normals[self.ind,0::2] *\n self.xmom_e[self.ind,:] +\n self.normals[self.ind, 1::2] *\n self.ymom_e[self.ind,:]) /\n self.depth_e[self.ind,:])\n\n \n edge_flux = (self.depth_e * self.edgelengths * normal_vels * self.dt)\n \n neighbour_conc = self.conc[self.neighbours]\n \n sed_flux = edge_flux * self.conc[:,num.newaxis]\n \n sed_flux[edge_flux < 0] = (\n edge_flux[edge_flux < 0] * neighbour_conc[edge_flux < 0])\n \n \n for k in self.bdry_indices:\n for i in range(3):\n\n n = self.neighbours[k,i]\n \n if n < 0:\n \n sed_flux[k,i] = edge_flux[k,i] * self.inflow_concentration\n \n \n sed_vol_change = num.sum(-sed_flux, axis=1)\n \n sed_vol_in_cell = self.conc * self.depth * self.areas\n new_sed_vol_in_cell = num.maximum(sed_vol_in_cell + sed_vol_change, 0)\n \n self.conc[:] = 0.\n self.conc[self.ind] = (new_sed_vol_in_cell[self.ind] /\n (self.depth[self.ind] * self.areas[self.ind]))\n\n self.conc[self.conc > 0.2] = 0.2\n \n self.domain.quantities['concentration'].\\\n set_values(self.conc, location = 'centroids')",
"title": ""
},
{
"docid": "5e42f5790082b25372043f5ecc5fa399",
"score": "0.57428986",
"text": "def dispersive_spread():\n\n def sod(f):\n return -1/2 * f**2\n\n def gamma(f):\n return 1\n\n def kerr(t, x, u):\n return abs(u)**2 * u\n\n t = numpy.linspace(0, 20, 2**9)\n x = numpy.linspace(-20, +20, 2**10)\n\n u0 = 0.5 * sech(x)\n result = gnlse(t, x, u0, sod, gamma, kerr)\n\n plt.figure(figsize=(4, 6))\n\n plt.pcolormesh(\n x, t, abs(result.u),\n cmap=\"magma\")\n plt.xlabel(r\"Coordinate $x$, a.u.\")\n plt.ylabel(r\"Time $t$, a.u.\")\n plt.tight_layout()\n\n plt.show()",
"title": ""
},
{
"docid": "ff93b96f10c7f4b258091be6de11b3ec",
"score": "0.5726917",
"text": "def __calc_sse(self):\r\n sse = 0\r\n for i in range(self.n_data):\r\n for j in range(self.n_cluster):\r\n sse += self.cluster[i][j] * np.sum((self.data[i] - self.mu[j])**2)\r\n return sse",
"title": ""
},
{
"docid": "37977d5dac1fe5f235047af3848966c9",
"score": "0.56725",
"text": "def __calc_sse(self):\r\n sse = 0\r\n for i in range(self.n_cluster):\r\n sse += np.sum((self.data[self.idx==i] - self.centroid[i])**2)\r\n return sse",
"title": ""
},
{
"docid": "7b0eb556f49a4d8d62238d2fdafa2f46",
"score": "0.5662908",
"text": "def derivative_of_scale_space(cutouts,sigmas):\n # part E\n kernel_x = np.array([[1,0,-1]]) # as specified in the asignment\n kernel_y = kernel_x.T\n \n dx_for_all_sigmas={} #this dictionary will contain all the images transformed by kernel_x \n dy_for_all_sigmas ={} #this dictionary will contain all the images transformed by kernel_y\n \n #apply botht the filters\n for i,(image,sigma) in enumerate(zip(cutouts,sigmas)):\n \n #apply the filter \n filter_x = cv2.filter2D(image,-1,kernel_x)\n filter_y = cv2.filter2D(image,-1,kernel_y)\n \n dx_for_all_sigmas[sigma] = filter_x\n dy_for_all_sigmas[sigma] = filter_y\n \n cv2.imshow(\"kernel_x on image \"+str(i),filter_x)\n cv2.imshow(\"kernel_y on image \"+str(i),filter_y)\n \n cv2.waitKey(0)\n cv2.destroyAllWindows()\n \n return dx_for_all_sigmas,dy_for_all_sigmas",
"title": ""
},
{
"docid": "18d0e6f0cfb6b165769418eb6731244d",
"score": "0.5648625",
"text": "def _e_step(self, X):\n n_samples = len(X)\n res = np.zeros((n_samples, self.n_components))\n\n for idx, data_point in enumerate(X):\n denominator = np.sum([self._weights[j] * self.density_func(data_point, self._means[j], self._vars[j]) for j in range(self.n_components)])\n for j in range (self.n_components):\n numerator = self._weights[j] * self.density_func(data_point, self._means[j], self._vars[j])\n res[idx][j] = numerator / denominator\n\n epslon = 1e-10\n for i in range(res.shape[0]):\n if (res[i,0] == 0):\n res[i,0] += epslon\n res[i,1] -= epslon\n if (res[i,1] == 0):\n res[i,0] -= epslon\n res[i,1] += epslon\n\n return res",
"title": ""
},
{
"docid": "d5a0cc566b1d16bb5bf800e68913e87d",
"score": "0.56152195",
"text": "def energy(f) :\n\n\toutput = 0.\n\t\n\toutput += 1/( 2* DeltaX ) * np.sum( np.multiply( xStaggered, np.square( f[1:,0] - f[:-1,0]) + np.square( f[1:,1] - f[:-1,1]) ) )\n\t\n\toutput += DeltaX * np.sum( np.divide( np.square( np.square(f[1:,0]) - np.square(f[1:,1])) , np.multiply( x[1:], np.square(f[1:,0]) + np.square(f[1:,1])) ) ) \n\t\n\treturn output",
"title": ""
},
{
"docid": "a00d995ca976abdc8f335d60c083cc92",
"score": "0.5599425",
"text": "def ecdf_representation(D, n):\n m = np.mean(D)\n X = []\n for d in xrange(D.shape[1] + 1):\n func = ECDF(([D[:, d] + np.random.randn(np.shape(D[:, d])) * 0.01]))\n ll = func(np.linspace(0, 1, n))\n X = [X, ll]\n X = [X, m]\n plt.plot(X)\n plt.show()\n return X",
"title": ""
},
{
"docid": "634e12ae636290e3b19db0e73ecdb18a",
"score": "0.55871993",
"text": "def gradEnergy(f) :\n\t\n\th = np.divide( np.square( f[:,0] ) - np.square( f[:,1] ), np.square( f[:,0] ) + np.square( f[:,1] ) )\n\t\n\tgrad = np.zeros((N,2))\n\t\n\tgrad[1:-1,0] -= np.multiply( xStaggered[1:], f[2:,0] - f[1:-1,0] ) / DeltaX + np.multiply( xStaggered[:-1], f[:-2,0] - f[1:-1,0] ) / DeltaX\n\t\n\tgrad[1:-1,1] -= np.multiply( xStaggered[1:], f[2:,1] - f[1:-1,1] ) / DeltaX + np.multiply( xStaggered[:-1], f[:-2,1] - f[1:-1,1] ) / DeltaX\n\n\tgrad[1:-1,0] +=\tnp.divide(4 * DeltaX * np.multiply( f[1:-1,0], h[1:-1] ) - 2 * DeltaX * np.multiply( f[1:-1,0], np.square(h[1:-1]) ), x[1:-1]) \n\t\n\tgrad[1:-1,1] +=\tnp.divide(4 * DeltaX * np.multiply( f[1:-1,1], - h[1:-1] ) - 2 * DeltaX * np.multiply( f[1:-1,1], np.square(h[1:-1]) ), x[1:-1]) \n\t\n\t# On ajoute la composante en 0. On suppose que f[0,0] = f[0,1]\n\tgrad[0,0] = xStaggered[0] * ( 2 * f[0,0] - f[1,0] - f[1,1] ) / DeltaX\n\t\n\treturn grad",
"title": ""
},
{
"docid": "11340756135fbb12dc3f95b7d9ae61d9",
"score": "0.55824184",
"text": "def get_electron_density(c_states, e_fermi, c_mass_array, npoints,degen, smearing, beta_eV, band_contribution = False, avg_eff_mass = False):\n # The 1D DOS is (including the factor of 2 for the spin):\n # g(E) = sqrt(2 * effmass)/(pi*hbar) * 1/sqrt(E-E0)\n # where effmass is the band effective mass, E0 is the band edge.\n #\n # I rewrite it as g(E) = D * sqrt(meff/m0) / sqrt(E-E0)\n # where (meff/m0) is simply the effective mass in units of the electron free mass,\n # and D=sqrt(2) / pi / sqrt(HBAR2OVERM0) and will be in units of 1/ang/sqrt(eV)\n D = n.sqrt(2.) / n.pi / n.sqrt(HBAR2OVERM0)\n\n el_density = n.zeros(npoints) \n \n contrib = n.zeros(len(degen)) \n \n avg_mass = n.zeros((1,3))\n \n # All the conduction band minima have to be taken into account with the appropriate degeneracy\n for j in range(len(degen)): #number of minima\n deg = degen[j]\n if j > 0 and avg_eff_mass == True:\n avg_mass = n.append(avg_mass,[[0.,0.,0.]],axis=0) # so that bands are separated by a line of zeros\n for state_energy, state in c_states[j]:\n energy_range = 20. # eV, to be very safe\n \n #if state_energy > e_fermi:\n # continue\n square_norm = sum((state)**2)\n # I average the inverse of the effective mass\n # Both state and c_mass_array should have the same length\n # NOT SURE: square_norm or sqrt(square_norm) ? AUGU: I'm pretty sure it's square_norm and I changed it\n averaged_eff_mass = 1./(sum(state**2 / c_mass_array[j]) / square_norm)\n if avg_eff_mass == True:\n avg_mass = n.append(avg_mass,[[state_energy,state_energy-e_fermi,averaged_eff_mass]],axis=0)\n \n if not smearing and state_energy < e_fermi:\n # At T=0, integrating from E0 to Ef the DOS gives\n # D * sqrt(meff) * int_E0^Ef 1/(sqrt(E-E0)) dE =\n # D * sqrt(meff) * 2 * sqrt(Ef-E0) [if Ef>E0, else zero]\n el_density += deg * D * n.sqrt(averaged_eff_mass) * 2. * n.sqrt(e_fermi - state_energy) * (\n state**2 / square_norm)\n contrib[j] += n.sum(deg * D * n.sqrt(averaged_eff_mass) * 2. * n.sqrt(e_fermi - state_energy) * (\n state**2 / square_norm))\n \n elif smearing and state_energy-e_fermi < energy_range: # more than enough margin\n # Need to numerically integrate the density of state times the occupation given by MV_smearing\n # to compute the integral, one uses the trick explained there to avoid singularities: http://math.stackexchange.com/questions/1351734/numerical-integration-of-divergent-function\n n_int = 10000. # number of intervals\n # change of variable E = state_energy + t**2\n dt = n.sqrt(energy_range)/n_int \n t = n.arange(0,n.sqrt(energy_range),dt) \n #plt.figure(1)\n #plt.plot(energy,deg * D * sqrt(averaged_eff_mass) * 1./n.sqrt(energy-state_energy),\"b\")\n #plt.plot(energy,MV_smearing(energy,beta_eV,e_fermi),\"r\")\n #plt.plot(energy,g_times_f,\"k\")\n #plt.title(\"%s\"%e_fermi)\n #plt.show()\n temp_dens = 2*deg * D * n.sqrt(averaged_eff_mass) * n.trapz(MV_smearing(state_energy+t**2,beta_eV,e_fermi),dx=dt) * (state**2 / square_norm)\n el_density += temp_dens\n contrib[j] += n.sum(temp_dens)\n \n # Up to now, el_density is in 1/ang; we want it in 1/cm\n if band_contribution == False:\n return el_density * 1.e8\n elif band_contribution == True and avg_eff_mass == False:\n return el_density * 1.e8, contrib * 1.e8\n else:\n return el_density * 1.e8, contrib * 1.e8, avg_mass",
"title": ""
},
{
"docid": "d7e79ef7f472ef5229bcf7422b86fa4d",
"score": "0.55435175",
"text": "def create_clumpy_system(num_concepts=200,n_dim=2,n_epicentres =1,linearsep = 1,plot=False,seed = 456):\n \n #Settings\n np.random.seed(seed)\n epicentre_range=1 \n \n #Check number of epicentres allows 40 items per epicentre\n min_concepts_per_epicentres = 40\n max_epicentres = num_concepts // min_concepts_per_epicentres\n assert (n_epicentres <= max_epicentres),\"Max number of epicentres is %d for %d concpets\" %(max_epicentres,num_concepts)\n \n #Get sigma\n #sigma = 1/linearsep\n #sigma = 1/math.exp(linearsep)\n sigma = 1/linearsep**3\n \n # Create X (from create_n_systems)\n X_cov = np.zeros((n_dim, n_dim), dtype = np.float32)\n np.fill_diagonal(X_cov, sigma)\n \n means = np.random.uniform(\n -epicentre_range, epicentre_range, size = (n_epicentres, n_dim)\n )\n\n X = []\n for i in range(num_concepts):\n mean = i % n_epicentres\n value = np.random.multivariate_normal(\n mean=means[mean], cov=X_cov, size=1\n )\n X.append(value)\n X = np.array(X,dtype = np.float32)\n X = np.squeeze(X)\n \n if n_dim == 2 and plot == True:\n fig = plt.figure()\n plt.scatter(\n X[:,0], X[:,1]\n ) \n plt.title(\"Synthetic data\")\n plt.show()\n \n X = np.float32(X)\n return X",
"title": ""
},
{
"docid": "8e36b33eb8457ecfd0736eaf7a6d81b2",
"score": "0.55198014",
"text": "def energy(img):\n raise NotImplementedError",
"title": ""
},
{
"docid": "c39620ea042ec12d4efccdc900809a10",
"score": "0.55122936",
"text": "def exc(self,n,der=0):\n return self.e_x(n,der=der)+self.e_corr(n,der=der)",
"title": ""
},
{
"docid": "c39620ea042ec12d4efccdc900809a10",
"score": "0.55122936",
"text": "def exc(self,n,der=0):\n return self.e_x(n,der=der)+self.e_corr(n,der=der)",
"title": ""
},
{
"docid": "eeca89241e66882039db4d79a290c56a",
"score": "0.5507301",
"text": "def get_hole_density(v_states, e_fermi, v_mass_array, npoints, degen, smearing, beta_eV, band_contribution = False, avg_eff_mass = False):\n D = n.sqrt(2.) / n.pi / n.sqrt(HBAR2OVERM0)\n \n h_density = n.zeros(npoints) \n \n avg_mass = n.zeros((1,3))\n \n contrib = n.zeros(len(degen))\n for j in range(len(degen)):\n deg = degen[j]\n if j > 0 and avg_eff_mass == True:\n avg_mass = n.append(avg_mass,[[0.,0.,0.]],axis=0) # so that bands are separated by a line of zeros\n for state_energy, state in v_states[j]:\n energy_range = 20. # eV to be extra safe\n \n # Note that here the sign is opposite w.r.t. the conduction case\n #if state_energy < e_fermi:\n # continue\n square_norm = sum((state)**2)\n averaged_eff_mass = 1./(sum(state**2 / v_mass_array[j]) / square_norm)\n if avg_eff_mass == True:\n avg_mass = n.append(avg_mass,[[state_energy,state_energy-e_fermi,averaged_eff_mass]],axis=0)\n \n if not smearing and state_energy > e_fermi:\n h_density += deg * D * n.sqrt(averaged_eff_mass) * 2. * n.sqrt(state_energy - e_fermi) * (\n state**2 / square_norm)\n contrib[j] += n.sum(deg * D * n.sqrt(averaged_eff_mass) * 2. * n.sqrt(state_energy - e_fermi) * (\n state**2 / square_norm)) \n \n \n elif smearing and e_fermi-state_energy < energy_range:\n \n n_int = 10000. # number of intervals\n # change of variable E = state_energy + t**2\n dt = n.sqrt(energy_range)/n_int \n t = n.arange(0,n.sqrt(energy_range),dt) \n temp_dens = 2*deg * D * n.sqrt(averaged_eff_mass) * n.trapz(MV_smearing(2*e_fermi-state_energy+t**2,beta_eV,e_fermi),dx=dt) * (state**2 / square_norm)\n h_density += temp_dens\n contrib[j] += n.sum(temp_dens)\n \n # to keep a trace of old work\n \"\"\"\n n_int = 100000. # 500 intervals\n delta_E = energy_range/n_int \n energy = n.arange(state_energy-energy_range,state_energy,delta_E)\n energy -= 1.e-8 # to avoid dividing by zero\n g_times_f = deg * D * sqrt(averaged_eff_mass) * 1./n.sqrt(state_energy-energy) * MV_smearing(2*e_fermi-energy,beta_eV,e_fermi)\n #plt.figure(2)\n #plt.plot(energy,deg * D * sqrt(averaged_eff_mass) * 1./n.sqrt(state_energy-energy),\"b\")\n #plt.plot(energy,MV_smearing(2*e_fermi-energy,beta_eV,e_fermi),\"r\")\n #plt.plot(energy,g_times_f,\"k\")\n #plt.title(\"%s\"%e_fermi)\n #plt.show()\n h_density += n.trapz(g_times_f,dx=delta_E) * (state**2 / square_norm)\n contrib[j] += sum(n.trapz(g_times_f,dx=delta_E) * (state**2 / square_norm))\n \"\"\" \n if band_contribution == False:\n return h_density * 1.e8\n elif band_contribution == True and avg_eff_mass == False:\n return h_density * 1.e8, contrib * 1.e8\n else:\n return h_density * 1.e8, contrib * 1.e8, avg_mass",
"title": ""
},
{
"docid": "0d11cc5570f55f3bad4e16547a84585f",
"score": "0.55023724",
"text": "def ecdf(data):\r\n\r\n # Number of data points: n\r\n n = len(data)\r\n\r\n # x-data for the ECDF: x\r\n x = np.sort(data)\r\n\r\n # y-data for the ECDF: y\r\n y = np.arange(1, len(x)+1) / n\r\n\r\n return x, y",
"title": ""
},
{
"docid": "84d93d981d6cd013de6968fe30628c3f",
"score": "0.5473807",
"text": "def main():\n\n # Create a dummy spectrum\n spec = np.zeros(100)\n wav = np.arange(len(spec))\n\n # Superimpose an emission line on the spectrum\n g = models.Gaussian1D(amplitude=3, mean=30.0, stddev=10.0)\n gauss_1d_emission = g(wav)\n\n spec = spec + gauss_1d_emission\n\n \"\"\"\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.plot(wav, spec)\n plt.show()\n \"\"\"\n\n # Now assign some width to the dummy galaxy\n # in pixels\n # say 1-sigma if it is shaped like a Gaussian\n # will deal with Sersic and other complicated profiles later\n galaxy_width = 10\n num_sigma = 2 # e.g., going from -2 to +2 sigma of galaxy width\n\n # Assume that each time the spectrum shifts \n # by some amount epsilon. I'm leaving this param\n # free for now.\n # in units of wavelength\n # this is saying that if a given pixel has the center of\n # the emission line at lambda then the adjacent pixel along\n # the dispersion direction will have the line center at\n # lambda + epsilon\n epsilon = 1.0\n # BE CAREFUL!! This currently means epsilon steps of wavelength\n # so that if the wavelenght is sampled at 1 A then epsilon is\n # 30 A but if the wavelength array is sampled at 10 A then epsilon\n # means 300 A.\n\n print(\"Width of galaxy provided:\", galaxy_width)\n print(\"Will consider between +- these many sigmas:\", num_sigma)\n print(\"Epsilon:\", epsilon)\n\n exten = int(epsilon * num_sigma * galaxy_width)\n print(\"Extension:\", exten)\n total_pix = num_sigma * galaxy_width\n print(\"Total pixels to consider:\", total_pix)\n\n final_spec = np.zeros(shape=(total_pix, (len(spec) + exten)))\n print(\"Shape of final spectra array:\", final_spec.shape)\n print(\"\\n\")\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n for pix in range(total_pix):\n\n # Create shifted spectrum\n i = int(epsilon*(1+pix)) # starting index for shift\n shifted_spec = np.zeros(len(spec) + i)\n shifted_spec[i:] = spec\n\n print(\"\\nAt pixel:\", pix+1)\n print(\"Shifting to index:\", i)\n print(\"len of shifted spec:\", len(shifted_spec))\n\n # Now fold it into the final spectrum\n final_spec[pix, :len(shifted_spec)] = shifted_spec\n\n ax.plot(shifted_spec)\n\n final_spec_comb = np.sum(final_spec, axis=0)\n print(\"Shape of final LSF broadened spectrum:\", final_spec_comb.shape)\n\n ax.plot(final_spec_comb, color='k')\n plt.show()\n\n # -----------\n fig1 = plt.figure()\n ax1 = fig1.add_subplot(111)\n ax1.imshow(final_spec)\n plt.show()\n\n return None",
"title": ""
},
{
"docid": "e9a7d39f1f79bb0d7d90532104f5a8df",
"score": "0.54614717",
"text": "def spectralEmissionDensity(self):\n return self.sed",
"title": ""
},
{
"docid": "ad29da81997d4ea6f22fb296cbb4f715",
"score": "0.5445146",
"text": "def erosion(self):\n \n self.Ke = 0.2e-6 / self.tau_crit**(0.5)\n shear_stress = self.rho_w * self.u_star**2\n \n# self.domain.set_quantity('shear_stress', shear_stress, location='centroids')\n \n \n edot = self.Ke * (shear_stress[self.ind] - self.tau_crit)\n edot[edot<0.0] = 0.0\n \n return edot",
"title": ""
},
{
"docid": "8a8f63223cb6f4c076dc3aa6faebd4df",
"score": "0.5443789",
"text": "def __energy(self, a, b, c, d):\n return (a + b - c) / (2. * d)",
"title": ""
},
{
"docid": "6b7eb9050f7193e88db86f1e325e7ed1",
"score": "0.54365504",
"text": "def calc_energy(img):\n img = img.astype('float32')\n\n R = img[:,:,0]\n G = img[:,:,1]\n B = img[:,:,2]\n\n filter_du = np.array([\n [1.0, 2.0, 1.0],\n [0.0, 0.0, 0.0],\n [-1.0, -2.0, -1.0],\n ])\n \n filter_dv = np.array([\n [1.0, 0.0, -1.0],\n [2.0, 0.0, -2.0],\n [1.0, 0.0, -1.0],\n ])\n\n # Sobel filter\n Gx = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]])\n Gy = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])\n convolved_r = np.absolute(convolve(R, filter_dv, mode='constant', cval=0)) + np.absolute(convolve(R, filter_du, mode='constant', cval=0))\n convolved_g = np.absolute(convolve(G, filter_dv, mode='constant', cval=0)) + np.absolute(convolve(G, filter_du, mode='constant', cval=0))\n convolved_b = np.absolute(convolve(B, filter_dv, mode='constant', cval=0)) + np.absolute(convolve(B, filter_du, mode='constant', cval=0))\n\n energy_map = convolved_r + convolved_g + convolved_b\n\n return energy_map",
"title": ""
},
{
"docid": "380ee9ca3c71ab499948c539daff84a9",
"score": "0.5423679",
"text": "def energy(self, data):\n return -be.dot(data, self.params.loc)",
"title": ""
},
{
"docid": "380ee9ca3c71ab499948c539daff84a9",
"score": "0.5423679",
"text": "def energy(self, data):\n return -be.dot(data, self.params.loc)",
"title": ""
},
{
"docid": "e092de3303767b748c8417ca5f2d978e",
"score": "0.53795356",
"text": "def energy_grad(self, x):\n return self.M*x - self.y",
"title": ""
},
{
"docid": "1b603cb20b69739a2d07596769a64d53",
"score": "0.5379365",
"text": "def generateDensities(self):\n densities = [] # storing them in a list\n for i in range(len(self.road)+1): \n densities.append(i/len(self.road))\n return densities",
"title": ""
},
{
"docid": "2fea24198f907b64540abc4de65b5c1b",
"score": "0.5378305",
"text": "def KDE(data_array, bandwidth=None):\n data = data_array\n d_range = data.max()-data.min()\n n = len(data)\n x_axis = np.linspace(data.min()-0.4*d_range,data.max()+0.4*d_range,100)\n\n sigma = np.std(data)\n if bandwidth is None:\n bandwidth = ((4*(sigma**5))/(3*n))**(0.2) # std dev of kernel distributions\n \n kernel_list = []\n for point in data:\n # normal distribution at each point\n kernel = stats.norm(point,bandwidth).pdf(x_axis)\n kernel_list.append(kernel)\n \n #Scale for plotting\n kernel = kernel / kernel.max()\n kernel = kernel *0.25\n plt.plot(x_axis,kernel,color = 'grey',alpha=0.5)\n #plt.ylim(0,0.6)\n\n sum_of_kernels = np.sum(kernel_list,axis=0)*(1/n) # total kernels\n \n plt.plot(x_axis,sum_of_kernels,color='indianred')\n sns.rugplot(data,c='indianred')\n plt.title('sum of the basis functions')",
"title": ""
},
{
"docid": "1f7da8a7947feb5ec731fb7dfb42ebd3",
"score": "0.53734314",
"text": "def ecdf(data):\r\n\r\n # Number of data points: n\r\n n = len(data)\r\n\r\n # x-data for the ECDF: x\r\n x = np.sort(data)\r\n\r\n # y-data for the ECDF: y\r\n y = np.arange(1, n+1) / n\r\n\r\n return x, y",
"title": ""
},
{
"docid": "ba649f537af85785059609138888b0cc",
"score": "0.53654814",
"text": "def _compute_se(self, data, centroids, assigned, n_samples):\r\n se = 0\r\n for idx in range(n_samples):\r\n se += np.sqrt((data[idx, :]-centroids[assigned[idx], :])**2).sum()\r\n return se/n_samples",
"title": ""
},
{
"docid": "2750e15839593227a497b1c19c7b61a2",
"score": "0.53645927",
"text": "def discretize(self, delta_t: float) -> None:",
"title": ""
},
{
"docid": "f9e225ff8a376e42f812254e6ef2c8d0",
"score": "0.53623915",
"text": "def ecdf(data):\n # Number of data points: n\n n = len(data)\n # x-data for the ECDF: x\n x = np.sort(data)\n # y-data for the ECDF: y\n y = np.arange(1, n + 1) / n\n return x, y",
"title": ""
},
{
"docid": "04620f2c03234d57938efe0bd8bcf893",
"score": "0.53555024",
"text": "def second_eccentricity(self):\n return self.e1",
"title": ""
},
{
"docid": "3ac782dc9fdf8aa36c91cdc654d1d019",
"score": "0.5352694",
"text": "def calculate_energy(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "960a254622eef1e11eddbadbbfe0704a",
"score": "0.53485847",
"text": "def det_hessian(self, img, box_size):\n #dxy = convolve(img, box_2nd_order('xy',box_size), mode='constant')\n #dxx = convolve(img, box_2nd_order('xx',box_size), mode='constant')\n #dyy = convolve(img, box_2nd_order('yy',box_size), mode='constant')\n dxx = self.box_xx(img, box_size)\n dxy = self.box_xy(img, box_size)\n dyy = self.box_yy(img, box_size)\n return dxx*dyy - (HESSIAN_WEIGHTS[box_size]*dxy)**2",
"title": ""
},
{
"docid": "46e90810b92bcb66f29cd928af9c0d50",
"score": "0.5347214",
"text": "def divergence(arr: np.ndarray, out: np.ndarray) -> None:\n # inner radial boundary condition\n for i in range(1, dim_r + 1): # iterate radial points\n out[i - 1] = (arr[0, i + 1] - arr[0, i - 1]) * scale_r\n out[i - 1] += arr[0, i] / rs[i - 1]",
"title": ""
},
{
"docid": "0bd0fff88ff42c813cf0dca1d0a4bec8",
"score": "0.53469586",
"text": "def ecdf(data):\n plt.plot(np.sort(data), np.linspace(0,1,len(data)))",
"title": ""
},
{
"docid": "0c273fb870eef966fc8be27c1d4abdb4",
"score": "0.53371334",
"text": "def emissivity(self, density: u.cm**(-3), **kwargs) -> u.erg * u.cm**(-3) / u.s:\n density = np.atleast_1d(density)\n g = self.contribution_function(density, **kwargs)\n return g * (density**2)[np.newaxis, :, np.newaxis]",
"title": ""
},
{
"docid": "36c6f9698fb1cd83cda646057f153fd5",
"score": "0.53329754",
"text": "def electron_line_average_density(self):\n elad = 1.45e+19\n\n return elad",
"title": ""
},
{
"docid": "8e995538d93d32413f8a1638a2093adc",
"score": "0.53268284",
"text": "def calc_sse(self,experimento):\n pass",
"title": ""
},
{
"docid": "1a088eefb743672096664d650b19c35f",
"score": "0.53135514",
"text": "def divergence(f):\n\tnum_dims = len(f)\n\tprint(f)\n\treturn np.ufunc.reduce(np.add, [np.gradient(f[i], axis=i) for i in range(num_dims)])",
"title": ""
},
{
"docid": "97b947491374e59e642db2be97bf5109",
"score": "0.53130823",
"text": "def ecdf(data):\n # Number of data points: n\n n = len(data)\n\n # x-data for the ECDF: x\n x = np.sort(data)\n\n # y-data for the ECDF: y\n y = np.arange(1, n + 1) / n\n\n return x, y",
"title": ""
},
{
"docid": "47af79ff39fb7e13328f6fac70898746",
"score": "0.5308772",
"text": "def ecdf(data):\n # Number of data points: n\n n = len(data)\n\n # x-data for the ECDF: x\n x = np.sort(data)\n\n # y-data for the ECDF: y\n y = np.arange(1, n+1) / n\n\n return x, y",
"title": ""
},
{
"docid": "47af79ff39fb7e13328f6fac70898746",
"score": "0.5308772",
"text": "def ecdf(data):\n # Number of data points: n\n n = len(data)\n\n # x-data for the ECDF: x\n x = np.sort(data)\n\n # y-data for the ECDF: y\n y = np.arange(1, n+1) / n\n\n return x, y",
"title": ""
},
{
"docid": "47af79ff39fb7e13328f6fac70898746",
"score": "0.5308772",
"text": "def ecdf(data):\n # Number of data points: n\n n = len(data)\n\n # x-data for the ECDF: x\n x = np.sort(data)\n\n # y-data for the ECDF: y\n y = np.arange(1, n+1) / n\n\n return x, y",
"title": ""
},
{
"docid": "4881e6ac96182b4cbcc2bd770a1d49b5",
"score": "0.5300936",
"text": "def sd(alpha=0.0002):\n print \"STEEPEST DESCENT: start\"\n # gradient\n g = [diff(obj, i) for i in m]\n # Initialize xs\n xs = [[0.0, 0.0]]\n xs[0] = x_start\n # Get gradient at start location (df/dx or grad(f))\n iter_s = 0\n while np.linalg.norm(xs[-1] - x_result) > target_precision:\n # print \"STEEPEST DESCENT: distance:\", np.linalg.norm(xs[-1] - x_result)\n gs = dfdx(xs[iter_s], g)\n # Compute search direction and magnitude (dx)\n # with dx = - grad but no line searching\n xs.append(xs[iter_s] - np.dot(alpha, gs))\n # print xs[-1]\n iter_s += 1\n if iter_s > 10000:\n break\n print \"STEEPEST DESCENT: result distance:\", np.linalg.norm(xs[-1] - x_result)\n xs = np.array(xs)\n plt.plot(xs[:, 0], xs[:, 1], 'g-o')",
"title": ""
},
{
"docid": "0143a2f4548ffdc607e38241b0b1edd5",
"score": "0.5298317",
"text": "def _compute_energy(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "efa1001944739b8f1b7356f6824798c0",
"score": "0.52960914",
"text": "def propagator(self):\r\n\t\th, m = self.hbar, self.m\r\n\t\tdt,dx = self.dt,self.dx\r\n\t\tnx,nt = self.nx, self.nt\r\n\t\tw = self.w\r\n\t\t\r\n\t\tS = np.zeros(shape = (nx,nx), dtype = self.dtype )\r\n\t\tdS = np.zeros(shape = (nx,nx), dtype = self.dtype )\r\n\r\n\t\tx = self.x\r\n\r\n\t\tc0 = (m * dt /(2* h))\r\n\t\tc1 = c0/(dt*dt)\r\n\t\tc2 = c0*w*w*.25\r\n\r\n\t\tl = len(x)\r\n\r\n\t\tfor ii in range(l):\r\n\t\t\tfor jj in range(ii,l):\r\n\t\t\t\ts = c1*(x[jj]-x[ii])**2 + c2*(x[jj]+x[ii])**2\r\n\t\t\t\tS[ii,jj] = s\r\n\t\t\t\tS[jj,ii] = s\r\n\r\n\t\tfor ii in range(l):\r\n\t\t\tfor jj in range(l):\r\n\t\t\t\tds = (S[ii,ii] - S[ii,jj])\r\n\t\t\t\tdS[ii,jj] = ds\r\n\r\n\t\treturn np.exp(dS)",
"title": ""
},
{
"docid": "52cb260a13b3756b2db709ab3968ccc4",
"score": "0.5291767",
"text": "def gkern(size=5, sigma=1.0):\n ax = np.arange(-size // 2 + 1.0, size // 2 + 1.0)\n xx, yy = np.meshgrid(ax, ax)\n kernel = np.exp(-(xx**2 + yy**2) / (2.0 * sigma**2))\n return kernel / np.sum(kernel)",
"title": ""
},
{
"docid": "b56cb682c40ec19c81282ec2fc83cfaa",
"score": "0.5291205",
"text": "def ec_disc(ainvs):\n a1, a2, a3, a4, a6 = ainvs\n b2 = a1*a1 + 4*a2\n b4 = a3*a1 + 2*a4\n b6 = a3*a3 + 4*a6\n c4 = b2*b2 - 24*b4\n c6 = -b2*b2*b2 + 36*b2*b4 - 216*b6\n return (c4*c4*c4 - c6*c6) / 1728",
"title": ""
},
{
"docid": "1c85dae29d30fe5ce67a22950816bde7",
"score": "0.5290065",
"text": "def K(self, x1, x2=0):\n diffs = (x1 - x2).reshape(self.D, 1) # Vector of differences\n #diffs = np.array([0] * self.D).reshape(self.D, 1)\n return e**(-.5 * (diffs.transpose() @ self.covMI @ diffs)[0][0]) / self.denom\n #return density",
"title": ""
},
{
"docid": "fd0da4be2f26428b2ed9c8672f259544",
"score": "0.5288685",
"text": "def electronDensity(self, separation):\n return self.electronDensityFunction(separation)",
"title": ""
},
{
"docid": "c3895f1ba9e89b0ab674f05f915cf232",
"score": "0.5278172",
"text": "def ecdf(data):\n data.dropna(inplace=True)\n\n # Number of data points: n\n n = len(data)\n\n # x-data for the ECDF: x\n x = np.sort(data)\n\n # y-data for the ECDF: y\n y = np.arange(1, n+1) / n\n\n return x, y",
"title": ""
},
{
"docid": "434076a188c42a3d7fba7c559c97e3e3",
"score": "0.5267953",
"text": "def energy(self, data):\n return be.dot(data, self.params.loc)",
"title": ""
},
{
"docid": "30ead6dd4621f1ed2bd8b9f7c6a87a8c",
"score": "0.52505195",
"text": "def FE_1D(self, xsamples, wsamples, nbins=100):\n # Take the projection.\n xproj = self.xproj(xsamples)\n # Procedure for calculating the free energy\n counts, bins = np.histogram(xproj, bins=nbins, weights=wsamples)\n p = counts / np.sum(counts) # the approximation of probability, px(x)\n centers = bins[:-1] + 0.5*(bins[1]-bins[0])\n FE = -np.log(p)\n E0 = np.min(FE)\n FE -= E0\n self.cutaway(zero=E0)\n plt.scatter(centers,FE, c='g')\n plt.ylabel(r'$\\Delta F$')\n plt.xlabel(r'$x_{1}$')",
"title": ""
},
{
"docid": "4157735a17f42821be441227ac549f3b",
"score": "0.52500194",
"text": "def dpsfdxc(i0,i1,xc,yc,sigma):\n a=1/(np.sqrt(2)*sigma)\n return -a*0.25*2/np.sqrt(np.pi)*(np.exp(-(a*(i1+0.5-xc))**2)-np.exp(-(a*(i1-0.5-xc))**2))*(erf(a*(i0+0.5-yc))-erf(a*(i0-0.5-yc)))",
"title": ""
},
{
"docid": "66a89674908fc712594c1572d2ba838c",
"score": "0.52474517",
"text": "def _gauss_kernel(self, kernel_size, nsig):\n\n x = np.linspace(-nsig, nsig, kernel_size + 1)\n kern1d = np.diff(st.norm.cdf(x))\n kern2d = np.outer(kern1d, kern1d)\n return kern2d / kern2d.sum()",
"title": ""
},
{
"docid": "053840603f16657c58a56e2fbab6b87f",
"score": "0.52409095",
"text": "def sse_calculation(self):\n sse = 0\n for row in self.data:\n sse += (row[0]-self.centroid[0])**2 + (row[1]-self.centroid[1])**2 + (row[2]-self.centroid[2])**2\n return sse",
"title": ""
},
{
"docid": "21d1749a7bd937f9ad5487847741f6b3",
"score": "0.5237992",
"text": "def se(self):\n \n sd = self.stdev()\n n = self.n()\n \n return sd / np.sqrt(n)",
"title": ""
},
{
"docid": "e7ab95afbc7faf0e56a7322f81dffc49",
"score": "0.52239615",
"text": "def __init_coefs(self, g):\n df = {}\n df[0] = g[5]\n df[1] = g[6]\n df[2] = g[9]\n df[3] = g[10]\n\n # Derivatives in the East direction and the North direction\n df[4] = (-g[7] + 4 * g[6] - 3 * g[5]) / 2\n df[5] = (3 * g[6] - 4 * g[5] + g[4]) / 2\n df[6] = (-g[11] + 4 * g[10] - 3 * g[9]) / 2\n df[7] = (3 * g[10] - 4 * g[9] + g[8]) / 2\n df[8] = (-g[13] + 4 * g[9] - 3 * g[5]) / 2\n df[9] = (-g[14] + 4 * g[10] - 3 * g[6]) / 2\n df[10] = (3 * g[9] - 4 * g[5] + g[1]) / 2\n df[11] = (3 * g[10] - 4 * g[6] + g[2]) / 2\n\n # Equations for the cross derivative\n df[12] = ((g[0] + g[10]) - (g[2] + g[8])) / 4\n df[13] = ((g[1] + g[11]) - (g[3] + g[9])) / 4\n df[14] = ((g[4] + g[14]) - (g[6] + g[12])) / 4\n df[15] = ((g[5] + g[15]) - (g[7] + g[13])) / 4\n\n self.__a[0] = df[0]\n self.__a[1] = df[4]\n self.__a[2] = -3 * df[0] + 3 * df[1] - 2 * df[4] - df[5]\n self.__a[3] = 2 * df[0] - 2 * df[1] + df[4] + df[5]\n self.__a[4] = df[8]\n self.__a[5] = df[12]\n self.__a[6] = -3 * df[8] + 3 * df[9] - 2 * df[12] - df[13]\n self.__a[7] = 2 * df[8] - 2 * df[9] + df[12] + df[13]\n self.__a[8] = -3 * df[0] + 3 * df[2] - 2 * df[8] - df[10]\n self.__a[9] = -3 * df[4] + 3 * df[6] - 2 * df[12] - df[14]\n self.__a[10] = (9 * df[0] - 9 * df[1] - 9 * df[2] + 9 * df[3] + 6 * df[4] + 3\n * df[5] - 6 * df[6] - 3 * df[7] + 6 * df[8] - 6 * df[9] + 3\n * df[10] - 3 * df[11] + 4 * df[12] + 2 * df[13] + 2 * df[14]\n + df[15])\n self.__a[11] = (-6 * df[0] + 6 * df[1] + 6 * df[2] - 6 * df[3] - 3 * df[4] - 3\n * df[5] + 3 * df[6] + 3 * df[7] - 4 * df[8] + 4 * df[9] - 2\n * df[10] + 2 * df[11] - 2 * df[12] - 2 * df[13] - df[14]\n - df[15])\n self.__a[12] = 2 * df[0] - 2 * df[2] + df[8] + df[10]\n self.__a[13] = 2 * df[4] - 2 * df[6] + df[12] + df[14]\n self.__a[14] = (-6 * df[0] + 6 * df[1] + 6 * df[2] - 6 * df[3] - 4 * df[4] - 2\n * df[5] + 4 * df[6] + 2 * df[7] - 3 * df[8] + 3 * df[9] - 3\n * df[10] + 3 * df[11] - 2 * df[12] - df[13] - 2 * df[14]\n - df[15])\n self.__a[15] = (4 * df[0] - 4 * df[1] - 4 * df[2] + 4 * df[3] + 2 * df[4] + 2\n * df[5] - 2 * df[6] - 2 * df[7] + 2 * df[8] - 2 * df[9] + 2\n * df[10] - 2 * df[11] + df[12] + df[13] + df[14] + df[15])",
"title": ""
},
{
"docid": "ca3065d3d91e7ea7d44cafab0eae7207",
"score": "0.52211946",
"text": "def s(self):\n xe = self._xe\n nobs = xe.shape[0]\n bw = self.bandwidth\n kernel = self._kernel\n kernel = KERNEL_LOOKUP[kernel]\n weights = kernel(bw, nobs - 1)\n out = _cov_kernel(xe, weights)\n\n return (out + out.T) / 2",
"title": ""
},
{
"docid": "35d4cc491b2e48738c2b48db33d27387",
"score": "0.522105",
"text": "def Energy():\n pot = 0\n kin = 0\n for j in range(ParticleAmount):\n pot1 = 0\n kin += (0.5) * (sum([x * x for x in Particles[j, 1]]))\n for k in range(ParticleAmount):\n if k != j:\n r = DistancePoints(Particles[j, 0], Particles[k, 0])\n pot1 += 4 * ((1 / r) ** 12 - (1 / r) ** 6)\n pot += pot1 / 2 # *0.5 to prevent double counting, can rewrite the range too to save time\n Epot.append(pot)\n Ekin.append(kin)\n Etot.append(pot + kin)",
"title": ""
},
{
"docid": "59ab17228bf4ee1b2a2cf26ccdf82ae5",
"score": "0.52159405",
"text": "def CalGen(self):\n # Energy dissipation mW/cm3-nm at each position and wavelength\n # (JAP Vol 86 p.487 Eq 22)\n if self.AbsRate is None:\n self.CalAbs()\n Q = self.AbsRate * self.AM15\n self.Gx = Q * 1e-12 / (h * c) * self.WL\n\n Gx_x = [np.sum(self.Gx[self.x_ind[i-1]:self.x_ind[i]])\n for i in range(1, len(self.layers))]\n self.Jsc = np.array(Gx_x) * self.WLstep * self.posstep * q * 1e-4\n\n return None",
"title": ""
},
{
"docid": "fb262161e2b6720b53dda0580da6603e",
"score": "0.5213868",
"text": "def lasso_g(datax,datay,w,alpha):\n return mse_g(datax,datay,w) + alpha * np.sign(w).reshape((-1,1))",
"title": ""
},
{
"docid": "dcb3e7c2bb7c19c7c709ee63678686ba",
"score": "0.5213698",
"text": "def get_energy(self):\n w = 0\n for i in range(3):\n for j in range(3):\n w = w + self.epsilon[i,j] * self.sigma[i,j]\n self.w = w",
"title": ""
},
{
"docid": "dcb3e7c2bb7c19c7c709ee63678686ba",
"score": "0.5213698",
"text": "def get_energy(self):\n w = 0\n for i in range(3):\n for j in range(3):\n w = w + self.epsilon[i,j] * self.sigma[i,j]\n self.w = w",
"title": ""
},
{
"docid": "6519c6a46cf4e1e9dcff86d413f8089a",
"score": "0.520436",
"text": "def ggprimeInScatter(g, l):\n return np.sum(skernel[l, g, :] * evalVecLegFlux(cell, l))",
"title": ""
},
{
"docid": "890684aeb4dec4d6d600488d444854b4",
"score": "0.5201224",
"text": "def Recompute_densities(self, Over_IDs, box_exp_multiplier):\n\t\tSlicedParts, SlicedIDs, SliceRanges = OF.Get_particle_box_slices(self.ParticlePos)\n\t\tNf_Distances = []\n\t\tNf_Segids = []\n\t\tNf_Tsolutions = []\n\t\tNf_ParticleBoxes = []\n\t\tNf_Masked_IDs = []\n\t\tfor number in Over_IDs:\n\t\t\tDist_new, SegID_new, Tsols_new, Pbox_new, MaskIDs_new = self.Mask_and_compute_distances_again(self.Filament_3DPos[self.Small_filaments][number],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbox_exp_multiplier*6, SlicedParts, SlicedIDs, SliceRanges)\n\t\t\tNf_Distances.append(Dist_new)\n\t\t\tNf_Segids.append(SegID_new)\n\t\t\tNf_Tsolutions.append(Tsols_new)\n\t\t\tNf_ParticleBoxes.append(Pbox_new)\n\t\t\tNf_Masked_IDs.append(MaskIDs_new)\n\t\tNf_Distances = np.array(Nf_Distances)\n\t\tNf_Segids = np.array(Nf_Segids)\n\t\tNf_Tsolutions = np.array(Nf_Tsolutions)\n\t\tNf_ParticleBoxes = np.array(Nf_ParticleBoxes)\n\t\tNf_Masked_IDs = np.array(Nf_Masked_IDs)\n\n\t\tIncluded_fils, Filtered_fils, OverThreshold = self.Filter_filament_density_threshold(self.Filament_3DPos[self.Small_filaments][Over_IDs], Nf_Distances,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.FilamentLength[self.Small_filaments][Over_IDs])\n\t\tReturn_included = Over_IDs[Included_fils] if Included_fils.any() else np.array([])\n\t\tReturn_excluded = Over_IDs[Filtered_fils] if Filtered_fils.any() else np.array([])\n\t\tReturn_overIDs = Over_IDs[OverThreshold] if OverThreshold.any() else np.array([])\n\t\treturn_dist = Nf_Distances[Included_fils] if Included_fils.any() else Nf_Distances\n\t\treturn_masks = Nf_Masked_IDs[Included_fils] if Included_fils.any() else Nf_Masked_IDs\n\t\treturn_segids = Nf_Segids[Included_fils] if Included_fils.any() else Nf_Segids\n\t\treturn Return_included, Return_excluded, Return_overIDs, return_dist, return_masks, return_segids",
"title": ""
},
{
"docid": "a4aa423a2c7b7f9a592905d8f14245f1",
"score": "0.51976824",
"text": "def energies(self):\n return self._pha[self._filter_idx]",
"title": ""
},
{
"docid": "84630f7b4385c7233eb8f8a0633001f7",
"score": "0.5187491",
"text": "def meas(self):\n meas = DubinsCarMeas(self.x, self.beacon_x, self.beacon_y)\n meas += self.meas_noise_coef @ np.random.standard_normal(self.meas_dim)\n return meas",
"title": ""
},
{
"docid": "f69a962661317491bb66b9e06eb07ae4",
"score": "0.5186381",
"text": "def divergence(u):\n return reduce(np.add,[np.gradient(u[i])[i] for i in range(len(u))])",
"title": ""
},
{
"docid": "30c03db0655c2dd521b33d248044b811",
"score": "0.51840013",
"text": "def elastic_spectrum(self,periods,acc,dt,damping):\n SD = []\n SV = []\n SA = []\n stiffness = []\n omega = []\n \n m = 1.0\n n = len(acc)\n for pp in range(len(periods)):\n # - Define aid variables \n if periods[pp] == 0.0:\n p = 5e-2\n else: \n p = periods[pp]\n \n w = 2*pi/p\n omega.append(w)\n \n k = m*(w**2)\n stiffness.append(k)\n c = 2*damping*(k*m)**0.5\n \n # - Initial conditions for Central differences\n u = []\n u0 = 0.0\n a0 = -1*acc[0]\n u1 = u0 + 0.5*a0*dt**2\n u.append(u0)\n u.append(u1)\n \n # - Run central differences\n for i in range(1,n-1):\n u_next = self.disp_next(u[i],u[i-1],acc[i],k,c,m,dt)\n u.append(u_next)\n \n \n sd = max(max(u),abs(min(u)))\n SD.append(sd)\n \n SD = np.array(SD)\n omega = np.array(omega)\n stiffness = np.array(stiffness)\n SV = omega*SD\n SA = SD*omega**2\n F_elastic = stiffness*SD\n return SA,SV,SD,F_elastic",
"title": ""
},
{
"docid": "4b9f0ece68460b9bcfa435619e9270c3",
"score": "0.51769805",
"text": "def bond_energy_np(self, x):\n d2 = (x[:, 0]-x[:, 2])**2 + (x[:, 1]-x[:, 3])**2\n d4 = d2**2\n d = np.sqrt(d2) # Here is the thorn\n return 1/4*self.a*d4 - 1/2*self.b*d2 + self.c*d",
"title": ""
},
{
"docid": "3fa481a61899674c89ee566d82f8c81a",
"score": "0.5175087",
"text": "def ecdf(data):\n x = np.sort(data)\n y = np.arange(1, 1+len(x)) /len(x)\n return x, y",
"title": ""
},
{
"docid": "493b564c5ea04ca341ab608cfcbe063d",
"score": "0.51748246",
"text": "def test_discrete5():\n geo = psci.geometry.Rectangular(space_origin=(0.0, -1.0), space_extent=(2.0, 1.0))\n pdes = psci.pde.NavierStokes(0.01, 1.0)\n pdes, geo = psci.discretize(pdes, geo, space_nsteps=(4, 3))\n bc = geo.get_bc_index()\n sd = geo.get_space_domain()\n\n sd_t, bc_t = geo_discrete([(0.0, -1.0), (2.0, 1.0)], (4, 3))\n assert np.allclose(bc, bc_t)\n assert np.allclose(sd, sd_t)",
"title": ""
},
{
"docid": "8287b48312fc13ece1193c6e5a88ba11",
"score": "0.51728",
"text": "def S(x):\n \n # return D/2 * (1-diam_narrow*(1+np.cos(2*np.pi*(x-x0)/L)))\n # L = 2*diam_steno_vessel\n return diam_steno_vessel/2 - diam_narrow/2*(1+np.cos(2*np.pi*(x)/length_steno))",
"title": ""
},
{
"docid": "9df29893afcbdf33c220643090ad8ca2",
"score": "0.5167013",
"text": "def oldvals():\n #plt.figure(0)\n #plt.scatter([ant1_px,ant2_px,ant3_px,ant4_px],[ant1_py,ant2_py,ant3_py,ant4_py])\n #plt.scatter(0.0,0.0)\n #plt.scatter(SPICE_x_guess,SPICE_y_guess)\n #plt.show()\n\n #print('try sample f:')\n #init_chi2 = f(ant1_pxb,ant1_pyb,ant1_pzb,ant1_pxt,ant1_pyt,ant1_pzt, ant2_px, ant2_py, ant2_pzb, ant2_pzt,ant3_px,ant3_py,ant3_pzb,ant3_pzt,ant4_px,ant4_py,ant4_pzb,ant4_pzt,cal_pz,SPICE_x_guess,SPICE_y_guess,c0p,c1p,c8p,c9p,c16p,c17p,c24p,c25p)\n #print('Inital chi2 is:', init_chi2)",
"title": ""
},
{
"docid": "bf3e630a0bdb88bf646fc8b5f9a88688",
"score": "0.5164112",
"text": "def evaluate_density(self, X):\n pass",
"title": ""
},
{
"docid": "d87294af9811a381b012f8c5adaafb2c",
"score": "0.51547587",
"text": "def kernel_density(x, kde):\r\n return kde.score_samples(np.array([[x]]))[0]",
"title": ""
},
{
"docid": "a2c40ac41dcc9dedd1dd3e6a55cfa10e",
"score": "0.5148907",
"text": "def Eccentricity(self, *args):\n return _gp.gp_Hypr2d_Eccentricity(self, *args)",
"title": ""
},
{
"docid": "87d70ddf2e62b8fa9e9d86ae54f4f0d2",
"score": "0.5140569",
"text": "def _demo_sweepD():\n abf = pyabf.ABF(PATH_DATA+\"/17o05026_vc_stim.abf\")\n epochTable = EpochTable(abf, 0) # channel 0\n sweepWaveform = epochTable.epochWaveformsBySweep[0] # sweep 0\n sweepD = sweepWaveform.getDigitalWaveform(4) # digital output 4\n print(\"SweepD\", sweepD)\n plt.plot(sweepD)\n plt.show()",
"title": ""
},
{
"docid": "5347d112ed9e6b4b51bb08e9f1553e2b",
"score": "0.5140415",
"text": "def delta_put(self):\r\n return (-norm.cdf(-self.d1))",
"title": ""
},
{
"docid": "1a96741ab555b8daf9b20c245f0715d7",
"score": "0.5138724",
"text": "def discretize(sample, grid):\n # TODO: Implement this\n pass",
"title": ""
},
{
"docid": "338b207987d5331e08bf473f0f018275",
"score": "0.5133485",
"text": "def compute_edges_dxdy(I):\n I = I.astype(np.float32)/255.\n \"\"\" Derivative Gaussian Filter \"\"\"\n # truncate calculation: t = (((w - 1)/2)-0.5)/s\n # using 5x5 filter here\n w = 5\n sigma = 10\n t = (((w - 1)/2)-0.5)/sigma\n dx = ndi.gaussian_filter(I,sigma,order=[1,0],truncate=t) # x Derivative\n dy = ndi.gaussian_filter(I,sigma,order=[0,1],truncate=t) # y Derivative\n\n # # \"\"\" original filter \"\"\"\n # dx = signal.convolve2d(I, np.array([[-1, 0, 1]]), mode='same', boundary='symm')\n # dy = signal.convolve2d(I, np.array([[-1, 0, 1]]).T, mode='same', boundary='symm')\n # dx = signal.convolve2d(I, np.array([[-1, 0, 1]]), mode='same')\n # dy = signal.convolve2d(I, np.array([[-1, 0, 1]]).T, mode='same')\n mag = np.sqrt(dx**2 + dy**2)\n mag = mag / np.max(mag)\n theta = np.arctan2(dy,dx)\n theta[theta < 0] += math.pi\n theta = theta*180/math.pi\n\n \"\"\" Non-maximum Suppression \"\"\"\n threshold = 0\n # NMS = np.copy(mag)\n for y in range(1, mag.shape[0]-1):\n for x in range(1, mag.shape[1]-1):\n if mag[y][x] > threshold:\n angle = theta[y][x]\n if (0 <= angle < 45):\n w = abs(dy[y][x])/abs(dx[y][x])\n p = w * mag[y-1][x-1] + (1-w) * mag[y][x-1]\n r = w * mag[y+1][x+1] + (1-w) * mag[y][x+1]\n\n elif (45 <= angle <= 90):\n w = abs(dx[y][x])/abs(dy[y][x])\n p = w * mag[y-1][x-1] + (1-w) * mag[y-1][x]\n r = w * mag[y+1][x+1] + (1-w) * mag[y+1][x]\n\n elif (90 < angle < 135):\n w = abs(dx[y][x])/abs(dy[y][x])\n p = w * mag[y-1][x+1] + (1-w) * mag[y-1][x]\n r = w * mag[y+1][x-1] + (1-w) * mag[y+1][x]\n\n elif (135 <= angle <= 180):\n w = abs(dy[y][x])/abs(dx[y][x])\n p = w * mag[y-1][x+1] + (1-w) * mag[y][x+1]\n r = w * mag[y+1][x-1] + (1-w) * mag[y][x-1]\n if mag[y][x] >= p and mag[y][x] >= r:\n # NMS[y][x] = mag[y][x]\n continue\n else:\n mag[y][x] = 0\n # NMS[y][x] = 0\n # # # mag = NMS\n mag = mag * 255.\n mag = np.clip(mag, 0, 255)\n mag = mag.astype(np.uint8)\n return mag",
"title": ""
},
{
"docid": "a02baba939fff0c72335b727a4479e73",
"score": "0.5133",
"text": "def cdf(self):\n _checkisfit(self)\n kern = self.kernel\n if kern.domain is None: # TODO: test for grid point at domain bound\n a, b = -np.inf, np.inf\n else:\n a, b = kern.domain\n\n def func(x, s):\n return np.squeeze(kern.density(s, x))\n\n support = self.support\n support = np.r_[a, support]\n gridsize = len(support)\n endog = self.endog\n probs = [\n integrate.quad(func, support[i - 1], support[i], args=endog)[0]\n for i in range(1, gridsize)\n ]\n return np.cumsum(probs)",
"title": ""
},
{
"docid": "e60b0fe9e0c03eb3b76a75c398b0ac94",
"score": "0.51325965",
"text": "def curvatures_fd(self, x: np.ndarray, eps=1e-6) -> np.ndarray:\n x = copy.deepcopy(x)\n f = self.compute_functional(x)\n curvs = np.zeros(x.shape)\n for i in range(x.size):\n x[i] += eps # x + eps\n fp = self.compute_functional(x)\n x[i] -= 2 * eps # x - eps\n fm = self.compute_functional(x)\n x[i] += eps # reset to original values\n curvs[i] += (fm - 2 * f + fp) / (eps**2)\n return curvs",
"title": ""
},
{
"docid": "326c603a4c0a27d3451e1dcf7c7911e3",
"score": "0.5127085",
"text": "def conedf(df, mu, N):\n return 1. / (df + 2. * np.sqrt(df) + float(mu) / N)",
"title": ""
},
{
"docid": "b54becf604e2ea6527fff11079f7ac2f",
"score": "0.5122607",
"text": "def cech_differential(self, start_coordinates, n_dim, deg):\n\n target_coordinates = []\n deg_sign = (-1)**deg\n # go through all dictionaries in input\n for i, coordinates in enumerate(start_coordinates):\n cech_image = {}\n # look at each nerve simplex on each dictionary\n for nerve_spx_index in iter(coordinates):\n points_IN = np.copy(self.points_IN[n_dim][nerve_spx_index])\n # Iterate over simplices in boundary of nerve simplex\n nerv_boundary_indx = np.nonzero(self.nerve_differentials[\n n_dim][:, nerve_spx_index])[0]\n nerv_boundary_coeff = self.nerve_differentials[n_dim][\n :, nerve_spx_index][nerv_boundary_indx]\n # go through all faces for each simplex in nerve\n for nerve_face_index, nerve_coeff in zip(\n nerv_boundary_indx, nerv_boundary_coeff):\n if deg == 0:\n # inclusions for points\n # Preallocate space\n if nerve_face_index not in cech_image:\n cech_image[nerve_face_index] = np.zeros(\n self.subcomplexes[n_dim - 1][\n nerve_face_index][0])\n # end if\n for point_idx, point_coeff in enumerate(coordinates[\n nerve_spx_index]):\n if point_coeff != 0:\n face_point_idx = np.argmax(self.points_IN[\n n_dim-1][nerve_face_index] == points_IN[\n point_idx])\n cech_image[nerve_face_index][\n face_point_idx\n ] += nerve_coeff * point_coeff * deg_sign\n cech_image[nerve_face_index][\n face_point_idx] %= self.p\n # end if\n # end for\n else:\n # inclusions for edges, 2-simplices and higher\n # simplices. Preallocate space as well.\n if nerve_face_index not in cech_image:\n cech_image[nerve_face_index] = np.zeros(len(\n self.subcomplexes[n_dim - 1][nerve_face_index][\n deg]))\n # end if\n # Iterate over nontrivial local simplices in domain\n spx_indices = np.nonzero(coordinates[\n nerve_spx_index])[0]\n spx_coefficients = coordinates[nerve_spx_index][\n spx_indices]\n for spx_index, spx_coeff in zip(\n spx_indices, spx_coefficients):\n # Obtain IN for vertices of simplex\n vertices_spx = points_IN[self.subcomplexes[n_dim][\n nerve_spx_index][deg][spx_index]]\n # Iterate over simplices in range to see which\n # one has vertices_spx as vertices.\n for im_indx, im_spx in enumerate(\n self.subcomplexes[n_dim-1][\n nerve_face_index][deg]):\n vertices_face = self.points_IN[n_dim-1][\n nerve_face_index][im_spx.astype(int)]\n # When the vertices coincide, break the loop\n if len(np.intersect1d(\n vertices_spx,\n vertices_face)) == deg + 1:\n cech_image[nerve_face_index][im_indx] += \\\n spx_coeff * nerve_coeff * deg_sign\n cech_image[nerve_face_index][im_indx] %= \\\n self.p\n break\n # end if\n # end for\n # end for\n # end else\n # end for\n # end for\n target_coordinates.append(cech_image)\n # end for\n\n return target_coordinates",
"title": ""
},
{
"docid": "7701d77ce5dfaa2901565acee37938d8",
"score": "0.51191807",
"text": "def e(self):\n e = 0\n for x in range(self.n):\n for i in range(self.n):\n for y in range(self.n):\n for j in range(self.n):\n e += - 0.5 * self.w[x][i][y][j] * self.s[x][i] * self.s[y][j]\n e += - self.b[x][i] * self.s[x][i]\n return e",
"title": ""
},
{
"docid": "e35dd859faecae1748b60ea82360451f",
"score": "0.51150864",
"text": "def evolve(self):",
"title": ""
},
{
"docid": "02c1e8c82a496669dd55c4ba7e450061",
"score": "0.5114417",
"text": "def disc_dist():\n fig = Figure(figsize=(0.5, 0.5), dpi=100)\n sub = fig.add_subplot(111)\n x_pts = [0, 0.99, 1, 1.01, 1.99, 2, 2.01, 3, 3.99, 4, 4.01, 5, 6]\n y_pts = [0, 0, 1, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0]\n sub.plot(x_pts, y_pts)\n return fig",
"title": ""
},
{
"docid": "5d741685cf46e767762950f2291aff0f",
"score": "0.5110884",
"text": "def test_discrete2():\n geo = psci.geometry.Rectangular(space_origin=(0.0, 0.0), space_extent=(1.0, 1.0))\n pdes = psci.pde.NavierStokes(0.01, 1.0)\n pdes, geo = psci.discretize(pdes, geo, space_nsteps=(20, 20))\n bc = geo.get_bc_index()\n sd = geo.get_space_domain()\n\n sd_t, bc_t = geo_discrete([(0.0, 0.0), (1.0, 1.0)], (20, 20))\n assert np.allclose(bc, bc_t)\n assert np.allclose(sd, sd_t)",
"title": ""
},
{
"docid": "c89caa2bc1611ec7253ad343b5536c5f",
"score": "0.5105004",
"text": "def CoreSersic2D(x, y, amplitude=1, r_eff=1, r_break=1, n=1, x_0=0, y_0=0,\n alpha=10, gamma=0.1, ellip=0, theta=0):\n bn = gammaincinv(2. * n, 0.5)\n a, b = r_eff, (1 - ellip) * r_eff\n cos_theta, sin_theta = np.cos(theta), np.sin(theta)\n x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta\n x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta\n r = np.sqrt((x_maj / a) ** 2 + (x_min / b) ** 2)\n\n r[np.where(r == 0)] = np.nan\n\n I = amplitude * (2 ** -(gamma / alpha)) * np.exp(\n bn * (2 ** (1 / alpha) * (r_break / r_eff)) ** (1 / n)\n )\n\n return I * (1 + (r_break / r) ** (alpha)) ** (gamma / alpha) * np.exp(\n - bn * ((r ** alpha + r_break ** alpha) / (r_eff) ** alpha) ** (1 / (alpha * n))\n )",
"title": ""
},
{
"docid": "53d462b951a0da3fc7642890f81aae7f",
"score": "0.5104522",
"text": "def gradientDivergence(c, xR, xL, yU, yD, vx, vy):#yU,yD,xR,xL, vx, vy):# y, x, vx, vy, r, norm):\n\n\n\t#print \"using gradDiv in observer\"\n\t#print \"VY: %s VX: %s\\nY: %s X: %s\" %(vy, vx, y, x)\n\n\t#print \"find gradient and Divergence\"\n\n\t#yU = self.concentration(y+2*r, x, r)\t* norm\n\t#yD = self.concentration(y-2*r, x, r)\t* norm\n\t#xU = self.concentration(y, x+2*r, r) * norm\n\t#xD = self.concentration(y, x-2*r, r) * norm\n\t#c = self.concentration(y,x,r) * norm\n\n\t#print \"yU: %s yD: %s\\nxU: %s xD: %s\\nc: %s \"%(yU, yD, xU, xD, c)\n\t#print \"vx: %s vy: %s\"%(vx,vy)\n\n\t#print \"concentrations: %s, %s, %s, %s\" %(yU, yD, xR, xL)\n\t###print vx, vy\n\t#print r\n\n\n\n\n\n\n\tDU_dy0 = (vy>=0)*(yU-c)/(r)+(vy<0)*(c-yD)/(r)\n \tDU_dx0 = (vx>=0)*(xR-c)/(r)+(vx<0)*(c-xL)/(r)\n\n\tD2U0 = (yU+yD-2*c)/r**2+(xR+xL-2*c)/r**2\n\treturn DU_dx0, DU_dy0, D2U0",
"title": ""
},
{
"docid": "59ee0ee61d4d63cdfeecace08bbe9b3c",
"score": "0.5094887",
"text": "def compute_energy(self):\n E=df.assemble(self.E)\n return E",
"title": ""
},
{
"docid": "024fb963828b3e7b9c7c47c22720be2a",
"score": "0.50926286",
"text": "def getenergies(self):\n\n\t\tself.data[\"eint\"] = np.zeros(len(self.data[\"rho\"]))\n\t\tself.data[\"edeg\"] = np.zeros(len(self.data[\"rho\"]))\n\t\tself.data[\"c_s\"] = np.zeros(len(self.data[\"rho\"]))\n\t\tfor i in range(len(self.data[\"rho\"])):\n\t\t\t[self.data[\"eint\"][i], self.data[\"edeg\"][i], self.data[\"c_s\"][i]] = self.gethelmeos_energies(self.data[\"rho\"][i], self.data[\"T\"][i])\n\t\tself.data[\"eth\"] = self.data[\"eint\"] - self.data[\"edeg\"]\n\t\tself.data[\"erot\"] = (2./3.)*self.data[\"R\"]**2*self.omega**2\t\t# specific rotational energy I*omega^2/m = 2/3*r^2*omega^2\n\n\t\tself.data[\"Edeg\"] = 4.*np.pi*scipyinteg.cumtrapz(self.data[\"edeg\"]*self.data[\"R\"]**2*self.data[\"rho\"], x=self.data[\"R\"], initial=0.)\n\t\tself.data[\"Eth\"] = 4.*np.pi*scipyinteg.cumtrapz(self.data[\"eth\"]*self.data[\"R\"]**2*self.data[\"rho\"], x=self.data[\"R\"], initial=0.)\n\t\t# Binding energy E_pot = \\int_0^m -GM_enc/r dm (http://farside.ph.utexas.edu/teaching/301/lectures/node153.html)\n\t\tself.data[\"Epot\"] = -4.*np.pi*self.grav*scipyinteg.cumtrapz(self.data[\"M\"]*self.data[\"R\"]*self.data[\"rho\"], x=self.data[\"R\"], initial=0.)\n\t\tself.data[\"Erot\"] = 0.5*self.getmomentofinertia(self.data[\"R\"], self.data[\"rho\"])*self.omega**2\n\t\tself.data[\"eb\"] = self.data[\"B\"]**2/8./np.pi/self.data[\"rho\"]\t# B^2/8pi is the magnetic energy density\n\t\tself.data[\"EB\"] = 0.5*scipyinteg.cumtrapz(self.data[\"B\"]**2*self.data[\"R\"]**2, x=self.data[\"R\"], initial=0.)",
"title": ""
},
{
"docid": "1b8055a169f5b35ea13f23b721ead370",
"score": "0.50918263",
"text": "def elemental_creep_strain_energy_density(self, **kwargs):\n self._check_elemental_location(**kwargs)\n return self._get_result_data_function_of_operator(\n \"ENL_CRWK\",\n self,\n self._data_sources,\n location=\"Elemental\",\n b_elem_average=True,\n **kwargs\n )",
"title": ""
},
{
"docid": "fdc63bf56a5f8fa3deb3c7764aab1ab3",
"score": "0.5089217",
"text": "def rse(cls):\n rss = sum((cls.orig_endog.values - cls.predict().values) ** 2)[0]\n n = cls.orig_endog.shape[0]\n p = len(cls.results.x) - 1 # excludes constant coefficient\n return np.sqrt(rss / (n - p - 1))",
"title": ""
},
{
"docid": "34c63c15d438ce099ab6dbf7cd863a5c",
"score": "0.5088638",
"text": "def calc_perigee(semi_major_axis, eccentricity):\n return float((1 - eccentricity) * semi_major_axis)",
"title": ""
},
{
"docid": "10571d1fad69fbf5d065c8d89cfcc83c",
"score": "0.5085874",
"text": "def calulate_SSE(df, fkm_cluster_centres, km_clusters_centres):\r\n df[\"fkm_SSE\"] = None\r\n df[\"km_SSE\"] = None\r\n for index, subject in df.iterrows():\r\n subject_dim = np.array(subject[[\"component_1\",\"component_2\"]])\r\n df.iloc[index, -2] = min([(np.sum(np.square(subject_dim - cluster))) for cluster in fkm_cluster_centres])\r\n df.iloc[index, -1] = min([np.sum(np.square(subject_dim - cluster)) for cluster in km_clusters_centres])\r\n return df",
"title": ""
}
]
4f6e8955cbc1ecda2a3c874da38e334e
Convert the dict of probabilities into a single numpy array. Used to pass the probabilities to the C numpy extension.
[
{
"docid": "7357f9b100e6c8991a9109526506d64c",
"score": "0.63100165",
"text": "def _prob_dict_to_np(self, nframes, naive_bayes=False):\n\n # Get a list of classes for results\n clses = self._classnames\n k = clses[0]\n\n # Allocate and fill each array\n sprobs = np.zeros((len(clses), np.shape(self._marg[k])[0], 2), dtype=np.float64)\n if not naive_bayes:\n jprobs = np.zeros((len(clses), np.shape(self._cond[k])[0],\n np.shape(self._cond[k])[1], 4), dtype=np.float64)\n likelihood = np.zeros((len(clses), nframes), dtype=np.float64)\n res = np.zeros((len(clses), nframes), dtype=np.float64)\n\n for i, key in enumerate(clses):\n sprobs[i] = self._marg[key]\n if not naive_bayes:\n jprobs[i] = self._cond[key]\n\n if not naive_bayes:\n return sprobs, jprobs, likelihood, res\n else:\n return sprobs, likelihood, res",
"title": ""
}
]
[
{
"docid": "00e96822a67220e93768623e53908805",
"score": "0.68825066",
"text": "def dict_to_array(dicti):\n vals = [i for i in dicti.keys()]\n counts = [i for _, i in dicti.items()]\n return np.repeat(vals, counts)",
"title": ""
},
{
"docid": "00e96822a67220e93768623e53908805",
"score": "0.68825066",
"text": "def dict_to_array(dicti):\n vals = [i for i in dicti.keys()]\n counts = [i for _, i in dicti.items()]\n return np.repeat(vals, counts)",
"title": ""
},
{
"docid": "f8438398831ab06fdd8fa15720323be4",
"score": "0.68129086",
"text": "def C_dict2array(C):\n return np.hstack([np.asarray(C[k]).ravel() for k in C_keys])",
"title": ""
},
{
"docid": "a0ec9debc8b93fc39c365abebf000cc2",
"score": "0.6796614",
"text": "def dict_to_array(self, d):\n n_fit_p = len(self.fit_parameters)\n n_wc = len(self.fit_wc_names)\n arr = np.zeros(n_fit_p + n_nui_p + n_wc)\n arr[:n_fit_p] = [d['fit_parameters'][p] for p in self.fit_parameters]\n arr[n_fit_p:] = [d['fit_wc'][c] for c in self.fit_wc_names]\n return arr",
"title": ""
},
{
"docid": "107b70e035320e15407a260d80cdfba4",
"score": "0.67675424",
"text": "def dict_to_array(self, d):\n n_fit_p = len(self.fit_parameters)\n n_nui_p = len(self.nuisance_parameters)\n n_wc = len(self.fit_wc_names)\n arr = np.zeros(n_fit_p + n_nui_p + n_wc)\n arr[:n_fit_p] = [d['fit_parameters'][p] for p in self.fit_parameters]\n arr[n_fit_p:n_fit_p+n_nui_p] = [d['nuisance_parameters'][p] for p in self.nuisance_parameters]\n arr[n_fit_p+n_nui_p:] = [d['fit_wc'][c] for c in self.fit_wc_names]\n return arr",
"title": ""
},
{
"docid": "8da945df3033cabbf07ff48881b1f23f",
"score": "0.65217113",
"text": "def to_numpy(self):\n return np.array([(x, *np.atleast_1d(y)) for x, y in sorted(self.data.items())])",
"title": ""
},
{
"docid": "5090c97ac430d73a28b3f1f10541e492",
"score": "0.6356121",
"text": "def kpoints_dict_to_array(kpoints_dict: dict, dim: int = 2):\n assert isinstance(kpoints_dict, dict)\n assert dim == 2 or dim == 3\n\n kpoints_list = []\n for key in _KP_NAMES:\n if key in kpoints_dict:\n kpoints_list.append(kpoints_dict[key])\n else:\n kpoints_list.append(np.full(shape=(dim,), fill_value=-1))\n\n kpoints_array = np.asarray(kpoints_list, dtype=float)\n return kpoints_array",
"title": ""
},
{
"docid": "9f638712c1c1a63f94f36d5c67401697",
"score": "0.63134253",
"text": "def array_dict_map(dictionary, keys):\n out = np.zeros((len(keys), 50))\n for i,key in enumerate(keys):\n out[i] = dictionary[key]\n return out",
"title": ""
},
{
"docid": "7d8ce5d62be38c032d27f72ef8d9bf80",
"score": "0.6284463",
"text": "def _get_matrix_values(self, seq_dict):\n return np.array([seq_dict[i] for i in seq_dict.keys()])",
"title": ""
},
{
"docid": "55360068b005e24661034de4f4d701e3",
"score": "0.60913867",
"text": "def fmt1darray(cdict):\n # Make sure this represents a 1d array\n if any(len(inds) != 1 for inds in cdict):\n raise ValueError(\n 'The keys of the input dictionary must be length-1 tuples')\n\n jmax = max((inds[-1] for inds in cdict), default=0)\n carr = np.zeros(jmax+1)\n for ((j,), c) in cdict.items():\n carr[j] = c\n return carr",
"title": ""
},
{
"docid": "1ddd8441a414241d3fef869ec20999c0",
"score": "0.6070513",
"text": "def arrayize(d):\r\n return np.array(d)",
"title": ""
},
{
"docid": "f1a4d3f6f363128cc1fe4e7624b20a13",
"score": "0.59904647",
"text": "def _get_ampdet_data_as_array(data: Dict[Any, AmpDetData], column: str) -> ArrayLike:\n return np.vstack([getattr(d, column) for d in data.values()])",
"title": ""
},
{
"docid": "a837cee92a4931c363365d451e8165a9",
"score": "0.5963877",
"text": "def dictToNpMatrix(d):\n return np.asmatrix([d[k] for k in sorted(d)])",
"title": ""
},
{
"docid": "8a5622c1c2ca190a6ad5f712c3215731",
"score": "0.58722794",
"text": "def pvary_levels_to_array(popt, pcmap={}):\n for pname, pconds in pcmap.items():\n popt[pname] = np.array([popt[c] for c in pconds])\n return popt",
"title": ""
},
{
"docid": "804e09f28bdc2df914577d67a5ccc46b",
"score": "0.58197963",
"text": "def array(values):\n return numpy.array(values, dtype=dtype)",
"title": ""
},
{
"docid": "1ef915815b4ad41329552defa962c064",
"score": "0.5807538",
"text": "def to_array_from_dict_set(self, N):\n\t\tA = np.zeros((N,N))\n\n\t\tfor key in self.dict_col2row_set:\n\t\t\tfor row_index in self.dict_col2row_set[key]:\n\t\t\t\tA[row_index, key] = 1\n\t\treturn A",
"title": ""
},
{
"docid": "a257d736617c4803d5c8cf2bbd7d7730",
"score": "0.5805067",
"text": "def dict_to_array(data, default_value, node_num):\n array = np.ones((node_num,))*default_value\n for i in xrange(node_num):\n if data.has_key(i+1):\n array[i] = data[i+1]\n return array",
"title": ""
},
{
"docid": "31227a2f533eb1b16387ec9b9a31c633",
"score": "0.5781736",
"text": "def convert_numpy(data):\n return np.array(data)",
"title": ""
},
{
"docid": "31b18e49df0999f9ab063becda624160",
"score": "0.57281286",
"text": "def extract_from_json_as_np_array(key, json_data):\n data_as_array = []\n for p in json_data:\n data_as_array.append(p[key])\n\n return np.array(data_as_array)",
"title": ""
},
{
"docid": "7b16d61a5eb614f48677abaa5bd28a22",
"score": "0.57124",
"text": "def _get_key_ndarray(self, key):\n return self.bstr_to_ndarray(self._client.get(key))",
"title": ""
},
{
"docid": "17406d8409d3b805aa6b255dd71a5d8b",
"score": "0.5694752",
"text": "def predict(self, input_array_dict):\n print(input_array_dict)\n return ({\"output_array1\": np.array([100, 200]).astype(np.float32),\n \"output_array2\": np.array([\"foo\".encode(),\"bar\".encode()]).astype(object), # you can get and pass strings encoded as bytes also\n })",
"title": ""
},
{
"docid": "bd9f98c9762ac87b95c654a07ba5682b",
"score": "0.5677484",
"text": "def _get_key_ndarray(self, key):\n return bstr_to_ndarray(self._client.get(key))",
"title": ""
},
{
"docid": "de8ee5824d937e15c4b24a32a5844fe1",
"score": "0.5666336",
"text": "def get_key_ndarray(self, key):\n return self.bstr_to_ndarray(self._client.get(key))",
"title": ""
},
{
"docid": "4e62d908b68ea7a40e35f443783490e2",
"score": "0.5632855",
"text": "def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct",
"title": ""
},
{
"docid": "4e62d908b68ea7a40e35f443783490e2",
"score": "0.5632855",
"text": "def json_numpy_obj_hook(dct):\n if isinstance(dct, dict) and '__ndarray__' in dct:\n data = base64.b64decode(dct['__ndarray__'])\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\n return dct",
"title": ""
},
{
"docid": "2769ee83b6ab0e2a8a3d7ff6b691968e",
"score": "0.562029",
"text": "def to_ndarray(self):",
"title": ""
},
{
"docid": "4aaf99a2f79fc57382ede1f74fa80983",
"score": "0.56192243",
"text": "def to_array(self, **kwargs):",
"title": ""
},
{
"docid": "425756974a3d5af9c1401d45796fa9bd",
"score": "0.56039935",
"text": "def cupy_ndarray(nb_arr):\n import cupy\n return cupy.ndarray(nb_arr.shape, dtype=cupy.uint8,\n strides=nb_arr.strides,\n memptr=cupy_cuda_MemoryPointer(nb_arr))",
"title": ""
},
{
"docid": "8099c53dceb67574de74a1c0516adf81",
"score": "0.5597376",
"text": "def to_numpy(self) -> None:\n for k, v in self.items():\n if isinstance(v, torch.Tensor):\n self.__dict__[k] = v.detach().cpu().numpy()\n elif isinstance(v, Batch):\n v.to_numpy()",
"title": ""
},
{
"docid": "64bc1be5622b95b0baaee0397ea2bcc9",
"score": "0.5572673",
"text": "def policy_to_array(policy, shape=(4,4)):\n array_policy = np.zeros(shape)\n for key, action in policy.iteritems():\n best_a = 0\n for a in xrange(nAction):\n if policy[key][a] > policy[key][best_a]:\n best_a = a\n\n array_policy[key[0]][key[1]] = best_a\n\n return array_policy",
"title": ""
},
{
"docid": "138941e6079a918134f3d88f3d59bba7",
"score": "0.55547583",
"text": "def _unwrap_aclu_epoch_values_dict_to_array(mean_epochs_all_frs):\n aclus = list(mean_epochs_all_frs.keys())\n values = np.array(list(mean_epochs_all_frs.values())) # \n return aclus, values # values.shape # (108, 36)",
"title": ""
},
{
"docid": "e3c8ffa8efaeef77a60f0775ed8034ac",
"score": "0.5538326",
"text": "def get_predictions(estimator, states):\n predict_input_fn = lambda: numpy_predict_fn(states)\n prediction = estimator.predict(input_fn=predict_input_fn)\n list_predictions = [p['logits'] for p in prediction]\n np_array_prediction_values = np.array(list_predictions)\n return np_array_prediction_values",
"title": ""
},
{
"docid": "d0196b6763262f969d75f8e6657171ca",
"score": "0.5500729",
"text": "def dict_to_array(dictionary, order):\n\tres = []\n\tfor key, data in dictionary.items():\n\t\tentry = [key]\n\t\tentry.extend([data[item] for item in order])\n\t\tres.append(entry)\n\treturn res",
"title": ""
},
{
"docid": "781bb9368a4d998befdcbf5da5076257",
"score": "0.54993504",
"text": "def gen_arr(embeddings, seq_id_to_label):\n # keys = embeddings.files\n output, labels, ids = [], [], []\n for key in embeddings:\n # print(embeddings[key])\n d = embeddings[key]\n labels.append(seq_id_to_label[key])\n output.append(d)\n ids.append(key)\n return np.array(output), labels, ids",
"title": ""
},
{
"docid": "ad4fb7d2a2454d5433056ad9795752d5",
"score": "0.54954565",
"text": "def to_np(var):\n return var.detach().cpu().numpy()",
"title": ""
},
{
"docid": "46f841666cb4073d1a922916043e594f",
"score": "0.5491247",
"text": "def return_np_array(args) -> np.array:\n\n return np.array(args)",
"title": ""
},
{
"docid": "5dc4fefa18ca1a5af40bda83ff181e43",
"score": "0.5470624",
"text": "def counter_to_array(counter):\n keys = counter.keys()\n vals = counter.values()\n tot = np.sum(vals)\n arr = np.zeros(tot, dtype=int)\n i = 0\n for key, val in zip(keys, vals):\n arr[i:i + val] = key\n i += val\n return arr",
"title": ""
},
{
"docid": "a992ba82d2b8673d9baab0065e26f463",
"score": "0.54561305",
"text": "def _arr(x, keys):\n arr = []\n for key in keys:\n val = x[key]\n try:\n arr.extend(val)\n except Exception:\n arr.append(val)\n arr = np.asarray(arr)\n return arr",
"title": ""
},
{
"docid": "ce57e140e00a25e53bf9720eae0412ae",
"score": "0.5448542",
"text": "def string_to_array(nts, lookup_dict=_DEFAULT_NTDICT_):\n labels = [lookup_dict[ch] for ch in nts if (ch in lookup_dict.keys())]\n return np.array(labels, dtype=np.int32)",
"title": ""
},
{
"docid": "a71acc1db5a1118bf55b876f8a92db78",
"score": "0.5445526",
"text": "def to_numpy(self, dtype=None, copy=False, na_value=None):\n out_data = self.__to_dict_impl(orient=\"list\", include_index=False)\n return np.array(list(out_data.values()), dtype=dtype).T",
"title": ""
},
{
"docid": "ee2a8ca4b64d95078f60def91da0ebc5",
"score": "0.5423703",
"text": "def as_array(counts, k):\n return np.array([counts[''.join(s)] for s in\n itertools.product('ACGT', repeat=k)])",
"title": ""
},
{
"docid": "033e728c6b3b057e87ce923896a77908",
"score": "0.5392687",
"text": "def toarray(arr):\n try:\n return arr.todense()\n except AttributeError:\n return asarray(arr)",
"title": ""
},
{
"docid": "777189737b6f8829422e5e706772819a",
"score": "0.5391778",
"text": "def to_numpy(self):\n return type(self)(ten2ar(self.mu), ten2ar(self.log_sigma))",
"title": ""
},
{
"docid": "56d36c1d03255d311b1eb5a785e7c18b",
"score": "0.53792375",
"text": "def to_numpy(po: pd.DataFrame) -> np.ndarray:\n try:\n return po.to_numpy()\n except AttributeError:\n return po.values",
"title": ""
},
{
"docid": "7b3eebf5f26d1646d187fab82911959e",
"score": "0.5375303",
"text": "def map_to_arrays(m):\n length = len(m)\n x = np.empty(length)\n y = np.empty(length)\n for k, i in zip(sorted(m.keys()), range(length)):\n x[i] = k\n y[i] = m[k]\n return x, y",
"title": ""
},
{
"docid": "52884181156754d2b3cd9d277e34ced5",
"score": "0.5363327",
"text": "def json_to_ndarray(payload: NdArray) -> np.ndarray:\n arr = np.array(payload.array)\n if payload.shape:\n arr = arr.reshape(*payload.shape)\n if payload.dtype:\n arr = arr.astype(payload.dtype)\n return arr",
"title": ""
},
{
"docid": "d786b4723a421af307b0e9c62babe4df",
"score": "0.5354856",
"text": "def to_numpy(in_array: Union[np.ndarray, torch.Tensor]) -> np.ndarray:\n if isinstance(in_array, torch.Tensor):\n in_array = in_array.cpu().numpy()\n\n np_array = cast(np.ndarray, in_array)\n\n return np_array",
"title": ""
},
{
"docid": "50d1f7ab787e9bd5ef0689261c8b0fa8",
"score": "0.53488183",
"text": "def posterior_predictive_to_xarray(self):\n data = self.posterior_predictive\n dims = {var_name: self.sample_dims + self.dims.get(var_name, []) for var_name in data}\n return dict_to_dataset(\n data, library=pymc, coords=self.coords, dims=dims, default_dims=self.sample_dims\n )",
"title": ""
},
{
"docid": "c3f30a929605830f535fccf2f047b3d7",
"score": "0.534839",
"text": "def get_predictions(predictions):\r\n pred_y, pred_scores = [], []\r\n val = next(predictions, None)\r\n while val is not None:\r\n pred_y.append(val['classes'])\r\n pred_scores.append(val['probabilities'])\r\n val = next(predictions, None)\r\n return np.array(pred_y), np.array(pred_scores)",
"title": ""
},
{
"docid": "ce161f83f56f98e9fe7171e339aaf8bb",
"score": "0.53476906",
"text": "def numpy_ndarray(nb_arr):\n return nb_arr.copy_to_host()",
"title": ""
},
{
"docid": "ae2bf98b5c8a696cdd12334870479786",
"score": "0.53096044",
"text": "def tensor_to_np(tensor):\n return tensor.permute(1, 2, 0).numpy()",
"title": ""
},
{
"docid": "5bba23b67a678aa539093b6e198d325f",
"score": "0.53067946",
"text": "def klasses_binary(self):\n (_, data, _, _) = self.data\n return np.asarray(data)",
"title": ""
},
{
"docid": "6bd29d79f29cf8b016268b86055551a8",
"score": "0.5296831",
"text": "def value_to_array(V, shape=(4,4)):\n array_V = np.zeros(shape)\n for key, value in V.iteritems():\n array_V[key[0]][key[1]]=value\n\n return array_V",
"title": ""
},
{
"docid": "e1b5e53caed2de5edf0060e191aecd8e",
"score": "0.5292031",
"text": "def toarray(arr):\n try:\n return arr.todense()\n except AttributeError:\n return _asarray(arr)",
"title": ""
},
{
"docid": "aaa70ef8790b88ebf0988866fb84a8a7",
"score": "0.52824736",
"text": "def __array__(self, dtype=None):\n return self.to_numpy()",
"title": ""
},
{
"docid": "f95fa954b56fddbe146b4c9bf37cecb8",
"score": "0.52746457",
"text": "def numpy_array(self):\n if self.arrC is not None:\n return self.arr.get()+self.arrC.get()*1.0j\n return self.arr.get()",
"title": ""
},
{
"docid": "60a442b612375938598410c75754c596",
"score": "0.5266783",
"text": "def _convert_evals_to_numpy(raw_evals, key):\n # get rid of dictionaries\n evals = [val[key] if isinstance(val, dict) else val for val in raw_evals]\n # get rid of pandas objects\n evals = [np.array(val) if isinstance(val, pd.Series) else val for val in evals]\n\n # find out the correct output shape\n try:\n array = next(x for x in evals if hasattr(x, \"shape\") or isinstance(x, dict))\n out_shape = array.shape\n except StopIteration:\n out_shape = \"scalar\"\n\n # convert to correct output shape\n if out_shape == \"scalar\":\n evals = [np.atleast_1d(val) for val in evals]\n else:\n for i in range(len(evals)):\n if isinstance(evals[i], float) and np.isnan(evals[i]):\n evals[i] = np.full(out_shape, np.nan)\n\n return evals",
"title": ""
},
{
"docid": "37e6033b3c4a70927d429a223e7834fc",
"score": "0.52635384",
"text": "def numpy_ndarray(pd_ser, nan_to_null=False):\n return pd_ser.to_numpy()",
"title": ""
},
{
"docid": "ba27b1258fc170f7438283a31f29ad1c",
"score": "0.5261366",
"text": "def to_numpy(self, **kwargs):\n return [p.to_numpy(**kwargs) for p in self.providers]",
"title": ""
},
{
"docid": "b7ada9dd259b32c956026059d4bb5771",
"score": "0.5259715",
"text": "def to_numpy(x: t.Union[np.ndarray, torch.tensor]) -> np.ndarray:\n return (x.cpu().numpy()).astype(np.float32) if torch.is_tensor(x) else x",
"title": ""
},
{
"docid": "c0486a3899af2ae129ef2068356ef7b5",
"score": "0.5244956",
"text": "def to_ndarray(item):\n \n return type(item), sp.array(item, sp.float64, ndmin=1)",
"title": ""
},
{
"docid": "9886b95dc17b9a732542cc8b3c6b9e27",
"score": "0.52355564",
"text": "def make_into_weight_arrays(result: dict):\n\n data = np.zeros((len(result), 3))\n\n for idx in range(len(result)):\n data[idx][0] = result[idx]['weights']['wto']\n data[idx][1] = result[idx]['weights']['wf']\n data[idx][2] = result[idx]['weights']['wpl']\n\n return data",
"title": ""
},
{
"docid": "9290171ced841fd1b75ae75aadef71c6",
"score": "0.5218645",
"text": "def _probabilities(\n self, input_data: Optional[np.ndarray], weights: Optional[np.ndarray]\n ) -> Union[np.ndarray, SparseArray]:\n raise NotImplementedError",
"title": ""
},
{
"docid": "9e678744c27d3e3d03665e094efacdaa",
"score": "0.52183974",
"text": "def to_array(x):\n if hasattr(x, 'todense'):\n return np.array(x.todense())\n if hasattr(x, 'cpu'):\n return x.data.cpu().numpy()\n return x",
"title": ""
},
{
"docid": "3f4b0db567f108d7d4d1cd2c4619ef7b",
"score": "0.52145636",
"text": "def vector_to_numpy_array(vector: Vector) -> np.array:\n if isinstance(vector, Vector):\n return np.array(vector.values)\n else:\n raise ValueError(\"Not a vector!\")",
"title": ""
},
{
"docid": "7852ebd07cc3dd944016ddac9933a708",
"score": "0.5213827",
"text": "def priors_to_xarray(self):\n if self.prior is None:\n return {\"prior\": None, \"prior_predictive\": None}\n if self.observations is not None:\n prior_predictive_vars = list(set(self.observations).intersection(self.prior))\n prior_vars = [key for key in self.prior.keys() if key not in prior_predictive_vars]\n else:\n prior_vars = list(self.prior.keys())\n prior_predictive_vars = None\n\n priors_dict = {}\n for group, var_names in zip(\n (\"prior\", \"prior_predictive\"), (prior_vars, prior_predictive_vars)\n ):\n priors_dict[group] = (\n None\n if var_names is None\n else dict_to_dataset(\n {k: np.expand_dims(self.prior[k], 0) for k in var_names},\n library=pymc,\n coords=self.coords,\n dims=self.dims,\n )\n )\n return priors_dict",
"title": ""
},
{
"docid": "537b15d7b798783b43b77b15d3b0ac3f",
"score": "0.52123576",
"text": "def var_to_np(x):\n if RUN_ON_GPU:\n x = x.cpu()\n return x.data.numpy()",
"title": ""
},
{
"docid": "537b15d7b798783b43b77b15d3b0ac3f",
"score": "0.52123576",
"text": "def var_to_np(x):\n if RUN_ON_GPU:\n x = x.cpu()\n return x.data.numpy()",
"title": ""
},
{
"docid": "1afbe15ace8ffde47d807132f9ae5c72",
"score": "0.52104324",
"text": "def _probs_and_log_probs_np(probs):\n to_return = ()\n p = np.array(probs)\n to_return += (1 - p, p)\n to_return += (np.log1p(-p), np.log(p))\n return to_return",
"title": ""
},
{
"docid": "84c2a327113f2adf8b285845e3471e02",
"score": "0.520424",
"text": "def to_numpy(tensor):\n return np.copy(tensor)",
"title": ""
},
{
"docid": "c01b3a31e22bbc02ca98cf8a3dc9e64a",
"score": "0.5183275",
"text": "def to_arrays(self):\n cdef int size = self._map.size()\n cdef np.ndarray[D_t, ndim=1] keys = np.empty(size, dtype=Dt)\n cdef np.ndarray[V_t, ndim=1] values = np.empty(size, dtype=np.float64)\n self._to_arrays(keys, values)\n return keys, values",
"title": ""
},
{
"docid": "61a6334b90eab994fbdc5f28ffd7cd79",
"score": "0.51803404",
"text": "def to_array(tensor):\n tensor_ = tensor.squeeze()\n\n unnormalize_transform = Compose([Normalize(mean=[0, 0, 0],\n std=[1 / 0.229, 1 / 0.224, 1 / 0.225]),\n Normalize(mean=[-0.485, -0.456, -0.406],\n std=[1, 1, 1])])\n arr_ = unnormalize_transform(tensor_)\n arr = arr_.permute(1, 2, 0).detach().numpy()\n\n return arr",
"title": ""
},
{
"docid": "c83f5c62ddc35610b33dbcadb668c0af",
"score": "0.51670134",
"text": "def _ndarray_values(self):\n # type: () -> np.ndarray\n return np.array(self)",
"title": ""
},
{
"docid": "8cd941a74bc49857f73c82913771ee11",
"score": "0.5164753",
"text": "def to_numpy(tensor: th.Tensor, flatten=False) -> np.ndarray:\n if flatten:\n tensor.squeeze_(-1)\n return tensor.detach().cpu().numpy()",
"title": ""
},
{
"docid": "867a1134d7e2ce93057f7adb3325515f",
"score": "0.51640046",
"text": "def sample(prediction):\n p = np.zeros(shape=[1, VOCABULARY_SIZE], dtype=np.float)\n p[0, sample_distribution(prediction[0])] = 1.0\n return p",
"title": ""
},
{
"docid": "5563c4378d8ee1ebcad7196e523055cd",
"score": "0.5161914",
"text": "def to_numpy(img: ndarray) -> ndarray:\n\n h = img.shape[0]\n w = img.shape[1]\n ret_img = np.zeros((h, w, 3))\n\n for y in range(0, h):\n for x in range(0, w):\n ret_img[y, x] = np.array(decimalToGCM(img[y, x]))\n\n # return the thresholded image\n return ret_img",
"title": ""
},
{
"docid": "41a719542ac33e7be54168a09cf2d173",
"score": "0.5161229",
"text": "def predictions_to_xarray(self):\n data = self.predictions\n dims = {var_name: self.sample_dims + self.dims.get(var_name, []) for var_name in data}\n return dict_to_dataset(\n data, library=pymc, coords=self.coords, dims=dims, default_dims=self.sample_dims\n )",
"title": ""
},
{
"docid": "f8f5a9492a8bb60d709f8b0082e42727",
"score": "0.5141708",
"text": "def wcxf2arrays(d):\n C = {}\n for k, v in d.items():\n name = k.split('_')[0]\n s = C_keys_shape[name]\n if s == 1:\n C[k] = v\n else:\n ind = k.split('_')[-1]\n if name not in C:\n C[name] = np.zeros(s, dtype=complex)\n C[name][tuple([int(i) - 1 for i in ind])] = v\n return C",
"title": ""
},
{
"docid": "7c85b5f2ea6eacadab17f3555c331eb9",
"score": "0.5125818",
"text": "def _C_as_ndarray(self, dataobj):\n shape = tuple(dataobj._obj.size[i] for i in range(self.ndim))\n ctype_1d = dtype_to_ctype(self.dtype) * int(reduce(mul, shape))\n buf = cast(dataobj._obj.data, POINTER(ctype_1d)).contents\n return np.frombuffer(buf, dtype=self.dtype).reshape(shape)",
"title": ""
},
{
"docid": "a2fe5ab7702ebad44650d76a21b8e8a3",
"score": "0.5121655",
"text": "def _as_array(nested):\n\n def __as_array(x):\n if np.isscalar(x):\n return np.array(x)\n return x\n\n return nest.map_structure(__as_array, nested)",
"title": ""
},
{
"docid": "5e0e888316c25ddd9538046d2581a2cf",
"score": "0.51154757",
"text": "def tensor_to_numpy(t):\n arr = t.detach().cpu().numpy()\n return arr",
"title": ""
},
{
"docid": "8f3ba374904db4e15ca38d5decd6c67f",
"score": "0.5104118",
"text": "def numpy(self) -> np.ndarray:\n return self.C",
"title": ""
},
{
"docid": "a27ec4d3b54a77c798f97596c29235f6",
"score": "0.5103691",
"text": "def to_numpy(self):\n\n return self.to_pandas().to_numpy()",
"title": ""
},
{
"docid": "08acadf66c0ea6c60ccb6fcc10d374f5",
"score": "0.5094192",
"text": "def convert_dict_to_matrix(gdf):\n\n samples = sp.concatenate(gdf.values())\n rval = sp.zeros((len(samples), 2))\n rval[:, 0] = samples\n idx = 0\n ids = sp.zeros(0)\n for key in sorted(gdf.keys()):\n ids = sp.concatenate((ids, sp.ones((len(gdf[key]))) * idx))\n idx += 1\n rval[:, 1] = ids\n rval = sortrows(rval)[:, [1, 0]]\n return rval",
"title": ""
},
{
"docid": "214f7628207547174a0ac38fcc5d87ec",
"score": "0.5089382",
"text": "def to_ndarray(self, **kwargs):\n return np.asarray(self.copy())",
"title": ""
},
{
"docid": "7838d19b3acfd930b3bac9eae4d81db7",
"score": "0.50893474",
"text": "def klasses_non_binary(self):\n (_, _, data, _) = self.data\n return np.asarray(data)",
"title": ""
},
{
"docid": "4270959670f9eba130a89b35a9b67610",
"score": "0.5086882",
"text": "def binary_entropy_np(probs):\n probs0, probs1, log_probs0, log_probs1 = _probs_and_log_probs_np(probs)\n return -1. * (\n np.where(probs0 == 0, 0, np.multiply(log_probs0, probs0)) +\n np.where(probs1 == 0, 0, np.multiply(log_probs1, probs1)))",
"title": ""
},
{
"docid": "2932d72269afe9364a3c86ab2f38e907",
"score": "0.50840074",
"text": "def gen_an_aug(self, results: Dict) -> np.ndarray:\n\n all_kps = results['keypoint'].astype(np.float32)\n kp_shape = all_kps.shape\n\n if 'keypoint_score' in results:\n all_kpscores = results['keypoint_score']\n else:\n all_kpscores = np.ones(kp_shape[:-1], dtype=np.float32)\n\n img_h, img_w = results['img_shape']\n\n # scale img_h, img_w and kps\n img_h = int(img_h * self.scaling + 0.5)\n img_w = int(img_w * self.scaling + 0.5)\n all_kps[..., :2] *= self.scaling\n\n num_frame = kp_shape[1]\n num_c = 0\n if self.with_kp:\n num_c += all_kps.shape[2]\n if self.with_limb:\n num_c += len(self.skeletons)\n\n ret = np.zeros([num_frame, num_c, img_h, img_w], dtype=np.float32)\n\n for i in range(num_frame):\n # M, V, C\n kps = all_kps[:, i]\n # M, C\n kpscores = all_kpscores[:, i] if self.use_score else \\\n np.ones_like(all_kpscores[:, i])\n\n self.generate_heatmap(ret[i], kps, kpscores)\n return ret",
"title": ""
},
{
"docid": "7e61f3dce3570c3384a9f532298e46d4",
"score": "0.50658816",
"text": "def as_c_array(self):\n pyarr = self.as_array()\n return (ctypes.c_double * len(pyarr))(*pyarr)",
"title": ""
},
{
"docid": "7e61f3dce3570c3384a9f532298e46d4",
"score": "0.50658816",
"text": "def as_c_array(self):\n pyarr = self.as_array()\n return (ctypes.c_double * len(pyarr))(*pyarr)",
"title": ""
},
{
"docid": "7e61f3dce3570c3384a9f532298e46d4",
"score": "0.50658816",
"text": "def as_c_array(self):\n pyarr = self.as_array()\n return (ctypes.c_double * len(pyarr))(*pyarr)",
"title": ""
},
{
"docid": "7e61f3dce3570c3384a9f532298e46d4",
"score": "0.50658816",
"text": "def as_c_array(self):\n pyarr = self.as_array()\n return (ctypes.c_double * len(pyarr))(*pyarr)",
"title": ""
},
{
"docid": "7e61f3dce3570c3384a9f532298e46d4",
"score": "0.50658816",
"text": "def as_c_array(self):\n pyarr = self.as_array()\n return (ctypes.c_double * len(pyarr))(*pyarr)",
"title": ""
},
{
"docid": "5fddf6570d7994f93fbd5f30ba36d25f",
"score": "0.5060462",
"text": "def _sample_from_policy(cls, policy: torch.FloatTensor) -> np.array:\n softmax = F.softmax(policy, dim=1)\n return torch.multinomial(softmax,\n num_samples=1).data",
"title": ""
},
{
"docid": "46ade162c3025b53ab686e5748e4f850",
"score": "0.5043946",
"text": "def _im2np(im):\n w = int(im['width'])\n h = int(im['height'])\n d = 1\n bp = int(im['bitpix'])\n dtype = _bp2np(bp)\n dlen = h * w * abs(bp) // 8\n if js9Globals['retrieveAs'] == 'array':\n s = im['data'][0:h*w]\n if d > 1:\n arr = numpy.array(s, dtype=dtype).reshape((d, h, w))\n else:\n arr = numpy.array(s, dtype=dtype).reshape((h, w))\n elif js9Globals['retrieveAs'] == 'base64':\n s = base64.decodebytes(im['data'].encode())[0:dlen]\n if d > 1:\n arr = numpy.frombuffer(s, dtype=dtype).reshape((d, h, w))\n else:\n arr = numpy.frombuffer(s, dtype=dtype).reshape((h, w))\n else:\n raise ValueError('unknown retrieveAs type for GetImageData()')\n return arr",
"title": ""
},
{
"docid": "7ae4bfdb3a6c874522e7453140770328",
"score": "0.50430226",
"text": "def dtype_torch_to_numpy(dtype: torch.dtype) ->np.dtype:\n return torch.empty([], dtype=dtype).numpy().dtype",
"title": ""
},
{
"docid": "057c0577eec40043ecb37ea0b4d2bfb5",
"score": "0.5043009",
"text": "def load_numpy(file_path: str) -> Union[numpy.ndarray, Dict[str, numpy.ndarray]]:\n file_path = clean_path(file_path)\n array = numpy.load(file_path)\n\n if not isinstance(array, numpy.ndarray):\n tmp_arrray = array\n array = OrderedDict()\n for key, val in tmp_arrray.items():\n array[key] = val\n\n return array",
"title": ""
},
{
"docid": "08291d48524b3b1bcce0aa624011eaa4",
"score": "0.5041989",
"text": "def observed_data_to_xarray(self):\n if self.predictions:\n return None\n return dict_to_dataset(\n self.observations,\n library=pymc,\n coords=self.coords,\n dims=self.dims,\n default_dims=[],\n )",
"title": ""
},
{
"docid": "bd3a0384641c43949f0e9bd322a5e956",
"score": "0.5037962",
"text": "def to_numpy(X):\n if isinstance(X, np.ndarray):\n return X\n\n if is_pandas_ndframe(X):\n return X.values\n\n if X.is_cuda:\n X = X.cpu()\n\n if isinstance(X, Variable):\n data = X.data\n else:\n data = X\n return data.numpy()",
"title": ""
},
{
"docid": "ad124aca9a9a8ef3bdff46e149d02257",
"score": "0.5036325",
"text": "def sitk2np(array_sitk):\n array_np = []\n for i in range(len(array_sitk)):\n array_np.append(sitk.GetArrayFromImage(array_sitk[i]))\n\n return array_np",
"title": ""
}
]
67185509529d539e7c34a5b449ea8449
Stores the given value via the specified ``overlay`` and ``key``.
Either pass name or enum itself, e.g `"local"` or `ChannelPicker.local`, or a list of one or more of those, e.g. `["national", "local"]`. If `remove_variants` is True, only keep the canonical form (the last) where there are multiple entries with the same title. Exclude the children's channel "CBeebies" (does not parse to catalogue for some reason and is not the same type as the other national channels anyway).
[
{
"docid": "66f31fb67bbbdca81de26329cd0c585a",
"score": "0.4037361",
"text": "def keys_by_category(cls, category, remove_variants=False, remove_cbeebies=True):\n keys = []\n if isinstance(category, list):\n for c in category:\n keys.extend(cls.keys_by_category(c, remove_variants=remove_variants))\n remove_variants = False # ensure not to re-remove at the end\n else:\n if isinstance(category, str): # name e.g. \"local\"\n category = cls[category]\n keys.extend(sorted([*category.value.__members__]))\n if remove_variants:\n keys = cls.remove_variants(keys, sort=True)\n if remove_cbeebies:\n if \"cr\" in keys:\n keys.remove(\"cr\")\n return keys",
"title": ""
}
]
[
{
"docid": "7e995dec9689c3c0d5f15c1a0d2c4a80",
"score": "0.49973446",
"text": "def filter_releases_by_channel(releases: List[dict], channel: str):\n if channel not in [\"stable\", \"beta\", \"alpha\"]:\n raise ValueError(\"Channel must be one of 'stable', 'beta', 'alpha'\")\n\n if channel == \"stable\":\n return [r for r in releases if \"alpha\" not in r[\"name\"] and \"beta\" not in r[\"name\"]]\n\n elif channel == \"beta\":\n return [r for r in releases if \"alpha\" not in r[\"name\"]]\n\n return list(releases)",
"title": ""
},
{
"docid": "0900e3b1675e2b414f12c8a8bcbd05d2",
"score": "0.4835335",
"text": "def filter(self, btype='', name=''):\n filtered = []\n for x in self.children:\n if name and isinstance(x, Key) and x.name == name:\n filtered.append(x)\n elif isinstance(x, Container) and x.__class__.__name__ == btype \\\n and x.value == name:\n filtered.append(x)\n elif not name and btype and x.__class__.__name__ == btype:\n filtered.append(x)\n return filtered",
"title": ""
},
{
"docid": "0900e3b1675e2b414f12c8a8bcbd05d2",
"score": "0.4835335",
"text": "def filter(self, btype='', name=''):\n filtered = []\n for x in self.children:\n if name and isinstance(x, Key) and x.name == name:\n filtered.append(x)\n elif isinstance(x, Container) and x.__class__.__name__ == btype \\\n and x.value == name:\n filtered.append(x)\n elif not name and btype and x.__class__.__name__ == btype:\n filtered.append(x)\n return filtered",
"title": ""
},
{
"docid": "df07b44c4c32bf17265a9bcf31583157",
"score": "0.47789168",
"text": "def clean_genre_category(soup_obj):\n genres = [get_string(e) for e in soup_obj.find_all(\"a\")]\n if not genres:\n assert get_string(soup_obj).strip() == \"N/A\"\n genres = genres[:-1] # Drop the \"see more thing\")\n return(genres)",
"title": ""
},
{
"docid": "4195ddb6c85d5f891084c548ed988b88",
"score": "0.46707535",
"text": "def test_filter_de_novos(self):\n \n # make a family without parents\n family = Family(\"fam_id\")\n child_gender = \"female\"\n family.add_child('child_id', 'mother_id', 'father_id', child_gender, '2', 'child_path')\n \n # set up an autosomal variant\n gender = \"M\"\n args = [\"1\", \"100\", \".\", \"T\", \"G\", \"1000\", \"PASS\", \".\", \"GT\", \"0/1\", gender]\n child_var = SNV(*args)\n \n # combine the variant into a list of TrioGenotypes\n child_vars = [child_var]\n mother_vars = []\n father_vars = []\n trio_variants = combine_trio_variants(family, child_vars, mother_vars, father_vars)\n \n # check that vars without parents get passed through automatically\n self.assertEqual(filter_de_novos(trio_variants, 0.9), trio_variants)\n \n # now add parents to the family\n family.add_mother(\"mother_id\", '0', '0', 'female', '1', \"mother_vcf_path\")\n family.add_father(\"father_id\", '0', '0', 'male', '1', \"father_vcf_path\")\n family = family\n \n # re-generate the variants list now that parents have been included\n trio_variants = combine_trio_variants(family, child_vars, mother_vars, father_vars)\n \n # check that vars with parents, and that appear to be de novo are\n # filtered out\n self.assertEqual(filter_de_novos(trio_variants, 0.9), [])\n \n # check that vars with parents, but which are not de novo, are retained\n mother_vars = child_vars\n trio_variants = combine_trio_variants(family, child_vars, mother_vars, father_vars)\n \n self.assertEqual(filter_de_novos(trio_variants, 0.9), trio_variants)",
"title": ""
},
{
"docid": "d718efedb4de38e37700d8b720a0c5e4",
"score": "0.4625562",
"text": "def filter_variants(df):\n \n waddell_list = ['missense_variant',\n 'stop_gained',\n 'frameshift_variant',\n 'splice_acceptor_variant',\n 'splice_donor_variant',\n 'start_lost',\n 'inframe_deletion',\n 'inframe_insertion',\n 'stop_lost']\n \n return df[df['One_Consequence'].isin(waddell_list)]",
"title": ""
},
{
"docid": "598315fe032a4a7509acb1deffdf7cf3",
"score": "0.46117678",
"text": "def filter_variants(self, variants):\n \n # if we have flagged CNVs on three different chroms, drop all CNVs,\n # since the sample is sufficiently anomalous\n# if self.count_cnv_chroms(variants) > 2:\n# variants = self.remove_cnvs(variants)\n \n # and filter by a lower MAF threshold\n\n variants = self.filter_by_maf(variants)\n variants = self.filter_polyphen(variants)\n variants = self.filter_exac(variants)\n \n return variants",
"title": ""
},
{
"docid": "fad45bd6306310c98f24b319f6ea877d",
"score": "0.4594125",
"text": "def prune(self, keep_channels=True, *, verbose=True):\n for v in self.variables:\n for var in wt_kit.flatten_list([ax.variables for ax in self._axes + self._constants]):\n if v == var:\n break\n else:\n self.remove_variable(v.natural_name, implied=False, verbose=verbose)\n if keep_channels is not True:\n try:\n if isinstance(keep_channels, str):\n raise TypeError\n indexes = tuple(keep_channels)\n except TypeError:\n indexes = (keep_channels,)\n\n for i, ch in enumerate(self.channels):\n if i not in indexes and not ch.natural_name in indexes:\n self.remove_channel(ch.natural_name, verbose=verbose)",
"title": ""
},
{
"docid": "4196db0a2bf1fcb8eac78a80527e505c",
"score": "0.45561054",
"text": "def _prune(self, name_parts, content):\n if not self.prune:\n return content\n for ep_filter in FILTERS:\n ep_filter_parts = ep_filter.split(\"/\")\n if len(name_parts) != len(ep_filter_parts):\n continue\n for filter_part, part in zip(ep_filter_parts, name_parts):\n if filter_part not in (\"*\", part):\n break\n else:\n for pth in FILTERS[ep_filter]:\n if isinstance(content, list):\n for o in content:\n pop_matches(pth, o)\n else:\n pop_matches(pth, content)\n return content",
"title": ""
},
{
"docid": "9f81cf1db570b4791444355f08a3fd43",
"score": "0.4537968",
"text": "def filter_variants(job, config, name, input_vcf):\n\n output_vcf = \"{}.filtered.vcf\".format(name)\n filter_log = \"{}.variantfiltration.log\".format(name)\n\n filter_command = [\"{}\".format(config['gatk-filter']['bin']),\n \"-T\",\n \"VariantFiltration\",\n \"-R\",\n \"{}\".format(config['reference']),\n \"--filterExpression\",\n \"'MQ0 > {}'\".format(config['mq0_threshold']),\n \"--filterName\",\n \"'HighMQ0'\",\n \"--filterExpression\",\n \"'DP < {}'\".format(config['coverage_threshold']),\n \"--filterName\",\n \"'LowDepth'\",\n \"--filterExpression\",\n \"'QUAL < {}'\".format(config['var_qual_threshold']),\n \"--filterName\",\n \"'LowQual'\",\n \"--filterExpression\",\n \"'MQ < {}'\".format(config['map_qual_threshold']),\n \"--filterName\",\n \"'LowMappingQual'\",\n \"--variant\",\n \"{}\".format(input_vcf),\n \"-o\",\n \"{}\".format(output_vcf)]\n\n job.fileStore.logToMaster(\"GATK VariantFiltration Command: {}\\n\".format(filter_command))\n pipeline.run_and_log_command(\" \".join(filter_command), filter_log)\n\n return output_vcf",
"title": ""
},
{
"docid": "1c733603bdac2925ff9dd6187c7dc270",
"score": "0.448448",
"text": "def remove(x, amount): # TO-DO: ONSET HANDLING (DO WE WANT IT TO NORMALIZE TO STARTING AT ZERO?)\n if amount<=0:\n return x # nothing to remove!\n #elif (x.__class__.__name__ == 'Music'):\n # remove(x.tree, amount)\n # return x\n elif (x.__class__.__name__ == 'Note' or x.__class__.__name__ == 'Rest'):\n if amount >= x.dur:\n x.dur = 0\n if amount < x.dur:\n x.dur = x.dur - amount\n return x\n elif (x.__class__.__name__ == 'Seq'):\n dLeft = amount\n newTree = []\n for t in x.trees:\n d = dur(t)\n newTree.append(remove(t, dLeft))\n dLeft = max(0,dLeft - d)\n x.trees = newTree\n return x\n elif isinstance(x, Par):\n newTrees = []\n for t in x.trees:\n newTrees.append(remove(t,amount))\n x.trees = newTrees\n return x\n elif (x.__class__.__name__ == 'Part'):\n #if (x.mod.__class__.__name__ == 'Tempo'):\n # remove(x.tree, amount*x.mod.value)\n #else:\n remove(x.tree, amount)\n return x\n else: raise MusEciException(\"Unrecognized musical structure: \" + str(x))",
"title": ""
},
{
"docid": "fb9c4d9f7561372c40ff1aeff3845279",
"score": "0.44445187",
"text": "def remove(self, name, parent=None):\n raise NotImplementedError",
"title": ""
},
{
"docid": "68152c3b86f3ef3200734d1357ac7da7",
"score": "0.44322827",
"text": "def remove_list_item(self, data):\n\n if isinstance(data, QJSValue):\n data = data.toVariant()\n\n rolename = \"displayName\"\n value = data[rolename]\n type = data[\"type\"]\n\n if type == \"Taxon\":\n\n idx = self.avFullModel.get_item_index(rolename=rolename, value=value)\n if idx >= 0:\n self.avFullModel.removeItem(idx)\n self.avFullSpecies = [x for x in self.avFullSpecies if x[\"displayName\"] != value]\n self.avFullSpeciesFiltered = [x for x in self.avFullSpeciesFiltered if x[\"displayName\"] != value]\n\n if data[\"isMostRecent\"] == \"True\":\n\n idx = self.avRecentModel.get_item_index(rolename=rolename, value=value)\n if idx >= 0:\n self.avRecentModel.removeItem(idx)\n self.avRecentSpecies = [x for x in self.avRecentSpecies if x[\"displayName\"] != value]\n self.avRecentSpeciesFiltered = [x for x in self.avRecentSpeciesFiltered if x[\"displayName\"] != value]\n\n elif type == \"Debris\":\n\n idx = self.avDebrisModel.get_item_index(rolename=rolename, value=value)\n if idx >= 0:\n self.avDebrisModel.removeItem(idx)\n self.avDebris = [x for x in self.avDebris if x[\"displayName\"] != value]\n self.avDebrisFiltered = [x for x in self.avDebrisFiltered if x[\"displayName\"] != value]",
"title": ""
},
{
"docid": "07c1e4440ab74a153155ce2e84b4c0b5",
"score": "0.4429736",
"text": "def absent_downstream_subtypes(subtype: str,\n subtypes: pd.Series,\n scheme_subtypes: List[str]) -> Optional[List[str]]:\n escaped_subtype = re.escape(subtype)\n re_subtype = re.compile(r'^{}\\.\\d+$'.format(escaped_subtype))\n downstream_subtypes = [s for s in scheme_subtypes if re_subtype.search(s)]\n absentees = [x for x in downstream_subtypes if not (subtypes == x).any()]\n return absentees if absentees else None",
"title": ""
},
{
"docid": "90507711d6c52a2f746f15c2b231d0c4",
"score": "0.44296724",
"text": "def remove_tree_item(self, index):\n if not isinstance(index, QModelIndex):\n return\n\n model = self.seModel\n\n # Get the existing catchId from the data - Do before deleting the actual row\n item = model.getItem(index)\n typeCol = model.getColumnNumber(\"type\")\n catchId = item.data(model.getColumnNumber(\"catchId\")).value()\n\n type = item.data(typeCol).value()\n if type == \"Taxon\":\n self.speciesCount -= 1\n\n elif type == \"Mix\":\n\n if isinstance(self._active_mix, QJSValue):\n self._active_mix = self._active_mix.toVariant()\n if catchId == self._active_mix[\"catchId\"]:\n self.activeMix = {\"displayName\": None, \"catchId\": None}\n\n # recurse to check all children + subchildren\n self.speciesCount -= len([x for x in item.children if x.data(typeCol).value() == \"Taxon\"])\n submixes = [x for x in item.children if x.data(typeCol).value() == \"Submix\"]\n for submix in submixes:\n self.speciesCount -= len([x for x in submix.children if x.data(typeCol).value() == \"Taxon\"])\n\n # If the submix is the activeMix and we're removing the submix, then set the activeMix to None\n if submix.data(model.getColumnNumber('catchId')).value() == self._active_mix[\"catchId\"]:\n self.activeMix = {\"displayName\": None, \"catchId\": None}\n\n elif type == \"Submix\":\n\n if isinstance(self._active_mix, QJSValue):\n self._active_mix = self._active_mix.toVariant()\n if catchId == self._active_mix[\"catchId\"]:\n self.activeMix = {\"displayName\": None, \"catchId\": None}\n\n # recurse to check all children\n self.speciesCount -= len([x for x in item.children if x.data(typeCol).value() == \"Taxon\"])\n\n\n # Remove the rows\n parentIdx = model.parent(index)\n status = model.removeRows(index.row(), 1, parentIdx)\n\n # Decrement the species count - this is shown in the upper right corner of the screen\n # self.speciesCount -= 1\n\n # Delete from the database\n if isinstance(catchId, int):\n\n catch_sql = \"\"\"\n WITH RECURSIVE subcatch(n) AS (\n SELECT CATCH_ID FROM CATCH WHERE CATCH_ID = ?\n UNION\n SELECT c.CATCH_ID FROM CATCH c, subcatch\n WHERE c.PARENT_CATCH_ID = subcatch.n\n )\n DELETE FROM CATCH WHERE CATCH_ID in subcatch;\n \"\"\"\n\n specimen_sql = \"\"\"\n WITH RECURSIVE subcatch(n) AS (\n SELECT CATCH_ID FROM CATCH WHERE CATCH_ID = ?\n UNION\n SELECT c.CATCH_ID FROM CATCH c, subcatch\n WHERE c.PARENT_CATCH_ID = subcatch.n\n ),\n subspecimens(n) AS (\n SELECT SPECIMEN_ID FROM SPECIMEN s INNER JOIN CATCH c\n ON c.CATCH_ID = s.CATCH_ID WHERE c.CATCH_ID in subcatch\n UNION\n SELECT s.SPECIMEN_ID FROM SPECIMEN s, subspecimens\n WHERE s.PARENT_SPECIMEN_ID = subspecimens.n\n )\n DELETE FROM SPECIMEN WHERE SPECIMEN_ID IN subspecimens;\n \"\"\"\n\n params = [catchId, ]\n self._db.execute(query=specimen_sql, parameters=params)\n self._db.execute(query=catch_sql, parameters=params)",
"title": ""
},
{
"docid": "d906e7b03fb499d2e52ea262ebf14bf4",
"score": "0.44267872",
"text": "def _unregister_propagate(self, types):\n channel = self\n while channel is not None:\n channel.registered_types.difference_update(types)\n channel = channel._parent",
"title": ""
},
{
"docid": "dda5a1a1aede81c7983cc66e7ce7b35d",
"score": "0.44106168",
"text": "def remove_child_banks(self, *args, **kwargs):\n # Implemented from kitosid template for -\n # osid.resource.BinHierarchyDesignSession.remove_child_bins\n self._get_provider_session('bank_hierarchy_design_session').remove_child_banks(*args, **kwargs)",
"title": ""
},
{
"docid": "f91daab046a23db26815af2cda4f421f",
"score": "0.4371158",
"text": "def process_variants(variant_titles: list[dict[str, Any]], variant_type: str, report_on_match: bool) -> None:\r\n\r\n variants: list[DatNode] = []\r\n\r\n for variant_title in variant_titles:\r\n if 'searchTerm' not in variant_title:\r\n printwrap(\r\n f'{Font.warning}* The following variants entry is missing a '\r\n f'{Font.bold}searchTerm{Font.warning} key and will be skipped:',\r\n 'error')\r\n eprint(f'\\n {variant_title}{Font.end}')\r\n\r\n if config.user_input.warningpause:\r\n eprint(f'\\n{Font.disabled}Press enter to continue{Font.end}')\r\n input()\r\n continue\r\n else:\r\n variant_name = variant_title['searchTerm']\r\n\r\n name_type: str = ''\r\n\r\n if 'nameType' in variant_title:\r\n name_type = variant_title['nameType']\r\n if not (name_type == 'full'\r\n or name_type == 'short'\r\n or name_type == 'regionFree'\r\n or name_type == 'tagFree'\r\n or name_type == 'regex'):\r\n name_type = 'short'\r\n else:\r\n name_type = 'short'\r\n\r\n # Look up the title in the dictionary, then process the required changes\r\n found_titles: set[DatNode] = set()\r\n\r\n if name_type == 'regex':\r\n valid_regex: list[str] = regex_test([variant_name], 'variants', 'clone list')\r\n\r\n if not valid_regex:\r\n continue\r\n\r\n found_titles = TitleTools.find_title(variant_name, name_type, processed_titles, missing_titles, config, deep_search=True)\r\n else:\r\n found_titles = TitleTools.find_title(variant_name, name_type, processed_titles, missing_titles, config)\r\n\r\n old_group_names: set[str] = set()\r\n\r\n for title in found_titles:\r\n old_group_names.add(title.group_name)\r\n\r\n new_group_name: str = TitleTools.get_group_name(value['group'], config)\r\n\r\n new_group_name = new_group_name.lower()\r\n\r\n if new_group_name not in processed_titles:\r\n processed_titles[new_group_name] = set()\r\n\r\n # If the title's not found in the DAT, add it to missing_titles,\r\n # otherwise add it to the delete list, then move it to the new\r\n # group\r\n if not found_titles:\r\n missing_titles.add(variant_name)\r\n else:\r\n if config.user_input.trace:\r\n report_on_match = TraceTools.trace_enable(set(found_titles), config.user_input.trace)\r\n\r\n for title in found_titles:\r\n for old_group_name in old_group_names:\r\n if title in processed_titles[old_group_name]:\r\n if (\r\n report_on_match\r\n and new_group_name != title.group_name\r\n and not is_includes):\r\n eprint('')\r\n TraceTools.trace_title('REF0055')\r\n eprint(f'* {title.full_name}')\r\n eprint(f' New group: {new_group_name}\\n{Font.disabled} Old group: {title.group_name}{Font.end}')\r\n eprint(f'\\n{Font.disabled}Press enter to continue{Font.end}')\r\n input()\r\n\r\n if (\r\n variant_type == 'title'\r\n or variant_type == 'superset'):\r\n new_title: DatNode = copy.deepcopy(title)\r\n\r\n if 'priority' in variant_title:\r\n if new_title.clonelist_priority == 1:\r\n new_title.clonelist_priority = variant_title['priority']\r\n\r\n if variant_type == 'superset':\r\n new_title.is_superset = True\r\n\r\n variants.append(new_title)\r\n\r\n for old_group_name in old_group_names:\r\n if title in processed_titles[old_group_name]:\r\n delete_titles.add((title, old_group_name))\r\n\r\n elif variant_type =='compilation':\r\n for compilation_title in processed_titles[old_group_name]:\r\n if compilation_title.full_name == title.full_name:\r\n title_position: int = 1\r\n if 'titlePosition' in variant_title:\r\n title_position = variant_title['titlePosition']\r\n\r\n if 'priority' in variant_title:\r\n clonelist_priority: int = variant_title['priority']\r\n else:\r\n clonelist_priority = 1\r\n\r\n compilation_title.contains_titles[value['group']] = {\"position\": title_position, \"priority\": clonelist_priority}\r\n\r\n if 'compilationPriority' in variant_title:\r\n compilation_title.clonelist_priority = variant_title['compilationPriority']\r\n\r\n # Set these variant properties after the processing, to make sure they\r\n # don't mess with title look ups\r\n for variant in variants:\r\n variant.group_name = new_group_name\r\n variant.short_name = value['group'].lower()\r\n processed_titles[new_group_name].add(variant)",
"title": ""
},
{
"docid": "2a50556d71444c30eb83ba99577ca164",
"score": "0.436203",
"text": "def variant(self, name):\n return filter(lambda s: s.name == name, self.variants)[0]",
"title": ""
},
{
"docid": "8c77687cfa248b2fe9f2e2ddc541d557",
"score": "0.43561602",
"text": "def filter_processes():\n \n foreground_process_category_list = []\n for process in db.getAll(Process):\n category = process.category\n if category is None:\n foreground_process_category_list.append(process) \n \n else:\n path = category.toPath()\n \n if not any(elem in path for elem in ECOINVENT_PARENT_CATEGORY_LIST):\n foreground_process_category_list.append(process)\n \n return foreground_process_category_list",
"title": ""
},
{
"docid": "18a74abde3d65bf841b822796b766cea",
"score": "0.43430316",
"text": "def filter_categories(self, raw_data):\n raw_categories = []\n\n for product in raw_data['products']:\n category_list = product['categories_tags']\n raw_categories += category_list\n\n fr_categories = []\n\n for category in raw_categories:\n if 'fr:' in category:\n fr_categories.append(category)\n\n keepers = []\n occurrence_limit = 2\n\n for category in fr_categories:\n if fr_categories.count(category) >= occurrence_limit:\n keepers.append(category)\n\n return keepers",
"title": ""
},
{
"docid": "8ccfb52fc20283f8f708685867829f80",
"score": "0.4340506",
"text": "def _filter_dact(self, dact, raw_utterance):\n slot_filter_priority = [\n Slots.GENRES, Slots.ACTORS, Slots.DIRECTORS, Slots.KEYWORDS,\n Slots.TITLE\n ]\n # first see if multiple genres lead to a plot keyword or title\n for slot in slot_filter_priority:\n params = [p for p in dact.params if p.slot == slot.value]\n for param in params:\n if any([\n re.search(r'\\b{0}\\b'.format(pattern), param.value)\n for pattern in self.dont_care_pattern +\n self.basic_patterns[UserIntents.ACKNOWLEDGE] +\n self.basic_patterns[UserIntents.DENY]\n ]):\n self._remove_param(param, dact)\n continue\n if param.slot == Slots.KEYWORDS.value:\n # remove the plot_keyword if it is also in other slot\n # values or is a sub-string\n if param.value in [\n p.value for p in dact.params if p.slot != param.slot\n ]:\n self._remove_param(param, dact)\n if param.slot in [\n Slots.GENRES.value, Slots.KEYWORDS.value,\n Slots.ACTORS.value, Slots.DIRECTORS.value\n ]:\n # remove genre if it is a sub-string of any other value\n values = param.value.strip().split() if param.slot == \\\n Slots.GENRES.value else [param.value]\n for value in values:\n for p in dact.params:\n if p.slot != param.slot and value in \\\n p.value.split() and len(p.value.split()) != \\\n len(value.split()):\n self._remove_param(param, dact)\n if param.slot == Slots.TITLE.value:\n # remove the title if it is also in other slot values\n if param.value in [\n p.value for p in dact.params if p.slot != param.slot\n ]:\n self._remove_param(param, dact)\n elif param.value in self.slot_values[Slots.KEYWORDS.value]:\n param.slot = Slots.KEYWORDS.value\n elif len([p for p in param.value.split() if p in self.slot_values[\n Slots.GENRES.value] or p in self.slot_annotator.genres_alternatives]) == \\\n len(param.value.split()):\n param.slot = Slots.GENRES.value\n\n # extra check for if an annotation is a sub-string of another\n for param in dact.params:\n if param.slot in [Slots.YEAR.value, Slots.GENRES.value]:\n continue\n param.value = self.slot_annotator.find_in_raw_utterance(\n raw_utterance, param.value, len(word_tokenize(param.value)))\n for param in deepcopy(dact.params):\n if any([param.value.lower() in p.value.lower() and param.value.lower() != \\\n p.value.lower() for p in dact.params if p.slot != param.slot]):\n self._remove_param(param, dact)\n\n self._filter_genres(dact)\n dual_persons = self._filter_person_names(dact)\n values_neg = self._get_annotation_relevance(dact, raw_utterance,\n dual_persons)\n for param in dact.params:\n if param.value in values_neg:\n if param.op == Operator.EQ:\n param.op = Operator.NE\n else:\n param.value = f'{param.op} {param.value}'\n param.op = Operator.NE",
"title": ""
},
{
"docid": "ed4f3c3bf11e705c08026e2ed359722a",
"score": "0.43332943",
"text": "def collect_children_type_of(parent, ch_type):\n collected = []\n\n for child in parent.winfo_children():\n if child.winfo_class() == ch_type:\n collected.append(child)\n\n return collected",
"title": ""
},
{
"docid": "5c8f84f63cc5153442c32918b3323faf",
"score": "0.43160313",
"text": "def choose_media_format(items):\n\n items_by_media_id = {}\n for item in items:\n media_items = items_by_media_id.get(item.media_id, list())\n media_items.append(item)\n items_by_media_id[item.media_id] = media_items\n\n pruned_items = []\n\n # Desired format in descending order\n desired_formats = [\n (smscsv.MediaFormat.VIDEO, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.MPEG4, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.MPEG4, smscsv.MediaQuality.HIGH_RES),\n (smscsv.MediaFormat.MPEG4, smscsv.MediaQuality.LOW_RES),\n (smscsv.MediaFormat.WMV, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.FLV, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.FLV, smscsv.MediaQuality.MEDIUM),\n (smscsv.MediaFormat.FLV, smscsv.MediaQuality.LOW),\n (smscsv.MediaFormat.IPOD, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.IPOD, smscsv.MediaQuality.MEDIUM),\n (smscsv.MediaFormat.IPOD, smscsv.MediaQuality.LOW),\n (smscsv.MediaFormat.AUDIO, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.AAC, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.MP3, smscsv.MediaQuality.HIGH),\n (smscsv.MediaFormat.AAC, smscsv.MediaQuality.MEDIUM),\n (smscsv.MediaFormat.MP3, smscsv.MediaQuality.MEDIUM),\n (smscsv.MediaFormat.AAC, smscsv.MediaQuality.LOW),\n (smscsv.MediaFormat.MP3, smscsv.MediaQuality.LOW),\n ]\n\n for media_items in items_by_media_id.values():\n if set(item.filename for item in media_items) == {''}:\n LOG.warning(\n 'Skipping item media_id=%s since it has no files at all', media_items[0].media_id)\n continue\n\n format_quality_pairs = {(item.format, item.quality): item for item in media_items}\n\n best_item = None\n for f in desired_formats:\n item = format_quality_pairs.get(f)\n if item is not None and item.filename != '':\n best_item = item\n break\n\n if best_item is not None:\n pruned_items.append(best_item)\n else:\n LOG.warning('Could not find format for item: media_id=%s', media_items[0].media_id)\n LOG.warning('Formats and filenames:')\n for item in media_items:\n LOG.warning(' %s', repr([(item.format, item.quality, item.filename)]))\n\n return pruned_items",
"title": ""
},
{
"docid": "dd27ae07fff4ed85ad53c81a72bf6f22",
"score": "0.43114892",
"text": "def channel_remove(runner, remove_info):\n db = runner.get_hn()\n tmp_params = runner.get_params()\n num_of_anchors = remove_info[\"num_of_anchors\"] if \"num_of_anchors\" in remove_info else None\n for idx, (layer, mask) in enumerate(zip(remove_info['layer_name'], remove_info['mask'])):\n assert layer in db['layers'], \"Layer does not exist in the HN\"\n assert db['layers'][layer]['type'] == 'output_layer', \"Chosen layer is not an output\"\n sh = db['layers'][layer]['output_shapes'][0]\n mask_tile = list(mask) if num_of_anchors is None else list(np.tile(mask, [num_of_anchors[idx]]))\n if not sh[3] == 1:\n # normal output\n assert len(mask_tile) == sh[3], \"One hot vector is not in the right length\"\n _channel_remove(db, layer, mask_tile, sh[3], tmp_params)\n else:\n # output layer after argmax\n pred_layer = db['layers'][layer]['input'][0]\n sh = db['layers'][pred_layer]['input_shapes'][0]\n assert len(mask) == sh[3], \"One hot vector is not in the right length\"\n _channel_remove(db, pred_layer, mask, sh[3], tmp_params)\n runner.set_hn(db)\n runner.load_params(tmp_params)",
"title": ""
},
{
"docid": "4aa8344063ccd901810a0ea8cd2e02f0",
"score": "0.42951268",
"text": "def __remove_nones_variant_data (self, variant_data):\r\n\t\tfor v in variant_data.keys():\r\n\t\t\tvariant_data[v] = [x for x in variant_data[v] if x is not None]\r\n\t\treturn variant_data",
"title": ""
},
{
"docid": "13a3047051446f3935246cf9e09f0458",
"score": "0.4283493",
"text": "def remove_steam_link_filter(value):\n\treturn _list_or_string(value, _remove_steam_link_filter)",
"title": ""
},
{
"docid": "196691121793488e5452d8632a4fbb50",
"score": "0.4277088",
"text": "def channel_remove(self):\n logger.info(f\"OSN: Channel remove start: name={self.channel_name}\")\n\n osn_admin = OSNAdmin(\n self.orderers_eps[-1],\n ORDERER_ADMIN_TLS_CERT,\n ORDERER_ADMIN_TLS_KEY,\n ORDERER_ADMIN_TLS_CA,\n )\n res = osn_admin.list_single_channel(self.channel_name)\n self.assertTrue(res)\n self.assertEqual(res[\"status\"], 'active')\n osn_admin.remove(self.channel_name)\n res = osn_admin.list_single_channel(self.channel_name)\n self.assertEqual(res[\"status\"], 'inactive')\n\n logger.info(f\"OSN: Channel remove done: name={self.channel_name}\")",
"title": ""
},
{
"docid": "fee8bc6ca702768975de62ba308ca269",
"score": "0.4267723",
"text": "def _build_categories(title):\n _, name, thumbnail = title.split(\"#\", 2)\n base = BrowseMediaSource(\n domain=DOMAIN,\n identifier=f\"{title}\",\n media_class=MediaClass.GAME,\n media_content_type=\"\",\n title=name,\n can_play=False,\n can_expand=True,\n children=[],\n children_media_class=MediaClass.DIRECTORY,\n thumbnail=thumbnail,\n )\n\n owners = [\"my\", \"community\"]\n kinds = [\"gameclips\", \"screenshots\"]\n for owner in owners:\n for kind in kinds:\n base.children.append(\n BrowseMediaSource(\n domain=DOMAIN,\n identifier=f\"{title}~~{owner}#{kind}\",\n media_class=MediaClass.DIRECTORY,\n media_content_type=\"\",\n title=f\"{owner.title()} {kind.title()}\",\n can_play=False,\n can_expand=True,\n children_media_class=MEDIA_CLASS_MAP[kind],\n )\n )\n\n return base",
"title": ""
},
{
"docid": "621d0daebe9467d5e766595121df96f8",
"score": "0.42472777",
"text": "def name_variants(self, key, value):\n if value.get('g'):\n self.setdefault('extra_words', [])\n self['extra_words'].extend(force_force_list(value.get('g')))\n\n values = self.get('name_variants', [])\n values.append({\n 'source': value.get('9'),\n 'value': force_force_list(value.get('a', [])),\n })\n\n return values",
"title": ""
},
{
"docid": "7f04abee56cc63dcdf24c79bfecd1536",
"score": "0.42433727",
"text": "def remove(self, name):\n found = [q for q in self.items if q.name == name]\n if len(found) == 1:\n self.items.remove(found[0])",
"title": ""
},
{
"docid": "5c6ac8705ae45dd05cf1e99b6173432c",
"score": "0.4217444",
"text": "def process_tree(self, channel_node):\n file_names = []\n self.process_tree_recur(file_names, channel_node)\n return [x for x in set(file_names) if x] # Remove any duplicate or None filenames",
"title": ""
},
{
"docid": "aeb23b0eb8c8d9fbef8859fce89d74d3",
"score": "0.42132205",
"text": "def removeChildren(self, *args):\n return _osg.Switch_removeChildren(self, *args)",
"title": ""
},
{
"docid": "b1d4598523d3fb06c2f62d5ffc155e13",
"score": "0.4200512",
"text": "def __init__(self, name, cuisine_type='ice_cream'):\n super().__init__(name, cuisine_type)\n self.flavors = []",
"title": ""
},
{
"docid": "812f0d135ca78842ec3f67a0b950f24e",
"score": "0.41887367",
"text": "def filter_variants(self, max_homol=None):\n\n\tfor variant in self.variants: \n\t for adj in variant.adjs:\n\t\tif 'N' in adj.probes[0].upper():\n\t\t adj.filtered_out = True\n\t\t if self.debug:\n\t\t\tsys.stdout.write('N_in_probe %s %s\\n' % (adj.contigs, adj.probes[0]))\n\t\t\t\n\t\tif not adj.filtered_out and adj.novel_seq is not None and adj.novel_seq != 'NA' and adj.novel_seq != '-' and\\\n\t\t re.search('[^ATGC]', adj.novel_seq, re.IGNORECASE):\n\t\t adj.filtered_out = True\n\t\t if self.debug:\n\t\t\tsys.stdout.write('non AGTC in novel sequence %s %s\\n' % (adj.contigs, adj.novel_seq))\n\t\t\t\n\t\tif not adj.filtered_out and (target_non_canonical(adj.chroms[0]) or target_non_canonical(adj.chroms[1])):\n\t\t adj.filtered_out = True\n\t\t if self.debug:\n\t\t\tsys.stdout.write('non canonical chromosome %s %s\\n' % (adj.contigs, adj.chroms))\n\t\t\t\n\t\tif not adj.filtered_out and adj.homol_seq and max_homol is not None:\n\t\t if len(adj.homol_seq[0]) > max_homol:\n\t\t\tadj.filtered_out = True\n\t\t\tif self.debug:\n\t\t\t sys.stdout.write('homolgous sequence length %s too long (>%s)\\n' % (len(adj.homol_seq[0]), max_homol))\n\t\t\t \n\t\tif adj.filtered_out:\n\t\t variant.filtered_out = True",
"title": ""
},
{
"docid": "defc55a118c7fcdeae9eef6c80176d58",
"score": "0.41869694",
"text": "def remove_lonely_choose_1(self,tree_original):\n\t\t\n\t\ttree= copy.deepcopy(tree_original)\n\t\tfor parent in tree.getiterator():\n\t\t\tfor child in list(parent):\t\t\t\t\n\t\t\t\tif child.tag ==\"choose_1\" and len(child) == 1:\n\t\t\t\t\tparent.remove(child)\n\t\t\t\t\tparent.extend(child)\n\t\treturn tree",
"title": ""
},
{
"docid": "e97266ab9146a747070617d8623030e9",
"score": "0.41719",
"text": "def test_concurrent_facet_change_via_uninstall(self):\n\n change_facets = [\n ['facet.A-incorp-sync',\n False, None, 'parent', False, False],\n ['facet.AA-sync', False, None, 'parent', False, False],\n ]\n remove_packages = self.__pkg_names_to_fmris([\n \"AB-sync@1\",\n ])\n change_packages = self.__pkg_name_tuples_to_fmris([\n [\"A-incorp-sync@1\", \"A-incorp-sync@3\"],\n [\"AA-sync@1\", \"AA-sync@4\"],\n [\"B-incorp-sync@1\", \"B-incorp-sync@2\"],\n [\"BA@1\", \"BA@2\"],\n [\"entire-sync@1\", \"entire-sync@2\"],\n ])\n self.__test_concurrent_facet_change_via_child_op(\n \"uninstall\", \"AB-sync\",\n extra_child_pkgs=[\"AB-sync@1\"],\n change_facets=change_facets,\n remove_packages=remove_packages,\n change_packages=change_packages)",
"title": ""
},
{
"docid": "c8b769698402ecc35f1aa8605b968f17",
"score": "0.41515467",
"text": "async def rmgallery(self, ctx: commands.Context, channel: discord.TextChannel):\n if channel.id in await self.config.guild(ctx.guild).channels():\n async with self.config.guild(ctx.guild).channels() as channels:\n channels.remove(channel.id)\n await ctx.send(\n f\"{channel.mention} has been removed from the Gallery channels list.\"\n )\n else:\n await ctx.send(\n f\"{channel.mention} already isn't in the Gallery channels list.\"\n )",
"title": ""
},
{
"docid": "92b40771d65177e61bcabb1abf2db6ba",
"score": "0.41502494",
"text": "def filter_variants(log, variants, retain=True):\r\n if check_is_dataframe(log):\r\n check_dataframe_columns(log)\r\n from pm4py.algo.filtering.pandas.variants import variants_filter\r\n return variants_filter.apply(log, [\",\".join(v) for v in variants],\r\n parameters={variants_filter.Parameters.POSITIVE: retain})\r\n else:\r\n from pm4py.algo.filtering.log.variants import variants_filter\r\n return variants_filter.apply(log, [\",\".join(v) for v in variants],\r\n parameters={variants_filter.Parameters.POSITIVE: retain})",
"title": ""
},
{
"docid": "7174854a0ebde31f853c7f266f52bd56",
"score": "0.4145753",
"text": "def prune(\n self,\n modifiers=None,\n modifier_types=None,\n samples=None,\n channels=None,\n measurements=None,\n ):\n # avoid mutable defaults\n modifiers = [] if modifiers is None else modifiers\n modifier_types = [] if modifier_types is None else modifier_types\n samples = [] if samples is None else samples\n channels = [] if channels is None else channels\n measurements = [] if measurements is None else measurements\n\n return self._prune_and_rename(\n prune_modifiers=modifiers,\n prune_modifier_types=modifier_types,\n prune_samples=samples,\n prune_channels=channels,\n prune_measurements=measurements,\n )",
"title": ""
},
{
"docid": "ba1680d3397a77c48f5a4fff8e2604e2",
"score": "0.4134821",
"text": "def __init__(self, name, cuisine_type):\r\n\t\tsuper().__init__(name, cuisine_type)\r\n\t\tself.flavors = []",
"title": ""
},
{
"docid": "17bdd0bbedc1abc2034935a1ce64f551",
"score": "0.41189975",
"text": "def single_prune(self, info, exclude=None):\n\n for module, name in self.conv_names.items():\n if exclude is not None and module in exclude:\n continue\n fisher = self.accum_fishers[module]\n in_mask = module.in_mask.view(-1)\n ancestors = self.conv2ancest[module]\n if self.delta == 'flops':\n # delta_flops is a value indicate how much flops is\n # reduced in entire forward process after we set a\n # zero in `in_mask` of a specific conv_module.\n delta_flops = self.flops[module] * module.out_mask.sum() / (\n module.in_channels * module.out_channels)\n for ancestor in ancestors:\n delta_flops += self.flops[ancestor] * ancestor.in_mask.sum(\n ) / (ancestor.in_channels * ancestor.out_channels)\n fisher /= (float(delta_flops) / 1e9)\n if self.delta == 'acts':\n delta_acts = 0\n for ancestor in ancestors:\n delta_acts += self.acts[ancestor] / ancestor.out_channels\n fisher /= (float(max(delta_acts, 1.)) / 1e6)\n info.update(\n self.find_pruning_channel(module, fisher, in_mask, info))\n return info",
"title": ""
},
{
"docid": "fa5619399e49084a85f8d6014856e724",
"score": "0.41178423",
"text": "def get_remove_filter() :\n\n return []",
"title": ""
},
{
"docid": "fa5619399e49084a85f8d6014856e724",
"score": "0.41178423",
"text": "def get_remove_filter() :\n\n return []",
"title": ""
},
{
"docid": "29462616611df69f7a5b3a8b25e68e7d",
"score": "0.40990835",
"text": "def remove_cats(self, df, name):\n cat_mapping: dict = self._lookup_mapping(name)\n drop: list = self._lookup_cleanup(name)\n self._remove_cats(df, drop, cat_mapping)",
"title": ""
},
{
"docid": "de0e7b2eac88ef38789cb9cf8f0f5db9",
"score": "0.4095526",
"text": "def filter_exac(self, variants):\n \n passed_vars = []\n \n for (var, check, inh, hgnc) in variants:\n \n # figure out what the het and hemi counts are in ExAC (if available)\n hemi, het = 0, 0\n if \"AC_Hemi\" in var.child.info and var.get_chrom() == \"X\":\n hemi = sum([int(x.replace('.', '0')) for x in var.child.info[\"AC_Hemi\"].split(\",\")])\n if \"AC_Het\" in var.child.info:\n het = sum([int(x.replace('.', '0')) for x in var.child.info[\"AC_Het\"].split(\",\")])\n \n geno = var.get_trio_genotype()\n # filter out hemizygous variants on chrX in males. Autosomal\n # and female chrX variants should pass through unfiltered.\n # We don't filter out de novo variants based on the ExAC hemizygous\n # count. We only apply this filter to inherited variants.\n if \"Hemizygous\" in inh and self.family.child.is_male() and hemi > 0 and \\\n geno != var.get_de_novo_genotype() and geno[1:] != (\"NA\", \"NA\"):\n inh.remove(\"Hemizygous\")\n \n # filter out monoallelic variants with high ExAC het counts.\n if var.get_chrom() != \"X\" and het > 4 and \"Monoallelic\" in inh:\n inh.remove(\"Monoallelic\")\n elif var.get_chrom() == \"X\" and (het + hemi) > 4 and \"X-linked dominant\" in inh:\n inh.remove(\"X-linked dominant\")\n \n if inh == []:\n log_str = \"{}\\t{} dropped from ExAC frequency count\".format(self.family.child.get_id(), var)\n logging.info(log_str)\n if var.get_chrom() == self.debug_chrom and var.get_position() == self.debug_pos:\n print(log_str)\n \n if inh != []:\n passed_vars.append((var, check, inh, hgnc))\n \n return passed_vars",
"title": ""
},
{
"docid": "37241e17629516eef6eb8bf1317a142f",
"score": "0.40910226",
"text": "def remove_cnvs(self, variants):\n \n # remove CNVs from the list of flagged variants\n passed_vars = []\n for (var, check, inh, hgnc) in variants:\n if not var.is_cnv():\n passed_vars.append((var, check, inh, hgnc))\n else:\n log_str = \"{}\\t{} dropped from excess CNVs in proband\".format(self.family.child.get_id(), var)\n logging.info(log_str)\n if var.get_chrom() == self.debug_chrom and var.get_position() == self.debug_pos:\n print(log_str)\n \n return passed_vars",
"title": ""
},
{
"docid": "3f64d0076c01b6133485d323a3e6e0f2",
"score": "0.408558",
"text": "def remove(self, *args):\n for x in args:\n self.children.remove(x)\n return self.children",
"title": ""
},
{
"docid": "3f64d0076c01b6133485d323a3e6e0f2",
"score": "0.408558",
"text": "def remove(self, *args):\n for x in args:\n self.children.remove(x)\n return self.children",
"title": ""
},
{
"docid": "8c0f550d65793098ec124581775ebdde",
"score": "0.40757728",
"text": "def filter_for_meta_sig_variants(testable_variants_file_name, meta_analysis_output_folder,\n final_meta_analysis_sig_variants_results):\n\n meta_global_counts_dict = {\n 'Biallelic_Testable_Variants': 0,\n 'Total_Tests': 0,\n 'Total_Sig_ASE_Variants': 0,\n 'Total_Biallelic_Samples' : 0,\n 'Total_Sig_Biallelic_Samples' : 0,\n 'Total_Sig_ASE_Ref': 0,\n 'Total_Sig_ASE_Alt': 0,\n 'Total_Biallelic_No_ASE': 0,\n 'Total_Passing_Homozygous': 0,\n 'Total_Passing_Homozygous_Ref': 0,\n 'Total_Passing_Homozygous_Alt': 0,\n 'Total_Non_Testable': 0} \n\n # Getting \n meta_analysis_sig_variants_list = final_meta_analysis_sig_variants_results['meta_analysis_sig_variants_list']\n\n # creating new filtered file\n sig_meta_file_name = meta_analysis_output_folder + \"/sig_meta_analysis_variants.txt\"\n\n # Create output folder for failing data for verification purposes\n not_sig_meta_file_name = meta_analysis_output_folder + \"/not_sig_meta_analysis_variants.txt\"\n\n # Open the files for writing\n sig_meta__file = open(sig_meta_file_name,'w')\n not_sig_meta_file = open(not_sig_meta_file_name,'w')\n \n #opening up variant file\n testable_variants_file = open(testable_variants_file_name, 'r')\n\n # reading file line by line\n for line in testable_variants_file:\n\n #Getting the header information from the file\n if line.startswith('#CHROM'):\n \n #Print header of file to new data_file\n sig_meta__file.write(line)\n not_sig_meta_file.write(line)\n continue\n \n else:\n #Tallying total testable variants\n meta_global_counts_dict['Biallelic_Testable_Variants']+= 1\n \n #Splitting the data\n parsed_line = line.rstrip().split('\\t')\n\n #Getting Variant Results Only\n variant_name = parsed_line[0] + \":\" + parsed_line[1]\n\n if variant_name in meta_analysis_sig_variants_list:\n sig_meta__file.write(line)\n meta_global_counts_dict['Total_Sig_ASE_Variants']+= 1\n\n else:\n not_sig_meta_file.write(line)\n\n # Close the files\n sig_meta__file.close()\n not_sig_meta_file.close()\n\n return{'sig_meta_file_name': sig_meta_file_name, 'meta_global_counts_dict':meta_global_counts_dict}",
"title": ""
},
{
"docid": "8ec7fc66d9e4c96a8513b49e4bf954fe",
"score": "0.40745944",
"text": "def test_channel_filters(self):\n # Include\n worker = Worker(None, only_channels=[\"yes.*\", \"maybe.*\"])\n self.assertEqual(\n worker.apply_channel_filters([\"yes.1\", \"no.1\"]),\n [\"yes.1\"],\n )\n self.assertEqual(\n worker.apply_channel_filters([\"yes.1\", \"no.1\", \"maybe.2\", \"yes\"]),\n [\"yes.1\", \"maybe.2\"],\n )\n # Exclude\n worker = Worker(None, exclude_channels=[\"no.*\", \"maybe.*\"])\n self.assertEqual(\n worker.apply_channel_filters([\"yes.1\", \"no.1\", \"maybe.2\", \"yes\"]),\n [\"yes.1\", \"yes\"],\n )\n # Both\n worker = Worker(None, exclude_channels=[\"no.*\"], only_channels=[\"yes.*\"])\n self.assertEqual(\n worker.apply_channel_filters([\"yes.1\", \"no.1\", \"maybe.2\", \"yes\"]),\n [\"yes.1\"],\n )",
"title": ""
},
{
"docid": "9b22ce43e94fbdf411eb9ca73948ba93",
"score": "0.40670216",
"text": "def children_of_kind(channel_id=None, content=None, kind=None, **kwargs):\n return content.get_descendants(include_self=False).filter(kind=kind).using(channel_id)",
"title": ""
},
{
"docid": "fbb1138bf9c1505f37e86c0e8b643d96",
"score": "0.40659022",
"text": "def removeChildren(self, *args):\n return _osg.SwitchRef_removeChildren(self, *args)",
"title": ""
},
{
"docid": "70d3bfb1dc64d16814d0c27df83b9153",
"score": "0.40645763",
"text": "def _densify_variants(self, variant, all_call_names):\n # type: (vcf_parser.Variant, List[str]) -> vcf_parser.Variant\n existing_call_name = {call.name: call for call in variant.calls}\n\n new_calls = []\n for call_name in all_call_names:\n if call_name in existing_call_name.keys():\n new_calls.append(existing_call_name.get(call_name))\n else:\n new_calls.append(\n vcfio.VariantCall(name=call_name,\n genotype=vcfio.MISSING_GENOTYPE_VALUE))\n variant.calls = new_calls\n\n return variant",
"title": ""
},
{
"docid": "a38310052c86f56e670b2b29062a0b18",
"score": "0.4063946",
"text": "def test_facet_inheritance_masked_preserve(self):\n\n # create parent (0), push child (1), and pull child (2)\n self._imgs_create(3)\n self._attach_child(0, [1])\n self._attach_parent([2], 0)\n\n # install a package with inheritable facets in the parent\n self._pkg([0], \"install -v {0}\".format(self.p_fmri[\"inc1@2\"]))\n\n for fv in [\"True\", \"False\"]:\n\n # set inheritable facet locally in children\n self._pkg([1, 2], \"change-facet 123456={0}\".format(fv))\n\n # disable inheritable facet in parent\n self._pkg([0], \"change-facet 123456=False\")\n self._pkg([2], \"sync-linked\")\n\n # verify inheritable facet is disabled in children\n output = \"facet.123456\\tFalse\\tparent\\n\"\n output_m = \\\n \"facet.123456\\tFalse\\tparent\\tFalse\\n\" + \\\n \"facet.123456\\t{0}\\tlocal\\tTrue\\n\".format(fv)\n for i in [1, 2]:\n self._pkg([i], \"facet -H -F tsv\", \\\n output_cb=self._assertEqual_cb(output))\n self._pkg([i], \"facet -H -F tsv -m\", \\\n output_cb=self._assertEqual_cb(output_m))\n\n # clear inheritable facet in the parent\n self._pkg([0], \"change-facet 123456=None\")\n self._pkg([2], \"sync-linked\")\n\n # verify the local child setting is restored\n output = \"facet.123456\\t{0}\\tlocal\\n\".format(fv)\n output_m = \"facet.123456\\t{0}\\tlocal\\tFalse\\n\".format(fv)\n for i in [1, 2]:\n self._pkg([i], \"facet -H -F tsv\", \\\n output_cb=self._assertEqual_cb(output))\n self._pkg([i], \"facet -H -F tsv -m\", \\\n output_cb=self._assertEqual_cb(output_m))",
"title": ""
},
{
"docid": "971505b0b6e18ca2c4e1d73af15d94bf",
"score": "0.40597397",
"text": "def removeChildren(self, *args):\n return _osg.Group_removeChildren(self, *args)",
"title": ""
},
{
"docid": "744cc58f8c3f4297fa38e5b5fe0673a5",
"score": "0.4051659",
"text": "def remove_item(self, src):\n ctg = self._items.__getitem__(src).category\n self._items.__delitem__(src)\n self._categories[ctg].remove(src)\n if not self._categories[ctg]:\n # avoid category with empty set\n self._categories.__delitem__(ctg)\n\n if ctg == self._main_detector_category:\n self._main_detector = ''",
"title": ""
},
{
"docid": "edb2ace143933faf07276a0b9ef6af26",
"score": "0.40428928",
"text": "async def bsdataset_remove(self, ctx: Context, *clantags):\n if not clantags:\n await send_cmd_help(ctx)\n return\n\n server = ctx.message.server\n bands = self.settings.get_bands_settings(server)\n\n for clantag in clantags:\n if clantag.startswith('#'):\n clantag = clantag[1:]\n\n removed = bands.pop(clantag, None)\n if removed is None:\n await self.bot.say(\"{} not in clan settings.\".format(clantag))\n return\n\n self.settings.set_bands_settings(server, bands)\n await self.bot.say(\"Removed #{} from bands.\".format(clantag))",
"title": ""
},
{
"docid": "822c40b977b12387acda710d371ebd09",
"score": "0.40162432",
"text": "def get_all_variants():\n return {\n 'BOOSTDESC': {\n 'desc': {\n 'BGM': 100,\n 'BGM_HARD': 101,\n 'BGM_BILINEAR': 102,\n 'LBGM': 200,\n 'BINBOOST_64': 300,\n 'BINBOOST_128': 301,\n 'BINBOOST_256': 302\n }\n },\n 'BRIEF': None,\n 'DAISY': {\n 'norm': {\n 'NRM_NONE': 100,\n 'NRM_PARTIAL': 101,\n 'NRM_FULL': 102,\n 'NRM_SIFT': 103\n }\n },\n 'FREAK': None,\n 'HarrisLaplace': None,\n 'LATCH': None,\n 'LUCID': None,\n 'STAR': None,\n 'AGAST': {\n 'type': {\n 'AGAST_5_8': 0,\n 'AGAST_7_12d': 1,\n 'AGAST_7_12s': 2,\n 'OAST_9_16': 3\n }\n },\n 'AKAZE': {\n 'diffusivity': {\n 'DIFF_PM_G1': 0,\n 'DIFF_PM_G2': 1,\n 'DIFF_WEICKERT': 2,\n 'DIFF_CHARBONNIER': 3\n },\n 'descriptor_type': {\n 'DESCRIPTOR_KAZE_UPRIGHT': 2,\n 'DESCRIPTOR_KAZE': 3,\n 'DESCRIPTOR_MLDB_UPRIGHT': 4,\n 'DESCRIPTOR_MLDB': 5\n }\n },\n 'BRISK': None,\n 'FAST': {\n 'type': {\n 'TYPE_5_8': 0,\n 'TYPE_7_12': 1,\n 'TYPE_9_16': 2\n }\n },\n 'GFTT': None,\n 'KAZE': {\n 'diffusivity': {\n 'DIFF_PM_G1': 0,\n 'DIFF_PM_G2': 1,\n 'DIFF_WEICKERT': 2,\n 'DIFF_CHARBONNIER': 3\n }\n },\n 'ORB': {\n 'scoreType': {\n 'HARRIS_SCORE': 0,\n 'FAST_SCORE': 1\n }\n }\n\n }",
"title": ""
},
{
"docid": "94f3ca0fd6625cd45f5366e8e0f3f2ae",
"score": "0.40150672",
"text": "def test_facet_inheritance_globs(self):\n\n # create parent (0), push child (1)\n self._imgs_create(2)\n self._attach_child(0, [1])\n\n self._pkg([0], \"change-facet\" +\n \" 123456=False\" +\n \" 456789=True\" +\n \" *456*=False\" +\n \" *789=True\" +\n \" 123*=True\")\n\n # verify that no facets are inherited\n output = \"\"\n self._pkg([1], \"facet -H -F tsv\", \\\n output_cb=self._assertEqual_cb(output))\n\n # install packages with inheritable facets in the parent\n self._pkg([0], \"install -v {0}\".format(self.p_fmri[\"inc1@2\"]))\n\n # verify that three facets are inherited\n output = \"\"\n output += \"facet.*456*\\tFalse\\tparent\\n\"\n output += \"facet.123*\\tTrue\\tparent\\n\"\n output += \"facet.123456\\tFalse\\tparent\\n\"\n self._pkg([1], \"facet -H -F tsv\", \\\n output_cb=self._assertEqual_cb(output))\n\n # install packages with inheritable facets in the parent\n self._pkg([0], \"install -v {0}\".format(self.p_fmri[\"inc2@2\"]))\n\n # verify that five facets are inherited\n output = \"\"\n output += \"facet.*456*\\tFalse\\tparent\\n\"\n output += \"facet.*789\\tTrue\\tparent\\n\"\n output += \"facet.123*\\tTrue\\tparent\\n\"\n output += \"facet.123456\\tFalse\\tparent\\n\"\n output += \"facet.456789\\tTrue\\tparent\\n\"\n self._pkg([1], \"facet -H -F tsv\", \\\n output_cb=self._assertEqual_cb(output))\n\n # remove packages with inheritable facets in the parent\n self._pkg([0], \"uninstall -v {0}\".format(self.p_fmri[\"inc1@2\"]))\n\n # verify that three facets are inherited\n output = \"\"\n output += \"facet.*456*\\tFalse\\tparent\\n\"\n output += \"facet.*789\\tTrue\\tparent\\n\"\n output += \"facet.456789\\tTrue\\tparent\\n\"\n self._pkg([1], \"facet -H -F tsv\", \\\n output_cb=self._assertEqual_cb(output))\n\n # remove packages with inheritable facets in the parent\n self._pkg([0], \"uninstall -v {0}\".format(self.p_fmri[\"inc2@2\"]))\n\n # verify that no facets are inherited\n output = \"\"\n self._pkg([1], \"facet -H -F tsv\", \\\n output_cb=self._assertEqual_cb(output))",
"title": ""
},
{
"docid": "b32fff1d585a7d61dee80d447129b251",
"score": "0.4014483",
"text": "def _filter_model(filter_text, data, type):\n if type == \"Debris\":\n return [d for d in data if filter_text.upper() in d['displayName'].upper()]\n\n else:\n filtered_list = [d for d in data\n if (filter_text.upper() in d['displayName'].upper() or\n filter_text.upper() in d['scientificName'].upper()or\n (d[\"commonName1\"] is not None and filter_text.upper() in d['commonName1'].upper()) or\n (d[\"commonName2\"] is not None and filter_text.upper() in d['commonName2'].upper()) or\n (d[\"commonName3\"] is not None and filter_text.upper() in d['commonName3'].upper()))]\n\n if filter_text == \"\":\n return filtered_list\n\n start_match_list = [x for x in filtered_list if x['displayName'].upper().startswith(filter_text.upper())]\n # start_match_list = sorted(start_match_list, key=lambda x: x[\"displayName\"].lower())\n\n remaining_list = [x for x in filtered_list if x not in start_match_list]\n # remaining_list = sorted(remaining_list, key=lambda x: x[\"displayName\"].lower())\n\n sorted_list = start_match_list + remaining_list\n\n return sorted_list",
"title": ""
},
{
"docid": "134decea10265357eda8c824d042aaf7",
"score": "0.40106404",
"text": "def filter_type(self, items, bclass):\n res = []\n for item in items:\n if self.g.query(f\"ASK {{ <{item}> rdf:type/rdfs:subClassOf* <{bclass}> }}\")[0]:\n res.append(item)\n return res",
"title": ""
},
{
"docid": "9b3b6c5322d150cf2d233a39424e3b98",
"score": "0.40089685",
"text": "def _filter_my_stocks(self):\n tickers_to_remove = []\n for tk in self.stock_picks.keys():\n if self.stock_picks[tk].errorCount > 0:\n tickers_to_remove.append(tk)\n \n for tk in tickers_to_remove:\n del self.stock_picks[tk]\n \n self.tickers_high_vol = list(self.stock_picks.keys())",
"title": ""
},
{
"docid": "6aefdcb40a27122a238f79ce743f27b1",
"score": "0.40069738",
"text": "def _filterExcludedGAVs(self, artifactList):\n\n logging.debug(\"Filtering artifacts with excluded GAVs.\")\n regExps = maven_repo_util.getRegExpsFromStrings(self.config.excludedGAVs)\n gavRegExps = []\n gatcvRegExps = []\n for regExp in regExps:\n if regExp.pattern.count(\":\") > 2:\n gatcvRegExps.append(regExp)\n else:\n gavRegExps.append(regExp)\n for ga in artifactList.keys():\n for priority in artifactList[ga].keys():\n for version in artifactList[ga][priority].keys():\n gav = \"%s:%s\" % (ga, version)\n if maven_repo_util.somethingMatch(gavRegExps, gav):\n logging.debug(\"Dropping GAV %s:%s from priority %i because it matches an excluded \"\n \"GAV pattern.\", ga, version, priority)\n del artifactList[ga][priority][version]\n else:\n artSpec = artifactList[ga][priority][version]\n for artType in copy.deepcopy(artSpec.artTypes.keys()):\n at = artSpec.artTypes[artType]\n for classifier in copy.deepcopy(at.classifiers):\n if classifier:\n gatcv = \"%s:%s:%s:%s\" % (ga, artType, classifier, version)\n else:\n gatcv = \"%s:%s:%s\" % (ga, artType, version)\n if maven_repo_util.somethingMatch(gatcvRegExps, gatcv):\n logging.debug(\"Dropping GATCV %s from priority %i because it matches an excluded \"\n \"GAV pattern.\", gatcv, priority)\n at.classifiers.remove(classifier)\n if not at.classifiers:\n logging.debug(\"Dropping GATV %s:%s:%s from priority %i because of no classifiers left.\",\n ga, artType, version, priority)\n del artSpec.artTypes[artType]\n if not artSpec.containsMain():\n logging.debug(\"Dropping GAV %s:%s from priority %i because of no main artifact left.\",\n ga, version, priority)\n del artifactList[ga][priority][version]\n if not artifactList[ga][priority]:\n logging.debug(\"Dropping GA %s from priority %i because of no version left.\", ga, priority)\n del artifactList[ga][priority]\n if not artifactList[ga]:\n logging.debug(\"Dropping GA %s because of no priority left.\", ga)\n del artifactList[ga]\n return artifactList",
"title": ""
},
{
"docid": "65a3a38e6b6dcc29e10c7ff1750d08c6",
"score": "0.4006651",
"text": "def test_change_list_remove_mixed(self):\r\n changed = self._remove_list_test_helper('alice, bob', 'bob, carol')\r\n self.assertEqual(changed, 'alice')",
"title": ""
},
{
"docid": "5cc573e94f4e7a9f674846adf5964921",
"score": "0.4005716",
"text": "def generate_null_variants(all_variants_file,significant_variants_file):\n all_variants_df = pd.read_csv(all_variants_file)\n all_variants_df = drop_duplicates(all_variants_df) \n\n significant_variants_df = pd.read_csv(significant_variants_file)\n significant_variants_df = drop_duplicates(significant_variants_df) \n\n null_variants = all_variants_df.loc[~all_variants_df[\"variant\"].isin(significant_variants_df[\"variant\"])].copy()\n return(null_variants)",
"title": ""
},
{
"docid": "588d52acdc41b0f89cc686446860d843",
"score": "0.40056813",
"text": "def removeGenre(self, genres, locked=True):\n return self.editTags('genre', genres, locked=locked, remove=True)",
"title": ""
},
{
"docid": "0b663675de8dc47a75c54d1a4f8d154c",
"score": "0.40054893",
"text": "def _del_sub(self, name, all=False, lang=None):\r\n path = self._fix_ns(name, split=True)\r\n original_target = path[-1]\r\n\r\n default_lang = self.get_lang()\r\n if not lang:\r\n lang = default_lang\r\n\r\n for level, _ in enumerate(path):\r\n # Generate the paths to the target elements and their parent.\r\n element_path = \"/\".join(path[:len(path) - level])\r\n parent_path = \"/\".join(path[:len(path) - level - 1])\r\n\r\n elements = self.xml.findall(element_path)\r\n parent = self.xml.find(parent_path)\r\n\r\n if elements:\r\n if parent is None:\r\n parent = self.xml\r\n for element in elements:\r\n if element.tag == original_target or not list(element):\r\n # Only delete the originally requested elements, and\r\n # any parent elements that have become empty.\r\n elem_lang = element.attrib.get('{%s}lang' % XML_NS,\r\n default_lang)\r\n if lang == '*' or elem_lang == lang:\r\n parent.remove(element)\r\n if not all:\r\n # If we don't want to delete elements up the tree, stop\r\n # after deleting the first level of elements.\r\n return",
"title": ""
},
{
"docid": "f69df88f1d37cee772eefc57e1484321",
"score": "0.40020126",
"text": "def variants(self):\n return self.list_to_instance_list(self.dict['variants'], ComicSummary)",
"title": ""
},
{
"docid": "93f46e2cf48383d5f6542d8ee5c2efaf",
"score": "0.40012228",
"text": "def parse_name(name):\n possible_names = []\n possible_addons = ['tv', 'ttv', '_tv', '_ttv']\n removables = ['you exiled ', 'you assisted in exiling ', 'you disrupted ', '\\n', '\\x0c']\n # convert to lowercase for easier parsing\n name = name.lower()\n # remove\n for removable in removables:\n name = name.replace(removable, '')\n # add the cleaned up name to the list of possible names\n possible_names.append(name)\n # remove any additional signifiers\n for addon in possible_addons:\n if addon in name:\n possible_names.append(name.replace(addon, ''))\n return possible_names",
"title": ""
},
{
"docid": "8278b42c829e9b7896c15256d2861d22",
"score": "0.4000646",
"text": "async def removeOldChannels():\n deleteChannelList = []\n #these two loops are split up, as the it raises an error when the dict changes.\n for i in client.get_all_channels():\n if \"-matchday-\" in i.name:\n logger.info(f\"Deleting old channel {i.name}\")\n deleteChannelList.append((i.server, i.name))\n\n for i in deleteChannelList:\n await deleteChannel(i[0], i[1])",
"title": ""
},
{
"docid": "fe2184062cac4dffe99cf91b2a4c2eed",
"score": "0.3998517",
"text": "def choose(part_of_name='', directory_path='.', types=[File, Directory], quiet=False):\n variants = list(filter(lambda item: part_of_name in item.name and\n any([isinstance(item, t) for t in types]), Directory(directory_path).get_items()))\n variants.sort(key=_item_name) \n if len(variants) == 1:\n return variants[0]\n elif len(variants) > 1:\n if not quiet:\n print(\"More than one matches\")\n do_print = input(\"Print all {}? (y/n) \".format(len(variants)))\n if 'y' in do_print or do_print.strip() == '':\n print()\n for i, item in enumerate(variants):\n print(i, item)\n choice = input(\"Choose #? (#/n) \")\n if choice.isnumeric():\n return variants[int(choice)]\n return variants\n else:\n if not quiet:\n print('No matches')",
"title": ""
},
{
"docid": "2a3fbddfd75f3618478da7e030d4e2c2",
"score": "0.39984125",
"text": "def removeChildren(self, *args):\n return _osg.Sequence_removeChildren(self, *args)",
"title": ""
},
{
"docid": "1dc342ed2715b38db6a2c05bcd8adfd2",
"score": "0.39962763",
"text": "def guess_category(name):\n if 'Battery' in name:\n return 'Battery'\n if 'Radio' in name or 'Zigbee' in name:\n return 'Radio'\n if 'Motion' in name:\n return 'Motion'\n if 'Door' in name:\n return 'Door'\n if 'LightSwitch' in name:\n return 'LightSwitch'\n if 'Light' in name:\n return 'Light'\n if 'Temperature' in name or 'Thermostat' in name:\n return 'Temperature'\n if 'Item' in name:\n return 'Item'\n return 'Other'",
"title": ""
},
{
"docid": "261aa348f981a418e5ac1bd8eb0435ff",
"score": "0.3991132",
"text": "def remove_component_from_favorites(self, component):",
"title": ""
},
{
"docid": "ee869df008f4b1c184f38297a67b1c94",
"score": "0.39907864",
"text": "def _RemoveFromCloneList(self, clone, attrNamesToClone):\n attrNamesToClone = super(SimpleComponentSplitter, self)._RemoveFromCloneList(clone, attrNamesToClone)\n dontClone = [\"splits\", \"_balance\", \"borrowedSplits\"]\n \n for name in dontClone:\n if name in attrNamesToClone:\n attrNamesToClone.remove(name)\n \n return attrNamesToClone",
"title": ""
},
{
"docid": "446e131f77020afba99d383b9eb726c2",
"score": "0.3990018",
"text": "def remove_channel(self, channel, *, verbose=True):\n channel_index = wt_kit.get_index(self.channel_names, channel)\n new = list(self.channel_names)\n name = new.pop(channel_index)\n del self[name]\n self.channel_names = new\n if verbose:\n print(\"channel {0} removed\".format(name))",
"title": ""
},
{
"docid": "0294f2538afb929f4310493fa385dfec",
"score": "0.3984516",
"text": "def form_molecule_type_filter(allowed=None, excluded=None):\n if allowed is None:\n allowed = []\n if excluded is None:\n excluded = []\n pdb_entry_type = pd.read_csv(PDB_ENTRY_TYPE_FILE, delimiter='\\t',\n names=['pdb_code', 'molecule_type', 'source'])\n pdb_entry_type['pdb_code'] = \\\n pdb_entry_type['pdb_code'].apply(lambda x: x.lower())\n pdb_entry_type = pdb_entry_type.set_index('pdb_code')\n molecule_type = pdb_entry_type['molecule_type']\n\n def filter_fn(df):\n pdb_codes = df['structure'].apply(lambda x: x[:4].lower())\n\n if len(allowed) > 0:\n to_keep = molecule_type[pdb_codes].isin(allowed)\n elif len(excluded) > 0:\n to_keep = ~molecule_type[pdb_codes].isin(excluded)\n else:\n to_keep = pd.Series([True] * len(df), index=df['structure'])\n return df[to_keep.values]\n return filter_fn",
"title": ""
},
{
"docid": "e990082266840ebbb5c541a5857275b8",
"score": "0.39794925",
"text": "def _remove(self, *members):\n from .treants import Treant\n\n abspaths = self._state\n remove = list()\n\n for member in members:\n if isinstance(member, int):\n remove.append(abspaths[member])\n elif isinstance(member, Treant):\n remove.append(member.abspath)\n elif isinstance(member, str):\n # try abspaths\n abspaths = fnmatch.filter(self.abspaths, member)\n paths = [m.abspath for m in self\n if m.abspath in abspaths]\n remove.extend(paths)\n # try names\n names = fnmatch.filter(self.names, member)\n paths = [m.abspath for m in self\n if m.name in names]\n remove.extend(paths)\n else:\n raise TypeError('Only a Treant, index, name, or absolute '\n 'path acceptable')\n\n self._del_members(remove)\n\n # remove from cache\n for abspath in remove:\n self._cache.pop(abspath, None)",
"title": ""
},
{
"docid": "8de7671bac4b3bd69fb2f637910ffd72",
"score": "0.39774927",
"text": "def _categorize(self, category):\n self.torrents = [result for result in self.torrents\n if result.category == category]",
"title": ""
},
{
"docid": "abee673efb4fb8eff73118936a8f80b3",
"score": "0.3977336",
"text": "def _filterExcludedGAVs(self, artifactList):\n\n logging.debug(\"Filtering artifacts with excluded GAVs.\")\n regExps = _getRegExpsFromStrings(self.config.excludedGAVs)\n for gat in artifactList.keys():\n ga = gat.rpartition(':')[0]\n for priority in artifactList[gat].keys():\n for version in artifactList[gat][priority].keys():\n gav = ga + \":\" + version\n if _somethingMatch(regExps, gav):\n del artifactList[gat][priority][version]\n if not artifactList[gat][priority]:\n del artifactList[gat][priority]\n if not artifactList[gat]:\n del artifactList[gat]\n return artifactList",
"title": ""
},
{
"docid": "b7619441a2ae4561d73ac874be66d801",
"score": "0.39750022",
"text": "def remove(self, name, partial_match = True):\n\n if not partial_match:\n for vrml_object, raw_object in zip(self.objects, self.raw_objects):\n if name==vrml_object.name:\n self.objects.remove(vrml_object)\n self.raw_objects.remove(raw_object)\n else:\n for vrml_object, raw_object in zip(self.objects, self.raw_objects):\n if name in vrml_object.name:\n self.objects.remove(vrml_object)\n self.raw_objects.remove(raw_object)",
"title": ""
},
{
"docid": "20f1d499dffdb0a6c6aaca1cda8a66f4",
"score": "0.3972678",
"text": "def test_remove_members_name(self, collection, tmpdir):\n with tmpdir.as_cwd():\n t1 = dtr.Treant('lark')\n t2 = dtr.Treant('elsewhere/lark')\n t3 = dtr.Treant('hark')\n g = dtr.Group('linus')\n\n stuff = [t1, t2, t3, g]\n\n # test removal by name\n collection.add(stuff)\n for item in stuff:\n assert item in collection\n\n # should remove both treants with name 'lark'\n collection.remove('lark')\n\n for item in (t3, g):\n assert item in collection\n\n for item in (t1, t2):\n assert item not in collection\n\n # test removal by a unix-style glob pattern\n collection.add(stuff)\n for item in stuff:\n assert item in collection\n\n # should remove 'lark' and 'hark' treants\n collection.remove('*ark')\n\n assert g in collection\n\n for item in (t1, t2, t3):\n assert item not in collection",
"title": ""
},
{
"docid": "30fa0788f25b4582a98df61eff431d9d",
"score": "0.39716175",
"text": "def remove_classification_value(self, obj, type, value):\n special = [\"lc_classifications\", \"dewey_decimal_class\"]\n if type in special and type in obj.keys():\n while value in obj[type]:\n obj[type].remove(value)\n if len(obj[type]) == 0:\n del obj[type]\n elif \"classifications\" in obj.keys() and type in obj[\"classifications\"].keys():\n while value in obj[\"classifications\"][type]:\n obj[\"classifications\"][type].remove(value)\n if len(obj[\"classifications\"][type]) == 0:\n del obj[\"classifications\"][type]\n if len(obj[\"classifications\"]) == 0:\n del obj[\"classifications\"]",
"title": ""
},
{
"docid": "39877248f007b0c470ae4323ee5b921e",
"score": "0.39677745",
"text": "def _prepare_remove(self, child):\n if child not in self.children():\n raise ValueError(\"cannot remove %r, hasn't been added\" % (child,))",
"title": ""
},
{
"docid": "7a5fd0c74c103c895fa025a79ad232c7",
"score": "0.39641193",
"text": "def remove(self, values: Union[str, Iterable[str]], *, wait: bool = False) -> None:\n return self._pass_call_to_attr(function_name=\"remove\", values=values, wait=wait)",
"title": ""
},
{
"docid": "4fdeaca17f7cdac37daaa8b69af47b4a",
"score": "0.3963481",
"text": "def _remove(item):\n\n # TODO: replace list with regex patterns per menu source\n removers = ('\\n', '(V)', '(V,P)', '(P)', '(V,FP)', '(FP)')\n removed = item\n for remover in removers:\n removed = removed.replace(remover, '')\n return removed",
"title": ""
},
{
"docid": "b9c8a691d4f8ba12b8b1f16405e90369",
"score": "0.39589578",
"text": "def test__parse_channels(input_value):\n guild_id = 202306080004\n guild = Guild.precreate(guild_id)\n \n return parse_channels(input_value, guild.channels, guild_id)",
"title": ""
},
{
"docid": "7b382351c002d6e9dfdde3984b362831",
"score": "0.39509356",
"text": "def test_remove_category_from_asset(self):\n pass",
"title": ""
},
{
"docid": "26b87545c8f510b41bee6dc4392640e5",
"score": "0.39507142",
"text": "def itemByName(self, name):\n return RemoveFeature()",
"title": ""
},
{
"docid": "f6e6e1bf427568adc1896a7fc282f8c8",
"score": "0.39422128",
"text": "def test_inheritance_matches_parental_affected_status_de_novo(self):\n \n cnv = self.create_variant(\"female\")\n \n cnv.child.format['INHERITANCE'] = \"de_novo\"\n cnv.child.format['CIFER_INHERITANCE'] = \"not_inherited\"\n \n self.inh.trio.mother.affected_status = \"1\"\n self.inh.trio.father.affected_status = \"1\"\n \n self.assertTrue(self.inh.inheritance_matches_parental_affected_status(cnv))",
"title": ""
},
{
"docid": "bb6cbf41e870e88e1fe2a8156a31fb17",
"score": "0.3936756",
"text": "def remove(self, composant):\n cible = None\n for bouton, indice, cote in self.composants:\n if composant == bouton:\n self.gui.remove(bouton)\n cible = bouton, indice, cote\n if cible!=None:\n self.composants.remove(cible)\n while self.boutons[0].count(composant)>0:\n self.boutons[0].remove(composant)\n while self.boutons[1].count(composant)>0:\n self.boutons[1].remove(composant)\n while self.boutons[2].count(composant)>0:\n self.boutons[2].remove(composant)\n while self.boutons[3].count(composant)>0:\n self.boutons[3].remove(composant)\n self.anime(0.0)",
"title": ""
},
{
"docid": "a52715d3556d1176881036d78a6d11d9",
"score": "0.393304",
"text": "def remove_pkg_debs(self, package, build_type):\n logger.debug(' '.join(['Remove all old version of debs for', package]))\n debs_clue = get_debs_clue(build_type)\n subdebs = debsentry.get_subdebs(debs_clue, package, logger)\n if not subdebs:\n logger.warning('Failed to get subdebs of %s from local debsentry cache', package)\n return False\n for deb in subdebs:\n pkg_item = deb.split('_')\n # if deb = name_version\n if len(pkg_item) > 1:\n msg = ''.join(['package ', pkg_item[0], '(', pkg_item[1], ')'])\n # if deb = name\n else:\n msg = ''.join(['package ', pkg_item[0]])\n logger.info(' '.join(['Searching for binary', msg, 'in repository', REPO_BUILD]))\n\n if self.kits['repo_mgr'].search_pkg(REPO_BUILD, pkg_item[0]):\n logger.info('Found binary %s in repository %s', msg, REPO_BUILD)\n if self.kits['repo_mgr'].delete_pkg(REPO_BUILD, pkg_item[0], 'binary', None, deploy=False):\n logger.info('Successfully deleted binary %s from repository %s',\n msg, REPO_BUILD)\n else:\n logger.info('Failed to delete binary %s from repository %s', msg,\n REPO_BUILD)\n ''' Fixme: not sure whether it's ok to skip self.publish_repo(REPO_BUILD) here\n '''\n return True",
"title": ""
},
{
"docid": "f976075e2735752549d36ae2cfa4b08d",
"score": "0.39292687",
"text": "def removeChildren(self, *args):\n return _osg.GroupRef_removeChildren(self, *args)",
"title": ""
},
{
"docid": "a9dc2e20d91f3de3e5d34cc1a756d870",
"score": "0.3924877",
"text": "def filter(self, options):\n if not options.category:\n self.showcategories = self.lists.keys()\n if not options.use_all_categories:\n self.showcategories = [ s for s in self.showcategories \n if s not in autohidden ]\n self.showcategories.sort()\n else:\n self.showcategories = options.category.split(':')\n\n if options.exclude:\n for c in options.exclude.split(':'):\n try:\n self.showcategories.remove(c)\n except ValueError:\n pass\n self.showcompleted = options.verbose",
"title": ""
},
{
"docid": "700ff8ab9f5bada01cedbdbfabb25e17",
"score": "0.39238182",
"text": "def _handle_removechannel(self, params):\n\t\tchannel = self.get_channel(params[\"chanid\"])\n\t\tif channel:\n\t\t\tself.channels.remove(channel)",
"title": ""
},
{
"docid": "e9c2e9027fc1c737bd4d7a516c364a3a",
"score": "0.39208168",
"text": "def test__ChannelType__name():\n for instance in ChannelType.INSTANCES.values():\n vampytest.assert_instance(instance.name, str)",
"title": ""
},
{
"docid": "09f829514c930bf5462c0988795e56c7",
"score": "0.3918251",
"text": "def remove_unavailable_variants(checkout):\n for line in checkout:\n try:\n add_variant_to_checkout(checkout, line.variant, line.quantity, replace=True)\n except InsufficientStock as e:\n quantity = e.item.quantity_available\n add_variant_to_checkout(checkout, line.variant, quantity, replace=True)",
"title": ""
},
{
"docid": "b7c804fa154c313a8cfc939164f2e6fc",
"score": "0.39179635",
"text": "def removeChildNamed(self, name):\n if name not in self.namedChildren:\n return False\n found = self.namedChildren[name]\n success = self._removeChildStructure(found)\n if not success:\n return False\n del self.namedChildren[name]\n return True",
"title": ""
},
{
"docid": "a237a184697c6dd4533bcf122b7af668",
"score": "0.39160612",
"text": "def removeSubD():\r\n\tif not pluginCheck():\r\n\t\treturn\r\n\r\n\tobjLs = mc.ls(sl=True, l=True)\r\n\tfor obj in objLs:\r\n\t\tobjSh = mc.listRelatives(obj, s=True, f=True)[0]\r\n\t\tmel.eval('vray addAttributesFromGroup %s vray_subdivision 0' % objSh)\r\n\t\tmel.eval('vray addAttributesFromGroup %s vray_subquality 0' % objSh)\r\n\t\tmel.eval('vray addAttributesFromGroup %s vray_displacement 0' % objSh)",
"title": ""
}
]
[
{
"docid": "3de819151e518daf959dce419ae9ac67",
"score": "0.65969247",
"text": "def daily():",
"title": ""
},
{
"docid": "962b7089a27f4c95efe8c39661c98eb8",
"score": "0.64363086",
"text": "def get_buy_day(self):\n # Write your code here\n profit = 0\n buy_day = 0\n day = 0\n\n while day < self.number_of_days and day != (self.number_of_days - 1):\n if self.prices[day] < self.prices[day + 1] and abs(self.prices[day] - self.prices[day + 1]) > profit:\n buy_day = day\n day += 1\n\n return buy_day",
"title": ""
},
{
"docid": "1bd817c4480583650a65068b311e02b3",
"score": "0.6181659",
"text": "def get_dates():\n # ---\n d1 = date(2015,8,31)\n #d1 = date(2018,8,25)\n d2 = date(2018,8,31)\n dates = [str(d1 + timedelta(days=x)) for x in range((d2-d1).days + 1)]\n\n # ---\n return dates",
"title": ""
},
{
"docid": "57f2e3232823f86a25b6ffd73579438f",
"score": "0.5986391",
"text": "def get_date_discount(driver_page):\n time_list = driver_page.find_elements_by_css_selector('time')\n time_start = time_list[0].text\n time_end = time_list[1].text\n finish_date = (time_start + '{}' + time_end).format(' - ').split()\n discount_date = get_start_end_date(finish_date)\n\n return discount_date",
"title": ""
},
{
"docid": "b79ca23b52a65bc5ca1f73ee14afcaec",
"score": "0.59722996",
"text": "def get_days_to_duedate(self):\n days = []\n for i,x in enumerate(self.active):\n days.append(utils.days_until_due(x[4]))\n self.days = days",
"title": ""
},
{
"docid": "97c02c7896e1f8cd83f13cca7c4a31ca",
"score": "0.5948199",
"text": "def get_historical_prices(symbol, chosen_date):\n import datetime\n from datetime import date\n from datetime import timedelta\n \n #Test\n # >>> chosen_date = '2016-05-10'\n # >>> year = int(chosen_date[:4])\n # >>> month = int(chosen_date[5:7])\n # >>> day = int(chosen_date[8:])\n # >>> end_date = datetime.date(year, month, day)\n # >>> start_date = str(end_date - datetime.timedelta(days=2))\n\n past_n_days = 10 #fixed because we only care about the stock price of the chosen day and the stock prices of the two previous days\n\n year = int(chosen_date[:4])\n month = int(chosen_date[5:7])\n day = int(chosen_date[8:])\n\n end_date = datetime.date(year, month, day)\n \n if end_date > datetime.date.today():\n statement = \"Choose any date before today: \" + str(datetime.date.today())\n d0 = end_date\n d1 = datetime.date.today()\n delta = d0 - d1\n past_n_days += delta.days\n # from datetime import date\n # d0 = date(2008, 8, 18)\n # d1 = date(2008, 9, 26)\n # delta = d0 - d1\n # print delta.days\n if end_date == datetime.date.today():\n past_n_days += 1\n \n\n #assert end_date < datetime.date.today(), \"chosen date must be any previous day from today: %r\" % end_date\n #assert num == 4, \"len of set is not 4: %r\" % num #example\n\n #List of dates:\n date_list = [end_date - datetime.timedelta(days=x) for x in range(0, 3)]\n\n # >>> date_list = [end_date - datetime.timedelta(days=x) for x in range(0, 3)]\n # >>> print date_list\n # [datetime.date(2016, 5, 10), datetime.date(2016, 5, 9), datetime.date(2016, 5, 8)]\n\n #start_date = str(end_date - datetime.timedelta(days=past_n_days)) #doesn't work when we previously put from datetime import datetime \n start_date = str(end_date - timedelta(days=past_n_days)) #code is always functional\n end_date = chosen_date\n\n # #month, day and year\n url = 'http://ichart.yahoo.com/table.csv?s=%s&' % symbol + \\\n 'd=%s&' % str(int(end_date[5:7]) - 1) + \\\n 'e=%s&' % str(int(end_date[8:10])) + \\\n 'f=%s&' % str(int(end_date[0:4])) + \\\n 'g=d&' + \\\n 'a=%s&' % str(int(start_date[5:7]) - 1) + \\\n 'b=%s&' % str(int(start_date[8:10])) + \\\n 'c=%s&' % str(int(start_date[0:4])) + \\\n 'ignore=.csv'\n print \"url\"\n print url\n days = urllib.urlopen(url).readlines()\n data = [day[:-2].split(',') for day in days]\n return data",
"title": ""
},
{
"docid": "81fd0021025722892014956f2b83a321",
"score": "0.58773535",
"text": "def get_workfree_dates (date):\n all_dates = get_same_month_dates (date)\n workfree_dates = []\n \n for date in all_dates:\n if holiday.is_workfree (date):\n workfree_dates.append (date)\n \n return workfree_dates",
"title": ""
},
{
"docid": "63584f5753a9b17c4cee5f78995ff04f",
"score": "0.5817576",
"text": "def get_sell_day(self):\n # Write your code here\n profit = 0\n sell_day = 0\n day = 0\n\n while day < self.number_of_days and day != (self.number_of_days - 1):\n if self.prices[day] > self.prices[day + 1] and abs(self.prices[day] - self.prices[day + 1]) > profit:\n sell_day = day\n day += 1\n\n return sell_day",
"title": ""
},
{
"docid": "8105d32b67fc2e098a3b735237ae34bf",
"score": "0.58046496",
"text": "def create_event_orders(self, events, sell_days, hold_days=5, amount=100):\n df_buy_events = pd.DataFrame(events, columns=[\"symbol\"], index=events.index)\n df_buy_events[\"action\"] =\"Buy\"\n df_buy_events[\"year\"] = events.index.year\n df_buy_events[\"month\"] = events.index.month\n df_buy_events[\"day\"] = events.index.day\n df_buy_events[\"amount\"] = 100\n df_sell_events = pd.DataFrame(events,columns=[\"symbol\"])\n df_sell_events.reset_index()\n df_sell_events.index = sell_days\n #sell_days = df_sell_events.index + timedelta(days=hold_days)\n df_sell_events[\"action\"] =\"Sell\"\n df_sell_events[\"year\"] = df_sell_events.index.year\n df_sell_events[\"month\"] = df_sell_events.index.month\n df_sell_events[\"day\"] = df_sell_events.index.day\n #df_sell_events.index = sell_days\n df_sell_events[\"amount\"] = 100\n df_orders = df_sell_events.append(df_buy_events).sort_index()\n df_orders.index.name = \"date\"\n return df_orders",
"title": ""
},
{
"docid": "e9f4fdde43c68a0450a15aa990e82a9a",
"score": "0.58044255",
"text": "def get_dates(self, repo_page, repo_name):\r\n dates = repo_page.find_all('relative-time', class_='no-wrap')\r\n return [date(d.attrs['datetime'], repo_name) for d in dates]",
"title": ""
},
{
"docid": "067d7f96f4fce9184f9a5c98079f8852",
"score": "0.5781324",
"text": "def get_semi_monthly_due_dates(table: element.ResultSet):\r\n\r\n if len(table) != 1:\r\n return []\r\n else:\r\n # Find tag that resembles <strong>\r\n strongs = table[0].find_all(\"strong\")\r\n due_dates = []\r\n for strong in strongs:\r\n strong_contents = strong.contents[0] # Should expect date to be in format: mm/dd/yyyy\r\n try:\r\n datetime_object = datetime.strptime(strong_contents, \"%m/%d/%Y\")\r\n due_dates.append(datetime_object)\r\n except:\r\n # strong tag did not contain a date, so skip\r\n continue\r\n\r\n return due_dates",
"title": ""
},
{
"docid": "590dc13b41ed44afc9b79a4c45da354f",
"score": "0.5779855",
"text": "def dates(self):\n return self._dates",
"title": ""
},
{
"docid": "056ee4b144da53cd260db72ce1ceb14e",
"score": "0.5776556",
"text": "def getDayPrice(self):\n ticker = self.ticker\n API = self.API\n dataset = API[\"dataset\"]\n name = dataset[\"name\"]\n # description = dataset[\"description\"]\n # newestAvailableDate = dataset[\"newest_available_date\"]\n # \"\"\"\n # [\"Date\",\"Open\",\"High\",\"Low\",\"Close\",\"Volume\",\"Dividend\",\"Split\",\"Adj_Open\",\"Adj_High\",\"Adj_Low\",\"Adj_Close\",\"Adj_Volume\"]\n # \"\"\"\n data = dataset[\"data\"]\n mostRecent = data[0]\n # date = mostRecent[0]\n open = mostRecent[1]\n # volume = mostRecent[5]\n # dividend = mostRecent[6]\n\n\n # print (\"newest_available_date:\" + newestAvailableDate)\n return open",
"title": ""
},
{
"docid": "b5adfd42c259f1cd9da3e6167c13a86d",
"score": "0.57355297",
"text": "def generateDates(start_date, end_date):\n\n #List to save all dates in\n dates = []\n\n #Difference in days between start and end date\n delta = end_date - start_date\n\n #Loop over total number of days between start and end date\n for i in range(delta.days):\n \n #Save the date, which is i dates from the start date\n dates.append(start_date + pd.Timedelta(i, unit=\"D\"))\n\n return dates",
"title": ""
},
{
"docid": "6b8f29907e0c13ec1085b0ff0f69068d",
"score": "0.5715112",
"text": "def dates_crawler(self):\r\n dates = []\r\n for url in self.repos:\r\n repo_page = crawl(master(url))\r\n repo_page = self.invalid_branch(repo_page, url)\r\n\r\n status(url)\r\n dates += self.get_dates(repo_page, url)\r\n button = self.get_button(repo_page)\r\n while scroll_down(button):\r\n sleep(self.slp)\r\n repo_page = crawl(button.attrs[LINK])\r\n dates += self.get_dates(repo_page, url)\r\n button = self.get_button(repo_page)\r\n return dates",
"title": ""
},
{
"docid": "d5639130a5c8bb86233a6898f22803d9",
"score": "0.5703914",
"text": "def generate_dates() -> List[date]:\n\tstart = date(2019, 12, 31)\n\tend = (datetime.today() - timedelta(1)).date()\n\tdates = [d.date() for d in pd.date_range(start, end)]\n\treturn dates",
"title": ""
},
{
"docid": "5dc16dd9d2160f8603839000149e2f6f",
"score": "0.5676596",
"text": "def get_dates_since_start(self):\n days = []\n start_data_date = date(2020, 3, 1)\n latest_data_date = date(2020, 5, 4)\n delta = timedelta(days=1)\n while start_data_date <= latest_data_date:\n days.append(start_data_date.strftime(\"%#m/%#d/%y\")) # Add dates to days list\n start_data_date += delta\n self.days = days\n self.start_data_date = date(2020, 3, 1).strftime(\"%#m/%#d/%y\")\n self.latest_data_date = date(2020, 5, 4).strftime(\"%#m/%#d/%y\")\n return days",
"title": ""
},
{
"docid": "5dc16dd9d2160f8603839000149e2f6f",
"score": "0.5676596",
"text": "def get_dates_since_start(self):\n days = []\n start_data_date = date(2020, 3, 1)\n latest_data_date = date(2020, 5, 4)\n delta = timedelta(days=1)\n while start_data_date <= latest_data_date:\n days.append(start_data_date.strftime(\"%#m/%#d/%y\")) # Add dates to days list\n start_data_date += delta\n self.days = days\n self.start_data_date = date(2020, 3, 1).strftime(\"%#m/%#d/%y\")\n self.latest_data_date = date(2020, 5, 4).strftime(\"%#m/%#d/%y\")\n return days",
"title": ""
},
{
"docid": "ef457189bd89e81bab355b91b456e6fc",
"score": "0.56671613",
"text": "def daily_sales (date):\n\n global log\n\n full_report = \"==%s SALES REPORT==\\n\" % date.strftime(\"%a, %b %-d %Y\")\n payments = get_payments(date)\n\n try:\n drawers = get_cash_drawer(date)\n except Exception as e:\n drawers = []\n ts = time.time()\n log += \"[%s]: %s\" % (datetime.datetime.fromtimestamp(ts).strftime(\"%Y-%m-%d %H:%M:%S\"), e)\n\n try:\n reportdate = datetime.datetime.strptime(date,\"%Y-%m-%d\")\n except:\n reportdate = date\n\n # @LOGAN? add some comments please.\n sales = sales_totals(payments, drawers, reportdate)\n full_report += report_string(sales)\n\n #####\n ## LAST YEAR\n #####\n\n last_year_report_date = reportdate + dateutil.relativedelta.relativedelta(years=-1, weekday=reportdate.weekday())\n last_year_payments = get_payments(last_year_report_date.strftime(\"%Y-%m-%d\"))\n\n try:\n last_year_drawers = get_cash_drawer(last_year_report_date.strftime(\"%Y-%m-%d\"))\n\n except Exception as e:\n last_year_drawers = []\n ts = time.time()\n log += \"[%s]: %s\"%(datetime.datetime.fromtimestamp(ts).strftime(\"%Y-%m-%d %H:%M:%S\"), e)\n\n full_report += \"\\n\"\n full_report += \"===LAST YEAR===\\n\"\n full_report += '==%s SALES REPORT==\\n'%last_year_report_date.strftime(\"%a, %b %-d %Y\")\n last_year_sales = sales_totals(last_year_payments,last_year_drawers,last_year_report_date)\n full_report += report_string(last_year_sales)\n full_report += \"\\n\"\n\n reportd = reportdate.strftime(\"%Y-%m-%d\")\n fill_db(reportd,sales, \"day\")\n \n full_report += \"===RECENT COMPARISONS===\\n\"\n full_report += \" =San Jac=\\n\"\n full_report += \"%s %-20s %-15s %-15s\\n\"%(\" \",date.strftime(\"%a, %b %-d %Y\"),\"Value\",\"Difference\")\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 3 Week Average:\",format_money(sales['sjs_total']),format_money(get_recent_average(reportdate,3)[0]),format_money(sales['sjs_total'] - get_recent_average(reportdate,3)[0]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\"3 Month Average:\",format_money(sales['sjs_total']),format_money(get_recent_average(reportdate,12)[0]),format_money(sales['sjs_total'] - get_recent_average(reportdate,12)[0]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 6 Month Best:\",format_money(sales['sjs_total']),format_money(get_recent_sales_best(reportdate,26)[0]),format_money(sales['sjs_total'] - get_recent_sales_best(reportdate,26)[0]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 6 Month Worst:\",format_money(sales['sjs_total']),format_money(get_recent_sales_worst(reportdate,26)[0]),format_money(sales['sjs_total'] - get_recent_sales_worst(reportdate,26)[0]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 12 Month Best:\",format_money(sales['sjs_total']),format_money(get_recent_sales_best(reportdate,52)[0]),format_money(sales['sjs_total'] - get_recent_sales_best(reportdate,52)[0]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 12 Month Worst:\",format_money(sales['sjs_total']),format_money(get_recent_sales_worst(reportdate,52)[0]),format_money(sales['sjs_total'] - get_recent_sales_worst(reportdate,52)[0]))\n full_report += \"\\n\"\n full_report += \" =Jack's=\\n\"\n full_report += \"%s %-20s %-15s %-15s\\n\"%(\" \",date.strftime(\"%a, %b %-d %Y\"),\"Value\",\"Difference\")\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 3 Week Average:\",format_money(sales['jacks_total']),format_money(get_recent_average(reportdate,3)[1]),format_money(sales['jacks_total'] - get_recent_average(reportdate,3)[1]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\"3 Month Average:\",format_money(sales['jacks_total']),format_money(get_recent_average(reportdate,12)[1]),format_money(sales['jacks_total'] - get_recent_average(reportdate,12)[1]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 6 Month Best:\",format_money(sales['jacks_total']),format_money(get_recent_sales_best(reportdate,26)[1]),format_money(sales['jacks_total'] - get_recent_sales_best(reportdate,26)[1]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 6 Month Worst:\",format_money(sales['jacks_total']),format_money(get_recent_sales_worst(reportdate,26)[1]),format_money(sales['jacks_total'] - get_recent_sales_worst(reportdate,26)[1]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 12 Month Best:\",format_money(sales['jacks_total']),format_money(get_recent_sales_best(reportdate,52)[1]),format_money(sales['jacks_total'] - get_recent_sales_best(reportdate,52)[1]))\n full_report += \"%s %-25s %-15s %-15s\\n\"%(\" 12 Month Worst:\",format_money(sales['jacks_total']),format_money(get_recent_sales_worst(reportdate,52)[1]),format_money(sales['jacks_total'] - get_recent_sales_worst(reportdate,52)[1]))\n\n return sales, full_report",
"title": ""
},
{
"docid": "88b1d9267f78a6736efeb27228f8f9d7",
"score": "0.5648886",
"text": "def get_workdays (date):\n return sorted (set (get_same_month_dates (date)) - set (get_workfree_dates (date)))",
"title": ""
},
{
"docid": "f7e04c06d076768952d4f6947877246e",
"score": "0.5640341",
"text": "def get_all_dates(self):\n dates = []\n\n dict_data = self.stock_dates\n\n for date_ in dict_data.keys():\n dates.append(date_)\n\n return dates",
"title": ""
},
{
"docid": "da41cf50f4db88f95d35c86536f59d39",
"score": "0.56070566",
"text": "def all_stock_values(start_date = \"01-01-1940\"):\n \n today = date.today()\n new_end_date = today - datetime.timedelta(days=1)\n new_end_date = new_end_date.strftime(\"%d-%m-%Y\")\n\n\n return extract_stock_values(start_date, new_end_date, get_stock_names())",
"title": ""
},
{
"docid": "46881249c278807c89894dde0bb2c137",
"score": "0.56048465",
"text": "def gen_burndown_data(self):\n # calculate sum of all backlog item estimates, and estimates of finished items on each date\n total_est = 0.0\n item_estimates = {} #{'2012-01-01': 12.5,...} #[['2012-01-01',4.5],...]\n for itm in self.backlog_set.get_query_set():\n itp = itm.date_points() #finished tasks ['2012-01-01', 4.5]\n total_est += itp[1]\n if itp[0] in item_estimates:\n item_estimates[itp[0]] += itp[1]\n else:\n item_estimates[itp[0]] = itp[1]\n # gen list of dates from self.start to self.end or today\n dt = self.start\n xdates = []\n while True:\n xdates.append([datetime.strftime(dt, '%Y-%m-%d')])\n dt += timedelta(days=1)\n if dt > datetime.now() or self.end and dt > self.end:\n break\n # populate xdates with remaining estimates\n remaining = total_est\n for xd in xdates:\n ie = item_estimates.get(xd, 0)",
"title": ""
},
{
"docid": "0e9e00af4110732cc07aa8849d6cd3a6",
"score": "0.5593276",
"text": "def get_dated_items(self):\n year = self.get_year()\n month = self.get_month()\n day = self.get_day()\n\n date = _date_from_string(\n year,\n self.get_year_format(),\n month,\n self.get_month_format(),\n day,\n self.get_day_format(),\n )\n\n return self._get_dated_items(date)",
"title": ""
},
{
"docid": "80c3795e2edc94b621400436cd2a0e93",
"score": "0.5572386",
"text": "def _GenerateBackfillDates(self, duration, start_date):\n # First find the nearest Wednesday before the start.\n crawl_date = start_date - timedelta(days=((start_date.weekday() + 5) % 7))\n days = []\n while crawl_date > start_date - duration:\n days.append(crawl_date)\n crawl_date = crawl_date - timedelta(weeks=1)\n return days",
"title": ""
},
{
"docid": "a517a2cf0fd4c059fc2d18ec7da132bb",
"score": "0.55685365",
"text": "def behind_the_wheel_date(self, data):\n payload = data.copy()\n payload.update({\n \"officeId\": self.id,\n \"numberItems\": \"1\",\n \"requestedTask\": \"DT\",\n \"resetCheckFields\": \"true\",\n })\n return get_dmv_date(payload)",
"title": ""
},
{
"docid": "47dae736cbb104e172758c77bc9a195e",
"score": "0.5563007",
"text": "def rec_stocks(s,df_close_prices):\n watch = []\n nearly = []\n buy = []\n for stock in s.symbols:\n tag = (stock + 'BUY')\n u_tag = (stock + 'TrigU')\n d_tag = (stock + 'TrigD')\n m_tag = (stock + 'MACD')\n \n #Calculates Reco dict for today\n up = df_close_prices.ix[-1,u_tag]\n down = df_close_prices.ix[-2,d_tag]\n down2 = df_close_prices.ix[-1,d_tag]\n macd = df_close_prices.ix[-1,m_tag]\n macd2 = df_close_prices.ix[-2,m_tag]\n y = up + down \n #Watch are if EMAs are in correct order going down\n if down == 1.0 and macd<4:\n watch.append(stock)\n #Nearly if Watch is true, MACD < 0% and < yesterday\n if down2 == 0 and macd<0 and macd2>macd:\n nearly.append(stock)\n #Buy if down and up triggers are both true\n if y ==11.0:\n buy.append(stock)\n else:\n continue \n print(\"\\nToday's stocks to watch and buy are as follows....\")\n print('Watch = ',watch)\n print('Nearly = ',nearly)\n print('Buy = ',buy)",
"title": ""
},
{
"docid": "0a6f1b1eb8f7039944ee3d15a1d6a19e",
"score": "0.55620915",
"text": "def getBusinessDaysBetween(date1: java.util.Date, date2: java.util.Date) -> int:\n ...",
"title": ""
},
{
"docid": "c7a3f45e482a0df1e4be0d39bbd03c2f",
"score": "0.55614525",
"text": "def _get_dated_items(self, date):\n allow_future = self.get_allow_future()\n allow_empty = self.get_allow_empty()\n first_date = self.get_first_date()\n\n if not allow_future and date > timezone_today():\n raise Http404(_(\"Future dates not available\"))\n\n if first_date and date < first_date:\n raise Http404(_(\"Dates this old not available\"))\n\n object_lists = {}\n\n # The order we add things to object_lists is the order in which\n # they'll appear in the page...\n\n for blog, posts in self._get_weblog_posts(date).items():\n object_lists.update({blog: posts})\n\n object_lists.update(self._get_flickr_photos(date))\n\n object_lists.update(self._get_pinboard_bookmarks(date))\n\n object_lists.update(self._get_twitter_tweets(date))\n\n object_lists.update(self._get_twitter_favorites(date))\n\n if not allow_empty:\n if len(object_lists) == 0:\n raise Http404(_(\"Nothing available\"))\n\n # Count the total number of ALL items are in the QuerySets:\n object_count = 0\n for key, object_list in object_lists.items():\n object_count += len(object_list)\n\n return (\n None,\n object_lists,\n {\n \"day\": date,\n \"previous_day\": self.get_previous_day(date),\n \"next_day\": self.get_next_day(date),\n \"object_count\": object_count,\n },\n )",
"title": ""
},
{
"docid": "58f62dc8c7f32aef0016f27d72136294",
"score": "0.55299747",
"text": "def getAllGameDates():\n start = date(2015, 11, 2)\n end = date(2015, 11, 11)\n\n delta = timedelta(days=1)\n\n date_array = []\n\n while start <= end:\n date_array.append(start.strftime(\"%Y-%m-%d\"))\n start += delta\n\n return date_array",
"title": ""
},
{
"docid": "99c27386e57436bdb7dcadeae2d405ed",
"score": "0.5506863",
"text": "def get_values_of_stock(stock_names: str, start_date = \"01-01-1940\"):\n \n today = date.today()\n new_end_date = today - datetime.timedelta(days=1)\n new_end_date = new_end_date.strftime(\"%d-%m-%Y\")\n\n\n return extract_stock_values(start_date, new_end_date, [stock_names])",
"title": ""
},
{
"docid": "44d5e6a4336857ce47fdb87b36c65844",
"score": "0.5498179",
"text": "def dates(date_info, air_visit_data, subTable):\n\n holidays = date_info.loc[date_info['holiday_flg'] == 1]\n holidays = holidays[['calendar_date']]\n holidays.columns = ['ds']\n holidays['holiday'] = 'Holiday'\n\n trainDays = pd.DataFrame(air_visit_data.visit_date.unique(),\n columns=['ds'])\n trainDays = trainDays.sort_values('ds')\n forecastDays = pd.DataFrame(subTable.ds.unique(), columns=['ds'])\n\n return holidays, trainDays, forecastDays",
"title": ""
},
{
"docid": "0721623c641fc04ea677008f0da968d5",
"score": "0.549523",
"text": "def generate_sales_invoice_daily():\n\n # Dates Query\n dates_query = \"\"\"SELECT DATE(date) AS daily_date FROM `tabReceipts` GROUP BY DATE(date)\"\"\"\n\n # Get all dates from tab receipts\n dates = frappe.db.sql(dates_query, as_dict=1)\n\n # Hey jude\n for date in dates:\n #print 'Creating for {0}'.format(date.daily_date)\n generate_sales_invoice_by_date(date.daily_date)",
"title": ""
},
{
"docid": "2cc137162dc123c47b1a907ef2d23af6",
"score": "0.5494386",
"text": "def check_in_out_dates(self):\n if self.checkout and self.checkin:\n #print \"=====\",self.checkin<self.date_order\n if self.checkin < self.date_order:\n raise except_orm(_('Warning'), _('Checkin date should be \\\n greater than the current date.'))\n if self.checkout < self.checkin:\n raise except_orm(_('Warning'), _('Checkout date \\\n should be greater than Checkin date.'))",
"title": ""
},
{
"docid": "dec0743e663d0db1eaec750cb67bbfa4",
"score": "0.5491623",
"text": "def get_working_days():\n\ttoday = date.today()\n\tlast_days = [\"{:%Y-%m-%d}\".format(today - timedelta(days=days_delta)) for days_delta in range(15)]\n\treturn last_days",
"title": ""
},
{
"docid": "87f1ef660943ed00dce550a1337af22d",
"score": "0.54909474",
"text": "def business_days_between(self, from_dates, to_dates):\n pass",
"title": ""
},
{
"docid": "fa35f40ff989db2081b0fc00a9018dd9",
"score": "0.54837537",
"text": "def get_buy_list(self, date):\n signaldate = QA_util_get_last_day(date)\n try:\n buy = self.preload.loc[parser.parse(signaldate).date(), :, :]\n buy.index = buy.index.remove_unused_levels()\n return buy.index.levels[1].tolist()\n except:\n return []",
"title": ""
},
{
"docid": "7d557d69aaed7f814327868ca7f5b21f",
"score": "0.5450726",
"text": "def get_prices(days, my_data, hotel):\n for day in days:\n if day.attrs['data-status'] == 'past-date':\n continue\n data_date = day.attrs[\"data-date\"]\n data_month = str(data_date.split('/')[0])\n data_year = str(data_date.split('/')[2])\n if not data_date:\n continue\n rate = day.find(\"span\", {\"class\": \"dateWrapper__button--rate\"})\n if not rate:\n continue\n try:\n rate = int(rate.text.split(\"$\")[1])\n except:\n rate = 9999\n date = data_date\n my_data.append({\"date\": date, \"price\": rate, \"hotel\": hotel})\n add_price_to_db(date, rate, hotel)\n return my_data",
"title": ""
},
{
"docid": "a43981bcbb2254cb40f196b8c41ddd23",
"score": "0.543319",
"text": "def backtest_strategy(symbol_price, trade_orders, capital):\n symbol = symbol_price.columns[1]\n df = t.sort(pd.merge(symbol_price, trade_orders, on=[\"date\"], how=\"outer\", indicator=True), \"date\", ascending=True)\n df = t.drop(df, \"_merge\")\n \n df[\"date\"] = pd.to_datetime(df[\"date\"])\n \n df[\"pct_change\"] = df[symbol].pct_change()\n df[\"invested_start_day\"] = 0\n df[\"invested_end_day\"] = 0\n df[\"account_cash_start_day\"] = 0\n df[\"account_cash_end_day\"] = 0 \n df[\"net_worth\"] = 0\n df[\"nb\"] = \"\"\n\n calendar = pd.Series( t.column(df, \"date\") )\n \n for date in calendar:\n \n invested_start_day, invested_end_day, account_cash_start_day, account_cash_end_day, net_worth = 0,0,0,0,0\n \n if t.column(df, \"date\")[0] == date: # first day\n invested_start_day = 0\n account_cash_start_day = capital\n\n else:\n invested_start_day = t.select(t.where(df, \"date\", date, op.lt), \"invested_end_day\").tail(1).values[0][0] + ( t.select(t.where(df, \"date\", date, op.lt), \"invested_end_day\").tail(1).values[0][0] * t.select(t.where(df, \"date\", date, op.eq), \"pct_change\").values[0][0])\n account_cash_start_day = t.select(t.where(df, \"date\", date, op.lt), \"account_cash_end_day\").tail(1).values[0][0]\n \n invested_end_day = invested_start_day + t.select(t.where(df, \"date\", date, op.eq), \"order_size\").values[0][0]\n account_cash_end_day = account_cash_start_day - t.select(t.where(df, \"date\", date, op.eq), \"order_size\").values[0][0]\n net_worth = invested_end_day + account_cash_end_day\n \n # update table\n df.loc[t.where(df, \"date\", date, op.eq).index[0], \"invested_start_day\"] = invested_start_day\n df.loc[t.where(df, \"date\", date, op.eq).index[0], \"invested_end_day\"] = invested_end_day\n df.loc[t.where(df, \"date\", date, op.eq).index[0], \"account_cash_start_day\"] = account_cash_start_day\n df.loc[t.where(df, \"date\", date, op.eq).index[0], \"account_cash_end_day\"] = account_cash_end_day\n df.loc[t.where(df, \"date\", date, op.eq).index[0], \"net_worth\"] = net_worth\n # include warning in case we spend more than we have (borrow)\n # or we sell more than we have (shorting)\n if account_cash_end_day < 0:\n df.loc[t.where(df, \"date\", date, op.eq).index[0], \"nb\"] = \"account_cash_end_day is negative\"\n if invested_end_day < 0:\n df.loc[t.where(df, \"date\", date, op.eq).index[0], \"nb\"] = df.loc[t.where(df, \"date\", date, op.eq).index[0], \"nb\"] + \" invested_end_day is negative\"\n \n \n # lift\n net_worth_start = t.column(df, \"net_worth\")[0]\n net_worth_end = t.column(df, \"net_worth\")[-1]\n lift = (net_worth_end - net_worth_start) / net_worth_start\n\n df[\"flux\"] = - df[\"order_size\"]\n df.loc[df.index[-1], \"flux\"] = t.column(df, \"flux\")[-1] + net_worth_end\n _irr = xirr(list(zip(df.date, df.flux)))\n \n return ({'transactions': df, 'ROI': lift, 'IRR': _irr, 'return': net_worth_end})",
"title": ""
},
{
"docid": "5573bc29411b3dc8c8b1f35ba925deed",
"score": "0.5431218",
"text": "def _compute_expected_date(self):\n for order in self:\n dates_list = []\n back_order_pickings = order.picking_ids.filtered(lambda r: r.picking_type_id == order.warehouse_id.pick_type_id and r.is_back_order and order.expected_date != r.scheduled_date)\n schedule_date_list = back_order_pickings and back_order_pickings.mapped('scheduled_date') or []\n if schedule_date_list:\n order.expected_date = schedule_date_list[0]\n continue\n for line in order.order_line.filtered(\n lambda x: x.state != 'cancel' and not x._is_delivery() and not x.display_type):\n dt = line._expected_date()\n dates_list.append(dt)\n if dates_list:\n order.expected_date = fields.Datetime.to_string(min(dates_list))\n else:\n order.expected_date = False",
"title": ""
},
{
"docid": "1fb147870542284312c4b28f8f621ea8",
"score": "0.54286754",
"text": "def get_pre_workfree_dates (date):\n this_month_workdays = set (get_workdays (date))\n pre_work_free_dates = []\n for workdate in this_month_workdays:\n if holiday.is_workfree (workdate + datetime.timedelta (days = 1)):\n pre_work_free_dates.append (workdate)\n \n return sorted (pre_work_free_dates)",
"title": ""
},
{
"docid": "facb772a86f071bbc603a7a7bce9d66a",
"score": "0.5426865",
"text": "def get_dates(cls, date_from=None, date_to=None):\n\n # Translate string format to the date object.\n if date_from and isinstance(date_from, str):\n date_from = datetime.datetime.strptime(date_from, \"%Y-%m-%d\").date()\n if date_to and isinstance(date_to, str):\n date_to = datetime.datetime.strptime(date_to, \"%Y-%m-%d\").date()\n\n if not date_to:\n date_to = datetime.date.today()\n\n if not date_from:\n date_from = date_to - datetime.timedelta(days=cls.DEFAULT_DATE_DELTA_DAYS)\n\n dates = []\n while date_from <= date_to:\n dates.append(date_from)\n date_from += datetime.timedelta(days=1)\n\n return dates",
"title": ""
},
{
"docid": "cb1d88c21bed319218eee7e56a811997",
"score": "0.5415063",
"text": "def get_dates(self):\n defaults = [self.date_created_at, self.date_updated_at]\n\n return self.__dates__ + defaults",
"title": ""
},
{
"docid": "f5985f6d2b641461c6091af63deeae33",
"score": "0.54135007",
"text": "def dates(self):\n # Return\n result = self._df_dates\n return result",
"title": ""
},
{
"docid": "4aafefbcbb353a5058e0db484a9fc5be",
"score": "0.54022646",
"text": "def bahai_date(major, cycle, year, month, day):\n return [major, cycle, year, month, day]",
"title": ""
},
{
"docid": "94758d47097b2b0cddd7ce560b11bf11",
"score": "0.5396541",
"text": "def by_date(items, yr, mo=None):\n\tres = []\n\tfor i in items:\n\t\tif fdate(i,yr,mo):\n\t\t\tres.append(i)\n\treturn res",
"title": ""
},
{
"docid": "ff9cf9bf67ef7bf9e96695817c6f78f1",
"score": "0.53943497",
"text": "def get_trans_datelist(self):\n return list(self.datelist)",
"title": ""
},
{
"docid": "5a836cca5a6b762301a8250c6909fc01",
"score": "0.5394073",
"text": "def processGetReportCard(start_date, end_date):\n stochasticknapsack.bootstrap()\n\n # check whether end date lesser than start date\n if end_date < start_date:\n return {\n 'status': 500,\n 'error_message': \"End date cannot be lesser than start date\"\n }\n\n reports = []\n today_date = datetime.datetime.now().date()\n\n try:\n for dt in rrule.rrule(rrule.MONTHLY,dtstart=start_date,until=end_date):\n date = dt.date()\n query_start_date = datetime.date(date.year, date.month, 1)\n query_end_date = datetime.date(date.year, date.month, calendar.monthrange(date.year, date.month)[1])\n\n # previous month's dates\n previous_month_start_date = query_start_date - relativedelta.relativedelta(months=1)\n previous_month_end_date = query_end_date - relativedelta.relativedelta(months=1)\n previous_month_end_date = datetime.date(previous_month_end_date.year, previous_month_end_date.month,\n calendar.monthrange(previous_month_end_date.year,\n previous_month_end_date.month)[1])\n\n try:\n report_card = ReportCard.objects.filter(\n date__month=date.month, date__year=date.year).latest('date')\n except ObjectDoesNotExist:\n if date.month < today_date.month:\n try:\n recommendation = Recommendation.objects.get(date__month=date.month, date__year=date.year)\n expected_demand = recommendation.expected_demand\n except:\n expected_demand = 0\n\n target_month = datetime.date(date.year, date.month, 1)\n t_1 = (target_month - datetime.timedelta(days=1)).replace(day=1)\n denominator = Order.objects.filter(\n shipping_date__month=t_1.month, shipping_date__year=t_1.year).count()\n\n if denominator == 0:\n percentageChangeDemand = 0\n else:\n percentageChangeDemand = (float(Order.objects.filter(\n shipping_date__month=date.month, shipping_date__year=date.year).count()) / Order.objects.filter(\n shipping_date__month=t_1.month, shipping_date__year=t_1.year).count() - 1) * 100\n report_card = ReportCard(\n date=datetime.date(date.year, date.month, 1),\n active_customers=getNumberOfActiveCustomersByDate(query_start_date, query_end_date),\n orders=countOrdersByDate(query_start_date, query_end_date),\n churn=getNumberOfChurnCustomersByDate(query_start_date, query_end_date),\n new_signups=getNumberOfNewSignupsByDate(query_start_date, query_end_date),\n actions={\n 'facebook_advertising_cost' : 0.00,\n 'blog_posts' : 0,\n 'email_campaigns' : 0,\n 'adwords_cost' : 0.00,\n 'roadshows': 0,\n 'new_coffees' : 0\n },\n percentage_changes={},\n expected_demand=expected_demand,\n demand_actualising=percentageChangeDemand,\n deviation=0.0\n )\n report_card.save()\n\n report_card.percentage_changes = getPercentageChange(previous_month_start_date, previous_month_end_date, report_card)\n\n report_card.save()\n\n else:\n reports.append(\n {\n 'date': '%s-%s' % (date.month, date.year),\n 'report_card': {\n 'report_id' : 0,\n 'active_customers': 0,\n 'orders': 0,\n 'churn': 0,\n 'new_signups': 0,\n 'percentage_active_customers' : 0,\n 'percentage_orders' : 0,\n 'percentage_churn' : 0,\n 'percentage_new_signups' : 0,\n 'actions' : {},\n 'percentage_changes' : {},\n 'expected_demand' : 0.0,\n 'demand_actualising' : 0.0,\n 'deviation' : 0.0\n }\n }\n )\n continue\n reports.append(\n {\n 'date': '%s-%s' % (date.month, date.year),\n 'report_card' : {\n 'report_id' : report_card.id,\n 'active_customers' : report_card.active_customers,\n 'orders' : report_card.orders,\n 'churn' : report_card.churn,\n 'new_signups' : report_card.new_signups,\n 'percentage_changes' : report_card.percentage_changes,\n 'actions' : report_card.actions,\n 'expected_demand': float(report_card.expected_demand),\n 'demand_actualising': float(report_card.demand_actualising),\n 'deviation': float(report_card.deviation)\n }\n }\n )\n except Exception as e:\n return {\n 'status': 500,\n 'error_message': str(e)\n }\n\n return {\n 'status' : 200,\n 'reports' : reports\n }",
"title": ""
},
{
"docid": "2b7086b0412bdeb3c3152a97eeb3962a",
"score": "0.5384157",
"text": "def run(self,):\n\n for date in QA_util_get_trade_range(self.start, self.end):\n buylist = self.get_buy_list(date)\n selllist = self.get_sell_list(date)\n self.tradetable[date] = {}\n\n if len(selllist) == 0:\n pass\n\n else:\n data = self.closepanel.loc[parser.parse(date).date(), selllist].map(lambda x: round(x,2)).to_dict()\n cashpre = self.cashpre/len(selllist)\n for code in selllist:\n\n volume = self.tradetable[QA_util_get_last_day(\n date, self.rolling-1)][code]\n if volume < 100:\n pass\n else:\n order = self.account.send_order(\n code[0:6], volume, price=data[code], datetime=date+' 15:00:00', towards=-1)\n self.account.make_deal(order)\n\n if len(buylist) != 0:\n\n d = self.datacenter.selects(\n buylist, date, date).open.map(lambda x: round(x, 2))\n\n d.index = d.index.droplevel(0)\n\n data = d.to_dict()\n cashpre = self.cashpre/len(buylist)\n for code in buylist:\n try:\n volume = int(\n 0.01*cashpre/data[code])*100 if data[code] != 0 else 0\n if volume < 100:\n pass\n else:\n order = self.account.send_order(\n code[0:6], volume, price=data[code], datetime=date+' 09:30:00', towards=1)\n self.account.make_deal(order)\n self.tradetable[date][code] = volume\n except:\n \"\"\"\n 主要是停牌买不入 直接放弃\n \n 此处买入未加入连续一字板的检测 rust 会增加此处的逻辑\n \n \"\"\"\n pass\n else:\n pass\n\n holdinglist = [QA_util_code_change_format(code)\n for code in list(self.account.positions.keys())]\n pricepanel = self.closepanel.loc[parser.parse(date).date(), holdinglist].map(lambda x: round(x,2))\n #pricepanel.index = pricepanel.index.droplevel(0)\n pricepanel =pricepanel.to_dict()\n for code in holdinglist:\n\n self.account.on_price_change(code[0:6], pricepanel[code])\n self.account.settle()",
"title": ""
},
{
"docid": "41a109cb50645c6644cc65f64fefab40",
"score": "0.5383977",
"text": "def dates(self):\n try:\n return self.data['datetime']\n except:\n return []",
"title": ""
},
{
"docid": "ec973e85fcbbb999facd33d792c524a1",
"score": "0.53811836",
"text": "def getListDates( date_start, date_end):\n duration_days = (date_end - date_start).days\n list_dates = list([(date_start+timedelta(days=i)) for i in range(duration_days+1)])\n return list_dates",
"title": ""
},
{
"docid": "3f66c08fd3c32936cb4b162efab86d8c",
"score": "0.5363678",
"text": "def history_commodity(commodity: str, start_date: datetime, stop_date: datetime) -> list:\n businessinsder_values = {v: k for k, v in businessinsder_key.items()}\n commodity_db = businessinsder_values[commodity]\n if commodity not in available_commodities():\n logger.error('commodity= {} not available in list'.format(commodity))\n raise NameError(commodity)\n commodity = available_commodities()[commodity]\n start = str(int(start_date.strftime('%d'))) + '.' + \\\n str(int(start_date.strftime('%m'))) + '.' + \\\n str(int(start_date.strftime('%Y')))\n stop = str(int(stop_date.strftime('%d'))) + '.' + \\\n str(int(stop_date.strftime('%m'))) + '.' + \\\n str(int(stop_date.strftime('%Y')))\n url = 'https://markets.businessinsider.com/commodities/historical-prices/' \\\n + commodity + '/usd/' + start + '_' + stop\n headers = {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate',\n 'Accept-Language': 'uk,en-US;q=0.8,en;q=0.5,ru;q=0.3',\n 'Cache-Control': 'no-cache',\n 'Connection': 'keep-alive',\n 'Content-Length': '0',\n 'DNT': '1',\n 'Host': 'markets.businessinsider.com',\n 'Pragma': 'no-cache',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:52.0) Gecko/20100101 Firefox/52.0',\n }\n\n responce_post1 = requests.post(url, headers=headers)\n logger.info('respoce_1= {}, from url= {}'.format(responce_post1.status_code, url))\n if responce_post1.status_code != 200:\n logger.error('wrong AUTH responce from businessinsider= {}'.format(responce_post1.status_code))\n logger.error('requested url= {}'.format(url))\n raise ValueError('bad responce')\n\n soup1 = BeautifulSoup(responce_post1.text, 'html.parser')\n # modify headers for data collection\n for key in ['__atts', '__ath', '__atcrv']:\n headers[key] = soup1.find('input', attrs={'name': key})['value']\n if key == '__atcrv':\n headers[key] = str(eval(headers[key]))\n\n########## 2-nd step ###############\n\n url2 = 'http://markets.businessinsider.com/Ajax/CommodityController_HistoricPriceList/' \\\n + commodity + '/USD/' + start + '_' + stop\n params = {'type': 'Brent'}\n\n responce_post2 = requests.post(url2, params=params, headers=headers)\n logger.info('respoce_2= {}, from url2= {}'.format(responce_post2.status_code, url2))\n if responce_post2.status_code != 200:\n logger.error('wrong DATA responce from businessinsider= {}'.format(responce_post2.status_code))\n logger.info('requested url= {}'.format(url2))\n raise ValueError('bad responce')\n\n soup2 = BeautifulSoup(responce_post2.text, 'html.parser')\n logger.debug('soup= {}'.format(soup2))\n logger.debug('soup text= {}'.format(soup2.div.get_text(strip=True)))\n # try another url\n if soup2.div.get_text(strip=True) == 'No data available':\n logger.warning('trying another url')\n url2 = 'http://markets.businessinsider.com/Ajax/CommodityController_HistoricPriceList/' \\\n + commodity + '/USc/' + start + '_' + stop\n responce_post2 = requests.post(url2, params=params, headers=headers)\n logger.info('respoce_2= {}, from url2= {}'.format(responce_post2.status_code, url2))\n if responce_post2.status_code != 200:\n logger.error('wrong DATA responce from businessinsider= {}'.format(responce_post2.status_code))\n logger.info('requested url= {}'.format(url2))\n raise ValueError('bad responce')\n soup2 = BeautifulSoup(responce_post2.text, 'html.parser')\n logger.debug('soup= {}'.format(soup2))\n\n result = []\n\n for row in soup2.find_all('tr', attrs={'class': False}):\n logger.debug('row= {}'.format(row))\n row_list = row.find_all('td')\n logger.debug('row_list= {}'.format(row_list))\n result.append({'time': pytz.utc.localize(datetime.strptime(row_list[0].get_text(strip=True), '%m/%d/%Y')),\n commodity_db: float(row_list[1].get_text(strip=True))})\n logger.debug('result= {}'.format(result))\n return result",
"title": ""
},
{
"docid": "384b7141635cd4d92ddaec6883db5a71",
"score": "0.5357981",
"text": "def dates_between(start, end):\n\n dates = []\n date = datetime.datetime.strptime(start, \"%Y/%m/%d\")\n date_end = datetime.datetime.strptime(end, \"%Y/%m/%d\")\n while date < date_end:\n dates.append(date.strftime(\"%Y/%m/%d\"))\n date += datetime.timedelta(days=1)\n dates.append(date_end.strftime(\"%Y/%m/%d\"))\n return dates",
"title": ""
},
{
"docid": "ed0408fafb8334b1b765a7ea20121156",
"score": "0.5355744",
"text": "def get_winners(soup):\n \n winning_bidders = soup.findAll(\"td\",class_='Ta-end')\n winners = []\n win_dates = []\n for winner in winning_bidders:\n winning_bidder = str(winner.find('a').text)\n winners.append(winning_bidder)\n win_date = str(winner.findAll('span',class_=\"Block F-timestamp Nowrap\")[0].text)\n win_dates.append(win_date)\n return winners,win_dates",
"title": ""
},
{
"docid": "d07d67b0bf6e238bd402d03a5363316a",
"score": "0.53534734",
"text": "def day_gains(self):\n open_price = Data.quote(self.symbol)['open']\n current_price = Data.price(self.symbol)\n return round((current_price * self.shares) - (open_price * self.shares),\n 2)",
"title": ""
},
{
"docid": "242bed9d1f298e15e91816c21b411525",
"score": "0.53501123",
"text": "def monthly():",
"title": ""
},
{
"docid": "3aaf2a9e5e5db60465ec53cad71eb406",
"score": "0.534938",
"text": "def get_publishing_date(self, selenium_driver):",
"title": ""
},
{
"docid": "b27be8a3b5f62b7287e007126ae8f3c2",
"score": "0.5345881",
"text": "def specificdates(country):\n if country == \"United-Kingdom\" or country == \"United-States\":\n print(\"This function is not available for \" + country + \" yet as I haven't configured all the different \"\n \"provinces, check back soon!\")\n else:\n date1 = input(\"Please enter a start date in the form dd/mm/yyyy:\\n\")\n date2 = input(\"Please enter an end date in the form dd/mm/yyyy:\\n\")\n date1 = date1.split(\"/\")\n date2 = date2.split(\"/\")\n\n date1 = datetime.datetime(int(date1[2]), int(date1[1]), int(date1[0]))\n date2 = datetime.datetime(int(date2[2]), int(date2[1]), int(date2[0]))\n\n temp = CountryAllStatus(country, date1, date2)\n r = CountryAllStatus.request(temp)\n\n firstitem = r[0]\n lastitem = r[-1]\n\n print(\"These are the results for \" + country + \" between\", date1.date(), \"and\", date2.date(), \":\\n\")\n print(\"Amount of New Cases:\\t\\t\" + f\"{int(lastitem['Confirmed']) - int(firstitem['Confirmed']):,d}\" +\n \"\\nAmount of New Deaths:\\t\\t\" + f\"{int(lastitem['Deaths']) - int(firstitem['Deaths']):,d}\" +\n \"\\nAmount of New Recoveries:\\t\" + f\"{int(lastitem['Recovered']) - int(firstitem['Recovered']):,d}\" +\n \"\\nAmount of New Active Cases:\\t\" + f\"{int(lastitem['Active']) - int(firstitem['Active']):,d}\" +\n \"\\nTotal amount of cases by\", date1.date(), \":\\t\\t\" + f\"{int(firstitem['Confirmed']):,d}\" +\n \"\\nTotal amount of cases by\", date2.date(), \":\\t\\t\" + f\"{int(lastitem['Confirmed']):,d}\")",
"title": ""
},
{
"docid": "764c436049bfc5f417c8bcdd9c2bf59c",
"score": "0.53347194",
"text": "def get_historical_prices_plus_one_day(symbol, date):\n#the date goes month(jan=0) day year\n url = 'http://ichart.yahoo.com/table.csv?s=%s&' % symbol + \\\n 'd=%s&' % str(int(date[5:7]) - 1) + \\\n 'e=%s&' % str(int(date[8:10]) + 1) + \\\n 'f=%s&' % str(int(date[0:4])) + \\\n 'g=d&' + \\\n 'a=%s&' % str(int(date[5:7]) - 1) + \\\n 'b=%s&' % str(int(date[8:10]) + 1) + \\\n 'c=%s&' % str(int(date[0:4])) + \\\n 'ignore=.csv'\n days = urllib.urlopen(url).readlines()\n data = [day[:-2].split(',') for day in days]\n return data",
"title": ""
},
{
"docid": "20905ebc926b3a1b70bdd9da48625c0c",
"score": "0.53295696",
"text": "def get_decays(self):\n return self.decays",
"title": ""
},
{
"docid": "c9bf956f237b612e5c47f646357ccdc9",
"score": "0.53212434",
"text": "def create_dates_list(start_date, end_date):\n\n delta = end_date - start_date\n dates = []\n for d in range(delta.days + 1):\n dates.append(start_date + timedelta(days = d))\n return dates",
"title": ""
},
{
"docid": "c158eace4f3d50e9531876d84d4001f2",
"score": "0.5315381",
"text": "def get_monthly_due_dates(table: element.ResultSet):\r\n\r\n if len(table) != 1:\r\n return []\r\n else:\r\n # Find tag that resembles <td style=\"font-weight: bold; color: red;\">\r\n tds = table[0].find_all(\"td\", {\"style\" : \"font-weight: bold; color: #ad0901;\"})\r\n due_dates = []\r\n for td in tds:\r\n td_contents = td.contents[0] # Should expect date to be in format: mm/dd/yyyy\r\n try:\r\n datetime_object = datetime.strptime(td_contents, \"%m/%d/%Y\")\r\n due_dates.append(datetime_object)\r\n except:\r\n # td tag did not contain a date, so skip\r\n continue\r\n\r\n return due_dates",
"title": ""
},
{
"docid": "2d3795dfda7ac8089b2ec866ae2e2f41",
"score": "0.53107476",
"text": "def weekly():",
"title": ""
},
{
"docid": "47586b6ab5781dc6dda3c32bfc1d9a27",
"score": "0.52951574",
"text": "def GetDates(self):\n begindate = datetime.datetime(1899, 12, 30)\n ntimes = range(self.nperiods)\n periods = [ntimes[0], ntimes[-1]]\n st_end = []\n for period in periods:\n date_offset = self.ResultsStartPos + period*self.bytesperperiod\n self.fp.seek(date_offset, 0)\n day = struct.unpack('d', self.fp.read(2*self.RECORDSIZE))[0]\n st_end.append(begindate + datetime.timedelta(days=int(day)))\n return st_end",
"title": ""
},
{
"docid": "5dd31faba637ab38d20eb42d1e080d86",
"score": "0.5291366",
"text": "def days_to_return_book(borrow_date):\n with open('config.json') as file:\n data = json.load(file)\n return borrow_date + timedelta(days=Parser.get_instance().days_to_return)",
"title": ""
},
{
"docid": "36f3d24713e63fe14c44c90cf874df8f",
"score": "0.5290857",
"text": "def daily_balances(transactions):\n dates = {}\n\n for trans in transactions:\n if trans['Date'] in dates:\n dates[trans['Date']] += float(trans['Amount'])\n else:\n dates[trans['Date']] = float(trans['Amount'])\n\n return dates",
"title": ""
},
{
"docid": "c0ff493d8f2bff77e87aa424b74e40ef",
"score": "0.52907073",
"text": "def get_purchase_date(self):\r\n return self.__purchase_date",
"title": ""
},
{
"docid": "087a7b2f068d06999f7ef14baf7a0c38",
"score": "0.5288863",
"text": "def dailysales():\n first = date2obj(total[0].date)\n last = date2obj(total[-1].date)\n daycount = (last - first).days + 1 #total days including the last and first\n increment = datetime.timedelta(days=1) # to count from first to the last day\n days = {}\n for i in range(daycount):\n now = first + increment * i\n nowstr = obj2date(now)\n days[nowstr] = 0\n for item in total: \n days[item.date] = days.get(item.date,0) +1\n return days",
"title": ""
},
{
"docid": "789613e65001be73655853c865bceb6c",
"score": "0.5283415",
"text": "def wealth_index(stock_name: str, date: str, \n end_date = datetime.datetime.today().strftime(\"%d-%m-%Y\")): \n rets = returns(stock_name)\n # get all returns for specified stock with function returns\n \n date_start = datetime.datetime.strptime(date, \"%d-%m-%Y\")\n # convert \"date\" entered as string to datetime type\n \n end_date = datetime.datetime.strptime(end_date, \"%d-%m-%Y\")\n # convert \"date\" entered as string to datetime type\n \n rets = rets.loc[(rets[\"HGDG_TARIH\"]> date_start) & (rets[\"HGDG_TARIH\"]< end_date)]\n \n rets_columns = rets[\"HGDG_KAPANIS\"]\n dates = rets[\"HGDG_TARIH\"]\n \n wealth_index = 1 * (1 + (rets_columns)).cumprod()\n \n wealth_index = pd.concat([dates, wealth_index], axis = 1)\n \n wealth_index = pd.DataFrame({\n \"Date\": wealth_index[\"HGDG_TARIH\"],\n \"Wealth_Index\": wealth_index[\"HGDG_KAPANIS\"] \n })\n\n return wealth_index",
"title": ""
},
{
"docid": "07fbf35ed97c41ec4aa30bf69724f7dd",
"score": "0.5274807",
"text": "def _detect_date(self):\n final_date_dict_list = []\n date_dict_list = self._detect_range()\n final_date_dict_list.extend(date_dict_list)\n self._update_processed_text(date_dict_list)\n date_dict_list = self._detect_return_date()\n final_date_dict_list.extend(date_dict_list)\n self._update_processed_text(date_dict_list)\n date_dict_list = self._detect_departure_date()\n final_date_dict_list.extend(date_dict_list)\n self._update_processed_text(date_dict_list)\n date_dict_list = self._detect_any_date()\n final_date_dict_list.extend(date_dict_list)\n self._update_processed_text(date_dict_list)\n\n return final_date_dict_list",
"title": ""
},
{
"docid": "4431b41fc09fdb952bcb497e4c32a1ff",
"score": "0.5270926",
"text": "def date(date):",
"title": ""
},
{
"docid": "b5b8795204b325210c59d7eda44959c4",
"score": "0.52700645",
"text": "def day(centuries):\n\td = centuries * 36525",
"title": ""
},
{
"docid": "dc64b2f86eecc5bf6a2bf3281e8bfdb8",
"score": "0.52671945",
"text": "def get_historical_prices(symbol, date):\n#the date goes month(jan=0) day year\n url = 'http://ichart.yahoo.com/table.csv?s=%s&' % symbol + \\\n 'd=%s&' % str(int(date[5:7]) - 1) + \\\n 'e=%s&' % str(int(date[8:10])) + \\\n 'f=%s&' % str(int(date[0:4])) + \\\n 'g=d&' + \\\n 'a=%s&' % str(int(date[5:7]) - 1) + \\\n 'b=%s&' % str(int(date[8:10])) + \\\n 'c=%s&' % str(int(date[0:4])) + \\\n 'ignore=.csv'\n days = urllib.urlopen(url).readlines()\n data = [day[:-2].split(',') for day in days]\n return data",
"title": ""
},
{
"docid": "77518f7e1ed66dc9746e247456e7cb1f",
"score": "0.52614725",
"text": "def Get_DateColumn(self):\r\n print(\"Length of stock_dates: %d\" % len(self.stock_dates)) \r\n return self.stock_dates",
"title": ""
},
{
"docid": "08e78b4c1777c147706a3714adbf1cd1",
"score": "0.5258313",
"text": "def date() -> list:\n return ['date']",
"title": ""
},
{
"docid": "d6c56a2a0fcd5fe8c8f9ea27d6eecf83",
"score": "0.52554756",
"text": "def _get_dattes(self, product_id=None,date=None):\n mapped_fields = {\n 'life_date': 'life_time',\n 'use_date': 'use_time',\n 'removal_date': 'removal_time',\n 'alert_date': 'alert_time'\n }\n \n str_date = str(date)\n print(str_date)\n date_r = datetime.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n res = dict.fromkeys(mapped_fields, False)\n product = self.env['product.product'].browse(product_id) or self.product_id\n if product:\n i=0\n for field in mapped_fields:\n duration = getattr(product, mapped_fields[field])\n if duration and field != 'life_date':\n date = date_r + datetime.timedelta(days=duration)\n res[field] = fields.Datetime.to_string(date) \n if field == 'life_date':\n date = date_r + datetime.timedelta(days=0)\n res[field] = fields.Datetime.to_string(date)\n i = i+1\n return res",
"title": ""
},
{
"docid": "86feeca616d2c78b72f5d4f3c1e51f11",
"score": "0.52535295",
"text": "def _find_dates(self):\n print(\"Checking for available dates...\")\n available_dates = []\n try:\n appointment_date_select = Select(self.driver.find_element_by_xpath('//*[@id=\"date\"]'))\n for option in appointment_date_select.options:\n # skip placeholder option\n if option.text != \"Please select a day\":\n available_dates.append(option.text)\n except Exception as e:\n print(\"Error checking available dates: \", e)\n self.driver.quit()\n return available_dates",
"title": ""
},
{
"docid": "2e5678afc51d1c6a92e13059234ecfd2",
"score": "0.5244338",
"text": "def populate_database (date):\n\n # @LOGAN? why this default date?\n if not date:\n date = \"2017-01-01\"\n\n try:\n date = datetime.datetime.strptime(date, \"%Y-%m-%d\")\n except:\n pass\n\n while date < datetime.datetime.today():\n daily_sales(date)\n date = date + datetime.timedelta(days=1)",
"title": ""
},
{
"docid": "24b6e872babd0a03ce2e36f9c9f5b040",
"score": "0.5242202",
"text": "def _filter_offer_dates(self, product=False, begin_date=None, end_date=None):\n\n begin_date = self._parse_to_datetime(begin_date)\n end_date = self._parse_to_datetime(end_date)\n\n begin_str = begin_date.strftime('%Y%m%d')\n end_str = end_date.strftime('%Y%m%d')\n\n # Filter by product if needed\n current = list(self._yield_product(self.current_files, product=product))\n historic = list(self._yield_product(self.historic_files, product=product))\n\n\n\n # Try the current files first\n if self._match_true(begin_str, current) and self._match_true(end_str, current):\n dates = [d.strftime(\"%Y%m%d\") for d in pd.date_range(begin_date, \n end_date)]\n\n search_results = [self._match_iterables(item, current) \n for item in dates]\n\n # See if it partially matches historic, partially matches current\n elif self._match_true(end_str, current) and not self._match_true(begin_str, current):\n \n daily_dates = [d.strftime(\"%Y%m%d\") for d in pd.date_range(begin_date, \n end_date)]\n monthly_dates = [d.strftime(\"%Y%m\") for d in pd.date_range(\n begin_date, \n end_date + timedelta(days=31), \n freq=\"M\", normalize=True)]\n\n daily_results = [self._match_iterables(item, current) \n for item in daily_dates]\n monthly_results = [self._match_iterables(item, historic) for item in\n monthly_dates]\n\n search_results = daily_results + monthly_results\n\n # Scrape the historic files\n elif begin_str not in current and end_str not in current:\n month_dates = [d.strftime(\"%Y%m\") for d in pd.date_range(\n begin_date, \n end_date + timedelta(days=31), \n freq=\"M\", normalize=True)]\n\n search_results = [self._match_iterables(item, historic) for item in\n month_dates]\n\n # Return the search results, save the last search to an internal\n # attribute for safe keeping\n search_results = filter(lambda x: x != None, search_results)\n self.search_results = search_results\n return search_results",
"title": ""
},
{
"docid": "f20fce6e92372ee38c8991a6ea5a704a",
"score": "0.52404726",
"text": "def days_accessible(user, coupon, week_start_date):\n\n start = max(week_start_date, user['REG_DATE'], coupon['DISPFROM'])\n end = min(week_start_date+datetime.timedelta(days=7), user['WITHDRAW_DATE'], coupon['DISPEND'])\n\n try:\n interval = (end-start).total_seconds()/86400.0\n\n except:\n print('end: ', end)\n print('start: ', start)\n print('end-start:', end-start)\n \n return interval if interval > 0 else 0",
"title": ""
},
{
"docid": "ce017821ddd152d4ac4b227a34e06bff",
"score": "0.5239177",
"text": "def get_daily_contracts(self):\n query = (SESSION.query(Contract.doc_cloud_id, Vendor.name)\n .filter(Contract.dateadded == TODAY_DATE)\n .filter(Contract.vendorid == Vendor.id)\n .all())\n\n SESSION.close()\n\n return query",
"title": ""
},
{
"docid": "b1aabb6bd46de9095b8953026e786ad9",
"score": "0.52351433",
"text": "def _all_date(self, *para):\n if para is not None and para != ():\n if para[0] != 0:\n all_date_list = db.session.query(PortfolioEsgScoreModel.date_key). \\\n filter_by(portfolio_id=para[0]). \\\n distinct(). \\\n order_by(PortfolioEsgScoreModel.date_key.desc()). \\\n all()\n else:\n all_date_list = db.session.query(PortfolioEsgScoreModel.date_key) \\\n .filter(PortfolioEsgScoreModel.esg_factor_id == para[1]).\\\n distinct(). \\\n order_by(PortfolioEsgScoreModel.date_key.desc()). \\\n all()\n\n else:\n all_date_list = db.session.query(PortfolioEsgScoreModel.date_key). \\\n distinct(). \\\n order_by(PortfolioEsgScoreModel.date_key.desc()). \\\n all()\n\n return all_date_list",
"title": ""
},
{
"docid": "77834e0a6b5564c17c495dc116d10cf6",
"score": "0.5234292",
"text": "def getAllDates(self):\n query = \"\"\"SELECT Date FROM AMZN LIMIT 100;\"\"\" \n cnx = mysql.connector.connect(**self._config)\n cursor = cnx.cursor()\n cursor.execute(query)\n\n try:\n result = cursor.fetchall()\n except Exception:\n logger.error('Unable to get all the dates: ', exc_info=True)\n\n return result",
"title": ""
},
{
"docid": "838cdf8570d513920fc8743d78323fa5",
"score": "0.5234204",
"text": "def make_day_requests(start_date: pendulum.DateTime, end_date: pendulum.DateTime, mailto: str) -> List[DayRequest]:\n\n events = []\n period = pendulum.period(start_date, end_date)\n for day in period.range(\"days\"):\n if day != end_date:\n events.append(DayRequest(day, mailto))\n return events",
"title": ""
},
{
"docid": "6692939e544013e5d0e4ecdcfbf0964f",
"score": "0.5226655",
"text": "def coupon_days_accessible(coupon, week_start_date):\n\n start = max(week_start_date, coupon['DISPFROM'])\n end = min(week_start_date+datetime.timedelta(days=7), coupon['DISPEND'])\n\n try:\n interval = (end-start).total_seconds()/86400.0\n\n except:\n print('end: ', end)\n print('start: ', start)\n print('end-start:', end-start)\n interval = 0.0\n \n return interval if interval > 0 else 0",
"title": ""
},
{
"docid": "6c16ccebbd260daea315adf7b31961bf",
"score": "0.52257305",
"text": "def ethiopic_date(year, month, day):\n return [year, month, day]",
"title": ""
},
{
"docid": "60db2763c1dceb8fec9c019d2efa33a1",
"score": "0.52246094",
"text": "def get_between_dates(self, start_date, end_date):\n\n sql = \"\"\"\n SELECT dt,open,high,low,close,vol FROM `ada-fd`.{table}\n WHERE tick = '{tick}' and dt between '{start_date}'\n and '{end_date}' and vol > 0\n order by dt ASC\n \"\"\".format(table=self.__table,\n tick=self.ticks,\n start_date=start_date,\n end_date=end_date)\n\n ret = self.__get_data(sql)\n return ret",
"title": ""
},
{
"docid": "54a080c909d39f0b88c097b263a8a80f",
"score": "0.522131",
"text": "def test_thanksgiving():\n # November 2005\n # Su Mo Tu We Th Fr Sa\n # 1 2 3 4 5\n # 6 7 8 9 10 11 12\n # 13 14 15 16 17 18 19\n # 20 21 22 23 24 25 26\n # 27 28 29 30\n\n nyse = xnys_cal\n good_dates = nyse.valid_days('2001-01-01', '2016-12-31')\n\n # If Nov has 4 Thursdays, {0} Thanksgiving is the last Thursday.\n thanksgiving_with_four_weeks = pd.Timestamp(\"2005-11-24\", tz='UTC')\n assert thanksgiving_with_four_weeks not in good_dates\n\n # November 2006\n # Su Mo Tu We Th Fr Sa\n # 1 2 3 4\n # 5 6 7 8 9 10 11\n # 12 13 14 15 16 17 18\n # 19 20 21 22 23 24 25\n # 26 27 28 29 30\n\n # If Nov has 5 Thursdays, {0} Thanksgiving is not the last week.\n thanksgiving_with_five_weeks = pd.Timestamp(\"2006-11-23\", tz='UTC')\n assert thanksgiving_with_five_weeks not in good_dates\n\n # If NYE falls on a weekend, {0} the Tuesday after is the first trading day.\n first_trading_day_after_new_years_sunday = pd.Timestamp(\"2012-01-03\", tz='UTC')\n assert first_trading_day_after_new_years_sunday in good_dates",
"title": ""
},
{
"docid": "94c6ec74eca30f6fabd689a37417aede",
"score": "0.5219331",
"text": "def getCurrentUpperairDate():",
"title": ""
},
{
"docid": "58e7c8b06439b01a2ec617c52624fc8e",
"score": "0.52113044",
"text": "def _all_date(self, *para):\n if para is not None and para != ():\n if para[0] != 0:\n all_date_list = db.session.query(PortfolioMetricDateModel.date_key). \\\n filter_by(portfolio_id=para[0]). \\\n distinct(). \\\n order_by(PortfolioMetricDateModel.date_key.desc()). \\\n all()\n else:\n all_date_list = db.session.query(PortfolioMetricDateModel.date_key) \\\n .filter(PortfolioMetricDateModel.portfolio_id == para[1]).\\\n distinct(). \\\n order_by(PortfolioMetricDateModel.date_key.desc()). \\\n all()\n\n else:\n all_date_list = db.session.query(PortfolioMetricDateModel.date_key). \\\n distinct(). \\\n order_by(PortfolioMetricDateModel.date_key.desc()). \\\n all()\n\n return all_date_list",
"title": ""
},
{
"docid": "0f21b9c6d8b0c39a574e2cbb5ddd7d12",
"score": "0.5210785",
"text": "def buy(self):\n pass",
"title": ""
},
{
"docid": "aedbe734aed0bfe3b87938c4b09e6c32",
"score": "0.5202451",
"text": "def test_Dates_After_Today_pass(self):\r\n self.assertTrue(test_dates_pass('2015-06-12','2019-05-11','1972-02-10','1975-03-15'))\r\n self.assertTrue(test_dates_pass('2015-04-12','2020-04-16','1999-02-10','2002-07-16'))\r\n self.assertTrue(test_dates_pass('2012-06-12','2018-05-11','1965-02-10','1970-04-15'))",
"title": ""
},
{
"docid": "a302ee1247b8f1925c37b7f1229c7d63",
"score": "0.52020496",
"text": "def daily( self ):\n return CashLedgerCollection( self.df.groupby( pd.Grouper( key=\"date\", freq='d' ) ) )",
"title": ""
},
{
"docid": "1b1682ac666b2e7a7c78cd19fe8df1b1",
"score": "0.5201697",
"text": "def list_all_expiration_dates(cls, params: Dict = {}) -> List[str]:\n contracts = cls.list_all(params)\n exp_dates = unique_values_from_key(contracts, \"date_expires\")\n return sorted(exp_dates)",
"title": ""
},
{
"docid": "97f6635e1a02b87001504d23569eb3ae",
"score": "0.5185713",
"text": "def get_DB_t_days(start, end, conn):\r\n \r\n query = \"\"\"\r\n SELECT TradingDate\r\n FROM JYDB..QT_TradingDayNew\r\n WHERE TradingDate >= '%s' AND TradingDate <= '%s' AND IfTradingDay = 1 AND SecuMarket=83\r\n \"\"\" % (start, end)\r\n\r\n t_days = pd.read_sql(query, conn)\r\n t_days = t_days[\"TradingDate\"].apply(lambda x:dt.datetime.strftime(x,'%Y%m%d'))\r\n return np.array(t_days)",
"title": ""
},
{
"docid": "edb0ab85888223f25de4b68867d1f425",
"score": "0.51834667",
"text": "def get_winning_days(fund_ts):\n return tsu.get_winning_days(tsu.daily(fund_ts))",
"title": ""
},
{
"docid": "1baf4db5ca51cc41996ac1227af9518f",
"score": "0.51804405",
"text": "def buy_expenses(self):\n\t\texpenses = 0\n\t\tlast_month_start = Scheduler().cur_tick - Scheduler().get_ticks_of_month()\n\t\tkeys_to_delete = []\n\t\tfor key, values in self.buy_history.iteritems():\n\t\t\tif key < last_month_start:\n\t\t\t\tkeys_to_delete.append(key)\n\t\t\telse:\n\t\t\t\texpenses += values[2]\n\t\t# remove old keys\n\t\tfor key in keys_to_delete:\n\t\t\tdel self.buy_history[key]\n\t\treturn expenses",
"title": ""
},
{
"docid": "91d0f63ed7bfeab87cbcfe6775ddc5f1",
"score": "0.51794463",
"text": "def get_date_specifics(service, now):\n dates = get_dates(service)\n for date in dates:\n start = decypher_date(date['start'])\n end = decypher_date(date['end'])\n if start <= now:\n if end > now:\n return date['templateID'], int(date['send_id'])\n else:\n continue\n else:\n log.info(\"We haven't started yet!\")\n exit(1)",
"title": ""
},
{
"docid": "56238f6ac35c3cc7a1a23dee07bfa00d",
"score": "0.51758796",
"text": "def calculate_trade_info(announce_date, ticker_info, market_info, stop_loss_rate=None, holding_days=None,\n sell_date=None):\n\n if sell_date is None and holding_days is None:\n raise Exception('Neither sell_date or holding_days has value')\n\n temp_result = {const.REPORT_RETURN_RATE: np.nan, const.REPORT_SELL_DATE: np.nan,\n const.REPORT_BUY_DATE: np.nan, const.REPORT_MARKET_TYPE: np.nan,\n const.REPORT_MARKET_TICKER: np.nan, const.REPORT_BUY_PRICE: np.nan}\n\n # Get buy day\n trading_days = trading_day_list[trading_day_list > announce_date].tolist()\n if len(trading_days) == 0:\n return pd.Series(temp_result)\n trade_day = trading_days[0]\n\n used_stock_data = load_stock_info(trade_day, ticker_info, market_info)\n if used_stock_data.empty:\n return pd.Series(temp_result)\n\n buy_price = used_stock_data.loc[used_stock_data.first_valid_index(), const.STOCK_ADJPRCWD]\n buy_date = used_stock_data.loc[used_stock_data.first_valid_index(), const.STOCK_DATE]\n\n # this means there are not enough days to finish this operation\n if holding_days is not None:\n if len(trading_days) == 0:\n return pd.Series(temp_result)\n elif len(trading_days) < holding_days:\n sell_date = trading_days[-1]\n else:\n sell_date = trading_days[holding_days - 1]\n\n for date in trading_days[1:]:\n stock_info = load_stock_info(date, ticker_info, market_info)\n if stock_info.empty:\n continue\n\n # print stock_info\n\n current_price = stock_info.loc[stock_info.first_valid_index(), const.STOCK_ADJPRCWD]\n rate = current_price / buy_price - 1\n\n if date >= sell_date or (stop_loss_rate is not None and rate < stop_loss_rate):\n sell_price = current_price\n temp_result[const.REPORT_RETURN_RATE] = sell_price / buy_price - 1\n temp_result[const.REPORT_SELL_DATE] = date\n temp_result[const.REPORT_MARKET_TICKER] = stock_info.loc[stock_info.first_valid_index(),\n const.STOCK_TICKER]\n # temp_result[const.REPORT_MARKET_TYPE] = stock_info.loc[stock_info.first_valid_index(),\n # const.STOCK_MARKET_TYPE]\n temp_result[const.REPORT_BUY_DATE] = buy_date\n temp_result[const.REPORT_BUY_PRICE] = buy_price\n return pd.Series(temp_result)\n\n return pd.Series(temp_result)",
"title": ""
},
{
"docid": "3b306778e9ffaaca696e43e47f21e542",
"score": "0.5175284",
"text": "def _all_date(self, *para):\n if para is not None and para != ():\n if para[0] != 0:\n all_date_list = db.session.query(PortfolioResultModel.date_key). \\\n filter_by(portfolio_id=para[0]). \\\n distinct(). \\\n order_by(PortfolioResultModel.date_key.desc()). \\\n all()\n else:\n all_date_list = db.session.query(PortfolioResultModel.date_key) \\\n .filter(PortfolioResultModel.portfolio_id == para[1]).\\\n distinct(). \\\n order_by(PortfolioResultModel.date_key.desc()). \\\n all()\n\n else:\n all_date_list = db.session.query(PortfolioResultModel.date_key). \\\n distinct(). \\\n order_by(PortfolioResultModel.date_key.desc()). \\\n all()\n\n return all_date_list",
"title": ""
},
{
"docid": "7136a52e3ef0234c26e69f875fce6f5d",
"score": "0.5171434",
"text": "def get_historical_prices_plus_one_day(symbol, date):\n url = 'http://ichart.yahoo.com/table.csv?s=%s&' % symbol + \\\n 'd=%s&' % str(int(date[4:6]) - 1) + \\\n 'e=%s&' % str(int(date[6:]) + 1) + \\\n 'f=%s&' % str(int(date[0:4])) + \\\n 'g=d&' + \\\n 'a=%s&' % str(int(date[4:6]) - 1) + \\\n 'b=%s&' % str(int(date[6:]) + 1) + \\\n 'c=%s&' % str(int(date[0:4])) + \\\n 'ignore=.csv'\n\n data=[] #python3 method ,\n try:\n days = urllib.request.urlopen(url).readlines() #urllib.urlopen --> py3k needs .request. in there\n for day in days: #day[0] holds the fields names, day[1+] holds the data values\n dayStr = str(day, encoding='utf8')\n data.append( dayStr[:-2].split(','))\n #print('his',data) #Need to fix this so that we get the close data that we want.\n except urllib.error.HTTPError as err:\n if err.code == 404: #try incrementing date again \n counter+=1\n if (counter > _CounterSentinel) :\n print(\"uh oh\")\n done=True\n data=[[\"error\"]]\n\n return data[1][6] #return the Adj Close value, this takes splits into acct #this is kinda willy nilly since we don't check that we get valid results.",
"title": ""
}
]
3f1bd3557a40150f6f3867fe96f4f28d
display all the assignments as per subjects
[
{
"docid": "18ffe5b655118cc61e341ba7eda2744a",
"score": "0.5649913",
"text": "def single(sub_id):\n db = con()\n \n assignments = db.execute(\"SELECT * FROM assignments WHERE subject_id =?\",(sub_id,)).fetchall()\n subject = db.execute(\"SELECT * FROM subjects WHERE subject_id =?\",(sub_id,)).fetchone()\n if len(assignments) < 1:\n \n return render_template(\"single.html\", message=\"No subject found\", subject=subject)\n return render_template(\"single.html\", assignments=assignments, subject=subject)",
"title": ""
}
]
[
{
"docid": "a8c318c38ea786352f82bfc466e06831",
"score": "0.7014018",
"text": "def printSubjects(subjects):\n totalVal, totalWork = 0,0\n if len(subjects) == 0:\n return 'Empty SubjectList'\n res = 'Course\\tValue\\tWork\\n======\\t====\\t=====\\n'\n subNames = subjects.keys()\n subNames.sort()\n for s in subNames:\n val = subjects[s][VALUE]\n work = subjects[s][WORK]\n res = res + s + '\\t' + str(val) + '\\t' + str(work) + '\\n'\n totalVal += val\n totalWork += work\n res = res + '\\nTotal Value:\\t' + str(totalVal) +'\\n'\n res = res + 'Total Work:\\t' + str(totalWork) + '\\n'\n print res",
"title": ""
},
{
"docid": "a8c318c38ea786352f82bfc466e06831",
"score": "0.7014018",
"text": "def printSubjects(subjects):\n totalVal, totalWork = 0,0\n if len(subjects) == 0:\n return 'Empty SubjectList'\n res = 'Course\\tValue\\tWork\\n======\\t====\\t=====\\n'\n subNames = subjects.keys()\n subNames.sort()\n for s in subNames:\n val = subjects[s][VALUE]\n work = subjects[s][WORK]\n res = res + s + '\\t' + str(val) + '\\t' + str(work) + '\\n'\n totalVal += val\n totalWork += work\n res = res + '\\nTotal Value:\\t' + str(totalVal) +'\\n'\n res = res + 'Total Work:\\t' + str(totalWork) + '\\n'\n print res",
"title": ""
},
{
"docid": "5e7879d229e00096aa74fd821d2530b5",
"score": "0.70125884",
"text": "def printSubjects(subjects):\n totalVal, totalWork = 0,0\n if len(subjects) == 0:\n return 'Empty SubjectList'\n res = 'Course\\tValue\\tWork\\n======\\t=====\\t====\\n'\n subNames = subjects.keys()\n subNames.sort()\n for s in subNames:\n val = subjects[s][VALUE]\n work = subjects[s][WORK]\n res = res + s + '\\t' + str(val) + '\\t' + str(work) + '\\n'\n totalVal += val\n totalWork += work\n res = res + '\\nTotal Value:\\t' + str(totalVal) +'\\n'\n res = res + 'Total Work:\\t' + str(totalWork) + '\\n'\n print res",
"title": ""
},
{
"docid": "f615bb4a8a23d6032a9c0b229ec625bf",
"score": "0.67587334",
"text": "def display_subject_details(data):\n for subject_details in data:\n print(\"{} is taught by {:1} and has {:2} students\".format(*subject_details))",
"title": ""
},
{
"docid": "490ba142e9243dcd720c6bc550134699",
"score": "0.6673414",
"text": "def printSubjects():\r\n mapping=loadSubjects(SUBJECT_FILENAME)\r\n totalVal, totalWork = 0,0\r\n if len(mapping) == 0:\r\n return 'Empty SubjectList'\r\n #\\t means tab\r\n res = 'Course\\tValue\\tWork\\n======\\t====\\t=====\\n'\r\n #dict.keys is a list of keys of a dict\r\n subNames = mapping.keys()\r\n ## values should be sorted with floats but not the first value of a float\r\n ## 10.09 should after 9.2 while if key=float is neglected, 10.09 comes before 9.2\r\n subNames.sort(key=float)\r\n print subNames\r\n for s in subNames:\r\n val = mapping[s][0]\r\n work = mapping[s][1]\r\n res = res + s + '\\t' + str(val) + '\\t' + str(work) + '\\n'\r\n totalVal += int(val)\r\n totalWork += int(work)\r\n res = res + '\\nTotal Value:\\t' + str(totalVal) +'\\n'\r\n res = res + 'Total Work:\\t' + str(totalWork) + '\\n'\r\n print res",
"title": ""
},
{
"docid": "0b957b2afbb67b2b00eae61c6e047a7c",
"score": "0.660154",
"text": "def renderSubjects(subjects):\n for subject_id in sorted(list(subjects.keys())):\n print(subject_id)\n for predicate in sorted(list(subjects[subject_id].keys())):\n print(\" \", predicate)\n for obj in subjects[subject_id][predicate]:\n print(\" \", obj)",
"title": ""
},
{
"docid": "081994b294f9ab941fb258f7063191ab",
"score": "0.63691497",
"text": "def currentSubjects(self):",
"title": ""
},
{
"docid": "9073d24fb9e977114a9a3205c20f967e",
"score": "0.6206391",
"text": "def assignments(ctx, *args, **kwargs):\n callbacks.list_subcommand(\n ctx, display_fields=ASSIGNMENT_FIELDS, grep_name='assignments'\n )",
"title": ""
},
{
"docid": "65ce29248318b95614e7cc348212d841",
"score": "0.61780787",
"text": "def students_assignments(request):\n\n user_tests = TestUserAssignment.objects.filter(user__username=request.user,\n assignment_completed=False)\n all_tests = Test.objects.all()\n return render(request, 'students/students-assigned-tests.html',\n context={'user_tests': user_tests,\n 'all_tests': all_tests})",
"title": ""
},
{
"docid": "5bd3bfd3abf9083f7f35783160266451",
"score": "0.6053379",
"text": "def get_all_in_subjects():\r\n q = db.get_col_subjects(\"category\", \"subject\", \"topic\")\r\n return q",
"title": ""
},
{
"docid": "a8c781a1efc6110ca61702449c13870b",
"score": "0.6010947",
"text": "def __get_aggregated_subjects__(self,workflow_id):\n stmt = \"select subject_id from aggregations where workflow_id = \" + str(workflow_id)\n postgres_cursor = self.postgres_session.cursor()\n postgres_cursor.execute(stmt)\n\n subjects = []\n\n for r in postgres_cursor.fetchall():\n subjects.append(r[0])\n\n return subjects",
"title": ""
},
{
"docid": "1cc014db630fdf3b3b161a8f5010e806",
"score": "0.58827966",
"text": "def session_assignments(session_id, course_id):\n session = sessions.get_session(session_id)\n course = courses.get_course(course_id)\n\n if session['course_id'] != course['course_num']:\n abort(403)\n\n cur = db.get_db().cursor()\n \n cur.execute(\"\"\"\n SELECT * FROM assignments\n WHERE sessions_id = %s\"\"\",\n (session_id,))\n\n assignments = cur.fetchall()\n\n cur.close()\n return render_template(\"student_views/your_assignments.html\", session=session, assignments=assignments, course=course)",
"title": ""
},
{
"docid": "1e7b5aa4a8794b6ac332e623fc5931d1",
"score": "0.58004814",
"text": "def get_subjects(self):\n return list(self.subjects.values())",
"title": ""
},
{
"docid": "39edab880ba21191b2b680860e467ee8",
"score": "0.57944715",
"text": "def get(self, request, *args, **kwargs):\n subject = Subject.objects.all()\n context = {\n \"subject_list\": subject\n }\n\n return render(request, self.template_name, context)",
"title": ""
},
{
"docid": "5c358dcfa6e6fb9d12f094aef9203acc",
"score": "0.5763922",
"text": "def subjects(self, subjects):\n\n self._subjects = subjects",
"title": ""
},
{
"docid": "4e2afb4d097b348b9f6d6c351539ed45",
"score": "0.5741078",
"text": "def main():\n data = get_data()\n display_subject_details(data)",
"title": ""
},
{
"docid": "c89749472fe6ab2319d4ed6f3255fc33",
"score": "0.573881",
"text": "def query_associated_subjects(self) -> models.QuerySet:\n Subject = get_subject_model()\n return Subject.objects.filter(\n mri_session_set__scan__study_groups__study=self.id\n ).distinct()",
"title": ""
},
{
"docid": "ac9d00ea3731651996d310b1b2943595",
"score": "0.57098675",
"text": "def get_category_subjects():\r\n q = db.get_col_subjects(\"category\", \"subject\")\r\n return q",
"title": ""
},
{
"docid": "6a1881011b8941d81efbf747a123453a",
"score": "0.5707057",
"text": "def instructorsummary1(self):\r\n\r\n for course,studentnumber in self.coursestudents.items():\r\n return [self.instructorcwid,self.instructorname,self.instructordept, course, studentnumber]",
"title": ""
},
{
"docid": "357e43eaf291abe339103bdd5a0f8bd8",
"score": "0.5626068",
"text": "def get_subjects(self, options=None):\n\n secure = self.options[\"force_ssl\"]\n try:\n secure = options[\"force_ssl\"]\n except:\n pass\n\n projectsToGet = [project for project in self.selectedProjectIds if not project in self.fetchedProjectIds]\n\n requests = (grequests.get(self.server + \"/data/projects/\" + project + \"/subjects\",\n headers={\"Cookie\": \"JSESSIONID=\" + self.JSESSIONID},\n params={\n \"format\": \"json\",\n \"columns\": \"\"\"age,birth_weight,dob,education,educationDesc,\n ethnicity,gender,gestational_age,group,handedness,\n height,insert_date,insert_user,last_modified,pi_firstname,\n pi_lastname,post_menstrual_age,race,ses,src,weight,yob\"\"\"\n },\n verify=secure) for project in projectsToGet)\n\n results = grequests.map(requests)\n\n # if self.data == None:\n # self.data = SubjectData({\"id_field\": \"ID\"})\n\n for count, result in enumerate(results):\n if result.status_code == 200:\n try:\n subjects = result.json()[\"ResultSet\"][\"Result\"]\n except:\n pass\n else:\n self.fetchedProjectIds.append(projectsToGet[count])\n for i in range(len(subjects)):\n subjects[i][\"project\"] = projectsToGet[count]\n subjects[i][\"experiments\"] = {}\n self.add_subject(subjects[i])\n\n self.add_group({}, \"All Subjects\")\n\n return self",
"title": ""
},
{
"docid": "11397855adecc841c36142ed15c09ce3",
"score": "0.5619291",
"text": "def subject_views(request):\n try:\n session = request.session.get(\"user_id\")\n if session:\n user_info_obj = UserInfo.objects.get(id=session)\n user_operation_obj = UserOperation.objects.filter(fk_user_role_id=user_info_obj.fk_user_role.id)\n subject_obj = Subject.objects.all()\n course_obj = Course.objects.all()\n semester_obj = Semester.objects.all()\n return render(request, \"master_subject.html\",\n {\"user_operation_obj\": user_operation_obj, \"user_info_obj\": user_info_obj,\n \"subject_obj\": subject_obj, \"course_obj\": course_obj, \"semester_obj\": semester_obj})\n else:\n return redirect(\"/\")\n except Exception:\n error_save(str(traceback.format_exc()))\n return redirect(\"error_handler_500\")",
"title": ""
},
{
"docid": "e44d58f1abed0a079e2089a10f2956f0",
"score": "0.5581853",
"text": "def show(self):\n centers = self.centers()\n subj = self.get_feature('subjects')\n prevalence = self.roi_prevalence()\n print \"index\", \"prevalence\", \"mean_position\", \"individuals\"\n for i in range(self.k):\n print i, prevalence[i], centers[i], np.unique(subj[i])",
"title": ""
},
{
"docid": "00ec49cda0eb2c2117b721a83f222ffe",
"score": "0.55746514",
"text": "def list_assignments(p):\n students_assignments = Assignment.get_assignments_by_student(p.student_id)\n students_assignments_dump = AssignmentSchema().dump(students_assignments, many=True)\n return APIResponse.respond(data=students_assignments_dump)",
"title": ""
},
{
"docid": "23d8accbe310597808d649321bc1e341",
"score": "0.55666864",
"text": "def get_assignments(course_id, header, host_site):\n \n path = '/api/v1/courses/%s/assignments' % (course_id)\n url = '%s%s%s' % (PROTOCOL, host_site, path)\n params = {'include[]':'submission',\n 'per_page': PAGE_PAGINATION_LIMIT\n }\n \n assignments = requests.get(url, headers=header, params=params).json() \n return sorted(assignments, key = lambda assignment: assignment['due_at'])",
"title": ""
},
{
"docid": "2068335873f23712018d564d8ac8f641",
"score": "0.5558842",
"text": "def subjects(concept_graph, concept, type=None):\n assert concept_graph.subjects('Sarah') == {'Peter'}\n assert concept_graph.subjects('Mary', 'likes') == {'John'}\n assert concept_graph.subjects('Mary', 'dislikes') == {'Peter'}",
"title": ""
},
{
"docid": "a0dfe96a2995b7cd021c51a11522e019",
"score": "0.55412096",
"text": "def instructor_table(self):\n print()\n print(\"Instructor Summary:\")\n # decided to display 'Dept' as well, though not required by assignment\n header = [\"CWID\", \"Name\", \"Dept\", \"Course\", \"Students\"]\n x = PT(header)\n for item in self.instructors:\n class_list = list(item.courses.keys())\n for subject in class_list:\n x.add_row([str(item.CWID), str(item.name), str(item.dept), subject, item.courses[subject]])\n print(x)\n return None",
"title": ""
},
{
"docid": "50f937f63f0c7150c92ea1d3da644039",
"score": "0.5535205",
"text": "def get_subject(self):\n for subject in self.subjects.keys():\n if self.subjects[subject].assigned:\n continue\n else:\n self.subjects[subject].assigned = True\n return self.subjects[subject]",
"title": ""
},
{
"docid": "40ef04f71cfc8ef9bf923cc6e4623e93",
"score": "0.553517",
"text": "def get_data(self, subjects):\n data = []\n for subject in subjects:\n data.append(self._get_single_subject_data(subject))\n return data",
"title": ""
},
{
"docid": "10273bb4694aa0b7ba8b46b62f690452",
"score": "0.5534084",
"text": "def get_subjects(subject_nodes):\n arXiv_subjects = []\n INSPIRE_subjects = []\n\n for subject in subject_nodes:\n category = get_subfield(subject, \"2\")\n subject = get_subfield(subject, \"a\")\n\n if category == \"arXiv\":\n arXiv_subjects.append(subject)\n elif category == \"INSPIRE\":\n INSPIRE_subjects.append(subject)\n\n arXiv_subjects = (arXiv_subjects + [np.nan] * SUBJECT_NCOLS)[:SUBJECT_NCOLS]\n INSPIRE_subjects = (INSPIRE_subjects + [np.nan] * SUBJECT_NCOLS)[:SUBJECT_NCOLS]\n\n subjects_df = pd.DataFrame(data=[arXiv_subjects + INSPIRE_subjects], columns=ARXIV_INSPIRE_SUBJECT_COLS)\n\n return subjects_df",
"title": ""
},
{
"docid": "64e17ea938cadb661919b810f09cb6ab",
"score": "0.55337095",
"text": "def read_subjects(contents):\n course_list = []\n for subj in contents.get(\"selectedSubjects\", []):\n if SUBJECT_ID_KEY in subj:\n course_list.append(subj[SUBJECT_ID_KEY])\n elif SUBJECT_ID_ALT_KEY in subj:\n course_list.append(subj[SUBJECT_ID_ALT_KEY])\n return course_list",
"title": ""
},
{
"docid": "a732e353e7ce21fc46bcf789bea7635b",
"score": "0.5526596",
"text": "def show_subjects(dir_path):\r\n\tdata_dir = os.listdir(dir_path)\r\n\treturn data_dir",
"title": ""
},
{
"docid": "b210428a8f7c047c5a2c0ac6e992331d",
"score": "0.550513",
"text": "def compare_subjects_within_student(subj1_all_students,\n subj2_all_students):\n for student, grades in subj1_all_students.items():\n if student in subj2_all_students.keys() and student != ('subject'):\n if max(subj1_all_students[student]) > max(subj2_all_students[student]):\n print (student, subj1_all_students['subject'])\n elif max(subj1_all_students[student]) < max(subj2_all_students[student]):\n print (student, subj2_all_students['subject'])\n elif max(subj1_all_students[student]) == max(subj2_all_students[student]):\n print (student, subj2_all_students['subject'], 'and', subj1_all_students['subject'] )",
"title": ""
},
{
"docid": "f704b732e2f007d47d5c103f541d1efd",
"score": "0.5490344",
"text": "def get_assignments(self, course_id):\n return self.api.get('/courses/%s/assignments' % course_id)",
"title": ""
},
{
"docid": "caf8b2afed8295174706136cafcda821",
"score": "0.546389",
"text": "def list(name_or_id=\"\", filter=None, human: bool = False):\n db_conf = configuration.get_configuration(key='management')\n manager_api_key = connect_from_env()\n _subjects_req = requests.get(\"{}/subjects\".format(db_conf.get(\"url\")),\n headers={\"x-api-key\": manager_api_key}\n )\n if _subjects_req.status_code == 200:\n if name_or_id:\n _subjects = None\n if name_or_id in _subjects_req.json().get(\"subjects\"):\n _subjects = _subjects_req.json().get(\"subjects\").get(name_or_id)\n else:\n for _subjects_key in _subjects_req.json().get(\"subjects\"):\n _name = _subjects_req.json().get(\"subjects\").get(_subjects_key).get(\"name\")\n if _name == name_or_id:\n _subjects = _subjects_req.json().get(\"subjects\").get(_subjects_key)\n name_or_id = _subjects_key\n break\n if not _subjects:\n raise Exception(\"Cannot find Subjects with name or ID {}\".format(name_or_id))\n else:\n if human:\n result = {\"subjects\": {name_or_id: _subjects}}\n else:\n result = {\"subjects\": [{name_or_id: _subjects}]}\n elif filter:\n return \"\\n\".join(\n [\"\\t\".join(_t) for _t in filter_dict(_subjects_req.json().get(\"subjects\"),\n filter.split(\",\"))]\n )\n else:\n result = _subjects_req.json()\n\n if human:\n return SubjectsCLI.human_display(result)\n else:\n return result\n LOGGER.error('Cannot list Subjects {}'.format(_subjects_req.status_code))",
"title": ""
},
{
"docid": "3846677f1c57e681c1e254c5ca2be05c",
"score": "0.54636914",
"text": "def show_submitted_courses(self):\n with open('../databases/users_db/students_info.json') as std_info:\n units = 0\n info = json.load(std_info)\n table = PrettyTable(\n ['course code', 'course name', 'units', 'teacher name', 'field code', 'total quantity'])\n for course in info[f'{self.user_id}'][1]:\n values = []\n for i in ['course_code', 'name', 'units', 'teacher_name', 'field_code', 'total_quantity']:\n values.append(course[i])\n table.add_row(values)\n units += course['units']\n print(f'TOTAL UNITS : {units}')\n print(table)",
"title": ""
},
{
"docid": "a611d41ea323ef1a9d7f8f3116404ea3",
"score": "0.54625446",
"text": "def public_assignments():\n\n # Get optional class filter from get query\n course_id = request.args.get(\"courseId\", default=None)\n\n # Get (possibly cached) assignment data\n assignment_data = get_assignments(current_user.netid, course_id)\n\n # Iterate over assignments, getting their data\n return success_response({\"assignments\": assignment_data})",
"title": ""
},
{
"docid": "bb9340e25767b1657be55e5a1e228693",
"score": "0.54577607",
"text": "def get_all_assignments(self):\n hits = self.connection.get_all_hits()\n assignments = []\n for hit in hits:\n page = 1\n while True:\n page_assignments = self.connection.get_assignments(hit.HITId, page_size=100, page_number=page)\n assignments.extend(page_assignments)\n page += 1\n if len(page_assignments) == 0:\n break\n return assignments",
"title": ""
},
{
"docid": "75bcc6a3fdd0549142a346b6b0aaf20f",
"score": "0.54342514",
"text": "def list_subjects_and_categories(command, root_path):\n subjects=[]\n categories=[]\n groups=[\"all\"]\n \n with open(command['fullpath'],'rt',encoding=\"utf-8-sig\") as fh:\n header=fh.readline().strip().split(',')\n subj_idx = header.index(\"id\")\n cat_idx = header.index(\"category\")\n try:\n group_idx = header.index(\"group\")\n except:\n group_idx = -1\n\n from csv import reader\n for line in reader(fh):\n if line[subj_idx] not in subjects:\n subjects.append(line[subj_idx])\n if line[cat_idx] not in categories:\n categories.append(line[cat_idx])\n if group_idx > -1:\n group_label = line[group_idx]\n if group_label not in groups:\n groups.append(group_label)\n return { \"type\": \"list_subjects_and_categories\",\n \"subjects\": subjects, \n \"categories\": categories,\n \"groups\": groups,\n \"subject\": subjects[0],\n \"category\": categories[0],\n \"group\": groups[0] }",
"title": ""
},
{
"docid": "3648fe0f497949953bfc8bb6beaa06fa",
"score": "0.54211396",
"text": "def assign_view(assign_id, session_id, course_id):\n assignment = assign.get_assignment(assign_id)\n session = sessions.get_session(session_id)\n course = courses.get_course(course_id)\n if session['course_id'] != course['course_num']:\n abort(403)\n if session['id'] != assignment['sessions_id']:\n abort(403)\n\n return render_template(\"student_views/assignment_details.html\", session=session, assignment=assignment, course=course)",
"title": ""
},
{
"docid": "d60883324d25fa431a5a90d6ceeda5b0",
"score": "0.5397596",
"text": "def filter_subjects(course_id=None, staff_id=None, request=None):\r\n # get list of submitted data.\r\n list_data = [course_id, staff_id]\r\n # give none value to all querysets as initial values.\r\n subjects_course_qs = None\r\n subjects_staff_qs = None\r\n if course_id:\r\n # filter subjects by selected course.\r\n subjects_course_qs = Subject.objects.filter(course__id=course_id)\r\n # if queryset doesn't exist.\r\n if not subjects_course_qs.exists():\r\n subjects_course_qs = 'Subject Course'\r\n if staff_id: \r\n # filter subjects by selected staff.\r\n subjects_staff_qs = Subject.objects.filter(staff__id=staff_id)\r\n # if queryset doesn't exist.\r\n if not subjects_staff_qs.exists():\r\n subjects_staff_qs = 'Subject Staff'\r\n # make a list of all filter querysets.\r\n filter_qs_list = [subjects_course_qs, subjects_staff_qs]\r\n # if course, session or gender is selected.\r\n if any(list_data):\r\n # return the intersection between all querysets.\r\n return filter_list(filter_qs_list) \r\n # if not.\r\n else:\r\n # get all staffs of current adminhod's courses.\r\n return Subject.objects.user_subjects(request.user)",
"title": ""
},
{
"docid": "e0c340a7fdb2576dff26c7a4cf286924",
"score": "0.5395771",
"text": "def get_subjects(datapath):\n df = read_tsv(datapath)\n subjects = df.participant_id.tolist()\n baseline = df.baseline.tolist()\n retest = df.retest.tolist()\n sub_ses = []\n for sub, bas, trt in zip(subjects, baseline, retest):\n sub_ses.append((sub, bas))\n sub_ses.append((sub, trt))\n return sub_ses",
"title": ""
},
{
"docid": "40c446ccfa8466074e29f5bc49364dc2",
"score": "0.53862315",
"text": "def subjects(self, subject_ids):\n return self.db.get_golds(subject_ids)",
"title": ""
},
{
"docid": "28248e8c679be52c14a29589a576e8dc",
"score": "0.5384386",
"text": "def parse_subject(self, response):\n current_term_id = self.determine_current_term(response)\n subject_id = re.search('\\?subject=(\\d+)', response.url).group(1)\n\n subject_name = response.xpath(\"//h2[@class='vac']\")[0].xpath('./text()').extract()\n subject_name = \"\".join([l.strip('\\n') for l in subject_name if l.strip('\\n')])\n\n for student in response.xpath(\"//td[@class='student-assessment']\"):\n student_id = student.xpath('./div/@id').extract()[0].split('_')[-1]\n if self.student_id and self.student_id != student_id:\n continue\n\n # First we have to set up and get the report section (subject) in the database\n item = PrimaryReportSectionItem()\n comment = response.xpath(\"//div[@id='user_comments_{}']\".format(student_id))\n comment = comment.xpath('./textarea/text()').extract()\n comment = comment[0] if comment else \"\"\n\n item['student_id'] = student_id\n item['course_id'] = response.meta.get('class_id')\n item['term_id'] = current_term_id\n\n item['subject_id'] = subject_id\n item['subject_name'] = subject_name\n item['comment'] = comment\n\n strands = response.xpath(\"//div[@id='user_strand_marks_{}']\".format(student_id))\n if not strands:\n # This is a single subject that has been changed from strands to just an overall comment\n overall_comment = response.xpath(\"//div[@id='user_final_grade_marks_{}']\".format(student_id))\n if overall_comment:\n overall_comment = overall_comment[0]\n selection = overall_comment.xpath(\"./table/tbody/tr/td/select/option[@selected='selected']/text()\").extract()\n selection = selection[0] if selection else \"\"\n item['overall_comment'] = {'G': 'Good', 'O':'Outstanding', 'N':'Needs Improvement'}.get(selection, '')\n else:\n item['overall_comment'] = \"N/A\"\n else:\n item['overall_comment'] = \"N/A\"\n\n yield item\n\n for student_strand in strands:\n which = 1\n for strand in student_strand.xpath('./table/tbody/tr'):\n strand_label = (strand.xpath('./td[1]/text()').extract()[0]).strip('\\n').strip()\n strand_label = strand_label[0].upper() + strand_label[1:]\n strand_label += \".\" if strand_label[-1] != '.' else \"\"\n value = strand.xpath(\"./td[2]/select/option[@selected='selected']/text()\").extract()\n strand_value = value[0] if value else \"\"\n \n item = PrimaryReportStrandItem()\n item['student_id'] = student_id\n item['course_id'] = response.meta.get('class_id')\n item['term_id'] = current_term_id\n item['subject_id'] = subject_id\n item['which'] = which\n\n item['strand_label'] = strand_label\n item['strand_label_titled'] = strand_label.title().replace('And', 'and')\n item['strand_text'] = strand_value\n\n if strand_label:\n yield item\n which += 1\n\n for student_outcome in response.xpath(\"//div[@id='user_learning_outcomes_marks_{}']\".format(student_id)):\n which = 1\n for outcome in student_outcome.xpath('./table'):\n\n # For this, we have to inspect the headers to see \n\n for section_num in range(len(outcome.xpath('./thead'))):\n\n strand_heading = outcome.xpath('./thead/tr/th/strong/text()').extract()[section_num * 2]\n outcome_body = outcome.xpath('./tbody')[section_num]\n\n for outcome_content in outcome_body.xpath('./tr'):\n\n cells = outcome_content.xpath('./td')\n if len(cells) == 2:\n outcome_label = (cells[0].xpath('./text()').extract()[0]).strip('\\n').strip()\n outcome_label = outcome_label[0].upper() + outcome_label[1:]\n outcome_label += '.' if outcome_label[-1] != \".\" else \"\"\n value = cells[1].xpath(\"./select/option[@selected='selected']/text()\").extract()\n\n outcome_value = value[0] if value else \"\"\n\n item = PrimaryReportOutcomeItem()\n item['student_id'] = student_id\n item['course_id'] = response.meta.get('class_id')\n item['term_id'] = current_term_id\n item['subject_id'] = subject_id\n item['heading'] = strand_heading.title().replace('And', 'and')\n item['which'] = which\n item['outcome_label'] = outcome_label\n item['outcome_label_titled'] = outcome_label.title().replace('And', 'and')\n item['outcome_text'] = outcome_value\n\n if outcome_label.strip('\\n'):\n yield item\n which += 1",
"title": ""
},
{
"docid": "5f9bbac8972ea23c44007e81f3a91216",
"score": "0.53774905",
"text": "def show_chosen_courses(self):\n if len(self.chosen_courses) == 0:\n print('No courses have been added yet.')\n\n else:\n table = PrettyTable(\n ['course code', 'course name', 'units', 'teacher name', 'field code', 'total quantity'])\n\n for lesson in self.chosen_courses:\n table.add_row(\n [lesson.course_code, lesson.name, lesson.units, lesson.teacher_name, lesson.field_code,\n lesson.total_quantity])\n print(table)",
"title": ""
},
{
"docid": "f5b8740e22088a47e45006e7695c29fc",
"score": "0.537559",
"text": "def __str__(self):\n return self.subject_name",
"title": ""
},
{
"docid": "51a9da0ced6340d1894761b738ad67eb",
"score": "0.5369715",
"text": "def handle_asklist(bot, event):\n event.reply('available subjects: ', experts.data.keys())",
"title": ""
},
{
"docid": "be36259696316828b2303f7956f63924",
"score": "0.5363911",
"text": "def get_assignments() -> list:\n return _assignments",
"title": ""
},
{
"docid": "eff4550afccd51d2b57263602d1b668a",
"score": "0.53589725",
"text": "def show_available_courses(self):\n\n table = PrettyTable(\n ['course code', 'course name', 'units', 'teacher name', 'field code', 'total quantity'])\n\n for lesson in self.defined_available_courses():\n table.add_row(\n [lesson.course_code, lesson.name, lesson.units, lesson.teacher_name, lesson.field_code,\n lesson.total_quantity])\n print(table)",
"title": ""
},
{
"docid": "4be05bd2766a16affe63f3c15f4ed69c",
"score": "0.53571683",
"text": "def get_all(cls):\n assignments = cls.query.all()\n return assignments",
"title": ""
},
{
"docid": "6af62829fcac9275703e0011a83d0850",
"score": "0.5352533",
"text": "def get_summary(self):\n if len(self.courses) > 0:\n print_array = []\n for course in self.courses:\n print_array.append([self.cwid,self.name,self.department,course,self.courses[course]])\n return print_array\n else:\n raise ValueError('Not enough courses')",
"title": ""
},
{
"docid": "ccc62b1dd243148cddb21b8ca25bc7ea",
"score": "0.5334023",
"text": "def get_assignments0(self, team_name):\n\n _cards = []\n _assignments = dict()\n\n if team_name == 'SysEng':\n for list in self.syseng_assignments.all_lists():\n if list.name == 'In Progress'.encode('utf-8'):\n _cards = self.syseng_assignments.get_list(list.id).list_cards()\n\n for _card in _cards:\n _label = 'default'\n\n for label in _card.labels:\n if label.name == b'Ok':\n _label = 'success'\n if label.name == b'Issues':\n _label = 'warning'\n if label.name == b'Blocked':\n _label = 'danger'\n\n _assignments[_card.id] = assignment.Assignment(_card.id, _card.name, None, _status = _label)\n\n return _assignments",
"title": ""
},
{
"docid": "31d0f81e924ceba0401beac23856ac5c",
"score": "0.53136635",
"text": "def show_subject(request,subject_slug,template_name=\"resources/subject.html\"): \r\n subject = get_object_or_404(Subject,slug=subject_slug)\r\n resources = subject.resource_set.all()\r\n page_title = subject.name\r\n is_ratted = False\r\n #dont't allow people to rate a subject more than once\r\n if request.user.subjectrate_set.filter(subject=subject):\r\n is_ratted=True\r\n rate_form = get_rate_form(Subject)()\r\n #save comment\r\n if request.method==\"POST\":\r\n if request.POST.get('action')=='comment':\r\n comments_form = CommentsForm(request.POST)\r\n if comments_form.is_valid():\r\n comments_form.save(user=request.user,subject=subject)\r\n comments= Comments.objects.filter(subject=subject).order_by('-date_comment')\r\n comments_form = CommentsForm()\r\n return render_to_response(template_name,locals(),context_instance=RequestContext(request))",
"title": ""
},
{
"docid": "2e6ccf933dba183c0f2f8d77d9ab0f6a",
"score": "0.5307436",
"text": "def view_announcements_student(request):\r\n # get list of all staffs' ids of current student's course.\r\n staff_ids_list = Subject.objects.staff_ids(request.user, is_student=True)\r\n # display all announcements of current student's course.\r\n announcements = Announcement.objects.filter(\r\n Q(adminhod=request.user.student.course.adminhod)|\r\n Q(staff__user__id__in=staff_ids_list) \r\n ).distinct()\r\n # update announcements from not seen to be seen. \r\n Announcement.objects.announcements_updated(request.user, staff_ids_list, is_student=True)\r\n context = {'announcements':announcements}\r\n return render(request, 'student/view_announcements.html', context)",
"title": ""
},
{
"docid": "a4a26eb7d7dc10461d0a364bea1672e2",
"score": "0.53056204",
"text": "def subjects(*reports) -> Iterator:\n for report in reports:\n yield from report.get(\"subjects\", {}).values()",
"title": ""
},
{
"docid": "864712af21aad61da766d66f4500a8d3",
"score": "0.5293205",
"text": "def instructorsummary(self):\r\n\r\n for course,studentnumber in self.coursestudents.items():\r\n yield [self.instructorcwid,self.instructorname,self.instructordept, course, studentnumber]",
"title": ""
},
{
"docid": "10368b3aaf3891162e89440019084cf1",
"score": "0.52924657",
"text": "def subject_set(self) -> models.QuerySet:\n return self.query_associated_subjects()",
"title": ""
},
{
"docid": "db79f4a4d6fd0e2875a5e6759b4cae80",
"score": "0.5276414",
"text": "def select_distinct_subjects(**kwargs):\n return select_distinct(M.SUBJECT, **kwargs)",
"title": ""
},
{
"docid": "98c4de9eec22f972e4a99dbf6a531ef4",
"score": "0.5271705",
"text": "def list_all(request):\n if \"full\" in request.GET:\n full = request.GET[\"full\"].lower() in TRUE_SET\n else:\n full = False\n return HttpResponse(json.dumps([c.to_json_object(full=full) for c in Course.public_courses().order_by(\"subject_id\")]), content_type=\"application/json\")",
"title": ""
},
{
"docid": "a4d3828bf82dc4919d034ce113193846",
"score": "0.5263679",
"text": "def display(self, assignment):\n # Subclasses can print in a prettier way, or display with a GUI\n print('CSP:', self, 'with assignment:', assignment)",
"title": ""
},
{
"docid": "1290ca40f5c22f428f5b2ef8444eb0bf",
"score": "0.52548254",
"text": "def get_subjects(fname_in):\n fin = open(fname_in, 'r')\n cin = csv.reader(fin)\n h = next(cin)\n\n subject_s = set()\n for l in cin:\n subject_s.add(l[3])\n\n subject_l = list(subject_s)\n pickle_data('subject_set.pkl', subject_s)\n fout = open('subjects.csv', 'w')\n cout = csv.writer(fout)\n cout.writerow(['Subject', 'In Allston'])\n for l in subject_l:\n cout.writerow([l, ''])\n\n fout.close()\n fin.close()",
"title": ""
},
{
"docid": "32a346bbee0158751cd6fea9f135d31d",
"score": "0.5251306",
"text": "def do_assignments(self, s):\n try:\n assignments = list(self.game.assign())\n except GameError as e:\n print('Cannot get assignments: {}'.format(e))\n else:\n if assignments:\n self._print_assignments(assignments)\n else:\n print('No new assignments available')",
"title": ""
},
{
"docid": "9801b5bddd8f98af7f0c97adde8248bd",
"score": "0.5214523",
"text": "def scheduleTeachingAssignmentHtml(request):\n getSchedule().sort(key=lambda x: x.instructor, reverse=False)\n assignment = sorted(getSchedule(), key=lambda x: x.instructor, reverse=False)\n additional_assignments = getAssignments()\n for instructor in additional_assignments:\n print(instructor.instructor)\n courses = getCourses()\n assignemnt_hours = list()\n total = 0\n final_total = 0\n currentinstructor = \"\"\n for instructor in assignment:\n if currentinstructor != instructor.instructor:\n final_total = total\n total = 0\n assignemnt_hours.append(final_total)\n for course in courses:\n currentinstructor = instructor.instructor\n if instructor.course == course.course:\n total = total + course.workloadhours\n assignemnt_hours.append(final_total)\n return render(request, \"polls/teaching_assignments.jinja\", {'assignment': assignment, 'courses': courses,\n 'assignment_hours': assignemnt_hours,\n 'additional_assignments': additional_assignments})",
"title": ""
},
{
"docid": "cc19fb4cce625b586095e683804d7cc4",
"score": "0.5196651",
"text": "def subjects(data, demo, cond, group):\r\n subs = list(set(data.index.values.tolist()))\r\n subs = [i for i in subs if (list(demo.loc[i])[0] == group)]\r\n return subs",
"title": ""
},
{
"docid": "453f9f6230ae2695d56373f986a8b19f",
"score": "0.5194091",
"text": "def dpAdvisor(subjects, maxWork):\r\n # TODO...\r",
"title": ""
},
{
"docid": "45925fb52bfc956f77864de8b2cb9c31",
"score": "0.5182091",
"text": "def get_subject_areas():\n result = requests.get(BASE_URL + '/course/api/subjectAreas', cookies=cookies)\n result.raise_for_status()\n return result",
"title": ""
},
{
"docid": "b0e220944cef6419a6d693f0fe5bd838",
"score": "0.5162329",
"text": "def list_assignments(assignments, upload):\n \n assignments_map ={}\n \n for i, assignment in enumerate(assignments, 1):\n if upload: \n assignments_map[i] = assignment['id'] \n else:\n assignments_map[i] = assignment['html_url'] \n print \"%s. %s\" % (str(i), assignment['name'])\n if upload:\n print 'Enter the number corresponding to the assignment you want to turn in: '\n return assignments_map[int(raw_input())]\n return assignments_map",
"title": ""
},
{
"docid": "3d230587bc09766e821a66d1920b525c",
"score": "0.51359594",
"text": "def get_all_assignment_ids(self):\n\n return self.assignments.keys()",
"title": ""
},
{
"docid": "1a962ec4e30f80efe5f0b11734f5d8f7",
"score": "0.5125519",
"text": "def get_students_list():\n\n titles = [\"Idx\", \"Password\", \"Name\", \"Surname\", \"Email\"]\n students = instances_manager.prepare_data_to_visualize(Student.student_list)\n codecooler_view.print_table(titles, students)\n get_students_grades()",
"title": ""
},
{
"docid": "eacaeec5b29408c7b89c24eb2049e30f",
"score": "0.5116473",
"text": "def n_subjects(self):\n return len(self.te.subjects_index)",
"title": ""
},
{
"docid": "3d924a66e5689cab3f6bec713610216c",
"score": "0.5108318",
"text": "def student_table(self):\n print()\n print(\"Student Summary:\")\n header = [\"CWID\", \"Name\", \"Major\", \"Courses Completed w/ grade >= C\", \"Remaining Required\", \"Remaining Electives\"]\n x = PT(header)\n for item in self.students:\n # First get difference between required and completed classes passed satisfactorily (grade >= 'C')\n passed_courses, required_remaining, elective_remaining = self.courses_remaining(item)\n x.add_row([str(item.CWID), str(item.name), str(item.major), sorted(passed_courses), sorted(required_remaining), sorted(elective_remaining)])\n print(x)\n return None",
"title": ""
},
{
"docid": "f190d3f0a99b3482263ca9f6eda1dda9",
"score": "0.50919473",
"text": "def find_subjects(experiment, rootdir=\"/\"):\n json_reader = JsonIndexReader(os.path.join(rootdir,\n \"protocols\",\n \"r1.json\"))\n subjects = json_reader.aggregate_values(\n 'subject_alias', experiment=experiment)\n return subjects",
"title": ""
},
{
"docid": "6750cb9a498fdd224f3d715723f94f9b",
"score": "0.5086884",
"text": "def associations_subjects(self, **kwargs) -> Iterator[CURIE]:\n # individual implementations should override this to be more efficient\n yielded = set()\n for a in self.associations(**kwargs):\n s = a.subject\n if s in yielded:\n continue\n yield s\n yielded.add(s)",
"title": ""
},
{
"docid": "0ec9fe4651bd7c208382943864489dbe",
"score": "0.5068389",
"text": "def n_subjects(self) -> int:\n return self.subject_set.count()",
"title": ""
},
{
"docid": "0d517e711d0eb2ca96d432911fcf48d6",
"score": "0.50627875",
"text": "def assign_users(request):\n teacher_created_assignments = AssignmentCreator.objects.filter(teacher=request.user)\n assignments_all_existing = TestUserAssignment.objects.filter(\n pk__in=teacher_created_assignments.values_list('assignment', flat=True))\n if request.method == 'POST':\n form = AssignmentsForm(request.POST)\n if form.is_valid():\n assignment_form = form.save()\n teacher = request.user\n creation = AssignmentCreator.objects.create(teacher=teacher,\n assignment=assignment_form)\n creation.save()\n teacher_created_assignments = \\\n AssignmentCreator.objects.filter(teacher=request.user)\n assignments_all_existing = TestUserAssignment.objects.filter(\n pk__in=teacher_created_assignments.values_list('assignment', flat=True))\n\n messages.info(request, \"Student {} was assigned a test {}\".format(form.cleaned_data[\"user\"],\n form.cleaned_data[\"test\"]))\n else:\n messages.info(request, \"Student {} is already assigned this test {}\".format(form.cleaned_data[\"user\"],\n form.cleaned_data[\"test\"]))\n\n else:\n form = AssignmentsForm()\n return render(request, 'teachers/assign-users-tests.html',\n {'form': form, 'existing_assignments': assignments_all_existing})",
"title": ""
},
{
"docid": "9a4ba812a269371e3607422d5fe9e261",
"score": "0.5043249",
"text": "def top_subjects(self,top_n = 25):\n return self._top_items(self.te.subjects_index,top_n)",
"title": ""
},
{
"docid": "1a59182dab6a872948f0aba87687f011",
"score": "0.5030182",
"text": "def get_all_classroom_data(url):\n for prefix in subjects:\n url = url + \"/\" + prefix\n html_data = get_all_html_content_from_url(url)\n extract_data_from_html(html_data)",
"title": ""
},
{
"docid": "a119ee699b7f8afc9650d68e9949f5bb",
"score": "0.50187707",
"text": "def index(course_name = None, assignment_name = None):\n courses = Course.accessible_by(request.username)\n course = None\n assignments = None\n assignment = None\n\n if course_name is not None:\n course = next((c for c in courses if c.name == course_name), None)\n if course is None:\n return abort(404)\n # Only show public unit tests\n assignments = course.assignments.filter_by(visible=True).filter(Assignment.testfiles.any(Testfile.unittests.any(Unittest.is_public)))\n if course is not None and assignment_name is not None:\n assignment = assignments.filter_by(name=assignment_name).first_or_404()\n\n return render_template(\"main.html\", courses=courses, course=course, assignments=assignments, assignment=assignment)",
"title": ""
},
{
"docid": "d58a86e75f7e3461a4d424b07a12f660",
"score": "0.50122947",
"text": "def getDFSubjects(self):\n return self.c_cells",
"title": ""
},
{
"docid": "b0154db1069d2a23685763d95a51729c",
"score": "0.50106895",
"text": "def score(self, article: Article, subjects: List[Subject]) -> List[Subject]:\n scores = self._calc_scores(article, subjects)\n for i, score in enumerate(scores):\n subjects[i].score = score\n return subjects",
"title": ""
},
{
"docid": "08672dfbfafbebf9e892e47190583def",
"score": "0.5009317",
"text": "def get_student_summary(self)-> Tuple[str, str, List[str]]:\n \n return [self.cwid, self.name,self.major,sorted(self.passed_courses),sorted(self.remaining_rc),sorted(self.remaining_ec),round(self.gpa,2)]",
"title": ""
},
{
"docid": "21342c3085f19ad0de2089c65ec59862",
"score": "0.5006905",
"text": "def studentcourses():\n rows = db.execute(\"select course_id, semester, year, grade from takes where ID = :ID\", ID = session[\"user_id\"])\n positions = []\n for row in rows:\n positions.append({\"course_id\": row['course_id'], \"semester\": row['semester'], \"year\": row['year'], \"grade\": row['grade']})\n\n return render_template(\"studentcourses.html\", positions = positions)",
"title": ""
},
{
"docid": "d7afc2567e2967e285133dea0dbb81b2",
"score": "0.50011986",
"text": "def subjects2rdfa(labels, subjects):\n html = [\"ul\"]\n for subject_id in sorted(list(subjects.keys())):\n html.append(subject2rdfa(labels, subject_id, subjects[subject_id]))\n return html",
"title": ""
},
{
"docid": "a3809f06612c69684a8439866d573b44",
"score": "0.4996725",
"text": "def _set_subject_variables(self, subject_dictionary):\n self.subject_column = subject_dictionary[\"subject_id_column\"]\n self.subject_info = subject_dictionary[\"subject_data_columns\"]\n self.subject_target = subject_dictionary[\"target_variable\"]\n self.subject_df = df_utils.get_subjects_data_frame(df=self.df,\n subject_column_name=self.subject_column,\n subject_info_columns=self.subject_info)",
"title": ""
},
{
"docid": "40596cbc81f75d0fd46f3b525bd30f50",
"score": "0.4993491",
"text": "def get(self, format=None):\n subjects = Subject.objects.all()\n serializer = SubjectSerializer(subjects, many=True)\n return Response(serializer.data)",
"title": ""
},
{
"docid": "c234350073b598332cae0c9b80fbda3b",
"score": "0.49841318",
"text": "def count_subjects_per_demographics(self):\n all_demographics = list(self.subject_demographic.values())\n\n # Number of subjects per demographic\n subjects_per_demographics = dict(\n [\n (d, sum(np.array(all_demographics) == d))\n for d in set(all_demographics)\n ]\n )\n\n return subjects_per_demographics",
"title": ""
},
{
"docid": "1749ddb15c85512431f21591ef87994d",
"score": "0.4983279",
"text": "def content_dist_courseinfo(self):\n for item in self.get_teaching_learning_courses():\n yield item",
"title": ""
},
{
"docid": "4e150542cf82b86a5c435b7f6d36fbae",
"score": "0.49807706",
"text": "def subjects(\n self,\n ids=None,\n types=None,\n slugs=None,\n levels=None,\n hidden=None,\n updated_after=None,\n fetch_all=False,\n ):\n\n url = self.url_builder.build_wk_url(\n constants.SUBJECT_ENDPOINT, parameters=locals()\n )\n return self._wrap_collection_in_iterator(\n self.authorized_request_maker(url), fetch_all\n )",
"title": ""
},
{
"docid": "7e8d89c0f57f1ff381ac060b228515e3",
"score": "0.4980363",
"text": "def courselist(self, indexed=True, standalone=False):\n self.asciiout.h1(\"Meine Veranstaltungen\")\n\n \"\"\" Prints a list of courses \"\"\"\n for i in range(0, len(self.courses)):\n key = self.courses.keys()[i]\n\n if indexed:\n print \"[\"+str(i)+\"] \"+self.courses[key]\n else:\n print self.courses[key]\n \n # courselist() is also utilized by other actions. the standalone flag prevents conflicts\n if standalone:\n courseId = selectId(\"Veranstaltungsdetails\", len(self.courses)) \n coursePage = pq(self.read(\"seminar_main.php?auswahl=\"+self.courses.keys()[courseId]))\n detailPage= pq(self.read(\"print_seminar.php\"))\n self.asciiout.h1(detailPage(\"h1\").eq(0).text())\n rows = detailPage(\"table\").eq(0).find(\"tr\")\n for i in range(1, len(rows)):\n tds = rows.eq(i).children(\"td\")\n self.asciiout.h2(tds.eq(0).text())\n self.asciiout.text(br2nl(tds.eq(1)).text())",
"title": ""
},
{
"docid": "d76f6c0a2370d98c6c1528b209f60963",
"score": "0.49786335",
"text": "def get_assignments(self, course_id, **kwargs):\n from canvasapi.assignment import Assignment\n\n return PaginatedList(\n Assignment,\n self._requester,\n 'GET',\n 'users/%s/courses/%s/assignments' % (self.id, course_id),\n _kwargs=combine_kwargs(**kwargs)\n )",
"title": ""
},
{
"docid": "f9e3a15ddd04e38185027387e3efef15",
"score": "0.49757263",
"text": "def testWebPage_setSubjectList(self):\n web_table = self.portal.web_page_module.newContent(portal_type=\"Web Table\")\n self.tic()\n self.changeSkin(\"UNGDoc\")\n web_table.WebPage_setSubjectList(\"VPN\")\n self.tic()\n subject_list = web_table.getSubjectList()\n self.assertEqual([\"VPN\"], subject_list)\n self.changeSkin(\"UNGDoc\")\n web_table.WebPage_setSubjectList(\"VPN,ERP5\")\n self.tic()\n self.changeSkin(\"UNGDoc\")\n subject_list = web_table.getSubjectList()\n self.assertEqual([\"ERP5\", \"VPN\"], sorted(subject_list))",
"title": ""
},
{
"docid": "2d8bee3718b4edecc01b7a11b5cebd93",
"score": "0.4973076",
"text": "def subject_page(subject, page_number):\n subject_data = dc.data_get(subject, page_number)\n button_data = dc.button_data_get(subject, subject_data)\n\n if subject == c.SUBJECT.PEOPLE:\n subject_data = dc.data_change_url_to_name(subject_data, c.KEY.People.HOMEWORLD)\n\n return render_template(\n 'index.html',\n subjects_list=c.SUBJECT_ORDER,\n subject_name=subject,\n subject_data=subject_data,\n button_data=button_data,\n column_names=dc.column_names_get(subject),\n pages_number=util.pagination_number_get(subject),\n page_active=page_number\n )",
"title": ""
},
{
"docid": "f7bff6fd2d1a33906b32dada0ad64e11",
"score": "0.4963504",
"text": "def group_assignments(self) -> Sequence[str]:\n return pulumi.get(self, \"group_assignments\")",
"title": ""
},
{
"docid": "47d12efe930433da8948b7d881dc7319",
"score": "0.49576584",
"text": "def list():\n\tif g.is_default_school:\n\t\tdiscussions = Discussion.objects.filter(published=True).order_by('-created')\n\telse:\n\t\tdiscussions = Discussion.objects.filter(schools=g.school, published=True).order_by('-created')\n\treturn render_template('discussion/list.html',\n\t\ttitle=_('Discussions'),\n\t\tdiscussions=discussions)",
"title": ""
},
{
"docid": "4f5d8c195c2634d294e123397180f306",
"score": "0.4957587",
"text": "def get_assignment_data(aid):\n matches = assignments.get_assignments(aid=aid)\n if len(matches) < 1:\n raise WebException(\"Assignment does not exist\")\n assignment = matches[0]\n assignment[\"groups\"] = [groups.get_group(gid=gid)[0] for gid in assignment.get(\"groups\", [])]\n return { \"success\": 1, \"data\": assignment }",
"title": ""
},
{
"docid": "8fa32a6e8dbd7102a5bcc7e3397ff007",
"score": "0.49546313",
"text": "def dpBruteForceAdvisor(subjects, maxWork):\n nameList = list(subjects.keys())\n tupleList = list(subjects.values())\n memo = {}\n bestSubset, bestSubsetValue = dpBruteForceAdvisorHelper(tupleList, maxWork,\n 0, None, None, [],\n 0, 0, memo)\n outputSubjects = {}\n\n for i in bestSubset:\n outputSubjects[nameList[i]] = tupleList[i]\n\n return outputSubjects",
"title": ""
},
{
"docid": "af50fce1e2e64969f08d272d4c6280a3",
"score": "0.49513468",
"text": "def loadSubjects(filename):\n\n # The following sample code reads lines from the specified file and prints\n # each one.\n inputFile = open(filename)\n output = {}\n for line in inputFile:\n data = line.rstrip().split(',')\n name = data[0]\n value = int(data[1])\n work = int(data[2])\n output[name] = (value, work)\n\n # print data\n\n return output",
"title": ""
},
{
"docid": "20c3faf143e90f76c28334af58aafa78",
"score": "0.49489135",
"text": "def get_subject_list(year_term):\n result = requests.get(URL_ROOT)\n soup = BeautifulSoup(result.text)\n select_box = soup.find('select', id='subject')\n subjects = select_box.find_all('option', class_=year_term)\n subject_str = [s['value'] for s in subjects]\n return subject_str",
"title": ""
},
{
"docid": "6e272554c2b2e048799aea7deffef1bc",
"score": "0.49430734",
"text": "def get_subjects(self, subject_group_id=None, **kwargs):\n params = dict((k, v) for k, v in kwargs.items() if k in\n ('subject_group', 'include_inactive'))\n\n return self._get('subjects', params=params)",
"title": ""
},
{
"docid": "99aadfe3874772525b6b2607b125bf41",
"score": "0.49289337",
"text": "def meeting_session_assignments(self, schedule : Schedule) -> Iterator[Assignment]:\n url = \"/api/v1/meeting/schedtimesessassignment/?schedule=\" + str(schedule.id)\n return self._retrieve_multi(url, Assignment)",
"title": ""
},
{
"docid": "eb58e62dec8a58983f4b361e662ff992",
"score": "0.49181697",
"text": "def _get_subject(params):\n params[\"SUBJECT_STATEMENT\"] = render_to_string(\"saml/xml/subject.xml\", params)",
"title": ""
}
]
14dc3b3ec7b206da8a0aac6584868cdb
Marks the list of barcodes as ready in the databse and sends email
[
{
"docid": "7e79f1f9bffed340ce4ad21cb22ac6c3",
"score": "0.7422296",
"text": "def mark_results_ready(self, barcodes, debug=False):\n debug = {}\n ready_sql = \"\"\"UPDATE ag.ag_kit_barcodes\n SET results_ready = 'Y'\n WHERE barcode IN %s\n AND (results_ready != 'Y' OR results_ready IS NULL)\n RETURNING barcode\"\"\"\n new_bcs = tuple(x[0] for x in\n self._con.execute_fetchall(\n ready_sql, [tuple(barcodes)]))\n debug['new_bcs'] = new_bcs\n if len(new_bcs) == 0:\n # No new barcodes, so no emails to send\n return\n\n bc_sql = \"\"\"UPDATE ag.ag_kit_barcodes\n SET date_of_last_email = '{0}'\n WHERE barcode IN %s\"\"\".format(datetime.now())\n subject = \"Your American/British Gut results are ready\"\n message = (\n \"Good afternoon American & British Gut participants!\\n\\n\"\n \"We are pleased to let you know that your results are now \"\n \"available. You may access them by signing onto \"\n \"microbio.me/americangut or microbio.me/britishgut. If you have \"\n \"forgotten your login credentials, you may retrieve them using \"\n \"the \\\"Forgot kit ID/password\\\" functions.\\n\\n\"\n \"We thank you for being a part of the project. While we emphasize \"\n \"getting results back to you, the participant, we and the broader \"\n \"American/British Gut scientific collaborative network are \"\n \"extremely excited about the population-scale microbiome \"\n \"observations that are for the first time becoming possible thanks\"\n \" to you and the other participants!\\n\\n\"\n \"Regards,\\n\"\n \"The American Gut Team\\n\")\n barcode_info = self.get_ag_barcode_details(new_bcs)\n # Make sure email only sent once if multiple barcodes with same email\n seen_emails = set(i['email'] for bc, i in viewitems(barcode_info))\n mail = send_email(message, subject, bcc=list(seen_emails), debug=debug)\n debug['mail'] = mail\n self._con.execute(bc_sql, [new_bcs])\n if debug:\n return debug",
"title": ""
}
]
[
{
"docid": "fb6f43761ac94070a64eefdf1c7b95a1",
"score": "0.76851845",
"text": "def __next__(self):\n for processor in self._processors:\n\n processor()",
"title": ""
},
{
"docid": "d91c4595fb0c24cff850e46a1bf542dc",
"score": "0.7611795",
"text": "def add_processor(self, processor):\n self.processors.append(processor)",
"title": ""
},
{
"docid": "fe113c26a9d288c85f8b41ce9dfe2bb9",
"score": "0.6942205",
"text": "def add_processor(self, processor):\n if not hasattr(processor, '__call__'):\n raise ValueError(\"LogScanner: add_processor(): processor not a \" +\n \"function or function object\")\n self._procs.append(processor)",
"title": ""
},
{
"docid": "a36cfde1fd38a787604157183dbfaa10",
"score": "0.6653939",
"text": "def processors(self, processors):\n self._processors = processors",
"title": ""
},
{
"docid": "e8f330368d3213dda0814a77c7631906",
"score": "0.64629006",
"text": "def register(self, processor):\n self.registry[processor.name] = processor",
"title": ""
},
{
"docid": "09e0e0db2cfe9d1e5d849d59e4cac835",
"score": "0.6329806",
"text": "def as_target_processor(self):\n self.current_processor = self.tokenizer\n yield\n self.current_processor = self.feature_extractor",
"title": ""
},
{
"docid": "5d8e0ca18d2e1b49bce3a81e87018c69",
"score": "0.62887657",
"text": "def my_processor():\n my_processor = MyProcessor()\n yield my_processor",
"title": ""
},
{
"docid": "ae979d2bd3ae74c54cd6acffe87889fa",
"score": "0.6263545",
"text": "def ProcessNext(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "a28c0e78628681877885b77ed9992823",
"score": "0.5928027",
"text": "def __setitem__(self, key, value):\n with self._condition:\n if key not in self._processors:\n proc_iterator = self._proc_iter_class()\n proc_iterator.add_processor(value)\n self._processors[key] = proc_iterator\n else:\n self._processors[key].add_processor(value)\n self._condition.notify_all()",
"title": ""
},
{
"docid": "eb434aa65b73cd6b0b0bb87bfb66d180",
"score": "0.5816772",
"text": "def add_processor(name, processor_object):\n globals()[name] = processor_object\n if name not in _PROCESSOR_NAMES:\n _PROCESSOR_NAMES.append(name)",
"title": ""
},
{
"docid": "38ca28daa7a791e4c80fe648de9a61c7",
"score": "0.58017886",
"text": "def add_post_processor(self, pp):\n self._pps.append(pp)\n pp.set_downloader(self)",
"title": ""
},
{
"docid": "e29a8036b43e6d2397de391b6d2ba2e7",
"score": "0.5766604",
"text": "def __getitem__(self, item):\n with self._condition:\n return self._processors[item].next_processor()",
"title": ""
},
{
"docid": "b5dfce14f9ee06da56b9b7b42cb06688",
"score": "0.5749241",
"text": "def __call__(self, *args, **kwargs):\n return self.current_processor(*args, **kwargs)",
"title": ""
},
{
"docid": "55bbd67eaf267d48c3051d14b30dd44e",
"score": "0.57464945",
"text": "def configure_processors(self, runtime_environment):\n if self.is_configured:\n return\n for processor in self:\n processor.configure(runtime_environment)\n self.is_configured = True",
"title": ""
},
{
"docid": "4d31f2f1793aafe93fe981aa1c062bea",
"score": "0.5706102",
"text": "def processor(self):\n return self._processor",
"title": ""
},
{
"docid": "7acfe698d8f9917466e0380639ef6526",
"score": "0.56905913",
"text": "def processors(self):\n return self._processors",
"title": ""
},
{
"docid": "7acfe698d8f9917466e0380639ef6526",
"score": "0.56905913",
"text": "def processors(self):\n return self._processors",
"title": ""
},
{
"docid": "2b6069bef7becaec5827d98d67ce58fd",
"score": "0.5676872",
"text": "def __next__(self):\n import itertools\n if self.current_batch_index >= self.num_batches:\n print(f'[Loader] Stopping Iteration')\n import sys; sys.exit(1)\n self.stop_loading()\n raise StopIteration\n else:\n # TODO: check processes are working\n if self.ii is None:\n self.ii = itertools.cycle(range(self.num_workers))\n print(f'[NEW BATCH] !!')\n examples = []\n for i in range(self.examples_per_batch):\n print(f' Getting example {i}..', end=''); start = time.time()\n example = None\n example = self.example_queues[next(self.ii)][1].recv()\n examples.append(example)\n print(f'.done ({time.time() - start} sec)')\n start = time.time()\n batch = self.batch_collate_fn(examples)\n print(f' (__next__) Collate took {time.time() - start:.2f} sec')\n self.current_batch_index += 1\n return batch",
"title": ""
},
{
"docid": "e4740c2013961a24a564806eadadc1e2",
"score": "0.56287396",
"text": "def __add__(self, next: Plug) -> Plug:\n if isinstance(next, FreePlug):\n return self\n\n # keep output not used by next pipeline and add next outputs\n next.outputs += [e for e in self.outputs if e not in next.inputs]\n return RestrictedPlug(inputs=self.inputs, outputs=next.outputs)",
"title": ""
},
{
"docid": "8a11d548de20a453f9ae7f98e9c139c9",
"score": "0.56010896",
"text": "def result_processor(self):\n return None",
"title": ""
},
{
"docid": "2f5a580b51bec249c09d3910d4fb39d7",
"score": "0.5571186",
"text": "def add_processor(self, module_name, content, session):\n\n filename = '/%s.py' % module_name\n self._add_file('processors', filename, content, session)",
"title": ""
},
{
"docid": "d2b7ddec1ce04ca0b50feef26ed7d9e4",
"score": "0.5564669",
"text": "def then(self, f, *args, **kw):\n assert callable(f)\n assert \"source\" not in kw\n # print(\"Processor\", args, kw)\n return Processor(self, f, *args, **kw)",
"title": ""
},
{
"docid": "27a91d150d917b16c03cd7228c5021d2",
"score": "0.55428576",
"text": "def _push_processor(self, proc, index=None):\n\n if index is None:\n self._procstack.append(proc)\n else:\n self._procstack.insert(index, proc)",
"title": ""
},
{
"docid": "3e59b32899238dd59ac2fdbbf5f1f135",
"score": "0.55407494",
"text": "def add_processor(self, consumer, is_error_consumer=False):\n for producer in self.producers:\n self.processor_graph.add_producer_and_consumer(producer, consumer, is_error_consumer)\n return ProcessorGraphBuilder(self.processor_graph, consumer)",
"title": ""
},
{
"docid": "acb7151480c72c391b3810741d157af1",
"score": "0.54978484",
"text": "def processors(self):\n return self._procs[:]",
"title": ""
},
{
"docid": "2622cfe2c2adb72c91010acb961fead6",
"score": "0.5463847",
"text": "def add(self, *var_processor_fns):\n for fn in var_processor_fns:\n self._processors.append(fn)\n return self",
"title": ""
},
{
"docid": "5bb8f014d9938de8894e0947fd49abe9",
"score": "0.54606146",
"text": "def next(self):\n super().next()\n self._method_next()",
"title": ""
},
{
"docid": "26346a212ea6fa768e332f308692f442",
"score": "0.54284114",
"text": "def set_post_processor(self, processor):\n self._post_processor = processor",
"title": ""
},
{
"docid": "f842cfe7e877bebb557bca6c91ac4aac",
"score": "0.5416073",
"text": "def bind_processor(self):\n return None",
"title": ""
},
{
"docid": "1550e6662673c2f343f3cbe1226f90b2",
"score": "0.54032487",
"text": "def _process(self, data):\n for pp in self._preprocessors:\n data = pp(data)\n\n return data",
"title": ""
},
{
"docid": "662f01c7e1873e2d8b6ffcbd0f0be61d",
"score": "0.5387171",
"text": "def as_target_processor(self):\n warnings.warn(\n \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n \"labels by using the argument `text` of the regular `__call__` method (either in the same call as \"\n \"your images inputs, or in a separate call.\"\n )\n self._in_target_context_manager = True\n self.current_processor = self.tokenizer\n yield\n self.current_processor = self.image_processor\n self._in_target_context_manager = False",
"title": ""
},
{
"docid": "e22bf7261ccd74e8dcc395c62c5072b6",
"score": "0.5284179",
"text": "def handle(self, to_process: typing.List[Input]) -> typing.Iterator[Output]:\n to_process.extend(self.iterable)\n yield from ()",
"title": ""
},
{
"docid": "1a3312952974c450b87f5b3890491a33",
"score": "0.52665395",
"text": "def flattener():\n for pg in list_all_process_groups():\n for proc in pg.nipyapi_extended.process_group_flow.flow.processors:\n yield proc",
"title": ""
},
{
"docid": "db9015057450d6bef36c82324c64bff3",
"score": "0.5254651",
"text": "def add_iter_to_generator_class(self, fn_info: FuncInfo) -> None:\n self.enter(fn_info)\n self_target = self.add_self_to_env(fn_info.generator_class.ir)\n self.add(Return(self.read(self_target, fn_info.fitem.line)))\n blocks, env, _, fn_info = self.leave()\n\n # Next, add the actual function as a method of the generator class.\n sig = FuncSignature((RuntimeArg(SELF_NAME, object_rprimitive),), object_rprimitive)\n iter_fn_decl = FuncDecl('__iter__', fn_info.generator_class.ir.name, self.module_name, sig)\n iter_fn_ir = FuncIR(iter_fn_decl, blocks, env)\n fn_info.generator_class.ir.methods['__iter__'] = iter_fn_ir\n self.functions.append(iter_fn_ir)",
"title": ""
},
{
"docid": "cbee7f3a7a629c488d6f48ddeaa869f1",
"score": "0.5245541",
"text": "def _run_cpu(self):\n if not self._built:\n raise RuntimeError(\"Pipeline must be built first.\")\n if not self._last_iter:\n self._pipe.RunCPU()\n self._cpu_batches_to_consume += 1",
"title": ""
},
{
"docid": "ce77f82a0a3a018a85304dd2546da8b4",
"score": "0.5187133",
"text": "def dispatch_next_process(self):\n # ...\n nextProc = self.processList[len(self.processList)-1]\n nextProc.event.set()\n nextProc.start()\n\n if len(self.processList) > 2:\n for x in range(len(self.processList) - 2):\n self.processList[x].event.clear()\n else:\n for x in range(len(self.processList) - 1):\n self.processList[x].event.clear()",
"title": ""
},
{
"docid": "9edee307a658d36c0ca027ec0efb35f0",
"score": "0.51868993",
"text": "def process(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "e4fc19f529a56c078db02279c2d2eaa0",
"score": "0.51866376",
"text": "def nextGenerator(self):\n \n pass",
"title": ""
},
{
"docid": "f90708bc176b2ee75fe4abe523c89a90",
"score": "0.5161887",
"text": "def __next__(self):\n pass",
"title": ""
},
{
"docid": "5253374ac0bb6b10e3b6abf5ea564032",
"score": "0.5156609",
"text": "def list_stream_processors(NextToken=None, MaxResults=None):\n pass",
"title": ""
},
{
"docid": "c68bba88497111ee9a1806076bea665c",
"score": "0.5156178",
"text": "def _push_next(self):\n # Optimally one would want to task worker that have none depleted iterators,\n # however, this does not seem to be possible with a worker pool\n async_ret = self._worker_pool.apply_async(\n self._worker_fn,\n (\n self.batch_size,\n self._batchify_fn,\n self.dtype,\n self.is_train,\n self.shuffle,\n self.cyclic,\n self.cycle_num,\n ),\n )\n self._data_buffer[self._sent_idx] = async_ret\n self._sent_idx += 1",
"title": ""
},
{
"docid": "df39f047f716d689bfb3fac618ca028c",
"score": "0.51334417",
"text": "def _process(self, packet):\n raise BirImplementationError(\"Processor %s: Super class %s \" %\n (self.name, str(type(self))) + \"of threaded_processor does \"\n \"not implement _process\")",
"title": ""
},
{
"docid": "27a4507793db10e93bbe6d1ed15026cb",
"score": "0.51197475",
"text": "def __init__(self, *processors):\n super().__init__()\n self._processors = tuple(processors)\n self.running = False",
"title": ""
},
{
"docid": "b02c3528de8cb6198569e0e0b2097783",
"score": "0.51169735",
"text": "def next(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "e21638d24cf393787c1583f8c8cc7553",
"score": "0.51024467",
"text": "def process(self, metric):\n if metric.source not in self.queues:\n self.queues[metric.source] = []\n self.queues[metric.source].append(metric)",
"title": ""
},
{
"docid": "6656075a2a258e01d1ca0bdab63d822b",
"score": "0.5098623",
"text": "def __next__(self):\n while self.index >= len(self.features) and not self.finished:\n new_features = self._fetch_features()\n self.features.extend(new_features)\n\n if self.index < len(self.features):\n self.index += 1\n return self.features[self.index - 1]\n\n raise StopIteration",
"title": ""
},
{
"docid": "4ddad9dd6cb545f6c96f60bb79e01012",
"score": "0.5096611",
"text": "def __next__(self):\n return self.__next__()",
"title": ""
},
{
"docid": "18554901c829c0d18f3337c475c165cb",
"score": "0.509258",
"text": "def _map_to_processors(self, f, objective):\n\n P = self._random_subprocessors()\n best = f(next(P))\n for p in P:\n x = f(p)\n if objective(best, x):\n best = x\n return best[1]",
"title": ""
},
{
"docid": "dee02fb0f9447eb7510047bf0f9916bd",
"score": "0.5092287",
"text": "def __next__(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "7248b835650dd95ffc5403205a7de260",
"score": "0.5092085",
"text": "def next(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "c29341d7ccd048f08528b6ac4b82b946",
"score": "0.5076727",
"text": "def get_processor():\n return _active_processor",
"title": ""
},
{
"docid": "a97f426dd682bdc73efbeb92b37065fa",
"score": "0.5054247",
"text": "def _process(self):\n return",
"title": ""
},
{
"docid": "c883b8b487e992db3f44bd71794739df",
"score": "0.50466424",
"text": "def _yield(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "9f223ae7ab82d2e277ce4734f6d8ddf9",
"score": "0.50385624",
"text": "def __add__(self, *args):\n return _pcbnew.SwigPyIterator___add__(self, *args)",
"title": ""
},
{
"docid": "46164425c4ddead95bd9f562600941d2",
"score": "0.5033553",
"text": "def __next__(self):\n raise NotImplementedError(\"implement in subclass\")",
"title": ""
},
{
"docid": "79900874293231e17d57dabf9e18dc2e",
"score": "0.50325733",
"text": "def __next__(self):\n return next(self.generator)",
"title": ""
},
{
"docid": "e802070faf045c07130bb93e1da25b7f",
"score": "0.5029876",
"text": "def __next__(self):\n return self._next()",
"title": ""
},
{
"docid": "11732417c29c99cf69c090e516f9506e",
"score": "0.5023839",
"text": "def next(*args, **kwargs):\n \n pass",
"title": ""
},
{
"docid": "899c169adea68e34b2e644c155949d8a",
"score": "0.5021767",
"text": "def next(self):\n raise NotImplementedError(\"This is not implemented yet\")",
"title": ""
},
{
"docid": "7a04d8cc4cc68220420f02314d9bb6f6",
"score": "0.50122577",
"text": "def _next():",
"title": ""
},
{
"docid": "a911c079a27118d60b4b1b1694481230",
"score": "0.50012296",
"text": "def _push(self, item):\n if self._logging == 'output':\n self._write_log(item)\n\n # The _process attribute will be set to the appropriate callable\n # when initializing the pipeline. I do this because I want the\n # chaining to be as efficient as possible. If logging is not set,\n # I don't want to have to hit that logic every push, so I just\n # invoke a callable attribute at each process that has been set\n # to the appropriate callable.\n for downstream in self._downstream_nodes:\n downstream._process(item)",
"title": ""
},
{
"docid": "3f972f6ed8a4ce9f3613298ef639e6b6",
"score": "0.49975964",
"text": "def next(self):\n pass",
"title": ""
},
{
"docid": "3f972f6ed8a4ce9f3613298ef639e6b6",
"score": "0.49975964",
"text": "def next(self):\n pass",
"title": ""
},
{
"docid": "3f972f6ed8a4ce9f3613298ef639e6b6",
"score": "0.49975964",
"text": "def next(self):\n pass",
"title": ""
},
{
"docid": "3f972f6ed8a4ce9f3613298ef639e6b6",
"score": "0.49975964",
"text": "def next(self):\n pass",
"title": ""
},
{
"docid": "7861390afaa38f6bfc4b2b87863de05e",
"score": "0.49942434",
"text": "def add(self, proc):\n self.set_update(self.wait_update)\n self.procs.append(proc)\n proc.cb_queue = self._cb_queue\n proc.done_queue = self._done_queue",
"title": ""
},
{
"docid": "5609341e241d3626ae9985092813ef8e",
"score": "0.49916098",
"text": "def GetNextProcessor(self, name, attrs):\n\n if self._handled == HandledDepth.SINGLE_TAG:\n handled = HandledDepth.NOT_HANDLED\n else:\n handled = self._handled\n\n return BaseHandler(context=self.context, handled=handled)",
"title": ""
},
{
"docid": "403ceeb5f448ee810a31fb3ae6706ad5",
"score": "0.49886668",
"text": "def next(self, *args):\n pass",
"title": ""
},
{
"docid": "46f2f911de7f690e96ba88b6ff07dbd8",
"score": "0.49844867",
"text": "def next(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "46f2f911de7f690e96ba88b6ff07dbd8",
"score": "0.49844867",
"text": "def next(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "46f2f911de7f690e96ba88b6ff07dbd8",
"score": "0.49844867",
"text": "def next(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "70235512fb99c9b8d5d406f51ebcfd6f",
"score": "0.49750322",
"text": "def processor(forType):\n MILLI = 1000\n try:\n processor = _processors[forType]\n except KeyError:\n def __init__(self, *a, **kw):\n item.Item.__init__(self, *a, **kw)\n self.store.powerUp(self, iaxiom.IBatchProcessor)\n\n attrs = {\n '__name__': 'Batch_' + forType.__name__,\n\n '__module__': forType.__module__,\n\n '__init__': __init__,\n\n '__repr__': lambda self: '<Batch of %s #%d>' % (reflect.qual(self.workUnitType), self.storeID),\n\n 'schemaVersion': 2,\n\n 'workUnitType': forType,\n\n 'scheduled': attributes.timestamp(doc=\"\"\"\n The next time at which this processor is scheduled to run.\n \"\"\", default=None),\n\n # MAGIC NUMBERS AREN'T THEY WONDERFUL?\n 'busyInterval': attributes.integer(doc=\"\", default=MILLI // 10),\n }\n _processors[forType] = processor = item.MetaItem(\n attrs['__name__'],\n (item.Item, _BatchProcessorMixin),\n attrs)\n\n registerUpgrader(\n upgradeProcessor1to2,\n _processors[forType].typeName,\n 1, 2)\n\n return processor",
"title": ""
},
{
"docid": "f7ce849aebf3a9a5ddcac26bf6964f0f",
"score": "0.49653915",
"text": "def next(self, next):\n\n self._next = next",
"title": ""
},
{
"docid": "f7ce849aebf3a9a5ddcac26bf6964f0f",
"score": "0.49653915",
"text": "def next(self, next):\n\n self._next = next",
"title": ""
},
{
"docid": "f7ce849aebf3a9a5ddcac26bf6964f0f",
"score": "0.49653915",
"text": "def next(self, next):\n\n self._next = next",
"title": ""
},
{
"docid": "6e5a1291b3b9a76d9d313673009792f2",
"score": "0.49625993",
"text": "def _connect_producer_to_consumers(self, processor):\n if not hasattr(processor, 'consumers'):\n e_msg = 'consumer-less processor: ' + repr(processor)\n detail = 'Did you invoke base.Processor\\'s __init__?'\n raise exceptions.ConfigurationError(e_msg, detail)\n for consumer, edge_data in self.processor_graph.consumers[processor].items():\n if edge_data['is_error_consumer']:\n processor.error_consumers.append(consumer)\n else:\n processor.consumers.append(consumer)",
"title": ""
},
{
"docid": "9ceb545b257c31a97f5e123348e1132e",
"score": "0.4955837",
"text": "def __next__(self):\n\t\treturn self.next()",
"title": ""
},
{
"docid": "bd3b69fcb8daa45ee9ac809b7a9242ba",
"score": "0.49549976",
"text": "def next(self):\n\n next_prime = self.sieve.next()\n self.sieve = _Sieve(next_prime, self.sieve)\n self.primes.append(next_prime)\n return next_prime",
"title": ""
},
{
"docid": "b8272f41509fc1d50feaa73dfa540538",
"score": "0.49496502",
"text": "def preprocessors(self):\n return super().preprocessors + [HugoPreprocessor]",
"title": ""
},
{
"docid": "cf72ac8d7996981c80ae6f0bc05f5744",
"score": "0.49449846",
"text": "def getNext(proc):\n\t\tnode = ProcTree.NODES[proc]\n\t\treturn [nextn.proc for nextn in node.next]",
"title": ""
},
{
"docid": "253bc642e3ccd27935c69463d98f41d4",
"score": "0.49301922",
"text": "def register_post_processor(self, post_processor: \"IPostProcessor\") -> None:\n ...",
"title": ""
},
{
"docid": "aff6ccfe6ce5bdad7a13ebccf295fe3f",
"score": "0.49210003",
"text": "def next_performer(lineup):",
"title": ""
},
{
"docid": "bf49c6f00d299737969112ac947a9f4f",
"score": "0.49039873",
"text": "def process(self):\n pass",
"title": ""
},
{
"docid": "bf49c6f00d299737969112ac947a9f4f",
"score": "0.49039873",
"text": "def process(self):\n pass",
"title": ""
},
{
"docid": "bf49c6f00d299737969112ac947a9f4f",
"score": "0.49039873",
"text": "def process(self):\n pass",
"title": ""
},
{
"docid": "bf49c6f00d299737969112ac947a9f4f",
"score": "0.49039873",
"text": "def process(self):\n pass",
"title": ""
},
{
"docid": "3901eddf869ccae23395af16dce91cd0",
"score": "0.4898005",
"text": "def setProcessorsToUse(self, processorlist):\n self._checkArgs( {\n 'processorlist' : types.ListType\n } )\n self.processorlisttouse = processorlist",
"title": ""
},
{
"docid": "68fa1ac40f7ed4d3eaeaad6060c600d6",
"score": "0.48902753",
"text": "def lookupProcessor(self, msg):",
"title": ""
},
{
"docid": "34228ecb534b829016125b768f48bd93",
"score": "0.48899725",
"text": "def process(self) -> None:\n pass",
"title": ""
},
{
"docid": "a0882775b4c9cd4274c6433d5dca60dd",
"score": "0.48893142",
"text": "def processor(self, processor_type):\n cache_entry = self._processor_cache.get((processor_type, self.world))\n\n if cache_entry is None:\n proc = self.world.get_processor(processor_type)\n if proc is not None:\n self._processor_cache[(processor_type, self.world)] = proc\n return proc\n\n return None\n\n return cache_entry",
"title": ""
},
{
"docid": "83da0dcae291e75b27d9e48ba09214a1",
"score": "0.48833093",
"text": "def _next_action_hook(self, a_next):\n return a_next",
"title": ""
},
{
"docid": "ea74f8704880ec88b8d2b71152b35d3b",
"score": "0.48823616",
"text": "def _make_processor(self, processor_configuration):\n assert not 'inline-pipeline' in processor_configuration, \"Unflattened configuration provided\"\n\n if 'existing' in processor_configuration:\n # Placeholder we'll replace when all processors have been made.\n return processor_configuration['existing']\n\n # We're going to modify the configuration, so don't modify the original.\n # Deep-copying with all the consumers is an expensive operation, so temporarily\n # remove them from the configuration:\n consumers = processor_configuration.pop('consumers', None)\n error_consumers = processor_configuration.pop('error_consumers', None)\n\n copied_processor_configuration = deepcopy(processor_configuration)\n\n # add the consumers and error_consumers back into the original configuration:\n if consumers is not None:\n processor_configuration['consumers'] = consumers\n if error_consumers is not None:\n processor_configuration['error_consumers'] = error_consumers\n\n # We pop away stuff because we want the remaining dictionary to define the\n # configuration values.\n plugin_name = copied_processor_configuration.pop('__processor__')\n processor_id = copied_processor_configuration.pop('id', None)\n\n plugin_factory = self._get_plugin_factory_or_fail(plugin_name)\n try:\n processor = plugin_factory(**self._ensure_configuration_keys_are_ascii(copied_processor_configuration))\n except TypeError, e:\n # If creating the processor blows up, the stacktrace isn't\n # particularly helpful, so put some effort in making a\n # helpful exception.\n self._explain_instantiation_typeerror(e, plugin_name, plugin_factory, copied_processor_configuration)\n\n processor.processor_configuration = copied_processor_configuration\n processor.id = processor_id\n\n return processor",
"title": ""
},
{
"docid": "c8cfaa210beaea19a0cb4a1d50b8de2a",
"score": "0.48794234",
"text": "def next(self):\n\t\treturn self.__next__()",
"title": ""
},
{
"docid": "68812a120432e779c036d61d5b6cc525",
"score": "0.48742604",
"text": "def process(self):\n raise NotImplementedError(\"override in a subclass\")",
"title": ""
},
{
"docid": "5b207e9e753d5f365733aa32ce9761a7",
"score": "0.48692426",
"text": "def add_next_to_generator_class(self,\n fn_info: FuncInfo,\n fn_decl: FuncDecl,\n sig: FuncSignature) -> None:\n self.enter(fn_info)\n self_reg = self.read(self.add_self_to_env(fn_info.generator_class.ir))\n none_reg = self.none_object()\n\n # Call the helper function with error flags set to Py_None, and return that result.\n result = self.add(Call(fn_decl, [self_reg, none_reg, none_reg, none_reg, none_reg],\n fn_info.fitem.line))\n self.add(Return(result))\n blocks, env, _, fn_info = self.leave()\n\n sig = FuncSignature((RuntimeArg(SELF_NAME, object_rprimitive),), sig.ret_type)\n next_fn_decl = FuncDecl('__next__', fn_info.generator_class.ir.name, self.module_name, sig)\n next_fn_ir = FuncIR(next_fn_decl, blocks, env)\n fn_info.generator_class.ir.methods['__next__'] = next_fn_ir\n self.functions.append(next_fn_ir)",
"title": ""
},
{
"docid": "b6be7e9f178fe3ea825548a58570e399",
"score": "0.4864786",
"text": "def __next__(self):\n # load a new page if needed\n if self.page is None or self.index >= len(self.page):\n self.index = 0\n self.page = self.load_next_page()\n self.pagenum += 1\n\n # if we have run out of results...\n if self.page is None or len(self.page) == 0:\n raise StopIteration\n\n # pull the next item from the current page\n item = self.page[self.index]\n\n # setup for the next call\n self.index += 1\n\n return item",
"title": ""
},
{
"docid": "10534975c837b867c47060c10db593d7",
"score": "0.4862352",
"text": "def test_get_processor_handler(self):\n pass",
"title": ""
},
{
"docid": "62f3ada61acc64fc540ac59dede6b829",
"score": "0.485444",
"text": "def process(self):\n\n pass",
"title": ""
},
{
"docid": "a071a60ce95b672dee1c7b45450525ff",
"score": "0.4852628",
"text": "def dispatch_next_process(self):\n # Starts the first two processes on the stack\n if len(self.run_stack) > 1:\n self.run_stack[-1].event.set()\n self.run_stack[-2].event.set()\n if len(self.run_stack) > 0:\n self.run_stack[-1].event.set()",
"title": ""
},
{
"docid": "cafaf78eea6dd9055840e02de747af29",
"score": "0.4846471",
"text": "def _process(self):",
"title": ""
}
]
a5b7cd94ad797db3374c86c3d84dd89e
Loads the dbt manifest.
[
{
"docid": "218f3c12ef21f4e5270383042bd1e288",
"score": "0.6806921",
"text": "def dbt_manifest(self):\n # Identity function used for macro hooks\n def identity(x):\n return x\n\n # Set dbt not to run tracking. We don't load\n # a dull project and so some tracking routines\n # may fail.\n from dbt.tracking import do_not_track\n\n do_not_track()\n\n if \"0.17\" in self.dbt_version:\n from dbt.parser.manifest import (\n load_internal_manifest as load_macro_manifest,\n load_manifest,\n )\n else:\n from dbt.parser.manifest import (\n load_macro_manifest,\n load_manifest,\n )\n\n load_macro_manifest = partial(load_macro_manifest, macro_hook=identity)\n\n dbt_macros_manifest = load_macro_manifest(self.dbt_config)\n self.dbt_manifest = load_manifest(\n self.dbt_config, dbt_macros_manifest, macro_hook=identity\n )\n return self.dbt_manifest",
"title": ""
}
]
[
{
"docid": "f7d9e68a6289002b2b30bb5e1dce5a7e",
"score": "0.6945915",
"text": "def read_manifest(self): # -> None:\n ...",
"title": ""
},
{
"docid": "99a973b3f287f2f28ff302065825d6f6",
"score": "0.66541666",
"text": "def load_manifest(path: Path):\n with open(path, \"rt\") as fin:\n data = json_load(fin)\n return Manifest.schema().load(data, many=True)",
"title": ""
},
{
"docid": "a55e8b437fdeeaeca62d7c70fc12a444",
"score": "0.6306055",
"text": "def _load_manifest(self, filename: Optional[str] = None) -> Dict[str, str]:\n filename = filename or self.manifest_filename\n if not os.path.isfile(filename):\n self.log.debug(f\"Manifest file '{filename}' doesn't exist and will be created.\")\n return {}\n with open(filename, \"r\") as f:\n manifest: Dict[str, str] = json.load(f)\n self.log.debug(f\"Reading manifest '{manifest}' from file '{filename}'\")\n return manifest",
"title": ""
},
{
"docid": "00a528a9695619693b05f27772004799",
"score": "0.6271859",
"text": "def load_manifest(filename):\n\n data = manifest.load(filename)\n for field in manifest.validate(data):\n name = field.cfg or ''\n if name and name[-1] != '.':\n name += '>'\n name += field.name\n for msg in field.warnings:\n print('WARNING: {}@{} {}'.format(filename, name, msg))\n for msg in field.errors:\n print('CRITICAL: {}@{} {}'.format(filename, name, msg))\n return data",
"title": ""
},
{
"docid": "20ab3df3ae8f83c433e556ca8eb3c21f",
"score": "0.61065125",
"text": "def _loadManifest(self, pkg):\r\n if pkg in self._packages:\r\n return\r\n\r\n sys.path = self._generatePythonPath(pkg) + sys.path",
"title": ""
},
{
"docid": "7106b78ba67fe84af9a50aa458a9b17d",
"score": "0.6054963",
"text": "def load_model_manifest(rel_path=\"model_manifest.json\"):\n manifest = []\n manifest_path = \"{}/{}\".format(Path(__file__).parents[1], rel_path)\n if path.exists(manifest_path):\n with open(manifest_path) as json_file:\n manifest = json.load(json_file)\n return manifest",
"title": ""
},
{
"docid": "7b0837b21bdc482f2140082be43eb954",
"score": "0.601994",
"text": "def get_manifest(self):\r\n if os.path.exists(self.manifestfile):\r\n return Manifest(json.loads(file(self.manifestfile).read()))\r\n return Manifest({})",
"title": ""
},
{
"docid": "0fe327ea9565fa552d000271058251f6",
"score": "0.5973812",
"text": "def manifest(ctx, config): # use as `schematic manifest ...`\n try:\n logger.debug(f\"Loading config file contents in '{config}'\")\n ctx.obj = CONFIG.load_config(config)\n except ValueError as e:\n logger.error(\"'--config' not provided or environment variable not set.\")\n logger.exception(e)\n sys.exit(1)",
"title": ""
},
{
"docid": "9d91603a30630396d778931d512ab0b8",
"score": "0.59669316",
"text": "def manifest():\n return setup((), _manifest=1)",
"title": ""
},
{
"docid": "604edbfa197a02bc70afdd197e5bb7d0",
"score": "0.58998394",
"text": "def fetch_manifest(self):\n manifest = self.open(self.urls['manifest'])\n return manifest.read()",
"title": ""
},
{
"docid": "7d158e9ac3eab40a72870cb9bcd49d5c",
"score": "0.5807419",
"text": "def readManifestEntries(context):\n return GenericMetadata._readEntriesForSection(context.projectDir, GenericMetadata.MANIFEST_SECTION)",
"title": ""
},
{
"docid": "cf7e2d23e65e75443297860439f5b6bb",
"score": "0.5764459",
"text": "def parse_manifest(manifest_path):\n with open(manifest_path, 'r') as f:\n data = f.read()\n if data:\n return json.loads(data)\n else:\n return {}",
"title": ""
},
{
"docid": "2e3f5340a45f0b014482c07a83c3295c",
"score": "0.57098615",
"text": "def _load(self):\n with qisys.sh.TempDir() as work_dir:\n pkg = portage.xpak.tbz2(self.path)\n pkg.decompose(work_dir, cleanup=0)\n arch, arch_variant = _get_pkg_arch(work_dir)\n with open(os.path.join(work_dir, 'PF'), 'r') as fpf:\n pf = fpf.readline().strip()\n name, version, revision = portage.versions.pkgsplit(pf)\n dependency = dict()\n for dep, dep_filename in _DEPENDENCY.items():\n dep_path = os.path.join(work_dir, dep_filename)\n if not os.path.exists(dep_path):\n dependency[dep] = list()\n continue\n with open(dep_path, 'r') as fdep:\n dependency[dep] = fdep.read().strip().split()\n dependency['all'] = list()\n for dep_list in _DEPENDENCY:\n dependency['all'].extend(dependency[dep_list])\n for dep, dep_list in dependency.items():\n dependency[dep] = list(set(dep_list))\n metadata = {\n 'name': name,\n 'version': version,\n 'revision': revision,\n 'arch': arch,\n 'arch_variant': arch_variant,\n 'dependencies': dependency,\n }\n self.metadata = metadata",
"title": ""
},
{
"docid": "6db07c5064499b68d5189f81035a1566",
"score": "0.5703096",
"text": "def plugin_manifest():\n\n # XXX: note, this doesn't get called. For an example of this working,\n # see the mockplugin unit test.\n\n filepath = importlib.resources.files(__package__) / \"plugin_manifest.json\"\n return manifest.manifest_from_string(\n filepath.read_text()\n )",
"title": ""
},
{
"docid": "7e4533fc64105a820b862ea9cdb02fd6",
"score": "0.56927377",
"text": "def load_manifest(manifest_path, use_relative_manifest_path=False):\n if use_relative_manifest_path:\n extra_path = os.path.dirname(manifest_path)\n manifest = []\n sample_names = set()\n with open(manifest_path, \"rt\") as manifest_file:\n for line in manifest_file:\n if line.startswith('#'):\n continue\n line = line.rstrip()\n element = ManifestElement()\n element.load(line)\n\n if use_relative_manifest_path:\n element.append_to_path(extra_path)\n elif not os.path.exists(element.path):\n # check paths relative to manifest\n element.append_to_path(os.path.dirname(\n os.path.abspath(manifest_path)))\n\n # check if file exists\n if not os.path.exists(element.path):\n raise FileNotFoundError(\n errno.ENOENT, os.strerror(errno.ENOENT), element.path)\n\n # duplicate sample names are bad because we name folders after them\n if element.name in sample_names:\n raise Exception(\n \"Duplicate sample name in manifest: %s\" % element.name)\n sample_names.add(element.name)\n\n manifest.append(element)\n return manifest",
"title": ""
},
{
"docid": "ae9cb021e1682f5997b4f51b3a1b1371",
"score": "0.5653137",
"text": "def _read_manifest_json(self):\n with open(os.path.join(self._crx_dir, \"manifest.json\")) as manifest:\n return json.load(manifest)",
"title": ""
},
{
"docid": "1ccb117ec29f6283c5057d9bdf655118",
"score": "0.5535802",
"text": "def load_app_manifests(self):\n self.app_manifests = []\n apps_lib_path = os.path.join(self.apps_dir_path, \"lib\")\n for app_dir in os.listdir(apps_lib_path):\n if app_dir not in (\"__init__.py\", \"__init__.pyc\"):\n if app_dir.find(\"_v\") > 1:\n app_name = app_dir[:app_dir.find(\"_v\")]\n self.app_manifests.append(json.load(file(os.path.join(self.apps_dir_path, 'lib', app_dir, \"manifest.json\"))))\n log.info(\"Manifest for %s app was loaded\" % (app_dir))\n else:\n log.info(\"Directory %s will be skipped from app loader . Doesn't match naming convention .\" % app_dir)",
"title": ""
},
{
"docid": "7d26c3828209e8535da49a32a2920b7e",
"score": "0.5523236",
"text": "def get_manifest(self):\n return self.manifest",
"title": ""
},
{
"docid": "adfc7abb294e33ba47c8708ec3767959",
"score": "0.5494579",
"text": "def load_manifest(self, *, user: str, identity_file: str):\n if not self.master_ip:\n return\n\n master_ssh_client = get_ssh_client(\n user=user,\n host=self.master_ip,\n identity_file=identity_file,\n wait=True,\n print_status=False)\n\n with master_ssh_client:\n manifest_raw = ssh_check_output(\n client=master_ssh_client,\n command=\"\"\"\n cat \"$HOME/.flintrock-manifest.json\"\n \"\"\")\n # TODO: Would it be better if storage (ephemeral and otherwise) was\n # implemented as a Flintrock service and tracked in the manifest?\n ephemeral_dirs_raw = ssh_check_output(\n client=master_ssh_client,\n # It's generally safer to avoid using ls:\n # http://mywiki.wooledge.org/ParsingLs\n command=\"\"\"\n shopt -s nullglob\n for f in /media/ephemeral*; do\n echo \"$f\"\n done\n \"\"\")\n\n manifest = json.loads(manifest_raw)\n\n self.ssh_key_pair = SSHKeyPair(\n public=manifest['ssh_key_pair']['public'],\n private=manifest['ssh_key_pair']['private'])\n\n services = []\n for [service_name, manifest] in manifest['services']:\n # TODO: Expose the classes being used here.\n service = globals()[service_name](**manifest)\n services.append(service)\n self.services = services\n\n storage_dirs = StorageDirs(\n root='/media/root',\n ephemeral=sorted(ephemeral_dirs_raw.splitlines()),\n persistent=None)\n self.storage_dirs = storage_dirs",
"title": ""
},
{
"docid": "7a6bd0e807055cc5264bd9bd0d41cead",
"score": "0.5488276",
"text": "def manifest_from_file(file_name):\n in_file = open(file_name, 'rb')\n try:\n return read_file_manifest(in_file)\n finally:\n in_file.close()",
"title": ""
},
{
"docid": "50b1436d4dad2dae6c6bfcbad316a521",
"score": "0.53888357",
"text": "def parse_manifest(location):\n mode = 'r'\n with open(location, mode) as manifest:\n return parse_manifest_data(manifest.read())",
"title": ""
},
{
"docid": "655118e9ba3dbba3270944ecf8a5725b",
"score": "0.53654754",
"text": "def read_manifest(manifest_fn):\n with open(manifest_fn, 'r') as csvfile:\n reader = csv.DictReader(csvfile, delimiter=';')\n dicts = list(reader)\n return dicts",
"title": ""
},
{
"docid": "1ce7638a5cd096b7433f1ade4ed970fa",
"score": "0.5360083",
"text": "def extract_manifest(path, resource_name):\n with LoadLibrary(path) as handle:\n try:\n return win32api.LoadResource(handle, RT_MANIFEST, resource_name)\n except pywintypes.error as error:\n if error.args[0] == winerror.ERROR_RESOURCE_DATA_NOT_FOUND:\n return None\n else:\n raise",
"title": ""
},
{
"docid": "1b0d63051ae40298e14d59e8684bcb16",
"score": "0.53290707",
"text": "def _set_manifest(self, manifest: Dict) -> None:\n if \"metadata\" not in manifest:\n manifest[\"metadata\"] = {}\n\n if \"files\" not in manifest:\n manifest[\"files\"] = {\n \"includes\": [],\n \"excludes\": [],\n }\n\n with open(self._manifest_path, \"w\", encoding=\"utf-8\") as file:\n # TODO: Exception handling\n self._yaml.dump(manifest, file)",
"title": ""
},
{
"docid": "c327e2f4872c12c438d696fd42567dbe",
"score": "0.53205585",
"text": "def _buildmanifest(self):\n\n man = self._parents[0].manifest().copy()\n copied = self._repo.dirstate.copies()\n is_exec = util.execfunc(self._repo.root,\n lambda p: man.execf(copied.get(p,p)))\n is_link = util.linkfunc(self._repo.root,\n lambda p: man.linkf(copied.get(p,p)))\n modified, added, removed, deleted, unknown = self._status[:5]\n for i, l in ((\"a\", added), (\"m\", modified), (\"u\", unknown)):\n for f in l:\n man[f] = man.get(copied.get(f, f), nullid) + i\n try:\n man.set(f, is_exec(f), is_link(f))\n except OSError:\n pass\n\n for f in deleted + removed:\n if f in man:\n del man[f]\n\n self._manifest = man",
"title": ""
},
{
"docid": "779a95c5259a1319f645df743eaa83a2",
"score": "0.53105235",
"text": "def manifest_dict(self):\n return self._parsed",
"title": ""
},
{
"docid": "779a95c5259a1319f645df743eaa83a2",
"score": "0.53105235",
"text": "def manifest_dict(self):\n return self._parsed",
"title": ""
},
{
"docid": "01a74a8067cf96208e5a6f84699f39a2",
"score": "0.53061396",
"text": "def require_manifest(func):\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n # Assume the manifest is in the current directory\n try:\n # If we are in a repository, we want to look in\n # the root of that repository for the manifest\n current_repo = vcs_git.RepoTool(Path.cwd(), search_parent=True)\n root_path = current_repo.get_root_path()\n except vcs_git.InvalidRepository:\n # Since we are not in a repository we will look\n # for the manifest in the current directory\n root_path = Path.cwd()\n\n manifest_path = root_path / manifest.MANIFEST_NAME\n\n try:\n loaded_manifest = manifest.load_manifest(manifest_path)\n return func(loaded_manifest, root_path, *args, **kwargs)\n except manifest.NotFound:\n ui.error(f\"Unable to load manifest: Not found: {str(manifest_path)}\")\n sys.exit(1)\n except manifest.ValidationFailed as exc:\n ui.error(f\"Unable to load manifest: Validation failed\")\n ui.error(str(exc))\n sys.exit(1)\n\n return wrapper",
"title": ""
},
{
"docid": "64e5d397d57a697e741ab40fd11081cc",
"score": "0.52327967",
"text": "def load(config, args):\n if not confirm(\"WARNING: This isn't considered production ready just yet. Continue?\"):\n return\n if not args.no_backup:\n timestamp = args.project.dump(args.node)\n print \"Pre-load backup: %s\" % args.project.dump_path(timestamp)\n args.project.load(args.node, args.timestamp)\n print \"Service data from %s loaded at %s\" % (args.timestamp, \n args.node.hostname)",
"title": ""
},
{
"docid": "338e16703a7a9f83dfbb260699665fc1",
"score": "0.52210414",
"text": "def build_manifest(self):\n return self._build_manifest",
"title": ""
},
{
"docid": "46ae0dc6c22bbc4256e6479eea8aee5a",
"score": "0.51629394",
"text": "def check_manifest(manifest):\n if not manifest:\n raise Exception('manifest is null')\n\n for key in ['dublin_core', 'checking', 'projects']:\n if key not in manifest:\n raise Exception('manifest missing key \"{0}\"'.format(key))\n\n # check checking\n for key in ['checking_entity', 'checking_level']:\n if key not in manifest['checking']:\n raise Exception('manifest missing checking key \"{0}\"'.format(key))\n\n if not isinstance(manifest['checking']['checking_entity'], list):\n raise Exception('manifest key checking.checking_entity must be an array')\n\n # check projects\n if not isinstance(manifest['projects'], list):\n raise Exception('manifest key projects must be an array')\n\n for key in ['categories', 'identifier', 'path', 'sort', 'title', 'versification']:\n for project in manifest['projects']:\n if key not in project:\n raise Exception('manifest missing project key \"{0}\"'.format(key))\n\n # check dublin_core\n for key in ['conformsto', 'contributor', 'creator', 'description', 'format', 'identifier', 'issued', 'language',\n 'modified', 'publisher', 'relation', 'rights', 'source', 'subject', 'title', 'type', 'version']:\n if key not in manifest['dublin_core']:\n raise Exception('manifest missing dublin_core key \"{0}\"'.format(key))\n\n expectedRCVersion = 'rc0.2'\n if manifest['dublin_core']['conformsto'].lower() != expectedRCVersion:\n raise Exception('unsupported rc version {}. Expected {}'.format(manifest['dublin_core']['conformsto'], expectedRCVersion))\n\n for key in ['direction', 'identifier', 'title']:\n if key not in manifest['dublin_core']['language']:\n raise Exception('manifest missing dublin_core.language key \"{0}\"'.format(key))\n\n if not isinstance(manifest['dublin_core']['source'], list):\n raise Exception('manifest key dublin_core.source must be an array')\n\n for key in ['version', 'identifier', 'language']:\n for source in manifest['dublin_core']['source']:\n if key not in source:\n raise Exception('manifest missing dublin_core.source key \"{0}\"'.format(key))",
"title": ""
},
{
"docid": "ce53a48bd5b93f86a7a69842682cb68d",
"score": "0.5162862",
"text": "def test_talbot_manifest_fetch():\n request, response = app.test_client.get(\"/iiif/manifest/452d6b51-949c-447d-9880-1108ffdfd96e.json\")\n assert response.status == 200",
"title": ""
},
{
"docid": "9e902dad1ecdaea377fed483c2c9b74c",
"score": "0.516229",
"text": "def manifest(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\n return pulumi.get(self, \"manifest\")",
"title": ""
},
{
"docid": "3f078b6b178c5d0e5bc424b6ec886675",
"score": "0.5146479",
"text": "def load(filename: Path) -> Optional[List[\"Downloadable\"]]:\n try:\n manifest = Manifest.load_manifest(filename)\n return Manifest.create_object_list(manifest)\n except FileNotFoundError as ex:\n logger.critical(f\"Error file not found: {ex.filename}\")\n except JSONDecodeError as ex:\n logger.critical(f\"format of manifest file is valid JSON: {ex.msg}\")\n\n return None",
"title": ""
},
{
"docid": "6c7e411c2c15bce45e103811676aa800",
"score": "0.5138625",
"text": "def print_manifest_contents(jar_file_path):\n print(get_manifest_contents(jar_file_path))",
"title": ""
},
{
"docid": "539856f10ee7afec85f51eb6bb5cac7b",
"score": "0.5104779",
"text": "def load_manifest_config(self, repository, revision):\n commit_log = self.m.gitiles.commit_log(\n repository, revision, step_name='Fetch manifest config',\n attempts=self._GITILES_ATTEMPTS)\n result = self.m.step.active_result\n\n # Handle missing/invalid response.\n if not (commit_log and commit_log.get('message')):\n self.m.python.failing_step('Fetch manifest config failure',\n 'Failed to fetch manifest config.')\n\n build_id = None\n loaded = []\n for line in reversed(commit_log['message'].splitlines()):\n # Automatic command?\n match = self._MANIFEST_CMD_RE.match(line)\n if match:\n self.c.chromite_branch = match.group(2)\n loaded.append('Chromite branch: %s' % (self.c.chromite_branch,))\n continue\n\n # Build ID?\n match = self._BUILD_ID_RE.match(line)\n if match:\n self.c.cbb.build_id = match.group(1)\n continue\n if loaded:\n loaded.insert(0, '')\n result.presentation.step_text += '<br/>'.join(loaded)",
"title": ""
},
{
"docid": "70ff872d42bb02f2d93a3b7e8712e6fc",
"score": "0.5101483",
"text": "def parse_data(self):\n\n try:\n if self.is_bytes:\n self.data = etree.XML(self.manifest)\n else:\n with open(self.manifest) as fh:\n self.data = etree.XML(fh.read().encode())\n except etree.XMLSyntaxError:\n raise InvalidManifest('Not an XML file')\n\n self.tree = etree.ElementTree(self.data)\n\n self.find_remotes()\n self.find_defaults()\n self.find_projects()\n\n return self.generate_manifest_dict()",
"title": ""
},
{
"docid": "c662582eda06e2c813e146a76349d4e4",
"score": "0.5074428",
"text": "def get_manifest(self):\n url = f'samples/{self.uuid}/manifest'\n return self.knex.get(url)",
"title": ""
},
{
"docid": "fa9f1c7ac1c1cf13b90bb3ca1efa6177",
"score": "0.505936",
"text": "def test_incorrect_dependency(self):\n load_manifest(StringIO(manifest_incorrect_dependency))",
"title": ""
},
{
"docid": "90c562ccc1e580b741c20fdb9872524c",
"score": "0.5049897",
"text": "def get_manifestfile(self):\r\n if self.__manifest_file is None:\r\n return os.path.join(self.cloudletdir, \"manifest\")\r\n return self.__manifest_file",
"title": ""
},
{
"docid": "179ceba762ccea795c47f14819cba941",
"score": "0.50358003",
"text": "def readManifest(fn):\n fp = open(fn)\n manifest = []\n for line in fp.readlines():\n work = line.strip()\n if work.startswith(\"#\"): continue\n if not work: continue\n if work.startswith('\"'):\n source = work.split('\"')[1]\n work = work[len(source)+2:].strip()\n else:\n source = work.split()[0]\n work = work[len(source):].strip()\n pass #end-else\n if not work or work.startswith(\"#\"):\n target = None\n elif work.startswith('\"'):\n target = work.split('\"')[1]\n work = work[len(target)+2:].strip()\n else:\n target = work.split()[0]\n work = work[len(target):].strip()\n pass #end-if\n if not work.startswith(\"#\") and work:\n raise Exception(\"Bad format line %s\" % line.strip())\n manifest.append((source,target))\n pass #end-for\n return manifest",
"title": ""
},
{
"docid": "b3847de5ff492d4c3321c741f0ee3fd0",
"score": "0.50304276",
"text": "def manifest(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\n return pulumi.get(self, \"manifest\")",
"title": ""
},
{
"docid": "b528a893ada959204fd0cdc9a92c02b9",
"score": "0.49886143",
"text": "def load(description: Dict, index: Index) -> Manifest:\n\n sources = description.get(\"sources\", {})\n pipelines = description.get(\"pipelines\", [])\n\n manifest = Manifest()\n source_refs = set()\n\n # load the sources\n for name, desc in sources.items():\n info = index.get_module_info(\"Source\", name)\n items = desc.get(\"items\", {})\n options = desc.get(\"options\", {})\n manifest.add_source(info, items, options)\n source_refs.update(items.keys())\n\n for desc in pipelines:\n load_pipeline(desc, index, manifest, source_refs)\n\n # The \"runner\" property in the manifest format is the\n # runner to the run the pipeline with. In osbuild the\n # \"runner\" property belongs to the \"build\" pipeline,\n # i.e. is what runner to use for it. This we have to\n # go through the pipelines and fix things up\n pipelines = manifest.pipelines.values()\n\n host_runner = Runner(index.detect_host_runner())\n runners = {\n pl.id: pl.runner for pl in pipelines\n }\n\n for pipeline in pipelines:\n if not pipeline.build:\n pipeline.runner = host_runner\n continue\n\n runner = runners[pipeline.build]\n pipeline.runner = runner\n\n return manifest",
"title": ""
},
{
"docid": "fd855874ea863136d944aa1c11523ccc",
"score": "0.49822238",
"text": "def manifest_file(self):\n return self._manifest_file",
"title": ""
},
{
"docid": "af5fa5b4cc493e43c7266c31c836630f",
"score": "0.4977194",
"text": "def load_from_disk(cls, path: Path) -> DirManifest:\n package = DirManifest()\n\n package.path = path\n packagepathstr = str(path)\n paths: List[str] = []\n\n # Simply return empty manifests if the given path isn't a dir.\n # (the server may intend to create it and is just asking what's\n # there already)\n if path.is_dir():\n # Build the full list of package-relative paths.\n for basename, _dirnames, filenames in os.walk(path):\n for filename in filenames:\n fullname = os.path.join(basename, filename)\n assert fullname.startswith(packagepathstr)\n paths.append(fullname[len(packagepathstr) + 1:])\n\n import hashlib\n from concurrent.futures import ThreadPoolExecutor\n from multiprocessing import cpu_count\n\n def _get_file_info(filepath: str) -> Tuple[str, DirManifestFile]:\n sha = hashlib.sha256()\n fullfilepath = os.path.join(packagepathstr, filepath)\n if not os.path.isfile(fullfilepath):\n raise Exception(f'File not found: \"{fullfilepath}\"')\n with open(fullfilepath, 'rb') as infile:\n filebytes = infile.read()\n filesize = len(filebytes)\n sha.update(filebytes)\n return (filepath,\n DirManifestFile(filehash=sha.hexdigest(),\n filesize=filesize))\n\n # Now use all procs to hash the files efficiently.\n with ThreadPoolExecutor(max_workers=cpu_count()) as executor:\n package.files = dict(executor.map(_get_file_info, paths))\n\n return package",
"title": ""
},
{
"docid": "ef1f539a2a3453b3a22fd53677e55abd",
"score": "0.49754578",
"text": "def read_manifest(manifest_path, max_duration=float('inf'), min_duration=0.0):\n manifest = []\n for json_line in codecs.open(manifest_path, 'r', 'utf-8'):\n try:\n json_data = json.loads(json_line)\n except Exception as e:\n raise IOError(\"Error reading manifest: %s\" % str(e))\n if (json_data[\"duration\"] <= max_duration and\n json_data[\"duration\"] >= min_duration):\n manifest.append(json_data)\n return manifest",
"title": ""
},
{
"docid": "8f63ce71ff8f5d666f99eab60b12c48c",
"score": "0.49737337",
"text": "def description(path='election-manifest.json'):\n from electionguard.manifest import Manifest\n with open(path, 'r') as manifest:\n string_representation = manifest.read()\n return Manifest.from_json(string_representation)",
"title": ""
},
{
"docid": "26d3f8774fc17313160112f9ca94151c",
"score": "0.49673423",
"text": "def fact():\n manifests = [x for x in os.walk(manifests_dir)]\n\n return { 'manifests': manifests }",
"title": ""
},
{
"docid": "bf25d8a4d47663a062d11dd5bc932a57",
"score": "0.4966431",
"text": "def __init__(self, settings, load = True):\n\t\tself.version = 2.0\n\t\tself.data = {'@meta':{'version': 0}}# Default to no version, which will be converted.\n\t\tself.file = stringutil.normalize_file(settings.save_base() + '/Manifest.json.gz')\n\t\tself._completed = []\n\t\tself._failed = []\n\t\tif load and os.path.isfile(self.file): #!cover\n\t\t\ttry:\n\t\t\t\twith gzip.GzipFile(self.file, 'rb') as data_file:\n\t\t\t\t\tself.data = json.loads(data_file.read().decode('utf8'))\n\t\t\texcept:\n\t\t\t\tstringutil.error('Failed to load Manifest at [%s]. Probably corrupt. Try removing the file.' % self.file)\n\t\t\t\traise\n\t\tchange, self.data = self._adapt(self.data)\n\t\twhile change:\n\t\t\tchange, self.data = self._adapt(self.data)\n\t\t#\n\t\tassert 'elements' in self.data\n\t\tassert 'completed' in self.data['elements']\n\t\tassert 'failed' in self.data['elements']\n\t\tself.og_count = len(self.data['elements']['completed']+ self.data['elements']['failed'])",
"title": ""
},
{
"docid": "39b9c8fa3c899df31acd93c613f2433b",
"score": "0.4961703",
"text": "def get_manifest_and_flags(self, manifest_id):\n if manifest_id == mercurial.node.nullid:\n return {}, {}\n revid = self._lookup_revision_by_manifest_id(manifest_id)\n return self.get_manifest_and_flags_by_revid(revid)",
"title": ""
},
{
"docid": "15d3fc219880ae9aab1996153f0188ac",
"score": "0.49540278",
"text": "def get_manifest(path: str):\n base_url = urlparse(path.strip(\"/\"))\n if base_url.scheme != \"s3\":\n raise click.UsageError(\n f\"URL scheme should be s3, but received {base_url.geturl()}\"\n )\n\n s3 = boto3.resource(\"s3\")\n manifest_filenames = [\"lecida__manifest.yml\", \"manifest.yml\"]\n\n def read_s3(base_url: ParseResult, filename: str) -> Optional[bytes]:\n try:\n obj = s3.Object(\n bucket_name=base_url.netloc,\n key=base_url.path.strip(\"/\") + f\"/{filename}\"\n )\n return obj.get()['Body'].read()\n except ClientError as e:\n # Only allow NoSuchKey errors, blow up on any other errors\n if e.response['Error']['Code'] == 'NoSuchKey':\n return None\n raise e\n\n body: Optional[bytes] = None\n for mf in manifest_filenames:\n body = read_s3(base_url, mf)\n if body is not None:\n break\n if body is None:\n raise click.ClickException(\n f\"Can't find any manifest files ({manifest_filenames}) in {path}\"\n )\n\n click.secho(\n f\"Found manifest in {base_url.geturl()}/{mf}\", fg='green', err=True\n )\n click.echo(body.decode(\"utf-8\"))",
"title": ""
},
{
"docid": "0c7388d807a35e9ab26a1facbde162d1",
"score": "0.488468",
"text": "def test_invalid_manifest_filepath(self):\n load_manifest(\"./ehiiehaiehnatheita\")",
"title": ""
},
{
"docid": "ef160d4aa653d8ff7d32935e7e93e39f",
"score": "0.48777252",
"text": "def testGetManifest(self):\n manifest = self.dl_object._GetManifest()\n self.assertEqual(manifest.get('mediaType'),\n 'application/vnd.docker.distribution.manifest.v2+json')\n self.assertIn('layers', manifest)",
"title": ""
},
{
"docid": "521b011e65109d6e46679e3ecc7be141",
"score": "0.48673728",
"text": "def read_manifest_xml(cls, document):\n manifest = []\n with zipfile.ZipFile(document, 'a') as open_document:\n for line in open_document.open(DOCUMENT_MANIFEST_PATH):\n manifest.append(line.decode('utf-8'))\n return manifest",
"title": ""
},
{
"docid": "cda8f03505a34525b3b7f5fd2172ee2d",
"score": "0.4864306",
"text": "def get_manifest_info(manifest_xml):\n\n manifest_info = mf_parse.Manifest(manifest_xml, is_bytes=True)\n manifest_data = manifest_info.parse_data()\n\n return mf_info.ManifestInfo(manifest_data)",
"title": ""
},
{
"docid": "3eff92cb969b3e149b8780d060a16d8d",
"score": "0.48551023",
"text": "def log_manifest(self):\n self._audit_log.log_json(\"manifest\", self.get_manifest_json())",
"title": ""
},
{
"docid": "679bdaadf36b807251d2a887342ce2d1",
"score": "0.4849527",
"text": "def parse_manifest_data(manifest):\n # normalize line endings then split each section: they are separated by two LF\n lines = '\\n'.join(manifest.splitlines(False))\n sections = re.split('\\n\\n+', lines)\n return [parse_section(s) for s in sections]",
"title": ""
},
{
"docid": "74f60b5a07651f651a7c49ce63faffcd",
"score": "0.48401114",
"text": "def manifestContent( self, pars, directory ):\n\n return None",
"title": ""
},
{
"docid": "5d70564a1b2f0bb0c5e47a1113cfb48d",
"score": "0.48191658",
"text": "def extract_storage_manifest(storage, manifest_path='manifest.xml'):\n\n try:\n raw_manifest = storage.file(manifest_path)\n except PathNotFoundError:\n raise ValueError\n return parse_manifest(raw_manifest)",
"title": ""
},
{
"docid": "b08d3e7f70d1c6ada6ad899818ab8518",
"score": "0.4815947",
"text": "def get_manifest_raw(args):\n # If we're given a path to a manifest file, use it\n if os.path.exists(args.manifest_location):\n manifest_fn = args.manifest_location\n log(\"INFO\", \"Using manifest file at location: {}\".format(manifest_fn))\n with open(manifest_fn, 'r') as manifest_file:\n manifest_raw = manifest_file.read()\n # Otherwise, use the CMake Magic manifest\n else:\n manifest_raw = _MANIFEST_CONTENTS\n log(\"TRACE\", \"Raw manifest contents: {}\".format(manifest_raw))\n return manifest_raw",
"title": ""
},
{
"docid": "66ad0744c0442b2c711141e707bca14b",
"score": "0.48058933",
"text": "def get_manifest_contents(jar_file_path):\n _is_valid_jar_file(jar_file_path)\n manifest_file_contents = _get_manifest_file_contents(jar_file_path)\n return _format_attributes(manifest_file_contents)",
"title": ""
},
{
"docid": "afb6a033b15a1e2f52a9d2f4201e2415",
"score": "0.47987786",
"text": "def update_manifest(self, filename: Optional[str] = None, manifest: Optional[Dict[str, str]] = None) -> None:\n filename = filename or self.manifest_filename\n manifest = manifest or {}\n self.log.debug(f\"Updating manifest '{manifest}' to file '{filename}'\")\n with open(filename, \"w\") as f:\n json.dump(manifest, f, indent=2)",
"title": ""
},
{
"docid": "cdfb81321b5baba6eb9068205bd7d81f",
"score": "0.47932842",
"text": "def test_load_manifest_no_inheritance(self):\n temp_directory = tempfile.mkdtemp()\n parent_file_path = os.path.join(temp_directory, \"parent.cfg\")\n child_file_path = os.path.join(temp_directory, \"child.cfg\")\n with open(parent_file_path, \"w\") as fh:\n fh.write(parent_manifest)\n\n with open(child_file_path, \"w\") as fh:\n fh.write(child_manifest.format(parent_file_path))\n\n manifest = load_manifest(child_file_path, do_inherit=False)\n\n assert not manifest.has_option(\"config\", \"namespace\")",
"title": ""
},
{
"docid": "f5beb29111b6e52c53c5cb75911a78db",
"score": "0.47867966",
"text": "def _ProcessManifest(manifest_path):\n doc = minidom.parse(manifest_path)\n manifests = doc.getElementsByTagName('manifest')\n assert len(manifests) == 1\n manifest = manifests[0]\n package = manifest.getAttribute('package')\n\n manifest.setAttribute('xmlns:%s' % _TOOLS_NAMESPACE_PREFIX, _TOOLS_NAMESPACE)\n\n tmp_prefix = os.path.basename(manifest_path)\n with tempfile.NamedTemporaryFile(prefix=tmp_prefix) as patched_manifest:\n doc.writexml(patched_manifest)\n patched_manifest.flush()\n yield patched_manifest.name, package",
"title": ""
},
{
"docid": "ee93a15988741f0c421c04a837f4eb6e",
"score": "0.47565392",
"text": "def test_CreatePackageManifest(tempdir: pathlib.Path):\n (tempdir / \"a\").touch()\n m = dpack.CreatePackageManifest(tempdir, [pathlib.Path(\"a\")])\n assert len(m.file) == 1\n assert m.file[0].comment == \"\"\n assert not m.file[0].size_in_bytes\n assert m.file[0].checksum_hash == dpack_pb2.SHA256\n assert m.file[0].checksum == SHA256_EMPTY_FILE",
"title": ""
},
{
"docid": "ed1efd6ba0fa7925a19d5452c9ede0ea",
"score": "0.47510195",
"text": "def supports_manifest(manifest):\n pass",
"title": ""
},
{
"docid": "f2bca57cfcc9985abe0749bffe5e91e3",
"score": "0.47421065",
"text": "def add_runtime(tm_env, manifest):\n _transform_services(manifest)\n\n app_manifest.add_linux_system_services(tm_env, manifest)\n app_manifest.add_linux_services(manifest)",
"title": ""
},
{
"docid": "1e23484f53d0f76ba1b209efa2a9bfe3",
"score": "0.4688811",
"text": "def load_yaml(self):\n env = self.state.document.settings.env\n relpath, abspath = env.relfn2path(directives.path(self.arguments[0]))\n\n env.note_dependency(relpath)\n\n encoding = self.options.get('encoding', env.config.source_encoding)\n with io.open(abspath, 'rt', encoding=encoding) as stream:\n spec = yaml.load(stream, _YamlOrderedLoader) # nosec\n self.spec = spec\n self.paths = spec[self.path_path]\n self.definitions = spec[self.models_path]\n self.openapi_version = spec.get('swagger', None) or spec['openapi']\n self.options.setdefault('uri', 'file://%s' % abspath)",
"title": ""
},
{
"docid": "4dca52d23b03dd673dd1c153329be492",
"score": "0.46530834",
"text": "def test_valid_and_empty_manifest(self):\n collector = PypiCollector()\n collector.parse_and_collect(MANIFEST_START + DEP_1, True)\n collector.parse_and_collect(None, True)\n packages = dict(collector.counter.most_common())\n assert packages == {\n 'daiquiri': 1\n }",
"title": ""
},
{
"docid": "1666b2b76538b5c07055126fdea7a473",
"score": "0.46519795",
"text": "def parse_manifest(self):\n import json\n import struct\n\n num = self.selection\n try:\n manifest = json.loads(self.cryptomattes[num].get(\"manifest\", \"{}\"))\n except:\n manifest = {}\n from_names = {}\n from_ids = {}\n\n unpacker = struct.Struct('=f')\n packer = struct.Struct(\"=I\")\n for name, value in manifest.iteritems():\n packed = packer.pack(int(value,16))\n packed = packed = '\\0' * (4 - len(packed)) + packed\n id_float = unpacker.unpack( packed )[0]\n name_str = str(name)\n from_names[name_str] = id_float\n from_ids[id_float] = name_str\n\n self.cryptomattes[num][\"names_to_IDs\"] = from_names\n self.cryptomattes[num][\"ids_to_names\"] = from_ids\n\n global g_cryptomatte_manf_from_names\n global g_cryptomatte_manf_from_IDs\n g_cryptomatte_manf_from_names = from_names\n g_cryptomatte_manf_from_IDs = from_ids\n\n return from_names",
"title": ""
},
{
"docid": "24e6739b94123b601a11685c9bda0545",
"score": "0.46446547",
"text": "def getAssemblies(pth):\n if pth.lower().endswith(\".manifest\"):\n return []\n # check for manifest file\n manifestnm = pth + \".manifest\"\n if os.path.isfile(manifestnm):\n with open(manifestnm, \"rb\") as fd:\n res = {RT_MANIFEST: {1: {0: fd.read()}}}\n else:\n # check the binary for embedded manifest\n try:\n res = GetManifestResources(pth)\n except winresource.pywintypes.error as exc:\n if exc.args[0] == winresource.ERROR_BAD_EXE_FORMAT:\n logger.info('Cannot get manifest resource from non-PE '\n 'file %s', pth)\n return []\n raise\n rv = []\n if RT_MANIFEST in res and len(res[RT_MANIFEST]):\n for name in res[RT_MANIFEST]:\n for language in res[RT_MANIFEST][name]:\n # check the manifest for dependent assemblies\n try:\n manifest = Manifest()\n manifest.filename = \":\".join([pth, str(RT_MANIFEST),\n str(name), str(language)])\n manifest.parse_string(res[RT_MANIFEST][name][language],\n False)\n except Exception as exc:\n logger.error(\"Can not parse manifest resource %s, %s\"\n \" from %s\", name, language, pth, exc_info=1)\n else:\n if manifest.dependentAssemblies:\n logger.debug(\"Dependent assemblies of %s:\", pth)\n logger.debug(\", \".join([assembly.getid()\n for assembly in\n manifest.dependentAssemblies]))\n rv.extend(manifest.dependentAssemblies)\n return rv",
"title": ""
},
{
"docid": "85b8cdd00a5eaecf3f96345d4373c615",
"score": "0.46384037",
"text": "def initFromFile(self):\n\n bootFilename = os.path.join(os.environ['CRAB3_BOOTSTRAP_DIR'], BOOTSTRAP_ENVFILE)\n if not os.path.isfile(bootFilename):\n msg = \"The CRAB3_BOOTSTRAP_DIR environment variable is set, but I could not find %s\" % bootFilename\n raise EnvironmentException(msg)\n else:\n with open(bootFilename) as fd:\n self.update(json.load(fd))",
"title": ""
},
{
"docid": "742677b54bdac63a9c37d33e91add012",
"score": "0.46214262",
"text": "def load_from_module_name(\n cls, module_name: str\n ) -> \"ThreatExchangeExtensionManifest\":\n try:\n module = importlib.import_module(module_name)\n except (ImportError, ValueError):\n raise ValueError(f\"No such module '{module_name}'\")\n\n try:\n manifest = module.TX_MANIFEST\n except AttributeError:\n raise ValueError(f\"Module is missing TX_MANIFEST\")\n\n if not isinstance(manifest, cls):\n raise ValueError(f\"TX_MANIFEST is not a {cls.__name__}!\")\n return manifest",
"title": ""
},
{
"docid": "7b95b1c76b42009ef2744f464bef6b1b",
"score": "0.46139222",
"text": "def test_load_manifest_inheritance(self):\n temp_directory = tempfile.mkdtemp()\n parent_file_path = os.path.join(temp_directory, \"parent.cfg\")\n child_file_path = os.path.join(temp_directory, \"child.cfg\")\n with open(parent_file_path, \"w\") as fh:\n fh.write(parent_manifest)\n\n with open(child_file_path, \"w\") as fh:\n fh.write(child_manifest.format(parent_file_path))\n\n manifest = load_manifest(child_file_path)\n\n assert (\n manifest.get(\"config\", \"namespace\") == \"inheritance\"\n ), \"Value not present in child should be pulled from parent!\"\n\n assert (\n manifest.get(\"parent_section\", \"parent\") == \"not me\"\n ), \"child value should override parent value!\"",
"title": ""
},
{
"docid": "e604535e64fb03b9bddd475f8ce90622",
"score": "0.46073496",
"text": "def _load(self):\n p = os.path.join(paths.setup_dir, 'system_health.yaml')\n if os.path.isfile(p):\n with open(p, 'r') as rfile:\n config = yaml.load(rfile)\n if config:\n self._values = config['values']\n self._conditionals = config['conditionals']\n\n general = config['general']\n self._limit = general['limit']",
"title": ""
},
{
"docid": "2efb06224e8d79a25e522cd0d1afac2c",
"score": "0.46070045",
"text": "def get_manifests(arcroot):\n manifests = []\n for root, dirs, files in os.walk(arcroot):\n if 'manifest.json' in files:\n manifests.append(os.path.join(root, 'manifest.json'))\n \n return manifests",
"title": ""
},
{
"docid": "70c274157e08161aa8e10e323cba88c8",
"score": "0.45951656",
"text": "def test_MergeManifests_file_attributes():\n d1 = dpack_pb2.DataPackage()\n f1 = d1.file.add()\n f1.relative_path = \"a\"\n f1.size_in_bytes = 1\n f1.checksum_hash = dpack_pb2.SHA1\n f1.checksum = \"abc\"\n d2 = dpack_pb2.DataPackage()\n f2 = d2.file.add()\n f2.relative_path = \"a\"\n f2.size_in_bytes = 2\n f2.checksum_hash = dpack_pb2.MD5\n f2.checksum = \"def\"\n dpack.MergeManifests(d1, d2)\n assert d1.file[0].size_in_bytes == 1\n assert d1.file[0].checksum_hash == dpack_pb2.SHA1\n assert d1.file[0].checksum == \"abc\"",
"title": ""
},
{
"docid": "3fa5d962e6a31bb79ca12b590566f748",
"score": "0.4583587",
"text": "def parse_manifest(manifest_contents):\n manifest = {}\n for line in manifest_contents.split('\\n'):\n line_unpacked = line.split()\n try:\n # Check that the line isn't empty or a comment\n if not line_unpacked or line.strip().startswith('#'):\n continue\n\n target, repo_hash, url, sha256_hash = line_unpacked\n manifest[target] = {\"repo_hash\": repo_hash,\n \"url\": url,\n \"sha256_hash\": sha256_hash,\n }\n except ValueError:\n log(\"WARN\", \"Warning: Invalid line in manifest file:\\n\"\n \" {}\".format(line))\n continue\n return manifest",
"title": ""
},
{
"docid": "10c9dab093ad302cb3acc204147d8723",
"score": "0.4579398",
"text": "def update_manifest(self, dst):\n # Read the current manifest into memory\n mpath = os.path.join(os.path.dirname(dst), \"manifest.json\")\n try:\n with open(mpath, 'r') as f:\n manifest = json.load(f)\n except IOError:\n manifest = {}\n\n name, _ = os.path.splitext(os.path.basename(dst))\n # Update the manifest record\n manifest[name] = {\n \"url\": os.path.basename(dst),\n \"signature\": sha256sum(dst),\n }\n\n # Write the manifest back to disk\n with open(mpath, 'w') as f:\n json.dump(manifest, f, indent=2)",
"title": ""
},
{
"docid": "22d1b2fceaeb43d570fdab95aaa9530b",
"score": "0.4572856",
"text": "async def resolve_manifest(\n self, resolve_manifest_request_body: ResolveManifestRequestBody = Body(None, description=\"\")\n ) -> ResolveManifest:\n try:\n return ResolveManifest(\n manifest=ManifestDeclarativeSource(\n resolve_manifest_request_body.manifest, construct_using_pydantic_models=True\n ).resolved_manifest\n )\n except Exception as error:\n self.logger.error(f\"Could not resolve manifest with error: {error.args[0]} - {self._get_stacktrace_as_string(error)}\")\n raise HTTPException(\n status_code=400,\n detail=f\"Could not resolve manifest with error: {error.args[0]}\",\n )",
"title": ""
},
{
"docid": "2acfb925f2be2f192c547a86e3bbfbf4",
"score": "0.45651814",
"text": "def build_manifest(self, build_manifest):\n\n self._build_manifest = build_manifest",
"title": ""
},
{
"docid": "df1b33c46b8108c0b33f40bb8ef8e9f2",
"score": "0.4555126",
"text": "def _load(self, directory):\n pass",
"title": ""
},
{
"docid": "2c6cfbc1ed390cefcd3c6552a3713be2",
"score": "0.45462036",
"text": "def update_manifest(explicit=False):\n if not os.path.exists(MANIFEST_FILENAME):\n return\n\n manifest_file = open(MANIFEST_FILENAME, 'r')\n parts = manifest_file.read().partition('\\n' + AUTOGEN_LINE)\n manifest_file.close()\n if parts[1] == '':\n if explicit:\n print \"%s has no AUTOGENERATE section\" % MANIFEST_FILENAME\n return\n\n commands = [line for line in parts[2].split('\\n') if line.startswith('#!')]\n excludes = []\n for command in commands:\n match = re.match(r'#!\\s*EXCLUDE:\\s*(.*)\\s*$', command)\n if options.verbose:\n print \"Excluding paths beginning with '%s'\" % match.group(1)\n if match:\n excludes.extend(re.split(r\",\\s*\", match.group(1)))\n\n cached_files = []\n hash_lines = []\n\n paths = options.local_listing.keys()\n paths.sort()\n size = 0\n for path in paths:\n info = options.local_listing[path]\n if path == MANIFEST_FILENAME or path == META_FILENAME or \\\n info['size'] > MAX_FILE_SIZE or \\\n is_data_path(path) or \\\n prefix_match(excludes, path):\n continue\n cached_files.append(path)\n hash_lines.append(\"%s=%s\" % (path, info['sha1']))\n size += info['size']\n\n manifest_lines = [parts[0], AUTOGEN_LINE, AUTOGEN_EXPLAIN]\n manifest_lines.extend(commands)\n manifest_lines.extend((\n \"# TOTAL FILES: %s (%s bytes)\" % (intcomma(len(cached_files)), intcomma(size)),\n \"# SIGNATURE: %s\" % hashlib.sha1('\\n'.join(hash_lines)).hexdigest(),\n \"CACHE:\",\n ))\n manifest_lines.extend(cached_files)\n\n manifest_file = open(MANIFEST_FILENAME, 'w')\n manifest_file.write('\\n'.join(manifest_lines) + '\\n')\n manifest_file.close()\n\n # Make sure the listing for the manifest file is up to date\n # so it will be uploaded if changed.\n update_local_listing(MANIFEST_FILENAME)",
"title": ""
},
{
"docid": "7348c687a4460382d2328a8765e7d7cd",
"score": "0.45457402",
"text": "def read_file_manifest(in_stream):\n count = struct.unpack(COUNT_FMT, checked_read(in_stream, COUNT_LEN))[0]\n name_map = {}\n for dummy in range(0, count):\n length, file_sha, history_sha = \\\n struct.unpack(MANIFEST_ENTRY_HDR_FMT,\n checked_read(in_stream,\n MANIFEST_ENTRY_HDR_LEN))\n\n length -= MANIFEST_ENTRY_HDR_LEN\n name = checked_read(in_stream, length)\n\n assert not name in name_map\n name_map[name] = (file_sha, history_sha)\n return name_map",
"title": ""
},
{
"docid": "fa27f7a4fe2ea88ba4b8f45fad5c15cf",
"score": "0.4543508",
"text": "def load_recipe(root: Path) -> None:\n module_name = 'dotops.recipes.' + root.name\n fpath = find_recipe(root)\n\n try:\n _ast = import_file_to_ast(fpath, module_name)\n mod = imp.new_module(module_name)\n\n mod.__file__ = fpath\n eval(ast_compile(_ast, fpath, \"exec\"), _globals(root, mod))\n except (HyTypeError, LexException) as e:\n if e.source is None:\n with open(fpath, 'rt') as fp:\n e.source = fp.read()\n e.filename = fpath\n raise\n except Exception:\n sys.modules.pop(module_name, None)\n raise",
"title": ""
},
{
"docid": "81a19b00fd269cb677b96fbe59973f31",
"score": "0.4528928",
"text": "def _load(self, funcdesc):\n # Get the module and function names\n funcdesc_elts = funcdesc.split(\".\")\n module_name = \".\".join(funcdesc_elts[:-1])\n func_name = funcdesc_elts[-1]\n\n # Get the absolute path the the xml file description\n # COMPATIBILITY: module not defined in python 2.6\n python_version = sys.version_info\n if python_version[:2] <= (2, 6):\n __import__(module_name)\n else:\n importlib.import_module(module_name)\n module = sys.modules[module_name]\n\n return getattr(module, func_name)",
"title": ""
},
{
"docid": "c6ec686d7331119f3062dc5d783e2ea3",
"score": "0.45259383",
"text": "def load(app, verbose, replay, exp_config=None):\n if replay:\n exp_config = exp_config or {}\n exp_config[\"replay\"] = True\n log(header, chevrons=False)\n loader = LoaderDeployment(app, Output(), verbose, exp_config)\n loader.run()",
"title": ""
},
{
"docid": "78d87336f2396342357242477fcc5652",
"score": "0.45210376",
"text": "def generateManifest(syn, allFiles, filename):\n keys, data = _extract_file_entity_metadata(syn, allFiles)\n _write_manifest_data(filename, keys, data)",
"title": ""
},
{
"docid": "3bbf55af9fb240f03d336162e60eb3ab",
"score": "0.45149973",
"text": "def load():\n # get (or create) config path\n p = initialize()\n return load_config(open(p['config']))",
"title": ""
},
{
"docid": "0e10fc44bf01aabbf4cff169e4240589",
"score": "0.45127457",
"text": "def load(self):\n self.suite.load()\n self.resource_map = {}\n dirlist = os.listdir(self.resources)\n for resource_name in (name for name in dirlist\n if os.path.isfile(os.path.join(self.resources,name)) and\n os.path.splitext(name)[1].lower() == '.fbr'):\n try:\n f = open(os.path.join(self.resources,resource_name),'rU')\n expr = f.read()\n d = eval(expr)\n resource_id = os.path.splitext(resource_name)[0].lower()\n d['id'] = resource_id\n kind = d['kind']\n del d['kind']\n self.resource_map[resource_id] = Resource.create(kind,**d)\n finally:\n f.close()",
"title": ""
},
{
"docid": "a913d923b861db64f34f4f861537a01e",
"score": "0.45092314",
"text": "def update_manifest(builder):\r\n\r\n manifest_path = join(builder.Config.SourceRootPath, builder.Config.WMAppManifest)\r\n dom = parse(manifest_path)\r\n\r\n #import pdb;pdb.set_trace()\r\n #version = make_version_string(builder)\r\n version = builder.AppVersion\r\n\r\n update_manifest_with_values(dom,\r\n Title = builder.CustomCfg.Title,\r\n #ProductID = builder.CustomCfg.ProductID,\r\n #PublisherID = builder.Config.PublisherID,\r\n Version = version,\r\n Languages = getattr(builder.CustomCfg, \"Languages\", None ) )\r\n\r\n with open(manifest_path, 'wb') as f:\r\n data = dom.toprettyxml(indent = \" \")\r\n # toprettyxml adds extra new lines\r\n lines = [ x for x in data.split(\"\\n\") if len(x.strip()) > 0]\r\n data = \"\\n\".join(lines)\r\n f.write(data)\r\n\r\n return True",
"title": ""
},
{
"docid": "db592431959e59a04623ec15dd0fb0cc",
"score": "0.45089307",
"text": "def load():\n out = load_as_root_module()\n parser = create_parser(os.path.basename(sys.argv[0]))\n opts = parser.parse_args(sys.argv[1:])\n load_env(opts, out.opt)\n\n return out",
"title": ""
},
{
"docid": "f6fc0df767ed0b990c78dd9a3d94f731",
"score": "0.45082822",
"text": "def load_descriptor(descriptor):\n\n try:\n data = yaml.safe_load(descriptor)\n except Exception as ex:\n raise CekitError('Cannot load descriptor', ex)\n\n if isinstance(data, basestring):\n LOGGER.debug(\"Reading descriptor from '{}' file...\".format(descriptor))\n\n if os.path.exists(descriptor):\n with open(descriptor, 'r') as fh:\n return yaml.safe_load(fh)\n\n raise CekitError(\n \"Descriptor could not be found on the '{}' path, please check your arguments!\".format(descriptor))\n\n LOGGER.debug(\"Reading descriptor directly...\")\n\n return data",
"title": ""
},
{
"docid": "1b20e6798345aea70ba11175f9875564",
"score": "0.45015812",
"text": "def parse_manifest_xml(manifest_path):\n dir_project_dict = {}\n parsed_xml = xml.dom.minidom.parse(manifest_path)\n projects = parsed_xml.getElementsByTagName('project')\n for project in projects:\n name = project.getAttribute('name')\n path = project.getAttribute('path')\n if path:\n dir_project_dict[path] = name\n else:\n dir_project_dict[name] = name\n return dir_project_dict",
"title": ""
},
{
"docid": "f0c83fd40ec198a1cd63f07acc61258d",
"score": "0.4491093",
"text": "def validate_manifest(\n request: ValidateManifestRequest = Body(...),\n schema: Any = Depends(get_description_schema),\n) -> ValidateManifestResponse:\n\n _, response = _validate_manifest(request, schema)\n return response",
"title": ""
},
{
"docid": "a6216f842ecc246653b25523d3e82310",
"score": "0.44865447",
"text": "def load():\n global tinyConfig\n if not tinyConfig:\n tinyConfig = CmdArgs()\n return tinyConfig",
"title": ""
},
{
"docid": "1abb28a2e46d4f7b412c56d5c01d386e",
"score": "0.4468919",
"text": "def check_app_manifest(api_docs_path, overrides, marketplace):\n if not os.path.exists(api_docs_path):\n raise exceptions.ValidationError(\n click.style(\"Could not find the manifest file at {}.\", fg=\"red\").format(api_docs_path))\n\n if os.path.isdir(api_docs_path):\n raise exceptions.ValidationError(\n click.style(\"{} is a directory. Please enter the direct path to the manifest file.\",\n fg=\"red\").format(api_docs_path))\n\n file_size = os.path.getsize(api_docs_path) / 1e6\n if file_size > 2:\n raise exceptions.ValidationError(\n click.style(\"The size of the manifest file at {} exceeds the maximum limit of 2MB.\", fg=\"red\")\n .format(api_docs_path))\n\n try:\n with open(api_docs_path, \"r\") as f:\n original_manifest_dict = yaml.load(f.read())\n\n manifest_dict = transform_manifest(original_manifest_dict, overrides, marketplace)\n\n # write back the manifest in case some clean up or overriding has happend\n with open(api_docs_path, \"w\") as f:\n yaml.dump(manifest_dict, f)\n\n return manifest_dict\n except (YAMLError, ValueError):\n raise exceptions.ValidationError(\n click.style(\"Your manifest file at {} is not valid YAML.\", fg=\"red\")\n .format(api_docs_path))",
"title": ""
},
{
"docid": "9dcb1e24d1e730d74793d0c10f533def",
"score": "0.4461114",
"text": "def load(path):\n pass",
"title": ""
},
{
"docid": "1b1b1add2dfd5fe7880d517df9599e9c",
"score": "0.44596192",
"text": "def load(config, contents, dynamic_templates):\n # recipe should be foo.bar.baz, not .foo.bar.baz or ..foo.bar.baz or foo/bar/baz\n path = config[\"lambdas_path\"] + os.sep\n modules = [imp.load_source(module_name, path + module_name + \".py\") for module_name in get_module_names(config)]\n for module in modules:\n module.config = config\n module.contents = contents\n module.dynamic_templates = dynamic_templates\n return {name: getattr(mod, name) for mod in modules for name in dir(mod)\n if not name.startswith(\"__\") and type(getattr(mod, name)) == types.FunctionType}",
"title": ""
},
{
"docid": "db59caa69c354306e4d49cb173a6dd5d",
"score": "0.4454742",
"text": "def get_manifest(\n ctx,\n title,\n data_type,\n jsonld,\n dataset_id,\n sheet_url,\n output_csv,\n use_annotations,\n oauth,\n json_schema,\n):\n # optional parameters that need to be passed to ManifestGenerator()\n # can be read from config.yml as well\n data_type = fill_in_from_config(\"data_type\", data_type, (\"manifest\", \"data_type\"))\n jsonld = fill_in_from_config(\"jsonld\", jsonld, (\"model\", \"input\", \"location\"))\n title = fill_in_from_config(\"title\", title, (\"manifest\", \"title\"), allow_none=True)\n json_schema = fill_in_from_config(\n \"json_schema\",\n json_schema,\n (\"model\", \"input\", \"validation_schema\"),\n allow_none=True,\n )\n\n # create object of type ManifestGenerator\n manifest_generator = ManifestGenerator(\n path_to_json_ld=jsonld,\n title=title,\n root=data_type,\n oauth=oauth,\n use_annotations=use_annotations,\n )\n\n # call get_manifest() on manifest_generator\n result = manifest_generator.get_manifest(\n dataset_id=dataset_id,\n sheet_url=sheet_url,\n json_schema=json_schema,\n )\n\n if sheet_url:\n logger.info(\"Find the manifest template using this Google Sheet URL:\")\n click.echo(result)\n\n elif isinstance(result, pd.DataFrame):\n if output_csv is None:\n prefix, _ = os.path.splitext(jsonld)\n prefix_root, prefix_ext = os.path.splitext(prefix)\n if prefix_ext == \".model\":\n prefix = prefix_root\n output_csv = f\"{prefix}.{data_type}.manifest.csv\"\n\n logger.info(\n f\"Find the manifest template using this CSV file path: {output_csv}\"\n )\n\n result.to_csv(output_csv, index=False)\n\n return result",
"title": ""
}
]
2c4f9f20da031ff4db9ea3dc8043a8b7
Perform forward pass of encoder. Returns mean with shape [batch_size, 784].
Gets a set of slides (.PDL1.mrxs) and divides each one of them into patches. The final patches are stored in a folder with the same name as the slide.
[
{
"docid": "64780dffa2afde7f412f5be70ad82c01",
"score": "0.6368157",
"text": "def patch_division(slides, outpath, level, tile_size=224, tissue_ratio=0.50, jobs=1):\n\n # Creates directory outpath if doesn't exist yet\n try:\n os.mkdir(outpath)\n print(\"Directory\", outpath, \"created\")\n print()\n except FileExistsError:\n print(\"Directory\", outpath, \"already exists\")\n print()\n\n # Collects all files in folder slide with the format *.PDL1.mrxs\n slides = os.path.join(slides, '*.PDL1.mrxs')\n slide_list = []\n start = time.time()\n n = 0\n n = Parallel(n_jobs=jobs)(delayed(get_patches)(\n s, outpath, level, tissue_ratio, tile_size) for s in glob.glob(slides))\n end = time.time()\n print('Total time patch extraction: {:.4f} s'.format(end - start))\n classifier = []\n for s in glob.glob(slides):\n slidename = os.path.basename(s)\n outpath_slide = os.path.join(outpath, slidename)\n image_path = os.path.join(outpath_slide, \"*.jpg\")\n patches = glob.glob(image_path)\n c = numpy.zeros((len(patches), 4))\n c = c.astype(int)\n for p in patches:\n name = os.path.basename(p)\n name = os.path.splitext(p)[0]\n number = name.split('#')[1]\n number = number.split('-')\n slide_number = int(number[0])\n x = int(number[2])\n y = int(number[3])\n # Loads data in classifier matrix\n c[slide_number][0] = slide_number\n c[slide_number][1] = x\n c[slide_number][2] = y\n # Positive column = 1 if patch is in list_positive\n c[slide_number][3] = 1\n classifier.append((s, c))\n\n pickle_save(classifier, outpath, 'class_{}_{}.p'.format(level, tile_size))\n\n n = sum(n)\n if not n == 0:\n print('Total number of patches extracted {}'.format(n))\n print()\n return classifier",
"title": ""
}
]
[
{
"docid": "119b53bb19bd42ac90c7ad21c6d8612e",
"score": "0.60560036",
"text": "def get_patches(slidepath, outpath, level=10, tissue_ratio=0.25, size=256):\n\n # Opens the slide with OpenSlide\n slide = OpenSlide(slidepath)\n\n # Gets deepzoom tile division\n slide_dz = deepzoom.DeepZoomGenerator(\n slide, tile_size=(size - 2), overlap=1)\n\n # Gets the name and number of the slide\n slidename = os.path.basename(slidepath)\n\n # Saves a preview of the slide under 'slidename.png'\n slide_preview(slide, slidename, outpath)\n\n # Asures that the chosen level is valid\n if level < slide_dz.level_count:\n tiles = slide_dz.level_tiles[level]\n print('Level {} contains {} tiles (empty tiles included)'.format(\n level, slide_dz.level_tiles[level][0] * slide_dz.level_tiles[level][1]))\n else:\n print('Invalid level')\n return 0\n\n # Creates new directory - where patches will be stored\n outpath = os.path.join(outpath, slidename)\n try:\n os.mkdir(outpath)\n print(\"Directory\", outpath, \"created\")\n except FileExistsError:\n print(\"Directory\", outpath, \"already exists\")\n print(\"Patches already extracted\")\n return 0\n\n # Saves tiles if detects tissue presence higher than tissue_ratio\n n = 0\n print(\"Saving tiles image \" + slidepath + \"...\")\n for i in tqdm(range(tiles[0])):\n for j in range(tiles[1]):\n # Gets the tile in position (i, j)\n tile = slide_dz.get_tile(level, (i, j))\n image = numpy.array(tile)[..., :3]\n mask = tissue.get_tissue_from_rgb(image, blacktol=10, whitetol=240)\n # Saves tile in outpath only if tissue ratio is higher than threshold\n if mask.sum() > tissue_ratio * tile.size[0] * tile.size[1]:\n tile_path = os.path.join(\n outpath, '{}#{}-level{}-{}-{}.jpg'.format(slidename, n, level, i, j))\n tile.save(tile_path)\n n = n + 1\n print('Total of {} tiles with tissue ratio >{} in slide {}'.format(\n n, tissue_ratio, slidepath))\n print()\n\n return n",
"title": ""
},
{
"docid": "09d5d6c7291388f01caf0f6f7809f850",
"score": "0.5605953",
"text": "def images_to_patches(self, patch_size=(80,96), max_patches=30):\n print 'Extracting patches from all images in the pipeline.'\n for img_list in self.img_lst2:\n for img_arr in img_list:\n patches = self.extract_patch(img_arr, patch_size=patch_size, max_patches=max_patches)\n self.patches_lst2.append(patches)",
"title": ""
},
{
"docid": "fca5356cf75785caacbfa5f20f15939d",
"score": "0.5595178",
"text": "def pickles_to_midi(self):\n for file_name in os.listdir(PREPROCESS_PICKLE_DIR):\n if file_name[-7:] == \".pickle\":\n # print(PICKLE_PATH+file_name)\n with open(PREPROCESS_PICKLE_DIR + file_name, \"rb\") as f:\n roll = pickle.load(f)\n midi = self.piano_roll_to_pretty_midi(\n roll, fs=400, program=self.program)\n midi.write(OUTPUT_PATH + file_name[:-7] + \".mid\")",
"title": ""
},
{
"docid": "d1dceef48aef01f002df60b5816b84e8",
"score": "0.5491693",
"text": "def extract_data(filename, num_images):\n imgs = []\n for i in range(1, num_images+1):\n imageid = \"satImage_%.3d\" % i\n image_filename = filename + imageid + \".png\"\n if os.path.isfile(image_filename):\n #print ('Loading ' + image_filename)\n img = mpimg.imread(image_filename)\n imgs.append(img)\n else:\n print ('File ' + image_filename + ' does not exist')\n\n num_images = len(imgs)\n IMG_WIDTH = imgs[0].shape[0]\n IMG_HEIGHT = imgs[0].shape[1]\n N_PATCHES_PER_IMAGE = (IMG_WIDTH/IMG_PATCH_SIZE)*(IMG_HEIGHT/IMG_PATCH_SIZE)\n\n img_patches = [img_crop(imgs[i], IMG_PATCH_SIZE, IMG_PATCH_SIZE) for i in range(num_images)]\n\n ######## ADD OFFSET\n if OFFSET_DATA[0]:\n for offset_size in OFFSET_DATA[1]:\n img_patches_offset = [img_crop_augmented(imgs[i], IMG_PATCH_SIZE, IMG_PATCH_SIZE,offset_size) for i in range(num_images)] \n img_patches += img_patches_offset\n \n ######\n\n data = [img_patches[i][j] for i in range(len(img_patches)) for j in range(len(img_patches[i]))]\n\n return numpy.asarray(data)",
"title": ""
},
{
"docid": "d0d27da381459bc556b5382c8f5ccf72",
"score": "0.5395526",
"text": "def _load_patches(self, img_files, name, patch_size, num_channels):\n patches_all = []\n # reduce the number of files to load if we are in testing mode\n img_files = img_files [0:self.n] if self.test else img_files\n # load patches\n pbar = tqdm(img_files)\n for file in pbar:\n pbar.set_description('Loading dataset %s' % name)\n # pick file name\n assert os.path.isfile(file), 'Not a file: %s' % file\n # load the image containing the patches and convert to float point\n # and make sure that que use only one single channel\n img = cv2.imread(file)[:,:,0] / 255.\n # split the image into patches and\n # add patches to buffer as individual elements\n patches_row = np.split(img, self.PATCHES_PER_ROW, axis=0)\n for row in patches_row:\n patches = np.split(row, self.PATCHES_PER_ROW, axis=1)\n for patch in patches:\n # resize the patch\n patch_resize = cv2.resize(patch, (patch_size,patch_size))\n # convert to tensor [w x h x d]\n patch_tensor = patch_resize.reshape(patch_size,\n patch_size,\n num_channels)\n patches_all.append(patch_tensor)\n return np.asarray(patches_all) if not self.test \\\n else np.asarray(patches_all[0:self.n])",
"title": ""
},
{
"docid": "84d9426d8293833e98a122fe5374b8f8",
"score": "0.53791505",
"text": "def sequential_patches(self, patch_size, step_size, normalize):\n img_shape = self.img.shape\n x = range(0, img_shape[0] - patch_size[0], step_size[0])\n y = range(0, img_shape[1] - patch_size[1], step_size[1])\n if (img_shape[0] <= patch_size[0]):\n x = [0]\n if (img_shape[1] <= patch_size[1]):\n y = [0]\n\n ic = (min(img_shape[0], patch_size[0]), min(img_shape[1], patch_size[1]))\n xy = [(i, j) for i in x for j in y]\n img_patches = []\n for i, j in xy:\n img_patch = self.getPatch(i, j, patch_size, ic, normalize)\n img_patches.append(img_patch)\n # print(len(img_patches))\n return (img_patches)",
"title": ""
},
{
"docid": "db276a3a80cb1ea1054f933dce7915bb",
"score": "0.53749466",
"text": "def split_patches(data_dir, save_dir, postfix=None):\n # 根据data_dir地址确定save_dir\n create_folder(save_dir)\n mode = os.path.basename(data_dir) # train/val\n\n save_dir = os.path.join(save_dir, mode)\n create_folder(save_dir)\n\n image_list = os.listdir(data_dir)\n for i_, image_name in enumerate(image_list):\n name = image_name.split('.')[0]\n if postfix and name[-len(postfix):] != postfix: # label\n continue\n image_path = os.path.join(data_dir, image_name)\n image = np.array(Image.open(image_path))\n # image = io.imread(image_path)\n seg_imgs = []\n\n # split into 16 patches of size 250x250\n h, w = image.shape[0], image.shape[1]\n patch_size = 250\n h_overlap = math.ceil((4 * patch_size - h) / 3)\n w_overlap = math.ceil((4 * patch_size - w) / 3)\n for i in range(0, h - patch_size + 1, patch_size - h_overlap):\n for j in range(0, w - patch_size + 1, patch_size - w_overlap):\n if len(image.shape) == 3:\n patch = image[i:i + patch_size, j:j + patch_size, :]\n else:\n patch = image[i:i + patch_size, j:j + patch_size]\n seg_imgs.append(patch)\n\n\n for k in range(len(seg_imgs)):\n\n if postfix:\n seg_imgs_pil = Image.fromarray(seg_imgs[k])\n seg_imgs_pil.save('{:s}/{:s}_{:d}_{:s}.png'.format(save_dir, name[:-len(postfix) - 1], k, postfix))\n # io.imsave('{:s}/{:s}_{:d}_{:s}.png'.format(save_dir, name[:-len(postfix) - 1], k, postfix), seg_imgs[k])\n else:\n seg_imgs_pil = Image.fromarray(seg_imgs[k])\n seg_imgs_pil.save('{:s}/{:s}_{:d}.png'.format(save_dir, name, k))\n # io.imsave('{:s}/{:s}_{:d}.png'.format(save_dir, name, k), seg_imgs[k])\n logging.info('{},{}/{}'.format(name, i_+1, k+1))",
"title": ""
},
{
"docid": "992e301df520c940d9668b67c280c5ac",
"score": "0.5373239",
"text": "def get_patches(xs, ys, nn_params, stride=49):\n patch_size = nn_params['patch_size']\n fully = nn_params['fully']\n assert stride >= 1, \"Stride must be at least 1\"\n assert (patch_size) >= 1, \"Patch size has to be >= 1\"\n assert patch_size % 2 == 1, \"Patch size should be odd\"\n assert xs[0].shape[0] >= patch_size and xs[0].shape[1] >= patch_size, \\\n (\"Patch is too big for this image: img.shape = %s\" % str(xs[0].shape))\n logging.info(\"Get patches of size: %i\", patch_size)\n patches, labels = [], []\n for X, y in zip(xs, ys):\n px_left_patchcenter = (patch_size - 1) / 2\n start_x = px_left_patchcenter\n end_x = X.shape[0] - px_left_patchcenter\n start_y = start_x\n end_y = X.shape[1] - px_left_patchcenter\n for patch_center_x in range(start_x, end_x + 1, stride):\n for patch_center_y in range(start_y, end_y + 1, stride):\n if fully:\n # Get Labels of the patch and flatt it to 1D\n # x1 = patch_center_x - px_left_patchcenter\n # x2 = patch_center_x + px_left_patchcenter + 1\n # y1 = patch_center_y - px_left_patchcenter\n # y2 = patch_center_y + px_left_patchcenter + 1\n l = y[patch_center_x - px_left_patchcenter:\n patch_center_x + px_left_patchcenter + 1,\n patch_center_y - px_left_patchcenter:\n patch_center_y + px_left_patchcenter + 1]\n\n labels.append(l.flatten())\n\n # Get patch from original image\n patches.append(X[patch_center_x - px_left_patchcenter:\n patch_center_x + px_left_patchcenter + 1,\n patch_center_y - px_left_patchcenter:\n patch_center_y + px_left_patchcenter + 1,\n :])\n else:\n labels.append(y[patch_center_x][patch_center_y])\n # Get patch from original image\n patches.append(X[patch_center_x - px_left_patchcenter:\n patch_center_x + px_left_patchcenter + 1,\n patch_center_y - px_left_patchcenter:\n patch_center_y + px_left_patchcenter + 1,\n :])\n assert len(patches) == len(labels), \"len(patches) != len(labels)\"\n logging.info(\"%i patches were generated.\", len(patches))\n if fully:\n return (np.array(patches, dtype=np.float32),\n np.array(labels, dtype=np.float32))\n else:\n return (np.array(patches, dtype=np.float32),\n np.array(labels, dtype=np.int32))",
"title": ""
},
{
"docid": "67ac7073b5b85bc3438681169d81b106",
"score": "0.5355265",
"text": "def get_DIV2k_data_patches(pLow, bs: int):\n# src = ImageImageList.from_folder(pLow, presort=True).split_by_idxs(\n# train_idx=list(range(0, 565407)), valid_idx=list(range(565408, 637408)))\n src = ImageImageList.from_folder(pLow, presort=True).split_by_rand_pct(valid_pct=0.05, seed=42)\n \n data = (src.label_from_func(lambda x: path_fullRes_patches/x.name)\n .transform(get_transforms(\n flip_vert = True\n ), tfm_y=True\n ).databunch(bs=bs, num_workers=2, no_check=True).normalize(imagenet_stats, do_y=True))\n data.c = 3\n return data",
"title": ""
},
{
"docid": "f1a384c62adeca071107412f22d80ce7",
"score": "0.5239801",
"text": "def montagePngs(png1, png2='', outname='', sidebyside=True, png3=None, geometry='auto',\n trim=False, spacing=2, background='black', png4=None,\n border2=None, montage='montage', crop2=None, splice2=None, \n tile='', png5=None, png6=None, convert='convert', density='',\n labelImages=False, pointsize=36, labelBasename=True, font='',\n verbose=False, cleanup=True, labelOverwrite=False, \n labelBackground='white', labelColor='black', addLabel='',\n png7=None, png8=None, filetype='png', removeSourcePngs=False,\n protectOriginals=False, png9=None, png10=None, png11=None, \n png12=None, png13=None, png14=None, png15=None, gravity='', forceLabel=False, \n order='', sort=True):\n if outname == '':\n outname = 'montagePngs.'+filetype\n if verbose: print(\"montagePngs using outname=%s\" % (outname))\n if png3 == '': png3 = None\n if png4 == '': png4 = None\n if png2 == '':\n if type(png1) == list:\n if len(png1) < 2:\n print(\"au.montagePngs received png1='%s', png2='%s'\" % (png1,png2))\n print(\"You must specify png2 or set png1 to a list of 2 or more files\")\n return\n else:\n if png1.find('*') < 0 and png1.find(',') < 0:\n print(\"You must specify png2 or include a wildcard in png1.\")\n return\n if png1.find('*') >= 0:\n pnglists = png1.split(',')\n png1 = []\n for mypnglist in pnglists:\n if mypnglist.find('!*') >= 0:\n png1listAll = glob.glob(os.path.join(os.path.dirname(mypnglist),'*.png'))\n png1listAvoid = glob.glob(mypnglist.replace('!',''))\n png1list = []\n for png in png1listAll:\n if png not in png1listAvoid:\n png1.append(png)\n else:\n png1 += sorted(glob.glob(mypnglist))\n if sort:\n png1 = sorted(png1)\n if order != '':\n if type(order) == str:\n order = order.split(',')\n if len(order) != len(png1):\n print(\"Number of items in order (%d) not equal to len of png1 (%d)\"% (len(order), len(png1)))\n return\n # convert from numpy.string to string\n indices = np.array([int(i) for i in order])\n print(\"indices = \", indices)\n png1 = [str(i) for i in np.array(png1)[indices]]\n print(\"New order: \", png1)\n else:\n png1 = png1.split(',')\n if sort:\n png1 = sorted(png1)\n if len(png1) == 0:\n print(\"No images found\")\n return\n print(\"png1 = \", png1)\n png = png1[:]\n if len(png) >= 2:\n png1 = png[0]\n png2 = png[1]\n if len(png) >= 3:\n png3 = png[2]\n if len(png) >= 4:\n png4 = png[3]\n if len(png) >= 5:\n png5 = png[4]\n if len(png) >= 6:\n png6 = png[5]\n if len(png) >= 7:\n png7 = png[6]\n if len(png) >= 8:\n png8 = png[7]\n if len(png) >= 9:\n png9 = png[8]\n if len(png) >= 10:\n png10 = png[9]\n if len(png) >= 11:\n png11 = png[10]\n if len(png) >= 12:\n png12 = png[11]\n if len(png) >= 13:\n png13 = png[12]\n if len(png) >= 14:\n png14 = png[13]\n if len(png) >= 15:\n png15 = png[14]\n elif os.path.isdir(png2):\n print(\"png2 is a directory, not an image file.\")\n return\n if type(png1) == str or type(png1) == np.string_: # png1 can already be a list (if png2=='')\n if png1.find('*') >= 0:\n png1list = glob.glob(png1)\n if len(png1list) > 1:\n print(\"You have specified png1 and png2, but the wildcard in png1 found more than 1 file.\")\n return\n elif len(png1list) == 0:\n print(\"You have specified png1 and png2, but the wildcard in png1 found zero files: \", png1)\n return\n else:\n png1 = png1list[0]\n else:\n print(\"type(png1) = %s\" % (type(png1)))\n png1list = png1\n print(\"Setting png1 to %s\" % (png1list[0]))\n png1 = png1list[0]\n if png2.find('*') >= 0:\n png2list = glob.glob(png2)\n if len(png2list) > 1:\n print(\"You have specified png1 and png2, but the wildcard in png2 found more than 1 file.\")\n return\n elif len(png2list) == 0:\n print(\"You have specified png1 and png2, but the wildcard in png2 found zero files: \", png2)\n return\n else:\n png2 = png2list[0]\n if (os.path.exists(png1) == False):\n print(\"Could not find file png1 = %s.\" % (png1))\n return\n if (os.path.exists(png2) == False):\n print(\"Could not find file png2 = \", png2)\n return\n if (png3 is not None):\n if (png4 is not None):\n if (png5 is not None):\n if (png6 is not None):\n if png7 is not None:\n if png8 is not None:\n if png9 is not None:\n if png10 is not None:\n if png11 is not None:\n if png12 is not None:\n if png13 is not None:\n if png14 is not None:\n if png15 is not None:\n npngs = 15\n else:\n npngs = 14\n else:\n npngs = 13\n if tile == '':\n tile = '3x5'\n else:\n npngs = 12\n if tile == '':\n tile = '3x4'\n else:\n npngs = 11\n if tile == '':\n tile = '3x4'\n else:\n npngs = 10\n if tile == '':\n tile = '3x4'\n else:\n npngs = 9\n if tile == '':\n tile = '3x3'\n else:\n npngs = 8\n if tile == '':\n tile = '2x4'\n else:\n npngs = 7\n if tile == '':\n tile = '2x4'\n else:\n npngs = 6\n if (tile == ''):\n tile = '2X3'\n else:\n npngs = 5\n if (tile == ''):\n tile = '2X3'\n else:\n npngs = 4\n if (tile == ''):\n tile = '2X2'\n else:\n npngs = 3\n if (tile == ''):\n if (sidebyside):\n tile = '3X1'\n else:\n tile = '1X3'\n else: # png3 is None\n npngs = 2\n if (tile == ''):\n if (sidebyside):\n tile = '2X1'\n else:\n tile = '1X2'\n if (geometry is None or filetype=='pdf'):\n geometry = '1000x1000+%d+%d' % (spacing,spacing)\n elif (geometry.find('auto') >= 0):\n gap = 0\n if (geometry.find('+') > 0):\n gap = int(geometry.split('+')[1])\n if png1.find('.png') >=0 :\n geometry = 'x'.join([str(i) for i in pngWidthHeight(png1, ignoreCommas=True)])\n else: # assume jpg\n geometry = 'x'.join([str(i) for i in jpgWidthHeight(png1)])\n if gap != 0:\n geometry += '+%d+%d' % (gap,gap)\n if verbose:\n print(\"geometry = \", geometry)\n if (trim):\n trim = '-trim'\n else:\n trim = ''\n removeFiles = []\n if border2 is not None:\n cmd = \"%s %s -bordercolor %s -border %d border_%s\" % (convert, png2, background, border2, png2)\n if verbose: print(\"Running %s\" % (cmd))\n os.system(cmd)\n png2 = \"border_\" + png2\n if cleanup:\n removeFiles.append(png2)\n if crop2 is not None:\n cmd = \"%s %s -crop %s crop_%s\" % (convert, png2, crop2, png2)\n if verbose: print(\"Running %s\" % (cmd))\n os.system(cmd)\n png2 = \"crop_\" + png2\n if cleanup:\n removeFiles.append(png2)\n if splice2 is not None:\n cmd = \"%s %s -background %s -splice %s splice_%s\" % (convert, png2, background, splice2, png2)\n if verbose: print(\"Running %s\" % (cmd))\n os.system(cmd)\n png2 = \"splice_\" + png2\n if cleanup:\n removeFiles.append(png2)\n if trim:\n path = os.path.dirname(png1)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s1.%s\" % (convert, png1, path, filetype,filetype)\n if verbose: print(\"running trim1: \", pcmd)\n mystatus = os.system(pcmd)\n if mystatus != 0:\n convert = '/opt/local/bin/convert'\n print(\"Switching to \", convert)\n pcmd = \"%s -trim %s %s%s1.%s\" % (convert, png1, path, filetype, filetype)\n print(\"running: \", pcmd)\n mystatus = os.system(pcmd)\n if mystatus != 0:\n convert = '/usr/local/bin/convert'\n print(\"Switching to \", convert)\n pcmd = \"%s -trim %s %s%s1.%s\" % (convert, png1, path, filetype, filetype)\n print(\"running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png1 = 'png1_trimmed.png'\n shutil.move('%s%s1.%s' % (path, filetype, filetype), png1)\n\n path = os.path.dirname(png2)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s2.%s\" % (convert, png2, path, filetype, filetype)\n if verbose: print(\"running trim2: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png2 = 'png2_trimmed.png'\n if verbose: print(\"moving: %s%s2.%s to %s\" % (path, filetype, filetype, png2))\n shutil.move('%s%s2.%s' % (path, filetype, filetype), png2)\n if density != '':\n density = '-density %s' % (str(density))\n if gravity != '':\n gravity = '-gravity ' + gravity\n if labelImages != False and labelImages != '':\n if len(font) > 0:\n font = '-font ' + font\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png1)\n else:\n label = png1\n else:\n label = labelImages[1-1]\n if len(grep(png1,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n newpng1 = png1.replace('.'+filetype,'_label01.'+filetype)\n # Note: -pointsize argument must come before label argument\n # +swap puts the label on the top of the image, otherwise it would be on the bottom.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png1, labelBackground, labelColor, font, pointsize, label, gravity, newpng1)\n# I tried the following to see if it would prevent also labeling in small font at bottom, but it did not change:\n# pcmd = '%s -background %s -fill %s %s -pointsize %d label:\"%s\" %s -gravity Center -append %s' % (convert, labelBackground, labelColor, font, pointsize, label, png1, newpng1)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if not os.path.exists(newpng1):\n tmpnewpng1 = '/tmp/' + newpng1\n pcmd = pcmd.replace(newpng1,tmpnewpng1)\n os.system(pcmd)\n newpng1 = tmpnewpng1\n print(\"Wrote \", tmpnewpng1)\n if labelOverwrite:\n os.remove(png1)\n os.rename(newpng1,png1)\n else:\n png1 = newpng1\n else:\n print(\"We have already labeled png1: %s. Set forceLabel=True.\" % (png1))\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png2)\n else:\n label = png2\n else:\n label = labelImages[2-1]\n if len(grep(png2,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n newpng2 = png2.replace('.'+filetype,'_label02.'+filetype)\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png2, labelBackground, labelColor, font, pointsize, label, gravity, newpng2)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if not os.path.exists(newpng2):\n tmpnewpng2 = '/tmp/' + newpng2\n pcmd = pcmd.replace(newpng2,tmpnewpng2)\n os.system(pcmd)\n newpng2 = tmpnewpng2\n print(\"Wrote \", tmpnewpng2)\n if labelOverwrite:\n os.remove(png2)\n os.rename(newpng2,png2)\n else:\n png2 = newpng2\n else:\n print(\"We have already labeled png2: %s. Set forceLabel=True\" % (png2))\n\n cmd = \"%s %s %s -background %s -tile %s %s -geometry %s %s %s \"%(montage,trim,density,background,tile,gravity,geometry,png1,png2)\n if removeSourcePngs:\n removeFiles.append(png1)\n removeFiles.append(png2)\n if type(labelImages) == bool:\n nLabelImages = 2\n for i in [png3,png4,png5,png6,png7,png8,png9,png10,png11,png12,png13,png14,png15]:\n if i is not None: nLabelImages += 1\n else:\n nLabelImages = len(labelImages)\n if (png3 is not None): \n if trim:\n path = os.path.dirname(png3)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s3.%s\" % (convert, png3, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png3 = 'png3_trimmed.png'\n shutil.move('%s%s3.%s'%(path,filetype,filetype), png3)\n if labelImages and nLabelImages > (3-1):\n newpng3 = png3.replace('.'+filetype,'_label03.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png3)\n else:\n label = png3\n else:\n label = labelImages[3-1].replace('//','////')\n if len(grep(png3,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png3, labelBackground, labelColor, font, pointsize, label, gravity, newpng3)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if not os.path.exists(newpng3):\n tmpnewpng3 = '/tmp/' + newpng3\n pcmd = pcmd.replace(newpng3,tmpnewpng3)\n os.system(pcmd)\n newpng3 = tmpnewpng3\n print(\"Wrote \", tmpnewpng3)\n if labelOverwrite:\n os.remove(png3)\n os.rename(newpng3,png3)\n else:\n png3 = newpng3\n cmd += png3 \n if removeSourcePngs:\n removeFiles.append(png3)\n if (png4 is not None): \n if trim:\n path = os.path.dirname(png4)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s4.%s\" % (convert, png4, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png4 = 'png4_trimmed.png'\n shutil.move('%s%s4.%s'%(path,filetype,filetype), png4)\n if labelImages and nLabelImages > (4-1):\n newpng4 = png4.replace('.'+filetype,'_label04.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png4)\n else:\n label = png4\n else:\n label = labelImages[4-1].replace('//','////')\n if len(grep(png4,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png4, labelBackground, labelColor, font, pointsize, label, gravity, newpng4)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if not os.path.exists(newpng4):\n tmpnewpng4 = '/tmp/' + newpng4\n pcmd = pcmd.replace(newpng4,tmpnewpng4)\n os.system(pcmd)\n newpng4 = tmpnewpng4\n print(\"Wrote \", tmpnewpng4)\n if labelOverwrite:\n os.remove(png4)\n os.rename(newpng4,png4)\n else:\n png4 = newpng4\n cmd += ' ' + png4\n if removeSourcePngs:\n removeFiles.append(png4)\n if (png5 is not None):\n if trim:\n path = os.path.dirname(png5)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s5.%s\" % (convert, png5, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png5 = 'png5_trimmed.png'\n shutil.move(path+filetype+'5.'+filetype, png5)\n if labelImages and nLabelImages > (5-1):\n newpng5 = png5.replace('.'+filetype,'_label05.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png5)\n else:\n label = png5\n else:\n label = labelImages[5-1].replace('//','////')\n if len(grep(png5,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png5, labelBackground, labelColor, font, pointsize, label, gravity, newpng5)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png5)\n os.rename(newpng5,png5)\n else:\n png5 = newpng5\n\n cmd += ' ' + png5\n if removeSourcePngs:\n removeFiles.append(png5)\n if (png6 is not None): \n if trim:\n path = os.path.dirname(png6)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s6.%s\" % (convert, png6, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png6 = 'png6_trimmed.png'\n shutil.move(path+filetype+'6.'+filetype, png6)\n if labelImages and nLabelImages > (6-1):\n newpng6 = png6.replace('.'+filetype,'_label06.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png6)\n else:\n label = png6\n else:\n label = labelImages[6-1].replace('//','////')\n if len(grep(png6,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png6, labelBackground, labelColor, font, pointsize, label, gravity, newpng6)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png6)\n os.rename(newpng6,png6)\n else:\n png6 = newpng6\n cmd += ' ' + png6\n if removeSourcePngs:\n removeFiles.append(png6)\n if (png7 is not None): \n if trim:\n path = os.path.dirname(png7)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s7.%s\" % (convert, png7, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png7 = 'png7_trimmed.png'\n shutil.move(path+'%s7.%s'%(filetype,filetype), png7)\n if labelImages and nLabelImages > (7-1):\n newpng7 = png7.replace('.'+filetype, '_label07.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png7)\n else:\n label = png7\n else:\n label = labelImages[7-1].replace('//','////')\n if len(grep(png7,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png7, labelBackground, labelColor, font, pointsize, label, gravity, newpng7)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png7)\n os.rename(newpng7,png7)\n else:\n png7 = newpng7\n cmd += ' ' + png7\n if removeSourcePngs:\n removeFiles.append(png7)\n if (png8 is not None): \n if trim:\n path = os.path.dirname(png8)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s8.%s\" % (convert, png8, path,filetype,filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png8 = 'png8_trimmed.png'\n shutil.move(path+'%s8.%s'%(filetype,filetype), png8)\n if labelImages and nLabelImages > (8-1):\n newpng8 = png8.replace('.'+filetype, '_label08.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png8)\n else:\n label = png8\n else:\n label = labelImages[8-1].replace('//','////')\n if len(grep(png8,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png8, labelBackground, labelColor, font, pointsize, label, gravity, newpng8)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png8)\n os.rename(newpng8,png8)\n else:\n png8 = newpng8\n cmd += ' ' + png8\n if removeSourcePngs:\n removeFiles.append(png8)\n if (png9 is not None): \n if trim:\n path = os.path.dirname(png9)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s9.%s\" % (convert, png9, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png9 = 'png9_trimmed.png'\n shutil.move(path+'%s9.%s'%(filetype,filetype), png9)\n if labelImages and nLabelImages > (9-1):\n newpng9 = png9.replace('.'+filetype, '_label09.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png9)\n else:\n label = png9\n else:\n label = labelImages[9-1].replace('//','////')\n if len(grep(png9,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png9, labelBackground, labelColor, font, pointsize, label, gravity, newpng9)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png9)\n os.rename(newpng9,png9)\n else:\n png9 = newpng9\n cmd += ' ' + png9\n if removeSourcePngs:\n removeFiles.append(png9)\n if (png10 is not None): \n if trim:\n path = os.path.dirname(png10)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s10.%s\" % (convert, png10, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png10 = 'png10_trimmed.png'\n shutil.move(path+'%s10.%s'%(filetype,filetype), png10)\n if labelImages and nLabelImages > (10-1):\n newpng10 = png10.replace('.'+filetype, '_label10.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png10)\n else:\n label = png10\n else:\n label = labelImages[10-1].replace('//','////')\n if len(grep(png10,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png10, labelBackground, labelColor, font, pointsize, label, gravity, newpng10)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png10)\n os.rename(newpng10,png10)\n else:\n png10 = newpng10\n cmd += ' ' + png10\n if removeSourcePngs:\n removeFiles.append(png10)\n if (png11 is not None): \n if trim:\n path = os.path.dirname(png11)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s11.%s\" % (convert, png11, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png11 = 'png11_trimmed.png'\n shutil.move(path+'%s11.%s'%(filetype,filetype), png11)\n if labelImages and nLabelImages > (11-1):\n newpng11 = png11.replace('.'+filetype, '_label11.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png11)\n else:\n label = png11\n else:\n label = labelImages[11-1].replace('//','////')\n if len(grep(png11,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png11, labelBackground, labelColor, font, pointsize, label, gravity, newpng11)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png11)\n os.rename(newpng11,png11)\n else:\n png11 = newpng11\n cmd += ' ' + png11\n if removeSourcePngs:\n removeFiles.append(png11)\n if (png12 is not None): \n if trim:\n path = os.path.dirname(png12)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s12.%s\" % (convert, png12, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png12 = 'png12_trimmed.png'\n shutil.move(path+'%s12.%s'%(filetype,filetype), png12)\n if labelImages and nLabelImages > (12-1):\n newpng12 = png12.replace('.'+filetype, '_label12.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png12)\n else:\n label = png12\n else:\n label = labelImages[12-1].replace('//','////')\n if len(grep(png12,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png12, labelBackground, labelColor, font, pointsize, label, gravity, newpng12)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png12)\n os.rename(newpng12,png12)\n else:\n png12 = newpng12\n cmd += ' ' + png12\n if removeSourcePngs:\n removeFiles.append(png12)\n if (png13 is not None): \n if trim:\n path = os.path.dirname(png13)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s12.%s\" % (convert, png13, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png13 = 'png13_trimmed.png'\n shutil.move(path+'%s12.%s'%(filetype,filetype), png13)\n if labelImages and nLabelImages > (13-1):\n newpng13 = png13.replace('.'+filetype, '_label13.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png13)\n else:\n label = png13\n else:\n label = labelImages[13-1].replace('//','////')\n if len(grep(png13,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png13, labelBackground, labelColor, font, pointsize, label, gravity, newpng13)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png13)\n os.rename(newpng13,png13)\n else:\n png13 = newpng13\n cmd += ' ' + png13\n if removeSourcePngs:\n removeFiles.append(png13)\n if (png14 is not None): \n if trim:\n path = os.path.dirname(png14)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s12.%s\" % (convert, png14, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png14 = 'png14_trimmed.png'\n shutil.move(path+'%s12.%s'%(filetype,filetype), png14)\n if labelImages and nLabelImages > (14-1):\n newpng14 = png14.replace('.'+filetype, '_label14.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png14)\n else:\n label = png14\n else:\n label = labelImages[14-1].replace('//','////')\n if len(grep(png14,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png14, labelBackground, labelColor, font, pointsize, label, gravity, newpng14)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png14)\n os.rename(newpng14,png14)\n else:\n png14 = newpng14\n cmd += ' ' + png14\n if removeSourcePngs:\n removeFiles.append(png14)\n if (png15 is not None): \n if trim:\n path = os.path.dirname(png15)\n if not os.access(path,os.W_OK):\n path = ''\n if len(path) > 0: path += '/'\n pcmd = \"%s -trim %s %s%s12.%s\" % (convert, png15, path, filetype, filetype)\n if verbose: print(\"Running: \", pcmd)\n mystatus = os.system(pcmd)\n if protectOriginals:\n png15 = 'png15_trimmed.png'\n shutil.move(path+'%s12.%s'%(filetype,filetype), png15)\n if labelImages and nLabelImages > (15-1):\n newpng15 = png15.replace('.'+filetype, '_label15.'+filetype)\n if labelImages == True:\n if labelBasename:\n label = os.path.basename(png15)\n else:\n label = png15\n else:\n label = labelImages[15-1].replace('//','////')\n if len(grep(png15,label)[0]) == 0 or forceLabel:\n # Then we have not already labeled this png.\n pcmd = '%s %s -background %s -fill %s %s -pointsize %d label:\"%s\" +swap %s -append %s' % (convert, png15, labelBackground, labelColor, font, pointsize, label, gravity, newpng15)\n if verbose: print(\"Running: \", pcmd)\n os.system(pcmd)\n if labelOverwrite:\n os.remove(png15)\n os.rename(newpng15,png15)\n else:\n png15 = newpng15\n cmd += ' ' + png15\n if removeSourcePngs:\n removeFiles.append(png15)\n cmd += ' ' + outname\n if verbose: print(\"Running %s\" % (cmd))\n mystatus = os.system(cmd)\n if (mystatus != 0 and mystatus != 256):\n # MacOS location of montage\n print(\"Switching to /opt/local/bin/montage\")\n cmd = cmd.replace('montage ','/opt/local/bin/montage ')\n if verbose: print(\"Running %s\" % (cmd))\n mystatus = os.system(cmd)\n if (mystatus != 0 and mystatus != 256):\n # MacOS location of montage\n print(\"Switching to /usr/local/bin/montage\")\n cmd = cmd.replace('/opt/local/bin/montage','/usr/local/bin/montage')\n if verbose: print(\"Running %s\" % (cmd))\n mystatus = os.system(cmd)\n if addLabel != '' and addLabel != False:\n print(\"Calling addTopLabelToPng\")\n if os.path.exists(addLabel):\n f = open(addLabel,'r')\n lines = f.readlines()\n f.close()\n for line in lines[::-1]:\n line = line.replace('#','')\n addTopLabelToPng(outname, line, pointsize, font='Courier-Bold', gravity=gravity)\n else:\n addTopLabelToPng(outname, addLabel, pointsize, font='Courier-Bold', gravity=gravity)\n for png2 in removeFiles:\n print(\"Removing \", png2)\n os.remove(png2)\n return outname",
"title": ""
},
{
"docid": "8d9d19bdf6b8eeabe17c85731e8875ba",
"score": "0.52351093",
"text": "def get_patches(raster_images, vector_images, *args, aug_random_slide = False, aug_color = False, aug_rotate = False, **kwargs):\n patch_size = kwargs['patch_size']\n # change ranster_images into 4d, vector_images into 3d\n if raster_images.ndim == 3:\n raster_images = np.expand_dims(raster_images, 0)\n if vector_images.ndim == 2:\n vector_images = np.expand_dims(vector_images, 0)\n \n image_num = raster_images.shape[0]\n raster_patch_list = []\n vector_patch_list = []\n \n for img_idx in range(image_num):\n raster_image = raster_images[img_idx]\n vector_image = vector_images[img_idx]\n \n rows_num, cols_num = raster_image.shape[0]//patch_size, raster_image.shape[1]//patch_size\n for i in range(rows_num):\n for j in range(cols_num):\n# idx = i * cols_num + j\n raster_patch = raster_image[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size]\n raster_patch_list.append(raster_patch)\n\n vector_patch = vector_image[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size]\n vector_patch_list.append(vector_patch)\n\n if aug_random_slide:\n patch_slide_num = args[0][0]\n is2 = int(1.0 * patch_size)\n xm, ym = raster_image.shape[0] - is2, raster_image.shape[1] - is2\n \n raster_patch_slide = np.zeros((patch_slide_num, is2, is2, 3))\n vector_patch_slide = np.zeros((patch_slide_num, is2, is2))\n \n for i in range(patch_slide_num):\n xc = random.randint(0, xm)\n yc = random.randint(0, ym)\n \n im = raster_image[xc:xc + is2, yc:yc + is2]\n mk = vector_image[xc:xc + is2, yc:yc + is2]\n \n if aug_color:\n if random.uniform(0, 1) > args[0][1]:\n im = im[:,:,::-1]\n\n if aug_rotate:\n if random.uniform(0, 1) > args[0][2]:\n im = im[::-1]\n mk = mk[::-1]\n\n raster_patch_slide[i] = im\n vector_patch_slide[i] = mk\n\n raster_patch_list.extend(raster_patch_slide)\n vector_patch_list.extend(vector_patch_slide)\n return raster_patch_list, vector_patch_list",
"title": ""
},
{
"docid": "6440caf221b7a951a6eed7cfac610e5b",
"score": "0.5225819",
"text": "def __patch_finder(self):\n tumor_patches = pd.DataFrame(columns=['patch_path', 'is_tumor'])\n normal_patches = pd.DataFrame(columns=['patch_path', 'is_tumor'])\n tumor_patch_paths_list = []\n normal_patch_paths_list = []\n\n for folder in os.listdir(self.image_patch_dir):\n\n if osp.basename(folder).startswith('tumor'):\n for subfolder in os.listdir(osp.join(self.image_patch_dir, folder)):\n # print(subfolder)\n\n tumor_patch_paths_in_folder = glob.glob(\n osp.join(self.image_patch_dir, folder, subfolder, '*.png'))\n # print(len(tumor_patch_paths_in_folder))\n tumor_patch_paths_list.extend(tumor_patch_paths_in_folder)\n # print(len(tumor_patch_paths_list))\n\n else:\n\n for subfolder in os.listdir(osp.join(self.image_patch_dir, folder)):\n # print(subfolder)\n normal_patch_paths_in_folder = glob.glob(\n osp.join(self.image_patch_dir, folder, subfolder, '*.png'))\n normal_patch_paths_list.extend(\n normal_patch_paths_in_folder)\n # print(normal_patch_paths_list)\n\n print(len(tumor_patch_paths_list))\n tumor_patch_paths_series = pd.Series(tumor_patch_paths_list)\n tumor_patches['patch_path'] = tumor_patch_paths_series.values\n tumor_patches['is_tumor'] = 1\n print(len(tumor_patches))\n\n normal_patch_paths_series = pd.Series(normal_patch_paths_list)\n normal_patches['patch_path'] = normal_patch_paths_series.values\n normal_patches['is_tumor'] = 0\n print(len(normal_patches))\n\n return (tumor_patches, normal_patches)",
"title": ""
},
{
"docid": "1f528d0eb31a60901c87a081b3450a6a",
"score": "0.5162926",
"text": "def create_patches(images, masks, size):\n\n patchesimages = image.extract_patches_2d(images, (size, size), 10, 0)\n patchesmasks = image.extract_patches_2d(masks, (size, size), 10, 0)\n return patchesimages, patchesmasks",
"title": ""
},
{
"docid": "c4f9afc148e743131f1c118d944616ab",
"score": "0.51229763",
"text": "def montageTwoPngLists(pnglist1, pnglist2, pnglist3=None, pnglist4=None,\n pdfname='', sidebyside=True, geometry='auto',\n filter='', trim=False, spacing=2, background='black',\n sourceFilter=[], maxpages=0, cleanup=True, filetype='png',\n mode='identical',labelImages='',labelOverwrite=False, startpage=0, \n verbose=False, computeDiff=False, diffThreshold=0, sortByTime=False, \n pointsize=36, removeString='', stopMatchAt='', listUnused=True, \n appendUnused=False, montage='montage', stopAtFailedMatch=False):\n print(\"Received filetype='%s'\" % (filetype))\n if (type(pnglist1) == str):\n # assume we want all pngs in this directory\n if (pnglist1.find('*') < 0 and os.path.isdir(pnglist1)):\n if sortByTime:\n pnglist1 = sorted(glob.glob(pnglist1+'/*.'+filetype), key=os.path.getmtime, reverse=False)\n else:\n pnglist1 = sorted(glob.glob(pnglist1+'/*.'+filetype))\n else:\n png1 = pnglist1\n if sortByTime:\n pnglist1 = sorted(glob.glob(pnglist1), key=os.path.getmtime, reverse=False)\n else:\n pnglist1 = sorted(glob.glob(pnglist1))\n if maxpages > 0:\n print(\"Found %d plots for pnglist1: %s\" % (len(pnglist1),pnglist1[:maxpages]))\n else:\n print(\"Found %d plots for pnglist1\" % (len(pnglist1)))\n if len(pnglist1) == 0:\n return\n print(pnglist1[:3])\n if (type(pnglist2) == str):\n # assume we want all pngs in this directory\n if (pnglist2.find('*') < 0):\n if (pnglist2.split(',')[0].find('replace') < 0):\n if sortByTime:\n pnglist2 = sorted(glob.glob(pnglist2+'/*.'+filetype), key=os.path.getmtime, reverse=False)\n else:\n pnglist2 = sorted(glob.glob(pnglist2+'/*.'+filetype))\n else:\n # only use files in common between 2 paths\n newpnglist1 = []\n newpnglist2 = []\n for png in pnglist1:\n png2 = png.replace(pnglist2.split(',')[1], pnglist2.split(',')[2])\n if (os.path.exists(png2)):\n newpnglist1.append(png)\n newpnglist2.append(png2)\n pnglist1 = newpnglist1\n pnglist2 = newpnglist2\n print(\"Found %d = %d common files\" % (len(pnglist1), len(pnglist2)))\n else:\n png2 = pnglist2\n if sortByTime:\n pnglist2 = sorted(glob.glob(pnglist2), key=os.path.getmtime, reverse=False)\n else:\n pnglist2 = sorted(glob.glob(pnglist2))\n if maxpages > 0:\n print(\"Found %d plots for pnglist2: %s\" % (len(pnglist2),pnglist2[:maxpages]))\n else:\n print(\"Found %d plots for pnglist2\" % (len(pnglist2)))\n if len(pnglist2) == 0:\n return\n print(pnglist2[:3])\n if (pnglist3 is not None):\n if (type(pnglist3) == str):\n # assume we want all pngs in this directory\n if (pnglist3.find('*') < 0):\n pnglist3 = glob.glob(pnglist3+'/*.'+filetype)\n else:\n pnglist3 = glob.glob(pnglist3)\n if sortByTime:\n pnglist3 = sorted(pnglist3, key=os.path.getmtime)\n else:\n pnglist3 = sorted(pnglist3)\n if (pnglist4 is not None):\n if (type(pnglist4) == str):\n # assume we want all pngs in this directory\n if (pnglist4.find('*') < 0):\n pnglist4 = glob.glob(pnglist4+'/*.'+filetype)\n else:\n pnglist4 = glob.glob(pnglist4)\n if sortByTime:\n pnglist4 = sorted(pnglist4, key=os.path.getmtime)\n else:\n pnglist4 = sorted(pnglist4)\n if (filter != '' or sourceFilter != []):\n if type(sourceFilter) == str:\n if not os.path.exists(sourceFilter):\n print(\"Could not find sourceFilter file = %s\" % (sourceFilter))\n return\n f = open(sourceFilter,'r')\n lines = f.readlines()\n f.close()\n sourceFilterFile = sourceFilter\n sourceFilter = []\n for line in lines:\n sourceFilter.append(line.replace('\\n',''))\n print(\"Read %d strings from %s\" % (len(sourceFilter),sourceFilterFile))\n newpnglist1 = []\n for png in pnglist1:\n if (sourceFilter != []):\n for src in sourceFilter:\n if (png.find(src) >= 0):\n newpnglist1.append(png)\n break\n else:\n if (png.find(filter) < 0):\n newpnglist1.append(png)\n pnglist1 = newpnglist1\n newpnglist2 = []\n for png in pnglist2:\n if (sourceFilter != []):\n for src in sourceFilter:\n if (png.find(src) >= 0):\n newpnglist2.append(png)\n break\n else:\n if (png.find(filter) < 0):\n newpnglist2.append(png)\n pnglist2 = newpnglist2\n if (pnglist3 is not None):\n newpnglist3 = []\n for png in pnglist3:\n if (sourceFilter != []):\n for src in sourceFilter:\n if (png.find(src) >= 0):\n newpnglist3.append(png)\n break\n else:\n if (png.find(filter) < 0):\n newpnglist3.append(png)\n pnglist3 = newpnglist3\n if (pnglist4 is not None):\n newpnglist4 = []\n for png in pnglist4:\n if (sourceFilter != []):\n for src in sourceFilter:\n if (png.find(src) >= 0):\n newpnglist4.append(png)\n break\n else:\n if (png.find(filter) < 0):\n newpnglist4.append(png)\n pnglist4 = newpnglist4\n if labelImages == 'dir':\n labelImages = [os.path.dirname(pnglist1[0]), os.path.dirname(pnglist2[0])]\n l1 = len(pnglist1)\n l2 = len(pnglist2)\n if (l1 != l2):\n if mode == 'identical':\n print(\"The two lists are not of equal length (%d vs. %d). Set mode='truncate' or 'match'.\" % (l1, l2))\n return\n elif mode == 'truncate':\n if l1 > l2:\n pnglist1 = pnglist1[:l2]\n else:\n pnglist2 = pnglist2[:l1]\n elif mode == 'match':\n pnglist2basename = [os.path.basename(i) for i in pnglist2]\n pnglist2basename_nopagenumber = []\n pnglist2stopMatchAt = []\n for p in pnglist2basename:\n if p.find('_') > 0:\n pnglist2basename_nopagenumber.append('_'.join(p.split('_')[1:]))\n else:\n pnglist2basename_nopagenumber.append(p)\n if stopMatchAt != '':\n if type(stopMatchAt) == list:\n newname = p\n for stopAt in stopMatchAt:\n print(\"A) Looking for %s in %s\" % (stopAt,p))\n if p.find(stopAt) >= 0:\n newname = p[:p.find(stopAt)]\n print(\"Set newname to %s\" % (newname))\n break\n pnglist2stopMatchAt.append(newname)\n else:\n pnglist2stopMatchAt.append(p[:p.find(stopMatchAt)])\n newpnglist1 = []\n newpnglist2 = []\n list2idx = []\n for ipng1,png1 in enumerate(pnglist1):\n if removeString != '':\n if png1.find(removeString) > 0:\n print(\"Removing string: %s from %s\" % (removeString,png1))\n matchName = os.path.basename(png1).replace(removeString,'')\n else:\n matchName = os.path.basename(png1)\n if ipng1 == 0:\n print(\"Set matchName to: \", matchName)\n if stopMatchAt == '':\n if matchName not in pnglist2basename:\n # check for match if either are preceeded by 'page001_'\n if png1.find('page') >= 0:\n print(\"Found 'page' in %s\" % (png1))\n checkname = '_'.join(os.path.basename(png1).split('_')[1:])\n if checkname not in pnglist2basename_nopagenumber:\n print(\"match failed for %s in list of %d\" % (checkname,len(pnglist2basename_nopagenumber)))\n continue\n elif matchName not in pnglist2basename_nopagenumber:\n print(\"match failed for %s in list of %d\" % (png1,len(pnglist2basename_nopagenumber)))\n continue\n \n else:\n if type(stopMatchAt) == list:\n foundMatch = False\n for sA,stopAt in enumerate(stopMatchAt):\n loc = matchName.find(stopAt)\n if loc > 0:\n giantList = []\n for i in pnglist2basename:\n loc = i.find(stopAt)\n giantList.append(i[:loc])\n if sA==0 and ipng1==0:\n print(\" pnglist2basename[0] = \", pnglist2basename[0])\n print(\"B) %s: Looking for %s in found list: %s\" % (stopAt,matchName[:loc], giantList[:1]))\n else:\n giantList = pnglist2basename\n if sA == 0:\n print(\"B) Looking for %s in %s\" % (matchName[:loc], giantList[:1]))\n if matchName[:loc] not in giantList:\n # check for match if either are preceeded by 'page001_'\n checkname = '_'.join(os.path.basename(png1).split('_')[1:])\n if checkname in pnglist2basename_nopagenumber:\n foundMatch = True\n break\n else:\n foundMatch = True\n break\n if not foundMatch:\n if listUnused:\n print(\"match failed for %s in list of %d\" % (checkname,len(pnglist2basename_nopagenumber)))\n if stopAtFailedMatch:\n return\n continue\n else:\n loc = matchName.find(stopMatchAt)\n giantList = []\n for i in pnglist2basename:\n myloc = i.find(stopMatchAt)\n giantList.append(i[:myloc])\n if matchName[:loc] not in giantList:\n # check for match if either are preceeded by 'page001_'\n checkname = '_'.join(os.path.basename(png1).split('_')[1:])\n if checkname not in pnglist2basename_nopagenumber:\n print(\"match failed for %s in list of %d\" % (checkname,len(pnglist2basename_nopagenumber)))\n if stopAtFailedMatch:\n return\n continue\n newpnglist1.append(png1)\n if stopMatchAt == '':\n list2idx.append(pnglist2basename.index(matchName))\n else:\n print(\"pnglist2stopMatchAt=\", pnglist2stopMatchAt)\n print(\"matchName=\", matchName)\n print(\"loc=%d = %s\" % (loc,matchName[:loc]))\n print(\"Looking for matchName[:loc]=\", matchName[:loc])\n print(\"len(pnglist2stopMatchAt) = \", len(pnglist2stopMatchAt))\n print(\"pnglist2stopMatchAt[0] = \", pnglist2stopMatchAt[0])\n list2idx.append(pnglist2stopMatchAt.index(matchName[:loc]))\n match = pnglist2[list2idx[-1]]\n print(\"Found match: %s = %s\" % (os.path.basename(png1), match))\n newpnglist2.append(match)\n unused = sorted(list(set(range(len(pnglist2basename))) - set(list2idx)))\n if verbose or listUnused:\n if len(unused) > 0:\n print(\"unused pngs: \", np.array(pnglist2basename)[unused])\n for i in unused:\n if listUnused:\n print(\"No match found in list2 for \", np.array(pnglist2basename)[i])\n if sortByTime:\n pnglist1 = sorted(newpnglist1, key=os.path.getmtime)\n idx = []\n for p in pnglist1:\n idx.append(newpnglist1.index(p))\n pnglist2 = list(np.array(newpnglist2)[idx])\n else:\n pnglist1 = newpnglist1\n pnglist2 = newpnglist2\n if appendUnused:\n for i in unused:\n print(\"Appending unused png: \", pnglist2basename[i])\n pnglist1.append(pnglist2basename[i])\n pnglist2.append(pnglist2basename[i])\n else:\n print(\"unrecognized mode: \", mode)\n return\n if (pnglist3 is not None):\n l3 = len(pnglist3)\n if (l1 != l3):\n print(\"The three lists are not of equal length (%d vs. %d vs. %d)\" % (l1, l2, l3))\n return\n if (pnglist4 is not None):\n l4 = len(pnglist4)\n if (l1 != l4):\n print(\"The four lists are not of equal length (%d vs. %d vs. %d vs %d)\" % (l1, l2, l3, l4))\n return\n \n plotfiles = []\n dirname = os.path.dirname(pdfname)\n if (dirname != ''):\n if (os.path.exists(dirname) == False):\n print(\"Creating directory: \", dirname)\n os.mkdir(dirname)\n dirname += '/'\n png3 = None\n png4 = None\n if (maxpages <= 0):\n npages = len(pnglist1)\n else:\n npages = maxpages\n print(\"Making %d-%d = %d pages\" % (npages,startpage, npages-startpage))\n if (pdfname == ''):\n pdfname = '%dpages.pdf' % (npages)\n for i in range(startpage,npages):\n page = dirname + os.path.basename(pdfname).rstrip('.pdf') + \".page%03d.%s\" % (i,filetype)\n plotfiles.append(page)\n if (pnglist3 is not None):\n png3 = pnglist3[i]\n if (pnglist4 is not None):\n png4 = pnglist4[i]\n if verbose: \n print(\"Calling montagePngs('%s','%s',outname='%s',filetype='%s',verbose=%s,trim=%s)\" % (pnglist1[i],pnglist2[i],page,filetype,verbose,trim))\n if computeDiff:\n if os.stat(pnglist1[i]).st_size < 1:\n print(\"WARNING: zero-size png1: \", pnglist1[i])\n elif os.stat(pnglist2[i]).st_size < 1:\n print(\"WARNING: zero-size png2: \", pnglist2[i])\n else:\n mydiff = comparePngs(pnglist1[i], pnglist2[i], cleanup=True)\n if mydiff > diffThreshold:\n print(\"%d: difference = %f. Rebuilding diff plot.\" % (i+1, mydiff))\n mydiff = comparePngs(pnglist1[i], pnglist2[i], cleanup=False)\n montagePngs(pnglist1[i], pnglist2[i], outname=page, background=background,\n sidebyside=sidebyside, spacing=spacing, geometry=geometry, \n png3=png3, png4=png4, labelImages=labelImages, trim=trim, pointsize=pointsize,\n labelOverwrite=labelOverwrite,verbose=verbose, filetype=filetype, montage=montage)\n if ((i+1) % 10 == 0):\n print(\"Done page %d\" % (i+1))\n buildPdfFromPngs(plotfiles, pdfname=pdfname, cleanup=cleanup, filetype=filetype)\n if cleanup:\n for i in plotfiles:\n if os.path.exists(i):\n os.remove(i)\n return(pdfname)",
"title": ""
},
{
"docid": "3d2bcc1e07be55a4215f5833b35c509e",
"score": "0.5097262",
"text": "def training_patch_paths(self):\n all_patches = self.__patch_finder()\n tumor_patches = all_patches[0]\n print(tumor_patches)\n # print(tumor_patches)\n\n normal_patches = all_patches[1]\n print(normal_patches)\n\n # exclude normal patches from not fully annotated tumor slides\n for i in range(len(self.exclude_normal_list)):\n normal_patches = normal_patches[\n ~normal_patches.patch_path.str.contains(self.exclude_normal_list[i])]\n\n print(len(normal_patches))\n\n # oversample the tumor patches\n\n tumor_patches = resample(tumor_patches, replace=True,\n n_samples=len(normal_patches), random_state=123)\n\n # get the time stamp\n\n time_of_saving = datetime.now().strftime(\"%d-%m-%Y_%I-%M-%S_%p\")\n tumor_patches.to_csv('%s/tumor_patches_%s.csv' %\n (self.path_to_save_model, time_of_saving))\n\n ###########################################################################\n # separate training and validation patches for both normal and tumor slides\n ###########################################################################\n training_patches_normal = normal_patches\n validation_patches_normal = pd.DataFrame(\n columns=['patch_path', 'is_tumor'])\n training_patches_tumor = tumor_patches\n validation_patches_tumor = pd.DataFrame(\n columns=['patch_path', 'is_tumor'])\n\n for i in range(len(self.validation_slides_normal)):\n training_patches_normal = training_patches_normal[\n ~training_patches_normal.patch_path.str.contains(self.validation_slides_normal[i])]\n\n for i in range(len(self.validation_slides_normal)):\n to_be_append_normal = normal_patches[\n normal_patches.patch_path.str.contains(self.validation_slides_normal[i])]\n validation_patches_normal = validation_patches_normal.append(\n to_be_append_normal, ignore_index=True)\n\n for i in range(len(self.validation_slides_tumor)):\n training_patches_tumor = training_patches_tumor[\n ~training_patches_tumor.patch_path.str.contains(self.validation_slides_tumor[i])]\n\n for i in range(len(self.validation_slides_tumor)):\n to_be_append_tumor = tumor_patches[\n tumor_patches.patch_path.str.contains(self.validation_slides_tumor[i])]\n validation_patches_tumor = validation_patches_tumor.append(\n to_be_append_tumor, ignore_index=True)\n # print(to_be_append_tumor)\n # print(validation_patches_tumor)\n\n # validation_patches_tumor = tumor_patches[~training_patches_tumor.patch_path]\n print(len(training_patches_tumor))\n print(len(training_patches_normal))\n print(len(validation_patches_tumor))\n print(len(validation_patches_normal))\n\n # ### keep only 3DHISTECH images################################\n if self.IIIdhistech_only:\n training_patches_tumor_3dhistech = training_patches_tumor[training_patches_tumor['patch_path'].map(\n lambda x: int(osp.splitext(osp.basename(x))[0][6:9]) < 71 or int(osp.splitext(osp.basename(x))[0][6:9]) > 100)]\n\n training_patches_normal_3dhistech = training_patches_normal[training_patches_normal['patch_path'].map(lambda x: int(osp.splitext(osp.basename(x))[0][6:9]) < 71 or int(\n osp.splitext(osp.basename(x))[0][6:9]) > 100 if osp.basename(x).startswith('tumor') else int(osp.splitext(osp.basename(x))[0][7:10]) < 101)]\n\n validation_patches_tumor_3dhistech = validation_patches_tumor[validation_patches_tumor['patch_path'].map(\n lambda x: int(osp.splitext(osp.basename(x))[0][6:9]) < 71 or int(osp.splitext(osp.basename(x))[0][6:9]) > 100)]\n\n validation_patches_normal_3dhistech = validation_patches_normal[validation_patches_normal['patch_path'].map(lambda x: int(osp.splitext(osp.basename(\n x))[0][6:9]) < 71 or int(osp.splitext(osp.basename(x))[0][6:9]) > 100 if osp.basename(x).startswith('tumor') else int(osp.splitext(osp.basename(x))[0][7:10]) < 101)]\n # get the number of patches\n print(len(training_patches_tumor_3dhistech))\n print(len(training_patches_normal_3dhistech))\n print(len(validation_patches_tumor_3dhistech))\n print(len(validation_patches_normal_3dhistech))\n\n # get the patches for training and validation\n\n training_patches = pd.concat(\n [training_patches_tumor_3dhistech, training_patches_normal_3dhistech])\n validation_patches = pd.concat(\n [validation_patches_tumor_3dhistech, validation_patches_normal_3dhistech])\n\n else:\n\n training_patches = pd.concat(\n [training_patches_tumor, training_patches_normal])\n validation_patches = pd.concat(\n [validation_patches_tumor, validation_patches_normal])\n\n return (training_patches, validation_patches)",
"title": ""
},
{
"docid": "5c82035841fa0ce75c1a8f0d796cd107",
"score": "0.50802016",
"text": "def rename_slide_parts(self, rIds):\n for idx, rId in enumerate(rIds):\n slide_part = self.related_parts[rId]\n slide_part.partname = PackURI(\n '/ppt/slides/slide%d.xml' % (idx+1)\n )",
"title": ""
},
{
"docid": "aa38572054083ca2632d31905ac806da",
"score": "0.5049692",
"text": "def make_patches(dataset_path, export_path, patch_size=(512, 512), no_overlap=False):\n # make output directories\n dataset_images_path = os.path.join(dataset_path, 'images')\n dataset_masks_path = os.path.join(dataset_path, 'masks')\n new_images_path = os.path.join(export_path, 'images')\n new_masks_path = os.path.join(export_path, 'masks')\n\n chk_mkdir(new_masks_path, new_images_path)\n\n for image_filename in os.listdir(dataset_images_path):\n # reading images\n im = io.imread(os.path.join(dataset_images_path, image_filename))\n masked_im = io.imread(os.path.join(dataset_masks_path, image_filename))\n # make new folders\n\n x_start = list()\n y_start = list()\n\n if no_overlap:\n x_step = patch_size[0]\n y_step = patch_size[1]\n else:\n x_step = patch_size[0] // 2\n y_step = patch_size[1] // 2\n\n for x_idx in range(0, im.shape[0] - patch_size[0] + 1, x_step):\n x_start.append(x_idx)\n\n if im.shape[0] - patch_size[0] - 1 > 0:\n x_start.append(im.shape[0] - patch_size[0] - 1)\n\n for y_idx in range(0, im.shape[1] - patch_size[1] + 1, y_step):\n y_start.append(y_idx)\n\n if im.shape[1] - patch_size[1] - 1 > 0:\n y_start.append(im.shape[1] - patch_size[1] - 1)\n\n for num, (x_idx, y_idx) in enumerate(product(x_start, y_start)):\n new_image_filename = os.path.splitext(image_filename)[0] + '_%d.png' % num\n # saving a patch of the original image\n io.imsave(\n os.path.join(new_images_path, new_image_filename),\n im[x_idx:x_idx + patch_size[0], y_idx:y_idx + patch_size[1], :]\n )\n # saving the corresponding patch of the mask\n io.imsave(\n os.path.join(new_masks_path, new_image_filename),\n masked_im[x_idx:x_idx + patch_size[0], y_idx:y_idx + patch_size[1]]\n )",
"title": ""
},
{
"docid": "66a557b49fee228cf6967fdd4572344a",
"score": "0.5040691",
"text": "def update_slides(self,score_month,score_year,brand_name,demense_name):\n pyear,pmonth = common.previous_period(year=score_year,month=score_month)\n content = prs.slides\n targets = {\n '<YYYY>': str(score_year),\n '<MONTH>': datetime.date(1900, score_month, 1).strftime('%B'),\n '<BRAND>': brand_name,\n '<DEMENSE>':demense_name,\n '<Y-1>': str(pyear),\n '<M-1>': datetime.date(1900, pmonth, 1).strftime('%B'),\n }\n for slide in content:\n self._update_slides(slide.shapes,targets)\n\n self._update_slides(prs.slide_master.shapes,targets,' ')\n prs.save(outputFile)",
"title": ""
},
{
"docid": "bffd1e091063e760780fad7cf92c7918",
"score": "0.50278246",
"text": "def generate_patches(self):\n num_patches = len(self.positive_sample_info) + len(self.negative_sample_info)\n\n debug('Extracting patches')\n start = time.process_time()\n\n self.extract_positive_patches()\n self.extract_negative_patches()\n\n end = time.process_time()\n debug(f'Extracted {num_patches} patches in {end-start} seconds')",
"title": ""
},
{
"docid": "e6d447b6b21d1889f470e6610201c81d",
"score": "0.50045466",
"text": "def genebmp(dirName,fn):\r\n print ('load dicom files in :',f, 'scan name:',fn)\r\n #directory for patches\r\n \r\n bmp_dir = os.path.join(dirName, scanbmp)\r\n# print bmp_dir\r\n# remove_folder(bmp_dir) \r\n if os.path.exists(bmp_dir) ==False :\r\n os.mkdir(bmp_dir) \r\n #list dcm files\r\n fileList = os.listdir(dirName)\r\n for filename in fileList:\r\n# print(filename)\r\n if \".dcm\" in filename.lower() and filename==fn: # check whether the file's DICOM\r\n FilesDCM =(os.path.join(dirName,filename)) \r\n# \r\n ds = dicom.read_file(FilesDCM)\r\n endnumslice=filename.find('.dcm')\r\n imgcore=filename[0:endnumslice]+'.'+typei\r\n# imgcore=filename[0:endnumslice]+'.dcm'\r\n\r\n# print imgcore\r\n bmpfile=os.path.join(bmp_dir,imgcore)\r\n scipy.misc.imsave(bmpfile, ds.pixel_array)\r\n if np.shape(ds.pixel_array)[0] !=dimtabx:\r\n orig= Image.open(bmpfile,'r') \r\n ncr=orig.resize((dimtabx,dimtaby),PIL.Image.ANTIALIAS)\r\n del orig\r\n ncr.save(bmpfile)\r\n# print np.shape(ds.pixel_array)\r\n \r\n# ds.save_as(bmpfile)\r\n\r\n #chek if lung mask present \r\n if lungmask == filename:\r\n \r\n lung_dir = os.path.join(dirName, lungmask)\r\n lung_bmp_dir = os.path.join(lung_dir,lungmaskbmp)\r\n lunglist = os.listdir(lung_dir)\r\n# remove_folder(lung_bmp_dir)\r\n# if lungmaskbmp not in lunglist:\r\n if os.path.exists(lung_bmp_dir) ==False :\r\n os.mkdir(lung_bmp_dir)\r\n# print(lung_bmp_dir)\r\n for lungfile in lunglist:\r\n# print(lungfile)\r\n if \".dcm\" in lungfile.lower(): # check whether the file's DICOM\r\n lungDCM =os.path.join(lung_dir,lungfile) \r\n dslung = dicom.read_file(lungDCM)\r\n endnumslice=lungfile.find('.dcm')\r\n lungcore=lungfile[0:endnumslice]+'.'+typei\r\n lungcoref=os.path.join(lung_bmp_dir,lungcore)\r\n scipy.misc.imsave(lungcoref, dslung.pixel_array)\r\n if np.shape(dslung.pixel_array)[0] !=dimtabx:\r\n orig= Image.open(lungcoref,'r')\r\n ncr=orig.resize((dimtabx,dimtaby),PIL.Image.ANTIALIAS)\r\n del orig\r\n ncr.save(lungcoref)",
"title": ""
},
{
"docid": "e4eb4418e7a64d1808dffa55a4c02564",
"score": "0.50043976",
"text": "def _extract_patches(tuples, max_per_frame=1, trials_per_tuple=100, flow_threshold=25.0, jumpcut_threshold=np.inf,\n workers=0):\n\n tick_t = timer()\n print('===> Extracting patches...')\n\n if workers != 0:\n parallel = Parallel(n_jobs=workers, backend='threading', verbose=5)\n tuples_per_job = len(tuples) // workers + 1\n result = parallel(\n delayed(_extract_patches_worker)(tuples[i:i + tuples_per_job], max_per_frame, trials_per_tuple,\n flow_threshold, jumpcut_threshold) for i in\n range(0, len(tuples), tuples_per_job))\n patches = sum(result, [])\n else:\n patches = _extract_patches_worker(tuples, max_per_frame, trials_per_tuple, flow_threshold, jumpcut_threshold)\n\n tock_t = timer()\n print(\"Done. Took ~{}s\".format(round(tock_t - tick_t)))\n\n return patches",
"title": ""
},
{
"docid": "693470b2346e1cdb09eb19c3b301c3d2",
"score": "0.49989954",
"text": "def slides_to_pic(slides, labels,original_pic_dim):#)num_pix_x, num_pix_y):\n # Setting up the basic dimonsions of the pictures.\n num_pix_x = slides.shape[2]\n num_pix_y = slides.shape[3]\n num_rows = original_pic_dim[0]\n num_cols = original_pic_dim[1]\n rows_float = num_rows / num_pix_x\n cols_float = num_cols / num_pix_y\n rows_odd = math.ceil((2*rows_float)%2) - 1\n cols_odd = math.ceil((2*cols_float)%2) - 1\n rows = math.floor(rows_float)\n cols = math.floor(cols_float)\n x_end = 2*rows + rows_odd\n y_end = 2*cols + cols_odd\n # Create empty list of boxes to draw\n boxes_tb_drawn =[]\n # Loading the pictures and in this case only adding the lowenergy part.\n #totalimsize = int((len(slides[0]) - 1)/2)\n #slides_re = slides[:,:totalimsize].reshape(-1,num_pix_x,num_pix_y)\n # Create the resulting initial picture.\n pics = []\n for channel in range(slides.shape[1]):\n pic = np.zeros((num_rows,num_cols))\n c=0\n for x in range(0,x_end):#2*rows-1):\n extendx = x== x_end-1\n xs =int( ((x%2)*num_pix_x/2 + math.floor(x/2)*num_pix_x) * (1-extendx) + extendx * (num_rows-num_pix_x))\n for y in range(0,y_end):#2*cols-1):\n extendy = y==y_end-1\n ys =int( ((y%2)*num_pix_y/2 + math.floor(y/2)*num_pix_y) * (1-extendy) + extendy * (num_cols-num_pix_y))\n pic[xs:xs+num_pix_x, ys:ys+num_pix_y] = slides[c,channel]\n if labels[c] ==1 and channel==0:\n boxes_tb_drawn.append([xs,ys])\n c+=1\n pics.append(pic)\n return np.array(pics), boxes_tb_drawn",
"title": ""
},
{
"docid": "d6c59cd223048b55597b3e461a1385a7",
"score": "0.4975566",
"text": "def extract_positive_patches(self):\n gsd = C.TRAIN.SAMPLES.GENERATOR.GSD\n progress = tqdm(self.positive_sample_info, desc='Generating positive patches')\n for i, row in enumerate(progress):\n name, center_x, center_y, original_angle, length, width = row\n\n path = os.path.join(C.TRAIN.SAMPLES.DIR, name)\n if os.path.isfile(path):\n warn(\"Skipping {}: file already exists. Remove existing files first to regenerate.\")\n continue\n dirname = os.path.dirname(path)\n os.makedirs(dirname, exist_ok=True)\n\n data = self.get_patch_xyr(center_x, center_y, 0, 0, -original_angle, radius_in_pixels=self.radius)\n obb = OrientedBoundingBox.from_rot_length_width((0, 0), 0, length / gsd, width / gsd)\n\n patch = Patch(\n name=name,\n obb=obb,\n volumetric=data[3:],\n rgb=data[:3],\n label=1,\n dr_dc_angle=(0, 0, 0),\n ori_xy=(center_x, center_y))\n\n with open(path, 'wb') as handle:\n pickle.dump(patch, handle, protocol=pickle.HIGHEST_PROTOCOL)",
"title": ""
},
{
"docid": "0ed6e878be9bcf733a30714df9b68d3b",
"score": "0.49720022",
"text": "def _cache_patches_worker(cache_dir, patches):\n for p in patches:\n patch_id = str(random.randint(1e10, 1e16))\n frames = load_patch(p)\n for i in range(3):\n file_name = '{}_{}.jpg'.format(patch_id, i)\n frames[i].save(join(cache_dir, file_name))",
"title": ""
},
{
"docid": "9c427caf68beffbca72412551c95099f",
"score": "0.497115",
"text": "def slice_folder(self, input_path):\n print(\"Parsing folder: \" + input_path)\n if input_path[-1] == self.sep:\n path_out = input_path[:-1] + suffix_sliced\n else:\n path_out = input_path + suffix_sliced\n try:\n os.mkdir(path_out)\n except OSError:\n pass\n for files in sorted(os.listdir(input_path)):\n ext = files[-3:].lower()\n if ext in images:\n file_to_add = os.path.join(input_path, files)\n im = Image.open(file_to_add)\n (w, h) = im.size\n if w > h:\n if self.verbose: # Note: If verbose\n print(\" Slicing \", file_to_add, str(w) + \"x\" + str(h))\n img_a, img_b = self.image_slice(file_to_add)\n if self.reverse: # If it's a manga, then the first page is the right page\n img_a, img_b = img_b, img_a\n for c, im in enumerate([img_a, img_b]):\n im.save(f\"{path_out}/{files[:-4]}_{c}.jpg\")\n else:\n shutil.copy(file_to_add, path_out)\n return path_out",
"title": ""
},
{
"docid": "617cd285a15113de7a2bc40a21b9770a",
"score": "0.49052998",
"text": "def _mdgel_series(self):\n # only a single page, scaled according to metadata in second page\n self.pages.useframes = False\n self.pages.keyframe = 0\n self.pages.load()\n md = self.mdgel_metadata\n if md[\"FileTag\"] in (2, 128):\n dtype = numpy.dtype(\"float32\")\n scale = md[\"ScalePixel\"]\n scale = scale[0] / scale[1] # rational\n if md[\"FileTag\"] == 2:\n # squary root data format\n def transform(a):\n return a.astype(\"float32\") ** 2 * scale\n\n else:\n\n def transform(a):\n return a.astype(\"float32\") * scale\n\n else:\n transform = None\n page = self.pages[0]\n return [\n TiffPageSeries(\n [page], page.shape, dtype, page.axes, transform=transform, stype=\"MDGel\"\n )\n ]",
"title": ""
},
{
"docid": "02f469dccda8f0047046ba3f29b17cd8",
"score": "0.49051732",
"text": "def patch_images(dark_images: np.ndarray,\n light_images: np.ndarray,\n size: int, stride: int) -> (np.ndarray, np.ndarray):\n dark_patches = []\n light_patches = []\n for i in range(dark_images.shape[0]):\n for p in range(0, dark_images.shape[1] - size + stride, stride):\n for q in range(0, dark_images.shape[2] - size + stride, stride):\n if (p + size <= dark_images.shape[1]) and (q + size <= dark_images.shape[2]):\n dark_patches.append(dark_images[i, p:p + size, q:q + size])\n light_patches.append(light_images[i, p:p + size, q:q + size])\n # print(dark_images[i, p:p + size, q:q + size].shape)\n\n return np.array(dark_patches), np.array(light_patches)",
"title": ""
},
{
"docid": "7df7ef25fae1a446f2fc08671d940e61",
"score": "0.49042118",
"text": "def images_example(path='train_images.pickle'):\r\n patch_size = (8, 8)\r\n\r\n with open('train_images.pickle', 'rb') as f:\r\n train_pictures = pickle.load(f)\r\n\r\n patches = sample_patches(train_pictures, psize=patch_size, n=20000)\r\n\r\n plt.figure()\r\n plt.imshow(train_pictures[0])\r\n plt.title(\"Picture Example\")\r\n\r\n plt.figure()\r\n for i in range(4):\r\n plt.subplot(2, 2, i + 1)\r\n plt.imshow(patches[:, i].reshape(patch_size), cmap='gray')\r\n plt.title(\"Patch Example\")\r\n plt.show()",
"title": ""
},
{
"docid": "7df7ef25fae1a446f2fc08671d940e61",
"score": "0.49042118",
"text": "def images_example(path='train_images.pickle'):\r\n patch_size = (8, 8)\r\n\r\n with open('train_images.pickle', 'rb') as f:\r\n train_pictures = pickle.load(f)\r\n\r\n patches = sample_patches(train_pictures, psize=patch_size, n=20000)\r\n\r\n plt.figure()\r\n plt.imshow(train_pictures[0])\r\n plt.title(\"Picture Example\")\r\n\r\n plt.figure()\r\n for i in range(4):\r\n plt.subplot(2, 2, i + 1)\r\n plt.imshow(patches[:, i].reshape(patch_size), cmap='gray')\r\n plt.title(\"Patch Example\")\r\n plt.show()",
"title": ""
},
{
"docid": "b9ad06ab878622ed4389133e5a2070c2",
"score": "0.48981017",
"text": "def images_example(path='train_images.pickle'):\n patch_size = (8, 8)\n\n with open(path, 'rb') as f:\n train_pictures = pickle.load(f)\n\n patches = sample_patches(train_pictures, psize=patch_size, n=20000)\n\n plt.figure()\n plt.imshow(train_pictures[0])\n plt.title(\"Picture Example\")\n\n plt.figure()\n for i in range(4):\n plt.subplot(2, 2, i + 1)\n plt.imshow(patches[:, i].reshape(patch_size), cmap='gray')\n plt.title(\"Patch Example\")\n plt.show()",
"title": ""
},
{
"docid": "62d490580e1b39beb3cda197ec9d17c3",
"score": "0.4897035",
"text": "def load_patch(patch):\n paths = (patch['left_frame'], patch['middle_frame'], patch['right_frame'])\n i, j = (patch['patch_i'], patch['patch_j'])\n imgs = [load_img(x) for x in paths]\n h, w = config.PATCH_SIZE\n return tuple(crop_image(x, i, j, h, w) for x in imgs)",
"title": ""
},
{
"docid": "5ad3150a652bab55763a4f7c5edf8e20",
"score": "0.48958212",
"text": "def getSlideshowImages():",
"title": ""
},
{
"docid": "9766e1f097a49598d8dffe6c2f239103",
"score": "0.48944816",
"text": "def get_patches(self, cv_kpts):\n # generate sampling grids.\n n_pixel = np.square(self.patch_size)\n self.output_grid = np.zeros((n_pixel, 3), dtype=np.float32)\n for i in range(n_pixel):\n self.output_grid[i, 0] = (i % self.patch_size) * 1. / self.patch_size * 2 - 1\n self.output_grid[i, 1] = (i / self.patch_size) * 1. / self.patch_size * 2 - 1\n self.output_grid[i, 2] = 1\n\n scale_index = [[] for i in range(len(self.pyr))]\n for idx, val in enumerate(cv_kpts):\n octave, layer, _ = unpack_octave(val)\n scale_val = (int(octave) - self.first_octave) * (self.n_octave_layers + 3) #+ int(layer)\n if scale_val >= len(self.pyr):\n print(\"Index[\", idx, \"] The scale value is \", scale_val, \", but the pyr is\", len(self.pyr))\n scale_val = len(self.pyr) - 1\n scale_index[scale_val].append(idx)\n\n all_patches = []\n for idx, val in enumerate(scale_index):\n tmp_cv_kpts = [cv_kpts[i] for i in val]\n scale_img = self.pyr[idx]\n # radius = self.sift_descr_scl_fctr * size * np.sqrt(2) * (self.sift_descr_width + 1) * 0.5\n patches = get_interest_region(scale_img, tmp_cv_kpts, self.output_grid,\n self.sift_descr_width, self.sift_descr_scl_fctr,\n self.patch_size)\n if patches is not None:\n all_patches.append(patches)\n\n if self.down_octave:\n all_patches = np.concatenate(all_patches[::-1], axis=0)\n else:\n all_patches = np.concatenate(all_patches, axis=0)\n assert len(cv_kpts) == all_patches.shape[0]\n return all_patches",
"title": ""
},
{
"docid": "2b93e4eb66499e5c4575b7c17d53c5fb",
"score": "0.48930573",
"text": "def genebmp(dirName):\r\n print ('generate bmp files from dicom files in :',f)\r\n #directory for patches\r\n bmp_dir = os.path.join(dirName, bmpname)\r\n remove_folder(bmp_dir) \r\n os.mkdir(bmp_dir)\r\n bgdirf = os.path.join(dirName, bgdir)\r\n remove_folder(bgdirf) \r\n os.mkdir(bgdirf)\r\n \r\n #list dcm files\r\n fileList = os.listdir(dirName)\r\n\r\n for filename in fileList:\r\n# print(filename)\r\n if \".dcm\" in filename.lower(): # check whether the file's DICOM\r\n FilesDCM =(os.path.join(dirName,filename)) \r\n# \r\n ds = dicom.read_file(FilesDCM)\r\n endnumslice=filename.find('.dcm')\r\n imgcore=filename[0:endnumslice]+'.'+typei\r\n# print imgcore\r\n bmpfile=os.path.join(bmp_dir,imgcore)\r\n scipy.misc.imsave(bmpfile, ds.pixel_array)\r\n \r\n posend=endnumslice\r\n while filename.find('-',posend)==-1:\r\n posend-=1\r\n debnumslice=posend+1\r\n slicenumber=int((filename[debnumslice:endnumslice])) \r\n namescan=os.path.join(sroidir,imgcore) \r\n textw='n: '+f+' scan: '+str(slicenumber)\r\n orign = Image.open(bmpfile)\r\n imscanc= orign.convert('RGB')\r\n tablscan = np.array(imscanc)\r\n if np.shape(tablscan)[0]==768L:\r\n# print ('size of image:',np.shape(tablscan)[0])\r\n if f not in listOverSize:\r\n print ('size of image:',np.shape(tablscan)[0])\r\n listOverSize.append(f)\r\n scipy.misc.imsave(namescan, tablscan)\r\n tagviews(namescan,textw,0,20) \r\n# print lungmask, filename\r\n \r\n \r\n lung_dir = os.path.join(dirName, lungmask)\r\n# print lung_dir\r\n lung_bmp_dir = os.path.join(lung_dir,lungmaskbmp)\r\n lunglist = os.listdir(lung_dir)\r\n remove_folder(lung_bmp_dir)\r\n# if lungmaskbmp not in lunglist:\r\n os.mkdir(lung_bmp_dir)\r\n# print(lung_bmp_dir)\r\n for lungfile in lunglist:\r\n# print(lungfile)\r\n if \".dcm\" in lungfile.lower(): # check whether the file's DICOM\r\n lungDCM =os.path.join(lung_dir,lungfile) \r\n dslung = dicom.read_file(lungDCM)\r\n endnumslice=lungfile.find('.dcm')\r\n lungcore=lungfile[0:endnumslice]+'.'+typei\r\n lungcoref=os.path.join(lung_bmp_dir,lungcore)\r\n scipy.misc.imsave(lungcoref, dslung.pixel_array)\r\n bgdirflm=os.path.join(bgdirf,lungcore)\r\n# print lungcoref,bgdirflm\r\n shutil.copyfile(lungcoref,bgdirflm)\r\n o = Image.open(bgdirflm,'r')\r\n t=np.array(o)\r\n# print bgdirflm\r\n del o\r\n np.putmask(t,t>0,100)\r\n scipy.misc.imsave(bgdirflm, t)",
"title": ""
},
{
"docid": "e8e286ef705e0f0b7044ca6362b19717",
"score": "0.48802644",
"text": "def extract_data(filename, num_images, preprocess=None):\n imgs = []\n for i in range(1, num_images+1):\n imageid = \"satImage_%.3d\" % i\n image_filename = filename + imageid + \".png\"\n if os.path.isfile(image_filename):\n #print ('Loading ' + image_filename)\n img = mpimg.imread(image_filename)\n if preprocess:\n img = preprocess(img)\n print('image ' + str(i) + ' of ' + str(num_images) + ' preprocessed')\n imgs.append(img)\n else:\n print ('File ' + image_filename + ' does not exist')\n\n num_images = len(imgs)\n IMG_WIDTH = imgs[0].shape[0]\n IMG_HEIGHT = imgs[0].shape[1]\n N_PATCHES_PER_IMAGE = (IMG_WIDTH/IMG_PATCH_SIZE)*(IMG_HEIGHT/IMG_PATCH_SIZE)\n\n img_patches = [img_crop(imgs[i], IMG_PATCH_SIZE, IMG_PATCH_SIZE) for i in range(num_images)]\n data = [img_patches[i][j] for i in range(len(img_patches)) for j in range(len(img_patches[i]))]\n\n return numpy.asarray(data)",
"title": ""
},
{
"docid": "d6c09bd2e3cfb54cbeb0e99bbb5df0be",
"score": "0.48777187",
"text": "def divide(self, res):\n \n lp = []\n for i in range(len(self.lp)-1):\n lp.extend(self._division2(self.lp[i], self.lp[i+1], res))\n \n #--- Remove doble points\n b = [lp[i] for i in range(len(lp)-1) if lp[i]!=lp[i+1]]\n b.append(lp[len(lp)-1]) #-- Add the last point\n \n return Figure(b)",
"title": ""
},
{
"docid": "0850daf5e1594f064689d4739a4a4bf9",
"score": "0.48749578",
"text": "def process_all_files(list_indices):\n patches = try_to_load_as_pickled_object('data/patches_' + str(list_indices[0]) + '.pkl')\n labels = try_to_load_as_pickled_object('data/labels_patches_' + str(list_indices[0]) + '.pkl')\n for i in list_indices[1:]:\n tmp1 = try_to_load_as_pickled_object('data/patches_' + str(i) + '.pkl')\n patches = np.append(patches, tmp1, axis=0)\n tmp2 = try_to_load_as_pickled_object('data/labels_patches_' + str(i) + '.pkl')\n labels = np.append(labels, tmp2, axis=0)\n print(\"loaded data \" + str(i))\n return patches, labels\n #save_obj(patches, 'data/patches')\n #save_obj(labels, 'data/labels_patches')",
"title": ""
},
{
"docid": "4d18989c0e0cd4d434e345728394c752",
"score": "0.4848752",
"text": "def _initialize_slides(self, prs, root_entry):\n\n # Initialize data structures\n self.slides = []\n self.layouts = []\n self.refs = {}\n\n # Iterate through slides\n for i,slide in enumerate(root_entry.data):\n # Get slide layout\n layout_vals = slide.get_values(tag=\"layout\", join=True)\n\n if (len(layout_vals) > 1):\n raise ValueError(\"slide may only have one layout attribute\"\\\n \"\\n{}\".format(self.ppp.error_info(slide)))\n\n # Add layout to list and remove layout entry from slide\n self.layouts.append(layout_vals[0])\n slide.remove(tag=\"layout\")\n\n # Ceate new slide with layout\n prs_layout = prs.slide_layouts[self.template[layout_vals[0]][\"idx\"]]\n self.slides.append(prs.slides.add_slide(prs_layout))\n\n # Get slide reference label\n label_vals = slide.get_values(tag=\"label\", join=True)\n\n # Reference label points to current slide and remove label entry\n if (len(label_vals) == 1):\n self.refs[label_vals[0]] = self.slides[-1]\n slide.remove(tag=\"label\")\n elif (len(label_vals) > 1):\n raise ValueError(\"slide may only have one label attribute\"\\\n \"\\n{}\".format(self.ppp.error_info(slide)))",
"title": ""
},
{
"docid": "73d77e26e48121481a18f9474b850ed0",
"score": "0.48472056",
"text": "def test():\n pics = glob.glob(\"*.png\")\n filename = 'test.pptx'\n img2ppt( pics, filename)",
"title": ""
},
{
"docid": "a8feb13ad2919a539daca4a954fb1ff2",
"score": "0.48357326",
"text": "def patches(self, stored=False) -> Generator['Patch', None, None]:\n\n for _, group in itertools.groupby(self.patch_elements(stored=stored), key=lambda e: e.version):\n # keep latest sub-patch version of each element\n elements = {}\n for elem in group:\n if elem.name not in elements:\n elements[elem.name] = elem\n yield Patch._create(list(elements.values()))",
"title": ""
},
{
"docid": "48c0f3bc4819490425d909758777b9e5",
"score": "0.4798717",
"text": "def getSlideshowFolder():",
"title": ""
},
{
"docid": "be68db6cbda7251e10b84ff3bc4cfdc5",
"score": "0.4794798",
"text": "def load_files(path, WIN_WIDTH=settings.WIN_WIDTH, extensions=settings.FILE_EXTENSIONS, return_notelists=False, overlap=True):\n\n extensions = settings.FILE_EXTENSIONS\n if WIN_WIDTH % 2 != 0:\n return None\n\n score_list = []\n melody_list = []\n map_score_window = []\n notelist_scores = []\n\n file_list = []\n # recurse all directories\n print(extensions)\n for root, subdirs, files in os.walk(path):\n # take just the files with the proper extension\n file_list += [os.path.join(root, fp)\n for fp in files if fp.endswith(extensions)]\n\n if len(file_list) == 0:\n raise Exception(\"No files found with this extension!\")\n\n # extract random files\n if settings.DATASET_PERC < 1:\n num_files = int(settings.DATASET_PERC * len(file_list))\n np.random.seed(1987)\n file_list = np.random.choice(\n file_list, num_files, replacement=False)\n\n for f in sorted(file_list):\n print(\"I've found a new file: \" + f)\n\n note_array = load_piece(f)\n # load pianorolls score and melody\n pr = utils.pianoroll_utils.make_pianorolls(\n note_array, output_idxs=return_notelists)\n score = pr[0]\n melody = pr[1]\n if len(pr) == 4:\n notelist_scores.append(pr[2])\n\n # split in windows\n score_splitted = split_windows(score,\n WIN_WIDTH, overlap)\n melody_splitted = split_windows(melody,\n WIN_WIDTH, overlap)\n # update the map\n counter = len(score_list)\n map_score_window.append(\n [c + counter for c in range(len(score_splitted))])\n\n # update the output list\n score_list += score_splitted\n melody_list += melody_splitted\n\n # reshaping output\n score_out = np.ndarray(shape=(len(score_list), 1, settings.WIN_HEIGHT, WIN_WIDTH),\n buffer=np.array(score_list), dtype=settings.floatX)\n\n melody_out = np.ndarray(shape=(len(melody_list), 1, settings.WIN_HEIGHT, WIN_WIDTH),\n buffer=np.array(melody_list), dtype=settings.floatX)\n\n if return_notelists:\n return score_out, melody_out, map_score_window, np.array(notelist_scores)\n else:\n return score_out, melody_out, map_score_window",
"title": ""
},
{
"docid": "695927e6f8026650988421e61a0ccd34",
"score": "0.4779739",
"text": "def extract_patch_batch(self, images, start_x, start_y):\n number_of_instances = images.shape[0]\n patch_ground_truth = np.zeros((number_of_instances, patch_width, patch_width, channels))\n\n for i in range(images.shape[0]):\n images[i], patch_ground_truth[i] = self.extract_patch(images[i], start_x, start_y)\n\n return images, patch_ground_truth",
"title": ""
},
{
"docid": "a0323d5661c76347b842bf265ce8a349",
"score": "0.47576848",
"text": "def _get_patches(bands, patch_dim=64):\n patches = image.extract_patches(bands,\n (patch_dim, patch_dim, 13),\n patch_dim)\n hs, ws = patches.shape[0], patches.shape[1]\n patches = patches.reshape(-1, patch_dim, patch_dim, 13)\n\n last_row = bands[bands.shape[0]-patch_dim:, :, :]\n last_column = bands[:, bands.shape[1]-patch_dim:, :]\n corner = np.asarray([bands[bands.shape[0]-patch_dim:,\n bands.shape[1]-patch_dim:,\n :]])\n\n last_column = image.extract_patches(last_column,\n (patch_dim, patch_dim, 13),\n patch_dim).reshape(-1,\n patch_dim,\n patch_dim,\n 13)\n last_row = image.extract_patches(last_row,\n (patch_dim, patch_dim, 13),\n patch_dim).reshape(-1,\n patch_dim,\n patch_dim,\n 13)\n\n lc = last_column.shape[0]\n lr = last_row.shape[0]\n\n patches = np.vstack((patches, last_column, last_row, corner))\n return patches, hs, ws, lc, lr, bands.shape[0], bands.shape[1]",
"title": ""
},
{
"docid": "a259de79f0fb1595ba1e3c704cecea56",
"score": "0.47515467",
"text": "def generate_patches(opt, validation_city):\n # load day 1 and 2 bands\n d1_path = get_path([opt.dataset_dir, 'images/', validation_city, '/imgs_1/*'])\n d2_path = get_path([opt.dataset_dir, 'images/', validation_city, '/imgs_2/*'])\n d1_bands = glob.glob(d1_path)\n d2_bands = glob.glob(d2_path)\n\n # sort bands to ensure that B01 -> B12 order\n d1_bands.sort()\n d2_bands.sort()\n\n # load band 2 from d1 bands to get template image dimensions\n template = rasterio.open(d1_bands[2])\n\n # TEMPORARY FIX: switching width and height seems to fix image generation\n city_dir = get_path([opt.dataset_dir, 'images/', validation_city])\n imgs_stacked = city_loader([city_dir, template.width, template.height, opt])\n\n d1 = imgs_stacked[0]\n d2 = imgs_stacked[1]\n\n # move image depth\n d1 = d1.transpose(1, 2, 0)\n d2 = d2.transpose(1, 2, 0)\n\n patches1, hs, ws, lc, lr, h, w = _get_patches(d1, patch_dim=opt.patch_size)\n patches1 = patches1.transpose(0, 3, 1, 2)\n\n print('Patches 1 Created')\n\n patches2, hs, ws, lc, lr, h, w = _get_patches(d2, patch_dim=opt.patch_size)\n patches2 = patches2.transpose(0, 3, 1, 2)\n\n print('Patches 2 Created')\n return patches1, patches2, hs, ws, lc, lr, h, w",
"title": ""
},
{
"docid": "db2abc0ad0eaa9af0a3214a64a9d9503",
"score": "0.47486103",
"text": "def load_left_right_pngs(dir_name, expected_number):\n left_images = []\n files = glob.glob(os.path.join(dir_name, \"left\", \"*.png\"))\n files.sort()\n for file in files:\n image = cv2.imread(file)\n left_images.append(image)\n assert len(left_images) == expected_number\n\n right_images = []\n files = glob.glob(os.path.join(dir_name, \"right\", \"*.png\"))\n files.sort()\n for file in files:\n image = cv2.imread(file)\n right_images.append(image)\n assert len(right_images) == expected_number\n\n return left_images, right_images",
"title": ""
},
{
"docid": "cee49886b91da00a1d7aeecab16dd1bf",
"score": "0.47476342",
"text": "def getAllWallpapers():\n\n \n # Find all current wallpapers\n mydir = getattr(sys, '_MEIPASS','.')+'/'\n myWallpapers = [mydir + image for image in os.listdir(mydir) if image.endswith(\".jpg\")]\n\n return myWallpapers",
"title": ""
},
{
"docid": "010ea2959b73515030cce5297936252d",
"score": "0.4739947",
"text": "def _extract_patches_worker(tuples, max_per_frame=1, trials_per_tuple=100, flow_threshold=0.0,\n jumpcut_threshold=np.inf):\n\n patch_h, patch_w = config.PATCH_SIZE\n n_tuples = len(tuples)\n all_patches = []\n jumpcuts = 0\n flowfiltered = 0\n total_iters = n_tuples * trials_per_tuple\n\n pil_to_numpy = lambda x: np.array(x)[:, :, ::-1]\n\n for tup_index in range(n_tuples):\n tup = tuples[tup_index]\n\n left, middle, right = (load_img(x) for x in tup)\n img_w, img_h = left.size\n\n left = pil_to_numpy(left)\n middle = pil_to_numpy(middle)\n right = pil_to_numpy(right)\n\n selected_patches = []\n\n for _ in range(trials_per_tuple):\n\n i = random.randint(0, img_h - patch_h)\n j = random.randint(0, img_w - patch_w)\n\n left_patch = left[i:i + patch_h, j:j + patch_w, :]\n right_patch = right[i:i + patch_h, j:j + patch_w, :]\n middle_patch = middle[i:i + patch_h, j:j + patch_w, :]\n\n if is_jumpcut(left_patch, middle_patch, jumpcut_threshold) or \\\n is_jumpcut(middle_patch, right_patch, jumpcut_threshold):\n jumpcuts += 1\n continue\n\n avg_flow = simple_flow(left_patch, right_patch)\n if random.random() > avg_flow / flow_threshold:\n flowfiltered += 1\n continue\n\n selected_patches.append({\n \"left_frame\": tup[0],\n \"middle_frame\": tup[1],\n \"right_frame\": tup[2],\n \"patch_i\": i,\n \"patch_j\": j,\n \"avg_flow\": avg_flow\n })\n\n sorted(selected_patches, key=lambda x: x['avg_flow'], reverse=True)\n all_patches += selected_patches[:max_per_frame]\n # print(\"===> Tuple {}/{} ready.\".format(tup_index+1, n_tuples))\n\n print('===> Processed {} tuples, {} patches extracted, {} discarded as jumpcuts, {} filtered by flow'.format(\n n_tuples, len(all_patches), 100.0 * jumpcuts / total_iters, 100.0 * flowfiltered / total_iters\n ))\n\n return all_patches",
"title": ""
},
{
"docid": "685986cc8b969136f0dbe3c2f522c89a",
"score": "0.4738939",
"text": "def processFile(fileName):\n global pnmflips\n global renumbers\n global nFiles\n\n # s = os.system('djpeg -greyscale -fast -scale 1/8 raw/' + fileName\n #+ ' | pnmfile')\n b = fileName[:-4].lower()\n n = b[-4:-1]\n if renumbers.has_key(n):\n n = renumbers[n]\n print b[-4:-1], '->', n\n else:\n n += '0' # multiply by 10 to make 4 figures\n\n if noExplode and b[-1] == 'm':\n return\n elif b[-1] == 'm':\n # Multipicture -- 3x3 grid of frames.\n print fileName, ' -- split'\n os.system('djpeg raw/' + fileName + ' >tmp.ppm')\n i = 0\n for y in [0, 240, 480]:\n for x in [0, 320, 640]:\n os.system('pnmcut %d %d 320 240 tmp.ppm' % (x, y)\n + ' | pnmtopng '\n '>' + b + '.f' + str(i) + '.png')\n i += 1\n nFiles += 1\n elif explodeOnly:\n return\n elif pnmflips.has_key(fileName[4:7]):\n flip = pnmflips[fileName[4:7]]\n print fileName, '-- flip', flip\n os.system('djpeg raw/' + fileName\n + ' | pnmflip ' + flip\n + ' | cjpeg -prog >' + n + '.' + b + 'r.jpg')\n # removed + ' | pnmscale 0.5'\n nFiles += 1\n else:\n print fileName\n os.system('djpeg raw/' + fileName\n + ' | cjpeg -prog >' + n + '.' + b + 'r.jpg')\n nFiles += 1",
"title": ""
},
{
"docid": "f8204b528a663162766c515e6efd6155",
"score": "0.4736585",
"text": "def img2ppt( pics, outfile ):\n pres = Presentation()\n for pic in pics:\n slide = pres.slides.add_slide( pres.slide_layouts[1] )\n slide.shapes.add_picture( pic, Inches(0.6), Inches(2))\n pres.save(outfile)",
"title": ""
},
{
"docid": "9649fe575e1518209191a6282d0c5ae5",
"score": "0.47363",
"text": "def render_slides(slides):\n handle, export_file = tempfile.mkstemp('.mp4', dir='./tmp')\n cmd = f'ffmpeg -v -1 -y -f image2pipe -vcodec ppm -i pipe: -r 30 -b 5000k \"{export_file}\"'\n ffmpeg = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n pipe = ffmpeg.stdin\n ffmpeg.stdout.close()\n\n prev_slide = slides[0]\n prev_slide.load()\n\n while slides:\n slide = slides.popleft()\n slide.load()\n\n for frame in range(TRANSITION_FRAMES):\n save_frame(Image.blend(prev_slide.frame(frame+TRANSITION_FRAMES+prev_slide.slide_frames),\n slide.frame(frame),\n frame/TRANSITION_FRAMES), pipe)\n for frame in range(slide.slide_frames):\n save_frame(slide.frame(frame+TRANSITION_FRAMES), pipe)\n\n prev_slide = slide\n\n pipe.close()\n time.sleep(5) # time to finsih writing to disk\n\n return mp.VideoFileClip(export_file)",
"title": ""
},
{
"docid": "8e42f0810053555183a8d32475f845c7",
"score": "0.47236136",
"text": "def warp_worms(expt_dir, positions):\n expt_dir = pathlib.Path(expt_dir)\n annotation_dir = expt_dir / 'annotations'\n warp_dir = expt_dir / 'warped_images'\n \n if not warp_dir.exists():\n warp_dir.mkdir(exist_ok = True)\n\n for worm, timepoints in positions.items():\n annotation_file = list(annotation_dir.glob('*'+worm+'.pickle'))[0]\n annotations = pickle.load(open(annotation_file, 'rb'))\n positions, tcks = annotations\n for tp, imgs in timepoints.items():\n center_tck, width_tck = tcks[tp]['pose']\n for img in imgs:\n warp_file = warp_dir / worm / (img.name)\n \n warp_image(center_tck, width_tck, img, warp_file)",
"title": ""
},
{
"docid": "18d7cca33c63785e55de57e696ba4427",
"score": "0.47192267",
"text": "def regroup_all_raw_data(path_src):\n try:\n os.mkdir(os.path.join(path_src, 'processed'))\n except OSError:\n sys.stdout.write('WARN: processed folder already exists\\n')\n\n if not os.path.exists(os.path.join(path_src, 'raw_data')):\n sys.stdout.write('*** ERROR: Make sure pictures are inside raw_data folder here %s ***\\n' % os.getcwd())\n os.mkdir(os.path.join(os.getcwd(), 'raw_data'))\n exit(0)\n\n output_cmd = subprocess.Popen('find raw_data/ -name *.CR2 -type f', shell=True, stdout=subprocess.PIPE).communicate()\n pictures_path = list(map(os.path.abspath, output_cmd[0].splitlines()))\n\n total_pics = len(pictures_path)\n pics = 1\n\n for pic_path in pictures_path:\n n = 1\n new_pic_name = rename_pictures(pic_path)\n while os.path.exists(os.path.join(path_src, 'processed', new_pic_name.replace('X', str(n)))):\n n += 1\n os.rename(pic_path, os.path.join(path_src, 'processed', new_pic_name.replace('X', str(n))))\n sys.stdout.write('({}/{}) MV {} to {}\\n'.format(pics, total_pics, pic_path, os.path.join(path_src, 'processed', new_pic_name.replace('X', str(n)))))\n pics += 1",
"title": ""
},
{
"docid": "6847c7cf81430f4ed5bfc3dbc5145566",
"score": "0.4706909",
"text": "def process_images(images: (list, np.ndarray), patch_size, stride):\n demo_patches = extract_patches_ong_img(images[0], patch_size=patch_size, stride=stride)\n n_patches = demo_patches.shape[0]\n X = np.zeros([len(images) * n_patches, *demo_patches.shape[1:]])\n del demo_patches\n for i, one_image in enumerate(images):\n X[i * n_patches: i * n_patches + n_patches] = extract_patches_ong_img(one_image, patch_size=patch_size, stride=stride)\n\n X = X.reshape(X.shape[0], -1)\n return X.T # n_features * n_samples",
"title": ""
},
{
"docid": "36a4a2b52820a3898b7b45b7ae514944",
"score": "0.4701995",
"text": "def generate_filename(self):\n folders = glob(os.path.join(self.path, \"Slide_*\"))\n\n for f in folders:\n organ = os.path.basename(f).split(\"Slide_\")[-1]\n organ = mapping[organ]\n f_gt = f.replace(\"Slide\", \"GT\")\n for raw_path in glob(os.path.join(f, \"*.png\")):\n basename = os.path.basename(raw_path)\n basename = basename.replace(\"Slide_\", \"image\")\n basename = basename.replace(\".png\", \"_mask.txt\")\n gt__path = os.path.join(f_gt, basename)\n yield raw_path, gt__path, organ",
"title": ""
},
{
"docid": "5b55e7060f164dcacc454074ea31bc40",
"score": "0.4694604",
"text": "def pavgene (namedirtopcf):\r\n print('generate patches on: ',f)\r\n# print namedirtopcf\r\n namemask1=os.path.join(namedirtopcf,lungmask)\r\n namemask=os.path.join(namemask1,lungmaskbmp)\r\n# print namemask\r\n bmpdir = os.path.join(namedirtopcf,scanbmp)\r\n# print bmpdir\r\n patchpathf=os.path.join(namedirtopcf,patchpath)\r\n jpegpathf=os.path.join(namedirtopcf,jpegpath)\r\n remove_folder(patchpathf)\r\n os.mkdir(patchpathf)\r\n remove_folder(jpegpathf)\r\n os.mkdir(jpegpathf)\r\n listbmp= os.listdir(bmpdir)\r\n# print(listbmp)\r\n if os.path.exists(namemask):\r\n listlungbmp= os.listdir(namemask) \r\n else:\r\n tflung=False\r\n listlungbmp=[]\r\n for img in listbmp:\r\n# print img\r\n endnumslice=img.find('.bmp')\r\n posend=endnumslice\r\n while img.find('-',posend)==-1:\r\n posend-=1\r\n debnumslice=posend+1\r\n slicenumber=(img[debnumslice:endnumslice])\r\n# print('sln:',slicenumber,'img:', img,debnumslice,endnumslice \r\n slns='_'+str(int(slicenumber))+'.'+typei\r\n# print(slns)\r\n for llung in listlungbmp:\r\n tflung=False\r\n# print(llung)\r\n# print(listlungbmp)\r\n\r\n if llung.find(slns) >0:\r\n tflung=True\r\n lungfile = os.path.join(namemask,llung)\r\n# print(lungfile)\r\n imlung = Image.open(lungfile)\r\n tablung = np.array(imlung)\r\n np.putmask(tablung,tablung>0,1)\r\n\r\n break\r\n if not tflung:\r\n errorfile.write('lung mask not found '+slns+' in: '+f) \r\n print('lung mask not found ',slns,' in: ',f)\r\n tablung = np.ones((dimtabx, dimtaby), dtype='i')\r\n \r\n bmpfile = os.path.join(bmpdir,img)\r\n im = Image.open(bmpfile)\r\n imc= im.convert('RGB')\r\n tabf = np.array(imc) \r\n# pavgene (im,tabim,tablung,slicenumber)\r\n \r\n nz= np.count_nonzero(tablung)\r\n if nz>0:\r\n \r\n atabf = np.nonzero(tablung)\r\n #tab[y][x] convention\r\n xmin=atabf[1].min()\r\n xmax=atabf[1].max()\r\n ymin=atabf[0].min()\r\n ymax=atabf[0].max()\r\n else:\r\n xmin=0\r\n xmax=0\r\n ymin=0\r\n ymax=0\r\n \r\n# atabf = np.nonzero(tablung)\r\n# #tab[y][x] convention\r\n# xmin=atabf[1].min()\r\n# xmax=atabf[1].max()\r\n# ymin=atabf[0].min()\r\n# ymax=atabf[0].max()\r\n i=xmin\r\n while i <= xmax:\r\n j=ymin\r\n # j=maxj\r\n while j<=ymax:\r\n # print(i,j)\r\n tabpatch=tablung[j:j+dimpavy,i:i+dimpavx]\r\n area= tabpatch.sum() \r\n \r\n# check if area above threshold\r\n targ=float(area)/pxy\r\n if targ>thrpatch:\r\n \r\n crorig = im.crop((i, j, i+dimpavx, j+dimpavy))\r\n imagemax=crorig.getbbox()\r\n # detect black patch\r\n # print (imagemax)\r\n if imagemax!=None:\r\n namepatch=patchpathf+'/p_'+slicenumber+'_'+str(i)+'_'+str(j)+'.'+typei\r\n if contrast:\r\n tabcont=normi(crorig)\r\n scipy.misc.imsave(namepatch, tabcont)\r\n else:\r\n crorig.save(namepatch)\r\n #we draw the rectange\r\n x=0\r\n while x < dimpavx:\r\n y=0\r\n while y < dimpavy:\r\n tabf[y+j][x+i]=[255,0,0]\r\n if x == 0 or x == dimpavx-1 :\r\n y+=1\r\n else:\r\n y+=dimpavy-1\r\n x+=1 \r\n j+=dimpavy \r\n i+=dimpavx\r\n # im = plt.matshow(tabf)\r\n # plt.colorbar(im,label='with pavage')\r\n scipy.misc.imsave(jpegpathf+'/'+'s_'+slicenumber+'.bmp', tabf)",
"title": ""
},
{
"docid": "4d10bf9e135ffd80e3ccb7429452a86b",
"score": "0.46922275",
"text": "def move_slides(self, no_of_extra_slide):\n index_map = {}\n ## Object for Input file\n inputFile = './templates/BDRC_2016_Input.pptx'\n inputFileprs = os.getcwd() + '/' + inputFile\n prs = Presentation(inputFileprs)\n max_length = prs.slides.__len__()\n outputFileprs = outputFile\n desprs = Presentation(outputFileprs)\n no_of_extra_slide.keys()\n ## Manage Slide index order sortted format\n source_index_list = no_of_extra_slide.keys()\n source_index_list.sort()\n\n for item, source_index in enumerate(source_index_list):\n if item != 0:\n extra_slide = no_of_extra_slide[\n source_index_list[item - 1]] - 1\n next_key = source_index + extra_slide\n else:\n next_key = source_index\n nos_of_new_record = no_of_extra_slide[source_index]\n for i in range(1, nos_of_new_record):\n key = next_key + i\n value = max_length + i - 1\n index_map[key] = value\n max_length = max_length + no_of_extra_slide[source_index] - 1\n\n ## Manage to arrange slide order with extra nos of slide count and index\n for new_index, old_index in index_map.iteritems():\n self.move_slide(desprs, old_index, new_index)\n desprs.save(outputFile)",
"title": ""
},
{
"docid": "c7f20f00893f5ca41933a203258abaf2",
"score": "0.46910876",
"text": "def convert_geotiff_folder(dir_path: Path,\n output_dir: Optional[Path]=None,\n block_size=256,\n max_nodata: float=0.1,\n unpaired=True):\n \n \n if output_dir is None:\n output_dir = dir_path.parent / (dir_path.name + \"_np\")\n \n output_dir.mkdir(parents=True)\n (output_dir / \"x\").mkdir()\n \n if not os.path.exists(dir_path / \"y\"):\n # Unlabelled setting\n it = MultiGeoTiffIterable(dir_path / \"x\", block_size=block_size, max_nodata=max_nodata)\n \n for i in trange(len(it)):\n np.save(output_dir / \"x\" / f\"{i}.npy\", it[i])\n \n elif unpaired == False:\n # Labelled setting\n (output_dir / \"y\").mkdir()\n it = MultiGeoTiffIterable(dir_path / \"x\", target_dir_path=dir_path / \"y\", block_size=block_size, max_nodata=max_nodata)\n \n for i in trange(len(it)):\n x, y = it[i]\n np.save(output_dir / \"x\" / f\"{i}.npy\", x)\n np.save(output_dir / \"y\" / f\"{i}.npy\", y)\n else:\n (output_dir / \"y\").mkdir()\n # Unpaired setting\n for folder in [\"x\", \"y\"]:\n print(folder)\n it = MultiGeoTiffIterable(dir_path / folder, block_size=block_size, max_nodata=max_nodata)\n for i in trange(len(it)):\n np.save(output_dir / folder / f\"{i}.npy\", it[i])",
"title": ""
},
{
"docid": "2917170ab8bf94ff59a9d26a45352ee8",
"score": "0.46903116",
"text": "def export_patches(self, output_dir: Path, data_root: Path = None) -> None:\n groups = self.df.groupby(\"setting\")\n for setting_idx, group in groups:\n s = self.settings[setting_idx]\n if data_root is not None:\n s.slide_path = data_root / s.slide_path\n self._export_patches_for_setting(\n group, output_dir, s.slide_path, s.level, s.patch_size, s.loader\n )",
"title": ""
},
{
"docid": "422a233c4be151469b6f4ff79b2de87b",
"score": "0.46814555",
"text": "def extract_all_patches(image, window_shape, stride, num_of_patches, rotations, output_path, im_name, rep_num, mode):\n non_doctored_windows = view_as_windows(image, window_shape, step=stride)\n non_doctored_patches = []\n for m in range(non_doctored_windows.shape[0]):\n for n in range(non_doctored_windows.shape[1]):\n non_doctored_patches += [non_doctored_windows[m][n][0]]\n # select random some patches, rotate and save them\n save_patches(non_doctored_patches, num_of_patches, mode, rotations, output_path, im_name, rep_num,\n patch_type='authentic')",
"title": ""
},
{
"docid": "f79358ea528e871b13a5e7be5b75de15",
"score": "0.46793705",
"text": "def process_images(imdir, extension='.jpg'):\n impaths = glob.glob(os.path.join(imdir, '*' + extension))\n im_patches = [extract_patches(ip) for ip in impaths]\n return np.concatenate(im_patches, 0)",
"title": ""
},
{
"docid": "ef87ce81caf7f71c51bc60742dcf1898",
"score": "0.46771538",
"text": "def do_extract_patches(self, layers, size=3, stride=1):\n results = []\n for l, f in layers:\n # Use a Theano helper function to extract \"neighbors\" of specific size, seems a bit slower than doing\n # it manually but much simpler!\n patches = theano.tensor.nnet.neighbours.images2neibs(f, (size, size), (stride, stride), mode='valid')\n # Make sure the patches are in the shape required to insert them into the model as another layer.\n patches = patches.reshape((-1, patches.shape[0] // f.shape[1], size, size)).dimshuffle((1, 0, 2, 3))\n # Calculate the magnitude that we'll use for normalization at runtime, then store...\n results.extend([patches] + self.compute_norms(T, l, patches))\n return results",
"title": ""
},
{
"docid": "89d0f0c36b714af649bf6bcd68550549",
"score": "0.4675163",
"text": "def mask_to_submission_lists(image_filenames):\n patch_size = 16\n labels = []\n for image_filename in image_filenames: \n im = mpimg.imread(image_filename)\n for j in range(0, im.shape[1], patch_size):\n for i in range(0, im.shape[0], patch_size):\n patch = im[i:i + patch_size, j:j + patch_size]\n label = patch_to_label(patch)\n labels.append(label)\n return labels",
"title": ""
},
{
"docid": "e10927f1b0db54772398e3fd7282ea00",
"score": "0.46710137",
"text": "def extract_patches(impath, step=2):\n img = imageio.imread(impath, as_gray=True).astype(np.float32)\n h, w = img.shape\n patches = []\n for i in range(0, h - 7, step):\n for j in range(0, w - 7, step):\n patch = np.reshape(img[i:i + 8, j:j + 8], (64,)) + np.random.rand(64)\n patches.append(patch)\n return np.array(patches)",
"title": ""
},
{
"docid": "104008eef9de7775fb342cedfe8ce573",
"score": "0.46545854",
"text": "def get_monthly_prism_ppt_data(year,month, plotPPTBounds = False):\n \n prism_dir = \"PRISM_ppt_stable_4kmM3_198101_202001_bil//\"\n \n if(month<10):\n prism_file_path = \"PRISM_ppt_stable_4kmM3_\"+str(year)+\"0\"+str(month)+\"_bil.bil\"\n else:\n prism_file_path = \"PRISM_ppt_stable_4kmM3_\"+str(year)+str(month)+\"_bil.bil\" \n \n ppt_data = read_prism_bil(join(root, prism_dir, prism_file_path))\n \n hdr_dict = read_prism_hdr(join(root, prism_dir, prism_file_path).replace('.bil', '.hdr'))\n \n hdr_dict[\"ULXMAP\"] = float(hdr_dict[\"ULXMAP\"])\n hdr_dict[\"ULYMAP\"] = float(hdr_dict[\"ULYMAP\"])\n hdr_dict['NROWS'] = int(hdr_dict['NROWS'])\n hdr_dict['NCOLS'] = int(hdr_dict['NCOLS'])\n hdr_dict['XDIM'] = float(hdr_dict['XDIM'])\n hdr_dict['YDIM'] = float(hdr_dict['YDIM'])\n \n p1 = (hdr_dict[\"ULXMAP\"] - (hdr_dict['XDIM']/2), \n hdr_dict[\"ULYMAP\"] + (hdr_dict['YDIM']/2))\n\n p2 = (hdr_dict[\"ULXMAP\"] + (hdr_dict['NCOLS']*hdr_dict['XDIM']),\n hdr_dict[\"ULYMAP\"] + (hdr_dict['XDIM']/2))\n\n p3 = (hdr_dict[\"ULXMAP\"] + (hdr_dict['NCOLS']*hdr_dict['XDIM']),\n hdr_dict[\"ULYMAP\"] - (hdr_dict['NROWS']*hdr_dict['YDIM']))\n\n p4 = (hdr_dict[\"ULXMAP\"] - (hdr_dict['XDIM']/2),\n hdr_dict[\"ULYMAP\"] - hdr_dict['NROWS']*hdr_dict['YDIM'])\n \n lon_point_list = (p1[0], p2[0], p3[0], p4[0])\n lat_point_list = (p1[1], p2[1], p3[1], p4[1])\n \n ppt_bounds = Polygon(zip(lon_point_list, lat_point_list))\n \n if(plotPPTBounds):\n crs = {'init': 'epsg:4326'}\n m = folium.Map(zoom_start=10, tiles='cartodbpositron')\n polygon = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[ppt_bounds]) \n \n folium.GeoJson(polygon).add_to(m)\n folium.LatLngPopup().add_to(m)\n m.save(\"mymap.html\")\n\n return ppt_bounds, ppt_data, hdr_dict",
"title": ""
},
{
"docid": "6a9b225e8e36eae6808bdd89309186dc",
"score": "0.46446556",
"text": "def get_patches_2(img_arr, size_h=None, stride_h=None, size_w=None, stride_w=None): \n # check size and stride\n if size_w % stride_w != 0:\n raise ValueError(\"size % stride must be equal 0\")\n\n if size_h % stride_h != 0:\n raise ValueError(\"size % stride must be equal 0\")\n\n patches_list = []\n overlapping_h = 0\n overlapping_w = 0\n\n if stride_h != size_h:\n overlapping_h = (size_h // stride_h) - 1\n\n if stride_w != size_w:\n overlapping_w = (size_w // stride_w) - 1\n\n if img_arr.ndim == 3:\n i_max = img_arr.shape[0] // stride_h - overlapping_h\n j_max = img_arr.shape[1] // stride_w - overlapping_w\n\n for i in range(i_max):\n for j in range(j_max):\n # print(i*stride_h, i*stride_h+size_h)\n # print(j*stride_w, j*stride_w+size_w)\n # img[starty:starty+cropy,startx:startx+cropx]\n patches_list.append(\n img_arr[\n i * stride_h: i * stride_h + size_h,\n j * stride_w : j * stride_w + size_w\n ]\n )\n\n elif img_arr.ndim == 4:\n i_max = img_arr.shape[1] // stride - overlapping\n for im in img_arr:\n for i in range(i_max):\n for j in range(i_max):\n # print(i*stride, i*stride+size)\n # print(j*stride, j*stride+size)\n patches_list.append(\n im[\n i * stride : i * stride + size,\n j * stride : j * stride + size,\n ]\n )\n\n else:\n raise ValueError(\"img_arr.ndim must be equal 3 or 4\")\n\n return np.stack(patches_list)",
"title": ""
},
{
"docid": "71af816a5a666ca6fc9523e606480f83",
"score": "0.463769",
"text": "def divide_into_patches(image: np.ndarray, number_of_patches_in_x: int,\n number_of_patches_in_y: int) -> List[Patch]:\n\n if not isinstance(number_of_patches_in_x, int) or not isinstance(\n number_of_patches_in_y, int):\n raise TypeError(\"number_of_patches_in_x and number_of_patches_in_y \"\n \"should be integers\")\n x_window_size, x_stride = divmod(image.shape[1], number_of_patches_in_x)\n y_window_size, y_stride = divmod(image.shape[0], number_of_patches_in_y)\n window_size = WindowSize(x_window_size + x_stride, y_window_size + y_stride)\n stride = Stride(x_window_size, y_window_size)\n patches = []\n for y_dim_patch_number in range(0, number_of_patches_in_y):\n for x_dim_patch_number in range(0, number_of_patches_in_x):\n left_border_x = int(0 + stride.x * x_dim_patch_number)\n right_border_x = int(window_size.x + stride.x * x_dim_patch_number)\n upper_border_y = int(0 + stride.y * y_dim_patch_number)\n lower_border_y = int(window_size.y + stride.y * y_dim_patch_number)\n patch = image[upper_border_y:lower_border_y,\n left_border_x:right_border_x]\n patch = Patch(patch)\n patches.append(patch)\n return patches",
"title": ""
},
{
"docid": "ea4a856f50a0b0c52ba5b3ef9b4c3559",
"score": "0.462963",
"text": "def get_sliced_images_path_pattern(self, imageid):",
"title": ""
},
{
"docid": "da38764b6850b49270e4bb52fc5ed99e",
"score": "0.46231073",
"text": "def convert_image_to_numpy(xls_no_polyp, xls_10_mm_polyp, xls_6_9_mm_polyp, folder, npy_dir):\n df_xls_no_polyp = pd.read_excel(xls_no_polyp)\n df_xls_10_mm_polyp = pd.read_excel(xls_10_mm_polyp)\n df_xls_6_9_mm_polyp = pd.read_excel(xls_6_9_mm_polyp)\n images_path = os.listdir(folder)\n tmp_np = []\n tmp_10_p = []\n tmp_6_9_p = []\n list_np = df_xls_no_polyp.iloc[:, 0].values.tolist()\n list_10_p = df_xls_10_mm_polyp.iloc[:, 0].values.tolist()\n list_6_9_p = df_xls_6_9_mm_polyp.iloc[:, 0].values.tolist()\n for l in images_path:\n if l in list_np:\n tmp_np.append(l)\n elif l in list_10_p:\n tmp_10_p.append(l)\n elif l in list_6_9_p:\n tmp_6_9_p.append(l)\n size_no_polyp, size_10_polyp, size_6_9_polyp = count_polyps_size(tmp_np, tmp_10_p, tmp_6_9_p, folder)\n print('size_10_polyp: ', size_10_polyp)\n print('size_no_polyp: ', size_no_polyp)\n print('size_6_9_polyp: ', size_6_9_polyp)\n on_convert_image_to_numpy(tmp_np, npy_dir, size_no_polyp, folder, 'no_polyp')\n print('finished converting no polyp')\n on_convert_image_to_numpy(tmp_10_p, npy_dir, size_10_polyp, folder, 'polyp_10')\n print('finished converting 10 mm polyp')\n on_convert_image_to_numpy(tmp_6_9_p, npy_dir, size_6_9_polyp, folder, 'polyp_6_9')",
"title": ""
},
{
"docid": "c23ee612577340aaeeaf16096a2409dd",
"score": "0.4618595",
"text": "def prepare_plots(self):\n MultiQubit_Spectroscopy_Analysis.prepare_plots(self)\n\n for qb_name in self.qb_names:\n\n # Copy the original plots in order to have both the analyzed and the\n # non-analyzed plots\n fig_id_original = f\"projected_plot_{qb_name}_Magnitude_{qb_name}_volt\"\n fig_id_analyzed = f\"ResonatorSpectroscopyFluxSweep_{fig_id_original}\"\n self.plot_dicts[fig_id_analyzed] = deepcopy(self.plot_dicts[\n f\"projected_plot_{qb_name}_Magnitude_Magnitude_{qb_name}_volt\"])\n\n # Change the fig_id of the copied plot in order to distinguish it\n # from the original\n self.plot_dicts[fig_id_analyzed]['fig_id'] = fig_id_analyzed\n\n # Plot the left dips\n self.plot_dicts[f\"{fig_id_analyzed}_left_dips\"] = {\n 'fig_id': fig_id_analyzed,\n 'plotfn': self.plot_line,\n 'xvals': self.analysis_data[qb_name]['left_dips_frequency'],\n 'yvals': self.analysis_data[qb_name]['volts'],\n 'marker': 'o',\n 'linestyle': 'none',\n 'color': 'C4',\n 'setlabel': 'Left dips',\n 'do_legend': True,\n 'legend_ncol': 2,\n 'legend_bbox_to_anchor': (1.2, -0.2),\n 'legend_pos': 'upper right'\n }\n\n # Plot the right dips\n self.plot_dicts[f\"{fig_id_analyzed}_right_dips\"] = {\n 'fig_id': fig_id_analyzed,\n 'plotfn': self.plot_line,\n 'xvals': self.analysis_data[qb_name]['right_dips_frequency'],\n 'yvals': self.analysis_data[qb_name]['volts'],\n 'marker': 'o',\n 'linestyle': 'none',\n 'color': 'C1',\n 'setlabel': 'Right dips',\n 'do_legend': True,\n 'legend_ncol': 2,\n 'legend_bbox_to_anchor': (1.2, -0.2),\n 'legend_pos': 'upper right'\n }\n\n # Plot the left LSS\n self.plot_dicts[f\"{fig_id_analyzed}_left_lss\"] = {\n 'fig_id': fig_id_analyzed,\n 'plotfn': self.plot_line,\n 'xvals': np.array([self.fit_res[qb_name]['left_lss_freq']]),\n 'yvals': np.array([\n self.fit_res[qb_name]['left_lss'],\n ]),\n 'marker': '<',\n 'linestyle': 'none',\n 'color': 'C4',\n 'line_kws': {\n 'ms': self.get_default_plot_params()['lines.markersize'] * 3\n },\n 'setlabel': 'Left LSS',\n 'do_legend': True,\n 'legend_ncol': 2,\n 'legend_bbox_to_anchor': (1.2, -0.2),\n 'legend_pos': 'upper right'\n }\n\n # Plot the left USS\n self.plot_dicts[f\"{fig_id_analyzed}_left_uss\"] = {\n 'fig_id': fig_id_analyzed,\n 'plotfn': self.plot_line,\n 'xvals': np.array([self.fit_res[qb_name]['left_uss_freq']]),\n 'yvals': np.array([self.fit_res[qb_name]['left_uss']]),\n 'marker': '>',\n 'linestyle': 'none',\n 'color': 'C4',\n 'line_kws': {\n 'ms': self.get_default_plot_params()['lines.markersize'] * 3\n },\n 'setlabel': 'Left USS',\n 'do_legend': True,\n 'legend_ncol': 2,\n 'legend_bbox_to_anchor': (1.2, -0.2),\n 'legend_pos': 'upper right'\n }\n\n # Plot the right LSS\n self.plot_dicts[f\"{fig_id_analyzed}_right_lss\"] = {\n 'fig_id': fig_id_analyzed,\n 'plotfn': self.plot_line,\n 'xvals': np.array([self.fit_res[qb_name]['right_lss_freq']]),\n 'yvals': np.array([self.fit_res[qb_name]['right_lss']]),\n 'marker': '<',\n 'linestyle': 'none',\n 'color': 'C1',\n 'line_kws': {\n 'ms': self.get_default_plot_params()['lines.markersize'] * 3\n },\n 'setlabel': 'Right LSS',\n 'do_legend': True,\n 'legend_ncol': 2,\n 'legend_bbox_to_anchor': (1.2, -0.2),\n 'legend_pos': 'upper right'\n }\n\n # Plot the right USS\n self.plot_dicts[f\"{fig_id_analyzed}_right_uss\"] = {\n 'fig_id': fig_id_analyzed,\n 'plotfn': self.plot_line,\n 'xvals': np.array([self.fit_res[qb_name]['right_uss_freq']]),\n 'yvals': np.array([self.fit_res[qb_name]['right_uss']]),\n 'marker': '>',\n 'linestyle': 'none',\n 'color': 'C1',\n 'line_kws': {\n 'ms': self.get_default_plot_params()['lines.markersize'] * 3\n },\n 'setlabel': 'Right USS',\n 'do_legend': True,\n 'legend_ncol': 2,\n 'legend_bbox_to_anchor': (1.2, -0.2),\n 'legend_pos': 'upper right'\n }\n\n # Mark the designated sweet spot\n self.plot_dicts[f\"{fig_id_analyzed}_designated_sweet_spot\"] = {\n 'fig_id': fig_id_analyzed,\n 'plotfn': self.plot_line,\n 'xvals': np.array(\n [self.fit_res[qb_name]\n [f'{qb_name}_sweet_spot_RO_frequency']]\n ),\n 'yvals': np.array(\n [self.fit_res[qb_name]\n [f'{qb_name}_sweet_spot']]\n ),\n 'marker': 'x',\n 'linestyle': 'none',\n 'color': 'C3',\n 'line_kws': {\n 'fillstyle': 'none',\n 'ms': self.get_default_plot_params()['lines.markersize'] * 3\n },\n 'setlabel': f'{qb_name} sweet spot',\n 'do_legend': True,\n 'legend_ncol': 2,\n 'legend_bbox_to_anchor': (1.2, -0.2),\n 'legend_pos': 'upper right'\n }\n\n # Plot textbox containing relevant information\n textstr = (\n f\"Left LSS = {self.fit_res[qb_name]['left_lss']:.3f} V\"\n f\"\\nLeft USS = {self.fit_res[qb_name]['left_uss']:.3f} V\"\n f\"\\nRight LSS = {self.fit_res[qb_name]['right_lss']:.3f} V\"\n f\"\\nRight USS = {self.fit_res[qb_name]['right_uss']:.3f} V\")\n\n self.plot_dicts[f'{fig_id_analyzed}_text_msg'] = {\n 'fig_id': fig_id_analyzed,\n 'ypos': -0.3,\n 'xpos': -0.2,\n 'horizontalalignment': 'left',\n 'verticalalignment': 'top',\n 'plotfn': self.plot_text,\n 'text_string': textstr\n }",
"title": ""
},
{
"docid": "7935e28c82804e4c14c1a8e59a7f6466",
"score": "0.46067646",
"text": "def extract_lab_and_boundary(file_name):\n t = get_time_from_filename(file_name)\n\n im = cv2.imread(file_name)\n im_lab = cv2.cvtColor(im, cv2.COLOR_BGR2LAB)\n im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n\n resized_y, resized_x = im_gray.shape\n slide_crop_coordinates = extraction_args['slide_coordinates']\n\n min_y = slide_crop_coordinates[0] * resized_y\n max_y = slide_crop_coordinates[1] * resized_y\n min_x = slide_crop_coordinates[2] * resized_x\n max_x = slide_crop_coordinates[3] * resized_x\n slide = im_gray[min_y: max_y, min_x: max_x]\n slidehist = cv2.calcHist([slide], [0], None, [256], [0, 256])\n\n # Plotting the slide in order to check the slide location once\n # if t < 2:\n # plt.subplot(2,1,1)\n # plt.imshow(slide, cmap=plt.cm.Greys_r)\n # plt.subplot(2,1,2)\n # plt.plot(slidehist)\n # plt.xlim([0,256])\n\n # plt.show()\n\n histogram_boundaries = get_histogram_min_max_with_percentile(slidehist, False)\n\n # return t, histogram_boundaries\n return t, im_lab, histogram_boundaries",
"title": ""
},
{
"docid": "d136340a1aafbb5342ea53fbeab0c51f",
"score": "0.4605846",
"text": "def allFigures(self):\n \n self.clean()\n self.saveData()\n self.figureImg(\"fig_01_img.png\")\n self.figureAvg(\"fig_02_avg.png\")\n self.figureDriftRAW(\"fig_03_drift1.png\")\n self.figureDriftDGOR(\"fig_04_drift2.png\")",
"title": ""
},
{
"docid": "170f903051ce383513e7b1cb8c2888a4",
"score": "0.4596534",
"text": "def read(root, start = 0):\n logger.debug(\"Read {0}\".format(root))\n patches = read_patches(open(find_patch_file(root)), start) \n projections = read_projections(\"{0}/txt/\".format(root), start)\n\n return patches, projections",
"title": ""
},
{
"docid": "6e98e44aed821a58291c464aca84a242",
"score": "0.45935205",
"text": "def load_mask_from_folder(self):\n try:\n foldername = QtWidgets.QFileDialog.getExistingDirectory(self, 'Select folder', './CoordinateManager/Images/')\n list_dir_raw = sorted(os.listdir(foldername))\n \n list_dir = [file for file in list_dir_raw if file[-3:] in ['png', 'jpg']]\n list_nr = len(list_dir)\n image_sequence = np.zeros([768, 1024, list_nr])\n \n for i in range(list_nr):\n single_mask =plt.imread(foldername + '/' + list_dir[i])\n # jpg has RGB channels\n single_mask_gray = rgb2gray(single_mask) \n check, valid_single_mask = self.check_mask_format_valid(single_mask_gray)\n if check:\n image_sequence[:,:,i] = valid_single_mask\n else: \n return\n \n self.DMD_actuator.send_data_to_DMD(image_sequence)\n \n self.load_mask_container_stack.setCurrentIndex(0)\n except:\n print(\"Fail to load.\")",
"title": ""
},
{
"docid": "b529759641b702d5a3230117447ddea8",
"score": "0.45907736",
"text": "def createMorph(selectedImages,selectedPMs):\n \n #save the temporary results here later:\n os_temp_path = tempfile.gettempdir()\n \n #get the measurements for the first image (our targets)\n mainImage = OriginalImage.objects.all().get(id=selectedImages[0])\n potentialids = [] \n #now get the associated measurements\n measures = Measurement.objects.all().filter(id__in=selectedPMs[0]).filter(mogelijkemeting__shapedefining=True)\n measures = [j for j in measures]\n measures.sort(key=lambda x: x.mogelijkemeting.name)\n \n coordsx = []\n coordsy = []\n for k, measurement in enumerate(measures):\n coordsx.append(float(measurement.x))\n coordsy.append(float(measurement.y))\n potentialids.append(measurement.mogelijkemeting.id)\n r1 = vtk.vtkJPEGReader()\n r1.SetFileName(settings.DATADIR + mainImage.id + \".jpg\")\n r1.Update() \n\n # flip y coord (VTK has opposite convention), create 3-d coords (z=0)\n ydim = r1.GetOutput().GetDimensions()[1]\n coords = [(x, ydim - y, 0) for (x,y) in zip(coordsx, coordsy)]\n \n # convert everything to vtkPoints\n lmt = vtk.vtkPoints()\n lmt.SetNumberOfPoints(len(coords))\n for i, coord in enumerate(coords):\n lmt.SetPoint(i,coord)\n \n #The target is clear, let's get to work, get the source images...\n images = []\n #we don't need the first image or its measures anymore, because they don't need to be transformed or morphed\n selectedImages.pop(0)\n selectedPMs.pop(0)\n for id in selectedImages:\n images.append(OriginalImage.objects.all().get(id=id))\n\n transformations = []\n morphtransformations = []\n \n #Create a new database object for the target image to associate the bitmaps with\n img = OriginalImage(project=mainImage.project, name='MorphedImage')\n img.save()\n imp = Image.open(settings.DATADIR + mainImage.id + '.jpg')\n imp.save(settings.DATADIR + img.id + '.jpg', 'JPEG') \n orig_bitmaps = Bitmap.objects.all().filter(image=mainImage)\n \n for bm in orig_bitmaps:\n #store bitmaps of mainImage as sub of img\n bitmap = Bitmap(project=img.project, name='warpedbitmap', image=img, \n mogelijkemeting=bm.mogelijkemeting, imagewidth=bm.imagewidth, \n imageheight=bm.imageheight, minx=bm.minx, miny=bm.miny, maxx=bm.maxx, maxy=bm.maxy)\n bitmap.save()\n \n bitmap_image = Image.open(settings.DATADIR + bm.id + '.gif')\n bitmap_image = bitmap_image.convert(\"RGBA\")\n bitmap_image.save(settings.DATADIR + bitmap.id + '.gif', transparency=0)\n \n #now get the other images and perform our transformations\n for i in range(len(images)):\n measures = Measurement.objects.all().filter(id__in=selectedPMs[i]).filter(mogelijkemeting__shapedefining=True)#get measurements\n measures = [j for j in measures]\n measures.sort(key=lambda x: x.mogelijkemeting.name)\n coordsx = []\n coordsy = [] \n for k, measurement in enumerate(measures):\n coordsx.append(float(measurement.x))\n coordsy.append(float(measurement.y))\n if potentialids[k] != measurement.mogelijkemeting.id: #the potentialmeasurements do not match up to the ones in the target image\n return img, 0\n r = vtk.vtkJPEGReader()\n r.SetFileName(settings.DATADIR + images[i].id + \".jpg\")\n r.Update()\n\n ydim = r.GetOutput().GetDimensions()[1]\n coordso = [(x, ydim - y, 0) for (x,y) in zip(coordsx, coordsy)]\n lms = vtk.vtkPoints()\n lms.SetNumberOfPoints(len(coordso))\n for k, coord in enumerate(coordso):\n lms.SetPoint(k,coord)\n\n transformation = vtk.vtkLandmarkTransform()\n transformation.SetTargetLandmarks(lmt) \n lmt.Modified()\n transformation.SetSourceLandmarks(lms)\n lms.Modified()\n #size matters, so set the mode to Rigid Body (also known as do not scale please)\n transformation.SetModeToRigidBody()\n transformation.Inverse()\n transformation.Update()\n out = vtk.vtkPoints()#this will be the source of our morph transform\n transformation.TransformPoints(lms,out)\n transformations.append(transformation)\n ir = vtk.vtkImageReslice()\n # we're not using linear, because we want to improve the quality of the bitmaps\n ir.SetInterpolationModeToNearestNeighbor()\n ir.SetResliceTransform(transformation)\n ir.SetInput(r.GetOutput())\n ir.SetInformationInput(r1.GetOutput())\n w = vtk.vtkJPEGWriter()\n w.SetFileName(os_temp_path+'/translated'+images[i].id+'.jpg')\n w.SetInput(ir.GetOutput())\n w.Write()\n r2 = vtk.vtkJPEGReader()\n r2.SetFileName(os_temp_path+'/translated'+images[i].id+'.jpg')\n r2.Update() \n \n # the mighty morphing ThinPlateSplineTransform\n morphtransform = vtk.vtkThinPlateSplineTransform()\n morphtransform.SetBasisToR2LogR()\n morphtransform.SetSourceLandmarks(lms)\n lms.Modified()\n morphtransform.SetTargetLandmarks(lmt)\n lmt.Modified()\n morphtransform.Inverse()\n morphtransform.Update()\n morphtransformations.append(morphtransform)\n\n #ir.SetInput(r2.GetOutput())\n #ir.SetInformationInput(r1.GetOutput())\n \n bitmaps = Bitmap.objects.all().filter(image=images[i])\n \n #now perform the total transformation on all bitmaps\n for bm in bitmaps:\n location = settings.DATADIR + bm.id + \".gif\"\n im = Image.open(location)\n im = im.convert(\"RGBA\")\n im.save(settings.DATADIR + bm.id + \".png\", \"PNG\")\n\n r3 = vtk.vtkPNGReader()\n r3.SetFileName(settings.DATADIR + bm.id + '.png')\n r3.Update()\n \n ir2 = vtk.vtkImageReslice()\n ir2.SetInterpolationModeToNearestNeighbor()\n ir2.SetResliceTransform(morphtransform)\n ir2.SetInput(r3.GetOutput())\n ir2.SetInformationInput(r2.GetOutput())\n \n w3 = vtk.vtkPNGWriter()\n w3.SetFileName(os_temp_path+'/morphed'+bm.id+'.png')\n w3.SetInput(ir2.GetOutput())\n w3.Write()\n \n bitmap = Bitmap(project=img.project, name='warpedbitmap', image=img, \n mogelijkemeting=bm.mogelijkemeting, imagewidth=bm.imagewidth, \n imageheight=bm.imageheight, minx=bm.minx, miny=bm.miny, maxx=bm.maxx, maxy=bm.maxy)\n bitmap.save()\n \n im = Image.open(os_temp_path+'/morphed'+bm.id+'.png')\n im = im.convert(\"RGBA\")\n im.save(settings.DATADIR + bitmap.id + '.gif', transparency=0)\n \n\n return img, 1",
"title": ""
},
{
"docid": "4654a734d1161324a4743ca5cddeda78",
"score": "0.45854816",
"text": "def _extract_pmids(pth):\n pmid_set_tmp = set()\n with open(pth.path, 'r', encoding='latin1') as f:\n rp_tmp = []\n for line in f:\n prefix, rest = _split2prefix_and_rest(line)\n if prefix == 'RP':\n rp_tmp.append(rest)\n elif prefix == 'RC':\n pass\n elif prefix == 'RX':\n if _is_small_scale_reference(rp_tmp):\n match = _search4pmid(rest)\n if match:\n pmid = _extract_pmid_from_match(match)\n pmid_set_tmp.add(pmid)\n else:\n app.logger.warn('Ignored LARGE SCALE ref: {}'.format(rest))\n app.logger.info('RP tokens for above reference: {}'.format(rp_tmp))\n else:\n rp_tmp = []\n known_pmids, new_pmids = _compare_pmid_sets(pmid_set_tmp)\n _log_known_pmids(known_pmids, pth)\n reference_model_list = _compile_reference_models(new_pmids, known_pmids, pth)\n return reference_model_list",
"title": ""
},
{
"docid": "d618a72c129ad3ecbb4a65d0cff8c00e",
"score": "0.45845008",
"text": "def slides(id):\n if request.method == \"POST\":\n save_song(id, request)\n song = Song.query.get(id)\n song = clean_lyrics(song)\n arrangement = clean_arrangement(song.default_arrangement)\n return render_template(\n \"slides_single_song.html.j2\", song=song, arrangement=arrangement, id=id\n )",
"title": ""
},
{
"docid": "8c2024d5ab7b17eb1bd166b4dca7310a",
"score": "0.45838833",
"text": "def extract_patches_from_image(data_path, label_name, patch_size, nb_samples):\n data = sio.loadmat(data_path)\n image = data['scan'].squeeze()\n\n label = data[label_name].squeeze()\n assert image.shape == label.shape\n\n image = per_image_standardization(image)\n image = image_padded(image, patch_size)\n\n coordinates = sampler(label, nb_samples)\n assert coordinates.shape[1] == nb_samples\n print('Number of voxels selected: ', coordinates.shape)\n\n patches_picked = []\n label_picked = label[coordinates[0, :], coordinates[1, :], coordinates[2, :]]\n\n for i in range(nb_samples):\n patches_picked.append(extract_orthogonal_patches(image, coordinates[:, i], patch_size))\n\n output_patches = np.array(patches_picked)\n\n return output_patches, label_picked",
"title": ""
},
{
"docid": "26f0822488b36d8261e64d04ca34f511",
"score": "0.45828974",
"text": "def buildRMDSSheets(self):\r\n l_fMDout = listdir(self.pr_MDout)\r\n\r\n # load ChEMBL table\r\n ldchem = toolbox.matrixToList(self.p_dataset)\r\n for dchem in ldchem:\r\n dfile = {}\r\n dfile[\"prot RMSD\"] = \"\"\r\n dfile[\"lig RMSD\"] = \"\"\r\n dfile[\"RMSF residue\"] = \"\"\r\n\r\n ChEMBL_id = dchem[\"CMPD_CHEMBLID\"]\r\n typeAff = dchem[\"STANDARD_TYPE\"]\r\n # extract RMSD files\r\n for fMDout in l_fMDout:\r\n ChEMBL_id_folder = fMDout.split(\"_\")[0]\r\n if ChEMBL_id == ChEMBL_id_folder:\r\n \r\n \r\n # protein\r\n p_RMSD_prot = self.pr_MDout + fMDout + \"/RMSDs/protein/protRMSD\"\r\n if path.exists(p_RMSD_prot):\r\n dfile[\"prot RMSD\"] = p_RMSD_prot\r\n else:\r\n print \"Error in \" + fMDout + \": prot RMSD missing\"\r\n self.computeRMSDProt(self.pr_MDout + fMDout + \"/\")\r\n\r\n # lig need to define here\r\n p_RMSD_lig = self.pr_MDout + fMDout + \"/RMSDs/ligand/ligRMSD\"\r\n if path.exists(p_RMSD_lig):\r\n dfile[\"lig RMSD\"] = p_RMSD_lig\r\n else:\r\n print \"Error in \" + fMDout + \": lig RMSD missing\"\r\n p_RMSD_lig = self.computeRMSDLig(self.pr_MDout + fMDout + \"/\")\r\n dfile[\"lig RMSD\"] = p_RMSD_lig\r\n\r\n \r\n # RMSF with the binding site represented\r\n p_RMSF_res = self.pr_MDout + fMDout + \"/RMSDs/residues/resRMSD_BS\"\r\n if path.exists(p_RMSF_res):\r\n dfile[\"RMSF residue\"] = p_RMSF_res\r\n else:\r\n print \"Error in \" + fMDout + \": lig RMSF BS missing\"\r\n p_RMSF_res = self.computeRMSFresBS(self.pr_MDout + fMDout + \"/\")\r\n dfile[\"lig RMSD\"] = p_RMSF_res\r\n \r\n # build figure 3 panels\r\n runExternalSoft.RMSD3panels(dfile[\"prot RMSD\"], dfile[\"lig RMSD\"], dfile[\"RMSF residue\"], ChEMBL_id, pathFolder.createFolder(self.pr_out + typeAff + \"/\"))",
"title": ""
},
{
"docid": "8944d0d9305159d36c0d523c7a89e5ca",
"score": "0.45788488",
"text": "def test_mask_based_patch_extractor_ndpi(\n sample_ndpi: Path,\n caplog: pytest.LogCaptureFixture,\n) -> None:\n res = 0\n patch_size = stride = (400, 400)\n input_img = Path(sample_ndpi)\n wsi = OpenSlideWSIReader(input_img=input_img)\n slide_dimensions = wsi.info.slide_dimensions\n\n # Generating a test mask to read patches from\n mask_dim = (int(slide_dimensions[0] / 10), int(slide_dimensions[1] / 10))\n wsi_mask = np.zeros(mask_dim[::-1], dtype=np.uint8) # reverse as dims are (w, h)\n # masking two column to extract patch from\n wsi_mask[:, :2] = 255\n\n # patch extraction based on the column mask\n patches = patchextraction.get_patch_extractor(\n input_img=input_img,\n input_mask=wsi_mask,\n method_name=\"slidingwindow\",\n patch_size=patch_size,\n resolution=res,\n units=\"level\",\n stride=None,\n )\n\n # read the patch from the second row (y) in the first column\n patch = wsi.read_rect(\n location=(0, int(patch_size[1])),\n size=patch_size,\n resolution=res,\n units=\"level\",\n )\n\n # because we are using column mask to extract patches, we can expect\n # that the patches[1] is the from the second row (y) in the first column.\n assert np.all(patches[1] == patch)\n assert patches[0].shape == (patch_size[0], patch_size[1], 3)\n\n # Test None option for mask\n _ = patchextraction.get_patch_extractor(\n input_img=input_img,\n input_mask=None,\n method_name=\"slidingwindow\",\n patch_size=patch_size,\n resolution=res,\n units=\"level\",\n stride=stride[0],\n )\n\n # Test passing a VirtualWSI for mask\n mask_wsi = VirtualWSIReader(wsi_mask, info=wsi._m_info, mode=\"bool\")\n _ = patchextraction.get_patch_extractor(\n input_img=wsi,\n input_mask=mask_wsi,\n method_name=\"slidingwindow\",\n patch_size=patch_size,\n resolution=res,\n units=\"level\",\n stride=None,\n )\n\n # Test `otsu` option for mask\n _ = patchextraction.get_patch_extractor(\n input_img=input_img,\n input_mask=\"otsu\",\n method_name=\"slidingwindow\",\n patch_size=patch_size[0],\n resolution=res,\n units=\"level\",\n stride=stride,\n )\n\n _ = patchextraction.get_patch_extractor(\n input_img=wsi_mask, # a numpy array to build VirtualSlideReader\n input_mask=\"morphological\",\n method_name=\"slidingwindow\",\n patch_size=patch_size,\n resolution=res,\n units=\"level\",\n stride=stride,\n )\n\n # Test passing an empty mask\n wsi_mask = np.zeros(mask_dim, dtype=np.uint8)\n\n _ = patchextraction.get_patch_extractor(\n input_img=input_img,\n input_mask=wsi_mask,\n method_name=\"slidingwindow\",\n patch_size=patch_size,\n resolution=res,\n units=\"level\",\n stride=stride,\n )\n assert \"No candidate coordinates left\" in caplog.text",
"title": ""
},
{
"docid": "f16f1ab71e00978256a637153f4f2b86",
"score": "0.4578261",
"text": "def to_tex_tikzpicture(self, output_dir=''):\n slides_file = os.path.join(output_dir, 'slides.tex')\n slides_output = []\n line_file_format = os.path.join(output_dir, 'line-{}{:02d}.tex')\n page_count = 0\n\n sections = self.merge_melody_lyrics()\n self.group_underlines(sections)\n\n for i, (tag, lines) in enumerate(sections):\n # new section\n line_count = 0\n for j, (nodes, bars, ties, slurs, underlines_list, triplets) in enumerate(lines):\n # new page\n if line_count % 2 == 0:\n page_count += 1\n if page_count > 1:\n slides_output.append('\\n')\n slides_output.append('%%%%% PAGE {} %%%%%'.format(page_count))\n slides_output.append(r'\\newpage')\n slides_output.append('')\n if j == 0: # first page in section\n slides_output.append('% <{}>'.format(tag))\n slides_output.append(r'\\begin{nmntag}')\n slides_output.append(r'\\textmd{$<$\\hspace{-0pt}' + tag + r'\\hspace{-0pt}$>$}')\n slides_output.append(r'\\end{nmntag}')\n else:\n slides_output.append(r'\\begin{nmnblank}')\n slides_output.append(r'\\end{nmnblank}')\n\n # new line\n line_lyrics = ''\n line_output = []\n line_output.append(r'\\begin{tikzpicture}')\n line_output.append(r\"\"\"\\tikzstyle{every node}=[inner sep=0pt]\n\\tikzstyle{dot}=[circle,fill=white,inner sep=0pt,text width=1.5pt]\n\\tikzstyle{lyrics}=[node distance=15pt]\n\\tikzstyle{tie}=[line width=0.5pt,bend left=45,min distance=4pt,max distance=5pt]\n\\tikzstyle{underline}=[line width=0.5pt]\n\\tikzstyle{tie0}=[line width=0.5pt,out=50,in=180,max distance=20pt]\n\\tikzstyle{tie1}=[line width=0.5pt,out=130,in=0,max distance=20pt]\"\"\")\n line_output.append('\\n\\n% nodes')\n line_output.append(r'\\node at (0pt, 12pt) {}; % for space adjustment')\n\n pos = 0\n first_text_idx = None\n for k, (time, start_beat, idx_list) in enumerate(bars):\n # new bar\n if k > 0:\n pos -= 2.5\n line_output.append(r'\\node at ({}pt,0) {{|}};'.format(pos))\n pos += 7.5\n for idx in idx_list:\n node = nodes[idx]\n note = node.value\n line_output.append('')\n if node.type != NodeType.NOTE:\n pos -= 2.5\n if node.type == NodeType.DASH:\n line_output.append(r'\\node at ({}pt,-1pt) {{-}};'.format(pos))\n elif node.type == NodeType.DOT:\n line_output.append(r'\\node[dot] at ({}pt,0) {{}};'.format(pos))\n pos += 7.5\n continue\n # name\n line_output.append(r'\\node (a{}) at ({}pt,0) {{{}}};'.format(idx, pos, note.name))\n # acc\n acc_dict = {-1: 'flat', 0: 'natural', 1: 'sharp'}\n if note.acc is not None:\n line_output.append(r'\\node at ($(a{}.north west)+(-1pt,0)$){{\\tiny$\\{}$}};'\n .format(idx, acc_dict[note.acc]))\n # octave\n if note.octave > 0:\n line_output.append(r'\\node[dot,above of=a{},node distance=6pt] {{}};'.format(idx))\n elif note.octave < 0:\n node_distance = 7\n if node.lines <= -3:\n node_distance = 10\n elif node.lines == -2:\n node_distance = 9\n elif node.lines == -1:\n node_distance = 8\n line_output.append(r'\\node[dot,below of=a{},node distance={}pt] {{}};'\n .format(idx, node_distance))\n # text\n height = -17\n if node.text:\n if node.text in '每悔':\n height += 1\n text = '{0}{1}{0}'.format('\\phantom{|}', node.text)\n if first_text_idx is None:\n first_text_idx = idx\n line_output.append(r'\\node[lyrics] (t{0}) at ($(a{0})+(0,{2}pt)$) {{{1}}};'\n .format(idx, text, height))\n line_lyrics += node.text\n elif first_text_idx is None:\n text = r'\\phantom{{{}}}'.format('天')\n line_output.append(r'\\node[lyrics] (t{0}) at ($(a{0})+(0,{2}pt)$) {{{1}}};'\n .format(idx, text, height))\n first_text_idx = idx\n pos += 10\n\n # ties\n line_output.append('\\n\\n% ties')\n for idx0, idx1 in ties:\n dis = 2\n if nodes[idx0].value.octave >= 1:\n dis = 5\n line_output.append(r'\\draw[tie] (a{}.north) ++(0,{}pt) coordinate (tmp) to (a{}.north |- tmp);'\n .format(idx0, dis, idx1))\n\n # underlines\n line_output.append('\\n\\n% underlines')\n for depth, underlines in enumerate(underlines_list):\n if depth == 0:\n continue\n for idx0, idx1 in underlines:\n line_output.append(r'\\draw[underline] (a{}.south west) ++(0,-{}pt)'.format(idx0, depth * 1.5)\n + r' coordinate (tmp) to (a{}.south east |- tmp);'.format(idx1))\n\n # triplets\n line_output.append('\\n\\n% triplets')\n for triplet in triplets:\n dis0, dis1 = 2, 9\n if nodes[triplet[0]].value.octave >= 1 or nodes[triplet[2]].value.octave >= 1:\n dis0 = 5\n if nodes[triplet[1]].value.octave >= 1:\n dis1 = 12\n line_output.append(r'\\node[above of=a{},node distance={}pt] (tri) {{\\tiny{{3}}}};'\n .format(triplet[1], dis1))\n line_output.append(r'\\draw[tie0] (a{}.north) +(0,{}pt) to ($(tri.west)+(-1pt,0)$);'\n .format(triplet[0], dis0))\n line_output.append(r'\\draw[tie1] (a{}.north) +(0,{}pt) to ($(tri.east)+(+1pt,0)$);'\n .format(triplet[2], dis0))\n\n line_output.append('')\n line_output.append(r'\\end{tikzpicture}')\n line_output.append('')\n assert line_output[0] == r'\\begin{tikzpicture}' and pos > 0\n line_output[0] = line_output[0] + '[xscale={}]'.format(110 / pos)\n\n line_file = line_file_format.format(chr(ord('a') + i), j)\n with open(line_file, 'w', encoding='utf-8') as f:\n f.write('\\n'.join(line_output))\n line_count += 1\n\n slides_output.append('\\n% {}'.format(line_lyrics))\n slides_output.append(r'\\begin{nmnline}')\n slides_output.append(r'\\input{{{}}}'.format(line_file.split('/')[-1]))\n slides_output.append(r'\\end{nmnline}')\n\n with open(slides_file, 'w', encoding='utf8') as f:\n f.write('\\n'.join(slides_output))",
"title": ""
},
{
"docid": "618008af2e4ed922f39a820a91f16f8f",
"score": "0.4574483",
"text": "def generate_powerpoint_pptx(self):\n # Generate the JSON for the report\n report_json = json.loads(self.generate_json())\n # Create document writer using the specified template\n if self.template_loc:\n try:\n self.spenny_ppt = Presentation(self.template_loc)\n except Exception:\n # TODO: Return error on webpage\n pass\n else:\n # TODO: Return error on webpage\n pass\n self.ppt_color_info = pptx.dml.color.RGBColor(\n self.informational_color_hex[0],\n self.informational_color_hex[1],\n self.informational_color_hex[2])\n self.ppt_color_low = pptx.dml.color.RGBColor(\n self.low_color_hex[0],\n self.low_color_hex[1],\n self.low_color_hex[2])\n self.ppt_color_medium = pptx.dml.color.RGBColor(\n self.medium_color_hex[0],\n self.medium_color_hex[1],\n self.medium_color_hex[2])\n self.ppt_color_high = pptx.dml.color.RGBColor(\n self.high_color_hex[0],\n self.high_color_hex[1],\n self.high_color_hex[2])\n self.ppt_color_critical = pptx.dml.color.RGBColor(\n self.critical_color_hex[0],\n self.critical_color_hex[1],\n self.critical_color_hex[2])\n # Loop through the dict of findings to create slides based on findings\n # Initialize findings stats dict\n findings_stats = {\n 'Critical': 0,\n 'High': 0,\n 'Medium': 0,\n 'Low': 0,\n 'Informational': 0\n }\n # Calculate finding stats\n for finding in report_json['findings'].values():\n findings_stats[finding['severity']] += 1\n # Slide styles (From Master Style counting top to bottom from 0..n)\n SLD_LAYOUT_TITLE = 0\n SLD_LAYOUT_TITLE_AND_CONTENT = 1\n SLD_LAYOUT_FINAL = 12\n # Add title slide\n slide_layout = self.spenny_ppt.slide_layouts[SLD_LAYOUT_TITLE]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n title_shape.text = 'Ghostwriter'\n text_frame = body_shape.text_frame\n # Use text_frame.text for first line/paragraph or\n # text_frame.paragraphs[0]\n text_frame.text = report_json['client']['full_name']\n p = text_frame.add_paragraph()\n p.text = report_json['client']['full_name']\n # Add Agenda slide\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n title_shape.text = 'Agenda'\n body_shape = shapes.placeholders[1]\n text_frame = body_shape.text_frame\n # Add Introduction slide\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n title_shape.text = 'Introduction'\n body_shape = shapes.placeholders[1]\n text_frame = body_shape.text_frame\n # Add Methodology slide\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n title_shape.text = 'Methodology'\n body_shape = shapes.placeholders[1]\n text_frame = body_shape.text_frame\n # Add Attack Path Overview slide\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n title_shape.text = 'Attack Path Overview'\n body_shape = shapes.placeholders[1]\n text_frame = body_shape.text_frame\n # Add Findings Overview Slide\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n title_shape.text = 'Findings Overview'\n text_frame = body_shape.text_frame\n for stat in findings_stats:\n p = text_frame.add_paragraph()\n p.text = '{} Findings'.format(stat)\n p.level = 0\n p = text_frame.add_paragraph()\n p.text = str(findings_stats[stat])\n p.level = 1\n # Add Findings Overview Slide 2\n # If there are findings then write a table of findings and\n # severity ratings\n if len(report_json['findings']) > 0:\n # Delete the default text placeholder\n textbox = shapes[1]\n sp = textbox.element\n sp.getparent().remove(sp)\n # Add a table\n rows = len(report_json['findings']) + 1\n columns = 2\n left = Inches(1.5)\n top = Inches(2)\n width = Inches(8)\n height = Inches(0.8)\n table = shapes.add_table(\n rows,\n columns,\n left,\n top,\n width,\n height).table\n # Set column width\n table.columns[0].width = Inches(9.0)\n table.columns[1].width = Inches(1.5)\n # Write table headers\n cell = table.cell(0, 0)\n cell.text = 'Finding'\n cell.fill.solid()\n cell.fill.fore_color.rgb = pptx.dml.color.\\\n RGBColor(0x2D, 0x28, 0x69)\n cell = table.cell(0, 1)\n cell.text = 'Severity'\n cell.fill.solid()\n cell.fill.fore_color.rgb = pptx.dml.color.\\\n RGBColor(0x2D, 0x28, 0x69)\n # Write findings rows\n row_iter = 1\n for finding in report_json['findings'].values():\n table.cell(row_iter, 0).text = finding['title']\n risk_cell = table.cell(row_iter, 1)\n # Set risk rating\n risk_cell.text = finding['severity']\n # Set cell color fill type to solid\n risk_cell.fill.solid()\n # Color the risk cell based on corresponding severity color\n if finding['severity'].lower() == \"informational\":\n risk_cell.fill.fore_color.rgb = self.ppt_color_info\n elif finding['severity'].lower() == \"low\":\n risk_cell.fill.fore_color.rgb = self.ppt_color_low\n elif finding['severity'].lower() == \"medium\":\n risk_cell.fill.fore_color.rgb = self.ppt_color_medium\n elif finding['severity'].lower() == \"high\":\n risk_cell.fill.fore_color.rgb = self.ppt_color_high\n elif finding['severity'].lower() == \"critical\":\n risk_cell.fill.fore_color.rgb = self.ppt_color_critical\n # Set cell's font color to white for better contrast with\n # dark background\n paragraph = risk_cell.text_frame.paragraphs[0]\n paragraph.font.color.rgb = pptx.dml.color.\\\n RGBColor(0xFF, 0xFF, 0xFF)\n row_iter += 1\n # Set all cells alignment to center and vertical center\n for cell in table.iter_cells():\n cell.text_frame.paragraphs[0].alignment = PP_ALIGN.CENTER\n cell.vertical_anchor = MSO_ANCHOR.MIDDLE\n else:\n p = text_frame.add_paragraph()\n p.text = 'No findings'\n p.level = 0\n # Create slide for each finding\n for finding in report_json['findings'].values():\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n title_shape.text = \"{} [{}]\".format(\n finding['title'],\n finding['severity'])\n text_frame = body_shape.text_frame\n text_frame.text = '{}'.format(finding['description'])\n bullets = finding['description'].splitlines()\n first_bullet = True\n for bullet in bullets:\n if first_bullet:\n text_frame.text = bullet\n first_bullet = False\n else:\n p = text_frame.add_paragraph()\n p.text = bullet\n p.level = 0\n # Add some detailed notes\n notes_slide = slide.notes_slide\n text_frame = notes_slide.notes_text_frame\n p = text_frame.add_paragraph()\n p.text = '{}: {}\\n'.format(\n finding['severity'].capitalize(),\n finding['title'])\n # Add observations slide\n # Observation 1\n # Bullet detail\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n title_shape.text = 'Positive Observations'\n text_frame = body_shape.text_frame\n # Add recommendations slide\n # Recommendation 1\n # Bullet detail\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n title_shape.text = \"Recommendations\"\n text_frame = body_shape.text_frame\n # Add Conclusion slide\n slide_layout = self.spenny_ppt.slide_layouts[\n SLD_LAYOUT_TITLE_AND_CONTENT]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n title_shape.text = 'Control Observations'\n text_frame = body_shape.text_frame\n # Add final slide\n slide_layout = self.spenny_ppt.slide_layouts[SLD_LAYOUT_FINAL]\n slide = self.spenny_ppt.slides.add_slide(slide_layout)\n shapes = slide.shapes\n # title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n text_frame = body_shape.text_frame\n text_frame.clear()\n p = text_frame.paragraphs[0]\n p.line_spacing = 0.7\n p.text = settings.COMPANY_NAME\n p = text_frame.add_paragraph()\n p.text = settings.COMPANY_TWITTER\n p.line_spacing = 0.7\n p = text_frame.add_paragraph()\n p.text = settings.COMPANY_EMAIL\n p.line_spacing = 0.7\n # Finalize document and return it for an HTTP response\n return self.spenny_ppt",
"title": ""
},
{
"docid": "edaee3289d9accda749851e2c910a401",
"score": "0.45582038",
"text": "def prepare_patches(self, calcs):\n # Make the full list of catalogs to run\n cats = set()\n\n # Use shear-shear and pos-pos only here as they represent\n # catalogs not pairs.\n for i, j, k in calcs:\n if k == SHEAR_SHEAR:\n cats.add((i, SHEAR_SHEAR))\n cats.add((j, SHEAR_SHEAR))\n elif k == SHEAR_POS:\n cats.add((i, SHEAR_SHEAR))\n cats.add((j, POS_POS))\n elif k == POS_POS:\n cats.add((i, POS_POS))\n cats.add((j, POS_POS))\n cats = list(cats)\n cats.sort(key=str)\n\n # This does a round-robin assignment to processes\n for (h, k) in self.split_tasks_by_rank(cats):\n\n print(f\"Rank {self.rank} making patches for {k}-type bin {h}\")\n\n # For shear we just have the one catalog. For position we may\n # have randoms also. We explicitly delete catalogs after loading\n # them to ensure we don't have two in memory at once.\n if k == SHEAR_SHEAR:\n cat = self.get_shear_catalog(h)\n cat.get_patches(low_mem=False)\n del cat\n else:\n cat = self.get_lens_catalog(h)\n cat.get_patches(low_mem=False)\n del cat\n ran_cat = self.get_random_catalog(h)\n\n # support use_randoms = False\n if ran_cat is None:\n continue\n\n ran_cat.get_patches(low_mem=False)\n del ran_cat\n\n # stop other processes progressing to the rest of the code and\n # trying to load things we have not written yet\n if self.comm is not None:\n self.comm.Barrier()",
"title": ""
},
{
"docid": "3d7d3a0f2fd92b5860bad018536d4af5",
"score": "0.45545024",
"text": "def multipage(filename, figs=None):\n\n pp = PdfPages(filename)\n if figs is None:\n figs = [plt.figure(n) for n in plt.get_fignums()]\n for fig in figs:\n fig.savefig(pp, format='pdf')\n pp.close()",
"title": ""
},
{
"docid": "35da644fb248999e8243ee74698348e6",
"score": "0.45540062",
"text": "def generate_md_files(self):\n for m in self.MODULES:\n rdf_figure_path = self.LINK_BASE_PATH + m.EXAMPLE_RDF[\"figure-file-path\"]\n rdf_file = self.BASE_PATH + m.EXAMPLE_RDF[\"file-path\"]\n rdf_txt = self.get_file_content(rdf_file)\n\n shex_figure_path = self.LINK_BASE_PATH + m.SHEX[\"figure-file-path\"]\n shex_file = self.BASE_PATH + m.SHEX[\"file-path\"]\n shex_txt = self.get_file_content(shex_file)\n\n markup_files = {}\n # create module markup\n with open(m.TEMPLATE_FILE, 'r') as f:\n md_text = chevron.render(f,\n {'text': m.EXAMPLE_RDF[\"text\"], 'semantic-model-figure-path': rdf_figure_path,\n 'example-rdf': rdf_txt, 'shex-figure-path': shex_figure_path,\n 'shex': shex_txt, 'title': m.NAME})\n print(md_text)\n output_file = self.OUTPUT_DIR + m.MD_FILE_NAME\n markup_files[output_file] = md_text\n\n for file_path, content in markup_files.items():\n file = open(file_path, \"w\")\n file.write(content)\n file.close()",
"title": ""
},
{
"docid": "9a8570e3a6ad962d16342db96f3296db",
"score": "0.45475274",
"text": "def loadStoredPictures(self):\n\t\timport StringIO\n\t\tif self.debug : logging.debug(\"Suche WMA-Bilder... %s\" % (self.filename))\n\t\tdiaMode = self.guiPlayer.slide_mode\n\t\tdatei = None\n\t\tif diaMode > 1:\n\t\t\tfor image in self.audio.get(\"WM/Picture\", []):\n\t\t\t\t(mime, data, type) = self.unpack_image(image.value)\n\t\t\t\tif not type == 3:\n\t\t\t\t\tif (diaMode == 3 or diaMode == 5) or (type > 3 and type < 7) :\n\t\t\t\t\t\tif self.debug : logging.debug('Bild gefunden. Typ {0}'.format(type) )\n\t\t\t\t\t\tself.setMiscPic(pygame.image.load(self.getTempPic(data)))\n\n\t\treturn",
"title": ""
},
{
"docid": "4913482ad2753acbbaf9cdc73fb3d2cb",
"score": "0.4545979",
"text": "def get_subimgs(self):\n\n subimgs = []\n for slide, bboxes_sl in zip(self.slides, self.bboxes):\n bb_arr = np.array([i[0] for i in bboxes_sl])\n lbl_arr = np.array([i[1] for i in bboxes_sl])\n for bbox in tqdm(bboxes_sl):\n subimg = self.get_random_crop_around(slide, bbox[0])\n other_bboxes, other_labels = self.get_all_landed_bboxes(subimg, bb_arr, lbl_arr)\n subimg = slide.get_patch(*subimg[0:2])\n subimg = tf.convert_to_tensor(subimg, dtype=tf.float32)\n other_bboxes = tf.convert_to_tensor(other_bboxes, dtype=tf.float32)\n other_labels = tf.convert_to_tensor(other_labels, dtype=tf.int32)\n subimgs.append({'image': subimg, 'objects': {'bbox': other_bboxes / 255, 'label': other_labels}})\n return subimgs",
"title": ""
},
{
"docid": "a833134c153c605a244dbc7e19b92b26",
"score": "0.45430592",
"text": "def get_presentation_elements_from_path(self):\n self.presentation_elements = []\n Logger.debug(\"Presentation: Creating the presentation elements from folder \" + self.media_path)\n for filename in self.presentation_filenames:\n extension = filename.split(\".\")[-1]\n relative_filename = self.media_path + \"/\" + filename\n Logger.debug(\"Relative filename: %s\", relative_filename)\n\n if extension in self.VIDEO_FORMATS:\n self.presentation_elements.append(PresentationElement(ContentType.Video, relative_filename))\n elif extension in self.IMAGE_FORMATS:\n self.presentation_elements.append(PresentationElement(ContentType.Image, relative_filename))\n elif extension in self.TEXT_FORMATS:\n self.presentation_elements.append(PresentationElement(ContentType.Text, relative_filename))\n else:\n Logger.error(\"Presentation: Unsupported filetype \\\"%s\\\" \", extension)",
"title": ""
},
{
"docid": "05b3b5410c5627f1afa77ffb41b9067a",
"score": "0.453223",
"text": "def chunker(self, pts, mod):\n\n outarrays = []\n newgrp = []\n for i,p in enumerate(pts):\n if i%mod==0:\n newgrp.append(p)\n if len(newgrp)>1:\n outarrays.append(newgrp)\n newgrp=[] \n else:\n newgrp.append(p)\n \n return outarrays",
"title": ""
},
{
"docid": "58f0318069750eff0e22f316aeef6770",
"score": "0.4531553",
"text": "def extract_test_patches(self, scan, db, pp, volumes, ind_part):\n \"\"\"\n Arguments:\n scan: selected scan\n db: DatabaseBRATS object\n pp: PreprocessorBRATS object\n volumes: scan volumes\n ind_part: list of voxel indices at which patches will be\n extracted\n Returns:\n extracted test patches\n \"\"\"\n raise NotImplementedError()",
"title": ""
},
{
"docid": "a1f638a16d6017e015e60ad226a8a328",
"score": "0.45289516",
"text": "def get_xpm_files():\n xpm_files = []\n filedir = get_image_directory()\n all_files = os.listdir(filedir)\n for filename in all_files:\n filepath = filedir + filename\n unused, ext = os.path.splitext(filepath)\n if ext == \".xpm\":\n xpm_files.append(filepath)\n xpm_files.sort()\n return xpm_files",
"title": ""
},
{
"docid": "655de99a45638703cb1fdd38a4718d3b",
"score": "0.45269948",
"text": "def get_PCPI_files(FRED_files, FIPS_L):\n Files_FIPS_L = [(fileI[0], fileI[0][-9:-4]) for fileI in FRED_files if re.match('PCPI\\d{5}',fileI[0].split('.')[0][-9:]) and fileI[0][-9:-4] in FIPS_L]\n return Files_FIPS_L",
"title": ""
},
{
"docid": "5620f6daa038a30fc76e47c7e4502136",
"score": "0.45142993",
"text": "def batchPNGtoGeotiff(folder):\n for image in glob.glob(folder + '/*.png'):\n base = os.path.splitext(image)[0]\n kml = base + '.kml'\n out = base + '.tif'\n pngToGeotiff(image, kml, out)",
"title": ""
},
{
"docid": "93c732887c9689224a4dae8398728bed",
"score": "0.45113245",
"text": "def _get_mpp_tiffslide(\n slide_path: str | Path,\n) -> tuple[float, float]:\n if not HAS_TIFFSLIDE:\n logger.critical(\n \"Cannot read MPP with TiffSlide because TiffSlide is not available\"\n )\n raise CannotReadSpacing()\n\n slide = tiffslide.TiffSlide(slide_path)\n mppx: float | None = None\n mppy: float | None = None\n if (\n tiffslide.PROPERTY_NAME_MPP_X in slide.properties\n and tiffslide.PROPERTY_NAME_MPP_Y in slide.properties\n ):\n mppx = slide.properties[tiffslide.PROPERTY_NAME_MPP_X]\n mppy = slide.properties[tiffslide.PROPERTY_NAME_MPP_Y]\n if mppx is None or mppy is None:\n raise CannotReadSpacing()\n else:\n try:\n mppx = float(mppx)\n mppy = float(mppy)\n return mppx, mppy\n except Exception as err:\n raise CannotReadSpacing() from err\n raise CannotReadSpacing()",
"title": ""
},
{
"docid": "c86fa07a78be8897902ec97bb63d7916",
"score": "0.4510664",
"text": "def main():\n figure_dir = os.path.dirname(plts.__file__)\n pngs = glob.glob(os.path.join(figure_dir, '*.png'))\n for png in pngs:\n print('copying {} to {}/'.format(os.path.basename(png), _this_dir))\n shutil.copy(png, _this_dir)",
"title": ""
},
{
"docid": "372bb7d0d7ea15ab00a33831406f3b72",
"score": "0.4496847",
"text": "def expertPaths (self, slist):\n\n # slicelist gets modified, slist doesn't\n slicelist = copy.copy(slist)\n template = self.getTemplate()\n\n # Use the name_in_file attribute to access files\n if hasattr(self, 'name_in_file'):\n realid = self.name_in_file\n else:\n realid = self.id\n\n # Handle rank-0 variables separately\n if self.rank() == 0:\n matchnames = [realid,None,None,None,None,None,None]\n filename = self.getFilePath(matchnames, template)\n\n result = (0, (), (filename, []))\n return result\n\n # Find the number of partitioned axes\n npart = 0\n ndim = 0\n for (axis,start,length,true_length) in self.domain:\n if hasattr(axis,'partition'):\n npart = npart+1\n if npart==1:\n part1 = axis\n npart1 = ndim\n elif npart==2:\n part2 = axis\n npart2 = ndim\n else:\n raise CDMSError, TooManyPartitions + variable.id\n ndim = ndim+1\n\n # If no partitioned axes, just read the data\n if npart==0:\n matchnames = [realid,None,None,None,None,None,None]\n filename = self.getFilePath(matchnames, template)\n result = (0, (), (filename, slicelist))\n\n # If one partitioned axes:\n elif npart==1:\n\n # intersect the slice and partition for that axis\n slice1 = slicelist[npart1]\n (axis,startelem,length,true_length) = self.domain[npart1]\n partition = slicePartition(slice1, self.getPartition(axis))\n if partition==[]:\n return (1, (npart1,), None)\n\n # For each (interval, partslice) in the partition:\n resultlist = []\n (firstinterval, firstslice) = partition[0]\n prevhigh = firstinterval[0]\n for (interval,partslice) in partition:\n\n # If the previous interval high is less than\n # the current interval low value, interpose\n # missing data.\n low = interval[0]\n if prevhigh<low:\n missing_interval = (prevhigh,low)\n missing_slice = sliceIntersect(slice1, missing_interval)\n\n # Note: if the slice has a stride>1, it might not intersect,\n # so don't interpose missing data in this case.\n if missing_slice is not None:\n slicelist[npart1] = missing_slice\n resultlist.append((None,copy.copy(slicelist)))\n prevhigh = interval[1]\n\n # generate the filename\n matchnames = [realid, None, None, None, None,None,None]\n matchnames = self.genMatch(axis, interval, matchnames)\n filename = self.getFilePath(matchnames, template)\n\n # adjust the partslice for the interval offset\n # and replace in the slice list\n filestart = partslice.start-interval[0]\n filestop = partslice.stop-interval[0]\n fileslice = slice(filestart,filestop,partslice.step)\n slicelist[npart1] = fileslice\n\n resultlist.append((filename,copy.copy(slicelist)))\n\n result = (1,(npart1,),resultlist)\n\n # If two partitioned axes, 2-D version of previous case\n if npart==2:\n slice1 = slicelist[npart1]\n slice2 = slicelist[npart2]\n (axis1,startelem1,length1,true_length1) = self.domain[npart1]\n (axis2,startelem2,length2,true_length2) = self.domain[npart2]\n partition1 = slicePartition(slice1, self.getPartition(axis1))\n partition2 = slicePartition(slice2, self.getPartition(axis2))\n if partition1==[] or partition2==[]:\n return (2, (npart1,npart2), None)\n\n # For each (interval, partslice) in the partition:\n resultlist = []\n (firstinterval1, firstslice1) = partition1[0]\n prevhigh1 = firstinterval1[0]\n for (interval1,partslice1) in partition1:\n\n # If the previous interval high is less than\n # the current interval low value, interpose\n # missing data.\n low = interval1[0]\n if prevhigh1<low:\n missing_interval = (prevhigh1,low)\n missing_slice = sliceIntersect(slice1, missing_interval)\n if missing_slice is not None:\n slicelist[npart1] = missing_slice\n resultlist.append( [(None,copy.copy(slicelist))] )\n prevhigh1 = interval1[1]\n\n # generate matchnames\n matchnames = [realid, None, None, None, None,None,None]\n matchnames = self.genMatch(axis1, interval1, matchnames)\n\n # adjust the partslice for the interval offset\n # and replace in the slice list\n filestart = partslice1.start-interval1[0]\n filestop = partslice1.stop-interval1[0]\n fileslice = slice(filestart,filestop,partslice1.step)\n slicelist[npart1] = fileslice\n\n chunklist = []\n (firstinterval2, firstslice2) = partition2[0]\n prevhigh2 = firstinterval2[0]\n for (interval2,partslice2) in partition2:\n\n # If the previous interval high is less than\n # the current interval low value, interpose\n # missing data.\n low = interval2[0]\n if prevhigh2<low:\n missing_interval = (prevhigh2,low)\n missing_slice = sliceIntersect(slice1, missing_interval)\n if missing_slice is not None:\n slicelist[npart2] = missing_slice\n chunklist.append((None,copy.copy(slicelist)))\n prevhigh2 = interval2[1]\n\n # generate the filename\n matchnames = self.genMatch(axis2, interval2, matchnames)\n filename = self.getFilePath(matchnames, template)\n\n filestart = partslice2.start-interval2[0]\n filestop = partslice2.stop-interval2[0]\n fileslice = slice(filestart,filestop,partslice2.step)\n slicelist[npart2] = fileslice\n\n chunklist.append((filename,copy.copy(slicelist)))\n\n resultlist.append(chunklist)\n\n result = (2,(npart1,npart2),resultlist)\n\n return result",
"title": ""
},
{
"docid": "da9c01bb418ced7adaa6bdaf3a03e81f",
"score": "0.44958374",
"text": "def get_gallery():\n\n # Examples path and required number of files\n web_path = \"/static/examples/\"\n examples_path = \"./web/static/examples\"\n result = []\n\n # Get all directories from path\n directories = os.listdir(examples_path)\n\n for _dir in directories:\n files = os.listdir(examples_path + \"/\" + _dir)\n chosen_files = sample(files, 2)\n\n result.append([{\n \"web\": web_path + _dir + \"/\" + fl,\n \"server\": examples_path + \"/\" + _dir + \"/\" + fl\n } for fl in chosen_files])\n\n return result",
"title": ""
},
{
"docid": "3b816b56981692cf6f9a1d59c01c4164",
"score": "0.4492419",
"text": "def _export_patches_for_setting(\n self,\n frame: pd.DataFrame,\n output_dir: Path,\n slide_path: Path,\n level: int,\n patch_size: int,\n loader: Loader\n ):\n def get_output_dir_for_label(label: str) -> Path:\n label_str = invert(loader.labels)[label]\n label_dir = Path(output_dir) / label_str\n return label_dir\n\n def make_patch_path(x: int, y: int, label: int) -> Path:\n filename = f\"{Path(slide_path).stem}-{x}-{y}-{level}-{patch_size}.png\"\n label_dir = get_output_dir_for_label(label)\n label_dir.mkdir(parents=True, exist_ok=True)\n return label_dir / filename\n\n def save_patch(region: Region, slide: SlideBase, filepath: Path) -> None:\n image = slide.read_region(region)\n opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)\n cv2.imwrite(str(filepath), np.array(opencv_image))\n\n with loader.load_slide(slide_path) as slide:\n for row in frame.itertuples():\n filepath = make_patch_path(row.x, row.y, row.label)\n region = Region.make(row.x, row.y, patch_size, level)\n save_patch(region, slide, filepath)",
"title": ""
},
{
"docid": "85eb9c99203fac2db0e8dc64bfa1435a",
"score": "0.4491039",
"text": "def _get_recal_plots(work_dir, align_bam):\n (base, _) = os.path.splitext(align_bam)\n reports = glob.glob(os.path.join(work_dir, \"reports\", \"images\",\n \"%s*-plot.pdf\" % base))\n reports.sort()\n return reports",
"title": ""
},
{
"docid": "be5d7648f11e083f11973e7a9f59d2bc",
"score": "0.44812915",
"text": "def load_data(data_dir):\n images = []\n labels = []\n\n for root, dirs, files in os.walk(data_dir):\n for file in files:\n if not file.endswith(\".ppm\"):\n continue\n img = cv2.imread(os.path.join(root, file), 1)\n img = cv2.resize(img, dsize=(30, 30))\n images.append(img / 255.)\n labels.append(os.path.basename(root))\n return images, labels",
"title": ""
}
]
Inline query handler for the bot. Returns the appropriate shows as a result.
[
{
"docid": "4389659bef73bc81b8f1323de6e6796e",
"score": "0.7259084",
"text": "def inline_query_handler(update, context):\n query_text = update.inline_query.query\n query_id = update.inline_query.id\n\n if not query_text:\n query_text = \"all\"\n\n logging.info(\"Query %s\", query_text)\n query_text = query_text.lower()\n query_args = query_text.split()\n\n keyword_arg = query_args[0]\n quality_arg = query_args[1] if len(query_args) > 1 else None\n input_message_content_arg = query_args[2] if len(query_args) > 2 else None\n answer = []\n\n for show in SHOW_CONFIG:\n if keyword_arg in show[\"keywords\"]:\n if quality_arg:\n try:\n quality = quality_arg\n feed = show[\"quality\"][quality]\n except KeyError:\n continue\n else:\n quality = show[\"default_quality\"]\n feed = show[\"quality\"][quality]\n\n if input_message_content_arg is not None:\n input_message_content = input_message_content_arg.lower() in [\n \"true\",\n \"1\",\n \"yes\",\n ]\n else:\n input_message_content = show[\"input_message_content\"]\n\n if quality == \"yt\":\n answer += get_newest_episode_from_yt_feed(feed)\n else:\n answer += get_newest_episode_from_podcast_feed(\n feed, input_message_content\n )\n\n if len(answer) > 0:\n logging.info(list(map(lambda x: x.video_url, answer)))\n context.bot.answer_inline_query(query_id, answer, cache_time=CACHE_TIME)",
"title": ""
}
]
[
{
"docid": "828ae1b9f8c8e39f5f7957021b31a780",
"score": "0.7084049",
"text": "async def on_inline_query(self, msg):\n\n async def compute_answer():\n \"\"\"\n Function generating the answer for the handler.\n :return: Lyrics as articles\n \"\"\"\n print(msg)\n return get_lyrics_as_inline_keyboard(msg['query'])\n\n self.answerer.answer(msg, compute_answer)",
"title": ""
},
{
"docid": "af631a08e7e8c40a69cc84d16a16dff8",
"score": "0.68604994",
"text": "def on_inline_query(self, msg):\n def compute():\n logger.debug(\"Computing inline\")\n queryId, fromId, queryString, offset = telepot.glance(msg, 'inline_query', True)\n offset = int(offset) if offset else 0\n command, msgText = input_parser.parseInlineCommand(queryString.lower())\n\n answer = request_manager.processInline(command, msgText, fromId, offset)\n resultList = []\n for inlineAnswer in answer.answerList:\n resultList.append(dict(type=\"article\", title=inlineAnswer.title, id=inlineAnswer.id_, input_message_content=dict(message_text=inlineAnswer.formattedAnswer, parse_mode=\"HTML\"), thumb_url=inlineAnswer.thumbUrl))\n return {\"results\": resultList, \"cache_time\": answer.cacheTime, \"is_personal\": answer.isPersonal, \"next_offset\": answer.nextOffset}\n \n self._answerer.answer(msg, compute)",
"title": ""
},
{
"docid": "4f96afd68aa99b952ed34e2547670ca3",
"score": "0.6734146",
"text": "def inlinequery(update: Update, context: CallbackContext) -> None:\n query = update.inline_query.query\n\n results = [\n InlineQueryResultArticle(\n id=str(uuid4()),\n title=\"Bot API response\",\n description=\"@responseJSONbot\",\n input_message_content=InputTextMessageContent(f\"{update}\")),\n InlineQueryResultArticle(\n id=str(uuid4()),\n title=\"About\",\n description=\"@responseJSONbot\",\n url=\"https://t.me/theostrich\",\n input_message_content=InputTextMessageContent(f\"{update}\"),\n ),\n ]\n\n update.inline_query.answer(results)",
"title": ""
},
{
"docid": "5336d6eabcf2ad7d1e70261c7a6fe426",
"score": "0.6485323",
"text": "def handle_inline(inline_query):\n if not nekowat.is_allowed(inline_query.from_user.id):\n nekowat.answer_inline_query(inline_query.id, [])\n return\n\n # Normalize expression\n expression = inline_query.query.lower().strip()\n\n if not expression:\n # Get all images\n wats = nekowat.get_all_wats()\n\n else:\n # Get by expression\n wats = nekowat.get_wats_by_expression(expression)\n\n try:\n responses = []\n\n for index, wat in enumerate(wats):\n r = telebot.types.InlineQueryResultCachedPhoto(\n str(index),\n # Get smallest file for inline reply\n wat['file_ids'][0],\n parse_mode='' # Workaround for Telegram API error\n )\n\n responses.append(r)\n\n nekowat.answer_inline_query(inline_query.id, responses)\n\n except Exception as e:\n print(e)",
"title": ""
},
{
"docid": "f52de476d06867031d83db5d700b8298",
"score": "0.6398506",
"text": "def inlinequery(update, context):\n user = update.inline_query.from_user['username']\n query = ''.join(c for c in update.inline_query.query if c.isalnum() or c is ' ')\n if query.strip() != '':\n logger.info(f\"User @{user} searched \\\"{query}\\\"\")\n query_results = bt.ask_mw_thesaurus(query)\n update.inline_query.answer(query_results)",
"title": ""
},
{
"docid": "fbd09c26f02a8000ccb05d045af8bda1",
"score": "0.6204197",
"text": "def query(self):\n # gets the query from the user or parameters\n h, r, t = self.capture_query()\n\n # get ranks for the query\n h_ranked, r_ranked, t_ranked = self.get_ranks(h, r, t)\n\n # output to terminal if query was made non-programatically\n self.show_title(h, r, t)\n self.show_results(h_ranked, \"( _, r, t )\")\n self.show_results(r_ranked, \"( h, _, t )\", 5)\n self.show_results(t_ranked, \"( h, r, _ )\")\n\n # retruns ranks for programatically evokeed queries\n return h_ranked, r_ranked, t_ranked",
"title": ""
},
{
"docid": "352c57be08173ae4df56de3b833f5106",
"score": "0.60998756",
"text": "def query():\n\n\t# Getting the query text\n\tuser_text = request.form.get(\"description\")\n\n\n\tdb = MySQLdb.connect(\"mysql-server\", \"root\", \"secret\", \"mydb\")\n\tcursor = db.cursor()\n\n\n\t# Default for before a user enters their own query \n\tif user_text is None:\n\t\tuser_query = \"SELECT ncid FROM voters LIMIT 1\"\n\t\tuser_answer = cursor.execute(user_query)\n\n\n\t# Running the query \n\telse:\n\t\tuser_answer = cursor.execute(user_text)\n\t\t\n\t\n\tuser_answer_table = cursor.fetchall()\t\n\n\treturn render_template('query.html', query_result=user_answer_table)",
"title": ""
},
{
"docid": "21eda1c8ed93759ea4b0bc14afb1949e",
"score": "0.6069543",
"text": "def query():\n return render_template('query_page.html')",
"title": ""
},
{
"docid": "45d5e6bb68a8b7405c430aa2706999a1",
"score": "0.5980653",
"text": "async def compute_answer():\n print(msg)\n return get_lyrics_as_inline_keyboard(msg['query'])",
"title": ""
},
{
"docid": "830dda012b68f01b73e4e56cd6b75608",
"score": "0.5975844",
"text": "def answer(self, query):\n pass",
"title": ""
},
{
"docid": "f1f6667dba2d437cadcbe0220e9d8a97",
"score": "0.5960328",
"text": "def query(self):\n # Clear scrolled text\n self.query_result.delete(1.0, END)\n\n # Check textboxes are not none\n assert self.user_id_txt.get() != None\n assert self.query_limit_txt.get() != None\n\n # Get user id and query limit from textboxes\n user_id = int(self.user_id_txt.get())\n query_limit = int(self.query_limit_txt.get())\n\n # Make query to recommender system.\n recommendation = self.system.query(user_id, query_limit)\n\n # Add header\n recommendation = \"Recommendations for user with id: {}\\n************************************\\n\\n\".format(user_id) + recommendation\n\n # Put text on ScrolledText area\n self.query_result.insert(INSERT, recommendation)",
"title": ""
},
{
"docid": "dec9e986ab9a389da997ed46d5aa6424",
"score": "0.59457755",
"text": "def query(self):\n pass",
"title": ""
},
{
"docid": "eae085f793541d9cb4eda76d41fdd8d4",
"score": "0.5884021",
"text": "def query(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "eae085f793541d9cb4eda76d41fdd8d4",
"score": "0.5884021",
"text": "def query(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "eae085f793541d9cb4eda76d41fdd8d4",
"score": "0.5884021",
"text": "def query(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "cd94726044815da2b4cf03f1bc2d2696",
"score": "0.58514106",
"text": "def echo(update, context):\n query = update.message.text\n update.message.reply_text(\"I got query : \" + query)",
"title": ""
},
{
"docid": "760ef48781cab03fd0e877db0fa94e96",
"score": "0.58022505",
"text": "async def show_result(self, ctx: Context):\n self.embed = discord.Embed()\n string = None\n\n if self.__result.status == AmadeusPromptStatus.INPUT_GIVEN:\n string = await s.get_string(ctx, \"amadeusPromptStatus\", \"INPUT_GIVEN\")\n elif self.__result.status == AmadeusPromptStatus.TIMEOUT:\n string = await s.get_string(ctx, \"amadeusPromptStatus\", \"TIMEOUT\")\n elif self.__result.status == AmadeusPromptStatus.CANCELLED:\n string = await s.get_string(ctx, \"amadeusPromptStatus\", \"CANCELLED\")\n elif self.__result.status == AmadeusPromptStatus.SHOWN:\n string = await s.get_string(ctx, \"amadeusPromptStatus\", \"SHOWN\")\n elif self.__result.status == AmadeusPromptStatus.NEW:\n string = await s.get_string(ctx, \"amadeusPromptStatus\", \"NEW\")\n if string is not None:\n self.embed.title = string.string\n await self.__prepare_footer(ctx)\n self.__result.message = await self.__result.message.edit(embed=self.embed)",
"title": ""
},
{
"docid": "5bd427cb18b729d125b8f33b57b4212d",
"score": "0.579944",
"text": "def query(self, *args, **kwargs):\n with result_manager(200, self._ResourcePB()) as result:\n self.__query(result, *args, **kwargs)\n return result",
"title": ""
},
{
"docid": "840e3cabfa7f44b4785e1d3ab9f8c2dd",
"score": "0.5762502",
"text": "def query(self, widget):\r\n query_str = self._frames['table'].get_entry_text()\r\n self._controller.query(query_str)",
"title": ""
},
{
"docid": "90ec86570207f4c34eaaf30a8fa4f26d",
"score": "0.56842554",
"text": "def __call__(self, query=None):\n\n console = Console()\n\n topics = list(self.topics.keys())\n if query:\n results = self.topicembed.search(query, 10)\n else:\n results = [(x, 1.0) for x in range(10)]\n\n for uid, score in results:\n if score >= 0.1:\n topic = topics[uid]\n console.print(f\"[bright_green]{topic}[/bright_green]\")\n\n # Print example question\n query = f\"select id, question from txtai where similar('{topic}')\"\n result = self.embeddings.search(query, 1)[0]\n console.print(f\"{result['question']} ({result['id']})\\n\")",
"title": ""
},
{
"docid": "46b374e6d870639218135517235746bd",
"score": "0.56692916",
"text": "def _query(self, *args, **kwargs):",
"title": ""
},
{
"docid": "a4b875875a548d52f2ed9b26a3a0f975",
"score": "0.5660484",
"text": "def show_result(self, *args, **kwargs):\n pass",
"title": ""
},
{
"docid": "e65c5fb6c649f66c26ddc17c3a394a1a",
"score": "0.5652235",
"text": "def query(self, **kwargs):\n result = self._get(path='/do/query', params=kwargs)\n return result.get('result')",
"title": ""
},
{
"docid": "17d628801d237a1ab94310a8050973cc",
"score": "0.56499946",
"text": "def query(self):\n return self.details[KEY_QUERY]",
"title": ""
},
{
"docid": "cb57fff35d066667534bfaaba7c96944",
"score": "0.56292784",
"text": "def query(self,query):\n # create request body\n req_body = {\n \"query\":query\n }\n # create request\n req = Request(\n method = \"post\",\n endpoint='/streamapps/query/{}'.format(self.name),\n data=json.dumps(req_body)\n )\n # create response handler\n def response_handler(resp):\n if resp.is_success is True:\n return resp.body\n print(resp.body)\n return False\n # call the api\n return self._execute(req,response_handler)",
"title": ""
},
{
"docid": "2c4367d919750741099bc25df0dba7e1",
"score": "0.561507",
"text": "def display_query(project, version, name):\n return query_service.get_versioned_query_as_html(project, version, name)",
"title": ""
},
{
"docid": "8dffa5ee30dccac5128edcdff46251a5",
"score": "0.5611329",
"text": "def execute(self):\n return SIAResults(self.execute_votable(), url=self.queryurl, session=self._session)",
"title": ""
},
{
"docid": "4c68b792c3244bfd1149a61c30396a27",
"score": "0.5603586",
"text": "def show_results(qry):\n\n result = runQuery(qry)\n resLen = len(result)\n\n # From this point, we need to make everything look pretty for the screen.\n print('<results>')\n print(' Imagine a pretty nifty list of devices here with links and all that.<br/> Here is what we know based on our current database:<br/><br/>')\n if DEBUGGING:\n print('Query as received: {}\\n'.format(qry))\n print('Result Length: {}\\n'.format(resLen))\n if resLen > 0:\n print('<table><tr><th>Brand</th><th>Model</th><th>Form</th><th>Digital<br/>Modes</th><th>MSRP</th><th>Vendor</th></tr>')\n for rig in result:\n model, brand, form, mode, msrp, vlink = rig[0], rig[1], rig[2], rig[3], rig[4], rig[5]\n print(f\"<tr><td> {brand} </td><td> {model} </td><td> {form} </td><td> {mode} </td><td> ${msrp} </td><td> <a href='{vlink}'>Link</a> </td></tr>\")\n print('</table>')\n else:\n print('<br/>There were no devices matching the criteria selected.')\n print('</results>')",
"title": ""
},
{
"docid": "2c1ca06021ca8d303157265c776be5e2",
"score": "0.5593014",
"text": "def query(self, query):\n pass",
"title": ""
},
{
"docid": "619f655bc8ae7eb5d09db804deca07a2",
"score": "0.559074",
"text": "def execute(self):\n return SSAResults(self.execute_votable(), self.getqueryurl())",
"title": ""
},
{
"docid": "c99fe403dcc8298c1c06e058128763dd",
"score": "0.5555807",
"text": "def query(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "ca7d1851b556d85bce09b4e763a22c9e",
"score": "0.55321777",
"text": "def query(self,name):\n return self._canonical[\"queries\"][name]",
"title": ""
},
{
"docid": "f5d3f6e37565f250fb26276e27ad1489",
"score": "0.5527054",
"text": "def execute(self):\n return DalResults(self.execute_votable(), self.getqueryurl())",
"title": ""
},
{
"docid": "7b32397366baf7a9b8a4da6ce2f8dd5d",
"score": "0.5520517",
"text": "def query(env):\n if not app.config['ENABLE_QUERY']:\n log.warning('Access to query interface disabled by administrator.')\n abort(403)\n\n envs = environments()\n if env != app.config['DEFAULT_ENVIRONMENT']:\n check_env(env, envs)\n\n form = QueryForm(meta={\n 'csrf_secret': app.config['SECRET_KEY'],\n 'csrf_context': session}\n )\n\n if form.validate_on_submit():\n if form.endpoints.data not in ENABLED_QUERY_ENDPOINTS:\n log.warning('Access to query endpoint %s disabled by administrator.',\n form.endpoints.data)\n abort(403)\n\n query = form.query.data.strip()\n\n # automatically wrap AST queries with [], if needed\n if form.endpoints.data != 'pql' and not query.startswith('['):\n query = f\"[{query}]\"\n\n try:\n result = get_or_abort_except_client_errors(\n puppetdb._query,\n form.endpoints.data,\n query=query)\n\n zero_results = (len(result) == 0)\n result = result if not zero_results else None\n\n if form.rawjson.data:\n # for JSON view pass the response from PuppetDB as-is\n return render_template('query.html',\n form=form,\n zero_results=zero_results,\n result=result,\n columns=None,\n envs=envs,\n current_env=env)\n else:\n # for table view separate the columns and the rows\n rows = []\n if not zero_results:\n columns = result[0].keys()\n for items in result:\n rows.append(list(items.values()))\n else:\n columns = []\n\n return render_template('query.html',\n form=form,\n zero_results=zero_results,\n result=rows,\n columns=columns,\n envs=envs,\n current_env=env)\n\n except HTTPError as e:\n error_text = e.response.text\n return render_template('query.html',\n form=form,\n error_text=error_text,\n envs=envs,\n current_env=env)\n\n return render_template('query.html',\n form=form,\n envs=envs,\n current_env=env)",
"title": ""
},
{
"docid": "ba08aa1fcb16f0ebfd549bbbdb59bec7",
"score": "0.54864115",
"text": "def answerInlineQuery(\n self,\n inline_query_id: str,\n results: List[InlineQueryResult],\n cache_time: int = None,\n is_personal: bool = None,\n next_offset: str = None,\n switch_pm_text: str = None,\n switch_pm_parameter: str = None,\n ):\n kwargs = {k:v for k,v in locals().items() if k!='self' and v!=None}\n return self(\"answerInlineQuery\", kwargs)",
"title": ""
},
{
"docid": "072ea70fb6d1fa1171354dbae434904e",
"score": "0.5484513",
"text": "def query(self) -> str:\n\n if len(self._raw) == 1: # string query\n return self._raw[0]\n\n # normal\n query_1, op, query_2 = self._raw\n\n return op.format(\n query_1.query,\n query_2.query if isinstance(query_2, Query) else query_1._callback(query_2),\n )",
"title": ""
},
{
"docid": "285223688f08c60eef80defa013d16c7",
"score": "0.5469835",
"text": "def query(self,*args, **kwargs):\n\n return self.database.query(*args, **kwargs)",
"title": ""
},
{
"docid": "72b0a2e8ef2dc87b780b0870d272aa00",
"score": "0.5461389",
"text": "def query(self, **options):\n\n return self._get(category='analysis', resource='query', subcategory='variant', **options)",
"title": ""
},
{
"docid": "128842ea9e6116aafff11fa93a23b635",
"score": "0.5443271",
"text": "def query(request, *args,**kwargs):\n l = livestatus(request)\n return l.query(*args, **kwargs)",
"title": ""
},
{
"docid": "5fbdc1c2f82cf790e0375704e7cf3fc1",
"score": "0.54014784",
"text": "def query(self,command):\n cmd=\"Q\"+command\n s=self.__query_string(cmd)\n return s",
"title": ""
},
{
"docid": "7fd4be8558155a55f7c6be2c18f82835",
"score": "0.5395507",
"text": "def show_hike_result():\n \n hike_id = request.args.get(\"hikeId\")\n result = HikeResult.query.filter((HikeResult.hike_id == hike_id) &\n (HikeResult.canceled_by_user == False)).first()\n if result:\n trail = Hike.query.filter_by(hike_id=hike_id).first()\n trail_id = trail.trail_id\n trail_details=Trail.query.filter_by(trail_id=trail_id).first()\n ascent_from_enum = str(result.ascent_rating).split('.')[1]\n ascent_from_enum = ascent_from_enum.replace(\"_\", \" \")\n distance_from_enum = str(result.distance_rating).split('.')[1]\n distance_from_enum = distance_from_enum.replace(\"_\", \" \")\n challenge_from_enum = str(result.challenge_rating).split('.')[1]\n challenge_from_enum = challenge_from_enum.replace(\"_\", \" \")\n result_details = {'name': trail_details.trail_name,\n 'summary': trail_details.description,\n 'difficulty': trail_details.difficulty,\n 'loc': trail_details.location,\n 'lat': trail_details.latitude,\n 'lng': trail_details.longitude,\n 'len': trail_details.distance_in_miles,\n 'asc': trail_details.total_ascent,\n 'dsc': trail_details.total_descent,\n 'date': trail_details.status_at,\n 'status': trail_details.status,\n 'details': trail_details.status_details,\n 'assessment': result.assessment,\n 'distance': result.distance_in_miles,\n 'hikedOn': result.hiked_on.strftime(\"%A %B %d, %Y\"),\n 'ascentRating': ascent_from_enum.lower(),\n 'distanceRating': distance_from_enum.lower(),\n 'challengeRating': challenge_from_enum.lower(),\n 'hikeTime': result.hike_time}\n return jsonify(result_details)\n else:\n return 'Add Hike Result'",
"title": ""
},
{
"docid": "f6e9b6b218585ae6671408ac5969262e",
"score": "0.53820723",
"text": "def echo(update, context):\r\n reply_markup = None\r\n text = update.message.text\r\n total = len(monster_names(text))\r\n if total == 0:\r\n message = '查無符合名稱的魔物！'\r\n elif total == 1:\r\n message = render_info(text, 'fuzzy')\r\n elif total <= 10:\r\n reply_markup = InlineKeyboardMarkup([\r\n [InlineKeyboardButton('模糊搜尋', callback_data = 'render {} fuzzy 0'.format(text))],\r\n [InlineKeyboardButton('精準搜尋', callback_data = 'query {} precise 0'.format(text))]\r\n ])\r\n message = '符合結果共 {}筆，請選擇搜尋方式：'.format(total)\r\n else:\r\n reply_markup = InlineKeyboardMarkup([[InlineKeyboardButton('精準搜尋', callback_data = 'query {} precise 0'.format(text))]])\r\n message = '符合筆數過多（{}筆），只支援精準搜索：'.format(total)\r\n update.message.reply_text(message, reply_markup = reply_markup)",
"title": ""
},
{
"docid": "e987cea1b2db337b0284c45269b6c83e",
"score": "0.5381155",
"text": "def show_query_results(query, format):\n print(\"\\n Results of:\" + query)\n\n for i in base.execute(query).fetchall() :\n print(format % i )\n print(\"-*\"*40)",
"title": ""
},
{
"docid": "777b09e83a27fdd302c2937c0ef5f1f2",
"score": "0.5365776",
"text": "def _query_button_fired(self): \n\n # debug print\n logging.debug(\"QUERY button pressed\")\n self.lastlog_string = \"QUERY button pressed\"\n\n # get required prefixes\n prefixes = \"\"\n if self.query_rdf_prefix:\n prefixes += RDF_PREFIX\n if self.query_rdfs_prefix:\n prefixes += RDFS_PREFIX\n if self.query_owl_prefix:\n prefixes += OWL_PREFIX\n if self.query_ns_prefix:\n prefixes += NS_PREFIX\n\n # multilevel\n multilevel = self.query_multilevel\n\n if not multilevel:\n\n # read the required value\n l = self.query_level_int \n \n # retrieve URI related to the query\n # execute the sparql query\n uri_list = []\n if self.query_reification:\n uri_list = self.kp.custom_query(q_reification)\n else:\n if len(self.query_string) > 0:\n uri_list = self.kp.custom_query(prefixes + self.query_string)\n\n # move objects!\n self.redraw(uri_list, l)\n\n else:\n\n # retrieve URI related to the query\n # execute the sparql query\n uri_list = []\n if self.query_reification:\n print \"here\"\n uri_list = self.kp.custom_multilevel_query(q_reification)\n print \"here\"\n print uri_list\n else:\n if len(self.query_string) > 0:\n uri_list = self.kp.custom_multilevel_query(prefixes + self.query_string)\n \n # move objects!\n level_counter = 0\n for level in uri_list:\n\n level_counter += 1\n self.redraw(level, level_counter)",
"title": ""
},
{
"docid": "1ea855d78f95a8cc7e491cc1dc42b888",
"score": "0.53617865",
"text": "async def showall(self, ctx: commands.Context):\n self.emojis = {str(e.id): e for e in self.bot.emojis}\n\n guild: discord.Guild = ctx.guild\n formatted = []\n async with self.config.guild(ctx.guild).wiggle() as wigglelist:\n for userid, emojiids in wigglelist.items():\n user: discord.Member = guild.get_member(int(userid))\n emojis: List[discord.Emoji] = [self.emojis[str(e)] for e in emojiids]\n line = f\"{' '.join([str(e) for e in emojis])} for {user.display_name}\"\n formatted.append(line)\n # await ctx.send(line)\n\n formatted = \"\\n\".join(formatted)\n pages = list(pagify(formatted))\n await menu(ctx, pages, DEFAULT_CONTROLS)\n # embedded_response = discord.Embed(\n # title=f\"Wiggle Emoji for {ctx.guild.name}\",\n # type=\"rich\",\n # description=formatted,\n # )\n # embedded_response = embed.randomize_colour(embedded_response)\n # await ctx.send(embed=embedded_response)",
"title": ""
},
{
"docid": "99216a8ba8d19ebcdfc22209dac2f831",
"score": "0.53615046",
"text": "def query(self,input_query):\n reply = self.open.query(str(input_query))\n return str(reply)",
"title": ""
},
{
"docid": "a250379d268919cf99c47c6399552818",
"score": "0.5361407",
"text": "def ng_query(self, request, *args, **kwargs):\n return self.build_json_response(self.get_query())",
"title": ""
},
{
"docid": "68073b38e65b556a77f7eada990359e4",
"score": "0.53519374",
"text": "def query(self):\r\n raise RuntimeError(\"Must implement query!\")",
"title": ""
},
{
"docid": "5a160dcd064bd666e48a4c031cf3f321",
"score": "0.53504527",
"text": "def query_data(instance_id, database_id):\n spanner_client = spanner.Client()\n instance = spanner_client.instance(instance_id)\n database = instance.database(database_id)\n\n results = database.execute_sql(\n 'SELECT SingerId, AlbumId, AlbumTitle FROM Albums')\n\n for row in results:\n print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row))",
"title": ""
},
{
"docid": "edb4f6e9b28feb6432154e3cab9e2f0e",
"score": "0.5350157",
"text": "def query(self, query_path):\n\t\t\n\t\tresponse = ''\n\t\tclient = httplib2.Http()\n\t\t\n\t\ttry:\n\t\t\tif self.server_os_type == NIX_ARCH:\n\t\t\t\tquery = '%s/%s/video/embed?botId=%s&video=&botnet=&' % (self.api_url, self.api_token, query_path)\n\t\t\t\n\t\t\telif self.server_os_type == WIN_ARCH:\n\t\t\t\tquery = '%s/%s/video/embed?botId=&video=%s&botnet=&' % (self.api_url, self.api_token, query_path)\n\t\t\t\n\t\t\telse:\n\t\t\t\tquery = '%s/%s/video/embed?botId=%s&video=&botnet=&' % (self.api_url, self.api_token, query_path)\n\t\t\t\n\t\t\tresponse = client.request(query)\n\t\t\t\n\t\texcept BaseException as e:\n\t\t\tprint '[X] %s' % (e.message)\n\t\t\t\n\t\tif response:\n\t\t\tresponse_body = response[1]\n\t\t\tpattern = re.compile( r'.*src=\"(.*?)\"')\n\t\t\tmatched = pattern.match(response_body)\n\t\t\t\n\t\t\tif matched:\n\t\t\t\tdata = matched.group(1)\n\t\t\t\tindex = stricmp(data, self.emit_data)\n\t\t\t\t\n\t\t\t\tif self.server_os_type == WIN_ARCH:\n\t\t\t\t\tindex -= 1\n\t\t\t\t\t\n\t\t\t\treturn data[index:]\n\t\t\t\t\n\t\treturn False",
"title": ""
},
{
"docid": "50cadc0ce961aa7c516ed6aa1a0011cb",
"score": "0.53350765",
"text": "def query(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "50cadc0ce961aa7c516ed6aa1a0011cb",
"score": "0.53350765",
"text": "def query(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "50cadc0ce961aa7c516ed6aa1a0011cb",
"score": "0.53350765",
"text": "def query(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "50cadc0ce961aa7c516ed6aa1a0011cb",
"score": "0.53350765",
"text": "def query(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"query\")",
"title": ""
},
{
"docid": "41c1df197145e76af49b99e344b3e5ac",
"score": "0.5322119",
"text": "def query_interface_run_query(\n *,\n db: Session = Depends(deps.get_db),\n resource_id: int,\n page: int = 0,\n page_size: int = 25,\n current_user: models.User = Depends(interface_read_validator),\n) -> List[Dict[str, Any]]:\n query = crud.query.get(db=db, id=resource_id)\n if not query:\n raise HTTPException(status_code=404, detail=\"Cannot find query.\")\n query_result = crud.query.run_query(\n db=db, id=resource_id, page=page, page_size=page_size\n )\n return jsonable_encoder(query_result)",
"title": ""
},
{
"docid": "a2c70b6faf4c45d24435240deeca3867",
"score": "0.53123814",
"text": "def query(self):\n return self.__structure.query",
"title": ""
},
{
"docid": "b8fdda858db01535049af578b9dd4a7b",
"score": "0.5310502",
"text": "def query_list(self) -> 'outputs.QueryListResponse':\n return pulumi.get(self, \"query_list\")",
"title": ""
},
{
"docid": "b8fdda858db01535049af578b9dd4a7b",
"score": "0.5310502",
"text": "def query_list(self) -> 'outputs.QueryListResponse':\n return pulumi.get(self, \"query_list\")",
"title": ""
},
{
"docid": "b8fdda858db01535049af578b9dd4a7b",
"score": "0.5310502",
"text": "def query_list(self) -> 'outputs.QueryListResponse':\n return pulumi.get(self, \"query_list\")",
"title": ""
},
{
"docid": "b8fdda858db01535049af578b9dd4a7b",
"score": "0.5310502",
"text": "def query_list(self) -> 'outputs.QueryListResponse':\n return pulumi.get(self, \"query_list\")",
"title": ""
},
{
"docid": "d524d922e5345b176ce3ff3cb559a9a9",
"score": "0.53021187",
"text": "def callback_query(call):\n chat_id = call.from_user.id\n weekdays = [\"Monday\", \"Tuesday\", \"Wednesday\",\n \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n\n if call.data == \"hafta_kun\":\n keyboard_l = types.InlineKeyboardMarkup()\n btn1 = types.InlineKeyboardButton(\"Dushanba\", callback_data=\"Monday\")\n btn2 = types.InlineKeyboardButton(\"Seshanba\", callback_data=\"Tuesday\")\n btn3 = types.InlineKeyboardButton(\"Chorshanba\", callback_data=\"Wednesday\")\n btn4 = types.InlineKeyboardButton(\"Payshanba\", callback_data=\"Thursday\")\n btn5 = types.InlineKeyboardButton(\"Juma\", callback_data=\"Friday\")\n btn6 = types.InlineKeyboardButton(\"Shanba\", callback_data=\"Saturday\")\n btn7 = types.InlineKeyboardButton(\"Yakshanba\", callback_data=\"Sunday\")\n btn8 = types.InlineKeyboardButton(\"🔜Keyingi bosqich\", callback_data=\"next\")\n back = types.InlineKeyboardButton(\"🔙Ortga\", callback_data=\"back\")\n keyboard_l.add(btn1, btn2)\n keyboard_l.add(btn3, btn4)\n keyboard_l.add(btn5, btn6, btn7)\n keyboard_l.add(back, btn8)\n msg = bot.send_message(chat_id,\n \"🗓 Xabarnoma jo\\'natilishi kerak bo\\'lgan hafta kuni(lari)ni belgilang.\",\n parse_mode='html', reply_markup=keyboard_l)\n bot.answer_callback_query(call.id, \"Endi xabarnomani jo\\'natish vaqtini belgilang\")\n bot.delete_message(call.message.chat.id, call.message.message_id)\n\n elif call.data == \"only_one_date\":\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n markup.row('🔙Ortga')\n msg = bot.send_message(chat_id,\n \"🎯 Xabarnomani jo\\'natish uchun sanani yuboring. \\nSanani namunadagi ko\\'rinishda yuboring:\\n✅ Namuna: <b>16</b>\",\n parse_mode='html',reply_markup=markup)\n bot.answer_callback_query(call.id, \"Endi xabarnomani jo\\'natish sanasini yuboring\")\n bot.delete_message(call.message.chat.id, call.message.message_id)\n bot.register_next_step_handler(msg, data_date_step)\n\n\n elif call.data == \"only_one\":\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n markup.row('🔙Ortga')\n msg = bot.send_message(chat_id,\n \"🎯 Xabarnomani jo\\'natish uchun sanani yuboring. \\nSanani namunadagi ko\\'rinishda yuboring:\\n✅ Namuna: <b>27.09.2020</b>\",\n parse_mode='html',reply_markup=markup)\n bot.answer_callback_query(call.id, \"Endi xabarnomani jo\\'natish sanasini yuboring\")\n bot.delete_message(call.message.chat.id, call.message.message_id)\n bot.register_next_step_handler(msg, data_sana_step) \n\n elif call.data == \"one_back\":\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n markup.row('📣 Yangi xabarnomani qo\\'shish')\n markup.row('🔕 O\\'rnatilgan xabarnomani o\\'chirish')\n bot.answer_callback_query(call.id, \"Ortga\")\n bot.send_message(chat_id, \"<i>Men </i>Groot<i>man 🌱</i>\",\n reply_markup=markup, parse_mode=\"html\")\n bot.delete_message(call.message.chat.id, call.message.message_id)\n\n\n\n elif call.data == \"back\":\n \n bot.answer_callback_query(call.id, \"Ortga\")\n keyboard_l = types.InlineKeyboardMarkup()\n btn1 = types.InlineKeyboardButton(\"🗓 Har hafta\", callback_data=\"hafta_kun\")\n btn2 = types.InlineKeyboardButton(\"📅 Har oyda\", callback_data=\"only_one_date\")\n btn3 = types.InlineKeyboardButton(\"🔂 Bir martalik\", callback_data=\"only_one\")\n back = types.InlineKeyboardButton(\"🔙Ortga\", callback_data=\"back\")\n keyboard_l.add(btn1)\n keyboard_l.add(btn2)\n keyboard_l.add(btn3)\n keyboard_l.add(back)\n msg = bot.send_message(call.from_user.id, \"🎯 Xabarnoma turini tanlang\",\n reply_markup=keyboard_l)\n bot.delete_message(call.message.chat.id, call.message.message_id)\n\n elif call.data in weekdays:\n if call.data == \"Monday\":\n bot.answer_callback_query(call.id, \"Dushanba kuni belgilandi\")\n if call.data == \"Tuesday\":\n bot.answer_callback_query(call.id, \"Seshanba kuni belgilandi\")\n if call.data == \"Wednesday\":\n bot.answer_callback_query(call.id, \"Chorshanba kuni belgilandi\")\n if call.data == \"Thursday\":\n bot.answer_callback_query(call.id, \"Payshanba kuni belgilandi\")\n if call.data == \"Friday\":\n bot.answer_callback_query(call.id, \"Juma kuni belgilandi\")\n if call.data == \"Saturday\":\n bot.answer_callback_query(call.id, \"Shanba kuni belgilandi\")\n if call.data == \"Sunday\":\n bot.answer_callback_query(call.id, \"Yakshanba kuni belgilandi\") \n \n\n try:\n session = Session()\n days = session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).order_by(Rejim.id.desc()).first()\n new_day = days.day\n if new_day is None:\n up_id = session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).order_by(Rejim.id.desc()).first().id\n session.query(Rejim).filter(Rejim.id == up_id).update({Rejim.day: call.data}, synchronize_session=False)\n \n else:\n new_day = new_day + ' ' + call.data\n up_id = session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).order_by(Rejim.id.desc()).first().id\n session.query(Rejim).filter(Rejim.id == up_id).update({Rejim.day: new_day}, synchronize_session=False)\n \n \n session.commit()\n session.close()\n\n except Exception as e:\n bot.send_message(config.admin_id, e)\n\n elif call.data == \"next\":\n session = Session()\n days = session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).order_by(Rejim.id.desc()).first()\n day = days.day\n need_day = day.split()\n kunlar=\"\"\n\n for i in need_day:\n if i == \"Monday\":\n kunlar = kunlar+\"Dushanba \"\n if i == \"Tuesday\":\n kunlar = kunlar+\"Seshanba \"\n if i == \"Wednesday\":\n kunlar = kunlar+\"Chorshanba \"\n if i == \"Thursday\":\n kunlar = kunlar+\"Payshanba \"\n if i == \"Friday\":\n kunlar = kunlar+\"Juma \"\n if i == \"Saturday\":\n kunlar = kunlar+\"Shanba \"\n if i == \"Sunday\":\n kunlar = kunlar+\"Yakshanba \"\n else:\n kunlar = kunlar+\"\"\n\n\n keyboard_l = types.InlineKeyboardMarkup()\n btn1 = types.InlineKeyboardButton(\"✅ Ha, vaqtni belgilash\", callback_data=\"yes_time\")\n btn2 = types.InlineKeyboardButton(\"🙅♂️ Yo\\'q, qayta tanlash\", callback_data=\"back_day\")\n keyboard_l.add(btn1)\n keyboard_l.add(btn2)\n bot.send_message(chat_id, \"<b>\" + kunlar + \"</b>\" + \"\\n\\nYuqoridagi kunlarga xabarnoma o'rnatmoqchimisiz?\",\n parse_mode=\"html\", reply_markup=keyboard_l)\n \n bot.delete_message(call.message.chat.id, call.message.message_id)\n\n session.close()\n \n\n elif call.data == \"back_day\":\n try:\n session = Session()\n up_id = session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).order_by(Rejim.id.desc()).first().id\n session.query(Rejim).filter(Rejim.id == up_id).update({Rejim.day: \"\"}, synchronize_session=False)\n # session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).update(\n # {Rejim.day: \"\"}, synchronize_session=False)\n session.commit()\n session.close()\n\n keyboard_l = types.InlineKeyboardMarkup()\n btn1 = types.InlineKeyboardButton(\"Dushanba\", callback_data=\"Monday\")\n btn2 = types.InlineKeyboardButton(\"Seshanba\", callback_data=\"Tuesday\")\n btn3 = types.InlineKeyboardButton(\"Chorshanba\", callback_data=\"Wednesday\")\n btn4 = types.InlineKeyboardButton(\"Payshanba\", callback_data=\"Thursday\")\n btn5 = types.InlineKeyboardButton(\"Juma\", callback_data=\"Friday\")\n btn6 = types.InlineKeyboardButton(\"Shanba\", callback_data=\"Saturday\")\n btn7 = types.InlineKeyboardButton(\"Yakshanba\", callback_data=\"Sunday\")\n btn8 = types.InlineKeyboardButton(\"🔜Keyingi bosqich\", callback_data=\"next\")\n back = types.InlineKeyboardButton(\"🔙Ortga\", callback_data=\"back\")\n keyboard_l.add(btn1, btn2)\n keyboard_l.add(btn3, btn4)\n keyboard_l.add(btn5, btn6, btn7)\n keyboard_l.add(back, btn8)\n msg = bot.send_message(chat_id,\n \"Xabarnomani jo\\'natish kerak bo\\'lgan hafta kunini belgilang.\",\n parse_mode='html', reply_markup=keyboard_l)\n bot.answer_callback_query(call.id, \"Endi xabarnomani jo\\'natish kunini belgilang\")\n\n except Exception as e:\n bot.send_message(config.admin_id, e)\n\n elif call.data == \"yes_time\":\n bot.answer_callback_query(call.id, \"Endi xabarnomani jo\\'natish vaqtini belgilang\")\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n markup.row('🔙Ortga')\n msg = bot.send_message(chat_id,\n \"🕙 Xabarnomani jo\\'natish kerak bo\\'lgan vaqtni namunadagi ko\\'rinishda yuboring\\n\\n<b>✅ Namuna: </b>16:00\",\n parse_mode='html',reply_markup=markup)\n bot.register_next_step_handler(msg, data_time_step)\n bot.delete_message(call.message.chat.id, call.message.message_id)\n\n elif call.data == \"yes_text\":\n bot.answer_callback_query(call.id, \"Endi xabarnoma uchun matnni yuboring\")\n msg = bot.send_message(chat_id,\n \"🎯 Xabarnomada ko'rsatilishi zarur bo\\'lgan xabar matnini yuboring.\",\n parse_mode='html')\n bot.register_next_step_handler(msg, data_text_step)\n bot.delete_message(call.message.chat.id, call.message.message_id)\n\n elif call.data == \"save\":\n session = Session()\n days = session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).order_by(Rejim.id.desc()).first()\n need_str = days.day\n need = need_str.split(\" \")\n for j in need:\n second = Second_db()\n second.user_id = days.user_id\n second.group_id = days.group_id\n second.day = j\n second.days_id = days.id\n second.time = days.time\n second.text = days.text\n second.finish = 0\n session.add(second)\n\n\n up_id = session.query(Rejim).filter(Rejim.user_id == chat_id).filter(Rejim.finish == 0).order_by(Rejim.id.desc()).first().id\n session.query(Rejim).filter(Rejim.id == up_id).update({Rejim.finish: 1}, synchronize_session=False) \n \n\n session.commit()\n session.close()\n bot.answer_callback_query(call.id, \"🟢 Xabarnoma tartibi o\\'rnatildi\")\n bot.delete_message(call.message.chat.id, call.message.message_id)\n \n\n if (call.data)[0:3] == \"del\":\n try:\n \n bot.answer_callback_query(call.id,\"O\\'chirildi\")\n del_id = int((call.data)[3:])\n session = Session()\n x = session.query(Rejim).filter(Rejim.id == del_id).one()\n session.delete(x)\n session.commit()\n session.close()\n bot.send_message(chat_id,\"Tanlangan xabarnoma o\\'chirildi\")\n bot.delete_message(call.message.chat.id, call.message.message_id)\n except Exception as e:\n bot.send_message(config.admin_id, e)",
"title": ""
},
{
"docid": "e8b139de96f810f0391ec7815df955df",
"score": "0.53020424",
"text": "def show(**kwargs):\n run_show(**kwargs)",
"title": ""
},
{
"docid": "7b570292a810bdaa78131b43ae860948",
"score": "0.52817607",
"text": "def demo_aql_search(self):\n logger.info(\"\\n+++++++++++++++++++++++++++++++AQL Search Start+++++++++++++++++++++++++++++++\")\n\n query_doc = ResourceUtility.generate_query_document(types=['dm_sysobject'], columns=['object_name'],\n sorts=[Sort('object_name', True, 'en', True)],\n expression_set=ExpSet('AND', FtExp('emc or rest')))\n logger.info('AQL search for keyword emc and parameters items-per-page=3,page=2,inline=true...')\n\n results = []\n try:\n results = self.client.aql_search(query_doc.dump(), {'items-per-page': '2', 'page': '1', 'inline': 'true'})\n except IOError:\n logger.info('Fail to search with AQL.')\n\n logger.info('Object names in page %d...', 2)\n for result in results.get_entries():\n logger.info(result.get('content').get('properties').get('object_name'))\n\n logger.info('Navigate to next page...')\n results = self.client.next_page(results)\n\n if results is None:\n logger.info('Next page does not exist.')\n else:\n logger.info('Object names in page %d...', 3)\n for result in results.get_entries():\n logger.info(result.get('content').get('properties').get('object_name'))\n\n # add facet definitions in search criteria\n self.step_separator('Facet against attribute {}'.format('r_object_type'))\n query_doc.facet_definitions = [\n FacetDefinition(facet_id='facet_r_object_type', attributes=['r_object_type'])]\n\n try:\n results = self.client.aql_search(query_doc.dump(), {'items-per-page': '2', 'page': '1', 'inline': 'true'})\n except IOError:\n logger.info('Fail to search with AQL.')\n\n logger.info('Facet results:')\n for facet in results.get('facets')[0].get('facet-value'):\n logger.info('group for %s has %s results and the navigation link is %s',\n facet.get('facet-value-constraint'), facet.get('facet-value-count'),\n facet.get('link').get('href'))\n\n logger.info(\"\\n+++++++++++++++++++++++++++++++AQL Search End+++++++++++++++++++++++++++++++\")",
"title": ""
},
{
"docid": "9c0745b7ee32511fc3371acf9e6fcc48",
"score": "0.5273036",
"text": "def run_query(self, request):\n ok, fields = forms.fetch(request, ['msg0_id', 'msg0_sent', 'msg0_locked', 'msg1_id',\n 'msg1_sent', 'msg1_locked', 'mis0_id', 'mis0_status', 'mis1_id', 'mis1_status'],\n blanks=['msg0_id', 'msg0_sent', 'msg0_locked', 'msg1_id', 'msg1_sent', 'msg1_locked',\n 'mis0_id', 'mis0_status', 'mis1_id', 'mis1_status'])\n if not ok:\n return {'error': 'Bad parameters.'}\n # For each message with a non-empty ID, create a subquery that returns only the count of that message type\n # with the given parameters.\n # e.g., (select count(*) from messages where messages.user_id=users.user_id and messages.msg_type=\"MSG_OBELISK01a\") as msg0\n # We'll simultaneously build a criteria_list that will be used to check the outputs of each subquery.\n # e.g., 'msg0=1 and msg1=1'\n subquery_list = ''\n criteria_list = ''\n for i in range(2):\n msg_type = fields['msg%d_id' % i]\n if msg_type != '':\n if subquery_list != '':\n subquery_list += ',\\n'\n criteria_list += ' and '\n subquery_list += '(select count(*) from messages where messages.user_id=users.user_id and messages.msg_type=\"%s\"' % (msg_type)\n if fields['msg%d_locked' % i] == 'TRUE':\n subquery_list += ' and messages.locked=1'\n elif fields['msg%d_locked' % i] == 'FALSE':\n subquery_list += ' and messages.locked=0'\n if fields['msg%d_sent' % i] == 'TRUE':\n criteria_list += 'msg%d=1' % (i)\n else:\n criteria_list += 'msg%d=0' % (i)\n subquery_list += ') as msg%d' % (i)\n # Append subqueries and subquery criteria for missions.\n for i in range(2):\n mis_def = fields['mis%d_id' % i]\n if mis_def != '':\n if subquery_list != '':\n subquery_list += ',\\n'\n criteria_list += ' and '\n subquery_list += '(select count(*) from missions where missions.user_id=users.user_id and missions.mission_definition=\"%s\"' % (mis_def)\n if fields['mis%d_status' % i] == 'STARTED':\n subquery_list += ' and missions.done=0'\n criteria_list += 'mis%d=1' % (i)\n elif fields['mis%d_status' % i] == 'DONE':\n subquery_list += ' and missions.done=1'\n criteria_list += 'mis%d=1' % (i)\n else:\n criteria_list += 'mis%d=0' % (i)\n subquery_list += ') as mis%d' % (i)\n\n if subquery_list == '':\n return {'error': 'All inputs were left blank.'}\n \n # Put it all together into the final query.\n query = 'select * from (select users.email,\\n%s\\nfrom users) as tbl_1 where %s' % (subquery_list, criteria_list)\n return { 'sql_query': query }",
"title": ""
},
{
"docid": "7e15842a32bd766570b11cab79ee767e",
"score": "0.52478856",
"text": "def query(self, **kwargs):\n\n raise NotImplemented()",
"title": ""
},
{
"docid": "9b8f6dc4db64cc552388f88fbc6a5590",
"score": "0.5245417",
"text": "def get_query_text(ibs, cm, aid2, truth, **kwargs):\n text_list = []\n if cm is not None:\n qaid = cm.qaid\n score = cm.get_annot_scores([aid2])[0]\n rawscore = cm.get_annot_scores([aid2])[0]\n aid2_raw_rank = cm.get_annot_ranks([aid2])[0]\n else:\n qaid = kwargs.get('qaid', None)\n score = kwargs.get('score', None)\n rawscore = kwargs.get('rawscore', None)\n aid2_raw_rank = kwargs.get('aid2_raw_rank', None)\n if kwargs.get('show_truth', False):\n truth_str = '*%s*' % ibs.const.EVIDENCE_DECISION.INT_TO_NICE.get(truth, None)\n text_list.append(truth_str)\n if kwargs.get('show_rank', aid2_raw_rank is not None or cm is not None):\n try:\n # aid2_raw_rank = cm.get_annot_ranks([aid2])[0]\n aid2_rank = aid2_raw_rank + 1 if aid2_raw_rank is not None else None\n rank_str = 'rank=%s' % str(aid2_rank)\n except Exception as ex:\n ut.printex(ex)\n # ut.embed()\n raise\n text_list.append(rank_str)\n if kwargs.get('show_rawscore', rawscore is not None or cm is not None):\n rawscore_str = 'rawscore=' + ut.num_fmt(rawscore)\n if len(text_list) > 0:\n rawscore_str = '\\n' + rawscore_str\n text_list.append(rawscore_str)\n if kwargs.get('show_score', score is not None or cm is not None):\n score_str = 'score=' + ut.num_fmt(score)\n if len(text_list) > 0:\n score_str = '\\n' + score_str\n text_list.append(score_str)\n name_score = kwargs.get('name_score', None)\n name_rank = kwargs.get('name_rank', None)\n if kwargs.get('show_name_score', True):\n if name_score is not None:\n text_list.append('name_score=' + ut.num_fmt(name_score))\n if kwargs.get('show_name_rank', True):\n if name_rank is not None:\n # Make display one based\n text_list.append('name_rank=#{}'.format(str(name_rank + 1)))\n # with ut.embed_on_exception_context:\n if kwargs.get('show_timedelta', True):\n assert qaid is not None, 'qaid cannot be None'\n # TODO: fixme\n if isinstance(aid2, list):\n aid2_ = aid2[0]\n else:\n aid2_ = aid2\n timedelta_str = '\\n' + get_timedelta_str(ibs, qaid, aid2_)\n text_list.append(timedelta_str)\n query_text = ', '.join(text_list)\n return query_text",
"title": ""
},
{
"docid": "326393a01b23c0ac92356371e1cef062",
"score": "0.5244869",
"text": "def _on_query_changed(self, evt):\n \n self.ShowArticles()",
"title": ""
},
{
"docid": "b08a3bf2f7a4bc3b4882fa333a7c1c70",
"score": "0.5242554",
"text": "def my_query(request):\n userprofile = UserProfileS.objects.filter(user=request.user.id)\n allquery = QueryS.objects.filter(user=request.user.id).order_by('-created_at')\n popular_query = QueryS.objects.order_by('-views')[:5]\n branch = ['CSE', 'IT', 'ECE', 'ME', 'CE', 'EN']\n title = request.user.username + ' Queries'\n context_dict = {\n 'userprofile': userprofile,\n 'allquery': allquery,\n 'popular_query': popular_query,\n 'branch': branch,\n 'title': title}\n return render(request, 'vtr/index.html', context_dict)",
"title": ""
},
{
"docid": "91e33ecbfd8017e374d99f625eb42f11",
"score": "0.52389663",
"text": "async def meta_query(self, meta, **kwargs: Any):\n if meta == 'siteinfo':\n async for json in self.query(meta='siteinfo', **kwargs):\n assert 'batchcomplete' in json\n assert 'continue' not in json\n return json['query']\n async for json in self.query(meta=meta, **kwargs):\n if meta == 'filerepoinfo':\n meta = 'repos'\n assert json['batchcomplete'] is True\n return json['query'][meta]",
"title": ""
},
{
"docid": "0544fb9d95ba5b0d4802b2c0376536c0",
"score": "0.5228882",
"text": "def handle_query():\n act_type = get(\"action\")\n req_type = get(\"type\")\n obj_type = OBJECT_TYPES.get(req_type)\n obj_name = get(\"name\")\n\n log.debug(\"responding to: %s, %s, %s\", act_type, req_type, obj_name)\n\n res = cached_special_query(act_type, req_type, obj_name)\n if res is not None:\n return res\n\n ## queries for each object type available\n if obj_type:\n if act_type == \"submit\":\n handle_submit(obj_type)\n return handle_response(obj_type, req_type)\n \n else:\n return json.dumps({\n \"error\" : \"unknown request: %s.\" % str((req_type, act_type, obj_type)),\n })",
"title": ""
},
{
"docid": "0e2eb312d23f77f5cb782026b1209edc",
"score": "0.5227536",
"text": "async def wiki(self, ctx, *, query: str):\n params = urllib.parse.urlencode({\"action\": \"opensearch\", \"search\": query})\n url = BASE_URL_WIKIPEDIA_API.format(params)\n async with ctx.bot.session.get(url) as response:\n if response.status == 200:\n data = await response.json()\n if not data[1]:\n await ctx.send(\"No results found. :<\")\n return\n embed = discord.Embed()\n for index in range(0, min(3, len(data[1]))):\n description = f\"{data[3][index]}\\n{data[2][index]}\"\n embed.add_field(name=data[1][index], value=description, inline=False)\n await ctx.send(embed=embed)\n else:\n message = \"Couldn't reach Wikipedia. x.x\"\n await ctx.send(message)",
"title": ""
},
{
"docid": "5f2a1b4c8f4c515834619a01f21118c5",
"score": "0.52245384",
"text": "def query(self,text_input,prefix='answer:'):\n raise NotImplementedError",
"title": ""
},
{
"docid": "d93c62512cc0748b0c2ca0f4f0552a9b",
"score": "0.5218709",
"text": "def clik_on_run(self):\n qry = self.q_box_manual.get(1.0, END)\n if(qry==\"\"):\n messagebox.showerror(\"Query Error\",\"qurey cant be empty\")\n return\n s_flag=self.semanticFlag.get() == 1\n stem_flag=self.stem_flag.get() == 1\n city_choise=self.get_choosen_cites()\n resualt_set = self.control.rum_custom_query(qry,s_flag,city_choise,stem_flag,self.res_path)\n display_flag=self.save_file_check.get() == 1\n if not display_flag:\n self.display_new_window(self.display(resualt_set))",
"title": ""
},
{
"docid": "a4cffdb07965339329bcaf9487a3bde1",
"score": "0.52054185",
"text": "def show_qry(title, querystr, fld_join='\\t', row_separator=None):\n\toutput_line('\\n\\n')\n\toutput_line('--- %s ---' % title)\n\trs = select_qry(querystr)\n\tif rs:\n\t\tfor row in rs:\n\t\t\toutput_line(fld_join.join([fld2str(s) for s in row]))\n\t\t\tif row_separator:\n\t\t\t\toutput_line(row_separator)\n\telse:\n\t\toutput_line(' -- NO DATA --')",
"title": ""
},
{
"docid": "d49b6e12bde82cfca34005cba62e522f",
"score": "0.52050996",
"text": "def _run_query(self, q):\n\n\t\tactions = ActionChains(self.driver)\n\n\t\tactions.move_to_element(self.driver.find_element_by_css_selector('div.ace_text-layer'))\n\t\tactions.click()\n\t\tactions.send_keys(q)\n\t\tactions.perform()\n\n\t\tget_answer_button = self.driver.find_element_by_css_selector('button.RunButton')\n\t\tself.click_and_wait(get_answer_button, secs=15)\n\n\t\trow_count = 0\n\n\t\ttry:\n\t\t\tfor _ in self.driver.find_element_by_css_selector('div.ShownRowCount').text.strip().split():\n\t\t\t\tif _.replace(',','').isdigit():\n\t\t\t\t\trow_count = int(_.replace(',',''))\n\t\t\t\t\tbreak\n\t\texcept:\n\t\t\tprint('cannot find row count!')\n\n\t\tprint(f'got {row_count} rows')\n\n\t\tself.rows_per_week.append(row_count)\n\n\t\tdownload_full_results = self.driver.find_element_by_css_selector('svg.Icon-downarrow')\n\t\tself.click_and_wait(download_full_results)\n\n\t\tcsv_option = self._find_by_text('button', 'Button', 'csv')\n\t\tself.click_and_wait(csv_option)\n\n\t\treturn self",
"title": ""
},
{
"docid": "ffd3374cf0e077d0a7abc12aa8930a83",
"score": "0.52003056",
"text": "def query(self):\n return self.__p['query']",
"title": ""
},
{
"docid": "97514463f2e417c4215abbc7960e63d4",
"score": "0.5198253",
"text": "def query_block(self):\n pass",
"title": ""
},
{
"docid": "fa9256ea7a6899afdc57cf9d1da6e080",
"score": "0.5195169",
"text": "def work(self):\n if self.options.client:\n client = Client.objects.get(slug=self.options.client)\n show = Show.objects.get(\n client=client, slug=self.options.show)\n else:\n show = Show.objects.get(slug=self.options.show)\n\n self.one_show(show)\n\n return",
"title": ""
},
{
"docid": "6a5dffec0ee4134b95cd5185ce9fc8cd",
"score": "0.518749",
"text": "async def wikiss13(self, ctx: commands.Context, *, query: str):\n async with ctx.typing():\n payload = self.generate_payload(query)\n async with aiohttp.ClientSession() as session:\n async with session.get(\n self.API_URL,\n params=payload,\n headers=self.HEADERS\n ) as res:\n result = await res.json(content_type=None)\n\n embed_tasks = []\n if \"query\" in result and \"pages\" in result[\"query\"]:\n result[\"query\"][\"pages\"].sort(\n key=lambda unsorted_page: unsorted_page[\"index\"]\n )\n pages = result[\"query\"][\"pages\"]\n if 'redirects' in result['query']:\n for redirect in result[\"query\"][\"redirects\"]:\n skip = False\n for page in pages:\n if page['title'] == redirect['to'] and not 'tofragment' in redirect:\n skip = True\n break\n if skip:\n continue\n page = {\n 'title': redirect['from'],\n 'fullurl': self.WIKI_URL + '/' + self.fix_fragment_urls(redirect['to']),\n 'redirect_title': redirect['to'],\n }\n if 'tofragment' in redirect:\n page['fullurl'] += '#' + self.fix_fragment_urls(redirect['tofragment'])\n page['tofragment'] = self.fix_fragment_urls(redirect['tofragment'])\n pages.append(page)\n pages.sort(key=lambda page: -self.similarity(page['title'], query))\n for page in pages:\n if (\n \"categories\" in page\n and page[\"categories\"]\n and \"title\" in page[\"categories\"][0]\n and page[\"categories\"][0][\"title\"]\n == self.DISAMBIGUATION_CAT\n ):\n continue # Skip disambiguation pages\n if not ctx.channel.permissions_for(ctx.me).embed_links:\n # No embeds here :(\n await ctx.send(\n warning(\n f\"I'm not allowed to do embeds here, so here's the first result:\\n{page['fullurl']}\"\n )\n )\n return\n embed_tasks.append(self.generate_embed(page, session))\n if not ctx.channel.permissions_for(ctx.me).add_reactions:\n break # Menu can't function so only show first result\n embeds = await asyncio.gather(*embed_tasks, return_exceptions=True)\n\n\n if not embeds:\n await ctx.send(\n error(f\"I'm sorry, I couldn't find \\\"{query}\\\" on SS13 Wiki\")\n )\n elif len(embeds) == 1:\n embeds[0].set_author(name=f\"Result 1 of 1\")\n await ctx.send(embed=embeds[0])\n else:\n count = 0\n for embed in embeds:\n count += 1\n embed.set_author(name=f\"Result {count} of {len(embeds)}\")\n await menu(ctx, embeds, DEFAULT_CONTROLS, timeout=60.0)",
"title": ""
},
{
"docid": "9dd6b2c67977d1a001e0c57836420ba6",
"score": "0.51872844",
"text": "def query(self, *, sparql: str) -> Result:\n return self.graph.query(sparql)",
"title": ""
},
{
"docid": "b34b1b51a131b8e7a1bab23b2c0e8f15",
"score": "0.51871115",
"text": "def query(self, qry, *args, **kwargs):\n self.qry = qry\n return QueryJob(self.qry)",
"title": ""
},
{
"docid": "1a79f98eec625064787b2b67aca25b09",
"score": "0.51815903",
"text": "def on_chosen_inline_result(self, msg):\n print(msg)\n print('inline_message_id' in msg)\n result_id, from_id, query_string = telepot.glance(msg, flavor='chosen_inline_result')\n print(from_id, \",\", self.id, ':', 'Chosen Inline Result:', result_id, from_id, query_string)\n print(\"Message sent to \" + str(from_id))",
"title": ""
},
{
"docid": "d98123f0b06761d789419be45e9f0218",
"score": "0.5178854",
"text": "def query(self, q):\n \n # create a query and get the results from the storage engine\n q = ReconcileQuery(q)\n results = self.storage.search(q)\n \n # sort the results by score\n if getattr(results, \"sort\", None):\n results.sort(key=lambda x: x.score, reverse=True)\n \n # if there's a limit on results only return those results\n if q.limit:\n results = results[0:q.limit]\n \n # prepare each result in the JSON return format\n for i in results:\n \n # check if it's an exact match\n match = q.query.lower()==i[self.search_field].lower() or i.score==100\n \n q.add_result({\n \"id\":i[self.id_field],\n \"name\":i[self.search_field],\n \"type\":[{\n \"id\": \"/\" + self.type,\n \"name\": self.type\n }],\n \"score\":i.score,\n \"match\":match,\n })\n \n # if we've got an exact match then just return it\n if match:\n return q.results\n \n return q.results",
"title": ""
},
{
"docid": "d0e2cbffb46459295f2c7eeee95138c4",
"score": "0.5178549",
"text": "def embed_query(self, text: str) -> List[float]:\n \n return self._embedding_func([text])[0]",
"title": ""
},
{
"docid": "278ec3aa046e257429e5901f73f801d1",
"score": "0.51777095",
"text": "def GetQuery(self):\n return self.dbHandlerModule.Query(self.dbHandler)",
"title": ""
},
{
"docid": "1748ef626855120f097b0e30bd2920d3",
"score": "0.51754",
"text": "def getAskResult(self,smw,ask,limit=20):\n #PrintRequest.debug=self.debug\n result=smw.query(ask,limit=limit)\n if self.debug:\n print (result) \n return result;",
"title": ""
},
{
"docid": "7b95a4831b75125b2f65b30a67b26d87",
"score": "0.5165777",
"text": "def do_run_sql(self):\n query, results = self._get_sql_results()\n if query and results:\n res = f\"QUERY\\n{'-' * 20}\\n{query}\\n{'-' * 20}\\nRESULT\\n\"\n res += tabulate(\n [[str(column) for column in row] for row in results],\n tablefmt=\"grid\",\n floatfmt=\".3f\",\n )\n res += \"\\n\"\n print(res)",
"title": ""
},
{
"docid": "8074e3c01f9caa090ff13f53c55c4f74",
"score": "0.5154938",
"text": "def show_search(self, search_q: str, user: discord.Member) -> None:\n\n # Get the search result from the client\n search_result: list = self.youtube_client.search_videos(search_q, self.max_result)\n\n # Store the user search\n user_name = user.name + \"#\" + user.discriminator\n self.user_search[user_name] = search_result\n\n # Create the search message\n search_message: str = user.display_name + \" here is the result for \\\"\" + search_q + \"\\\" :\\n\"\n search_message += \"```\\n\"\n for i in range(len(search_result)):\n search_message += str(i + 1) + \". \"\n search_message += search_result[i][\"title\"] + \" - \" + search_result[i][\"channel_title\"]\n search_message += \" [\" + search_result[i][\"duration\"] + \"]\\n\"\n search_message += \"```\"\n search_message += \"Type `!choose <ID>` to add a song to the queue\"\n\n # Send the message\n self.send_message(search_message)",
"title": ""
},
{
"docid": "82c9dd1acb59e6a9ea741b29dba1ac68",
"score": "0.5141633",
"text": "def __query(self, q, r=False):\n\t\tdb = self.bot_name\n\t\tconn = sql.connect(db)\n\n\t\ttry:\n\t\t\tc = conn.cursor()\n\t\t\tc.execute(q)\n\t\t\tconn.commit()\n\n\t\t\tif r:\n\t\t\t\tresults = c.fetchone()\n\t\t\t\tc.close()\n\t\t\t\treturn results\n\t\t\telse:\n\t\t\t\tc.close()\n\t\t# todo - add a proper exception message\n\t\texcept:\n\t\t\tpass",
"title": ""
},
{
"docid": "36f18eab68af72c63d02748e7e6ca45a",
"score": "0.5134174",
"text": "def query(self, query_statement, args, context=None):\n pass",
"title": ""
},
{
"docid": "536f5d3334f0c1eb958848a230f061a1",
"score": "0.5112002",
"text": "async def _arun(self, query: str) -> str:\n return (await self.wrapper.aresults(query, self.num_results)).__str__()",
"title": ""
},
{
"docid": "7673e7e77975371fbfe0c2781b18c623",
"score": "0.51080906",
"text": "def show_results(self, result, delta_wealth):\n\n pass",
"title": ""
},
{
"docid": "e5122663690f0d6072babaa92fb622fa",
"score": "0.51037693",
"text": "def query(self,comando):\n \n time.sleep(0.5) \n try:\n return self.interface.query(comando)\n except Exception as e:\n raise Exception",
"title": ""
},
{
"docid": "5640beb1a24194b190ca94012dfcaa1e",
"score": "0.5101341",
"text": "def coreQuery(self):\n\n\t\twebquery = {'query':self.query, 'resulttype': 'core', 'format': 'json'}\n\t\tr = requests.get(self.epmc_basequeryurl, params=webquery)\n\t\tself.rawresults = r.json()\n\t\tif len(self.rawresults['resultList']['result']) == 1:\n\t\t\tself.singleresult = self.rawresults['resultList']['result'][0]",
"title": ""
},
{
"docid": "3f6692069622122f8fb3aef2715dc7f4",
"score": "0.50959855",
"text": "def show_all_hike_results():\n \n user_id = session.get(\"current_user\", None)\n if user_id:\n results = HikeResult.query.filter((HikeResult.user_id == user_id) &\n (HikeResult.canceled_by_user == False)).all()\n return jsonify(results)\n return 'No active results found'",
"title": ""
},
{
"docid": "09ae8fb01a55bbca6d78397cb8f8d5ae",
"score": "0.509199",
"text": "def query(self):\n return self._query",
"title": ""
},
{
"docid": "48baeafa1018463686baa54df6b7e729",
"score": "0.50915104",
"text": "async def run_query(*, github_access_token, query):\n endpoint = \"https://api.github.com/graphql\"\n query = json.dumps({\"query\": query})\n client = ClientWrapper()\n resp = await client.post(\n endpoint,\n data=query,\n headers={\"Authorization\": f\"Bearer {github_access_token}\"},\n )\n resp.raise_for_status()\n return resp.json()",
"title": ""
},
{
"docid": "19d6dfa72b6d737429d27175753e6844",
"score": "0.5082662",
"text": "def get_query():\n return CiscoC2900Query",
"title": ""
},
{
"docid": "e4182878b215aec5cddbf4560f8ce2fe",
"score": "0.5071671",
"text": "def set_queries(self) -> None:",
"title": ""
},
{
"docid": "b18885c94f02e4278b04b96808e81f43",
"score": "0.5064763",
"text": "def query_function():\n args = get_args(\n request_args=request.args,\n allowed_str_args=['text', 'entry_name'],\n allowed_int_args=['limit']\n )\n\n return jsonify(query.function(**args))",
"title": ""
}
]
e5d0c52e17e4e5e5e0f1ed744b6195bc
Compute the probability of the different services contained in the data using the given classifier.
[
{
"docid": "31822774ceb7b6860b32167010e772e7",
"score": "0.7801408",
"text": "def services(self, data, classifier):\n values = self.__generate_values(data, classifier)\n max_value = 0\n # Search max index in probabilities (number of categories)\n for val in values:\n if val[0] > max_value:\n max_value = val[0]\n # Add element to the array (start at 0)\n max_value += 1\n # List of probabilities for each category\n probabilities = [0] * max_value\n for val in values:\n if (val[1]*100) > probabilities[val[0]]:\n probabilities[val[0]] = (100*val[1])\n\n return probabilities",
"title": ""
}
]
[
{
"docid": "c13a61aa62c3aa58c2f8926ba1620388",
"score": "0.6992532",
"text": "def probability(self, data, classifier):\n values = self.__generate_values(data, classifier)\n max_value = 0\n # Search max index in probabilities (number of categories)\n for val in values:\n if val[0] > max_value:\n max_value = val[0]\n # Add element to the array (start at 0)\n max_value += 1\n # List of probabilities for each category\n probabilities = [0] * max_value\n for val in values:\n if val[1] > probabilities[val[0]]:\n probabilities[val[0]] = val[1]\n data = 0\n for prob in probabilities:\n if (prob*100) > data:\n data = (prob*100)\n return data",
"title": ""
},
{
"docid": "2fbbf9f79d55bc7330e697f898cbadc7",
"score": "0.66785395",
"text": "def _calculate_probablility(self, trained_data: dict, classification: str, tokens: list, delta=0.01):\n if classification not in trained_data:\n return 0\n\n token_counts = trained_data[classification]\n total_words_in_category = sum(val for val in token_counts.values())\n vocab_size = len(token_counts.keys())\n probability = Decimal('1')\n for token in tokens:\n counts = Decimal(token_counts[token] + delta)\n token_probability = counts / Decimal(total_words_in_category + (delta * vocab_size))\n probability = probability * token_probability\n\n return probability",
"title": ""
},
{
"docid": "9ea3ee3534840321fda2898c5961e22c",
"score": "0.63413054",
"text": "def PredictProbModel(classifier,X_test):\n Y_test_pred_proba = classifier.predict_proba(X_test)\n \n return Y_test_pred_proba*100",
"title": ""
},
{
"docid": "6a4f1563ce9514e09a59d80b7ac71e50",
"score": "0.61598575",
"text": "def __calc_probabilities__(self, dataset):\n prior_prob = defaultdict(float)\n # Format for this dict is: First dict key refers to attribute name, second dict key refers\n # attribute value and third dict key refers to class name. E.g. P[0]['20-29']['recurrence-events\\n']\n posterior_prob = defaultdict(lambda: defaultdict(lambda: defaultdict(float)))\n\n # Calculating the prior probabilities by dividing by the number of instances\n for class_name, value in self.prior_counts.items():\n prior_prob[class_name] = value/dataset.get_num_rows()\n\n # Now calculate the posterior probabilities by dividing the fractional counts\n # by the fractional class counts\n for attr_name, val_dict in self.posterior_counts.items():\n for attr_val, class_dict in self.posterior_counts[attr_name].items():\n for class_name, count in self.posterior_counts[attr_name][attr_val].items():\n numerator = count\n # Subtract the fractional counts from the total if this attribute of\n # the instance is missing\n denominator = self.prior_counts[class_name] - self.missing_counts[attr_name][class_name]\n posterior_prob[attr_name][attr_val][class_name] = numerator/denominator\n\n return prior_prob, posterior_prob",
"title": ""
},
{
"docid": "17616a3c345c08625da43fde4d5fadfc",
"score": "0.61435264",
"text": "def __generate_values(self, data, classifier):\n # Load default parameters\n parameters = self.__load_default()\n\n # Load classifiers\n classifiers = []\n z = 0\n while True:\n try:\n classifier.classify([], '_'.join(parameters) + '_' + str(z))\n z += 1\n except FileNotFoundError:\n break\n\n # The classifier is not trained\n if z < 2:\n raise FileNotFoundError\n\n # OpenCV Selective Search has a bug which generates bounding boxes outside the border of the image.\n # Therefore, a big border is needed to solve the problem.\n bordersize = 200\n # List for all the Histograms\n all = []\n # Load vocabulary generated in the training process\n self.vocabulary = np.loadtxt(os.path.join(self.feature_directory, 'default_' + '_'.join(parameters), 'voc.out'))\n # Load standard scaler generated in the training process\n self.std_slr = pickle.load(\n open(os.path.join(self.feature_directory, 'default_' + '_'.join(parameters), \"std.out\"), \"rb\"))\n # Loop over all images\n for img in data:\n probabilities = [0] * z\n # Apply white border\n border = cv2.copyMakeBorder(cv2.imread(img), top=bordersize,\n bottom=bordersize, left=bordersize, right=bordersize,\n borderType=cv2.BORDER_CONSTANT, value=[255, 255, 255])\n # Create Selective Search Segmentation Object using default parameters\n ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()\n # Set input image on which we will run segmentation\n ss.setBaseImage(border)\n # Use quality Search\n ss.switchToSelectiveSearchQuality()\n # Run selective search segmentation on input image\n rects = ss.process()\n # Iterate over all the region proposals\n for j, rect in enumerate(rects):\n # Compare rectangle for region proposal till numShowRects\n if j < 500: # 500 is the best value found in the Bachelor Thesis Logos recognition for website services\n # Get rectangle coordinates\n x, y, w, h = rect\n probs = []\n # Loop over the different logos classes to compute the histogram\n for i in range(z):\n hist = self.__compute_hist(border[y:y + h, x:x + w])\n if hist is not None:\n # Compute probability that the histogram is the logo i\n prob = classifier.classify([hist], '_'.join(parameters) + '_' + str(i))[0]\n # Save probability and logo type\n probs.append((prob[0], i))\n # Sort the probabilities to have the highest as the first element\n probs.sort(key=lambda tup: tup[0], reverse=True)\n # loop over all probabilities (one for each class) and take the highest until now\n for i in range(len(probs)):\n # if prob is higher than the probability until now replace it\n if probs[i][0] > probabilities[probs[i][1]]:\n probabilities[probs[i][1]] = probs[i][0]\n # print image with random name\n break\n else:\n break\n for i in range(z):\n all.append((i, probabilities[i]))\n return all",
"title": ""
},
{
"docid": "87e2b3d985277858a46ca84a0ae6e629",
"score": "0.6140995",
"text": "def model_probs(self, model):\n if not model:\n model = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True), n_jobs=-1)\n model.fit(self.X_train, self.y_train).decision_function(self.X_test)\n predictions = model.predict_proba(self.X)\n return predictions",
"title": ""
},
{
"docid": "3592a7f9f754fbed554ed77430bbdff0",
"score": "0.60972387",
"text": "def calculate_prob(input_data,feature_values):\n m = input_data.shape[0] #total sample size\n n = input_data.shape[1] #total feature(attribute) number\n \n total_feature_value_number = len(feature_values)\n probs = np.zeros((total_feature_value_number,n))\n #if all values are the same --> Assuming we have two labels only!!!\n if check_all_values_same(input_data,feature_values):\n for i in range(total_feature_value_number):\n if input_data[0] == feature_values[i]:\n probs[i,0] = 1.0\n else: \n for i in range(n):\n current_attribute_data = input_data[:,i]\n total_prob = 0\n for j in range(total_feature_value_number):\n # Calculate how many times we have specific value for attribute\n total = current_attribute_data[current_attribute_data == feature_values[j]].shape[0]\n probs[j,i] = total/m\n total_prob = total_prob + probs[j,i]\n #print(\"Total prob: \", total_prob)\n\n # Total probability must be equal to 1.0\n epsilon = 0.001\n if (total_prob - 1.0) > epsilon:\n print(\"Total prob. is not equal to 1.0!\")\n \n return probs",
"title": ""
},
{
"docid": "2aca7cabb48c22dd66b562aad61417c6",
"score": "0.60691977",
"text": "def train_classifier(self):\n for c_class in self.classes:\n self.class_prob[c_class] = math.log(float(len(self.result_set[c_class])) / self.total_docs, 2)\n #print \"Class: %s, prob: %d\" %(c_class, self.class_prob[c_class])\n\n \"\"\"Find the probability of each document\"\"\"\n \n \"\"\"count the total number of unique terms\"\"\"\n words_map = {}\n unique_word = 0\n for c_class in self.classes:\n data = self.result_set[c_class]\n words = re.split(r'\\W+', data)\n for word in words:\n if word not in words_map:\n unique_word += 1\n words_map[word] = 1\n\n for c_class in self.classes:\n data = self.result_set[c_class]\n words = re.split(r'\\W+', data)\n local_word_count = {}\n for word in words:\n if word not in local_word_count:\n local_word_count[word] = 0\n local_word_count[word] += 1\n\n num_words = len(words)\n for word in local_word_count:\n if word not in self.word_prob:\n self.word_prob[word] = {}\n self.word_prob[word][c_class] = math.log((float)(local_word_count[word] + 1) / (num_words + unique_word), 2)\n self.nan[c_class] = math.log((float) (1)/ (num_words + unique_word), 2)\n #print c_class, num_words, unique_word\n \n \n \n \"\"\"\n for word in self.word_prob:\n print \"%s, %s\" %(word, self.word_prob[word])\n \"\"\"",
"title": ""
},
{
"docid": "decf7de5aead797d88d7a1ed119b6e9f",
"score": "0.60660297",
"text": "def classify(templates, data, index, classifier):\n\n features = featurize(data, index, templates)\n return classifier.predict_proba(features)",
"title": ""
},
{
"docid": "5fa8ed84e08b343a2e3f4369e63f35ed",
"score": "0.60084236",
"text": "def _conditional_prob(self, X_splitted):\n y_unique = list(X_splitted.keys()) # number of classes\n n = X_splitted[y_unique[0]].shape[1] # number of features\n counts = []\n totals = []\n for y in X_splitted.keys():\n temp_count = np.sum(X_splitted[y], axis=0).tolist()\n total = np.sum(X_splitted[y])\n counts.append(temp_count)\n totals.append(total)\n counts = np.array(counts)\n totals = np.array(totals).reshape(len(y_unique), 1)\n self.conditional = (counts + self.smooth) / (totals + n)\n # print(counts)\n # print(totals)",
"title": ""
},
{
"docid": "e5dc0690d17b77de3a5dbfcdd340bb7b",
"score": "0.600323",
"text": "def predictClassifier(classifierObj, data):\n\n \n result = classifierObj.predict(data)\n return result",
"title": ""
},
{
"docid": "cbd7e308ddd32509828f1895583b6f35",
"score": "0.5993264",
"text": "def dice_coef_metric_per_classes(\n probabilities: np.ndarray,\n truth: np.ndarray,\n eps: float = 1e-9,\n classes: list = ['WT', 'TC', 'ET']) -> np.ndarray:\n scores = {key: list() for key in classes}\n num_classes = probabilities.shape[0]\n predictions = probabilities\n assert (predictions.shape == truth.shape)\n\n for class_ in range(num_classes):\n prediction = predictions[class_]\n truth_ = truth[class_]\n intersection = 2.0 * (truth_ * prediction).sum()\n union = truth_.sum() + prediction.sum()\n if truth_.sum() == 0 and prediction.sum() == 0:\n scores[classes[class_]].append(1.0)\n else:\n scores[classes[class_]].append((intersection + eps) / union)\n\n return scores",
"title": ""
},
{
"docid": "ebb06930723730d60b31fb22d5423a15",
"score": "0.59763265",
"text": "def predict_probability(*args, **kwargs):\n return _algorithms.svm_01_predict_probability(*args, **kwargs)",
"title": ""
},
{
"docid": "f777190f58a73947ac4e55fa827bf172",
"score": "0.5939167",
"text": "def compute_priors(data, protected_index, prediction_index):\n counts = np.zeros((2, 2))\n for batch in list(data):\n _, labels = batch[0], batch[1]\n\n for label in labels:\n prot_value = label[protected_index]\n pred_value = label[prediction_index]\n counts[prot_value][pred_value] += 1\n total = sum(sum(counts))\n\n prot_rate = np.round(counts[1][1]/sum(counts[1]), 4)\n unprot_rate = np.round(counts[0][1]/sum(counts[0]), 4)\n\n print('Prob. protected class:', np.round(sum(counts[1])/total, 4))\n print('Prob. positive outcome:', np.round(sum(counts[:, 1])/total, 4))\n print('Prob. positive outcome given protected class', prot_rate)\n print('Prob. positive outcome given unprotected class', unprot_rate)",
"title": ""
},
{
"docid": "4bdd63fcea34210611355677a63afc30",
"score": "0.5928846",
"text": "def prob(dataset):\n\n # bin by the number of different values per feature\n num_rows, num_columns = dataset.shape\n bins = [len(np.unique(dataset[:, ci])) for ci in range(num_columns)]\n\n prob, _ = np.histogramdd(dataset, bins)\n return prob / np.sum(prob)",
"title": ""
},
{
"docid": "bf18f129e3421257faf98d462e441f20",
"score": "0.59250057",
"text": "def svc_cl_pred(self, x):\n \n # Validate\n pred_proba = self.svc_classif.predict_proba(x)\n probabilities = np.zeros((len(x), self.class_count), float)\n\n rownum = 0\n for row in pred_proba: \n c = 0\n for i in row:\n col = int(self.svc_classif.classes_[c]-1)\n probabilities[rownum, col] = i\n c += 1\n rownum += 1\n \n # print probabilities\n # print \"Found following classes:\"\n # print self.svc_classif.classes_\n \n pred_class = self.svc_classif.predict(x)\n \n return pred_class, probabilities",
"title": ""
},
{
"docid": "5f6366f94af59a869bca88914a52aaa4",
"score": "0.5883085",
"text": "def compute_initial_distribution(training_data, order):\n\tlength = len(training_data)\n\ttags_list = [training_data[idx][1] for idx in range(0, length)]\n\n\tif order == 2:\n\t\tcount_dict = defaultdict(int)\n\t\tpi_dict = defaultdict(float)\n\t\t# Count the tags that appear in the first of the sentence.\n\t\tcount_dict[tags_list[0]] += 1\n\t\tfor idx1 in range(0, length-1):\n\t\t\tif tags_list[idx1] == \".\":\n\t\t\t\tif tags_list[idx1+1] != \".\":\n\t\t\t\t\tcount_dict[tags_list[idx1+1]] += 1\n\t\ttotal_counts = sum(count_dict.values())\n\t\t# Calculate the probability.\n\t\tfor tag_i in count_dict.keys():\n\t\t\tpi_dict[tag_i] += float(count_dict[tag_i]) / total_counts\n\n\t\treturn pi_dict\n\n\telif order == 3:\n\t\tcount_dict = defaultdict(lambda: defaultdict(int))\n\t\tpi_dict = defaultdict(lambda: defaultdict(float))\n\t\t# Count the tags that appear in the first of the sentence.\n\t\tcount_dict[tags_list[0]][tags_list[1]] += 1\n\t\tfor idx1 in range(0, length-2):\n\t\t\tif tags_list[idx1] == \".\":\n\t\t\t\tif tags_list[idx1+1] != \".\":\n\t\t\t\t\tif tags_list[idx1+2] != \".\":\n\t\t\t\t\t\tcount_dict[tags_list[idx1+1]][tags_list[idx1+2]] += 1\n\t\ttotal_counts = 0\n\t\tfor tag1 in count_dict.keys():\n\t\t\ttotal_counts += sum(count_dict[tag1].values())\n\t\t# Calculate the probability.\n\t\tfor tag_i in count_dict.keys():\n\t\t\tfor tag_j in count_dict[tag_i].keys():\n\t\t\t\tpi_dict[tag_i][tag_j] += float(count_dict[tag_i][tag_j]) / total_counts\n\n\t\treturn pi_dict\n\n\telse:\n\t\traise Exception('The order must be 2 or 3.')",
"title": ""
},
{
"docid": "6677aae65df136f925718fd2011bd0fb",
"score": "0.58715874",
"text": "def _predict_probability_from_features(self, features):\n return LogisticRegression.predict_proba(self.target_model, features)",
"title": ""
},
{
"docid": "5e1ee0fbc236b0f6cbdb165d1f07866f",
"score": "0.5856548",
"text": "def test_clf(clf, train_data, train_target, test_data, test_target):\r\n clf.fit(train_data, train_target)\r\n pred = clf.predict(test_data)\r\n acc = np.sum(pred==test_target) / test_data.shape[0]\r\n return acc",
"title": ""
},
{
"docid": "3ba28f4c74c8d744f0eabc62627a8342",
"score": "0.58552",
"text": "def computeProbabilities(self):\n pass",
"title": ""
},
{
"docid": "02012cd35cba409248a09e2143d4c53d",
"score": "0.5841066",
"text": "def evaluate(model, ds):\n results = ds.map(lambda x: (model.predict(x.features), x.label))\n truePositives = results.filter(lambda x: x[0] == x[1])\n result = float(truePositives.count()) / results.count()\n truePositives.unpersist()\n results.unpersist()\n return result",
"title": ""
},
{
"docid": "80c9442d72caae7dab26e06dd6269350",
"score": "0.5815622",
"text": "def train(self):\n total = 0\n classes = {} \n counts = {}\n # determine size of a training vector\n size = len(self.data[0])\n #\n # iterate through training instances\n for instance in self.data:\n total += 1\n category = instance[0]\n classes.setdefault(category, 0)\n counts.setdefault(category, {})\n classes[category] += 1\n # now process each column in instance\n col = 0\n for columnValue in instance[1:]:\n col += 1\n tmp = {}\n if col in counts[category]:\n tmp = counts[category][col]\n if columnValue in tmp:\n tmp[columnValue] += 1\n else:\n tmp[columnValue] = 1\n counts[category][col] = tmp\n ## print(classes) {'i500': 9, 'i100': 6}\n ## print(counts)\n #############################\n ## counts数据结构: 但该DS有一个不足就是：如果数据量特别大的时候，数据结构的构造非常麻烦。\n # {'i500': {1: {'appearance': 3, 'health': 4, 'both': 2}, 2: {'active': 4, 'sedentary': 2, 'moderate': 3}, 3: {'aggressive': 6, 'moderate': 3}, 4: {'yes': 6, 'no': 3}},\n # 'i100': {1: {'both': 3, 'health': 1, 'appearance': 2}, 2: {'active': 2, 'sedentary': 3, 'moderate': 1}, 3: {'aggressive': 1, 'moderate': 5}, 4: {'yes': 2, 'no': 4}}}\n #############################\n\n\n # ok. done counting. now compute probabilities\n #\n # first prior probabilities\n # \n for (category, count) in classes.items():\n self.prior[category] = count / total\n # now compute conditional probabilities 算法改进\n for (category, columns) in counts.items():\n tmp = {}\n for (col, valueCounts) in columns.items():\n tmp2 = {}\n for (value, count) in valueCounts.items():\n tmp2[value] = count / classes[category]\n tmp[col] = tmp2\n #convert tmp to vector\n tmp3 = []\n for i in range(1, size):\n tmp3.append(tmp[i])\n self.conditional[category] = tmp3\n ## print(self.conditional)\n #############################\n ## conditional数据结构\n # {'i500': [{'health': 0.4444444444444444, 'appearance': 0.3333333333333333, 'both': 0.2222222222222222}, \n # {'active': 0.4444444444444444, 'sedentary': 0.2222222222222222, 'moderate': 0.3333333333333333}, \n # {'aggressive': 0.6666666666666666, 'moderate': 0.3333333333333333}, \n # {'yes': 0.6666666666666666, 'no': 0.3333333333333333}], \n # 'i100': [{'both': 0.5, 'health': 0.16666666666666666, 'appearance': 0.3333333333333333}, \n # {'active': 0.3333333333333333, 'sedentary': 0.5, 'moderate': 0.16666666666666666}, \n # {'aggressive': 0.16666666666666666, 'moderate': 0.8333333333333334}, \n # {'yes': 0.3333333333333333, 'no': 0.6666666666666666}]}\n #############################",
"title": ""
},
{
"docid": "38baada5f959da0f875b11f902fbf0e5",
"score": "0.5800794",
"text": "def learn_discrete(dataset,virtual_count=1,domain=None):\r\n res = dict()\r\n # print \"DATA:\", dataset, \"Domain: \", domain\r\n if(domain==None):\r\n domain = set(dataset)\r\n for elem in domain:\r\n res[elem] = 0\r\n #TODO: generate a distribution over the domain from the data and\r\n #taking into account the virtual counts\r\n denom = len(dataset) + (virtual_count*len(domain))\r\n for elem in dataset:\r\n res[elem] = res.get(elem, 0) + 1\r\n for k,v in res.iteritems():\r\n res[k] = (v+virtual_count)/float(denom)\r\n # print res\r\n return res\r\n # NOTE: currently assumes that same virtual count applies to every entry in domain\r",
"title": ""
},
{
"docid": "a2c7d609a9ec2aa39efbbe7b6b90e708",
"score": "0.57777405",
"text": "def classifier(train_pca, val_pca, test_pca):\n \n clf = RandomForestClassifier(n_estimators=100, \n max_depth=None, \n min_samples_split=2, \n random_state=0)\n clf.fit(train_pca, train_labels.values.ravel())\n \n accuracy = clf.score(val_pca, val_labels)\n val_predictions = clf.predict(val_pca)\n negative, positive = class_sample_count(val_predictions)\n \n print(\"-\"*40)\n print(\"Classifier Accuracy: {0}\".format(accuracy) + \"\\n\")\n print(\"Validation Set Predictions\")\n print(\"Number of Data Points for Class -1: {0}\".format(negative))\n print(\"Number of Data Points for Class 1: {0}\".format(positive))\n print(\"-\"*40)\n \n test_labels = clf.predict(test_pca)\n # Convert label numpy array to dataframe\n test_labels_df = pd.DataFrame(test_labels)\n \n negative, positive = class_sample_count(test_labels) \n print(\"-\"*40)\n print(\"Test Set Predictions\")\n print(\"Number of Data Points for Class -1: {0}\".format(negative))\n print(\"Number of Data Points for Class 1: {0}\".format(positive))\n print(\"-\"*40)\n\n return test_labels_df",
"title": ""
},
{
"docid": "38671734fca341e79cc0306a45615b6c",
"score": "0.57676584",
"text": "def classify(self, features):\n spamProb = self.document_probability(features, 'spam')\n return spamProb",
"title": ""
},
{
"docid": "e0e9596c5bdfbd01f70f5487710dbb2f",
"score": "0.576644",
"text": "def classifier_test(classifier, feature_vector, mu_ft, std_ft):\n \n # Normalize feature_vector\n x = (feature_vector - mu_ft) / std_ft \n y_hat = classifier.predict(x)\n #y_hat = None\n return y_hat",
"title": ""
},
{
"docid": "6d76a51b453f1eabe97406be2f29c232",
"score": "0.5761093",
"text": "def compute_distributions(data, labels, discrete_features, continuous_features):\n\t# obtain counts for discrete features given needs work\n\tdiscrete_positive = [discrete_conditional(data, labels, feature, class_=1) \n\t\t\t\t\t\t\t\tfor feature in discrete_features]\n\t# obtain counts for discrete features given not needs work\n\tdiscrete_negative = [discrete_conditional(data, labels, feature, class_=-1) \n\t\t\t\t\t\t\t\tfor feature in discrete_features]\n\t# obtain probability distribution for continuous features given needs work\n\tcontinuous_positive = []\n\tfor feature in continuous_features:\n\t\tif feature != 'writing':\n\t\t\tcontinuous_positive.append(\n\t\t\t\tcontinuous_conditional(data, labels, feature, class_=1, distribution='normal')\n\t\t\t\t)\n\t\telse:\n\t\t\tcontinuous_positive.append(\n\t\t\t\tcontinuous_conditional(data, labels, feature, class_=1, distribution='negativeBinomial')\n\t\t\t\t)\t\n\t# obtain probability distribution for continuous features given not needs work\n\tcontinuous_negative = []\n\tfor feature in continuous_features:\n\t\tif feature != 'writing':\n\t\t\tcontinuous_negative.append(\n\t\t\t\tcontinuous_conditional(data, labels, feature, class_=-1, distribution='normal')\n\t\t\t\t)\n\t\telse:\n\t\t\tcontinuous_negative.append(\n\t\t\t\tcontinuous_conditional(data, labels, feature, class_=-1, distribution='negativeBinomial')\n\t\t\t\t)\t\n\treturn (discrete_positive, discrete_negative, continuous_positive, continuous_negative)",
"title": ""
},
{
"docid": "a91e0570619171ca46e928259fe4d179",
"score": "0.5758035",
"text": "def learn_naive_bayes(class_key,feature_keys,\r\n dataset,\r\n class_prior_count=1,feature_posterior_count=1,\r\n class_domain=None,feature_domains=None):\r\n if class_domain == None:\r\n #compute the set of values that the class can take on\r\n class_domain = set([instance[class_key] for instance in dataset])\r\n if feature_domains == None:\r\n #compute the set of values that the features can take on\r\n feature_domains = dict()\r\n for f in feature_keys:\r\n if f not in feature_domains:\r\n feature_domains[f] = set([instance[f] for instance in dataset])\r\n\r\n #create a uniform class prior\r\n # PCuniform = uniform(class_domain)\r\n # PFuniform = dict()\r\n # #create uniform feature priors\r\n # for f in feature_keys:\r\n # PFf = dict()\r\n # #for all values v in the class domain, PFf[v] is a distribution over f's\r\n # #domain\r\n # for class_v in class_domain:\r\n # PFf[class_v] = uniform(feature_domains[f])\r\n # PFuniform[f] = PFf\r\n # return (PCuniform,PFuniform)\r\n\r\n\r\n PClearned = learn_discrete([instance[class_key] for instance in dataset],\r\n class_prior_count,\r\n class_domain)\r\n PFlearned = dict()\r\n # TODO what here?\r\n for f in feature_keys:\r\n PFf = dict()\r\n for class_v in class_domain:\r\n # print \"CurrClass: \", class_v, \"CurrFeat: \", f\r\n PFf[class_v] = learn_discrete([instance[f] for instance in dataset if instance[class_key]==class_v], \r\n feature_posterior_count,\r\n feature_domains[f])\r\n PFlearned[f] = PFf\r\n return (PClearned,PFlearned)",
"title": ""
},
{
"docid": "1575a8533dfcc28a99e132a3d52dc571",
"score": "0.5757652",
"text": "def Probability(self, doc, dclass=\"\"):\r\n if dclass:\r\n sum_dclass = self.sum_words_in_class(dclass)\r\n prob = 0\r\n\r\n d = Document(self.__vocabulary)\r\n d.add_text_to_doc(doc)\r\n\r\n for j in self.__document_classes:\r\n sum_j = self.sum_words_in_class(j)\r\n prod = 1\r\n for i in d.Words():\r\n wf_dclass = 1 + self.__document_classes[dclass].WordFreq(i)\r\n wf = 1 + self.__document_classes[j].WordFreq(i)\r\n r = wf * sum_dclass / (wf_dclass * sum_j)\r\n prod *= r\r\n prob += prod * self.__document_classes[j].NumberOfDocuments() / self.__document_classes[\r\n dclass].NumberOfDocuments()\r\n if prob != 0:\r\n return 1 / prob\r\n else:\r\n return -1\r\n else:\r\n prob_list = []\r\n for dclass in self.__document_classes:\r\n prob = self.Probability(doc, dclass)\r\n prob_list.append([dclass, prob])\r\n prob_list.sort(key=lambda x: x[1], reverse=True)\r\n return prob_list",
"title": ""
},
{
"docid": "d289107e62fe2a6e2eaf298e9cfbf64f",
"score": "0.57522213",
"text": "def n_validator(data, p, classifier, *args):\n\n # if args are passed it is synthetic data\n if len(args) > 0:\n # merge the two synthetic data\n data = data + args[0]\n\n # randomized the data\n random.shuffle(data)\n #split the data in p groups\n chunked_data = chunks(data, p)\n\n n = len(chunked_data)\n training = chunked_data[-1] # p-1 part of data is training data\n score = 0\n for i in range(n - 1):\n lt = len(chunked_data[i])\n labels_list = []\n test_list = []\n\n for j in range(lt):\n # separate test data from its label\n labels_list.append(chunked_data[i][j][1])\n test_list.append(chunked_data[i][j][0])\n #classifier is nn_classifier, method passed as param\n labels = classifier(training, test_list)\n\n #compared the label with the one from classifier\n for t in range(len(labels)):\n if labels[t] == labels_list[t]:\n score += 1\n # return performance of classifier\n return score / len(data)",
"title": ""
},
{
"docid": "400d8c8b9042bb42d3de1ad1494b24cf",
"score": "0.5744106",
"text": "def predict(self, feature_matrix):\r\n classifications = []\r\n for propability in self.predict_probability(feature_matrix):\r\n if propability > self.cut_off:\r\n classifications.append(1)\r\n else:\r\n classifications.append(0)\r\n return classifications",
"title": ""
},
{
"docid": "7226a09c09ad1e3e450129fcd34d40da",
"score": "0.5730646",
"text": "def predict(self, cls, point, examples):\n value = point[self.name]\n if cls not in self.probs:\n self.probs[cls] = dict()\n if value in self.probs[cls]:\n return self.probs[cls][value]\n else:\n examples = examples[self.name]\n self.probs[cls][value] = self._prob(point[self.name], examples.value_counts(), len(examples))\n return self.probs[cls][value]",
"title": ""
},
{
"docid": "cb6e42dc00c6b20245025a3aacc2d513",
"score": "0.5726601",
"text": "def predict_probability(*args, **kwargs):\n return _algorithms.svm_dense_predict_probability(*args, **kwargs)",
"title": ""
},
{
"docid": "289f021bc06b62ddd06a3a349305f0fd",
"score": "0.5713281",
"text": "def classify(self, document, discrete=True):\n v = self._vector(document)[1]\n i = self._iteration or 1\n i = float(i)\n p = defaultdict(float)\n for type, w in self._weight.iteritems():\n #p[type] = sum(w[f][0] for f in v if f in w) # Without averaging.\n s = 0\n for f in v:\n if f in w:\n w0, w1, j = w[f]\n s += ((i-j) * w0 + w1) / i\n p[type] = s\n # Normalize probability estimates.\n m = min(chain(p.itervalues(), (0,)))\n s = sum(x-m for x in p.itervalues()) or 1\n for type in p:\n p[type] -= m\n p[type] /= s\n if not discrete:\n return p\n try:\n # Ties are broken in favor of the majority class\n # (random winner for majority ties).\n m = max(p.itervalues())\n p = sorted((self._classes[type], type) for type, w in p.iteritems() if w == m > 0)\n p = [type for frequency, type in p if frequency == p[0][0]]\n return choice(p)\n except:\n return self.baseline",
"title": ""
},
{
"docid": "a1b91b7bfc69fe5737dc4d0594d44e2e",
"score": "0.5709207",
"text": "def __init__(self, pred_class, pred_class_count, total_count): \n self.pred_class = pred_class\n self.pred_class_count = pred_class_count\n self.total_count = total_count\n self.prob = pred_class_count / total_count # probability of having the class label",
"title": ""
},
{
"docid": "53f242ef2b343e378dbcfa0ade0b4655",
"score": "0.5703653",
"text": "def cprob(self, f, cat):\n clf = self.fprob(f, cat)\n if clf == 0: return 0.0 # else testOneCategory() fails\n freqsum = sum([self.fprob(f, c) for c in self.categories()])\n p = clf/freqsum\n return p",
"title": ""
},
{
"docid": "b95793b24ab5a41a5e8357914ec8581f",
"score": "0.56769687",
"text": "def Naive_Bayes_classifier(headline, word_list, training_set, training_label, m, p):\n #headline = headline.split()\n \n #calculate P(fake) and P(real)\n n = len(train_real) + len(train_fake)\n count_real = len(train_real)\n count_fake = len(train_fake)\n prob_fake = len(train_fake) / float(n)\n prob_real = 1.0 - prob_fake\n \n prob_word_real = []\n prob_word_fake = [] \n for i in word_list.keys():\n #P(word_i|real)\n P_word_i_real = (word_list[i][0]+m*p)/float(count_real + 1)\n #P(word_i|fake)\n P_word_i_fake = (word_list[i][1]+m*p)/float(count_fake + 1)\n \n if i in headline:\n prob_word_real.append(P_word_i_real)\n prob_word_fake.append(P_word_i_fake) \n elif i not in headline:\n prob_word_real.append(1. - P_word_i_real)\n prob_word_fake.append(1. - P_word_i_fake) \n \n #conditional independence is assumed by Naive Bayes\n #do multiplication to get P(words|real) and P(words|fake)\n multi_real = 0\n for p in prob_word_real:\n multi_real += math.log(p)\n multi_real = math.exp(multi_real)\n \n multi_fake = 0\n for p in prob_word_fake:\n multi_fake += math.log(p)\n multi_fake = math.exp(multi_fake)\n \n #compute P(class)*P(words|class)\n prob_real_words = prob_real * multi_real\n prob_fake_words = prob_fake * multi_fake\n \n #compute P(class)*(1 - P(words|class)) for part 3\n prob_real_not_words = prob_real * (1. - multi_real)\n prob_fake_not_words = prob_fake * (1. - multi_fake) \n \n #probability that the given headline is fake, P(fake|words)\n prob = prob_fake_words/ (prob_fake_words + prob_real_words)\n \n #probability that the headline is fake when the word absence, P(fake|~words), for part 3\n prob_absence = prob_fake_not_words/ (prob_fake_not_words + prob_real_not_words) \n \n result = \"real\"\n if prob > 0.5:\n result = \"fake\"\n \n return result, prob, prob_absence",
"title": ""
},
{
"docid": "78c2f9405143d8564f0cdf7334a50859",
"score": "0.5674112",
"text": "def classifier(point):\n kernel_v = np.array([kernel_func(point, v) \\\n for v in support_vectors])\n predict_class = sum(alpha_y_nz*kernel_v)\n if predict_class>0:\n return 1\n else:\n return 0",
"title": ""
},
{
"docid": "581d7009f10ab1d61f3b55649aa91631",
"score": "0.5670493",
"text": "def classify(self, instances):\n\n priors = [0.0] * len(self.estimators)\n probabilities = [[0.0 for x in range(len(priors))] for y in range(len(instances))]\n\n # For all class labels estimate the prior of the class\n for i in range(len(self.estimators)):\n priors[i] = len(self.estimators[i].getSamples())\n\n # Now we iterate over all instances to determine the kernel densities\n for i in range(len(instances)):\n densities = [0] * len(self.estimators)\n\n # Then estimate the kernel density based on the stored instances with that label\n # TODO: variable kernel, incorporate bandwidth!\n for j in range(len(self.estimators)):\n densities[j] = self.estimators[j].estimateDensity(instances[i], self.bandwidths[j])\n # Compute probabilities as product of estimated prior and density\n for j in range(len(densities)):\n probabilities[i][j] = priors[j]*densities[j]\n\n # Normalize probabilities\n if(sum(probabilities[i]) == 0):\n # Choose class with highest prior\n probabilities[i][np.argmax(priors)] = 1.0\n else:\n probabilities[i] /= sum(probabilities[i])\n\n return probabilities",
"title": ""
},
{
"docid": "c41e9f48666405dda26fc24296745627",
"score": "0.56640023",
"text": "def calculate_accuracies(X_train, X_test, y_train, y_test,\r\n classifier, num_features=None):\r\n X_train, y_train = X_train, y_train\r\n X_test, y_test = X_test, y_test\r\n pipe = Pipeline([('vectorizer', TfidfVectorizer(max_features=num_features)),\r\n ('classifier', classifier)])\r\n pipe.fit(X_train, y_train)\r\n y_pred = pipe.predict(X_test)\r\n y_pred_train = pipe.predict(X_train)\r\n return accuracy_score(y_pred, y_test), accuracy_score(y_pred_train,\r\n y_train)",
"title": ""
},
{
"docid": "06da4f3891a26ec39c0070ffb55c2699",
"score": "0.5661928",
"text": "def predict(algo):\n\n X_train, X_test = vectorize.ngrams_vectorize(train_sentences=train_passages,\n test_sentences=test_passages,\n ngram_range=NGRAMS,\n max_features=MAX_FEATURES)\n\n clf = algo.fit(X_train, Y_train)\n assert clf.classes_.tolist()[0] == 'fic' # make sure that the class ordering is ['fic' 'non']\n preds_with_probs = clf.predict_proba(X_test) # for AUROC & AUPRC\n\n print(\"Train: {} & {} | Test: {}\".format(X_train.shape, Y_train.shape, X_test.shape))\n print(\"Ordering:\", clf.classes_)\n print(\"Y test shape:\", preds_with_probs.shape)\n\n return preds_with_probs",
"title": ""
},
{
"docid": "30b5c1154a113024c63eb05c5fd02d90",
"score": "0.5658066",
"text": "def _classify(self, document, probability=False):\n if self._model is None:\n return None\n M = self._model[0]\n H1 = self._model[1]\n H2 = self._model[2]\n H3 = self._model[3]\n n = len(H1)\n v = self._vector(document)[1]\n v = dict(map(lambda (i, k): (H1.get(k, n+i+1), v[k]), enumerate(v)))\n # For linear SVC, use LIBLINEAR which is 10x faster.\n # For kernel SVC, use LIBSVM.\n if self.extension == LIBLINEAR:\n f = self._svm.liblinearutil.predict\n o = \"-b 0 -q\"\n else:\n f = self._svm.libsvmutil.svm_predict\n o = \"-b %s -q\" % int(probability)\n p = f([0], [v], M, o)\n # Note: LIBLINEAR does not currently support probabilities for classification.\n if self._type == CLASSIFICATION and probability is True and self.extension == LIBLINEAR:\n return {}\n if self._type == CLASSIFICATION and probability is True:\n return defaultdict(float, ((H3[i], w) for i, w in enumerate(p[2][0])))\n if self._type == CLASSIFICATION:\n return H3.get(int(p[0][0]))\n if self._type == REGRESSION:\n return p[0][0]\n if self._type == DETECTION:\n return p[0][0] > 0 # -1 = outlier => return False\n return p[0][0]",
"title": ""
},
{
"docid": "6198bb977c74adcc47a3968702fea1d2",
"score": "0.56548846",
"text": "def n_validator(data, p, classifier, *args):\n\n # if args aren't passed k = 1\n k = 1\n dist_type = 1\n if args:\n k = args[0]\n\n # if second args is passed\n if len(args) > 1:\n dist_type = args[1]\n \"\"\"if a 3er args and its type is array\n is passed merge the two synthetic data\"\"\"\n if len(args) > 2 and isinstance(args[2], list):\n data = data + args[2]\n\n # randomized the data\n random.shuffle(data)\n # split the data in p groups\n chunked_data = chunks(data, p)\n\n n = len(chunked_data)\n training = chunked_data[-1] # p-1 part of data is training data\n score = 0\n for i in range(n - 1):\n lt = len(chunked_data[i])\n labels_list = []\n test_list = []\n\n for j in range(lt):\n # separate test data from its label\n labels_list.append(chunked_data[i][j][1])\n test_list.append(chunked_data[i][j][0])\n # classifier is knn_classifier, method passed as param\n labels = classifier(training, test_list, k, dist_type)\n\n # compared the label with the one from classifier\n for t in range(len(labels)):\n if labels[t] == labels_list[t]:\n score += 1\n # return performance of classifier\n return score / len(data)",
"title": ""
},
{
"docid": "9458ddc564a1ec6563d8466014fe899d",
"score": "0.56402564",
"text": "def classify(data, labels, discrete_features, continuous_features, alpha):\n\t# compute total number of projects labelled \"needs work\"\n\tnum_needsWork = num_positive(labels)\n\t# obtain all the necessary probability distributions\n\tdis_pos, dis_neg, cont_pos, cont_neg = compute_distributions(data, labels, \n\t\t\t\t\t\t\t\t\t\t\t\tdiscrete_features, continuous_features)\n\t# initialize numpy array to store predicted labels\n\tpredicted_labels = np.zeros(len(labels), dtype=int)\n\n\t# iterate over rows in data set\n\tfor i in range(len(labels)):\n\t\t# compute number of training instances labelled positive (needs work)\n\t\tif labels[i]==1: num_pos = num_needsWork-1\n\t\telse: num_pos = num_needsWork\n\n\t\t# compute number of training instances labelled negative (not needs work)\n\t\tnum_neg = len(labels)-1-num_pos\n\n\t\t# compute unconditional probability of positive class (needs work)\n\t\tP_needsWork = num_pos/(len(labels)-1)\n\n\t\t# initialize scores for positive class (needs work) and negative class (not needs work)\n\t\tscore_pos, score_neg = math.log(P_needsWork), math.log(1-P_needsWork)\n\t\t\n\t\t# for each discrete feature\n\t\tfor j in range(len(discrete_features)):\n\t\t\t# access feature value\n\t\t\tfeature_value = data[discrete_features[j]][i]\n\t\t\t# obtain conditional count for that feature value given positive\n\t\t\tcount_pos = dis_pos[j].get(feature_value, 0) \n\t\t\t# if test case had true label positive, subtract 1 from this count\n\t\t\tif labels[i]==1: count_pos -= 1 \n\t\t\t# find conditional probability, applying Laplace smoothing\n\t\t\tprob_pos = (count_pos + alpha)/(num_pos + alpha*len(dis_pos[j]))\n\t\t\t# update score for positive class\n\t\t\tscore_pos += math.log(prob_pos)\n\t\t\t# obtain conditional count for feature value given negative\n\t\t\tcount_neg = dis_neg[j].get(feature_value, 0)\n\t\t\t# if test case had true label negative, subtract 1 from this count\n\t\t\tif labels[i]==-1: count_neg -= 1 \n\t\t\t# find conditional probability, applying Laplace smoothing\n\t\t\tprob_neg = (count_neg + alpha)/(num_neg + alpha*len(dis_neg[j]))\n\t\t\t# update score for negative class\n\t\t\tscore_neg += math.log(prob_neg)\n\n\t\t# for each continuous feature\n\t\tfor k in range(len(continuous_features)):\n\t\t\t# access feature value\n\t\t\tfeature_value = data[continuous_features[k]][i]\n\t\t\t# obtain conditional probability for feature value given positive\n\t\t\tprob_pos = cont_pos[k](feature_value)\n\t\t\t# update score for positive class\n\t\t\tscore_pos += math.log(prob_pos)\n\t\t\t# obtain conditional probability for feature value given negative\n\t\t\tprob_neg = cont_neg[k](feature_value)\n\t\t\t# update score for negative class\n\t\t\tscore_neg += math.log(prob_neg)\n\t\t# classify current row\n\t\tif score_pos > score_neg: predicted_labels[i] = 1\n\t\telse: predicted_labels[i] = -1\n\n\t# return vector of predicted labels\n\treturn(predicted_labels)",
"title": ""
},
{
"docid": "b2dc41e1b63211fa19eb9cf38c8b1fca",
"score": "0.56321377",
"text": "def test_generate_classifier(templates_path_pattern, persist_clf_filename):\n global HYPERPARAMS\n clf, X_test, y_test = generate_classifier(templates_path_pattern, hyperparams=HYPERPARAMS)\n print('Test Accuracy of SVC = ', round(clf.score(X_test, y_test), 4))\n n_predict = 10\n print('My SVC predicts: ', clf.predict(X_test[0:n_predict]))\n print('For these', n_predict, 'labels: ', y_test[0:n_predict])\n if persist_clf_filename is not None:\n persist_classifier(clf, X_test, y_test, pickle_file=persist_clf_filename)",
"title": ""
},
{
"docid": "ca7b8dc0ae7e1f130cb05d3dad94749a",
"score": "0.56172806",
"text": "def evaluate(filename, repeat=1, train_percent=.7):\n total = 0\n correct = 0\n \n for i in range(repeat):\n data = load_data(filename, num_train_per_cat)\n \n classifier = ParagraphClassifier(training_data, feature_extractor)\n \n for (data, bio_type) in test_data:\n total += 1\n if classifier.classify(data) == bio_type:\n correct += 1\n\n print \"Paragraph classifier on all data, results over \" + str(repeat) + \" runs: \" + str(float(correct)/total)\n\n classifier.classifier.show_most_informative_features(50)\n return classifier",
"title": ""
},
{
"docid": "0cff0535ced2fcbf3f8caae6a6e1c15e",
"score": "0.5617273",
"text": "def score(self, feature_values):\n fv_vector = vectorize_values(feature_values)\n scaled_fv_vector = self.apply_scaling(fv_vector)\n\n prediction = self.estimator.predict([scaled_fv_vector])[0]\n labels = self.estimator.classes_\n probas = self.estimator.predict_proba([scaled_fv_vector])[0]\n probability = {label: proba for label, proba in zip(labels, probas)}\n\n doc = {'prediction': prediction, 'probability': probability}\n return util.normalize_json(doc)",
"title": ""
},
{
"docid": "8c809498a68eb1cb05dde0d9819959e0",
"score": "0.5604324",
"text": "def calc_cond_prob(self):\n for key in self.class_labels:\n denominator = self.class_labels[key] + 2*config.COND_PROB_DELTA\n for feature in config.FEATURE_DICT:\n numerator = config.COND_PROB_DELTA + config.FEATURE_DICT[feature][key]\n if feature not in self.cond_probs:\n self.cond_probs[feature] = {}\n self.cond_probs[feature][key] = numerator/denominator",
"title": ""
},
{
"docid": "368d346da4ebf3646c4694348e12f926",
"score": "0.5603966",
"text": "def compute_score(train, test):\n return sum([classify(sample, train) == sample[4] for sample in test]) / len(test)",
"title": ""
},
{
"docid": "2a9a25934594045c684eca4608a715c4",
"score": "0.56034255",
"text": "def predict_knn(classifier, X_test):\n\n pred_classes = classifier.predict(X_test)\n pred_probs = classifier.predict_proba(X_test)\n\n return pred_classes, pred_probs",
"title": ""
},
{
"docid": "e9129226f50ffdeef2d4096665f9f82c",
"score": "0.5603181",
"text": "def feature_probability(self, feature, label):\n total = self.features.get(feature, 0)\n if total:\n return Decimal(self.feature_counts.get(feature, {}).get(label, 0))/total\n\n return total",
"title": ""
},
{
"docid": "438ac9a8928da99882b42b34d9a40fde",
"score": "0.5600129",
"text": "def calculateProbabilityDensityFunc(self):\n\n trainErrorSetosa = 0\n trainErrorVirginica = 0\n trainErrorVersicolor = 0\n\n testErrorSetosa = 0\n testErrorVirginica = 0\n testErrorVersicolor = 0\n\n i = 1/(((2*pi)**(self.numOfFeatures/2))*sqrt(linalg.det(self.sigma)))\n z = linalg.inv(self.sigma)\n\n iTwo = 1/(((2*pi)**(self.numOfFeatures/2))*sqrt(linalg.det(self.sigmaTwo)))\n zTwo = linalg.inv(self.sigmaTwo)\n\n iThree = 1/(((2*pi)**(self.numOfFeatures/2))*sqrt(linalg.det(self.sigmaThree)))\n zThree = linalg.inv(self.sigmaThree)\n\n (m, n) = self.testData.shape\n for x in range(m):\n diff = subtract(self.testData[x], self.muOne) # This will give us a 4x1\n diff = diff.reshape((self.numOfFeatures, 1))\n diffTransposed = transpose(diff)\n\n k = exp(-0.5 * dot(dot(diffTransposed, z), diff))\n probSetosa = i * k\n\n diffTwo = subtract(self.testData[x], self.muTwo) # This will give us a 4x1\n diffTwo = diffTwo.reshape((self.numOfFeatures, 1))\n diffTwoTransposed = transpose(diffTwo)\n\n k = exp(-0.5 * dot(dot(diffTwoTransposed, zTwo), diffTwo))\n probVersicolor = iTwo * k\n\n diffThree = subtract(self.testData[x], self.muThree) # This will give us a 4x1\n diffThree = diffThree.reshape((self.numOfFeatures, 1))\n diffThreeTransposed = transpose(diffThree)\n\n k = exp(-0.5 * dot(dot(diffThreeTransposed, zThree), diffThree))\n probVirginica = iThree * k\n\n maxVal = max([probSetosa[0], probVersicolor[0], probVirginica[0]])\n\n answer = \"\"\n if maxVal == probSetosa:\n answer = \"Iris-setosa\"\n elif maxVal == probVersicolor:\n answer = \"Iris-versicolor\"\n elif maxVal == probVirginica:\n answer = \"Iris-virginica\"\n trueVal = self.data[\"class\"][x].decode(\"utf-8\")\n if answer != trueVal:\n if trueVal == \"Iris-setosa\":\n if x >= 40 and x < 50:\n testErrorSetosa += 1\n else:\n trainErrorSetosa += 1\n elif trueVal == \"Iris-versicolor\":\n if x >= 90 and x < 100:\n testErrorVersicolor += 1\n else:\n trainErrorVersicolor += 1\n elif trueVal == \"Iris-virginica\":\n if x >= 140 and x < 150:\n testErrorVirginica += 1\n else:\n trainErrorVirginica += 1\n\n trainError = (trainErrorVirginica + trainErrorVersicolor + trainErrorSetosa) / 120\n testError = (testErrorSetosa + testErrorVersicolor + testErrorVirginica) / 30\n\n testErrorSetosa /= 10\n testErrorVirginica /= 10\n testErrorVersicolor /= 10\n\n trainErrorSetosa /= 40\n trainErrorVirginica /= 40\n trainErrorVersicolor /= 40\n print(\"The test error rate was {}\".format(testError))\n print(\"The training error rate was {}\".format(trainError))\n\n print(\"The Setosa test error rate was {}\".format(testErrorSetosa))\n print(\"The Versicolor test error rate was {}\".format(testErrorVersicolor))\n print(\"The Virginica test error rate was {}\".format(testErrorVirginica))\n\n print(\"The Setosa training error rate was {}\".format(trainErrorSetosa))\n print(\"The Versicolor training error rate was {}\".format(trainErrorVersicolor))\n print(\"The Virginica training error rate was {}\".format(trainErrorVirginica))",
"title": ""
},
{
"docid": "6e9595d14ac8cdedb47b916dac4887fd",
"score": "0.5560794",
"text": "def train(self, corpus): \n\n for sentence in corpus.corpus:\n last_token = None\n for datum in sentence.data:\n token = datum.word\n if not self.counts[token]:\n self.v += 1\n self.counts[token] += 1\n if last_token:\n self.counts[(last_token, token)] += 1\n last_token = token\n\n for sentence in corpus.corpus:\n last_token = None\n for datum in sentence.data:\n token = datum.word\n if last_token:\n self.probs[(last_token, token)] = 1.0 * (self.counts[(last_token, token)] + 1) / (self.counts[last_token] + self.v)\n last_token = token",
"title": ""
},
{
"docid": "8132db5ee03171a9ace1ca3d49cf69cf",
"score": "0.55428374",
"text": "def classify(self, document, discrete=True):\n # Given red & round, what is the likelihood that it is an apple?\n # p = p(red|apple) * p(round|apple) * p(apple) / (p(red) * p(round))\n # The multiplication can cause underflow so we use log() instead.\n # For unknown features, we smoothen with an alpha value.\n v = self._vector(document)[1]\n m = self._method\n a = self._alpha\n n = self._classes.itervalues()\n n = float(sum(n))\n p = defaultdict(float)\n for type in self._classes:\n if m == MULTINOMIAL:\n if not type in self._cache: # 10x faster\n self._cache[type] = float(sum(self._likelihood[type].itervalues()))\n d = self._cache[type]\n if m == BINOMIAL \\\n or m == BERNOUILLI:\n d = float(self._classes[type])\n L = self._likelihood[type]\n g = sum(log((L[f] if f in L else a) / d) for f in v)\n g = exp(g) * self._classes[type] / n # prior\n p[type] = g\n # Normalize probability estimates.\n s = sum(p.itervalues()) or 1\n for type in p:\n p[type] /= s\n if not discrete:\n return p\n try:\n # Ties are broken in favor of the majority class\n # (random winner for majority ties).\n m = max(p.itervalues())\n p = sorted((self._classes[type], type) for type, g in p.iteritems() if g == m > 0)\n p = [type for frequency, type in p if frequency == p[0][0]]\n return choice(p)\n except:\n return self.baseline",
"title": ""
},
{
"docid": "20425f88f95749ba95750439cbdfff12",
"score": "0.5542399",
"text": "def predict_probability(self, estimator, x_tr, weight):\n sample_distribution = []\n for (i, tree_in_rf) in enumerate(estimator.estimators_):\n tr_samples_class_dist = tree_in_rf.predict_proba(x_tr) * weight[i]\n sample_distribution.append(tr_samples_class_dist)\n\n sample_distribution = np.asarray(sample_distribution)\n\n return np.sum(sample_distribution, axis=0)",
"title": ""
},
{
"docid": "1610dac215487016b2c510dbd91adf32",
"score": "0.5533235",
"text": "def fit_predict(X_train, label_nums, test_data, classes_name, vectorizer):\n clf = MLPClassifier(verbose=True, early_stopping=True, activation='tanh', learning_rate='adaptive')\n clf.fit(X_train, label_nums)\n pred = clf.predict(vectorizer.transform(test_data))\n generate_results(pred, classes_name)\n return pred",
"title": ""
},
{
"docid": "dfe33ed5fed89d18f2253588947d74c8",
"score": "0.553313",
"text": "def classify(self, document, discrete=True):\n # Distance is calculated between the document vector and all training instances.\n # This will make KNN slow in higher dimensions.\n classes = {}\n v1 = self._vector(document)[1]\n D = ((distance(v1, v2, method=self.distance), type) for type, v2 in self._vectors)\n D = ((d, type) for d, type in D if d < 1) # Nothing in common if distance=1.0.\n D = heapq.nsmallest(self.k, D) # k-least distant.\n # Normalize probability estimates.\n s = sum(1 - d for d, type in D) or 1\n p = defaultdict(float)\n for d, type in D:\n p[type] += (1 - d) / s\n if not discrete:\n return p\n try:\n # Ties are broken in favor of the majority class\n # (random winner for majority ties).\n m = max(p.itervalues())\n p = sorted((self._classes[type], type) for type, w in p.iteritems() if w == m > 0)\n p = [type for frequency, type in p if frequency == p[0][0]]\n return choice(p)\n except:\n return self.baseline",
"title": ""
},
{
"docid": "195ae6ab0f3e031c15c1b349bdad2fb1",
"score": "0.55322266",
"text": "def svcClassifier(self):\r\n name = 'SVC'\r\n svc = SVC()\r\n svc.fit(self.X_train, self.y_train)\r\n score = svc.score(self.X_train, self.y_train)\r\n\r\n print(\"***** Accuracy Score ******* \\n\")\r\n print(\"Score: \", score)\r\n\r\n ## Cross-validaton using 10-fold cross validation\r\n cv_scores = cross_val_score(svc, self.X_train, self.y_train, cv=10)\r\n print(\"Cross-Validation average score: %.2f \\n\" % cv_scores.mean())\r\n\r\n ypredicted = svc.predict(self.X_test)\r\n\r\n cm = confusion_matrix(self.y_test, ypredicted)\r\n print(\"***** Confusion Matrix ******* \\n\")\r\n print(cm)\r\n\r\n # Save the model\r\n self.saveModel(svc, name)\r\n \r\n # Append value to the results\r\n self.results.append((name, cv_scores.mean()))\r\n \r\n return cv_scores",
"title": ""
},
{
"docid": "7756fcd46151bd2dc30f161704ae759f",
"score": "0.5531835",
"text": "def predict_proba(self, test_data):\r\n\r\n try:\r\n getattr(self, \"model\")\r\n except AttributeError:\r\n raise RuntimeError(\"Please train your classifier/model\")\r\n\r\n obj_mu=self.model.get('obj_mu')\r\n obj_sigma=self.model.get('obj_sigma')\r\n\r\n bkg_mu=self.model.get('bkg_mu')\r\n bkg_sigma=self.model.get('bkg_sigma')\r\n\r\n obj_prob=np.zeros(len(test_data[:,0]))\r\n bkg_prob=np.zeros(len(test_data[:,0]))\r\n\r\n for n in range(len(test_data[:,0])): # n data samples\r\n for m in range(len(test_data[0,:])): # m features\r\n # print (normpdf(test_data[n,m], obj_mu[m], obj_sigma[m]))\r\n obj_prob[n]=obj_prob[n]+math.log(self.normpdf(test_data[n,m], obj_mu[m], obj_sigma[m])+10e-10)\r\n bkg_prob[n]=bkg_prob[n]+math.log(self.normpdf(test_data[n,m], bkg_mu[m], bkg_sigma[m])+10e-10)\r\n\r\n # calculate the log-likelihood ratio and normalize to [0 1] probabilities\r\n prob=obj_prob-bkg_prob\r\n prob=(prob-prob.min())/(prob.max()-prob.min())\r\n\r\n # for two classes\r\n prob_k=[]\r\n for i in range(len(prob)):\r\n prob_k.append([1-prob[i], prob[i]])\r\n return np.array(prob_k)",
"title": ""
},
{
"docid": "da70da2a4a87c84c155ab0a5e3bc3d0e",
"score": "0.55207586",
"text": "def accuracy_on_data_set(self, dataset):\n tp_or_fn = 0.0\n fp_or_tn = 0.0\n for example, tag in dataset:\n pred = self.predict_on_single_example(example)\n if pred == tag:\n tp_or_fn += 1\n else:\n fp_or_tn += 1\n return tp_or_fn / (tp_or_fn + fp_or_tn)",
"title": ""
},
{
"docid": "6022f8892c67757ca24fc44e13922164",
"score": "0.5519891",
"text": "def teach_all_frequency(classifier):\n N = classifier.input_dimension\n positive_sample_count = 1000\n X = np.random.randn(2 * positive_sample_count, N)\n Y = np.random.randint(low=0, high=1, size=[2 * positive_sample_count])\n scores = []\n base_labels = ['cos_', 'sin_']\n for base_label in base_labels:\n for f in range(N):\n label = base_label + str(f)\n wave = np.sin([f * i * (2 * math.pi) / N for i in range(N)])\n X[:positive_sample_count, :] = np.array([wave, ] * positive_sample_count)\n Y[:positive_sample_count] = np.ones(shape=positive_sample_count)\n x_train, x_test, y_train, y_test = train_test_split(X, Y)\n classifier.fit(x_train, y_train, label)\n scores.append(classifier.score(x_test, y_test, label))\n return np.mean(scores)",
"title": ""
},
{
"docid": "4e59fc88ac51c000a88c31b72f40d195",
"score": "0.55097884",
"text": "def evaluate_classifier_accuracy(self):\n correct = 0.0\n total = 0.0\n\n with open(\"data/SemEval/test2.txt\") as tsv:\n for tweet in csv.reader(tsv, dialect=\"excel-tab\"):\n self.tokenize_and_update_model(tweet[1], tweet[0])\n content = tweet[1]\n bow = self.tokenize_doc(content)\n label = tweet[0]\n if self.classify(bow) == label:\n correct += 1.0\n total += 1.0\n return 100 * correct / total",
"title": ""
},
{
"docid": "dcfcfc8383b159ab986e3a7a597563d9",
"score": "0.55000794",
"text": "def probability(*args, **kwargs):",
"title": ""
},
{
"docid": "3ce1dd5d5b783c81b185630028a4d16a",
"score": "0.5499486",
"text": "def naive_bayes(train_label, train_word_dict, test_words, m, p_hat):\n real_count = train_label.count(1)\n fake_count = train_label.count(0)\n total_count = len(train_label)\n\n # Get priors\n p_real = float(real_count) / float(total_count)\n p_fake = float(fake_count) / float(total_count)\n real_probs, fake_probs = [], []\n\n for word, word_count in train_word_dict.iteritems():\n if word in test_words:\n real_probs.append((float(word_count[0]) + m * p_hat) / float(real_count + m))\n fake_probs.append((float(word_count[1]) + m * p_hat) / float(fake_count + m))\n else:\n real_probs.append(1. - (float(word_count[0]) + m * p_hat) / float(real_count + m))\n fake_probs.append(1. - (float(word_count[1]) + m * p_hat) / float(fake_count + m))\n\n # Get the likelihoods and calculate the probability of test being real and fake\n p_real_likelihood = small_product(real_probs)\n p_real_prob = p_real_likelihood * p_real\n\n p_fake_likelihood = small_product(fake_probs)\n p_fake_prob = p_fake_likelihood * p_fake\n\n\n # prediction = np.argmax([p_real_prob, p_fake_prob])\n # print \"prediction:\", prediction\n if p_real_prob >= p_fake_prob:\n return 1\n else:\n return 0",
"title": ""
},
{
"docid": "9bc94eda4dd798ba4ec3a631bf2d3099",
"score": "0.54976195",
"text": "def _predict_proba(self, X):\n dists = np.zeros((X.shape[0], self.n_classes_))\n\n # Call predict proba on each classifier, multiply the probabilities by the\n # classifiers weight then add them to the current HC2 probabilities\n dists = np.add(\n dists,\n self._stc.predict_proba(X) * (np.ones(self.n_classes_) * self.stc_weight_),\n )\n dists = np.add(\n dists,\n self._drcif.predict_proba(X)\n * (np.ones(self.n_classes_) * self.drcif_weight_),\n )\n dists = np.add(\n dists,\n self._arsenal.predict_proba(X)\n * (np.ones(self.n_classes_) * self.arsenal_weight_),\n )\n dists = np.add(\n dists,\n self._tde.predict_proba(X) * (np.ones(self.n_classes_) * self.tde_weight_),\n )\n\n # Make each instances probability array sum to 1 and return\n return dists / dists.sum(axis=1, keepdims=True)",
"title": ""
},
{
"docid": "dcf9c035d27f1f816bcbbcbfa03fe8e9",
"score": "0.5491038",
"text": "def test_separable_100perc():\n\n separable_ds = make_fully_separable_classes(max_class_size=100,\n max_dim=np.random.randint(2,\n max_dim))\n separable_ds.description = 'fully_separable_dataset'\n out_path_sep = os.path.join(out_dir, 'two_separable_classes.pkl')\n out_dir_sep = os.path.join(out_dir, 'fully_separable_test')\n os.makedirs(out_dir_sep, exist_ok=True)\n separable_ds.save(out_path_sep)\n\n nrep = 10\n gsl = 'none' # to speed up the process\n for clf_name in cfg.classifier_choices:\n for fs_name in cfg.all_dim_red_methods:\n\n cli_str = 'neuropredict -y {} -t {} -n {} -c {} -g {} -o {} -e {} -dr ' \\\n '{}' \\\n ''.format(out_path_sep, train_perc, nrep, 1, gsl, out_dir_sep,\n clf_name, fs_name)\n sys.argv = shlex.split(cli_str)\n cli()\n\n cv_results = neuropredict.reports.load_results_from_folder(out_dir_sep)\n for sg, result in cv_results.items():\n raise_if_mean_differs_from(result['accuracy_balanced'],\n result['target_sizes'],\n reference_level=1.0,\n # comparing to perfect\n eps_chance_acc=0.5,\n method_descr='{} {}'.format(fs_name,\n clf_name))",
"title": ""
},
{
"docid": "ac34b7e589eb620b50a778efe1736150",
"score": "0.5487508",
"text": "def test_naive_bayes(self, test_set, classes, class_probabilities, class_feature_probs):\n\n print('[ INFO ]: Testing with Naive Bayes Classifier...')\n\n class_results = {}\n scores = {}\n true_samples_total = 0\n n_samples = 0\n\n for cl in classes:\n\n # Create new column for class predictions\n feature_set = test_set.drop(classes, axis=1)\n feature_set['pred_class'] = 0\n true_class = test_set[cl]\n\n for row in range(len(feature_set)):\n\n # Initialize probability sums for each class\n true_probs_sum = 1\n false_probs_sum = 1\n true_conditional_prob_sum = 1\n false_conditional_prob_sum = 1\n\n for col in feature_set.columns:\n\n if col != 'pred_class':\n\n # Calculate probabilities assuming the class is present or 1\n if feature_set[col].iloc[row] == 1:\n\n # Compute conditional feature probabilities based on\n # wether or not the feature is present (1 or 0)\n true_prob = class_feature_probs[cl][0].get(col)\n false_prob = 1 - class_feature_probs[cl][1].get(col)\n\n else:\n\n # Calculate probabilities assuming the class is not present or 0\n true_prob = 1 - class_feature_probs[cl][0].get(col)\n false_prob = class_feature_probs[cl][1].get(col)\n\n # Multiply all feature probabilities together for each record\n true_probs_sum = true_probs_sum * true_prob\n false_probs_sum = false_probs_sum * false_prob\n\n # Multiply class conditional probabilities by conditional feature probabilities\n true_conditional_prob_sum = class_probabilities[cl] * true_probs_sum\n false_conditional_prob_sum = (1 - class_probabilities[cl]) * false_probs_sum\n\n # Determine which probability is highest - highest one is selected as the prediction value\n if true_conditional_prob_sum > false_conditional_prob_sum:\n feature_set['pred_class'].iloc[row] = 1\n\n # Place the results into a data frame for comparison\n results = pd.concat([feature_set['pred_class'], true_class], axis=1)\n results.columns = ['pred_class', 'true_class']\n class_results[cl] = results\n\n # Calculate the number of TP, TN, FP, FN\n true_positives = len(results.loc[(results['true_class'] == 1) & (results['pred_class'] == 1)])\n true_negatives = len(results.loc[(results['true_class'] == 0) & (results['pred_class'] == 0)])\n false_positives = len(results.loc[(results['true_class'] == 0) & (results['pred_class'] == 1)])\n false_negatives = len(results.loc[(results['true_class'] == 1) & (results['pred_class'] == 0)])\n\n scores[cl] = {\n 'TP' : true_positives,\n 'TN' : true_negatives,\n 'FP' : false_positives,\n 'FN' : false_negatives\n }\n\n true_samples_total = true_samples_total + true_positives + true_negatives\n n_samples = n_samples + len(results)\n\n classification_accuracy = true_samples_total / n_samples\n\n return class_results, scores, classification_accuracy",
"title": ""
},
{
"docid": "266b5a9615ff6cb439920131bac93377",
"score": "0.54754114",
"text": "def calculateAccuracy(self, classifier, data):\r\n #initialize\r\n accuracy = 0 \r\n \r\n if data.SETS == 2: \r\n #calculate the accuracy\r\n testSamplesCount = data.testX.shape[0]\r\n for sampleIndex in range(testSamplesCount):\r\n label = classifier.predictClass(data.testX[sampleIndex, :])\r\n if label == data.testy[sampleIndex]:\r\n accuracy += 1\r\n accuracy = float(accuracy)/testSamplesCount\r\n print (\"accuracy\", accuracy)",
"title": ""
},
{
"docid": "07a07bf9350de95a31a97ab204c89ccf",
"score": "0.5475121",
"text": "def evaluate_classifier(classifier, X_validation, y_validation):\n return classifier.score(X_validation, y_validation)",
"title": ""
},
{
"docid": "7ac7789c1631599150ad25cc618b734a",
"score": "0.54631627",
"text": "def predict (trained_classifier, test_vectors):\n t0 = time.time()\n prediction=trained_classifier.predict(test_vectors)\n t1 = time.time()\n time_predict=t1-t0\n print(\"Prediction Results for \"+str(type(trained_classifier).__name__))\n print(\"Prediction time: %fs\" % (time_predict))\n return prediction",
"title": ""
},
{
"docid": "2573483d8d2ace4dfc9807c81769ff7c",
"score": "0.545273",
"text": "def calculate_probabilities(words_occurrence_dict):\n total = sum(words_occurrence_dict.values())\n probability_dict = {}\n for word, occurrence in words_occurrence_dict.items():\n prob = occurrence/total\n# print(f'occurrence: {occurrence}, total: {total}, prob: {prob}')\n probability_dict[word] = prob\n return probability_dict",
"title": ""
},
{
"docid": "679b99ac5c3a70755f2c710ea9d00e20",
"score": "0.5444537",
"text": "def prob_cal(self,price):\n prob=0\n for i in range(self.components):\n # extract the weights and the distribution parameters for each mixed Guassian component\n weight=self.GMM.weights_[i]\n mu=self.GMM.means_[i][0]\n std=np.sqrt(self.GMM.covariances_[i][0][0])\n # calculate the cumulative probability by composing all the Guassian components\n prob=prob+weight*norm.cdf(price,loc=mu,scale=std)\n \n return prob",
"title": ""
},
{
"docid": "ee29484fd54feada76ebbbe85912c0e7",
"score": "0.5443583",
"text": "def predict_classes(probas, cut):\n N = len(probas)\n res = np.zeros(N)\n for i in range(N):\n if probas[i] < cut:\n res[i] = 0\n else:\n res[i] = 1\n return res",
"title": ""
},
{
"docid": "1c99b44867a0750ded56c3a547ed278f",
"score": "0.54432774",
"text": "def predict(data):\n ids = [x['id'] for x in data]\n\n # get feats\n X1 = np.concatenate([get_feats1(x) for x in data])\n X2 = np.concatenate([get_feats2(x) for x in data])\n\n # classifiers\n svc1 = joblib.load('pan20/auth/svc1.model')\n rf1 = joblib.load('pan20/auth/rf1.model')\n nb1 = joblib.load('pan20/auth/nb1.model')\n svc2 = joblib.load('pan20/auth/svc2.model')\n rf2 = joblib.load('pan20/auth/rf2.model')\n nb2 = joblib.load('pan20/auth/nb2.model')\n bst = joblib.load('pan20/auth/bst.model')\n\n # preds\n p_svc1 = get_preds(svc1, X1)\n p_rf1 = get_preds(rf1, X1)\n p_nb1 = get_preds(nb1, X1)\n p_svc2 = get_preds(svc2, X2)\n p_rf2 = get_preds(rf2, X2)\n p_nb2 = get_preds(nb2, X2)\n\n # to text\n to_txt(p_svc1, p_rf1, p_nb1, p_svc2, p_rf2, p_nb2)\n\n # xgboost\n dmatrix = xgb.DMatrix(xgb_in_path)\n preds = bst.predict(dmatrix)\n\n # form into expected dictionary and return\n preds = [{'id': ids[i], 'value': float(preds[i])}\n for i in range(len(preds))]\n\n return preds",
"title": ""
},
{
"docid": "b446c7ff4ba0427f516ab2fbfbc753bd",
"score": "0.54369456",
"text": "def calc_priors(self):\n label_counts = sum(self.class_labels.values())\n denominator = label_counts + len(self.class_labels)*config.CLASS_PRIOR_DELTA\n for key in self.class_labels:\n numerator = config.CLASS_PRIOR_DELTA + self.class_labels[key]\n self.priors[key] = np.math.log(numerator/denominator, 10)",
"title": ""
},
{
"docid": "9f7ffc324186c52ea11fd39989974cc2",
"score": "0.5436108",
"text": "def train(self, corpus): \n # TODO your code here\n for sentence in corpus.corpus: # iterate over sentences in the corpus\n for datum in sentence.data: # iterate over datums in the sentence\n word = datum.word # get the word\n self.tokens.append(word)\n self.words.add(word)\n\n self.freqs = Counter(self.tokens) # create dict with help of\n\n for key in self.freqs:\n self.probs[key] = (self.freqs[key] + 1) / (len(self.tokens) + len(self.words))\n\n pass",
"title": ""
},
{
"docid": "ca5552cd9d6e65e22b9cea04f1fc8c7a",
"score": "0.54291344",
"text": "def specificity_metric_per_classes(\n probabilities: np.ndarray,\n truth: np.ndarray,\n eps: float = 1e-9,\n classes: list = ['WT', 'TC', 'ET']) -> np.ndarray:\n scores = {key: list() for key in classes}\n num_classes = probabilities.shape[0]\n predictions = probabilities\n assert (predictions.shape == truth.shape)\n\n for class_ in range(num_classes):\n prediction = predictions[class_]\n truth_ = truth[class_]\n tp = l_and(prediction, truth_).sum()\n tn = l_and(l_not(prediction), l_not(truth_)).sum()\n fp = l_and(prediction, l_not(truth_)).sum()\n if truth_.sum() == 0 and prediction.sum() == 0:\n scores[classes[class_]].append(tn / (tn + fp))\n else:\n scores[classes[class_]].append(tn / (tn + fp))\n\n return scores",
"title": ""
},
{
"docid": "7d769740f8f0bc9cc0c9ecd3843ff808",
"score": "0.54272044",
"text": "def construct_probability_vector():\n # We count the labels\n counter = Counter()\n for label in self.y:\n counter.update(label)\n\n # We divide the count of each label by the total number of data points in the set\n len_dataset = float(len(self.x))\n probability_vector = [count / len_dataset for count in counter.values()]\n\n return probability_vector",
"title": ""
},
{
"docid": "208e6b16bf1435b5a36663dc38ddd395",
"score": "0.5425743",
"text": "def cal_cpt_4(train, f_a, f_b, features):\n n = len(features[f_a])\n cpt = train.groupby([f_a, f_b, 'class']).size()\n den = train.groupby([f_b,'class']).size() \n for v_a in features[f_a]:\n for v_b in features[f_b]:\n for label in features['class']:\n if (v_a,v_b,label) in cpt.index:\n cpt.loc[(v_a,v_b,label)] = (cpt.loc[(v_a,v_b,label)]+1)/\\\n (den.loc[v_b,label]+n)\n elif (v_a,v_b,label) not in cpt.index and (v_b,label) in den.index:\n cpt.loc[(v_a,v_b,label)] = 1/(den.loc[v_b,label]+n)\n elif (v_b,label) not in den.index:\n cpt.loc[(v_a,v_b,label)] = 1/n\n else:\n pass\n return cpt",
"title": ""
},
{
"docid": "4445274b7ea2ab0a5eb91d3fee726feb",
"score": "0.54226005",
"text": "def compute_probabilities():\r\n\tword_tag_probabilities = {} # {(word,tag): p(tag|word)}\r\n\t# calculate the probability for each word-tag pair\r\n\tfor word_tag in word_tag_count:\r\n\t\tword_tag_probabilities[word_tag] = word_tag_count[word_tag] / word_count[word_tag[WORD_INDEX]]\r\n\treturn word_tag_probabilities",
"title": ""
},
{
"docid": "a6ce1fb894b977dd66b27ad46c75e62a",
"score": "0.54222006",
"text": "def evaluate_multilabel_classifier(model: Model, loss_fn: Callable, dataset: Dataset) -> Dict[str, float]:\n samples = iter(OneShotDataLoader(dataset)).next()\n with torch.no_grad():\n features = Variable(samples['features'])\n labels = Variable(samples['label'])\n model_output = model(features)\n loss = loss_fn(model_output, labels).data.item()\n\n predictions = functional.sigmoid(model_output)\n precision = average_precision_score(labels.data.numpy(), predictions.data.numpy())\n\n return {'loss': loss, 'average_precision_score': precision}",
"title": ""
},
{
"docid": "9a1ca06ccac294102c9dbaead2625a51",
"score": "0.5422043",
"text": "def classify_multi(classifiers, X_train, y_train, X_test, y_test=None, groups=None):\n \n num_samples = X_test.shape[0]\n num_classes = 9\n num_classifiers = len(classifiers)\n print(num_samples, num_classes, num_classifiers)\n probabilities = np.zeros((num_samples, num_classes, num_classifiers))\n predictions = np.zeros((num_samples, num_classifiers))\n scores = np.zeros(num_classifiers)\n \n for i in range(num_classifiers):\n pred, score, clf, proba = classify(classifiers[i], X_train, y_train, X_test, y_test, groups)\n predictions[:, i] = pred\n scores[i] = score\n probabilities[:, :, i] = proba\n \n return predictions, scores, classifiers, probabilities",
"title": ""
},
{
"docid": "5935d2daf450034d127e3890c274ea8b",
"score": "0.54180753",
"text": "def predict_prob(self, X_test):\n # res = np.dot(X_test, self.feat_prob.T) * self.posteriori\n res = self.predict(X_test)\n return res / res.sum(axis=1)[:, None]",
"title": ""
},
{
"docid": "95c99810a8f972c4e6a847c3f39fb3a3",
"score": "0.54173476",
"text": "def predict_probability(self, feature_matrix):\r\n scores = Core.dot_product_matrix_vector(feature_matrix, self.coefficients)\r\n # Compute P(y_i = +1 | x_i, w)\r\n probabilities = [1.0 / (1 + math.exp(-score)) for score in scores]\r\n return probabilities",
"title": ""
},
{
"docid": "3e2b2a52404aedcad93b65006335380b",
"score": "0.5416358",
"text": "def _probability_for_class(self, x, k):\n p = self._p_y[k] / self._n\n for i, j in enumerate(x):\n p_xj_y = (self._p_x[k][i][j] + 1) / (self._p_y[k] + 2)\n p *= p_xj_y\n return p",
"title": ""
},
{
"docid": "c34923162b35f3714d4842f9be1fee41",
"score": "0.54104936",
"text": "def sensitivity_metric_per_classes(\n probabilities: np.ndarray,\n truth: np.ndarray,\n eps: float = 1e-9,\n classes: list = ['WT', 'TC', 'ET']) -> np.ndarray:\n scores = {key: list() for key in classes}\n num_classes = probabilities.shape[0]\n predictions = probabilities\n assert (predictions.shape == truth.shape)\n\n for class_ in range(num_classes):\n prediction = predictions[class_]\n truth_ = truth[class_]\n intersection = (truth_ * prediction).sum()\n union = truth_.sum()\n if truth_.sum() == 0 and prediction.sum() == 0:\n scores[classes[class_]].append(np.nan)\n else:\n scores[classes[class_]].append((intersection + eps) / union)\n\n return scores",
"title": ""
},
{
"docid": "1495bf4d29d683ee01294a592713ad47",
"score": "0.53951234",
"text": "def calculate_prob_scores(test_labels, probabilities, min_label=1):\n results = {}\n\n thres = max(int(0.2*len(test_labels)), 1)\n results['p_top20'] = float(np.sum(test_labels[:thres] == min_label)/thres)\n results['brier'] = float(np.mean((probabilities - test_labels)**2))\n results['log_loss'] = float(log_loss(test_labels, probabilities))\n results['auc'] = float(roc_auc_score(test_labels, probabilities))\n\n return results",
"title": ""
},
{
"docid": "9e3f2071fc970a3bb6ada0f5c481befa",
"score": "0.53816223",
"text": "def get_classwise_dice(predict, soft_y):\n y_vol = torch.sum(soft_y, dim = 0)\n p_vol = torch.sum(predict, dim = 0)\n intersect = torch.sum(soft_y * predict, dim = 0)\n dice_score = (2.0 * intersect + 1e-5)/ (y_vol + p_vol + 1e-5)\n return dice_score",
"title": ""
},
{
"docid": "3c60ab4b1b906da32cef154414a8545f",
"score": "0.537611",
"text": "def classify(self, traindata, trainlabels, testdata, testlabels):\n # Put your code below\n # running the above 3 classifiers turns out that the SVC is the best classifier\n classifier = SVC(C=8, gamma=0.125)\n classifier.fit(traindata, trainlabels)\n testError = 1 - classifier.score(testdata, testlabels)\n # Do not change this sequence!\n return (classifier, testError)",
"title": ""
},
{
"docid": "497b269c8825ee3be2bf4529c9b9eefd",
"score": "0.53722775",
"text": "def SVMPrediciton(data_trn,label_trn,data_tst,label_tst):\n if all([label_trn[i] == label_trn[0] for i in range(len(label_trn))]):\n label_pred = label_trn[0] * np.ones(data_tst.shape[0])\n pred_accuracy = sum([1 for i in range(len(label_pred)) if label_pred[i] == label_tst[i]]) / len(label_pred)\n return pred_accuracy\n clf = svm.SVC()\n clf.fit(data_trn, label_trn)\n label_pred = clf.predict(data_tst)\n pred_accuracy = sum([1 for i in range(len(label_pred)) if label_pred[i] == label_tst[i]])/len(label_pred)\n return pred_accuracy",
"title": ""
},
{
"docid": "140ffaef8dbd9bf60453941ba7e427f3",
"score": "0.5365233",
"text": "def train_classifier(classifier, vectorizer, base, language):\n print(\"============ {} =============\".format(base))\n with open(os.path.join(base, \"seeds.json\"), 'r') as json_posts:\n posts = json.load(json_posts)\n data = [(item, 1.0) for item in posts['political']]\n data.extend([(item, 0.0) for item in posts['not_political']])\n print(\"num seeds: {}\".format(len(data)))\n data.extend(load_ads_from_psql(language))\n print(\"num unique samples: {}\".format(len(data)))\n train, test = train_test_split(data, test_size=0.1)\n x_train, y_train = zip(*train)\n x_test, y_test = zip(*test)\n x_train = vectorizer.transform(x_train)\n x_test = vectorizer.transform(x_test)\n x_train, y_train = equalize_classes(x_train, y_train)\n print(\"final size of training data: %s\" % x_train.shape[0])\n classifier.fit(x_train, y_train)\n print(classification_report(y_test, classifier.predict(x_test)))\n return classifier",
"title": ""
},
{
"docid": "ad65553346206b25cf45602f6ae0c392",
"score": "0.53630364",
"text": "def predict_proba(self, X):\n check_is_fitted(self, [\"classes_\", \"calibrators_\"])\n\n # Y[i, j] gives the probability that sample i has the label j.\n Y = np.array([c.predict_proba(\n np.column_stack([np.sum(np.delete(X, obj=i, axis=1), axis=1), X[:, self.classes_[i]]]))[:, 1] for i, c in\n enumerate(self.calibrators_)]).T\n\n if len(self.calibrators_) == 1:\n # Only one estimator, but we still want to return probabilities for two classes.\n Y = np.concatenate(((1 - Y), Y), axis=1)\n\n # Pad with zeros for classes not in training data\n if np.shape(Y)[1] != np.shape(X)[1]:\n p_pred = np.zeros(np.shape(X))\n p_pred[:, self.classes_] = Y\n Y = p_pred\n\n # Normalize probabilities to 1.\n Y = sklearn.preprocessing.normalize(Y, norm='l1', axis=1, copy=True, return_norm=False)\n return np.clip(Y, a_min=0, a_max=1)",
"title": ""
},
{
"docid": "7a6c1a57f3169cd3c3cf6dedb12d7ea4",
"score": "0.53597957",
"text": "def fit(self, X_train, y_train):\n # dictionary where the key is the class label and value is the probability\n self.X_train = X_train\n self.y_train = y_train\n self.priors = {}\n # dictionary where key is the class label and value is the number of occurences\n class_dict = myutils.create_dictionary(y_train)\n total_instances = len(y_train)\n\n # copy class_dict into priors\n self.priors = copy.deepcopy(class_dict)\n # divide value by total instances to get percent \n for item in self.priors:\n self.priors[item] = self.priors[item]/total_instances\n #print(self.priors)\n # value of nested dictionary is probability of class label, parallel to labels\n labels = self.priors.keys()\n self.posteriors = {}\n\n # create a dictionary of attributes and how often they appear \n # attributes = {att0 : {1 : 5, 2 : 10}, att1 : {3 : 5, 2 : 6, 1: 4}}\n attribute_totals = {}\n for i in range(len(X_train[0])):\n name = \"att{}\".format(i)\n attribute_totals[name] = {}\n for j in range(len(X_train)):\n if X_train[j][i] in attribute_totals[name]:\n attribute_totals[name][X_train[j][i]] += 1\n else:\n attribute_totals[name][X_train[j][i]] = 1\n\n # create a dictionary with posterior probabilities\n # self.posteriors = {att0 : {1 : {\"yes\" : 0, \"no\" : 0}, 2 : {\"yes\" : 0, \"no\" : 0}}\n self.posteriors = copy.deepcopy(attribute_totals)\n for attribute in self.posteriors:\n for item in self.posteriors[attribute]:\n self.posteriors[attribute][item] = {}\n for label in labels:\n self.posteriors[attribute][item][label] = 0\n \n \n for i in range(len(X_train[0])): # iterate through attributes\n for item in self.posteriors[\"att{}\".format(i)]: # iterate through dictionary at attribute\n for row in range(len(X_train)): # iterate down rows\n if X_train[row][i] == item:\n self.posteriors[\"att{}\".format(i)][item][y_train[row]] += 1\n for label in labels:\n self.posteriors[\"att{}\".format(i)][item][label] = self.posteriors[\"att{}\".format(i)][item][label] / (self.priors[label] * total_instances)\n\n #print(self.posteriors)",
"title": ""
},
{
"docid": "10b0a2a48190bab8a3687ae37a4f91ea",
"score": "0.53541857",
"text": "def predict(self, cls, point, examples):\n value = point[self.name]\n if cls not in self.stats:\n self.stats[cls] = dict()\n if value not in self.stats[cls]:\n examples = examples[self.name]\n self.stats[cls][value] = (examples.mean(), examples.std())\n mean, std = self.stats[cls][value]\n return self.normal_prob(mean, std, value)",
"title": ""
},
{
"docid": "c78816f37c5f5234a2bed3d095c6ba3a",
"score": "0.5350316",
"text": "def decision_function(self, dataset, parallel=True):\n # handle NaNs\n if np.any(np.isnan(dataset)):\n dataset = np.nan_to_num(dataset, copy=False)\n\n # set up variables\n if self.batch_size > 0 and parallel:\n return self.decision_function_batch(dataset, self.batch_size)\n else:\n pairs_proba = np.empty((len(dataset), len(self.root_nodes)), float) # indexes of data points\n\n # get all clusters for all points in all trees\n for d_idx, d in enumerate(dataset):\n # traverse all trees\n for t_idx, tree in enumerate(self.root_nodes):\n d_mean, d_pct, d_pdf_mean, d_cov_det, d_cov_inv = descend_density_tree(d, tree)\n if d_pct > self.thresh_traverse:\n if self.method == 'normal':\n pairs_proba[d_idx, t_idx] = d_pct * my_normal(d, d_mean, d_cov_det, d_cov_inv)\n if self.standardize:\n pairs_proba[d_idx, t_idx] /= d_pdf_mean # standardize by max. probability\n else:\n pairs_proba[d_idx, t_idx] = euclidean(d_mean, d)\n if self.standardize:\n pairs_proba[d_idx, t_idx] /= d_pdf_mean # standardize by max. probability\n else:\n pairs_proba[d_idx, t_idx] = np.nan\n self.scores = np.log(np.nanmean(pairs_proba, axis=-1))\n return self.scores",
"title": ""
},
{
"docid": "638bbbef4f3367c6f80c60b165e119db",
"score": "0.53485787",
"text": "def rfc_cl_pred(self, x, feat_imp_plot_filename = None):\n \n pred_proba = self.rfc_classif.predict_proba(x)\n probabilities = np.zeros((len(x), self.class_count), float)\n\n rownum = 0\n for row in pred_proba: \n c = 0\n for i in row:\n col = int(self.rfc_classif.classes_[c]-1)\n probabilities[rownum, col] = i\n c += 1\n rownum += 1\n \n # print probabilities\n # print \"Found following classes:\"\n # print self.rfc_classif.classes_\n \n pred_class = self.rfc_classif.predict(x)\n \n return pred_class, probabilities",
"title": ""
},
{
"docid": "4585456eeed51ac5b766eb71aaad02e0",
"score": "0.5348072",
"text": "def __calculate_score(y_pred_class, y_pred_prob):\n if y_pred_class == 0:\n MAX = 0.5\n scaled_percentage = (y_pred_prob * MAX) / 100\n return MAX - scaled_percentage\n else:\n MAX = 1\n scaled_percentage = (y_pred_prob * MAX) / 100\n return scaled_percentage",
"title": ""
},
{
"docid": "1582336a87bffa99b2e0c2a4f6b4d1bd",
"score": "0.5342883",
"text": "def _compute_diversity(self, targets, prediction_matrix):\n diversity = np.zeros(self.n_classifiers)\n\n for clf_index in range(self.n_classifiers):\n for clf_index2 in range(clf_index + 1, self.n_classifiers):\n this_diversity = self.diversity_func(targets,\n prediction_matrix[:, clf_index],\n prediction_matrix[:, clf_index2])\n\n diversity[clf_index] += this_diversity\n diversity[clf_index2] += this_diversity\n return diversity",
"title": ""
},
{
"docid": "4e9dab93431fef193aec5cb2871f82ce",
"score": "0.534066",
"text": "def feature_scalars(d, features_list, test_size, random_state=42):\n data = featureFormat(d, features_list, sort_keys=True)\n # Split between labels (poi) and the rest of features\n target, features = targetFeatureSplit(data)\n\n # Create both training and test sets through split_data()\n features_train, features_test, labels_train, labels_test = train_test_split(\n features,\n target,\n test_size=test_size,\n random_state=random_state)\n \n classifier = [\"ADA\", \"SVC\"]\n for c in classifier:\n if c == \"ADA\":\n clf = AdaBoostClassifier()\n elif c == \"SVM\":\n clf = SVC(kernel='linear', max_iter=1000)\n\n result = []\n clf.fit(features_train, labels_train)\n pred = clf.predict(features_test)\n importances = clf.feature_importances_\n\n for i in range(len(importances)):\n t = [features_list[i], importances[i]]\n result.append(t)\n\n result = sorted(result, key=lambda x: x[1], reverse=True)\n\n print (result)\n\n\n return None",
"title": ""
},
{
"docid": "8478eb9e8dcb911cc9ba1f1e50446d8e",
"score": "0.53401184",
"text": "def implement_classifiers(self, X_train, X_test, y_train, y_test):\n clf_LR = LogisticRegression(random_state=1, max_iter=2000)\n clf_LR.fit(X_train, y_train)\n clf_RF = RandomForestClassifier(n_estimators=40, max_depth=7, random_state=2)\n clf_RF.fit(X_train, y_train)\n prob_train_LR = clf_LR.predict_proba(X_train)\n prob_test_LR = clf_LR.predict_proba(X_test)\n prob_train_RF = clf_RF.predict_proba(X_train)\n prob_test_RF = clf_RF.predict_proba(X_test)\n probs_LR = [prob_train_LR, prob_test_LR]\n probs_RF = [prob_train_RF, prob_test_RF]\n return probs_LR, probs_RF",
"title": ""
}
]
pillar['master']['file_roots'] is overwritten by the master in order to use the fileclient interface to read the pillar files. We should restore the actual file_roots when we send the pillar back to the minion.
[
{
"docid": "1bd53991876d9a27761e854c675850d0",
"score": "0.6599424",
"text": "def test_issue_5449_report_actual_file_roots_in_pillar(\n salt_call_cli, pillar_tree, base_env_state_tree_root_dir\n):\n ret = salt_call_cli.run(\"pillar.data\")\n assert ret.returncode == 0\n assert ret.data\n file_roots = ret.data[\"master\"][\"file_roots\"][\"base\"]\n assert pathlib.Path(base_env_state_tree_root_dir).resolve() in [\n pathlib.Path(p).resolve() for p in file_roots\n ]",
"title": ""
}
]
[
{
"docid": "ad44959dc2368d29845f68104be29acc",
"score": "0.5513161",
"text": "def changeRoot(self, root):\n self.path_holder.root = root\n for s in self.subfiles:\n s.path_holder.root = root",
"title": ""
},
{
"docid": "fd02d487b6e859a684615b8454404306",
"score": "0.5375802",
"text": "def backup_files(self):\r\n for host in self._hosts:\r\n # local filesystem\r\n toml_bak = os.path.join(self._l_toml_bak, host)\r\n tmpl_bak = os.path.join(self._l_tmpl_bak, host)\r\n conf_bak = os.path.join(self._l_conf_bak, host)\r\n remove_folder(toml_bak)\r\n remove_folder(tmpl_bak)\r\n remove_folder(conf_bak)\r\n get_folder(toml_bak)\r\n get_folder(tmpl_bak)\r\n get_folder(conf_bak)\r\n # minio server\r\n toml_pre = '%s/' % os.path.join('toml', self._folder_pre, host)\r\n tmpl_pre = '%s/' % os.path.join('tmpl', self._folder_pre, host)\r\n conf_pre = '%s/' % os.path.join('conf', self._folder_pre, host)\r\n objs = self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=toml_pre, recursive=False)\r\n for x in objs:\r\n self.minio.remove_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=x.object_name.encode('utf-8'))\r\n objs = self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=tmpl_pre, recursive=False)\r\n for x in objs:\r\n self.minio.remove_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=x.object_name.encode('utf-8'))\r\n objs = self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=conf_pre, recursive=False)\r\n for x in objs:\r\n self.minio.remove_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=x.object_name.encode('utf-8'))\r\n aapi = Ansible2API(hosts=[host], **self._ansible_kwargs)\r\n # 1. backup toml to minio server\r\n tomls = self.get_tomls(host=host)\r\n for x in tomls:\r\n state, state_sum, results = ansible_safe_run(\r\n aapi=aapi, module='fetch',\r\n args=dict(\r\n dest='%s/' % toml_bak,\r\n src=os.path.join(self._r_toml, x),\r\n flat='yes'))\r\n msg = 'Toml File Backup: %s' % state_sum\r\n app.logger.debug(logmsg(msg))\r\n msg = 'Toml File Backup: %s' % results\r\n app.logger.info(logmsg(msg))\r\n self.minio.fput_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=os.path.join(toml_pre, x),\r\n file_path=os.path.join(toml_bak, x))\r\n # 2. backup tmpl to minio server\r\n tmpls = self.get_tmpls(host=host)\r\n for x in tmpls:\r\n state, state_sum, results = ansible_safe_run(\r\n aapi=aapi, module='fetch',\r\n args=dict(\r\n dest='%s/' % tmpl_bak,\r\n src=os.path.join(self._r_tmpl, self._folder_pre, x),\r\n flat='yes'))\r\n msg = 'Tmpl File Backup: %s' % state_sum\r\n app.logger.debug(logmsg(msg))\r\n msg = 'Tmpl File Backup: %s' % results\r\n app.logger.info(logmsg(msg))\r\n self.minio.fput_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=os.path.join(tmpl_pre, x),\r\n file_path=os.path.join(tmpl_bak, x))\r\n # 3. backup conf to minio server\r\n # files should include (name, dir, mode, owner)\r\n for x in self._files:\r\n src = os.path.join(x['dir'], x['name'])\r\n file_name = '%s%s%s' % (\r\n '@@'.join([x['mode'], x['owner']['name'], x['owner']['group']]),\r\n self._broken_word_2,\r\n src.replace('/', self._broken_word_1))\r\n state, state_sum, results = ansible_safe_run(\r\n aapi=aapi, module='fetch',\r\n args=dict(\r\n dest=os.path.join(conf_bak, file_name),\r\n src=src, flat='yes'))\r\n msg = 'Conf File Backup: %s' % state_sum\r\n app.logger.debug(logmsg(msg))\r\n msg = 'Conf File Backup: %s' % results\r\n app.logger.info(logmsg(msg))\r\n file_path = os.path.join(conf_bak, file_name)\r\n if os.path.isfile(file_path):\r\n self.minio.fput_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=os.path.join(conf_pre, file_name),\r\n file_path=file_path)\r\n # 4. check if toml/tmpl/conf have been backuped to minio server\r\n objs = [os.path.basename(x.object_name.encode('utf-8')) for x in\r\n self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=toml_pre,\r\n recursive=False)]\r\n for x in tomls:\r\n if x not in objs:\r\n raise Exception('Toml Backup Failed: %s.' % x)\r\n objs = [os.path.basename(x.object_name.encode('utf-8')) for x in\r\n self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=tmpl_pre,\r\n recursive=False)]\r\n for x in tmpls:\r\n if x not in objs:\r\n raise Exception('Tmpl Backup Failed: %s.' % x)",
"title": ""
},
{
"docid": "62593542fbd2029336126d78b58cfe72",
"score": "0.53749573",
"text": "def load_master(self):\n ret=self._handle.MasterConfLoadFile(self.conf_path+\"/\"+self._mcf_file)\n return ret",
"title": ""
},
{
"docid": "392007e234b8f5c9c5670b26a31d24db",
"score": "0.5369088",
"text": "def __update_root(self) -> None:\n # 5.2.1. Let N denote the version number of the trusted root metadata\n # file.\n prev_root = self.__root\n curr_root = prev_root\n n = curr_root.version\n\n # 5.2.8. Repeat steps 5.2.1 to 5.2.8.\n for _ in range(self.config.MAX_ROOT_ROTATIONS):\n # 5.2.2. Try downloading version N+1 of the root metadata file.\n n += 1\n remote_filename = self.__remote_metadata_filename(self.ROOT_ROLENAME, n)\n remote_path = self.__remote_metadata_path(remote_filename)\n try:\n tmp_file = self.download(\n remote_path, self.config.MAX_ROOT_LENGTH, self.config\n )\n except DownloadNotFoundError:\n break\n self.__check_length(tmp_file, self.config.MAX_ROOT_LENGTH)\n\n # 5.2.3. Check for an arbitrary software attack.\n metadata = self.read_from_file(tmp_file)\n metadata.signed = cast(Root, metadata.signed)\n self.__check_signatures(curr_root.root, metadata)\n self.__check_signatures(metadata.signed.root, metadata)\n\n # 5.2.4. Check for a rollback attack.\n if metadata.signed.version != n:\n raise RollbackAttack(\n f\"{metadata.signed.version} != {n} in {remote_path}\"\n )\n\n # 5.2.5. Note that the expiration of the new (intermediate) root\n # metadata file does not matter yet.\n\n # 5.2.6. Set the trusted root metadata file to the new root metadata\n # file.\n curr_root = metadata.signed\n\n # 5.2.9. Check for a freeze attack.\n self.__check_expiry(curr_root)\n\n if prev_root < curr_root:\n # 5.2.11. Set whether consistent snapshots are used as per the\n # trusted root metadata file.\n # NOTE: We violate the spec in checking this *before* deleting local\n # timestamp and/or snapshot metadata, which I think is reasonable.\n if not curr_root.consistent_snapshot:\n raise NoConsistentSnapshotsError\n\n # 5.2.10. If the timestamp and / or snapshot keys have been rotated,\n # then delete the trusted timestamp and snapshot metadata files.\n if (\n self.__root.timestamp != curr_root.timestamp\n or self.__root.snapshot != curr_root.snapshot\n ):\n filename = self.__local_metadata_filename(self.SNAPSHOT_ROLENAME)\n if self.file_exists(filename):\n self.rm_file(filename)\n\n filename = self.__local_metadata_filename(self.TIMESTAMP_ROLENAME)\n if self.file_exists(filename):\n self.rm_file(filename)\n\n # 5.2.7. Persist root metadata.\n # NOTE: We violate the spec in persisting only *after* checking\n # everything, which I think is reasonable.\n self.mv_file(tmp_file, self.__local_metadata_filename(self.ROOT_ROLENAME))\n self.__root = curr_root",
"title": ""
},
{
"docid": "060a31bb9b639b810ebbb50da3bbcf27",
"score": "0.5350123",
"text": "def __load_root(self) -> None:\n # NOTE: we must parse the root metadata file on disk in order to get\n # the keys to verify itself in the first place.\n filename = self.__local_metadata_filename(self.ROOT_ROLENAME)\n metadata = self.read_from_file(filename)\n\n # FIXME: the following line is purely to keep mypy happy; otherwise,\n # it complains that the .signed.root attribute does not exist.\n metadata.signed = cast(Root, metadata.signed)\n\n # Verify self-signatures on previous root metadata file.\n self.__check_signatures(metadata.signed.root, metadata)\n\n # NOTE: the expiration of the trusted root metadata file does not\n # matter, because we will attempt to update it in the next step.\n\n # We do not support non-consistent-snapshot repositories.\n if not metadata.signed.consistent_snapshot:\n raise NoConsistentSnapshotsError\n\n # Now that we have verified signatures, throw them away, and set the\n # current root to the actual metadata of interest.\n self.__root = metadata.signed",
"title": ""
},
{
"docid": "a2a9d107f4683c123883aca61c05208e",
"score": "0.53284204",
"text": "def post_master_init(self, master):\n\n if self.connected:\n self.opts[\"pillar\"] = yield salt.pillar.get_async_pillar(\n self.opts,\n self.opts[\"grains\"],\n self.opts[\"id\"],\n saltenv=self.opts[\"saltenv\"],\n pillarenv=self.opts.get(\"pillarenv\"),\n ).compile_pillar()\n\n # Ensure that the value of master is the one we passed in.\n # if pillar_opts is enabled then master could be overwritten\n # when compile_pillar is run.\n self.opts[\"master\"] = master\n\n tag = \"salt/deltaproxy/start\"\n self._fire_master(tag=tag)\n\n if \"proxy\" not in self.opts[\"pillar\"] and \"proxy\" not in self.opts:\n errmsg = (\n \"No proxy key found in pillar or opts for id {}. Check your pillar/opts \"\n \"configuration and contents. Salt-proxy aborted.\".format(self.opts[\"id\"])\n )\n log.error(errmsg)\n self._running = False\n raise SaltSystemExit(code=-1, msg=errmsg)\n\n if \"proxy\" not in self.opts:\n self.opts[\"proxy\"] = self.opts[\"pillar\"][\"proxy\"]\n\n self.opts = salt.utils.dictupdate.merge(\n self.opts,\n self.opts[\"pillar\"],\n strategy=self.opts.get(\"proxy_merge_pillar_in_opts_strategy\"),\n merge_lists=self.opts.get(\"proxy_deep_merge_pillar_in_opts\", False),\n )\n\n if self.opts.get(\"proxy_mines_pillar\"):\n # Even when not required, some details such as mine configuration\n # should be merged anyway whenever possible.\n if \"mine_interval\" in self.opts[\"pillar\"]:\n self.opts[\"mine_interval\"] = self.opts[\"pillar\"][\"mine_interval\"]\n if \"mine_functions\" in self.opts[\"pillar\"]:\n general_proxy_mines = self.opts.get(\"mine_functions\", [])\n specific_proxy_mines = self.opts[\"pillar\"][\"mine_functions\"]\n try:\n self.opts[\"mine_functions\"] = general_proxy_mines + specific_proxy_mines\n except TypeError as terr:\n log.error(\n \"Unable to merge mine functions from the pillar in the opts, for proxy %s\",\n self.opts[\"id\"],\n )\n\n fq_proxyname = self.opts[\"proxy\"][\"proxytype\"]\n\n # Need to load the modules so they get all the dunder variables\n (\n self.functions,\n self.returners,\n self.function_errors,\n self.executors,\n ) = self._load_modules()\n\n # we can then sync any proxymodules down from the master\n # we do a sync_all here in case proxy code was installed by\n # SPM or was manually placed in /srv/salt/_modules etc.\n self.functions[\"saltutil.sync_all\"](saltenv=self.opts[\"saltenv\"])\n\n # Pull in the utils\n self.utils = salt.loader.utils(self.opts)\n\n # Then load the proxy module\n self.proxy = salt.loader.proxy(self.opts, utils=self.utils)\n\n # And re-load the modules so the __proxy__ variable gets injected\n (\n self.functions,\n self.returners,\n self.function_errors,\n self.executors,\n ) = self._load_modules()\n self.functions.pack[\"__proxy__\"] = self.proxy\n self.proxy.pack[\"__salt__\"] = self.functions\n self.proxy.pack[\"__ret__\"] = self.returners\n self.proxy.pack[\"__pillar__\"] = self.opts[\"pillar\"]\n\n # Reload utils as well (chicken and egg, __utils__ needs __proxy__ and __proxy__ needs __utils__\n self.utils = salt.loader.utils(self.opts, proxy=self.proxy)\n self.proxy.pack[\"__utils__\"] = self.utils\n\n # Reload all modules so all dunder variables are injected\n self.proxy.reload_modules()\n\n # Start engines here instead of in the Minion superclass __init__\n # This is because we need to inject the __proxy__ variable but\n # it is not setup until now.\n self.io_loop.spawn_callback(\n salt.engines.start_engines, self.opts, self.process_manager, proxy=self.proxy\n )\n\n proxy_init_func_name = \"{}.init\".format(fq_proxyname)\n proxy_shutdown_func_name = \"{}.shutdown\".format(fq_proxyname)\n if (\n proxy_init_func_name not in self.proxy\n or proxy_shutdown_func_name not in self.proxy\n ):\n errmsg = (\n \"Proxymodule {} is missing an init() or a shutdown() or both. \"\n \"Check your proxymodule. Salt-proxy aborted.\".format(fq_proxyname)\n )\n log.error(errmsg)\n self._running = False\n raise SaltSystemExit(code=-1, msg=errmsg)\n\n self.module_executors = self.proxy.get(\n \"{}.module_executors\".format(fq_proxyname), lambda: []\n )()\n proxy_init_fn = self.proxy[proxy_init_func_name]\n proxy_init_fn(self.opts)\n\n self.opts[\"grains\"] = salt.loader.grains(self.opts, proxy=self.proxy)\n\n self.mod_opts = self._prep_mod_opts()\n self.matchers = salt.loader.matchers(self.opts)\n self.beacons = salt.beacons.Beacon(self.opts, self.functions)\n uid = salt.utils.user.get_uid(user=self.opts.get(\"user\", None))\n self.proc_dir = salt.minion.get_proc_dir(self.opts[\"cachedir\"], uid=uid)\n\n if self.connected and self.opts[\"pillar\"]:\n # The pillar has changed due to the connection to the master.\n # Reload the functions so that they can use the new pillar data.\n (\n self.functions,\n self.returners,\n self.function_errors,\n self.executors,\n ) = self._load_modules()\n if hasattr(self, \"schedule\"):\n self.schedule.functions = self.functions\n self.schedule.returners = self.returners\n\n if not hasattr(self, \"schedule\"):\n self.schedule = salt.utils.schedule.Schedule(\n self.opts,\n self.functions,\n self.returners,\n cleanup=[salt.minion.master_event(type=\"alive\")],\n proxy=self.proxy,\n _subprocess_list=self.subprocess_list,\n )\n\n # add default scheduling jobs to the minions scheduler\n if self.opts[\"mine_enabled\"] and \"mine.update\" in self.functions:\n self.schedule.add_job(\n {\n \"__mine_interval\": {\n \"function\": \"mine.update\",\n \"minutes\": self.opts[\"mine_interval\"],\n \"jid_include\": True,\n \"maxrunning\": 2,\n \"run_on_start\": True,\n \"return_job\": self.opts.get(\"mine_return_job\", False),\n }\n },\n persist=True,\n fire_event=False,\n )\n log.info(\"Added mine.update to scheduler\")\n else:\n self.schedule.delete_job(\"__mine_interval\", persist=True, fire_event=False)\n\n # add master_alive job if enabled\n if self.opts[\"transport\"] != \"tcp\" and self.opts[\"master_alive_interval\"] > 0:\n self.schedule.add_job(\n {\n salt.minion.master_event(type=\"alive\", master=self.opts[\"master\"]): {\n \"function\": \"status.master\",\n \"seconds\": self.opts[\"master_alive_interval\"],\n \"jid_include\": True,\n \"maxrunning\": 1,\n \"return_job\": False,\n \"kwargs\": {\"master\": self.opts[\"master\"], \"connected\": True},\n }\n },\n persist=True,\n fire_event=False,\n )\n if (\n self.opts[\"master_failback\"]\n and \"master_list\" in self.opts\n and self.opts[\"master\"] != self.opts[\"master_list\"][0]\n ):\n self.schedule.add_job(\n {\n salt.minion.master_event(type=\"failback\"): {\n \"function\": \"status.ping_master\",\n \"seconds\": self.opts[\"master_failback_interval\"],\n \"jid_include\": True,\n \"maxrunning\": 1,\n \"return_job\": False,\n \"kwargs\": {\"master\": self.opts[\"master_list\"][0]},\n }\n },\n persist=True,\n fire_event=False,\n )\n else:\n self.schedule.delete_job(\n salt.minion.master_event(type=\"failback\"),\n persist=True,\n fire_event=False,\n )\n else:\n self.schedule.delete_job(\n salt.minion.master_event(type=\"alive\", master=self.opts[\"master\"]),\n persist=True,\n fire_event=False,\n )\n self.schedule.delete_job(\n salt.minion.master_event(type=\"failback\"),\n persist=True,\n fire_event=False,\n )\n\n # proxy keepalive\n proxy_alive_fn = fq_proxyname + \".alive\"\n if (\n proxy_alive_fn in self.proxy\n and \"status.proxy_reconnect\" in self.functions\n and self.opts.get(\"proxy_keep_alive\", True)\n ):\n # if `proxy_keep_alive` is either not specified, either set to False does not retry reconnecting\n self.schedule.add_job(\n {\n \"__proxy_keepalive\": {\n \"function\": \"status.proxy_reconnect\",\n \"minutes\": self.opts.get(\n \"proxy_keep_alive_interval\", 1\n ), # by default, check once per minute\n \"jid_include\": True,\n \"maxrunning\": 1,\n \"return_job\": False,\n \"kwargs\": {\"proxy_name\": fq_proxyname},\n }\n },\n persist=True,\n fire_event=False,\n )\n self.schedule.enable_schedule(fire_event=False)\n else:\n self.schedule.delete_job(\n \"__proxy_keepalive\",\n persist=True,\n fire_event=False,\n )\n\n # Sync the grains here so the proxy can communicate them to the master\n self.functions[\"saltutil.sync_grains\"](saltenv=\"base\")\n self.grains_cache = self.opts[\"grains\"]\n # Now setup the deltaproxies\n self.deltaproxy = {}\n self.deltaproxy_opts = {}\n self.deltaproxy_objs = {}\n self.proxy_grains = {}\n self.proxy_pillar = {}\n self.proxy_context = {}\n self.add_periodic_callback(\"cleanup\", self.cleanup_subprocesses)\n\n _failed = list()\n if self.opts[\"proxy\"].get(\"parallel_startup\"):\n log.debug(\"Initiating parallel startup for proxies\")\n waitfor = []\n for _id in self.opts[\"proxy\"].get(\"ids\", []):\n waitfor.append(\n subproxy_post_master_init(\n _id,\n uid,\n self.opts,\n self.proxy,\n self.utils,\n )\n )\n\n try:\n results = yield tornado.gen.multi(waitfor)\n except Exception as exc: # pylint: disable=broad-except\n log.error(\"Errors loading sub proxies: %s\", exc)\n\n _failed = self.opts[\"proxy\"].get(\"ids\", [])[:]\n for sub_proxy_data in results:\n minion_id = sub_proxy_data[\"proxy_opts\"].get(\"id\")\n if minion_id in _failed:\n _failed.remove(minion_id)\n\n if sub_proxy_data[\"proxy_minion\"]:\n self.deltaproxy_opts[minion_id] = sub_proxy_data[\"proxy_opts\"]\n self.deltaproxy_objs[minion_id] = sub_proxy_data[\"proxy_minion\"]\n\n if self.deltaproxy_opts[minion_id] and self.deltaproxy_objs[minion_id]:\n self.deltaproxy_objs[\n minion_id\n ].req_channel = salt.channel.client.AsyncReqChannel.factory(\n sub_proxy_data[\"proxy_opts\"], io_loop=self.io_loop\n )\n else:\n log.debug(\"Initiating non-parallel startup for proxies\")\n for _id in self.opts[\"proxy\"].get(\"ids\", []):\n try:\n sub_proxy_data = yield subproxy_post_master_init(\n _id, uid, self.opts, self.proxy, self.utils\n )\n except Exception as exc: # pylint: disable=broad-except\n log.info(\n \"An exception occured during initialization for %s, skipping: %s\",\n _id,\n exc,\n )\n _failed.append(_id)\n continue\n minion_id = sub_proxy_data[\"proxy_opts\"].get(\"id\")\n\n if sub_proxy_data[\"proxy_minion\"]:\n self.deltaproxy_opts[minion_id] = sub_proxy_data[\"proxy_opts\"]\n self.deltaproxy_objs[minion_id] = sub_proxy_data[\"proxy_minion\"]\n\n if self.deltaproxy_opts[minion_id] and self.deltaproxy_objs[minion_id]:\n self.deltaproxy_objs[\n minion_id\n ].req_channel = salt.channel.client.AsyncReqChannel.factory(\n sub_proxy_data[\"proxy_opts\"], io_loop=self.io_loop\n )\n\n if _failed:\n log.info(\"Following sub proxies failed %s\", _failed)\n self.ready = True",
"title": ""
},
{
"docid": "2cf0f0fcee484304651511099d841b88",
"score": "0.49817708",
"text": "def getRoots(self) -> List[java.io.File]:\n ...",
"title": ""
},
{
"docid": "1e3e5c16886f6b3e12aea103fdbaf125",
"score": "0.4979484",
"text": "def refresh_remote_folders_from_log(root_binding):\n # TODO\n raise NotImplementedError()",
"title": ""
},
{
"docid": "764389d5e26bf4f724d85c6be0667ebf",
"score": "0.495386",
"text": "def update_root_list_files():\n\n\tfrom gsi.models import HomeVariables as Home\n\tfrom gsi.models import ListTestFiles\n\n\thome_var = Home.objects.all()\n\troot_path = home_var[0].RF_AUXDATA_DIR\n\n\ttry:\n\t\tfiles, errors = get_files(root_path, '.tif')\n\t\ttif_files = filter(lambda x: x.endswith('.tif'), files)\n\t\tfiles_exclude = ListTestFiles.objects.filter(input_data_directory=None).exclude(name__in=tif_files).delete()\n\t\tfiles_include = ListTestFiles.objects.filter(input_data_directory=None).values_list('name')\n\n\t\tfor f in tif_files:\n\t\t\tfile_path = os.path.join(root_path, f)\n\n\t\t\tif (f,) not in files_include:\n\t\t\t\tobj = ListTestFiles.objects.create(name=f, input_data_directory=None)\n\t\t\t\tobj.size = convert_size_file(os.path.getsize(file_path))\n\t\t\t\tobj.date_modified = datetime.fromtimestamp(os.path.getmtime(file_path))\n\t\t\t\tobj.save()\n\texcept StopIteration, e:\n\t\tpass\n\texcept OSError, e:\n\t\tpass",
"title": ""
},
{
"docid": "a352e3e3b646c8815510e64b654e24a4",
"score": "0.49312118",
"text": "def test_pillar_refresh_pillar_raw(salt_cli, salt_minion, key_pillar):\n key = \"issue-54941-raw\"\n\n # We do not expect to see the pillar because it does not exist yet\n ret = salt_cli.run(\"pillar.raw\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert val == {}\n\n with key_pillar(key) as key_pillar_instance:\n # The pillar exists now but raw reads it from in-memory pillars\n ret = salt_cli.run(\"pillar.raw\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert val == {}\n\n # Calling refresh_pillar to update in-memory pillars\n key_pillar_instance.refresh_pillar()\n\n # The pillar can now be read from in-memory pillars\n ret = salt_cli.run(\"pillar.raw\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert val is True, repr(val)",
"title": ""
},
{
"docid": "90a197cdf6479cd942dec99130ed7348",
"score": "0.48707634",
"text": "def _unpack_rootstrap(self):\n\n ext2compr = {'tgz': 'gz', 'gz': 'gz', 'bz2': 'bz2'}\n\n try:\n extension = self._rootstrap.split('.')[-1]\n try:\n compr_flag = ext2compr[extension]\n except KeyError:\n compr_flag = ''\n rootstrap = tarfile.open(self._rootstrap, \"r:%s\" % compr_flag)\n tmpdir = tempfile.mkdtemp(prefix='rootstrap-')\n LOG.info(\"Unpacking rootstrap to '%s'\" % tmpdir)\n rootstrap.extractall(tmpdir)\n self.path = tmpdir\n except Exception, error:\n msg = \"Failed to unpack rootstrap: '%s'\" % str(error)\n LOG.error(msg)\n raise ConductorError(msg, \"311\")",
"title": ""
},
{
"docid": "e61f48461a3105aa117acee457b88050",
"score": "0.4861662",
"text": "def configure_postload(self, conf):\n super().configure_postload(conf)\n base_directory = conf.get('base_directory')\n # where repos are cloned on the slave\n conf.set('repo_directory', join(base_directory, 'repos', 'slave'))\n # where the slave's result artifacts should be stored\n conf.set('artifact_directory', join(base_directory, 'artifacts'))\n # where to store results on the slave\n conf.set('results_directory', join(base_directory, 'results', 'slave'))\n conf.set('timings_directory', join(base_directory, 'timings', 'master')) # timing data",
"title": ""
},
{
"docid": "65cb9f9f2a9233d694eaae4ca0594877",
"score": "0.48539564",
"text": "def push_files(self, rollback=False):\r\n for host in self._hosts:\r\n aapi = Ansible2API(hosts=[host], **self._ansible_kwargs)\r\n toml_folder = '%s/' % (\r\n os.path.join(self._l_toml_bak, host)\r\n if rollback else os.path.join(self._l_toml, host))\r\n tmpl_folder = '{}/'.format(\r\n os.path.join(self._l_tmpl_bak, host)\r\n if rollback else self._l_tmpl)\r\n if rollback:\r\n conf_folder = '%s/' % os.path.join(self._l_conf_bak, host)\r\n # clear folders\r\n remove_folder(toml_folder)\r\n remove_folder(tmpl_folder)\r\n remove_folder(conf_folder)\r\n get_folder(toml_folder)\r\n get_folder(tmpl_folder)\r\n get_folder(conf_folder)\r\n # download latest tomls/tmpls from minio\r\n toml_pre = '%s/' % os.path.join('toml', self._folder_pre, host)\r\n objs = self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=toml_pre, recursive=False)\r\n for x in objs:\r\n object_name = x.object_name.encode('utf-8')\r\n self.minio.fget_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=object_name,\r\n file_path=os.path.join(\r\n toml_folder, os.path.basename(object_name)))\r\n tmpl_pre = '%s/' % os.path.join('tmpl', self._folder_pre, host)\r\n objs = self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=tmpl_pre, recursive=False)\r\n for x in objs:\r\n object_name = x.object_name.encode('utf-8')\r\n self.minio.fget_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=object_name,\r\n file_path=os.path.join(\r\n tmpl_folder, os.path.basename(object_name)))\r\n conf_pre = '%s/' % os.path.join('conf', self._folder_pre, host)\r\n objs = self.minio.list_objects(\r\n bucket_name=self._minio_bucket, prefix=conf_pre, recursive=False)\r\n for x in objs:\r\n object_name = x.object_name.encode('utf-8')\r\n self.minio.fget_object(\r\n bucket_name=self._minio_bucket,\r\n object_name=object_name,\r\n file_path=os.path.join(\r\n conf_folder, os.path.basename(object_name)))\r\n # push conf files to remote/local confd client\r\n for x in os.listdir(conf_folder):\r\n config = x.split(self._broken_word_2)\r\n file_path = config[1].replace(self._broken_word_1, '/')\r\n info = config[0].split('@@')\r\n state, state_sum, results = ansible_safe_run(\r\n aapi=aapi, module='copy',\r\n args=dict(\r\n mode=info[0],\r\n src=os.path.join(conf_folder, x),\r\n dest=file_path,\r\n group=info[2],\r\n owner=info[1]))\r\n msg = 'Conf File Updated: %s' % state_sum\r\n app.logger.debug(logmsg(msg))\r\n msg = 'Conf File Updated: %s' % results\r\n app.logger.info(logmsg(msg))\r\n # 1. push toml files to remote/local confd client\r\n state, state_sum, results = ansible_safe_run(\r\n aapi=aapi, module='copy',\r\n args=dict(\r\n mode=self._confd_file_mode,\r\n src=toml_folder,\r\n dest=self._r_toml,\r\n group=self._confd_owner[1],\r\n owner=self._confd_owner[0]))\r\n msg = 'Toml File Updated: %s' % state_sum\r\n app.logger.debug(logmsg(msg))\r\n msg = 'Toml File Updated: %s' % results\r\n app.logger.info(logmsg(msg))\r\n # 2. push tmpl files to remote/local confd client\r\n r_tmpl_folder = os.path.join(self._r_tmpl, self._folder_pre)\r\n state, state_sum, results = ansible_safe_run(\r\n aapi=aapi, module='copy',\r\n args=dict(\r\n mode=self._confd_file_mode,\r\n src=tmpl_folder,\r\n dest=r_tmpl_folder,\r\n group=self._confd_owner[1],\r\n owner=self._confd_owner[0]))\r\n msg = 'Tmpl File Updated: %s' % state_sum\r\n app.logger.debug(logmsg(msg))\r\n msg = 'Tmpl File Updated: %s' % results\r\n app.logger.info(logmsg(msg))",
"title": ""
},
{
"docid": "82699850f5dc6d73115927e877f5a9ec",
"score": "0.4845694",
"text": "def master_config():\n\n pass",
"title": ""
},
{
"docid": "e02a95f8f4d4bb3d96b13d800f4d617b",
"score": "0.48365924",
"text": "def RefRpcFile(self):\n pass",
"title": ""
},
{
"docid": "408926e7827627c6816c4a0723b86ac5",
"score": "0.48189354",
"text": "def _do_restore(all_masters: Set[Node], backup_local_path: Path) -> None:\n backup_name = backup_local_path.name\n backup_remote_path = Path('/etc/') / backup_name\n\n for master in all_masters:\n master.send_file(\n local_path=backup_local_path,\n remote_path=backup_remote_path,\n )\n\n for master in all_masters:\n master.run(args=['systemctl', 'stop', 'dcos-exhibitor'])\n\n for master in all_masters:\n master.run(\n args=[\n '/opt/mesosphere/bin/dcos-shell',\n 'dcos-zk', 'restore', str(backup_remote_path), '-v',\n ],\n output=Output.LOG_AND_CAPTURE,\n )\n\n for master in all_masters:\n master.run(args=['systemctl', 'start', 'dcos-exhibitor'])",
"title": ""
},
{
"docid": "0f3b2bef2290f599b8ac35f6cac2ecd0",
"score": "0.477597",
"text": "def load(self, root):\n self.__rootdir = root",
"title": ""
},
{
"docid": "f8b26fb5786b8a5cbb0b0f060745d1da",
"score": "0.4741134",
"text": "def test_issue_54755(tmp_path, state_tree, modules):\n file_path = tmp_path / \"issue-54755\"\n sls_contents = \"\"\"issue-54755:\n file.managed:\n - name: {{ pillar['file_path'] }}\n - contents: issue-54755\n - unless: /bin/bash -c false\n \"\"\"\n\n with pytest.helpers.temp_file(\"issue-54755.sls\", sls_contents, state_tree):\n ret = modules.state.sls(mods=\"issue-54755\", pillar={\"file_path\": file_path})\n key = \"file_|-issue-54755_|-{}_|-managed\".format(file_path)\n assert key in ret.raw\n assert ret.raw[key][\"result\"] is True\n with salt.utils.files.fopen(str(file_path), \"r\") as fp:\n assert fp.read().strip() == \"issue-54755\"",
"title": ""
},
{
"docid": "87cfefe32bb6aa1a44ec253fa9af55b0",
"score": "0.4737973",
"text": "def reset_root_dir(self):\n self.root_directory = os.getcwd()",
"title": ""
},
{
"docid": "be52fe9b6f4d0173f4d393c635d095cc",
"score": "0.47192565",
"text": "def __init__(self, root_path):\n self._root_path = os.path.realpath(root_path)",
"title": ""
},
{
"docid": "d9590b54a8b88dea809b3bd437abb6c9",
"score": "0.47020742",
"text": "def setinitial(self, args):\r\n current = py.path.local()\r\n for arg in args + [current]:\r\n anchor = current.join(arg, abs=1)\r\n if anchor.check(): # we found some file object \r\n self._path2confmods[None] = self.getconftestmodules(anchor)\r\n break",
"title": ""
},
{
"docid": "e286cc377c2cd840357f6826080a0977",
"score": "0.47016668",
"text": "def __init__(self):\r\n self.file_list = self.update()",
"title": ""
},
{
"docid": "36716c424b9afb41cad69a2c3d181b3f",
"score": "0.46904516",
"text": "def sync(self) -> None:\n tarred_files = self._get_all_tarred_filenames()\n untarred_files = self._get_all_untarred_filepaths()\n new_files = _get_missing_tarred_files(tarred_files, untarred_files)\n\n files_with_extensions = [f'{filename}.tar.gz' for filename in new_files]\n\n if not files_with_extensions:\n pprint('no new scan files to untar')\n\n for filename in files_with_extensions:\n pprint(('untarring file: ', filename))\n self._untar_file(filename)\n pprint(('untarred file: ', filename))",
"title": ""
},
{
"docid": "24ecd95ff533b756f56f76bd9b59020d",
"score": "0.4687472",
"text": "def sync_files(self):\n while True:\n try:\n time.sleep(10)\n for client in self.clients:\n logger.debug( \"list of files for client %s, availability %s\",client.mfiles.list(), client.available)\n if client.available:\n for file in client.mfiles.list():\n rpc_status = rpc.pull_file(client.ip, client.port, file, self.username, self.ip)\n\n if rpc_status is None:\n client.available = False\n continue\n client.mfiles.remove(file)\n logger.debug(\"actual sync\")\n except KeyboardInterrupt:\n break",
"title": ""
},
{
"docid": "c789aa2538335812c9ee9df76e2ad184",
"score": "0.4685471",
"text": "def ls_master_path(path):\n return ls_pc_path(path) if ISMASTER else ls_s3_path(path)",
"title": ""
},
{
"docid": "4c8ae07339bfc66cac43dd2ec385afd6",
"score": "0.46796742",
"text": "def tmp_bdb_root(mocker, tmp_path):\n for dot_path in ('nog.bdb._get_bdb_root','impl.nog.bdb._get_bdb_root',):\n mocker.patch(\n dot_path, return_value=(tmp_path / 'minters').resolve(),\n )\n\n return tmp_path",
"title": ""
},
{
"docid": "86a7bfd9878a990d0964bc8b4546bbc0",
"score": "0.46720725",
"text": "def read_root ( fname ) :\n \n import ostap.io.root_file\n with ROOT.TFile.Open ( fname , 'READ' ) as f :\n \n f.ls()\n keys = f.GetListOfKeys()\n \n for k in keys :\n key = \"%s;%d\" % ( k.GetName() , k.GetCycle() ) \n obj = f.Get( key ) \n logger.info ( 'Read key/object %s/%s' % ( key , type ( obj ) ) )",
"title": ""
},
{
"docid": "b0cc1736db5757407837b033345b43d0",
"score": "0.46643278",
"text": "def root(self):\n str_treeRoot = '/'\n self.ml_cwd = [str_treeRoot]\n self.msnode_current = self.msnode_root\n self.msbranch_current = self.msbranch_root",
"title": ""
},
{
"docid": "ccc53978234f813df3e15ab1a28617b7",
"score": "0.46580508",
"text": "def _update_roots(self, server_binding, session, local_roots,\n remote_roots, repository):\n local_roots_by_id = dict((r.remote_root, r) for r in local_roots)\n local_root_ids = set(local_roots_by_id.keys())\n\n remote_roots_by_id = dict((r.uid, r) for r in remote_roots)\n remote_root_ids = set(remote_roots_by_id.keys())\n\n to_remove = local_root_ids - remote_root_ids\n to_add = remote_root_ids - local_root_ids\n\n for ref in to_remove:\n self._local_unbind_root(local_roots_by_id[ref], session)\n\n for ref in to_add:\n # get a client with the right base folder\n rc = self.get_remote_client(server_binding,\n repository=repository,\n base_folder=ref)\n self._local_bind_root(server_binding, remote_roots_by_id[ref],\n rc, session)",
"title": ""
},
{
"docid": "a7b9e87e1319f2c8d1d3c7cd83655376",
"score": "0.46576884",
"text": "def hard_refresh(self):\r\n for sys in self.fs:\r\n sys.refresh_files()\r\n self.refresh_files()",
"title": ""
},
{
"docid": "50d4a42720e5b0477230752f8a9aa19d",
"score": "0.46575683",
"text": "def read_master_file(self, filename):\n temp = []\n \"\"\"Read the cache file from disk\"\"\"\n with open(\"roothints.md\") as file_:\n lines = file_.readlines()\n for l in lines:\n #remove comments\n b = re.sub(r';[^\\n]*', \"\", l)\n te = l.split()\n if(te[0] != ';'):\n temp.append(te)\n if(\".\" in te[0]):\n type_ = self.getType(te[2])\n if(type_ == Type.A):\n rdata = ARecordData(te[3])\n elif(type_ == Type.NS):\n rdata = NSRecordData(te[3])\n elif(type_ == Type.CNAME):\n rdata = CNAMERecordData(te[3])\n rr = ResourceRecord(te[0], type_, Class.IN, te[1], rdata)\n self.records.update({te[0] : rr})",
"title": ""
},
{
"docid": "10181a53ad62517b829a3201cbf0c055",
"score": "0.46558937",
"text": "def test_projects_project_id_restore_files_post(self):\n pass",
"title": ""
},
{
"docid": "95257532723ba4842eaa1b8e643c2138",
"score": "0.46411076",
"text": "def _overrideMasterSettings(self):\n return",
"title": ""
},
{
"docid": "cb1f51a6a59f282b54de317003af50fa",
"score": "0.46361974",
"text": "def reindex(self):\n # check raw detector images\n logger.info('Reindexing already present exposures...')\n for subdir, extension in [\n ('images', 'cbf'), ('images_local', 'cbf'), ('param', 'pickle'), ('param_override', 'pickle'),\n ('eval2d', 'npz'), ('eval1d', 'txt')]:\n # find all subdirectories in `directory`, including `directory`\n # itself\n directory = self.getSubDir(subdir)\n logger.debug(f'Reindexing subdirectory {directory}')\n filename_regex = re.compile(rf'^(?P<prefix>\\w+)_(?P<fsn>\\d+)\\.{extension}$')\n for folder, subdirs, files in os.walk(str(directory)):\n logger.debug(f'Looking in folder {folder}')\n # find all files\n matchlist = [m for m in [filename_regex.match(f) for f in files] if m is not None]\n # find all file prefixes, like 'crd', 'tst', 'tra', 'scn', etc.\n prefixes = {m.group('prefix') for m in matchlist}\n for prefix in prefixes:\n logger.debug(f'Checking prefix {prefix}')\n if prefix not in self._lastfsn:\n self._lastfsn[prefix] = None\n # find the highest available FSN of the current prefix in\n # this directory\n maxfsn = max([int(m.group('fsn')) for m in matchlist if m.group('prefix') == prefix])\n logger.debug(f'Maxfsn is {maxfsn}')\n if self._lastfsn[prefix] is None or (maxfsn > self._lastfsn[prefix]):\n logger.debug(f'Updating lastfsn for prefix {prefix} to {maxfsn}')\n self._lastfsn[prefix] = maxfsn\n logger.debug(f'All prefixes done in this folder ({folder})')\n logger.debug('All folders done.')\n\n logger.debug('Creating empty prefixes')\n # add known prefixes to self._lastfsn if they were not yet added.\n for prefix in self.config['path']['prefixes'].values():\n if prefix not in self._lastfsn:\n self._lastfsn[prefix] = None\n else:\n self.lastFSNChanged.emit(prefix, self._lastfsn[prefix])\n\n # update self._nextfsn\n logger.debug('Updating nextfsn.')\n for prefix in self._lastfsn:\n self._nextfsn[prefix] = self._lastfsn[prefix] + 1 if self._lastfsn[prefix] is not None else 0\n self.nextFSNChanged.emit(prefix, self._nextfsn[prefix])\n logger.info('Reindexing done.')",
"title": ""
},
{
"docid": "3af606379e182d778d17badd5eeb1a0e",
"score": "0.4634852",
"text": "def test_relative_root_map(RE, tmpdir):\n directory = str(tmpdir)\n\n serializer = Serializer(directory)\n RE(count([img]), serializer)\n serializer.close()\n dest = shutil.copytree(img.save_path, pathlib.Path(directory, 'external_data'))\n relative_d = str(pathlib.Path(dest.relative_to(directory)))\n root_map = {img.save_path: relative_d}\n\n # At this point root map maps the original absolute path to one relative to\n # the diretory containing the catalog.\n\n CATALOG_FILE = f\"\"\"\nsources:\n test_relative_root_map:\n driver: bluesky-jsonl-catalog\n args:\n paths:\n - {directory}/*.jsonl\n root_map:\n {img.save_path}: {relative_d}\"\"\"\n catalog_path = str(pathlib.Path(directory, \"catalog.yml\"))\n with open(catalog_path, \"w\") as file:\n file.write(CATALOG_FILE)\n\n catalog = intake.open_catalog(catalog_path)\n subcatalog = catalog[\"test_relative_root_map\"]()\n # At init time, Broker should resolve the relative path to an absolute one.\n assert subcatalog.root_map[img.save_path] == str(dest)\n\n # But it can only do this if it has a catalog *file* to interpret the path\n # relative to.\n with pytest.raises(ValueError):\n BlueskyJSONLCatalog(f'{directory}/*.jsonl', root_map=root_map)",
"title": ""
},
{
"docid": "fc886b7abe11ea31a8471eb2cbf9e223",
"score": "0.46324226",
"text": "def update_file_references(self):\n\n self.copies = {copy for copy in self.yield_unchanged_copies()}\n\n self.symlinks = {symlink for symlink in self.yield_unchanged_symlinks()}",
"title": ""
},
{
"docid": "291e8af0d38c2837da2fe2b5bc034fc0",
"score": "0.4628297",
"text": "def _add_project_root_path(self):\n\n check_path = os.path.abspath(os.getcwd())\n while check_path:\n if check_path == '/':\n break\n\n for marker_file in FILES.values():\n marker_path = os.path.join(check_path, marker_file)\n if os.path.isfile(marker_path):\n self._paths['pwd'] = check_path\n return\n check_path = os.path.dirname(check_path)",
"title": ""
},
{
"docid": "875a7a77f5f0f1cd1ed785daf7f188aa",
"score": "0.46257824",
"text": "def roots(self, roots):\n\n self._roots = roots",
"title": ""
},
{
"docid": "f8a2f52a575d661dc128652112b56a2a",
"score": "0.46132347",
"text": "def test_write_io_mount_point_resumed_quorum_restored_x3(self):\n # pylint: disable=too-many-locals,too-many-statements,too-many-branches\n # set cluster.quorum-type to auto\n options = {\"cluster.quorum-type\": \"auto\"}\n g.log.info(\"setting cluster.quorum-type to auto on volume %s\",\n self.volname)\n ret = set_volume_options(self.mnode, self.volname, options)\n self.assertTrue(ret, (\"Unable to set volume option %s for\"\n \"volume %s\" % (options, self.volname)))\n g.log.info(\"Successfully set %s for volume %s\",\n options, self.volname)\n\n # Creating files on client side\n for mount_obj in self.mounts:\n g.log.info(\"Generating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"python %s create_files -f 30 %s\"\n % (self.script_upload_path, mount_obj.mountpoint))\n\n proc = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n self.all_mounts_procs.append(proc)\n\n # Validate IO\n self.io_validation_complete = False\n self.assertTrue(\n validate_io_procs(self.all_mounts_procs, self.mounts),\n \"IO failed on some of the clients\"\n )\n self.io_validation_complete = True\n\n # Do IO and check on subvols with nodes to reboot\n subvols_dict = get_subvols(self.mnode, self.volname)\n for subvol in subvols_dict['volume_subvols']:\n # define nodes to reboot\n brick_list = subvol[0:2]\n nodes_to_reboot = []\n for brick in brick_list:\n node, brick_path = brick.split(':')\n nodes_to_reboot.append(node)\n\n # get files to delete/create for nodes to be offline\n node, brick_path = brick_list[0].split(':')\n ret, brick_file_list, _ = g.run(node, 'ls %s' % brick_path)\n self.assertFalse(ret, 'Failed to ls files on %s' % node)\n file_list = brick_file_list.splitlines()\n\n # delete files from mountpoint\n for mount_obj in self.mounts:\n g.log.info(\"Deleting data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n cmd = ('cd %s/ ; rm -rf %s'\n % (mount_obj.mountpoint, ' '.join(file_list)))\n ret, _, _ = g.run(mount_obj.client_system, cmd)\n self.assertFalse(ret, 'Failed to rm file on %s'\n % mount_obj.client_system)\n g.log.info('Files %s are deleted', file_list)\n\n # reboot nodes on subvol and wait while rebooting\n g.log.info(\"Rebooting the nodes %s\", nodes_to_reboot)\n ret = reboot_nodes(nodes_to_reboot)\n self.assertTrue(ret, 'Failed to reboot nodes %s '\n % nodes_to_reboot)\n\n # Creating files on nodes while rebooting\n self.all_mounts_procs = []\n for mount_obj in self.mounts:\n g.log.info(\"Creating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"cd %s/ ;\"\n \"touch %s\"\n % (mount_obj.mountpoint, ' '.join(file_list)))\n\n proc = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n self.all_mounts_procs.append(proc)\n\n # Validate IO\n self.io_validation_complete = False\n g.log.info(\"Validating if IO failed with read-only filesystem\")\n ret = is_io_procs_fail_with_rofs(self, self.all_mounts_procs,\n self.mounts)\n self.assertTrue(ret, (\"Unexpected error and IO successful\"\n \" on read-only filesystem\"))\n self.io_validation_complete = True\n g.log.info(\"EXPECTED: \"\n \"Read-only file system in IO while creating file\")\n\n # check if nodes are online\n counter = 0\n timeout = 300\n _rc = False\n while counter < timeout:\n ret, reboot_results = are_nodes_online(nodes_to_reboot)\n if not ret:\n g.log.info(\"Nodes are offline, Retry after 5 seconds ... \")\n time.sleep(5)\n counter = counter + 5\n else:\n _rc = True\n break\n\n if not _rc:\n for node in reboot_results:\n if reboot_results[node]:\n g.log.info(\"Node %s is online\", node)\n else:\n g.log.error(\"Node %s is offline even after \"\n \"%d minutes\", node, timeout / 60.0)\n else:\n g.log.info(\"All nodes %s are up and running\", nodes_to_reboot)\n\n # Wait for volume processes to be online\n g.log.info(\"Wait for volume processes to be online\")\n ret = wait_for_volume_process_to_be_online(self.mnode,\n self.volname)\n self.assertTrue(ret,\n (\"Failed to wait for volume %s processes to \"\n \"be online\", self.volname))\n g.log.info(\"Successful in waiting for volume %s processes to be \"\n \"online\", self.volname)\n\n # Verify volume's all process are online\n g.log.info(\"Verifying volume's all process are online\")\n ret = verify_all_process_of_volume_are_online(self.mnode,\n self.volname)\n self.assertTrue(ret, (\"Volume %s : All process are not online\"\n % self.volname))\n g.log.info(\"Volume %s : All process are online\", self.volname)\n\n # Creating files on nodes after rebooting\n self.all_mounts_procs = []\n for mount_obj in self.mounts:\n g.log.info(\"Creating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"cd %s/ ;\"\n \"touch %s\"\n % (mount_obj.mountpoint, ' '.join(file_list)))\n\n proc = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n self.all_mounts_procs.append(proc)\n\n # Validate IO\n self.io_validation_complete = False\n self.assertTrue(\n validate_io_procs(self.all_mounts_procs, self.mounts),\n \"IO failed on some of the clients\"\n )\n self.io_validation_complete = True\n\n # Do IO and check on subvols without nodes to reboot\n subvols_dict = get_subvols(self.mnode, self.volname)\n for subvol in subvols_dict['volume_subvols']:\n # define nodes to reboot\n brick_list = subvol[0:2]\n nodes_to_reboot = []\n for brick in brick_list:\n node, brick_path = brick.split(':')\n nodes_to_reboot.append(node)\n\n # get files to delete/create for nodes to be online\n new_subvols_dict = get_subvols(self.mnode, self.volname)\n subvol_to_operate = new_subvols_dict['volume_subvols']\n subvol_to_operate.remove(subvol)\n brick_list_subvol_online = subvol_to_operate[0]\n\n node, brick_path_vol_online = \\\n brick_list_subvol_online[0].split(':')\n ret, brick_file_list, _ = g.run(node,\n 'ls %s' % brick_path_vol_online)\n self.assertFalse(ret, 'Failed to ls files on %s' % node)\n file_list = brick_file_list.splitlines()\n\n # delete files from mountpoint\n for mount_obj in self.mounts:\n g.log.info(\"Deleting data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n cmd = ('cd %s/ ; rm -rf %s'\n % (mount_obj.mountpoint, ' '.join(file_list)))\n ret, _, _ = g.run(mount_obj.client_system, cmd)\n self.assertFalse(ret, 'Failed to rm file on %s'\n % mount_obj.client_system)\n g.log.info('Files %s are deleted', file_list)\n\n # reboot nodes on subvol and wait while rebooting\n g.log.info(\"Rebooting the nodes %s\", nodes_to_reboot)\n ret = reboot_nodes(nodes_to_reboot)\n self.assertTrue(ret, 'Failed to reboot nodes %s '\n % nodes_to_reboot)\n\n # Creating files on nodes while rebooting\n self.all_mounts_procs = []\n for mount_obj in self.mounts:\n g.log.info(\"Creating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"cd %s/ ;\"\n \"touch %s\"\n % (mount_obj.mountpoint, ' '.join(file_list)))\n\n proc = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n self.all_mounts_procs.append(proc)\n\n # Validate IO\n self.io_validation_complete = False\n self.assertTrue(\n validate_io_procs(self.all_mounts_procs, self.mounts),\n \"IO failed on some of the clients\"\n )\n self.io_validation_complete = True\n\n # check if nodes are online\n counter = 0\n timeout = 300\n _rc = False\n while counter < timeout:\n ret, reboot_results = are_nodes_online(nodes_to_reboot)\n if not ret:\n g.log.info(\"Nodes are offline, Retry after 5 seconds ... \")\n time.sleep(5)\n counter = counter + 5\n else:\n _rc = True\n break\n\n if not _rc:\n for node in reboot_results:\n if reboot_results[node]:\n g.log.info(\"Node %s is online\", node)\n else:\n g.log.error(\"Node %s is offline even after \"\n \"%d minutes\", node, timeout / 60.0)\n else:\n g.log.info(\"All nodes %s are up and running\", nodes_to_reboot)\n\n # Wait for volume processes to be online\n g.log.info(\"Wait for volume processes to be online\")\n ret = wait_for_volume_process_to_be_online(self.mnode,\n self.volname)\n self.assertTrue(ret,\n (\"Failed to wait for volume %s processes to \"\n \"be online\", self.volname))\n g.log.info(\"Successful in waiting for volume %s processes to be \"\n \"online\", self.volname)\n\n # Verify volume's all process are online\n g.log.info(\"Verifying volume's all process are online\")\n ret = verify_all_process_of_volume_are_online(self.mnode,\n self.volname)\n self.assertTrue(ret, (\"Volume %s : All process are not online\"\n % self.volname))\n g.log.info(\"Volume %s : All process are online\", self.volname)\n\n # Do IO and check and reboot nodes on all subvols\n subvols_dict = get_subvols(self.mnode, self.volname)\n nodes_to_reboot = []\n file_list_for_all_subvols = []\n for subvol in subvols_dict['volume_subvols']:\n # define nodes to reboot\n brick_list = subvol[0:2]\n for brick in brick_list:\n node, brick_path = brick.split(':')\n nodes_to_reboot.append(node)\n\n # get files to delete/create for nodes to be offline\n node, brick_path = brick_list[0].split(':')\n ret, brick_file_list, _ = g.run(node, 'ls %s' % brick_path)\n self.assertFalse(ret, 'Failed to ls files on %s' % node)\n file_list = brick_file_list.splitlines()\n file_list_for_all_subvols.append(file_list)\n\n # delete files from mountpoint\n for mount_obj in self.mounts:\n g.log.info(\"Deleting data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n cmd = ('cd %s/ ; rm -rf %s'\n % (mount_obj.mountpoint, ' '.join(file_list)))\n ret, _, _ = g.run(mount_obj.client_system, cmd)\n self.assertFalse(ret, 'Failed to rm file on %s' % node)\n g.log.info('Files %s are deleted', file_list)\n\n # reboot nodes on subvol and wait while rebooting\n g.log.info(\"Rebooting the nodes %s\", nodes_to_reboot)\n ret = reboot_nodes(nodes_to_reboot)\n self.assertTrue(ret, 'Failed to reboot nodes %s '\n % nodes_to_reboot)\n\n # Creating files on nodes while rebooting\n all_mounts_procs, all_mounts_procs_1, all_mounts_procs_2 = [], [], []\n # Create files for 1-st subvol and get all_mounts_procs_1\n for mount_obj in self.mounts:\n g.log.info(\"Creating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"cd %s/ ;\"\n \"touch %s\"\n % (mount_obj.mountpoint,\n ' '.join(file_list_for_all_subvols[0])))\n\n proc = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n all_mounts_procs_1.append(proc)\n all_mounts_procs.append(all_mounts_procs_1)\n\n # Create files for 2-st subvol and get all_mounts_procs_2\n for mount_obj in self.mounts:\n g.log.info(\"Creating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"cd %s/ ;\"\n \"touch %s\"\n % (mount_obj.mountpoint,\n ' '.join(file_list_for_all_subvols[1])))\n\n proc2 = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n all_mounts_procs_2.append(proc2)\n all_mounts_procs.append(all_mounts_procs_2)\n\n for mounts_procs in all_mounts_procs:\n # Validate IO\n self.io_validation_complete = False\n g.log.info(\"Validating if IO failed with read-only filesystem\")\n ret = is_io_procs_fail_with_rofs(self, mounts_procs,\n self.mounts)\n self.assertTrue(ret, (\"Unexpected error and IO successful\"\n \" on read-only filesystem\"))\n self.io_validation_complete = True\n g.log.info(\"EXPECTED: \"\n \"Read-only file system in IO while creating file\")\n\n # check if nodes are online\n counter = 0\n timeout = 300\n _rc = False\n while counter < timeout:\n ret, reboot_results = are_nodes_online(nodes_to_reboot)\n if not ret:\n g.log.info(\"Nodes are offline, Retry after 5 seconds ... \")\n time.sleep(5)\n counter = counter + 5\n else:\n _rc = True\n break\n\n if not _rc:\n for node in reboot_results:\n if reboot_results[node]:\n g.log.info(\"Node %s is online\", node)\n else:\n g.log.error(\"Node %s is offline even after \"\n \"%d minutes\", node, timeout / 60.0)\n else:\n g.log.info(\"All nodes %s are up and running\", nodes_to_reboot)\n\n # Wait for volume processes to be online\n g.log.info(\"Wait for volume processes to be online\")\n ret = wait_for_volume_process_to_be_online(self.mnode,\n self.volname)\n self.assertTrue(ret,\n (\"Failed to wait for volume %s processes to \"\n \"be online\", self.volname))\n g.log.info(\"Successful in waiting for volume %s processes to be \"\n \"online\", self.volname)\n\n # Verify volume's all process are online\n g.log.info(\"Verifying volume's all process are online\")\n ret = verify_all_process_of_volume_are_online(self.mnode,\n self.volname)\n self.assertTrue(ret, (\"Volume %s : All process are not online\"\n % self.volname))\n g.log.info(\"Volume %s : All process are online\", self.volname)\n\n # Creating files on nodes after rebooting\n all_mounts_procs, all_mounts_procs_1, all_mounts_procs_2 = [], [], []\n # Create files for 1-st subvol and get all_mounts_procs_1\n for mount_obj in self.mounts:\n g.log.info(\"Creating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"cd %s/ ;\"\n \"touch %s\"\n % (mount_obj.mountpoint,\n ' '.join(file_list_for_all_subvols[0])))\n\n proc = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n all_mounts_procs_1.append(proc)\n all_mounts_procs.append(all_mounts_procs_1)\n\n # Create files for 2-st subvol and get all_mounts_procs_2\n for mount_obj in self.mounts:\n g.log.info(\"Creating data for %s:%s\",\n mount_obj.client_system, mount_obj.mountpoint)\n\n # Creating files\n cmd = (\"cd %s/ ;\"\n \"touch %s\"\n % (mount_obj.mountpoint,\n ' '.join(file_list_for_all_subvols[1])))\n\n proc2 = g.run_async(mount_obj.client_system, cmd,\n user=mount_obj.user)\n all_mounts_procs_2.append(proc2)\n all_mounts_procs.append(all_mounts_procs_2)\n\n for mounts_procs in all_mounts_procs:\n # Validate IO\n self.io_validation_complete = False\n self.assertTrue(\n validate_io_procs(self.all_mounts_procs, self.mounts),\n \"IO failed on some of the clients\"\n )\n self.io_validation_complete = True",
"title": ""
},
{
"docid": "0c0bc85a87fc61879b24444581869f95",
"score": "0.46022454",
"text": "def _load_file_system(self):\n \n #a = time.time()\n # parse the paths so we get full paths\n root_dir = self.FILE_SYSTEM.open_dir(inode=self.ROOT_INUM)\n self.fs_inode_to_path[self.ROOT_INUM] = \"/\"\n self._parse_paths(root_dir,\"/\")\n \n #print \"Total time to process path structure: %f s\" % (time.time() - a)\n \n # parse all the MFT entries in order\n last_inum = self.FILE_SYSTEM.info.last_inum\n for inode_num in xrange(0, last_inum+1):\n self._load_file_entry(inode_num)",
"title": ""
},
{
"docid": "445349ba3d3f9959f417c2757717d259",
"score": "0.46003407",
"text": "def _set_current_data(self, file_name: str) -> None:\n if self.has_multiple_files:\n self.file_name = file_name\n\n self.error_prefix = \"File {}\".format(self.file_name)\n self.file_path = Nitpick.current_app().root_dir / self.file_name\n\n # Configuration for this file as a TOML dict, taken from the style file.\n self.file_dict = Nitpick.current_app().config.style_dict.get(TomlFormat.group_name_for(self.file_name), {})\n\n # Nitpick configuration for this file as a TOML dict, taken from the style file.\n self.nitpick_file_dict = search_dict(\n 'files.\"{}\"'.format(self.file_name), Nitpick.current_app().config.nitpick_section, {}\n )",
"title": ""
},
{
"docid": "b75f9c1cee3e19028125597cb8151585",
"score": "0.45966762",
"text": "def substitute_project_root(yaml_file):\n for key in yaml_file.keys():\n current_key = yaml_file[key]\n # If we are dealing with a File object\n if (\n isinstance(current_key, ruamel.yaml.comments.CommentedMap)\n and \"class\" in current_key\n and current_key[\"class\"] == \"File\"\n ):\n new_value = yaml_file[key][\"path\"].replace(\n PIPELINE_ROOT_PLACEHOLDER, ROOT_DIR\n )\n yaml_file[key][\"path\"] = new_value\n\n # If we are dealing with a string\n # Todo: these should be replaced with File types\n if type(yaml_file[key]) == str:\n new_value = yaml_file[key].replace(PIPELINE_ROOT_PLACEHOLDER, ROOT_DIR)\n yaml_file[key] = new_value\n\n return yaml_file",
"title": ""
},
{
"docid": "5567a64ad571fc977f3a2a8fafb8a95a",
"score": "0.45780694",
"text": "def restore_files(self, file_list: Sequence[str]) -> Mapping[str, Union[str, bytes]]:\n file_index = self.get_file_ids(file_list)\n result = {}\n for path, file_id in file_index.items():\n result[path] = self.restore_single_file(file_id)\n return result",
"title": ""
},
{
"docid": "9f96e615f1a61d7983c3704af2ae3ce7",
"score": "0.45726028",
"text": "def test_pillar_refresh_pillar_item(salt_cli, salt_minion, key_pillar):\n key = \"issue-54941-item\"\n\n # We do not expect to see the pillar because it does not exist yet\n ret = salt_cli.run(\"pillar.item\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert key in val\n assert val[key] == \"\"\n\n with key_pillar(key) as key_pillar_instance:\n # The pillar exists now but get reads it from in-memory pillars, no\n # refresh happens\n ret = salt_cli.run(\"pillar.item\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert key in val\n assert val[key] == \"\"\n\n # Calling refresh_pillar to update in-memory pillars\n key_pillar_instance.refresh_pillar()\n\n # The pillar can now be read from in-memory pillars\n ret = salt_cli.run(\"pillar.item\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert key in val\n assert val[key] is True",
"title": ""
},
{
"docid": "16845454ebc098491b06351c14b566c5",
"score": "0.45724306",
"text": "def _flash_rootfs(self, ROOTFS):\n raise Exception(\"Not implemented!\")",
"title": ""
},
{
"docid": "90e1c65360519916db0dd7ab96a8143f",
"score": "0.45709443",
"text": "def test_pillar_refresh_pillar_ping(salt_cli, salt_minion, key_pillar):\n key = \"issue-54941-ping\"\n\n # We do not expect to see the pillar because it does not exist yet\n ret = salt_cli.run(\"pillar.item\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert key in val\n assert val[key] == \"\"\n\n with key_pillar(key) as key_pillar_instance:\n ret = salt_cli.run(\"test.ping\", minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert val is True\n\n # The pillar exists now but get reads it from in-memory pillars, no\n # refresh happens\n ret = salt_cli.run(\"pillar.item\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert key in val\n assert val[key] == \"\"\n\n # Calling refresh_pillar to update in-memory pillars\n key_pillar_instance.refresh_pillar()\n\n # The pillar can now be read from in-memory pillars\n ret = salt_cli.run(\"pillar.item\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert key in val\n assert val[key] is True",
"title": ""
},
{
"docid": "4a2b3d21dfe9aca17cb50ed043c81d98",
"score": "0.45405373",
"text": "def __init__(self, repo_root,\n debug, quiet, no_color,\n out=None):\n\n super(MKCTFAPI, self).__init__()\n self.logger = Logger(debug, quiet, no_color, out)\n\n self.repo_root = Path(repo_root)\n self.logger.debug('repo_root: {}'.format(self.repo_root))\n\n self.glob_conf_path = Path.home() / '.config/mkctf.yml'\n self.logger.debug('glob_conf_path: {}'.format(self.glob_conf_path))\n\n self.glob_conf = load_config(self.glob_conf_path)\n self.logger.debug('glob_conf: {}'.format(self.glob_conf))\n\n self.repo_conf_path = self.repo_root / self.glob_conf['files']['config']['repository']\n self.logger.debug('repo_conf_path: {}'.format(self.repo_conf_path))\n\n self.repo = Repository(self.logger, self.repo_conf_path, self.glob_conf)",
"title": ""
},
{
"docid": "bf19c519496235d0f421260715155cee",
"score": "0.45368007",
"text": "def _push_file_main(self, file_path):\n with open(file_path) as file_pointer:\n hashes = get_hashes(\n file_pointer,\n settings.UPLOAD_HASH_CHUNK_SIZE,\n hash_funcs,\n )\n file_pointer.seek(0)\n primary_hash = hashes[settings.UPLOAD_PRIMARY_HASH.__name__]\n with RetryUpload(self):\n container = storage.storage_container_proxy.get()\n obj, created = container.get_or_upload_file(file_pointer, primary_hash)\n md5 = hashes.get(hashlib.md5.__name__)\n if md5 != obj.md5:\n raise errors.HashMismatchError\n copy_completed_file(file_path, primary_hash)\n cleaned_hashes = clean_hash_names(hashes)\n return serialize_object(obj, **cleaned_hashes), created",
"title": ""
},
{
"docid": "6e415d9244ff9a64a1059885f67f8c4d",
"score": "0.45354697",
"text": "def __setup_master_specific_conf_files():\n import params\n\n params.File(hawq_constants.hawq_check_file,\n content=params.hawq_check_content)\n\n params.File(hawq_constants.hawq_slaves_file,\n content=InlineTemplate(\"{% for host in hawqsegment_hosts %}{{host}}\\n{% endfor %}\"))",
"title": ""
},
{
"docid": "9687fca4f32fc19691d47711581c1c11",
"score": "0.4530055",
"text": "def test_filemanager_setup_valid_with_inplace_data(self):\n print '\\n'; print_message('---- Starting Test: {} ----'.format(inspect.stack()[0][3]), 'ok')\n config_path = 'tests/test_configs/e3sm_diags_complete.cfg'\n config = ConfigObj(config_path)\n db = '{}.db'.format(inspect.stack()[0][3])\n\n filemanager = FileManager(\n database=db,\n event_list=EventList(),\n config=config)\n filemanager.populate_file_list()\n filemanager.update_local_status()\n\n self.assertTrue(isinstance(filemanager, FileManager))\n self.assertTrue(os.path.exists(db))\n self.assertTrue(filemanager.all_data_local())\n os.remove(db)",
"title": ""
},
{
"docid": "79441f886b18df34862b1eb48da0e9e7",
"score": "0.45227385",
"text": "def masterUFOPaths(self):\n return [sourceDescriptor.path for sourceDescriptor in self.sources]",
"title": ""
},
{
"docid": "32477dbc02a4fd8b914aeba391da4241",
"score": "0.45136926",
"text": "def sync_workdir_from_guest(self):\n pass",
"title": ""
},
{
"docid": "064cf081837913bc427e36ea4cdee7ff",
"score": "0.4511163",
"text": "def populate_tree(self):\n\n def create_file(root):\n \"\"\"\n Function used in os.walk via create_files\n\n Parameters\n ----------\n root : string\n This is where the files will be written\n \"\"\"\n\n file_path = os.path.join(root, self.random_string())\n while(os.path.exists(file_path)):\n file_path = os.path.join(root, self.random_string())\n\n delta_time = self.config[\"end\"] - self.config[\"start\"]\n atime = self.config[\"start\"] + int(random.choice(xrange(delta_time)))\n mtime = self.config[\"start\"] + int(random.choice(xrange(delta_time)))\n\n self.debug(\"Creating file %s\" % file_path, blue)\n file = open(file_path, 'w')\n\n max_size = self.config[\"size\"] * 1024\n chars_to_use = self.legal_chars + \"\\n\"\n content = self.random_string(max_size,legal_chars=chars_to_use)\n file.write(content)\n\n file.close()\n os.utime(file_path, (atime, mtime))\n self.fileCount+=1\n\n def create_files(root):\n \"\"\"\n Function used in os.walk to manage creation of files\n\n Parameters\n ----------\n root : string\n Current root\n \"\"\"\n\n num_files_to_create = random.choice(xrange(self.config[\"files\"]))\n for _ in xrange(0, num_files_to_create):\n create_file(root)\n\n for root, _ , _ in os.walk(self.config[\"target\"]):\n create_files(root)",
"title": ""
},
{
"docid": "79515513d521eba51279976fbd609183",
"score": "0.4510647",
"text": "def localfiles(self):\n\n for shape in self.loc_fl.keys():\n for ota in info.mel_ota_list:\n local_lyr = paths.ktima(ota, shape, ext=True)\n\n if os.path.exists(local_lyr):\n self.loc_fl[shape].add(str(ota))",
"title": ""
},
{
"docid": "063dc179e0f86e6f9ea2ca2e4a734f28",
"score": "0.45057848",
"text": "def rsync_done_opener(main_path): \n\n # Creates a list to store \"rsync_done\" files descriptor\n rsync_done_files = list()\n\n for file_path in recursive_walk(main_path, files_only=True):\n if basename(file_path) == RSYNC_PATTERN:\n # Opens each file\n try:\n rsync_done_files.append(open(file_path, \"r\"))\n except IOError:\n print >> stderr, \"Invalid argument - [\" + path + \"] is not an openable file.\"\n continue\n\n return rsync_done_files",
"title": ""
},
{
"docid": "fd45492ebaefa8a4b85c2a49e0a720bf",
"score": "0.45017883",
"text": "def __refresh(self) -> None:\n try:\n self.__load_root()\n self.__update_root()\n self.__update_timestamp()\n self.__update_snapshot()\n except Exception:\n self.close()\n raise",
"title": ""
},
{
"docid": "cd9a41610f617d77d3c2ee829bc309d9",
"score": "0.44998658",
"text": "def alignToMakeMaster(self):\n # Define a running variable that contains the refList. This\n # will be updated after each loop.\n refList = self.firstRefList\n\n\n for ii in range(0, len(self.images)):\n alignRoot = self.alignDir + 'align_'\n alignRoot += str(ii) + '_' + self.fields[ii]\n\n alignRootBoot = self.alignDir + 'align_boot_'\n alignRootBoot += str(ii) + '_' + self.fields[ii]\n\n starList = self.images[ii] + '_rms_dist.lis'\n\n # ==========\n # Re-order refList to shift common source to the top.\n # ==========\n newRefList = self.lisDir + 'ref_%d_%s' % (ii, refList)\n newStarList = self.lisDir + 'nirc2_%d_%s' % (ii, starList)\n\n # Ref List\n shiftToTopOfList(self.lisDir + refList, newRefList, \n self.refStars[self.fields[ii]])\n\n # Star List\n shiftToTopOfList(self.lisDir + starList, newStarList, \n self.refStars[self.fields[ii]])\n\n # Get the align data type\n fitsFile = self.dataDir + '../%s.fits' % (self.images[ii])\n alignType = dataUtil.get_align_type(fitsFile, errors=True)\n\n # Get the angle\n posAngle = dataUtil.get_pos_angle(fitsFile)\n if posAngle != 0 and self.alignOrder == 0:\n print 'You must allow for rotation with alignOrder > 0'\n\n # ==========\n # Align\n # ==========\n _list = open(alignRoot + '.list', 'w')\n _list.write('%s %s ref\\n' % (newRefList, alignType))\n _list.write('%s %d\\n' % (newStarList, alignType))\n _list.close()\n\n print '\\n*** Aligning %s ***' % starList\n cmd = 'java align -v -p -a %d -r %s %s.list' % (self.alignOrder, alignRoot, alignRoot)\n os.system(cmd)\n\n # Bootstrap\n print '\\n*** Aligning %s (bootstrap) ***' % starList\n ntrials = 100\n cmd = 'java align -v -p -a %d -n %d -r %s %s.list' % \\\n (self.alignOrder, ntrials, alignRootBoot, alignRoot)\n os.system(cmd)\n\n # ==========\n # Make a new reference list out of the previous align results\n # This builds up a list after each alignment\n # ==========\n refList = 'aligned_%d_%s' % (ii, starList)\n starPrefix = 'ep%d' % ii\n\n makeNewRefList(alignRootBoot, self.lisDir + refList, \n starPrefix=starPrefix)\n\n shutil.copyfile(self.lisDir + refList, self.lisDir + 'master.lis')",
"title": ""
},
{
"docid": "62154abd99604418ff3d9c23d83dfa02",
"score": "0.44965872",
"text": "def _populatedata(self):\n file_basename = os.path.basename(self._filename)\n\n # FIXME - this code is crazy\n path = self._filename.replace(self._root, '')\n path = path.replace(os.path.basename(self._filename), '')\n path = path[:-1]\n\n absolute_path = self._filename.replace(self._datadir, '')\n absolute_path = self._filename.replace(self._datadir, '', 1)\n absolute_path = absolute_path.replace(file_basename, '')\n absolute_path = absolute_path[1:][:-1]\n\n if absolute_path and absolute_path[-1] == \"/\":\n absolute_path = absolute_path[0:-1]\n\n filenamenoext = os.path.splitext(file_basename)[0]\n if absolute_path == '':\n file_path = filenamenoext\n else:\n file_path = '/'.join((absolute_path, filenamenoext))\n\n tb_id = '%s/%s' % (absolute_path, filenamenoext)\n tb_id = re.sub(r'[^A-Za-z0-9]', '_', tb_id)\n\n self._metadata.update({\n 'path': path,\n 'absolute_path': absolute_path,\n 'file_path': file_path,\n 'tb_id': tb_id,\n 'basename': filenamenoext,\n 'filename': self._filename\n })\n\n self.set_time(self._timetuple)\n\n config = self._request.get_configuration()\n\n fileext = os.path.splitext(self._filename)\n if fileext:\n fileext = fileext[1][1:]\n eparser = config['extensions'][fileext]\n entrydict = eparser(self._filename, self._request)\n\n # Update the _metadata directly skipping over this class'\n # dict-like stuff. Otherwise we end up in a vicious loop!\n self._metadata.update(entrydict)\n self.__populated = 1",
"title": ""
},
{
"docid": "c055d1bad6d6d88cfda91a11b5bda6b7",
"score": "0.4490046",
"text": "def referance_duplicate_to_master(master_file, duplicate_file):\n\n duplicate_file['real_path'] = master_file['real_path']\n duplicate_file['version_id'] = master_file['version_id']\n\n return duplicate_file",
"title": ""
},
{
"docid": "81cf05068ea44b13c772597da08201b0",
"score": "0.44894654",
"text": "def _readPath(self):\n try:\n name = nuke.root().name()\n if name == \"Root\":\n self.path = os.path.join(os.getcwd(), \"untitled.nk\")\n else:\n self.path = name\n except:\n # in the PLE, nuke.root() returns None\n self.path = os.path.join(os.getcwd(), \"untitled.nk\")",
"title": ""
},
{
"docid": "99bbe7cebc6e0f8348ae920f032b7c70",
"score": "0.44876128",
"text": "def all_codes(self):\n from q import Q\n if not self.settings.WORKDIR:\n raise QError(\"Ticket storage directory WORKDIR is not set.\")\n if not os.path.isdir(self.settings.WORKDIR):\n Q.wr(\"Initialize\", \"Creating ticket directory '%s'.\", self.settings.WORKDIR)\n mkpath(self.settings.WORKDIR)\n ret = []\n for p in os.listdir(self.settings.WORKDIR):\n if os.path.isfile(self.settings.WORKDIR+\"/\"+p+\"/README\"):\n ret.append(p)\n return ret",
"title": ""
},
{
"docid": "1a9e48c2a37df142cf18bdc28ca241ad",
"score": "0.4479799",
"text": "def _init_1d_correlations_hists_from_root_file(self) -> None:\n self._init_hists_from_root_file(hists = self.correlation_hists_delta_phi)\n self._init_hists_from_root_file(hists = self.correlation_hists_delta_eta)",
"title": ""
},
{
"docid": "1a9e48c2a37df142cf18bdc28ca241ad",
"score": "0.4479799",
"text": "def _init_1d_correlations_hists_from_root_file(self) -> None:\n self._init_hists_from_root_file(hists = self.correlation_hists_delta_phi)\n self._init_hists_from_root_file(hists = self.correlation_hists_delta_eta)",
"title": ""
},
{
"docid": "50752ee90167f2acf046dec9a7ac0bb0",
"score": "0.4477912",
"text": "def root(self, import_path):",
"title": ""
},
{
"docid": "cd1ba8147809695c423aad0633b2d7a0",
"score": "0.44775957",
"text": "def backup_keys(self):\r\n dir_pre = os.path.join('/', self._key_bak_pre, self._folder_pre)\r\n if dir_pre in self.etcd:\r\n self.etcd.delete(key=dir_pre, dir=True, recursive=True)\r\n for x in self._files:\r\n items = self.get_keys(cfg_name=x['name'])\r\n for k, v in items.items():\r\n ret = self.etcd.write(\r\n key=os.path.join(dir_pre, x['name'], k), value=v)\r\n msg = 'Etcd Key Backup: %s.' % ret\r\n app.logger.info(logmsg(msg))",
"title": ""
},
{
"docid": "eb8cdf79b0043d6725034bc0563101da",
"score": "0.44770062",
"text": "def _fetch_root_resources(self):\n status, headers, service_root_resp = (\n self._conn._rest_get(self._root_prefix))\n self._root_resp = service_root_resp",
"title": ""
},
{
"docid": "6765a2a41a9fca6174bdd37b77d57c23",
"score": "0.44691765",
"text": "def rehash(self):\n\t\tself.load(self.fileName)\n\t\tlog(\"Reloading configuration.\")",
"title": ""
},
{
"docid": "a7d4f70c2752213d243d2864244422a2",
"score": "0.44657946",
"text": "def test_file_root_hash(self):\n root = ipfs_file(b'definetly not a dir')\n with self.assertRaises(InvalidIPFSPathException):\n with ipfs_mounted(root, ipfs_client):\n pass",
"title": ""
},
{
"docid": "36df502f15ac7431e7d165bf2af92466",
"score": "0.44648266",
"text": "def test_pull_local_old(self, app, client):\n\n repo_dir = os.path.join(self.testing_repos['simple'], 'subdir')\n if os.path.exists(repo_dir):\n shutil.rmtree(repo_dir)\n\n self.pull_and_wait(client, repo_dir)\n\n with open(repo_dir + '/subfile.txt', 'r') as local_file:\n assert local_file.readline() == 'subfile content\\n'\n\n # Modify in local repo and change its modification time\n with open(repo_dir + '/subfile.txt', 'w') as local_file:\n local_file.write('This was touched locally\\n')\n\n self.set_old_atime(repo_dir + '/subfile.txt', age=3600 * 30000, recursive=False)\n\n # Try to pull a dir already pulled just before\n self.pull_and_wait(client, repo_dir)\n\n assert os.path.exists(repo_dir + '/subfile.txt')\n assert os.path.isdir(repo_dir + '/subsubdir')\n assert os.path.exists(repo_dir + '/subsubdir/subsubfile.txt')\n assert os.path.exists(repo_dir + '/subsubdir/poutrelle.xml')\n\n with open(repo_dir + '/subfile.txt', 'r') as local_file:\n assert local_file.readline() == 'This was touched locally\\n'\n\n if os.path.exists(repo_dir):\n shutil.rmtree(repo_dir)",
"title": ""
},
{
"docid": "0d2035b64f96d041e84717151e7f175e",
"score": "0.44598183",
"text": "def upload_local_changes():\n\n while not mqueue.modified.empty():\n file = mqueue.modified.get()\n \n if file in mqueue.client_modified:\n continue\n\n print \"Detect %s modified\" % file\n \n url = \"%s/files/%s/hashes\" % (server_url, file)\n try:\n fd = urllib2.urlopen(url)\n except URLError, e:\n if hasattr(e, 'reason'):\n mqueue.postponed_modified.append(file)\n continue\n except HTTPError:\n # The server may return us an error if things have gone south during\n # the file creation process.\n # If so, mark the file as added instead of modified.\n print \"mark as added instead\"\n mqueue.added.put(file)\n continue\n\n hashes = json.load(fd)\n try:\n patchedfile = open(secure_path(cagibi_folder, file), \"rb\")\n deltas = encode_deltas(rsyncdelta(patchedfile, hashes))\n patchedfile.close()\n except IOError:\n print \"IOError - continuing\"\n # The file may be locked by another process\n # Add it to the postponed list. \n mqueue.postponed_modified.append(file)\n continue\n\n # Send the deltas to the server.\n post_data = {}\n post_data[\"deltas\"] = json.dumps(deltas)\n post_string = urllib.urlencode(post_data)\n fd = urllib2.urlopen(url, post_string)\n results = json.load(fd)\n \n local_files = load_config(\"files.json\")\n local_files[file][\"rev\"] = results[\"rev\"]\n save_config(local_files, filename=\"files.json\") \n\n opener = urllib2.build_opener(urllib2.HTTPHandler)\n\n while not mqueue.added.empty():\n file = mqueue.added.get()\n print \"Detected %s added\" % file\n\n put_data = {}\n try:\n fd = open(secure_path(cagibi_folder, file), \"r\")\n put_data[\"contents\"] = fd.read()\n put_string = urllib.urlencode(put_data)\n fd.close()\n except IOError, e:\n print e\n continue\n\n url = \"%s/files/%s\" % (server_url, file)\n request = urllib2.Request(url, data=put_string)\n request.add_header('Content-Type', 'application/json')\n request.get_method = lambda: 'PUT'\n try:\n url = opener.open(request)\n local_files = load_config(\"files.json\")\n local_files[file] = {\"rev\": 1}\n save_config(local_files, filename=\"files.json\") \n\n except URLError, e:\n if hasattr(e, 'reason'):\n mqueue.postponed_added.append(file)\n continue\n except HTTPError:\n continue\n\n while not mqueue.removed.empty():\n file = mqueue.removed.get()\n print \"Detect %s removed\" % file\n\n url = \"%s/files/%s\" % (server_url, file)\n request = urllib2.Request(url)\n request.get_method = lambda: 'DELETE'\n try:\n url = opener.open(request)\n except URLError, e:\n if hasattr(e, 'reason'):\n mqueue.postponed_removed.append(file)\n continue\n except HTTPError:\n continue",
"title": ""
},
{
"docid": "01bb4abc520bfa57a0740e6637646ebf",
"score": "0.44562474",
"text": "def load_from_poscar(self, filename):\n atomic_set.load_from_poscar(self, filename)\n self.forces = zeros(self.atoms.shape)\n self.energy = 0",
"title": ""
},
{
"docid": "7a04651ae9e11ec83c362317966dbc49",
"score": "0.44556227",
"text": "def form_transfer(xmap, as_subform=0):\n initialize = 0\n info = create_obj_xmap_mode(xmap)\n\n print_text(TXT_TRANSFER_INFO.format(**info))\n\n # not use info[] for 'ignore' and 'files' because in info[] are string\n origin = info['origin']['path'] \n destination = info['destination']['path']\n ignore_test = ignore_rules(xmap.get(\"ignore\"))\n objects = xmap.get('files')\n is_path_transfer = not objects\n if not exists_master_path(info['origin']['path']):\n print_error(TXT_ERR_MASTER_OBJECT_NOT_EXISTS.format(storage=info['origin']['name'],path = info['origin']['path']))\n objects = ()\n\n elif is_path_transfer: \n # get all objects in master path\n objects = ls_master_path(origin)\n else:\n # if not 'is_master_path'\n # check existence of all files specified in xmap['files']\n all_exists = True\n for file in ( \"/\".join([info['origin']['path'], obj]) for obj in objects):\n if not exists_master_file(file):\n print_error(TXT_ERR_MASTER_OBJECT_NOT_EXISTS.format(storage=info['origin']['name'], path=file))\n all_exists = False\n if not all_exists:\n # remove all objects, stop transfer\n objects = iter(())\n\n for obj in objects:\n # Objects for cicle on s3 master return error message and iter(())\n # if bucket name is wrong!\n #\n # Objects is generator, don't give error until first access\n # for this reason\n # '_init_fun' is inside the loop:\n # is useful to NOT delete local slave folder\n # if bucket objects not exists!\n obj_data = None if obj.endswith(\"/\") else get_master_file(\"/\".join([origin, obj]))\n obj_fullpath = \"/\".join([destination, obj])\n if not initialize:\n initialize = 1\n _init_fun = _init_path_transfer if is_path_transfer else _init_files_transfer\n if not _init_fun(info):\n break # objects transfer\n if ignore_test(obj):\n print_blue(TXT_TRANSFER_IGNORE.format(storage=info['destination']['name'], object=obj))\n else:\n print_text(TXT_SAVE_OBJECT.format(storage=info['destination']['name'],root=info['destination']['path'], object=obj))\n if not mk_slave_object(obj_fullpath, obj_data):\n break # objects transfer\n else: # and complete cicle of for obj in objects\n if initialize:\n if CONFIG['MAIN'].getboolean('show_transfer_detail'):\n print_success(TXT_TRANSFER_COMPLETE)\n if as_subform:\n return 1\n else:\n return\n\n # here if cicle inclomplete or not 'initializate' == 0\n print_warning(TXT_WARNING_EXIT_TASK_WITH_ERROR)\n if as_subform:\n return 0\n # else return None",
"title": ""
},
{
"docid": "a8a1a102cb1aced09d3232a6b01fe488",
"score": "0.4454129",
"text": "def set_root(self, root):\n self.root.key = root.key",
"title": ""
},
{
"docid": "45aee2077f7df3697c6c15aa35b3b970",
"score": "0.445254",
"text": "def _update_stage_paths(self):\n\n # Get the expanded path for the 'MAST_HLSP' directory.\n cwd = os.getcwd()\n self._root = os.path.join(cwd.split(self._root_dir, 1)[0],\n self._root_dir,\n )\n\n # Default filename should be in the root directory named hlsp_name.hlsp\n default_name = self._get_filename()\n self._default_path = os.path.join(self._root, default_name)\n\n # Construct file path for check_file_names.py results.\n cfn_name = self._get_filename(self._check_file_names_out)\n self._cfn_path = os.path.join(self._root,\n self._check_file_names_dir,\n cfn_name,\n )\n\n # Construct file path for precheck_data_format.py reults.\n pcdf_name = self._get_filename(self._precheck_metadata_format_out)\n self._pcdf_path = os.path.join(self._root,\n self._check_metadata_format_dir,\n pcdf_name\n )\n\n # Construct file path for check_metadata_format.py results.\n cmd_name = self._get_filename(self._check_metadata_format_out)\n self._cmd_path = os.path.join(self._root,\n self._check_metadata_format_dir,\n cmd_name,\n )",
"title": ""
},
{
"docid": "4085693ee35d7e1c1551521c9c7adb4c",
"score": "0.44417578",
"text": "def get_all_pickles(self):\n if self.__pickle_files:\n # self.pickles = {pickle.load(file) for file in self.__pickle_files]\n return self.pickles\n else:\n raise FileNotFoundError",
"title": ""
},
{
"docid": "d78372c659d8249b7cda9feed02dceab",
"score": "0.44313928",
"text": "def set_master_dir(set_dir_to):\n global MASTER_DIR\n\n MASTER_DIR = set_dir_to\n # TODO - remove this function",
"title": ""
},
{
"docid": "109acfa1d5db3bc630981196865c0d4c",
"score": "0.44312644",
"text": "def _set_all_owners(self):\n direct_dependency = get_folders(self.changed_files)\n all_folders = set(direct_dependency)\n for folder in direct_dependency:\n folder_name = folder.replace(self.repo_root, \"\")\n dependencies = {\n f\"{self.repo_root}{f}\"\n for f in self.transitive_dependencies.get(folder_name, [])\n }\n all_folders = all_folders.union(dependencies)\n\n for folder in all_folders:\n folder_name = folder.replace(self.repo_root, \"\")\n self.owners[folder_name] = self._get_folder_owners(folder)",
"title": ""
},
{
"docid": "3e434c0c55e656f936cc54185f416a1a",
"score": "0.44302025",
"text": "def restore_user_files(logged_in, client):\n\n try:\n folder = read_bytes_until(client[0], \"\\n\")\n print_connection_event(client[1], \"Upload args: \", folder, \" \")\n except:\n print_connection_event(client[1], \"Error in request for restoration\", \"RBR ERR\", \"<-\")\n client[0].sendall(\"RBR ERR\\n\".encode())\n exit(2)\n\n dirpath = os.path.join(logged_in, folder)\n if not os.path.isdir(dirpath):\n print_connection_event(client[1], \"Directory not found\", \"RBR EOF\", \"<-\")\n client[0].sendall(\"RBR EOF\\n\".encode())\n exit(1)\n\n file_list = [f for f in os.scandir(dirpath) if f.is_file()]\n message = \"RBR {}\".format(len(file_list))\n\n print_connection_event(client[1], \"Start sending back files\", message, \"<-\")\n client[0].sendall(message.encode())\n\n for user_file in file_list:\n\n f_stat = user_file.stat()\n f_time = strftime(\"%d.%m.%Y %H:%M:%S\", gmtime(f_stat.st_mtime))\n mess_part = \" {} {} {} \".format(user_file.name, f_time, f_stat.st_size)\n print_connection_event(client[1], \" Sending {}\".format(user_file.name), \"\", \" \")\n client[0].sendall(mess_part.encode())\n\n filefd = os.open(user_file.path, os.O_RDONLY)\n for data in chunked_read_fd(filefd, f_stat.st_size, 4096):\n client[0].sendall(data)\n print_connection_event(client[1], \" Sent {}\".format(user_file.name), \"\", \" \")\n\n client[0].sendall(\"\\n\".encode())\n print_connection_event(client[1], \"Finished sending back files\", message, \"<-\")",
"title": ""
},
{
"docid": "60f64198dfebe7daddc362c9b12e5a14",
"score": "0.44281995",
"text": "def test_pillar_refresh_pillar_get(salt_cli, salt_minion, key_pillar):\n key = \"issue-54941-get\"\n\n # We do not expect to see the pillar because it does not exist yet\n ret = salt_cli.run(\"pillar.get\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert val == \"\"\n\n with key_pillar(key) as key_pillar_instance:\n # The pillar exists now but get reads it from in-memory pillars, no\n # refresh happens\n ret = salt_cli.run(\"pillar.get\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert val == \"\"\n\n # Calling refresh_pillar to update in-memory pillars\n key_pillar_instance.refresh_pillar()\n\n # The pillar can now be read from in-memory pillars\n ret = salt_cli.run(\"pillar.get\", key, minion_tgt=salt_minion.id)\n assert ret.returncode == 0\n val = ret.data\n assert val is True, repr(val)",
"title": ""
},
{
"docid": "3c427165f25dffff7f08c043cd4dffda",
"score": "0.44203442",
"text": "def __load_processed_files(self):\n if self.correctDir.get() is not None and self.display_hocr is not None:\n self.gui['hocr_list'].delete(0, tk.END)\n for k, v in self.display_hocr.get_file_listing().items():\n self.gui['hocr_list'].insert(tk.END, k)\n self.__poll_processed_list()",
"title": ""
},
{
"docid": "a7825aff335266e7e09e79a58467bc8e",
"score": "0.44156718",
"text": "def __init__(self, root):\n self.root = root\n self.listdir = os.listdir(self.root)\n # self.args = args",
"title": ""
},
{
"docid": "1a8dd66568bc1a407a839e3268e52295",
"score": "0.4412801",
"text": "def restore_config_file(self):\n if PS == \"Windows\":\n common_config_file = world.config[\"sd_server\"][\"common_config_file_win\"]\n common_config_file_backup = world.config[\"sd_server\"][\"common_config_file_backup_win\"]\n else:\n common_config_file = world.config[\"sd_server\"][\"common_config_file\"]\n common_config_file_backup = world.config[\"sd_server\"][\"common_config_file_backup\"]\n shutil.copy2(common_config_file_backup, common_config_file)\n world.config_file_backed_up = False",
"title": ""
},
{
"docid": "954ab868dacb74549f52e1c73e2aa4de",
"score": "0.44106334",
"text": "def paths_update(self, al_branchNodes):\n for node in al_branchNodes:\n #print \"appending %s\" % node\n l_pwd = self.ml_cwd[:]\n l_pwd.append(node)\n #print \"l_pwd: %s\" % l_pwd\n #print \"ml_cwd: %s\" % self.ml_cwd\n self.ml_allPaths.append(l_pwd)",
"title": ""
},
{
"docid": "7486bd9e563bc290b4dee44c09ae6fe4",
"score": "0.44104803",
"text": "def GitConfigRebaseMaster(cwd):\n proc.check_call(\n ['git', 'config', 'branch.master.rebase', 'true'], cwd=cwd)",
"title": ""
},
{
"docid": "c1804939a76345fdb971d8e45cef6982",
"score": "0.4406382",
"text": "def sync_workdir_to_guest(self):\n pass",
"title": ""
},
{
"docid": "abda81b089649217106040067c65cd92",
"score": "0.4406118",
"text": "def _phon_poscar_setup(self):\n name = self.keywords['name']\n pospath = os.path.join(name, \"POSCAR\")\n prepath = os.path.join(name, \"POSCAR_prePHON\")\n if os.path.isfile(pospath): #Already done. Return.\n return\n my_poscar = Poscar.from_file(prepath) \n my_poscar.selective_dynamics=None #unset SD if it is set\n my_poscar.velocities=None #unset velocities\n dirutil.lock_directory(name)\n my_poscar.write_file(pospath)\n dirutil.unlock_directory(name)\n #pick up a copy and strip out the elements line.\n mypfile = MASTFile(pospath)\n myline6=mypfile.get_line_number(6)\n if myline6.strip().split()[0].isalpha:\n mypfile.modify_file_by_line_number(6,\"D\")\n mypfile.to_file(pospath)\n return",
"title": ""
},
{
"docid": "4f4b534a756edc052c871dbb64fbed1a",
"score": "0.44045126",
"text": "async def restore(self) -> None:\n if self._core_config_changed:\n await reset_hass_config(self._hass)\n self._core_config_changed = False\n _LOGGER.debug(\"Finished exam: %s\", self.config_file_full)",
"title": ""
},
{
"docid": "63b27fa09dc08c006b0c4df770763c54",
"score": "0.44001788",
"text": "def init_folders(self):\n filename = pkg_resources.resource_filename('pub2', 'skel')\n pathname = os.path.dirname(os.path.abspath(__file__))\n os.system(\"mrbob -w {0} -O {1}\".format(opj(pathname, filename), self.working_dir))\n os.remove(opj(self.working_dir, \".mrbob.ini\"))\n os.remove(opj(self.working_dir, \"_pubs/_assets/.gitignore\"))\n os.remove(opj(self.working_dir, \"_data/.gitignore\"))",
"title": ""
},
{
"docid": "0555b26754304148dd07b0656cd5e199",
"score": "0.43957016",
"text": "def _initialize(self):\r\n status, out, err = git.ls_tree('--full-tree', '-r', '-t', '-z',\r\n self.ref)\r\n if status != 0:\r\n Interaction.log_status(status, out, err)\r\n return\r\n\r\n if not out:\r\n return\r\n\r\n for line in out[:-1].split('\\0'):\r\n # .....6 ...4 ......................................40\r\n # 040000 tree c127cde9a0c644a3a8fef449a244f47d5272dfa6\trelative\r\n # 100644 blob 139e42bf4acaa4927ec9be1ec55a252b97d3f1e2\trelative/path\r\n objtype = line[7]\r\n relpath = line[6 + 1 + 4 + 1 + 40 + 1:]\r\n if objtype == 't':\r\n parent = self.dir_entries[utils.dirname(relpath)]\r\n self.add_directory(parent, relpath)\r\n elif objtype == 'b':\r\n self.add_file(relpath)",
"title": ""
},
{
"docid": "f8c6f5f13538dd5c9b59a5241e278869",
"score": "0.43914732",
"text": "def merge_rootfile(filename=None,RunList='',caltag=None): \n\n if filename == None:\n return None\n\n if caltag == None:\n return None\n\n flags='hadd '+filename+' '\n for run in RunList:\n name=os.path.splitext(os.path.basename(run))[0]\n flags=flags+'root-files/X0-'+name+'-'+caltag+'-reco.root '\n\n if os.path.isfile(filename):\n os.remove(filename)\n subprocess.call(flags, shell=True)\n\n return None",
"title": ""
},
{
"docid": "15cabb85f6b60af409dacfd0041986f2",
"score": "0.43858448",
"text": "def rootfs(self):\n return self._rootfs",
"title": ""
},
{
"docid": "4b572a3eceb1b26ae17521983fc6ff58",
"score": "0.43759075",
"text": "def refresh_file_dir(self):\n res = cmd_get_free_space(self.hid)\n if res:\n self.filedir.bytesLeft = uint32_toint(res[3:7])\n capacity = uint32_toint(res[7:11])\n self.filedir.bytesUsed = capacity - self.filedir.bytesLeft\n res = cmd_get_num_files(self.hid)\n if res:\n self.filedir.files = []\n self.filedir.numFiles = uint16_toint(res[3:5])\n for i in range(64):\n res = cmd_get_dir_entry(self.hid, i+1)\n d = PFxFile()\n d.from_bytes(res)\n if d.id < 0xFF:\n self.filedir.files.append(d)",
"title": ""
},
{
"docid": "44fdd0352492dcce31a3e876a40f8399",
"score": "0.43650916",
"text": "def get_pillar(self, ext_pillar_conf):\n with patched_environ(GIT_SSH=self.git_ssh):\n return super().get_pillar(ext_pillar_conf)",
"title": ""
},
{
"docid": "9f0722c90a09d70a2c0e39c3f89c4112",
"score": "0.4360991",
"text": "def setup(self):\n self.temporary_file_list = False\n # store current pysat directory\n self.saved_data_path = pysat.data_dir\n\n pysat.data_dir = ''\n re_load(pysat._files)",
"title": ""
},
{
"docid": "81af24b700a1eccf7ed66a2d2867b830",
"score": "0.43600908",
"text": "def repair_master(self):\n\n # change into the right directory\n self.change_path()\n\n branches = self.git_branch()\n # unneeded, if the master branch already exists\n if \"master\" in branches:\n return\n\n # we have to use this\n # (http://git-annex.branchable.com/direct_mode/)\n self.execute_command([\"git\", \"-c\", \"core.bare=false\", \"commit\", \"--allow-empty\", \"-m\", \"empty commit\"])",
"title": ""
},
{
"docid": "d453f63c7cde4624fe7a81fa53aaaa53",
"score": "0.43585685",
"text": "def files_root(self):\n _s3 = self._s3_connection()\n\n files = []\n bucket = _s3.Bucket(self.bucket_name)\n\n for f in bucket.objects.all():\n files.append(f.key)\n\n return files",
"title": ""
},
{
"docid": "ff6096a1758d0a4183a5ece162fe9b73",
"score": "0.43564025",
"text": "def _initialize_master_working_set():\n working_set = WorkingSet._build_master()\n _declare_state('object', working_set=working_set)\n\n require = working_set.require\n iter_entry_points = working_set.iter_entry_points\n add_activation_listener = working_set.subscribe\n run_script = working_set.run_script\n # backward compatibility\n run_main = run_script\n # Activate all distributions already on sys.path with replace=False and\n # ensure that all distributions added to the working set in the future\n # (e.g. by calling ``require()``) will get activated as well,\n # with higher priority (replace=True).\n tuple(dist.activate(replace=False) for dist in working_set)\n add_activation_listener(\n lambda dist: dist.activate(replace=True),\n existing=False,\n )\n working_set.entries = []\n # match order\n list(map(working_set.add_entry, sys.path))\n globals().update(locals())",
"title": ""
},
{
"docid": "5d747b54b624e20417c7b0438f8f98dc",
"score": "0.43535233",
"text": "def readfiles(self):\n x = clprocessfiles()",
"title": ""
},
{
"docid": "fad49a4820d857e735ebcfff89fde2c1",
"score": "0.43533996",
"text": "def set_master(self, master_server, get_cur_log_pos = True):\n\n self.wsrep_cluster_addr = '127.0.0.1:%d' %(master_server.galera_listen_port)",
"title": ""
},
{
"docid": "bcd8b2b67a025446ee35550fc3bc02e4",
"score": "0.4343609",
"text": "def find_roots(cls, root: typing.Union[str, bytes, PathLike, PurePath]):\n root = Path(root)\n actual_roots = {}\n for sequence_number in range(11):\n try:\n actual_roots[sequence_number] = kitti_loader.find_root(root, sequence_number)\n except FileNotFoundError:\n continue\n return actual_roots",
"title": ""
}
]
fe8646ab98b360f3a828c884c49582f2
Creates a new instance of data page and assigns its values.
[
{
"docid": "18e8b588f7434cdc93fc9c7d71063882",
"score": "0.5780925",
"text": "def __init__(self, data: List[Any], token: str = None, total: int = None):\n # The total amount of items in a request.\n self.total: int = total\n # The starting point for the next search.\n self.token: str = token\n # The items of the retrieved page.\n self.data: List[Any] = data",
"title": ""
}
]
[
{
"docid": "0f5b0e3516d0e736cb18463ee0e55493",
"score": "0.6318198",
"text": "def export_kml():\n folder = 'cad'\n name = get_dataset_filename()\n \n # Create a cad folder in the temp directory if it does not exist\n working_folder = catalog_publish_folder + name + \"//\" + temp_folder + \"//\" + folder\n create_folder(working_folder, True)\n \n # Export the shapefile to the folder\n source = database_connection + \"\\\\\" + args.feature_class\n destination = working_folder + \"\\\\\" + name + \".kmz\"\n \n # Make a feature layer (in memory)\n debug(' - Generating KML file in memory from \"' + source + '\"')\n gp.MakeFeatureLayer_management(source, name, \"\", \"\")\n \n # Convert the layer to KML\n debug(' - Exporting KML file (KMZ) to \"' + destination + '\"')\n gp.LayerToKML_conversion(name, destination, \"20000\", \"false\", \"DEFAULT\", \"1024\", \"96\")\n\n # Publish the zipfile to the download folder\n publish_file(working_folder, name + \".kmz\",\"kml\")",
"title": ""
},
{
"docid": "0ddd20f320a99ece84fd00a11ed1e6a5",
"score": "0.6048646",
"text": "def output_path(self):",
"title": ""
},
{
"docid": "70de662a1197bbfb3ab348f44769df01",
"score": "0.59567255",
"text": "def output_path(self) -> Path:\n return self.path / OUTPUT_FILE_NAME",
"title": ""
},
{
"docid": "e46576c2df4472a9368be645c677392d",
"score": "0.59310794",
"text": "def get_feature_object_name_and_path(cls, input_path, feature_dir):\n new_filename = os.path.basename(input_path)\n new_filename = scrub_underscore_suffix(new_filename)\n\n # Append model_name along with \"features\" to demarcate\n # different models when saving the feature vectors.\n new_filename = add_suffix_to_filename(\n new_filename, \"_features\" + \"_\" + cls.class_feature_name\n )\n if not os.path.isdir(os.path.join(feature_dir, cls.class_feature_name)):\n os.makedirs(os.path.join(feature_dir, cls.class_feature_name))\n feature_path = os.path.join(feature_dir, cls.class_feature_name, new_filename)\n feature_path = os.path.abspath(feature_path)\n return feature_path",
"title": ""
},
{
"docid": "557bd66077bbaaa2a03ff10ecfe7067d",
"score": "0.5896151",
"text": "def get_kpath(self):\n if self._kpath is None:\n from matdb.kpoints import parsed_kpath\n self._kpath = parsed_kpath(self.atoms)\n\n return self._kpath",
"title": ""
},
{
"docid": "853f345c4cec8a31b2c85b65168eff5f",
"score": "0.58727056",
"text": "def output_path(self) -> pathlib.Path:\n return self.path / self.file",
"title": ""
},
{
"docid": "a6bbf47045cb4a28f187f624e1247c01",
"score": "0.58236724",
"text": "def CreatePath(self):",
"title": ""
},
{
"docid": "a6bbf47045cb4a28f187f624e1247c01",
"score": "0.5823312",
"text": "def CreatePath(self):",
"title": ""
},
{
"docid": "a2d391eba31ac5431ead5cc999b785ec",
"score": "0.5614006",
"text": "def outputPath(self):\n if self.path == []:\n print('Nothing in path. Skipping output...')\n return\n\n makedirs('output', exist_ok=True)\n # Scaling factor to gradually change colour\n ptImg = self.img.copy()\n ptPix = ptImg.load()\n l = 120.0 / len(self.path)\n for i, coords in enumerate(self.path):\n ptPix[coords] = (0, 255-int(i*l), 0)\n ptImg.save('output/Path-'+self.fileName)\n ptImg.close()",
"title": ""
},
{
"docid": "087c13c591abd19f1dd1f0b4a1024b46",
"score": "0.5428924",
"text": "def output_path(self):\n return '%s/%s' % (os.path.abspath(os.path.dirname(__file__) + '/../../outputs'),\n self.identifier)",
"title": ""
},
{
"docid": "d79015d50969186b6744b4afe3406c70",
"score": "0.53893644",
"text": "def make_output_structure(output_path):\n\n Path(f\"{output_path}/mosaics\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/chips/pre\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/chips/post\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/loc\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/dmg\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/over\").mkdir(parents=True, exist_ok=True)\n Path(f\"{output_path}/shapes\").mkdir(parents=True, exist_ok=True)\n\n return True",
"title": ""
},
{
"docid": "349676cc477dc4d38ea8fa2c5bab68be",
"score": "0.5386216",
"text": "def as_path(self):\n return os.path.join(self.package.as_path(), self.jclass.get())",
"title": ""
},
{
"docid": "61f1cf81b68c6f3e369b89a2f09b4d45",
"score": "0.5385024",
"text": "def output_path(self):\n return self.files_dir() + '/output/'",
"title": ""
},
{
"docid": "5f3630ae005cc7a2499f79d0fe8b3503",
"score": "0.5379766",
"text": "def output(filename):\n return os.path.join(*[os.sep, 'opt', 'ml', 'model', filename])",
"title": ""
},
{
"docid": "5fbc05403b439b5cf469ccff2e30f569",
"score": "0.5374211",
"text": "def generate_outpath(in_path: PathType,\n out_path: Optional[PathType],\n suffix: str = None) -> PathType:\n in_path = pathlib.Path(in_path)\n if suffix is None:\n default_fname = '{}'.format(in_path.stem)\n else:\n default_fname = '{}.{}'.format(in_path.stem, suffix)\n\n if out_path is None:\n out_path = pathlib.Path(default_fname)\n else:\n out_path = pathlib.Path(out_path)\n if not out_path.suffix:\n out_path = out_path.joinpath(default_fname)\n if not out_path.parent.is_dir():\n msg = f'Error. Path \"{out_path.parent!s}\" does not exist.'\n raise ValueError(msg)\n return out_path",
"title": ""
},
{
"docid": "60381abcc58f993ea380acfe99027bb6",
"score": "0.5370048",
"text": "def mk_output_path(odir, k):\n relpath = educe.pdtb.id_to_path(k)\n ofile_dirname = os.path.join(odir, os.path.dirname(relpath))\n ofile_basename = os.path.basename(relpath)\n if not os.path.exists(ofile_dirname):\n os.makedirs(ofile_dirname)\n return os.path.join(ofile_dirname, ofile_basename)",
"title": ""
},
{
"docid": "b45ce2c6d56f8661fd6cc54ce39d04d5",
"score": "0.53249043",
"text": "def build_output_path(self):\n return self.data_file_path().join('..', '%s_output' % self.slug)",
"title": ""
},
{
"docid": "0243007d45751b0b65c9f5e94d469d8a",
"score": "0.5276601",
"text": "def get_model_path(self):\n return self.root_path + \"model.ckpt\"",
"title": ""
},
{
"docid": "987a81d72f5b4a474ced3e6665beb305",
"score": "0.52739507",
"text": "def create_model_output_folder(outputName):\n\tif \"\\\\\" in outputName:\n\t\toutputName=outputName.replace('\\\\','/')\n\tfolder= outputName.replace(outputName.split('/')[-1],'')\n\t\n\tif \"/\" in folder:\n\t\tif not os.path.exists(folder):\n\t\t\tos.makedirs(folder)",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52730644",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "7b6ea84420a883c7f02f0fda697a508f",
"score": "0.52726805",
"text": "def GetPath(self):",
"title": ""
},
{
"docid": "1fdd53478ef295ff08d5abf97d9b2abe",
"score": "0.52659833",
"text": "def output_dir(self) -> Path:\n return paths.OUT_DIR / self.name",
"title": ""
},
{
"docid": "3ccf3742670772eec594a27f10aabe23",
"score": "0.5261726",
"text": "def create_output_footprints(self):\n self.messages.addMessage(\"creating output footprint file\")\n out_path = os.path.dirname(self.outfootprints)\n out_name = os.path.basename(self.outfootprints)\n out_sref = arcpy.SpatialReference('Geographic Coordinate Systems/World/WGS 1984')\n arcpy.CreateFeatureclass_management(out_path, out_name, 'POLYGON', spatial_reference=out_sref)\n arcpy.AddField_management(self.outfootprints, \"rasterid\", \"SHORT\")\n arcpy.AddField_management(self.outfootprints, \"name\", \"TEXT\", 255)\n arcpy.AddField_management(self.outfootprints, \"date\", \"DATE\")\n arcpy.DeleteField_management (self.outfootprints, \"id\")",
"title": ""
},
{
"docid": "ec8bbc62dab33cdd7487c737f9f5b4fd",
"score": "0.5254749",
"text": "def _m0_path(self):",
"title": ""
},
{
"docid": "e2a9a76d19aa5939db539171694d634e",
"score": "0.52338636",
"text": "def get_output_path(self):\n return os.path.join(self.outdir, self.outfile)",
"title": ""
},
{
"docid": "039a4d6efd3194afd2e85e0b4f5caf2a",
"score": "0.5231837",
"text": "def getOutPath(self, key, courselab):\n labPath = self.getDirPath(key, courselab)\n return \"%s/%s\" % (labPath, self.OUTPUT_FOLDER)",
"title": ""
},
{
"docid": "fb416ec06ed3382f9f035648eaa76c54",
"score": "0.5231334",
"text": "def get_output_paths(self):\n pass",
"title": ""
},
{
"docid": "24ad3b7dc63687db2b08455b2fa48a79",
"score": "0.52274966",
"text": "def to_readable_class_path(self, path, as_class_path=False):\n if not as_class_path:\n path = self.to_package(path).as_class_path()\n if not path:\n if StateProperty().is_project():\n return \"(Default Package)\"\n else:\n return \"(Unknown Package)\"\n return path",
"title": ""
},
{
"docid": "433f2ca7ed09d99052416994d6231cf0",
"score": "0.5224498",
"text": "def construct_output_path(\n name: str,\n relative_path: Path,\n output_directory: Path,\n input_path: Path = None,\n output_format: str = TextgridFormats.SHORT_TEXTGRID,\n) -> Path:\n if isinstance(output_directory, str):\n output_directory = Path(output_directory)\n if output_format.upper() == \"LAB\":\n extension = \".lab\"\n elif output_format.upper() == \"JSON\":\n extension = \".json\"\n elif output_format.upper() == \"CSV\":\n extension = \".csv\"\n else:\n extension = \".TextGrid\"\n if relative_path:\n relative = output_directory.joinpath(relative_path)\n else:\n relative = output_directory\n output_path = relative.joinpath(name + extension)\n if output_path == input_path:\n output_path = relative.joinpath(name + \"_aligned\" + extension)\n os.makedirs(relative, exist_ok=True)\n relative.mkdir(parents=True, exist_ok=True)\n return output_path",
"title": ""
},
{
"docid": "8990eb93c4fcc59860631d057ea9051a",
"score": "0.5177936",
"text": "def __init__(self, out_path):\n self.out_path = out_path",
"title": ""
},
{
"docid": "a83db3ea7e3c55a4572f289025d9c551",
"score": "0.51744777",
"text": "def get_output(self, path):\n assert self._as_parameter_\n return Output._new_from_handle(zzub_archive_get_output(self, path))",
"title": ""
},
{
"docid": "a1957a1f7b656a9d6ab71c1d31cfca50",
"score": "0.51505685",
"text": "def test_make_output_path():\n\n step = Step()\n output_path = step.make_output_path('junk_uncal.fits')\n assert output_path == 'junk_jwststep.fits'\n\n output_path = step.make_output_path('junk_uncal.fits', idx=1)\n assert output_path == 'junk_1_jwststep.fits'\n\n step.output_ext = '.asdf'\n output_path = step.make_output_path('junk_uncal')\n assert output_path == 'junk_jwststep.asdf'\n\n output_path = step.make_output_path('junk_uncal.fits', ext='asdf')\n assert output_path == 'junk_jwststep.asdf'\n\n step.output_dir = '/junk'\n step.output_ext = None\n output_path = step.make_output_path('junk_uncal.fits')\n assert output_path == path.join(step.output_dir, 'junk_jwststep.fits')",
"title": ""
},
{
"docid": "8204f66e82d90cd419557a9b86250d8d",
"score": "0.5133523",
"text": "def create_output_paths(set_id):\n\n out_dir_path = OUT_DIRECTORY_PATTERN.format(set_id)\n\n # Create needed paths.\n train_data_path = out_dir_path + \"train_data\"\n train_labels_path = out_dir_path + \"train_labels\"\n validation_data_path = out_dir_path + \"validation_data\"\n validation_labels_path = out_dir_path + \"validation_labels\"\n test_data_path = out_dir_path + \"test_data\"\n test_labels_path = out_dir_path + \"test_labels\"\n\n return train_data_path, train_labels_path, \\\n validation_data_path, validation_labels_path, \\\n test_data_path, test_labels_path",
"title": ""
},
{
"docid": "a508f48cb278bc7e872738092145e9ad",
"score": "0.51319027",
"text": "def path(self):\n\n return Path(self.full_name)",
"title": ""
},
{
"docid": "6862e875e49ec148996804bb3ecb60f9",
"score": "0.51157045",
"text": "def generate_path_tree(self):\n pass",
"title": ""
},
{
"docid": "c9e5215a8f5902913983d098729c644c",
"score": "0.5114615",
"text": "def write_labels(output_path):",
"title": ""
},
{
"docid": "3723615668ac4d631e495caa002ad3d7",
"score": "0.50953186",
"text": "def path(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "d159ef8f7741c6a8feee60565e48a486",
"score": "0.50796866",
"text": "def get_path(self):",
"title": ""
},
{
"docid": "7b0a16c75f08c7255e27c9c373c4ac85",
"score": "0.5074151",
"text": "def modelpath(self, which, code2=None):\n path = '{0}.{1}.txt.gz'.format(self.sjexbase, which)\n if code2 is None:\n return path\n path2 = self.fname2('{0}.txt.gz'.format(which),code2, category='read')\n if os.path.exists(path2):\n return path2\n return path",
"title": ""
},
{
"docid": "74a46eff778ec10f144f3c71328aa431",
"score": "0.5065377",
"text": "def get_img_output_path(self):\n return '/'.join([self.base_folder, self.img_output])",
"title": ""
},
{
"docid": "fc2ced558bcfa420a109f7dc8a3343b9",
"score": "0.5055187",
"text": "def serving_model_path(output_uri: Text) -> Text:\n model_dir = serving_model_dir(output_uri)\n export_dir = os.path.join(model_dir, 'export')\n if tf.io.gfile.exists(export_dir):\n # TODO(b/160795287): Deprecate estimator based executor.\n absl.logging.warning(\n 'Support for estimator-based executor and model export'\n ' will be deprecated soon. Please use export structure '\n '<ModelExportPath>/serving_model_dir/saved_model.pb\"')\n model_dir = io_utils.get_only_uri_in_dir(export_dir)\n return io_utils.get_only_uri_in_dir(model_dir)\n else:\n # If dir doesn't match estimator structure, use serving model root directly.\n return model_dir",
"title": ""
},
{
"docid": "e2c44ec449cf45f56259d097c40b538f",
"score": "0.5040054",
"text": "def ExportMIFileName(self) -> str:",
"title": ""
},
{
"docid": "403d970e2d2a0a8011dfa31422d75814",
"score": "0.5039584",
"text": "def save_fit_paths(self, suffix=\"_fpaths.pckle\", save_mode = 1):\n try:\n if( save_mode):\n omode = \"wb\"\n else:\n omode = \"w\"\n fo = open(self.outputFile+self.vsuffix+suffix,omode)\n cPickle.dump([self.fpaths,self.paths,self.ftrees],fo,save_mode)\n fo.close()\n except Exception, error:\n print \"failed in save_fit_paths()\",error\n sys.exit()",
"title": ""
},
{
"docid": "5336769d06cb9c30c1d51e9633474f83",
"score": "0.50380933",
"text": "def Path(self) -> str:",
"title": ""
},
{
"docid": "5336769d06cb9c30c1d51e9633474f83",
"score": "0.50380933",
"text": "def Path(self) -> str:",
"title": ""
},
{
"docid": "5336769d06cb9c30c1d51e9633474f83",
"score": "0.50380933",
"text": "def Path(self) -> str:",
"title": ""
},
{
"docid": "74d53f68ffcd90eca739317e8dd9d3c3",
"score": "0.5013982",
"text": "def __fspath__(self):\n return osp.join(self.base_dir, self.env_name, self.algo_name, self.exp_id)",
"title": ""
},
{
"docid": "c20bc355a09bc1cf18a6d92a1f39f901",
"score": "0.5011293",
"text": "def get_path(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "81e4c5962e3140e13a48dd8a27e3435d",
"score": "0.5006212",
"text": "def to_path(self):\n return self.directory + \"/\" + self.name",
"title": ""
},
{
"docid": "846872a30505478f0564a35cc0ee6334",
"score": "0.49624977",
"text": "def get_output_path(node=None):\n if node==None: node = get_system()\n\n path = os.path.join(get_base_path(),'data','processed')\n \n return path",
"title": ""
},
{
"docid": "08d37034bec83a0e8e9491db66dfd17d",
"score": "0.49517703",
"text": "def as_class_path(self):\n return \".\".join(self.package_paths)",
"title": ""
},
{
"docid": "94acc0cc41fb6aa367ca5a1ee85123cf",
"score": "0.49513608",
"text": "def image_output_path(self, output_path, sequence_number=None):\n split = path.splitext(output_path)[0]\n base = path.basename(output_path)\n if sequence_number:\n sequence_number_left_padded = str(sequence_number).zfill(6)\n newname = f\"{base}.{sequence_number_left_padded}\"\n else:\n newname = base\n output_path = path.join(split, newname)\n return Path(f\"{output_path}.png\")",
"title": ""
},
{
"docid": "d1ebfc70f9a7686dff41ea16945694d7",
"score": "0.49452424",
"text": "def OutputRootPath(self):\r\n\t\treturn self._get_attribute('outputRootPath')",
"title": ""
},
{
"docid": "fcd6b984f0aa7662d43333e503f95600",
"score": "0.4940185",
"text": "def shp_to_kml():\t\n\tfolders = os.listdir('data/' + my.DATA_FOLDER + '/shp/')\n\tif '.DS_Store' in folders:\n\t\tfolders.remove('.DS_Store')\n\t#print folders\n\tfor folder in folders:\n\t\tprint '\\n' + folder + '\\n'\n\t\tpath = 'data/' + my.DATA_FOLDER + 'shp/' + folder + '/'\n\t\tkml_path = 'data/' + my.DATA_FOLDER + 'kml/' + \\\n\t\t\t\t\tfolder.replace(' Division Gangs', '') + '/'\n\t\tif not os.path.exists(kml_path):\n\t\t\tos.makedirs(kml_path)\n\n\t\tfiles = os.listdir(path)\n\t\tif '.DS_Store' in files:\n\t\t\tfiles.remove('.DS_Store')\n\t\tfiles_ = []\n\t\tfor file in files:\n\t\t\tfile = file.split('.')\n\t\t\tfiles_.append(file[0])\n\t\tfiles_ = list(set(files_))\n\t\t#pprint(files_)\n\n\t\tfor file in files_:\n\t\t\tshp = path + file + '.shp'\n\t\t\tkml = kml_path + file.replace('_', ' ') + '.kml'\n\t\t\togr2ogr.main([\"\",\"-f\", \"KML\", kml, shp])",
"title": ""
},
{
"docid": "466e299648c97f0729e276c8e135172a",
"score": "0.49392816",
"text": "def PathProfile(self) -> Profile3d:",
"title": ""
},
{
"docid": "9a2e9d354cc003d7deeb2f95ae3b23cb",
"score": "0.49385214",
"text": "def establish_new_path(self, output_folder_path: Path):\n self.new_path = output_folder_path.joinpath(self.title+\".txt\")",
"title": ""
},
{
"docid": "85f5438877420e745ffb7939dfe3322d",
"score": "0.49374145",
"text": "def as_class_path(self):\n if self.package.is_empty():\n return self.jclass.get()\n elif self.jclass.is_empty():\n return self.package.as_class_path()\n return \".\".join([\n x for x in\n [self.package.as_class_path(), self.jclass.get()]\n if x\n ])",
"title": ""
},
{
"docid": "d22cd7a430b03c81fc621f0a69dd784c",
"score": "0.49321896",
"text": "def make_output_file(self):\n sc = self\n if not sc.output_fn:\n if sc.verbose:\n g.note('no output file')\n return\n cmd = (\n sc.inkscape_bin,\n \"--without-gui\",\n \"--export-png=\" + sc.output_fn,\n \"--export-area-drawing\",\n \"--export-area-snap\",\n sc.working_fn)\n proc = subprocess.Popen(cmd, stderr=subprocess.PIPE)\n proc.communicate() # Wait for Inkscape to terminate.\n if sc.verbose:\n g.note('wrote: %s' % g.shortFileName(sc.output_fn))\n if Image: # trim transparent border\n try:\n img = Image.open(sc.output_fn)\n img = sc.trim(img, (255, 255, 255, 0))\n img.save(sc.output_fn)\n except IOError:\n g.trace('can not open %s' % sc.output_fn)\n sc.make_at_url_node_for_output_file()",
"title": ""
},
{
"docid": "72512f1a120d02094e2a99a195d58fa1",
"score": "0.4922218",
"text": "def GetNewPath(self):",
"title": ""
},
{
"docid": "f9143b1c28de6b9b3145d9e6cc833a5c",
"score": "0.4919029",
"text": "def getExportPath():\n return '/final/path/'",
"title": ""
},
{
"docid": "d0aebe235c9df1d36f51d59fd6b5aeb7",
"score": "0.49185914",
"text": "def write_output(self, filename, output_format='csv'):\n try:\n filename = join(self.folder_absolute_path, filename) + '.' + output_format\n\n if output_format == 'json':\n self.features.to_json(path_or_buf=filename, index=False)\n else:\n if output_format == 'sql':\n self.features.to_sql(path_or_buf=filename, index=False)\n else:\n\n self.features.columns = self.features.columns.str.replace(\"-\", \"_\")\n self.features['id'] = self.features['id'].str.replace('-','_')\n self.features.to_csv(path_or_buf=filename, index=False)\n except:\n logging.error(\"Unexpected error on writing output\")",
"title": ""
},
{
"docid": "c32f98ef37ffa3bd8cf54451c653d954",
"score": "0.49160412",
"text": "def toPath(prefix, metric):\n\n m = metric.replace(\".\", \"/\") + \".wsp\"\n return os.path.join(prefix, m)",
"title": ""
},
{
"docid": "b76db82d225f3edee226fa8c532b299b",
"score": "0.49143124",
"text": "def path(self):\n return self.pad_path",
"title": ""
},
{
"docid": "02914f93612bbb9cc51305afcbf16628",
"score": "0.4908661",
"text": "def export(self, path):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "c5251d1499b9b588b42096f6a2fcf935",
"score": "0.49081084",
"text": "def export_area_as_SHP(self, filename_out,area_id, epsgOUT):\n\t\n\t\t\n self.cur.execute(\"SELECT ST_AsText(geom) FROM areas WHERE id = %s;\" % (area_id))\n areaST = self.cur.fetchall() \n geom = areaST[0][0]\n\t\t\n area = []\n if (geom.find('POLYGON') >= 0):\n i = geom.find('POLYGON((')\n j = geom.find('))')\n pts = geom[i+9:j]\n pts = pts.replace(',',' ').split()\n i = 0\n while i < len(pts):\n area.append([float(pts[i]), float(pts[i+1])])\n i += 2\n\n\t\t\n\t\tsrs_in = osr.SpatialReference()\n\t\tsrs_in.ImportFromEPSG(self.epsg)\n\t\tsrs_out = osr.SpatialReference()\t\t\t\n\t\tsrs_out.ImportFromEPSG(epsgOUT)\n\t\t\n\t\t#write the polygon data to KML\n driver = ogr.GetDriverByName('ESRI Shapefile')\n outfile = driver.CreateDataSource(filename_out)\n layerPolygons = outfile.CreateLayer(\"Polygons\", srs_out, ogr.wkbPolygon)\t\n featurePolygons = ogr.Feature(layerPolygons.GetLayerDefn())\n '''\n layerLinestring = outfile.CreateLayer(\"Linestring\", srs_out, ogr.wkbLineString) \n featureLinestring = ogr.Feature(layerLinestring.GetLayerDefn())\n line = ogr.Geometry(type=ogr.wkbLineString)\n '''\n #print outfile.GetLayerCount()\n \n #NOTE: To create a polygon you first need to create a ring\n #\t\tthen add it to the polygon\n ptCount = len(area)\n firstPt = area[0]\n lastPt = area[ptCount-1]\n polygon = ogr.Geometry(type=ogr.wkbPolygon)\t\t\t#create polygon\n ring = ogr.Geometry(type=ogr.wkbLinearRing)\t\t\t#create ring\n\t\t\n #Only convert the feature to polygon if is a closed linestring\n if firstPt[0] == lastPt[0] and firstPt[1] == lastPt[1]:\n print \"Valid LINESTRING\"\t \n i = 0\n while i < ptCount-1:\n p = area[i]\n ring.AddPoint(p[0],p[1])\n i += 1\n ring.CloseRings()\n polygon.AddGeometry(ring)\t\t\t\t\t\t#add ring to polygon\t\n polygon.AssignSpatialReference(srs_in)\n polygon.TransformTo(srs_out)\n featurePolygons.SetGeometry(polygon)\n layerPolygons.CreateFeature(featurePolygons) \n \n '''\n for p in area:\n line.AddPoint(p[0],p[1])\n\n line.AssignSpatialReference(srs_in)\n line.TransformTo(srs_out)\n featureLinestring.SetGeometry(line)\n layerLinestring.CreateFeature(featureLinestring) \n \n featureLinestring.Destroy()\n \n '''\n \n outfile = None\n featurePolygons.Destroy()",
"title": ""
},
{
"docid": "dd21a47ababa283ecef422fa18dc4015",
"score": "0.49046147",
"text": "def PathName(self) -> str:",
"title": ""
},
{
"docid": "6f3267569a449afab63c3ac971b33923",
"score": "0.4900609",
"text": "def export_shapefile():\n folder = 'shape'\n name = get_dataset_filename()\n \n # Create a shape folder in the temp directory if it does not exist\n working_folder = catalog_publish_folder + name + \"//\" + temp_folder + \"//\" + folder\n create_folder(working_folder, True)\n\n # Create a folder for the shapefile (since it is a folder)\n create_folder(working_folder + \"\\\\\" + name)\n \n # Export the shapefile to the folder\n source = database_connection + \"\\\\\" + args.feature_class\n destination = working_folder + \"\\\\\" + name + \"\\\\\" + name + \".shp\"\n \n # Export the shapefile\n debug(' - Exporting to shapefile from \"' + source + '\" to \"' + destination + '\"')\n gp.CopyFeatures_management(source, destination, \"\", \"0\", \"0\", \"0\")\n \n # Zip up the files\n debug(' - Zipping the shapefile')\n zip = zipfile.ZipFile(working_folder + \"\\\\\" + name + \".zip\", \"w\")\n \n for filename in glob.glob(working_folder + \"/\" + name + \"/*\"):\n zip.write(filename, os.path.basename(filename), zipfile.ZIP_DEFLATED)\n \n zip.close()\n \n # Publish the zipfile to the download folder\n publish_file(working_folder, name + \".zip\",\"shape\")",
"title": ""
},
{
"docid": "4e7c5643bded7b28ec00165bf6182ce5",
"score": "0.4892126",
"text": "def set_paths(self, output_dir: Optional[Union[str, Path]] = None) -> None:\n if output_dir is None:\n output_dir = os.path.join(os.getcwd(), f\"amici-{self.model_name}\")\n\n self.model_path = os.path.abspath(output_dir)\n self.model_swig_path = os.path.join(self.model_path, \"swig\")",
"title": ""
},
{
"docid": "dba4900497a173fbef677ac8f92ee80d",
"score": "0.48878017",
"text": "def save_to_feature_class(self, output_feature_class):\n # dictionary to look up geometry types\n geometry_type = {\n 'esriGeometryPoint': 'POINT',\n 'esriGeometryMultipoint': 'MULTIPOINT',\n 'esriGeometryPolyline': 'POLYLINE',\n 'esriGeometryPolygon': 'POLYGON'\n }\n\n # create feature class\n fc = arcpy.CreateFeatureclass_management(\n out_path=os.path.dirname(output_feature_class),\n out_name=os.path.basename(output_feature_class),\n geometry_type=geometry_type[self.properties['geometryType']],\n spatial_reference=arcpy.SpatialReference(self.properties['extent']['spatialReference']['latestWkid'])\n )[0]\n\n # dictionary to look up field types\n field_type = {\n 'esriFieldTypeString': 'TEXT',\n 'esriFieldTypeFloat': 'FLOAT',\n 'esriFieldTypeDouble': 'DOUBLE',\n 'esriFieldTypeSmallInteger': 'SHORT',\n 'esriFieldTypeInteger': 'LONG',\n 'esriFieldTypeDate': 'DATE',\n 'esriFieldTypeGlobalID': 'GUID'\n }\n\n # fields variable for insert cursor later\n insert_field_list = []\n\n # add fields\n for field in self.properties['fields']:\n\n # if the field is not the object id or geometry field\n if self._validate_field(field):\n\n # if this is a text field, look up the length and use it, otherwise just use a blank placeholder\n if field_type[field['type']] == 'TEXT':\n length = field['length']\n else:\n length = \"\"\n\n # add this field name to the fields name list\n insert_field_list.append(field['name'])\n\n # add field with properties\n arcpy.AddField_management(\n in_table=fc,\n field_name=field['name'],\n field_type=field_type[field['type']],\n field_alias=field['alias'],\n field_length=length,\n )\n\n # if the max batch query is less than 100, set this to the batch size, otherwise use 100 since retrieving\n # greater than 100 records from feature services backed by enterprise SDE's using ObjectIDs is documented\n # to suffer from performance issues\n if self.properties['maxRecordCount'] > 100:\n batch_size = 100\n else:\n batch_size = self.properties['maxRecordCount']\n\n # get the list of all feature id's\n fid_list = self.get_fid_list()\n\n # create a list of lists (redundant?) to use for making query requests to the REST endpoint\n fid_batch_list = [fid_list[i: i + int(batch_size)] for i in range(0, len(fid_list)-1, int(batch_size))]\n\n # add geometry to the fields list\n insert_field_list.append('SHAPE@')\n\n # use an insert cursor to insert features\n with arcpy.da.InsertCursor(fc, insert_field_list) as insert_cursor:\n\n # iterate the batch lists\n for fid_batch in fid_batch_list:\n\n # make the rest call to get the features\n feature_list = self._get_feature_list(fid_batch)\n\n # for every feature in the feature list\n for feature in feature_list:\n\n # get a list of attribute values using iterator to match up dictionary values matching the fields\n # being used\n attribute_list = []\n\n # iterate the validated field names\n for attribute_name in insert_field_list:\n\n # do not get geometry\n if attribute_name != 'SHAPE@':\n\n # retrieve the value from the dictionary for the attribute name and add it to the list\n attribute_list.append(feature['attributes'][attribute_name])\n\n # add geometry to the list\n attribute_list.append(feature['geometry'])\n\n # use the insert cursor to insert a record\n insert_cursor.insertRow(attribute_list)\n\n # return the path to the feature class\n return fc",
"title": ""
},
{
"docid": "f32359035aa47f4c48fe0e1a0a3ac710",
"score": "0.48847365",
"text": "def export(self, path):\n pass",
"title": ""
},
{
"docid": "58d8fe7087757dda87cf56fc51e9680a",
"score": "0.48847118",
"text": "def get_path(geojson_path: str, action: str):\n file_and_extension = os.path.basename(geojson_path).split(\".\")\n new_file = f\"{'.'.join(file_and_extension[:-1])}_{action}.{file_and_extension[-1]}\"\n return os.path.join(os.path.dirname(geojson_path), new_file)",
"title": ""
},
{
"docid": "3cb22b6f5ced4797e2560bdacacbe49b",
"score": "0.48618275",
"text": "def route2kml(route, fname):\n\n kml = simplekml.Kml()\n\n for city in route:\n kml.newpoint(\n name=city.name,\n coords=[\n math.degrees(city.lat),\n math.degrees(city.lat)\n ]\n )\n\n kml.save(fname)",
"title": ""
},
{
"docid": "a3e174027ca4392bd3dc47da4b3c030f",
"score": "0.4858324",
"text": "def add_h5paths_out(outpaths, info):\n\n (h5path_wsmask, MAdilation,\n h5path_dist, h5path_lmm, sigmoidweighting) = info\n\n root, ds_main = outpaths['out'].split('.h5')\n for dsname, outpath in outpaths.items():\n grpname = ds_main + \"_steps\"\n outpaths[dsname] = os.path.join(root + '.h5' + grpname, dsname)\n\n if not h5path_wsmask:\n outpaths['wsmask'] = ''\n if MAdilation:\n outpaths['madil{:02d}'.format(MAdilation)] = ''\n if not h5path_dist:\n if sigmoidweighting:\n if not h5path_lmm:\n outpaths['distance_simple'] = ''\n outpaths['sheaths_simple'] = ''\n outpaths['distance_sigmod'] = ''\n else:\n outpaths['distance_simple'] = ''\n\n return outpaths",
"title": ""
},
{
"docid": "b8b6a084c934ab3531c5fd8e0154a902",
"score": "0.48450223",
"text": "def kpath(self):\n from matdb.database import Database\n if isinstance(self.parent, Database):\n return self.get_kpath()\n elif isinstance(self.parent, Hessian):\n return self.parent.get_kpath()",
"title": ""
},
{
"docid": "002df1c36df5fa7a7524292c9646b9dc",
"score": "0.4844066",
"text": "def getPath(self) -> unicode:\n ...",
"title": ""
},
{
"docid": "4bf91423d00910301290f696ebbcf9f2",
"score": "0.48383176",
"text": "def input_path(self) -> Path:\n return self.path / INPUT_DIR_NAME",
"title": ""
},
{
"docid": "2929e84d8f8f9de3a6f56bec6ae2db3b",
"score": "0.4837608",
"text": "def outdir(self):\n funcname = '.'.join(self.id().split('.')[-3:])\n return path.join(self.outputdir, funcname)",
"title": ""
},
{
"docid": "ede72a5196f287db29f4077fc25d9baf",
"score": "0.48357865",
"text": "def __init__(self, folder_input_path: str, folder_output_path: str = None, output_type: str = \"png\"):\n\n self.folder_input_path, self.folder_output_path = self.check_paths(folder_input_path=folder_input_path,\n folder_output_path=folder_output_path)\n\n self.wmf_folder = self.folder_input_path / \"wmf_files\"\n\n self.wmf_folder.mkdir(parents=True, exist_ok=True)\n\n self.output_type = output_type",
"title": ""
},
{
"docid": "4e700925be47835b8bdad3b9607fc252",
"score": "0.48347443",
"text": "def get_output_path(self, input_path=None, output_ext=None):\n found_input = bool(input_path)\n\n if found_input and input_path in self._filepath_map:\n return self._filepath_map[input_path]\n\n self._idx += 1\n\n if not found_input:\n input_path = self._default_filename_patt % self._idx\n\n filename = os.path.basename(input_path)\n name, ext = os.path.splitext(filename)\n\n # URL handling\n # @todo improve this, while still maintaining Unix/Windows path support\n name = name.replace(\"%\", \"-\")\n ext = ext.split(\"?\")[0]\n\n if output_ext is not None:\n ext = output_ext\n\n filename = name + ext\n\n key = name if self.ignore_exts else filename\n self._filename_counts[key] += 1\n\n count = self._filename_counts[key]\n if count > 1:\n filename = name + (\"-%d\" % count) + ext\n\n output_path = os.path.join(self.output_dir, filename)\n\n if found_input:\n self._filepath_map[input_path] = output_path\n\n return output_path",
"title": ""
},
{
"docid": "9d6896f1e39a2302816d425e1767a345",
"score": "0.4832463",
"text": "def get_path(model_name: str, path_type=None) -> str:\n\n if model_name == 'dlib-lmk68':\n return ModelAPI._dlib_lmk68_router(path_type)\n\n elif model_name == 'openface-embed':\n return ModelAPI._openface_embed_router(path_type)",
"title": ""
},
{
"docid": "e0c89aced230990fa09556a92a2ad143",
"score": "0.48307207",
"text": "def build_single_output(\n feature_config: BaseOutputFeatureConfig, output_features: Optional[Dict[str, OutputFeature]]\n ) -> OutputFeature:\n logger.debug(f\"Output {feature_config.type} feature {feature_config.name}\")\n output_feature_class = get_from_registry(feature_config.type, get_output_type_registry())\n output_feature_obj = output_feature_class(feature_config, output_features=output_features)\n return output_feature_obj",
"title": ""
},
{
"docid": "3d97ffe88c714b354567a1f315a45848",
"score": "0.48275766",
"text": "def path(cls):\n return cls.__name__.lower()",
"title": ""
},
{
"docid": "e9e9566a95bbf004de7e33416be68ab7",
"score": "0.4825473",
"text": "def generate_temp_filename(self, output, suffix=None):\n if not suffix:\n suffix = self._temp_suffix\n return Path(\"temp_{t}_{o}_{n:03d}\".format(\n t=self._TYPE, o=Path(output).stem,\n n=self.segment_number)).with_suffix(suffix)",
"title": ""
},
{
"docid": "a9e78a69c99b3a77cce4233ed2020ac2",
"score": "0.48145795",
"text": "def serialize(self, outf):\n\n # create output dir if not exist\n os.makedirs(os.path.dirname(outf), exist_ok=True)\n # serialize CE CORE into RDF format (turtle)\n print('Serialize CE CORE KB to {} with turtle format...'.format(outf))\n self.graph.serialize(destination=outf, format='turtle')\n print('CE CORE KB serialized to {} with turtle format!'.format(outf))\n return True",
"title": ""
},
{
"docid": "afcb1e5e33e88561988f45dca545577b",
"score": "0.47968724",
"text": "def get_path(self):\n # The path is generated in display coordinates, then converted back to\n # data coordinates.\n _path, fillable = self._get_path_in_displaycoord()\n if np.iterable(fillable):\n _path = Path.make_compound_path(*_path)\n return self.get_transform().inverted().transform_path(_path)",
"title": ""
},
{
"docid": "dd37a5e576b80b69d5228f8a48481835",
"score": "0.47918037",
"text": "def generate_filepath(self, instance):\n\n # create filename based on instance and field name\n # we do not want to end up with StravaRoute or SwitzerlandMobilityRoute\n if apps.get_model(\"routes\", \"Route\") in getmro(instance.__class__):\n class_name = \"Route\"\n else:\n class_name = instance.__class__.__name__\n\n # generate unique id from unique fields:\n unique_id_values = []\n for field in self.unique_fields:\n unique_field_value = getattr(instance, field)\n\n # get field value or id if the field value is a related model instance\n unique_id_values.append(\n str(getattr(unique_field_value, \"id\", unique_field_value))\n )\n\n # filename, for example: route_data_<uuid>.h5\n filename = \"{class_name}_{field_name}_{unique_id}.h5\".format(\n class_name=class_name.lower(),\n field_name=self.name,\n unique_id=\"\".join(unique_id_values),\n )\n\n # generate filepath\n if callable(self.upload_to):\n filepath = Path(self.upload_to(instance, filename))\n else:\n dirname = self.upload_to\n filepath = Path(dirname, filename)\n return self.storage.generate_filename(filepath)",
"title": ""
},
{
"docid": "fb542de0c12c01721e0b5bc225bf5a66",
"score": "0.4787515",
"text": "def path(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"path\")",
"title": ""
},
{
"docid": "5bf48c5bd017aaa7efee656acbfcdcc4",
"score": "0.47866398",
"text": "def out_filename(self):\n file = path.join(self.working_directory, str(self.label))\n if self.add_dim:\n file += '_{:0.0f}'.format(self.height)\n if self.width:\n file += 'x{:0.0f}'.format(self.width)\n\n if self.add_dn:\n file += '_DN{:0.0f}'.format(self.add_dn)\n return file",
"title": ""
},
{
"docid": "8c62ec5a1ef88b92c4c5621b520c101c",
"score": "0.47860977",
"text": "def creat_xs_fc(outpath, outname, spatial_ref, is_shp=True):\r\n shp = None\r\n if not is_shp:\r\n shp = os.path.join (outpath , '{0}.shp'.format (outname))\r\n if arcpy.Exists(shp):\r\n arcpy.Delete_management(shp)\r\n arcpy.CreateFeatureclass_management (outpath , '{0}.shp'.format (outname) , geometry_type=\"POLYLINE\" ,\r\n has_m=\"DISABLED\" ,\r\n has_z=\"DISABLED\" , spatial_reference=spatial_ref)\r\n else:\r\n shp = os.path.join (outpath , '{0}'.format (outname))\r\n if arcpy.Exists(shp):\r\n arcpy.Delete_management(shp)\r\n arcpy.CreateFeatureclass_management (outpath , '{0}'.format (outname) , geometry_type=\"POLYLINE\" ,\r\n has_m=\"DISABLED\" ,\r\n has_z=\"DISABLED\" , spatial_reference=spatial_ref)\r\n\r\n arcpy.AddField_management (shp , field_name=\"Tributary\" , field_alias=\"Tributary\" , field_type=\"TEXT\" ,\r\n field_length=25 , )\r\n\r\n arcpy.AddField_management (shp , field_name=\"Station\" , field_alias=\"Station\" , field_type=\"TEXT\" ,\r\n field_length=25 , )\r\n\r\n return shp",
"title": ""
},
{
"docid": "810d7db5cc4e2adb524281a624f3c9db",
"score": "0.47825044",
"text": "def PathProfile(self) -> LoftProfile:",
"title": ""
},
{
"docid": "2e4bf0c28ea299f9fd5f0ea05ee76a74",
"score": "0.47806406",
"text": "def test_pathy_export_spacy_model(temp_folder: Path) -> None:\n import spacy\n\n use_fs(temp_folder)\n bucket = Pathy(\"gs://my-bucket/\")\n bucket.mkdir(exist_ok=True)\n model = spacy.blank(\"en\")\n output_path = Pathy(\"gs://my-bucket/models/my_model\")\n model.to_disk(output_path)\n sorted_entries = sorted([str(p) for p in output_path.glob(\"*\")])\n expected_entries = [\n \"gs://my-bucket/models/my_model/config.cfg\",\n \"gs://my-bucket/models/my_model/meta.json\",\n \"gs://my-bucket/models/my_model/tokenizer\",\n \"gs://my-bucket/models/my_model/vocab\",\n ]\n assert sorted_entries == expected_entries",
"title": ""
},
{
"docid": "71a7bc5c4c6f9cedf82ff0d18f123e9e",
"score": "0.47784424",
"text": "def GetPath(self, *args, **kw):",
"title": ""
},
{
"docid": "40ef1f7dc0fbde3fd4ed7e35d313018d",
"score": "0.47706366",
"text": "def path(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"path\")",
"title": ""
}
]
d15c16502af6ce62d6e0a6aa48a02c38
First implentation of the two precedent algorithm
[
{
"docid": "b1f85f94a850720f0d292914e99787b4",
"score": "0.0",
"text": "def master_algorithme(set_point, heta):\n #find the perfect hash table\n hash_table = epsilon_variation_algo(set_point, len(set_point))\n \n #define the minimal number of rectangle\n min_nb_rectangle = sqrt(len(set_point))\n \n #convert the hash table in a set of rectangles\n set_rectangle = [minimum_rect(hash_table[key]) for key in hash_table.keys()]\n #apply the NN algorithm while the condition is not False\n i = 0 \n while True:\n #find the NN\n afficher_plsr_pts_rect_1(set_rectangle, None, i)\n nearest_neighboor = naive_nearest_neighboor(set_rectangle)\n i+=1\n \n if len(set_rectangle) > 2:\n #merge the NN\n set_rectangle = merge_rectangle(nearest_neighboor, set_rectangle)\n #stop the algorithm\n else:\n return set_rectangle",
"title": ""
}
]
[
{
"docid": "a8dcf31285714d06d3927c1343a0f07a",
"score": "0.70782053",
"text": "def algorithm(self):",
"title": ""
},
{
"docid": "75f9f33adb7fc5ee9bca5067ef34a714",
"score": "0.58826745",
"text": "def next_trick(self):",
"title": ""
},
{
"docid": "f5cd32a98def5a9aeaf4aa06e0d6235f",
"score": "0.5818549",
"text": "def precompute(self,x1,x2):\n pass",
"title": ""
},
{
"docid": "d315e80eb7eefd4f0fdc1bc80f0f84de",
"score": "0.57278717",
"text": "def session_00018_line_318():",
"title": ""
},
{
"docid": "135fa61e8ea998d21ad0c7e88d306398",
"score": "0.56644154",
"text": "def __mul__(self, other):\n \"\"\" WARNING: do not try to impliment this function on alarm... \"\"\"\n\n new_scope = []\n for scope in self.scope:\n new_scope.append(scope)\n for scope in other.scope:\n if scope not in self.scope:\n new_scope.append(scope)\n # print \"new_scope\", new_scope\n\n\n new_ranges = {}\n for i in new_scope:\n if (i in self.scope):\n # print \"self.ranges: \", self.ranges # testing\n new_ranges[i] = self.ranges[i]\n elif (i in other.scope):\n new_ranges[i] = other.ranges[i]\n\n\n # print \"new_range: \", new_ranges # testing\n\n\n\n\n x1Ux2_scope = len(new_scope)\n # print \"x1Ux2_scope\", x1Ux2_scope # testing\n\n x1Ux2_cardinality_values = 1\n for key in new_ranges:\n x1Ux2_cardinality_values *= new_ranges[key]\n # print \"x1Ux2_cardinality_values\", x1Ux2_cardinality_values # testing\n\n\n \"\"\" This is the start the implimentation of Alogithm 10.A.1 on pg. 359 \"\"\"\n\n j, k = 0, 0 # Line 1\n assignment = []\n psi_values = []\n\n for l in range(x1Ux2_scope): # Line 2\n assignment.append(0) # Line 3\n\n for i in range(x1Ux2_cardinality_values - 1): # Line 4\n psi_values.append(self.vals[j] * other.vals[k]) # Line 5\n\n for l in new_scope: # Line 6 (modified from the actual algoithem)\n\n assignment[new_scope.index(l)] += 1 # Line 7\n\n if assignment[new_scope.index(l)] == new_ranges[l]: # Line 8\n assignment[new_scope.index(l)] = 0 # Line 9\n\n j = j - (new_ranges[l] - 1) * Factor.stride(self, l) # Line 10\n k = k - (new_ranges[l] - 1) * Factor.stride(other, l) # Line 11\n\n else: # Line 12\n j = j + Factor.stride(self, l) # Line 13\n k = k + Factor.stride(other, l) # Lin3 14\n break # Line 15\n\n\n # print psi_values # testing\n\n psi_values.append(self.vals[j] * other.vals[k])\n new_scope.reverse()\n # END PLACEHOLDER CODE\n return Factor(new_scope, psi_values, new_ranges) # Line 16",
"title": ""
},
{
"docid": "2859810b10b652b5c43604cab0be7747",
"score": "0.5631979",
"text": "def solution(self):\n pass",
"title": ""
},
{
"docid": "1ce6b293babf58c50644b5eb546b5241",
"score": "0.5536008",
"text": "def nextP(A, B):\n global a2, b2\n V1 = B - A\n\n t = Vecteur([1, -1 * ((b2) / (a2)) * B.coord[0] / B.coord[1]])\n n = Vecteur([((b2) / (a2)) * B.coord[0] / B.coord[1], 1])\n\n V2 = ((V1 * t) * t) - ((V1 * n) * n)\n\n q1 = V2.coord[0]\n q2 = V2.coord[1]\n k = (-2 * q1 * B.coord[0] / (a2) - 2 * q2 *\n B.coord[1] / (b2)) / (q1 * q1 / a2 + q2 * q2 / b2)\n # print(type(k))\n\n return B + k * V2",
"title": ""
},
{
"docid": "bf462020843ba6f0b46f9c2eac0fe753",
"score": "0.5491115",
"text": "def compute(self):\r\n\r\n a,b = len(self.A),len(self.B)\r\n\r\n #On crée la matrice chemin qui donne pour chaque case M la(les) case(s) précédente(s) à partir de laquelle elle a été calculée\r\n\r\n chemin = [[[] for i in range(a+2)] for i in range (b+2)]\r\n\r\n for j in range(2, a+2):\r\n chemin[1][j] = [2]\r\n\r\n for i in range(2,b+2):\r\n chemin[i][1] = [1]\r\n\r\n #On remplit la matrice de similarité M\r\n\r\n M = [[0 for i in range(a+2)] for i in range(b+2)]\r\n\r\n for j in range(2,a+2):\r\n M[0][j] = self.A[j-2]\r\n\r\n for i in range(2,b+2):\r\n M[i][0] = self.B[i-2]\r\n\r\n for j in range(1,a+2):\r\n M[1][j] = (j-1)*self.g\r\n\r\n for i in range(1,b+2):\r\n M[i][1] = (i-1)*self.g\r\n\r\n for j in range(2,a+2):\r\n for i in range(2,b+2):\r\n diag = self.m* (self.A[j-2] != self.B[i-2]) + M[i-1][j-1]\r\n haut = M[i-1][j] + self.g\r\n bas = M[i][j-1] + self.g\r\n\r\n l = [diag,haut,bas]\r\n M[i][j] = int(min(l))\r\n\r\n for k in range(3):\r\n if l[k] == min(l):\r\n chemin[i][j].append(k)\r\n\r\n i,j = b+1,a+1\r\n distance = M[i][j]\r\n\r\n #On crée deux tableaux qui A2 et B2 qui contiendront les chaines alignées par l'algorithme.\r\n A2,B2 = [],[]\r\n\r\n #On crée le tableau parcours qui remonte la matrice M\r\n parcours = []\r\n\r\n while ((i,j) != (2,2) and (i,j) != (2,1) and (i,j) != (1,2)):\r\n if chemin[i][j][0] == 0: #Remarque : parfois, plusieurs chemins sont possibles mais on n'en calcule qu'un seul\r\n parcours.append(0)\r\n i-=1\r\n j-=1\r\n elif chemin[i][j][0] == 1:\r\n parcours.append(1)\r\n i-=1\r\n elif chemin[i][j][0] == 2:\r\n parcours.append(2)\r\n j-=1\r\n else:\r\n break\r\n\r\n parcours = parcours[::-1]\r\n\r\n #Selon la dernière case atteinte on ajoute le premier caractère de A2 et B2\r\n\r\n if (i,j) == (2,2):\r\n A2.append(self.A[0])\r\n B2.append(self.B[0])\r\n\r\n elif (i,j) == (2,1):\r\n A2.append('=')\r\n B2.append(self.B[0])\r\n\r\n elif (i,j) == (1,2):\r\n A2.append(self.A[0])\r\n B2.append('=')\r\n\r\n #On suit ensuite le parcours que l'on a trouvé pour remplir A2 et B2\r\n\r\n for a in parcours:\r\n if a == 0:\r\n i+=1\r\n j+=1\r\n A2.append(self.A[j-2])\r\n B2.append(self.B[i-2])\r\n\r\n elif a == 1:\r\n i+=1\r\n A2.append('=')\r\n B2.append(self.B[i-2])\r\n\r\n elif a == 2:\r\n j+=1\r\n A2.append(self.A[j-2])\r\n B2.append('=')\r\n\r\n A2=\"\".join(A2)\r\n B2=\"\".join(B2)\r\n\r\n #On crée les attributs\r\n\r\n self.A2 = A2\r\n self.B2 = B2\r\n self.distance = distance",
"title": ""
},
{
"docid": "c053d62355c05e23f4e999b4d150af0d",
"score": "0.54583997",
"text": "def dmp1(self):",
"title": ""
},
{
"docid": "08a3fd3ddfab086f29c9b91351101572",
"score": "0.54527193",
"text": "def problem71():",
"title": ""
},
{
"docid": "6e28626beacb18d4efe422b3002499ea",
"score": "0.5451952",
"text": "def __reduce__():",
"title": ""
},
{
"docid": "d9f2602cbdeb4a767539061d1eef6197",
"score": "0.5442272",
"text": "def _pre_of_new(n1, n2):\n n1_class_counts = n1.class_counts()\n n2_class_counts = n2.class_counts()\n tp, fp = 0.0, 0.0\n n1_total = sum([count for c, count in n1_class_counts.items()\n if c != 'None'])\n n2_total = sum([count for c, count in n2_class_counts.items()\n if c != 'None'])\n\n for c1, count1 in n1_class_counts.items():\n if c1 != 'None':\n tp += (count1 * (count1 - 1.0)) / 2.0\n fp += count1 * (n1_total - count1) / 2.0\n for c2, count2 in n2_class_counts.items():\n if c2 != 'None':\n tp += (count2 * (count2 - 1.0)) / 2.0\n fp += count2 * (n2_total - count2) / 2.0\n\n for c1, count1 in n1_class_counts.items():\n for c2, count2 in n2_class_counts.items():\n if c1 != 'None' and c1 == c2:\n tp += count1 * count2\n elif c1 != 'None' and c2 != 'None' and c1 != c2:\n fp += count1 * count2\n\n # TODO(AK): probably want more assertions.\n assert tp + fp > 0.0\n assert tp / (tp + fp) <= 1.0\n assert tp / (tp + fp) >= 0.0\n\n return tp / (tp + fp)",
"title": ""
},
{
"docid": "11725e0d4aa23fdfc0173c1231d04274",
"score": "0.5415706",
"text": "def _compute(self):",
"title": ""
},
{
"docid": "f45a6995f008fe558e0b1a713dee2ae9",
"score": "0.540002",
"text": "def _algorithm(head, rel, tail):\n\t\thead = layers.l2_normalize(head, axis=-1)\n\t\trel = layers.l2_normalize(rel, axis=-1)\n\t\ttail = layers.l2_normalize(tail, axis=-1)\n\t\tscore = head + rel - tail\n\t\treturn score",
"title": ""
},
{
"docid": "b4f08e760333598e2f7fc5ae9c2501cf",
"score": "0.5388665",
"text": "def algorithm():\n list_how_much_0_owes_1_with_amount_owed, list_friend = generate_data()\n\n print(\"#\" * 100)\n print(\"Algorithm\")\n print(\"#\" * 100)\n print()\n\n dict_k_pair_v_owed = defaultdict(int)\n\n # Algorithm part 1\n for i in list_how_much_0_owes_1_with_amount_owed:\n dict_k_pair_v_owed[i[0]] += i[1]\n\n # Print to show work\n print(\"Dict of Who owes Who (total amount owed)\")\n for pair, amount_owed in dict_k_pair_v_owed.items():\n print(pair, amount_owed)\n print()\n\n dict_k_pair_v_owed_total = dict()\n\n set_frozenset_used = set()\n\n # Algorithm part 2\n for pair, amount_owed in dict_k_pair_v_owed.items():\n\n frozenset_pair = frozenset(pair)\n\n if frozenset_pair not in set_frozenset_used:\n set_frozenset_used.add(frozenset_pair)\n dict_k_pair_v_owed_total[pair] = amount_owed\n continue\n\n else:\n\n pair_reversed = tuple(reversed(pair))\n\n value_of_pair_reversed = dict_k_pair_v_owed_total.get(pair_reversed)\n\n if amount_owed > value_of_pair_reversed:\n dict_k_pair_v_owed_total.pop(pair_reversed)\n dict_k_pair_v_owed_total[pair] = amount_owed - value_of_pair_reversed\n\n else:\n dict_k_pair_v_owed_total[pair_reversed] = dict_k_pair_v_owed_total[pair_reversed] - amount_owed\n\n # Print to show solution\n print(\"How much money friend index 0 owes friend index 1\")\n for pair, amount_owed in dict_k_pair_v_owed_total.items():\n print(pair, amount_owed)\n print()\n\n print(\"Proof that the Maximum amount transactions needed total is <= Combinations of size 2\")\n print(\"Combination: {} CHOOSE {} = {}\".format(len(list_friend), 2,\n math.comb(len(list_friend), 2)))\n print(\"Size of the result of my algorithm {}\".format(len(dict_k_pair_v_owed_total)))",
"title": ""
},
{
"docid": "a0af3ed211bb6078ebf77275abc84820",
"score": "0.5388352",
"text": "def simplifyForFc(self):\n# nTdebug('Starting simplifyForFc for\\n:%r' % ( self ) )\n atomPairIdxJ = len(self.atomPairs) # starting from the end.\n while atomPairIdxJ > 1:\n atomPairIdxJ -= 1\n atomPairJ = self.atomPairs[atomPairIdxJ]\n atomPairJset = set(atomPairJ) # Important to use api of unsorted atoms in pair (left right will not matter)\n atom0J = atomPairJ[0]\n atom1J = atomPairJ[1]\n\n# nTdebug('For atomPairIdxJ %d using atoms J %s and %s' % ( atomPairIdxJ, atom0J, atom1J) )\n # speed up check on J as an early abort clause.\n if not (atom0J.hasPseudoAtom() or atom1J.hasPseudoAtom()):\n if not (atom0J.getPseudoOfPseudoAtom() or atom1J.getPseudoOfPseudoAtom()):\n# nTdebug('Skipping restraint without pseudo representing J atoms')\n continue\n\n for atomPairIdxI in range(atomPairIdxJ): # Compare only with the previous atom pairs\n atomPairI = self.atomPairs[atomPairIdxI]\n _atom0I = atomPairI[0]\n _atom1I = atomPairI[1]\n# nTdebug(' Using atoms I %s and %s' % ( atom0I, atom1I) )\n atomPairIset = set(atomPairI)\n atomPairIntersection = atomPairIset.intersection(atomPairJset)\n if not atomPairIntersection:\n# nTdebug(' No intersection')\n continue\n\n# At this point it is certain that there is an intersection of at least one atom between the two pairs.\n if len(atomPairIntersection) != 1:\n# nTdebug('More than one atom in atom set intersection: %s' % atomPairIntersection)\n continue\n\n atomInCommon = atomPairIntersection.pop() # get arbitrary element of set.\n atomIinCommonIdx = 0\n atomJinCommonIdx = 0\n atomItoMergeIdx = 1\n atomJtoMergeIdx = 1\n if atomPairI[atomIinCommonIdx] != atomInCommon:\n atomIinCommonIdx = 1\n atomItoMergeIdx = 0\n if atomPairJ[atomJinCommonIdx] != atomInCommon:\n atomJinCommonIdx = 1\n atomJtoMergeIdx = 0\n\n # Now we know which atoms are in common and consequently the others should be tried to merge.\n# nTdebug(' atominCommonIdx I %d and J %d for %s' % ( atomIinCommonIdx, atomJinCommonIdx, atomInCommon) )\n\n atomItoMerge = atomPairI[atomItoMergeIdx]\n atomJtoMerge = atomPairJ[atomJtoMergeIdx]\n\n atomIinCommon = atomPairI[atomIinCommonIdx]\n atomJinCommon = atomPairJ[atomJinCommonIdx]\n\n# nTdebug(' atomIinCommon %s == atomJinCommon %s' % ( atomIinCommon, atomJinCommon ))\n if atomIinCommon != atomJinCommon:\n nTcodeerror(' atoms toMerge I %s and J %s differ.' % ( atomItoMerge, atomJtoMerge) )\n continue\n # end if\n\n if atomItoMerge.getStereoPartner() != atomJtoMerge:\n# nTdebug(' atoms toMerge I %s and J %s have different parent if at all related.' % ( atomItoMerge, atomJtoMerge) )\n continue\n # end if\n\n pseudoOfAtom = atomItoMerge.pseudoAtom()\n if not pseudoOfAtom:\n# nTdebug(' no pseudo for this atom %s' % atomItoMerge)\n pseudoOfAtom = atomItoMerge.getPseudoOfPseudoAtom()\n if not pseudoOfAtom:\n nTwarning(' no pseudo of pseudoatom %s' % atomItoMerge) # happens in 1y0j for <Atom A.VAL205.CG1>\n continue\n # end if\n # end if\n\n# nTdebug( \" New pop atom: %s\" % pseudoOfAtom)\n # Change I maintaining order\n atomPairINewList = list(atomPairI)\n atomPairINewList[atomItoMergeIdx] = pseudoOfAtom\n self.atomPairs[atomPairIdxI] = tuple(atomPairINewList)\n# nTdebug(\"Now self.atomPairs[atomPairIdxI]: %s\" % str(self.atomPairs[atomPairIdxI]))\n # Remove J\n# nTdebug(\"Removing self.atomPairs[atomPairIdxJ]: %s\" % str(self.atomPairs[atomPairIdxJ]))\n del self.atomPairs[atomPairIdxJ]\n # Return quickly to keep code to the left (keep it simple).\n# nTdebug('Simplified.')\n return self.STATUS_SIMPLIFIED\n # end for\n # end while\n# nTdebug('Not simplified.')\n return self.STATUS_NOT_SIMPLIFIED",
"title": ""
},
{
"docid": "07dcb95bbef275a97b442069bf6c8503",
"score": "0.53762484",
"text": "def session_00015_line_271():",
"title": ""
},
{
"docid": "28152b516c23824e3c845d7e214752ab",
"score": "0.53493476",
"text": "def session_00016_line_284():",
"title": ""
},
{
"docid": "acf9d8d1a0ee3271da4fc4caa5c018f1",
"score": "0.5344391",
"text": "def experiment16(l):\n s = {x:() for x in l}\n s[l[0]] = (0,l[0])\n s[l[1]] = (0,l[1])\n for a in l:\n for x in s:\n if(a-x in s and a-x != x):\n s[a] = (x,a-x) if x < a-x else (a-x,x)\n break\n\n #print(s)\n ans = {l[0]:(1,0),l[1]:(0,1)}\n for a in l[2:]:\n first = ans[s[a][0]]\n second = ans[s[a][1]]\n ans[a] = (first[0]+second[0],first[1]+second[1])\n for a in l:\n print(a,ans[a],ans[a][0]+ans[a][1],(ans[a][0]/(ans[a][0]+ans[a][1]),ans[a][1]/(ans[a][0]+ans[a][1])) if ans[a][0]+ans[a][1] > 0 else 0)",
"title": ""
},
{
"docid": "35ae811da44ebf915cc56d74d00527b2",
"score": "0.53190964",
"text": "def apply(self):",
"title": ""
},
{
"docid": "7c8658c79593f0f6a735f770582e580c",
"score": "0.5302779",
"text": "def adderExample():\n class Adder(Algorithm):\n _inputs_=('a','b')\n _outputs_=('c',)\n def __init__(self,name):\n self.name=name\n self.a=Variable(0)\n self.b=Variable(0)\n self.c=Variable(0)\n Algorithm.__init__(self)\n self.enabled.value=True\n \n def update(self):\n self.c.value=self.a.value+self.b.value\n\n a1=Adder(\"a1\")\n a2=Adder(\"a2\")\n i1,i2,i3=Variable(0),Variable(0),Variable(0)\n\n a2.c.observe(pp(\"a2.c value\"))\n a2.c.blocked.observe(pp(\"a2.c blocked\"))\n a1.c.observe(pp(\"a1.c value\"))\n a1.c.blocked.observe(pp(\"a1.c blocked\"))\n\n a2.a.track_variable(a1.c)\n a1.a.track_variable(i1)\n a1.b.track_variable(i2)\n a2.b.track_variable(i3)\n\n return ((i1,i2,i3),(a1,a2))",
"title": ""
},
{
"docid": "2f3ffaa7d856f2c0a6f8092d299351a9",
"score": "0.52935946",
"text": "def C(n):\n rep = set()\n #nbre = 0\n if n%2 == 0:\n n2 = n//2\n for k in range(1, int(((n-2)/4)**0.5)+1):\n for a in range(1, (n2-1-2*k*(k-1))//(2*k)+1):\n if k == 1:\n for b in range(1, a+1):\n num = n2 - a*b\n if num%(a+b) == 0 and num >= (a+b) and num//(a+b) <= b:\n c = (n2-a*b)//(a+b)\n rep.add((a,b,c))\n #nbre += 1\n else:\n \n alpha = (a+2*(k-1))*4\n beta = (2*(k-1)*(a+k-2)-n2)*4\n #print(k, a, n2, alpha, beta)\n #D = s**2 + s*alpha + beta\n Dmin = max(2**2 + 2*alpha + beta, 1)\n Dmax = max((2*a)**2 + (2*a)*alpha + beta, 1)\n #print(Dmin, Dmax)\n for d, D in iterCarres(Dmin, Dmax):\n discr = alpha**2-4*(beta-D)\n sqdiscr = int(discr**0.5)\n if discr >= 0 and discr == sqdiscr**2 and sqdiscr%2 == 0:\n s = (sqdiscr-alpha)//2\n if (s+d)%2 == 0:\n c1 = (s+d)//2\n b = s-c1\n if N(a,b, c1, k) == n:\n if c1>=1 and b >= c1 and a >= b:\n rep.add((a,b,c1))\n\n c2 = (s-d)//2\n b = s-c2\n if N(a,b, c2, k) == n:\n if c2>= 1 and b >= c2 and a >= b:\n rep.add((a,b,c2))\n \n return len(rep) #nbre",
"title": ""
},
{
"docid": "a4fd3a08868433e46ed0e4b23c847786",
"score": "0.5275122",
"text": "def get_proof_recursive(self, a, b, P, g, h, n):\n if n == 1:\n #return the tuple: a', b', L[], R[]\n #note total size is 2 * scalar_size + log(n) * 2 * point_size\n return (a[0], b[0], self.L, self.R)\n #Split the existing vectors into halves\n aL, aR = halves(a)\n bL, bR = halves(b)\n gL, gR = halves(g)\n hL, hR = halves(h)\n self.L.append(IPC(aL, bR, g=gR, h=hL, u=self.U).get_commitment())\n self.R.append(IPC(aR, bL, g=gL, h=hR, u=self.U).get_commitment())\n x, xb, x_sq, x_sqb, xinv, xinvb, x_sq_inv, x_sq_invb = self.fiat_shamir(\n self.L[-1], self.R[-1], P)\n #Construct change of coordinates for base points, and for vector terms\n gprime = []\n hprime = []\n aprime = []\n bprime = []\n for i in range(n/2):\n gprime.append(add_pubkeys([multiply(xinvb, g[i], False),\n multiply(xb, g[i+n/2], False)], False))\n hprime.append(add_pubkeys([multiply(xb, h[i], False),\n multiply(xinvb, h[i+n/2], False)], False))\n aprime.append(encode((x * decode(a[i],\n 256) + xinv * decode(a[i + n/2], 256)) % N, 256, 32))\n bprime.append(encode((xinv * decode(b[i],\n 256) + x * decode(b[i + n/2], 256)) % N, 256, 32))\n \n Pprime = add_pubkeys([P, multiply(x_sqb, self.L[-1], False),\n multiply(x_sq_invb, self.R[-1], False)], False)\n return self.get_proof_recursive(aprime, bprime, Pprime, gprime, hprime, n/2)",
"title": ""
},
{
"docid": "3de60c7cd308e819780acb450afe72b1",
"score": "0.52667046",
"text": "def session_00019_line_331():",
"title": ""
},
{
"docid": "868643b5fd5c2c91f5ca74c33e0a98e9",
"score": "0.52657175",
"text": "def jaro_comp(val1, val2):\n\n # If at least one of the values is empty return 0\n #\n if (val1 == '') or (val2 == ''):\n return 0.0\n\n # If both attribute values exactly match return 1\n #\n elif (val1 == val2):\n return 1.0\n\n len1 = len(val1) # Number of characters in val1\n len2 = len(val2) # Number of characters in val2\n\n halflen = int(max(len1, len2) / 2) - 1\n\n assingment1 = '' # Characters assigned in val1\n assingment2 = '' # Characters assigned in val2\n\n workstr1 = val1 # Copy of original value1\n workstr2 = val2 # Copy of original value1\n\n common1 = 0 # Number of common characters\n common2 = 0 # Number of common characters\n\n for i in range(len1): # Analyse the first string\n start = max(0, i - halflen)\n end = min(i + halflen + 1, len2)\n index = workstr2.find(val1[i], start, end)\n if (index > -1): # Found common character, count and mark it as assigned\n common1 += 1\n assingment1 = assingment1 + val1[i]\n print(assingment1)\n for i in range(len2): # Analyse the second string\n start = max(0, i - halflen)\n end = min(i + halflen + 1, len1)\n index = workstr1.find(val2[i], start, end)\n if (index > -1): # Found common character, count and mark it as assigned\n common2 += 1\n assingment2 = assingment2 + val2[i]\n print(assingment2)\n\n if (common1 != common2):\n common1 = float(common1 + common2) / 2.0\n\n if (common1 == 0): # No common characters within half length of strings\n return 0.0\n\n transposition = 0 # Calculate number of transpositions\n\n for i in range(len(assingment1)):\n if (assingment1[i] != assingment2[i]):\n print(assingment1[i],assingment2[i])\n transposition += 1\n print(transposition)\n transposition = transposition / 2.0\n\n\n common1 = float(common1)\n\n jaro_sim = 1. / 3. * (common1 / float(len1) + common1 / float(len2) + \\\n (common1 - transposition) / common1)\n\n assert (jaro_sim >= 0.0) and (jaro_sim <= 1.0), \\\n 'Similarity weight outside 0-1: %f' % (jaro_sim)\n\n return jaro_sim",
"title": ""
},
{
"docid": "28968c35b13c6be24bdb84e08c4929b0",
"score": "0.52598137",
"text": "def exo2():\n M1 = MWT\n for j in log2(n): -1: 1:\n p = n/ 2^(j)\n sel = 1: p\n sel1 = 1: 2*p\n selw = p + 1: 2*p\n % average/ difference along X\n A = M1(sel, sel1, sel1)\n D = M1(selw, sel1, sel1)\n M1(1: 2: 2*p, sel1, sel1) = (A + D)/ sqrt(2)\n M1(2: 2: 2*p, sel1, sel1) = (A-D)/ sqrt(2)\n % average/ difference along Y\n A = M1(sel1, sel, sel1)\n D = M1(sel1, selw, sel1)\n M1(sel1, 1: 2: 2*p, sel1) = (A + D)/ sqrt(2)\n M1(sel1, 2: 2: 2*p, sel1) = (A-D)/ sqrt(2)\n % average/ difference along Z\n A = M1(sel1, sel1, sel)\n D = M1(sel1, sel1, selw)\n M1(sel1, sel1, 1: 2: 2*p) = (A + D)/ sqrt(2)\n M1(sel1, sel1, 2: 2: 2*p) = (A-D)/ sqrt(2)",
"title": ""
},
{
"docid": "7a6c9f4d3281606a633eebb790432840",
"score": "0.52573293",
"text": "def _compute_shunts(self):",
"title": ""
},
{
"docid": "1b92579afc41b08f1858d2c3d72151b7",
"score": "0.52570015",
"text": "def tql2(n, V, d, e):\n for i in range(1, n):\n e[i-1] = e[i]\n\n e[n-1] = 0.0\n\n f = 0.0\n tst1 = 0.0\n eps = math.pow(2.0, -52.0)\n\n for l in range(n):\n tst1 = max(tst1, abs(d[l]) + abs(e[l]))\n m = l\n\n while m < n:\n if abs(e[m]) <= eps*tst1:\n break\n m += 1\n\n if m > l:\n iter = 0\n\n while True:\n iter += 1\n g = d[l]\n p = (d[l+1] - g) / (2.0*e[l])\n r = (p**2 + 1)**0.5\n #r = hypot(p, 1.0)\n\n if p < 0:\n r = -r\n\n d[l] = e[l] / (p + r)\n d[l+1] = e[l] * (p + r)\n dl1 = d[l+1]\n h = g - d[l]\n\n for i in range(l+2, n):\n d[i] -= h\n\n f += h\n p = d[m]\n c = 1.0\n c2 = c\n c3 = c\n el1 = e[l+1]\n s = 0.0\n s2 = 0.0\n\n for i in range(m-1, l-1, -1):\n c3 = c2\n c2 = c\n s2 = s\n g = c*e[i]\n h = c*p\n r = (p**2 + e[i]**2)**0.5\n #r = hypot(p, e[i])\n e[i+1] = s*r\n s = e[i] / r\n c = p / r\n p = c*d[i] - s*g\n d[i+1] = h + s*(c*g + s*d[i])\n\n for k in range(n):\n h = V[k][i+1]\n V[k][i+1] = s*V[k][i] + c*h\n V[k][i] = c*V[k][i] - s*h\n\n p = -s*s2*c3*el1*e[l] / dl1\n e[l] = s*p\n d[l] = c*p\n\n if abs(e[l]) <= eps*tst1:\n break\n\n d[l] = d[l] + f\n e[l] = 0.0\n\n for i in range(n-1):\n k = i\n p = d[i]\n\n for j in range(i+1, n):\n if d[j] < p:\n k = j\n p = d[j]\n\n if k != i:\n d[k] = d[i]\n d[i] = p\n\n for j in range(n):\n p = V[j][i]\n V[j][i] = V[j][k]\n V[j][k] = p\n\n return V, d, e",
"title": ""
},
{
"docid": "cd5220d5508a48e8737105db31a86ac5",
"score": "0.5245568",
"text": "def ProdScalaire(V1, V2):\r\n (Vx1, Vy1, Vz1) = V1\r\n (Vx2, Vy2, Vz2) = V2\r\n return(Vx1 * Vx2 + Vy1 * Vy2 + Vz1 * Vz2)",
"title": ""
},
{
"docid": "7526d12deeebb0ea2f8f662a7ccbd451",
"score": "0.52440774",
"text": "def arrGen(P,V): #Let P and V be sorted lists\n\t#Does so using the recursive method described in the paper.\n\tif len(P) == 0: yield V; return\n\tif len(P) == 1: [v1,v2] = V; yield [v1,*P,v2]; return #This is for optimization.\n\tP = P.copy()\n\tp = P.pop(0)#P is now P', stored under the same variable for efficiency\n\tV1 = list(filter(lambda i: i < p, V)) #this is Vp from the notes\n\tV2 = V.copy() #this is just for optimization\n\tV2.insert(V.index(max(V1)) + 1, p)\n\tfor [v1,v2] in subsetIter(V1, 2):\n\t\tV3 = V2.copy() #this will be V' from the notes\n\t\tV3.remove(v1)\n\t\tV3.remove(v2)\n\t\tfor a in arrGen(P,V3): #here a' is selected\n\t\t\tj = a.index(p)\n\t\t\ta.insert(j+1,v2) #this line, and the following produce a, corresponding to a'.\n\t\t\ta.insert(j,v1)\n\t\t\tyield a",
"title": ""
},
{
"docid": "92e933f1d02b3385ea7afcd7f1e1d33f",
"score": "0.52235883",
"text": "def compute_C(self):",
"title": ""
},
{
"docid": "9a68215d37a9be7e0ff915870d129ea6",
"score": "0.52072394",
"text": "def Map( V, S1, S2, D1, D2):\r\n if (abs(S2 - S1) <= Epsilon):\r\n # print( (S1, S2, D1, D2))\r\n a = 1\r\n b = (D1 + D2) / 2.\r\n else :\r\n a = (D2 - D1) / (S2 - S1)\r\n b = D1 - a * S1\r\n return(a*V+b)",
"title": ""
},
{
"docid": "be8db9369933e27e9da228826a115a82",
"score": "0.51992214",
"text": "def _k( s1, s2, embs, maxlen, _gap_decay, _match_decay, _order_coefs,D,dD_dgap):\n\tS = embs[s1] @ embs[s2].T\n\t# Main loop, where Kp, Kpp values are calculated.\n\tKp = np.ones(shape=(len(_order_coefs), maxlen, maxlen))\n\tdKp_dgap = np.zeros(shape=(len(_order_coefs), maxlen, maxlen))\n\tdKp_dmatch = np.zeros(shape=(len(_order_coefs), maxlen, maxlen))\n\tmatch_sq = _match_decay * _match_decay\n\n\n\tfor i in range(len(_order_coefs)-1):\n\t\taux1 = S * Kp[i]\n\t\taux2 = np.dot(aux1,D)\n\t\tKpp = match_sq * aux2\n\t\tKp[i + 1] = Kpp.T.dot(D).T\n\n\t\tdaux1_dgap = S * dKp_dgap[i]\n\t\tdaux2_dgap = daux1_dgap.dot(D) + aux1.dot(dD_dgap)\n\t\tdKpp_dgap = match_sq * daux2_dgap\n\t\tdKp_dgap[i + 1] = dKpp_dgap.T.dot(D).T + Kpp.T.dot(dD_dgap).T\n\n\t\tdaux1_dmatch = S * dKp_dmatch[i]\n\t\tdaux2_dmatch = daux1_dmatch.dot(D)\n\t\tdKpp_dmatch = (match_sq * daux2_dmatch) + (2 * _match_decay * aux2)\n\t\tdKp_dmatch[i + 1] = dKpp_dmatch.T.dot(D).T\n\t\t\t\n\n\t\t\t\n\t#Final calculation\n\tfinal_aux1 = S * Kp\n\tfinal_aux2 = np.sum(final_aux1, axis=1)\n\tfinal_aux3 = np.sum(final_aux2, axis=1)\n\tKi = match_sq * final_aux3\n\tk = Ki.dot(_order_coefs)\n\n\tfinal_daux1_dgap = S * dKp_dgap\n\tfinal_daux2_dgap = np.sum(final_daux1_dgap, axis=1)\n\tfinal_daux3_dgap = np.sum(final_daux2_dgap, axis=1)\n\tdKi_dgap = match_sq * final_daux3_dgap\n\tdk_dgap = dKi_dgap.dot(_order_coefs)\n\t\n\tfinal_daux1_dmatch = S * dKp_dmatch\n\tfinal_daux2_dmatch = np.sum(final_daux1_dmatch, axis=1)\n\tfinal_daux3_dmatch = np.sum(final_daux2_dmatch, axis=1)\n\tdKi_dmatch = match_sq * final_daux3_dmatch + (2 * _match_decay *final_aux3)\n\tdk_dmatch = dKi_dmatch.dot(_order_coefs)\n\n\tdk_dcoefs = Ki\n\treturn k, dk_dgap, dk_dmatch, dk_dcoefs",
"title": ""
},
{
"docid": "f61dff72706e2134fc21c365b4e959ff",
"score": "0.51923126",
"text": "def wordseg_soft_acc(list_a: List[Tuple[str, str]],\n list_b: List[Tuple[str, str]]) -> float:\n i, j = 0, 0\n acc = 0.0\n a_l, b_l = 0, 0\n while i < len(list_a) and j < len(list_b):\n a_r = a_l + len(list_a[i][0]) - 1\n b_r = b_l + len(list_b[j][0]) - 1\n if a_r < b_l:\n i += 1\n a_l = a_r + 1\n continue\n if b_r < a_l:\n j += 1\n b_l = b_r + 1\n continue\n if a_l == b_l and a_r == b_r:\n acc += 1.0\n a_l, b_l = a_r + 1, b_r + 1\n i, j = i + 1, j + 1\n continue\n if a_l == b_l and a_r < b_r:\n cnt = 0.0\n tmp_a_r = a_r\n for k in range(i + 1, len(list_a)):\n tmp_a_r += len(list_a[k])\n cnt += 1.0\n if tmp_a_r == b_r:\n acc += cnt\n i, j = k + 1, j + 1\n a_l, b_l = tmp_a_r + 1, b_r + 1\n break\n i += 1\n continue\n if a_l == b_l and a_r > b_r:\n tmp_b_r = b_r\n for k in range(j + 1, len(list_b)):\n tmp_b_r += len(list_b[k])\n if tmp_b_r == a_r:\n acc += 1.0\n i, j = i + 1, k + 1\n a_l, b_l = a_r + 1, tmp_b_r + 1\n break\n j += 1\n continue\n i += 1\n return acc",
"title": ""
},
{
"docid": "ef663699bebafa139a1bba305061b5c7",
"score": "0.5191531",
"text": "def RBO(l1, l2, p=0.9):\n if l1 == None: l1 = []\n if l2 == None: l2 = []\n \n sl,ll = sorted([(len(l1), l1),(len(l2),l2)])\n s, S = sl\n l, L = ll\n if s == 0: return 0\n\n # Calculate the overlaps at ranks 1 through l \n # (the longer of the two lists)\n ss = set([]) # contains elements from the smaller list till depth i\n ls = set([]) # contains elements from the longer list till depth i\n x_d = {0: 0}\n sum1 = 0.0\n for i in range(l):\n x = L[i]\n y = S[i] if i < s else None\n d = i + 1\n \n # if two elements are same then \n # we don't need to add to either of the set\n if x == y: \n x_d[d] = x_d[d-1] + 1.0\n # else add items to respective list\n # and calculate overlap\n else: \n ls.add(x) \n if y != None: ss.add(y)\n x_d[d] = x_d[d-1] + (1.0 if x in ss else 0.0) + (1.0 if y in ls else 0.0) \n #calculate average overlap\n sum1 += x_d[d]/d * pow(p, d)\n \n sum2 = 0.0\n for i in range(l-s):\n d = s+i+1\n sum2 += x_d[d]*(d-s)/(d*s)*pow(p,d)\n\n sum3 = ((x_d[l]-x_d[s])/l+x_d[s]/s)*pow(p,l)\n\n # Equation 32\n rbo_ext = (1-p)/p*(sum1+sum2)+sum3\n return rbo_ext",
"title": ""
},
{
"docid": "47ef706b1db43359e2d82e0a3a59b00b",
"score": "0.51862395",
"text": "def alias_setup(probs):\r\n\r\n K = len(probs)\r\n\r\n q = np.zeros(K)\r\n\r\n J = np.zeros(K, dtype=np.int)\r\n\r\n\r\n\r\n smaller = []\r\n\r\n larger = []\r\n\r\n for kk, prob in enumerate(probs):\r\n\r\n q[kk] = K * prob\r\n\r\n if q[kk] < 1.0:\r\n\r\n smaller.append(kk)\r\n\r\n else:\r\n\r\n larger.append(kk)\r\n\r\n\r\n\r\n while len(smaller) > 0 and len(larger) > 0:\r\n\r\n small = smaller.pop()\r\n\r\n large = larger.pop()\r\n\r\n\r\n\r\n J[small] = large\r\n\r\n q[large] = q[large] + q[small] - 1.0\r\n\r\n if q[large] < 1.0:\r\n\r\n smaller.append(large)\r\n\r\n else:\r\n\r\n larger.append(large)",
"title": ""
},
{
"docid": "1055e573c00b7c7ac204856f290fdec0",
"score": "0.51753867",
"text": "def SOL_2(x,y):\n n = len(x)\n m = len(y)\n if (n>1) and (m>=1):\n i = abs(n)//2\n j = coupure(x,y)\n # print(\"la coupure pour x={} y={} i:{} j:{}\".format(x,y,i,j))\n SOL_2(x[0:i],y[0:j])\n SOL_2(x[i:],y[j:])\n elif(n==1) and (m>1):\n a=align_lettre_mot(x,y)\n for letterX in a[0]:\n XA.append(letterX)\n for letterY in y:\n YA.append(letterY)\n else:\n # print(\"fina; n:{} m:{} {} {}\".format(n,m,x,y))\n if(n==0):\n for letterY in y:\n XA.append('-')\n else:\n for letterX in x:\n XA.append(letterX)\n if(m==0):\n for letterX in x:\n YA.append('-')\n else:\n for letterY in y:\n YA.append(letterY)",
"title": ""
},
{
"docid": "d4107ab43c798a5efb09b250d79f4741",
"score": "0.5173423",
"text": "def subproblem2(p, q, k1, k2):\n \n eps = np.finfo(np.float64).eps\n norm = np.linalg.norm\n \n k12 = np.dot(k1, k2)\n pk = np.dot(p, k2)\n qk = np.dot(q, k1)\n \n # check if solution exists\n if (np.abs( 1 - k12**2) < eps):\n warnings.warn(\"No solution - k1 != k2\")\n return []\n \n a = np.matmul([[k12, -1], [-1, k12]],[pk, qk]) / (k12**2 - 1)\n \n bb = (np.dot(p,p) - np.dot(a,a) - 2*a[0]*a[1]*k12)\n if (np.abs(bb) < eps): bb=0\n \n if (bb < 0):\n warnings.warn(\"No solution - no intersection found between cones\")\n return []\n \n gamma = np.sqrt(bb) / norm(np.cross(k1,k2))\n if (np.abs(gamma) < eps):\n cm=np.array([k1, k2, np.cross(k1,k2)]).T\n c1 = np.dot(cm, np.hstack((a, gamma)))\n theta2 = subproblem1(k2, p, c1)\n theta1 = -subproblem1(k1, q, c1)\n return [(theta1, theta2)]\n \n cm=np.array([k1, k2, np.cross(k1,k2)]).T\n c1 = np.dot(cm, np.hstack((a, gamma)))\n c2 = np.dot(cm, np.hstack((a, -gamma)))\n theta1_1 = -subproblem1(q, c1, k1)\n theta1_2 = -subproblem1(q, c2, k1)\n theta2_1 = subproblem1(p, c1, k2)\n theta2_2 = subproblem1(p, c2, k2)\n return [(theta1_1, theta2_1), (theta1_2, theta2_2)]",
"title": ""
},
{
"docid": "a6c7324e35da0dc2054d14493a83dca8",
"score": "0.5165419",
"text": "def solve(self):\n raise NotImplementedError('This is the method every algorithm has to implement')",
"title": ""
},
{
"docid": "7b4b46718148a90cc0e80aa62cf09580",
"score": "0.51627547",
"text": "def ration(a, b, line: int, spot: int, iii: int, target: str) -> float:\n\n d1 = pd.read_csv(\"line \"+str(line)+\" Point \"+str(spot)+\" iteration \"+str(iii)+\" foreground1D.csv\")\n d2 = pd.read_csv(\"line \"+str(line)+\" Point \"+str(spot)+\" iteration \"+str(iii)+\" foreground2D.csv\")\n d1_ = pd.read_csv('../../data/background1D.csv')\n d2_ = pd.read_csv('../../data/background1D.csv')\n \n d1['I'] = d1['I'] - d1_['I']\n base1 = peakutils.baseline(d1['I'], 1)\n d1['I_base'] = d1['I'] - base1\n d1 = d1[(d1['W'] > 1220) & (d1['W'] < 1750)]\n\n d2['I'] = d2['I'] - d2_['I']\n d2 = d2[(d2['W'] > 2550) & (d2['W'] < 2850)]\n d2 = d2[(np.abs(stats.zscore(d2)) < 3).all(axis = 1)]\n base2 = peakutils.baseline(d2['I'], 1)\n d2['I_base'] = d2['I'] - base2\n \n def PseudoVoigtFunction(WavNr, Pos, Amp, GammaL, FracL):\n SigmaG = GammaL / np.sqrt(2*np.log(2)) # Calculate the sigma parameter for the Gaussian distribution from GammaL (coupled in Pseudo-Voigt)\n LorentzPart = Amp * (GammaL**2 / ((WavNr - Pos)**2 + GammaL**2)) # Lorentzian distribution\n GaussPart = Amp * np.exp( -((WavNr - Pos)/SigmaG)**2) # Gaussian distribution\n Fit = FracL * LorentzPart + (1 - FracL) * GaussPart # Linear combination of the two parts (or distributions)\n return Fit\n\n def one_pv(pars, x, data=None, eps=None): #Function definition\n # unpack parameters, extract .value attribute for each parameter\n a3 = pars['a3'].value\n c3 = pars['c3'].value\n s3 = pars['s3'].value\n f3 = pars['f3'].value\n\n peak1 = PseudoVoigtFunction(x.astype(float),c3, a3, s3, f3)\n\n model = peak1 # The global model is the sum of the Gaussian peaks\n\n if data is None: # if we don't have data, the function only returns the direct calculation\n return model, peak1\n if eps is None: # without errors, no ponderation\n return (model - data)\n return (model - data)/eps # with errors, the difference is ponderated\n\n def three_pv(pars, x, data=None, eps=None): #Function definition\n # unpack parameters, extract .value attribute for each parameter\n a1 = pars['a1'].value\n c1 = pars['c1'].value\n s1 = pars['s1'].value\n f1 = pars['f1'].value\n \n a4 = pars['a4'].value\n c4 = pars['c4'].value\n s4 = pars['s4'].value\n f4 = pars['f4'].value\n \n a2 = pars['a2'].value\n c2 = pars['c2'].value\n s2 = pars['s2'].value\n f2 = pars['f2'].value\n \n\n peak1 = PseudoVoigtFunction(x.astype(float), c1, a1, s1, f1)\n peak3 = PseudoVoigtFunction(x.astype(float), c4, a4, s4, f4)\n peak2 = PseudoVoigtFunction(x.astype(float), c2, a2, s2, f2)\n\n model = peak1 + peak3 + peak2 # The global model is the sum of the Gaussian peaks\n\n if data is None: # if we don't have data, the function only returns the direct calculation\n return model, peak1, peak3, peak2\n if eps is None: # without errors, no ponderation\n return (model - data)\n return (model - data)/eps # with errors, the difference is ponderated\n\n ps1 = Parameters()\n\n # (Name, Value, Vary, Min, Max, Expr)\n ps1.add_many(('a1', 1 , True, 0, None, None),\n ('c1', 1350, True, 1330, 1370, None),\n ('s1', 20, True, 10, 200, None), # 200 so that we get proper fit width of unpatterned peak \n ('f1', 0.5, True, 0, 1, None),\n ('a4', 1 , True, 0, None, None), # peak middle of GD\n ('c4', 1500, True, 1480, 1520, None),\n ('s4', 20, True, 10, 200, None), \n ('f4', 0.5, True, 0, 1, None),\n ('a2', 1, True, 0, None, None),\n ('c2', 1600, True, 1560, 1640, None),\n ('s2', 20, True, 10, 200, None),\n ('f2', 0.5, True, 0, 1, None))\n\n ps2 = Parameters()\n\n # (Name, Value, Vary, Min, Max, Expr)\n ps2.add_many(('a3', 1, True, 0, None, None),\n ('c3', 2700, True, 2650, 2750, None),\n ('s3', 20, True, 10, 200, None),\n ('f3', 0.5, True, 0, 1, None))\n\n x = d1['W']\n y = d1['I_base']\n out = minimize(three_pv, ps1, method = 'leastsq', args=(x, y))\n\n x2 = d2['W']\n y2 = d2['I_base']\n out2 = minimize(one_pv, ps2, method = 'leastsq', args=(x2, y2))\n\n df1 = pd.DataFrame({key: [par.value] for key, par in out.params.items()})\n df2 = pd.DataFrame({key: [par.value] for key, par in out2.params.items()})\n\n df = pd.concat([df1,df2],axis=1)\n\n if df['s1'].values > 300:\n df[['a1','c1','s1','f1']] = 0\n\n if df['s2'].values > 120:\n df[['a2','c2','s2','f2']] = 0\n\n if df['s3'].values > 120:\n df[['a3','c3','s3','f3']] = 0\n \n df.columns= ['D','PD','WD','FD','D1','PD1','WD1','FD1','G','PG','WG','FG','2D','P2D','W2D','F2D']\n \n df['GD']=df['G']/df['D']\n df['2DG']=df['2D']/df['G']\n df['file'] = ','.join(str(x) for x in [line, spot, iii])\n\n # print(df)\n # fit_results = pd.read_csv('fit_results.csv')\n # print(f\"Before FIT RESULTS:\\n {fit_results}\\n\")\n df.to_csv('fit_results.csv', header=False, index=False, mode = 'a')\n # print(f\"Appended FIT RESULTS:\\n {fit_results}\\n\")\n\n if df['WD'].values>120:\n if (df['D'].values>.3*df['G'].values or df['D1'].values > df['D'].values):\n print(\"Width D > 120 : patterning not done\")\n\n elif (df['WG'].values>120):\n print(\"Width G > 120: patterning not done\")\n df3=pd.read_csv('dataset.csv')\n df3['ratio'].replace(' ',np.nan, inplace=True)\n df4=df3.dropna(subset=[\"ratio\"])\n a=df4['ratio'].shape\n if target == '2DG':\n df3.loc[a[0],'ratio'] = df['2DG'].values[0]\n else:\n df3.loc[a[0],'ratio'] = df['GD'].values[0]\n df3.to_csv('dataset.csv',index=False)\n \n \n elif (np.mean(d1[d1['W']<1255]['I_base']) > 0.7*np.mean(d1[(d1['W']>1340) & (d1['W']<1350)]['I_base'])\\\n or np.mean(d1[(d1['W']>1400) & (d1['W']<1550)]['I_base']) > 0.7*np.mean(d1[(d1['W']>1340) & (d1['W']<1350)]['I_base'])) \\\n and (df['GD'].values[0] <= 1.2) :\n print(\"Intensity @ 1255 / 1500 abnormally high : patterning not done\")\n \n else:\n\n df3=pd.read_csv('dataset.csv')\n df3['ratio'].replace(' ',np.nan, inplace=True)\n df4=df3.dropna(subset=[\"ratio\"])\n a=df4['ratio'].shape\n if target == '2DG':\n print(f\"Extracting {target} to dataset.csv\")\n df3.loc[a[0],'ratio'] = df['2DG'].values[0]\n else:\n print(f\"Extracting {target} to dataset.csv\")\n df3.loc[a[0],'ratio'] = df['GD'].values[0]\n df3.to_csv('dataset.csv',index=False)\n \n return df['GD'].values[0]",
"title": ""
},
{
"docid": "6c7d05c68545e26ea5cda6a20ee0879f",
"score": "0.51607394",
"text": "def experiment17(l,k,m):\n s = {x:() for x in l}\n s[l[0]] = (0,l[0])\n s[l[1]] = (0,l[1])\n for a in l:\n for x in s:\n if(a-x in s and a-x != x):\n s[a] = (x,a-x) if x < a-x else (a-x,x)\n break\n\n #print(s)\n coms = {x:[] for x in [0]+l}\n weird = [];\n weird2 = [];\n for a in l:\n coms[s[a][0]] += [s[a][1]]\n coms[s[a][1]] += [s[a][0]]\n for a in l:\n lo = 0\n hi = 0\n for c in coms[a]:\n if (k*c)%m < m/2:\n lo += 1\n else:\n hi += 1\n low = (k*a)%m < m/2\n if(lo != 0 and hi != 0):\n weird += [(a,lo,hi)]\n if ((low and hi > 0) or (not low and lo > 0)) and hi*lo == 0:\n weird2 += [(a,lo,hi)]\n #print(a,lo,hi,coms[a])\n print(\"WEIRD\")\n for w in weird:\n print(w)\n print(\"WEIRDER\")\n for w in weird2:\n print(w)",
"title": ""
},
{
"docid": "a348e632b0028e6bec1239e2970714eb",
"score": "0.5138175",
"text": "def first_prime_over():",
"title": ""
},
{
"docid": "301725575c75bae39124d23ee3560e96",
"score": "0.5136804",
"text": "def productoInternoV(v1,v2):\r\n d= daga(v1)\r\n return productoM(d,v2)",
"title": ""
},
{
"docid": "a8294ec79b4cccfb86d57c907aaad5f9",
"score": "0.5134629",
"text": "def sol3(limtit) -> int:\n \n // implementation \n \n pass",
"title": ""
},
{
"docid": "aec1ba07a9bf4264a60300ee06ad1862",
"score": "0.51252824",
"text": "def solve(self):\r\n pass",
"title": ""
},
{
"docid": "aec1ba07a9bf4264a60300ee06ad1862",
"score": "0.51252824",
"text": "def solve(self):\r\n pass",
"title": ""
},
{
"docid": "9cd2b705d6da14bb2e62c4c32589c575",
"score": "0.511769",
"text": "def pid(\n almA,\n almB,\n mode=2\n ):\n\n zipped = zip(almA,almB)\n idn_pos = 0\n int_gps = 0\n aln_pos = 0\n\n for charA,charB in zipped:\n tmp = [charA,charB].count('-')\n if tmp == 1:\n int_gps += 1\n elif tmp == 0 and charA == charB:\n idn_pos += 1\n aln_pos += 1\n elif tmp == 0:\n aln_pos += 1\n\n if mode == 2:\n try:\n return idn_pos / (aln_pos + int_gps)\n except ZeroDivisionError:\n #print('\\t'.join(almA))\n #print('\\t'.join(almB))\n #print('-----')\n return 0\n\n elif mode == 1: \n try:\n return idn_pos / aln_pos\n except ZeroDivisionError:\n #print('\\t'.join(almA))\n #print('\\t'.join(almB))\n #print('-----')\n return 0\n\n elif mode == 3:\n srt_seq = min(\n len(\n [i for i in almA if i != '-']\n ),\n len(\n [i for i in almB if i != '-']\n )\n )\n try:\n return idn_pos / srt_seq\n except ZeroDivisionError:\n #print('\\t'.join(strA))\n #print('\\t'.join(strB))\n #print('-----')\n return 0\n\n elif mode == 4:\n srt_seq = min(\n len(\n ''.join([i[0] for i in almA]).strip('-')\n ),\n len(\n ''.join([i[0] for i in almB]).strip('-')\n )\n )\n try:\n return idn_pos / srt_seq\n except ZeroDivisionError:\n print('\\t'.join(almA))\n print('\\t'.join(almB))\n print('-----')\n return 0\n\n elif mode == 5:\n \n return idn_pos / len(almA)",
"title": ""
},
{
"docid": "7e973dd74aea32ed82aca60b17ad68e2",
"score": "0.51157886",
"text": "def conjecture1(n,k,r):\n vavb = sum([1/r[i] for i in range(n-2)]) >= 1/r[n-1]+1/r[n-2]-2/(r[n-1]+r[n-2])\n vavc = sum([1/(r[i]+r[n-1]) for i in range(n-1)]) >= 1/r[n-1]\n vbvc = sum([1/(r[i]+r[n-1]) for i in range(n-2)])+1/r[n-2] >= sum([1/r[i] for i in range(n-2)])+1/(r[n-2]+r[n-1])\n vbva = sum([1/r[i] for i in range(n-2)]) <= 1/r[n-1]+1/r[n-2]-2/(r[n-1]+r[n-2])\n vcva = sum([1/(r[i]+r[n-1]) for i in range(n-1)]) <= 1/r[n-1]\n vcvb = sum([1/(r[i]+r[n-1]) for i in range(n-2)])+1/r[n-2] <= sum([1/r[i] for i in range(n-2)])+1/(r[n-2]+r[n-1])\n if vavb and vavc:\n #Strategy A\n game_value = 2/sum([1/r[i] for i in range(n)])\n if game_value*10000//1 != v.varValue*10000//1:\n #*10000//1 to avoid rounding errors\n print(f\"Ouch... Value A: {game_value} != {v.varValue}\")\n for i in range(size_s):\n if len(searcher_strats[i]) == 1:\n pi = game_value/(2*r[i])\n if (x[i].varValue*10000//1)!=(pi*10000//1):\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != {pi}\\n\")\n return False\n else:\n if int(x[i].varValue*10000)!=0:\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != 0\\n\")\n return False\n print(\"Checked !\\n\")\n return True\n elif vbva and vbvc:\n #Strategy B\n game_value = 1/(sum([1/r[i] for i in range(n-2)])+1/(r[n-2]+r[n-1]))\n if game_value*10000//1 != v.varValue*10000//1:\n #*10000//1 to avoid rounding errors\n print(f\"Ouch... Value B: {game_value} != {v.varValue}\")\n for i in range(size_s):\n if len(searcher_strats[i]) == 1 and i<n-2:\n pi = game_value/r[i]\n if (x[i].varValue*10000//1)!=(pi*10000//1):\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != {pi}\\n\")\n return False\n elif searcher_strats[i]==[n-2,n-1]:\n pi = game_value/(r[n-2]+r[n-1])\n if (x[i].varValue*10000//1)!=(pi*10000//1):\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != {pi}\\n\")\n return False\n else:\n if int(x[i].varValue*10000)!=0:\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != 0\\n\")\n return False\n print(\"Checked !\\n\")\n return True\n elif vcva and vcvb:\n #Strategy C\n game_value = 2/(sum([1/r[i] for i in range(n-1)])+sum([1/(r[i]+r[n-1]) for i in range(n-1)]))\n if game_value*10000//1 != v.varValue*10000//1:\n #*10000//1 to avoid rounding errors\n print(f\"Ouch... Value C: {game_value} != {v.varValue}\")\n for i in range(size_s):\n if len(searcher_strats[i]) == 1 and i!=n-1:\n pi = game_value/(2*r[i])\n if (x[i].varValue*10000//1)!=(pi*10000//1):\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != {pi}\\n\")\n return False\n elif len(searcher_strats[i]) == 2 and searcher_strats[i][1]==n-1:\n pi = game_value/(2*(r[searcher_strats[i][0]]+r[searcher_strats[i][1]])) \n if (x[i].varValue*10000//1)!=(pi*10000//1):\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != {pi}\\n\")\n return False\n else:\n if int(x[i].varValue*10000)!=0:\n print(\"Ouch...\")\n print(f\"{searcher_strats[i]} : {x[i].varValue} != 0\\n\")\n return False\n print(\"Checked !\\n\")\n return True",
"title": ""
},
{
"docid": "e5ded93f33c86090f850283eff706d83",
"score": "0.5114497",
"text": "def pijav_bnb(problem, eps, solinfo, maxsteps):\n class Sub:\n def __init__(self, s1, s2, ival):\n self.s1 = s1\n self.s2 = s2\n self.ival = ival\n a = ival[0]\n b = ival[1]\n La = s1.S[0]\n Lb = s2.S[1]\n va = s1.value\n vb = s2.value\n self.c = (vb - va + La * a - Lb * b)/(La - Lb)\n self.bound = va + La * (self.c - a)\n boundcheck = vb + Lb * (self.c - b)\n if abs(self.bound - boundcheck) > 0.01:\n print(\"bound = \", self.bound, \", check \", boundcheck)\n print(\"a = \", a, \", b = \", b, \", va = \", va, \", vb = \", vb, \", La = \", La, \", Lb = \", Lb)\n print(\"s1 = \", s1)\n print(\"s2 = \", s2)\n\n def bnd(self):\n return self.bound\n\n def __repr__(self):\n return \"s1 = \" + str(self.s1) + \", s2 = \" + str(self.s2) + \", interval = \" + str(self.ival)\\\n + \"c =\" + str(self.c) + \", bound = \" + str(self.bound)\n\n\n f = smp.lambdify(problem.xvar, problem.fexpr)\n fslp = smp.lambdify(problem.xvar, problem.fexpr, slp)\n P = []\n ival = interval.Interval(problem.range)\n s1 = fslp(slp.Slope(ival, ival[0]))\n s2 = fslp(slp.Slope(ival, ival[1]))\n P.append(Sub(s1, s2, ival))\n fr = solinfo.value\n steps = 0\n\n # Expr.flagRecompRange = True\n while len(P) > 0 and steps <= maxsteps:\n steps = steps + 1\n sub = P.pop(0)\n if fr - sub.bnd() > eps:\n x1 = interval.Interval([sub.ival[0], sub.c])\n x2 = interval.Interval([sub.c, sub.ival[1]])\n\n # ns = fslp(slp.Slope(sub.ival, sub.c))\n # ns1 = ns\n # ns2 = ns\n\n ns1 = fslp(slp.Slope(x1, sub.c))\n ns2 = fslp(slp.Slope(x2, sub.c))\n\n\n # print(\"at \", sub.c, \" ns = \", ns)\n if ns1.value < fr:\n fr = ns1.value\n xr = sub.c\n P.append(Sub(sub.s1, ns1, x1))\n P.append(Sub(ns2, sub.s2, x2))\n solinfo.value = fr\n solinfo.x = xr\n return steps",
"title": ""
},
{
"docid": "529ce01eb8b32601062e4cdd0fba7053",
"score": "0.51092744",
"text": "def A3(fn):\n#want to find a key that matches (M1, M2, M3) into (C1,C2,C3)\n\n returned_message1 = random_string(n_bytes)\n returned_message2 = random_string(n_bytes)\n returned_message3 = random_string(n_bytes)\n\n C1 = fn(returned_message1) \n C2 = fn(returned_message2) \n C3 = fn(returned_message3) \n\n #check that key is consistent with (M1,C1),(M2,C2), (M3,C3)\n for i in range(string_to_int((k_bytes)* \"\\xFF\")): #i is key\n key1 = int_to_string(i,1)\n key2_1 = xor_strings(E_I(key1,C1), returned_message1)\n key2_2 = xor_strings(E_I(key1,C2), returned_message2)\n key2_3 = xor_strings(E_I(key1,C3), returned_message3)\n if(key2_1 == key2_2 and key2_2 == key2_3):\n return key1+key2_1\n \n\n return i",
"title": ""
},
{
"docid": "ebb9475143bdfa377f733590ad05785f",
"score": "0.5108019",
"text": "def main(*args):\n\n \"\"\"\n l1 = [\"1\", \"2\", \"3\", \"4\", \"2\", \"2\", \"4\", \"5\"]\n l2 = [\"3\", \"4\", \"5\"]\n l3 = [\"2\", \"4\", \"2\", \"1\", \"3\", \"3\", \"5\"]\n \"\"\"\n # Case 1 \n #l1 = [\"4\", \"4\", \"3\", \"3\", \"3\", \"2\", \"2\", \"1\"]\n #l2 = [\"3\", \"2\", \"2\"]\n #l3 = [\"4\", \"3\", \"2\", \"1\"]\n #l4 = [\"4\", \"4\", \"3\", \"3\", \"3\" ]\n #lt = [l1, l2, l3, l4]\n\n \"\"\"\n l1 = [\"2\", \"3\", \"4\"]\n l2 = [\"2\", \"4\"]\n lt = [l1, l2]\n \"\"\"\n \"\"\"\n l1 = [\"3\", \"2\", \"4\"]\n l2 = [\"4\", \"3\", \"2\"]\n lt = [l1, l2]\n \"\"\"\n \"\"\"\n l1 = [\"4\", \"2\", \"2\", \"2\", \"3\"]\n l2 = [\"2\", \"4\", \"2\", \"3\", \"2\"]\n lt = [l1, l2]\n \"\"\"\n l1 = [\"2\", \"3\", \"3\", \"3\", \"4\", \"5\"]\n l2 = [\"4\", \"3\", \"3\", \"3\", \"2\"]\n lt = [l1, l2]\n\n n = len(lt)\n jw = \"Jaro Winkler\\n\"\n st = \"Soft tf idf\\n\"\n sl = \"Sequence with t-norm lukasiewicz\\n\"\n sp = \"Sequence with t-norm product\\n\"\n for i in range(n):\n l1 = lt[i]\n for j in range(n):\n l2 = lt[j]\n jw += str((l1, l2))+\"\\t\"\n st += str((l1, l2))+\"\\t\"\n sl += str((l1, l2))+\"\\t\"\n sp += str((l1, l2))+\"\\t\"\n print(\"--------------------------------\")\n print((l1, l2))\n print(\"Jaro Winkler\", jaro_winkler(l1, l2))\n jw += str(jaro_winkler(l1, l2)) + \"\\t\"\n print(\"soft_tf_idf\", soft_tf_idf(l1, l2))\n st += str(soft_tf_idf(l1, l2)) + \"\\t\"\n print(\"sim_subseq lukasiewicz\", sim_subseq_t_norm_l(l1, l2))\n sl += str(sim_subseq_t_norm_l(l1, l2)) + \"\\t\"\n print(\"sim_subseq tnorm\", sim_subseq(l1, l2))\n sp += str(sim_subseq(l1, l2)) + \"\\t\"\n jw += \"\\n\"\n st += \"\\n\"\n sl += \"\\n\"\n sp += \"\\n\"\n\n jw +=\"\\n\"+st+\"\\n\"+sl+\"\\n\"+sp+\"\\n\"\n with open(\"results_caso4.csv\", \"w\") as fd:\n fd.write(jw)\n\n p1 = [43,96,74,38,35,43,22,56,35,80]\n p2 = [30,94,84,13,30,18,30,41,48,95]\n\n cl = SimList(p1, p2)\n print(\"testing s_rho_nomrmalized\")\n z = cl.spearmanr_rho() \n print(z)",
"title": ""
},
{
"docid": "2c09fcd92e60c2fd508060aebfb30f19",
"score": "0.5104186",
"text": "def routing_two(x, P1, C0, r, rho):\n\n \"\"\" Stage one calculations \"\"\"\n \"\"\" ([xa, ya]) and ([xb, yb]) are treated as origin and intermediate point respectively \"\"\"\n\n A1 = x[0] ** 2 + x[1] ** 2\n\n B1 = 2 * ((-C0[0] * x[0]) + (-C0[1] * x[1]))\n\n C1 = C0[0] ** 2 + C0[1] ** 2 - r ** 2\n\n D1 = (B1 ** 2) - (4 * A1 * C1)\n\n v1 = 0\n\n if D1 > 0:\n s11 = (-B1 + D1 ** 0.5) / (2 * A1)\n\n s21 = (-B1 - D1 ** 0.5) / (2 * A1)\n\n if s11 >= s21:\n t1 = s21\n s21 = s11\n s11 = t1\n\n\n if (s11 < 1 and s21 < 1) or (s11 < 0 and s21 < 0):\n\n v1 = (min(s21, 1) - max(s11, 0)) * (A1 ** 0.5)\n\n else:\n\n v1 = 0\n\n\n F1 = (A1 ** 0.5) + (rho * v1 ** 3) # Function for the first stage\n\n\n \"\"\"Second Stage Calculations\"\"\"\n \"\"\" ([xa, ya]) and ([xb, yb]) are treated as intermediate point and the input point respectively \"\"\"\n\n A2 = (P1[0] - x[0]) ** 2 + (P1[1] - x[1]) ** 2\n\n B2 = 2 * ((x[0] - C0[0]) * (P1[0] - x[0]) + (x[1] - C0[1]) * (P1[1] - x[1]))\n\n C2 = (x[0] - C0[0]) ** 2 + (x[1] - C0[1]) ** 2 - r ** 2\n\n D2 = (B2 ** 2) - (4 * A2 * C2)\n\n v2 = 0\n\n if D2 > 0:\n s12 = (-B2 + D2 ** 0.5) / (2 * A2)\n\n s22 = (-B2 - D2 ** 0.5) / (2 * A2)\n\n if s12 >= s22:\n t2 = s22\n s22 = s12\n s12 = t2\n\n\n if (s12 < 1 and s22 < 1) or (s12 < 0 and s22 < 0):\n\n v2 = (min(s22, 1) - max(s12, 0)) * (A2 ** 0.5)\n\n else:\n\n v2 = 0\n\n\n F2 = (A2 ** 0.5) + (rho * v2 ** 3) # Function for the second stage\n\n F = F1 + F2 # Final objective function for intermediate point\n\n return F",
"title": ""
},
{
"docid": "69a3e652d5b49f1c5e335858a3df0695",
"score": "0.51013213",
"text": "def _compute(self):\n # first, find all first order matches (matches depending only on the attr tuples)\n l.info(\"Phase 1: Coarse statistical matching\")\n for lib, lib_lmds in self.lmdb.lib_lmds.items():\n self._compute_first_order_matches(lib, lib_lmds)\n l.info(\"Phase 2: FunctionDiff\")\n for lib in self.lmdb.lib_lmds:\n self._compute_second_order_matches(lib)\n l.info(\"Phase 3: Callee context\")\n self._compute_third_order()\n l.info(\"Phase 4: Caller context\")\n self._compute_fourth_order()\n l.info(\"Phase 5: Cleanup\")\n self._dedup()",
"title": ""
},
{
"docid": "6dc6d023fd32904806d6c2e16a63aea5",
"score": "0.5095904",
"text": "def findvertex_rp(tstart0, tend0, tstart1, tend1,\n tstart0_new, tend0_new, tstart1_new, tend1_new,\n phi0, eta0, q0, pt0, dxy0, dz0, pvx0, pvy0, pvz0,\n phi1, eta1, q1, pt1, dxy1, dz1, pvx1, pvy1, pvz1):\n\n global N\n # determine the length of scanned range in rp space\n range0 = tend0_new-tstart0_new\n range1 = tend1_new-tstart1_new\n \n # sampling running parameters for the two helices\n rp0 = np.linspace(tstart0_new, tend0_new, num=N)#,dtype=np.double)\n rp1 = np.linspace(tstart1_new, tend1_new, num=N)#,dtype=np.double)\n \n #sampling points as (N,3) array\n points0 = np.transpose(helix(rp0, phi0, eta0, q0, pt0,\n dxy0, dz0, pvx0, pvy0, pvz0))\n points1 = np.transpose(helix(rp1, phi1, eta1, q1, pt1,\n dxy1, dz1, pvx1, pvy1, pvz1))\n \n # matrices with sampling points, one with copied rows, one with with copied coloumns\n p0_mat = np.tile(points0,(N,1,1))\n p1_mat = np.transpose(np.tile(points1,(N,1,1)),(1, 0, 2))\n \n ds = np.sum((p0_mat-p1_mat)**2, axis=-1)\n min_of_ds = np.amin(ds)\n #ind_sort_x, ind_sort_y = np.unravel_index(np.argsort(ds.flatten())[:4],ds.shape)\n ind_sort_x_0, ind_sort_y_0 = np.unravel_index(np.argsort(ds.ravel()),\n (N,N),order='F')\n x_no_duplicates = []\n y_no_duplicates = []\n\n for i in ind_sort_x_0:\n if i not in x_no_duplicates:\n x_no_duplicates.append(i)\n \n for i in ind_sort_y_0:\n if i not in y_no_duplicates:\n y_no_duplicates.append(i)\n\n ind_sort_x_1, ind_sort_y_1 = (x_no_duplicates[:points_n],\n y_no_duplicates[:points_n])\n\n poca_min = np.sqrt(ds[ind_sort_x_1[0],ind_sort_y_1[0]])\n \n t0 = rp0[ind_sort_x_0[0]]\n t1 = rp1[ind_sort_y_0[0]]\n\n t0_min_d_cand = rp0[ind_sort_x_1]\n t1_min_d_cand = rp1[ind_sort_y_1]\n\n ''' How to return new bounds: take 10 min distances and take\n min and max of the running parameters of this set\n '''\n\n t0_min = min(t0_min_d_cand)\n t0_max = max(t0_min_d_cand)\n t1_min = min(t1_min_d_cand)\n t1_max = max(t1_min_d_cand)\n\n if t1 > tend1_new or t1 < tstart1_new:\n print(\"Alarm!\")\n\n len0 = abs(t0_max - t0_min)*intervall_frac #range0 * 0.33\n len1 = abs(t1_max - t1_min)*intervall_frac #range1 * 0.33\n \n '''\n len0 = range0 * 0.12\n len1 = range1 * 0.12\n '''\n '''\n print \"Ratio of intervall which contains close points: \",len0 / range0\n print \"Ratio of total intervall which contains close points: \",len0 / (tend0-tstart0)\n '''\n \n #adjust len0 and len1 for pathologic cases\n if len0 == 0 or len0 > range0 * 0.45:\n len0 = range0 * 0.45\n\n if len1 == 0 or len1 > range1 * 0.45:\n len1 = range1 * 0.45\n \n #get new search intervalls \n tstart0_newn = 0.\n tstart1_newn = 0.\n tend0_newn = 0.\n tend1_newn = 0.\n #min sourounded with margin\n '''\n if t0 - len0 > tstart0_new:\n tstart0_newn = t0 - len0\n else:\n tstart0_newn = tstart0_new\n \n if t0 + len0 < tend0_new:\n tend0_newn = t0 + len0\n else:\n tend0_newn = tend0_new\n\n if t1 - len1 > tstart1_new:\n tstart1_newn = t1 - len1\n else:\n tstart1_newn = tstart1_new\n \n if t1 + len1 < tend1_new:\n tend1_newn = t1 + len1\n else:\n tend1_newn = tend1_new\n '''\n #intervall sourounded with margin \n #'''\n if t0_min - len0 > tstart0_new:\n tstart0_newn = t0_min - len0\n else:\n tstart0_newn = tstart0_new\n \n if t0_max + len0 < tend0_new:\n tend0_newn = t0_max + len0\n else:\n tend0_newn = tend0_new\n\n if t1_min - len1 > tstart1_new:\n tstart1_newn = t1_min - len1\n else:\n tstart1_newn = tstart1_new\n \n if t1_max + len1 < tend1_new:\n tend1_newn = t1_max + len1\n else:\n tend1_newn = tend1_new\n #'''\n #Min sourounded with global limits as scope\n '''\n if t0 - len0 > tstart0:\n tstart0_newn = t0 - len0\n else:\n tstart0_newn = tstart0\n \n if t0 + len0 < tend0:\n tend0_newn = t0 + len0\n else:\n tend0_newn = tend0\n\n if t1 - len1 > tstart1:\n tstart1_newn = t1 - len1\n else:\n tstart1_newn = tstart1\n \n if t1 + len1 < tend1:\n tend1_newn = t1 + len1\n else:\n tend1_newn = tend1\n ''' \n\n return t0, t1, tstart0_newn, tend0_newn, tstart1_newn, tend1_newn,t0_min_d_cand,t1_min_d_cand, poca_min",
"title": ""
},
{
"docid": "aba09951bc167657c0199672ce676923",
"score": "0.5093684",
"text": "def test_consistency():\n prior_fisher_params = { 'row_strip' :np.array([3,5,6,7]),\n 'fisher_source' :'data/F_Planck_tau0.01.dat',\n 'n_full' :45,\n 'n_de' :36,\n 'z_step' :0.025\n }\n mat = np.random.rand(45,45)\n mat = np.dot(mat.T,mat)\n priors = np.random.rand(45,45)\n priors = np.dot(priors.T,priors)\n assert np.all(np.linalg.eigh(mat)[0]>=0.)\n assert np.all(np.linalg.eigh(priors)[0]>=0.)\n\n fp1 = PriorFisher('jdem',prior_fisher_params,fisher_in=mat,labels_in=JDEM_LABELS)\n fp2 = PriorFisher('w0wa',prior_fisher_params,fisher_in=mat,labels_in=JDEM_LABELS)\n fp3 = PriorFisher('constant_w',prior_fisher_params,fisher_in=mat,labels_in=JDEM_LABELS)\n res0 = fp3.get_fisher().get_fisher()\n res1 = pf.project_w0wa_to_w0(fp2.get_fisher().get_fisher(),prior_fisher_params,fp2.processed_labels)[0]\n res2 = pf.project_w0(fp1.get_fisher().get_fisher(),prior_fisher_params,fp1.processed_labels)[0]\n res3_int = pf.project_w0wa(fp1.get_fisher().get_fisher(),prior_fisher_params,fp1.processed_labels)[0]\n res3 = pf.project_w0wa_to_w0(res3_int,prior_fisher_params,fp2.processed_labels)[0]\n assert np.allclose(res3_int[0:6,0:6],res3)\n assert np.all(np.linalg.eigh(res0)[0]>=0.)\n assert np.all(np.linalg.eigh(res1)[0]>=0.)\n assert np.all(np.linalg.eigh(res2)[0]>=0.)\n assert np.all(np.linalg.eigh(res3_int)[0]>=0.)\n assert np.all(np.linalg.eigh(res3)[0]>=0.)\n assert np.allclose(res0,res1)\n assert np.allclose(res0,res2)\n assert np.allclose(res0,res3)\n assert np.allclose(fp2.get_fisher().get_fisher(),res3_int)\n assert np.isclose(np.linalg.det(res0),np.linalg.det(res1))\n assert np.isclose(np.linalg.det(res0),np.linalg.det(res2))\n assert np.isclose(np.linalg.det(res0),np.linalg.det(res3))\n priors = priors\n fp1p = PriorFisher('jdem',prior_fisher_params,fisher_in=priors,labels_in=JDEM_LABELS)\n fp2p = PriorFisher('w0wa',prior_fisher_params,fisher_in=priors,labels_in=JDEM_LABELS)\n fp3p = PriorFisher('constant_w',prior_fisher_params,fisher_in=priors,labels_in=JDEM_LABELS)\n res0p = fp3p.get_fisher().get_fisher()\n res1p = pf.project_w0wa_to_w0(fp2p.get_fisher().get_fisher(),prior_fisher_params,fp2p.processed_labels)[0]\n res2p = pf.project_w0(fp1p.get_fisher().get_fisher(),prior_fisher_params,fp1p.processed_labels)[0]\n res3_intp = pf.project_w0wa(fp1p.get_fisher().get_fisher(),prior_fisher_params,fp1p.processed_labels)[0]\n res3p = pf.project_w0wa_to_w0(res3_intp,prior_fisher_params,fp2p.processed_labels)[0]\n assert np.allclose(res3_intp[0:6,0:6],res3p)\n assert np.all(np.linalg.eigh(res0p)[0]>=0.)\n assert np.all(np.linalg.eigh(res1p)[0]>=0.)\n assert np.all(np.linalg.eigh(res2p)[0]>=0.)\n assert np.all(np.linalg.eigh(res3_intp)[0]>=0.)\n assert np.all(np.linalg.eigh(res3p)[0]>=0.)\n assert np.allclose(res0p,res1p)\n assert np.allclose(res0p,res2p)\n assert np.allclose(res0p,res3p)\n assert np.allclose(fp2p.get_fisher().get_fisher(),res3_intp)\n assert np.isclose(np.linalg.det(res0p),np.linalg.det(res1p))\n assert np.isclose(np.linalg.det(res0p),np.linalg.det(res2p))\n assert np.isclose(np.linalg.det(res0p),np.linalg.det(res3p))\n\n\n alt_prior_fisher_params = { 'row_strip' :np.array([]),\n 'fisher_source' :'data/F_Planck_tau0.01.dat',\n 'n_full' :41,\n 'n_de' :36,\n 'z_step' :0.025\n }\n prior_mat = priors+mat\n fp1c = PriorFisher('jdem',prior_fisher_params,fisher_in=prior_mat,labels_in=JDEM_LABELS)\n fp2c = PriorFisher('w0wa',prior_fisher_params,fisher_in=prior_mat,labels_in=JDEM_LABELS)\n fp3c = PriorFisher('constant_w',prior_fisher_params,fisher_in=prior_mat,labels_in=JDEM_LABELS)\n fp1c2 = PriorFisher('jdem',alt_prior_fisher_params,fisher_in=fp1p.get_fisher().get_fisher()+fp1.get_fisher().get_fisher(),labels_in=fp1.processed_labels)\n fp2c2 = PriorFisher('w0wa',alt_prior_fisher_params,fisher_in=fp1p.get_fisher().get_fisher()+fp1.get_fisher().get_fisher(),labels_in=fp1.processed_labels)\n fp3c2 = PriorFisher('constant_w',alt_prior_fisher_params,fisher_in=fp1p.get_fisher().get_fisher()+fp1.get_fisher().get_fisher(),labels_in=fp1.processed_labels)\n sum_in1 = fp1p.get_fisher().get_fisher()+fp1.get_fisher().get_fisher()\n sum_in2 = fp2p.get_fisher().get_fisher()+fp2.get_fisher().get_fisher()\n sum_in3 = fp3p.get_fisher().get_fisher()+fp3.get_fisher().get_fisher()\n res4 = fp1c.get_fisher().get_fisher()\n res5 = fp2c.get_fisher().get_fisher()\n res6 = fp3c.get_fisher().get_fisher()\n res7 = pf.project_w0wa_to_w0(fp2c.get_fisher().get_fisher(),alt_prior_fisher_params,fp2c.processed_labels)[0]\n res8 = pf.project_w0wa(fp1c.get_fisher().get_fisher(),alt_prior_fisher_params,fp3c.processed_labels)[0]\n res9 = pf.project_w0wa_to_w0(res8,alt_prior_fisher_params,fp2c.processed_labels)[0]\n res10 = fp1c2.get_fisher().get_fisher()\n res11 = fp2c2.get_fisher().get_fisher()\n res12 = fp3c2.get_fisher().get_fisher()\n assert np.allclose(res8[0:6,0:6],res9)\n assert np.all(np.linalg.eigh(res4)[0]>=0.)\n assert np.all(np.linalg.eigh(res5)[0]>=0.)\n assert np.all(np.linalg.eigh(res6)[0]>=0.)\n assert np.all(np.linalg.eigh(res7)[0]>=0.)\n assert np.all(np.linalg.eigh(res8)[0]>=0.)\n assert np.all(np.linalg.eigh(res9)[0]>=0.)\n assert np.all(np.linalg.eigh(res10)[0]>=0.)\n assert np.all(np.linalg.eigh(res11)[0]>=0.)\n assert np.all(np.linalg.eigh(res12)[0]>=0.)\n assert np.allclose(res4,sum_in1)\n assert np.allclose(res5,sum_in2)\n assert np.allclose(res6,sum_in3)\n assert np.allclose(res6,res7)\n assert np.allclose(res5,res8)\n assert np.allclose(res6,res9)\n assert np.allclose(res4,res10)\n assert np.allclose(res5,res11)\n assert np.allclose(res6,res12)\n\n #interlace tests\n eig2 = np.linalg.eigh(res2)[0]\n eig3 = np.linalg.eigh(res3)[0]\n eig_diff = (eig2[::-1][1:eig3.size]-eig3[::-1][0:eig3.size-1])\n eig2p = np.linalg.eigh(res2p)[0]\n eig3p = np.linalg.eigh(res3p)[0]\n eig_diffp = (eig2p[::-1][1:eig3p.size]-eig3p[::-1][0:eig3p.size-1])\n assert np.all(eig_diffp<=0.)\n eig2c = np.linalg.eigh(res5)[0]\n eig3c = np.linalg.eigh(res6)[0]\n eig_diffc = (eig2c[::-1][1:eig3c.size]-eig3c[::-1][0:eig3c.size-1])\n assert np.all(eig_diffc<=0.)",
"title": ""
},
{
"docid": "223db33425e0a7293ab9a606bd7ba8fa",
"score": "0.5092296",
"text": "def eq02():",
"title": ""
},
{
"docid": "96b3461b00e68c1acf13075f11745038",
"score": "0.50801736",
"text": "def Exp1(A,m,r,k):\n\n B = int(A*r) # number of patients satisfying certain criteria\n expectation = Decimal(0)\n alpha = 1 - 1 / (2 * m)\n # Restrit an interval for single bucket size (|A1| in formula) with probability greater than 1-alpha\n rv_a = binom(A, 1/m)\n (lb_a, ub_a) = rv_a.interval(alpha)\n rv_b = hypergeom(A, int(lb_a), B)\n (lb_b, ub_b) = rv_b.interval(alpha)\n # Rule out the case that there is no collision\n if lb_b == 0 or lb_a == 0:\n for a in range(int(lb_a), int(ub_a)+1):\n if a > k:\n # Find lowerbound and upperbound for B1\n rv_b = hypergeom(A, a, B)\n (lb_b, ub_b) = rv_b.interval(alpha)\n # Rule out the case that there is no collision\n lb_b = max(1, lb_b)\n # Compute P(|e| < k | |A1|)\n p = P(lb_b, ub_b, k, rv_b, a)\n #Compute Expectation\n expectation = expectation + p*Decimal(rv_a.pmf(a))\n else:\n rv_b = hypergeom(A, a, B)\n expectation = expectation + Decimal(rv_a.pmf(a))*(1-Decimal(rv_b.pmf(0)))\n else:\n for a in range(int(lb_a), int(ub_a)+1):\n # when |A1| < k, P(|e| <= k | A1,B1) = 0\n if a > k:\n # Restrit an interval for B1 with probability greater than 0.99995\n rv_b = hypergeom(A, a, B)\n (lb_b, ub_b) = rv_b.interval(0.99995)\n # Compute P(|e|<=k | |A1|)\n p = P(lb_b, ub_b, k, rv_b, a)\n #Compute Expectation\n expectation = expectation + p*Decimal(rv_a.pmf(a))\n else:\n expectation = expectation + Decimal(rv_a.pmf(a))\n return round(expectation*m, 5)",
"title": ""
},
{
"docid": "b1b8f6d7e9104309d19e2e7b9ba5954f",
"score": "0.50781476",
"text": "def __rtruediv__(self, other):\n \n pass",
"title": ""
},
{
"docid": "8650ac8c92b5644c216f0bea91515fb4",
"score": "0.5076354",
"text": "def coupure(x,y):\n n = len(x)\n m = len(y)\n iStar = abs(n) // 2\n\n D = [ [ 0 for j in range(len(y)+1)] for i in range(2)]\n I = [ [ 0 for j in range(len(y)+1)] for i in range(2)]\n\n for j in range(1,m+1):\n D[0][j] = c_ins*j\n I[0][j] = j #initialisation 0 1 2 3 4 5\n\n for i in range(1,n+1):\n D[1][0] = c_del*i\n I[1][0] = 0\n for j in range(1,m+1):\n D[1][j] = min(D[0][j] + c_ins, D[1][j-1] + c_del, D[0][j-1] + c_sub(x[i-1],y[j-1]))\n if(i>iStar):\n\n if(D[1][j] == D[1][j-1] + c_del):\n # print(\"element {} {} {}\".format(D[1][j],D[1][j-1],I[0][j-1]))\n I[1][j] = I[1][j-1]\n elif(D[1][j] == D[0][j] + c_ins):\n # print(\"element {} {} {}\".format(D[1][j],D[0][j],I[0][j]))\n I[1][j] = I[0][j]\n elif(D[1][j] == D[0][j-1] + c_sub(x[i-1],y[j-1])):\n # print(\"element {} {} {}\".format(D[1][j],D[0][j-1],I[0][j-1]))\n I[1][j] = I[0][j-1]\n\n\n if(i>iStar and i!=n):\n I[0], I[1] = I[1], I[0]\n\n if(i!=n):\n\n D[0], D[1] = D[1], D[0]\n # print(I[1])\n\n return I[1][-1]",
"title": ""
},
{
"docid": "4540741d0599128a68765629e922c4b6",
"score": "0.50758624",
"text": "def algorithm(values, replace1, replace2):\n values[1], values[2] = replace1, replace2\n pos = 0\n while True:\n op = values[pos]\n if op == 99:\n return values[0]\n inputs = values[values[pos + 1]], values[values[pos + 2]]\n values[values[pos + 3]] = (inputs[0] * inputs[1]) if op == 2 else (inputs[0] + inputs[1])\n pos += 4",
"title": ""
},
{
"docid": "12b08b3b1099c0e00573fb306986571f",
"score": "0.5073931",
"text": "def computeProbaCCouples(strat_1, strat_2, nb_states, rounds):\n init_weight = 1\n proba_c1 = 0\n proba_c2 = 0\n if hasOnlyOneAction(strat_1):\n proba_c1 = strat_1[2]\n if hasOnlyOneAction(strat_2):\n proba_c2 = strat_2[2]\n else:\n # Build tree knowing that proba_c1 is fixed\n for state in range(nb_states):\n if hasOnlyOneDirection(strat_2):\n direction = strat_2[0]\n if direction == 1 and state == 0: # Left\n proba_c2 += strat_2[2 + state]\n elif direction == 0 and state == nb_states - 1: # Right\n proba_c2 += strat_2[2 + state]\n else:\n root_2 = Tree(state, strat_2, init_weight, nb_states - 1)\n cur_prob_c2 = buildTreeOptimised(proba_c1, root_2, rounds)\n proba_c2 += cur_prob_c2\n else:\n root_2 = Tree(state, strat_2, init_weight, nb_states - 1)\n cur_prob_c2 = buildTreeOptimised(proba_c1, root_2, rounds)\n proba_c2 += cur_prob_c2\n\n proba_c2 /= nb_states\n else: # Strat_1 has different possible actions\n if hasOnlyOneAction(strat_2):\n proba_c2 = strat_2[2]\n # Build tree knowing that proba_c2 is fixed\n for state in range(nb_states):\n if hasOnlyOneDirection(strat_1):\n direction = strat_1[0]\n if direction == 1 and state == 0: # Left\n proba_c1 += strat_1[2 + state]\n elif direction == 0 and state == nb_states - 1:\n proba_c1 += strat_1[2 + state]\n else:\n root_1 = Tree(state, strat_1, init_weight, nb_states - 1)\n cur_prob_c1 = buildTreeOptimised(proba_c2, root_1, rounds)\n proba_c1 += cur_prob_c1\n else:\n root_1 = Tree(state, strat_1, init_weight, nb_states - 1)\n cur_prob_c1 = buildTreeOptimised(proba_c2, root_1, rounds)\n proba_c1 += cur_prob_c1\n\n proba_c1 /= nb_states\n\n else: # Have to build two decision tree without having any fixed probability of cooperation\n for state_1 in range(nb_states):\n for state_2 in range(nb_states):\n root_1 = Tree(state_1, strat_1, init_weight, nb_states - 1)\n root_2 = Tree(state_2, strat_2, init_weight, nb_states - 1)\n cur_prob_c1, cur_prob_c2 = buildTrees(root_1, root_2, rounds)\n proba_c1 += cur_prob_c1\n proba_c2 += cur_prob_c2\n proba_c1 /= np.power(nb_states, 2)\n proba_c2 /= np.power(nb_states, 2)\n\n return proba_c1, proba_c2",
"title": ""
},
{
"docid": "ca783ff53c6a73d7cedffe19b13b47bd",
"score": "0.50730383",
"text": "def test1(self):\r\n root_2 = math.sqrt(2)\r\n x1 = ureal(0,2)\r\n x2 = ureal(0,6)\r\n\r\n y = fn.mul2(x1,x2)\r\n\r\n self.assertTrue( equivalent(y.x,0) )\r\n\r\n uc = x1.u * x2.u\r\n self.assertTrue( equivalent(y.u,uc) )\r\n\r\n self.assertTrue(\r\n equivalent( component(y,x1), uc/root_2 )\r\n )\r\n\r\n x3 = ureal(0,4)\r\n uc *= x3.u\r\n y = fn.mul2(y,x3)\r\n\r\n self.assertTrue( equivalent(y.u,uc) )\r\n\r\n self.assertTrue(\r\n equivalent( component(y,x1), uc/root_2**2 )\r\n )\r\n self.assertTrue(\r\n equivalent( component(y,x3), uc/root_2 )\r\n )\r\n\r\n # a different order should not affect uc\r\n y = fn.mul2(x1,fn.mul2(x2,x3))\r\n self.assertTrue( equivalent(y.u,uc) )",
"title": ""
},
{
"docid": "518e78e45a0f94046a4f5ee960f857e1",
"score": "0.50729614",
"text": "def solve(board1, pents):\n\n\n board = 1 - board1\n # print(board)\n # print(pents[0])\n # print(pent_id(pents[0]))\n # print(pents[0][0][0])\n # return\n all_pents = []\n temp = cp.deepcopy(pents)\n # print(get_pent_from_hashed(pent_hash(pents[1], 2, 2, board), board))\n # return\n for i in range(4):\n for p in pents:\n all_pents.append(np.flipud(np.rot90(p, i)))\n all_pents.append(np.rot90(p, i))\n # print(len(all_pents))\n # for i in all_pents:\n # if (get_pent_idx(i) == 2):\n # print(i)\n repeat = []\n for i in range(len(all_pents)):\n for j in range(len(all_pents)):\n if i < j and np.array_equal(all_pents[i], all_pents[j]):\n repeat.append(j)\n repeat = list(set(repeat))\n repeat.sort()\n repeat.reverse()\n for i in repeat:\n all_pents.pop(i)\n for i in all_pents:\n pent_id(i)\n y = get_all_subsets(board, all_pents, pents)\n x = range(len(pents) + len(board) * len(board[0]))\n x2 = {j: set(filter(lambda i: j in y[i], y)) for j in x}\n # print(len(y))\n # print(len(x))\n solutions = solve_x(x2, y)\n # print(sol)\n # for i in sol:\n # print(get_pent_from_hashed(i, board))\n # print(len(all_pents))\n # for j in range(1, 13):\n # for i in all_pents:\n # if (get_pent_idx(i) == j):\n # print(i)\n\n # print(sum)\n for i in solutions:\n sol = i\n break\n # print(sol)\n res = [get_pent_from_hashed(r, board) for r in sol]\n b = cp.deepcopy(board)\n\n for i in res:\n add_pentomino(b, i[0], i[1])\n # print(b)\n return res",
"title": ""
},
{
"docid": "61b3e7c234cef2a85297cf7ea2d46b0b",
"score": "0.50680363",
"text": "def ransac(x,y,funcFindF,funcDist,minPtNum,iterNum,thDist,thInlrRatio):\n\n ptNum = len(x)\n thInlr = round(thInlrRatio*ptNum)\n\n\n inlrNum = np.zeros([iterNum,1])\n fLib= np.zeros(shape=(iterNum,3,3))\n for i in range(iterNum):\n permut = np.random.permutation(ptNum)\n sampleIdx = permut[range(minPtNum)]\n f1 = funcFindF(x[sampleIdx,:],y[sampleIdx,:])\n dist = funcDist(x,y,f1)\n b = dist<=thDist\n r = np.array(range(len(b)))\n inlier1 = r[b]\n inlrNum[i] = len(inlier1)\n if len(inlier1) < thInlr: continue\n fLib[i] = funcFindF(x[inlier1,:],y[inlier1,:])\n\n idx = inlrNum.tolist().index(max(inlrNum))\n f = fLib[idx]\n dist = funcDist(x,y,f);\n b = dist<=thDist\n r = np.array(range(len(b)))\n inlierIdx = r[b]\n return f, inlierIdx",
"title": ""
},
{
"docid": "f79c684c579ae0164feae756673e5ca9",
"score": "0.506603",
"text": "def session_00013_line_249():",
"title": ""
},
{
"docid": "1da2c1a2c783069b1ca3d28db4027496",
"score": "0.5056336",
"text": "def problem76():\n from time import clock\n partitions = {}\n def p(n):\n if n < 0: return 0\n if n == 0: return 1\n if n not in partitions:\n partitions[n] = sum([(-1)**(k+1) * (p(n - (k * (3 * k - 1)/2))\n + p(n - (k * (3 * k + 1) / 2))) for k in xrange(1, n+1)])\n return partitions[n]\n\n def main():\n t0 = clock()\n print p(100) - 1\n print clock() - t0\n\n if __name__ == \"__main__\":\n main()",
"title": ""
},
{
"docid": "27929fd0bae9112084fc9d03b91d7eae",
"score": "0.5055975",
"text": "def compute_matching(x, y):\n\n pass",
"title": ""
},
{
"docid": "24995310ea8ee997700e8ed101f1e7ab",
"score": "0.50551796",
"text": "def test_big_better_algorithm():\n pass",
"title": ""
},
{
"docid": "6ead608373e997a811506ea417fbd8b3",
"score": "0.50513583",
"text": "def foiling(b_one: List[str], b_two: List[str], var_type: str) -> List[str]:\n #print(b_one, b_two, var_type)\n b_one_string = stringify(b_one)\n b_one_obj = Algebra(b_one_string+\"=0\")\n\n b_two_string = stringify(b_two)\n b_two_obj = Algebra(b_two_string+\"=0\")\n\n # Create array with only coefficients\n b_one_obj.get_coeff()\n b_two_obj.get_coeff()\n b_one_coeff = copy.deepcopy(b_one_obj.coeff)\n b_two_coeff = copy.deepcopy(b_two_obj.coeff)\n b_one_obj, b_two_obj = None, None\n # print(\"b_one_coeff = \", b_one_coeff.eqn, \"b_two_coeff = \", b_two_coeff.eqn)\n\n ans_coeff = []\n ans_powers = []\n b_one_pow = len(b_one_coeff) - 1\n b_two_pow = len(b_two_coeff) - 1\n\n # Foiling\n for i in range(0, len(b_one_coeff)):\n\n for j in range(0, len(b_two_coeff)):\n ans_coeff.append(b_one_coeff[i] * b_two_coeff[j])\n ans_powers.append((b_one_pow - i) + (b_two_pow - j))\n\n # print(\"ans_coeff = \", ans_coeff)\n # Simplifying the answer\n simp_ans_coeff = []\n temp_ind = []\n temp = 0\n for i in range(ans_powers[0], -1, -1):\n\n for j in range(0, len(ans_powers)):\n\n if ans_powers[j] == i:\n temp += ans_coeff[j]\n\n simp_ans_coeff.append(temp)\n temp = 0\n\n # print(\"simp_ans_coeff = \", simp_ans_coeff)\n ans = []\n j = 0\n highest_deg_ans = len(simp_ans_coeff) - 1\n\n if highest_deg_ans > 1:\n\n for i in range(highest_deg_ans, 1, -1):\n\n if not ans:\n\n if simp_ans_coeff[j].imag == 0:\n\n if simp_ans_coeff[j] == 1:\n\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n elif simp_ans_coeff[j] == 0:\n\n pass\n j += 1\n\n elif simp_ans_coeff[j] < 0:\n\n if simp_ans_coeff[j] == -1:\n\n ans.append(\"-\" + var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n else:\n\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n else:\n\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n # simp_coeff_ans[j] is a complex number\n else:\n\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n else:\n\n if simp_ans_coeff[j].imag == 0:\n\n if simp_ans_coeff[j] == 1:\n\n ans.append(\"+\")\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n elif simp_ans_coeff[j] == 0:\n\n pass\n j += 1\n\n elif simp_ans_coeff[j] < 0:\n\n if simp_ans_coeff[j] == -1:\n\n ans.append(\"-\")\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n else:\n\n ans.append(\"-\")\n ans.append(str(abs(simp_ans_coeff[j])))\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n # simp_coeff_ans[j] is a complex number\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n ans.append(\"^\")\n ans.append(str(float(i)))\n j += 1\n\n if simp_ans_coeff[j].imag == 0:\n\n if simp_ans_coeff[j] == 1:\n\n ans.append(\"+\")\n ans.append(var_type)\n j += 1\n\n elif simp_ans_coeff[j] == 0:\n\n pass\n j += 1\n\n elif simp_ans_coeff[j] < 0:\n\n if simp_ans_coeff[j] == -1:\n\n ans.append(\"-\")\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(\"-\")\n ans.append(str(abs(simp_ans_coeff[j])))\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n j += 1\n\n if simp_ans_coeff[j].imag == 0:\n\n if simp_ans_coeff[j] == 0:\n\n pass\n\n else:\n\n if simp_ans_coeff[j] < 0:\n\n ans.append(\"-\")\n ans.append(str(abs(simp_ans_coeff[j])))\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n\n else:\n\n if not ans:\n\n if simp_ans_coeff[j].imag == 0:\n\n if simp_ans_coeff[j] == 1:\n\n ans.append(var_type)\n j += 1\n\n elif simp_ans_coeff[j] == 0:\n\n pass\n j += 1\n\n elif simp_ans_coeff[j] < 0:\n\n if simp_ans_coeff[j] == -1:\n\n ans.append(\"-\")\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(\"-\")\n ans.append(str(abs(simp_ans_coeff[j])))\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n j += 1\n\n else:\n\n if simp_ans_coeff[j].imag == 0:\n\n if simp_ans_coeff[j] == 1:\n\n ans.append(\"+\")\n ans.append(var_type)\n j += 1\n\n elif simp_ans_coeff[j] == 0:\n\n pass\n j += 1\n\n elif simp_ans_coeff[j] < 0:\n\n if simp_ans_coeff[j] == -1:\n\n ans.append(\"-\")\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(\"-\")\n ans.append(str(abs(simp_ans_coeff[j])))\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n j += 1\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n ans.append(var_type)\n j += 1\n\n if simp_ans_coeff[j].imag == 0:\n\n if simp_ans_coeff[j] == 0:\n\n pass\n\n else:\n\n if simp_ans_coeff[j] < 0:\n\n ans.append(\"-\")\n ans.append(str(abs(simp_ans_coeff[j])))\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n\n else:\n\n ans.append(\"+\")\n ans.append(str(simp_ans_coeff[j]))\n\n return ans",
"title": ""
},
{
"docid": "f6038987bef58ff3fa661afc41ddce01",
"score": "0.50459707",
"text": "def clause_b_5_1_2_T_z_1():\n\n pass",
"title": ""
},
{
"docid": "82be65ce20341777894c01f529d32afd",
"score": "0.5044495",
"text": "def problem2(self, s):\n\n points = 0\n # Test problem 2 here.\n return points",
"title": ""
},
{
"docid": "b73b09889263ec4104e2f4fc4c4b74ba",
"score": "0.5042088",
"text": "def calculate_rbo(recommendations1, recommendations2, p=0.98):\n if recommendations1 == None: recommendations1 = []\n if recommendations2 == None: recommendations2 = []\n\n sl, ll = sorted([(len(recommendations1), recommendations1), (len(recommendations2), recommendations2)])\n s, S = sl\n l, L = ll\n if s == 0: return 0\n\n # Calculate the overlaps at ranks 1 through l\n # (the longer of the two lists)\n ss = set([]) # contains elements from the smaller list till depth i\n ls = set([]) # contains elements from the longer list till depth i\n x_d = {0: 0}\n sum1 = 0.0\n for i in range(l):\n x = L[i]\n y = S[i] if i < s else None\n d = i + 1\n\n # if two elements are same then\n # we don't need to add to either of the set\n if x == y:\n x_d[d] = x_d[d - 1] + 1.0\n # else add items to respective list\n # and calculate overlap\n else:\n ls.add(x)\n if y != None: ss.add(y)\n x_d[d] = x_d[d - 1] + (1.0 if x in ss else 0.0) + (1.0 if y in ls else 0.0)\n # calculate average overlap\n sum1 += x_d[d] / d * pow(p, d)\n\n sum2 = 0.0\n for i in range(l - s):\n d = s + i + 1\n sum2 += x_d[d] * (d - s) / (d * s) * pow(p, d)\n\n sum3 = ((x_d[l] - x_d[s]) / l + x_d[s] / s) * pow(p, l)\n\n # Equation 32\n rbo_ext = (1 - p) / p * (sum1 + sum2) + sum3\n return rbo_ext",
"title": ""
},
{
"docid": "01c5996683001e47ba10a2f351370ae5",
"score": "0.5042032",
"text": "def NSGA2(function1, function2, max_gen, pop_size, dim, pf_target):\n\n gen_no = 0\n igd_values = []\n\n n_obj = 2\n pf_ref = copy.deepcopy(pf_target)\n pf_ref_len = len(pf_ref)\n max_obj = [0] * n_obj\n min_obj = [0] * n_obj\n for i in range(n_obj):\n pf_ref = sorted(pf_ref, key=lambda obj: obj[i])\n max_obj[i] = pf_ref[pf_ref_len - 1][i]\n min_obj[i] = pf_ref[0][i]\n for i in range(pf_ref_len):\n for j in range(n_obj):\n pf_ref[i][j] = (pf_ref[i][j] - min_obj[j]) / (max_obj[j] - min_obj[j])\n pf_ref_f1 = []\n pf_ref_f2 = []\n for i in range(pf_ref_len):\n pf_ref_f1.append(pf_ref[i][0])\n pf_ref_f2.append(pf_ref[i][1])\n\n # Initialize search population\n solution = [[random.random() for _ in range(dim)] for _ in range(0, pop_size)]\n function1_values = [function1(solution[i]) for i in range(0, pop_size)]\n function2_values = [function2(solution[i]) for i in range(0, pop_size)]\n\n while (gen_no < max_gen):\n non_dominated_sorted_solution = fast_non_dominated_sort(function1_values[:], function2_values[:])\n print(\"NSGA-II Output for Generation \", gen_no, \" :\")\n parent_front_f11 = []\n parent_front_f22 = []\n non_dominated_sorted_solution[0].sort()\n for index in non_dominated_sorted_solution[0]:\n parent_front_f11.append(function1_values[index])\n parent_front_f22.append(function2_values[index])\n\n # Compute IGD values\n parent_front_f1 = []\n parent_front_f2 = []\n for i in range(len(parent_front_f11)):\n parent_front_f1.append((parent_front_f11[i] - min_obj[0]) / (max_obj[0] - min_obj[0]))\n parent_front_f2.append((parent_front_f22[i] - min_obj[1]) / (max_obj[1] - min_obj[1]))\n sum_dist = 0\n for i in range(pf_ref_len):\n min_dist = math.inf\n for j in range(len(parent_front_f1)):\n dist2 = pow(parent_front_f1[j] - pf_ref_f1[i], 2.0) + pow(parent_front_f2[j] - pf_ref_f2[i], 2.0)\n dist = math.sqrt(dist2)\n if dist < min_dist:\n min_dist = dist\n sum_dist += min_dist\n igd = sum_dist / pf_ref_len\n igd_values.append(igd)\n print('IGD = ', igd)\n\n # Generating offsprings\n solution2 = solution[:]\n while (len(solution2) < 2 * pop_size):\n a1 = random.randint(0, pop_size - 1)\n a2 = random.randint(0, pop_size - 1)\n a = binary_tournament(a1, a2, function1_values[:], function2_values[:])\n b1 = random.randint(0, pop_size - 1)\n b2 = random.randint(0, pop_size - 1)\n b = binary_tournament(b1, b2, function1_values[:], function2_values[:])\n c1, c2 = SBX_crossover(solution[a], solution[b])\n c1_mutated, c2_mutated = polynomial_mutation(c1, c2)\n solution2.append(c1_mutated)\n solution2.append(c2_mutated)\n function1_values2 = function1_values[:]\n function2_values2 = function2_values[:]\n for i in range(pop_size, 2 * pop_size):\n function1_values2.append(function1(solution2[i]))\n function2_values2.append(function2(solution2[i]))\n non_dominated_sorted_solution2 = fast_non_dominated_sort(function1_values2[:], function2_values2[:])\n crowding_distance_values2 = []\n for i in range(0, len(non_dominated_sorted_solution2)):\n crowding_distance_values2.append(crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))\n\n # Environmental selection\n new_solution = []\n function1_values = []\n function2_values =[]\n for i in range(0, len(non_dominated_sorted_solution2)):\n non_dominated_sorted_solution2[i].sort()\n front = sort_distance(non_dominated_sorted_solution2[i], crowding_distance_values2[i])\n front.reverse()\n for index in front:\n new_solution.append(solution2[index])\n function1_values.append(function1_values2[index])\n function2_values.append(function2_values2[index])\n if (len(new_solution) == pop_size):\n break\n if (len(new_solution) == pop_size):\n break\n\n solution = new_solution[:]\n gen_no = gen_no + 1\n print(\"\\n\")\n\n return igd_values",
"title": ""
},
{
"docid": "a5ec347c651217b0499eafc78060a0b3",
"score": "0.5037284",
"text": "def _postsolve(self):",
"title": ""
},
{
"docid": "94e389dcd2cca8e465f31514884aa7b8",
"score": "0.5032518",
"text": "def cycle(c):\n \n \"\"\"\n -1 Initial input parameters\n -2 Mutation\n -3 child a\n -4 child b\n -5 best\n \"\"\"\n time_start = time.time()\n \n# Counter\n pf.this_cycle = pf.this_cycle + 1\n pf.this_gen = 0\n \n# Start Cycle (calculate width,\n pf.start_cycle()\n \n# SAVE Starting Parameters\n a = 0\n for fn in range(len(g.pot_functions['functions'])): \n if(g.pot_functions['functions'][fn]['fit_type'] == 1): # TABULATED \n b = a + g.pot_functions['functions'][fn]['fit_size'] \n pf.ps[-1,a:b] = numpy.zeros((g.pot_functions['functions'][fn]['fit_size'],),)\n a = b\n elif(g.pot_functions['functions'][fn]['fit_type'] == 2): # ANALYTIC \n b = a + g.pot_functions['functions'][fn]['fit_size'] \n pf.ps[-1,a:b] = g.pot_functions['functions'][fn]['a_params'][:]\n a = b\n\n# Update and calculate starting rss\n pf.update_potential(pf.ps[-1,:])\n pf.rss[-1] = pf.get_rss() \n \n# Try within the range provided\n \n# Create initial population - First half\n pf.stage = 'Initialising population - First Half'\n for p in range(pf.pop_size):\n if(p == 0):\n pf.ps[p,:] = pf.ps[-1,:]\n else:\n pf.ps[p,:] = pf.ps[-1,:]\n \n# Calculate and save RSS\n pf.ps[p,:] = pf.random_p(0.0, 1.0)\n pf.update_potential(pf.ps[p,:])\n pf.rss[p] = pf.get_rss() \n pf.check_improvement(pf.ps[p,:], pf.rss[p])\n\n# Look in a wider range\n\n# Create initial population - Second half\n pf.stage = 'Initialising population - Second Half'\n m = 0.5\n m_inc = (10.0-0.5) / (pf.pop_size - 1)\n for p in range(pf.pop_size, pf.pop_size_d):\n loop = True\n while(loop): \n pf.ps[p,:] = pf.random_p(0.0, m)\n pf.update_potential(pf.ps[p,:])\n pf.rss[p] = pf.get_rss() \n if(not numpy.isnan(pf.rss[p])):\n loop = False\n pf.check_improvement(pf.ps[p,:], pf.rss[p])\n m = m + m_inc\n\n###################################\n# LOOP THROUGH GENERATIONS\n###################################\n \n gens = pf.generations\n if(c > 1): \n gens = pf.spline_generations\n pf.since_improvement = 0\n for gen in range(gens):\n pf.this_gen = gen + 1\n \n parents = numpy.arange(pf.pop_size_d)\n numpy.random.shuffle(parents)\n\n pf.stage = 'Looping population'\n \n#######################\n# Parents + Parents\n#######################\n \n ca = 0\n# Loop through population\n for p in range(pf.pop_size): \n pa = parents[p]\n pb = parents[p + pf.pop_size]\n \n# Breed\n pf.breed(pa, pb, ca, ca + 1)\n \n# Run calculations\n pf.update_potential(pf.p_children[ca, :]) \n pf.rss_children[ca] = pf.get_rss() \n pf.check_improvement(pf.p_children[ca,:], pf.rss_children[ca]) \n \n pf.update_potential(pf.p_children[ca+1, :]) \n pf.rss_children[ca+1] = pf.get_rss() \n pf.check_improvement(pf.p_children[ca+1,:], pf.rss_children[ca+1])\n \n# Increment\n ca = ca + 2\n \n# MAKE FRESH PARAMETERS\n m = 0.5\n m_inc = (10.0-0.5) / (pf.fresh_size_d - 1)\n for p_fresh in range(pf.pop_size_d, pf.pop_size_d + pf.fresh_size_d): \n pf.ps[p_fresh,:] = pf.random_p(pf.ps[-5,:], m)\n m = m + m_inc\n \n#######################\n# Parents + Fresh\n#######################\n \n# Pick parents\n parents = numpy.arange(pf.pop_size_d)\n numpy.random.shuffle(parents)\n \n pf.stage = 'Looping population + fresh pool'\n# Breed random parents with fresh pool\n pa_n = 0\n for p_fresh in range(pf.pop_size_d, pf.pop_size_d + pf.fresh_size_d):\n pa = parents[pa_n]\n \n# Breed\n pf.breed(pa, p_fresh, ca, ca + 1)\n \n# Run calculations\n pf.update_potential(pf.p_children[ca, :]) \n pf.rss_children[ca] = pf.get_rss() \n pf.check_improvement(pf.p_children[ca, :], pf.rss_children[ca]) \n \n pf.update_potential(pf.p_children[ca+1, :]) \n pf.rss_children[ca + 1] = pf.get_rss() \n pf.check_improvement(pf.p_children[ca+1, :], pf.rss_children[ca+1])\n \n# Increment\n ca = ca + 2\n \n#######################\n# Select Best\n#######################\n \n pf.rss_list[:pf.pop_size_d] = pf.rss[:pf.pop_size_d]\n pf.rss_list[pf.pop_size_d:] = pf.rss_children[:]\n\n rss_sorted = sort.sort_1d_dp_asc(pf.rss_list)\n rt = rss_sorted[pf.pop_size_d-1]\n\n# SELECT BEST FROM PARENTS AND CHILDREN FOR NEXT GEN\n n = 0\n for i in range(pf.pop_size_d):\n if(pf.rss[i] < rt):\n pf.p_temp[n,:] = pf.ps[i,:]\n pf.rss_temp[n] = pf.rss[i]\n n = n + 1 \n for i in range(len(pf.rss_children)):\n if(pf.rss_children[i] < rt):\n pf.p_temp[n,:] = pf.p_children[i,:]\n pf.rss_temp[n] = pf.rss_children[i]\n n = n + 1\n for i in range(pf.pop_size_d):\n if(pf.rss[i] == rt and n < pf.pop_size_d):\n pf.p_temp[n,:] = pf.ps[i,:]\n pf.rss_temp[n] = pf.rss[i]\n n = n + 1 \n for i in range(len(pf.rss_children)):\n if(pf.rss_children[i] == rt and n < pf.pop_size_d):\n pf.p_temp[n,:] = pf.p_children[i,:]\n pf.rss_temp[n] = pf.rss_children[i]\n n = n + 1\n \n# UPDATE RSS AND PS\n pf.rss[1:pf.pop_size_d] = numpy.copy(pf.rss_temp[1:pf.pop_size_d]) \n pf.ps[1:pf.pop_size_d] = numpy.copy(pf.p_temp[1:pf.pop_size_d]) \n\n# RUN EXTINCTION EVENT\n if((gen % pf.extinction_frequency) == 0 and gen > 0 and gen < pf.generations-1):\n pf.stage = 'Extinction event'\n pf.extinction()\n\n# RUN ENHANCE EVENT\n if(((gen + 1) % pf.enhance_frequency) == 0):\n pf.stage = 'Enhancing Top 10% With Steepest Descent'\n pf.enhance()\n \n# SAVE POTENTIAL\n pf.output()\n \n# Run end function\n pf.stage = 'End' \n results = pf.end() \n pf.progress = 100\n pf.time_remaining = 0.0\n progress.display(results)",
"title": ""
},
{
"docid": "70522a0b1fa464e3d11ae4c124df489a",
"score": "0.5031848",
"text": "def assign01_main():\n # a sorted list of ~20 items\n list1 = [2, 3, 6, 10, 11, 17, 20, 23, 24, 29, 31, 34, 38, 39, 42, 47, 53]\n item_to_find = 34\n\n ls_res1 = linearSearch(list1, item_to_find)\n bs_res1 = binarySearch(list1, item_to_find)\n\n print(f\"\\nlist1 (size = {len(list1)}) results\")\n if ls_res1[0]:\n print(\" linear search found the item and required:\")\n else:\n print(\" linear search did not find the item and required:\")\n print(\" \", ls_res1[1], \"comparisons\")\n print(f\" {ls_res1[2]:.4f} seconds\")\n\n if bs_res1[0]:\n print(\" binary search found the item and required:\")\n else:\n print(\" binary search did not find the item and required:\")\n print(\" \", bs_res1[1], \"comparisons\")\n print(f\" {bs_res1[2]:.4f} seconds\")\n\n # a sorted list of odds from 1 to 9999\n list2 = list(range(1, 10000, 2))\n item_to_find = -1\n\n ls_res2 = linearSearch(list2, item_to_find)\n bs_res2 = binarySearch(list2, item_to_find)\n\n print(f\"\\nlist2 (size = {len(list2)}) results\")\n if ls_res2[0]:\n print(\" linear search found the item and required:\")\n else:\n print(\" linear search did not find the item and required:\")\n print(\" \", ls_res2[1], \"comparisons\")\n print(f\" {ls_res2[2]:.4f} seconds\")\n\n if bs_res2[0]:\n print(\" binary search found the item and required:\")\n else:\n print(\" binary search did not find the item and required:\")\n print(\" \", bs_res2[1], \"comparisons\")\n print(f\" {bs_res2[2]:.4f} seconds\")",
"title": ""
},
{
"docid": "a1ff54492bc2edb6cf6f33c76b13835d",
"score": "0.5031396",
"text": "def cantor_composition_simple(D1,D2,f,genus):\n a1, b1 = D1\n a2, b2 = D2\n if a1 == a2 and b1 == b2:\n # Duplication law:\n d, h1, h3 = a1.xgcd(2*b1)\n a = (a1 // d)**2\n b = (b1 + h3*((f - b1**2) // d)) % (a)\n else:\n d0, _, h2 = a1.xgcd(a2)\n if d0 == 1:\n a = a1*a2\n b = (b2 + h2*a2*(b1-b2)) % (a)\n else:\n d, l, h3 = d0.xgcd(b1 + b2)\n a = (a1*a2) // (d**2)\n b = ((b2 + l*h2*(b1-b2)*(a2 // d)) + h3*((f - b2**2) // d)) % (a)\n a =a.monic()\n return (a, b)",
"title": ""
},
{
"docid": "e865dec34e559e5075f9478ac13d88fb",
"score": "0.5025185",
"text": "def main2():\n s = set()\n m = None\n r3 = 0\n r0 = 0\n while True:\n r4 = r3 | 65536 # pow(2, 16)\n r3 = 2176960\n while True:\n r3 = (((r3 + (r4 & 0xff)) & 0xffffff) * 65899) & 0xffffff\n if 256 > r4:\n break\n r4 = r4 // 256\n if not s:\n print('Part 1:', r3)\n if r3 in s:\n print('Part 2:', m)\n break\n else:\n s.add(r3)\n m = r3\n #if r3 == r0:\n # break",
"title": ""
},
{
"docid": "30f33a83c6aa73b70d5dc120b115fe91",
"score": "0.5023245",
"text": "def second():\n inp = get_input()\n nanobots = [parse_nanobot_data(s) for s in inp]\n\n origins = [nb.origin for nb in nanobots]\n\n x_min, x_max = min(origins, key=lambda o: o.x).x, max(origins, key=lambda o: o.x).x\n y_min, y_max = min(origins, key=lambda o: o.y).y, max(origins, key=lambda o: o.y).y\n z_min, z_max = min(origins, key=lambda o: o.z).z, max(origins, key=lambda o: o.z).z\n\n r = None\n queue = []\n heapq.heappush(queue,\n Region(x_min, y_min, z_min, x_max - x_min + 1, y_max - y_min + 1, z_max - z_min + 1, nanobots))\n while r is None:\n region = heapq.heappop(queue)\n if region.width == 1: # Assume cube here. Whatever ....\n r = region\n else:\n regions = region.split()\n [heapq.heappush(queue, region) for region in regions]\n\n # Should return distance: 98565591, bots in range: 975, pos: (17304966, 29121001, 52139624)\n return Point.manhattan_distance_to_point(Point(0, 0, 0), r.origin)",
"title": ""
},
{
"docid": "5ba36145a9938013ea21fac30b987708",
"score": "0.5023203",
"text": "def main():\n sum = 0\n testvalues = list(range(1,10001))\n primes = e_sieve(10001)\n dlist1 = []\n dlist2 = []\n amicables = []\n\n for i in testvalues:\n if i in primes:\n testvalues.remove(i)\n\n for i in testvalues:\n dlist1.append((i, d(i)))\n\n for j in dlist1:\n dlist2.append((d(j[1]), j[1]))\n \n for i in range(len(dlist1)):\n if dlist1[i] == dlist2[i] and dlist1[i][0] != dlist1[i][1]:\n amicables.append(dlist1[i])\n \n for i in amicables:\n sum += i[0] + i[1]\n\n print (sum/2)",
"title": ""
},
{
"docid": "9fa51976be7e4dafdc212c6cfc7d1ff5",
"score": "0.50210065",
"text": "def _compute_quotient(self):\n generators=set([])\n genus=self.genus()\n num_edges=0\n self.get_embedding_matrix(prec = 1)\n p=self._p\n num_verts=0\n v0=Vertex(self,num_verts,self._Mat_22([1,0,0,1]),determinant = 1,valuation = 0)\n V=collections.deque([v0])\n S=Graph(0,multiedges=True,weighted=True)\n Sfun = Graph(0)\n edge_list=[]\n vertex_list=[v0]\n self._num_edges=0\n num_verts+=1\n n_units=len(self.get_units_of_order())\n while len(V)>0:\n v=V.popleft()\n # found_edges=sum([len(e.links) for e in v.leaving_edges])\n # v_stabilizer=len(self._stabilizer(v.rep,as_edge=False))\n # total_edges=(p+1)/v_stabilizer\n\n E=self._BT.leaving_edges(v.rep)\n\n # print 'V = %s, E = %s, G = %s (target = %s), lenV = %s'%(num_verts,num_edges,1+num_edges-num_verts,genus,len(V))\n for e in E: #ii in range(p+1):\n # if found_edges == total_edges:\n # break\n\n #e=E[ii]\n edge_det=e.determinant()\n edge_valuation=edge_det.valuation(p)\n # if v_stabilizer == 1 and ii == len(v.leaving_edges):\n # e1 = None\n # else:\n # g,e1=self._find_equivalent_edge(e,v.leaving_edges,valuation=edge_valuation)\n\n g,e1=self._find_equivalent_edge(e,v.leaving_edges,valuation=edge_valuation)\n\n if e1 is not None: # The edge is old. We just update the links\n e1.links.append(g)\n target = self._BT.target(e)\n if e1.parity == 0:\n Sfun.add_edge(v.rep,target,label = e1.label)\n else:\n Sfun.add_edge(v.rep,target,label = e1.opposite.label)\n\n Sfun.set_vertex(target,e1.target)\n \n else: # The edge is new.\n target=self._BT.target(e)\n target.set_immutable()\n new_det=target.determinant()\n new_valuation=new_det.valuation(p)\n new_parity=new_valuation%2\n g1,v1=self._find_equivalent_vertex(target,V,valuation=new_valuation)\n if v1 is None:\n #The vertex is also new\n v1=Vertex(self,num_verts,target,determinant = new_det,valuation = new_valuation)\n vertex_list.append(v1)\n num_verts+=1\n #Add the vertex to the list of pending vertices\n V.append(v1)\n else:\n generators.add(g1[0])\n\n\n # Add the edge to the list\n new_e=Edge(self,num_edges,e,v,v1,determinant = edge_det,valuation = edge_valuation)\n new_e.links.append(self.B_one())\n Sfun.add_edge(v.rep,target,label = num_edges)\n Sfun.set_vertex(target,v1)\n\n # Add the edge to the graph\n S.add_edge(v.rep,v1.rep,num_edges)\n S.set_vertex(v.rep,v)\n S.set_vertex(v1.rep,v1)\n\n # Find the opposite edge\n opp=self._BT.opposite(e)\n opp_det=opp.determinant()\n new_e_opp=Edge(self,num_edges,opp,v1,v,opposite = new_e)\n new_e.opposite=new_e_opp\n\n if new_e.parity == 0:\n edge_list.append(new_e)\n else:\n edge_list.append(new_e_opp)\n\n v.leaving_edges.append(new_e)\n v.entering_edges.append(new_e_opp)\n v1.entering_edges.append(new_e)\n v1.leaving_edges.append(new_e_opp)\n num_edges += 1\n # found_edges+=ZZ(v_stabilizer/len(self._stabilizer(e,as_edge=True)))\n # if genus == 1 - len(vertex_list) +num_edges:\n # break\n computed_genus=Integer(1- len(vertex_list)+num_edges)\n if computed_genus != genus:\n print 'You found a bug! Please report!'\n print 'Computed genus =',computed_genus\n print 'Theoretical genus =', genus\n raise RuntimeError\n\n self._generators=generators\n self._boundary= {v.rep:v for v in vertex_list}\n self._edge_list=edge_list\n self._vertex_list=vertex_list\n self._S=S\n self._Sfun=Sfun",
"title": ""
},
{
"docid": "b494c4d679e386d29cee6c8f7b461673",
"score": "0.5020426",
"text": "def __truediv__(self, other):\n \n pass",
"title": ""
},
{
"docid": "b70fea3a4a3dac60e5ade13272c23cde",
"score": "0.5020366",
"text": "def Arraydiscarder(arrayx, arrayy, args):\n\tL, l, w, delta = args\n\tj=0\n\tfor j in range(len(arrayx)):\n\t\tx = arrayx[j]\n\t\ty = arrayy[j]\n\t\tif y > -L/2 - delta and y < -L/2 + delta:\n\t\t\tif x > l - w/2 and x < l + w/2: \t\t\n\t\t\t\tu = 1\n\t\t\telse:\n\t\t\t\tu = 0\n\t\t\t\tbreak\n\t\tif y > L/2 - delta and y < L/2 + delta:\n\t\t\tif x > l - w/2 and x < l + w/2: \n\t\t\t\tu = 1\n\t\t\telse:\n\t\t\t\tu = 0\n\t\t\t\tbreak\t\n\t\tif x > 0 - delta and x < 0 + delta:\n\t\t\tif y > l - w/2 and y < l + w/2: \n\t\t\t\tu = 1\n\t\t\telse:\n\t\t\t\tu = 0\n\t\t\t\tbreak\n#\t\tif x > L/2 - delta and x < L/2 + delta:\n#\t\t\tif y > l - w/2 and y < l + w/2: \n#\t\t\t\tu = 1\n#\t\t\telse:\n#\t\t\t\tu = 0\n#\n#\t\t\t\tbreak\n#\t\tif x > -L/2 - delta and x < -L/2 + delta:\n#\t\t\tif y > l - 2*w/3 and y < l + w/3: \n#\t\t\t\tu = 1\n#\t\t\telse:\n#\t\t\t\tu = 0\n#\t\t\t\tbreak\n\t\tj= j+1\n\treturn u, j",
"title": ""
},
{
"docid": "49a9c11ea5e24061031e3a26df6e83c5",
"score": "0.5016446",
"text": "def LUP_decompose(self):\n if self.m != self.n:\n print(\"Nije kvadratna! Nema dekompozicije!\")\n self.a = None\n self.p = None\n return\n\n self.a = self.copy()\n self.p = self.identity()\n self.s = 0\n\n for i in range(0, self.n):\n tmp_max = -math.inf\n r = i\n for j in range(i, self.n):\n if math.fabs(self.a[j, i]) > tmp_max:\n tmp_max = math.fabs(self.a[j, i])\n r = j\n if tmp_max < self.epsilon:\n print(\"Matrica je singularna! Prekidam dekompoziciju\")\n self.a = None\n self.p = None\n return\n else:\n if r != i:\n self.a.switch_rows(r, i)\n self.p.switch_rows(r, i)\n self.s += 1\n for j in range(i+1, self.m):\n if math.fabs(self.a[i, i]) < self.epsilon:\n print(\"Matrica je singularna! Prekidam dekompoziciju\")\n self.a = None\n self.p = None\n return\n self.a[j, i] = self.a[j, i] / self.a[i, i]\n for k in range(i+1, self.n):\n self.a[j, k] = self.a[j, k] - self.a[j, i] * self.a[i, k]\n return",
"title": ""
},
{
"docid": "fcd83622e5f7fa2b71ac05750e236dc0",
"score": "0.50153434",
"text": "def other(G, fs):\n raise ValueError(\"not yet implemented\") ",
"title": ""
},
{
"docid": "ebba63df8a9229b008955f2fae175816",
"score": "0.5011193",
"text": "def test_pred_construction(self):\n security_parameter = 10\n one_way_perm = blum_blum_shub.blum_blum_shub(security_parameter)\n hardcorePred = blum_blum_shub.parity",
"title": ""
},
{
"docid": "555d6211df2d3d92dcc104209d385ee1",
"score": "0.50092953",
"text": "def compute_k(self):",
"title": ""
},
{
"docid": "91b0f5fcda92a08c141e4c6d4e3e9872",
"score": "0.5009154",
"text": "def P(alpha, m):\n if alpha >= 2*m-1:\n raise Exception\n if m%2==0:\n if alpha < m:\n if alpha%2 == 0:\n b = alpha // 2\n return [(2*a, (2*a + 2*b + 1)%(2*m)) for a in range(m)]\n else:\n b = (alpha-1) // 2\n return [(2*a, (2*a - 2*b - 1)%(2*m)) for a in range(m)]\n else:\n y = alpha - m\n pairs = [(b,(2*y-b)%(2*m)) for b in range(y)]\n pairs += [(c,(2*m+2*y-c-2)%(2*m)) for c in range(2*y+1,m+y-1)]\n pairs += [(2*m+int(-1.5-.5*(-1)**y),y),(2*m+int(-1.5+.5*(-1)**y),m+y-1)]\n return pairs\n else:\n if alpha < m-1:\n if alpha % 2 == 0:\n b = alpha // 2\n return [(2*a,(2*a+2*b+1)%(2*m)) for a in range(m)]\n else:\n b = (alpha-1) // 2\n return [(2*a,(2*a-2*b-1)%(2*m)) for a in range(m)]\n else:\n y = alpha-m+1\n pairs = [(b,2*y-b) for b in range(y)]\n pairs += [(c,2*m+2*y-c) for c in range(2*y+1,m+y)]\n pairs += [(y,m+y)]\n return pairs",
"title": ""
},
{
"docid": "43c08286c4ed462dac13db8b66bc84b8",
"score": "0.50077325",
"text": "def extra(maze):\n # astar_multi(maze)\n # TODO: Write your code here\n class State:\n def __init__(self, position, gs, lastPosition):\n self.position = position\n self.gs = gs\n self.lastPosition = lastPosition\n\n objective = maze.getObjectives()\n\n def geths(a, p):\n return abs(a[0] - p[0]) + abs(a[1] - p[1])\n\n def multiAstar(fromPosition, toPosition):\n start = State(fromPosition, 0, -1)\n priqueue = [start]\n link = [start]\n explored = [fromPosition]\n\n def addPriqueue(f):\n index = 0\n while index in range(len(priqueue)):\n if (priqueue[index].gs + geths(priqueue[index].position, toPosition)) > \\\n (f.gs + geths(f.position, toPosition)):\n break\n index = index + 1\n priqueue.insert(index, f)\n\n def indexPriqueue(pos):\n index = 0\n while index in range(len(priqueue)):\n if priqueue[index].position == pos:\n return index\n index = index + 1\n return -1\n\n def indexLink(pos):\n index = 0\n while index in range(len(link)):\n if link[index].position == pos:\n return index\n index = index + 1\n return -1\n\n while priqueue[0].position != toPosition:\n neighbors = maze.getNeighbors(priqueue[0].position[0], priqueue[0].position[1])\n for indexN in range(len(neighbors)):\n if neighbors[indexN] not in explored:\n explored.append(neighbors[indexN])\n frontier = State(neighbors[indexN], priqueue[0].gs + 1, priqueue[0].position)\n addPriqueue(frontier)\n link.append(frontier)\n else:\n updateIndex = indexPriqueue(neighbors[indexN])\n # if the update state is in the priqueue, then update gs directly\n if updateIndex != -1:\n if priqueue[updateIndex].gs > priqueue[0].gs + 1:\n frontier = State(neighbors[indexN], priqueue[0].gs + 1, priqueue[0].position)\n priqueue.pop(updateIndex)\n addPriqueue(frontier)\n indexL = indexLink(neighbors[indexN])\n link.pop(indexL)\n link.append(frontier)\n else:\n indexL = indexLink(neighbors[indexN])\n if link[indexL].gs > priqueue[0].gs + 1:\n frontier = State(neighbors[indexN], priqueue[0].gs + 1, priqueue[0].position)\n addPriqueue(frontier)\n link.pop(indexL)\n link.append(frontier)\n priqueue.pop(0)\n\n astarReturn = []\n i = len(link) - 1\n while link[i].position != toPosition:\n i = i - 1\n while i != -1:\n astarReturn.append(link[i].position)\n i = indexLink(link[i].lastPosition)\n astarReturn.reverse()\n return astarReturn\n\n\n objective.insert(0, maze.getStart())\n # print(objective)\n fromList = []\n toList = []\n path = []\n length = []\n indexa = 0\n while indexa in range(len(objective)):\n indexb = indexa + 1\n while indexb < len(objective):\n fromList.append(objective[indexa])\n toList.append(objective[indexb])\n if (abs(objective[indexa][0] - objective[indexb][0]) == 1 and objective[indexa][1] == objective[indexb][1]) \\\n or (abs(objective[indexa][1] - objective[indexb][1]) == 1 and (objective[indexa][0] == objective[indexb][0])):\n result = [objective[indexa], objective[indexb]]\n # if objective[indexa] == (15, 14) and objective[indexb] == (15, 1):\n # print(\"not aaa\")\n else:\n result = multiAstar(objective[indexa], objective[indexb])\n # if objective[indexa] == (15, 14) and objective[indexb] == (15, 1):\n # print(\"aaaa\")\n path.append(result)\n length.append(len(result))\n indexb = indexb + 1\n indexa = indexa + 1\n # print(indexa,\"+1 astar\")\n\n def mst(a):\n nodeList = []\n for i in range(len(a)):\n if a[i] not in nodeList:\n nodeList.append(a[i])\n minLength = 500\n indexMin = 0\n for i in range(len(fromList)):\n if nodeList[len(nodeList)-1] == fromList[i] or nodeList[len(nodeList)-1] == toList[i]:\n if minLength > length[i]:\n minLength = length[i]\n indexMin = i\n return len(nodeList) - 1 + length[indexMin] - 1\n\n\n\n\n class Node:\n def __init__(self, position, gs, route, hs):\n self.position = position\n self.gs = gs\n self.route = route\n self.hs = hs\n\n start = Node(maze.getStart(), 0, [maze.getStart()], 0)\n multiPriqueue = []\n\n def findLength(f, t):\n index = 0\n while index in range(len(fromList)):\n if (fromList[index] == f and toList[index] == t) or (toList[index] == f and fromList[index] == t):\n return length[index] - 1\n index = index + 1\n return -1\n\n def findNodeList(list):\n nodeList = []\n for ii in range(len(objective)):\n if objective[ii] not in list:\n nodeList.append(objective[ii])\n nodeList.append(list[len(list)-1])\n\n # remove redundant\n a = []\n for i in range(len(nodeList)):\n if nodeList[i] not in a:\n a.append(nodeList[i])\n\n return a\n\n for index in range(len(objective)):\n if objective[index] != start.position:\n # for i in range(len(multiPriqueue)):\n # print(multiPriqueue[i].position)\n indexN = 0\n while indexN in range(len(multiPriqueue)):\n\n if multiPriqueue[indexN].gs > findLength(start.position, objective[index]):\n\n break\n indexN = indexN + 1\n\n multiPriqueue.insert(indexN, Node(objective[index], start.gs + findLength(start.position, objective[index]), [start.position, objective[index]], 0))\n\n\n # for i in range(len(multiPriqueue)):\n # multiPriqueue[i].hs = mst(findNodeList(multiPriqueue[i].route))\n # print(multiPriqueue[i].position, multiPriqueue[i].route, multiPriqueue[i].gs + mst(findNodeList(multiPriqueue[i].route)), multiPriqueue[i].gs, mst(findNodeList(multiPriqueue[i].route)))\n\n maxLength = 0\n maxLengthRoute = []\n trashNum = 0\n\n while 1:\n host = multiPriqueue.pop(0)\n if len(host.route) > maxLength:\n # print(host.route)\n maxLength = len(host.route)\n maxLengthRoute = host.route\n\n\n\n finish = 0\n for i in range(len(objective)):\n if objective[i] not in host.route:\n finish = 0\n break\n else:\n finish = 1\n if finish == 1:\n break\n\n # try to improve searching speed\n trashRoute = 0\n # if len(host.route) < 20:\n # restriction = round(maxLength / 3)\n # elif len(host.route) < 50:\n # restriction = maxLength - 15\n # elif len(host.route) < 90:\n # restriction = maxLength - 10\n # elif len(host.route) < 120:\n # restriction = maxLength - 10\n # elif len(host.route) < 150:\n # restriction = maxLength - 10\n # elif len(host.route) < 190:\n # restriction = maxLength - 15\n # else:\n # restriction = maxLength - 15\n\n # restriction = round(maxLength / 2)\n restriction = maxLength\n for i in range(min(restriction, len(host.route))):\n if host.route[i] != maxLengthRoute[i]:\n trashRoute = 1\n break\n if trashRoute == 1:\n # print(\"delete\", trashNum, \"trash\")\n trashNum = trashNum + 1\n continue\n\n # originalNeighbors = []\n neighbors = []\n minNeighbors = 500\n if len(neighbors) == 0:\n # print(\"find the nearest\")\n for index in range(len(objective)):\n if objective[index] != host.position and objective[index] not in host.route:\n tempNb = findLength(host.position, objective[index])\n if minNeighbors > tempNb:\n minNeighbors = tempNb\n minIndex = index\n neighbors.append(objective[minIndex])\n\n\n for index in range(len(neighbors)):\n\n\n\n temp = Node(neighbors[index], host.gs + findLength(host.position, neighbors[index]), host.route+[neighbors[index]], 0)\n temp.hs = mst(findNodeList(temp.route))\n # t0 = time.clock()\n\n if len(multiPriqueue):\n for i in range(len(multiPriqueue)):\n if multiPriqueue[i].gs + multiPriqueue[i].hs >= temp.gs + temp.hs:\n break\n\n multiPriqueue.insert(i, temp)\n\n finalPath = host.route\n # print(finalPath, len(multiPriqueue), \"final\")\n\n finalReturn = []\n\n\n\n for i in range(len(finalPath)-1):\n for ii in range(len(path)):\n if fromList[ii] == finalPath[i] and toList[ii] == finalPath[i+1]:\n finalReturn = finalReturn + path[ii]\n finalReturn.pop()\n break\n elif toList[ii] == finalPath[i] and fromList[ii] == finalPath[i+1]:\n\n path[ii].reverse()\n finalReturn = finalReturn + path[ii]\n path[ii].reverse()\n finalReturn.pop()\n break\n\n\n finalReturn.append(finalPath[len(finalPath)-1])\n # print(finalReturn)\n\n\n return finalReturn",
"title": ""
},
{
"docid": "8284513df19115b1059f755f1cc8acb7",
"score": "0.50072235",
"text": "def eq0405():",
"title": ""
},
{
"docid": "a8745c8d5a4558a30d9efb77fccf661f",
"score": "0.5005822",
"text": "def reconcile_layers_iter(a1=[5, 7, 3], a2=[7, 8]):\n res_arr = []\n l_arr = []\n r_arr = []\n while sum(a1) > 0 or sum(a2) > 0:\n for i in range(len(a1)):\n if a1[i] > 0:\n num1 = a1[i]\n break\n for j in range(len(a2)):\n if a2[j] > 0:\n num2 = a2[j]\n break\n minn = min(num1, num2)\n a1[i] -= minn\n a2[j] -= minn\n res_arr.append(minn)\n l_arr.append(i+1)\n r_arr.append(j+1)\n return res_arr, l_arr, r_arr",
"title": ""
},
{
"docid": "bc5c576d9bc7596637cf857f7e3ec1c8",
"score": "0.4996447",
"text": "def applyRp(A,B, Rup, Rvp, Leg = \"I\", forwhat = 'trg', RABs = [1]*6):\n if forwhat == 'trg':\n Alist = [A, B, A, B]\n [Arot1, Arot2, _, _], _ = rotateLattice(Alist, Leg)\n Arot1mod = ncon([Arot1, Rup],[[-1,-2,1,-4],[1,-3]])\n Arot2mod = ncon([Arot2, Rvp],[[1,-2,-3,-4],[-1,1]])\n Amodlist = [Arot1mod, Arot2mod, Arot1mod, Arot2mod]\n revLeg = {\"I\":\"I\", \"II\":\"IV\", \"III\":\"III\", \"IV\":\"II\"}\n [Amod, Bmod, _, _], _ = rotateLattice(Amodlist, revLeg[Leg])\n return Amod, Bmod\n elif forwhat == 'hotrg':\n # Alist = [A, A, B, B]\n # [Arot1, Arot2, _, _], _ = rotateLattice(Alist, Leg)\n # Arot1mod = ncon([Arot1, Rup],[[-1,-2,1,-4],[1,-3]])\n # Arot2mod = ncon([Arot2, Rvp],[[1,-2,-3,-4],[-1,1]])\n # Amodlist = [Arot1mod, Arot2mod, Arot2mod, Arot1mod]\n # revLeg = {\"II\":\"IV\", \"IV\":\"II\"}\n # Amod = rotateLattice(Amodlist, revLeg[Leg])[0][0]\n # Bmod = rotateLattice(Amodlist, revLeg[Leg])[0][2]\n RA, RB, RAl, RAr, RBl, RBr = RABs\n if Leg == \"II\":\n Amod = ncon([A, Rup], [[-1, -2, -3, 1],[1, -4]])\n Bmod = ncon([B, Rvp], [[-1, 1, -3, -4],[-2, 1]])\n # update RA and RB matrices\n if type(RA).__module__.split(\".\")[0] != 'abeliantensors':\n RA = 1.0 * Rup\n else:\n RA = ncon([RA, Rup], [[-1,1],[1,-2]])\n if type(RB).__module__.split(\".\")[0] != 'abeliantensors':\n RB = 1.0 * Rvp.transpose([1,0])\n else:\n RB = ncon([RB, Rvp], [[-1,1],[-2,1]])\n elif Leg == \"IV\":\n Amod = ncon([A, Rvp], [[-1, -2, -3, 1],[-4, 1]])\n Bmod = ncon([B, Rup], [[-1, 1, -3, -4],[1, -2]])\n # update RA and RB matrices\n if type(RA).__module__.split(\".\")[0] != 'abeliantensors':\n RA = 1.0 * Rvp.transpose([1,0])\n else:\n RA = ncon([RA, Rvp], [[-1,1],[-2,1]])\n if type(RB).__module__.split(\".\")[0] != 'abeliantensors':\n RB = 1.0 * Rup\n else:\n RB = ncon([RB, Rup], [[-1,1],[1,-2]])\n elif Leg == \"I\":\n Amod = ncon([A, Rup, Rvp], [[1, -2, 2, -4], [2, -3], [-1, 1]])\n Bmod = B * 1.0\n # update RAl, RAr matrices\n if type(RAl).__module__.split(\".\")[0] != 'abeliantensors':\n RAl = 1.0 * Rvp.transpose([1,0])\n else:\n RAl = ncon([RAl, Rvp], [[-1,1],[-2,1]])\n if type(RAr).__module__.split(\".\")[0] != 'abeliantensors':\n RAr = 1.0 * Rup\n else:\n RAr = ncon([RAr, Rup], [[-1,1],[1,-2]])\n elif Leg == \"III\":\n Amod = A * 1.0\n Bmod = ncon([B, Rup, Rvp], [[1,-2, 2, -4], [1, -1], [-3, 2]])\n # update RBl, RBr matrices\n if type(RBl).__module__.split(\".\")[0] != 'abeliantensors':\n RBl = 1.0 * Rup\n else:\n RBl = ncon([RBl, Rup], [[-1,1],[1,-2]])\n if type(RBr).__module__.split(\".\")[0] != 'abeliantensors':\n RBr = 1.0 * Rvp.transpose([1,0])\n else:\n RBr = ncon([RBr, Rvp], [[-1,1],[-2,1]])\n else:\n raise ValueError(\"Leg can only be chosen from [II, IV, I, III]\" +\n \"in this gilt-hotrg-imp version\")\n RABs = [RA, RB, RAl, RAr, RBl, RBr]\n return Amod, Bmod, RABs\n else:\n raise ValueError(\"forwhat should be chosen between trg or hotrg\")",
"title": ""
},
{
"docid": "1c8b9269279ae3c517b6925198883e6f",
"score": "0.49918652",
"text": "def D(z):",
"title": ""
},
{
"docid": "9431f55afcd37a2a6831ab6c50584bd9",
"score": "0.49877673",
"text": "def work(self):",
"title": ""
},
{
"docid": "b6c751385a3c827e90abc12f2a0dd137",
"score": "0.4980513",
"text": "def Solve(self):\n pass",
"title": ""
},
{
"docid": "a9e8267bc51ca8a5ad636fd2734d816d",
"score": "0.49778596",
"text": "def clno(A, a, B, b):\n logging.debug(\"---------------------------------------------------\")\n logging.debug(\"clno(): In python implmentation of Tim's derivation\")\n logging.debug(\"---------------------------------------------------\")\n #logging.debug(\"Inputs are:\")\n #logging.debug(\" A:\\n{}\".format(A))\n #logging.debug(\" a:\\n{}\".format(a))\n #logging.debug(\" B:\\n{}\".format(B))\n #logging.debug(\" b:\\n{}\".format(b))\n ApB = (A + B)\n logging.debug(\"ApB: \\n{}\".format(ApB))\n ApB_det = np.linalg.det(ApB)\n\n ApB_i = np.linalg.inv(ApB)\n amb = a - b\n\n result = 6 * np.log(2*np.pi)\n logging.debug(\"Added 6log(2pi): {}\".format(result))\n lndet = np.log(ApB_det)\n logging.debug(\"Log of det(ApB): {}\".format(lndet))\n result += np.log(ApB_det)\n logging.debug(\"result so far: {}\".format(result))\n exponent = (np.dot(amb, np.dot(ApB_i, amb)))\n logging.debug(\"matrix exponent is: {}\".format(exponent))\n result += exponent\n logging.debug(\"result so far: {}\".format(result))\n result *= -0.5\n\n# lnoverlap = -0.5 * (np.dot(amb, np.dot(ApB_i, amb)))\n# lnoverlap -= 3 * np.log(2 * np.pi)\n# lnoverlap -= 0.5 * np.log(ApB_det)\n\n #logging.debug(\"Final result is: {}\".format(lnoverlap))\n\n logging.debug(\"---------------------------------------------------\")\n logging.debug(\"Final result is: {}\".format(result))\n logging.debug(\"---------------------------------------------------\")\n return result",
"title": ""
},
{
"docid": "6aa1322220f9f50973886ef25d2f9b1a",
"score": "0.49754128",
"text": "def thomas1d(N, b, a, c, q):\n\n l = np.zeros(N, dtype='float64')\n u = np.zeros(N, dtype='float64')\n d = np.zeros(N, dtype='float64')\n y = np.zeros(N, dtype='float64')\n x = np.zeros(N, dtype='float64')\n\n #print 'b = ', b\n #print 'a = ', a\n #print 'c = ', c\n\n d[0] = a[0]\n u[0] = c[0]\n for i in np.arange(N - 2):\n l[i] = b[i] / d[i]\n #print 'b[i] = %f, d[i] = %f, l[i] = %f' % (b[i], d[i], l[i])\n d[i + 1] = a[i + 1] - l[i] * u[i]\n #print 'a[i+1] = %f, l[i] = %f, u[i] = %f => d[i+1 ] %f' % (a[i + 1],\n # l[i], u[i],\n # d[i + 1])\n u[i + 1] = c[i + 1]\n\n l[N - 2] = b[N - 2] / d[N - 2]\n d[N - 1] = a[N - 1] - l[N - 2] * u[N - 2]\n\n # Forward substitution\n y[0] = q[0]\n for i in np.arange(1, N):\n y[i] = q[i] - l[i - 1] * y[i - 1]\n #print 'q[i] = %f, l[i-1] = %f, y[i-1] = %f => y[i] = q - l[i-1] * y[i-1] = %f' %\\\n # (q[i], l[i - 1], y[i - 1], y[i])\n\n # Backward substitution\n x[N - 1] = y[N - 1] / d[N - 1]\n for i in np.arange(N - 2, -1, -1):\n x[i] = (y[i] - u[i] * x[i + 1]) / d[i]\n #print 'y[i] = %f, u[i] * x[i + 1] = %f, d[i] = %f => x=(y-ux)/d = %f' %\\\n # (y[i], u[i] * x[i + 1], d[i], x[i])\n\n return x, (l, u, d, y)",
"title": ""
},
{
"docid": "7f1c12681ad259b3cb64cb0f183c1fdd",
"score": "0.49683738",
"text": "def match_features(features1, features2, x1, y1, x2, y2):\n #############################################################################\n # TODO: YOUR CODE HERE\n\n #hyper parameter\n threshold = 0.9\n Sorting = True\n\n ratio = np.zeros([features1.shape[0], 1])\n indexer = np.zeros ([features1.shape[0], 1])\n indexer[:] = np.NaN\n #for order in range(0, 1):\n\n\n for order in range (0,features1.shape[0]):\n sys.stdout.write('\\r' +\" \"+ str(int(order/features1.shape[0]*100+1)) + \"%\")\n\n Loss = np.zeros([features2.shape[0], 1])\n for order2 in range (0,features2.shape[0]):\n Loss[order2, 0] = sum(abs(features1[order,:]-features2[order2,:]))\n '''\n for order3 in range(0,128):\n Loss[order2,0] = Loss[order2,0] + abs(features1[order,order3]-features2[order2,order3])\n #Loss[order2, 0] = Loss[order2, 0] + features1[order, order3] - features2[order2, order3]\n '''\n\n Loss2 = np.sort(Loss.flatten())\n #Loss2 = np.sort(Loss)\n\n a= Loss2[0]/Loss2[1]\n\n #print(a)\n if a <threshold:\n ratio[order, 0] = a\n indexer[order, 0] = np.argmin(Loss)\n print()\n\n dummy = np.arange(indexer.shape[0]).T\n dummy = np.reshape(dummy,(dummy.shape[0],1))\n\n indexer = np.concatenate((dummy,indexer,ratio),axis=1)\n\n for num in range(indexer.shape[0]-1, -1,-1):\n if indexer [num,2]==0:\n indexer = np.delete(indexer,num,0)\n\n if Sorting ==True:\n indexer = indexer[indexer[:,2].argsort()]\n\n matches = indexer[:,0:2].astype(int)\n confidences = indexer[:,2]\n\n\n #print(matches)\n\n\n\n\n\n\n\n\n\n\n #############################################################################\n\n #raise NotImplementedError('`match_features` function in ' +\n # '`student_feature_matching.py` needs to be implemented')\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n return matches, confidences",
"title": ""
},
{
"docid": "c98c4c757ceafd66c160a204f853d257",
"score": "0.49674457",
"text": "def double(a):\n \"\"\"{ pre True\n post forall 0 <= i < len(a), b[i] == a[i] * 2\n return b\n }\"\"\"\n b = []\n x = 0\n \"\"\"{ 1.OK forall 0 <= i < x, b[i] == a[i] * 2 foralli\n 2.OK x == len(b) algebra\n }\"\"\"\n while x != len(a) :\n \"\"\"{ invariant (forall 0 <= i < x, b[i] == a[i] * 2) and (x == len(b))\n modifies x, b\n }\"\"\"\n b.append(a[x]*2)\n \"\"\"{ 1.OK (forall 0 <= i < x, b[i] == a[i] * 2) and (x == len(b_old)) premise\n 2.OK x == len(b_old) ande 1\n 10.OK forall 0 <= i < x, b[i] == a[i] * 2 ande 1\n 3.OK len(b) == len(b_old) + 1 premise\n 4.OK x == len(b)-1 algebra 2 3\n 5.OK b[len(b)-1] == a[x]*2 premise\n 6.OK b[x] == a[x]*2 subst 4 5\n 7.OK forall 0 <= i < x+1, b[i] == a[i] * 2 foralli 10 6\n 8.OK len(b) == x + 1 algebra 4\n 9.OK return 7 8\n }\"\"\"\n x = x + 1\n \"\"\"{ 1.OK forall 0 <= i < x_old +1, b[i] == a[i] * 2 premise\n 2.OK x == x_old +1 premise\n 3.OK forall 0 <= i < x, b[i] == a[i] * 2 subst 2 1\n 4.OK len(b) == x_old + 1 premise\n 5.OK len(b) == x subst 2 4\n 6.OK x == len(b) algebra 5\n #7.OK (forall 0 <= i < x, b[i] == a[i] * 2) and (x == len(b)) andi 3 6\n 8. return 3 6\n }\"\"\"\n\n \"\"\"{ 1.OK (forall 0 <= i < x, b[i] == a[i] * 2) and (x == len(b)) premise\n 3.OK not(x != len(a)) premise\n 4.OK x == len(a) algebra\n 6.OK forall 0 <= i < x, b[i] == a[i] * 2 ande 1\n 5.OK forall 0 <= i < len(a), b[i] == a[i] * 2 subst 4 6\n }\"\"\"\n return b",
"title": ""
},
{
"docid": "bdd7ebdabf4b54ad2a6a7bccef9a5a74",
"score": "0.49661937",
"text": "def solve(s, a, b, c):\n def score(a, b, _taken, turn, prevFailed=False):\n \"\"\"\n taken is a set of unusable rooms.\n It includes both rooms under construction and already-painted rooms.\n turn is 0 for Alma and 1 for Berthe.\n \"\"\"\n #print(a, b, _taken, turn)\n taken = set(_taken)\n possible = [] # Possible next moves.\n \n if turn == 0:\n x = a\n else:\n x = b\n \n if x[1] > 1:\n new = (x[0], x[1] - 1)\n if new not in taken:\n possible.append(new)\n \n if x[1] < x[0] * 2 - 1:\n new = (x[0], x[1] + 1)\n if new not in taken:\n possible.append(new)\n \n if x[1] % 2 == 0:\n if x[0] > 1:\n new = (x[0] - 1, x[1] - 1)\n if new not in taken:\n possible.append(new)\n else:\n if x[0] < s:\n new = (x[0] + 1, x[1] + 1)\n #print(\"bOOOOOOOOOm\", new in taken)\n if new not in taken:\n possible.append(new)\n \n #print(\" possible:\", possible)\n \n p = len(possible)\n if p == 0:\n if prevFailed:\n return 0\n return score(a, b, tuple(taken), 1 - turn, True)\n \n if turn == 0:\n scores = []\n for i in range(p):\n ac = possible[i]\n taken.add(ac)\n scores.append(score(ac, b, tuple(taken), 1 - turn))\n taken.remove(ac)\n return max(scores) + 1\n \n else:\n scores = []\n for i in range(p):\n bc = possible[i]\n taken.add(bc)\n scores.append(score(a, bc, tuple(taken), 1 - turn))\n taken.remove(bc)\n return min(scores) - 1\n \n taken = [a, b]\n taken.extend(list(c))\n ans = score(a, b, tuple(taken), 0)\n \n return ans",
"title": ""
},
{
"docid": "811ffcd6bcd84f21006749643f7fdc19",
"score": "0.4965829",
"text": "def create_modulus(risk_funs, N1, N2, n1, n2, margin, upper_bound, g, x1, x2):\n for index, function in enumerate(risk_funs):\n if function == \"kaplan_markov\":\n if index == 0:\n T1 = lambda delta: 2*n1*np.log(1 + margin/(2*upper_bound-margin)* \\\n (N1 + N2)/N1*delta)\n else:\n T2 = lambda delta: 2*n2*np.log(1 + (N1 + N2)/N2*delta)\n\n elif function == \"kaplan_kolmogorov\":\n if index == 0:\n T1 = lambda delta: 2*sum(np.log(1 + np.divide(margin*(N1+N2), \\\n (2*upper_bound - margin)*np.multiply(np.array(x1) + \\\n g, N1 - np.array(range(len(x1)))))*delta))\n else:\n T2 = lambda delta: 2*sum(np.log(1 + np.divide(N1 + N2, np.multiply(np.array(x2) \\\n + g, N2 - np.array(range(len(x2)))))*delta))\n \n elif function == \"kaplan_wald\": \n if index == 0:\n T1 = lambda delta: 2*n1*(np.log(1 + margin/(2*upper_bound - margin)* \\\n (N1 + N2)/N1*delta) + np.log(1 + g*margin/(2* \\\n upper_bound - margin)*(N1 + N2)/N1*delta))\n else:\n T2 = lambda delta: 2*n2*(np.log(1+(N1 + N2)/N2*delta) + \\\n np.log(1 + g*(N1 + N2)/N2*delta))\n else: \n return None\n\n if N1 == 0:\n T1 = lambda delta: 0\n\n if N2 == 0: \n T2 = lambda delta: 0\n\n return lambda delta: T1(delta) + T2(delta)",
"title": ""
}
]
8ffc57df12cae62a251b68661b73e7b5
Construct an object from a parsed response.
[
{
"docid": "0e9dde3a2dac743da0d5c1f024147fc1",
"score": "0.0",
"text": "def from_json(cls, attributes):\n return cls(**{to_snake_case(k): v for k, v in attributes.items()})",
"title": ""
}
]
[
{
"docid": "38fb22532f23588d56b0cb3828560113",
"score": "0.71406657",
"text": "def __init__(self, res):\n self.fromResponseObj(res)",
"title": ""
},
{
"docid": "38fb22532f23588d56b0cb3828560113",
"score": "0.71406657",
"text": "def __init__(self, res):\n self.fromResponseObj(res)",
"title": ""
},
{
"docid": "38fb22532f23588d56b0cb3828560113",
"score": "0.71406657",
"text": "def __init__(self, res):\n self.fromResponseObj(res)",
"title": ""
},
{
"docid": "c73792bd0fba0062485fe08cdbe17a80",
"score": "0.6973285",
"text": "def from_str(cls, response_str):\n #import pdb; pdb.set_trace()\n response_splited = response_str.split('\\n')\n if len(response_splited) < 3:\n raise ResponseError()\n response_splited = response_splited[:-2]\n status = response_splited[0]\n \n status = cls.check_status(status)\n msg = ''\n metrics = []\n \n if status:\n data_lines = response_splited[1:]\n metrics = cls.parse_data(data_lines)\n assert metrics is not None, 'Response class got none metrics from {:}'.format(response_splited)\n else:\n msg = response_splited[1]\n return Response(status, metrics, msg)",
"title": ""
},
{
"docid": "6e32306b49fbdbcaec5a03cccd5b9b9b",
"score": "0.6627292",
"text": "def __init__(self, oResponse):\n if oResponse is not None:\n # Read the whole response (so we can log it).\n sBody = oResponse.read();\n\n # Check the content type.\n sContentType = oResponse.getheader('Content-Type');\n if sContentType is None or sContentType != 'application/x-www-form-urlencoded; charset=utf-8':\n testboxcommons.log('SERVER RESPONSE: Content-Type: %s' % (sContentType,));\n testboxcommons.log('SERVER RESPONSE: %s' % (sBody.rstrip(),))\n raise testboxcommons.TestBoxException('Invalid server response type: \"%s\"' % (sContentType,));\n\n # Parse the body (this should be the exact reverse of what\n # TestBoxConnection.postRequestRaw).\n ##testboxcommons.log2('SERVER RESPONSE: \"%s\"' % (sBody,))\n self._dResponse = urlparse.parse_qs(sBody, strict_parsing=True);\n\n # Convert the dictionary from 'field:values' to 'field:value'. Fail\n # if a field has more than one value (i.e. given more than once).\n for sField in self._dResponse:\n if len(self._dResponse[sField]) != 1:\n raise testboxcommons.TestBoxException('The field \"%s\" appears more than once in the server response' \\\n % (sField,));\n self._dResponse[sField] = self._dResponse[sField][0]\n else:\n # Special case, dummy response object.\n self._dResponse = dict();\n # Done.",
"title": ""
},
{
"docid": "c77314265a9822753c9b75c1cedae329",
"score": "0.6600541",
"text": "def __init__(self, response_dict={}):\n self.code = response_dict.get('code')\n self.name = response_dict.get('name')\n self.series = response_dict.get('series')\n self.total_cards = response_dict.get('totalCards')\n self.standard_legal = response_dict.get('standardLegal')\n self.release_date = response_dict.get('releaseDate')",
"title": ""
},
{
"docid": "ef5ce99c9dfd9b7d877aff561376bcfe",
"score": "0.6592119",
"text": "def __init__(self, resp):\n self.resp_json = None\n try:\n self.resp_json = resp.json()\n except ValueError:\n pass\n self.resp_text = resp.text",
"title": ""
},
{
"docid": "7cb162c9b79340b9d9e49bde3da23107",
"score": "0.6589386",
"text": "def make(cls, gitkit_response):\n gitkit_response = dict(gitkit_response) # Make a copy for mutations.\n matches, error = cls.match(gitkit_response)\n if not matches:\n raise ValueError(\n 'Unable to create %s; error: %s' % (cls.__name__, error))\n\n # response_body must be present. It contains a json string we must\n # convert to plain old Python values.\n response_body = gitkit_response.get(_GITKIT_RESPONSE_KEY_RESPONSE_BODY)\n if response_body is None:\n raise ValueError(\n 'Unable to parse GITKit OOB response: response_body missing')\n\n try:\n gitkit_response[_GITKIT_RESPONSE_KEY_RESPONSE_BODY] = (\n transforms.loads(response_body))\n except Exception, e:\n raise ValueError(\n 'Unable to parse %s from GITKit OOB response: got %s' % (\n _GITKIT_RESPONSE_KEY_RESPONSE_BODY, response_body))\n\n return cls(**gitkit_response)",
"title": ""
},
{
"docid": "080833dd3fd269d4ff82227c6d9ef898",
"score": "0.65586996",
"text": "def fromResponseObj(self, res):\n if type(res) is dict and res.get('data') is not None:\n self._id = res[\"data\"][\"_id\"]\n self.trackingNumber = res[\"data\"][\"trackingNumber\"]\n self.message = res[\"data\"][\"message\"]\n else:\n self.message = str(res)\n self._id = self.trackingNumber = None",
"title": ""
},
{
"docid": "c42ddc1348dbbf9fd232ecb45f1fc804",
"score": "0.6558021",
"text": "def __init__(\n self,\n response\n ):\n self.__npod_uuid = read_value(\n \"nPod.uuid\", response, str, False)\n self.__spu_serial = read_value(\n \"spu.serial\", response, str, False)\n self.__wwn = read_value(\n \"wwn\", response, str, True)\n self.__old_firmware_rev = read_value(\n \"oldFirmwareRev\", response, str, True)\n self.__new_firmware_rev = read_value(\n \"newFirmwareRev\", response, str, True)\n self.__vendor = read_value(\n \"vendor\", response, str, True)\n self.__model = read_value(\n \"model\", response, str, True)\n self.__eula_url = read_value(\n \"eulaURL\", response, str, True)",
"title": ""
},
{
"docid": "494c1a8c982acbe610d70ee3216b83dc",
"score": "0.6505576",
"text": "def fromResponseObj(self, res):\n if type(res) is dict and res.get(\"data\") is not None:\n self.data = res[\"data\"]\n self.message = res.get(\"message\")\n else:\n self.message = str(res)\n self.data = None",
"title": ""
},
{
"docid": "818de5889c406056908e974962e06856",
"score": "0.6446237",
"text": "def deserialize(self, string):\n result = json.loads(string)\n for k in ['url', 'status_code', 'matched', 'response_time', 'error', 'scrape_time']:\n setattr(self, k, result[k])\n return self",
"title": ""
},
{
"docid": "52135e66becbf08040f9b192e09418e5",
"score": "0.6426448",
"text": "def parse(self, response):",
"title": ""
},
{
"docid": "52135e66becbf08040f9b192e09418e5",
"score": "0.6426448",
"text": "def parse(self, response):",
"title": ""
},
{
"docid": "e40fa6cc9ac094effbd1646b87c6b970",
"score": "0.64180404",
"text": "def __init__(\n self,\n response: dict\n ):\n\n self.__uuid = read_value(\n \"uuid\", response, str, True)\n self.__name = read_value(\n \"name\", response, str, True)\n self.__note = read_value(\n \"note\", response, str, True)\n self.__email = read_value(\n \"email\", response, str, True)\n self.__first_name = read_value(\n \"firstName\", response, str, True)\n self.__last_name = read_value(\n \"lastName\", response, str, True)\n self.__mobile_phone = read_value(\n \"mobilePhone\", response, str, True)\n self.__business_phone = read_value(\n \"businessPhone\", response, str, True)\n self.__inactive = read_value(\n \"inactive\", response, bool, True)\n self.__group_uuids = read_value(\n \"groups.uuid\", response, str, False)\n self.__preferences = read_value(\n \"preferences\", response, UserPreferences, False)\n self.__support_contact_id = read_value(\n \"supportContactID\", response, str, False)\n self.__policy_uuids = read_value(\n \"policies.uuid\", response, str, False)\n self.__change_password = read_value(\n \"changePassword\", response, bool, False)\n self.__change_password_reason = read_value(\n \"changePasswordReason\", response, ChangePasswordReason, False)",
"title": ""
},
{
"docid": "dad1692329a7948d4e7388576f3ab15c",
"score": "0.63826615",
"text": "def parse(cls, json_data):\n dictionary = json.loads(json_data)\n return cls.build(dictionary)",
"title": ""
},
{
"docid": "afe1147b366a839c15693431bdd23d25",
"score": "0.6380625",
"text": "def __init__(self, resp):\n\n self.resp = resp\n self.name = resp['name']\n self.type = resp['metadata']['operationType']\n\n self._parse_timing()",
"title": ""
},
{
"docid": "8b29141e105f8a79e6b6d8a1ce58c16a",
"score": "0.63520694",
"text": "def __init__(self, response):\n self.response = response",
"title": ""
},
{
"docid": "576ab0bbb5d4e2bc3f9fe85ebcd5ccc2",
"score": "0.62402546",
"text": "def from_dict(cls, data):\n response = cls()\n response.sender = data.get('from')\n response.content = data.get('text')\n response.subject = data.get('subject')\n return response",
"title": ""
},
{
"docid": "785113835c1f61b18cdd4127b7da8910",
"score": "0.62264836",
"text": "def __init__(self, graph, response, profile=False):\n self.graph = graph\n self.header = []\n self.result_set = []\n\n # in case of an error an exception will be raised\n self._check_for_errors(response)\n\n if len(response) == 1:\n self.parse_statistics(response[0])\n elif profile:\n self.parse_profile(response)\n else:\n # start by parsing statistics, matches the one we have\n self.parse_statistics(response[-1]) # Last element.\n self.parse_results(response)",
"title": ""
},
{
"docid": "7a394c2bbb8a4f870902b51e5b04e836",
"score": "0.621284",
"text": "def __init__(\n self,\n response: dict\n ):\n self.__spu_serial = read_value(\n \"spu.serial\", response, str, False)\n self.__wwn = read_value(\n \"wwn\", response, str, True)\n self.__media_type = read_value(\n \"mediaType\", response, str, True)\n self.__position = read_value(\n \"position\", response, int, True)\n self.__state = read_value(\n \"stateEnum\", response, PhysicalDriveState, True)\n self.__unadmitted = read_value(\n \"unadmitted\", response, bool, True)\n self.__size_bytes = read_value(\n \"sizeBytes\", response, int, True)\n self.__vendor = read_value(\n \"vendor\", response, str, True)\n self.__model = read_value(\n \"model\", response, str, True)\n self.__serial = read_value(\n \"serial\", response, str, True)\n self.__firmware_revision = read_value(\n \"firmwareRevision\", response, str, True)\n self.__interface_type = read_value(\n \"interfaceType\", response, str, True)\n self.__update_failure = read_value(\n \"updateFailure\", response, str, False)",
"title": ""
},
{
"docid": "dc520044947159cc4401d26572c0f57b",
"score": "0.61814946",
"text": "def __init__(self, response, stream):\n\t\tself.response = response\n\t\tself.stream = stream",
"title": ""
},
{
"docid": "9a508985520ddc7d8a00a42f86e370dd",
"score": "0.6142405",
"text": "def parse_response(self, resp):\n p, u = self.getparser()\n p.feed(resp.text)\n p.close()\n return u.close()",
"title": ""
},
{
"docid": "c53c234f6617eb3ed01fd9d0f866ecb0",
"score": "0.6111229",
"text": "def parse_response(self, response):\n return xml.to_dict(response, self.responses, self.do_raise,\n self.delimiter, self.encapsulator, self.uniform)",
"title": ""
},
{
"docid": "50b550033ad387bf154ce2232ff46087",
"score": "0.61028934",
"text": "def _parseResponse(self, data):\r\n return self._parseAndReturnResult(data)",
"title": ""
},
{
"docid": "7861f98ef8154b838fafceceb634b5c3",
"score": "0.6091204",
"text": "def __init__(\n self,\n response: dict\n ):\n self.__items = read_value(\n \"items\", response, User, False)\n self.__more = read_value(\n \"more\", response, bool, False)\n self.__total_count = read_value(\n \"totalCount\", response, int, False)\n self.__filtered_count = read_value(\n \"filteredCount\", response, int, False)",
"title": ""
},
{
"docid": "c1027f59f44a17046d4e27733f52bbcc",
"score": "0.6081564",
"text": "def load(self, response):\n self._doc_id = response.get_elements()[\"_id\"]\n self._rev_id = response.get_elements()[\"_rev\"]\n self._values = response.get_elements()[\"value\"]",
"title": ""
},
{
"docid": "2706b3219a7e22002bf1379e64816146",
"score": "0.60575765",
"text": "def _unpack_response(response, cursor_id=None, as_class=dict, tz_aware=False):\r\n response_flag = struct.unpack(\"<i\", response[:4])[0]\r\n if response_flag & 1:\r\n # Shouldn't get this response if we aren't doing a getMore\r\n assert cursor_id is not None\r\n\r\n raise InterfaceError(\"cursor id '%s' not valid at server\" %\r\n cursor_id)\r\n elif response_flag & 2:\r\n error_object = bson.BSON(response[20:]).decode()\r\n if error_object[\"$err\"] == \"not master\":\r\n raise DatabaseError(\"master has changed\")\r\n raise DatabaseError(\"database error: %s\" %\r\n error_object[\"$err\"])\r\n\r\n result = {}\r\n result[\"cursor_id\"] = struct.unpack(\"<q\", response[4:12])[0]\r\n result[\"starting_from\"] = struct.unpack(\"<i\", response[12:16])[0]\r\n result[\"number_returned\"] = struct.unpack(\"<i\", response[16:20])[0]\r\n result[\"data\"] = bson.decode_all(response[20:], as_class, tz_aware)\r\n assert len(result[\"data\"]) == result[\"number_returned\"]\r\n return result",
"title": ""
},
{
"docid": "e220c5044bf505d42213666b8d673c53",
"score": "0.6053045",
"text": "def fromResponseObj(self, res):\n if type(res) is dict and res.get('message') is not None:\n self.message= res[\"message\"]\n else:\n self.message = str(res)",
"title": ""
},
{
"docid": "9b549981aaeda38589405e58051a084b",
"score": "0.6036484",
"text": "def _from_parsed_uri(cls, result):\n\n # decode label from uri path\n label = result.path\n if label.startswith(\"/\") and len(label) > 1:\n label = unquote(label[1:])\n else:\n raise cls._uri_error(\"missing label\")\n\n # extract old-style issuer prefix\n if \":\" in label:\n try:\n issuer, label = label.split(\":\")\n except ValueError: # too many \":\"\n raise cls._uri_error(\"malformed label\")\n else:\n issuer = None\n if label:\n label = label.strip() or None\n\n # parse query params\n params = dict(label=label)\n for k, v in parse_qsl(result.query):\n if k in params:\n raise cls._uri_error(\"duplicate parameter (%r)\" % k)\n params[k] = v\n\n # synchronize issuer prefix w/ issuer param\n if issuer:\n if \"issuer\" not in params:\n params['issuer'] = issuer\n elif params['issuer'] != issuer:\n raise cls._uri_error(\"conflicting issuer identifiers\")\n\n # convert query params to constructor kwds, and call constructor\n return cls(**cls._adapt_uri_params(**params))",
"title": ""
},
{
"docid": "8526a5a9dcf51db0d56d7ac46887a443",
"score": "0.6030578",
"text": "def _parse_bing_response(self, query, results, offset):\n response = Response()\n response.version = 'json'\n response.feed.setdefault('title', \"Results from %s for: %s\" % (self.engineName, query.search_terms))\n response.feed.setdefault('link', \"\")\n response.feed.setdefault('description', \"%s results from %s\" % (self.source, self.engineName))\n response.namespaces.setdefault(\"opensearch\", \"http://a9.com/-/spec/opensearch/1.1/\")\n \n \n if self.source == \"Web\":\n entries = self._parse_web_results(results) \n \n elif self.source == \"Image\":\n entries = self._parse_image_results(query, results)\n \n elif self.source == \"News\":\n entries = self._parse_news_results(results)\n \n elif self.source == \"RelatedSearch\":\n entries = self._parse_related_results(query, results)\n \n elif self.source == \"Video\":\n entries = self._parse_video_results(query, results)\n \n elif self.source == \"SpellingSuggestions\":\n entries = self._parse_spelling_results(query, results)\n \n for entry in entries:\n response.entries.append(entry)\n \n response.feed.setdefault('opensearch_totalresults', len(entries))\n response.feed.setdefault('opensearch_startindex', offset)\n response.feed.setdefault('opensearch_itemsperpage', self.resultsPerPage)\n\n return response",
"title": ""
},
{
"docid": "553ac2e8a41c61ff390158db50431ca8",
"score": "0.6029541",
"text": "def parse_response(response):\n status = response.status\n produces = [response.content.mimeType]\n schema = json.loads(response.content.text, cls=YAMLSchemaDecoder)\n return dict(status=status, produces=produces, schema=schema)",
"title": ""
},
{
"docid": "78b69ea24ff8a7c2939d5086c9e23bcb",
"score": "0.60275006",
"text": "def __init__(self, response=None, status=200, headers=None, mimetype=None,\n content_type=None):\n if response is None:\n self.response = []\n elif isinstance(response, basestring):\n self.response = [response]\n else:\n self.response = iter(response)\n if not headers:\n self.headers = Headers()\n elif isinstance(headers, Headers):\n self.headers = headers\n else:\n self.headers = Headers(headers)\n if content_type is None:\n if mimetype is None and 'Content-Type' not in self.headers:\n mimetype = self.default_mimetype\n if mimetype is not None and mimetype.startswith('text/'):\n mimetype += '; charset=' + self.charset\n content_type = mimetype\n if content_type is not None:\n self.headers['Content-Type'] = content_type\n if isinstance(status, (int, long)):\n self.status_code = status\n else:\n self.status = status",
"title": ""
},
{
"docid": "bf6a44f612a4afc6e5fb89ca9c14be21",
"score": "0.60080504",
"text": "def parse(self, data: dict) -> T:\n kwargs = self.get_kwargs(data)\n return self.object_class(**kwargs)",
"title": ""
},
{
"docid": "56d4351e74f22395b18452d3b7381f87",
"score": "0.59892863",
"text": "def DecodeResponse( self, xmlResponse ):\n\t\tdom = xml.dom.minidom.parseString( xmlResponse )\n\t\ttaskNode = dom.getElementsByTagName( \"task\" )[0]\n\t\ttask = Task()\n\t\ttask.Id = taskNode.getAttribute( \"id\" )\n\t\ttask.Status = taskNode.getAttribute( \"status\" )\n\t\tif task.Status == \"Completed\":\n\t\t\ttask.DownloadUrl = taskNode.getAttribute( \"resultUrl\" )\n\t\treturn task",
"title": ""
},
{
"docid": "e2dfeedc03fd38dad796099b9d24222f",
"score": "0.5979086",
"text": "def __init__(self, resp):\n\n self.resp = resp\n self.id = '{projectId}:{jobId}'.format(**resp['reference'])\n self.state = resp['status']['state']\n\n self.job_type = set(tuple(resp.keys()))\\\n .intersection({'hadoopJob', 'sparkJob', 'pysparkJob', 'hiveJob', 'pigJob', 'sparkSqlJob'})\\\n .pop()\n\n self._parse_timing()",
"title": ""
},
{
"docid": "b0b639a9d05393735095c8bdc7ee1ae7",
"score": "0.5969583",
"text": "def parse_response(resp):\n filter_only = ['url', 'title', 'features', 'status']\n parsed_resp = {key: val for key, val in resp.items() if key in filter_only}\n\n parsed_resp['scraped_date'] = [date.today().isoformat()]\n parsed_resp['price'] = [resp['price']['value']]\n parsed_resp['price_pm'] = [resp['areaPrice']['value']]\n parsed_resp['added_date'] = [resp['dateCrated']]\n parsed_resp['updated_date'] = [resp['dateModified']]\n\n parsed_resp['_id'] = resp['id']\n parsed_resp['address'] = resp['addresses']['pl']\n parsed_resp['coordinates'] = f\"{resp['coordinates']['latitude']}+{resp['coordinates']['longitude']}\"\n parsed_resp['characteristics'] = {char['key']: char['value_translated'] for char in resp['characteristics']}\n parsed_resp['photos'] = [(photo['thumbnail'], photo['large']) for photo in resp['photos'].values()]\n\n return parsed_resp",
"title": ""
},
{
"docid": "4b93bdd55727ae5e3769c3f102645048",
"score": "0.59676665",
"text": "def parse(cls, response: requests.Response) -> APIResponse:\n try:\n data = response.json()\n except (ValueError, TypeError) as e:\n raise APIResponseParsingException(exc=e, response=response)\n else:\n cls.check_for_errors(data)\n return APIResponse(data)",
"title": ""
},
{
"docid": "948f1baad0809d4994678abc371bfb6f",
"score": "0.5943018",
"text": "def _deserialize_response(data: dict, t: Type[T] = BaseResponse) -> T:\n return parse_obj_as(t, data)",
"title": ""
},
{
"docid": "c7edb0a8ea01812eb23efec5a8b02dd8",
"score": "0.5912667",
"text": "def _parse_response(self, str_response):\n try:\n resp = json.loads(str_response)\n status = resp['status']\n message = resp['message']\n except (ValueError, KeyError):\n raise SBusClientMalformedResponse('Got malformed response')\n\n # NOTE(takashi): task_id is currently used only in EXECUTE command, so\n # we don't fail here even if the given response doesn't\n # have task_id.\n task_id = resp.get('task_id')\n\n return SBusResponse(status, message, task_id)",
"title": ""
},
{
"docid": "6b4ff981fbff4302712a1998d03b9fb0",
"score": "0.5907516",
"text": "def from_api_response(cls, reddit_session, json_dict):\r\n if cls == WikiPage: # Temporary HACK for WikiPage\r\n # pylint: disable-msg=W0212\r\n parts = reddit_session._request_url.split('/', 6)\r\n # pylint: enable-msg=W0212\r\n subreddit = parts[4]\r\n page = parts[6].split('.', 1)[0]\r\n return cls(reddit_session, subreddit, page, json_dict=json_dict)\r\n return cls(reddit_session, json_dict=json_dict)",
"title": ""
},
{
"docid": "65e0e6054744bac5c2f83023c0cc8b88",
"score": "0.5899166",
"text": "def __init__(self, line, *args, **kwargs):\n\n if re.search(r\"Response\", line) is None:\n raise InitializationError()\n self.main_id = None\n self.line = line\n\n self.time = self._get_time()\n self.mac = self._get_mac()\n self.ip = self._get_ip()",
"title": ""
},
{
"docid": "0df00bc329c74e673e4f72c7f5b67993",
"score": "0.58825713",
"text": "def __init__(self, s):\n self._parse(s)",
"title": ""
},
{
"docid": "484d992535ff5504bcb4f5a37ddfa4ae",
"score": "0.5876787",
"text": "def __init__(self, response):\n self._total_speech_time = response.get(self._TOTAL_SPEECH_TIME, None)\n self._remaining_speech_time = response.get(self._REMAINING_SPEECH_TIME, None)\n self._speech_time = response.get(self._SPEECH_TIME, None)\n self._enrollment_status = response.get(self._ENROLLMENT_STATUS, None)",
"title": ""
},
{
"docid": "34181215e67d4297b4fea58cda52088b",
"score": "0.5869378",
"text": "def parse (cls, data, *args, **kwargs):\n return cls().load(data, *args, **kwargs)",
"title": ""
},
{
"docid": "b47641062d96fc8ce00311367442e03d",
"score": "0.5864493",
"text": "def parse_response(self, response):\n\n def custom_json_decoder():\n return load_from_json_str(response.text)\n\n response.json = custom_json_decoder\n try:\n return ServerBase.parse_response(response)\n except ProtocolError as exc:\n if self._response_error_handler:\n return self._response_error_handler(exc)\n raise",
"title": ""
},
{
"docid": "f17102899c07fc3d9077a18bea771b56",
"score": "0.5841206",
"text": "def parse_response(resp, endpoint=None, tag=None):\n try:\n try:\n content = resp.content\n root = ET.fromstring(content)\n code = root.find('./Code').text\n msg = root.find('./Message').text\n request_id = root.find('./RequestId').text\n host_id = root.find('./HostId').text\n except ETParseError:\n request_id = resp.headers.get('x-odps-request-id', None)\n if len(resp.content) > 0:\n obj = json.loads(resp.text)\n msg = obj['Message']\n code = obj.get('Code')\n host_id = obj.get('HostId')\n if request_id is None:\n request_id = obj.get('RequestId')\n else:\n raise\n clz = globals().get(code, ODPSError)\n return clz(\n msg, request_id=request_id, code=code, host_id=host_id, endpoint=endpoint, tag=tag\n )\n except:\n # Error occurred during parsing the response. We ignore it and delegate\n # the situation to caller to handle.\n LOG.debug(utils.stringify_expt())\n\n if resp.status_code == 404:\n return NoSuchObject('No such object.', endpoint=endpoint, tag=tag)\n else:\n text = resp.content.decode() if six.PY3 else resp.content\n if text:\n if resp.status_code == 502 and _nginx_bad_gateway_message in text:\n return BadGatewayError(\n text, code=str(resp.status_code), endpoint=endpoint, tag=tag\n )\n else:\n return ODPSError(text, code=str(resp.status_code), endpoint=endpoint, tag=tag)\n else:\n return ODPSError(str(resp.status_code), endpoint=endpoint, tag=tag)",
"title": ""
},
{
"docid": "a37799518218ddb865d4c2f586c90830",
"score": "0.58384097",
"text": "def __init__(\n self,\n response: dict\n ):\n self.__items = read_value(\n \"items\", response, PhysicalDrive, False)\n self.__more = read_value(\n \"more\", response, bool, False)\n self.__total_count = read_value(\n \"totalCount\", response, int, False)\n self.__filtered_count = read_value(\n \"filteredCount\", response, int, False)",
"title": ""
},
{
"docid": "61dc6f78d7c3daed3b656fc02fcf6679",
"score": "0.583705",
"text": "def _parse_result(self, response, verbose=False):\n\n arr = np.atleast_1d(np.genfromtxt(io.BytesIO(response.content),\n names=True, dtype=None, delimiter=',',\n skip_header=1))\n\n if len(arr) == 0:\n return None\n else:\n return Table(arr)",
"title": ""
},
{
"docid": "bc060cc5aa007d7b797bcb3f8673e104",
"score": "0.58370167",
"text": "def parse_response(cls, should_close=False):\n return cls(should_close=should_close)",
"title": ""
},
{
"docid": "2a5f4a77d43215c5ff585e7789b0aa29",
"score": "0.58326125",
"text": "def from_response(cls, text: str):\n #text = '\\n0. azurenightwalker, 76561198056214819 \\n '\n if text == 'No Players Connected \\n ':\n return cls(0,())\n names = tuple(filter(None, map(lambda name: name.groups()[0], REGEX.finditer(text))))\n return cls(len(names), names)",
"title": ""
},
{
"docid": "1a0a40fa498890259499600ad89a418c",
"score": "0.5814047",
"text": "def __init__(self, response):\n Exception.__init__(self, \"Expected to get Response object, received {}\".format(type(response)))",
"title": ""
},
{
"docid": "d9364a9460ed565a5c3b2590f5ab8405",
"score": "0.5809993",
"text": "def parse_response(response):\n dash_app.server.logger.debug(\"parse_response - input: {}\".format(response.text))\n parsed_response = json.loads(response.text)\n dash_app.server.logger.debug(\"parse_response - parsed: {}\".format(parsed_response))\n return parsed_response",
"title": ""
},
{
"docid": "cccfb9fdbd10eae81ceda72012c3b6fe",
"score": "0.58010197",
"text": "def __init__(self, resp, data):\n self.aiohttp_response = resp\n self.status = resp.status\n self.reason = resp.reason\n self.data = data",
"title": ""
},
{
"docid": "1f660f8c5d89004f9b1afc2125e425c0",
"score": "0.57980204",
"text": "def parse_response(response):\n d = {}\n d[\"full_name\"] = response[\"input\"][\"full_name\"]\n d[\"samples\"] = response[\"details\"][\"samples\"]\n d[\"first_name\"] = response[\"first_name\"]\n d[\"probability\"] = response[\"probability\"]\n d[\"gender\"] = response[\"gender\"]\n\n return d",
"title": ""
},
{
"docid": "b806de66d903793d2100e74a1257b65e",
"score": "0.5796085",
"text": "def __init__(\n self,\n response: dict\n ):\n\n self.__send_notification = read_value(\n \"sendNotification\", response, SendNotificationType, True)\n self.__time_zone = read_value(\n \"timeZone\", response, str, True)\n self.__show_base_two = read_value(\n \"showBaseTwo\", response, bool, True)\n self.__date_format = read_value(\n \"dateFormat\", response, DateFormat, True)",
"title": ""
},
{
"docid": "eb6add536feacea28e26a4b8a33ecf05",
"score": "0.57925516",
"text": "def load_client_response(self, rs_body):\n\n try:\n self._rs_dict = CustomDict(json.loads(rs_body))\n except JSONDecodeError:\n try:\n self._rs_dict = CustomDict(xml2dict(rs_body))\n except XMLSyntaxError:\n logger.warning('Response str could be neither parsed to json nor xml obj')\n return self",
"title": ""
},
{
"docid": "84b79088472d2199b81926f873e9f9c3",
"score": "0.57809347",
"text": "def from_response(response, method=None):\n if response.status_code:\n cls, enhanced_classes = _code_map.get(response.status_code,\n (ClientException, []))\n\n req_id = response.headers.get(\"x-openstack-request-id\")\n content_type = response.headers.get(\"Content-Type\", \"\").split(\";\")[0]\n\n kwargs = {\n 'code': response.status_code,\n 'method': method,\n 'url': response.url,\n 'request_id': req_id,\n }\n\n if \"retry-after\" in response.headers:\n kwargs['retry_after'] = response.headers.get('retry-after')\n\n if content_type == \"application/json\":\n try:\n body = response.json()\n except ValueError:\n pass\n else:\n if 'description' in body:\n # Gnocchi json\n desc = body.get('description')\n if desc and isinstance(desc, six.text_type):\n for enhanced_cls in enhanced_classes:\n if enhanced_cls.match.match(desc):\n cls = enhanced_cls\n break\n kwargs['message'] = desc\n elif isinstance(body, dict) and isinstance(body.get(\"error\"),\n dict):\n # Keystone json\n kwargs['message'] = body[\"error\"][\"message\"]\n else:\n kwargs['message'] = response.text\n elif content_type.startswith(\"text/\"):\n kwargs['message'] = response.text\n\n if not kwargs['message']:\n del kwargs['message']\n return cls(**kwargs)",
"title": ""
},
{
"docid": "f589baf0dc218ecc9d37cc57091ceb1e",
"score": "0.57767695",
"text": "def fromJson(json):\n if 'type' in json:\n if json['type'] == PaillierResponse.ADD_RESP:\n return PaillierResponse.parseAddResponse(json)\n if json['type'] == PaillierResponse.MUL_RESP:\n return PaillierResponse.parseMulResponse(json)\n if json['type'] == PaillierResponse.ERROR:\n return PaillierResponse.parseErrorReponse(json)\n if json['type'] == PaillierResponse.SUB_RESP:\n return PaillierResponse.parseSubResponse(json)\n\n raise MessageParseError('No \"type\" field in message')",
"title": ""
},
{
"docid": "c58c9f259ed165a558254a45d54ff342",
"score": "0.5769329",
"text": "def __init__(self, api_response: Dict[str, Any], config: ShipEngineConfig) -> None:\n self.events = list()\n result = api_response[\"result\"]\n for event in result[\"events\"]:\n self.events.append(TrackingEvent(event=event))\n\n self.shipment = (\n Shipment(\n shipment=result[\"shipment\"],\n actual_delivery_date=self.get_latest_event().date_time,\n config=config,\n )\n if \"shipment\" in result\n else None\n )\n self.package = Package(result[\"package\"]) if \"package\" in result else None",
"title": ""
},
{
"docid": "73259b8ab76b01eba64a3f685950bc7e",
"score": "0.57637143",
"text": "def from_string(self, s):\n ss = s.split('.')\n self.name = json.loads(ss[0])\n self.id = json.loads(ss[1])\n self.cards = json.loads(ss[2])\n self.free_armies = json.loads(ss[3])\n self.conquered_territory = json.loads(ss[4])",
"title": ""
},
{
"docid": "c48d8de77e9c8a91bbfb69c8cf7c46a5",
"score": "0.57604486",
"text": "def from_data(cls, data):\n self = object.__new__(cls)\n self.description = parse_description(data)\n self.welcome_channels = parse_welcome_channels(data)\n return self",
"title": ""
},
{
"docid": "0be36a5354c049c001a02e3b9381d1ed",
"score": "0.57442486",
"text": "def from_json(cls, data):\n self = cls.__new__(cls)\n\n self.kind = data.get('kind')\n self.brand = data['brand']\n self.name = data['name']\n self.price = data['price']\n self.main = data['main']\n self.stars = data['stars']\n self.image = data.get('image')\n self.frequent_skill = data.get('frequent_skill')\n return self",
"title": ""
},
{
"docid": "0f143461d36b7af0d29d7375db3807f4",
"score": "0.57423466",
"text": "def _parse_response(self, res):\n #decoded = '' # Referenced before assignment protection\n # content_type we get with nHaystack is Content_type : application/json; charset=UTF-8\n content_type = res.headers['Content-Type']\n if ';' in content_type:\n # Separate encoding from content type\n (content_type, encoding) = content_type.split(';',1)\n content_type = content_type.strip()\n # TODO: do we need to convert to Unicode, of so, how?\n\n if content_type in ('text/zinc', 'text/plain'):\n decoded = hszinc.parse(res.text, mode=hszinc.MODE_ZINC)[0]\n elif 'application/json' in content_type:\n decoded = hszinc.parse(res.text, mode=hszinc.MODE_JSON)\n else:\n raise NotImplementedError(\"Don't know how to parse type %s\" \\\n % content_type)\n if 'err' in decoded.metadata:\n raise HaystackError(decoded.metadata.get('dis', 'Unknown error'),\n traceback=decoded.metadata.get('traceback',None))\n return decoded",
"title": ""
},
{
"docid": "807e9893ac2e3700faa84d46cdd76c30",
"score": "0.5724377",
"text": "def from_response(cls, url, message, response):\n if isinstance(response, tuple):\n # The response has been unrolled into a (status_code,\n # headers, body) 3-tuple.\n status_code, headers, content = response\n else:\n status_code = response.status_code\n content = response.content\n return BadResponseException(\n url, message, \n debug_message=\"Status code: %s\\nContent: %s\" % (\n status_code,\n content,\n )\n )",
"title": ""
},
{
"docid": "e5d6f5254534b035c32bf77e862968c3",
"score": "0.5723599",
"text": "def parse(self, response):\n final_data = {}\n\n item = {}\n\n if self.__avoid_unwanted_responses(response.status):\n doc = html.fromstring(response.body)\n domain = response.meta['domain']\n site_variable, site_name = parse_site_variable(response.url)\n final_data[self.get_domain(domain)] = []\n \n \n #phone Number only \n phone_logic = PhoneParser(response)\n phone_number = phone_logic.process_response(response)\n if phone_number:\n phone_no = phone_number\n else:\n phone_no = []\n self.total_item.append(phone_no)",
"title": ""
},
{
"docid": "91bcf115ba537f688f47190b93f8f823",
"score": "0.57228917",
"text": "def from_data(cls, data):\n self = object.__new__(cls)\n self.distributor = parse_distributor(data)\n self.id = parse_id(data)\n self.sku = parse_sku(data)\n return self",
"title": ""
},
{
"docid": "903b750ec7d8e5f42957a590994c000c",
"score": "0.5718594",
"text": "def from_data(data):\n if data is None or len(data) == 0:\n return PAR()\n\n f = PAR()\n\n from Acquire.Client import Location as _Location\n from Acquire.Client import ACLRule as _ACLRule\n from Acquire.ObjectStore import string_to_datetime \\\n as _string_to_datetime\n\n f._location = _Location.from_data(data[\"location\"])\n f._aclrule = _ACLRule.from_data(data[\"aclrule\"])\n f._expires_datetime = _string_to_datetime(data[\"expires_datetime\"])\n f._uid = data[\"uid\"]\n\n return f",
"title": ""
},
{
"docid": "c6902616027b299d5c986520423e237d",
"score": "0.57115",
"text": "def loads(cls, buffer_):\n return cls(**json.loads(buffer_))",
"title": ""
},
{
"docid": "6f79efffdf19664c08e59def378f8cfb",
"score": "0.57100546",
"text": "def parse(self, response):\n \n loader = ItemLoader(item=RealEstateItem(), response=response)\n\n loader.add_xpath('title', '//*[@id=\"ls_title_10024233\"][1]/text()')\n loader.add_xpath('price', '(//div[contains(@id, \"ls_price\")])[1]/font/text()')\n loader.add_xpath('description', '//*[@id=\"ls_property_link_10024233\"]/div/div[2]/div[2]/div/div[1]/div[2]/div[2]/text()')\n loader.add_xpath('address', '//*[@id=\"ls_loc_9260436\"]/text()')\n loader.add_xpath('image_urls', '//*[@id=\"ls_image_10024233\"]/img/@src')\n \n # Housekeeping fields\n loader.add_value('url', response.url)\n loader.add_value('project', self.settings.get('BOT_NAME'))\n loader.add_value('spider', self.name)\n loader.add_value('server', socket.gethostname())\n loader.add_value('date', datetime.datetime.now())\n\n return loader.load_item()",
"title": ""
},
{
"docid": "41a7a52d7113e48b1e90163493e07f8e",
"score": "0.56709975",
"text": "def _parse_response(self, response):\n\n response_dict = {}\n for line in response.splitlines():\n key, value = response.split(\"=\", 1)\n response_dict[key] = value\n return response_dict",
"title": ""
},
{
"docid": "9c61ed248b01089ee57adca62db98f81",
"score": "0.5653755",
"text": "def parse(self, response, shape):\n ...",
"title": ""
},
{
"docid": "7154abd803bb5c353f923a858bd945e2",
"score": "0.5649987",
"text": "def nal_response(response):\n cls = _nal_code_map.get(response['error-code'], NalApiException)\n # Iterate over the nested objects and retreive the \"message\" attribute.\n details = response['message']\n return cls(details=details)",
"title": ""
},
{
"docid": "8a1b47877c58bfa31dc0e476378fe638",
"score": "0.5633628",
"text": "def __init__(self, soup=None):\n if soup is not None:\n self.parse(soup)",
"title": ""
},
{
"docid": "584ca56607076c69eafe3950c8834715",
"score": "0.56273276",
"text": "def __init__(self, response=None, status=None, headers=None, mimetype=None,\n content_type=None, direct_passthrough=False, max_age=0,\n private=None):\n max_age = max_age or 0\n try:\n max_age = int(max_age)\n except ValueError:\n max_age = 0\n self.max_age = max_age\n if private is None:\n if self.max_age == 0:\n # The most common reason for max_age to be set to 0 is that a resource\n # is _also_ private.\n private = True\n else:\n private = False\n self.private = private\n\n body = response\n if isinstance(body, etree._Element):\n body = etree.tostring(body)\n elif not isinstance(body, (bytes, str)):\n body = str(body)\n\n super(Response, self).__init__(\n response=body,\n status=status,\n headers=self._headers(headers or {}),\n mimetype=mimetype,\n content_type=content_type,\n direct_passthrough=direct_passthrough\n )",
"title": ""
},
{
"docid": "2fc19d2dde76330ce161c0b4b75a8e84",
"score": "0.5625901",
"text": "def _parse_response(response):\n if response is None:\n raise APIException('Failed to read data from API.')\n try:\n return json.loads(response)\n except:\n raise APIException('Failed to parse data from API.')",
"title": ""
},
{
"docid": "48f4cc90d87718bdaf934823c8410a42",
"score": "0.5625173",
"text": "def __init__(self: ResourceResult):\n self.results: List[Dict] = \"\"\n self.type: int = 0\n self.title: str = \"\"\n return",
"title": ""
},
{
"docid": "170fad8fc38865e058d2da327e3115c1",
"score": "0.5620945",
"text": "def _parse_body(self, robj, response, expected_statuses):\n # If no response given, then return.\n if response is None:\n return None\n\n status, headers, data = response\n\n # Check if the server is down(status==0)\n if not status:\n m = 'Could not contact Riak Server: http://{0}:{1}!'.format(\n self._node.host, self._node.http_port)\n raise RiakError(m)\n\n # Make sure expected code came back\n self.check_http_code(status, expected_statuses)\n\n if 'x-riak-vclock' in headers:\n robj.vclock = VClock(headers['x-riak-vclock'], 'base64')\n\n # If 404(Not Found), then clear the object.\n if status == 404:\n robj.siblings = []\n return None\n # If 201 Created, we need to extract the location and set the\n # key on the object.\n elif status == 201:\n robj.key = headers['location'].strip().split('/')[-1]\n # If 300(Siblings), apply the siblings to the object\n elif status == 300:\n ctype, params = parse_header(headers['content-type'])\n if ctype == 'multipart/mixed':\n if six.PY3:\n data = bytes_to_str(data)\n boundary = re.compile('\\r?\\n--%s(?:--)?\\r?\\n' %\n re.escape(params['boundary']))\n parts = [message_from_string(p)\n for p in re.split(boundary, data)[1:-1]]\n robj.siblings = [self._parse_sibling(RiakContent(robj),\n part.items(),\n part.get_payload())\n for part in parts]\n\n # Invoke sibling-resolution logic\n if robj.resolver is not None:\n robj.resolver(robj)\n\n return robj\n else:\n raise Exception('unexpected sibling response format: {0}'.\n format(ctype))\n\n robj.siblings = [self._parse_sibling(RiakContent(robj),\n headers.items(),\n data)]\n\n return robj",
"title": ""
},
{
"docid": "94389282f3fd8c4b20cfab7ce205fc5b",
"score": "0.562015",
"text": "def parse_response(self, body):\n p, u = self.getparser()\n p.feed(body)\n p.close()\n return u.close()",
"title": ""
},
{
"docid": "49ead695b0dc90d4e7d63550bc424dcf",
"score": "0.56131077",
"text": "def from_dict(cls, d: Dict[str, Any]) -> object:\n try:\n return ResponseRPC(result=d[\"result\"], error=d[\"error\"])\n except KeyError:\n raise Exception(\n \"The RPC dictionary must have both the 'result' and 'error' keys\"\n )",
"title": ""
},
{
"docid": "a15e6e5690668e3432fd086dc18fde65",
"score": "0.560839",
"text": "def __init__(self, raw, user):\n self.body = raw[\"body\"]\n self.content = raw[\"content\"]\n self.count = raw[\"count\"]\n self.created_time = raw[\"created\"]\n self.key = raw[\"key\"]\n self.notif_id = raw[\"_id\"]\n self.priority = raw[\"priority\"]\n self.read = raw[\"subject\"]\n self.reason = raw[\"reason\"]\n self.status = raw[\"status\"]\n self.subject = raw[\"subject\"]\n self.thing_id = raw[\"thingID\"]\n self.thing_type = raw[\"thingType\"]\n self.updated_time = raw[\"updated\"]\n self.user = user\n try:\n self.reply_id = raw[\"replyId\"]\n except KeyError:\n self.reply_id = None\n try:\n self.hash_key = raw[\"hash_key\"]\n self.__v = raw[\"__v\"]\n except KeyError:\n self.hash_key = None\n self.__v = None",
"title": ""
},
{
"docid": "7b60cf62cf58ec1e4844d2cd672c7190",
"score": "0.55986893",
"text": "def factory(cls, response):\n valid_responses_map = {\n 'CpChanged': 'speaker.service.changed',\n 'SignInStatus': 'speaker.service.logged_in',\n 'SignOutStatus': 'speaker.service.logged_out',\n }\n\n if response.name not in valid_responses_map:\n return None\n\n return cls(valid_responses_map[response.name], response.data['cpname'])",
"title": ""
},
{
"docid": "882ee6162776da65d5d7cedb828f9492",
"score": "0.5573996",
"text": "def parse (self):\n\n xmlobj = xml2obj(self.apml_content)\n\n # create a structure similar to the one \n # defined by Jon Ciancillo in his php version. \n return Apml(xmlobj)",
"title": ""
},
{
"docid": "bfc9dbc343381399a01224430421ce72",
"score": "0.5569944",
"text": "def parse(obj):\n return InfoChainResult(\n version=obj[\"version\"],\n protocolVersion=obj[\"protocolversion\"],\n blocks=obj[\"blocks\"],\n timeOffset=obj[\"timeoffset\"],\n connections=obj[\"connections\"],\n proxy=obj[\"proxy\"],\n difficulty=obj[\"difficulty\"],\n testNet=obj[\"testnet\"],\n relayFee=obj[\"relayfee\"],\n errors=obj[\"errors\"],\n )",
"title": ""
},
{
"docid": "0931ebb300e275b227fd1fa75345e093",
"score": "0.5569687",
"text": "def parse_book(self, response):\n\n book_loader = BookLoader(\n item=Book(),\n response=response\n )\n\n # Book url\n book_loader.add_value(\n 'book_url',\n response.url\n )\n\n # Book title\n book_loader.add_xpath(\n 'title',\n \"//div[@class='prodtitle']/h1/text()\"\n )\n\n # Book price\n book_loader.add_xpath(\n 'price',\n \"//div[@class='buying-priceold-val']/span/text()\"\n )\n\n # Book publishing house\n book_loader.add_xpath(\n 'publishing_house',\n \"//div[@class='publisher']/a/text()\"\n )\n\n # Book series\n book_loader.add_xpath(\n 'series_of_books',\n \"//div[@class='series']/a/text()\"\n )\n\n return book_loader.load_item()",
"title": ""
},
{
"docid": "c0793e3b5c41d902e2e9ff3b77cf8286",
"score": "0.5568101",
"text": "def __init__(self, data, region):\n # Parse response data\n self.name = data['name']\n self.id = data['id']\n self.account_id = data['accountId']\n self.puuid = data['puuid']\n self.iconId = data['profileIconId']\n self.level = data['summonerLevel']\n self.region = region\n\n # This is only for later creating a Ranked Summoner object\n self._raw = data",
"title": ""
},
{
"docid": "851ae551f5608dfc5810d283b4a086c5",
"score": "0.5557714",
"text": "def makeParser(self, msg=None):\n if msg:\n self.msg = msg\n\n self.parser = self.parseResponse() # make generator",
"title": ""
},
{
"docid": "fa1b20b492f7543a824664b7b9e13e98",
"score": "0.5555741",
"text": "def from_dict(cls, d):\n return cls(\n name=d[\"name\"],\n word=d[\"word\"],\n misses=d[\"misses\"],\n hits=d[\"hits\"],\n host=d[\"host\"],\n guesser=d[\"guesser\"],\n state=d[\"state\"]\n )",
"title": ""
},
{
"docid": "e5f039e68cf61d903573993f82ddc694",
"score": "0.5553371",
"text": "def _parse_response(self, response):\n myStr = StringIO.StringIO(response)\n\n for line in myStr:\n if string.find(line, '#__GR2PROTO__') != -1:\n break\n\n # make sure the 1st line is #__GR2PROTO__\n if string.find(line, '#__GR2PROTO__') == -1:\n raise Exception(\"Bad response: %r\" % response)\n\n resDict = {}\n\n for myS in myStr:\n myS = myS.strip()\n strList = string.split(myS, '=', 2)\n\n try:\n resDict[strList[0]] = strList[1]\n except:\n resDict[strList[0]] = ''\n\n return resDict",
"title": ""
},
{
"docid": "3e7be474b5927c4ee06796cb00f05b20",
"score": "0.55440974",
"text": "def __init__(self, response, url_suffix, request_data):\n resp_json = None\n try:\n resp_json = response.json()\n except:\n resp_json = {}\n if response.status_code == 500:\n if resp_json and not resp_json.get('reason'):\n resp_json = {'reason': 'Internal Server Error', 'suggestion': 'Contact DDS support.'}\n Exception.__init__(self, 'Error {} on {}\\nReason:{}\\nSuggestion:{}'.format(\n response.status_code, url_suffix, resp_json.get('reason', resp_json.get('error', '')),\n resp_json.get('suggestion', '')\n ))\n self.response = resp_json\n self.url_suffix = url_suffix\n self.request_data = request_data\n self.status_code = response.status_code",
"title": ""
},
{
"docid": "165734ffbd768caad43b22c60c7404ea",
"score": "0.5536647",
"text": "def from_dict(cls, dikt) -> 'InlineResponse20012':\n return deserialize_model(dikt, cls)",
"title": ""
},
{
"docid": "76d4bccc1df939d73e0283406afe009a",
"score": "0.55364615",
"text": "def from_dict(cls, dikt) -> 'InlineResponse200':\n return deserialize_model(dikt, cls)",
"title": ""
},
{
"docid": "37a23897b6ecdfb25bb3d051c162f7fd",
"score": "0.55358404",
"text": "def from_dict(cls, dikt) -> 'InlineResponse200':\n return util.deserialize_model(dikt, cls)",
"title": ""
},
{
"docid": "05b0f9c3678863451b6df4e1477209d1",
"score": "0.5533961",
"text": "def from_xml_str(cls, _xml: str):\n xml2_obj_converter = Xml2Obj()\n el = xml2_obj_converter.ParseString(_xml)\n return cls.from_xml(el)",
"title": ""
},
{
"docid": "98e9b23745cad062d80629c68aa60c4d",
"score": "0.5527031",
"text": "def _unjsonify(self, response):\n return objectify(json.loads(response))",
"title": ""
},
{
"docid": "9387318ce5e5d05d0fa1ac7111456f20",
"score": "0.55053926",
"text": "def _parse_result(self, response, *, verbose=False):\n formats = parse_readme(self.FORMATFILE)\n\n dtypes = [entry['dtype'] for entry in formats.values()]\n\n rows = []\n for line in response.text.split('\\n'):\n if line.strip():\n row = []\n start = 0\n for key, entry in formats.items():\n formatter = entry['formatter']\n length = entry['length']\n value = formatter(line[start:start+length])\n row.append(value)\n start = start + length\n rows.append(row)\n\n result = Table(rows=rows, names=formats.keys(), dtype=dtypes)\n\n return result",
"title": ""
},
{
"docid": "bc0f8db7f1dca7814abd15e53d2a86cf",
"score": "0.5496888",
"text": "def _parse_response(self, response, api=False):\n data = response.json()\n if 'error' in data:\n raise PyYouTubeException(response)\n if api:\n return self._parse_data(data)\n return data",
"title": ""
},
{
"docid": "c01a7cc82da81502f6a7e22af16e9aff",
"score": "0.5493553",
"text": "def from_dict(cls, data):\n return from_dict(cls, data)",
"title": ""
},
{
"docid": "2e73e61bb1610d7cb6f73948de388099",
"score": "0.5487599",
"text": "def parse_from_json(self, json):\n self.name = json[\"name\"]\n self.ID = json[\"id\"]\n self.type = json[\"type\"]\n self.group = json[\"group\"]\n self.params = Params(json[\"params\"])",
"title": ""
},
{
"docid": "a3f2bdac8b60640080abcf0226a26e28",
"score": "0.5480331",
"text": "def __init__(self, *, raw_request=None, parsed_request=None):\n if raw_request is None and parsed_request is None:\n raise ValueError('Either \"raw_request\" or \"parsed_request\" must be set for Request.')\n\n self._raw_request = raw_request\n \"\"\"str|None: Raw request json, if given.\"\"\"\n\n self._parsed_request = parsed_request\n \"\"\"dict|None: Parsed request json, if given or after being parsed from `_raw_request`.\"\"\"\n\n self._dict = None\n \"\"\"dict|None: Parsed request json after being checked for correct type, used to skip repeated checks.\"\"\"",
"title": ""
},
{
"docid": "82fdf129b4a05b9fc8f8b0e56fd0b5cb",
"score": "0.5474289",
"text": "def __init__(\n self,\n response: dict\n ):\n self.__items = read_value(\n \"items\", response, PhysicalDriveUpdate, True)\n self.__more = read_value(\n \"more\", response, bool, True)\n self.__total_count = read_value(\n \"totalCount\", response, int, True)\n self.__filtered_count = read_value(\n \"filteredCount\", response, int, True)",
"title": ""
}
]
20c21940fc9dd704dacf8ea88aa5ee69
Check the type of address (v4, v6, mac) and split out the address, prefix, and port. Values are None if they don't exist.
[
{
"docid": "0ab101390fc832573508d0eda37e7ee3",
"score": "0.692085",
"text": "def _split_addr(addr_str: str) -> Tuple:\n address = possible_addr = prefix = port = possible_port = None\n\n try:\n address, prefix = addr_str.rsplit('/', maxsplit=1)\n except Exception:\n address = addr_str\n\n # is this a mac address? then stop\n if re.match(r'(?:\\S\\S\\:){5}\\S\\S', address):\n return address, prefix, port\n\n # is it an ipv4 with port or just ipv6?\n if ':' in address:\n try:\n possible_addr, possible_port = address.rsplit(':', maxsplit=1)\n _ = ipaddress.IPv4Address(possible_addr)\n address = possible_addr\n port = possible_port\n # assume it was an IPv6 address\n except Exception:\n pass\n\n return address, prefix, port",
"title": ""
}
]
[
{
"docid": "4f797d46fe6ac632ffd5a9a1ab83eeeb",
"score": "0.64934886",
"text": "def parse_address(address):\n ipv6 = 0\n for i in range(12):\n if address[i] != 0:\n ipv6 = 1\n break\n if ipv6 == 1:\n return socket.inet_ntop(\n socket.AF_INET6, struct.pack('!16B', *address))\n else:\n return socket.inet_ntop(\n socket.AF_INET,\n struct.pack('!BBBB', *address[12:]))",
"title": ""
},
{
"docid": "e88b61dadbf9a1c043830b9c6d4d296d",
"score": "0.620578",
"text": "def split_host_port(hostport: str):\n if not hostport:\n # wtf do you want to split?\n raise AddressError(hostport, \"missing host and port in address\")\n\n missing_port = \"missing port in address\"\n too_many_colons = \"too many colons in address\"\n host = ''\n port = ''\n\n i = hostport.rfind(':')\n # if there is no ':' in hostport or ':' is the last thing\n # hostport then throw an error into the callers face\n if i < 0 or len(hostport) == i+1:\n raise AddressError(hostport, missing_port)\n\n if '[' in hostport and ']' in hostport:\n # we are treading ipv6 zone\n end = hostport.rfind(']') # get index of ]\n if end+1 == len(hostport):\n # if index of ] is the last thing then there is no port\n raise AddressError(hostport, missing_port)\n elif end+1 == i:\n # this is what we expect\n pass\n else:\n if hostport[end+1] == ':':\n # either ']' is followed by a colon or it is\n # but its not the last one\n raise AddressError(hostport, too_many_colons)\n raise AddressError(hostport, missing_port)\n # host port is worthy ipv6 and should be stripped now.\n host, port = hostport[0 : i], hostport[i+1:]\n host = host.strip('[]')\n elif '[' in hostport or ']' in hostport:\n # contains only one of '[' ']'\n raise AddressError(hostport, \"address has only one of '[' and ']'\")\n else:\n # not string representation of ipv6 but has more than one ':'\n host, port = hostport[0 : i], hostport[i+1:]\n if ':'in host:\n raise AddressError(hostport, too_many_colons)\n \n # we've made it this far and its cool. we can now start the splitting.\n return host, port",
"title": ""
},
{
"docid": "5278c4eb8334b8bea16a151741de233f",
"score": "0.62054735",
"text": "def parse_address(readfunc):\n address_message = readfunc(1)\n address_type, = unpack('B', address_message)\n if address_type == socks5.IPV4:\n raw_address = readfunc(4)\n address = ipaddress.IPv4Address(raw_address).compressed\n elif address_type == socks5.IPV6:\n raw_address = readfunc(16)\n address = ipaddress.IPv6Address(raw_address).compressed\n elif address_type == socks5.DOMAIN:\n n = readfunc(1)\n address_message += n\n n, = unpack('B', n)\n address = raw_address = readfunc(n)\n else:\n return address_type, None, None, address_message\n address_message += raw_address\n raw_port = readfunc(2)\n address_message += raw_port\n port, = unpack('!H', raw_port)\n return address_type, address, port, address_message",
"title": ""
},
{
"docid": "ef220017c8c667745404e59727faef02",
"score": "0.6021543",
"text": "def _parse_addresses(self, data: bytes):\n result = []\n try:\n stream = io.BytesIO(data)\n while stream.tell() < len(data):\n _type = int.from_bytes(stream.read(1), byteorder='big')\n if _type == 1: # IPv4 length 6\n ip = socket.inet_ntoa(stream.read(4))\n port = int.from_bytes(stream.read(2), byteorder='big')\n result.append(f\"{ip}:{port}\")\n elif _type == 2: # IPv6 length 18\n ip = socket.inet_ntop(socket.AF_INET6, stream.read(16))\n port = int.from_bytes(stream.read(2), byteorder='big')\n result.append(f\"[{ip}]:{port}\")\n elif _type == 3: # TORv2 length 12 (deprecated)\n stream.read(12)\n elif _type == 4: # TORv3 length 37\n addr = base64.b32encode(stream.read(35)).decode('ascii').lower()\n port = int.from_bytes(stream.read(2), byteorder='big')\n result.append(f\"{addr}.onion:{port}\")\n elif _type == 5: # DNS up to 258\n hostname_len = int.from_bytes(stream.read(1), byteorder='big')\n hostname = stream.read(hostname_len).decode('ascii')\n port = int.from_bytes(stream.read(2), byteorder='big')\n result.append(f\"{hostname}:{port}\")\n else: # Stop parsing at the first unknown type\n break\n # we simply pass exceptions and return what we were able to read so far\n except Exception:\n pass\n self.addresses = result",
"title": ""
},
{
"docid": "feed03cdcb02466e08861cf523abb169",
"score": "0.5922379",
"text": "def resolve_ipaddress(address:str) -> Tuple[str, int, bool]:\n\tparts = address.split(\":\")\n\tip = parts[0]\n\ttry:\n\t\tinet_pton(AF_INET, ip)\n\t\tport = int(parts[1])\n\t\treturn ip, port, port >= 0 and port <= 65535\n\n\texcept OSError as e: #illegal ip address\n\t\tstderr.write(f\"{e}: {ip}\\n\")\n\n\texcept IndexError: #missing port\n\t\tstderr.write(f\"Bad NAMESERVER format (expected IPv4:PORT, got {address}).\\n\")\n\treturn None, None, False",
"title": ""
},
{
"docid": "b4b68e8c402b0287f642a645fa0fefd1",
"score": "0.59146124",
"text": "def parse_addr(addr, port=20000):\n if addr == '':\n # no address given (default: localhost IPv4 or IPv6)\n return \"\", port, 0\n elif ']:' in addr:\n # IPv6 address with port\n ip, port = addr.rsplit(':', 1)\n return ip.strip('[]'), int(port), 6\n elif ']' in addr:\n # IPv6 address without port\n return addr.strip('[]'), port, 6\n elif addr.count(':') > 1:\n # IPv6 address without port\n return addr, port, 6\n elif ':' in addr:\n # IPv4 address with port\n ip, port = addr.split(':')\n return ip, int(port), 4\n else:\n # IPv4 address without port\n return addr, port, 4",
"title": ""
},
{
"docid": "3b23ee4f7688bf0e21cc863c4b65a5a3",
"score": "0.5900799",
"text": "def _get_addr_info(self, host, port):\r\n addresses = getaddrinfo(host, port)\r\n v4_addresses = [info for info in addresses if info[0] == AF_INET]\r\n if has_ipv6:\r\n v6_addresses = [info for info in addresses if info[0] == AF_INET6]\r\n if v6_addresses and not v4_addresses:\r\n # The only time we return a v6 address is if it's the only option\r\n return v6_addresses[0]\r\n return v4_addresses[0]",
"title": ""
},
{
"docid": "c435997053ca249ff2617f8afd19ed31",
"score": "0.5773865",
"text": "def toAddress(self, data):\n #data = data#.decode()\n data = data.decode()\n ip, port = data.split(':')\n return (ip, int(port))",
"title": ""
},
{
"docid": "f5d2473f4e2d78bfd6d3ec4b581f695f",
"score": "0.57585955",
"text": "def _router_address(self, data):\n args = data.split()[1:]\n try:\n self._relay_attrs['ip_v6'].extend(args)\n except KeyError:\n self._relay_attrs['ip_v6'] = list(args)",
"title": ""
},
{
"docid": "a37e0b13854baef2575a8638bf9e0312",
"score": "0.5728167",
"text": "def splitaddr(line):\n p = r\"IP:(?P<srcip>[a-fx0-9.]*):(?P<srcport>\\d+) --> (?P<dstip>[a-fx0-9.]*):(?P<dstport>\\d+)\"\n\n try:\n d = re.search(p, line).groupdict()\n except:\n return {\"srcip\": \"\", \"srcport\": None, \"dstip\": \"\", \"dstport\": None}\n\n if \"x\" in line:\n d[\"srcip\"] = \".\".join(str(int(x, 16)) for x in\n wrap(d[\"srcip\"][2:].zfill(8), 2))\n d[\"dstip\"] = \".\".join(str(int(x, 16)) for x in\n wrap(d[\"dstip\"][2:].zfill(8), 2))\n d[\"srcport\"] = int(d[\"srcport\"])\n d[\"dstport\"] = int(d[\"dstport\"])\n return d",
"title": ""
},
{
"docid": "9978c60c60cd62cb4270ddc40cdf2c44",
"score": "0.57269293",
"text": "def parse_address(self, address):\n postcode = self.get_postcode(address)\n street = self.get_street(address)\n paon = self.get_paon(address)\n\n return paon, street, postcode",
"title": ""
},
{
"docid": "dbf38b5417ebded7b8f2456e24144041",
"score": "0.57154655",
"text": "def _ParseAddr(addr_str):\n result = re.search(r'(.+):(\\d+)', addr_str)\n if not result:\n _Usage()\n ip = result.group(1)\n port = int(result.group(2))\n return (ip, port)",
"title": ""
},
{
"docid": "63d03d8ae6587d093566b80af99d107b",
"score": "0.57064843",
"text": "def get_host_and_port(address: str, default_port: int) -> tuple:\n host_port = address.split(\":\")\n host = host_port[0]\n if len(host_port) == 2:\n port = int(host_port[1])\n else:\n port = default_port\n return host, port",
"title": ""
},
{
"docid": "4bd87cc983565f92264c332a81e3a4ac",
"score": "0.56836426",
"text": "def split(addrinfo):\n ...",
"title": ""
},
{
"docid": "42dad3286bd8415708cb56a179277420",
"score": "0.56821144",
"text": "def parse_ip_address_or_subnet(value):\r\n address, sep, subnet = value.partition(\"/\")\r\n if sep != \"\":\r\n subnet = int(subnet)\r\n assert 0 <= subnet < 32\r\n fields = address.split(\".\")\r\n assert len(fields) == 4\r\n for field in fields:\r\n num = int(field)\r\n assert 0 <= num < 256\r\n return value",
"title": ""
},
{
"docid": "c8b45b21fb7958ce589f2b166c2746cb",
"score": "0.5669089",
"text": "def parse_address(self, *args, **kwargs):\n raise NotImplementedError",
"title": ""
},
{
"docid": "1880e2617d5f9c5641869f0210741560",
"score": "0.56525695",
"text": "def get_address(source):\n chunks = source.split(':')\n return chunks[0], int(chunks[1]) if len(chunks) > 1 else NTP_PORT",
"title": ""
},
{
"docid": "10f61713343e018ac5ffeeb7380a435f",
"score": "0.56420726",
"text": "def _parse_ipv4(ip):\n\n addr, port = ip.split(':')\n return addr, port",
"title": ""
},
{
"docid": "aaf3419e0c1c3fc2e78b44bee94046d5",
"score": "0.5618106",
"text": "def parse(self, data, offset, indent=0):\n if len(data) <= offset:\n raise ParseError(\"Field %s is missing\" % self.name)\n mode = data[offset]\n if mode == 0:\n print_field(indent, self.name, \"Address not present\")\n return offset + 1\n if mode == 1:\n if len(data) < offset + 3:\n raise ParseError(\"Field %s is missing or short\" %\n self.addr_name)\n print_field(indent, self.name, \"Group address\")\n print_field(indent, self.addr_name,\n \"%04x\" % (data[offset+1] |\n (data[offset+2] << 8)))\n return offset + 3\n if mode == 2:\n if len(data) < offset + 3:\n raise ParseError(\"Field %s is missing or short\" %\n self.addr_name)\n print_field(indent, self.name, \"16-bit\")\n print_field(indent, self.addr_name,\n \"%04x\" % (data[offset+1] |\n (data[offset+2] << 8)))\n return offset + 3\n if mode == 3:\n if len(data) < offset + 9:\n raise ParseError(\"Field %s is missing or short\" %\n self.addr_name)\n if len(data) == offset + 9:\n raise ParseError(\"Field %s is missing\" % self.ep_name)\n print_field(indent, self.name, \"64-bit\")\n print_field(indent, self.addr_name,\n \":\".join(\"%02x\" % b\n for b in data[offset+8:offset:-1]))\n print_field(indent, self.ep_name, \"%02x\" % data[offset+9])\n return offset + 10\n if mode == 0xff:\n if len(data) < offset + 3:\n raise ParseError(\"Field %s is missing or short\" %\n self.addr_name)\n print_field(indent, self.name, \"Broadcast\")\n print_field(indent, self.addr_name,\n \"%04x\" % (data[offset+1] |\n (data[offset+2] << 8)))\n return offset + 3\n raise ParseError(\"Invalid field %s (%02x)\" % (self.name,\n data[offset]))",
"title": ""
},
{
"docid": "e2d01d23d01e86d3099258266b139320",
"score": "0.5573",
"text": "def parse_bind_address(address, default=('localhost', 8080)):\n if ':' in address:\n host, port = address.split(':', 1)\n port = int(port)\n elif re.match(r'^\\d+$', address):\n host = default[0]\n port = int(address)\n else:\n host = address\n port = default[1]\n return host, port",
"title": ""
},
{
"docid": "7901436a5905976cce9373652e11be4d",
"score": "0.5545493",
"text": "def parse_mac_address(cls, proposed_address: str) -> str:\n uppercase = proposed_address.upper()\n cleaned = uppercase.replace(\"-\", \"\") \\\n .replace(\".\", \"\") \\\n .replace(\":\", \"\")\n result = None\n if len(cleaned) == (2 * RmNetUtils.MAC_ADDRESS_LENGTH):\n match = re.match(r\"^[A-Z0-9]+$\", cleaned)\n if match is not None:\n result = cleaned\n return result",
"title": ""
},
{
"docid": "eb791d951c594da301ef354d6c85483e",
"score": "0.55362225",
"text": "def pull_and_validate_addrs():\n print(\"Pulling and validating MAC addresses\")\n\n # Throw an error on a bad MAC address or add it to the global MAC address storage\n # TODO: The validation likely won't go in this script, but we'll keep it here for now\n global mac_addrs\n for addr in run_config.mac_addrs:\n if not re.match(\"[0-9a-f]{2}([:])[0-9a-f]{2}(\\\\1[0-9a-f]{2}){4}$\", addr.lower()):\n print(\"Provided address \" + addr + \" is not a vaid MAC address.\")\n mac_addrs.append(addr.lower())",
"title": ""
},
{
"docid": "bd58451e31a60cc3a971cba6315ba3c3",
"score": "0.5531523",
"text": "def parse(self, addr):\n zip = self.regex_lib.zip_regex.search(addr)\n if zip:\n self.zip = zip.group(0).strip()\n addr = addr.replace(self.zip, '')\n else:\n self.zip = ''\n\n state = self.regex_lib.state_regex.search(addr)\n if state:\n self.state = state.group(0).strip().upper()\n addr = addr[0:state.span()[0]] \n \n # State Standardization - to abbreviation\n if (self.state) and (self.state.upper() in self.standards.states):\n self.state = self.standards.states[self.state.upper()]\n else:\n self.state = ''\n\n\n # TODO: What if pre text?\n # What if number LIKE A701?\n number = self.regex_lib.number_regex.search(addr)\n if number:\n self.number = number.group(0).strip()\n addr = addr.replace(self.number, '')\n if '-' in self.number:\n self.number = self.number[:self.number.index('-')]\n else:\n self.number = ''\n \n pobox = self.regex_lib.po_regex.search(addr)\n if pobox:\n self.po_box_flag = True\n\n\n # TODO: Apt Regex needs to be more flexible\n apt = self.regex_lib.apt_regex.search(addr.strip())\n if apt:\n self.apartment = apt.group(0).strip()\n addr = addr.replace(apt.group(0), '')\n # search for line 2 address\n apt = self.regex_lib.secondary_str_regex.search(addr.strip())\n if apt:\n self.apartment = apt.group(0).strip()\n addr = addr.replace(apt.group(0), '')\n\n # ISSUE: If no/incorrect zip: need street/city value...\n # HACK: Cannot standardize Street direction until 100% positive\n # that City is not in street - EAST HAMPTON , EAST HANOVER, ETC\n #intersection_test = re.search(r'(?:\\s(AT|@|AND|&)\\s)', addr)\n intersection_test = self.regex_lib.intersection_test.search(addr)\n if intersection_test:\n self.intersection_flag = True\n self.street1 = addr[0:intersection_test.span()[0]]\n addr = addr.replace(intersection_test.group(0), '')\n addr = addr.replace(self.street1, '')\n self.street1 = self.street1.upper()\n\n street = self.regex_lib.street_regex.search(addr)\n if street:\n street = street.group(0).strip().split(' ')\n if self.intersection_flag:\n self.street2 = ' '.join(street)\n else:\n self.street1 = ' '.join(street).upper().strip()\n self.delta = addr",
"title": ""
},
{
"docid": "4253537d3006eb5cccfcf3942322e5aa",
"score": "0.5527876",
"text": "def _ip_from_options(self, option_name_prefix):\n ip = \"\"\n for i in [1, 2, 3, 4]:\n option = option_name_prefix + str(i)\n octet = self._get_option(option)\n if octet is None or len(str(octet)) < 1:\n return None\n\n ip = ip + str(octet)\n if i < 4:\n ip = ip + \".\"\n\n return ip",
"title": ""
},
{
"docid": "631172bc33954af43af2ae43b0155e79",
"score": "0.549989",
"text": "def validate(value):\n try:\n wtypes.IPv4AddressType.validate(value)\n return value\n except ValueError:\n try:\n wtypes.IPv6AddressType.validate(value)\n return value\n except ValueError as e:\n error = 'Value should be IPv4 or IPv6 format'\n raise ValueError(error) from e",
"title": ""
},
{
"docid": "304995f9b648c6f18691ee537a11cbb8",
"score": "0.54927886",
"text": "def ip_obj(addr: Text) -> Tuple[Text, Text]:\n\n try:\n # Is address an ipv4 or ipv6?\n ip = ipaddress.ip_address(addr.strip())\n\n if ip.version not in (4, 6):\n raise ValueError('Unknown IP version: %s' % ip.version)\n\n return (\"ipv{}\".format(ip.version), ip.exploded)\n except ValueError:\n raise ValueError('Invalid IP address: %s' % addr)",
"title": ""
},
{
"docid": "10c6c9fdbdda38450d64c29efba1e21a",
"score": "0.54877573",
"text": "def _format_address(\n address: Any, output_format: str, must_contain: Tuple[str, ...], split: bool, errors: str\n) -> Any:\n address_dict, status = _check_address(address, must_contain, True)\n outputs = _address_dict_to_string(address_dict, output_format, split)\n\n if status == \"null\":\n return (np.nan,) * len(_get_column_names(output_format, split)) + (0,)\n\n elif status == \"unknown\":\n if errors == \"raise\":\n raise ValueError(f\"unable to parse value {address}\")\n return tuple(\n np.nan if not value else value if errors == \"ignore\" else np.nan for value in outputs\n ) + (1,)\n\n if len(outputs) == 1 and address == outputs[0]:\n code = 3\n else:\n code = 2\n return tuple(np.nan if not value else value for value in outputs) + (code,)",
"title": ""
},
{
"docid": "1bedde1df918fc8def509adfd269cd67",
"score": "0.5468448",
"text": "def ipaddr(value, options=None):\n ipv4_obj = ipv4(value, options=options)\n ipv6_obj = ipv6(value, options=options)\n if ipv4_obj is None or ipv6_obj is None:\n # an IP address can be either IPv4 either IPv6\n # therefofe if the value passed as arg is not a list, at least one of the calls above will return None\n # if one of them is none, means that we should return only one of them\n return ipv4_obj or ipv6_obj # one of them\n else:\n return ipv4_obj + ipv6_obj # extend lists",
"title": ""
},
{
"docid": "67626eb606b0722883bec7f265b2205d",
"score": "0.5459971",
"text": "def _guess_address_type(address) -> str:\n # the fewest number of : in an ipv6 is for ::1. A domain or IP should never have one so checking for 2 is safe\n if address.count(\":\") > 1:\n return \"ipv6\"\n\n # IPv4 must be 4 octets and no TLD can be a number so checking for both gives us a good guess between the two\n parts = address.split(\".\")\n if len(parts) == 4:\n if parts[3].isnumeric():\n if 0 <= int(parts[3]) < 256:\n return \"ipv4\"\n\n return \"domain\"",
"title": ""
},
{
"docid": "391ef66b3e8e6fdd29a65a3db8e71de8",
"score": "0.5458938",
"text": "def is_netaddr(form, value):\n items = value.split()\n if len(items) > 1:\n raise ValidationError(\n lazy_gettext(u'You have to enter a valid net address.')\n )\n items = items[0].split(':')\n if len(items) not in (1, 2):\n raise ValidationError(\n lazy_gettext(u'You have to enter a valid net address.')\n )\n elif len(items) == 2 and not items[1].isdigit():\n raise ValidationError(lazy_gettext(u'The port has to be numeric'))",
"title": ""
},
{
"docid": "3b3b2de9ea557449c2cfc124295fa132",
"score": "0.54492474",
"text": "def extract_ipv4(data):\r\n results = []\r\n flattened_data = flatten_list(data)\r\n addr, mask = None, None\r\n for i, v in enumerate(flattened_data):\r\n if v == 'ip' and (i < len(flattened_data) - 1):\r\n addr = flattened_data[i+1]\r\n if addr:\r\n addr = addr.strip('\"')\r\n elif v == 'mask' and (i < len(flattened_data) - 1):\r\n mask = flattened_data[i+1]\r\n if addr and mask:\r\n if re.match(r'\\d+\\.\\d+\\.\\d+\\.\\d+', addr) and (isinstance(mask, int) or re.match(r'\\d+', mask)):\r\n results.append((addr, str(mask)))\r\n addr, mask = None, None\r\n return results",
"title": ""
},
{
"docid": "bd1bf4816d96c2245478146178d78e72",
"score": "0.54396546",
"text": "def _check_address(address: Any, must_contain: Tuple[str, ...], clean: bool) -> Any:\n if address in NULL_VALUES:\n return (None, \"null\") if clean else False\n\n address = re.sub(r\"[().]\", \"\", str(address))\n\n try:\n address, _ = tag(address, TAG_MAPPING)\n\n except RepeatedLabelError:\n return (None, \"unknown\") if clean else False\n\n status = _check_status(address, must_contain)\n\n if status:\n return (address, \"success\") if clean else True\n\n return (address, \"unknown\") if clean else False",
"title": ""
},
{
"docid": "23a0fc2440614ac90a36c766eda9b685",
"score": "0.54298544",
"text": "def _extract_addr_info(self, xpath_sel):\n\n text_list = [a.strip() for a in xpath_sel.xpath(\"./text()\").extract() if a.strip()]\n if not text_list:\n #sometimes they send a longer list hidden in html !\n text_list = [a.strip() for a in xpath_sel.xpath(\"./div[@class='address']/text()\").extract() if a.strip()]\n\n name = xpath_sel.xpath(\".//strong/text()\")[0].extract()\n\n ma = get_fresh_merchant_address()\n\n if name:\n ma[\"address_name\"] = name\n\n #try to match the whole address\n addr_text = \" \".join(text_list)\n #print \"ADDR_TEXT : \",addr_text\n res = re.search(\"(.*)(\\d{3}\\-\\d{3}\\-\\d{4})\", addr_text)\n\n if res:\n final_address = res.group(1).strip()\n ma[\"address\"] = final_address\n ma[\"phone_number\"] = res.group(2).strip()\n else:\n ma[\"address\"] = addr_text.strip()\n\n return ma",
"title": ""
},
{
"docid": "adc026c6bed04a193f997284b7f1a319",
"score": "0.5422981",
"text": "def post_parse_dev(self):\n \"\"\" \n Prefix Type-some of these are 2+ words. \n need to be able to handle this. Maybe via regex with anchor in front?\n If No City is found -> educated guess by chopping off chunks?\n \"\"\"\n try:\n\n street = self.street1.upper().strip()\n # Pre-Street Type\\\n pretype = self.regex_lib.street_prefix_regex.search(street.strip())\n if pretype:\n self.street1_pretype = pretype.group(0).strip().title()\n street = street.replace(pretype.group(0), '')\n \n addr_tokens = street.split(' ')\n \n if len(addr_tokens) > 2:\n # Post-Direction\n if addr_tokens[-1] in self.standards.street_direction:\n self.street1_postdir = self.standards.street_direction[addr_tokens[-1]]\n # remove addr_tokens[-1] \n addr_tokens[-1] = self.standards.street_direction[addr_tokens[-1]]\n del addr_tokens[-1]\n\n # Post Street Type\n if addr_tokens[-1] in self.standards.cannonical_types:\n self.street1_type = self.standards.cannonical_types[addr_tokens[-1]].strip()\n self.street1_type = self.street1_type.strip()\n del addr_tokens[-1]\n\n # Pre-Direction\n if len(addr_tokens) > 1 and addr_tokens[0] in self.standards.street_direction:\n street1_predir = self.standards.street_direction[addr_tokens[0]]\n del addr_tokens[0]\n\n else: \n # Post Street Type\n if addr_tokens[-1] in self.standards.cannonical_types:\n self.street1_type = self.standards.cannonical_types[addr_tokens[-1]].strip()\n del addr_tokens[-1]\n self.street1 = ' '.join(addr_tokens).strip()\n\n # State Standardization\n if (self.state) and (self.state.upper() in self.standards.states_v2):\n self.state = self.standards.states_v2[self.state.upper()]\n # self.state = self.state.title()\n self.state = self.state\n except:\n #print '\\t\\tERROR IN POST_PARSE_DEV: %s\\n%s' % (sys.exc_info()[1], sys.exc_info()[2] ) \n return",
"title": ""
},
{
"docid": "8e538c53fb7b1b9a7b458a200d155a34",
"score": "0.54136336",
"text": "def ipv4_address(self):",
"title": ""
},
{
"docid": "15d7d72a76c646bfadc605a0412d6370",
"score": "0.5404541",
"text": "def unpack_address(self, b):\n bytes_read = 0\n (length, r) = unpack_uint8_t(b[bytes_read])\n bytes_read+=r\n #addr = 0\n # an embedded (non-referenced) address\n if length != 0:\n (typ, r) = unpack_uint8_t(b[bytes_read])\n bytes_read+=r\n addr = b[bytes_read:bytes_read+length]\n bytes_read+=length\n addr_id = len(self.referenced_address)\n self.referenced_address[addr_id] = addr \n # a referenced address\n else:\n (addr_id, r) = unpack_uint32_t(b[bytes_read:])\n bytes_read+=r\n try:\n addr = self.referenced_address[addr_id]\n except:\n print(\"Die: couldn't find referenced address %d\" % addr_id)\n sys.exit(-1)\n if len(addr) == 4:\n return (socket.inet_ntop(socket.AF_INET, addr), bytes_read)\n elif len(addr) == 16:\n return (socket.inet_ntop(socket.AF_INET6, addr), bytes_read)\n else:\n assert False",
"title": ""
},
{
"docid": "37c62b93c2156c97a70f6a795b8704f7",
"score": "0.53985095",
"text": "def prefix_to_network(prefix):\n ipaddr = ipaddress.ip_interface(prefix) # turn into ipaddress object\n address = ipaddr.ip\n mask = ipaddr.netmask\n return address, mask",
"title": ""
},
{
"docid": "d93381c8be5837c35ff84373baa4e563",
"score": "0.5387671",
"text": "def test_get_family_from_address():",
"title": ""
},
{
"docid": "9e2fff88e3e138af171216592ad0ff09",
"score": "0.5380194",
"text": "def mac_parser(mac_address_table_output):\n mac_table_regex = '(\\d*)\\s*(([0-9a-fA-F]{4}[.-:|]?){3})\\s*(\\w*)\\s*(.*)'\n list_of_mac_address_info = []\n for item in mac_address_table_output:\n if re.search(mac_table_regex, item):\n found = re.search(mac_table_regex, item)\n if found.group(5) == 'CPU': #Ignores entries in MAC address table with PORT as CPU\n pass\n else:\n dict = {}\n dict['VLAN'] = found.group(1)\n dict['MAC_ADDRESS'] = found.group(2)\n dict['LEARNED_VIA']= found.group(4) \n dict['INTERFACE'] = found.group(5)\n list_of_mac_address_info.append(dict)\n return list_of_mac_address_info",
"title": ""
},
{
"docid": "33051f321f54c8dc9c40a282e58eba41",
"score": "0.5377643",
"text": "def __init__ (self, addr):\r\n # Always stores as a 6 character string\r\n if isinstance(addr, bytes) or isinstance(addr, basestring):\r\n if len(addr) == 6:\r\n # raw\r\n pass\r\n elif len(addr) == 17 or len(addr) == 12 or addr.count(':') == 5:\r\n # hex\r\n if len(addr) == 17:\r\n if addr[2::3] != ':::::' and addr[2::3] != '-----':\r\n raise RuntimeError(\"Bad format for ethernet address\")\r\n # Address of form xx:xx:xx:xx:xx:xx\r\n # Pick out the hex digits only\r\n addr = ''.join((addr[x*3:x*3+2] for x in xrange(0,6)))\r\n elif len(addr) == 12:\r\n pass\r\n else:\r\n # Assume it's hex digits but they may not all be in two-digit\r\n # groupings (e.g., xx:x:x:xx:x:x). This actually comes up.\r\n addr = ''.join([\"%02x\" % (int(x,16),) for x in addr.split(\":\")])\r\n # We should now have 12 hex digits (xxxxxxxxxxxx).\r\n # Convert to 6 raw bytes.\r\n addr = b''.join((chr(int(addr[x*2:x*2+2], 16)) for x in range(0,6)))\r\n else:\r\n raise RuntimeError(\"Expected ethernet address string to be 6 raw \"\r\n \"bytes or some hex\")\r\n self._value = addr\r\n elif isinstance(addr, EthAddr):\r\n self._value = addr.toRaw()\r\n elif type(addr) == list or (hasattr(addr, '__len__') and len(addr) == 6\r\n and hasattr(addr, '__iter__')):\r\n self._value = b''.join( (chr(x) for x in addr) )\r\n elif addr is None:\r\n self._value = b'\\x00' * 6\r\n else:\r\n raise RuntimeError(\"Expected ethernet address to be a string of 6 raw \"\r\n \"bytes or some hex\")",
"title": ""
},
{
"docid": "4a24bd5eb10ddfb823af38f88c7d865b",
"score": "0.5375611",
"text": "def parse_network(value, cols):\n brd = None\n scope = None\n if \"/\" in value: # we have a CIDR in this address\n ip, cidr = value.split(\"/\")\n else:\n ip = value\n cidr = 32\n\n if type_ == \"inet\":\n mask = cidr_to_ipv4_netmask(int(cidr))\n if \"brd\" in cols:\n brd = cols[cols.index(\"brd\") + 1]\n elif type_ == \"inet6\":\n mask = cidr\n if \"scope\" in cols:\n scope = cols[cols.index(\"scope\") + 1]\n return (ip, mask, brd, scope)",
"title": ""
},
{
"docid": "4c40ebd1caf6f7f6210192ea74d8c884",
"score": "0.5372666",
"text": "def test_part2 (self):\r\n h = '\\xfe\\x80\\x00\\x00\\x00\\x00\\x00\\x00\\xba\\x8d\\x12\\xff\\xfe\\x2a\\xdd\\x6e'\r\n a = IPAddr6.from_raw(h)\r\n assert str(a) == 'fe80::ba8d:12ff:fe2a:dd6e'\r\n assert a.raw == h\r\n\r\n assert a.num == 0xfe80000000000000ba8d12fffe2add6e\r\n assert IPAddr6.from_num(a.num) == a\r\n\r\n assert a.is_multicast is False\r\n assert IPAddr6(\"FF02:0:0:0:0:0:0:1\").is_multicast\r\n\r\n assert IPAddr6('2001:db8:1:2::').set_mac('00:1D:BA:06:37:64') \\\r\n == '2001:db8:1:2:021d:baff:fe06:3764'\r\n\r\n assert IPAddr6('0:0:0:0:0:FFFF:222.1.41.90') == '::ffff:222.1.41.90'\r\n assert IPAddr6('::ffff:C0A8:5') == '::ffff:192.168.0.5'\r\n assert IPAddr6('::ffff:192.168.0.5') == '::ffff:c0a8:5'",
"title": ""
},
{
"docid": "823a7aa07e6314a875e7e29f191d28e6",
"score": "0.5355483",
"text": "def parse(self, data, offset, indent=0):\n if len(data) < offset:\n raise ParseError(\"Field %s is missing\" % self.name)\n if len(data) < offset + 9:\n raise ParseError(\"Field %s is missing or short\" %\n self.addr_name)\n mode = data[offset]\n if mode == 3:\n print_field(indent, self.name, \"64-bit\")\n print_field(indent, self.addr_name,\n \":\".join(\"%02x\" % b\n for b in data[offset+8:offset:-1]))\n elif mode == 2:\n print_field(indent, self.name, \"16-bit\")\n print_field(indent, self.addr_name,\n \"%04x\" % (data[offset+1] |\n (data[offset+2] << 8)))\n elif mode == 1:\n print_field(indent, self.name, \"Group address\")\n print_field(indent, self.addr_name,\n \"%04x\" % (data[offset+1] |\n (data[offset+2] << 8)))\n elif mode == 0:\n print_field(indent, self.name, \"Address not present\")\n elif mode == 0xff:\n print_field(indent, self.name, \"Broadcast\")\n print_field(indent, self.addr_name,\n \"%04x\" % (data[offset+1] |\n (data[offset+2] << 8)))\n else:\n print_field(indent, self.name, \"Unknown(%02x)\" % mode)\n print_field(indent, self.addr_name,\n \" \".join(\"0x%02x\" % b\n for b in data[offset+1:offset+9]))\n return offset + 9",
"title": ""
},
{
"docid": "501c98d8cf2d3df3a67b590b7326c90f",
"score": "0.5355263",
"text": "def address(self, address, delimiter = \".\"):\n def list_addr(addr):\n \"\"\"list_addr(addr)\n Takes a list of digits representing an Ipv4 address and converts\n it to a single long\"\"\"\n if len(addr) != 4:\n raise ValueError('Expected 4 octet address, got %i in (%s)' %\n (len(addr), address))\n # converts a list into a single long\n # [255, 255, 255, 255] -> 0b11111111 11111111 11111111 11111111\n _addr = sum(\n [addr[octet] << (8 * (3 - octet)) for octet in range(4)]\n )\n return _addr\n\n def string_addr(addr):\n \"\"\"string_addr(addr)\n Takes a string representing an Ipv6 address, and converts it to a\n single long\"\"\"\n return list_addr(\n map(int, addr.split(self._delimiter))\n )\n\n self._delimiter = delimiter\n if isinstance(address, str):\n self._address = string_addr(address)\n elif isinstance(address, tuple) or isinstance(address, list):\n self._address = list_addr(address)\n elif isinstance(address, int) or isinstance(address, long):\n self._address = address\n elif isinstance(address, Ipv4):\n self._address = list_addr(list(address))\n else:\n raise TypeError(\n 'Unsupported type for address initialization %s, (%s)' % (\n type(address), address)\n )",
"title": ""
},
{
"docid": "ef62aca307931dcd1ae01992f96e5346",
"score": "0.5349561",
"text": "def do_ipaddr(self, line):\n args = line.split(\" \")\n valid = 1\n preferred = 1\n flags = 0\n prefix_len = 64 # always use /64, as prefix.network.prefixlen returns /128.\n\n num = len(args)\n if (num > 1):\n ipaddr = args[1]\n prefix = ipaddress.IPv6Interface(unicode(ipaddr))\n arr = prefix.ip.packed\n\n if args[0] == \"\":\n v = self.prop_get_value(SPINEL_PROP_IPV6_ADDRESS_TABLE)\n # TODO: clean up table parsing to be less hard-coded magic.\n sz = 0x1B\n addrs = [v[i:i+sz] for i in xrange(0, len(v), sz)]\n for addr in addrs:\n addr = addr[2:18]\n print str(ipaddress.IPv6Address(addr))\n\n elif args[0] == \"add\":\n arr += pack('B', prefix_len) \n arr += pack('<L', valid)\n arr += pack('<L', preferred)\n arr += pack('B', flags)\n value = self.prop_insert_value(SPINEL_PROP_IPV6_ADDRESS_TABLE, \n arr, str(len(arr))+'s')\n if self.wpanApi.tun_if:\n self.wpanApi.tun_if.addr_add(ipaddr)\n\n elif args[0] == \"remove\":\n value = self.prop_remove_value(SPINEL_PROP_IPV6_ADDRESS_TABLE, \n arr, str(len(arr))+'s')\n if self.wpanApi.tun_if:\n self.wpanApi.tun_if.addr_del(ipaddr)\n\n print(\"Done\")",
"title": ""
},
{
"docid": "dbcc9006161fac6d267c5b637684444d",
"score": "0.53353196",
"text": "def get_address_type(self, idx: int):\n if not self.announced or len(self.addresses) <= idx:\n return None\n addrstr = self.addresses[idx]\n if \".onion:\" in addrstr:\n return 'tor'\n if addrstr[0].isdigit():\n return 'ipv4'\n if addrstr.startswith(\"[\"):\n return 'ipv6'\n return 'dns'",
"title": ""
},
{
"docid": "37e235573a746a3b02fd4a20671953a5",
"score": "0.5334579",
"text": "def parse_address(self, address):\n num = len(address)\n if num == 0:\n return {}\n if isinstance(address[num-1], dict):\n map = address[num-1].copy()\n num -= 1\n else:\n map = {}\n if num == 1:\n map[\"host\"] = address[0]\n elif num == 2:\n map[\"host\"] = address[0]\n map[\"port\"] = address[1]\n elif num > 2:\n raise ValueError\n return map",
"title": ""
},
{
"docid": "e8667f5a2fa677d57e24308cdb7dd837",
"score": "0.5328319",
"text": "def _process_binding_address(self, binding, is_ipv6, is_anycast, vip_exempt):\n addr = binding[\"address\"]\n if is_ipv6:\n # we need to translate the netmask6 exposed by puppet into a prefix length since ipaddress\n # library does not support this case.\n nm = self._get_ipv6_prefix_length(binding[\"netmask\"])\n else:\n nm = binding[\"netmask\"]\n\n address = ipaddress.ip_interface(f\"{addr}/{nm}\")\n\n if ((address.is_link_local) or (address.is_loopback)):\n # We skip link local and loopback addresses\n return None\n\n # Warn the user if one of the IP is an auto-config IP and skip it\n if is_ipv6 and address.exploded[27:32] == 'ff:fe':\n self.log_warning(f\"{address}: skipping SLAAC IP\")\n return None\n\n if vip_exempt:\n # FIXME\n # this is a bug in our deployment of certain servers where some service addresses have\n # an incorrect netmask and aren't actually VIPs\n # figure out the actual netmask from the prefix\n prefixq = PrefixFilterSet().search_contains(Prefix.objects.all(), \"\", str(address))\n if not prefixq:\n self.log_failure(f\"Can't find matching prefix for {address} when fixing netmask!\")\n return None\n realnetmask = max([i.prefix.prefixlen for i in prefixq])\n address = ipaddress.ip_interface(f\"{addr}/{realnetmask}\")\n self.log_info(\"VIP exempt: Overriding provided netmask\")\n\n if (is_anycast or (address.network.prefixlen in (32, 128))):\n self._handle_vip(address, is_anycast)\n return None\n\n return address",
"title": ""
},
{
"docid": "97ec0d934c2c510915978b447ccada2f",
"score": "0.5315125",
"text": "def parse_address(addr_entry):\n label = get_label_from_schema(addr_entry)\n if addr_entry.po_box:\n street = safe_text(addr_entry.po_box)\n else:\n street = safe_text(addr_entry.street)\n\n # figure out neighborhood/city distinction.\n neighborhood = None\n if addr_entry.neighborhood:\n if not addr_entry.city:\n city = addr_entry.neighborhood.text\n else:\n neighborhood = addr_entry.neighborhood.text\n city = addr_entry.city.text\n else:\n city = safe_text(addr_entry.city)\n\n region = safe_text(addr_entry.region)\n postcode = safe_text(addr_entry.postcode)\n country = safe_text(addr_entry.country)\n return (label, street, neighborhood, city, region, postcode, country)",
"title": ""
},
{
"docid": "a8882691a50ea88fc6cf11fa460e5ea3",
"score": "0.53118026",
"text": "def get_ipv4_info(**connection_info):\n ipv4_info = {}\n res = {}\n if connection_info['IPv4']['method'] == 'auto':\n ipv4_info['method'] = 'auto'\n if connection_info['IPv4']['ignore-auto-dns']:\n ipv4_info['ignore-auto-dns'] = connection_info['IPv4']['ignore-auto-dns']\n if connection_info['IPv4']['never-default']:\n ipv4_info['never-default'] = connection_info['IPv4']['never-default']\n if connection_info['IPv4']['dns']:\n # Check if dns is valid\n if check_ipv4(*connection_info['IPv4']['dns']):\n ipv4_info['dns'] = connection_info['IPv4']['dns']\n else:\n res[conststr.RET_TYPE] = conststr.FAILED_RES\n res[conststr.RET_MSG] = conststr.INVALID_DNS_ADDR\n res[conststr.RET_CODE] = -1\n elif connection_info['IPv4']['method'] == 'manual':\n ipv4_info['method'] = 'manual'\n if connection_info['IPv4']['addresses']:\n # Check if ipv4 address is valid.\n ips = [x[0] for x in connection_info['IPv4']['addresses']] + [x[2] for x in connection_info['IPv4']['addresses']]\n pfs = [x[1] for x in connection_info['IPv4']['addresses']]\n if not check_ipv4(*ips):\n res[conststr.RET_TYPE] = conststr.FAILED_RES\n res[conststr.RET_MSG] = conststr.INVALID_IPV4_ADDR\n res[conststr.RET_CODE] = -1\n elif not check_pf4(*pfs):\n res[conststr.RET_TYPE] = conststr.FAILED_RES\n res[conststr.RET_MSG] = conststr.INVALID_IPV4_PREFIX\n res[conststr.RET_CODE] = -1\n else:\n ipv4_info['addresses'] = connection_info['IPv4']['addresses']\n else:\n res[conststr.RET_TYPE] = conststr.FAILED_RES\n res[conststr.RET_MSG] = conststr.EMPTY_IPV4_ADDR\n res[conststr.RET_CODE] = -1\n if connection_info['IPv4']['dns']:\n # Check if dns is valid\n if check_ipv4(*connection_info['IPv4']['dns']):\n ipv4_info['dns'] = connection_info['IPv4']['dns']\n else:\n res[conststr.RET_TYPE] = conststr.FAILED_RES\n res[conststr.RET_MSG] = conststr.INVALID_DNS_ADDR\n res[conststr.RET_CODE] = -1\n if connection_info['IPv4']['ignore-auto-dns']:\n ipv4_info['ignore-auto-dns'] = connection_info['IPv4']['ignore-auto-dns']\n if connection_info['IPv4']['never-default']:\n ipv4_info['never-default'] = connection_info['IPv4']['never-default']\n elif connection_info['IPv4']['method'] == 'link-local':\n ipv4_info['method'] = 'link-local'\n if connection_info['IPv4']['ignore-auto-dns']:\n ipv4_info['ignore-auto-dns'] = connection_info['IPv4']['ignore-auto-dns']\n if connection_info['IPv4']['never-default']:\n ipv4_info['never-default'] = connection_info['IPv4']['never-default']\n elif connection_info['IPv4']['method'] == 'disabled':\n ipv4_info['method'] = 'disabled'\n return ipv4_info, res",
"title": ""
},
{
"docid": "40a34061855701db58d67ae646c32de4",
"score": "0.528599",
"text": "def parseAddress(self, address):\n add_list = []\n for i in range(4):\n add_list.append(int(address.hex()[(i * 2): (i + 1) * 2], 16))\n add_str = (\n str(add_list[0])\n + \".\"\n + str(add_list[1])\n + \".\"\n + str(add_list[2])\n + \".\"\n + str(add_list[3])\n )\n return add_str",
"title": ""
},
{
"docid": "3aebee88b657a52ad47daf580fd393f0",
"score": "0.52794445",
"text": "def splitaddr(line):\n pattern = r\"(IN|OUT): ([0-9.]*):(\\d+) --> ([0-9.]*):(\\d+) \\((\\D+)\\)\"\n keys = (\"direction\", \"srcip\", \"srcport\", \"dstip\", \"dstport\", \"proto\")\n m = re.search(pattern, line)\n try:\n return dict((k, v) for k, v in zip(keys, m.groups()))\n except:\n return dict((k, None) for k in keys)",
"title": ""
},
{
"docid": "677cbd42ab236ac8a957610fa272e212",
"score": "0.52793294",
"text": "def _parse_host_header(self):\n host = self.host_header\n if not host:\n return None, None\n port = None\n m = host_header_re.match(host)\n if m:\n host = m.group(\"host\").strip(\"[]\")\n if m.group(\"port\"):\n port = int(m.group(\"port\"))\n return host, port",
"title": ""
},
{
"docid": "983e37b57b7d8bd83691c5a4126cf838",
"score": "0.5267389",
"text": "def _parse_address(key):\n return key.split('/')[-2]",
"title": ""
},
{
"docid": "0ad177d2b9b24a907510d914fa6e36aa",
"score": "0.5260803",
"text": "def get_address_components(full_address=None, short_address=None):\n raw_address = None\n address_dict = {}\n from_full_address = False\n\n if full_address:\n raw_address = full_address\n from_full_address = True\n\n elif short_address:\n raw_address = short_address\n\n if raw_address:\n if ',' not in raw_address:\n address_dict['address1'] = raw_address\n else:\n address_componets = raw_address.split(',')\n if len(address_componets) == 4:\n address_dict['address1'] = address_componets[0]\n address_dict['address2'] = address_componets[1].strip()\n address_dict['city'] = address_componets[2].strip().title()\n elif len(address_componets) == 3 and from_full_address:\n address_dict['address1'] = address_componets[0]\n address_dict['city'] = address_componets[1].strip().title()\n elif not from_full_address:\n address_dict['city'] = address_componets[0].strip().title()\n \n # Try to get zip code\n zip_code = re.search(r'(\\d{5})([-])?(\\d{4})?', raw_address)\n address_dict['zip_code'] = ''.join(match for match in zip_code.groups() if match) if zip_code else None\n\n state = re.search(r'([A-Z]{2})', raw_address)\n address_dict['state'] = state.group(0) if state else None\n\n if 'United States'.lower() in raw_address.lower() or state or zip_code:\n address_dict['country'] = 'US'\n address_dict['full_address'] = raw_address\n return address_dict",
"title": ""
},
{
"docid": "4fa9a97e4141783de5522dd293186a5f",
"score": "0.5260441",
"text": "def _parse_request_ipv4(self, data: bytes) -> None:\n host = socket.inet_ntoa(data[:4])\n port = struct.unpack(\"!H\", data[4:6])[0]\n self._done_parsing(host, port)",
"title": ""
},
{
"docid": "58f3129f17f999c0ce47323432bac75d",
"score": "0.525287",
"text": "def address_prefixes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"address_prefixes\")",
"title": ""
},
{
"docid": "58f3129f17f999c0ce47323432bac75d",
"score": "0.525287",
"text": "def address_prefixes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"address_prefixes\")",
"title": ""
},
{
"docid": "58f3129f17f999c0ce47323432bac75d",
"score": "0.525287",
"text": "def address_prefixes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"address_prefixes\")",
"title": ""
},
{
"docid": "6037eff73a9fe2443cabc05f21299a91",
"score": "0.5245369",
"text": "def full_address(self, separator=\", \"):\n addr = separator.join(filter(None, (self.addr_street, self.addr_line2,\n self.addr_town, self.addr_postcode)))\n if not addr.strip():\n return 'Address unknown'\n return addr",
"title": ""
},
{
"docid": "635861155cbb74fdfa2c80e64e0bf156",
"score": "0.5241696",
"text": "def _structured_addr(match):\r\n\r\n res = (match.group('name'), {\r\n 'addr': match.group('addr'),\r\n 'type': match.group('type'),\r\n 'scope': match.group('scope'),\r\n })\r\n\r\n brd = match.group('brd')\r\n alias = match.group('alias')\r\n extra = match.group('extra')\r\n\r\n if brd:\r\n res[1]['brd'] = brd\r\n if alias:\r\n res[1]['alias'] = alias\r\n if extra:\r\n res[1]['settings'] = _dict_from_spaced_kv(extra)\r\n\r\n return res",
"title": ""
},
{
"docid": "602e8e17afc84f74c9f02034ae7bfdb9",
"score": "0.52396375",
"text": "def valid_ip_addresses():\r\n temp = []\r\n\r\n for i in psutil.net_if_addrs().items():\r\n for j in i[1]:\r\n if re.match(r\"192\\.168\\..+\\..+\",j[1]):\r\n temp.append(j[1])\r\n \r\n return temp",
"title": ""
},
{
"docid": "9327cc0ff5af097fbe212839d195a06c",
"score": "0.5239225",
"text": "def _prom_addr_of_element(element):\n if not element:\n # this is sciond\n return '127.0.0.1', 32040\n (addrs_selector, public_keyword, bind_keyword, port_keyword) = \\\n ('InternalAddrs','PublicOverlay','BindOverlay', 'OverlayPort') if 'InternalAddrs' in element.keys() \\\n else ('Addrs','Public','Bind', 'L4Port')\n addrs = next(iter(element[addrs_selector].values()))\n addr_type = bind_keyword if bind_keyword in addrs.keys() else public_keyword\n ip = addrs[addr_type]['Addr']\n port = addrs[addr_type][port_keyword] + PROM_PORT_OFFSET\n return ip, port",
"title": ""
},
{
"docid": "f3e94547f9098f8ff2d8fca6f4c4e5fd",
"score": "0.5236579",
"text": "def urlsafe_address(address):\n addr, port, *rest = address\n\n if rest:\n # An IPv6 address needs to be surrounded by square brackets\n addr = f'[{addr}]'\n\n return addr, port",
"title": ""
},
{
"docid": "b53e2d269f9a772227ebca3b1684bb71",
"score": "0.52343327",
"text": "def resolve_router():\n gws = netifaces.gateways()\n gateway_ip = gws['default'][netifaces.AF_INET][0]\n output = check_output([\"nmap\", \"-sP\", \"-n\", gateway_ip]).decode(sys.stdout.encoding)\n regmatch = re.compile(\"MAC Address: ([0-9A-F:]+) ((.*))\", re.MULTILINE)\n matches = regmatch.findall(output)\n if matches is None or len(matches) > 0:\n return matches[0][1]\n return \"Unknown\"",
"title": ""
},
{
"docid": "d63311dfb459233f47df9cc4aff8a4e7",
"score": "0.5233224",
"text": "def extract_address(search_response):\n if type(search_response) is not SearchResponse:\n raise CaprotoTypeError(\"expected SearchResponse, not {!r}\"\n \"\".format(type(search_response).__name__))\n if search_response.header.parameter1 == 0xffffffff:\n # The CA spec tells us that this sentinel value means we\n # should fall back to using the address of the sender of\n # the UDP datagram.\n address = search_response.sender_address[0]\n else:\n address = search_response.ip\n return (address, search_response.port)",
"title": ""
},
{
"docid": "5f8d6d60fcba1940db2642197e6f65cd",
"score": "0.52272934",
"text": "def addrstr(addr):\n sz = len(addr)\n if sz == 4: # IP\n return inet_ntoa(addr)\n elif sz == 6: # MAC\n return hexstr(addr)\n else:\n logging.warning('unexpected address length: %d' % sz)\n return hexstr(addr)",
"title": ""
},
{
"docid": "80cd965d2ed720856372215c2b181095",
"score": "0.5224687",
"text": "def handleLine(ip4, ip6, names, outp, generate):\n\n #print(\"_{}_\\t_{}_\\t_{}_\".format(ip4, ip6, ' '.join(names)), file=debugOut)\n\n # at least one address has to be supplied\n if ip4 is None and ip6 is None:\n print(\"Neither IPv4 nor IPv6 address were specified for name(s) \" + ','.join(names), file=debugOut)\n return False\n\n # there must be at least one name (currently this is handled by the line-parser, so this should never happen)\n if len(names) <= 0:\n print(\"No names were specified for {}, {}\".format(ip4, ip6), file=debugOut)\n return False\n\n try:\n if ip4 is None:\n ip4addr = None\n else:\n ip4addr = ipaddress.IPv4Address(ip4)\n if not ip4addr in ip4_range:\n print(\"IPv4 address not in permitted range \" + str(ip4_range), file=debugOut)\n return False\n\n if ip6 is None:\n ip6addr = None\n else:\n ip6addr = ipaddress.IPv6Address(ip6)\n if not ip6addr in ip6_range:\n print(\"IPv6 address not in permitted range \" + str(ip6_range), file=debugOut)\n return False\n\n except Exception as e:\n print(\"Invalid IP address: \" + repr(e), file=debugOut)\n return False\n\n for name in names:\n if nameRegex.match(name) is None:\n print(\"Invalid name \\\"{}\\\", does not match {}\".format(name, nameRegex.pattern), file=debugOut)\n return False\n\n #TODO: check for double addresses\n #TODO: check for double DNS names\n\n if generate:\n primaryName = names[0]\n\n if not ip4addr is None:\n for name in names:\n outp['arecords'] += \"{name}.{domain}\\tA\\t{ip} ~\\n\".format(name=name, ip=ip4, domain=domain)\n ip4_r = ipaddress.IPv4Address(ip4addr.packed[::-1])\n outp['aptrs'] += \"{rip}.in-addr.arpa.\\tPTR\\t{name}.{domain} ~\\n\".format(rip=ip4_r, domain=domain, name=primaryName)\n\n if not ip6addr is None:\n for name in names:\n outp['arecords'] += \"{name}.{domain}\\tAAAA\\t{ip} ~\\n\".format(name=name, ip=ip6, domain=domain)\n ip6_r = '.'.join(ip6addr.exploded.translate({ord(\":\"): None})[::-1])\n outp['aaaaptrs'] += \"{rip}.ip6.arpa.\\tPTR\\t{name}.{domain} ~\\n\".format(rip=ip6_r, domain=domain, name=primaryName)\n\n return True",
"title": ""
},
{
"docid": "359b536cdca42fad074bb06aa60b3037",
"score": "0.5221294",
"text": "def parse_address(address):\n province = None\n region1 = None\n region2 = None\n\n if not address or not isinstance(address, basestring):\n return (province, region1, region2)\n \n addresses = address.split() \n\n # province\n try:\n province = addresses[0]\n except IndexError, e:\n pass\n\n # region1\n try:\n region1 = addresses[1]\n except IndexError, e:\n pass\n\n # region2\n try:\n region2 = addresses[2]\n except IndexError, e:\n pass\n\n return (province, region1, region2)",
"title": ""
},
{
"docid": "4dd7abf2b8256758d48a9c953782b5a8",
"score": "0.52157223",
"text": "def IpAddress(address, version=None):\n\n if version:\n if version == 4:\n return Ipv4Address(address)\n elif version == 6:\n return Ipv6Address(address)\n\n try:\n return Ipv4Address(address)\n except (ValueError):\n pass\n\n try:\n return Ipv6Address(address)\n except (ValueError):\n pass\n\n raise ValueError('%r does not appear to be an IPv4 or IPv6 address' % address)",
"title": ""
},
{
"docid": "d3d6eb686a4050d0563ed05817c449df",
"score": "0.5211757",
"text": "def resolver(host: str, port: str, network: str) -> tuple[list, AddrConfig]:\n config = config_inetaddr(host, port, network)\n addr_list = inet_addr_list(config.get_config(), network)\n return addr_list, config",
"title": ""
},
{
"docid": "07ecb155d696090c85f2e9730d1ec2cd",
"score": "0.5211693",
"text": "def test_basics_part1 (self):\r\n a = IPAddr6('2001:0db8:85a3:0000:0000:8a2e:0370:7334')\r\n assert str(a) == '2001:db8:85a3::8a2e:370:7334','minimal repr'\r\n assert a.to_str(zero_drop=False) == \\\r\n '2001:0db8:85a3::8a2e:0370:7334', 'no zero drop'\r\n assert a.to_str(section_drop=False) == \\\r\n '2001:db8:85a3:0:0:8a2e:370:7334', 'no section drop'\r\n assert a.to_str(section_drop=False, zero_drop=False) == \\\r\n '2001:0db8:85a3:0000:0000:8a2e:0370:7334', 'full length'\r\n assert str(IPAddr6('0:0:0:0:0:0:0:1')) == '::1', 'loopback'\r\n assert str(IPAddr6('0:0:0:0:0:0:0:0')) == '::', 'unspecified'\r\n assert str(IPAddr6('2001:db8:0:0:0:0:2:1')) == '2001:db8::2:1'\r\n assert str(IPAddr6('2001:db8:0000:1:1:1:1:1')) == '2001:db8:0:1:1:1:1:1'\r\n assert str(IPAddr6('2001:db8:0:0:1:0:0:1')) == '2001:db8::1:0:0:1'\r\n assert str(IPAddr6('1:0:0:2:0:0:0:3')) == '1:0:0:2::3'",
"title": ""
},
{
"docid": "6e2ebe96979672f574af780d1ab37930",
"score": "0.51968217",
"text": "def find_macaddr2(debug=False):\n pairs=[]\n sas=[]\n for ts_sec,ts_usec,ts_nsec,dbytes,ch in self.pkts:\n ehdr=self.eth_hdr(dbytes,debug=False)\n if ehdr.sa not in sas:\n sas.append(ehdr.sa)\n if (ehdr.sa,ehdr.da) not in pairs:\n pairs.append((ehdr.sa,ehdr.da))\n for macaddr in sas:\n for pair in pairs: \n if pair[1]=='ffffffffffff':\n continue\n if macaddr not in pair:\n break\n if debug:\n print pairs\n print filter(lambda x: x[0]==macaddr,pairs)\n print filter(lambda x: x[0]!=macaddr,pairs)\n return macaddr\n return None",
"title": ""
},
{
"docid": "e5187f7a95249385b78b6141d7abe2b6",
"score": "0.51743704",
"text": "def segwit_addr_decode(hrp, addr):\n hrpgot, data = bech32_decode(addr)\n if hrpgot != hrp:\n return None, None\n decoded = convertbits(data[1:], 5, 8, False)\n if decoded is None or len(decoded) < 2 or len(decoded) > 40:\n return None, None\n if data[0] > 16:\n return None, None\n if data[0] == 0 and len(decoded) != 20 and len(decoded) != 32:\n return None, None\n return data[0], decoded",
"title": ""
},
{
"docid": "76b9b2a65ba96b8cffc168d6d8b82185",
"score": "0.5166014",
"text": "def extract_registration_address(self, input_dict):\n\n # columns to create address, in order\n address_components = [\n 'HOME-NO',\n 'HOME-STREET',\n 'HOME-APT',\n 'HOME-DEV',\n ]\n # create address string for usaddress.tag\n address_str = ' '.join([\n input_dict[x] for x in address_components if input_dict[x] is not None\n ])\n\n raw_dict = {\n 'RAW_ADDR1': address_str,\n # Including Raw Addr 2 as same because not as clear of a division\n 'RAW_ADDR2': address_str,\n 'RAW_CITY': input_dict['HOME-CITY'],\n 'RAW_ZIP': input_dict['HOME-ZIPCODE']\n }\n\n usaddress_dict = self.usaddress_tag(address_str)[0]\n\n if usaddress_dict:\n converted_addr = self.convert_usaddress_dict(usaddress_dict)\n\n converted_addr.update({\n 'PLACE_NAME': input_dict['HOME-CITY'],\n 'ZIP_CODE': input_dict['HOME-ZIPCODE'],\n 'VALIDATION_STATUS': '2'\n })\n converted_addr.update(raw_dict)\n else:\n converted_addr = self.constructEmptyResidentialAddress()\n converted_addr.update(raw_dict)\n converted_addr.update({\n 'STATE_NAME': input_dict['STATE'],\n 'VALIDATION_STATUS': '1'\n })\n\n return converted_addr",
"title": ""
},
{
"docid": "657720069a71bfe3df5f3baa63d83028",
"score": "0.51587725",
"text": "def getIPv4Addresses():\n \n STRUCT_SIZE = 40 # 64bit environment - each name/IP pair is 40 bytes\n SIOCGIFCONF = 35090 # addr from http://pydoc.org/1.6/SOCKET.html\n BYTES = 4096 # buffer size to use\n IGNORED_IFACES = ('lo','virbr','tun','tap')\n \n sck = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n \n names = array.array('B', '\\0' * BYTES)\n outbytes = struct.unpack('iL', fcntl.ioctl(\n sck.fileno(),\n SIOCGIFCONF, \n struct.pack('iL', BYTES, names.buffer_info()[0])))[0]\n\n namestr = names.tostring()\n\n if_list = [(namestr[i:i+16].split('\\0', 1)[0],\n socket.inet_ntoa(namestr[i+20:i+24])) \n for i in range(0, outbytes, STRUCT_SIZE)] \n\n return [ifaddr for ifname,ifaddr in if_list \n if not ifname.startswith(IGNORED_IFACES) ]",
"title": ""
},
{
"docid": "8c610b93f55f212c937e65b2da5f96f7",
"score": "0.5157077",
"text": "def serialize_network_address_values_given(self, address):\n logging.info('SER Serialize network address values given.')\n\n ip = address[2]\n if '.' in ip:\n # ipv4; unused (12 bytes) + ipv4 (4 bytes) = ipv4-mapped ipv6 address\n host = bytearray.fromhex(\"00000000000000000000ffff\") + socket.inet_aton(ip)\n elif ':' in ip and not ip.endswith('.onion'):\n # ipv6; ipv6 (16 bytes)\n host = socket.inet_pton(socket.AF_INET6, ip)\n elif ip.endswith(\".onion\"):\n # convert .onion address to its ipv6 equivalent (6 + 10 bytes)\n host = ONION_PREFIX + b32decode(ip[:-6], True)\n else:\n # the address was not in the correct format, therefore return empty bytes\n return b''\n\n return struct.pack('<I', int(address[0])) + struct.pack(\"<Q\", int(address[1])) + struct.pack('>16sH', host, int(address[3]))",
"title": ""
},
{
"docid": "07714853de2e2aee7fb6b7e53561c00c",
"score": "0.513481",
"text": "def test_format_address():\n protocol = 'tcp'\n host = '127.0.0.1'\n port = 5555\n address = ZMQComm.format_address(protocol, host, port)\n result = ZMQComm.parse_address(address)\n nt.assert_equal(result['protocol'], protocol)\n nt.assert_equal(result['host'], host)\n nt.assert_equal(result['port'], port)\n nt.assert_raises(ValueError, ZMQComm.parse_address, 'INVALID')\n nt.assert_raises(ValueError, ZMQComm.parse_address, 'INVALID://')",
"title": ""
},
{
"docid": "ae74498f8cd4e8f3b39de709e0ec2f22",
"score": "0.5126181",
"text": "def parse_prefix(prefix, default_length=24):\n if '/' in prefix:\n network, pfxlen = prefix.split('/')\n else:\n network = prefix\n pfxlen = default_length\n return network, int(pfxlen)",
"title": ""
},
{
"docid": "22a261e1e31f7a9a4ec855b59207d4e6",
"score": "0.5111533",
"text": "def format_address(address):\n host, port = address[:2]\n if port is not None:\n return f\"[{host}]:{port}\"\n else:\n return f\"[{host}]\"",
"title": ""
},
{
"docid": "b5738ae143286a659406d65eb8d3c5d1",
"score": "0.5109787",
"text": "def _split_address(self, address):\n\n if address < 0:\n raise ValueError(\"address must be non-negative!\")\n\n # The offset is contained in the lowest 'self.offset_bits' bits.\n offset_mask = (1 << self.offset_bits) - 1\n offset = address & offset_mask\n\n # The slot id is contained in the middle 'self.slot_bits' bits.\n offset_and_slot_mask = (1 << (self.slot_bits + self.offset_bits)) - 1 \n slot = (address & offset_and_slot_mask) >> self.offset_bits\n\n # The tag is contained in the remaining bits above the offset and slot id.\n tag_mask = ~offset_and_slot_mask\n tag = (address & tag_mask) >> (self.slot_bits + self.offset_bits)\n\n return tag, slot, offset",
"title": ""
},
{
"docid": "ac4970a3e321bdbd4ba0e9b7b817c063",
"score": "0.5100607",
"text": "def parse_daemon_addr(self, daemon_addr):\n p = self.mk_generic_daemon_addr_pattern_regexp()\n m = p.search(daemon_addr)\n if m is None: return None,None,None\n prefix,gupid,pid = m.group(\"prefix\", \"gupid\", \"pid\")\n pid = self.safe_atoi(pid, None)\n if pid is None: return None,None,None\n return prefix,gupid,pid",
"title": ""
},
{
"docid": "a710db806929bd7ad409cd4492021927",
"score": "0.5094197",
"text": "def ipv6_address(self):",
"title": ""
},
{
"docid": "ad43bcc751db2888aa4638b42b1a0b3d",
"score": "0.50940126",
"text": "def convert_address(row, me, alias_map):\n if isinstance(me, str): me = me.decode('utf-8')\n address = format_phone(row['address'])\n if alias_map and row['group_id'] in alias_map:\n other = alias_map[row['group_id']]\n else:\n other = address\n \n if row['flags'] == 2:\n from_addr = other\n to_addr = me\n elif row['flags'] == 3:\n from_addr = me\n to_addr = other\n \n return (from_addr, to_addr)",
"title": ""
},
{
"docid": "de96db539c8ffc85af3170d40cfc52fe",
"score": "0.5090147",
"text": "def _FormatInAddrExToken(self, token_data):\n protocol = bsmtoken.BSM_PROTOCOLS.get(token_data.net_type, 'UNKNOWN')\n if token_data.net_type == 4:\n ip_address = self._FormatPackedIPv6Address(token_data.ip_address[:4])\n elif token_data.net_type == 16:\n ip_address = self._FormatPackedIPv6Address(token_data.ip_address)\n return {\n 'protocols': protocol,\n 'net_type': token_data.net_type,\n 'address': ip_address}",
"title": ""
},
{
"docid": "b136952dd47b4a55ed419cc28814e0d7",
"score": "0.5085396",
"text": "def mac_address():\n # This procedure has given us problems in the past, so sorround with try-except\n try:\n # Get the IP of servcinf-sql\n sql_ip = socket.gethostbyname('servcinf-sql')\n # Get the route for the servcinf-sql ip, it will look like one of these:\n #10.54.6.26 dev eth0 src 10.54.6.43 \\ cache\n #130.225.86.27 via 10.54.6.1 dev eth0 src 10.54.6.43 \\ cache\n interface_string = subprocess.check_output(\n ['ip', '-o', 'route', 'get', sql_ip]\n ).split()\n\n # The interface name e.g. \"eth0\" is the first item after \"dev\"\n ifname = interface_string[interface_string.index('dev') + 1]\n\n # Get an infostring for a socket connection of this interface name\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n info = fcntl.ioctl(sock.fileno(), 0x8927, struct.pack(b'256s', ifname[:15]))\n if sys.version < '3':\n return ':'.join(['%02x' % ord(char) for char in info[18:24]])\n else:\n return ':'.join(['%02x' % char for char in info[18:24]])\n except: # pylint: disable=bare-except\n return 'MAC ADDRESS UNKNOWN'",
"title": ""
},
{
"docid": "e1ec2f6c8a7f2c83910515701af9f4be",
"score": "0.50825006",
"text": "def address_prefixes(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"address_prefixes\")",
"title": ""
},
{
"docid": "d6cb8d1f28fa00012479a1204472e275",
"score": "0.5081402",
"text": "def __init__(self, address):\n super(Ipv4, self).__init__(address)",
"title": ""
},
{
"docid": "cf1a7a59f22ac36d2582564729ca73ce",
"score": "0.5075956",
"text": "def parse_ip4_address(cls, proposed_value: str) -> [int]:\n result: [int] = None\n accumulate: [int] = []\n tokens: [str] = proposed_value.split(\".\")\n if len(tokens) == RmNetUtils.IP4_ADDRESS_LENGTH:\n valid: bool = True\n for this_token in tokens:\n try:\n token_parsed: int = int(this_token)\n if (token_parsed >= 0) & (token_parsed <= 255):\n accumulate.append(token_parsed)\n else:\n # Number is out of acceptable range\n valid = False\n break\n except ValueError:\n valid = False\n break\n finally:\n pass\n if valid:\n result = accumulate\n return result",
"title": ""
},
{
"docid": "508297c665377fa39da6df5c2ce03711",
"score": "0.5072287",
"text": "def prepare_addresses(addresses, usage=\"header\"):\n result = []\n for address in re.split('[,;]', addresses):\n if not address:\n continue\n name, addr = parseaddr(address)\n if name and usage == \"header\":\n result.append(\"%s <%s>\" % (Header(name, 'utf8'), addr))\n else:\n result.append(addr)\n if usage == \"header\":\n return \",\".join(result)\n return result",
"title": ""
},
{
"docid": "6c2ef96d22069e715fd67b14e51f1eb8",
"score": "0.5067478",
"text": "def extract_address(request):\n # First find and strip out /geocode/\n header = '/geocode/'\n if not request.startswith(header):\n return None\n request = request.partition(header)[2]\n\n splits = request.split('&')\n for s in splits:\n parts = s.split('=')\n if len(parts) != 2:\n continue\n tag, value = parts\n if tag == 'address':\n return value\n return None",
"title": ""
},
{
"docid": "8788c1205270d0513ff1103e9ad79d09",
"score": "0.5065744",
"text": "def parse(cls, nlri_data):\n nlri_list = []\n while nlri_data:\n if nlri_data == b'\\x00\\x00':\n # Note: Fix wrong decoding to [\"0.0.0.0/0\", \"0.0.0.0/0\"]\n nlri_data = nlri_data[2:]\n continue\n if isinstance(nlri_data[0], int):\n prefix_bit_len = int(nlri_data[0])\n else:\n prefix_bit_len = ord(nlri_data[0:1])\n if prefix_bit_len % 8 == 0:\n prefix_byte_len = prefix_bit_len // 8\n else:\n prefix_byte_len = prefix_bit_len // 8 + 1\n offset = prefix_byte_len + 1\n prefix_bit = nlri_data[1:offset]\n # append zero\n zero_len = (128 - prefix_bit_len) // 8\n for i in range(0, zero_len):\n prefix_bit += b'\\x00'\n\n prefix_addr = str(netaddr.IPAddress(int(binascii.b2a_hex(prefix_bit), 16))) + '/%s' % prefix_bit_len\n nlri_list.append(prefix_addr)\n nlri_data = nlri_data[offset:]\n\n return nlri_list",
"title": ""
},
{
"docid": "5a352bfa33d3cc7bcc044ef5d3f9608d",
"score": "0.50614583",
"text": "def pack_address(host, port):\n ip = ipaddress.ip_address(host)\n if ip.version == 4:\n msg = pack('B', socks5.IPV4)\n elif ip.version == 6:\n msg = pack('B', socks5.IPV6)\n else:\n assert False\n msg += ip.packed + pack('!H', port)\n return msg",
"title": ""
},
{
"docid": "03746db2effbfcef85658f1bc59bf2ce",
"score": "0.5059381",
"text": "def reassemble_addresses(seq):\n reassembled = []\n prev = ''\n\n for s in seq:\n if prev.isdigit():\n try:\n end, port = s.split('_')\n except ValueError:\n end, port = '', ''\n\n if s.isdigit():\n reassembled[-1] += '.{}'.format(s)\n elif end.isdigit() and port.isdigit():\n reassembled[-1] += '.{}:{}'.format(end, port)\n else:\n reassembled.append(s)\n else:\n reassembled.append(s)\n\n prev = s\n\n return reassembled",
"title": ""
},
{
"docid": "bdfb28d3f7e70f4b8663f6e546a1dcff",
"score": "0.5057183",
"text": "def addr_tuple(self) -> tuple[str, int]:\n return self.ip_addr, self.port",
"title": ""
},
{
"docid": "a2c17786cccbedd3edb4c69ee969a4ca",
"score": "0.5055029",
"text": "def _parse_addresses(self, addresses):\r\n\r\n # start off with an empty list\r\n address_list = []\r\n\r\n # we could be getting a list or a string\r\n if addresses:\r\n if isinstance(addresses, (tuple, list)):\r\n for address in addresses:\r\n address_list += address.split(';')\r\n else:\r\n address_list = addresses.split(';')\r\n\r\n # return the final list, removing blanks\r\n return [address for address in address_list if len(address.strip()) > 0]",
"title": ""
},
{
"docid": "f97134d43cb399c26a3721489e7831e6",
"score": "0.504879",
"text": "def _splitnport(host, defport=-1):\n host, delim, port = host.rpartition(':')\n if not delim:\n host = port\n elif port:\n try:\n nport = int(port)\n except ValueError:\n nport = None\n return host, nport\n return host, defport",
"title": ""
},
{
"docid": "e3a24fab6f0ec3aaf92a9371f0a1d1a6",
"score": "0.5047713",
"text": "def destination_address_prefixes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"destination_address_prefixes\")",
"title": ""
},
{
"docid": "b3166c44cb94e3672aa40b0cd86fa48c",
"score": "0.5047621",
"text": "def decode_addr(v):\n if len(v) not in [0, 20]:\n raise Exception(\"Serialized addresses must be empty or 20 bytes long!\")\n return encode_hex(v)",
"title": ""
},
{
"docid": "480b592465bd307ffed8195103ac99b5",
"score": "0.5047279",
"text": "def get_interface_addresses(version=AF_INET):\n\n for interface in netifaces.interfaces():\n try:\n addresses = netifaces.ifaddresses(interface)\n except ValueError:\n # some interfaces are given that are invalid, we encountered one called ppp0\n yield Interface(interface, None, None, None)\n else:\n if version == AF_INET:\n for option in addresses.get(netifaces.AF_INET, []):\n try:\n yield Interface(interface, option.get(\"addr\"), option.get(\"netmask\"), option.get(\"broadcast\"))\n except TypeError:\n # some interfaces have no netmask configured, causing a TypeError when\n # trying to unpack _l_netmask\n pass\n elif version == AF_INET6:\n for option in addresses.get(netifaces.AF_INET6, []):\n try:\n yield Interface(interface, option.get(\"addr\").split(\"%\")[0], option.get(\"netmask\"), option.get(\"broadcast\"), version=AF_INET6)\n except TypeError:\n # some interfaces have no netmask configured, causing a TypeError when\n # trying to unpack _l_netmask\n pass\n else:\n logger.warning(\"Unknown version %s\", version)",
"title": ""
}
]
e7f79e2f79009c5fe91885bf8e3476a1
Get the route location and customer location of a customer.
[
{
"docid": "826d0525eebbe19e2e0d8df2fd11fe00",
"score": "0.5834146",
"text": "def cust_loc(self, sol, cust):\n cust_ind = [] # [route_loc, cust_loc]\n for i, rt in enumerate(sol):\n if cust in rt:\n cust_ind.append(i)\n cust_ind.append(rt.index(cust))\n return cust_ind\n\n print('Costomer not in the solution: ', cust)",
"title": ""
}
]
since gateways don't always start at the same time, some secods don't have corresponding rssi and hence have NaN instead. Currenty machine learning lagorithms don't know how to handle NaN. As a temp fix, we're removing all records that have NaN in them.
[
{
"docid": "8361c7d6bb8408ccdc93d98e06906cb5",
"score": "0.63010675",
"text": "def _removerawnans(self):\n for band in self.data[self.root]:\n for i, f in enumerate(self.data[self.root][band]['frequency']):\n if math.isnan(f):\n for param in self.data[self.root][band]:\n if len(self.data[self.root][band][param]) == \\\n len(self.data[self.root][band]['frequency']):\n np.delete(self.data[self.root][band][param],i)",
"title": ""
},
{
"docid": "96cebc2aed0cbec825c4d54bfa1ccdd9",
"score": "0.62637925",
"text": "def cleanNaN (raw_df):\n #first step:cleaning the NaN values when the whole row or whole columns is empty:\n raw_df.dropna(axis=1, how='all' )\n raw_df.dropna(axis=0, how='all')\n \n\n while True:\n\n #removing indicators (columns)\n NaN_per_col = raw_df.apply(numMissing, axis=0).to_frame().T #axis=0 defines that function is to be applied on each column\n max_NaN_per_col = np.amax(NaN_per_col.iloc[0,:].values)\n min_NaN_per_col=np.amin(NaN_per_col.iloc[0,:].values)\n if max_NaN_per_col == min_NaN_per_col+2 or max_NaN_per_col == min_NaN_per_col+1 : \n threshold1=len(raw_df.index)-max_NaN_per_col+1\n else:\n threshold1=len(raw_df.index)-max_NaN_per_col+3 #if rate of 2 here, check the condition at +1\n raw_df=raw_df.dropna(axis=1, thresh=threshold1)\n \n \n # check conditions to stop the while loop\n if raw_df.isnull().sum().sum() == 0:\n break\n \n \n #removing countries (rows)\n NaN_per_row = raw_df.apply(numMissing, axis=1).to_frame().T #axis=1 defines that function is to be applied on each row\n max_NaN_per_row = np.amax(NaN_per_row.iloc[0,:].values)\n threshold0=len(raw_df.columns)-max_NaN_per_row +1\n raw_df=raw_df.dropna(axis=0, thresh=threshold0)\n \n\n # check conditions to stop the while loop\n if raw_df.isnull().sum().sum() == 0:\n break\n \n return raw_df",
"title": ""
},
{
"docid": "c7414a0fef1fec27eac411ce030507b3",
"score": "0.6119339",
"text": "def _remove_nan_inf(self):\n # infinite and nan check\n self.TB10A[np.isfinite(self.TB10A) is False] = self.bad_data\n self.TB19A[np.isfinite(self.TB19A) is False] = self.bad_data\n self.TB37A[np.isfinite(self.TB37A) is False] = self.bad_data\n self.TB85A[np.isfinite(self.TB85A) is False] = self.bad_data\n if hasattr(self, 'TB10B'): # Assume if one, all are there\n self.TB10B[np.isfinite(self.TB10B) is False] = self.bad_data\n self.TB19B[np.isfinite(self.TB19B) is False] = self.bad_data\n self.TB37B[np.isfinite(self.TB37B) is False] = self.bad_data\n self.TB85B[np.isfinite(self.TB85B) is False] = self.bad_data\n else: # Set all the B channels to bad, these are single-channel data\n for freq in FREQS:\n setattr(self, 'TB' + freq + 'B',\n self.bad_data * np.ones((self.nscans, self.ncross),\n dtype='float'))\n # Address geolocations if available\n if hasattr(self, 'Latitude'):\n self.Latitude[np.isfinite(self.Latitude) is False] = self.bad_data\n self.Longitude[np.isfinite(self.Longitude) is False] = \\\n self.bad_data",
"title": ""
},
{
"docid": "756ddf8bd32157a47110f845a19de702",
"score": "0.6115779",
"text": "def removeNans(data: Dict):\n # find nan in the dataset and removes the values\n for chan in data:\n data[chan] = removeNansSingle(data[chan])\n return data",
"title": ""
},
{
"docid": "77edef415c0e04d3ef1580f96c9c6df4",
"score": "0.6110685",
"text": "def handle_missing_values(df):\n print(\"just drop all the samples with missing values, consider a better approach\")\n newdf= df.dropna()\n return newdf",
"title": ""
},
{
"docid": "c3266ab1d07f9f838365bd8fbde7a1f7",
"score": "0.604752",
"text": "def remove_nans(self):\n s = \"::: removing NaNs from %s :::\" % self.name\n print_text(s, self.color)\n\n self.x = self.x[self.good_x]\n self.y = self.y[self.good_y]\n self.x_min = self.x.min()\n self.x_max = self.x.max()\n self.y_min = self.y.min()\n self.y_max = self.y.max()\n self.nx = len(self.x)\n self.ny = len(self.y)\n\n for i in self.data.keys():\n self.data[i] = self.data[i][self.good_y, : ]\n self.data[i] = self.data[i][:, self.good_x]",
"title": ""
},
{
"docid": "c8e963843fe13ec0998f958dfce64dfc",
"score": "0.5992643",
"text": "def dropna(df):\n df = df[df < math.exp(709)] # big number\n df = df[df != 0.0]\n df = df.dropna()\n return df",
"title": ""
},
{
"docid": "deb517438e75d14790c556d6c26530e4",
"score": "0.5991817",
"text": "def dropna(self):\n self.transformed_data = list(\n filter(lambda line_obj: line_obj.has_null is False, self.orig_data))\n return self",
"title": ""
},
{
"docid": "52259136ad5716d8e4e3adceb77bace3",
"score": "0.5938397",
"text": "def remove_nans(sig):\n\n sig_nans = np.isnan(sig)\n sig_removed = sig[np.where(~np.isnan(sig))]\n\n return sig_removed, sig_nans",
"title": ""
},
{
"docid": "59b786a4ada6804cec21d78e75eb0698",
"score": "0.59266084",
"text": "def dropna(df):\n df = df[df < math.exp(709)] # big number\n df = df[df != 0.0]\n df = df.dropna()\n return df",
"title": ""
},
{
"docid": "b5c0c67abd61e6060f97e87d779570eb",
"score": "0.5856776",
"text": "def clean_nans(record):\n floats = [field for field in record if isinstance(record[field], float)]\n for field in floats:\n if np.isnan(record[field]):\n del record[field]",
"title": ""
},
{
"docid": "82fb941e9652d6c2263194543d36ee88",
"score": "0.57872057",
"text": "def prune_records(self,rec):\n return {k: v for k, v in json.loads(rec).items() if not (v is None or (isinstance(v, list) and sum(pd.isnull(v) > 0)) or\n (isinstance(v, float) and math.isnan(v))\n or isinstance(v, pd.tslib.NaTType))}",
"title": ""
},
{
"docid": "9ccc1c304ac258099fe94a5f4329c384",
"score": "0.5760188",
"text": "def _strip(session) -> tuple:\n eeg, *rest = session\n ind = -next(i for i, value in enumerate(eeg[0, ::-1]) if not np.isnan(value))\n if ind == 0:\n ind = None\n return tuple((eeg[:, :ind], *rest))",
"title": ""
},
{
"docid": "cc1c465dc06a8c0c07afedbcc6d05af9",
"score": "0.57554376",
"text": "def remove_null_values(dataframe):\r\n dataframe.dropna()",
"title": ""
},
{
"docid": "968d84511f33f982e53d2dced989779c",
"score": "0.57538193",
"text": "def filter_values_1(self):\n inf = float(\"inf\")\n last_mea = list(self.scan_values[-1])\n for i in range(len(last_mea)):\n if last_mea[i] == inf:\n for k in reversed(xrange(MEASUREMENTS-1)):\n if self.scan_values[k][i] != inf:\n last_mea[i] = self.scan_values[k][i]\n break\n scan = LaserScan()\n scan.ranges = last_mea\n self.laser_filtered_pub.publish(scan)\n self.counter = 0\n self.scan_values = []",
"title": ""
},
{
"docid": "d3d79578ca01616242fae7179a5eef34",
"score": "0.5741",
"text": "def prune (self):\r\n #Remove excess zero data points characteristic of a multi-event spectrum acquisition structure that occur inside peaks.\r\n #Thermo data sometimes does this depending on acquisition parameters.\r\n if self.msevents > 1:\r\n zstart = None\r\n zend = None\r\n for x,intensity in enumerate(self.i):\r\n if intensity == 0:\r\n if not zstart:\r\n zstart = x\r\n zend = x\r\n else:\r\n if zend and zstart:\r\n length = zend-zstart+1\r\n if length == self.msevents-1:\r\n self.i[zstart:zend+1] = list(itertools.repeat(-1,length))\r\n zstart = None\r\n zend = None\r\n while True:\r\n try:\r\n where = self.i.index(-1)\r\n except:\r\n break\r\n self.i.pop(where)\r\n self.t.pop(where)\r\n self.time.pop(where)",
"title": ""
},
{
"docid": "413b28d3b07ead5cb86d8f3729fcefd2",
"score": "0.57369703",
"text": "def nan_filter(self):\r\n \r\n # filter out nan values in numerical attributes\r\n for att in self.catalog_atts:\r\n if getattr(self, att).shape[0] == 0:\r\n pass\r\n elif (type(getattr(self, att)[0]) == str) or (type(getattr(self, att)[0]) == bytes):\r\n # FIXME: intent here unclear: \r\n # note float('nan') is an IEEE NaN, getattr(.) is a str, and != on NaNs is special\r\n i = np.where(getattr(self, att) != float('nan'))[0]\r\n self.revise_lists(i)\r\n # exclude non-numerical types\r\n elif type(getattr(self, att)[0]) not in (np.unicode_, np.string_, np.bool_, bytes):\r\n if att == 'coords':\r\n i1 = np.where(~np.isnan(self.coords.ra.to('deg').value))[0]\r\n i2 = np.where(~np.isnan(self.coords.dec.to('deg').value))[0]\r\n i = np.intersect1d(i1,i2)\r\n else:\r\n i = np.where(~np.isnan(getattr(self, att)))[0]\r\n self.revise_lists(i)",
"title": ""
},
{
"docid": "1763b3f28d3ff59fd197556ba56b060a",
"score": "0.5727697",
"text": "def remove_data_for_training_from_dataframe(data_df,Mask_NA,missing_fraction=0.3):\n\n def convert_series_to_diag_df(Series):\n return pd.DataFrame(columns=Series.index, index=Series.index, data=np.diag(Series.values))\n \n #removed_vals = (Mask_NA == 1).any(axis=0).astype(int)\n NAs = (Mask_NA == 1).any(axis=1).astype(int)\n NA_diag = convert_series_to_diag_df(NAs)\n\n NA_map = PandasFunctions.CompressDataFrame(NA_diag,[1])\n Val_map = PandasFunctions.CompressDataFrame(Mask_NA,[1])\n #############################################################\n np.random.seed(0)\n remover = np.random.rand(data_df.shape[0], NA_map.shape[0])\n remover = np.where( missing_fraction>remover , 1, 0)\n ############################################################# \n NA_1s = np.matmul(remover, NA_map.values)\n Values_0s = np.matmul(remover, Val_map.values)\n ############################################################# \n data_missing_df = data_df.copy()\n matrix = data_missing_df.values\n matrix[NA_1s ==1] = 1\n matrix[Values_0s==1] = 0\n data_missing_df[:] = matrix\n #############################################################\n return data_missing_df",
"title": ""
},
{
"docid": "ed86b9e8b99ac7b505b79ccb5753c9a8",
"score": "0.57137537",
"text": "def remove_nan_rows(micro_df: pd.DataFrame):\n data_cols = micro_df.columns.tolist()[1:]\n micro_df.dropna(how = 'all', subset = data_cols, inplace=True)\n micro_df.reset_index(inplace=True, drop=True)",
"title": ""
},
{
"docid": "13cf93e8603b92c72a804674e53a47cc",
"score": "0.5667109",
"text": "def filter_nan(s,o):\n '''\n data = np.array([s.flatten(),o.flatten()])\n data = np.transpose(data)\n #data = data[~np.isnan(data).any(1)]\n data = data[~pd.isnull(data)]\n #data = data[~np.isnan(data)]\n return data[:,0],data[:,1]\n #return data\n '''\n s = s.flatten()\n o = o.flatten()\n s = np.transpose(s)\n o = np.transpose(o)\n s[np.invert(~np.isnan(o))] = np.nan\n o[np.invert(~np.isnan(s))] = np.nan\n s = s[~pd.isnull(s)]\n o = o[~pd.isnull(o)]\n return s,o",
"title": ""
},
{
"docid": "7bbdbe56e3df32572a4fa12f73449eb4",
"score": "0.56610817",
"text": "def wash_out_all_nan(data):\n # wash out the data with nan\n return data[~np.isnan(np.sum(data, 1))]",
"title": ""
},
{
"docid": "689966188b7db502832026a5b59f8021",
"score": "0.5639008",
"text": "def clean_data(df):\n df = df[~df.location.isnull()]\n return df",
"title": ""
},
{
"docid": "88cb616fb2e42ac45b5a8d4bd9062a4f",
"score": "0.56378675",
"text": "def process_na_data_in_df(df: 'DataFrame') -> 'DataFrame':\n\n # Remove Weekends: Sat & Sun\n df = df[(df.index.dayofweek != 5)&(df.index.dayofweek != 6)]\n\n # Removing all columns with NA values > 10% entries\n df = df.loc[:, df.isna().sum()/df.shape[0] <= 0.1]\n\n # Dropping rows with all NA values\n df = df.dropna(axis=0,how='all')\n\n return df",
"title": ""
},
{
"docid": "208cc3adf0e73a81cd66b4d8257283ab",
"score": "0.56377816",
"text": "def clean_dataset(df):\n assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n df.dropna(inplace=True)\n indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n return df[indices_to_keep].astype(np.float64)",
"title": ""
},
{
"docid": "b2d825919fec8aa76095fd7978826676",
"score": "0.5636558",
"text": "def _filter_na_for_fit(self, X: pd.DataFrame, y):\n # let's also treat infinite values as NA\n # scikit-learn's check_estimator might throw those at us\n with pd.option_context(\"mode.use_inf_as_na\", True):\n flt = pd.isna(X).values\n X_out = X[~flt]\n if y is not None and len(y) > 0:\n y_out = y[~flt]\n else:\n y_out = y\n return X_out, y_out",
"title": ""
},
{
"docid": "d8d89a4a2ee9109d29df82591e9344b0",
"score": "0.5592618",
"text": "def clean_null_values(data_frame):\n half_count = len(data_frame) / 2\n # axis = 1 indicates remove of cols, and axis = 0 indicates remove of rows\n data_frame = data_frame.dropna(thresh=half_count, axis=1) # Drop any column with more than 50% missing values\n # drop any row with null values\n data_frame = data_frame.dropna(axis=0, how='any')\n return data_frame",
"title": ""
},
{
"docid": "f2cf87fea3e49c01afa9f06ca623a419",
"score": "0.5588972",
"text": "def remove_nan_entries(df):\n\tdf_initsize = len(df)\n\tprint('Initial dataset size: ', df.shape)\n\tdisplay(HTML(df.tail().to_html()))\n\tprint('\\nRemoving the NaN values...')\n\t\n\tfor col in df.columns:\n\t col_type = df[col].dtype\n\t #print('col infos: {} dtype={}'.format(col, col_type))\n\t if(col_type != np.dtype('int64')):\tdf.drop(df[~df[col].notna()].index, inplace=True)\n\n\tdisplay(HTML(df.tail().to_html()))\n\tprint('\\nDataset size after NaN removing: ', df.shape)\n\tred_per = 100*(df_initsize - len(df)) / df_initsize\n\tprint(\"=> %.0f%% reduction of data.\" % red_per)\n\n\treturn df",
"title": ""
},
{
"docid": "90230baf2be6f9623288c70f2a631e38",
"score": "0.5581642",
"text": "def _getnonconnectedsensors(self):\n currentcams=[]\n currenttemp=[]\n currentni=[]\n overcams=[]\n overtemp=[]\n overni=[]\n for par in self._paramlist:\n if par[\"type\"]==\"camera\":\n currentcams.append((par[\"vendor\"],par[\"camid\"]))\n if par[\"type\"]==\"temperature\":\n currenttemp.append(par[\"handle\"])\n if par[\"type\"]==\"nianalog\":\n currentni.append(par[\"devstr\"])\n \n currentcamsset=set(currentcams)\n currenttempset=set(currenttemp)\n currentniset=set(currentni)\n \n if not self._connectedcams is None:\n camintersection=currentcamsset.intersection(self._connectedcams)\n for cam in list(camintersection):\n currentcamsset.remove(cam)\n\n if not self._connectedtemp is None:\n tempintersection=currenttempset.intersection(self._connectedtemp)\n for temp in list(tempintersection):\n currenttempset.remove(temp)\n \n if not self._connectedni is None:\n niintersection=currentniset.intersection(self._connectedni)\n for ni in list(niintersection):\n currentniset.remove(ni)\n \n missing={\"camera\":list(currentcamsset),\"temperature\":list(currenttempset),\"nianalog\":list(currentniset)}\n \n return missing",
"title": ""
},
{
"docid": "ff1dbb732594bd0adf24b12f262abf13",
"score": "0.55662936",
"text": "def test_filter_missing_default(self, meas):\n print(meas.data.columns)\n meas.set_regression_cols(\n power='meter_power',\n poa='met1_poa_refcell',\n t_amb='met2_amb_temp',\n w_vel='met1_windspeed',\n )\n assert all(meas.rview('all', filtered_data=True).isna().sum() == 0)\n assert meas.data_filtered.shape[0] == 1440\n meas.data_filtered.loc['10/9/90 12:00', 'meter_power'] = np.NaN\n meas.data_filtered.loc['10/9/90 12:30', 'met1_poa_refcell'] = np.NaN\n meas.data_filtered.loc['10/10/90 12:35', 'met2_amb_temp'] = np.NaN\n meas.data_filtered.loc['10/10/90 12:50', 'met1_windspeed'] = np.NaN\n meas.filter_missing()\n assert meas.data_filtered.shape[0] == 1436",
"title": ""
},
{
"docid": "50765df3afe4062e887fd495f7d20620",
"score": "0.5561974",
"text": "def eliminate_singles(files):\n # allocate memory for the output\n filtered_traces = files.copy()\n # for all the columns\n for col in np.arange(files.shape[1]):\n # get the target trace\n original_trace = files.iloc[:, col]\n # find the derivative of the nan trace\n nan_positions = np.diff(np.isnan(original_trace).astype(np.int32), n=2)\n # find the coordinates of the singles\n single_positions = np.argwhere(nan_positions == 2) + 1\n # single_positions = np.argwhere((nan_positions[:-1] == 1) & (nan_positions[1:] == -1))\n # nan the singles\n filtered_traces.iloc[single_positions, col] = np.nan\n\n return filtered_traces",
"title": ""
},
{
"docid": "b7d28291e59ab3392cb75585f375154b",
"score": "0.5544502",
"text": "def clean_df(self, df):\n df = df.dropna(axis='columns', how='all')\n df = df.dropna(axis='rows', how='any')\n df = df.astype('float64')\n df.index = df.index.astype('int64')\n return df",
"title": ""
},
{
"docid": "7beeecf20699561c694c5c237529de88",
"score": "0.5533206",
"text": "def filter_nan(s,o):\n data = np.array([s.flatten(),o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n #data = data[~np.isnan(data)]\n return data[:,0],data[:,1]",
"title": ""
},
{
"docid": "6deb83b721aaaea9e4f2f2aac5032b3f",
"score": "0.5526761",
"text": "def drop_na(self):\n self.df = self.df.dropna(axis=0, how='any')",
"title": ""
},
{
"docid": "535115677c38ae302efaebcf05b89078",
"score": "0.55242294",
"text": "def clean_non_targets(self, df):\n # drop contaminants\n df = df[df[\"Potential contaminant\"] != \"+\"]\n # drop reverse\n df = df[df[\"Reverse\"] != \"+\"]\n return df",
"title": ""
},
{
"docid": "302ac812d6a4d6da03d1689fbe1ac5e8",
"score": "0.55231535",
"text": "def clean_nan_inf(ts):\n ts = to_np_array(ts)\n search = (np.isinf(ts) | np.isnan(ts))\n ts[search] = 0\n\n return ts",
"title": ""
},
{
"docid": "d584df708be962da48a56ddd6b8d43d2",
"score": "0.5511816",
"text": "def remove_nulls(self):\n \n print('Removing Null Data...')\n \n for column in ['Open', 'Close']:\n for index in self.df[column].index[self.df[column].apply(np.isnan)].tolist():\n self.df.drop(labels=index,axis=0,inplace=True)\n print('NULL ELEMENT REMOVED!')\n\n print('All Null Data Removed Successfully!')",
"title": ""
},
{
"docid": "a224e4e86808e5779ba93a7e2c895b22",
"score": "0.5505433",
"text": "def remove_rows_with_wrong_values(self):\n print(\" removing rows with wrong values...\")\n self.df.dropna(inplace=True)",
"title": ""
},
{
"docid": "0945fb57da5b13eac3a5116d028644a8",
"score": "0.54922837",
"text": "def drop_row_with_NA(self):\n\n # self.main_df = self.main_df[self.main_df[columns] != 0]\n self.main_df = self.main_df.dropna()",
"title": ""
},
{
"docid": "310836c18424bdaa13136de14364ea51",
"score": "0.54829514",
"text": "def no_modify(data):\n # wash out the data with nan\n return data",
"title": ""
},
{
"docid": "e827c22aa3c1ef2d5d904975bd256876",
"score": "0.5474302",
"text": "def remove_empty_features(self, tx):\n tx = np.where(tx == -999, np.NaN, tx)\n tx = tx[:, ~np.all(np.isnan(tx), axis=0)]\n return tx",
"title": ""
},
{
"docid": "da6d97796efe7051da7f69e82026797c",
"score": "0.54705954",
"text": "def drop_na_reactions_or_single_row(df):\n new_df = df.dropna(subset=['reaction'])\n new_df = new_df[new_df.reaction_count_equiv > 0]\n if len(new_df) == 0:\n new_df = df.head(1)\n new_df['reaction'] = np.nan\n new_df['label'] = new_df['gene']\n return new_df",
"title": ""
},
{
"docid": "8f15ae29fe533b404dc846a9c9957e64",
"score": "0.5465945",
"text": "def drop_nan(self):\n self.df = self.df.dropna(axis=0, how='any')\n return self.df",
"title": ""
},
{
"docid": "c2d52c882873b9b394ba08aa17a39614",
"score": "0.54497117",
"text": "def remove_rows_with_missing_values(self):\n clean_data = []\n for row in self.data:\n hasMissing = False\n for col in row:\n if col == \"NA\" or col == \"\":\n hasMissing = True\n if not hasMissing:\n clean_data.append(row)\n self.data = clean_data",
"title": ""
},
{
"docid": "ae8ead52a7b3c87ce6d0aa2874c477a8",
"score": "0.54421103",
"text": "def make_log_no_trips(self):\n self.df['log_trips'] = self.df[(self.df['trips_in_first_30_days'] != 0)].trips_in_first_30_days.apply(np.log)\n self.df['log_trips'] = self.df['log_trips'].apply(lambda x: 0 if np.isnan(x) else x)",
"title": ""
},
{
"docid": "6a2a3655355cff1d9e36930017d6a14d",
"score": "0.5433729",
"text": "def remove_empty_values_marks(self, data):\n\n data_new = np.ndarray(shape=data.shape, dtype=data.dtype)\n\n __ACCURACY = 0.00001\n\n sample_number = 0\n for features in data:\n feature_index = 0\n for value in features:\n if abs(self.__EMPTY_VALUE - value) > __ACCURACY:\n data_new[sample_number][feature_index] = value\n else:\n data_new[sample_number][feature_index] = None\n feature_index += 1\n sample_number += 1\n print(\"Remove marks about empty values has done!\")\n return data_new",
"title": ""
},
{
"docid": "2ddec8050279af60f2721fd30cf43fe3",
"score": "0.54108644",
"text": "def _remove_nan(values: TypeValList) -> TypeValList:\n values = np.asarray(values, dtype=float)\n return values[~np.isnan(values)]",
"title": ""
},
{
"docid": "85ffd26d43c6c9bd7e9ff00c77464868",
"score": "0.541016",
"text": "def check_for_nan(df):\n return df.dropna()",
"title": ""
},
{
"docid": "f070ce783a1e1cc0ab8923b612b26e5e",
"score": "0.54039186",
"text": "def filter_all(data):\n #filter individual blocks of continuous data\n datablocks = [list(v) for k,v in groupby(data,np.isfinite) if k]\n filtereddatablocks = [filterfunction(v) for v in datablocks]\n if np.any(np.isnan(data)): #if there are gaps\n #get information about gaps\n gap_indeces = np.where(np.isnan(data))[0]\n gap_locs = []\n gap_lengths = []\n for k, g in groupby(enumerate(gap_indeces), lambda ix:ix[1]-ix[0]):\n temprun = list(map(itemgetter(1), g))\n gap_lengths.append(np.size(temprun))\n gap_locs.append(temprun[0])\n #build filtered data with gaps\n filteredrun = np.array([]);\n if gap_locs[0]==0: #if the first space is a gap, append and remove\n filteredrun = np.append(filteredrun, np.full(gap_lengths[0],np.NaN) )\n gap_locs = np.delete(gap_locs,0)\n gap_lengths = np.delete(gap_lengths,0)\n for i in range(np.size(filtereddatablocks)):\n filteredrun = np.append(filteredrun,filtereddatablocks[i])\n if i<np.size(gap_locs):\n filteredrun = np.append(filteredrun,np.full(gap_lengths[i],np.NaN))\n else: #if no gaps\n filteredrun = filterfunction(data)\n return filteredrun",
"title": ""
},
{
"docid": "8ff79d1cf1232b2cd2bee9677a15f802",
"score": "0.5403428",
"text": "def drop_missing_data(df):\n\n return df.dropna()",
"title": ""
},
{
"docid": "58ac718730e57ad72fbd52481e985402",
"score": "0.5393061",
"text": "def drop_irrelevant(self):\r\n self.df = self.df.drop(['id2', 'state', 'year', 'asthma_rate'], axis=1)",
"title": ""
},
{
"docid": "0a9a5ca95990bcb1ddfe7419ab9b2409",
"score": "0.5385711",
"text": "def drop_lowz(df):\n df = df.dropna(subset=['FIELD'])\n return df",
"title": ""
},
{
"docid": "bfb57879edba1a2b3b4ef96649e53326",
"score": "0.53795296",
"text": "def clean_data(df):\n # removes first row time = 0\n df.dropna(how='any', inplace=True)\n df.drop_duplicates(inplace=True)\n\n # remove points where speed is greater than > 100 mph\n df = df[df['speed'] < 100]\n\n # replace unknowns with mean of column\n df = df.replace(-99999, np.nan)\n df = df.fillna(df.mean())\n return df",
"title": ""
},
{
"docid": "0430e6d2ce3529d2c69f6a112c139784",
"score": "0.5377258",
"text": "def _clean_missing(data, params):\n attributes = params['attributes']\n cleaning_mode = params['cleaning_mode']\n frag = params['id_frag']\n\n if cleaning_mode == \"REMOVE_COLUMN\":\n\n thresh = params['thresh']\n if thresh:\n subset = []\n cols = params['columns_drop']\n for c in cols.index:\n if cols.loc[c] > thresh:\n subset.append(c)\n else:\n subset = params['columns_drop']\n data = data.drop(subset, axis=1)\n\n elif cleaning_mode == \"MEAN\":\n\n sizes, sums = params['values']\n values = np.divide(sums, sizes)\n\n for v, a in zip(values, attributes):\n data[a] = data[a].fillna(value=v)\n\n elif cleaning_mode == \"MODE\":\n dict_mode = params['dict_mode']\n for att in dict_mode:\n mode = dict_mode[att].idxmax()\n data[att] = data[att].fillna(value=mode)\n\n elif cleaning_mode == 'MEDIAN':\n medians = params['values']\n for att in medians:\n data[att] = data[att].fillna(value=medians[att])\n\n data.reset_index(drop=True, inplace=True)\n info = generate_info(data, frag)\n return data, info",
"title": ""
},
{
"docid": "bc3286b60c67caabbf08b38675cf0de7",
"score": "0.53675956",
"text": "def drop_missing(X):\n nonmissing=X[0].copy()\n nonmissing['Nonmissing']=True\n nonmissing=nonmissing['Nonmissing']\n for x in X:\n nonmissing.where(pd.notnull(x).all(axis=1),False,inplace=True)\n\n for i in range(len(X)):\n X[i] = X[i].loc[nonmissing,:]\n\n return tuple(X)",
"title": ""
},
{
"docid": "ed07a105be7b32edd490f39e6279fde8",
"score": "0.53658754",
"text": "def flag_na(df,remove_rows=False):\n df = df.copy()\n n_bad = sum(np.ravel(df.isnull().values))\n print('{} bad values in the dataset.')\n if remove_rows == True:\n df.dropna()\n return(df)",
"title": ""
},
{
"docid": "d3ccf0fca21cb7d020e01721e6d73f4f",
"score": "0.5357542",
"text": "def _getmissingsensors(self):\n currentcams=[]\n currenttemp=[]\n currentni=[]\n missingcams=[]\n missingtemp=[]\n missingni=[]\n for par in self._paramlist:\n if par[\"type\"]==\"camera\":\n currentcams.append((par[\"vendor\"],par[\"camid\"]))\n if par[\"type\"]==\"temperature\":\n currenttemp.append(par[\"handle\"])\n if par[\"type\"]==\"nianalog\":\n currentni.append(par[\"devstr\"])\n \n for cam in self._connectedcams:\n if not cam in currentcams:\n missingcams.append(cam)\n if not self._connectedtemp is None:\n for temp in self._connectedtemp:\n if not temp in currenttemp:\n missingtemp.append(temp)\n if not self._connectedni is None:\n for ni in self._connectedni:\n if not ni in currentni:\n missingni.append(ni)\n missing={\"camera\":missingcams, \"temperature\":missingtemp,\"nianalog\":missingni}\n return missing",
"title": ""
},
{
"docid": "1c24a661c654de184a93b6d3fcc684cf",
"score": "0.53526807",
"text": "def nan_trim(data):\r\n data_diff = np.diff(data)\r\n\r\n for i, diff in reversed(list(enumerate(data_diff))):\r\n if diff == 1:\r\n data.pop(i)\r\n\r\n return data",
"title": ""
},
{
"docid": "d773f973c4e24a2274159b9e08fef1b8",
"score": "0.53520674",
"text": "def nans_remove(self):\n msk = np.isfinite(self.y)\n return self.__class__(self.x[msk], self.y[msk])",
"title": ""
},
{
"docid": "11f98e0c9343893178195cbd3f034352",
"score": "0.5342439",
"text": "def remove_na_lines(self, max_na_proportion:float=None):\n\n if max_na_proportion:\n drop_lines = list(self.df.loc[self.df.isna().sum(axis=1)>len(self.df.columns)*max_na_proportion].index) \n else:\n drop_lines = self.na_lines\n #print(f'Lines removed:{drop_lines[0:4]}...')\n self.df = self.df.drop(drop_lines)\n return self.df",
"title": ""
},
{
"docid": "075d66d00dfa6a68a225f3dacc5ab7fe",
"score": "0.5338691",
"text": "def cleanData(dataToClean):\n for i in range(len(dataToClean)):\n for j in range(len(dataToClean[0])):\n if np.isnan(dataToClean[i][j]):\n dataToClean[i][j]=0",
"title": ""
},
{
"docid": "a12091d6b20c49b7725690d923460001",
"score": "0.5331909",
"text": "def clean_df(df):\n df = df.where(pd.notnull(df), None)\n df = df.fillna(value=0)\n return df",
"title": ""
},
{
"docid": "3d6bb36d5febd036ea6d7c4c4267a6a5",
"score": "0.533119",
"text": "def _drop_obs(df):\n drop_subj = []\n for sid, row in df.iterrows():\n try:\n if sum(row.dropna()) == 0:\n drop_subj.append(sid)\n elif row.count() == 0:\n drop_subj.append(sid)\n except TypeError: # compatible to ados\n if sum(row.astype(float).dropna()) == 0:\n drop_subj.append(sid)\n elif row.astype(float).count() == 0:\n drop_subj.append(sid)\n return drop_subj",
"title": ""
},
{
"docid": "260be91c0b8510b71ae7a1d740960b03",
"score": "0.53306866",
"text": "def NaN_cleaning(df):\n df = df.replace(np.nan, 'unknown')\n return df.reset_index(drop=True)",
"title": ""
},
{
"docid": "5be06db39233a32605860ac8b3689a5c",
"score": "0.53283554",
"text": "def _remove_missing_data(\n data_values: NDArray,\n geometry_array: geopandas.GeoSeries,\n) -> tuple[NDArray, geopandas.GeoSeries]:\n not_missing_data = ~pandas.isnull(data_values)\n geometry_array = geometry_array[not_missing_data]\n data_values = data_values[not_missing_data]\n return data_values, geometry_array",
"title": ""
},
{
"docid": "aeb9b42a6eaa6253ee3b8d523139e299",
"score": "0.5324406",
"text": "def quick_clean(data):\n rows_missing_data = (data == 0).sum(1)\n return data[rows_missing_data == 0, :]",
"title": ""
},
{
"docid": "82d9e2a4681978a2e091275a72005b3c",
"score": "0.5316683",
"text": "def clean_nan(data, data_column):\r\n data.dropna(subset=[data_column, \\\r\n 'Quality'], inplace=True)\r\n return data",
"title": ""
},
{
"docid": "a7f38a2777c41e92eecb61343ef10b22",
"score": "0.53023773",
"text": "def filter_zeroes(self):\n self.U[self.U == 0] = np.nan\n self.V[self.V == 0] = np.nan\n self.W[self.W == 0] = np.nan",
"title": ""
},
{
"docid": "985aa5c0aa5f067935e58f73b2100dbc",
"score": "0.5297176",
"text": "def remove_nans(array_like):\n data = array_like.copy()\n \n # Astropy table object\n if type(data) is Table:\n data = data.filled(NAN_VALUE)\n # Pandas DataFrame object\n elif type(data) is pd.DataFrame:\n data = data.fillna(NAN_VALUE)\n else:\n print(\"Unrecognized data format:\", type(data))\n return None\n \n # Return final data structure\n return data",
"title": ""
},
{
"docid": "914a197db781cb12a5fe19ab43161b5d",
"score": "0.5292508",
"text": "def test_DataCleaner_big_nan_handler_warning(self):\n dc = DataCleaner(max_na_frac=0.01)\n df = pd.DataFrame(\n np.random.randint(0, 100, size=(100, 4)), columns=list(\"ABCD\")\n )\n dc.fit(df, \"D\")\n self.assertEqual(len(dc.warnings), 0)\n\n df[\"A\"].iloc[10:20] = np.nan\n df[\"B\"].iloc[:99] = np.nan\n dc.fit(df, \"D\")\n self.assertEqual(len(dc.warnings), 1)",
"title": ""
},
{
"docid": "dc851de57a11012e4155217bb31adc96",
"score": "0.52870226",
"text": "def delete_rows(df):\r\n\r\n df = df.drop(df[df.img.isna()].index)\r\n df = df.drop(df[df.cost.isna()].index)\r\n df = df.drop(df[df.rarity.isna()].index)\r\n return df",
"title": ""
},
{
"docid": "7fbcab9e0a3ed7e49af6f0c03573e528",
"score": "0.5284864",
"text": "def test_nans_replaced_none():\n\n #generate test data\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\n dataframe['A'][5] = numpy.nan\n\n #run function on dataframe\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\"none\", removal_time_frame=\"day\",\n fault_placement=\"calendar\")\n\n assert dataframe.size == result_dataframe.size, \"nan has not been dropped\"\n assert result_dataframe['A'][5] != numpy.nan, \"nan was removed but shouldn't have\"",
"title": ""
},
{
"docid": "08006fa93818a2f08ef6539d0e78d6b8",
"score": "0.5269665",
"text": "def remove_duplicates_and_full_nulls(data):\n data = data.copy()\n data = data.drop_duplicates()\n data = data.dropna(how ='all')\n return data",
"title": ""
},
{
"docid": "d9cd6b277461a674b517515b1a4294eb",
"score": "0.52642363",
"text": "def remove_null(dataframe):\n\n dataframe = dataframe.dropna()\n return dataframe",
"title": ""
},
{
"docid": "52409db8b7b10f8ebaa0732ca7d73c16",
"score": "0.52546704",
"text": "def _rmDataWithoutBrightStar(self, neighborStarMap, starMap,\n wavefrontSensors):\n\n # Collect the sensor list without the bright star\n noStarSensorList = []\n for detector, stars in neighborStarMap.items():\n if (len(stars.getId()) == 0):\n noStarSensorList.append(detector)\n\n # Remove the data in map\n for detector in noStarSensorList:\n neighborStarMap.pop(detector)\n starMap.pop(detector)\n wavefrontSensors.pop(detector)",
"title": ""
},
{
"docid": "3392f154927a0b3ea4e7a68b784f6179",
"score": "0.5254399",
"text": "def filter_nan(s,o):\n data = np.array([s.flatten(),o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n\n return data[:,0],data[:,1]",
"title": ""
},
{
"docid": "bbe7cf61ecf67b86675bc79f599d13c2",
"score": "0.5251705",
"text": "def test_filter_by_NaN(self):\n test_scope = scope.Scope(-4.9, -4.70, 48, 49, 0, 1500000000)\n test_preprocessing = preprocessing.Preprocessing(test_scope, self.test_ships)\n\n self.assertEqual(len(self.test_ships), 15)\n test_preprocessing.filter_by_NaN()\n self.assertEqual(len(self.test_ships), 7)\n\n number_states = {'245257000': 29, '228037600': 259, '304091000': 1,\n '227705102': 249, '227443000': 92, '228931000': 21,\n '228131600': 350, '228064900': 4, '37100300': 7}\n\n for ship in self.test_ships:\n self.assertEqual(len(ship.ais_states), number_states[ship.sourcemmsi],\n msg=\"sourcemmsi is \" + ship.sourcemmsi)",
"title": ""
},
{
"docid": "9ee4fe5bb4730d0e6d8c78730fa48d55",
"score": "0.52495563",
"text": "def remove_rows_with_missing_values(self):\n row_length = len(self.column_names)\n table_copy = []\n \n for i in range(len(self.data)):\n has_NA = False\n for j in range(row_length):\n if self.data[i][j] == \"NA\" or self.data[i][j] == \"\":\n has_NA = True\n if has_NA == False: \n table_copy.append(self.data[i])\n \n self.data.clear()\n self.data = copy.deepcopy(table_copy) \n pass # TODO: fix this",
"title": ""
},
{
"docid": "16ead744219067e1a2c3a93e51f6bfe2",
"score": "0.5245453",
"text": "def _clean_useless_rows(self):\r\n self.temp_stock_data = pandas.DataFrame()\r\n dates = []\r\n for i in xrange(0, self.stock_data[self.company_list.Companies.irow(0)].count()):\r\n #print i\r\n row = self.stock_data.irow(i)\r\n if len(row[numpy.isnan(row)]) == len(row):\r\n dates.append(self.date_list[i])\r\n \r\n for single_date in dates:\r\n self.stock_data = self.stock_data.drop(pandas.Timestamp(single_date))\r\n print \"Data cleaned from useless rows.\"",
"title": ""
},
{
"docid": "6fc475ee4cc92954c4f70406962b6150",
"score": "0.52325326",
"text": "def data_cleaning(df: DataFrame) -> DataFrame:\n print(\"Cleaning the data to remove problems\\n \")\n\n nulls = df.isnull().sum()\n nulls = nulls[nulls != 0]\n\n if not nulls.empty:\n print(f\"The columns below some 'NaN' values:\\n{nulls}\")\n\n # for index_with_nan in enumerate(nulls.keys()[:]):\n # print(f\"Column: {index_with_nan} \\n \")\n\n if 'Birth Year' in df.columns.values.tolist():\n df = dob_trim(df)\n\n time.sleep(2)\n\n no_user_type: float = format(df['User Type'].isna().sum()/df.shape[0]*100, '.2g')\n print(f\"A few rows don't have any information for 'User Type' but it is only {no_user_type}% so we'll just drop them\")\n df = df.dropna(axis = 0, subset=['User Type'])\n\n if 'Gender' in df.columns.values.tolist():\n gender_neutral: float = format(df['Gender'].isna().sum()/df.shape[0]*100, '.2g')\n print(f\"{gender_neutral}% of trips don't have any information for 'Gender' so we'll forward-fill.\")\n df.fillna(method=\"ffill\")\n # gender_neutral: float = format(nulls['Gender']/df.shape[0]*100, '.2g')\n # print(f\"A few rows don't have any information for 'Gender' but it is only {gender_neutral}% so we'll just drop them\")\n # df = df.dropna(axis = 0, subset=['Gender'], inplace=True)\n \n return df",
"title": ""
},
{
"docid": "658b7ab760716acff016b703ca146fca",
"score": "0.5225665",
"text": "def removeNansSingle(data):\n # set an x array\n x = np.arange(data.size)\n # find locations of nans - this is a bool array with True in locations with nan values\n nanLocs = np.isnan(data)\n # if no nans, do nothing\n if not np.any(nanLocs):\n return data # no nans to remove\n # create mask\n mask = np.ones(data.size, np.bool)\n mask[nanLocs] = 0 # using numpy indexing with bool arrays\n # no need to group, want to remove every nan\n data[nanLocs] = np.interp(x[nanLocs], x[mask], data[mask])\n return data",
"title": ""
},
{
"docid": "5cb6066ce4a1f9dc1a890971b21dec1f",
"score": "0.5221285",
"text": "def remove_notes(data):\n return data[pd.to_numeric(data.iloc[:, 0], errors='coerce').notnull()]",
"title": ""
},
{
"docid": "6a937fac4a9481e0cde52d043b27035f",
"score": "0.52128845",
"text": "def filter_spikes(df:pd.DataFrame):\n \"\"\"first return argument is non-spike signals, the second one for the spikes\"\"\"\n new_df=pd.DataFrame()\n spike_cluster=pd.DataFrame()\n for i in range(len(df.columns)):\n tmp=df.iloc[:, i] \n uniques=tmp.unique()\n uniques = [x for x in uniques if str(x) != 'nan']\n uniques = [x for x in uniques if x not in [0.0,1.0]]\n if(len(uniques)<=1): #spike like\n spike_cluster.insert(len(spike_cluster.columns),tmp.name,tmp)\n else:\n new_df.insert(len(new_df.columns),tmp.name,tmp)\n return new_df,spike_cluster",
"title": ""
},
{
"docid": "4a372da4cfe63a9a92a7f200bb86f20a",
"score": "0.5209513",
"text": "def nan_clean(array):\n for i in range(len(array)):\n if np.isnan(array[i]) == True:\n array[i] = 0.0\n return array",
"title": ""
},
{
"docid": "6a41fc2d468a3aa9657429af2f10331b",
"score": "0.52041674",
"text": "def remove_nan_observations(x, y, z):\n x_ = x[~np.isnan(z)]\n y_ = y[~np.isnan(z)]\n z_ = z[~np.isnan(z)]\n return x_, y_, z_",
"title": ""
},
{
"docid": "7b824e07c74f481cca378320af481262",
"score": "0.5198521",
"text": "def filterNaN(instr, datacol, outfile, logfile, verbose):\n try:\n nanclean = instr[1].header['NANCLEAN']\n except:\n naxis2 = 0\n for i in range(len(instr[1].columns.names)):\n if 'time' in instr[1].columns.names[i].lower():\n timecol = instr[1].columns.names[i]\n try:\n instr[1].data.field(datacol)\n except:\n msg = (\"ERROR -- KEPIO.FILTERNAN: cannot find column {}\"\n \"in the infile\".format(datacol))\n kepmsg.err(logfile, msg, verbose)\n try:\n for i in range(len(instr[1].data.field(0))):\n if (str(instr[1].data.field(timecol)[i]) != '-inf' and\n str(instr[1].data.field(datacol)[i]) != '-inf'):\n instr[1].data[naxis2] = instr[1].data[i]\n naxis2 += 1\n instr[1].data = instr[1].data[:naxis2]\n comment = 'NaN cadences removed from data'\n kepkey.new('NANCLEAN', True, comment, instr[1], outfile, logfile,\n verbose)\n except:\n errmsg = ('ERROR -- KEPIO.FILTERNAN: Failed to filter NaNs from '\n + outfile)\n kepmsg.err(logfile, errmsg, verbose)\n return instr",
"title": ""
},
{
"docid": "e14120618e0caa9c1a1adf8bf742894f",
"score": "0.51975524",
"text": "def fix_na_values(df):\n ignored = [\"SUBJECT_ID\", \"HADM_ID\", \"ADMITTIME\", \"DISCHTIME\"]\n df_core = df.drop(ignored, axis=1)\n\n while df_core.isna().sum().sum():\n # get column with least amount of missing values\n cols_with_na = df_core.isna().sum()\n col = cols_with_na[cols_with_na > 0].idxmin()\n # impute that column\n df_core.loc[df_core[col].isna(), col] = impute_column(df_core, col)\n\n return pd.concat([df_core, df[ignored]], axis=1)",
"title": ""
},
{
"docid": "13995298c15ddffef72188fbf4ae9e9e",
"score": "0.51944923",
"text": "def test_nans_dropped():\n\n #generate test data\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\n dataframe['A'][5] = numpy.nan\n\n #run function on dataframe\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\"drop\", removal_time_frame=\"day\", fault_placement=\"calendar\")\n assert dataframe.size != result_dataframe.size, \"nan have not been dropped\"",
"title": ""
},
{
"docid": "9105baa116f0859c24acfe0688351a8b",
"score": "0.51941323",
"text": "def drop_missing(self):\n df1 = self.df\n df2 = df1.dropna(axis=0)\n\n self.df = df2",
"title": ""
},
{
"docid": "fe78430afcdbf63973f5fb1eef658c15",
"score": "0.5192824",
"text": "def remove_missing_values(x):\n x_clean = x.copy()\n x_clean = x_clean[:, [np.count_nonzero(x.T[i] == -999) == 0 for i in range (x.shape[1])]]\n return x_clean",
"title": ""
},
{
"docid": "26e1f1380c66022fe6c1a3408b34b606",
"score": "0.51923764",
"text": "def remove_rcna(df):\n df.dropna(how = 'all', axis = 'columns', inplace = True)\n df.dropna(how = 'all', axis = 'rows', inplace = True)\n return df",
"title": ""
},
{
"docid": "97e3227a5225488f9568a8c415801ea9",
"score": "0.51922977",
"text": "def generate_query_ratesnull(self):\n pass",
"title": ""
},
{
"docid": "934058862a1706b0deffe110b8780021",
"score": "0.518677",
"text": "def _series_cleaner(self, obs_data: Union[dict, list]) -> pd.DataFrame:\n series = pd.json_normalize(obs_data[\"observations\"])\n series.drop(columns=[\"realtime_start\", \"realtime_end\"], inplace=True)\n series[\"value\"] = series[\"value\"].replace(self.nan_char, np.nan)\n return series",
"title": ""
},
{
"docid": "eabc39526af7b6e52689bfe97d78ffba",
"score": "0.5184404",
"text": "def drop_missing(self):\n self.miss = np.rint(len(self.data.columns)*self.miss)\n self.data = self.data[self.data.isnull().sum(axis=1)<=self.miss]",
"title": ""
},
{
"docid": "b6e550833aaa3e203af1bd5115ecd7f9",
"score": "0.5179664",
"text": "def clean_data(df):\n data_types = get_missing_columns()\n \n for idx in data_types.index:\n column = idx\n if column in df.columns:\n df[column] = df[column].replace(data_types.loc[column]['nan_vals'], np.nan)\n\n \n # remove selected columns\n df.drop(['AGER_TYP', 'ALTER_HH', 'ALTER_KIND1', 'ALTER_KIND2', 'ALTER_KIND3', 'ALTER_KIND4', 'ALTERSKATEGORIE_FEIN',\\\n 'D19_BANKEN_ONLINE_QUOTE_12', 'D19_GESAMT_ONLINE_QUOTE_12', 'D19_KONSUMTYP', 'D19_LETZTER_KAUF_BRANCHE',\\\n 'D19_LOTTO', 'D19_SOZIALES', 'D19_TELKO_ONLINE_QUOTE_12', 'D19_VERSAND_ONLINE_QUOTE_12',\\\n 'D19_VERSI_ONLINE_QUOTE_12', 'EXTSEL992','GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'TITEL_KZ', 'LNR'],\n axis=1, inplace=True)\n \n print(\"Removal of Columns with over 20% null values complete...\")\n \n # remove selected rows\n df = df[df.isnull().sum(axis=1) < 9].reset_index(drop=True)\n \n # remove selected columns again\n features_not_in_feat_info_42 = ['AKT_DAT_KL', 'ANZ_KINDER', 'ANZ_STATISTISCHE_HAUSHALTE', 'CJT_KATALOGNUTZER','CJT_TYP_1',\\\n 'CJT_TYP_2', 'CJT_TYP_3', 'CJT_TYP_4', 'CJT_TYP_5', 'CJT_TYP_6', 'DSL_FLAG',\\\n 'EINGEZOGENAM_HH_JAHR','FIRMENDICHTE', 'GEMEINDETYP', 'HH_DELTA_FLAG', 'KBA13_ANTG1',\\\n 'KBA13_ANTG2', 'KBA13_ANTG3','KBA13_ANTG4', 'KBA13_BAUMAX', 'KBA13_CCM_1401_2500',\\\n 'KBA13_GBZ', 'KBA13_HHZ', 'KBA13_KMH_210','KONSUMZELLE', 'MOBI_RASTER', 'RT_KEIN_ANREIZ',\\\n 'RT_SCHNAEPPCHEN', 'RT_UEBERGROESSE', 'STRUKTURTYP','UMFELD_ALT', 'UMFELD_JUNG',\\\n 'UNGLEICHENN_FLAG', 'VERDICHTUNGSRAUM', 'VHA', 'VHN', 'VK_DHT4A','VK_DISTANZ',\\\n 'VK_ZG11','D19_KONSUMTYP_MAX', 'KOMBIALTER','EINGEFUEGT_AM']\n \n df.drop(features_not_in_feat_info_42,axis=1, inplace=True)\n \n print(\"Removal of undocumented columns complete...\")\n \n \n # select, re-encode, and engineer column values.\n # # feature engineering of categorical features\n\n df['OST_WEST_KZ'].replace(['W','O'], [1, 0], inplace=True)\n \n multi = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CJT_GESAMTTYP', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',\\\n 'D19_BANKEN_DATUM','D19_BANKEN_OFFLINE_DATUM', 'D19_BANKEN_ONLINE_DATUM', 'D19_GESAMT_ANZ_12',\\\n 'D19_GESAMT_ANZ_24', 'D19_GESAMT_DATUM','D19_GESAMT_OFFLINE_DATUM', 'D19_GESAMT_ONLINE_DATUM',\\\n 'D19_TELKO_DATUM', 'D19_TELKO_OFFLINE_DATUM', 'D19_TELKO_ONLINE_DATUM','D19_VERSAND_DATUM',\\\n 'D19_VERSAND_OFFLINE_DATUM', 'D19_VERSAND_ONLINE_DATUM', 'D19_VERSI_DATUM', 'D19_VERSI_OFFLINE_DATUM',\\\n 'D19_VERSI_ONLINE_DATUM', 'FINANZTYP', 'GEBAEUDETYP', 'GFK_URLAUBERTYP','LP_FAMILIE_FEIN','LP_FAMILIE_GROB',\\\n 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP']\n \n df = pd.get_dummies(df, columns=multi, prefix=multi)\n \n \n # feature engineering of mixed features\n df['PRAEGENDE_JUGENDJAHRE_decade'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_decade)\n df['PRAEGENDE_JUGENDJAHRE_movement'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_movement)\n df.drop('PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)\n \n print(\"Feature Engineering PRAEGENDE_JUGENDJAHRE complete...\")\n \n \n df['CAMEO_INTL_2015_wealth'] = df['CAMEO_INTL_2015'].apply(make_wealth) \n df['CAMEO_INTL_2015_life_stage'] = df['CAMEO_INTL_2015'].apply(make_life_stage)\n df.drop('CAMEO_INTL_2015', axis=1, inplace=True)\n \n print(\"Feature Engineering CAMEO_INTL_2015 complete...\")\n \n df['WOHNLAGE_rural'] = df['WOHNLAGE'].map({0:0,1:0,2:0,3:0,4:0,5:0,7:1,8:1})\n df['WOHNLAGE_rating_class'] = df['WOHNLAGE'].map({0:0,1:1,2:2,3:3,4:4,5:5,7:0,8:0})\n \n print(\"Feature Engineering WOHNLAGE complete...\")\n \n df = df.drop(['LP_LEBENSPHASE_FEIN','LP_LEBENSPHASE_GROB','WOHNLAGE','PLZ8_BAUMAX'], axis=1)\n \n print(\"Finished!\")\n \n # Return the cleaned dataframe.\n return df",
"title": ""
},
{
"docid": "50b15f9a5bc9f58c3f1fda20bd23a98b",
"score": "0.5174193",
"text": "def clean_data(self):\n # Mask files with lat = 0 or lon = 0\n mask = np.where(np.logical_or(self.data['VEH_LAT']==0, \n self.data['VEH_LONG']==0))\n\n for k in self.data.keys():\n if isinstance(self.data[k][0], float):\n self.data[k][mask] = np.nan",
"title": ""
},
{
"docid": "0fb2a849eb9288c2238968e9235b9752",
"score": "0.51717937",
"text": "def drop_null_rows(self, x: bool):\n if x:\n total_null_rows = self.df[self.target].isin(\n [' ', 'NULL', np.nan]).sum()\n if total_null_rows > 0:\n print(\"Dropping rows having [' ', 'NULL', numpy.nan] values\")\n self.df.dropna(inplace=True)\n self.df.reset_index(drop=True, inplace=True)\n self.null_values_present = False\n print_in_red(f\"Total Null rows dropped: {total_null_rows}\\n\")\n self._start_analysis()\n else:\n print(colored(\"There is no null rows present.\\n\", \"green\"))",
"title": ""
},
{
"docid": "52449af6e301d2d873cf47553c2b38b8",
"score": "0.5167622",
"text": "def filter_nan(s, o):\n data = np.array([s.flatten(), o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n return data[:, 0], data[:, 1]",
"title": ""
},
{
"docid": "6512e72d0658c2f1c0378e699cc56170",
"score": "0.51613533",
"text": "def clean_timeseries(self):\n if 'tb_incidence' in self.timeseries_log.keys():\n self.timeseries_log['tb_incidence'][0] = 0.",
"title": ""
},
{
"docid": "248a5d7d28357321ce39d2c77fe712a7",
"score": "0.5160788",
"text": "def filter_values_2(self, counter):\n inf = float(\"inf\")\n last_mea = list(self.scan_values_2[counter])\n for i in range(len(last_mea)):\n if last_mea[i] == inf:\n for k in range(1, MEASUREMENTS):\n if self.scan_values_2[counter-k] != inf:\n last_mea[i] = self.scan_values_2[k][i]\n break\n scan = LaserScan()\n scan.ranges = last_mea\n self.laser_filtered_pub.publish(scan)",
"title": ""
},
{
"docid": "16cc667bcf39fc41ca499a7580cf48d0",
"score": "0.51605296",
"text": "def _get_instr_after_remove(self, remove_size_null, remove_adj_cap_nan):\n if any([remove_size_null, remove_adj_cap_nan]): # will update instr_df\n instr_df = self.concat_instr_df.copy()\n else: # nothing will be changed in self.concat_instr_df so no need to copy\n instr_df = self.concat_instr_df\n if instr_df.empty:\n raise Exception('concat_instr_df is empty. Cannot display metrics')\n if remove_size_null:\n no_instrs = len(instr_df)\n idx_size_null = instr_df['stake'] == 0\n if sum(idx_size_null) > 0:\n self.logger.warning('Dropping {}/{} instructions with stake 0'.format(\n sum(idx_size_null), no_instrs))\n instr_df = instr_df.loc[~idx_size_null]\n if remove_adj_cap_nan:\n if 'adj_cap' not in instr_df.columns:\n raise Exception('concat_instr_df do not have column '\n 'adj_cap and so cannot remove_adj_cap_nan')\n no_instrs = len(instr_df)\n idx_nan = instr_df['adj_cap'].isnull()\n if sum(idx_nan) > 0:\n self.logger.warning('Keeping {}/{} instructions with known capital adjustment'\n .format(no_instrs - sum(idx_nan), no_instrs))\n # remove nan in adjst_capital\n instr_df = instr_df.loc[~idx_nan]\n return instr_df",
"title": ""
}
]
014a5481438d0e9b71966a48d4d8bbf6
Generate a NL or LP file from Pyomo, and then do subsequent conversions.
[
{
"docid": "b50fba180d6eee76d9713189ba7dcb27",
"score": "0.56642395",
"text": "def apply(self, *args, **kwds):\n\n import pyomo.scripting.convert\n\n capabilities = kwds.pop(\"capabilities\", None)\n\n # all non-consumed keywords are assumed to be options\n # that should be passed to the writer.\n io_options = {}\n for kwd, value in iteritems(kwds):\n io_options[kwd] = value\n kwds.clear()\n\n # basestring is gone in Python 3.x, merged with str.\n if PY3:\n compare_type = str\n else:\n compare_type = basestring\n\n if isinstance(args[2], compare_type):\n instance = None\n else:\n instance = args[2]\n\n if args[1] == ProblemFormat.cpxlp:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.lp')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.cpxlp,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.cpxlp,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2lp(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2lp conversion \"\n \"returned nonzero error code \"\n \"(%s)\" % ans.errorcode)\n\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n return (problem_filename,),symbol_map\n\n elif args[1] == ProblemFormat.bar:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.bar')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.bar,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.bar,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2bar(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2bar conversion \"\n \"returned nonzero error code \"\n \"(%s)\" % ans.errorcode)\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n return (problem_filename,),symbol_map\n\n elif args[1] in [ProblemFormat.mps, ProblemFormat.nl]:\n if args[1] == ProblemFormat.nl:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.nl')\n if io_options.get(\"symbolic_solver_labels\", False):\n pyutilib.services.TempfileManager.add_tempfile(\n problem_filename[:-3]+\".row\",\n exists=False)\n pyutilib.services.TempfileManager.add_tempfile(\n problem_filename[:-3]+\".col\",\n exists=False)\n else:\n assert args[1] == ProblemFormat.mps\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.mps')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=args[1],\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=args[1],\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2nl(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2nl conversion \"\n \"returned nonzero error \"\n \"code (%s)\" % ans.errorcode)\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n\n if args[1] == ProblemFormat.nl:\n return (problem_filename,),symbol_map\n #\n # Convert from NL to MPS\n #\n # TBD: We don't support a variable map file when going\n # from NL to MPS within the PICO converter.\n # NOTE: this is a problem with the MPS writer that is\n # provided by COIN-OR\n # NOTE: we should generalize this so it doesn't strictly\n # depend on the PICO converter utility.\n #\n ans = self.pico_converter.apply(ProblemFormat.nl,\n ProblemFormat.mps,\n problem_filename)\n os.remove(problem_filename)\n return ans\n\n elif args[1] == ProblemFormat.osil:\n if False:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix='pyomo.osil')\n if instance:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.osil,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.osil,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), None\n else:\n raise NotImplementedError(\n \"There is currently no \"\n \"script conversion available from \"\n \"Pyomo to OSiL format.\")",
"title": ""
}
]
[
{
"docid": "67db92a1a3821a68008dc249af915482",
"score": "0.5871873",
"text": "def create_model(self):\r\n\r\n self.try_load_model()\r\n\r\n self.set_correct_problem_instance_name()\r\n\r\n self.add_objective_and_budget_constraints()\r\n self.add_starting_ending_nodes_constraints()\r\n\r\n self.docplex_model.print_information()\r\n\r\n # DEBUG\r\n # for constraint in self.docplex_model.iter_constraints():\r\n # print(constraint)\r",
"title": ""
},
{
"docid": "95e1197a8a29532e1bb1bc651f420051",
"score": "0.5701343",
"text": "def formula_generator(known_args, other_args): # pylint: disable=W0613\n poly = Poly(*known_args.format)\n\n if known_args.smt2:\n with open(known_args.smt2, 'w') as smt2_file:\n with redirect_stdout(smt2_file):\n poly.problem(known_args.degree)\n else:\n poly.problem(known_args.degree)",
"title": ""
},
{
"docid": "1717949860fb45cd73863fd5f97acc0e",
"score": "0.56868994",
"text": "def __init__(self, model, output_path, wanted_lorentz = [],\n wanted_couplings = [], replace_dict={}):\n\n self.model = model\n self.model_name = export_cpp.ProcessExporterCPP.get_model_name(model['name'])\n self.aloha_model = create_aloha.AbstractALOHAModel(self.model_name)\n\n\n self.dir_path = output_path\n self.default_replace_dict = dict(replace_dict)\n # List of needed ALOHA routines\n self.wanted_lorentz = wanted_lorentz\n self.wanted_couplings = wanted_couplings\n\n # For dependent couplings, only want to update the ones\n # actually used in each process. For other couplings and\n # parameters, just need a list of all.\n self.coups_dep = {} # name -> base_objects.ModelVariable\n self.coups_indep = [] # base_objects.ModelVariable\n self.params_dep = [] # base_objects.ModelVariable\n self.params_indep = [] # base_objects.ModelVariable\n self.p_to_cpp = None \n\n # Prepare parameters and couplings for writeout in C++\n self.prepare_parameters()\n self.prepare_couplings(wanted_couplings)",
"title": ""
},
{
"docid": "c5525bfc1f28e0a667830ec014bdb725",
"score": "0.56353825",
"text": "def convertProcessToLcia(O,p):\r\n \r\n # the players\r\n P = O.findUuid(os.path.splitext(p)[0])\r\n print 'Importing: ' + P.El('baseName').text\r\n L = O.createLciaFromTemplate()\r\n \r\n lcia_ns = L.getroot().nsmap\r\n \r\n # the basic data\r\n L.El('name','common').text = P.El('baseName').text\r\n L.El('methodology').text = 'TRACI 2.0'\r\n L.El('impactCategory').text = selectFromList(impactCategories)\r\n L.El('impactIndicator').text = raw_input('Impact Indicator: ')\r\n L.El('referenceYear').text = P.El('referenceYear','common').text\r\n L.El('duration').text = 'indefinite'\r\n L.El('interventionLocation').text = 'US'\r\n L.El('impactLocation').text = 'US'\r\n\r\n L.El('typeOfDataSet').text = selectFromList(indicatorTypes)\r\n L.El('normalisation').text= 'false'\r\n L.El('weighting').text = 'false'\r\n \r\n QR=L.El('quantitativeReference')\r\n CF=L.El('characterisationFactors')\r\n \r\n # the exchanges\r\n Xs = P.El('exchanges').getchildren()\r\n for x in Xs:\r\n flow_el = nonemap(x,'referenceToFlowDataSet')\r\n flow_uuid = flow_el.attrib['refObjectId']\r\n flow_comment = flow_el.getchildren()[0]\r\n if nonemap(x, 'exchangeDirection').text == 'Input':\r\n # Input exchange = reference flow\r\n ref_uuid = getReferenceFlow(O.findUuid(flow_uuid), nsmap = lcia_ns)\r\n RQ = ET.Element('referenceQuantity',\r\n attrib = {'type':'flow property data set',\r\n 'refObjectId':ref_uuid,\r\n 'uri':'../flowproperties/{0}.xml'.format(ref_uuid)},\r\n nsmap = lcia_ns)\r\n RQ.append(flow_comment)\r\n QR.append(RQ)\r\n print 'reference flow: {0} {1}'.format(ref_uuid,flow_comment.text)\r\n \r\n else:\r\n # Output exchange = new factor\r\n mean_value = 1 / float(nonemap(x, 'resultingAmount').text) # note inversion!\r\n F = ET.Element('factor',nsmap = lcia_ns)\r\n FL = ET.Element('referenceToFlowDataSet', \r\n attrib={'type':'flow data set',\r\n 'refObjectId':flow_uuid,\r\n 'uri':'../flows/{0}.xml'.format(flow_uuid)},\r\n nsmap = lcia_ns)\r\n FL.append(flow_comment)\r\n F.append(FL)\r\n ET.SubElement(F, 'exchangeDirection',nsmap = lcia_ns).text='Output'\r\n ET.SubElement(F, 'meanValue', nsmap = lcia_ns).text = str( mean_value ) \r\n CF.append(F)\r\n print 'exchange: {0} {1}'.format(flow_uuid,flow_comment.text)\r\n\r\n return L",
"title": ""
},
{
"docid": "b0f2020c341a65e6214adebd68e7903e",
"score": "0.56281185",
"text": "def generate_and_save_all_interpretations_for_model(pci_obj, X, y, mdl, mdl_name):\n \n ## Generate dataset-level interpretation for Logistic Regression model\n print('Dataset-level interpretation generation is in process for ' + mdl_name + '.')\n dataset_level_interpretation = pci_obj.dataset_level_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_dataset_level_interpretation_' + mdl_name + '.txt', dataset_level_interpretation, fmt='%1.8e')\n print('Dataset-level interpretation generation is finished for' + mdl_name + '.')\n \n \n ## Generate whole dataset signed interpretation for Logistic Regression model\n print('Whole dataset signed interpretation generation is in process for ' + mdl_name + '.')\n whole_dataset_signed_interpretation = pci_obj.whole_dataset_signed_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_whole_dataset_signed_interpretation_' + mdl_name + '.txt', whole_dataset_signed_interpretation, fmt='%1.8e')\n print('Whole dataset signed interpretation generation is finished for ' + mdl_name + '.')\n \n ## Generate whole dataset signed interpretation for Logistic Regression model\n print('Whole dataset unsigned interpretation generation is in process for ' + mdl_name + '.')\n whole_dataset_unsigned_interpretation = pci_obj.whole_dataset_unsigned_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_whole_dataset_unsigned_interpretation_' + mdl_name + '.txt', whole_dataset_unsigned_interpretation, fmt='%1.8e')\n print('Whole dataset unsigned interpretation generation is finished for ' + mdl_name + '.')",
"title": ""
},
{
"docid": "95c5748803c3d19cc5d921a875e0b086",
"score": "0.5581633",
"text": "def convert(prototxt_path, caffemodel_path, phase='test', output_model_name='freeze_graph_model.pb'):\n assert_exist_files(prototxt_path, caffemodel_path)\n\n data_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.mat')\n code_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.py')\n standalone_output_path = os.path.join(os.path.dirname(prototxt_path), output_model_name)\n\n try:\n with tf.Session() as sess:\n transformer = TensorFlowTransformer(prototxt_path, caffemodel_path, phase=phase)\n print_stderr('Converting data...')\n if data_output_path is not None:\n data = transformer.transform_data()\n print_stderr('Saving data...')\n with open(data_output_path, 'wb') as data_out:\n np.save(data_out, data)\n if code_output_path is not None:\n print_stderr('Saving source...')\n with open(code_output_path, 'wb') as src_out:\n src_out.write(transformer.transform_source())\n\n if standalone_output_path:\n filename, _ = os.path.splitext(os.path.basename(standalone_output_path))\n temp_folder = os.path.join(os.path.dirname(standalone_output_path), 'tmp')\n\n if not os.path.exists(temp_folder):\n os.makedirs(temp_folder)\n\n if data_output_path is None:\n data = transformer.transform_data()\n print_stderr('Saving data...')\n data_output_path = os.path.join(temp_folder, filename) + '.npy'\n with open(data_output_path, 'wb') as data_out:\n np.save(data_out, data)\n\n if code_output_path is None:\n print_stderr('Saving source...')\n code_output_path = os.path.join(temp_folder, filename) + '.py'\n with open(code_output_path, 'wb') as src_out:\n src_out.write(transformer.transform_source())\n\n checkpoint_path = os.path.join(temp_folder, filename + '.ckpt')\n graph_name = os.path.basename(standalone_output_path)\n graph_folder = os.path.dirname(standalone_output_path)\n input_node = transformer.graph.nodes[0].name\n output_node = transformer.graph.nodes[-1].name\n tensor_shape = transformer.graph.get_node(input_node).output_shape\n tensor_shape_list = [tensor_shape.batch_size, tensor_shape.height, tensor_shape.width, tensor_shape.channels]\n\n sys.path.append(os.path.dirname(code_output_path))\n module = os.path.splitext(os.path.basename(code_output_path))[0]\n class_name = transformer.graph.name\n KaffeNet = getattr(__import__(module), class_name)\n\n data_placeholder = tf.placeholder(tf.float32, tensor_shape_list, name=input_node)\n net = KaffeNet({input_node: data_placeholder})\n\n # load weights stored in numpy format\n net.load(data_output_path, sess)\n\n print_stderr('Saving checkpoint...')\n\n saver = tf.train.Saver()\n saver.save(sess, checkpoint_path)\n\n print_stderr('Saving graph definition as protobuf...')\n tf.train.write_graph(sess.graph.as_graph_def(), graph_folder, graph_name, as_text=False)\n\n input_graph_path = standalone_output_path\n input_saver_def_path = \"\"\n input_binary = True\n input_checkpoint_path = checkpoint_path\n output_node_names = output_node\n restore_op_name = 'save/restore_all'\n filename_tensor_name = 'save/Const:0'\n output_graph_path = standalone_output_path\n clear_devices = True\n\n print_stderr('Saving standalone model...')\n freeze_graph(input_graph_path, input_saver_def_path,\n input_binary, input_checkpoint_path,\n output_node_names, restore_op_name,\n filename_tensor_name, output_graph_path,\n clear_devices, '')\n\n shutil.rmtree(temp_folder)\n\n print_stderr('Done.')\n except KaffeError as err:\n fatal_error('Error encountered: {}'.format(err))",
"title": ""
},
{
"docid": "870fd0325523f72544b469d902b7c2cc",
"score": "0.5544181",
"text": "def convert_odml():\n f_in = join(ROOT, \"tmp.xml\")\n f_out = join(ROOT, \"tmpout.xml\")\n\n o_ver_conv = VersionConverter(f_in)\n\n # either write to file\n o_ver_conv.write_to_file(f_out)\n\n # or pass it to the python xml parser\n doc = ODMLReader(parser='XML').from_string(str(o_ver_conv))\n doc.pprint()",
"title": ""
},
{
"docid": "5d270ed9448ab6ff02368268de72a91b",
"score": "0.5542094",
"text": "def compare_lp_files(self, filename, ignored=None, my_om=None):\n if my_om is None:\n om = self.get_om()\n else:\n om = my_om\n tmp_filename = filename.replace('.lp', '') + '_tmp.lp'\n new_filename = ospath.join(self.tmppath, tmp_filename)\n om.write(new_filename, io_options={'symbolic_solver_labels': True})\n logging.info(\"Comparing with file: {0}\".format(filename))\n with open(ospath.join(self.tmppath, tmp_filename)) as generated_file:\n with open(ospath.join(ospath.dirname(ospath.realpath(__file__)),\n \"lp_files\",\n filename)) as expected_file:\n\n def chop_trailing_whitespace(lines):\n return [re.sub(r'\\s*$', '', ln) for ln in lines]\n\n def remove(pattern, lines):\n if not pattern:\n return lines\n return re.subn(pattern, \"\",\n \"\\n\".join(lines))[0].split(\"\\n\")\n\n expected = remove(ignored,\n chop_trailing_whitespace(\n expected_file.readlines()))\n generated = remove(ignored,\n chop_trailing_whitespace(\n generated_file.readlines()))\n\n def normalize_to_positive_results(lines):\n negative_result_indices = [\n n for n, line in enumerate(lines)\n if re.match(\"^= -\", line)]\n equation_start_indices = [\n [n for n in reversed(range(0, nri))\n if re.match('.*:$', lines[n])][0]+1\n for nri in negative_result_indices]\n for (start, end) in zip(\n equation_start_indices,\n negative_result_indices):\n for n in range(start, end):\n lines[n] = (\n '-'\n if lines[n] and lines[n][0] == '+'\n else '+'\n if lines[n]\n else lines[n]) + lines[n][1:]\n lines[end] = '= ' + lines[end][3:]\n return lines\n\n expected = normalize_to_positive_results(expected)\n generated = normalize_to_positive_results(generated)\n\n eq_(generated, expected,\n \"Failed matching expected with generated lp file:\\n\" +\n \"\\n\".join(unified_diff(expected, generated,\n fromfile=ospath.relpath(\n expected_file.name),\n tofile=ospath.basename(\n generated_file.name),\n lineterm=\"\")))",
"title": ""
},
{
"docid": "bffa7248d281833024e2e2237090aa94",
"score": "0.55059963",
"text": "def model_to_owl(model, fname):\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\n\n try:\n fileOS = autoclass('java.io.FileOutputStream')(fname)\n except JavaException:\n logger.error('Could not open data file %s' % fname)\n return\n l3_factory = autoclass('org.biopax.paxtools.model.BioPAXLevel').L3.getDefaultFactory()\n model_out = l3_factory.createModel()\n for r in model.getObjects().toArray():\n model_out.add(r)\n io.convertToOWL(model_out, fileOS)\n\n fileOS.close()",
"title": ""
},
{
"docid": "016e6167d18373b2c352dac2e0aac10f",
"score": "0.54671896",
"text": "def run(in_obo_filename=IN_OBO_FILENAME, in_pkl_filename=IN_PKL_FILENAME, in_tsv_filename=IN_TSV_FILELANE,\n in_monomers_filename=IN_MONOMERS_FILENAME,\n max_num_proteins=None,\n out_pickle_filename=OUT_PICKLE_FILENAME, out_pickle_filename_2=OUT_PICKLE_FILENAME_2,\n out_tsv_filename=OUT_TSV_FILENAME, out_fasta_filename=OUT_FASTA_FILENAME,\n out_fig_filename=OUT_FIG_FILENAME, out_structure_dirname=OUT_STRUCTURE_DIRNAME,\n out_viz_dirname=OUT_VIZ_DIRNAME):\n # get the PRO ontology and extract the modified proteins from the ontology\n # proteins = get_pro_from_obo(obo_filename=in_obo_filename, pkl_filename=in_pkl_filename, max_num_proteins=max_num_proteins)\n proteins = get_pro_from_tsv(in_tsv_filename, max_num_proteins=max_num_proteins)\n\n # parse the modified proteins and retrieve their sequences\n if not os.path.isfile(out_pickle_filename):\n # parse the modified proteins and retrieve their sequences\n parsed_proteins = []\n for i_protein, protein in enumerate(proteins):\n if i_protein % 100 == 0:\n print('Parsing protein {} of {}'.format(i_protein + 1, len(proteins)))\n parsed_proteins.append(parse_protein(protein))\n\n # save the parsed proteins in pickle format\n with open(out_pickle_filename, 'wb') as file:\n pickle.dump(parsed_proteins, file)\n else:\n # load saved parsed proteins in pickle format\n with open(out_pickle_filename, 'rb') as file:\n parsed_proteins = pickle.load(file)\n\n # read list of monomers\n monomers = {}\n with open(in_monomers_filename, 'r') as file:\n reader = csv.DictReader(file, dialect='excel')\n for row in reader:\n monomers[row['PRO id']] = {\n 'mod': bpforms.protein_alphabet.monomers.get(row['BpForms code'], None),\n 'origin': [],\n }\n if row['Base monomer']:\n monomers[row['PRO id']]['origin'] = row['Base monomer'].split(', ')\n\n # generate list of modified monomeric forms\n for protein in parsed_proteins:\n for modification in protein['modifications']:\n if modification['monomer'] not in monomers:\n monomers[modification['monomer']] = {\n 'mod': None,\n 'origin': [],\n }\n\n # print list of unmapped monomers\n unmapped_monomers = []\n for monomer, code in monomers.items():\n if not code['mod']:\n unmapped_monomers.append(monomer)\n unmapped_monomers.sort()\n if unmapped_monomers:\n print('Several PRO monomeric forms have not been mapped to BpForms monomeric forms:\\n {}'.format(\n '\\n '.join(unmapped_monomers)))\n\n # check for inconsistencies between residue and modified monomeric form\n monomer_codes = {}\n for code, monomer in bpforms.protein_alphabet.monomers.items():\n monomer_codes[monomer] = code\n\n for protein in parsed_proteins:\n for modification in protein.get('modifications', []):\n if modification['residue'] and modification['monomer']:\n monomer = monomers.get(modification['monomer'], None)\n if (monomer['mod'] and monomer['mod'].get_canonical_code(monomer_codes) != modification['residue']) \\\n or (monomer['origin'] and modification['residue'] not in monomer['origin']):\n codes = set(monomer['origin'])\n if monomer['mod']:\n codes.add(monomer['mod'].get_canonical_code(monomer_codes))\n msg = 'Modified monomeric form {} potentially inconsistent with residue {} != {}'.format(\n modification['monomer'], modification['residue'],\n ', '.join(codes))\n print(protein['id'] + ': ' + msg)\n\n # generate BpForms for each protein\n if not os.path.isdir(out_structure_dirname):\n os.mkdir(out_structure_dirname)\n\n if not os.path.isdir(out_viz_dirname):\n os.mkdir(out_viz_dirname)\n\n if not os.path.isfile(out_pickle_filename_2):\n for i_protein, protein in enumerate(parsed_proteins):\n if i_protein % 100 == 0:\n print('Generating BpForms {} of {}'.format(i_protein + 1, len(parsed_proteins)))\n\n protein['modified_seq'] = None\n if not protein['uniprot_id']:\n continue\n if not protein['seq']:\n continue\n if protein['pro_errors']:\n continue\n\n processed_form = gen_bpform(protein, monomers, monomer_codes, apply_modifications=False)\n protein['processed_seq'] = str(processed_form)\n if not processed_form.validate():\n processed_formula = processed_form.get_formula()\n protein['processed_formula'] = str(processed_formula)\n protein['processed_mol_wt'] = processed_form.get_mol_wt()\n protein['processed_charge'] = processed_form.get_charge()\n\n if not protein['modifications']:\n continue\n\n modified_form = gen_bpform(protein, monomers, monomer_codes, include_annotations=False)\n protein['modified_seq'] = str(modified_form)\n\n modified_form = gen_bpform(protein, monomers, monomer_codes)\n if not modified_form.validate():\n modified_formula = modified_form.get_formula()\n protein['modified_full_seq'] = str(modified_form)\n protein['modified_formula'] = str(modified_formula)\n protein['modified_mol_wt'] = modified_form.get_mol_wt()\n protein['modified_charge'] = modified_form.get_charge()\n\n protein['modifications_formula'] = str(modified_formula - processed_formula)\n protein['modifications_mol_wt'] = protein['modified_mol_wt'] - protein['processed_mol_wt']\n protein['modifications_charge'] = protein['modified_charge'] - protein['processed_charge']\n\n # with open(os.path.join(out_structure_dirname, protein['id'] + '.cml'), 'w') as file:\n # file.write(modified_form.export('cml'))\n\n form = gen_bpform(protein, monomers, monomer_codes,\n apply_processing=False, include_annotations=True)\n seq_features = []\n if protein['processing']:\n seq_features.append({\n 'label': 'Processed',\n 'color': '#cccccc',\n 'positions': [],\n })\n last = 0\n for p in protein['processing']:\n seq_features[0]['positions'].append([last + 1, p['start'] - 1])\n last = p['end']\n seq_features[0]['positions'].append([\n protein['processing'][-1]['end'] + 1,\n len(form.seq),\n ])\n\n if protein['processing'][0]['start'] == 1:\n seq_features[0]['positions'].pop(0)\n if protein['processing'][-1]['end'] == len(form.seq):\n seq_features[0]['positions'].pop(len(seq_features[0]['positions']) - 1)\n\n with open(os.path.join(out_viz_dirname, protein['id'] + '.svg'), 'w') as file:\n file.write(form.get_genomic_image(seq_features, width=910))\n\n if modified_form.get_canonical_seq(monomer_codes) != protein['processed_seq']:\n protein['pro_errors'].append('Modified sequence for {} not compatible with the processed sequence'.format(\n protein['id']))\n\n # save the parsed proteins in pickle format\n with open(out_pickle_filename_2, 'wb') as file:\n pickle.dump(parsed_proteins, file)\n else:\n with open(out_pickle_filename_2, 'rb') as file:\n parsed_proteins = pickle.load(file)\n\n # save the proteoforms in TSV format\n with open(out_tsv_filename, 'w') as file:\n writer = csv.writer(file, dialect='excel-tab')\n writer.writerow(['PRO id', 'UniProt id', 'Organism',\n 'Unmodified sequence (IUBMB)',\n 'Processing', 'Deletions', 'Processsed sequence (IUBMB)', 'Processsed formula',\n 'Processsed molecular weight', 'Processsed charge',\n 'Modifications', 'Crosslinks', 'Modified sequence (abbreviated BpForms)', 'Modified sequence (BpForms)',\n 'Is modified sequence concrete', 'Modified formula', 'Modified molecular weight', 'Modified charge',\n 'Modifications formula', 'Modifications molecular weight', 'Modifications charge',\n 'PRO issues', 'Monomeric form issues'])\n\n for parsed_protein in parsed_proteins:\n if parsed_protein.get('pro_errors', None):\n pro_errors = '. '.join(parsed_protein['pro_errors']) + '.'\n else:\n pro_errors = None\n\n if parsed_protein.get('modified_errors', None):\n modified_errors = '. '.join(parsed_protein['modified_errors']) + '.'\n else:\n modified_errors = None\n\n writer.writerow([\n parsed_protein['id'],\n parsed_protein.get('uniprot_id', None),\n parsed_protein.get('organism', None),\n parsed_protein.get('seq', None),\n ', '.join('{}-{}'.format(p['start'], p['end']) for p in parsed_protein['processing']),\n ', '.join('{}-{}'.format(deletion[0], deletion[1]) for deletion in parsed_protein.get('deletions', [])),\n parsed_protein.get('processed_seq', None),\n parsed_protein.get('processed_formula', None),\n parsed_protein.get('processed_mol_wt', None),\n parsed_protein.get('processed_charge', None),\n ', '.join('{} --> {} ({})'.format(m['residue'] or '?', m['monomer'], ', '.join(str(p) for p in m['positions']))\n for m in parsed_protein['modifications']),\n ', '.join('{}{}-{}{}'.format(xlink[0][1], xlink[0][0], xlink[1][1], xlink[1][0])\n for xlink in parsed_protein.get('crosslinks', [])),\n parsed_protein.get('modified_seq', None),\n parsed_protein.get('modified_full_seq', None),\n parsed_protein.get('modified_concrete', False),\n parsed_protein.get('modified_formula', None),\n parsed_protein.get('modified_mol_wt', None),\n parsed_protein.get('modified_charge', None),\n parsed_protein.get('modifications_formula', None),\n parsed_protein.get('modifications_mol_wt', None),\n parsed_protein.get('modifications_charge', None),\n pro_errors,\n modified_errors,\n ])\n\n # save the proteoforms in FASTA format\n seqs = (SeqRecord(id='{} | {}'.format(protein['id'], protein['uniprot_id']),\n seq=Seq(protein['modified_seq']),\n description='')\n for protein in parsed_proteins\n if protein['modified_seq'])\n SeqIO.write(seqs, out_fasta_filename, \"fasta\")\n\n # analyze frequency of modifications\n plot_modifications(parsed_proteins, fig_filename=out_fig_filename)\n\n # return proteins\n return proteins, parsed_proteins",
"title": ""
},
{
"docid": "776ae1cf149e78ae0a4114b21438d138",
"score": "0.54315245",
"text": "def build_LM(in_file):\r\n print 'building language models...'\r\n # This is an empty method\r\n # Pls implement your code in below\r\n\r\n # Open in_file and split into a list of sentences\r\n input_file = open(in_file, 'r')\r\n train_input_list = input_file.read().split(\"\\r\\n\")\r\n\r\n # Set up dictionaries to store the 4grams generated from input.train\r\n # ngram_count stores the total number of ngrams each language has generated\r\n four_gram_table = {}\r\n ngram_count = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\r\n\r\n '''\r\n NGRAM GENERATION\r\n Multiple for loops through each sentence and then each character for\r\n 4gram generation. Each sentence is padded with 3 'START's and 3 'END's\r\n '''\r\n for sentence in train_input_list:\r\n sentence_parts = sentence.split(None, 1)\r\n # To handle possible empty strings or weird strings in training input\r\n if (len(sentence_parts) != 2):\r\n break\r\n language = sentence_parts[0]\r\n sentence_body = sentence_parts[1]\r\n char_list = list(sentence_body)\r\n for index in range(3):\r\n char_list.insert(0, 'START')\r\n char_list.append('END')\r\n for index in range(len(char_list) - 4):\r\n four_gram = (char_list[index], char_list[index + 1],\r\n char_list[index + 2], char_list[index + 3])\r\n if four_gram in four_gram_table:\r\n four_gram_table[four_gram][language] += 1\r\n ngram_count[language] += 1\r\n else:\r\n # Add-one smoothing for new ngrams\r\n four_gram_table[four_gram] = {'malaysian': 1, 'indonesian': 1,\r\n 'tamil': 1}\r\n for value in ngram_count.itervalues():\r\n value += 1\r\n four_gram_table[four_gram][language] += 1\r\n ngram_count[language] += 1\r\n return [four_gram_table, ngram_count]\r\n\r\n input_file.close()",
"title": ""
},
{
"docid": "8776c3c0636ee64e48b9236afde29d96",
"score": "0.54051876",
"text": "def run(args: argparse.Namespace) -> None:\n assert WaymoDataFileReader is not None, (\n \"Please install the requirements in scripts/optional.txt to use\"\n \"Waymo conversion.\"\n )\n if not os.path.exists(args.output):\n os.makedirs(args.output)\n result = from_waymo(\n args.input,\n args.output,\n args.save_images,\n args.use_lidar_labels,\n args.nproc,\n )\n save(os.path.join(args.output, \"scalabel_anns.json\"), result)",
"title": ""
},
{
"docid": "89aa08012b425e9517160341b3b4c2b8",
"score": "0.5397848",
"text": "def __init__(self,pedfname = None,title='title',outfname='rgMakeGenModel.phe',\n xtradir=\"/foo/bar\", linkage=True, logf = None, model=additive,\n labels=['FounderNonAff','FounderAff','OffspringNonAff','OffspringAff']):\n ehet = '1'\n ehom1 = '0'\n ehom2 = '2'\n missval = '.'\n missing = ['N','0','.','-']\n if model == additive: # we have ehom2 = 2 major alleles\n modeldict = {ehom1:'2',ehet:'1',ehom2:'0',missval:missval}\n elif model == zadditive:\n modeldict = {ehom1:'1',ehet:'0',ehom2:'-1',missval:missval}\n elif model == dominant:\n modeldict = {ehom1:'1',ehet:'1',ehom2:'0',missval:missval}\n else:\n modeldict = {ehom1:'2',ehet:'0',ehom2:'0',missval:missval}\n # model code translation dicts\n\n swapdict = {ehom1:ehom2,ehom2:ehom1,ehet:ehet,missval:missval} # if initial ref allele was wrong, use these to swap\n mdict = dict(zip(missing,missing))\n pedf = '%s.ped' % pedfname\n f = file(pedf,'r')\n if linkage: # read map file\n map = readMap(pedfname)\n rslist = [x[1] for x in map] # get rs numbers\n else:\n head = f.next().strip()\n rslist = head.split()\n nrs = len(rslist) # number of markers\n elen = 2*nrs + 6 # expected # elements on each line\n logf.write('%s %s: found %d for nrs\\n' % (thisprog,timenow(),nrs))\n gm = {}\n gm['founders'] = [] # array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\n gm['offspring'] = [] # [array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\n adicts = [{} for x in xrange(nrs)] # count of alleles in a dict for each marker\n refallele = [None for x in xrange(nrs)] # list of first observed alleles\n nsubj = 0\n indiv = {'founders':[],'offspring':[]}\n for lnum,l in enumerate(f):\n ll = l.strip().split()\n if (lnum+1) % 200 == 0:\n logf.write('%s %s: Processing line %d\\n' % (thisprog, timenow(),lnum+1))\n if len(ll) < elen: # ? short ?\n logf.write('%s %s: Line %d is %d long, expected %d\\n' % (thisprog, timenow(),\n lnum,len(ll),elen))\n else:\n garray = array.array('c',[missval,]*nrs)\n nsubj += 1\n sid = '%s_%s' % (ll[0],ll[1])\n if sid == '1_1': # eesh\n sid = '%d_%d' % (nsubj,nsubj)\n isFounder = isOff = False\n status = labels[0] # founder unaff\n if ll[2] <> '0' and ll[3] <> '0': # has parent ids\n iclass = 'offspring'\n status = labels[2] # unaffected offspring\n if ll[5] == '2':\n status = labels[3] # affected offspring\n else:\n iclass = 'founders'\n if ll[5] == '2':\n status = labels[1] #change from unaff to aff founder label\n gender = 'M'\n if ll[4] == '2':\n gender = 'F'\n ped = ll[:6]\n indiv[iclass].append(ped) # for row wise output\n for snp in xrange(nrs):\n pos = 2*snp + 6 # first\n g1,g2 = ll[pos],ll[pos+1] # pair of genos\n if mdict.get(g1,None) or mdict.get(g2,None): # one or both missing\n esnp = missval # missing value\n else:\n if not refallele[snp]:\n refallele[snp] = g1 # first one we saw!\n for g in (g1,g2):\n n = adicts[snp].get(g,0)\n n += 1\n adicts[snp][g] = n\n if g1 == g2: # hom\n if g1 == refallele[snp]:\n esnp = ehom2 # 2 copies of current reference allele\n else:\n esnp = ehom1 # no copies\n else:\n esnp = ehet # het - always has one copy of reference allele\n garray[snp] = esnp\n gm[iclass].append(garray) # append genos for this new subject\n for ek in gm.keys():\n lek = len(gm[ek])\n if len(gm[ek]) > 0:\n lek0 = len(gm[ek][0])\n s = 'for %s, have %d subjects with %d markers' % (ek,lek,lek0)\n print s\n logf.write(s)\n for x in range(lek):\n if len(gm[ek][x]) <> lek0:\n s = 'for row %d, len = %d, not %d' % (x, len(gm[ek][x]),lek0)\n print s\n logf.write(s)\n logf.write('%s %s: Now checking major allele assignment and fixing as needed\\n' % (thisprog,timenow()))\n for iclass in gm.keys():\n for subject in xrange(len(gm[iclass])): # for each subject\n for snp in xrange(nrs): # now check to see if reference = major allele\n major = majAllele(adicts[snp])\n if major <> refallele[snp]: # either None or we need to change all the codes\n if major <> None:\n gm[iclass][subject][snp] = swapdict[gm[iclass][subject][snp]]\n for iclass in gm.keys():\n for subject in xrange(len(gm[iclass])): # now convert to genetic model wanted\n gm[iclass][subject] = [modeldict[x] for x in gm[iclass][subject]] # translate to model\n self.gm = gm\n self.model = model\n self.indiv = indiv\n self.nrs = nrs\n self.nsubj = nsubj\n self.logf = logf\n self.basename = title\n self.outfname = outfname\n self.rslist = rslist\n pedhead = 'famid\\tiid\\tfid\\tmid\\tgender\\taffection\\t'\n rs = '\\t'.join(rslist)\n self.outhead = '%s%s\\n' % (pedhead,rs)",
"title": ""
},
{
"docid": "ce63391b0ba53484bcdbfa3c177f5093",
"score": "0.5392235",
"text": "def test_LM(in_file, out_file, LM):\n print(\"testing language models...\")\n\n # Parse inputs\n data = read_by_line(in_file)\n model = LM[0]\n languages = LM[1]\n\n # Create an empty probability reference sheet\n basic_probability_ref = dict()\n for l in languages:\n basic_probability_ref[l] = np.float64(1)\n\n # Clear file of any results, to prepare the output file for eval.py\n open(out_file, 'w').close()\n \n with open(out_file, 'a') as f:\n # Process each sentence and make a guess\n for sentence in data:\n p_ref = basic_probability_ref.copy()\n tokens = tokenise(sentence, token_size)\n num_undetected = 0\n\n # Multiple token probabilities for each token\n for t in tokens:\n if t in model:\n # Multiply the respective probability of each language\n counts = model[t]\n for l in languages:\n p_ref[l] += counts[l]\n else:\n num_undetected += 1\n\n # Find the best prediction using the maximum product\n if num_undetected / len(tokens) > other_threshold:\n prediction = other_language\n else:\n prediction = max(p_ref.items(), key=operator.itemgetter(1))[0]\n\n # Write the prediction to the output file\n f.write(prediction + \" \" + sentence + \"\\n\")",
"title": ""
},
{
"docid": "11937e2179eb9e5be7829ede94d45adb",
"score": "0.5387432",
"text": "def test_issue4707():\n nlp = English()\n nlp.add_pipe(\"sentencizer\")\n nlp.add_pipe(\"entity_ruler\")\n assert nlp.pipe_names == [\"sentencizer\", \"entity_ruler\"]\n exclude = [\"tokenizer\", \"sentencizer\"]\n with make_tempdir() as tmpdir:\n nlp.to_disk(tmpdir, exclude=exclude)\n new_nlp = load_model_from_path(tmpdir, disable=exclude)\n assert \"sentencizer\" not in new_nlp.pipe_names\n assert \"entity_ruler\" in new_nlp.pipe_names",
"title": ""
},
{
"docid": "fe5676fbca6769a80c88f1d8e1109454",
"score": "0.5377364",
"text": "def main(argv):\n parser = argparse.ArgumentParser(\n description='Create all possible determinizations of this domain.')\n parser.add_argument('-d', '--domain', required=True)\n parser.add_argument('-o', '--output', required=True) \n args = parser.parse_args()\n domain_file_name = args.domain\n output_file_name = args.output\n \n # Reading domain file.\n domain_str = ''\n try:\n with open(domain_file_name, 'r') as domain_file:\n for line in domain_file:\n if line.startswith(';;') or line.isspace():\n continue;\n domain_str += line\n except IOError:\n print \"Could not read file:\", domain_file_name\n sys.exit(-1) \n \n # Parsing the domain tree.\n domain_tree = ppddl_util.parse_sexp(domain_str)\n all_prob_effects_list = []\n \n # Getting all possible probabilistic effects.\n ppddl_util.get_all_probabilistic_effects(domain_tree, all_prob_effects_list)\n \n # Getting the most likely outcome determinization.\n mlo_determinization = []\n for probabilistic_effect_info in all_prob_effects_list:\n mlo_determinization.append(\n ppddl_util.get_mlo_determinization_effect(probabilistic_effect_info))\n \n # Writing the MLO determinization to a PPDDL file.\n determinization_ppddl_tree = copy.deepcopy(domain_tree)\n description_text = \"\"\n for effect_info in mlo_determinization:\n description_text += \"%s %d\\n\" % (effect_info[0], effect_info[1][0])\n ppddl_util.determinize_tree(mlo_determinization, determinization_ppddl_tree)\n ppddl_util.clean_up_tree(determinization_ppddl_tree)\n f = open('%s_mlo_det.pddl' % (output_file_name), 'w')\n fd = open('%s_mlo_det.desc' % (output_file_name), 'w')\n f.write(ppddl_util.make_str(determinization_ppddl_tree[0]))\n fd.write(description_text)\n f.close()\n fd.close()\n \n print 'Created MLO determinization for %s' % domain_file_name",
"title": ""
},
{
"docid": "2b6f6722ce9ac5c08f4612e25ef7aec0",
"score": "0.5376005",
"text": "def make_model(pbm):\n for stmt in pbm.statements:\n try:\n # Skip statements with no subject\n if stmt.agent_list()[0] is None and \\\n not isinstance(stmt, Conversion):\n continue\n # Assemble statements\n if isinstance(stmt, Modification):\n pbm._assemble_modification(stmt)\n elif isinstance(stmt, RegulateActivity):\n pbm._assemble_regulate_activity(stmt)\n elif isinstance(stmt, RegulateAmount):\n pbm._assemble_regulate_amount(stmt)\n elif isinstance(stmt, Gef):\n pbm._assemble_gef(stmt)\n elif isinstance(stmt, Gap):\n pbm._assemble_gap(stmt)\n elif isinstance(stmt, ActiveForm):\n pbm._assemble_active_form(stmt)\n elif isinstance(stmt, Complex):\n pbm._assemble_complex(stmt)\n elif isinstance(stmt, Conversion):\n pbm._assemble_conversion(stmt)\n elif isinstance(stmt, Autophosphorylation):\n pbm._assemble_autophosphorylation(stmt)\n elif isinstance(stmt, Transphosphorylation):\n pbm._assemble_transphosphorylation(stmt)\n else:\n logger.info('Unhandled statement: %s' % stmt)\n except Exception:\n pass\n return pbm.model",
"title": ""
},
{
"docid": "9045c7c73ca55f814b38834d2149df8b",
"score": "0.5342216",
"text": "def try_load_model(self):\r\n filename = 'constraints_problem_size_{0}'.format(self.get_real_n())\r\n path = '../cache_constraints/'\r\n\r\n solution_is_loop = self.sol_is_loop()\r\n\r\n if solution_is_loop:\r\n filename += '_loop'\r\n path_filename = path + filename + '.lp'\r\n\r\n model_reader = ModelReader()\r\n\r\n self.outer_model_interface.check_dir(path)\r\n\r\n try:\r\n # Load the constraints\r\n self.docplex_model = model_reader.read_model(path_filename)\r\n except:\r\n # If they are not cached, create them\r\n print(\"\\nFile {} not found :(\\nI'm gonna create it! :D\".format(path_filename))\r\n self.docplex_model = self.generate_general_constraints(path, filename)",
"title": ""
},
{
"docid": "3c879101600ab140261ce79e742c8479",
"score": "0.5335524",
"text": "def generateModel(self):\n\t\tglobal startLoadModel, doneLoadModel\n\t\tprint(\"model type:\" + self.modeltype)\n\t\tstartLoadModel = time.time()\n\t\tif not os.path.exists(self.dictionaryfile):\n\t\t\tprint(\"creating dictionary \" + self.dictionaryfile)\n\t\t\tself.dictionary = self.makeDictionaryMemoryFriendly(self.corpusfile)\n\t\telse:\n\t\t\tprint(\"reading dictionary \" + self.dictionaryfile)\n\t\t\tself.dictionary = self.loadDictionary(self.dictionaryfile)\n\t\tif not os.path.exists(self.corpusmatrixfile):\n\t\t\tprint(\"serializing corpus \" + self.corpusmatrixfile)\n\t\t\tself.serializeCorpusMemoryFriendly(self.corpusfile, self.dictionary)\n\t\tif self.modeltype == \"LDA\":\n\t\t\tprint(\"creating lda model with \" + str(self.topics) + \" topics.\")\n\t\t\tif self.online:\n\t\t\t\tprint(\"Running in online mode with chunksize: \" + str(self.chunksize))\n\t\t\telse:\n\t\t\t\tprint(\"Running in offline mode with \" + str(self.passes) + \" passes\")\n\t\t\tif self.distributed:\n\t\t\t\tprint(\"Running in distributed mode\")\n\t\t\tself.generateLDAModel(self.topics, self.passes, self.distributed)\n\t\telif self.modeltype == \"LSI\":\n\t\t\tprint(\"creating lsi model\")\n\t\t\tself.generateLSIModel(topics)\n\t\telif self.modeltype == \"HDP\":\n\t\t\tprint(\"creating hdp model\")\n\t\t\tself.generateHDPModel()\n\t\tdoneLoadModel = time.time()\n\t\tprint(\"Time to generate model:\" + str(doneLoadModel - startLoadModel))",
"title": ""
},
{
"docid": "91c5431c7473abefa61a82391d7c11c8",
"score": "0.5323186",
"text": "def test_LM(in_file, out_file, LM):\r\n print \"testing language models...\"\r\n # This is an empty method\r\n # Pls implement your code in below\r\n\r\n # Setting up data provided from given LM\r\n four_gram_table = LM[0]\r\n ngram_count = LM[1]\r\n\r\n # Open in_file and split into a list of sentences\r\n # Open out_file for writing predictions\r\n input_file = open(in_file, 'r')\r\n test_input_list = input_file.read().split(\"\\r\\n\")\r\n output_file = open(out_file, 'w')\r\n\r\n for sentence in test_input_list:\r\n # To handle possible empty strings or invalid input\r\n if sentence == \"\":\r\n break\r\n # Ngram probability score for current sentence\r\n ngram_score = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\r\n # Counters to keep track of number of unencountered ngrams generated by\r\n # the current sentence.\r\n invalid_ngram_count = 0\r\n total_ngram_count = 0\r\n # Sentence is padded with 3 'START's and 3 'END's\r\n char_list = list(sentence)\r\n for index in range(3):\r\n char_list.insert(0, 'START')\r\n char_list.append('END')\r\n for index in range(len(char_list) - 4):\r\n four_gram = (char_list[index], char_list[index + 1],\r\n char_list[index + 2], char_list[index + 3])\r\n # Log10 is used to prevent underflow of individual ngram_scores\r\n if four_gram in four_gram_table:\r\n for language, count in four_gram_table[four_gram].iteritems():\r\n ngram_score[language] += math.log10(count/ngram_count[language])\r\n else:\r\n invalid_ngram_count += 1\r\n total_ngram_count += 1\r\n # invalid_ngram_ratio represents the proportion of unencountered ngrams\r\n # out of all ngrams generated from current sentence\r\n invalid_ngram_ratio = invalid_ngram_count / total_ngram_count\r\n # The set threshold of 0.6 indicates that the sentence might not be a\r\n # valid language in the given LM\r\n if max(ngram_score.itervalues()) is 0 or invalid_ngram_ratio > 0.6:\r\n predicted_language = 'other'\r\n else:\r\n predicted_language = max(ngram_score.iterkeys(), key=(lambda language: ngram_score[language]))\r\n output_file.write(predicted_language + \" \" + sentence + \"\\n\")\r\n input_file.close\r\n output_file.close",
"title": ""
},
{
"docid": "3c8230e8f56aabea2a2d18b33a22dab9",
"score": "0.5304721",
"text": "def generate(args):\n if args.cuda and torch.cuda.is_available():\n device = 0\n use_cuda = True\n elif args.cuda and not torch.cuda.is_available():\n print(\"You do not have CUDA, turning cuda off\")\n device = -1 \n use_cuda = False\n else:\n device = -1\n use_cuda=False\n\n #Load the vocab\n vocab = du.load_vocab(args.vocab)\n eos_id = vocab.stoi[EOS_TOK]\n pad_id = vocab.stoi[PAD_TOK]\n\n if args.ranking: # default is HARD one, the 'Inverse Narrative Cloze' in the paper\n dataset = du.NarrativeClozeDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, LM=False)\n # Batch size during decoding is set to 1\n batches = BatchIter(dataset, 1, sort_key=lambda x:len(x.actual), train=False, device=-1)\n else:\n dataset = du.SentenceDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, add_eos=False) #put in filter pred later\n # Batch size during decoding is set to 1\n batches = BatchIter(dataset, args.batch_size, sort_key=lambda x:len(x.text), train=False, device=-1)\n\n data_len = len(dataset)\n\n #Create the model\n with open(args.load, 'rb') as fi:\n if not use_cuda:\n model = torch.load(fi, map_location=lambda storage, loc : storage)\n else:\n model = torch.load(fi, map_location=torch.device('cuda'))\n\n if not hasattr(model.latent_root, 'nohier'):\n model.latent_root.set_nohier(args.nohier) #for backwards compatibility\n\n model.decoder.eval()\n model.set_use_cuda(use_cuda)\n\n #For reconstruction\n if args.perplexity:\n loss = calc_perplexity(args, model, batches, vocab, data_len)\n print(\"Loss = {}\".format(loss))\n elif args.schema:\n generate_from_seed(args, model, batches, vocab, data_len)\n elif args.ranking:\n do_ranking(args, model, batches, vocab, data_len, use_cuda)\n else:\n# sample_outputs(model, vocab)\n reconstruct(args, model, batches, vocab)",
"title": ""
},
{
"docid": "6b20588231ce0fcb6d4f7434d73e68c3",
"score": "0.52846843",
"text": "def generateModel(modelName, pack):\n\n (params, run, cost, optim_date, var_set) = pack\n\n with open('%s_optimized.mo' % modelName, 'w') as file:\n header = \"\"\"model {mn}_optimized \"Generated by PostProcess/postprocess_optim.py {d} with cost {c} lowest at run {r}\"\n extends {mn} (\n \\n\"\"\".format(mn=modelName, d = optim_date, c = '%.6f' % cost, r = '%d' % run)\n footer = ');\\nend %s_optimized;\\n' % modelName\n\n param_lines = (' %s = %.6e' % param for param in params)\n # use join to avoid placing the comma after the last\n lines = ',\\n'.join(param_lines)\n\n\n file.write(header)\n file.writelines(lines)\n file.write(footer)",
"title": ""
},
{
"docid": "4d6897ba3959406b0ee3b87b094e823e",
"score": "0.5258285",
"text": "def load_model(length=MAX_UTTERENCE_LENGTH):\n return torch.load(os.path.join(PATH_TO_SAVE, 'generator_{}.pt'.format(length))).to(DEVICE)",
"title": ""
},
{
"docid": "078acbdaf7a42c95ccc2c6c684279376",
"score": "0.5254087",
"text": "def compile_po_file(po_path: Path) -> None:\n output_path = po_path.parent\n\n target_json = po_path.with_suffix(\".json\")\n if target_json.exists():\n target_json.unlink()\n convert_catalog_to_json(po_path, output_path, po_path.stem)\n\n target_mo = po_path.with_suffix(\".mo\")\n if target_mo.exists():\n target_mo.unlink()\n compile_to_mo(po_path)",
"title": ""
},
{
"docid": "b3a0443fbd71bfab66c479b31441d52a",
"score": "0.52386135",
"text": "def test_LM(in_file, out_file, LM):\r\n print \"testing language models...\"\r\n # This is an empty method\r\n # Pls implement your code in below\r\n malay_LM = LM['malaysian']\r\n indon_LM = LM['indonesian']\r\n tamil_LM = LM['tamil']\r\n\r\n FD_in = open(in_file, 'r')\r\n FD_out = open(out_file, 'w')\r\n\r\n lines = FD_in.read()\r\n lines_array = lines.replace('\\r', ' ').split('\\n')\r\n lines_array.pop(len(lines_array) - 1)\r\n\r\n for line in lines_array:\r\n malay_count = 1\r\n indon_count = 1\r\n tamil_count = 1\r\n\r\n total_count = 0\r\n miss_count = 0\r\n\r\n four_char_array = get_four_char_array(line)\r\n\r\n for four_char in four_char_array:\r\n # key in malayLM = key in all LMs due to add one smoothing\r\n if four_char in malay_LM:\r\n malay_count += math.log10(malay_LM[four_char])\r\n indon_count += math.log10(indon_LM[four_char])\r\n tamil_count += math.log10(tamil_LM[four_char])\r\n else:\r\n miss_count += 1\r\n total_count += 1\r\n\r\n if (miss_count / total_count > 0.7):\r\n FD_out.write(\"other \" + line + \"\\n\")\r\n elif malay_count > indon_count and malay_count > tamil_count:\r\n FD_out.write(\"malaysian \" + line + \"\\n\")\r\n elif indon_count > malay_count and indon_count > tamil_count:\r\n FD_out.write(\"indonesian \" + line + \"\\n\")\r\n else:\r\n FD_out.write(\"tamil \" + line + \"\\n\")\r\n\r\n FD_in.close()\r\n FD_out.close()",
"title": ""
},
{
"docid": "5a76f0d38ddb8ab17ec18597b44bc149",
"score": "0.5236154",
"text": "def to_lalinference(self, **kwargs):\n return self.write(file_format=\"lalinference\", package=\"gw\", **kwargs)",
"title": ""
},
{
"docid": "35a86959eb9fc6a9c3356b7be538a93d",
"score": "0.5229652",
"text": "def make_input(\n wel_qin,\n wel_qin_1,\n wel_qout,\n wel_qout_1,\n wel_coordsin,\n wel_coordsin_1,\n wel_coordsout,\n wel_coordsout_1,\n model_path,\n name,\n exe_name='mf6',\n verbosity_level=0):\n # created to load, build and save the simulation\n sim = flopy.mf6.MFSimulation(\n sim_name=name,\n sim_ws=model_path, #working folder\n exe_name=exe_name,\n verbosity_level=verbosity_level, #standard output to be written - error messages should be included (=1)\n )\n # time discretization package\n times = (10.0, 120, 1.0)\n tdis_rc = [(1.0, 1, 1.0), times, times, times]\n flopy.mf6.ModflowTdis(\n sim, pname=\"tdis\",\n time_units=\"DAYS\",\n nper=4, # number of stress periods\n perioddata=tdis_rc, # [perlen(len stress period), nstp(time-steps in the stressperiod), tsmult(multiplier of of the lenght of sucessive time-steps]\n )\n #iterative model solution package\n flopy.mf6.ModflowIms(sim) # default solver - nearly linear models - confined or a single unconfined layer that is tick enough to contain the water table within a single layer\n # groundwater flow model\n gwf = flopy.mf6.ModflowGwf(sim, modelname=name, save_flows=True)\n # spatial discretization package\n flopy.mf6.ModflowGwfdis(gwf, nrow=10, ncol=10) # number ir rows and cols since the model has only one layer\n # Initial conditions package\n flopy.mf6.ModflowGwfic(gwf) #models using steady state in the beginning won't be affected\n # node property flow package pane - default is harmonic mean\n flopy.mf6.ModflowGwfnpf(\n gwf,\n save_flows=True,\n save_specific_discharge=True,\n icelltype=[0], # 0 unconfined, -1 confined\n k=[0.5], # initial value of k\n k33=[0.1], # vertical anisotropy\n )\n # storage package specifying sy - specific yield bigger than 0 means the cell is convertible\n sy = flopy.mf6.ModflowGwfsto.sy.empty(\n gwf,\n default_value=0.2 # convertible cells\n )\n #stirage package specifying ss - specific storage\n ss = flopy.mf6.ModflowGwfsto.ss.empty(\n gwf, default_value=0.000001\n )\n # storage package\n flopy.mf6.ModflowGwfsto(\n gwf,\n pname=\"sto\",\n save_flows=True,\n save_specific_discharge=True,\n iconvert=1, # flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top\n ss=ss,\n sy=sy,\n transient={0: True}, # keyword to indicate that stress period IPER is transient. Transient conditions will apply until the STEADY-STATE keyword is specified in a subsequent BEGIN PERIOD block\n )\n #dictionary of boundaries Each well is defined through definition of layer (int), row (int), column (int), flux (float). The simplest form is a dictionary with a lists of boundaries for each stress period, where each list of boundaries itself is a list of boundaries. Indices of the dictionary are the numbers of the stress period\n # if the number of stress periods is larger than the dic than the last speficied will apply until the end\n stress_period_data = {\n 1: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\n 2: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\n 3: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0]\n }\n # defines the well package\n flopy.mf6.ModflowGwfwel(\n gwf,\n stress_period_data=stress_period_data,\n )\n # constant head package\n flopy.mf6.ModflowGwfchd(\n gwf,\n stress_period_data=[\n [(0, 0, 0), 1.], # top left cell [cell_id, head, aux, boundname]\n [(0, 9, 9), 1.]], # bottom right cell\n )\n # variable for the output control\n budget_file = name + '.bud'\n head_file = name + '.hds'\n # output control package\n flopy.mf6.ModflowGwfoc(\n gwf,\n budget_filerecord=budget_file,\n head_filerecord=head_file,\n saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL')])\n sim.write_simulation()",
"title": ""
},
{
"docid": "ef942e4bbae34ac5e0e916536c3fd820",
"score": "0.5222133",
"text": "def main():\n args = parse_arguments()\n\n if args.output_name:\n output_name = args.output_name\n else:\n output_name = args.xodr_file.rpartition(\".\")[0]\n output_name = f\"{output_name}.xml\" # only name of file\n\n if os.path.isfile(output_name) and not args.force_overwrite:\n print(\n \"Not converting because file exists and option 'force-overwrite' not active\",\n file=sys.stderr,\n )\n sys.exit(-1)\n\n with open(\"{}\".format(args.xodr_file), \"r\") as file_in:\n opendrive = parse_opendrive(etree.parse(file_in).getroot())\n\n scenario = convert_opendrive(opendrive)\n\n if not args.osm:\n writer = ExtendedCommonRoadFileWriter(\n scenario, source=\"OpenDRIVE 2 Lanelet Converter\"\n )\n\n with open(f\"{output_name}\", \"w\") as file_out:\n writer.write_scenario_to_file_io(file_out)\n\n else:\n l2osm = OSMConverter(args.osm)\n osm = l2osm(scenario)\n with open(f\"{output_name}\", \"w\") as file_out:\n file_out.write(etree.tostring(osm, encoding=\"unicode\", pretty_print=True))",
"title": ""
},
{
"docid": "f23cd82b5a7d336e4b8f27a862f78256",
"score": "0.52209806",
"text": "def main(model=Path(\"L:\\\\BigData\\\\Env\\\\SavedModels\"), output_dir=Path(\"L:\\\\BigData\\\\Env\\\\SavedModels\")):\n if model is not None:\n nlp = spacy.load(model) # load existing spaCy model\n print(\"Loaded model '%s'\" % model)\n\n ner = nlp.get_pipe(\"ner\")\n\n ner.add_label(\"Email\")# add new entity label to entity recognizer\n ner.add_label(\"Full Name\")\n ner.add_label(\"First Name\")\n ner.add_label(\"Updatedtime\")\n ner.add_label(\"City\")\n ner.add_label(\"Postcode\")\n ner.add_label(\"Date\")\n ner.add_label(\"Phone\")\n ner.add_label(\"State\")\n ner.add_label(\"Country\")\n ner.add_label(\"Street\")\n ner.add_label(\"Id\")\n ner.add_label(\"Last Name\")\n ner.add_label(\"username\")\n ner.add_label(\"Transaction Id\")\n\n move_names = list(ner.move_names)\n\n # test the trained model\n test_text = \"[email protected] , Glenna Olson , Corbin , 1999-07-05T12:33:47.066Z , Kingshire 77787 1/8/2013 | (695)464-6165 x9467 , Iowa Indonesia 038 Schumm Walks 0 Ortiz Frida 362 \"\n doc = nlp(test_text)\n print(\"Entities in '%s'\" % test_text)\n for ent in doc.ents:\n print(ent.label_, ent.text)\n\n # save model to output directory\n if output_dir is not None:\n output_dir = Path(output_dir)\n if not output_dir.exists():\n output_dir.mkdir() # rename model\n nlp.to_disk(output_dir)\n print(\"Saved model to\", output_dir)\n\n # test the saved model\n print(\"Loading from\", output_dir)\n nlp2 = spacy.load(output_dir)\n # Check the classes have loaded back consistently\n assert nlp2.get_pipe(\"ner\").move_names == move_names\n doc2 = nlp2(test_text)\n for ent in doc2.ents:\n print(ent.label_, ent.text)",
"title": ""
},
{
"docid": "ba506d586a69c49c3403983dbd1f5025",
"score": "0.52202463",
"text": "def GenFile(DSLModel, Table, View):",
"title": ""
},
{
"docid": "8666e838db88654723d809adce0d3d19",
"score": "0.52056324",
"text": "def generateModel(self,startPoint,start,end,tol,vel):\r\n\t\t\r\n\t\t(code,levels,cantPoints) = Loader().load(self.path,startPoint)\r\n\t\thaveParser = False\r\n\r\n\t\tadded = 0\r\n\t\tself.start = start\r\n\t\tself.end = end\r\n\t\t\r\n\t\tif( tol > 0):\t\t\r\n\t\t\tself.parser = PointsAdder(code,levels,cantPoints,tol)\r\n\t\t\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\r\n\t\t\thaveParser = True\r\n\t\t\t\r\n\t\tif( vel > 0):\t\t\r\n\t\t\tself.parser = VelocityChanger(code,levels,cantPoints,vel)\r\n\t\t\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\r\n\t\t\thaveParser = True\r\n\t\t\t\r\n\t\tif (not haveParser):\r\n\t\t\tself.parser = Parser(code,levels,cantPoints)\r\n\t\t\t(code,levels,cantPoints) = self.parser.parse()\t\r\n\t\r\n\t\tself.modifiedGCode = code\r\n\t\tself.levels = levels\r\n\t\tself.cantPoints = cantPoints",
"title": ""
},
{
"docid": "e9a39b116920aebf857a1c3b136837b0",
"score": "0.5203123",
"text": "def compile():\n\n m = dir(modules)\n\n words = []\n for module_name in m:\n try:\n eval(\"words.extend(modules.%s.WORDS)\" % module_name)\n except:\n pass # module probably doesn't have the property\n\n words = list(set(words))\n\n # for spotify module\n words.extend([\"MUSIC\",\"SPOTIFY\"])\n\n # create the dictionary\n pronounced = g2p.translateWords(words)\n zipped = zip(words, pronounced)\n lines = [\"%s %s\" % (x, y) for x, y in zipped]\n\n with open(\"../client/dictionary.dic\", \"w\") as f:\n f.write(\"\\n\".join(lines) + \"\\n\")\n\n # create the language model\n with open(\"../client/sentences.txt\", \"w\") as f:\n f.write(\"\\n\".join(words) + \"\\n\")\n f.write(\"<s> \\n </s> \\n\")\n f.close()\n\n # make language model\n os.system(\n \"text2idngram -vocab ../client/sentences.txt < ../client/sentences.txt -idngram temp.idngram\")\n os.system(\n \"idngram2lm -idngram temp.idngram -vocab ../client/sentences.txt -arpa ../client/languagemodel.lm\")",
"title": ""
},
{
"docid": "49e798a22449fe4ec327a89f2378b7cd",
"score": "0.51998425",
"text": "def main() -> None:\n\n args = get_args()\n package = Package()\n ontology = get_ontology(args.ontology_file)\n\n for i, fh in enumerate(args.data_file, start=1):\n print(f'{i:3}: {os.path.basename(fh.name)}')\n fh.close()\n package.infer(fh.name)\n\n for res in package.resources:\n if args.missing_values:\n res.descriptor['schema']['missingValues'].extend(\n args.missing_values)\n\n for field in res.descriptor['schema']['fields']:\n name = field.get('name')\n if not name:\n continue\n\n ont = ontology.get(name)\n if not ont:\n continue\n\n data_type = ont.get('frictionless type', field.get('type'))\n if data_type:\n field['type'] = data_type\n\n data_format = ont.get('frictionless format', field.get('format'))\n if data_format:\n field['format'] = data_format\n\n rdf_type = ont.get('rdf type purl', field.get('rdfType'))\n if rdf_type:\n field['rdfType'] = rdf_type\n\n units = ont.get('units purl', field.get('pm:unitRdfType'))\n if units:\n field['pm:unitRdfType'] = units\n\n source_url = ont.get('pm:source url', field.get('pm:sourceUrl'))\n if source_url:\n field['pm:source url'] = source_url\n\n measure_source = ont.get('measurement source purl',\n field.get('pm:measurementSourceRdfType'))\n if measure_source:\n field['pm:measurementSourceRdfType'] = measure_source\n\n measure_url = ont.get('pm:measurement source protocol',\n field.get('pm:measurement source protocol'))\n if measure_url:\n field['pm:measurementSourceProtocolUrl'] = measure_url\n\n # TODO: Not sure about this\n # is_searchable = ont.get('pm:searchable', field.get('pm:searchable'))\n # if is_searchable:\n # field['pm:searchable'] = is_searchable\n\n package.remove_resource(res.name)\n package.add_resource(res.descriptor)\n\n package.save(args.out_file)\n\n print(f'Done, see \"{args.out_file}\"')",
"title": ""
},
{
"docid": "a3fe6b4a1d97a21eaef6d6f4d02a796d",
"score": "0.51963997",
"text": "def actualSolve(self, lp):\n\n if not self.executable(self.path):\n raise PulpSolverError(\"PuLP: cannot execute \" + self.path)\n tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, \"lp\", \"sol\", \"mst\")\n vs = lp.writeLP(tmpLp, writeSOS=1)\n try:\n os.remove(tmpSol)\n except:\n pass\n cmd = self.path\n options = self.options + self.getOptions()\n if self.timeLimit is not None:\n options.append((\"TimeLimit\", self.timeLimit))\n cmd += \" \" + \" \".join([f\"{key}={value}\" for key, value in options])\n cmd += f\" ResultFile={tmpSol}\"\n if self.optionsDict.get(\"warmStart\", False):\n self.writesol(filename=tmpMst, vs=vs)\n cmd += f\" InputFile={tmpMst}\"\n\n if lp.isMIP():\n if not self.mip:\n warnings.warn(\"GUROBI_CMD does not allow a problem to be relaxed\")\n cmd += f\" {tmpLp}\"\n if self.msg:\n pipe = None\n else:\n pipe = open(os.devnull, \"w\")\n\n return_code = subprocess.call(cmd.split(), stdout=pipe, stderr=pipe)\n\n # Close the pipe now if we used it.\n if pipe is not None:\n pipe.close()\n\n if return_code != 0:\n raise PulpSolverError(\"PuLP: Error while trying to execute \" + self.path)\n if not os.path.exists(tmpSol):\n # TODO: the status should be infeasible here, I think\n status = constants.LpStatusNotSolved\n values = reducedCosts = shadowPrices = slacks = None\n else:\n # TODO: the status should be infeasible here, I think\n status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)\n self.delete_tmp_files(tmpLp, tmpMst, tmpSol, \"gurobi.log\")\n if status != constants.LpStatusInfeasible:\n lp.assignVarsVals(values)\n lp.assignVarsDj(reducedCosts)\n lp.assignConsPi(shadowPrices)\n lp.assignConsSlack(slacks)\n lp.assignStatus(status)\n return status",
"title": ""
},
{
"docid": "148556ac6767aa89118518bfbccc312b",
"score": "0.5196136",
"text": "def create_pyomo_model(self, debug_mode=False, debug_cost=2e7):\n\n # work on a local copy of the dataframe\n if not debug_mode and self.df.index.str.contains('DBUG').any():\n # previously ran in debug mode, but now done\n df = self.remove_debug_links()\n df.to_csv(self.linksfile + '-final.csv')\n else:\n df = self.df\n\n self.log.info('Creating Pyomo Model (debug=%s)' % debug_mode)\n\n model = ConcreteModel()\n\n model.N = Set(initialize=self.nodes)\n model.k = Set(initialize=range(15))\n model.A = Set(within=model.N*model.N*model.k, \n initialize=self.links, ordered=True)\n model.source = Param(initialize='SOURCE')\n model.sink = Param(initialize='SINK')\n\n def init_params(p):\n if p == 'cost' and debug_mode:\n return (lambda model,i,j,k: debug_cost \n if ('DBUG' in str(i)+'_'+str(j))\n else 1.0)\n else:\n return lambda model,i,j,k: df.loc[str(i)+'_'+str(j)+'_'+str(k)][p]\n\n model.u = Param(model.A, initialize=init_params('upper_bound'), mutable=True)\n model.l = Param(model.A, initialize=init_params('lower_bound'), mutable=True)\n model.a = Param(model.A, initialize=init_params('amplitude'))\n model.c = Param(model.A, initialize=init_params('cost'))\n\n # The flow over each arc\n model.X = Var(model.A, within=Reals)\n\n # Minimize total cost\n def obj_fxn(model):\n return sum(model.c[i,j,k]*model.X[i,j,k] for (i,j,k) in model.A)\n model.total = Objective(rule=obj_fxn, sense=minimize)\n\n # Enforce an upper bound limit on the flow across each arc\n def limit_rule_upper(model, i, j, k):\n return model.X[i,j,k] <= model.u[i,j,k]\n model.limit_upper = Constraint(model.A, rule=limit_rule_upper)\n\n # Enforce a lower bound limit on the flow across each arc\n def limit_rule_lower(model, i, j, k):\n return model.X[i,j,k] >= model.l[i,j,k]\n model.limit_lower = Constraint(model.A, rule=limit_rule_lower)\n\n # To speed up creating the mass balance constraints, first\n # create dictionaries of arcs_in and arcs_out of every node\n # These are NOT Pyomo data, and Pyomo does not use \"model._\" at all\n arcs_in = {}\n arcs_out = {}\n\n def arc_list_hack(model, i,j,k):\n if j not in arcs_in:\n arcs_in[j] = []\n arcs_in[j].append((i,j,k))\n\n if i not in arcs_out:\n arcs_out[i] = []\n arcs_out[i].append((i,j,k))\n return [0]\n\n model._ = Set(model.A, initialize=arc_list_hack)\n\n # Enforce flow through each node (mass balance)\n def flow_rule(model, node):\n if node in [value(model.source), value(model.sink)]:\n return Constraint.Skip\n outflow = sum(model.X[i,j,k]/model.a[i,j,k] for i,j,k in arcs_out[node])\n inflow = sum(model.X[i,j,k] for i,j,k in arcs_in[node])\n return inflow == outflow\n model.flow = Constraint(model.N, rule=flow_rule)\n\n model.dual = Suffix(direction=Suffix.IMPORT)\n\n self.model = model",
"title": ""
},
{
"docid": "f1d525b72f37243510024f58395907b3",
"score": "0.5173822",
"text": "def makeLibSource(self):\n\n # Make vector field (and event) file for compilation\n # This sets the field self._eventNames\n self._prepareEventSpecs()\n\n # Write the model.m file\n allfilestr = self._prepareModelContents()\n modelfile = os.path.join(self._target_dir, self._model_file)\n try:\n file = open(modelfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._model_file+\" for writing\")\n raise IOError(e)\n\n # Write the events.m file\n if len(self._eventNames) > 0:\n allfilestr = self._prepareEventsFileContents() + self._prepareAuxContents()\n eventsfile = os.path.join(self._target_dir, self._events_file)\n try:\n file = open(eventsfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._events_file+\" for writing\")\n raise IOError(e)\n\n\n # Write the initialconditions.m file\n allfilestr = self._prepareICContents()\n icfile = os.path.join(self._target_dir, self._ic_file)\n try:\n file = open(icfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._ic_file+\" for writing\")\n raise IOError(e)\n\n # Write the pars.m file\n allfilestr = self._prepareParamContents()\n paramfile = os.path.join(self._target_dir, self._param_file)\n try:\n file = open(paramfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._param_file+\" for writing\")\n raise IOError(e)\n\n # Write the get.m file\n allfilestr = self._prepareGetFileContents()\n getfile = os.path.join(self._target_dir, self._get_file)\n try:\n file = open(getfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._get_file+\" for writing\")\n raise IOError(e)\n\n # Write the set.m file\n allfilestr = self._prepareSetFileContents()\n setfile = os.path.join(self._target_dir, self._set_file)\n try:\n file = open(setfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._set_file+\" for writing\")\n raise IOError(e)\n\n # Write the vfield.m file\n# vfdefines = self._prepareVfieldDefines()\n# allfilestr = self._prepareVfieldContents(vfdefines)\n allfilestr = self.funcspec.spec[0] + self._prepareAuxContents()\n vffile = os.path.join(self._target_dir, self._vfield_file)\n try:\n file = open(vffile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._vfield_file+\" for writing\")\n raise IOError(e)",
"title": ""
},
{
"docid": "870c6fd27ca456de9582a6aa9f045d53",
"score": "0.51735485",
"text": "def buildSolverModel(self, lp):\n log.debug(\"create the gurobi model\")\n lp.solverModel = gurobipy.Model(lp.name)\n log.debug(\"set the sense of the problem\")\n if lp.sense == constants.LpMaximize:\n lp.solverModel.setAttr(\"ModelSense\", -1)\n if self.timeLimit:\n lp.solverModel.setParam(\"TimeLimit\", self.timeLimit)\n gapRel = self.optionsDict.get(\"gapRel\")\n logPath = self.optionsDict.get(\"logPath\")\n if gapRel:\n lp.solverModel.setParam(\"MIPGap\", gapRel)\n if logPath:\n lp.solverModel.setParam(\"LogFile\", logPath)\n\n log.debug(\"add the variables to the problem\")\n for var in lp.variables():\n lowBound = var.lowBound\n if lowBound is None:\n lowBound = -gurobipy.GRB.INFINITY\n upBound = var.upBound\n if upBound is None:\n upBound = gurobipy.GRB.INFINITY\n obj = lp.objective.get(var, 0.0)\n varType = gurobipy.GRB.CONTINUOUS\n if var.cat == constants.LpInteger and self.mip:\n varType = gurobipy.GRB.INTEGER\n var.solverVar = lp.solverModel.addVar(\n lowBound, upBound, vtype=varType, obj=obj, name=var.name\n )\n if self.optionsDict.get(\"warmStart\", False):\n # Once lp.variables() has been used at least once in the building of the model.\n # we can use the lp._variables with the cache.\n for var in lp._variables:\n if var.varValue is not None:\n var.solverVar.start = var.varValue\n\n lp.solverModel.update()\n log.debug(\"add the Constraints to the problem\")\n for name, constraint in lp.constraints.items():\n # build the expression\n expr = gurobipy.LinExpr(\n list(constraint.values()), [v.solverVar for v in constraint.keys()]\n )\n if constraint.sense == constants.LpConstraintLE:\n relation = gurobipy.GRB.LESS_EQUAL\n elif constraint.sense == constants.LpConstraintGE:\n relation = gurobipy.GRB.GREATER_EQUAL\n elif constraint.sense == constants.LpConstraintEQ:\n relation = gurobipy.GRB.EQUAL\n else:\n raise PulpSolverError(\"Detected an invalid constraint type\")\n constraint.solverConstraint = lp.solverModel.addConstr(\n expr, relation, -constraint.constant, name\n )\n lp.solverModel.update()",
"title": ""
},
{
"docid": "f807685d80245425900a66f5f81872f5",
"score": "0.5164706",
"text": "def import_model(self, file):\n felems = os.path.splitext(file)\n ext = felems[1].lower()\n if ext in (\".gz\", \".zip\",):\n ext = os.path.splitext(felems[0])[1].lower()\n\n if ext == \".cpo\":\n import docplex.cp.cpo.cpo_parser as cpo_parser\n prs = cpo_parser.CpoParser(self)\n prs.parse(file)\n\n elif ext == \".fzn\":\n import docplex.cp.fzn.fzn_parser as fzn_parser\n prs = fzn_parser.FznParser(self)\n prs.parse(file)\n # Get model to force compilation\n prs.get_model()\n\n elif ext == \".lp\":\n import docplex.cp.lp.lp_parser as lp_parser\n prs = lp_parser.LpParser(self)\n prs.parse(file)\n\n else:\n raise CpoException(\"Unknown '{}' file format. Only .cpo, .fzn and .lp are supported.\".format(ext))",
"title": ""
},
{
"docid": "a3c04ce9f517817a518f872f20d83633",
"score": "0.51574755",
"text": "def to_automl(data, path='.', name='autodata'):\n # check if folder exists\n dir = os.path.join(path, name+'_automl')\n if not os.path.exists(dir):\n # create folder\n os.mkdir(dir)\n data.descriptors().to_csv(os.path.join(dir, name+'.info'), header=True) # some information\n if data.has_class():\n pd.DataFrame(data.indexes['X']).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\n pd.DataFrame(data.indexes['y']).to_csv(os.path.join(dir, name+'_label.name'), index=False, header=False) # label name\n if 'train' in data.indexes: # train/test and X/y splits\n data.get_data('X_train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\n data.get_data('X_test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\n data.get_data('y_train').to_csv(os.path.join(dir, name+'_train.solution'), sep=' ', index=False, header=False) # train solution\n data.get_data('y_test').to_csv(os.path.join(dir, name+'_test.solution'), sep=' ', index=False, header=False) # test solution\n else: # only X/y split\n data.get_data('X').to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data\n data.get_data('y').to_csv(os.path.join(dir, name+'.solution'), sep=' ', index=False, header=False) # solution\n else:\n pd.DataFrame(data.columns).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\n if 'train' in data.indexes: # only train/test split\n data.get_data('train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\n data.get_data('test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\n else: # no split at all\n data.to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data",
"title": ""
},
{
"docid": "311b06e5d22497504730712619f4da06",
"score": "0.5155293",
"text": "def _make_input_file(self, teff_lims, teff_step, logg_lims, logg_step,\n feh_lims, feh_step, vsini_lims, vsini_step, \n vmicro_lims, vmicro_step, resolution):\n output_string = '{:.1f} {:.0f} {:.1f}\\n'.format(teff_lims[0], \n teff_step, \n teff_lims[-1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(logg_lims[0],\n logg_step,\n logg_lims[1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(vmicro_lims[0],\n vmicro_step,\n vmicro_lims[1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(vsini_lims[0],\n vsini_step,\n vsini_lims[1])\n\n output_string += \"skip 0.03 0.02 0.07 !dilution factor\\n\"\n\n output_string += 'skip {:.1f} {:.1f} {:.1f}\\n'.format(feh_lims[0],\n feh_step,\n feh_lims[1])\n \n output_string += 'He 0.04 0.005 0.06 ! Individual abundance\\n'\n\n output_string += '0.0 {:.0f}\\n'.format(resolution)\n\n output_string += '{}\\n{}\\n'.format(self.abundance_table, self.model_dir)\n\n output_string += '2 1 !atmosphere model vmicro and mass\\n'\n output_string += 'ST ! model atmosphere chemical composition flag\\n'\n\n dx = self.data.x[1] - self.data.x[0]\n output_string += '1 {:.5f} fit\\n'.format(dx)\n\n output_string += 'data_sets/{}.txt\\n'.format(self.output_basename)\n\n output_string += '0.5 0.99 0.0 adjust ! RV determination stuff\\n'\n\n xmin, xmax = self.data.x[0]-1, self.data.x[-1]+1\n output_string += '{:.1f} {:.1f}\\n'.format(xmin, xmax)\n\n outfilename = '{}.inp'.format(self.output_basename)\n with open(outfilename, 'w') as outfile:\n outfile.write(output_string)\n\n return outfilename",
"title": ""
},
{
"docid": "8ae6ddb4fe2249629c719de91f2cabee",
"score": "0.51552427",
"text": "def write(self, filename='pflotran.in'):\n\n if filename:\n self.filename = filename\n outfile = open(self.filename, 'w')\n\n # Presumes simulation.simulation_type is required\n if self.simulation.simulation_type:\n self._write_simulation(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'simulation is required, it is currently reading as empty')\n\n if self.simulation.subsurface_flow or \\\n self.simulation.subsurface_transport:\n self._write_subsurface_simulation_begin(outfile)\n\n if self.reference_pressure:\n self._write_reference_pressure(outfile)\n\n if self.reference_temperature:\n self._write_reference_temperature(outfile)\n\n if self.co2_database:\n self._write_co2_database(outfile)\n\n if self.regression.cells \\\n or self.regression.cells_per_process \\\n or self.regression.all_cells:\n self._write_regression(outfile)\n\n if self.uniform_velocity.value_list:\n self._write_uniform_velocity(outfile)\n\n if self.specified_velocity:\n self._write_specified_velocity(outfile)\n\n if self.update_flow_permeability:\n self._write_update_flow_permeability(outfile)\n\n if self.reference_liquid_density:\n self._write_reference_liquid_density(outfile)\n\n if self.minimum_hydrostatic_pressure:\n self._write_minimum_hydrostatic_pressure(outfile)\n\n if self.reference_stress_state.value_list:\n self._write_reference_stress_state(outfile)\n\n if self.eoslist:\n self._write_eos(outfile)\n\n if self.klinkenberg_effect:\n self._write_klinkenberg_effect(outfile)\n\n if self.creep_closure_table:\n self._write_creep_closure_table(outfile)\n\n if self.nonuniform_velocity.filename:\n self._write_nonuniform_velocity(outfile)\n\n if self.simulation.mode.upper() == 'MPHASE' and self.co2_database:\n self._write_co2_database(outfile)\n\n if self.multiple_continuum:\n self._write_multiple_continuum(outfile)\n\n if self.overwrite_restart_flow_params:\n self._write_overwrite_restart_flow(outfile)\n\n if self.overwrite_restart_transport:\n self._write_overwrite_restart_transport(outfile)\n\n if self.initialize_flow_from_file is not None:\n self._write_initialize_flow_from_file(outfile)\n\n if self.initialize_transport_from_file is not None:\n self._write_initialize_transport_from_file(outfile)\n\n if self.isothermal:\n self._write_isothermal(outfile)\n\n if self.reference_porosity:\n self._write_reference_porosity(outfile)\n\n if self.datasetlist:\n self._write_dataset(outfile)\n # else: print 'info: dataset name not detected\\n'\n\n if self.chemistry:\n self._write_chemistry(outfile)\n # else: print 'info: chemistry not detected\\n'\n\n if self.grid:\n self._write_grid(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'grid is required, it is currently reading as empty!')\n\n if self.timestepper_flow or self.timestepper_transport:\n self._write_timestepper(outfile)\n # else: print 'info: timestepper not detected\\n'\n\n if self.time:\n self._write_time(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'time is required, it is currently reading as empty!')\n\n if self.proplist:\n self._write_prop(outfile)\n else:\n PyFLOTRAN_WARNING(\n 'material property list is empty! ' +\n ' Using default material property settings')\n self.add(pmaterial())\n self._write_prop(outfile)\n\n if self.lsolverlist:\n self._write_lsolver(outfile)\n # else: print 'info: lsolverlist (linear solver list) not detected\\n'\n\n if self.nsolverlist:\n self._write_nsolver(outfile)\n # else: print 'info: nsolverlist (newton solver list) not detected\\n'\n\n if self.output:\n self._write_output(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'output is required, it is currently reading as empty!')\n\n if self.fluidlist:\n self._write_fluid(outfile)\n\n if self.saturationlist:\n self._write_saturation(outfile)\n elif self.charlist:\n self._write_characteristic_curves(outfile)\n else:\n if self.simulation.subsurface_flow:\n self.add(pcharacteristic_curves())\n self._write_characteristic_curves(outfile)\n PyFLOTRAN_WARNING(\n 'characteristic_curves list or saturation list ' +\n 'is required, it is currently reading as empty! ' +\n ' Using default characteristic_curves settings')\n\n if self.regionlist:\n self._write_region(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'regionlist is required, it is currently reading as empty!')\n\n if self.integral_flux_list:\n self._write_integral_flux(outfile)\n\n if self.observation_list:\n self._write_observation(outfile)\n\n if self.flowlist:\n self._write_flow(outfile)\n\n if self.transportlist:\n self._write_transport(outfile)\n\n if self.initial_condition_list:\n self._write_initial_condition(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'initial_condition_list is required,' +\n 'it is currently reading as empty!')\n\n if self.boundary_condition_list:\n self._write_boundary_condition(outfile)\n\n if self.source_sink_list:\n self._write_source_sink(outfile)\n\n if self.source_sink_sandbox_list:\n self._write_source_sink_sandbox(outfile)\n\n if self.reference_saturation:\n self._write_reference_saturation(outfile)\n\n if self.strata_list:\n self._write_strata(outfile)\n else:\n PyFLOTRAN_WARNING(\n 'stratigraphy_coupler is required, ' +\n 'it is currently reading as empty! ' +\n 'Using default settings')\n self.add(pstrata())\n self._write_strata(outfile)\n\n if self.constraint_list:\n self._write_constraint(outfile)\n\n if self.rt_mass_transfer_list:\n self._write_rt_mass_transfer(outfile)\n\n if self.flow_mass_transfer_list:\n self._write_flow_mass_transfer(outfile)\n\n if self.simulation.subsurface_flow or \\\n self.simulation.subsurface_transport:\n self._write_subsurface_simulation_end(outfile)\n\n\n # -----------------------------------------------\n\n if self.simulation.surface_subsurface:\n self._write_surface_subsurface_begin(outfile)\n\n if self.surface_flow:\n self._write_surface_flow(outfile)\n\n self._write_surface_subsurface_end(outfile)\n\n # -----------------------------------------------\n\n if self.simulation.simulation_type == 'hydroquake':\n self._write_hydroquake(outfile)\n\n if self.simulation.simulation_type.lower() == 'geomechanics_subsurface':\n self._write_geomechanics(outfile)\n\n if self.waste_form_general:\n self._write_wasteform_general(outfile)\n\n if self.ufd_decay:\n self._write_ufd_decay(outfile)\n\n if self.ufd_biosphere:\n self._write_ufd_biosphere(outfile)\n\n if self.wipp_source_sink:\n self._write_wipp_source_sink(outfile)\n\n outfile.close()",
"title": ""
},
{
"docid": "6217bff10682c1129e9efb6eb05446d3",
"score": "0.5149605",
"text": "def write(solution):\n trimmed_solution = solution\n input_file_name_stripped = trimmed_solution.name\n cwd = os.getcwd()\n output_file_name = os.path.join(cwd,\n 'pym_' +\n input_file_name_stripped +\n '.inp')\n f = open(output_file_name, 'w+')\n\n #Work functions\n\n calories_constant = 4184.0 #number of calories in 1000 Joules of energy\n def eliminate(input_string, char_to_replace, spaces='single'):\n \"\"\"\n Eliminate characters from a string\n\n :param input_string\n string to be modified\n :param char_to_replace\n array of character strings to be removed\n \"\"\"\n for char in char_to_replace:\n input_string = input_string.replace(char, \"\")\n if spaces == 'double':\n input_string = input_string.replace(\" \", \" \")\n return input_string\n\n def section_break(title):\n \"\"\"\n Insert break and new section title into cti file\n\n :param title:\n title string for next section_break\n \"\"\"\n f.write('!'+ \"-\"*75 + '\\n')\n f.write('! ' + title +'\\n')\n f.write('!'+ \"-\"*75 + '\\n')\n\n def replace_multiple(input_string, replace_list):\n \"\"\"\n Replace multiple characters in a string\n\n :param input_string\n string to be modified\n :param replace list\n list containing items to be replaced (value replaces key)\n \"\"\"\n for original_character, new_character in replace_list.items():\n input_string = input_string.replace(original_character,\n new_character)\n return input_string\n\n def build_arrhenius(equation_object, equation_type):\n \"\"\"\n Builds Arrhenius coefficient string\n\n :param equation_objects\n cantera equation object\n :param equation_type:\n string of equation type\n \"\"\"\n coeff_sum = sum(equation_object.reactants.values())\n pre_exponential_factor = equation_object.rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.rate.activation_energy / calories_constant)\n if equation_type == 'ElementaryReaction':\n if coeff_sum == 1:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor))\n if coeff_sum == 2:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**3))\n if coeff_sum == 3:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**6))\n if equation_type == 'ThreeBodyReaction':\n if coeff_sum == 1:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**3))\n if coeff_sum == 2:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**6))\n if (equation_type != 'ElementaryReaction'\n and equation_type != 'ThreeBodyReaction'):\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor))\n arrhenius = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius\n\n def build_modified_arrhenius(equation_object, t_range):\n \"\"\"\n Builds Arrhenius coefficient strings for high and low temperature ranges\n\n :param equation_objects\n cantera equation object\n :param t_range:\n simple string ('high' or 'low') to designate temperature range\n \"\"\"\n if t_range == 'high':\n pre_exponential_factor = equation_object.high_rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.high_rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.high_rate.activation_energy/calories_constant)\n if len(equation_object.products) == 1:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**3))\n else:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor))\n arrhenius_high = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius_high\n if t_range == 'low':\n\n pre_exponential_factor = equation_object.low_rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.low_rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.low_rate.activation_energy/calories_constant)\n if len(equation_object.products) == 1:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**6))\n else:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**3))\n\n arrhenius_low = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius_low\n\n\n def build_nasa(nasa_coeffs, row):\n \"\"\"\n Creates string of nasa polynomial coefficients\n\n :param nasa_coeffs\n cantera species thermo coefficients object\n :param row\n which row to write coefficients in\n \"\"\"\n line_coeffs = ''\n lines = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]]\n line_index = lines[row-2]\n for ix, c in enumerate(nasa_coeffs):\n if ix in line_index:\n if c >= 0:\n line_coeffs += ' '\n line_coeffs += str('{:.8e}'.format(c))\n return line_coeffs\n\n def build_species_string():\n \"\"\"\n formats species string for writing\n \"\"\"\n species_list_string = ''\n line = 1\n for sp_index, sp_string in enumerate(trimmed_solution.species_names):\n sp = ' '\n #get length of string next species is added\n length_new = len(sp_string)\n length_string = len(species_list_string)\n total = length_new + length_string + 3\n #if string will go over width, wrap to new line\n if total >= 70*line:\n species_list_string += '\\n'\n line += 1\n species_list_string += sp_string + ((16-len(sp_string))*sp)\n return species_list_string\n\n #Write title block to file\n section_break('Chemkin File converted from Solution Object by pyMARS')\n\n #Write phase definition to file\n element_names = eliminate(str(trimmed_solution.element_names),\n ['[', ']', '\\'', ','])\n element_string = Template(\n 'ELEMENTS\\n' +\n '$element_names\\n' +\n 'END\\n')\n f.write(element_string.substitute(element_names=element_names))\n species_names = build_species_string()\n species_string = Template(\n 'SPECIES\\n' +\n '$species_names\\n'+\n 'END\\n')\n f.write(species_string.substitute(species_names=species_names))\n\n #Write species to file\n section_break('Species data')\n f.write('THERMO ALL' + '\\n' +\n ' 300.000 1000.000 5000.000' +'\\n')\n phase_unknown_list = []\n\n #write data for each species in the Solution object\n for sp_index in xrange(len(trimmed_solution.species_names)):\n d = 3.33564e-30 #1 debye = d coulomb-meters\n species = trimmed_solution.species(sp_index)\n name = str(trimmed_solution.species(sp_index).name)\n nasa_coeffs = trimmed_solution.species(sp_index).thermo.coeffs\n #Species attributes from trimmed solution object\n t_low = '{0:.3f}'.format(species.thermo.min_temp)\n t_max = '{0:.3f}'.format(species.thermo.max_temp)\n t_mid = '{0:.3f}'.format(species.thermo.coeffs[0])\n temp_range = str(t_low) + ' ' + str(t_max) + ' ' + t_mid\n species_comp = ''\n for atom in species.composition:\n species_comp += '{:<4}'.format(atom)\n species_comp += str(int(species.composition[atom]))\n if type(species.transport).__name__ == 'GasTransportData':\n species_phase = 'G'\n else:\n phase_unknown_list.append(name)\n species_phase = 'G'\n line_1 = (\n '{:<18}'.format(name) +\n '{:<6}'.format(' ') +\n '{:<20}'.format(species_comp) +\n '{:<4}'.format(species_phase) +\n '{:<31}'.format(temp_range) +\n '{:<1}'.format('1') +\n '\\n')\n f.write(line_1)\n line_2_coeffs = build_nasa(nasa_coeffs, 2)\n line_2 = line_2_coeffs + ' 2\\n'\n f.write(line_2)\n line_3_coeffs = build_nasa(nasa_coeffs, 3)\n line_3 = line_3_coeffs + ' 3\\n'\n f.write(line_3)\n line_4_coeffs = build_nasa(nasa_coeffs, 4)\n line_4 = line_4_coeffs + ' 4\\n'\n f.write(line_4)\n\n f.write('END\\n')\n\n #Write reactions to file\n section_break('Reaction Data')\n f.write('REACTIONS\\n')\n #write data for each reaction in the Solution Object\n for reac_index in xrange(len(trimmed_solution.reaction_equations())):\n equation_string = str(trimmed_solution.reaction_equation(reac_index))\n equation_string = eliminate(equation_string, ' ', 'single')\n equation_object = trimmed_solution.reaction(reac_index)\n equation_type = type(equation_object).__name__\n m = str(reac_index+1)\n if equation_type == 'ThreeBodyReaction':\n arrhenius = build_arrhenius(equation_object, equation_type)\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arrhenius[0]) +\n '{:>9}'.format(arrhenius[1]) +\n '{:>11}'.format(arrhenius[2]) +\n '\\n')\n f.write(main_line)\n #trimms efficiencies list\n efficiencies = equation_object.efficiencies\n trimmed_efficiencies = equation_object.efficiencies\n for s in efficiencies:\n if s not in trimmed_solution.species_names:\n del trimmed_efficiencies[s]\n replace_list_2 = {\n '{':'',\n '}':'/',\n '\\'':'',\n ':':'/',\n ',':'/'}\n efficiencies_string = replace_multiple(\n str(trimmed_efficiencies),\n replace_list_2)\n secondary_line = str(efficiencies_string) + '\\n'\n if bool(efficiencies) is True:\n f.write(secondary_line)\n if equation_type == 'ElementaryReaction':\n arrhenius = build_arrhenius(equation_object, equation_type)\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arrhenius[0]) +\n '{:>9}'.format(arrhenius[1]) +\n '{:>11}'.format(arrhenius[2]) +\n '\\n')\n f.write(main_line)\n if equation_type == 'FalloffReaction':\n arr_high = build_modified_arrhenius(equation_object, 'high')\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arr_high[0]) +\n '{:>9}'.format(arr_high[1]) +\n '{:>11}'.format(arr_high[2]) +\n '\\n')\n f.write(main_line)\n arr_low = build_modified_arrhenius(equation_object, 'low')\n second_line = (\n ' LOW /' +\n ' ' + arr_low[0] +\n ' ' + arr_low[1] +\n ' ' + arr_low[2] + '/\\n')\n f.write(second_line)\n j = equation_object.falloff.parameters\n #If optional Arrhenius data included:\n try:\n third_line = (\n ' TROE/' +\n ' ' + str(j[0]) +\n ' ' + str(j[1]) +\n ' ' + str(j[2]) +\n ' ' + str(j[3]) +' /\\n')\n f.write(third_line)\n except IndexError:\n pass\n #trimms efficiencies list\n efficiencies = equation_object.efficiencies\n trimmed_efficiencies = equation_object.efficiencies\n for s in efficiencies:\n if s not in trimmed_solution.species_names:\n del trimmed_efficiencies[s]\n replace_list_2 = {\n '{':'',\n '}':'/',\n '\\'':'',\n ':':'/',\n ',':'/'}\n efficiencies_string = replace_multiple(\n str(trimmed_efficiencies),\n replace_list_2)\n\n fourth_line = str(efficiencies_string) + '\\n'\n if bool(efficiencies) is True:\n f.write(fourth_line)\n #dupluicate option\n if equation_object.duplicate is True:\n duplicate_line = ' DUPLICATE' +'\\n'\n f.write(duplicate_line)\n f.write('END')\n f.close()\n\n #Test mechanism file\n\n original_solution = solution\n #convert written chemkin file to cti, and get solution\n parser = ck2cti.Parser()\n outName = 'test_file.cti'\n parser.convertMech(output_file_name, outName=outName)\n new_solution = ct.Solution(outName)\n\n #test new solution vs original solutoin\n #test(original_solution, new_solution)\n os.remove(outName)\n return output_file_name\n\n\n \"\"\"\n def build_falloff(j):\n\n Creates falloff reaction Troe parameter string\n\n param j:\n Cantera falloff parameters object\n\n falloff_str = str(',\\n falloff = Troe(' +\n 'A = ' + str(j[0]) +\n ', T3 = ' + str(j[1]) +\n ', T1 = ' + str(j[2]) +\n ', T2 = ' + str(j[3]) +') )\\n\\n')\n return falloff_str\n \"\"\"",
"title": ""
},
{
"docid": "bb9fe475543f8a500b17238e2c01e2a8",
"score": "0.5131358",
"text": "def writeDataFile(self):\n model = pe.PEChainBuilder(self.lenPa, self.numPa, self.lenPc, self.numPc,\\\n self.volRatio, self.chargeFraction)\n model.genChains()\n \n \n model.writeFiles(self.directory)\n self.dataFileName = \"data.pe.la{0}.na{1}.lc{2}.nc{3}.rho{4}.r{5}.lammps\".\\\n format(self.lenPa, self.numPa, self.lenPc, self.numPc, \\\n self.volRatio, self.chargeRepeat)",
"title": ""
},
{
"docid": "dc59f59e013fe69fd099020e5707bc62",
"score": "0.51224095",
"text": "def createPrismModel(states, transitions, target, mode, nr_actions, reset_transitions, reset_cost):\n out_file = open(TMP_MODEL_PATH, 'w')\n # module\n out_file.write(\"mdp\\n\\nmodule tmp\\n\\n\")\n\n # number of state and initial state\n out_file.write(\"\\ts : [0..\" + str(len(states)) + \"] init \" + str(abs(mode)) + \";\\n\\n\")\n\n # transitions\n for state in range(len(transitions)):\n for action in range(len(transitions[state])):\n if sum(transitions[state][action]) != 0:\n out_file.write(\"\\t[\" + chr(97 + action) + \"] s=\" + str(state) + \"-> \")\n destinations = []\n for dest in range(len(transitions[state][action])):\n if transitions[state][action][dest] != 0:\n destinations.append(str(transitions[state][action][dest]) + \":(s'=\" + str(dest) + \")\")\n out_file.write(\" + \".join(destinations))\n out_file.write(\";\\n\")\n out_file.write(\"\\nendmodule\\n\\n\")\n\n # label target\n out_file.write('label \"target\" = ')\n target = list(set(target))\n target = [\"(s=\" + str(x) + \")\" for x in target]\n if len(target) == 0:\n out_file.write(\"(s=\" + str(len(states) + 1) + \");\\n\")\n else:\n out_file.write(\" | \".join(target))\n out_file.write(\";\\n\")\n\n # label states\n if mode == MODE_MAX:\n out_file.write('label \"sink\" = (s=0);\\n')\n\n for i in range(mode, len(states)):\n out_file.write('label \"s' + str(states[i][0]) + '_obs' + str(states[i][1]) + '\" = (s=' + str(i) + ');\\n')\n\n if mode == MODE_MIN:\n out_file.write(\"\\nrewards\\n\")\n if reset_cost == 1:\n out_file.write(\"\\ttrue:1;\\n\")\n else:\n for i in range(len(states)):\n for j in range(nr_actions):\n if j in reset_transitions[i]:\n out_file.write(\"\\t[\" + chr(97 + j) + \"] (s=\" + str(i) + \") : \" + str(reset_cost) + \";\\n\")\n else:\n out_file.write(\"\\t[\" + chr(97 + j) + \"] (s=\" + str(i) + \") : 1;\\n\")\n\n out_file.write(\"endrewards\\n\")\n\n out_file.close()",
"title": ""
},
{
"docid": "f391c50f49d5b0eae5ebbf6c5f12961a",
"score": "0.5118555",
"text": "def build_LM(in_file):\r\n print 'building language models...'\r\n # This is an empty method\r\n # Pls implement your code in below\r\n LMs = {'malaysian': {}, 'indonesian': {}, 'tamil': {}}\r\n malay_LM = LMs['malaysian']\r\n indon_LM = LMs['indonesian']\r\n tamil_LM = LMs['tamil']\r\n\r\n FD = open(in_file, 'r')\r\n\r\n lines = FD.read()\r\n lines_array = lines.replace('\\r', ' ').split('\\n')\r\n lines_array.pop(len(lines_array) - 1)\r\n\r\n for line in lines_array:\r\n line_array = line.split(' ', 1)\r\n lang = line_array[0]\r\n text = line_array[1]\r\n current_LM = LMs[lang]\r\n four_char_array = get_four_char_array(text)\r\n\r\n for four_char in four_char_array:\r\n if four_char not in current_LM:\r\n # performs add one smoothing\r\n malay_LM[four_char] = 1\r\n indon_LM[four_char] = 1\r\n tamil_LM[four_char] = 1\r\n current_LM[four_char] += 1\r\n\r\n # calculates ratio of set frequency over total frequency\r\n # for all sets in each of the three LMs\r\n for LM in LMs.values():\r\n count = 0\r\n for freq_value in LM.values():\r\n count += freq_value\r\n for four_char_key in LM.keys():\r\n LM[four_char_key] /= count\r\n return LMs\r\n\r\n FD.close()",
"title": ""
},
{
"docid": "c862647fcd5ba7f408d748abaf7ab617",
"score": "0.5115003",
"text": "def test_ImportUFONoSideEffectNLO(self):\n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('loop_sm'),False)\n original_all_particles = copy.copy(ufo_model.all_particles)\n original_all_vertices = copy.copy(ufo_model.all_vertices)\n original_all_couplings = copy.copy(ufo_model.all_couplings)\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\n original_all_parameters = copy.copy(ufo_model.all_parameters)\n original_all_orders = copy.copy(ufo_model.all_orders)\n original_all_functions = copy.copy(ufo_model.all_functions)\n original_all_CTvertices = copy.copy(ufo_model.all_CTvertices)\n original_all_CTparameters = copy.copy(ufo_model.all_CTparameters)\n\n\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\n model = ufo2mg5_converter.load_model()\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \n\n self.assertEqual(original_all_particles,ufo_model.all_particles)\n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\n# self.assertEqual(original_all_couplings,ufo_model.all_couplings)\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\n self.assertEqual(original_all_orders,ufo_model.all_orders)\n self.assertEqual(original_all_functions,ufo_model.all_functions)\n self.assertEqual(original_all_CTvertices,ufo_model.all_CTvertices)\n self.assertEqual(original_all_CTparameters,ufo_model.all_CTparameters)",
"title": ""
},
{
"docid": "dfe119951be54a560fcefe9057a921e1",
"score": "0.5112634",
"text": "def main(models, output_dir):\n\tfor model in models:\n\t\tobj = ParseOmsa(model)\n\t\tobj.bios_version()\n\t\tobj.perc_versions()\n\t\tobj.write_yaml(output_dir)",
"title": ""
},
{
"docid": "00e7eaf7ed485ca9233fa3bbe1a1b96e",
"score": "0.5110691",
"text": "def solve_lp(self):\n self._initialize_model()\n self._initialize_node_variables_lp()\n self._initialize_edge_variables_lp()\n self._initialize_objective()\n self._initialize_contraint_nodes()\n self._initialize_constraint_edges()\n self._initialize_constraint_terminals()\n self._run_solver()\n self._calculate_cut_value()\n self._calculate_source_sets()\n self._calculate_possible_terminals_by_node_weak()\n self._calculate_possible_terminals_by_node_strong()",
"title": ""
},
{
"docid": "6586aa3830f43b54dce72f69cbc0963d",
"score": "0.5104416",
"text": "def loadLoopToyModel():\n \n mypartlist = base_objects.ParticleList()\n myinterlist = base_objects.InteractionList()\n myloopmodel = loop_base_objects.LoopModel()\n\n # A gluon\n mypartlist.append(base_objects.Particle({'name':'g',\n 'antiname':'g',\n 'spin':3,\n 'color':8,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'G',\n #'antitexname':'G',\n 'line':'curly',\n 'charge':0.,\n 'pdg_code':21,\n #'propagating':True,\n 'propagator':0, \n 'is_part':True,\n 'counterterm':{('QCD', ((6,),)): {0: 'UVWfct_G_1', -1: 'UVWfct_G_1_1eps'}, ('QCD', ((5,),)): {0: 'UVWfct_G_0', -1: 'UVWfct_G_0_1eps'}},\n 'self_antipart':True}))\n \n # A quark U and its antiparticle\n mypartlist.append(base_objects.Particle({'name':'u',\n 'antiname':'u~',\n 'spin':2,\n 'color':3,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'u',\n #'antitexname':'u',\n 'line':'straight',\n 'charge':2. / 3.,\n 'pdg_code':2,\n #'propagating':True,\n 'propagator':'', \n 'is_part':True,\n 'self_antipart':False}))\n antiu = copy.copy(mypartlist[1])\n antiu.set('is_part', False)\n\n # A quark D and its antiparticle\n mypartlist.append(base_objects.Particle({'name':'d',\n 'antiname':'d~',\n 'spin':2,\n 'color':3,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'d',\n #'antitexname':'d',\n 'line':'straight',\n 'charge':-1. / 3.,\n 'pdg_code':1,\n #'propagating':True,\n 'propagator':'',\n 'is_part':True,\n 'self_antipart':False}))\n antid = copy.copy(mypartlist[2])\n antid.set('is_part', False)\n\n myloopmodel.set('particles', mypartlist)\n myloopmodel.set('couplings', ['QCD']) \n myloopmodel.set('interactions', myinterlist)\n myloopmodel.set('perturbation_couplings', ['QCD'])\n myloopmodel.set('order_hierarchy', {'QCD':1})\n\n return myloopmodel",
"title": ""
},
{
"docid": "695e98b499f902207ba35ccac8891bda",
"score": "0.5104137",
"text": "def generate(genconf_model, project_folder):\n\n gendesc = get_generator_desc(genconf_model.gen_name)\n meta = get_language(gendesc.lang)\n\n # Path for templates overrides\n templates_path = os.path.join(project_folder, 'templates',\n genconf_model.gen_name)\n\n output_root = os.path.join(project_folder, genconf_model.output)\n\n def _create_folder(output_file):\n try:\n os.makedirs(os.path.dirname(output_file))\n except FileExistsError:\n pass\n\n # For each model configured in the current genconf\n for model_path in genconf_model.models:\n\n click.echo('Processing model \"{}\"'.format(model_path))\n model = meta.model_from_file(os.path.join(project_folder, 'model',\n model_path))\n\n # Validate model using generator specific validation.\n if gendesc.validate:\n gendesc.validate(model)\n\n params = {}\n # Adding generator params\n for p in genconf_model.params:\n params[p.name] = p.value\n\n # Processing all rules\n for rule in genconf_model.rules:\n\n # Sanity check\n if len(rule.types) != len(rule.var_names):\n raise TextXToolsError('Number of variables don\\'t match'\n ' number of types in rule \"{}\"'\n .format(rule.name))\n\n # Collect all object of each given type\n type_objs = []\n for t in rule.types:\n type_objs.append(children_of_type(model, t))\n\n if rule.all:\n context = {'__builtins__':{}}\n for ind, obj in enumerate(type_objs):\n params[rule.var_names[ind]] = obj\n context.update(params)\n for t in rule.trans:\n target_path = eval(t.python_path_expr, context)\n output_file = os.path.join(output_root, target_path)\n\n click.echo(\"Generating {}\".format(output_file))\n\n _create_folder(output_file)\n with open(output_file, 'w') as f:\n f.write(gendesc.render(t.template_path, params,\n templates_path))\n else:\n if len(rule.types) > 1:\n raise TextXToolsError('Multiple types/variables are not'\n ' possible for \"non-all\" rules.')\n for obj in type_objs[0]:\n params[rule.var_names[0]] = obj\n context = {'__builtins__': {}}\n context.update(params)\n for t in rule.trans:\n target_path = eval(t.python_path_expr, context)\n output_file = os.path.join(output_root, target_path)\n click.echo(\"Generating {}\".format(output_file))\n _create_folder(output_file)\n\n with open(output_file, 'w') as f:\n f.write(gendesc.render(t.template_path, params,\n templates_path))",
"title": ""
},
{
"docid": "8a1835d042cf66486ed099ce14f71906",
"score": "0.51036656",
"text": "def generate_pymods(the_parsed_topology_xml, xml_filename, opt):\n if the_parsed_topology_xml.get_namespace():\n if VERBOSE:\n print(\n \"Generating pymods for topology %s::%s\"\n % (\n the_parsed_topology_xml.get_namespace(),\n the_parsed_topology_xml.get_name(),\n )\n )\n else:\n if VERBOSE:\n print(\n \"Generating pymods for topology %s\"\n % (the_parsed_topology_xml.get_name())\n )\n model = TopoFactory.TopoFactory.getInstance()\n topology_model = model.create(the_parsed_topology_xml)\n\n # create list of used parsed component xmls\n parsed_xml_dict = {}\n for comp in the_parsed_topology_xml.get_instances():\n if comp.get_type() in topology_model.get_base_id_dict():\n parsed_xml_dict[comp.get_type()] = comp.get_comp_xml()\n else:\n if VERBOSE:\n print(\n \"Components with type {} aren't in the topology model.\".format(\n comp.get_type()\n )\n )\n\n #\n # Hack to set up deployment path for instanced dictionaries (if one exists remove old one)\n #\n if opt.dict_dir is None:\n os.environ[\"DICT_DIR\"] = os.getcwd()\n else:\n os.environ[\"DICT_DIR\"] = opt.dict_dir\n\n xml_list = []\n for parsed_xml_type in parsed_xml_dict:\n if parsed_xml_dict[parsed_xml_type] is None:\n print(\n \"ERROR: XML of type {} is being used, but has not been parsed correctly. Check if file exists or add xml file with the 'import_component_type' tag to the Topology file.\".format(\n parsed_xml_type\n )\n )\n raise Exception()\n xml_list.append(parsed_xml_dict[parsed_xml_type])\n temp_comp = parsed_xml_dict[parsed_xml_type].get_component()\n if VERBOSE:\n print(\n \"Generating component dicts for %s::%s\"\n % (temp_comp.get_namespace(), temp_comp.get_name())\n )\n write_pymods_from_comp(parsed_xml_dict[parsed_xml_type], opt, topology_model)\n if VERBOSE:\n print(\n \"Generated component dicts for %s::%s\"\n % (temp_comp.get_namespace(), temp_comp.get_name())\n )\n\n topology_model.set_instance_xml_list(xml_list)\n\n if the_parsed_topology_xml.get_namespace():\n if VERBOSE:\n print(\n \"Generated pymods for topology %s::%s\"\n % (\n the_parsed_topology_xml.get_namespace(),\n the_parsed_topology_xml.get_name(),\n )\n )\n else:\n if VERBOSE:\n print(\n \"Generated pymods for topology %s\"\n % (the_parsed_topology_xml.get_name())\n )",
"title": ""
},
{
"docid": "8b7d04b25bee59ebf17f61215a787437",
"score": "0.51035786",
"text": "def main(input_dir: str, output_dir: str) -> None:\n input_dir = Path(input_dir)\n output_dir = Path(output_dir)\n output_dir.mkdir(parents=True, exist_ok=True)\n\n spacy_formatted_data = {\"train\": [], \"test\": []}\n\n for partition in spacy_formatted_data:\n note_dir = input_dir / partition / f\"{partition}_note\"\n if partition == \"train\":\n norm_dir = input_dir / partition / f\"{partition}_norm\"\n else:\n # TODO (John): Is this the name of the directory in the official download?\n norm_dir = input_dir / partition / f\"{partition}_norm_cui_replaced_with_unk\"\n\n for note_filepath in note_dir.iterdir():\n if not note_filepath.name.endswith(\".txt\"):\n continue\n\n norm_filepath = norm_dir / f\"{note_filepath.stem}.norm\"\n\n with open(note_filepath, \"r\") as f:\n spacy_formatted_data[partition].append((f.read(), {\"entities\": []}))\n with open(norm_filepath, \"r\") as f:\n for line in f:\n start, end = tuple(map(int, line.strip().split(\"||\")[-2:]))\n spacy_formatted_data[partition][-1][-1][\"entities\"].append((start, end, ENTITY_LABEL))\n\n with open(output_dir / \"spacy_formatted_data.pickle\", \"wb\") as f:\n pickle.dump(spacy_formatted_data, f)",
"title": ""
},
{
"docid": "df99f942c3c7b8f03f573780597898e3",
"score": "0.5099381",
"text": "def pmodel(self):\r\n if not hasattr(self, '_pmodel'):\r\n pmodelname = self.get('pmodel-name')\r\n if pmodelname == 'ISO9613':\r\n # create environment\r\n G = tuple([float(x) for x in self.get('pmodel-iso9613-ground-coeffs').strip('()').split(',')])\r\n p = self.getFloat('pmodel-iso9613-atmospheric-pressure')\r\n t = self.getFloat('pmodel-iso9613-atmospheric-temperature')\r\n r = self.getFloat('pmodel-iso9613-atmospheric-humidity')\r\n self._environment = ISO9613Environment(G = G, p = p, t = t, r = r)\r\n # create propagation model\r\n self._pmodel = ISO9613Model(environment = self._environment)\r\n self._pmodel.correction['geometricDivergence'] = self.getBool('pmodel-iso9613-flag-geometric-divergence')\r\n self._pmodel.correction['atmosphericAbsorption'] = self.getBool('pmodel-iso9613-flag-atmospheric-absorption')\r\n self._pmodel.correction['groundEffect'] = self.getBool('pmodel-iso9613-flag-ground-effect')\r\n self._pmodel.correction['sourceDirectivity'] = self.getBool('pmodel-iso9613-flag-source-directivity')\r\n else:\r\n raise Exception('configuration file: Propagation model \"%s\" not known - use \"ISO9613\"' % pmodelname)\r\n return self._pmodel",
"title": ""
},
{
"docid": "7bb2d23548d39b59677cfcfc9082e026",
"score": "0.50863206",
"text": "def main():\n\n parser = argparse.ArgumentParser(description='Duolingo shared task baseline model')\n parser.add_argument('--language', default='en_es', help='choose from [es_en, en_es, fr_en]', required=False)\n parser.add_argument('--dataset_path', default='../data/%s/', required=False)\n parser.add_argument('--outputs_path', default='./outputs/', required=False)\n args = parser.parse_args()\n\n dataset_path = args.dataset_path % args.language\n assert os.path.isdir(dataset_path)\n\n train_path = dataset_path + '%s.slam.20190204.train' % args.language\n dev_path = dataset_path + '%s.slam.20190204.dev' % args.language\n test_path = dataset_path + '%s.slam.20190204.test' % args.language\n assert os.path.isfile(train_path)\n assert os.path.isfile(dev_path)\n assert os.path.isfile(test_path)\n\n if not os.path.isdir(args.outputs_path): os.mkdir(args.outputs_path)\n\n # ============================== Hyper Parameter ==============================\n dbg = False\n from_path = None\n # from_path = './saved_model/seq2seq_nomlp_20'\n # from_path = './saved_model/seq2seq_exp_20'\n # from_path = './saved_model/attention_v2_20'\n # from_path = './saved_model/cnn_3'\n # from_path = './saved_model/seq2seq_c_7'\n # from_path = './saved_model/transformer_1'\n epochs = 10 if dbg else 10\n lang = Lang()\n\n # ============================== Data Loading ==============================\n\n print('Begin Data Loading')\n start_time = time.time()\n training_data, training_labels = load_data(train_path, lang, dbg=dbg, use_all_features=True)\n dev_data = load_data(dev_path, lang, use_all_features=True)\n test_data = load_data(test_path, lang, use_all_features=True)\n users = list(get_users(training_data).union(get_users(dev_data)).union(get_users(test_data)))\n lang.addUsers(users)\n for i in range(len(lang.letters)): lang.letter2Index[lang.letters[i]] = i\n end_time = time.time()\n print('Data Loaded\\t Time Taken %0.2fm' % ((end_time - start_time)/60))\n\n model = Model(lang)\n\n # ============================== Training ==============================\n if from_path == None:\n print('Begin Training')\n train_loader = get_dataloader(training_data, lang, training_labels)\n model.train(train_loader, epochs)\n\n # ============================== Inference ==============================\n # print('Begin Inference-Dev', end=' ')\n # start_time = time.time()\n # dev_loader = get_dataloader(dev_data, lang)\n # predictions = model.predict_for_set(dev_loader, from_path)\n # with open(args.outputs_path + '%s_dev_predictions.pred' % args.language, 'wt') as f:\n # for instance_id, prediction in iteritems(predictions):\n # f.write(instance_id + ' ' + str(prediction) + '\\n')\n # end_time = time.time()\n # print('| %0.2fm' % ((end_time-start_time)/60))\n \n print('Begin Inference-Test', end=' ')\n start_time = time.time()\n test_loader = get_dataloader(test_data, lang)\n predictions = model.predict_for_set(test_loader, from_path)\n with open(args.outputs_path + '%s_test_predictions.pred' % args.language, 'wt') as f:\n for instance_id, prediction in predictions.items():\n f.write(instance_id + ' ' + str(prediction) + '\\n')\n end_time = time.time()\n print('| %0.2fm' % ((end_time-start_time)/60))",
"title": ""
},
{
"docid": "aa7982cf4b80468889131323fa94e9d7",
"score": "0.50660026",
"text": "def generate_language_model(**kwargs):\n\n lm = Session.query(model.MorphemeLanguageModel).get(kwargs['morpheme_language_model_id'])\n trie_path = lm.get_file_path('trie')\n trie_mod_time = lm.get_modification_time(trie_path)\n lm.generate_succeeded = False\n try:\n lm.write_corpus()\n except Exception, e:\n lm.generate_message = u'Error writing the corpus file. %s' % e\n try:\n lm.write_vocabulary()\n except Exception, e:\n lm.generate_message = u'Error writing the vocabulary file. %s' % e\n try:\n lm.write_arpa(kwargs['timeout'])\n except Exception, e:\n lm.generate_message = u'Error writing the ARPA file. %s' % e\n try:\n lm.generate_trie()\n except Exception, e:\n lm.generate_message = u'Error generating the LMTrie instance. %s' % e\n else:\n if lm.get_modification_time(trie_path) != trie_mod_time:\n lm.generate_succeeded = True\n lm.generate_message = u'Language model successfully generated.'\n else:\n lm.generate_message = u'Error generating the LMTrie instance.'\n lm.generate_attempt = unicode(uuid4())\n lm.modifier_id = kwargs['user_id']\n lm.datetime_modified = h.now()\n Session.commit()",
"title": ""
},
{
"docid": "2cff1d19062392a49928573882a530bd",
"score": "0.5064492",
"text": "def load_model(model_path):\r\n nlp = spacy.blank('en')\r\n if 'ner' not in nlp.pipe_names:\r\n ner = nlp.create_pipe('ner')\r\n nlp.add_pipe(ner)\r\n\r\n ner = nlp.from_disk(model_path)\r\n return ner",
"title": ""
},
{
"docid": "fd82b28979eb3edddc68ff36415c4e0c",
"score": "0.5058911",
"text": "def _write_imports(\n cls,\n pickle_type: Optional[str] = None,\n mojo_model: Optional[bool] = False,\n binary_h2o_model: Optional[bool] = False,\n tf_model: Optional[bool] = False,\n binary_string: Optional[str] = None,\n ) -> None:\n pickle_type = pickle_type if pickle_type else \"pickle\"\n cls.score_code += (\n f\"import math\\nimport {pickle_type}\\nimport pandas as pd\\n\"\n \"import numpy as np\\nfrom pathlib import Path\\n\\n\"\n )\n \"\"\"\nimport math\nimport pickle\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\n\n \"\"\"\n\n try:\n if current_session().version_info() != 3.5:\n cls.score_code += \"import settings\\n\\n\"\n \"\"\"\nimport settings\n \n \n \"\"\"\n except AttributeError:\n warn(\n \"No current session connection was found to a SAS Viya server. Score \"\n \"code will be written under the assumption that the target server is \"\n \"SAS Viya 4.\"\n )\n\n if mojo_model or binary_h2o_model:\n cls.score_code += \"import h2o\\n\\nh2o.init()\\n\\n\"\n \"\"\"\nimport h2o\n\nh2o.init()\n\n \"\"\"\n elif tf_model:\n cls.score_code += \"import tensorflow as tf\\n\\n\"\n \"\"\"\nimport tensorflow as tf\n\n \"\"\"\n elif binary_string:\n cls.score_code += (\n f'import codecs\\n\\nbinary_string = \"{binary_string}\"'\n f\"\\nmodel = {pickle_type}.loads(codecs.decode(binary_string\"\n '.encode(), \"base64\"))\\n\\n'\n )\n \"\"\"\nimport codecs\n\nbinary_string = \"<binary string>\"\nmodel = pickle.load(codecs.decode(binary_string.encode(), \"base64\"))\n\n \"\"\"",
"title": ""
},
{
"docid": "2c1108513704f5cbba7b473aa2906d50",
"score": "0.50567424",
"text": "def write_pymods_from_comp(the_parsed_component_xml, opt, topology_model):\n global BUILD_ROOT\n global DEPLOYMENT\n global VERBOSE\n\n parsed_port_xml_list = []\n parsed_serializable_xml_list = []\n # uses the topology model to process the items\n # checks if the topology model exists\n if topology_model is None:\n PRINT.info(\n \"Topology model was not specified. Please also input a topology model when running this command.\"\n )\n raise OSError\n\n port_type_files_list = the_parsed_component_xml.get_port_type_files()\n\n for port_file in port_type_files_list:\n port_file = search_for_file(\"Port\", port_file)\n xml_parser_obj = XmlPortsParser.XmlPortsParser(port_file)\n parsed_port_xml_list.append(xml_parser_obj)\n del xml_parser_obj\n\n serializable_type_files_list = (\n the_parsed_component_xml.get_serializable_type_files()\n )\n for serializable_file in serializable_type_files_list:\n serializable_file = search_for_file(\"Serializable\", serializable_file)\n xml_parser_obj = XmlSerializeParser.XmlSerializeParser(\n serializable_file\n ) # Telemetry/Params can only use generated serializable types\n # check to make sure that the serializables don't have things that channels and parameters can't have\n # can't have external non-xml members\n if len(xml_parser_obj.get_include_header_files()):\n print(\n \"ERROR: Component include serializables cannot use user-defined types. file: \"\n % serializable_file\n )\n sys.exit(-1)\n\n parsed_serializable_xml_list.append(xml_parser_obj)\n del xml_parser_obj\n\n model = CompFactory.CompFactory.getInstance()\n component_model = model.create(\n the_parsed_component_xml, parsed_port_xml_list, parsed_serializable_xml_list\n )\n\n instChannelWriter = InstChannelWriter.InstChannelWriter()\n instCommandWriter = InstCommandWriter.InstCommandWriter()\n instEventWriter = InstEventWriter.InstEventWriter()\n\n if opt.dict_dir is None:\n if VERBOSE:\n print(\"Dictionary output directory not specified!, defaulting to cwd\")\n opt.dict_dir = os.getcwd()\n os.environ[\"DICT_DIR\"] = opt.dict_dir\n\n # iterate through command instances\n for command_model in component_model.get_commands():\n if VERBOSE:\n print(\"Generating command dict %s\" % command_model.get_mnemonic())\n instCommandWriter.DictStartWrite(command_model, topology_model)\n instCommandWriter.DictHeaderWrite(command_model, topology_model)\n instCommandWriter.DictBodyWrite(command_model, topology_model)\n\n for parameter_model in component_model.get_parameters():\n if VERBOSE:\n print(\"Generating parameter dict %s\" % parameter_model.get_name())\n instCommandWriter.DictStartWrite(parameter_model, topology_model)\n instCommandWriter.DictHeaderWrite(parameter_model, topology_model)\n instCommandWriter.DictBodyWrite(parameter_model, topology_model)\n\n # iterate through command instances\n for event_model in component_model.get_events():\n if VERBOSE:\n print(\"Generating event dict %s\" % event_model.get_name())\n instEventWriter.DictStartWrite(event_model, topology_model)\n instEventWriter.DictHeaderWrite(event_model, topology_model)\n instEventWriter.DictBodyWrite(event_model, topology_model)\n\n # iterate through command instances\n for channel_model in component_model.get_channels():\n if VERBOSE:\n print(\"Generating channel dict %s\" % channel_model.get_name())\n instChannelWriter.DictStartWrite(channel_model, topology_model)\n instChannelWriter.DictHeaderWrite(channel_model, topology_model)\n instChannelWriter.DictBodyWrite(channel_model, topology_model)",
"title": ""
},
{
"docid": "dbac7e9dbefed924c07d9e4bb763e9cb",
"score": "0.5051691",
"text": "def convert(self, path, version, target):\n source = self.comparer.get_representation(path)\n lines = [ '# <fortpy version=\"{}\"></fortpy>\\n'.format(version) ]\n\n for line in self.comparer.template.contents[version].preamble:\n lines.append(line.write(source.preamble, source.version, source.stored) + \"\\n\")\n\n for line in self.comparer.template.contents[version].body:\n for valueset in source.body:\n lines.append(line.write(valueset, source.version, source.stored) + \"\\n\")\n\n with open(os.path.expanduser(target), 'w') as f:\n f.writelines(lines)",
"title": ""
},
{
"docid": "34c2bef9339db6de5f9e061ddef3fb47",
"score": "0.5050992",
"text": "def convert_to_onnx(pytorch_model, input_shape, output_file, input_names, output_names):\n\n pytorch_model.eval()\n onnx_input_shape = torch.randn(input_shape)\n torch.onnx.export(pytorch_model, onnx_input_shape, output_file,\n verbose=True, input_names=input_names, output_names=output_names)\n\n # Model check after conversion\n import onnx\n model_from_onnx = onnx.load(output_file)\n try:\n onnx.checker.check_model(model_from_onnx)\n print('ONNX check passed successfully.')\n except onnx.onnx_cpp2py_export.checker.ValidationError as exc:\n sys.exit('ONNX check failed with error: ' + str(exc))",
"title": ""
},
{
"docid": "5f4a2a2fd203b35f20cc76a1f1693031",
"score": "0.5048336",
"text": "def _ppo(self, m):\n\n m[\"ppo1\"] = m[\"MemoryOp <: po_loc :> Write\"]\n m[\"ppo2\"] = m[\"rdw\"]\n m[\"ppo3\"] = m[\"(AMO + StoreConditional) <: (rf & po)\"]\n m[\"ppo4rr\"] = m[\"(Read <: po :> (FencePR & FenceSR)).(po :> Read)\"]\n m[\"ppo4rw\"] = m[\"(Read <: po :> (FencePR & FenceSW)).(po :> Write)\"]\n m[\"ppo4wr\"] = m[\"(Write <: po :> (FencePW & FenceSR)).(po :> Read)\"]\n m[\"ppo4ww\"] = m[\"(Write <: po :> (FencePW & FenceSW)).(po :> Write)\"]\n m[\"ppo4tso1\"] = m[\"(Read <: po :> (FenceTSO)).(po :> MemoryOp)\"]\n m[\"ppo4tso2\"] = m[\"(MemoryOp <: po :> (FenceTSO)).(po :> Write)\"]\n m[\"ppo4\"] = m[\"ppo4rr + ppo4rw + ppo4wr + ppo4ww + ppo4tso1 + ppo4tso2\"]\n m[\"ppo5\"] = m[\"Aq <: po :> MemoryOp\"]\n m[\"ppo6\"] = m[\"MemoryOp <: po :> Rl\"]\n m[\"ppo7\"] = m[\"RCsc <: po :> RCsc\"]\n m[\"ppo8\"] = m[\"pair\"]\n m[\"ppo9\"] = m[\"addrdep\"]\n m[\"ppo10\"] = m[\"datadep\"]\n m[\"ppo11\"] = m[\"ctrldep.po :> Write\"]\n m[\"ppo12\"] = m[\"(addrdep + datadep).(rf & po)\"]\n m[\"ppo13\"] = m[\"addrdep.po :> Write\"]\n\n return m[\n \"ppo1 + ppo2 + ppo3 + ppo4 + ppo5 + ppo6 + ppo7 + \"\n \"ppo8 + ppo9 + ppo10 + ppo11 + ppo12 + ppo13\"\n ]",
"title": ""
},
{
"docid": "7ac89b0a71c9943d58dd2e15032afde8",
"score": "0.504813",
"text": "def makeFile(m, model):\n tempfile = ['#include \"lime.h\"\\n', '#include \"math.h\"\\n',\n '#include \"stdio.h\"\\n', '#include \"stdlib.h\"\\n',\n '#include \"%s\"\\n\\n' % model.header.fn]\n writeModelProperties(tempfile, model)\n writeWeighting(tempfile, model)\n writeImageParameters(tempfile, m, model)\n writeInterpolationFuncs(tempfile, model)\n writeDensity(tempfile, model)\n writeTemperatures(tempfile, model)\n writeAbundance(tempfile, model)\n if model.dust is not None:\n writeGastoDust(tempfile, model)\n writeDoppler(tempfile, model)\n writeVelocityStructure(tempfile, model)\n with open('model_%d.c' % m, 'w') as tosave:\n for line in tempfile:\n tosave.write('%s' % line)\n return",
"title": ""
},
{
"docid": "06ef0a20cae196e2de5c67f6c87df504",
"score": "0.5047686",
"text": "def _save_prototxt(self):\n solver_path = osp.dirname(self._solver_prototxt)\n if not osp.exists(solver_path):\n os.makedirs(solver_path)\n with open(self._solver_prototxt, 'w') as f:\n f.write(text_format.MessageToString(self._solver))",
"title": ""
},
{
"docid": "9127df10abf73188e578bcc4dcd4d09c",
"score": "0.50475544",
"text": "def owl_to_model(fname):\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\n\n try:\n file_is = autoclass('java.io.FileInputStream')(fname)\n except JavaException:\n logger.error('Could not open data file %s' % fname)\n return\n try:\n biopax_model = io.convertFromOWL(file_is)\n except JavaException as e:\n logger.error('Could not convert data file %s to BioPax model' % fname)\n logger.error(e)\n return\n\n file_is.close()\n\n return biopax_model",
"title": ""
},
{
"docid": "b084295606442608e4e95a1201da837c",
"score": "0.5046919",
"text": "def export_model(self, out=None, **kwargs):\n # Remove all code transformations but respect those provided explicitly\n kwargs.setdefault('length_for_alias', None)\n kwargs.setdefault('name_all_constraints', False)\n\n CpoCompiler(self, **kwargs).write(out)",
"title": ""
},
{
"docid": "380e97eb5048bd83b762af621acf5fd7",
"score": "0.5038669",
"text": "def main(model=None, output_dir=None, n_iter=100):\n if model is not None:\n nlp = spacy.load(model) # load existing spaCy model\n print(\"Loaded model '%s'\" % model)\n else:\n nlp = spacy.blank(\"en\") # create blank Language class\n print(\"Created blank 'en' model\")\n \n # create the built-in pipeline components and add them to the pipeline\n # nlp.create_pipe works for built-ins that are registered with spaCy\n if \"ner\" not in nlp.pipe_names:\n ner = nlp.create_pipe(\"ner\")\n nlp.add_pipe(ner, last=True)\n # otherwise, get it so we can add labels\n else:\n ner = nlp.get_pipe(\"ner\")\n \n # add labels\n for _, annotations in TRAIN_DATA:\n for ent in annotations.get(\"entities\"):\n ner.add_label(ent[2])\n\n # get names of other pipes to disable them during training\n other_pipes = [pipe for pipe in nlp.pipe_names if pipe != \"ner\"]\n with nlp.disable_pipes(*other_pipes): # only train NER\n # reset and initialize the weights randomly – but only if we're\n # training a new model\n if model is None:\n nlp.begin_training()\n for itn in range(n_iter):\n random.shuffle(TRAIN_DATA)\n losses = {}\n # batch up the examples using spaCy's minibatch\n batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))\n for batch in batches:\n texts, annotations = zip(*batch)\n nlp.update(\n texts, # batch of texts\n annotations, # batch of annotations\n drop=0.5, # dropout - make it harder to memorise data\n losses=losses,\n )\n #print(\"Losses\", losses)\n\n # test the trained model\n for text in TEST_DATA:\n doc = nlp(text)\n print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])\n print(\"Tokens\", [(t.text, t.ent_type_, t.ent_iob) for t in doc])\n \n # save model to output directory\n if output_dir is not None:\n output_dir = Path(output_dir)\n if not output_dir.exists():\n output_dir.mkdir()\n nlp.to_disk(output_dir)\n print(\"Saved model to\", output_dir)\n \n # test the saved model\n print(\"Loading from\", output_dir)\n nlp2 = spacy.load(output_dir)\n for text, _ in TRAIN_DATA:\n doc = nlp2(text)\n print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])",
"title": ""
},
{
"docid": "4dfa7950183441b0441fb0c52fd47749",
"score": "0.5037608",
"text": "def convert2or():\n # parse converter arguments\n args = get_args_convert2or()\n\n # input data parameters\n try:\n workspace_dir, exec_file_name, data_uri, data_path, output_path = get_params()\n entry_filename = os.path.splitext(os.path.basename(exec_file_name))[0]\n except Exception as e:\n print('Invalid arguments, {}'.format(e))\n sys.exit(1)\n\n else:\n # Generate requirements.txt\n try:\n import pipreqs\n except ImportError as e:\n raise RuntimeError('Package installation failed: {}'.format(e))\n\n else:\n time.sleep(1)\n try:\n p = subprocess.Popen([\"pipreqs\", \"--force\", workspace_dir])\n p.wait()\n time.sleep(1)\n\n # fix the bug raising from 'tensorflow', 'tensorflow_gpu'\n filename = os.path.join(workspace_dir, \"requirements.txt\")\n\n rw_file(filename, matplotlib=\"matplotlib\", tensorflow_gpu=\"\", tensorflow=\"tensorflow-gpu\")\n remove_empty_lines(filename)\n print(\"Generated 'requirements.txt' successfully!\")\n\n except Exception as e:\n raise RuntimeError(\"Generating 'requirements.txt' failed: {}\".format(e))\n\n else:\n # Generate params.json\n try:\n exec_file_name_v = os.path.relpath(exec_file_name, start=workspace_dir)\n data_path_v = \"\" if data_path == \"\" else os.path.relpath(data_path, start=workspace_dir)\n output_path_v = \"\" if output_path == \"\" else os.path.relpath(output_path, start=workspace_dir)\n params_json = json.dumps({\"exec_file_name\": exec_file_name_v,\n \"data_uri\": data_uri,\n \"data_path\": data_path_v,\n \"output_path\": output_path_v,\n })\n with open(os.path.join(workspace_dir, \"params.json\"), 'w+') as f:\n f.write(params_json)\n print(\"Generated 'params.json' successfully!\")\n\n except Exception as e:\n raise IOError(\"Generating 'params.json' failed: {}\".format(e))\n\n else:\n time.sleep(2)\n try:\n zip_folder_path = os.path.join(workspace_dir, os.pardir, \"task_files\")\n\n if not os.path.exists(zip_folder_path):\n os.makedirs(zip_folder_path)\n\n output_filename = str(entry_filename) + \"_orion.zip\"\n zip_folder(workspace_dir, os.path.join(zip_folder_path, output_filename))\n print('Zipped files successfully!')\n except Exception as e:\n raise RuntimeError('Zipping files failed: {}'.format(e))\n else:\n try:\n os.remove(os.path.join(workspace_dir, \"params.json\"))\n os.remove(os.path.join(workspace_dir, \"requirements.txt\"))\n except Exception as e:\n raise RuntimeError('Removing files failed: {}'.format(e))",
"title": ""
},
{
"docid": "78aca13d4302fac740b8dddb5e1eb7e6",
"score": "0.5032639",
"text": "def descout_to_input( # noqa: C901 - fxn too complex\n outfile,\n infile,\n objective=\"force\",\n optimizer=\"lsq-exact\",\n header=\"#DESC-generated input file\",\n ftol=1e-2,\n xtol=1e-6,\n gtol=1e-6,\n maxiter=100,\n ):\n f = open(outfile, \"w+\")\n\n f.seek(0)\n\n eq = load(infile)\n try:\n eq0 = eq[-1]\n except TypeError:\n eq0 = eq\n\n f.write(header + \"\\n\")\n\n f.write(\"# global parameters\\n\")\n f.write(\"sym = {:1d} \\n\".format(eq0.sym))\n f.write(\"NFP = {:3d} \\n\".format(int(eq0.NFP)))\n f.write(\"Psi = {:.8f} \\n\".format(eq0.Psi))\n\n f.write(\"\\n# spectral resolution\\n\")\n for key, val in {\n \"L_rad\": \"L\",\n \"M_pol\": \"M\",\n \"N_tor\": \"N\",\n \"L_grid\": \"L_grid\",\n \"M_grid\": \"M_grid\",\n \"N_grid\": \"N_grid\",\n }.items():\n f.write(f\"{key} = {getattr(eq0, val)}\\n\")\n\n f.write(\"\\n\\n# solver tolerances\\n\")\n f.write(f\"ftol = {ftol}\\n\")\n f.write(f\"xtol = {xtol}\\n\")\n f.write(f\"gtol = {gtol}\\n\")\n f.write(f\"maxiter = {maxiter}\\n\")\n\n f.write(\"\\n\\n# solver methods\\n\")\n f.write(f\"optimizer = {optimizer}\\n\")\n f.write(f\"objective = {objective}\\n\")\n f.write(\"spectral_indexing = {}\\n\".format(eq0._spectral_indexing))\n f.write(\"node_pattern = {}\\n\".format(eq0._node_pattern))\n\n f.write(\"\\n# pressure and rotational transform/current profiles\\n\")\n\n if eq0.iota:\n assert (\n eq0.pressure.__class__.__name__ == \"PowerSeriesProfile\"\n and eq0.iota.__class__.__name__ == \"PowerSeriesProfile\"\n ), \"Equilibrium must have power series profiles for ascii io\"\n char = \"i\"\n iseven_pres = int(eq0._pressure.basis.sym == \"even\") + 1\n iseven_iota = int(eq0._iota.basis.sym == \"even\") + 1\n pres_profile = np.zeros((eq0.L + 1,))\n iota_profile = np.zeros((eq0.L + 1,))\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\n iota_profile[: eq0.L + 1 : iseven_iota] = eq0._iota.params\n\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\n for l in idxs:\n f.write(\n \"l: {:3d}\\tp = {:16.8E}\\t{} = {:16.8E}\\n\".format(\n int(l), pres_profile[l], char, iota_profile[l]\n )\n )\n else:\n assert (\n eq0.pressure.__class__.__name__ == \"PowerSeriesProfile\"\n and eq0.current.__class__.__name__ == \"PowerSeriesProfile\"\n ), \"Equilibrium must have power series profiles for ascii io\"\n char = \"c\"\n iseven_pres = int(eq0._pressure.basis.sym == \"even\") + 1\n iseven_curr = int(eq0._current.basis.sym == \"even\") + 1\n pres_profile = np.zeros((eq0.L + 1,))\n curr_profile = np.zeros((eq0.L + 1,))\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\n curr_profile[: eq0.L + 1 : iseven_curr] = eq0._current.params\n\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\n for l in idxs:\n f.write(\n \"l: {:3d}\\tp = {:16.8E}\\t{} = {:16.8E}\\n\".format(\n int(l), pres_profile[l], char, curr_profile[l]\n )\n )\n\n f.write(\"\\n\")\n\n f.write(\"\\n# fixed-boundary surface shape\\n\")\n # boundary paramters\n if eq0.sym:\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\n if abs(eq0.Rb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, eq0.Rb_lmn[k], 0\n )\n )\n for k, (l, m, n) in enumerate(eq0.surface.Z_basis.modes):\n if abs(eq0.Zb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, 0, eq0.Zb_lmn[k]\n )\n )\n else:\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\n if abs(eq0.Rb_lmn[k]) > 1e-8 or abs(eq0.Zb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, eq0.Rb_lmn[k], eq0.Zb_lmn[k]\n )\n )\n\n f.close()",
"title": ""
},
{
"docid": "aac8d9a34c982c21d64ee69aa9b8aada",
"score": "0.50321543",
"text": "def models(self):\r\n # make model directory, removing the old one if necessary\r\n rm_rf(self.cfg.model_dir)\r\n mkdir_p(self.cfg.model_dir)\r\n\r\n # run generator script\r\n gen_script = os.path.join(self._prj_root, 'gen.py')\r\n\r\n if 'PYTHON_MSDSL' in os.environ:\r\n python_name = os.environ['PYTHON_MSDSL']\r\n else:\r\n python_name = which('python')\r\n \r\n call([python_name, gen_script, '-o', self.cfg.model_dir, '--dt', str(self.dt)])",
"title": ""
},
{
"docid": "37e59d1ec3cd22e83b1a98ae5bb15330",
"score": "0.502148",
"text": "def write_model_to_file(self) -> str:\n\n model_file_content = self.interpreter.get_model_file_content()\n\n db_logger.info(f\"Writing {self.model_file_url}\")\n\n with open(self.model_file_location, 'w') as model:\n model.write(model_file_content)\n\n file_name = os.path.basename(self.model_file_location)\n project_dir = self.model_file_location.replace(file_name, '')\n\n helpers.process_includes(\n self.model_file_url, project_dir, self.interpreter\n )\n\n if not self.no_score:\n self.model_file_location = self.setup_protocol(self.score)\n\n return self.model_file_location",
"title": ""
},
{
"docid": "5a4533cb8662178a596b89ed1d73420b",
"score": "0.50117064",
"text": "def test_ImportUFONoSideEffectLO(self): \n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('sm'),False)\n original_all_particles = copy.copy(ufo_model.all_particles)\n original_all_vertices = copy.copy(ufo_model.all_vertices)\n original_all_couplings = copy.copy(ufo_model.all_couplings)\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\n original_all_parameters = copy.copy(ufo_model.all_parameters)\n original_all_orders = copy.copy(ufo_model.all_orders)\n original_all_functions = copy.copy(ufo_model.all_functions)\n\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\n model = ufo2mg5_converter.load_model()\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \n\n self.assertEqual(original_all_particles,ufo_model.all_particles) \n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\n self.assertEqual(original_all_couplings,ufo_model.all_couplings)\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\n self.assertEqual(original_all_orders,ufo_model.all_orders)\n self.assertEqual(original_all_functions,ufo_model.all_functions)",
"title": ""
},
{
"docid": "7e8c2e02b0ee94ddd321ca2db4033cd0",
"score": "0.50004613",
"text": "def exe_dyn_mdl(fv, usr):\n\n exe = os.chdir(prg_path + '/dynamic_mdl-0.1') \n exe = os.getcwd() \n pcs = sbp.Popen([exe + '/a.out', str(fv)], stdout=sbp.PIPE, stderr=sbp.PIPE)\n out, error = pcs.communicate()\n if out:\n print('OK', out)\n if error:\n print('Error', error.strip()) \n if not pcs.poll():\n print(\"Dynamic Module execute finish\")\n p_nme = 'Dynamic'\n dest_pth = fles_dir + usr + '/' + p_nme\n dest_pth = dest_pth + '/' + time.get_date_time_hm()\n dummy_sys_file.crt_dir(dest_pth)\n \"\"\"Creating directory for program execution\"\"\"\n\n \"\"\"Names of the generated files\"\"\"\n fle_name1 = 'sk'\n fle_name2 = 'f_self'\n fle_name3 = 'coefficient'\n \"\"\"full path of the outputs of program\"\"\"\n out_fle1 = exe + '/' + 'sk.dat'\n out_fle2 = exe + '/' + 'fself.dat'\n out_fle3 = exe + '/' + 'coeficiente.dat' \n dummy_sys_file.cpy_file_to_pth_dir(out_fle1, dest_pth, fle_name1)\n dummy_sys_file.cpy_file_to_pth_dir(out_fle2, dest_pth, fle_name2)\n dummy_sys_file.cpy_file_to_pth_dir(out_fle3, dest_pth, fle_name3)",
"title": ""
},
{
"docid": "10bf29315de0dab52975584c75ef2dfb",
"score": "0.49911758",
"text": "def generate_package(self):\n\n # Create a directory (mymodel)\n cwd = Path(self.dir)\n directory=cwd/'csharp_model'\n if (directory).isdir() :\n self.dir = directory\n else:\n self.dir = directory.mkdir()\n\n files = []\n count = 0\n\n for model in self.models:\n\n\n self.generate_component(model)\n\n ext = '' if count == 0 else str(count)\n filename = self.dir/\"%s.cs\"%signature(model)\n\n with open(filename, \"w\") as python_file:\n python_file.write(self.code.encode('utf-8','ignore'))\n files.append(filename)\n\n model.module_name = str(Path(filename).namebase)\n\n count += 1\n\n return files",
"title": ""
},
{
"docid": "22f27813e63fc901b230aaee14b21dfe",
"score": "0.49885112",
"text": "def new_IDP_gen(subj,LUT_txt): #,fix4melviewtxt\n\n\n #remove trailing forward slashes in subject paths\n if subj.endswith(\"/\"):\n subj = subj[:-1]\n\n if not os.path.exists(subj + \"/IDP_files/\"):\n os.makedirs(subj + \"/IDP_files/\")\n\n subjName = subj[subj.rfind(\"/\") + 1 :]\n\n\n # IDP_FC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_FC_dist.txt\")\n # IDP_SC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_SC_dist.txt\")\n # IDP_MELODIC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_MELODIC_SNR.txt\")\n # IDP_MCFLIRT_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_MCFLIRT_disp.txt\")\n # IDP_homotopic_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_homotopic.txt\")\n # new_IDP_list_file = os.path.join(subj + \"/IDP_files/\", \"tvb_new_IDPs.txt\")\n\n new_IDP_file = os.path.join(subj + \"/IDP_files/\", \"tvb_new_IDP.txt\")\n\n\n # IDP_output_files = [IDP_FC_file,IDP_SC_file,IDP_MELODIC_file,IDP_MCFLIRT_file,IDP_homotopic_file]\n\n # for file in IDP_output_files:\n # with open(file, 'w') as fp:\n # pass\n\n with open(new_IDP_file, 'w') as fp:\n line = '\\t'.join([\"short\",\"num\",\"category\",\"num_in_cat\",\"long\",\"unit\",\"dtype\",\"description\",\"value\"])\n\n fp.write(line)\n\n\n\n fix4melviewtxt=\"\"\n\n FC_distribution(subj)\n SC_distribution(subj)\n MELODIC_SNR(subj,fix4melviewtxt)\n MCFLIRT_displacement(subj) \n\n homotopic(subj,LUT_txt)\n \n\n\n\n\n #TODO TL/distance, or ts.txt",
"title": ""
},
{
"docid": "cde366ecb10bf14e7b028bb5eeb70a26",
"score": "0.497974",
"text": "def produce_layout(eoi, graph):\n\n subprocess.run((\"/usr/bin/dot\", \"{}\".format(graph.filename), \"-o\", \n \"{}/layout.dot\".format(eoi)))",
"title": ""
},
{
"docid": "42db4bc248db8077aaa00be0cc702c70",
"score": "0.4975401",
"text": "def load_model_output(text):\r\n # uncomment when working in linux and remove subsequent two lines\r\n # nlp = spacy.load(\"../models/quick-spacy/\")\r\n model_path = Path(__file__).parent.absolute() / \"models/quick-spacy/\"\r\n nlp = spacy.load(model_path)\r\n doc = nlp(text)\r\n sentence = [ent.text for ent in doc.ents]\r\n labels = [ent.label_ for ent in doc.ents]\r\n return sentence, labels",
"title": ""
},
{
"docid": "d71b59de5a8a54084e51dfb95444b719",
"score": "0.49555877",
"text": "def generate_theory(\n grammar,\n config,\n theory_op_file,\n theorem_prover,\n max_depth,\n min_depth\n):\n # Get Theorem Prover Config and initialize Theorem Prover\n theorem_prover_config = TheoremProverConfig(\n grammar, **config[\"theory\"][\"theorem_prover\"]\n )\n\n num_examples = config[\"theory\"][\"num_examples\"]\n min_num_positive_examples = config[\"theory\"][\"min_num_positive_examples\"]\n max_num_negative_examples = num_examples - min_num_positive_examples\n statement_types = config[\"theory\"][\"statement_types_per_example\"]\n assertion_start_symbol = config[\"assertion\"][\"start_symbol\"]\n example_id_prefix = config.get(\"example_id_prefix\", \"\")\n\n # Generate examples for every required type of statement (Start Symbol type)\n num_true_labels = 0\n num_false_labels = 0\n curr_num_examples = 0\n progress_tracker = tqdm(total=num_examples)\n progress_tracker.set_description(desc=\"Generating Examples...\")\n while (\n curr_num_examples < num_examples and num_true_labels < min_num_positive_examples\n ):\n example = generate_random_example(\n curr_num_examples + 1,\n example_id_prefix,\n grammar,\n theorem_prover_config,\n statement_types,\n assertion_start_symbol,\n theorem_prover,\n max_depth,\n min_depth\n )\n if example is not None:\n if example.theory_assertion_instance.label:\n num_true_labels += 1\n else:\n if num_false_labels == max_num_negative_examples:\n continue\n else:\n num_false_labels += 1\n json.dump(example.to_json(), theory_op_file)\n theory_op_file.write(\"\\n\")\n curr_num_examples += 1\n progress_tracker.update()\n progress_tracker.close()\n print(f\"Generated {curr_num_examples} examples.\")\n print(f\" No. with True label: {num_true_labels}\")\n print(f\" No. with False label: {num_false_labels}\")",
"title": ""
},
{
"docid": "6abcb45148b5a15227491491ca106d5b",
"score": "0.49505714",
"text": "def polish_and_save(model_filename,\n save_filename='',\n suffix='.polished',\n *args,\n **kwargs):\n\n save_filename = save_filename or model_filename.replace(\n '.onnx', suffix + '.onnx')\n\n model = onnx.load(model_filename)\n model = polish_model(model, *args, **kwargs)\n onnx.save(model, save_filename)\n logger.info('polished model saved to: %s', save_filename)\n return save_filename",
"title": ""
},
{
"docid": "c70ae932b14d6c0ac6415942b0994419",
"score": "0.49496683",
"text": "def output_perplexity(task_letter):\n output_file = open(\"output/group02.perplexity\" + task_letter, mode=\"w\")\n word_dictionary = get_word_dict()\n test_sentences = open(\"data/sentences.eval\", mode=\"r\")\n\n for test_sentence in test_sentences.readlines():\n # predicted_softmax_vecs: 30 X 20.000 X 1 Output Vector with softmax probabilities\n predicted_softmax_vecs = eval_neural_network(test_sentence)\n perp = perplexity(predicted_softmax_vecs, test_sentence, word_dictionary)\n output_file.write(str(perp) + \"\\n\")\n\n output_file.close()",
"title": ""
},
{
"docid": "a208cbaa76b296741b5c20e88440925f",
"score": "0.49453568",
"text": "def __init__(self,\n fileName,\n realFileName=None,\n prequelFileName=None,\n preErrorMessages=(), #TODO: check the type\n readFileLater=False,\n fillImportBoxLater=False,\n parseFileLater=False,\n noSymbolChecking=False,\n allowedFeatures=(),\n recognizeUSEOCLNativeModelDefinition=False):\n #type: (Text, Optional[Text], Optional[Text], List[Any], bool, bool, bool, bool, List[Text], bool) -> None\n\n\n # if readFileLater or fillImportBoxLater or parseFileLater:\n # assert finalizeLater\n\n # Create an empty model\n # Not to be moved after super\n # This should be done in all case so that\n # the model attribute always exist even if there\n # are some error in reading the file\n self.model = self.emptyModel() # type: Model\n\n\n\n\n # Call the super class, read the file or not\n try:\n # This can raise an exception for instance if\n # there is a problem reading the file\n super(ModelSourceFile, self).__init__(\n fileName=fileName,\n realFileName=realFileName,\n prequelFileName=prequelFileName,\n preErrorMessages=preErrorMessages,\n doNotReadFiles=readFileLater,\n allowedFeatures=allowedFeatures\n )\n except FatalError:\n pass # an error as already been registered\n\n from modelscripts.megamodels import Megamodel\n Megamodel.registerSource(self)\n Megamodel.registerModel(self.model)\n\n\n # Backward link\n self.model.source=self\n\n # Link issue box\n self.model._issueBox.addParent(self._issueBox)\n\n # Source to ModelElement Mapping\n self._modelMapping=_ModelSourceMapping()\n\n\n # Create first an empty ImportBox.\n self.importBox=ImportBox(self)\n\n # Then fill it by reading megamodel statements,\n # unless specified.\n try:\n if not fillImportBoxLater:\n parseToFillImportBox(\n self,\n noSymbolChecking,\n recognizeUSEOCLNativeModelDefinition)\n\n if not parseFileLater:\n self.parseToFillModel()\n self.finalize()\n\n except FatalError:\n pass # nothing to do, the issue has been registered",
"title": ""
},
{
"docid": "2e5aa18489143959b9fdd129d63b51d0",
"score": "0.49399826",
"text": "def preflight(self):\n self.params[\"translations\"][\"mgf_input_file\"] = os.path.join(\n self.params[\"input_dir_path\"], self.params[\"input_file\"]\n )\n self.params[\"translations\"][\"output_file_incl_path\"] = os.path.join(\n self.params[\"output_dir_path\"], self.params[\"output_file\"]\n )\n self.param_file_name = os.path.join(\n self.params[\"translations\"][\"output_file_incl_path\"].strip(\".csv\")\n + \"pipi_params.def\"\n )\n self.created_tmp_files.append(self.param_file_name)\n\n # pprint.pprint(self.params['translations']['_grouped_by_translated_key'])\n # pprint.pprint(self.params)\n # sys.exit(1)\n self.params_to_write = {\n \"version\": self.META_INFO[\"version\"],\n \"output_percolator_input\": 1,\n \"mod10\": \"0.0@X?\",\n \"pepNterm\": 0.0,\n \"pepCterm\": 0.0,\n \"proNterm\": 0.0,\n \"proCterm\": 0.0,\n }\n symbols = [\"~\", \"!\", \"%\", \"^\", \"&\", \"*\", \"+\", \"<\", \">\"]\n for x, element in enumerate(symbols):\n self.params_to_write[\"mod0{0}\".format(x + 1)] = \"0.0@X{0}\".format(element)\n for aa in \"ACDEFGHIKLMNOPQRSTUVWYnc\":\n self.params_to_write[aa] = 0\n\n write_exclusion_list = [\n # 'label',\n \"-Xmx\",\n \"frag_mass_tolerance_unit\",\n \"base_mz\",\n # 'header_translations',\n # 'validation_score_field'\n ]\n\n # additional_15N_modifications = []\n # if self.params['translations']['label'] == '15N':\n # for aminoacid, N15_Diff in ursgal.ukb.DICT_15N_DIFF.items():\n # existing = False\n # for mod_dict in self.params['mods']['fix']:\n # if aminoacid == mod_dict['aa']:\n # mod_dict['mass'] += N15_Diff\n # mod_dict['name'] += '_15N_{0}'.format(aminoacid)\n # existing = True\n # if existing == True:\n # continue\n # else:\n # self.params['mods']['fix'].append(\n # {\n # 'aa': aminoacid,\n # 'mass': N15_Diff,\n # 'name': '_15N_{0}'.format(aminoacid),\n # 'pos': 'any',\n # }\n # )\n\n if self.params[\"translations\"][\"frag_mass_tolerance_unit\"] == \"ppm\":\n self.params[\"translations\"][\"_grouped_by_translated_key\"][\n \"ms2_tolerance\"\n ] = {\n \"frag_mass_tolerance\": ursgal.ucore.convert_ppm_to_dalton(\n self.params[\"translations\"][\"frag_mass_tolerance\"],\n base_mz=self.params[\"base_mz\"],\n )\n }\n\n for pipi_param_name in self.params[\"translations\"][\n \"_grouped_by_translated_key\"\n ].keys():\n for ursgal_param_name, param_value in self.params[\"translations\"][\n \"_grouped_by_translated_key\"\n ][pipi_param_name].items():\n if pipi_param_name in write_exclusion_list:\n continue\n elif pipi_param_name == \"frag_clear_mz_range\":\n min_mz, max_mz = param_value\n self.params_to_write[\"min_clear_mz\"] = min_mz\n self.params_to_write[\"max_clear_mz\"] = max_mz\n elif type(pipi_param_name) is tuple:\n for pn in pipi_param_name:\n self.params_to_write[pn] = param_value\n elif pipi_param_name == \"enzyme\":\n enz, site, aa, inh = param_value.split(\";\")\n self.params_to_write[\"enzyme\"] = \"{0} {1} {2} {3}\".format(\n enz, site, aa, inh\n )\n elif pipi_param_name == \"modifications\":\n \"\"\"\n # specify additional mass and amino acid. DO NOT change the last character.\n # maximum number is 10\n # empty entries must start with 0.0\n mod01 = 79.966331@S~ # Phosphorylation\n mod02 = 79.966331@T! # Phosphorylation\n mod03 = 79.966331@Y% # Phosphorylation\n mod04 = 42.010565@K^ # Acetylation\n mod05 = 15.994915@M& # Oxidation\n mod06 = 14.015650@K* # Methylation\n mod07 = 0.0@X+\n mod08 = 0.0@X<\n mod09 = 0.0@X>\n mod10 = 0.0@X?\n \"\"\"\n n = 0\n for mod_dict in self.params[\"mods\"][\"opt\"]:\n \"\"\"\n {'_id': 0,\n 'aa': '*',\n 'composition': {'C': 2, 'H': 2, 'O': 1},\n 'id': '1',\n 'mass': 42.010565,\n 'name': 'Acetyl',\n 'org': '*,opt,Prot-N-term,Acetyl',\n 'pos': 'Prot-N-term',\n 'unimod': True},\n \"\"\"\n if mod_dict[\"pos\"] == \"Prot-N-term\":\n self.params_to_write[\"proNterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"Prot-C-term\":\n self.params_to_write[\"proCterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"N-term\":\n self.params_to_write[\"pepNterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"C-term\":\n self.params_to_write[\"pepCterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"any\":\n pass\n else:\n print(\n \"\"\"\n Unknown positional argument for given modification:\n {0}\n PIPI cannot deal with this, please use one of the follwing:\n any, Prot-N-term, Prot-C-term, N-term, C-term\n \"\"\".format(\n mod_dict[\"org\"]\n )\n )\n sys.exit(1)\n n += 1\n assert (\n n <= 10\n ), \"\"\"\n A maximum of 10 optional modifications is allowed in PIPI.\n You specified more than 10.\n \"\"\"\n if n < 10:\n mod_n = \"mod0{0}\".format(n)\n elif n == 10:\n mod_n = \"mod{0}\".format(n)\n else:\n print(\n \"\"\"\n A maximum of 10 optional modifications is allowed in PIPI.\n You specified more than 10.\n \"\"\"\n )\n sys.exit(1)\n self.params_to_write[mod_n] = \"{0}@{1}{2}\".format(\n mod_dict[\"mass\"], mod_dict[\"aa\"], symbols[n - 1]\n )\n\n for mod_dict in self.params[\"mods\"][\"fix\"]:\n if \"N-term\" in mod_dict[\"pos\"]:\n self.params_to_write[\"n\"] = mod_dict[\"mass\"]\n elif \"C-term\" in mod_dict[\"pos\"]:\n self.params_to_write[\"c\"] = mod_dict[\"mass\"]\n else:\n self.params_to_write[mod_dict[\"aa\"]] = mod_dict[\"mass\"]\n else:\n self.params_to_write[pipi_param_name] = param_value\n self.write_params_file()\n\n self.params[\"command_list\"] = [\n \"java\",\n \"-Xmx{0}\".format(\n self.params[\"translations\"][\"_grouped_by_translated_key\"][\"-Xmx\"][\n \"-xmx\"\n ]\n ),\n \"-jar\",\n self.exe,\n self.param_file_name,\n self.params[\"translations\"][\"mgf_input_file\"],\n ]\n\n return self.params",
"title": ""
},
{
"docid": "45b7bcbb2c25384bb8bd0c4ba1275413",
"score": "0.49382108",
"text": "def gen_pov(ldraw_path, pov_path):\n model, parts = get_model(ldraw_path)\n\n with open(pov_path, \"w\") as pov_file:\n pov_file.write(POV_HEADER + \"\\n\")\n writer = POVRayWriter(parts, pov_file)\n writer.write(model)\n pov_file.write(POV_TRAILER + \"\\n\")",
"title": ""
},
{
"docid": "b0f8306823fe036ca17dd5e9585dab03",
"score": "0.49359792",
"text": "def django_run(target, opt=\"XTAL\"):\r\n # Set up the OpenBaebel conversion modules\r\n sdconv = OBConversion()\r\n ligref = OBMol()\r\n # Define the residues and the proteisn to analyse\r\n if os.path.isfile(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')):\r\n \tres_d = [trans_res(x) for x in ast.literal_eval(open(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')).read())[target.title].split()]\r\n print res_d\r\n# Molecules\r\n\t # Now read in the ligand\r\n plif_method = PlifMethod()\r\n plif_method.text= \"PYPLIF\"\r\n feature_list = [\"POLAR\",\"FACE\",\"EDGE\",\"ACCEPTOR\",\"DONOR\",\"NEGATIVE\",\"POSITIVE\"]\r\n try:\r\n plif_method.validate_unique()\r\n plif_method.save()\r\n except ValidationError:\r\n \t plif_method = PlifMethod.objects.get(text=\"PYPLIF\")\r\n out_d = {}\r\n counter = 0\r\n# Create a file for the protein\r\n t = tempfile.NamedTemporaryFile(suffix=\".pdb\",delete=False)\r\n my_prot = Protein.objects.get(code=target.title+\"TEMP\")\r\n t.write(my_prot.pdb_info.name)\r\n t.close()\r\n protref = read_prot(t.name, res_d)\r\n t = tempfile.NamedTemporaryFile(suffix=\".sdf\",delete=False)\r\n t.close()\r\n sdconv.SetInFormat(\"sdf\")\r\n if opt == \"XTAL\":\r\n mols = Molecule.objects.exclude(prot_id__code__contains=target.title).filter(prot_id__target_id=target)\r\n elif opt == \"LLOOMMPPAA\":\r\n \t mols = []\r\n \t sps = SynthPoint.objects.filter(target_id=target)\r\n \t for s in sps:\r\n \t \t mols.extend([m for m in s.mol_id.all()])\r\n else:\r\n \t print \"UNKNOWN OPTION\"\r\n \t return\r\n for dj_mol in mols:\r\n \t out_sd = Chem.SDWriter(t.name)\r\n \t out_sd.write(Chem.MolFromMolBlock(str(dj_mol.sdf_info)))\r\n \t out_sd.close()\r\n \t sdconv.ReadFile(ligref, t.name)\r\n \t # Now make the new plif\r\n \t new_plif = Plif()\r\n \t new_plif.mol_id = dj_mol\r\n \t new_plif.prot_id = my_prot\r\n \t new_plif.method_id = plif_method\r\n \t try:\r\n \t \t new_plif.validate_unique()\r\n \t \t new_plif.save()\r\n \t except ValidationError:\r\n \t \t new_plif = Plif.objects.get(mol_id=dj_mol,prot_id=my_prot,method_id=plif_method)\r\n \t lig_name = ligref.GetTitle().strip(\",\")\r\n \t prot_name = lig_name.split(\"_\")[0]\r\n \t ligref.AddHydrogens()\r\n \t counter +=1\r\n \t refresdict = pp.getresiduedict(protref, res_d)\r\n \t new_d = get_fp(protref,ligref, res_d)\r\n \t for res in new_d:\r\n \t \t new_res = PlifRes()\r\n \t \t new_res.res_name = res[:3]\r\n \t \t new_res.res_num = int(res[3:])\r\n \t \t new_res.prot_id = my_prot\r\n \t \t try:\r\n \t \t \t new_res.validate_unique()\r\n \t \t \t new_res.save()\r\n \t \t except ValidationError:\r\n \t \t \t new_res = PlifRes.objects.get(res_name=res[:3],res_num=int(res[3:]),prot_id=my_prot)\r\n \t \t new_plif.res_id.add(new_res)\r\n \t \t for bit_num, bit in enumerate(new_d[res]):\r\n \t \t \t new_bit = PlifBit()\r\n \t \t \t new_bit.feature = feature_list[bit_num]\r\n \t \t \t new_bit.method_id = plif_method\r\n \t \t \t new_bit.res_id = new_res\r\n \t \t \t try:\r\n \t \t \t \t new_bit.validate_unique()\r\n \t \t \t \t new_bit.save()\r\n \t \t \t \t my_fun(dj_mol,new_bit,new_plif,bit)\r\n \t \t \t except ValidationError:\r\n\t\t\t\t\t\t\t\t\t\tnew_bit = PlifBit.objects.get(feature=feature_list[bit_num],method_id=plif_method,res_id=new_res)\r\n\t\t\t\t\t\t\t\t\t\tnew_bit.save()\r\n\t\t\t\t\t\t\t\t\t\tnew_plif.bit_id.add(new_bit)\r\n\t\t\t\t\t\t\t\t\t\tmy_fun(dj_mol,new_bit,new_plif,bit)\r\n\t\t\t\t\t\t\t\t\t\t\r\n \t ligref = OBMol()\r\n \t notatend = sdconv.Read(ligref)",
"title": ""
},
{
"docid": "2cea0abf4708ac80030bc376d139c8b2",
"score": "0.49342123",
"text": "def writeLP(item_count, capacity, items):\n lpfile = checkpath + '/knapsack.lp'\n f = open(lpfile, 'w')\n f.write(\"Maximize\\n\")\n f.write(\"Objective:\\n\")\n cns = \"\"\n cnt = 0\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n cns = cns + \" + \" + str(items[i].value) + \" \" + xVar\n cnt = cnt + 1\n if cnt % 5 == 4:\n f.write(cns + \"\\n\")\n cns = \"\"\n cnt = 0\n f.write(cns + \"\\n\")\n\n f.write(\"Subject To\\n\")\n cns = \"\"\n cnt = 0\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n cns = cns + \" + \" + str(items[i].weight) + \" \" + xVar\n cnt = cnt + 1\n if cnt % 5 == 4:\n f.write(cns + \"\\n\")\n cns = \"\"\n cnt = 0\n f.write(cns + \" <= \" + str(capacity) + \"\\n\")\n\n f.write(\"Binaries\\n\")\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n f.write(xVar + \"\\n\")\n f.write(\"End\")\n f.close()",
"title": ""
},
{
"docid": "aa711305cbfff319951e389f1f16131b",
"score": "0.49325135",
"text": "def createModelHandler(self):\n className = self.model.name.upper()\n filename = self.model.name + \".pk\"\n\n return f\"\"\"\n# Copyright 2017 Battelle Energy Alliance, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport sys\nimport pickle\nimport os\nfrom pythonfmu.fmi2slave import Fmi2Type, Fmi2Slave, Fmi2Causality, Fmi2Variability, Integer, Real, Boolean, String\n\nclass {className}(Fmi2Slave):\n #\n # RAVEN (raven.inl.gov) Model-based Python-driven simulator\n #\n author = \"RAVEN Team\"\n description = \"RAVEN Model-based Python-driven simulator\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.inputVariables = {self.inputVars}\n self.outputVariables = {self.outVars}\n # set path to raven and the serialized model\n self.raven_path = r\"{self.ravenDir}\"\n # model_path is by default the path to this model that is exported as FMU (serialized). It is stored in the resource folder\n self.model_path = self.resources + \"/\" + \"{filename}\"\n #os.path.sep\n sys.path.append(self.model_path)\n # this flag activates the initialization at the begin of the solve\n self.initialized = False\n # register raven_path variables if needed to be changed\n self.register_variable(String(\"raven_path\", causality=Fmi2Causality.parameter, variability=Fmi2Variability.tunable))\n # register input variables\n for var in self.inputVariables:\n # set var\n self.__dict__[var] = 0.0\n self.register_variable(Real(var, causality=Fmi2Causality.input))\n for var in self.outputVariables:\n # set var\n self.__dict__[var] = 0.0\n self.register_variable(Real(var, causality=Fmi2Causality.output))\n\n def setup_experiment(self, start_time: float):\n self.start_time = start_time\n if not self.initialized:\n sys.path.append(self.raven_path)\n # find the RAVEN framework\n if os.path.isdir(os.path.join(self.raven_path,\"ravenframework\")):\n # we import the Driver to load the RAVEN enviroment for the un-pickling\n try:\n import ravenframework.Driver\n except RuntimeError as ae:\n # we try to add the framework directory\n raise RuntimeError(\"Importing or RAVEN failed with error:\" +str(ae))\n else:\n print(\"framework not found in\",self.raven_path)\n # de-serialize the model\n print(\"model_path\", self.model_path)\n self.model = pickle.load(open(self.model_path, mode='rb'))\n self.initialized = True\n\n def do_step(self, current_time: float, step_size: float) -> bool:\n request = dict()\n for var in self.inputVariables:\n request[var] = self.__dict__[var]\n request['current_time'] = current_time\n request['step_size'] = step_size\n\n return_var = self.model.{self.executeMethod}(request)\n outs = return_var if isinstance(return_var,dict) else return_var[{self.indexReturnDict}]\n\n for var in outs:\n self.__dict__[var] = outs[var]\n return True\n\n\"\"\"",
"title": ""
},
{
"docid": "bfc1b2a35b7e5fd939b8a36de23ec2a2",
"score": "0.49318784",
"text": "def buildSolverModel(self, lp):\n log.debug(\"create the glpk model\")\n prob = glpk.glp_create_prob()\n glpk.glp_set_prob_name(prob, lp.name)\n log.debug(\"set the sense of the problem\")\n if lp.sense == constants.LpMaximize:\n glpk.glp_set_obj_dir(prob, glpk.GLP_MAX)\n log.debug(\"add the constraints to the problem\")\n glpk.glp_add_rows(prob, len(list(lp.constraints.keys())))\n for i, v in enumerate(lp.constraints.items(), start=1):\n name, constraint = v\n glpk.glp_set_row_name(prob, i, name)\n if constraint.sense == constants.LpConstraintLE:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_UP, 0.0, -constraint.constant\n )\n elif constraint.sense == constants.LpConstraintGE:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_LO, -constraint.constant, 0.0\n )\n elif constraint.sense == constants.LpConstraintEQ:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_FX, -constraint.constant, -constraint.constant\n )\n else:\n raise PulpSolverError(\"Detected an invalid constraint type\")\n constraint.glpk_index = i\n log.debug(\"add the variables to the problem\")\n glpk.glp_add_cols(prob, len(lp.variables()))\n for j, var in enumerate(lp.variables(), start=1):\n glpk.glp_set_col_name(prob, j, var.name)\n lb = 0.0\n ub = 0.0\n t = glpk.GLP_FR\n if not var.lowBound is None:\n lb = var.lowBound\n t = glpk.GLP_LO\n if not var.upBound is None:\n ub = var.upBound\n t = glpk.GLP_UP\n if not var.upBound is None and not var.lowBound is None:\n if ub == lb:\n t = glpk.GLP_FX\n else:\n t = glpk.GLP_DB\n glpk.glp_set_col_bnds(prob, j, t, lb, ub)\n if var.cat == constants.LpInteger:\n glpk.glp_set_col_kind(prob, j, glpk.GLP_IV)\n assert glpk.glp_get_col_kind(prob, j) == glpk.GLP_IV\n var.glpk_index = j\n log.debug(\"set the objective function\")\n for var in lp.variables():\n value = lp.objective.get(var)\n if value:\n glpk.glp_set_obj_coef(prob, var.glpk_index, value)\n log.debug(\"set the problem matrix\")\n for constraint in lp.constraints.values():\n l = len(list(constraint.items()))\n ind = glpk.intArray(l + 1)\n val = glpk.doubleArray(l + 1)\n for j, v in enumerate(constraint.items(), start=1):\n var, value = v\n ind[j] = var.glpk_index\n val[j] = value\n glpk.glp_set_mat_row(prob, constraint.glpk_index, l, ind, val)\n lp.solverModel = prob\n # glpk.glp_write_lp(prob, None, \"glpk.lp\")",
"title": ""
},
{
"docid": "90dd741a4cce0ae2de8a4b272b8dd9f3",
"score": "0.4929237",
"text": "def to_modflow(model, fname):\n try:\n import flopy\n except ImportError as e:\n raise type(e)(str(e) + \", modflow output not possible!\")\n # For modflow we want to create a new folder instead of only a file. The folder name is the base\n # name of the passed filename\n realization_dir = pathlib.Path(fname).parent\n runname = pathlib.Path(fname).name\n mfdir = realization_dir / 'MODFLOW'\n mfdir.mkdir(parents=True, exist_ok=True)\n mfname = str(mfdir / runname)\n\n # Assign name and create modflow model object\n mf = flopy.modflow.Modflow(mfname, exe_name='mf2005')\n\n # Create the discretization object\n ztop = model.grid.z0 + model.grid.lz\n zbot = model.grid.z0\n botm = np.linspace(ztop, zbot, model.grid.nz + 1)\n dis = flopy.modflow.ModflowDis(mf, model.grid.nz, model.grid.nx, model.grid.ny, \n delr=model.grid.dx, delc=model.grid.dy,\n top=ztop, botm=botm[1:])\n\n # Variables for the BAS package\n ibound = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.int32)\n ibound[:, :, 0] = -1\n ibound[:, :, -1] = -1\n\n strt = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.float32)\n strt[:, :, 0] = model.flowtrans['hin'][0]\n strt[:, :, -1] = model.flowtrans['hout'][0]\n\n bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=strt)\n\n # Assign hydraulic conductivity\n hyvr_hk = np.transpose(model.data['k_iso'], (2, 0, 1))\n hyvr_layvka = 1 # VKA dataset is ratio of horizontal K\n if 'anirat' in model.data.keys():\n hyvr_vka = np.transpose(model.data['anirat'], (2, 0, 1))\n\n # Add LPF package to the MODFLOW model\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\n hk=hyvr_hk, # Horizontal hydraulic conductivity\n layvka=hyvr_layvka, # Flag for each layer of anisotropic ratio\n vka=hyvr_vka) # Anisotropy ratios.\n else:\n # Add LPF package to the MODFLOW model\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\n hk=hyvr_hk) # Horizontal hydraulic conductivity\n\n oc = flopy.modflow.ModflowOc(mf) # Add OC package to the MODFLOW model\n pcg = flopy.modflow.ModflowPcg(mf) # Add PCG package to the MODFLOW model\n mf.write_input() # Write the MODFLOW model input files",
"title": ""
},
{
"docid": "486cabbe9fb6d10c75ea06f7ee5cf1a7",
"score": "0.49249116",
"text": "def write_production_mdp(prefix, rep, temp):\n\n fn = prefix + '_%d.mdp'%rep\n fout = open(fn, 'w')\n fout.write(\"\"\"title = prod_GBSA\ncpp = /lib/cpp\ninclude = -I../top\ndefine = \nintegrator = sd\ndt = 0.002\nnsteps = 25000000 ; 50 ns\nnstxout = 500000 ; every 1 ns \nnstvout = 500000\nnstlog = 5000 ; every 10 ps\nnstxtcout = 5000 ; every 10 ps\nnstenergy = 5000 ; every 10 ps\n\n\nimplicit_solvent = GBSA\ngb_algorithm = OBC\nnstgbradii = 1\ngb_epsilon_solvent = 80.0\nsa_algorithm = Ace-approximation\n\ncomm_mode = ANGULAR\n\nrgbradii = 0.9\ncoulombtype = Cut-off\nrvdw = 0.9\nrlist = 0.9\nrcoulomb = 0.9\n\n; CONSTRAINTS\nconstraints = hbonds\nconstraint_algorithm = LINCS\n\n; other stuff\nbd_fric = 1.0\nnstcomm = 10\ncomm_grps = System\nxtc_grps = System\nenergygrps = System\nnstlist = 10\nns_type = grid\ntc-grps = System \ntau_t = 0.0109\n\nref_t = %d \ncompressibility = 4.5e-5\nref_p = 1.0\ngen_vel = yes\ngen_temp = %d \ngen_seed = 1729\npbc = no\"\"\"%(temp, temp))\n\n fout.close()\n\n return fn",
"title": ""
},
{
"docid": "6bfcf30894e9237e9a45c1cbf4059801",
"score": "0.49182045",
"text": "def write_aloha_routines(self):\n \n\n self.aloha_model.add_Lorentz_object(self.model.get('lorentz'))\n self.aloha_model.compute_subset(self.wanted_lorentz)\n # Write out the aloha routines in Python\n aloha_routines = []\n \n # First add the default external wavefunction routines\n wavefunction_routines = open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read() \n open(pjoin(self.dir_path,'wavefunctions.py'),'w').write(\n 'from __future__ import division\\n'+wavefunction_routines)\n\n #aloha_routines.append(open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read())\n\n # Now write the process-depenent Feynman rules ones\n for routine in self.aloha_model.values():\n aloha_routines.append(routine.write(output_dir = None, \n mode='mg5',\n language = 'Python'))\n\n for routine in self.aloha_model.external_routines:\n aloha_routines.append(\n open(self.aloha_model.locate_external(routine, 'Python')).read())\n\n # Now collect imports\n python_imports = []\n new_aloha_routines = []\n for aloha_routine in aloha_routines:\n new_aloha_routine = []\n for line in aloha_routine.split('\\n'):\n if any(line.startswith(token) for token in ['from','import']):\n if line not in python_imports:\n python_imports.append(line)\n else:\n new_aloha_routine.append(line)\n new_aloha_routines.append('\\n'.join(new_aloha_routine))\n aloha_routines = new_aloha_routines\n \n # Veto some imports\n vetoed_imports = ['import aloha.template_files.wavefunctions as wavefunctions']\n python_imports = [pi for pi in python_imports if pi not in vetoed_imports]\n python_imports.insert(0, 'import wavefunctions')\n\n aloha_output = open(pjoin(self.dir_path,'aloha_methods.py'),'w')\n aloha_output.write('from __future__ import division\\n')\n # Write imports\n aloha_output.write('\\n'.join(python_imports))\n aloha_output.write('\\n'*2)\n\n # Write routines\n aloha_output.write('\\n'.join(aloha_routines))\n\n aloha_output.close()",
"title": ""
},
{
"docid": "f73793368d062ed8010c45c9ae955d77",
"score": "0.49119902",
"text": "def run(self):\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\n self._openScriptFile()\n self.scriptf.write(\"from phycas import *\\n\\n\")\n\n if self.opts.seed > 0:\n self.scriptf.write('setMasterSeed(%d)\\n\\n' % self.opts.seed)\n\n if self.opts.model == 'jc':\n self.model_jc(0)\n elif self.opts.model == 'jc+i':\n self.model_jc(1)\n elif self.opts.model == 'jc+g':\n self.model_jc(2)\n elif self.opts.model == 'jc+i+g':\n self.model_jc(3)\n elif self.opts.model == 'hky':\n self.model_hky(0)\n elif self.opts.model == 'hky+i':\n self.model_hky(1)\n elif self.opts.model == 'hky+g':\n self.model_hky(2)\n elif self.opts.model == 'hky+i+g':\n self.model_hky(3)\n elif self.opts.model == 'gtr':\n self.model_gtr(0)\n elif self.opts.model == 'gtr+i':\n self.model_gtr(1)\n elif self.opts.model == 'gtr+g':\n self.model_gtr(2)\n elif self.opts.model == 'gtr+i+g':\n self.model_gtr(3)\n elif self.opts.model == 'codon':\n self.model_codon()\n\n self.model_edgelen()\n\n if self.opts.datafile is None:\n self.scriptf.write(\"mcmc.data_source = None\\n\")\n elif self.opts.datafile == 'sample.nex':\n self.saveSampleData()\n self.scriptf.write(\"mcmc.data_source = '%s'\\n\\n\" % self.opts.datafile)\n else:\n self.scriptf.write(\"mcmc.data_source = '%s'\\n\\n\" % self.opts.datafile)\n\n if self.opts.analysis == 'mcmc':\n self.mcmc_analysis()\n self.sump_analysis()\n self.sumt_analysis()\n if self.opts.analysis == 'poly':\n self.polytomy_analysis()\n elif self.opts.analysis == 'cpo':\n self.cpo_analysis()\n #elif self.opts.analysis == 'idr':\n # self.idr_analysis()\n elif self.opts.analysis == 'ss':\n self.steppingstone_analysis()\n\n self._closeScriptFile()",
"title": ""
},
{
"docid": "c3e42d57ee79ba479dae9b3b96b226d2",
"score": "0.49086118",
"text": "def build_model(out: ModelOutput = ModelOutput(\"malt/swemalt-1.7.2.mco\"),\n _maltjar: Binary = Binary(\"[malt.jar]\")):\n out.download(\"http://maltparser.org/mco/swedish_parser/swemalt-1.7.2.mco\")",
"title": ""
},
{
"docid": "e9a026d51eff7c21f70c849e56481fa5",
"score": "0.49045718",
"text": "def to_alien(self, file_pointer, format='opb', comments=None):\n\n if self.atms and format == 'smt':\n raise NotImplementedError('SMT-LIB2 does not support PB constraints directly; you may want to use Z3\\'s API instead')\n\n cchars = {'lp': '\\\\', 'opb': '*', 'smt': ';'}\n\n # saving formula's internal comments\n for c in self.comments:\n print(cchars[format], c, file=file_pointer)\n\n # saving externally specified comments\n if comments:\n for c in comments:\n print(cchars[format], c, file=file_pointer)\n\n # normalized soft clauses\n soft, hard = [], []\n topv = self.nv + 1\n for cl in self.soft:\n if len(cl) == 1:\n soft.append(cl)\n else:\n hard.append([topv] + cl)\n soft.append([topv])\n topv += 1\n\n if format == 'opb':\n print('* #variable= {0} #constraint= {1}'.format(self.nv, len(self.hard) + len(hard)),\n file=file_pointer)\n print('min:',\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\n ';', file=file_pointer)\n elif format == 'lp':\n print('Minimize', file=file_pointer)\n print('obj:',\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\n file=file_pointer)\n print('Subject To', file=file_pointer)\n elif format == 'smt':\n for v in range(1, self.nv + 1):\n print('(declare-fun x{0} () Bool)'.format(v), file=file_pointer)\n\n for i, cl in enumerate(self.hard + hard, 1):\n line, neg = [], 0\n for l in cl:\n if l > 0:\n if format == 'smt':\n line.append('x{0}'.format(l))\n else:\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', l))\n else:\n if format == 'smt':\n line.append('(not x{0})'.format(-l))\n else:\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', -l))\n neg += 1\n\n if format == 'smt':\n print('(assert (or {0}))'.format(' '.join(line)), file=file_pointer)\n else:\n print('{0}{1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}: '.format(i),\n ' '.join(l for l in line),\n 1 - neg, ';' if format == 'opb' else ''),\n file=file_pointer)\n\n for i, am in enumerate(self.atms, len(self.hard) + len(hard) + 1):\n line, neg = [], 0\n for l in am[0]:\n if l > 0:\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', l))\n neg += 1\n else:\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', -l))\n\n print('{0} {1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}:'.format(i),\n ' '.join(l for l in line),\n len(am[0]) - am[1] - neg, ';' if format == 'opb' else ''),\n file=file_pointer)\n\n if format == 'lp':\n print('Bounds', file=file_pointer)\n for v in range(1, topv):\n print('0 <= x{0} <= 1'.format(v), file=file_pointer)\n print('Binary', file=file_pointer)\n for v in range(1, topv):\n print('x{0}'.format(v), file=file_pointer)\n print('End', file=file_pointer)\n elif format == 'smt':\n for cl, w in zip(soft, self.wght):\n l = 'x{0}'.format(cl[0]) if cl[0] > 0 else '(not x{0})'.format(-cl[0])\n print('(assert-soft {0} :weight {1})'.format(l, w), file=file_pointer)\n\n print('(check-sat)', file=file_pointer)\n print('(get-model)', file=file_pointer)\n print('(get-objectives)', file=file_pointer)\n print('(exit)', file=file_pointer)",
"title": ""
},
{
"docid": "1bed09b7d57987e01641bd6feb172467",
"score": "0.4902589",
"text": "def copy_files_export_model(geomodel_dir, geomodeller_file, new_dir, nx, ny, nz, **kwds):\n \n \n \n os.chdir(geomodel_dir)\n \n \n # first step: determine all Geomodeller files in original directory\n geomodel_files = []\n for l in os.listdir(\".\"):\n if os.path.splitext(l)[1] == \".sec\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".s3d\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".md5\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".wsp\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".xml\":\n # This is the original file - keep for later\n geomodel_xml_ori = l\n \n print geomodel_xml_ori\n os.chdir(new_dir)\n\n for f in geomodel_files:\n shutil.copyfile(os.path.join(geomodel_dir, f), os.path.join(\".\",f))\n\n for l2 in os.listdir(\".\"):\n if l2 == geomodeller_file:\n print l2\n shutil.copyfile(l2,os.path.join(\".\", geomodel_xml_ori))\n if \"original\" in l2:\n new_grid = \"exported_grid_original.txt\"\n new_delxyz = \"delxyz_original.txt\"\n else:\n new_grid = \"exported_grid_\" + os.path.splitext(l2)[0] + \".txt\"\n new_delxyz = \"delxyz_\" + os.path.splitext(l2)[0] + \".txt\"\n \n if kwds.has_key('nz_list'): # cell discretization study\n try:\n i = int(l2[-7:-4])\n except ValueError:\n os.chdir('..') \n continue # original model\n subprocess.call(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, kwds['nz_list'][i]), shell=True)\n else:\n\n while True:\n proc = subprocess.Popen(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\n for i in range(10):\n time.sleep(5)\n if proc.poll() == 0:\n print(\"Process finished successfully\")\n break\n if proc.poll() == None:\n proc.kill()\n print(\"\\n\\n\\tProcess killed, restart!\\n\\n\")\n continue\n elif proc.poll() == 0:\n print(\"Process finished successfully\")\n break\n print(\"\\n\\n\\tProcess not finished correctly!\\n\\n\\n\")\n break\n # subprocess.call(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\n shutil.copyfile(\"exported_grid.txt\", os.path.join(\".\", new_grid))\n shutil.copyfile(\"delxyz.txt\", os.path.join(\".\", new_delxyz))",
"title": ""
},
{
"docid": "8dc902536351b3d417ec8d2e8f7e209e",
"score": "0.48992693",
"text": "def convert2lite(save_dir,\n lite_valid_places=\"arm\",\n lite_model_type=\"naive_buffer\"):\n\n from paddlelite.lite import Opt\n opt = Opt()\n opt.set_model_dir(save_dir + \"/inference_model\")\n opt.set_valid_places(lite_valid_places)\n opt.set_model_type(lite_model_type)\n opt.set_optimize_out(save_dir + \"/opt\")\n opt.run()",
"title": ""
},
{
"docid": "38860f271edd9ad00978731b25db1a2b",
"score": "0.48990908",
"text": "def makefile2dot(**kwargs):\n\n direction = kwargs.get('direction', \"BT\")\n if direction not in [\"LR\", \"RL\", \"BT\", \"TB\"]:\n raise ValueError('direction must be one of \"BT\", \"TB\", \"LR\", RL\"')\n\n output = kwargs.get('output', '')\n view = kwargs.get('view', False)\n\n graph = build_graph(stream_database(), direction=direction)\n if output == \"\":\n if view:\n graph.view()\n else:\n print(graph)\n else:\n with open(output, 'w') as file:\n file.write(str(graph))\n if view:\n graph.view()",
"title": ""
},
{
"docid": "7c4d103572b36b3f5587fd6cccb6325d",
"score": "0.48969853",
"text": "def load_model(fp: str):\n pass",
"title": ""
},
{
"docid": "6128a8fd86ce81b50e836d0234155722",
"score": "0.48924553",
"text": "def writemodelfile(self, classpy=None):\n if not classpy is None:\n classpy.close()\n pyfile = '%s.py' % os.path.join(self.modelspath, self.thismodule)\n classpy = open(pyfile, 'r')\n lines = classpy.readlines()\n lines.insert(2, '\\n')\n classpy.close()\n froms = list()\n for line in lines:\n prefix = ''\n if 'ForeignKey' in line or 'OneToOneField' in line \\\n or 'ManyToManyField' in line:\n idx = lines.index(line)\n third = ''\n paren = False\n comma = False\n first, second = line.split('(', 1)\n if not 'self' in second: # do nothing if it is\n if ',' in second: # some options exist\n second, third = second.split(',', 1)\n third = ', %s' % third\n if ')' in second: # that's all in the line\n paren = True # so it can be put back\n second = second.replace(')', '').strip()\n if \"'\" in second:\n second = second.replace(\"'\", '').strip()\n if '\"' in second:\n second = second.replace('\"', '').strip()\n relatedmodel = second # totally naked\n assert len(second.split()) == 1\n module = self.checkthisimport(relatedmodel)\n if not module is None:\n if module.startswith('.'):\n prefix = '#'\n line = \"%s('%s'%s\" % (first, second, third)\n else:\n line = \"%s(%s%s\" % (first, second, third)\n if paren:\n line = '%s)\\n' % line.rstrip()\n if not line.endswith('\\n'):\n line = '%s\\n' % line\n if idx:\n lines[idx] = line\n if module:\n impline = '%sfrom %s import %s\\n' % (prefix, module,\n relatedmodel)\n else:\n impline = 'import %s\\n' % model\n if not impline in froms:\n #print(impline)\n froms.insert(0, impline)\n # now find all the other imported items\n chunk = line.replace('=', ' ')\n chunk = chunk.replace('[', ' ')\n chunk = chunk.replace('(', ' ')\n chunk = chunk.replace(')', ' ')\n chunk = chunk.replace('.', ' ')\n chunk = chunk.replace(']', ' ')\n chunk = chunk.replace(',', ' ')\n bits = chunk.split()\n for bit in bits:\n if self.seemsok(bit, line):\n module = self.checkthisimport(bit)\n if not module is None:\n if module == '':\n impline = 'import %s\\n' % bit\n else:\n impline = 'from %s import %s\\n' % (module, bit)\n if not impline in froms:\n froms.insert(0, impline)\n\n clean = list()\n for item in froms:\n if not '__future__' in item:\n if not item in clean:\n clean.insert(0, item)\n cleaner = self.removethismodule(clean)\n del(clean)\n cleaner.sort()\n cleaner.reverse()\n if aggregate:\n cleanest = self.aggregatefroms(cleaner)\n else:\n cleanest = cleaner\n for item in cleanest:\n lines.insert(self.startline, item)\n classpy = open(pyfile, 'w')\n classpy.writelines(lines)\n classpy.close()",
"title": ""
},
{
"docid": "6b24a82db4e8a7271fd5aaa542794f09",
"score": "0.48900214",
"text": "def _translate_and_simulate(self, simulate):\n import os\n import shutil\n import jinja2\n import datetime\n\n # Delete output files\n self.deleteOutputFiles()\n\n worDir = self._outputDir_\n\n # Construct the model instance with all parameter values\n # and the package redeclarations\n dec = self._declare_parameters()\n dec.extend(self._modelModifiers_)\n\n if len(dec) == 0:\n model_modifier = \"\"\n else:\n model_modifier = '{dec}'.format(mn=self.modelName, dec=','.join(dec))\n\n file_name = \"{}.py\".format(self.modelName.replace(\".\", \"_\"))\n## self._time_stamp_old_files = datetime.datetime.now()\n with open(os.path.join(worDir, file_name), mode=\"w\", encoding=\"utf-8\") as fil:\n path_to_template = os.path.join(\n os.path.dirname(__file__), os.path.pardir, \"development\")\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(path_to_template))\n\n template = env.get_template(\"optimica_run.template\")\n\n # Note that filter argument must respect glob syntax ([ is escaped with []]) + OPTIMICA mat file\n # stores matrix variables with no space e.g. [1,1].\n txt = template.render(\n model=self.modelName,\n model_modifier=model_modifier,\n ncp=self._simulator_.get('numberOfIntervals'),\n rtol=self._simulator_.get('eps'),\n solver=self._simulator_.get('solver'),\n start_time=self._simulator_.get('t0') if self._simulator_.get(\n 't0') is not None else 'mod.get_default_experiment_start_time()',\n final_time=self._simulator_.get('t1') if self._simulator_.get(\n 't1') is not None else 'mod.get_default_experiment_stop_time()',\n result_file_name=f\"{self._simulator_.get('resultFile')}.mat\",\n simulate=simulate,\n time_out=self._simulator_.get('timeout'),\n filter=self._result_filter,\n generate_html_diagnostics=self._generate_html_diagnostics,\n debug_solver=self._debug_solver,\n debug_solver_interactive_mode=self._debug_solver_interactive_mode)\n\n fil.write(txt)\n shutil.copyfile(\n os.path.join(\n os.path.dirname(__file__),\n \"OutputGrabber.py\"),\n os.path.join(\n worDir,\n \"OutputGrabber.py\"))\n\n try:\n super()._runSimulation([\"jm_ipython.sh\", file_name],\n self._simulator_.get('timeout'),\n worDir)\n\n self._check_simulation_errors(worDir=worDir, simulate=simulate)\n# self._copyNewFiles(worDir)\n# self._deleteTemporaryDirectory(worDir)\n\n except Exception as e: # Catch all possible exceptions\n em = f\"Simulation failed in '{worDir}'\\n Exception: {e}.\\n You need to delete the directory manually.\\n\"\n self._reporter.writeError(em)\n raise e\n os.remove(os.path.join(worDir, \"OutputGrabber.py\"))",
"title": ""
},
{
"docid": "989010fe3938e83cdafa116524121a61",
"score": "0.4889075",
"text": "def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):\n from pathlib import Path\n\n from stamp_processing.module.yolov5.models.experimental import attempt_load\n from stamp_processing.module.yolov5.models.yolo import Model\n from stamp_processing.module.yolov5.utils.torch_utils import select_device\n\n file = Path(__file__).absolute()\n\n save_dir = Path(\"\") if str(name).endswith(\".pt\") else file.parent\n path = (save_dir / name).with_suffix(\".pt\") # checkpoint path\n try:\n device = select_device((\"0\" if torch.cuda.is_available() else \"cpu\") if device is None else device)\n\n if pretrained and channels == 3 and classes == 80:\n model = attempt_load(path, map_location=device) # download/load FP32 model\n else:\n cfg = list((Path(__file__).parent / \"models\").rglob(f\"{name}.yaml\"))[0] # model.yaml path\n model = Model(cfg, channels, classes) # create model\n if autoshape:\n model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS\n return model.to(device)\n\n except Exception as e:\n help_url = \"https://github.com/ultralytics/yolov5/issues/36\"\n s = \"Cache may be out of date, try `force_reload=True`. See %s for help.\" % help_url\n raise Exception(s) from e",
"title": ""
},
{
"docid": "1163e643e5b668503b7ff3340a328791",
"score": "0.48880547",
"text": "def make_dot(aut_fname, dot_fname):\n coll = collection.Collection()\n aut = load_automaton(coll, aut_fname, False, False)\n\n make_backup_file(dot_fname)\n\n dot_handle = open(dot_fname, 'w')\n dot_handle.write(aut.to_dot())\n dot_handle.close()",
"title": ""
}
]
129bf3d37fc164404295d1647290238f
Set an instance variable to test that this method gets called
If the same item appears in the list, sums the quantities and create a unique Food object.
[
{
"docid": "fc122672b927400040c2b8b6fa8b33a7",
"score": "0.5547891",
"text": "def eraseDubloons(foodList):\n resList = []\n foodNames = [f.codsougr_name for f in foodList]\n uniqueFoodNames = list(set(foodNames))\n for name in uniqueFoodNames:\n duplicates = [f for f in foodList if f.codsougr_name == name]\n qte_nette = sum([f.qte_nette for f in duplicates])\n codsougr = [f.codsougr for f in duplicates][0]\n \n newFood = Food(codsougr, name, qte_nette)\n resList.append(newFood)\n return resList",
"title": ""
}
]
[
{
"docid": "4f021ae31218d83063fc7b7fe95882c6",
"score": "0.6519353",
"text": "def __add_muilt(self,food_name,quantity):\n\t\tif (not food_name in self.items):\n\t\t\tself.items[food_name] =0\n\t\tself.items[food_name] = self.items[food_name] + quantity",
"title": ""
},
{
"docid": "232d5321f12a8823e62ae2f05ee7bab2",
"score": "0.5817523",
"text": "def addFood(self, num):\n self.camp.data[\"foodQuantity\"] += int(num)",
"title": ""
},
{
"docid": "3f7d71c67883675ad8cf3a3be6f640a0",
"score": "0.575201",
"text": "def add(self, name, quantity):\n item = Item(name, quantity)\n if item in self.items:\n # we already have this, so add to it\n self.items[self.items.index(item)] += item\n else:\n self.items.append(item)",
"title": ""
},
{
"docid": "5d5bf92df7dcf5cd7b9da5461a553cbf",
"score": "0.5631031",
"text": "def condense_grocery_list(list_of_grocery_items):\n condensed_list=[]\n condensed_dict=dict()\n for grocery_item in list_of_grocery_items:\n dict_key=grocery_item.unit+\"-\"+grocery_item.name\n if dict_key in condensed_dict:\n condensed_dict[dict_key].number = str(float(condensed_dict[dict_key].number) + float(grocery_item.number))\n else:\n condensed_dict[dict_key]=grocery_item\n\n for item in condensed_dict:\n condensed_list.append(condensed_dict[item])\n\n condensed_list.sort(key=lambda x: x.name)\n condensed_dict={}\n for item in condensed_list:\n if item.name in condensed_dict:\n condensed_dict[item.name] = condensed_dict[item.name] + \", \" + item.number.strip() + \" \" + item.unit.strip()\n else:\n condensed_dict[item.name]=item.name + \": \" + item.number.strip() + \" \" + item.unit.strip()\n condensed_list=[]\n for item in condensed_dict:\n new_item=shopping_item()\n new_item.name=item\n new_item.grocery_list_line=condensed_dict[item]\n condensed_list.append(new_item)\n\n return condensed_list",
"title": ""
},
{
"docid": "615ee899602c1129b4b2df5f3f783058",
"score": "0.5625155",
"text": "def createMeal(jour, tyrep, itemsName, itemsDict, minItem, maxItem, meanQuantities):\n nItems = random.randint(minItem,maxItem)\n items = random.sample(itemsName, nItems)\n foodItems = [Food(itemsDict[x], x, meanQuantities[x]) for x in items]\n meal = Meal(foodItems, jour=jour, tyrep=tyrep)\n meal.reorder()\n return meal",
"title": ""
},
{
"docid": "ff3c0498b926385879c47b0567a49a81",
"score": "0.5587279",
"text": "def consume_food(self, food):\n quantity = self.__items['food'].get(food, 0)\n if quantity > 0:\n self.__items['food'][food] = quantity - 1",
"title": ""
},
{
"docid": "0139cce57cee1ffab79707a72f831baf",
"score": "0.55362856",
"text": "def add_to_inventory_v2(inventory, added_items):\n\n for item in added_items:\n if item not in inventory:\n my_inventory.setdefault(item, 1)\n else:\n my_inventory[item] += 1\n return my_inventory",
"title": ""
},
{
"docid": "7d1cce71848b886fcbf253344f3c75a8",
"score": "0.5494977",
"text": "def add_to_inventory(inventory, added_items):\n for key in list(inventory):\n for item in added_items:\n if key == item:\n inventory[key] += 1\n else:\n inventory.setdefault(item, 1)\n\n return my_inventory",
"title": ""
},
{
"docid": "21510822cd79307c80383341d84fa004",
"score": "0.53571904",
"text": "def add(self, item1, item2):\n for sumField in self.sumFields:\n item1[sumField] = item1[sumField] + item2[sumField]",
"title": ""
},
{
"docid": "ae3d284c0f2807c357fd971d08a6f7f8",
"score": "0.5330261",
"text": "def addDish(self,dish):\r\n\t\tself.items[dish.name] = dish\r\n\t\tfor nutrient in TARGET_NUTRIENTS:\r\n\t\t\tself.totals[nutrient]+=dish.nutrition[nutrient]",
"title": ""
},
{
"docid": "f1159106f3c256affb95ba2576b99a40",
"score": "0.5326817",
"text": "def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\n pass",
"title": ""
},
{
"docid": "9fa86c34f680edfb331c7e63841be1d7",
"score": "0.5321849",
"text": "def update_world_totals(new_item):\n # TODO: Can definitely optimize this search if performance becomes an issue\n for item in world_inv:\n if new_item.equals(item):\n item.set_count(item.count + new_item.count)\n return\n\n if (new_item.damage != 0 and (new_item.full_name in degradeable_tools or new_item.full_name in degradeable_armor)):\n world_inv.append(Item(new_item.full_name, new_item.count, -1, new_item.lore))\n else:\n world_inv.append(Item(new_item.full_name, new_item.count, new_item.damage, new_item.lore))",
"title": ""
},
{
"docid": "90ff6a56e8a808fc9686f492e012e077",
"score": "0.52610236",
"text": "def feeding_animal(self):\n for food in self.get_food_set():\n for animal in self.get_animal_set():\n if (abs(animal.position[0] - food.position[0]) < Constants.ANIMAL_WIDTH / 2 + Constants.FOOD_WIDTH / 2 and \\\n abs(animal.position[1] - food.position[1]) < Constants.ANIMAL_HEIGHT / 2 + Constants.FOOD_HEIGHT / 2):\n self.animal_set.remove(animal)\n self.money += 1\n food.used = True\n if food.used:\n self.food_set.remove(food)",
"title": ""
},
{
"docid": "f300c9707bf682e3a6b1d5677eba5aa9",
"score": "0.52460706",
"text": "def execute_item_creation(self)->None:\n [ingedient.use_quantity(quantity) for ingedient,quantity in self.ingredient_quantity]",
"title": ""
},
{
"docid": "70b286ea3b1c733d10122c24d15eb782",
"score": "0.52455187",
"text": "def digest_fat(self):\n can_move = min(self.population - self.food, self.fat_storage)\n self.fat_storage -= can_move\n self.food += can_move",
"title": ""
},
{
"docid": "ae9736db106bb68027158dacec819627",
"score": "0.5245462",
"text": "def add_fruit(inventory, fruit, quantity=0):\n #print fruit, quantity\n inventory[fruit]=quantity\n #return inventory",
"title": ""
},
{
"docid": "bf0a041ec85a5bcb7a4fa271d76f533e",
"score": "0.5186193",
"text": "def add_item(self, item_name, item_price):\n if 'none' in self.items_with_price:\n self.items_with_price.clear()\n self.items_with_price.update({str(item_name): float(item_price)})",
"title": ""
},
{
"docid": "379782037ed753eb6be9b48c0ea5b3b7",
"score": "0.51835597",
"text": "def add_to_inventory(inventory, added_items):\n for i in added_items:\n if i in inventory.keys():\n inventory[i] += 1\n else:\n inventory[i] = 1\n return inventory",
"title": ""
},
{
"docid": "8fd6eb79281380c997d0ddf13a3b1137",
"score": "0.51719004",
"text": "def updateCart(self, replaceItem):\n\t\ttopFoods = self.dashFilter(replaceItem)\n\t\trestOfCart = [item for item in self.foods if item[\"id\"] != replaceItem]\n\t\t#topCombo = self.comboFilter(topFoods, restOfCart)\n\t\ttopCombo = self.randomFilter(topFoods)\n\t\tself.foods = [{\"id\":topCombo[\"id\"],\"name\":topCombo['name'],\"groups\":item['groups'],\"combos\":topCombo['combos']} if item[\"id\"] == replaceItem else item for item in self.foods]\n\t\treturn {'id':topCombo['id'],'name':topCombo['name'].upper(),'img':topCombo['id']+'.jpg'}",
"title": ""
},
{
"docid": "242d58e8f298df51464e9f6224d850aa",
"score": "0.51659745",
"text": "def addFood(surface) -> Food:\n\n newFood = Food(\n coord=[randint(0, screen_resolution[0]), randint(0, screen_resolution[1])],\n energy=2,\n surface=surface\n )\n foodHolder.append(newFood)\n\n return newFood",
"title": ""
},
{
"docid": "3c319e9376a5acfcd6692426a6106298",
"score": "0.51233023",
"text": "def test_bulk_inventory_quantity_combined(self):\n category = ['Clothing', 'Unisex Adult Clothing', 'Pants']\n\n expected_left_bulk_header = 'Individual Quantity'\n\n new_quantities = ['200', '300', '400', '500']\n\n expected_product_offerings = [\n ['1', '500.00', '550', '200', 't', 'L', '1', 'Black', '1'],\n ['1', '500.00', '550', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['1', '500.00', '550', '400', 't', 'M', '2', 'Black', '1'],\n ['1', '500.00', '550', '500', 't', 'M', '2', 'Pinkish', '2'],\n ['2', '1.00', '111', '200', 't', 'L', '1', 'Black', '1'],\n ['2', '1.00', '111', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['2', '1.00', '111', '400', 't', 'M', '2', 'Black', '1'],\n ['2', '1.00', '111', '500', 't', 'M', '2', 'Pinkish', '2'],\n ['3', '10.00', '222', '200', 't', 'L', '1', 'Black', '1'],\n ['3', '10.00', '222', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['3', '10.00', '222', '400', 't', 'M', '2', 'Black', '1'],\n ['3', '10.00', '222', '500', 't', 'M', '2', 'Pinkish', '2']\n ]\n\n expected_variation_properties = [\n ['1', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['2', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['3', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\n ]\n\n expected_variation_options = [\n ['1', 't', '2119', 'L', '1'],\n ['1', 't', '2116', 'M', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '', 'Pinkish', '2'],\n ['2', 't', '2119', 'L', '1'],\n ['2', 't', '2116', 'M', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '', 'Pinkish', '2'],\n ['3', 't', '2119', 'L', '1'],\n ['3', 't', '2116', 'M', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '', 'Pinkish', '2']\n ]\n\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\n\n # Select category in bulk edit area\n bpiv.select_category(category)\n\n bulk_row = bpiv.bulk_edit_row\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\n\n # Set first variation property\n bpiv.set_property(bulk_row, 0, 'Size', \"Men's\")\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 0, 'L', displayed_name=\"L Men's\")\n bpiv.add_option(bulk_row, 0, 'M', displayed_name=\"M Men's\")\n\n # Set second variation property\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 1, 'Black')\n bpiv.add_custom_option(bulk_row, 1, 'Pinkish')\n\n bpiv.select_inventory_tab(bulk_row, \"Quantity\")\n\n # Switch to individual quantities, check the header is changed\n click(bpiv.bulk_header_checkbox(bulk_row, i=0))\n click(bpiv.bulk_header_checkbox(bulk_row, i=1), delay=0.5)\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\n\n # Enter individual quantities for the property combinations\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=0)\n for i, quantity in enumerate(new_quantities):\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\n\n # Check that only titles are in listing preview as preview is disabled\n check_simplified_preview(bpiv)\n\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\n\n # Apply\n assert bpiv.operation_apply().is_enabled() is True\n click(bpiv.operation_apply())\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\n 'Sync button is not enabled')\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\'t show up'\n\n # Check bulk edit area texts and that Apply button is disabled\n check_bulk_properties_disabled(bpiv)\n\n # Sync updates\n click(bpiv.sync_updates_button())\n\n # Check data in DB\n check_db_state(expected_product_offerings, expected_variation_properties, expected_variation_options)",
"title": ""
},
{
"docid": "8ea81c57ff2db743346ab37434f24574",
"score": "0.51203",
"text": "def calculateAmount(self):\n return sum([item.price for item in self.items])",
"title": ""
},
{
"docid": "e5115be39649d3f3029e91969f33dab0",
"score": "0.5108897",
"text": "def add_to_inventory(player_inventory, added_items):\n # For each new piece of loot...\n for new_loot_piece in added_items:\n if new_loot_piece in player_inventory:\n # if the loot already existed in inventory, increment it's count\n player_inventory[new_loot_piece] += 1\n else:\n # If the loot was not already in the inventory, create a new piece of loot\n # in the inventory and se it's default value to 1.\n player_inventory.setdefault(new_loot_piece, 1)\n\n return player_inventory",
"title": ""
},
{
"docid": "5e4b677c435c587a9cfddc38115a3c4f",
"score": "0.5098413",
"text": "def test_bulk_inventory_single_quantity_individual(self, property_number):\n category = ['Home & Living', 'Furniture']\n\n expected_left_bulk_header = 'Individual Quantity'\n\n new_quantities = ['22', '33']\n\n expected_product_offerings = [\n [\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'White', '2'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'White', '2'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'White', '2'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\n ],\n [\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'wool', '1', 'White', '2'],\n ['1', '500.00', '550', '22', 't', 'cotton', '2', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'wool', '1', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'cotton', '2', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'wool', '1', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'cotton', '2', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\n ]\n ]\n\n expected_variation_properties = [\n [\n ['1', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\n ['2', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\n ['3', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f']\n ],\n [\n ['1', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['2', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['3', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\n ]\n ]\n\n expected_variation_options = [\n [\n ['1', 't', '', 'wool', '1'],\n ['1', 't', '', 'cotton', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '10', 'White', '2'],\n ['2', 't', '', 'wool', '1'],\n ['2', 't', '', 'cotton', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '10', 'White', '2'],\n ['3', 't', '', 'wool', '1'],\n ['3', 't', '', 'cotton', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '10', 'White', '2']\n ],\n [\n ['1', 't', '', 'wool', '1'],\n ['1', 't', '', 'cotton', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '10', 'White', '2'],\n ['2', 't', '', 'wool', '1'],\n ['2', 't', '', 'cotton', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '10', 'White', '2'],\n ['3', 't', '', 'wool', '1'],\n ['3', 't', '', 'cotton', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '10', 'White', '2']\n ]\n ]\n\n\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\n\n # Select category in bulk edit area\n bpiv.select_category(category)\n\n bulk_row = bpiv.bulk_edit_row\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\n\n # Set first variation property\n bpiv.set_property(bulk_row, 0, 'Fabric')\n # Add two options to first property, one by one\n bpiv.add_custom_option(bulk_row, 0, 'wool')\n bpiv.add_custom_option(bulk_row, 0, 'cotton')\n\n # Set second variation property\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 1, 'Black')\n bpiv.add_option(bulk_row, 1, 'White')\n\n bpiv.select_inventory_tab(bulk_row, \"Quantity\")\n\n # Switch to individual quantities, check the header is changed\n click(bpiv.bulk_header_checkbox(bulk_row, i=property_number), delay=0.5)\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\n\n # Enter individual quantities\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=property_number)\n for i, quantity in enumerate(new_quantities):\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\n\n # Check that only titles are in listing preview as preview is disabled\n check_simplified_preview(bpiv)\n\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\n\n # Apply\n assert bpiv.operation_apply().is_enabled() is True\n click(bpiv.operation_apply())\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\n 'Sync button is not enabled')\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\'t show up'\n\n # Check bulk edit area texts and that Apply button is disabled\n check_bulk_properties_disabled(bpiv)\n\n # Sync updates\n click(bpiv.sync_updates_button())\n\n # Check data in DB\n check_db_state(expected_product_offerings[property_number],\n expected_variation_properties[property_number],\n expected_variation_options[property_number])",
"title": ""
},
{
"docid": "ddde5fc2882d117d910fb6854f8c507f",
"score": "0.50927",
"text": "def concatenate_items(self) -> dict:\n test = []\n for text in self.receipt_text:\n if self.text_is_number(text) | self.remove_pound_symbol(text) | self.change_o_to_zero(text):\n text = text[1:]\n else:\n test.append(text)\n\n if self.text_is_number(text):\n if test[-1] in self.dict_to_return:\n self.dict_to_return[test[-1]]['quantity'] += 1\n else:\n self.dict_to_return[test[-1]] = {'name': text, 'quantity': 1}\n test = []\n\n return self.dict_to_return",
"title": ""
},
{
"docid": "28e1c26cb444d691a6d757c89aed7d2b",
"score": "0.5092597",
"text": "def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\n self.ingredient_quantity.append((ingredient,quantity))",
"title": ""
},
{
"docid": "0a68a1b31438ff6224ac04a9bb4f05d1",
"score": "0.50659823",
"text": "def scan_onto_shelf(self):\n if hasattr(self, \"shelf\"):\n items_added = []\n for item in self.items_stored:\n for product in self.shelf.compartments:\n if product[\"Product Number\"] == item.number:\n if product[\"Items Stored\"] is None:\n product[\"Items Stored\"] = []\n product[\"Items Stored\"].append(item)\n product[\"Quantity\"] += 1\n items_added.append(item)\n Item.sorted_items.append(item)\n Item.shipment.remove(item)\n for item in items_added:\n self.items_stored.remove(item)",
"title": ""
},
{
"docid": "484270c6a6c051655cc77f321d243802",
"score": "0.50581247",
"text": "def add_to_inventory(inventory, added_items):\n if added_items in inventory:\n inventory[added_items] += 1\n else:\n inventory[element] = 1",
"title": ""
},
{
"docid": "a4d21353320ad10efa03007bf3481510",
"score": "0.50539666",
"text": "def add(self, product, quantity):\n product_id = product.id\n if product_id not in self.basket:\n self.basket[product_id] = {\"price\": float(product.regular_price), \"quantity\": quantity}\n else:\n self.basket[product_id][\"quantity\"] = quantity\n self.save()",
"title": ""
},
{
"docid": "35cf195a68cb0eebf880a0671ed3bb9d",
"score": "0.50376934",
"text": "def _add_dict_inventory(self, d, item_pouch): # item_pouch used to find which dictionary to look in\r\n for group in d:\r\n if group in item_pouch:\r\n if item_pouch[group] is None:\r\n item_pouch[group] = d[group]\r\n elif isinstance(item_pouch[group], list):\r\n item_pouch[group].extend(d[group])\r\n elif isinstance(item_pouch[group], dict):\r\n self._add_dict_inventory(d[group], item_pouch[group])\r\n elif isinstance(item_pouch[group], int):\r\n item_pouch[group] = item_pouch.setdefault(group, 0)+d[group]\r\n else:\r\n item_pouch[group] = d[group]",
"title": ""
},
{
"docid": "3ca2e8c63e53f9ab69061677cf963375",
"score": "0.50190413",
"text": "def test_adding_item_to_order_model_is_add_one_to_quantity(self):\n cart_item_1 = CartItem.objects.create(order=self.order, item=self.item_1)\n cart_item_1.Add() # quantity = 1 ** payout = self.item_1.price * 1\n cart_item_1.Add() # quantity = 2 ** payout = self.item_1.price * 2\n\n self.assertEqual(CartItem.objects.get(order=self.order, item=self.item_1).quantity, 2)\n self.assertEqual(cart_item_1.total_price, self.item_1.price * 2)\n self.assertEqual(self.order.payout, self.item_1.price * 2)",
"title": ""
},
{
"docid": "f019318a90a39d1d4baa58c66b55a7e8",
"score": "0.50127226",
"text": "def uniq_add(my_list=[]):\n\n return sum(set(my_list))",
"title": ""
},
{
"docid": "85016ea7f6bcc15054e712a84e7aaa66",
"score": "0.5005258",
"text": "def create_order_item(self, food_item, quantity):\n OrderItem.objects.create(\n user=self.customer,\n food_item=food_item,\n quantity=quantity,\n )",
"title": ""
},
{
"docid": "3f95d8bb1a0a822a0a89287235821bd2",
"score": "0.50050193",
"text": "def calculateAmount(self):\n summa = 0\n for item in self.items:\n summa += item.price\n return summa",
"title": ""
},
{
"docid": "0a975cec92256ad6ac4119086b89bcaf",
"score": "0.50043887",
"text": "def add(self, product, quantity=None, update_quantity=False):\n if product.price_with_taxes > 0 and quantity != \"0\":\n self.cart[str(product.id)] = {\n 'user_id': self.request.user.id,\n 'article_id': str(product.id),\n 'name': product.name,\n 'quantity': quantity,\n 'price_type': product.price_type,\n 'price_with_taxes': str(product.price_with_taxes),\n 'article_code': product.article_code,\n }\n self.save()",
"title": ""
},
{
"docid": "e946d19a98dd4708d5ea172fdab839a3",
"score": "0.49940917",
"text": "def fortune(player):\n random.seed()\n \n # (inventory item, amount received)\n treasures = [\n ('food',random.randint(100,500)),\n ('money',random.randint(200,500)),\n ('bullets',random.randint(30,100)),\n ('kits',random.randint(2,3)),\n ('parts',random.randint(2,3)),\n ]\n \n # Determine random item.\n found = random.choice(treasures)\n \n # Handle special message for food.\n if found[0] == 'food':\n current_food = player.get_from_inventory('food')\n # Handle food inventory limit.\n if current_food + found[1] > 1000:\n added_food = 1000 - current_food\n print(\"You found {} pounds of food in an abandoned wagon\".format(found[1]))\n print(\"You can only add {} pounds to your wagon\".format(added_food))\n player.update_inventory('food',1000)\n else:\n print(\"You found {} pounds of food in an abandoned wagon\".format(found[1]))\n player.add_to_inventory('food',found[1])\n \n # Handle special message for money.\n elif found[0] == 'money':\n print(\"You found ${} in an abandoned wagon\".format(\"%.2f\" %found[1]))\n player.add_to_inventory('money',found[1])\n \n # Handle all other items.\n else:\n print(\"You found {} {} in an abandoned wagon\".format(found[1],found[0]))\n player.add_to_inventory(found[0],found[1])",
"title": ""
},
{
"docid": "1e056e6e443126ef305397e1edf9f00e",
"score": "0.49848992",
"text": "def calc_items_total(cls, items):\n shipping_charge = 0\n subtotal = 0\n ad_charge_amt = 0\n ad_charge_breakup = {}\n\n for item in items:\n shipping_charge += item.shipping_charge\n subtotal += item.subtotal\n ad_charge_amt += item.additional_charge\n\n for charge in item.cost_breakup['additional_charge']:\n k = slugify(charge.get('name')).replace('-', '_')\n v = charge.get('amt')\n # Initialize value\n if ad_charge_breakup.get(k, None) is None:\n ad_charge_breakup[k] = 0.0\n ad_charge_breakup[k] += v\n\n return {\n 'shipping_charge': helper.round_off(shipping_charge),\n 'subtotal': helper.round_off(subtotal),\n 'additional_charge': helper.round_off(ad_charge_amt),\n 'cost_breakup': {\n 'additional_charge': ad_charge_breakup\n }\n }",
"title": ""
},
{
"docid": "5ba05c3eea1691cbb2cf7dd27f2811a3",
"score": "0.49668226",
"text": "def add(self, comida, quantity=1, update_quantity=False):\n product_id = str(comida.id)\n if product_id not in self.cart:\n self.cart[product_id] = {'quantity': 0,\n 'precio': str(comida.precio)}\n if update_quantity:\n self.cart[product_id]['quantity'] = quantity\n else:\n self.cart[product_id]['quantity'] += quantity\n self.save()",
"title": ""
},
{
"docid": "2a55875101d7602488fcb4f6054d62ad",
"score": "0.49663317",
"text": "def initializeFoods(self):\n for i in range(0, self.numFoods):\n self.addFood()",
"title": ""
},
{
"docid": "9da1b12f109a9a6aef2dab62544176d0",
"score": "0.49556127",
"text": "def add(self, product, qty):\n product_id = str(product.id)\n\n if product_id in self.basket:\n self.basket[product_id][\"qty\"] = qty\n else:\n self.basket[product_id] = {\"price\": str(product.regular_price), \"qty\": qty}\n\n self.save()",
"title": ""
},
{
"docid": "ffc752d2ca3326fdbd563d1bbeac4d05",
"score": "0.49489328",
"text": "def add(self, product, quantity=1, update_quantity=False):\n product_id = str(product.id) # converted to string because JSON use only string data\n if product_id not in self.cart:\n self.cart[product_id] = {'quantity': 0,\n 'price': str(product.price)\n }\n if update_quantity:\n self.cart[product_id]['quantity'] = quantity\n else:\n self.cart[product_id]['quantity'] += quantity # increment the quantity\n self.save() # saving the state of the quantity after update",
"title": ""
},
{
"docid": "3e122449c4307f51ed5cb01667c9a9d1",
"score": "0.49486324",
"text": "def eatFood(self):\n # if a prey is in contact with a food, then eat the food by removing the food and increment hunger.\n objIDInContactList = super().getContactObjects()\n # if there are objects in contact with this prey, then determine if they are food and if so eat them!\n foodEaten = 0\n if objIDInContactList:\n for objID in objIDInContactList:\n food = hsm.objIDToObject[objID]\n if food in hsm.foodList:\n hsm.foodList.remove(food)\n hsm.destroy(objID)\n foodEaten += 1\n # incrememnt hunger accordingly\n self.hunger += mv.PREY_INCREMENT_HUNGER \n # throw away excess, prey can only eat so much food at a time.\n if self.hunger > mv.PREY_MAX_HUNGER:\n self.hunger = mv.PREY_MAX_HUNGER\n if foodEaten > 0:\n self.foodTimeStamps.append([hsm.frameCount, foodEaten])",
"title": ""
},
{
"docid": "892adb19f630ba769f49adfe54589f4c",
"score": "0.4945769",
"text": "def add_item(self, x):\n self._items_with_price.update(x)",
"title": ""
},
{
"docid": "8b3b75d718473bad062e7134b4e43dd2",
"score": "0.49438444",
"text": "def add_item_to_cart(self, product_id, quantity):\n product = Product.objects.filter(product_id=product_id, store=self.customer_id.store_id, deleted_at=None).first()\n if product is None or product.inventory < quantity:\n raise ProductNotFoundException\n\n product.inventory -= quantity\n product.save()\n\n existing_cart_item = CartItem.objects.filter(cart_id=self.cart_id, product_id=product.product_id\n , removed_at=None).first()\n\n self.customer_id.last_cart_activity_at = datetime.now()\n self.customer_id.updated_at = datetime.now()\n self.customer_id.save()\n if existing_cart_item:\n existing_cart_item.quantity += quantity\n existing_cart_item.save()\n return existing_cart_item\n else:\n cart_item = CartItem(\n cart_item_id=str(uuid.uuid4().int),\n product_id=product.product_id,\n cart_id=self.cart_id,\n quantity=quantity\n ).save()\n return cart_item",
"title": ""
},
{
"docid": "106331337e2a2c1d307ba926e7184437",
"score": "0.49325597",
"text": "def calculate_total_nutrition_fulldayofeating2(\n specificingredient2_dict_list,\n all_nutrients_and_default_units,\n set_to_zero_if_none,\n):\n\n # Initialize the dictionary which will store the results\n result_total_nutrition_fulldayofeating = {}\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n new_dict = {nutrient_name: 0}\n result_total_nutrition_fulldayofeating.update(\n new_dict\n )\n\n # Go through all the SpecificIngredients related to the FullDayOfEating.\n # For each SpecificIngredient, calculate its Nutrition by\n # multiplying the calculated_amount divided by the reference amount\n # multiplied by each nutrient value in the associated RawIngredient.\n\n # Sum up the total nutrition\n for k in range(len(specificingredient2_dict_list)):\n # For each nutrient, calculate the amount of that nutrient contained\n # in the calculated_amount of the SpecificIngredient\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n result_total_nutrition_fulldayofeating[nutrient_name] = \\\n result_total_nutrition_fulldayofeating[nutrient_name] \\\n + set_to_zero_if_none(specificingredient2_dict_list[k][\n 'raw_ingredient'][nutrient_name]) \\\n * float(specificingredient2_dict_list[k]['calculated_amount'])\\\n / float(specificingredient2_dict_list[k][\n 'raw_ingredient']['reference_amount'])\n\n # Round the values in the result_total_nutrition_fulldayofeating\n # Initialize the dictionary containing the rounded values.\n result_total_nutrition_fulldayofeating_rounded = {}\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n new_dict = {nutrient_name: 0}\n result_total_nutrition_fulldayofeating_rounded.update(\n new_dict\n )\n\n # Round the values and save them to the respective dictionary.\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n result_total_nutrition_fulldayofeating_rounded[nutrient_name] = \\\n round(result_total_nutrition_fulldayofeating[nutrient_name], 1)\n\n return result_total_nutrition_fulldayofeating,\\\n result_total_nutrition_fulldayofeating_rounded",
"title": ""
},
{
"docid": "6e950cf94ff01f7be16cc47e54da41ba",
"score": "0.4931627",
"text": "def _update_total_nutrition_values(self, unique_id, date, plan, add):\n table = self.dynamodb.Table(c.TABLE_NUTRITIONAL_NEEDS_DAY)\n week = form.get_week_by_date(date)\n\n item_nutrients_for_week = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.WEEK: week\n },\n ProjectionExpression='#toplevel',\n ExpressionAttributeNames={\"#toplevel\": c.NUTRIENTS_FOR_WEEK}\n )\n\n item_nutrients_for_day = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date\n },\n ProjectionExpression='#toplevel',\n ExpressionAttributeNames={\"#toplevel\": c.NUTRIENTS_FOR_DAY}\n )\n\n for container, container_content in plan.iteritems():\n item_nutrients_for_container = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id, c.DATE: date},\n ProjectionExpression='#toplevel.#container',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_CONTAINER,\n \"#container\": container\n }\n )\n for meal_key, meal_content in container_content.iteritems():\n\n\n\n for n in params.nutrientList:\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\n (1 if add else -1)\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\n (1 if add else -1)\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['VAL'] += \\\n meal_content['nutrients'][n]['VAL'] * (1 if add else -1)\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['UNIT'] = \\\n meal_content['nutrients'][n]['UNIT']\n\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date},\n UpdateExpression='SET #toplevel.#cat = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_CONTAINER,\n \"#cat\": container\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container]\n }\n )\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date},\n UpdateExpression='SET #toplevel = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_DAY\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY]\n }\n )\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.WEEK: week\n },\n UpdateExpression='SET #toplevel = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_WEEK\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK]\n }\n )",
"title": ""
},
{
"docid": "c7dfb948b8301a0430651d0b5bb487bb",
"score": "0.49241003",
"text": "def player_eats_food(self, snake, food):\n head = snake[0]\n head_x, head_y = head\n for food_item in food:\n food_x, food_y = food_item\n #Return whether or not the player's received food, adds fat if they have\n if head_x == food_x and head_y == food_y:\n #We've eaten food, update our score\n self.score += int(150 + int(len(snake)/10)*5)\n\n #Add more fat to the user if they already have some or they have a decent score\n self.fat += 8 + int(self.fat/4) + int(self.score/500)\n return True\n return False",
"title": ""
},
{
"docid": "7e5cd209b264fb2dd585ad69bbc06220",
"score": "0.49199784",
"text": "def createConsumptionSeq(df):\n mealList = []\n M = {} #Contient les aliments d'un repas \n nomen = df.nomen.iloc[0]\n jour = df.jour.iloc[0]\n tyrep = df.tyrep.iloc[0]\n \n for index, row in df.iterrows(): # Scan tous les aliments de consommation\n #print(row.nomen, row.jour, row.tyrep)\n if (row.nomen == nomen) & (row.jour == jour) & (row.tyrep == tyrep):\n name = str(row.nomen) + '_' + str(index)\n M[name] = Food(row.codsougr, row.codsougr_name, row.qte_nette) # Crée l'objet FoodItem\n else:\n m = list(set(M.values())) #Set of food items in a meal\n uniqueM = eraseDubloons(m)\n meal = Meal(uniqueM, jour = jour, tyrep = tyrep)\n meal.reorder()\n logging.debug('{}'.format(meal.nameTuple))\n if meal:\n mealList.append(meal)\n\n M = {}\n nomen = row.nomen\n jour = row.jour\n tyrep = row.tyrep\n \n meals = [m for m in mealList if m]\n return meals",
"title": ""
},
{
"docid": "c830b9fef7a64c1f072cecdf121412c0",
"score": "0.4918213",
"text": "def create_dico(item_list):\n assert type(item_list) is list\n dico = {}\n for items in item_list:\n for item in items:\n if item not in dico:\n dico[item] = 1\n else:\n dico[item] += 1\n return dico",
"title": ""
},
{
"docid": "c830b9fef7a64c1f072cecdf121412c0",
"score": "0.4918213",
"text": "def create_dico(item_list):\n assert type(item_list) is list\n dico = {}\n for items in item_list:\n for item in items:\n if item not in dico:\n dico[item] = 1\n else:\n dico[item] += 1\n return dico",
"title": ""
},
{
"docid": "6ddaa4351c2892173251704a2ad04d77",
"score": "0.49181578",
"text": "def sum_list(new_list):\n return sum(new_list)\n # return None",
"title": ""
},
{
"docid": "9ee3ad439b54ccc0e9043295f07ea1cd",
"score": "0.49134657",
"text": "def saved_meal_item_add(\n self, meal_id, food_id, food_entry_name, serving_id, num_units\n ):\n params = {\n \"method\": \"saved_meal_item.add\",\n \"format\": \"json\",\n \"saved_meal_id\": meal_id,\n \"food_id\": food_id,\n \"food_entry_name\": food_entry_name,\n \"serving_id\": serving_id,\n \"number_of_units\": num_units,\n }\n\n response = self.session.get(self.api_url, params)\n return self.valid_response(response)",
"title": ""
},
{
"docid": "a46b885d595c7bea38ce23fc1d6daa4d",
"score": "0.4910791",
"text": "def calculateInventory(self):\n from collections import Counter\n orders = self.model[0]\n shipping = self.model[1]\n inventory = {key:dict() for key in shipping}\n citys = {o.dest for o in orders}\n months = {o.orderTime.month for o in orders}\n products = {o.pro for o in orders}\n #print shipping\n for pro in products:\n for city in citys:\n inventory[pro][city] = Counter()\n for o in orders:\n o.ship = self.chooseBestShipping(o,citys)\n\n inventory[o.pro][o.ship][o.orderTime.month] += o.num\n #self.view.showInventory(inventory)\n self.inventory = inventory\n self.citys = citys\n self.months = months\n self.products = products\n return inventory",
"title": ""
},
{
"docid": "dcbca7ea38a938ec591a33b34ace0ede",
"score": "0.49077475",
"text": "def save(self, *args, **kwargs):\n if self.pk is None:\n item = self.item\n item.quantity += self.quantity\n item.save()\n return super(Entry, self).save(*args, **kwargs)",
"title": ""
},
{
"docid": "cbf4880010aa81907064f104b9d60f03",
"score": "0.49064472",
"text": "def subtotal(self):\n sub = 0.0\n for i in self.items:\n sub += i.quantity * INVENTORY[i.name]\n return sub",
"title": ""
},
{
"docid": "442dae3041bd85957abe5451209e318b",
"score": "0.48951203",
"text": "def _initilizeItemInventory(self):\n for itemName in itemPrice.keys():\n for j in range(5):\n self._itemInventory.add(itemName)",
"title": ""
},
{
"docid": "2bf1960258a00848bdd160a6a903b421",
"score": "0.48922408",
"text": "def find_food(self):\n \n if self.check_knife(1):\n self.knife_dur -= 1\n self.food += 1",
"title": ""
},
{
"docid": "4280fe8c4dcd8681a0e6d972b181946f",
"score": "0.48690817",
"text": "def createFoodDict(food_item_list):\n food_dict = {}\n for food_item in food_item_list:\n words = food_item.split(' ')\n first_word = words[0]\n food_dict[first_word] = food_item\n return food_dict",
"title": ""
},
{
"docid": "31e19cfc7f9cc7ac082b4629f8df71cb",
"score": "0.48465908",
"text": "def scan_onto_cart(self):\n if hasattr(self, \"shelf\"):\n for item in Item.shipment:\n if len(self.items_stored) < self.max_capacity:\n for product in self.shelf.compartments:\n if item.number == product[\"Product Number\"]:\n self.items_stored.append(item)",
"title": ""
},
{
"docid": "bb607bc5bfc19f80144df53d7174c4b2",
"score": "0.4835511",
"text": "def returnfrequentitemsets(itemSet, transactionList, minSupport, freqSet):\n\t\t_itemSet = set()\n\t\tlocalSet = defaultdict(int)\n\n\t\tfor item in itemSet:\n\t\t\tfor transaction in transactionList:\n\t\t\t\tif item.issubset(transaction):\n\t\t\t\t\tfreqSet[item] += 1\n\t\t\t\t\tlocalSet[item] += 1\n\n\t\tfor item, count in localSet.items():\n\t\t\t\tsupport = float(count)/len(transactionList)\n\n\t\t\t\tif support >= minSupport:\n\t\t\t\t\t\t_itemSet.add(item)\n\n\t\treturn _itemSet",
"title": ""
},
{
"docid": "f64d95b9e2dbd365c3e3c875f63c4ec1",
"score": "0.48311788",
"text": "def add_item(self, item):\n self.items_with_price.update(item)",
"title": ""
},
{
"docid": "b1dea7e98dd8eb0a30e38b7120376a69",
"score": "0.4829422",
"text": "def process_item(self, item, spider):\n # looks for a duplicate\n duplicate_check = self.db[self.collection_name].find({'name': item['name'], 'store': item['store'], 'category': item['category'], 'animal': item['animal']}).count()\n if duplicate_check == 0:\n self.db[self.collection_name].insert(dict(item))\n logging.debug('Product added to MongoDB')\n return item\n else:\n # logging.debug('Duplicate found. Skipping product')\n raise DropItem('Duplicate found. Skipping product: %s' % item['name'])\n # return item",
"title": ""
},
{
"docid": "616c81c4094a7fd6dab55793794e5f42",
"score": "0.48247102",
"text": "def generate_food(self):\n num_food = ENV_N_FOOD\n\n pos = [(random() * self.W, random() * self.H) for i in range(num_food)]\n\n return [Food(x, y, self.W, self.H) for (x, y) in pos]",
"title": ""
},
{
"docid": "0bdff644a55ea47546734621b9954d60",
"score": "0.4823412",
"text": "def budget(data,productsIds, quantity):\r\n \r\n data=data.fillna(0)\r\n budg=0\r\n products=productsIds\r\n quantity=quantity\r\n \r\n dataFilter = data[data['No. Item'].isin(products)]\r\n prices=dataFilter['Precio Unitario'].tolist()\r\n \r\n for i in range(len(quantity)):\r\n budg+=quantity[i]*prices[i]\r\n return budg",
"title": ""
},
{
"docid": "b9b6f3b617d4f2917091b725f1b1b61a",
"score": "0.48086756",
"text": "def get_total(self):\n\n base_price = 5\n if self.species == \"Christmas melon\":\n base_price = base_price * 1.5\n\n total = (1 + self.tax) * self.qty * base_price\n \n # if qty is less than ten melons and international, then +$3 to international_order\n if self.qty < 10 and self.order_type == 'international':\n total = total + 3\n\n return total",
"title": ""
},
{
"docid": "a15150d7171093c8a2769cd73df5a8ab",
"score": "0.4804091",
"text": "def fruits(fruit_list, fruit):\n\n updated_fruits_list = []\n for item in fruit_list:\n updated_fruits_list.append(item)\n updated_fruits_list.append(fruit)\n\n return updated_fruits_list",
"title": ""
},
{
"docid": "17ff7340eee41c80ad913f6610332c1b",
"score": "0.47999802",
"text": "def add_to_cart(self, selection_id, selection_qty):\n # read the data from data storage\n for keys, values in ShoppingCart.__ecommerce_data.items():\n # check if the product contain necessary data before proceed\n if \"products\" and \"sku\" and \"prices\" and \"stock\" in values:\n # check if it is a correct sku code from the data storage\n if selection_id in values[\"sku\"]:\n # add product information to the instance variables\n self.__seller_id.append(keys)\n product_id = values[\"sku\"].index(selection_id)\n self.__seller.append(values[\"first_name\"] + \" \" + values[\"last_name\"])\n self.__sku.append(values[\"sku\"][product_id])\n self.__price.append(values[\"prices\"][product_id])\n self.__product_name.append(values[\"products\"][product_id])\n self.__pos.append(product_id)\n self.__check_out_qty.append(selection_qty)",
"title": ""
},
{
"docid": "c1ac6afebd277ad77e422efa246f2ad9",
"score": "0.47989768",
"text": "def addItem(self, itemId, itemQty):\r\n self.items[itemId] += itemQty\r\n return self",
"title": ""
},
{
"docid": "d10ce6669d3c4fce854bab14bf47b1b7",
"score": "0.47970992",
"text": "def add_item(self, item):\n LOG.info('Add Product')\n LOG.info(\n \"Add the item: %s , price: %s, quantity: %s\"\n , item.get_name(), item.get_price(), item.get_quantity())\n if not item.get_name() in self.store_stock:\n self.db_manager.insert_element(item)\n else:\n current_item_quantity = \\\n self.store_stock[item.get_name()].get_quantity() \\\n + item.get_quantity()\n self.db_manager.update_quantity_field(quantity=current_item_quantity,\n id=self.store_stock[item.get_name()].get_id())\n LOG.info(\"Complete to add a item successful\")",
"title": ""
},
{
"docid": "1f5be3b43a6445bef2e20fac0d8943eb",
"score": "0.4792217",
"text": "def combine_the_anon_cart(self, request):\n cart = request.session.get('cart')\n if cart:\n for item in cart.items():\n product = Product.objects.get(id=item[0])\n if product.user == request.user:\n messages.error(request, 'You can\\'t buy your product')\n else:\n quantity = item[1]['quantity']\n self.add(product, quantity)\n del request.session['cart']",
"title": ""
},
{
"docid": "5f77055a0765db17ee93d22d7bb315d6",
"score": "0.47913775",
"text": "def total_items_sold(self, given_date):\n # ensure given_date is a date\n assert isinstance(given_date, date), \"given_date must be datetime.date()\"\n\n # get quantity from order_lines for given_date\n quantities = [\n i[0]\n for i in cur.execute(\"\"\"\n SELECT quantity\n FROM orders\n INNER JOIN order_lines ON orders.id=order_lines.order_id\n WHERE created_at LIKE ?\n \"\"\", (given_date.isoformat()+'%',))\n ]\n\n # sum quantity\n return sum(quantities)",
"title": ""
},
{
"docid": "2a07d888277c16437ea1ed144e02e719",
"score": "0.47881973",
"text": "def oh_quantity_by_location(self):\n location_list = self.transactions.all().values(\"location\").distinct().order_by(\"location\")\n my_dict = dict()\n for l in location_list:\n location = Location.objects.get(pk=l[\"location\"])\n my_dict[location] = self.get_oh_quantity(location)\n return my_dict",
"title": ""
},
{
"docid": "b7a2e2d6e3ad23e24d00b97541db3187",
"score": "0.47833753",
"text": "def returnItemsWithMinSupport(itemSet, transactionList, minSupport, freqSet):\n _itemSet = set()\n localSet = defaultdict(int)\n\n for item in itemSet:\n for transaction in transactionList:\n if item.issubset(transaction):\n freqSet[item] += 1\n localSet[item] += 1\n\n # print 'localSet',localSet\n for item, count in localSet.items():\n # print item,count\n support = float(count)/len(transactionList)\n\n if support >= minSupport:\n _itemSet.add(item)\n\n return _itemSet",
"title": ""
},
{
"docid": "8e7d505ef1815ef26d24544b025773ca",
"score": "0.47816607",
"text": "def main():\r\n group_of_list = []\r\n group = {}\r\n count = 0\r\n dict_input_food_and_price = input()\r\n dict_input_food_and_price = json.loads(dict_input_food_and_price)\r\n while True:\r\n yourname = input()\r\n if yourname == \"End\":\r\n break\r\n group[yourname] = 0\r\n for food, price in dict_input_food_and_price.items():\r\n while True:\r\n yourname = input()\r\n if yourname == \"End\":\r\n break\r\n group_of_list.append(yourname)\r\n count += 1\r\n total = price/count\r\n for food in group_of_list:\r\n group[food] = group.get(food)+total\r\n count = 0\r\n group_of_list = []\r\n print(group)",
"title": ""
},
{
"docid": "57fee8f9f7f940bfc0f162909b7ee2b2",
"score": "0.47804844",
"text": "def reducer(L):\n\tdistribution = {}\n\tfor item in L:\n\t\tfor record in item:\n\t\t\tif record in distribution:\n\t\t\t\tdistribution[record] += item[record]\n\t\t\telse:\n\t\t\t\tdistribution[record] = item[record]\n\t\titem.clear()\n\treturn distribution",
"title": ""
},
{
"docid": "7dbbe3574385be484fb2e72fb6016bb1",
"score": "0.47785863",
"text": "def add_item(self, new_item):\n self.items_with_price.update(new_item)",
"title": ""
},
{
"docid": "301be53ed8845a4768b33a75ed332e78",
"score": "0.4775439",
"text": "def getFood( self, amount ) :\n\t\treturn self.getSupply( amount )",
"title": ""
},
{
"docid": "6e98031becdd3b82c96afc040c16348c",
"score": "0.47707295",
"text": "def get_total(self, **kwargs):\n return self.get_price_per_item(**kwargs) * self.get_quantity(**kwargs)",
"title": ""
},
{
"docid": "99bf9dc41926c19f7c1798b9a962799d",
"score": "0.47650927",
"text": "def cardlist_sum(cardlist1, cardlist2):\n cards = deepcopy(cardlist1)\n for name, amount in cardlist2.items():\n if name not in cards:\n cards[name] = 0\n cards[name] = cards[name] + amount\n return cards",
"title": ""
},
{
"docid": "af3e40827b1596cd982bb10623718ab9",
"score": "0.47612268",
"text": "def food_add_favorite(self, food_id, serving_id=None, number_of_units=None):\n\n params = {\"method\": \"food.add_favorite\", \"format\": \"json\", \"food_id\": food_id}\n\n if serving_id and number_of_units:\n params[\"serving_id\"] = serving_id\n params[\"number_of_units\"] = number_of_units\n\n response = self.session.get(self.api_url, params=params)\n return self.valid_response(response)",
"title": ""
},
{
"docid": "46d8b637023ae9e022ee3a1e96d883cd",
"score": "0.47490332",
"text": "def food_insert(self, name, calories, protein, fat, carbs, fiber):\n pass",
"title": ""
},
{
"docid": "7d8c0504c732838f7986103ecf4d9fb0",
"score": "0.47435322",
"text": "def add(self, product, quantity=1, update_quantity=False):\n product_vencode = str(product.vencode)\n if product_vencode not in self.cart:\n self.cart[product_vencode] = {'quantity': 0,\n 'price': str(product.price)}\n if update_quantity:\n self.cart[product_vencode]['quantity'] = quantity\n else:\n self.cart[product_vencode]['quantity'] += quantity\n self.save()",
"title": ""
},
{
"docid": "abd12d614dd01fa7d1dc8d540d13a970",
"score": "0.4740142",
"text": "def add(self, item):",
"title": ""
},
{
"docid": "96d3c09b955b186c27327828c5c875b7",
"score": "0.47401217",
"text": "def test_unique_quantities(self):\n collection = Collection(['air__temperature', 'water__temperature'])\n\n quantities = collection.quantities()\n\n self.assertEqual(len(quantities), 1)\n self.assertTrue('temperature' in quantities)",
"title": ""
},
{
"docid": "3f0904ee6421b4e1fba7c11b177be256",
"score": "0.47290277",
"text": "def get_sum(items=[]):\n\t\tresult = 0\n\t\tif items:\n\t\t\tresult = reduce(lambda x, y: x+y, items)\n\t\treturn result",
"title": ""
},
{
"docid": "baa64214b750820c4bb4b85c71e8520d",
"score": "0.47211656",
"text": "def _amount_all(self):\n for order in self:\n amount_untaxed = 0.0\n for line in order.depence:\n amount_untaxed += line.montant\n\n order.update({\n\n 'Montant_total': amount_untaxed,\n })",
"title": ""
},
{
"docid": "105d02d685d7278913bf8246ba61d913",
"score": "0.4696378",
"text": "def union(a, b):\n c = a.copy()\n for item, quantity in b.items():\n if item in c:\n c[item] += quantity\n else:\n c[item] = quantity\n return c",
"title": ""
},
{
"docid": "43a8f10d46c49f4fc02930e22a6c1f53",
"score": "0.46955162",
"text": "def sum_list(input_list: typing.List[float]) -> float:\n return sum(input_list)",
"title": ""
},
{
"docid": "6b6714d61699a90caccf507a1439ba0b",
"score": "0.46904996",
"text": "def add_food(self, food: FoodProp) -> None:\n if not self.can_place_food(food):\n raise Exception('Cannot place a food with that color on the colorful plate')\n\n self.foods.append(food)",
"title": ""
},
{
"docid": "68e5cbf07cac2a1423f45e8e5ac6e692",
"score": "0.46781105",
"text": "def __len__(self):\n return sum(item[\"qty\"] for item in self.basket.values())",
"title": ""
},
{
"docid": "0b38093e769550aa59eb195f38c23eb6",
"score": "0.46772686",
"text": "def add_item(self, product, price):\n if not product in self.items_in_cart:\n self.items_in_cart[product] = price\n print (product + \" added.\")\n else:\n print (product + \" is already in the cart.\")",
"title": ""
},
{
"docid": "1690b8513ce9e630d2fbdaaf6b81ca8d",
"score": "0.4674813",
"text": "def sum_mixed_list(input_list: List[Union[int, float]]) -> float:\n return reduce(lambda x, y: x+y, input_list)",
"title": ""
},
{
"docid": "8d97e4fa24429dc883f373bd0ca99355",
"score": "0.46693996",
"text": "def _product_association(self):\n # Find orders that bought the target item\n orders = self.data[self.data[self.item_col]==self.target][self.order_col]\n \n # Find all items in those orders\n items = self.data[self.data[self.order_col].isin(orders)]\n \n # Recommend items frequently bought with the target item \n self._find_recs(items.groupby(self.item_col).size())\n self._print_recs(\"popular items other customers bought\")",
"title": ""
},
{
"docid": "e92b39dd58dbb549782499edade31dac",
"score": "0.46683574",
"text": "def autoPopulateInventory(self):\n if not self._permanent:\n self.clearInventory()\n\n # Randomly select the itemCount\n itemCount = int(getRandomItemFromList(self._numOfItemsCarried))\n\n if not itemCount: # 0 items carried\n return True\n\n for itemNum in range(1, itemCount + 1): # +1 due to exclusive ranges\n itemId = getRandomItemFromList(self._itemCatalog)\n if not itemId:\n continue\n if \"/\" not in itemId:\n continue\n\n dLog(\"creature.autoPopulateInventory: obj = \" + itemId)\n oType, oNum = itemId.split(\"/\")\n obj1 = ObjectFactory(oType, oNum)\n obj1.load()\n self.addToInventory(obj1)\n return True",
"title": ""
},
{
"docid": "4025849a832f2176dc523dd3048e8ffc",
"score": "0.46618393",
"text": "def raw_ingredients_exact(item_set):\n remaining = item_set.copy()\n raw_ingredients = {}\n while any(remaining):\n item, quantity = list(remaining.items())[0]\n item_object = items[item]\n del remaining[item]\n if item_object.raw:\n raw_ingredients = union(raw_ingredients, {item: quantity})\n else:\n item_list = item_object.recipe.ingredients.items()\n for ingredient, ingredient_quantity in item_list:\n remaining = union(\n remaining,\n multiply(\n {ingredient: ingredient_quantity},\n quantity / item_object.recipe.quantity,\n ),\n )\n return raw_ingredients",
"title": ""
},
{
"docid": "5a586b690c5f4ad80786ce24861767f9",
"score": "0.46590447",
"text": "def frequentOneItem(self):\n\n candidate = {}\n # global finalPatterns, minSup, Database\n # self.minSup = self.minSup\n for i in range(len(self.Database)):\n for j in range(len(self.Database[i])):\n if self.Database[i][j] not in candidate:\n candidate[self.Database[i][j]] = 1\n else:\n candidate[self.Database[i][j]] += 1\n self.finalPatterns = {keys: value for keys, value in candidate.items() if value >= self.minSup}",
"title": ""
},
{
"docid": "cac81ccf764ad1c0b428a32da2a2677e",
"score": "0.46445563",
"text": "def add_item(request):\n\n if request.method == \"GET\":\n return HttpResponseRedirect(reverse(\"index\"))\n \n # Variable definition\n try:\n dish = Dish.objects.get(pk=(request.POST[\"dish\"]))\n except:\n return HttpResponseRedirect(reverse(\"index\"))\n\n extra = request.POST.getlist('extra')\n\n # No toppings Pizza validator\n if (dish.pk == 1 or dish.pk == 2 or dish.pk == 11 or dish.pk == 12) and len(extra) != 0:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 1 topping Pizza validator\n if (dish.pk == 3 or dish.pk == 4 or dish.pk == 13 or dish.pk == 14) and len(extra) != 1:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 2 toppings Pizza validator\n if (dish.pk == 5 or dish.pk == 6 or dish.pk == 15 or dish.pk == 16) and len(extra) != 2:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 3 toppings Pizza validator\n if (dish.pk == 7 or dish.pk == 8 or dish.pk == 17 or dish.pk == 18) and len(extra) != 3:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # Special Pizza Pizza validator\n if (dish.pk == 9 or dish.pk == 10 or dish.pk == 19 or dish.pk == 20) and len(extra) != 5:\n return HttpResponseRedirect(reverse(\"index\"))\n \n # Item variables\n extras = []\n price = dish.price\n\n for item in extra:\n new_extra = Extra.objects.get(pk=(item))\n extras.append(new_extra)\n price += new_extra.price\n\n new_item = Item(dish=dish, price=price)\n new_item.save()\n for item in extras:\n new_item.extras.add(item)\n\n # Cart Handling\n try:\n cart = Cart.objects.get(user=request.user)\n except:\n cart = Cart(user=request.user)\n cart.save()\n cart.items.add(new_item)\n cart.save()\n\n return HttpResponseRedirect(reverse(\"index\"))",
"title": ""
},
{
"docid": "a923dccac387cb688c107df06e72d941",
"score": "0.4643559",
"text": "def sum(items):\n if items is None or len(items) == 0:\n return None\n try:\n return sum(items)\n except:\n msg = \"Error while addition: %s\" %sys.exc_info()[0]\n sys.stderr.write(msg)\n raise",
"title": ""
},
{
"docid": "e5e463dda3a19d276a1fe9f6ee356c72",
"score": "0.46426684",
"text": "def test_add_item(self):\n before = len(self.shopping_list_1.items)\n self.shopping_list_1.add_item(self.item_3)\n after = len(self.shopping_list_1.items)\n self.assertEqual(1, after - before,\n msg='The object should add an item to the instance')",
"title": ""
},
{
"docid": "a0d6261296989b18c47c219f2b8c7f83",
"score": "0.46423283",
"text": "def get_total(self):\n\n # self.base_price = self.get_base_price()\n\n if self.species == \"Christmas\":\n self.base_price *= 1.5\n total = (1 + self.tax) * self.qty * self.base_price\n return total",
"title": ""
}
]
17ff9d02a0b166bd8a9c253d3fe795a0
Update details of an Experiment. Update details of an Experiment with specific Experiment Id.
[
{
"docid": "a90932de5b0dd8cc3450221c5e50b85a",
"score": "0.7416664",
"text": "def update_experiment(request, pk):\n\n required_data = request.data.get(\"requiredData\")\n tests_config = request.data.get(\"testsConfig\")\n expiration = request.data.get(\"expiration\")\n allow_multiple_answers = request.data.get(\"allowMultipleAnswers\")\n disabled = request.data.get(\"disabled\")\n name = request.data.get(\"name\")\n\n try:\n print('ENTERED')\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if tests_config is None or len(tests_config) == 0:\n return Response('At least one test must be provided', status=HTTP_400_BAD_REQUEST)\n\n if expiration is None:\n return Response('Valid expiration date must be provided', status=HTTP_400_BAD_REQUEST)\n\n try:\n experiment.name = name\n experiment.requiredDataConfig = required_data\n experiment.testsConfig = tests_config\n experiment.expiration = expiration\n experiment.allowMultipleAnswers = allow_multiple_answers\n experiment.disabled = disabled\n experiment.save()\n except Exception as e:\n return Response(str(e), HTTP_400_BAD_REQUEST)\n\n serialized = ExperimentSerializer(experiment).data\n\n return Response(serialized, HTTP_200_OK)",
"title": ""
},
{
"docid": "0aace5c2053a4192cc2f99b73768ad86",
"score": "0.67339313",
"text": "def update_experiment_metadata(\n writer_client, experiment_id, name=None, description=None\n):\n logger.info(\"Modifying experiment %r\", experiment_id)\n request = write_service_pb2.UpdateExperimentRequest()\n request.experiment.experiment_id = experiment_id\n if name is not None:\n logger.info(\"Setting exp %r name to %r\", experiment_id, name)\n request.experiment.name = name\n request.experiment_mask.name = True\n if description is not None:\n logger.info(\n \"Setting exp %r description to %r\", experiment_id, description\n )\n request.experiment.description = description\n request.experiment_mask.description = True\n try:\n grpc_util.call_with_retries(writer_client.UpdateExperiment, request)\n except grpc.RpcError as e:\n if e.code() == grpc.StatusCode.NOT_FOUND:\n raise ExperimentNotFoundError()\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\n raise PermissionDeniedError()\n if e.code() == grpc.StatusCode.INVALID_ARGUMENT:\n raise InvalidArgumentError(e.details())\n raise",
"title": ""
},
{
"docid": "546f21b219d724d0964dc0327c6c25f2",
"score": "0.66571933",
"text": "def test_submit_edit_experiment(self):\n experiment = self.create_test_experiment(name=\"old_name\")\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.name, \"new_name\")",
"title": ""
},
{
"docid": "3cfc1af097dea409e1e342c357e44dae",
"score": "0.61671877",
"text": "def refresh(self) -> None:\n self.update_from_remote_data(self._api_client.experiment_get(self.uuid))",
"title": ""
},
{
"docid": "9e7efd2d39f68200aa5173115ccf47fd",
"score": "0.6142266",
"text": "def test_edit_experiment_view(self):\n experiment = self.create_test_experiment()\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")",
"title": ""
},
{
"docid": "6c08855f31c71f29263100fbc5d63505",
"score": "0.59430104",
"text": "def update_emp(db, id, hoursTrained, trainedStatus):\r\n trainingInfo = {\"Hours Trained\" : hoursTrained,\r\n \"Training Completed\" : trainedStatus}\r\n\r\n db.collection(\"Employees\").document(id).set(trainingInfo)",
"title": ""
},
{
"docid": "58e74d8d26fab10630d7b37236175eee",
"score": "0.5902595",
"text": "def test_submit_edit_started_experiment_changes_name_and_notes(self):\n experiment = self.create_test_experiment(name=\"old_name\", notes=\"old_notes\",\n tracks_finalized=True)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n old_track = self.create_test_track(experiment=experiment, name=\"old_name_track\")\n experiment_json = {\n \"name\": \"new_name\", \"notes\": \"new_notes\", \"tracks\": [{\"id\": old_track.id,\n \"name\": \"new_track_name\"}],\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment_json)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.name, \"new_name\")\n self.assertEquals(experiment.notes, \"new_notes\")\n self.assertEquals(experiment.tracks.all()[0].name, \"new_track_name\")",
"title": ""
},
{
"docid": "5b3b67f1d59d87115dd9fc81a90aab86",
"score": "0.58990973",
"text": "def update(self, collab_id: str, project_id: str, expt_id: str, **updates):\n return self._execute_operation(\n operation=\"put\",\n url=self._generate_single_url(\n collab_id=collab_id,\n project_id=project_id, \n expt_id=expt_id\n ),\n payload=updates\n )",
"title": ""
},
{
"docid": "4272881909e59c29f6e7e94aae595276",
"score": "0.5827913",
"text": "def update(id_exp=None):\n\n current_app.logger.info(\"PUT /sync route with id: %s\" % id_exp)\n\n (gfg_id, exp_name, session_id), session, resp = parse_id_exp(id_exp)\n\n # Check JSON validity\n if utils.check_valid_json(request.get_data()):\n valid_json = json.loads(request.get_data())\n # session.datastring = valid_json\n else:\n resp = {\"status\": \"bad request\"}\n current_app.logger.error(\"Invalid JSON\")\n\n current_app.logger.info(\n \"Current trial: %s, unique_id: %s, experiment name: %s, session id: %s \" % (valid_json['currenttrial'],\n valid_json['uniqueid'], valid_json['experimentname'], valid_json['sessionid']))\n\n # For each trial, pass to appropriate parser, if not in db\n for json_trial in valid_json['data']:\n if exp_name == \"category_switch\":\n experiment_class = CategorySwitch\n elif exp_name == \"keep_track\":\n experiment_class = KeepTrack\n else:\n current_app.logger.error(\"%s does not exist\" % (exp_name))\n resp = {\"status\": \"bad request\"}\n\n db_trial, new = db_utils.get_or_create(db.session,\n experiment_class, gfg_id=gfg_id, session_id=session_id,\n trial_num=json_trial['current_trial'])\n\n # If the trial is new, add data\n if new:\n db_trial.add_json_data(json_trial)\n db.session.commit()\n\n # For each event, pass to parser, if not in db\n for json_event in valid_json['eventdata']:\n db_event, new = db_utils.get_or_create(db.session, EventData,\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name, \n timestamp = utils.convert_timestamp(json_event['timestamp']))\n\n if new:\n db_event.add_json_data(json_event)\n db.session.commit()\n\n # For the QuestionData, pass to parser, if not in db\n for json_ques in valid_json['questiondata']:\n db_ques, new = db_utils.get_or_create(db.session, QuestionData,\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name)\n\n if new:\n db_ques.add_json_data(json_ques)\n db.session.commit()\n\n if resp is None:\n resp = {\"status\": \"user data saved\"}\n return jsonify(**resp)",
"title": ""
},
{
"docid": "6b05deea5431803e1eac4738e4cc6a36",
"score": "0.5811839",
"text": "def update(self, request, pk=None):\n selected_routine = request.query_params.get('routine', None) \n\n try: \n exercise = Exercise.objects.get(pk=pk)\n\n exercise.routine = Routine.objects.get(pk=selected_routine)\n exercise.description = request.data['description']\n exercise.save()\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)\n \n except ValidationError as ex:\n return Response({\"reason\": ex.message}, status=status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "e1ea28e67ff7a830d61e1851c7da9f33",
"score": "0.56713605",
"text": "async def edit_training_examples(\n intent: str,\n id: str,\n request_data: TextData,\n current_user: User = Depends(auth.get_current_user),\n):\n mongo_processor.edit_training_example(\n id, request_data.data, intent.lower(), current_user.get_bot(), current_user.get_user()\n )\n return {\"message\": \"Training Example updated!\"}",
"title": ""
},
{
"docid": "b610548abb62bf8777e59ea744a47f65",
"score": "0.5661236",
"text": "def put(self, exploration_id):\n exploration = exp_services.get_exploration_by_id(exploration_id)\n version = self.payload.get('version')\n _require_valid_version(version, exploration.version)\n\n commit_message = self.payload.get('commit_message')\n change_list = self.payload.get('change_list')\n\n try:\n exp_services.update_exploration(\n self.user_id, exploration_id, change_list, commit_message)\n except utils.ValidationError as e:\n raise self.InvalidInputException(e)\n\n self.values.update(self._get_exploration_data(exploration_id))\n self.render_json(self.values)",
"title": ""
},
{
"docid": "4f444f6fc62b19e72dd10f8ea281178b",
"score": "0.5650311",
"text": "def update(self, entity_id, details, delete_existing):",
"title": ""
},
{
"docid": "829b7025e64765315044ed5f1182fe55",
"score": "0.5622866",
"text": "def causx_edit_an_entity(id):\n project = get_project()\n updated_entry = request.get_json()[\"updated_entry\"]\n entity = causx_set_variable(project, id, updated_entry)\n response=dict(entity=entity)\n calc_params = get_calc_params(project, data_required=False)\n if calc_params:\n response[\"layers\"] = get_qnodes_layer(calc_params)\n return response, 200",
"title": ""
},
{
"docid": "d3a677e8ac2b6e770914054e27e7250c",
"score": "0.56208825",
"text": "def test_submit_edit_started_experiment_changes_existing_tracks(self):\n experiment = self.create_test_experiment(name=\"old_name\", tracks_finalized=False,\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n track1 = self.create_test_track(experiment=experiment, name=\"A\")\n track2 = self.create_test_track(experiment=experiment, name=\"B\")\n self.create_test_track_weight(experiment=experiment, track=track1)\n self.create_test_track_weight(experiment=experiment, track=track2)\n track_count = experiment.tracks.count()\n experiment_json = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": False,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": track1.id, \"weighting\": 30, \"name\": \"C\"},\n {\"id\": track2.id, \"weighting\": 70, \"name\": \"D\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment.id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment_json)\n )\n self.assertOkay(response)\n experiment = Experiment.objects.get(id=experiment.id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.tracks.count(), track_count)\n track1 = experiment.tracks.get(id=track1.id)\n track2 = experiment.tracks.get(id=track2.id)\n self.assertEquals(track1.name, \"C\") #Checks name has changed\n self.assertEquals(track2.name, \"D\")\n self.assertEquals(track1.weight.weighting, 30) #Checks weighting has changed\n self.assertEquals(track2.weight.weighting, 70)",
"title": ""
},
{
"docid": "02d99f571b3fa51ed6afda42fff2838c",
"score": "0.56005096",
"text": "def put(self, id):\n data = request.get_json()\n return update_a_reporter(id=id, data=data)",
"title": ""
},
{
"docid": "3eca505c37f3bd0881bd1ed010599a0a",
"score": "0.559187",
"text": "def test_edit_experiment_view_started_experiment(self):\n experiment = self.create_test_experiment()\n experiment.tracks_finalized = True\n experiment.save()\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")",
"title": ""
},
{
"docid": "e11f93fd1499f93fcb17b45a21891a2d",
"score": "0.55208725",
"text": "def test_submit_edit_started_experiment_does_not_change_tracks(self):\n experiment = self.create_test_experiment(name=\"old_name\", tracks_finalized=True,\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_tracks = experiment.tracks.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"},\n {\"id\": None, \"weighting\": None, \"name\": \"B\"},\n {\"id\": None, \"weighting\": None, \"name\": \"C\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.tracks.count(), no_tracks)",
"title": ""
},
{
"docid": "2db940d31dbabec99f670e73eaba4b31",
"score": "0.5507888",
"text": "def test_submit_edit_experiment_nonexistent(self):\n experiment_id = NONEXISTENT_EXPERIMENT_ID\n experiment = {\"name\": \"new_name\", \"notes\": \"\"}\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertEquals(response.status_code, 404)",
"title": ""
},
{
"docid": "214e821c70df6edb4046bfd747a7248d",
"score": "0.5484202",
"text": "def edit(project):\n if g.user:\n session['exp'] = project\n abstract = s.get_experiment_data(g.user, project, 'abstract')\n gl = s.get_experiment_data(g.user, project, 'gl')\n ec = s.get_experiment_data(g.user, project, 'ec')\n fn = s.get_experiment_data(g.user, project, 'fn')\n return render_template('edit.html', project=project, abstract=abstract, gl=gl, ec=ec, fn=fn)\n return redirect(url_for('login'))",
"title": ""
},
{
"docid": "f70ff7a5bde05f7ffc13d2ef2cf7f6ab",
"score": "0.5455811",
"text": "def update(self, entity_id, details, delete_existing=False):\n self._validate_entity_id(entity_id)\n\n if not isinstance(details, dict):\n msg = (_(\"Provided details %s is not valid dict.\")\n % details)\n raise ValueError(msg)\n\n return self._storage.update(\n entity_id, details, delete_existing)",
"title": ""
},
{
"docid": "95f73067644b0b8d431330b8b573696d",
"score": "0.54527766",
"text": "def update(self, event_id, event_time=None, **kwargs):\n pass",
"title": ""
},
{
"docid": "95f73067644b0b8d431330b8b573696d",
"score": "0.54527766",
"text": "def update(self, event_id, event_time=None, **kwargs):\n pass",
"title": ""
},
{
"docid": "95f73067644b0b8d431330b8b573696d",
"score": "0.54527766",
"text": "def update(self, event_id, event_time=None, **kwargs):\n pass",
"title": ""
},
{
"docid": "5ba50b0f354cac4d2eb92581c8b79298",
"score": "0.5451184",
"text": "def update(self, id, data):\n return self.make_request('update', '/' + id, data)",
"title": ""
},
{
"docid": "53e08e1e5e63528ecaa3e670768710da",
"score": "0.54415154",
"text": "def put(user_id,self):\n form = ResearchInfoUpdateForm(request.form)\n if form.validate():\n \n research_info = ResearchInformation.query.filter((ResearchInformation.id == form.research_id.data) & (ResearchInformation.user_id == user_id))\n if research_info is None:\n return make_response(jsonify({'error':'Please give an appropriate research ID'}),400)\n update_dict = {}\n for key, value in form.data.items():\n if value and key != 'research_id':\n update_dict[key] = value\n try:\n research_info.update(update_dict)\n db.session.commit()\n except:\n return make_response(jsonify({'error' : 'Database Connection Problem'}),500)\n return make_response(jsonify({'msg' : ' Successfully changed'}),201)\n\n else:\n return make_response(jsonify({'error':'Wrong input format'}),400)",
"title": ""
},
{
"docid": "83b67ad4ce2e3e31eb6207122f1a66f3",
"score": "0.5403792",
"text": "def put(self):\n return employee_service.edit_employee(get_jwt_identity(), get_uuid(request), **api.payload), 201",
"title": ""
},
{
"docid": "d2ac49e54bcfac440fb624d369239316",
"score": "0.53886133",
"text": "def _add_experiment(self, experiment_key, variation_id):\n\n experiment_id = self.config.get_experiment_id(experiment_key)\n self.params[EXPERIMENT_PARAM_FORMAT.format(experiment_prefix=Params.EXPERIMENT_PREFIX,\n experiment_id=experiment_id)] = variation_id",
"title": ""
},
{
"docid": "0fd8bb0e660cd252ad87d2e7d500422a",
"score": "0.53784376",
"text": "def put(self, id):\n\t\ttoken_parser.parse_args()\n\t\trecord = Record.query.get(id)\n\t\tif not record:\n\t\t\treturn error_response('RECORD NOT FOUND', 404) \n\t\t# PERMISSION_DENIED because don't want user to know this record id exists\n\t\tif record.runner.id!=int(get_jwt_identity()) and not min_access_level(self, ROLES['admin']):\n\t\t\treturn error_response('RECORD NOT FOUND', 404) \n\n\t\tdata = request.get_json() or {}\n\t\ttry:\n\t\t\trecord.update(data)\n\t\texcept ValueError as error:\n\t\t\treturn error_response('RECORD NOT FOUND', 404)\n\t\texcept Exception as error:\n\t\t\treturn error_response('PERMISSION DENIED', 403)\n\t\tdb.session.commit()\n\t\treturn make_response(jsonify(record.to_dict()), 200)",
"title": ""
},
{
"docid": "7a8c7feb5e4f073a780db8763a8c3a47",
"score": "0.53570676",
"text": "def test_edit_report(self):\n report = self.create_report()\n pk = report[0].pk\n my_url = report_url(pk)\n response = self.client.put(\n my_url,\n self.updated_report,\n **self.headers,\n format='json'\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)",
"title": ""
},
{
"docid": "3c6b8e8628b2258d34781373f3756603",
"score": "0.5355502",
"text": "def put(self, id):\n # Get the json object from the request body\n data = request.get_json()\n # Check all fields for availability and get the new data if the field is available\n if 'email' not in data:\n email = None\n else:\n email = data[\"email\"]\n if 'first_name' not in data:\n first_name = None\n else:\n first_name = data[\"first_name\"]\n if 'last_name' not in data:\n last_name = None\n else:\n last_name = data[\"last_name\"]\n if 'availability' not in data:\n availability = None\n else:\n availability = data[\"availability\"]\n\n # Update all requested fields of the requested interviewer, if there is an interviewer with the id, else return\n # an error\n interviewer = interviewer_ctrl.update_interviewer(id, email, first_name, last_name, availability)\n if not interviewer:\n response = \"Interviewer with id %s does not exist or email %s is already taken by another interviewer\" % (\n id, email), 400\n else:\n response = interviewer, 200\n return response",
"title": ""
},
{
"docid": "15c1f7722e5e0c8697db10072db3f404",
"score": "0.53356797",
"text": "def put(self, id):\n data = request.json\n args = request.args\n return update_unit(id, data, args)",
"title": ""
},
{
"docid": "f5a0bd39cf438b04b13e0fd2dad2b75f",
"score": "0.5282175",
"text": "def update_project(id=None, name=None, description=None):\n pass",
"title": ""
},
{
"docid": "7da3e669c6d3439e143564ac0ff1622b",
"score": "0.5282065",
"text": "def test_edit_experiment_view_last_modified_updated(self):\n experiment = self.create_test_experiment()\n experiment.name += \" (updated)\"\n response = self.client.post(reverse(\"ab_testing_tool_submit_edit_experiment\",\n args=(experiment.id,)),\n content_type=\"application/json\",\n data=experiment.to_json())\n self.assertEquals(response.content, \"success\")\n updated_experiment = Experiment.objects.get(id=experiment.id)\n self.assertLess(experiment.updated_on, updated_experiment.updated_on,\n response)",
"title": ""
},
{
"docid": "a63400c45bfcc9bce89953c8ae6b3701",
"score": "0.5281295",
"text": "def put(self, id):\n data = request.get_json()\n if 'email' not in data:\n email = None\n else:\n email = data[\"email\"]\n if 'first_name' not in data:\n first_name = None\n else:\n first_name = data[\"first_name\"]\n if 'last_name' not in data:\n last_name = None\n else:\n last_name = data[\"last_name\"]\n if 'availability' not in data:\n availability = None\n else:\n availability = data[\"availability\"]\n interviewee = interviewee_ctrl.update_interviewee(id, email, first_name, last_name, availability)\n if not interviewee:\n response = \"Interviewee with id %s does not exist or email %s is already taken by another interviewee\" % (\n id, email), 400\n else:\n response = interviewee, 200\n return response",
"title": ""
},
{
"docid": "f85bb5f0f4fbddd046d215c53c2b171b",
"score": "0.52504665",
"text": "def put(self,request,employee_id):\n try:\n get_data = Employee.objects.get(pk=employee_id)\n employee_data = EmployeeSerializer(get_data,data=request.data)\n if not(employee_data.is_valid()):\n return ApiResponse().error(\"Error while update employee details\",400)\n employee_data.save()\n return ApiResponse().success(employee_data.data,200)\n except Exception as err:\n print(err)\n return ApiResponse().success(\"Error\",500)",
"title": ""
},
{
"docid": "ecba54809858f5347c6dc258ddbd143b",
"score": "0.5228171",
"text": "def update_testimonial():\n data = request.get_json()\n description = data.get('description')\n title = data.get('title')\n image = data.get('image')\n rank = data.get('rank')\n testimonial_id = data.get('id')\n\n if (not title) or (not description) or (not rank) or (not testimonial_id):\n return jsonify(result={'failed_msg': \"Unanle to save testimonial with missing fields\"})\n\n return jsonify(result=save_testimonial(title, description, rank, image, _id=testimonial_id))",
"title": ""
},
{
"docid": "997ffa7f45afa19ce3c55b4b84f758f6",
"score": "0.5227724",
"text": "async def update(\n self,\n id: int,\n data: EnvironmentUpdateSchema\n ) -> Record:\n\n async with self.database.transaction():\n query = (\n environments_table.update()\n .where(environments_table.c.id == id)\n .values(\n name=data.name,\n description=data.description,\n updated_at=datetime.now()\n )\n .returning(\n environments_table.c.id,\n environments_table.c.name,\n environments_table.c.code,\n environments_table.c.description,\n environments_table.c.created_at,\n environments_table.c.updated_at,\n environments_table.c.deleted_at,\n environments_table.c.is_deleted\n )\n )\n\n return await self.database.fetch_one(query)",
"title": ""
},
{
"docid": "1b7edbfabd11852be3e16d3e6569fbe4",
"score": "0.52266294",
"text": "def update(self, model, id, update_with):\n raise NotImplementedError('You have not implemented an update func in your client class')",
"title": ""
},
{
"docid": "15f18479a5de36268b4534b705d906ee",
"score": "0.5196483",
"text": "def test_put_experiment_valid_permission(self):\n url = '/' + version + '/collection/unittestcol/experiment/unittestexp/'\n data = {'description': 'A new experiment for unit tests. Updated'}\n\n response = self.client.put(url, data=data)\n self.assertEqual(response.status_code, 200)",
"title": ""
},
{
"docid": "cf3a3156e9bbff73bb66d428cba3cc4a",
"score": "0.5194518",
"text": "def test_submit_edit_experiment_changes_assignment_method_to_weighted(self):\n experiment = self.create_test_experiment(name=\"old_name\")\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_track_weights = experiment.track_probabilites.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": False,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": 20, \"name\": \"A\"},\n {\"id\": None, \"weighting\": 80, \"name\": \"B\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.track_probabilites.count(), no_track_weights + 2)",
"title": ""
},
{
"docid": "3ca66dab826730162433142988260ed8",
"score": "0.5188264",
"text": "def test_submit_edit_experiment_changes_assignment_method_to_uniform(self):\n experiment = self.create_test_experiment(\n name=\"old_name\", assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_tracks = experiment.tracks.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"},\n {\"id\": None, \"weighting\": None, \"name\": \"B\"},\n {\"id\": None, \"weighting\": None, \"name\": \"C\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.UNIFORM_RANDOM)\n self.assertEquals(experiment.tracks.count(), no_tracks + 3)",
"title": ""
},
{
"docid": "7565e22ff55c1a4dbfaef25b7830ba89",
"score": "0.5184008",
"text": "def put(self, id):\n report = self.mydb.get_by_id(id)\n \n if report:\n data = request.get_json()\n report.createdBy = data['username']\n report.type = data['flag']\n report.location = data['location']\n report.status = data['status']\n report.image = data['image']\n report.video = data['video']\n report.comment = ['comment']\n return make_response(jsonify({\n \"message\":\"Report edited successfully\",\n \"data\": report.serialize()\n }), 201)\n else:\n return make_response(jsonify({\n \"message\": \"ID invalid, no report found\"\n }),400)",
"title": ""
},
{
"docid": "160cb1102a31e22433b0a95d1b19c287",
"score": "0.5183066",
"text": "def upload_experiment(self, experiment: Experiment) -> None:\n data = {\n 'device_name': experiment.backend_name,\n 'type': experiment.type,\n 'extra': experiment.extra,\n }\n if experiment.start_datetime:\n data['start_time'] = local_to_utc_str(experiment.start_datetime)\n if experiment.tags:\n data['tags'] = experiment.tags\n if experiment.uuid:\n data['uuid'] = experiment.uuid\n response_data = self._api_client.experiment_upload(data)\n experiment.update_from_remote_data(response_data)",
"title": ""
},
{
"docid": "a0650d7cb60a6083e8c9359cba962c35",
"score": "0.517982",
"text": "def update(self, typ, id, **kwargs):\n return self._load(self._request(self._url(typ, id), 'PUT', kwargs))",
"title": ""
},
{
"docid": "8b5e1688a20dad535a152c61484ada45",
"score": "0.517151",
"text": "def update(self, step_name, worker_id):\n record = ReportClient().get_record(step_name, worker_id)\n logging.debug(\"Get Record=%s\", str(record))\n self.search_alg.update(record.serialize())\n ParameterSharing().remove()\n logging.info(f\"Update Success. step_name={step_name}, worker_id={worker_id}\")\n logging.info(\"Best values: %s\", ReportServer().print_best(step_name=General.step_name))",
"title": ""
},
{
"docid": "4cc0ccaf21cc95495b47366271ce2579",
"score": "0.51659554",
"text": "def post_experiment(self):\n if not self.has_prepared_out:\n self.prepare_experiment()\n\n for exp, prod, desc in self.experiment.overview:\n print exp, prod, desc\n dt = json.dumps(\\\n {'name': exp,\n 'desc': desc})\n resp = requests.post(\n self.server_url + 'experiments/',\n data=dt,\n headers={'Content-type':'application/json'})\n print resp",
"title": ""
},
{
"docid": "3a1a25ea3994e5d6663400ef4dc22a54",
"score": "0.5152818",
"text": "def edit_quiz(request_ctx, course_id, id, quiz_notify_of_update, **request_kwargs):\n\n path = '/v1/courses/{course_id}/quizzes/{id}'\n payload = {\n 'quiz[notify_of_update]' : quiz_notify_of_update,\n }\n url = request_ctx.base_api_url + path.format(course_id=course_id, id=id)\n response = client.put(request_ctx, url, payload=payload, **request_kwargs)\n\n return response",
"title": ""
},
{
"docid": "76aca4be1ce01e6f0e21a2963567cc01",
"score": "0.5147998",
"text": "def test_edit_experiment_view_nonexistent(self):\n e_id = NONEXISTENT_EXPERIMENT_ID\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(e_id,)))\n self.assertTemplateNotUsed(response, \"ab_tool/edit_experiment.html\")\n self.assertEquals(response.status_code, 404)",
"title": ""
},
{
"docid": "ddb24b14a3442c8472d5c6d09956b062",
"score": "0.51298386",
"text": "def update_project(self, id: str, name: str = None, description: str = None) -> Dict:\n pass",
"title": ""
},
{
"docid": "9609d770f7c1fe4197f40d9c80ed4b91",
"score": "0.5129037",
"text": "def update_environment(context: click.Context, application_id: str, environment_id: str, name: str, url: str) -> None:\n api = context.obj\n application = api.application_by_id(application_id)\n environment = api.environment_by_id(application, environment_id)\n updated_environment = api.update_environment(application, environment, name, url)\n table = [[\"ID\", \"Name\", \"URL\"], [updated_environment.id, updated_environment.name, updated_environment.url]]\n click.echo(tabulate.tabulate(table, headers=\"firstrow\"))",
"title": ""
},
{
"docid": "da739f61d8dd8506648f0f235b47402d",
"score": "0.51289636",
"text": "def edit_experience(request, id):\r\n experience = Experience.objects.get(pk=id)\r\n context = {\r\n 'experience': experience\r\n }\r\n if request.method == 'POST':\r\n experience.user_id = request.POST['user_id' or None]\r\n experience.company_name = request.POST['company_name' or None]\r\n experience.job_title = request.POST['job_title' or None]\r\n experience.job_description = request.POST['job_description' or None]\r\n experience.year_from = request.POST['year_from' or None]\r\n experience.year_to = request.POST['year_to' or None]\r\n\r\n experience.save()\r\n messages.success(request, 'Experience Updated Successfully')\r\n return redirect('dashboard', request.user.pk)\r\n else:\r\n return render(request, 'accounts/edit_experience.html', context)",
"title": ""
},
{
"docid": "1d62bd10f0ddf13b60680d431d1214af",
"score": "0.5126918",
"text": "def update_teacher(email=None, zipcode=None, bio=None, \n mobile_number=None, days_of_week=None,teaching_experience_in_hours=None,\n pay_rate_per_hour=None, pod_id=None, img_url=None, covid_risk_profile_id=None):\n\n teacher = db.session.query(Teacher).filter(Teacher.email==email).one()\n teacher.zipcode=zipcode \n teacher.bio=bio \n teacher.mobile_number=mobile_number\n teacher.days_of_week=days_of_week\n teacher.teaching_experience_in_hours=teaching_experience_in_hours\n teacher.pay_rate_per_hour=pay_rate_per_hour\n teacher.pod_id=pod_id\n teacher.img_url=img_url\n teacher.covid_risk_profile_id=covid_risk_profile_id\n\n db.session.commit()\n\n return teacher",
"title": ""
},
{
"docid": "608457875b3bc5272855818c41796e9c",
"score": "0.5124847",
"text": "def edit(obj, index, description, duration, code, task_id, project_id):\n with ots_filestore(obj) as timesheet_storage:\n timesheet_edited = timesheet_storage.edit_timesheet(\n index,\n description=description,\n duration=duration,\n task_code=code,\n task_id=task_id,\n project_id=project_id,\n )\n if timesheet_edited:\n click.echo('Timesheet updated.')\n else:\n click.echo('Nothing to update.')",
"title": ""
},
{
"docid": "3cbff9690ae2f28c9373856792e9b190",
"score": "0.50883085",
"text": "def update(self, data, id):\n with db.session.begin_nested():\n self.query.filter_by(id=id).update(data)",
"title": ""
},
{
"docid": "f165c0fcebdc404642a446e6277bbe24",
"score": "0.5067855",
"text": "def test_submit_edit_experiment_wrong_course(self):\n experiment = self.create_test_experiment(name=\"old_name\",\n course_id=TEST_OTHER_COURSE_ID)\n data = {\"name\": \"new_name\", \"notes\": \"\"}\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment.id,)),\n content_type=\"application/json\", data=json.dumps(data)\n )\n self.assertError(response, UNAUTHORIZED_ACCESS)",
"title": ""
},
{
"docid": "a61125735c80419b89b3c2ef8fdb6cc7",
"score": "0.50544035",
"text": "def update_suite(suiteId):\n\t res = requests.post('https://api.ghostinspector.com/v1/suites/'+suiteId+'/?apiKey='+apiKey)\n\t return res",
"title": ""
},
{
"docid": "67143faae1b601cdc1166defe338b29d",
"score": "0.505348",
"text": "def step_impl(context, post_id):\n context.payload = constants.POST_INFORMATION_TO_UPDATE\n context.response = request.put(context.url + \"/%s\" % post_id, context.payload)",
"title": ""
},
{
"docid": "c923e8f9fc820942ebc8185747323eb5",
"score": "0.5051238",
"text": "def get_experiment(experiment_id):\n req = requests.get(\"{}experiments/{}/?format=json\".format(__base_url__, experiment_id))\n metadata = req.json()\n if metadata['status'] != 'error':\n save_metadata(metadata)\n else:\n print 'Error fetching metadata for {}'.format(experiment_id)",
"title": ""
},
{
"docid": "0c7f7bf6fb01612d985fa2b143d8134b",
"score": "0.5051149",
"text": "def wxt_update_person(person_id, data, myheaders):\n update_person_url = 'https://api.ciscospark.com/v1/people/{0}'\n response = requests.put(update_person_url.format(person_id),\n data=json.dumps(data),\n headers=myheaders)\n return response",
"title": ""
},
{
"docid": "4a558e28f4fcb1556894539bd864dab7",
"score": "0.5049372",
"text": "def experiment_detail(request, exp_name):\n exp = get_object_or_404(Experiment, name=exp_name)\n reports = ExperimentReport.objects.filter(experiment=exp)\n\n return render_to_response(\n \"splango/experiment_detail.html\",\n {\"title\": exp.name, \"exp\": exp, \"reports\": reports},\n RequestContext(request))",
"title": ""
},
{
"docid": "0758bd2dc137efe10c77c05015e4b113",
"score": "0.50357556",
"text": "def update_movie_details(db: Session, sl_id: int, details: schema.UpdateMovie):\n db.query(model.Movies).filter(model.Movies.id == sl_id).update(vars(details))\n db.commit()\n return db.query(model.Movies).filter(model.Movies.id == sl_id).first()",
"title": ""
},
{
"docid": "e912a37963fc2345ab045aa5222960ba",
"score": "0.5034493",
"text": "def update_by_id(self, id=None, **kwargs):\n if not id:\n raise PardotAPIArgumentError('id is required to update a prospect.')\n response = self._post(path='/do/update/id/{id}'.format(id=id), params=kwargs)\n return response",
"title": ""
},
{
"docid": "bec9d8e407070c9a77a984f35bc91736",
"score": "0.5032699",
"text": "def update_test_run(self, run_update_model, project, run_id):\n route_values = {}\n if project is not None:\n route_values['project'] = self._serialize.url('project', project, 'str')\n if run_id is not None:\n route_values['runId'] = self._serialize.url('run_id', run_id, 'int')\n content = self._serialize.body(run_update_model, 'RunUpdateModel')\n response = self._send(http_method='PATCH',\n location_id='cadb3810-d47d-4a3c-a234-fe5f3be50138',\n version='5.0',\n route_values=route_values,\n content=content)\n return self._deserialize('TestRun', response)",
"title": ""
},
{
"docid": "41e64bd368f6696cc8782c9dac2067f6",
"score": "0.50115055",
"text": "def update(self, request, pk=None):\n player = Player.objects.get(user=request.auth.user)\n suspect = Suspect.objects.get(pk=pk)\n suspect.player = player\n suspect.name = request.data[\"name\"]\n suspect.description = request.data[\"description\"]\n guilty = Guilty.objects.get(pk=request.data[\"guiltyId\"])\n suspect.guilty = guilty\n movie = Movie.objects.get(pk=request.data[\"movieId\"])\n suspect.suspect_image_url = request.data[\"suspectImageUrl\"]\n suspect.movie = movie\n \n suspect.save()\n\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)",
"title": ""
},
{
"docid": "489dce730c6e0515e0c8902d35851c78",
"score": "0.49720645",
"text": "def update_test(testId):\n\t res = requests.post('https://api.ghostinspector.com/v1/tests/'+testId+'/?apiKey='+apiKey)\n\t return res",
"title": ""
},
{
"docid": "6b1c0e0826bdbdb476676fb3b9967ce7",
"score": "0.49566084",
"text": "def test_put_experiment_no_permissions(self):\n url = '/' + version + '/collection/col1/experiment/exp1/'\n data = {'description': 'A new experiment for unit tests. Updated'}\n\n response = self.client.put(url, data=data)\n self.assertEqual(response.status_code, 403)",
"title": ""
},
{
"docid": "60ee48539f966381bc6e042c165a54a8",
"score": "0.49527475",
"text": "def put(self, id):\n return DAO.update(id, api.payload)",
"title": ""
},
{
"docid": "3c8a67ef451a5b9d3cfbff45c9f08289",
"score": "0.49498895",
"text": "def delete_experiment(writer_client, experiment_id):\n logger.info(\"Deleting experiment %r\", experiment_id)\n request = write_service_pb2.DeleteExperimentRequest()\n request.experiment_id = experiment_id\n try:\n grpc_util.call_with_retries(writer_client.DeleteExperiment, request)\n except grpc.RpcError as e:\n if e.code() == grpc.StatusCode.NOT_FOUND:\n raise ExperimentNotFoundError()\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\n raise PermissionDeniedError()\n raise",
"title": ""
},
{
"docid": "60ebeb2bb4b736d0d454c31d04209770",
"score": "0.49268797",
"text": "def test_edit_experiment_view_with_tracks_weights(self):\n experiment = self.create_test_experiment()\n experiment.assignment_method = Experiment.WEIGHTED_PROBABILITY_RANDOM\n track1 = self.create_test_track(name=\"track1\", experiment=experiment)\n track2 = self.create_test_track(name=\"track2\", experiment=experiment)\n self.create_test_track_weight(experiment=experiment, track=track1)\n self.create_test_track_weight(experiment=experiment, track=track2)\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")",
"title": ""
},
{
"docid": "1a83bb2f35b27541623f09db41fce462",
"score": "0.49165675",
"text": "def update_results(student_id, experiment_id, student_response):\n\n replies = get_experiment_replies(experiment_id)\n replies_dict = json.loads(replies)\n\n replies_dict['replies'].append({\"id\": int(student_id), \"response\": student_response})\n\n new_replies = json.dumps(replies_dict)\n\n con = sql.connect(DB_PATH)\n cur = con.cursor()\n\n cur.execute(\"UPDATE experiments SET replies = ? WHERE id = ?\", (new_replies, experiment_id))\n\n cur.execute(\"DELETE FROM experiments_students \"\n \"WHERE student_id = ? AND experiment_id = ?\", (student_id, experiment_id))\n\n con.commit()\n\n con.close()",
"title": ""
},
{
"docid": "d8b1a5247263b3eaa3ed81d6c8455d38",
"score": "0.49110433",
"text": "def update(self, kwargs):\n if self._status in [DEFAULT, DETACHED]:\n if any([k not in self.param_keys() and k in self._specials for k in kwargs]):\n raise ValueError('Key not recognised!')\n else:\n self.__dict__.update(kwargs)\n else:\n raise ValueError('Parameters of a created experiment cannot be updated.')",
"title": ""
},
{
"docid": "b058a3ccc0c492a3a2054e916ba606a3",
"score": "0.4910217",
"text": "def update(self, model, rec_id, data):\n\n url = self.base_url + model + '/' + str(rec_id)\n data = format_write_data(data)\n return self.session.put(url, data=data)",
"title": ""
},
{
"docid": "9c3a3f85820ec6698cda066db2a4dae5",
"score": "0.4900522",
"text": "def detail_put(self, request, id, *args, **kwargs):\n raise NotAllowed()",
"title": ""
},
{
"docid": "84ef2def248ce0213ba3a195c608662a",
"score": "0.48950928",
"text": "def edit_education(request, id):\r\n education = Education.objects.get(pk=id)\r\n context = {\r\n 'education': education\r\n }\r\n if request.method == 'POST':\r\n education.user_id = request.POST['user_id' or None]\r\n education.school = request.POST['school' or None]\r\n education.field_of_study = request.POST['field_of_study' or None]\r\n education.qualification = request.POST['qualification' or None]\r\n education.year = request.POST['year' or None]\r\n\r\n education.save()\r\n messages.success(request, 'Education Updated Successfully, you may add another !')\r\n return redirect('dashboard', request.user.pk)\r\n else:\r\n return render(request, 'accounts/edit_education.html', context)\r\n\r\n # DELETE EDUCATION\r",
"title": ""
},
{
"docid": "1f9b95546a734ec60c96a828783d5784",
"score": "0.4894346",
"text": "def test_experiment_specification(self, client, ephemeral_storage):\n\n storage = ephemeral_storage.storage\n\n _add_experiment(storage, name=\"a\", version=1, _id=1)\n _add_trial(storage, experiment=1, id_override=\"ae8\", status=\"completed\")\n\n response = client.simulate_get(\"/experiments/status/a\")\n assert response.status == \"200 OK\"\n assert response.json == {\n \"trials_completed\": 1,\n \"best_trials_id\": \"7bbfdb8c684aa5f4be324a09d7da94af\",\n \"best_evaluation\": 0.05,\n \"start_time\": \"0001-01-01 00:00:00\",\n \"finish_time\": \"0001-01-02 00:00:00\",\n \"max_trials\": 10,\n \"nb_trials\": 1,\n \"progress\": 0.1,\n \"trial_status_count\": {\"completed\": 1},\n \"elapsed_time\": \"23:59:50\",\n \"sum_of_trials_time\": \"23:59:50\",\n \"eta\": \"8 days, 23:58:30\",\n \"eta_milliseconds\": 777510000.0,\n }",
"title": ""
},
{
"docid": "527afa47e52ca91f15e0bf2975749899",
"score": "0.48926717",
"text": "def edit(id):\n\n pet = Pet.query.get_or_404(id)\n form = EditPetForm(obj=pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.data[\"photo_url\"]\n pet.notes = form.data[\"notes\"]\n pet.available = form.data['available']\n db.session.commit()\n flash(f\"{pet.name} updated.\", 'success')\n return redirect(url_for('index'))\n\n else:\n return render_template(\"edit_pet_form.html\", form=form, pet=pet)",
"title": ""
},
{
"docid": "f73c9c211624982ebce62505144b3717",
"score": "0.48906887",
"text": "def update(self):\n response = self.api(self.id).get()\n self._update_fields(response)",
"title": ""
},
{
"docid": "3aac6ddcfb59a9b9059b7ceec9897878",
"score": "0.48723197",
"text": "def put(self, id):\n data = request.json\n if data[\"entityID\"] != id:\n return {\"message\": \"entityID property in the incoming json object and id parameter in the URL path are\"\n \"not matched\"}, 400\n evn = EventService.get_by_id(id)\n if not evn:\n return {\"message\": \"The event does not exist\"}, 404\n else:\n return EventService.update(data, id)",
"title": ""
},
{
"docid": "4ee013b5fc5b6473163ae2bdd9f275cd",
"score": "0.4865083",
"text": "def test_experiment_specification(self, client, ephemeral_storage):\n\n storage = ephemeral_storage.storage\n\n _add_experiment(storage, name=\"a\", version=1, _id=1)\n _add_trial(storage, experiment=1, id_override=\"ae8\", status=\"completed\")\n\n response = client.simulate_get(\"/experiments/a\")\n\n assert response.status == \"200 OK\"\n\n assert response.json[\"name\"] == \"a\"\n assert response.json[\"version\"] == 1\n assert response.json[\"status\"] == \"not done\"\n assert response.json[\"trialsCompleted\"] == 1\n assert response.json[\"startTime\"] == \"0001-01-01 00:00:00\" # TODO\n assert response.json[\"endTime\"] == \"0001-01-02 00:00:00\" # TODO\n assert len(response.json[\"user\"])\n assert response.json[\"orionVersion\"] == \"x.y.z\"\n\n _assert_config(response.json[\"config\"])\n _assert_best_trial(response.json[\"bestTrial\"])",
"title": ""
},
{
"docid": "d7d6f7bd49709c1e26e4af92b13dda56",
"score": "0.48624727",
"text": "def api_employee_edit(request):\r\n\r\n if request.method.lower() == 'post':\r\n\r\n data = request.POST.dict()\r\n employee_id = int(request.POST['id'])\r\n\r\n try:\r\n employee = Employee.objects.get(id=employee_id)\r\n\r\n form_employee = EmployeeEditForm(data=data, instance=employee)\r\n\r\n if form_employee.is_valid():\r\n form_employee.save()\r\n return ApiResponse.http(status=ApiResponse.ST_SUCCESS, message='Employee successfully updated!', employee_id=employee_id)\r\n else:\r\n errors = dict(form_employee.errors)\r\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Validation errors.', errors=errors)\r\n except Employee.DoesNotExist:\r\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Invalid employee.')\r\n else:\r\n return ApiResponse.http(status=ApiResponse.ST_FORBIDDEN, message='Use Post!')",
"title": ""
},
{
"docid": "9bcea40c6b9b0fdaa251a9fb9aff1687",
"score": "0.4860615",
"text": "def delete_experiment(request, pk):\n\n try:\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.deleted is True:\n return Response('Experiment with given ID is already deleted', status=HTTP_400_BAD_REQUEST)\n\n experiment.deleted = True;\n experiment.save()\n\n return Response(HTTP_200_OK)",
"title": ""
},
{
"docid": "c3d2f2168376364b60de974564c9a455",
"score": "0.48573977",
"text": "def patch(self, incident_id):\n data = request.get_json()\n\n # script to allow only location request.body\n key_list = [*data]\n if (len(key_list) == 1 and key_list[0] == \"location\"):\n\n new_incident_instance = IncidentsModel()\n res = new_incident_instance.edit_incident(incident_id, data)\n\n if res:\n return {\n \"status\": 200,\n \"data\": [{\n \"id\": res[\"id\"],\n \"message\": \"incident record has been updated\"\n }]\n }, 200\n else:\n return {\n \"status\": 404,\n \"error\": \"Not found for id {}\".format(incident_id)\n }, 404\n else:\n return {\n \"status\": 403,\n \"error\": \"Bad Request only location is allowed\"\n }, 403",
"title": ""
},
{
"docid": "b76af9e574ad062068ef873994d3d48a",
"score": "0.48331708",
"text": "def update_teste_cmd(teste_id, **teste_properties):\n return UpdateTesteCommand(teste_id, **teste_properties)",
"title": ""
},
{
"docid": "b77d5e6d015b83efb6dd8d19931b53fe",
"score": "0.48324943",
"text": "def get_experiment(request, pk):\n\n try:\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.deleted is True:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.disabled is True:\n return Response('This experiment is currently unavailable', status=HTTP_423_LOCKED)\n\n if experiment.expiration < timezone.now():\n return Response('Experiment with given ID already expired', status=HTTP_400_BAD_REQUEST)\n\n serialized = ExperimentSerializer(experiment).data\n\n return Response(serialized, status=HTTP_200_OK)",
"title": ""
},
{
"docid": "834ae376dc46243ee0e99aa11693c662",
"score": "0.48319104",
"text": "def test_update_resolution(self):\n self.client = APIClient()\n login = self.client.login(username=self.username, password=self.password)\n self.assertEqual(login, True)\n url = reverse('resolution-detail', args=[str(self.upd_pk)])\n\n data = {\n 'description': 'Updated description',\n }\n response = self.client.put(url, data=data, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution update Failed')\n self.assertDictContainsSubset(data, response.data, \"Modified Resolution didn't contain the updated description\")\n\n #lets doublecheck we get updated data back\n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution failed to be Retrieved')\n self.assertDictContainsSubset(data, response.data, \"Resolution didn't contain the updated description\")",
"title": ""
},
{
"docid": "4601be0b78971f6a8aa160d438fd449c",
"score": "0.48254716",
"text": "def test_modify_report(self):\n print \"(\"+self.test_modify_report.__name__+\")\", self.test_modify_report.__doc__\n resp = self.client.put(self.url,\n data=json.dumps(self.report_1_req),\n headers={\"Content-Type\": JSON})\n self.assertEquals(resp.status_code, 204)\n #Check that the report has been modified\n resp2 = self.client.get(self.url)\n self.assertEquals(resp2.status_code, 200)\n data = json.loads(resp2.data)\n #Check that the report has been modified with the new data\n self.assertEquals(data[\"details\"], self.report_1_req[\"details\"])",
"title": ""
},
{
"docid": "747712a9fe8b9ca2a54838b34b33ff28",
"score": "0.48183075",
"text": "def edit(id):\n sensor = Sensor.query.get(id)\n form = SensorForm(request.form, obj=sensor)\n if request.method == 'POST':\n form.populate_obj(sensor)\n if form.validate():\n db.session.commit()\n flash('You have successfully modified the sensor.', 'success')\n return redirect(url_for('sensors.edit', id=sensor.id))\n return render_template('sensors/edit.html', form=form, sensor=sensor)",
"title": ""
},
{
"docid": "a19c62d0f4ff880e9eb69dbb30f04d74",
"score": "0.48122746",
"text": "def update(self, obj): \n if isinstance(obj, Entity):\n url_filter = \"v1/ai/skillEntity/name/{}\"\n else:\n url_filter = \"v1/ai/skill/name/{}\"\n url_filter = url_filter.format(obj.name)\n obj._update_json_data()\n response = self.carol.call_api(url_filter, method = 'PUT', data=obj._json_data)\n return response",
"title": ""
},
{
"docid": "6175d3e3097744ca2912d63fc9461e1a",
"score": "0.48087907",
"text": "def put(self, id):\n pass",
"title": ""
},
{
"docid": "07dd4375dada7f132f3e9aad7897d557",
"score": "0.48071754",
"text": "def update(ctx, name, image, ingredients, id):\n repo = getDrinkRepo()\n\n updateArgs = {}\n if image is not None:\n updateArgs[\"image\"] = image\n\n if ingredients is not None:\n updateArgs[\"ingredients\"] = ingredients\n\n if name is not None and name != \"\":\n updateArgs[\"name\"] = name\n\n success = False if len(updateArgs) <= 0 else repo.updateDrink(id, **updateArgs)\n\n if success:\n ctx.invoke(describe, drink=id)\n else:\n click.echo(\"Unable to update drink. Make sure the ID is right.\")",
"title": ""
},
{
"docid": "568612e39e13d479cbcd2c0e0e449a7d",
"score": "0.4804053",
"text": "def update_research_plan(self, id, title, description, assigned_to_id, objective, evaluation_process):\n if (id is None or id == '' or id == 0) or \\\n (title is None or title == '') or \\\n (assigned_to_id is None or assigned_to_id == '' or assigned_to_id == 0):\n raise Exception('id and title, assigned_to_id are required.')\n\n op = Operation(silverpond_schema.Mutation)\n parent = op.update_research_plan(id=id, title=title, description=description, assigned_to_id=assigned_to_id,\n objective=objective, evaluation_process=evaluation_process)\n\n # Field Set\n parent.errors()\n researchplan = parent.research_plan()\n fieldset_research_plan(researchplan)\n fieldset_experiment(researchplan.experiments())\n fieldset_research_plan_metrics(researchplan.research_plan_metrics())\n\n ql_endpoint = HTTPEndpoint(self.GRAPHQL_API_ENDPOINT, self.headers)\n data = ql_endpoint(op)\n obj = op + data\n if len(obj.update_research_plan.errors) > 0:\n raise Exception(obj.update_research_plan.errors[0])\n return obj.update_research_plan.research_plan",
"title": ""
},
{
"docid": "9b0ef8b4263c6629412f32c2845466ca",
"score": "0.47978824",
"text": "def edit_recipe(id):\n recipe = mongo.db.recipes.find_one({\"_id\": ObjectId(id)})\n\n if session[\"user\"] != recipe['created_by']:\n return render_template(\"403.html\")\n\n if request.method == \"POST\":\n edit = {\n \"created_by\": session[\"user\"],\n \"image_url\": request.form.get(\"image_url\"),\n \"name\": request.form.get(\"name\"),\n \"desc\": request.form.get(\"desc\"),\n \"instructions\": request.form.getlist(\"step\"),\n \"ingredients\": request.form.getlist(\"ingredient\"),\n \"amount\": request.form.getlist(\"amount\"),\n \"measure\": request.form.getlist(\"measurement\"),\n }\n mongo.db.recipes.update({\"_id\": ObjectId(id)}, edit)\n flash(\"Changes Successfully Saved\")\n\n return render_template(\"edit_recipe.html\", recipe=recipe)",
"title": ""
},
{
"docid": "f62bedbce7972cef7d5388864810ea47",
"score": "0.47965991",
"text": "def update(self, request, pk=None):\n raise MethodNotAllowed(method=\"PUT\")",
"title": ""
},
{
"docid": "f62bedbce7972cef7d5388864810ea47",
"score": "0.47965991",
"text": "def update(self, request, pk=None):\n raise MethodNotAllowed(method=\"PUT\")",
"title": ""
},
{
"docid": "db18595a922a3022c241d41ec0dd8953",
"score": "0.47930816",
"text": "def put(self, id):\n\n data = request.json\n update_service(id, data)\n return None, 204",
"title": ""
},
{
"docid": "0e53e28a07124d9371460d81cf2a1079",
"score": "0.4790036",
"text": "def get_experiment_id(self):\n return self.experiment_id",
"title": ""
},
{
"docid": "53e553e91fdc6114550e464616fdb09f",
"score": "0.47860163",
"text": "def experiment_id(self) -> str:\n return self._experiment_id",
"title": ""
},
{
"docid": "8f101fff17f778b28c18fdd5714ef7e6",
"score": "0.47858354",
"text": "def _update_details(self, details, obj):\n pass",
"title": ""
},
{
"docid": "8a81408420c0f78ee0daaee351debc12",
"score": "0.4765387",
"text": "def put(self, id):\n data = request.json\n Team.query.filter(Team.id == id).update({'name': data.get('name')})\n team = Team.query.filter(Team.id == id).one()\n enrollments = data.get('enrollments')\n team = add_enrollments(enrollments, team)\n\n db.session.commit()\n return team, 204",
"title": ""
}
]
97251e022090c1c096acfb522379fe37
Check to see if the tolerance has been met. Returns False if tolerance is met.
[
{
"docid": "18dae62af5d8fcb325b8c556954aefee",
"score": "0.0",
"text": "def check_tol(g, tol, log_scale_param):\n s = np.exp(log_scale_param)\n for tolerance, gradient in zip(tol, g):\n if abs(gradient) > tolerance / s:\n return True\n return False",
"title": ""
}
]
[
{
"docid": "ce055bab6287f8377db1128076fa364f",
"score": "0.8071129",
"text": "def _tolerance_check(self, tolerance, value):\n if tolerance != None and np.linalg.norm(value) < tolerance:\n print(\"Variable update tolerance was reached. Terminating Search.\")\n return True\n return False",
"title": ""
},
{
"docid": "352bced7ca72b726560ef38f52549699",
"score": "0.7769892",
"text": "def __bool__(self):\n return bool(abs(self) > tolerance)",
"title": ""
},
{
"docid": "a1971746879f68f3648bf7f1b42f064d",
"score": "0.76606834",
"text": "def reached_tolerance(self):\n if sum(self.dp) < self.tolerance:\n rospy.logwarn('[%s] Tolerance of %f achieved kp=%f, ki=%f, kd=%f with best error: %f',\n self.name, self.tolerance, self.p[0], self.p[1], self.p[2], self.best_error)\n return True\n return False",
"title": ""
},
{
"docid": "f482c72478d2317b312759080e7312ba",
"score": "0.7599516",
"text": "def passed(self):\n return self.value_diff <= self.tolerance",
"title": ""
},
{
"docid": "5e0066709419ead4f4919127aa2b7f3d",
"score": "0.7546813",
"text": "def tolerance(computedValue,targetValue,tolerance):\n\n if abs(computedValue-targetValue)<tolerance:\n return True\n return False",
"title": ""
},
{
"docid": "f7a93d71559ef30aa4a60c670707cb2c",
"score": "0.7538815",
"text": "def within_tolerance(self, value: float, reference: float) -> bool:",
"title": ""
},
{
"docid": "0b83b4cce34827842e7ef27ba067fd22",
"score": "0.69806385",
"text": "def tolerance(self):\n return self._tolerance",
"title": ""
},
{
"docid": "0b83b4cce34827842e7ef27ba067fd22",
"score": "0.69806385",
"text": "def tolerance(self):\n return self._tolerance",
"title": ""
},
{
"docid": "0b83b4cce34827842e7ef27ba067fd22",
"score": "0.69806385",
"text": "def tolerance(self):\n return self._tolerance",
"title": ""
},
{
"docid": "0b83b4cce34827842e7ef27ba067fd22",
"score": "0.69806385",
"text": "def tolerance(self):\n return self._tolerance",
"title": ""
},
{
"docid": "0b83b4cce34827842e7ef27ba067fd22",
"score": "0.69806385",
"text": "def tolerance(self):\n return self._tolerance",
"title": ""
},
{
"docid": "fdc569d7b54c21a1edbc947696b74106",
"score": "0.6949747",
"text": "def CheckTolerance(self, value):\n tolerance = self._unitop.GetTolerance()\n error = self.CalculateError(value)\n if error > tolerance:\n self._unitop.PushConsistencyError(self, value)",
"title": ""
},
{
"docid": "5bb76f3f9c3cbbc265d428d33cda9b7b",
"score": "0.67973673",
"text": "def is_satisfied(self, tol=1.0e-6):\n return abs(self()) < tol",
"title": ""
},
{
"docid": "01eed1f9e6d4c494b70631c89a5020eb",
"score": "0.67568636",
"text": "def check_tolerance(self, last_obj, new_obj=0.0):\n if last_obj is not None:\n delta_obj = abs(float(new_obj) - float(last_obj))\n self.mention('delta obj ratio: %.2e' % (delta_obj / self.TOLERANCE))\n return delta_obj < self.TOLERANCE\n return False",
"title": ""
},
{
"docid": "1f484cc19e63f22b759133eb18b11f88",
"score": "0.6700461",
"text": "def inTolerance(self):\n ...",
"title": ""
},
{
"docid": "78cb818baf81b4d40eeb15a3d8f3427e",
"score": "0.6625738",
"text": "def within(a, b, tolerance=1e-5):\n if a==0 and b==0:\n return True\n elif a!=0 and b==0:\n #Avoid divide by zero\n return False\n else:\n return abs((a/b)-1) < tolerance",
"title": ""
},
{
"docid": "df68bc8c4fad2af46f1f98168c37eb99",
"score": "0.6526349",
"text": "def check_difference(shift1, shift2, tolerance=0.05):\r\n mean_diff = (shift1[1] - shift2[1]) ** 2\r\n sigma_diff = (shift1[2] + shift2[2]) ** 2\r\n res = mean_diff > sigma_diff\r\n if abs(shift1[1] - shift2[1]) < tolerance:\r\n res = False\r\n return res",
"title": ""
},
{
"docid": "052e05a351af76d3d07aabb0d11920b3",
"score": "0.6519439",
"text": "def check_difference(shift1, shift2, tolerance=0.05):\n mean_diff = (shift1[1] - shift2[1]) ** 2\n sigma_diff = (shift1[2] + shift2[2]) ** 2\n res = mean_diff > sigma_diff\n if abs(shift1[1] - shift2[1]) < tolerance:\n res = False\n return res",
"title": ""
},
{
"docid": "54b7b3c683e3287d731d3fc658b0b49e",
"score": "0.65151095",
"text": "def approx_eq(x, y, tolerance=1e-5):\n return abs(x - y) < tolerance",
"title": ""
},
{
"docid": "b21b7db657d408a524edb16b0a76a8c5",
"score": "0.64969355",
"text": "def relaxed_allclose(x, y, atol=0.01, pct_wrong_allowable=0.1):\n res = torch.isclose(x, y, atol=atol)\n n_wrong = res.numel() - res.sum()\n n_wrong_allowable = pct_wrong_allowable * res.numel()\n return n_wrong <= n_wrong_allowable",
"title": ""
},
{
"docid": "c98b6d633c65381e53c549fda9118dc5",
"score": "0.64799714",
"text": "def match(self, other, tolerance=3, amtdev=0.00):\n if (tolerance == ()):\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev)\n else:\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev) and \\\n (abs(self.date-other.date) <= datetime.timedelta(days=tolerance))",
"title": ""
},
{
"docid": "66b0b45511093d926ef07d9c350ee676",
"score": "0.64740145",
"text": "def check_convergence(self):\n return False",
"title": ""
},
{
"docid": "f179475d3fa9daa37883a197df0fde02",
"score": "0.647181",
"text": "def _check_convergence(self, tol):\n if hasattr(self, 'coeffs_prev'):\n if torch.abs(self.coeffs - self.coeffs_prev).max() < tol:\n return True\n self.coeffs_prev = self.coeffs.detach().clone()\n return False",
"title": ""
},
{
"docid": "cafa9423f1cf921e7a02d68925ddd74a",
"score": "0.6433718",
"text": "def check_convergence(new_measure, old_measure, direction, threshold):\n\n sign = 1.0 if direction == 'higher' else -1.0\n\n if sign * (new_measure - old_measure) / old_measure < threshold:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "c897e612f977361f4bbbb71eeb1adec3",
"score": "0.6422954",
"text": "def converged(self):\n\n top_lnls = [abs(x['lnL']) for x in self.estimates()][0:2]\n\n # dif = (top_lnls[0] - top_lnls[1]) / (0.5 * (top_lnls[0] + top_lnls[1]))\n dif = top_lnls[0] - top_lnls[1]\n\n if abs(dif) < 0.1:\n return True\n\n else:\n return False",
"title": ""
},
{
"docid": "fae09a0e92a3b5f68c494935dbbc3b46",
"score": "0.6420245",
"text": "def tolerance(*args, **kwargs):\n pass",
"title": ""
},
{
"docid": "dfa65081f2e310884cc7d5b8c35c0003",
"score": "0.63993156",
"text": "def _check_tolerance(self, content, name, value, mos_file):\n\n if (name + \"=\" == \"tolerance=\" and float(value) > 1e-6):\n self._wrong_parameter(mos_file, name, value)",
"title": ""
},
{
"docid": "97ba0fccb0fd64a1b60f0c57d8f1ae49",
"score": "0.6392067",
"text": "def is_not_close_to(self, other, tolerance):\n self._validate_close_to_args(self.val, other, tolerance)\n\n if self.val >= (other-tolerance) and self.val <= (other+tolerance):\n if type(self.val) is datetime.datetime:\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\n h, rem = divmod(tolerance_seconds, 3600)\n m, s = divmod(rem, 60)\n self._err('Expected <%s> to not be close to <%s> within tolerance <%d:%02d:%02d>, but was.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\n else:\n self._err('Expected <%s> to not be close to <%s> within tolerance <%s>, but was.' % (self.val, other, tolerance))\n return self",
"title": ""
},
{
"docid": "598e173b3b57da6b873a58422c1b89d2",
"score": "0.6380607",
"text": "def check_below_threshold(distance, epsilon):\n return distance < epsilon",
"title": ""
},
{
"docid": "ad4e1852afe57b0efaf7eb41c4d73868",
"score": "0.63688385",
"text": "def test_equal_time(t1, t2, tol=1E-3):\n if abs(t1) <= tol:\n res = abs(t1 - t2) <= tol\n else:\n res = abs(t1 - t2) <= tol * abs(t1)\n return res",
"title": ""
},
{
"docid": "55450f03a699b012ba500303f637fc20",
"score": "0.6359971",
"text": "def almost_less(x, y, rtol=1e-14, atol=1e-14):\n return x - y <= atol + np.abs(y) * rtol",
"title": ""
},
{
"docid": "f0ba06fef13eccb4c5e30f8990ea616b",
"score": "0.6354376",
"text": "def get_tolerance():\n return parameters.TOLERANCE",
"title": ""
},
{
"docid": "ea156158ab9f5d2aaf9fc4bdbf001e45",
"score": "0.6347623",
"text": "def tolerance(self, tol):\n self._tolerance = tol",
"title": ""
},
{
"docid": "ea156158ab9f5d2aaf9fc4bdbf001e45",
"score": "0.6347623",
"text": "def tolerance(self, tol):\n self._tolerance = tol",
"title": ""
},
{
"docid": "25409b090e1ef08c7cd44cce91d0d556",
"score": "0.6339426",
"text": "def require_accurate_evaluation(self, noisy_val):\n # Check if the point improves over existing points, \n # or if it could be optimal according to tolerances. \n # In this case, perform a double evaluation.\n best_possible = self.fmin\n if ((noisy_val <= best_possible -\n self.l_settings.eps_impr*max(1.0, abs(best_possible))) or\n (noisy_val <= self.l_settings.target_objval +\n self.l_settings.eps_opt*abs(self.l_settings.target_objval))):\n return True\n else:\n return False",
"title": ""
},
{
"docid": "c7e85303ea78fbed7177461921ba142e",
"score": "0.63144785",
"text": "def assertNowish(self, timestamp, tolerance=1):\n delta = datetime.now() - timestamp \n self.assertTrue(delta.seconds <= tolerance)",
"title": ""
},
{
"docid": "66fe57c29b7f2dc32be82220469ad628",
"score": "0.63110167",
"text": "def is_almost_equal(num_1, num_2):\n threshold = 0.03\n if abs(num_1 - num_2) < threshold:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "e96566cf853f4af55a1845bc40f16ae7",
"score": "0.6305576",
"text": "def close_to(self, other, tol=1e-3):\n for diff in self.iter_differences(other):\n if abs(diff) > tol:\n return False\n return True",
"title": ""
},
{
"docid": "97ec08d14fe9c90904c986522080af42",
"score": "0.62932694",
"text": "def validate(self, maze, tolerance):\n pos = maze.turtlebot_pos\n\n # all negative positions are invalid\n if any(np.asarray(pos) < 0):\n return False\n elif self.state_pixel is None:\n return True\n # if the position does not change dramatically, then it is valid\n elif np.sum(abs(self.state_pixel - pos)) < tolerance:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "c729962a8c959955b7ce8295b5bcdf55",
"score": "0.6289345",
"text": "def tolerance(self):\n return self.settings.hu_tolerance",
"title": ""
},
{
"docid": "c729962a8c959955b7ce8295b5bcdf55",
"score": "0.6289345",
"text": "def tolerance(self):\n return self.settings.hu_tolerance",
"title": ""
},
{
"docid": "59c0b8ed0b1ce02b75778415e3dc7bb9",
"score": "0.628875",
"text": "def tol(self):\n\n return self._tol",
"title": ""
},
{
"docid": "4e00eb39f18c5d9c9b7cfb437e6f9f4d",
"score": "0.62810886",
"text": "def is_goal(self, tolerance):\n \n # if self.goal_pixel is not None:\n # print(\"Distance to goal: \", np.linalg.norm(self.goal_pixel - self.state_pixel))\n return (self.goal_pixel is not None) and np.linalg.norm(self.goal_pixel - self.state_pixel) < tolerance",
"title": ""
},
{
"docid": "e0b5989ad0bf9e75eb987728af518a83",
"score": "0.6276838",
"text": "def make_check_values(self, value_calculated:np.array, value_returned:np.array) -> np.ndarray:\n return all(abs(x-y) <= self.eps for x, y in zip(value_calculated, value_returned))",
"title": ""
},
{
"docid": "4e4c8aba4006b0dfda076ed8c32293b3",
"score": "0.62742877",
"text": "def converged(self, rtol, atol):\n return self.err < rtol * abs(self.mean) + atol",
"title": ""
},
{
"docid": "81a8fd864474efc6c9860a1953ce10be",
"score": "0.6266887",
"text": "def checkAnswer(comment,value,expected,tol=1e-7,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True",
"title": ""
},
{
"docid": "7c499c9f239a76df6adfde8157564e6c",
"score": "0.6262354",
"text": "def close_to_exceeding(self) -> bool:\n if self.max_cuts is not None and self.num_cuts >= self.max_cuts:\n return True\n\n thresh = self.longest_seen\n\n if self.max_duration is not None:\n return self.current + thresh >= self.max_duration - 1e-3 # float precision\n return False",
"title": ""
},
{
"docid": "ca048b2f7eed20824b9ec4952b296ff1",
"score": "0.626226",
"text": "def testEqualityInvalid(self):\n self.assertNotEqual(mox.IsAlmost(1.899), 1.9)",
"title": ""
},
{
"docid": "3aa76e22b72faa026c4886b1c1042820",
"score": "0.62560344",
"text": "def approx_less_eq(number1, number2):\n if number1 < number2 or abs(number1-number2) < 0.0001:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "b20ed3b6e514e81eed5fc1f427ae2ed8",
"score": "0.62403435",
"text": "def is_fulfilled(self, tolerance=1e-2) -> bool:\r\n self.satisfied = True\r\n ianionsite = 0\r\n for isite, site in enumerate(self.lse.structure):\r\n if self.lse.valences[isite] < 0:\r\n if (abs(self.anions_bond_strengths[ianionsite]['bond_strengths_sum'] - 2.0)) > tolerance:\r\n self.satisfied = False\r\n\r\n ianionsite = ianionsite + 1\r\n\r\n return self.satisfied",
"title": ""
},
{
"docid": "7ba8649b0078c87521af46929105746a",
"score": "0.6238806",
"text": "def should_stop(cls, interval_delta, middle_point, epsilon_rel, epsilon_abs):\n return abs(interval_delta) <= epsilon_rel * abs(middle_point) + epsilon_abs",
"title": ""
},
{
"docid": "16a938f0aa9ae53b0660ef67c3505ce2",
"score": "0.6237505",
"text": "def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True",
"title": ""
},
{
"docid": "16a938f0aa9ae53b0660ef67c3505ce2",
"score": "0.6237505",
"text": "def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True",
"title": ""
},
{
"docid": "bf8e86ebc3665ab1a22e210725724cc2",
"score": "0.6235275",
"text": "def first_failure(self) -> bool:\n if self.error_count == (self._tolerance + 1):\n return True\n return False",
"title": ""
},
{
"docid": "862ceb422d3e20a1aa4297ca1f61debc",
"score": "0.6231477",
"text": "def overTolerance(self):\n ...",
"title": ""
},
{
"docid": "761e8922afafb75e52b936db13da0302",
"score": "0.62229717",
"text": "def _check_convergence(self, ll_old = None, ll_new = None):\n\t\tif np.isnan(ll_old) or np.isnan(ll_new):\n\t\t\tif np.sum(np.fabs(self.score)) < self.tol:\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0\n\t\telse:\n\t\t\tif np.abs(ll_old - ll_new) < self.tol:\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0",
"title": ""
},
{
"docid": "42818455aef80a63675e73aa5aa1ce48",
"score": "0.62129897",
"text": "def passed(self):\n return self.nominal_length_mm - self.tolerance < self.length_mm < self.nominal_length_mm + self.tolerance",
"title": ""
},
{
"docid": "ff6845c40999f73e9c0068282d2d8d90",
"score": "0.6206111",
"text": "def satisfied(self):\n if self.current_t is None:\n return False\n return self.current_t >= self.target_t",
"title": ""
},
{
"docid": "9e43890b5778c1c8e01bd2caef100059",
"score": "0.62052995",
"text": "def check_within_epsilon(true_values, estimated_values, epsilon):\n for i in range(len(true_values)):\n if not is_within_epsilon(true_values[i], estimated_values[i], epsilon):\n return False\n return True",
"title": ""
},
{
"docid": "6003c157d1137bfc396a53043442dd09",
"score": "0.6186162",
"text": "def _check_termination(self):\n convergence_radius = 0.1\n\n abs_dist = self._get_dist_to_goal()\n if (abs_dist < convergence_radius):\n logging.debug(\"done - success!\")\n return True\n if (self.num_steps > self.MAX_STEPS):\n logging.debug(\"done - max steps reached\")\n logging.debug(\"final delta to goal \\t{}\".format(abs_dist))\n return True\n else:\n return False",
"title": ""
},
{
"docid": "c3968786a87d2361ac9a3411b5ce4ad9",
"score": "0.6183106",
"text": "def is_convergence(self, eps=1e-6):\n return 1.0 - self.convergence() < eps",
"title": ""
},
{
"docid": "81a478e3f5c74597d60defb839359e63",
"score": "0.6168033",
"text": "def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n\treturn abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)",
"title": ""
},
{
"docid": "6297d904a6c5ecb1de360644656c890f",
"score": "0.61669457",
"text": "def check(self, point):\n value = self.calculate_value(point)\n\n if self._equation:\n if math.fabs(value) > EPSILON:\n return False\n return True\n else:\n if value >= 0:\n return True\n return False",
"title": ""
},
{
"docid": "3c466c5bf1a782b9ebeae9e830d5d875",
"score": "0.61604327",
"text": "def result_is_close_enough(self, result):\n return (np.linalg.norm(result) / np.linalg.norm(self.b)) < self.precision",
"title": ""
},
{
"docid": "4648393a7a9f827347aea27d99f85684",
"score": "0.6160404",
"text": "def converged(self):\n # This insures that the cost has been computed at least twice without checking iterations\n try:\n is_converged = abs(self.cost[-2] - self.cost[-1]) <= self.tol\n except IndexError:\n is_converged = False\n return is_converged",
"title": ""
},
{
"docid": "3ce06aa43bb5ad73239467f4103b9ed0",
"score": "0.6154088",
"text": "def withinEpsilon(x, y, epsilon):\r\n return abs(x - y) <= epsilon",
"title": ""
},
{
"docid": "b2fc040f3290c78528ec01bdab21f953",
"score": "0.6151508",
"text": "def check_answer(self, x, ftol):\n if (self.lb is not None and np.any(x < self.lb) or\n self.ub is not None and np.any(x > self.ub)):\n return False\n\n f = np.sum(self.fun(x) ** 2)\n return f < (1 + ftol) * self.fopt",
"title": ""
},
{
"docid": "3751932a2738cc6efda700136490a635",
"score": "0.6150774",
"text": "def withinToleranceOfGoal(self, possible_node, goal_point, tolerance_m):\n dx, dy = goal_point.x - possible_node.point.x, goal_point.y - possible_node.point.y\n dist = math.sqrt(dx * dx + dy * dy)\n if dist <= tolerance_m:\n return True\n return False",
"title": ""
},
{
"docid": "7877ad34d7fdb9f8246a47f4d0f4651c",
"score": "0.6150306",
"text": "def pass_tol(self):\n pass_tol = self.stat < self.tol\n return pass_tol",
"title": ""
},
{
"docid": "9b5665e2b8bf3a5e9a7c07c7467ccc21",
"score": "0.6146118",
"text": "def on_curve(self, curve, tol=1e-6):\n return self.distance_to_point(curve.closest_point(self)) < tol",
"title": ""
},
{
"docid": "d7d2de72638ff94b890520db4a3ee527",
"score": "0.614382",
"text": "def is_close_to(self, other, tolerance):\n self._validate_close_to_args(self.val, other, tolerance)\n\n if self.val < (other-tolerance) or self.val > (other+tolerance):\n if type(self.val) is datetime.datetime:\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\n h, rem = divmod(tolerance_seconds, 3600)\n m, s = divmod(rem, 60)\n self._err('Expected <%s> to be close to <%s> within tolerance <%d:%02d:%02d>, but was not.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\n else:\n self._err('Expected <%s> to be close to <%s> within tolerance <%s>, but was not.' % (self.val, other, tolerance))\n return self",
"title": ""
},
{
"docid": "1d3859dbee4ccea0f8a16f0893fceca7",
"score": "0.61364233",
"text": "def tolerance(focuser):\n return 0",
"title": ""
},
{
"docid": "ea3c1cea00539b49b223f0de89c48038",
"score": "0.6133284",
"text": "def win(self):\n return abs(self) < 0.4",
"title": ""
},
{
"docid": "e0075ced987e952c0fe93c9be7232991",
"score": "0.6107463",
"text": "def is_satisfied(\n self,\n x: \"CartesianCoordinates\",\n tol: float = 1e-4,\n ) -> bool:\n return abs(self.delta(x)) < tol",
"title": ""
},
{
"docid": "b7d54b5fe2b7b828ebba13bce6f9f414",
"score": "0.61056906",
"text": "def approx_equal(x, y, atol=.01):\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol",
"title": ""
},
{
"docid": "b7d54b5fe2b7b828ebba13bce6f9f414",
"score": "0.61056906",
"text": "def approx_equal(x, y, atol=.01):\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol",
"title": ""
},
{
"docid": "3a1f4bdc249cb1e97c3ff44c4588e500",
"score": "0.6102711",
"text": "def check_convergence(\n prev_loss,\n cur_loss,\n cur_step,\n iter_below_tol,\n abs_loss_chg_tol=0.001,\n min_num_iter=100,\n max_iter_below_tol=50\n):\n has_converged = False\n\n # Check if we have reached the desired loss tolerance.\n loss_diff = abs(prev_loss - cur_loss)\n if loss_diff < abs_loss_chg_tol:\n iter_below_tol += 1\n else:\n iter_below_tol = 0\n\n if iter_below_tol >= max_iter_below_tol:\n has_converged = True\n\n if cur_step < min_num_iter:\n has_converged = False\n\n return has_converged, iter_below_tol",
"title": ""
},
{
"docid": "eff80409fd268c1870d26d9ad330a405",
"score": "0.6100981",
"text": "def same(x,y):\n print x,y\n if abs(x-y) < 0.000001:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "666dc76b0995415179aa619056de6e3d",
"score": "0.6099752",
"text": "def test_rmsd_convergence(self):\n\n # http://en.wikipedia.org/wiki/\n # Non-linear_least_squares#Convergence_criteria\n try:\n r1 = self.history[\"rmsd\"][-1]\n r2 = self.history[\"rmsd\"][-2]\n except IndexError:\n return False\n\n tests = [\n abs((r2[0] - r1[0]) / r2[0]) < self._rmsd_tolerance if r2[0] > 0 else True\n ]\n\n return all(tests)",
"title": ""
},
{
"docid": "f56d59064290fa3d889a3e8c5b3948dc",
"score": "0.6090564",
"text": "def withinEpsilon(x, y, epsilon):\n\t\t return abs(x - y) <= epsilon",
"title": ""
},
{
"docid": "3b136615e4b36f758caaa630be12e493",
"score": "0.60889125",
"text": "def is_time_around(expected_time, loop=None, delta=.01):\n now = loop.time() if loop else time.time()\n return (expected_time - delta) <= now <= (expected_time + delta)",
"title": ""
},
{
"docid": "53bc44057f3e8cfc7bf7c71e9074e729",
"score": "0.608647",
"text": "def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:\n return abs(value_1 - value_2) <= delta",
"title": ""
},
{
"docid": "5a4e870bbde087be74dc9e5b654a6cda",
"score": "0.6082421",
"text": "def check_below_threshold(distance, epsilon):\n\n accepted = False\n for i in range(len(epsilon)):\n if epsilon[i] >= distance[i] >= 0:\n accepted = True\n else:\n accepted = False\n break\n return accepted",
"title": ""
},
{
"docid": "fcd33f8d6dc55e034f7e449659c687d8",
"score": "0.60819095",
"text": "def isViolated(self):\n upVar = self._findValue(\"upVar\")\n lowVar = self._findValue(\"lowVar\")\n freeVar = self._findValue(\"freeVar\")\n result = abs(upVar + lowVar) >= const.EPS\n if result:\n log.debug(\n \"isViolated %s, upVar %s, lowVar %s, freeVar %s result %s\"\n % (self.name, upVar, lowVar, freeVar, result)\n )\n log.debug(f\"isViolated value lhs {self.findLHSValue()} constant {self.RHS}\")\n return result",
"title": ""
},
{
"docid": "3e153b1e868caa0e2263a65e3d9fd63d",
"score": "0.60777473",
"text": "def check_pass_or_fail(self,\n epsilon_failure_tolerance =np.nan, epsilon_failure_tolerance_default =None,\n non_finite_data_tolerance =np.nan, non_finite_data_tolerance_default =None,\n total_data_failure_tolerance=np.nan, total_data_failure_tolerance_default=None,\n min_acceptable_r_squared =np.nan, min_acceptable_r_squared_default =None\n ) :\n\n passValues = [ ]\n \n # test the epsilon value tolerance\n \n # get the tolerance for failures compared to epsilon\n epsilonTolerance = epsilon_failure_tolerance if epsilon_failure_tolerance is not np.nan else epsilon_failure_tolerance_default\n\n # did we fail based on the epsilon?\n failed_fraction = self.comparison.diff_outside_epsilon_fraction\n passed_epsilon = None if (epsilonTolerance is None) else (failed_fraction <= epsilonTolerance)\n passValues.append(passed_epsilon)\n\n # test the nonfinite tolerance\n \n # get the tolerance for failures in amount of nonfinite data (in spatially valid areas)\n nonfiniteTolerance = non_finite_data_tolerance if non_finite_data_tolerance is not np.nan else non_finite_data_tolerance_default\n \n # did we fail based on nonfinite data\n non_finite_diff_fraction = self.finiteData.finite_in_only_one_fraction\n passed_nonfinite = None if (nonfiniteTolerance is None) else (non_finite_diff_fraction <= nonfiniteTolerance)\n passValues.append(passed_nonfinite)\n\n # test if the total failed percentage is acceptable\n \n # get the total percentage of failed data that is acceptable\n totalFailTolerance = total_data_failure_tolerance if total_data_failure_tolerance is not np.nan else total_data_failure_tolerance_default\n \n # did we fail based on all data failures?\n passed_all_percentage = None if (totalFailTolerance is None) else ((non_finite_diff_fraction + failed_fraction) <= totalFailTolerance)\n passValues.append(passed_all_percentage)\n\n # test the r-squared correlation coefficent\n \n # get the minimum acceptable r-squared correlation coefficient\n min_r_squared = min_acceptable_r_squared if (min_acceptable_r_squared is not np.nan) else min_acceptable_r_squared_default\n\n # did we fail based on the r-squared correlation coefficient?\n r_squared_value = None if (min_r_squared is None) else self.comparison.r_squared_correlation\n passed_r_squared = None if (min_r_squared is None) else (r_squared_value >= min_r_squared)\n passValues.append(passed_r_squared)\n\n # figure out the overall pass/fail result\n didPass = None\n for passValue in passValues :\n # if passValue isn't none, we need to update didPass\n if passValue is not None :\n if didPass is not None :\n didPass = passValue and didPass\n else :\n didPass = passValue\n \n return didPass, failed_fraction, non_finite_diff_fraction, r_squared_value",
"title": ""
},
{
"docid": "dd3456078c758c8906845f50e0b61fcb",
"score": "0.60761505",
"text": "def check_distractor_distance_threshold(self, goal, gripper):\n self.current_norm_distance = self.calc_distance(goal, gripper)\n threshold = 0.1\n return self.current_norm_distance < threshold",
"title": ""
},
{
"docid": "3355029114166bf81231fd0d2d801c5d",
"score": "0.6064035",
"text": "def isclose(a, b, rel_tol=1e-04, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)",
"title": ""
},
{
"docid": "32e334610a88894d3aaa5bac1639704b",
"score": "0.6063143",
"text": "def testEqualityValid(self):\n self.assertEqual(mox.IsAlmost(1.8999999999), 1.9)",
"title": ""
},
{
"docid": "92458285d0dafe3180d047c506589c65",
"score": "0.6051033",
"text": "def assert_approximately_equal(computed_potential, expected_potential, tolerance=ENERGY_TOLERANCE):\n\n # Compute error.\n error = (computed_potential - expected_potential)\n\n # Raise an exception if the error is larger than the tolerance.\n if abs(error) > tolerance:\n raise Exception(\"Computed potential %s, expected %s. Error %s is larger than acceptable tolerance of %s.\" % (computed_potential, expected_potential, error, tolerance))\n \n return",
"title": ""
},
{
"docid": "a1b3eccca005b97c8d678bf85206ff99",
"score": "0.6048701",
"text": "def approx_equal(x,y):\n if abs(x-y) < 0.00001:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "fcbdd67840da82380b3179d13588b9e7",
"score": "0.60423213",
"text": "def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)",
"title": ""
},
{
"docid": "fcbdd67840da82380b3179d13588b9e7",
"score": "0.60423213",
"text": "def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)",
"title": ""
},
{
"docid": "bb6d4c5d6a88c6cdeaabba4604521bb3",
"score": "0.6019731",
"text": "def is_almost_equal(x, y, epsilon=1 * 10 ** (-8)):\r\n return abs(x - y) <= epsilon",
"title": ""
},
{
"docid": "c6f0f831144f4f4bc4e2b444dda49c07",
"score": "0.6016614",
"text": "def terminate(fitness, tolerance):\n # for i in fitness:\n # if abs((2**(1.0 / 2)) - i) < tolerance:\n # return True\n return False",
"title": ""
},
{
"docid": "4736fe1b1e1ae33fa99ac5411b20e523",
"score": "0.6011596",
"text": "def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)",
"title": ""
},
{
"docid": "45f49e14c0c463fd5b66be61780bae72",
"score": "0.60109454",
"text": "def isViolated(self):\n if abs(value(self.denominator)) >= const.EPS:\n if self.lowTarget is not None:\n if self.lowTarget > self.findLHSValue():\n return True\n if self.upTarget is not None:\n if self.findLHSValue() > self.upTarget:\n return True\n else:\n # if the denominator is zero the constraint is satisfied\n return False",
"title": ""
},
{
"docid": "2f14dead98b6ba6800efd45cdbe674d5",
"score": "0.6010444",
"text": "def float_cmp(x, y, rtol=1e-14, atol=1e-14):\n return np.abs(x - y) <= atol + np.abs(y) * rtol",
"title": ""
},
{
"docid": "636a908b2b82f32903e61aae0d60938c",
"score": "0.5998174",
"text": "def approx_equal(a, b, tol=1e-9):\n return fabs(a-b) <= max(fabs(a), fabs(b)) * tol",
"title": ""
},
{
"docid": "ced2eec7247f91ef01d988715d9d6ab9",
"score": "0.5985774",
"text": "def is_close_enough(x, y):\n\n return abs(x - y) < 0.0001",
"title": ""
},
{
"docid": "4d7d4ae19a201d3f531de04477e5da05",
"score": "0.5979146",
"text": "def _check_epsilon(epsilon):\n if not (isinstance(epsilon, float) and 1e-5 <= epsilon <= 0.3):\n print(\"> ERROR: epsilon must be a float between 0.3 and 10^-5\")\n return False\n\n return True",
"title": ""
},
{
"docid": "ffcf0711a0d3f286558a560e3298c417",
"score": "0.59740263",
"text": "def getTolerance(self):\n ...",
"title": ""
}
]
3267fcb6ce40a8d0595e6979dd8f0ae1
Return filenames that have a spcified set of extensions.
[
{
"docid": "841a54f260628de898d042118a6d2804",
"score": "0.671288",
"text": "def filenameMatchesAListOfExtensions(filename, extensionList=None):\n if extensionList is not None:\n for currExt in extensionList:\n if filename.lower().endswith(currExt.lower()):\n return True\n return False",
"title": ""
}
]
[
{
"docid": "980d3d0380131c6070595da9574d0e44",
"score": "0.7548063",
"text": "def get_files(dirname, extensions=['.png', '.tif', '.jpg']):\n dir_path = Path(dirname)\n\n files = dir_path.glob('**/*')\n\n files = [path.resolve() for path in files]\n\n match = [f for f in files if f.suffix in extensions]\n return match",
"title": ""
},
{
"docid": "756bf2887c3883a51543e44a722ba14b",
"score": "0.74919146",
"text": "def ext_filter(directory, exts, ABS_PATHS = True):\n if isinstance(exts,list) or isinstance(exts,tuple):\n exts = list(exts)\n if ABS_PATHS:\n return [opj(directory,file) for file in os.listdir(directory) if \n os.path.splitext(file)[1] in exts]\n else:\n return [file for file in os.listdir(directory) if \n os.path.splitext(file)[1] in exts]",
"title": ""
},
{
"docid": "59861aab08ffbe5d5f4534e9f1ede444",
"score": "0.73087174",
"text": "def filter_by_extension(ext):\n def filt(filename):\n return filename.endswith(ext)\n return filt",
"title": ""
},
{
"docid": "1418fdb60218040b473b134833d3322a",
"score": "0.7305053",
"text": "def get_files_with_extension(dir, names, ext):\n list = []\n for name in names:\n e = os.path.splitext(name)[1]\n if e == ext:\n list.append(name)\n list.sort()\n return [os.path.join(dir, n) for n in list]",
"title": ""
},
{
"docid": "b4aa8be8863df9601ac1abf7504c66b8",
"score": "0.726645",
"text": "def _get_allowed_extensions():\n return [\n ext.lower()\n for ext, file_type in Image.EXTENSION.items()\n if file_type.upper() in MIME_TYPE_TO_PIL_IDENTIFIER.values()\n ]",
"title": ""
},
{
"docid": "aaae5bf503325a9a14f507df67b18904",
"score": "0.7261796",
"text": "def filter_by_suffix(files):\n if includesuffix:\n for insuffix in includesuffix:\n extname = '*.' + insuffix\n if fnmatch.fnmatch(files, extname):\n return True\n\n if excludesuffix:\n for exsuffix in excludesuffix:\n extname = '*.' + exsuffix\n if fnmatch.fnmatch(files, extname):\n return False\n\n if includedefault:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "f6ad56e049784f05cd7a715e0f302633",
"score": "0.7199362",
"text": "def filter_files(directory, extensions):\n\n return map(lambda x: os.path.join(directory, x),\n filter(lambda x: re.search('\\.({0})$'.format(\n '|'.join(extensions)), x), sorted(os.listdir(directory))))",
"title": ""
},
{
"docid": "069c2ef98f7e6ab26e42a762fee47e2c",
"score": "0.71652",
"text": "def allowed_file_exts(files: List, extensions: List[str]) -> bool:\n for f in files:\n ext = get_file_extension(f.filename)\n if ext not in extensions:\n return False\n return True",
"title": ""
},
{
"docid": "1cf1ff4052794423d8f2d4f6e16fb27f",
"score": "0.71020085",
"text": "def file_extensions_get(fname_list):\n return [os.path.splitext(fname)[-1] for fname in fname_list]",
"title": ""
},
{
"docid": "df91497740c894b23886e74235c15f45",
"score": "0.69904",
"text": "def list_files_with_extensions(directory, ext_list):\n filepath_list = sum(\n [\n glob(\"{0}/*{1}\".format(directory, ext))\n for ext in ext_list\n ],\n [],\n )\n filenames = [os.path.basename(filepath) for filepath in filepath_list]\n return filenames",
"title": ""
},
{
"docid": "8c7239a42c8c11c31617909d28b183ed",
"score": "0.6975149",
"text": "def get_file_list(path, ext):\n return [f for f in os.listdir(path) if \n os.path.isfile(os.path.join(path, f)) and f.endswith(ext)]",
"title": ""
},
{
"docid": "65c3dee1bd54f7da56b4079011cad679",
"score": "0.69076073",
"text": "def getFileNames():\n included_extenstions = ['json' ]\n file_names = [fn for fn in os.listdir(os.getcwd()) \n if any([fn.endswith(ext) \n for ext in included_extenstions])] \n return file_names",
"title": ""
},
{
"docid": "276a32c3bedac93c824d12a57fa12d6c",
"score": "0.6899496",
"text": "def _generate_filename_list(self):\n fq_filename, extension = os.path.splitext(self._fq_infilename)\n\n la_ext_list = [\n 'la{0}'.format(char) for char in string.lowercase]\n da_ext_list = [\n 'da{0}'.format(char) for char in string.lowercase]\n\n extension_list = la_ext_list + da_ext_list\n\n filename_list = [fq_filename + '.{0}'.format(ext)\n for ext in extension_list\n if os.path.exists(fq_filename + '.{0}'.format(ext))]\n\n return filename_list",
"title": ""
},
{
"docid": "f8ec45f3da344c3e14e5a94841f8ca67",
"score": "0.68790245",
"text": "def _file_has_extensions(self, filestr, extensions):\r\n for extension in extensions:\r\n if filestr.find(extension) > -1:\r\n return True\r\n return False",
"title": ""
},
{
"docid": "1dd879bf8ef32a15b5c6accde3a3c28d",
"score": "0.6872849",
"text": "def get_exts(filepath, all_exts=True):\n \n ext = 42 # initialize and set to non-0 value\n out = [] # the output array of extensions\n \n while ext:\n filepath, ext = path.splitext(filepath)\n\n if ext:\n clean_ext = ext[1:] # strip the \".\" off the front of ext\n out = [clean_ext] + out # append to output array\n if not all_exts: break # if all_exts is False then end the\n # loop after grabbing the first ext\n return out",
"title": ""
},
{
"docid": "b01cc22ff929e71d1968875d4d7497db",
"score": "0.6780839",
"text": "def has_file_allowed_extension(filename, extensions):\r\n return filename.lower().endswith(extensions)",
"title": ""
},
{
"docid": "799cf33845c24e316c0f6012022c9d39",
"score": "0.6763899",
"text": "def get_files(dir_path, extension):\n files = []\n for file in os.listdir(dir_path):\n if file.endswith(f\".{extension}\"):\n files.append(file)\n return files",
"title": ""
},
{
"docid": "dc9a2178e41285df4b777ad0ee69655b",
"score": "0.6762181",
"text": "def test_find_files_returns_only_files_with_correct_extensions(tmpdir):\n folder = tmpdir.mkdir('sub')\n txt = folder.join('file1.txt').write('foo') #noqa\n py = folder.join('file2.py').write('foo') # noqa\n\n exts = ('.txt')\n files = find_files(folder, exts=exts)\n\n # only keep the extensions - makes the assert simpler.\n files = [f[-4:] for f in files]\n\n assert '.txt' in files",
"title": ""
},
{
"docid": "b0dee152af03710c76a66f7b2baa3081",
"score": "0.67420954",
"text": "def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)",
"title": ""
},
{
"docid": "b0dee152af03710c76a66f7b2baa3081",
"score": "0.67420954",
"text": "def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)",
"title": ""
},
{
"docid": "b0dee152af03710c76a66f7b2baa3081",
"score": "0.67420954",
"text": "def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)",
"title": ""
},
{
"docid": "b0dee152af03710c76a66f7b2baa3081",
"score": "0.67420954",
"text": "def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)",
"title": ""
},
{
"docid": "2c50aae55f171a1e973c614e2ca4de44",
"score": "0.67118245",
"text": "def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\r\n return filename.lower().endswith(extensions)",
"title": ""
},
{
"docid": "5421761bd07d486d4c06a376bbeae8b2",
"score": "0.67003095",
"text": "def list_files_with_extension(directory, extension):\n tmp = os.listdir(directory)\n logging.debug(tmp)\n extens = [f for f in tmp if os.path.splitext(f)[1] == \".\"+extension]\n return [os.path.join(directory, f) for f in extens]",
"title": ""
},
{
"docid": "f5eab32a1febb229cc347dceb867e23d",
"score": "0.667502",
"text": "def getFiles(self):\n fileList = [os.path.normcase(f) for f in os.listdir(self.directory)]\n fileList = [os.path.join(self.directory, f) for f in fileList if os.path.splitext(f)[1] in self.extList ]\n return [self.getAssertion(f) for f in fileList]",
"title": ""
},
{
"docid": "19726e185e58dfb46ca6cecbfa3fab44",
"score": "0.66600364",
"text": "def list():\r\n return [stem for stem, extension in [\r\n os.path.splitext(filename) for filename in os.listdir(_dirname)\r\n ] if extension == '.dat' and stem != 'root']",
"title": ""
},
{
"docid": "81ebd23d3bf8322b15b144d2fafb50b7",
"score": "0.6659508",
"text": "def list_of_files_in_dir_for_ext(dir, ext='.xtc') :\n if dir is None : return []\n if not os.path.exists(dir) : return [] \n return sorted([f for f in os.listdir(dir) if os.path.splitext(f)[1] == ext])",
"title": ""
},
{
"docid": "645da5d74ffa9a97b65fa0142e44c844",
"score": "0.6638439",
"text": "def file_list(directory, extension='jpg'):\n return [os.path.join(directory,f) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory,f)) and f.endswith(\".{}\".format(extension))]",
"title": ""
},
{
"docid": "7916ced72f4dfb18ecf328bee7073f74",
"score": "0.6617648",
"text": "def remove_unwanted_files(files: List[str], extensions: List[str]=['.DS_Store', '.gitignore']):\n for source_file in files:\n for ex in extensions:\n if ex in source_file:\n files.remove(source_file)\n break\n return files",
"title": ""
},
{
"docid": "4318629eeb540df122d4835547b3180e",
"score": "0.6605068",
"text": "def has_file_allowed_extension(filename, extensions):\r\n filename_lower = filename.lower()\r\n return any(filename_lower.endswith(ext) for ext in extensions)",
"title": ""
},
{
"docid": "acef0f93bcfae5beca5c606154193b84",
"score": "0.66009",
"text": "def filter_srt(files):\n\n out = []\n for filename in files:\n splitted = os.path.splitext(filename)\n srt_identifier = \".srt\"\n if splitted[1] == srt_identifier: \n out.append(filename)\n\n return out",
"title": ""
},
{
"docid": "b0f7c84fe8b6dfd6c72f8c698d1a9204",
"score": "0.6594698",
"text": "def search_file(path, ext):\n to_search = []\n for local_files in os.listdir(path):\n if local_files.endswith(ext):\n to_search.append(local_files)\n return to_search",
"title": ""
},
{
"docid": "8bd9f2583a013c4869bd150f748a48cf",
"score": "0.6586023",
"text": "def file_extensions_valid(ext_list):\n ext_list_valid = ['.csv','.txt','.xls','.xlsx']\n return ext_list[0] in ext_list_valid",
"title": ""
},
{
"docid": "7658e9c1622a6e670e73581653f33cb5",
"score": "0.657985",
"text": "def has_file_allowed_extension(filename: PATH_TYPE, extensions: Tuple[str, ...]) -> bool:\n return str(filename).lower().endswith(extensions)",
"title": ""
},
{
"docid": "e5d429d8ce86144952e4aeb00b3126b7",
"score": "0.65746367",
"text": "def get_file_names(path: str, extension: Union[List, str]) -> List:\n if isinstance(extension, List):\n \n images: list = []\n for ext in extension:\n request = os.path.join(path, f'*.{ext}')\n images += glob(request) # to avoid merging opperation after\n\n return images\n\n elif isinstance(extension, str):\n\n request = os.path.join(path, f'*.{extension}')\n return glob(request) #list\n\n else:\n\n raise TypeError('Only list or str are expected')",
"title": ""
},
{
"docid": "300624ac6baeeb201c602b5784d31828",
"score": "0.6562819",
"text": "def FileNameList(basepath='.', name='*.*', extension=True, excludename=''):\n files = fnmatch.filter(os.listdir(basepath), name)\n\n if not extension:\n files = [file.split('.')[0] for file in files]\n\n files = ExcludeFromList(files, excludename)\n\n return sorted(files)",
"title": ""
},
{
"docid": "8d86f8d2d9d70ddaf014e1bd0ced7a58",
"score": "0.65608174",
"text": "def has_file_allowed_extension(filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)",
"title": ""
},
{
"docid": "8d86f8d2d9d70ddaf014e1bd0ced7a58",
"score": "0.65608174",
"text": "def has_file_allowed_extension(filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)",
"title": ""
},
{
"docid": "6ba5686840208032086ef18a90ec8b73",
"score": "0.6553543",
"text": "def get_filenames_names_only(*filenames: str) -> List[str]:\n return [os.path.splitext(file)[0] for file in filenames]",
"title": ""
},
{
"docid": "0e5acc64ff532175cfdabccea7afe32a",
"score": "0.6542695",
"text": "def _find_files(self, chapter, extension):\n find_pattern = os.path.join(build_config.IMAGES_DIRECTORY,\n chapter, '*%s' % extension)\n found_files = Glob(find_pattern, strings=True)\n # Strip off the extensions.\n length_extension = len(extension)\n found_files = [x[x.index(os.sep) + 1:-length_extension]\n for x in found_files]\n return found_files",
"title": ""
},
{
"docid": "91091c1aac2c6f91219737ba2f01e1a4",
"score": "0.65398926",
"text": "def list_files(path, extension=\".cpp\", exclude=\"S.cpp\"):\n return [\"%s/%s\" % (path, f) for f in listdir(path) if f.endswith(extension) and (not f.endswith(exclude))]",
"title": ""
},
{
"docid": "7b35943bad645aa56528ba618476ed27",
"score": "0.6533559",
"text": "def _get_files(paths, types):\n files = set()\n for path in paths:\n for dirpath, dirnames, filenames in os.walk(path):\n for filename in filenames:\n try:\n # try-except because [1] and [1:] can fail\n file_ext = os.path.splitext(filename)[1].decode('utf8')[1:]\n if file_ext.lower() in types:\n full_path = os.path.join(dirpath, filename)\n files.add(full_path)\n except:\n pass\n return files",
"title": ""
},
{
"docid": "5077294894066df8af31e946ce04b105",
"score": "0.6518748",
"text": "def get_dir_filenames(dir_path, extension='.txt'):\n return [ i for i in listdir(dir_path) if i.find(extension) > 0 ]",
"title": ""
},
{
"docid": "ff5fc702b37c7878ca58605e8048dea1",
"score": "0.65176266",
"text": "def allowed_file(filename):\n return '.' in filename and \\\n filename.split('.')[-1].lower() in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "c2ede6286900cf09fee7270c3fa7bda3",
"score": "0.64979035",
"text": "def check_extensions(x, elist):\n if not elist: # if no qualifier then we are good\n return True\n\n extension = os.path.splitext(x)[1].lower()\n\n for y in elist: # for each extension we just have to match one\n if '.'+ y == extension:\n return True\n return False",
"title": ""
},
{
"docid": "6abc209052dcc275c09192e3081b8755",
"score": "0.6495629",
"text": "def get_filenames(self, prefix, ext='gz'):\n if self.langs:\n return [f'{prefix}.{lang}.{ext}' for lang in self.langs]\n num = len(self.files)\n return [f'{prefix}.{i}.{ext}' for i in range(1, num + 1)]",
"title": ""
},
{
"docid": "651ee5bc19ff5d41c7290c1fb1ddd1c7",
"score": "0.64897215",
"text": "def list_of_pathes_in_dir_for_ext(dir, ext='.xtc') :\n return [os.path.join(dir,f) for f in list_of_files_in_dir_for_ext(dir, ext)]",
"title": ""
},
{
"docid": "f3ba78dd868419a763fabf267dbd907d",
"score": "0.6488085",
"text": "def get_files(fpath,extensions,recursive=False):\n p = Path(fpath)\n if recursive:\n filelist = [str(x) for x in p.rglob('*') if x.suffix in extensions]\n else:\n filelist = [str(x) for x in p.glob('*') if x.suffix in extensions]\n assert len(filelist) > 0, f\"No files matching extensions {extensions} found in {fpath}\"\n return(filelist)",
"title": ""
},
{
"docid": "1bf7d95d9cbbf7cbbb7487588a5548c1",
"score": "0.6485401",
"text": "def filter_lst_files(files):\r\n return [lst_file for lst_file in files if lst_file.endswith(\".lst\")]",
"title": ""
},
{
"docid": "d75973d2539760dbb06cee8f1505f558",
"score": "0.6484452",
"text": "def listFiles(dir, ext, ignoreExt=None):\n matches = []\n for root, dirs, files in os.walk(dir):\n for f in files:\n if f.endswith(ext):\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\n matches.append(os.path.join(root, f))\n return matches",
"title": ""
},
{
"docid": "d75973d2539760dbb06cee8f1505f558",
"score": "0.6484452",
"text": "def listFiles(dir, ext, ignoreExt=None):\n matches = []\n for root, dirs, files in os.walk(dir):\n for f in files:\n if f.endswith(ext):\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\n matches.append(os.path.join(root, f))\n return matches",
"title": ""
},
{
"docid": "0bd1deb96d18b65d580a0899b7268dec",
"score": "0.64773446",
"text": "def mfilter(f):\n return os.path.isfile(f) and f.endswith(ext)",
"title": ""
},
{
"docid": "afbcff433711999ef61b24b30b431e91",
"score": "0.64706737",
"text": "def _list_dir(ext):\n return [os.path.join(upload_dir, name) for name in os.listdir(upload_dir) if\n os.path.isfile(os.path.join(upload_dir, name)) and name.endswith(ext)]",
"title": ""
},
{
"docid": "75cee534d7e9514a87d45c139de47294",
"score": "0.6459723",
"text": "def get_file_extension_list(self, tpc_type=None):\n file_names = []\n file_extensions = []\n for data_file_name in self.get_data_file_list(tpc_type=tpc_type):\n datum = data_file_name.split(\".\")\n if datum[0] is None or datum[1] is None or datum[0] == \"\" or datum[1] == \"\":\n raise ValueError(\"An attempt was made to parse non eligible files!\")\n file_names.append(datum[0])\n file_extensions.append(datum[1])\n if len(file_names) != len(file_extensions):\n raise ValueError(\"File name list does not match list of file extensions!\")\n return file_names, file_extensions",
"title": ""
},
{
"docid": "954e5050c09733909745fff458aaed44",
"score": "0.6458051",
"text": "def lookupExtension(filename):",
"title": ""
},
{
"docid": "d3194b2c6b22e636b9ed62b4210f7497",
"score": "0.6446683",
"text": "def _get_fortran_files(srcfiles, extensions=False):\n files_out = []\n for srcfile in srcfiles:\n ext = os.path.splitext(srcfile)[1]\n if ext.lower() in [\".f\", \".for\", \".f90\", \".fpp\"]:\n if extensions:\n # save unique extension\n if ext not in files_out:\n files_out.append(ext)\n else:\n files_out.append(srcfile)\n if len(files_out) < 1:\n files_out = None\n return files_out",
"title": ""
},
{
"docid": "f4291c27f6503ca61ac99590c229b061",
"score": "0.64121467",
"text": "def ext_ambiguous_file_list(fn, ext, do_sub_dirs = None) :\n\n ext = ext or \"\"\n if ext and (ext[0] != '.') :\n ext = '.' + ext\n\n if os.path.isdir(fn) :\n fn = os.path.join(fn, '*' + ext)\n else :\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs)\n if not len(fns) : # if there are no ambiguous files to find,\n fn += '*' + ext # then add the extension in case it's not there.\n pass\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs) # find the files\n\n return(fns)",
"title": ""
},
{
"docid": "42aa6b0a35ee1c729ca08ab4246b9dec",
"score": "0.6408415",
"text": "def list_all_files(dir_name, exts=[\"jpg\", \"bmp\", \"png\"]):\n result = []\n for dir_, subdirs, file_names in os.walk(dir_name):\n for file_name in file_names:\n if any(file_name.endswith(ext) for ext in exts):\n result.append(os.path.join(dir_, file_name)) \n return result",
"title": ""
},
{
"docid": "c86969a31ae3ab540e6cf0fb67d500bc",
"score": "0.6398939",
"text": "def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "c86969a31ae3ab540e6cf0fb67d500bc",
"score": "0.6398939",
"text": "def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "c86969a31ae3ab540e6cf0fb67d500bc",
"score": "0.6398939",
"text": "def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "4693c015a2f86460acdecbf08217851b",
"score": "0.63641214",
"text": "def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "e45a64d233e1122b068e3d50407b9620",
"score": "0.63629353",
"text": "def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1] \\\n in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "f107566b36eb4d52c54343511a9eb42f",
"score": "0.63594925",
"text": "def test_filter_to_preferred_ext(image_files):\n tiff_files = list(Path(image_files).glob(\"*.tif\"))\n assert len(tiff_files) == 2000\n jpeg_files = list(Path(image_files).glob(\"*.jpg\"))\n assert len(jpeg_files) == 1000\n image_files = list(Path(image_files).iterdir())\n return_files = list(core.filter_to_preferred_ext(image_files, [\".jpg\"]))\n assert len(return_files) == 2000",
"title": ""
},
{
"docid": "d5f2936129dd4078f5c2d4712324b785",
"score": "0.6341543",
"text": "def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "d5f2936129dd4078f5c2d4712324b785",
"score": "0.6341543",
"text": "def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "d5f2936129dd4078f5c2d4712324b785",
"score": "0.6341543",
"text": "def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "530c663dd2481cfb9df2f98b2e798f24",
"score": "0.633947",
"text": "def has_file_allowed_extension(self, filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)",
"title": ""
},
{
"docid": "9b9cc413fd83ab2c29e4e9db0769890a",
"score": "0.633573",
"text": "def _kwik_filenames(filename):\n basename, ext = op.splitext(filename)\n return {ext: '{basename}.{ext}'.format(basename=basename, ext=ext)\n for ext in _KWIK_EXTENSIONS}",
"title": ""
},
{
"docid": "e3472c2429f376eda94d88e246754f0d",
"score": "0.63155866",
"text": "def allowed_file_extension(filename):\n\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['UPLOAD_EXTENSIONS']",
"title": ""
},
{
"docid": "d3ec7c26f73886c8002746eda9cbb2fd",
"score": "0.63105655",
"text": "def allowedFile(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS",
"title": ""
},
{
"docid": "1ec4943a2176c4c5d643e1c0a952924b",
"score": "0.63059044",
"text": "def get_files(path, extensions='*'):\n if path[-1] is not '/' and path[-1] is not '\\\\':\n path = path + '/'\n\n if isinstance(extensions, str) or isinstance(extensions, unicode):\n extensions = [extensions]\n\n all_files = [os.path.join(root, filename)\n for root, dirnames, filenames in os.walk(path)\n for filename in filenames]\n\n filtered_files = [\n filename for filename in all_files\n if get_extension(filename) in extensions or '*' in extensions\n ]\n\n return filtered_files",
"title": ""
},
{
"docid": "2a4add0b8a0cf1a8a2dcb24bf22e8f9a",
"score": "0.6276475",
"text": "def GetImageExtWildcard():",
"title": ""
},
{
"docid": "b93b2a34639ddd4c3bf1281667a59826",
"score": "0.62707496",
"text": "def file_extensions_all_equal(ext_list):\n return len(set(ext_list))==1",
"title": ""
},
{
"docid": "e55ab259acc0438e8e1a7c98a5e1a18c",
"score": "0.62676054",
"text": "def find_by_ext(folder, extension):\n found_files = [path.join(dirpath, f)\n for dirpath, dirnames, files in walk(folder)\n for f in files if f.endswith('.' + extension)]\n return found_files",
"title": ""
},
{
"docid": "6a65a86abf2079431459ba72d2a3cf19",
"score": "0.6267129",
"text": "def is_valid_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\n\n return any(filename.lower().endswith(ext) for ext in extensions)",
"title": ""
},
{
"docid": "1afcce354f31a092af483f465d296d42",
"score": "0.62572294",
"text": "def split_matching_extensions_and_other(self, srcs, exts):\n\n matches = []\n leftovers = []\n\n for src in (srcs or []):\n base, ext = os.path.splitext(src)\n if ext in exts:\n matches.append(src)\n else:\n leftovers.append(src)\n\n return (matches, leftovers)",
"title": ""
},
{
"docid": "8fa9688926f891714b18fb38bf371e1c",
"score": "0.6253049",
"text": "def list_files_with_ext(*paths, ext, maxdepth=-1):\n needed_files = []\n for path in paths:\n if ext in path:\n needed_files.append(path)\n continue\n if not os.path.isdir(path):\n print_red(\n qte(path) + \" is not a directory nor does it contain \" +\n qte(ext) + \". Skipping.\"\n )\n continue\n path = os.path.realpath(path)\n start_level = path.count(os.sep)\n # pylint: disable=unused-variable\n # Need dirs to iterate\n for root, dirs, files in os.walk(path):\n depth = root.count(os.sep) - start_level\n if depth > maxdepth and (maxdepth != -1):\n break\n for filename in files:\n if ext in filename:\n needed_files.append(os.path.join(root, filename))\n return needed_files",
"title": ""
},
{
"docid": "b6340c4e87b1f4f711576f1699c15b5c",
"score": "0.62498426",
"text": "def get_filtered_files(root_folder, extensions, keywords, strict=False):\n result = []\n unfiltered_files = UtilityBox.get_all_files(root_folder, *extensions)\n for file_path in unfiltered_files:\n if (not strict and any(kw.lower() in file_path.lower() for kw in keywords))\\\n or (strict and all(kw.lower() in file_path.lower() for kw in keywords)):\n result.append(file_path)\n return result",
"title": ""
},
{
"docid": "9073547ed295dc0f959d4a012820c136",
"score": "0.6249226",
"text": "def get_paths_by_ext(directory, exts=['JPEG']):\n if not directory.endswith('/'):\n directory += '/'\n\n img_paths = []\n for ext in exts:\n img_paths = img_paths + glob(os.path.join(directory) + '**/*.' + ext, recursive=True)\n return sorted(img_paths)",
"title": ""
},
{
"docid": "e54f73d0236ebe7aad1f4c86f4fca73f",
"score": "0.624537",
"text": "def get_file_info(d, extensions):\n for key in extensions:\n for f in os.listdir(d):\n if f.endswith(key):\n pass",
"title": ""
},
{
"docid": "ff03cf7eb8834954b337babf077d85a7",
"score": "0.622903",
"text": "def get_related_files(filename):\n related_files = []\n if filename.endswith(\".img\") or filename.endswith(\".hdr\"):\n path, name, ext = split_filename(filename)\n for ext in ['.hdr', '.img', '.mat']:\n related_files.append(os.path.join(path, name + ext))\n elif filename.endswith(\".BRIK\") or filename.endswith(\".HEAD\"):\n path, name, ext = split_filename(filename)\n for ext in ['.BRIK', '.HEAD']:\n related_files.append(os.path.join(path, name + ext))\n if not len(related_files):\n related_files = [filename]\n return related_files",
"title": ""
},
{
"docid": "73a2dceed81f4f2da1fc9fb3e5c52545",
"score": "0.62266505",
"text": "def grab_names():\n\n output = []\n original = os.listdir()\n \n for filename in original:\n if \".\" in filename:\n continue\n else:\n output.append(filename)\n\n return output",
"title": ""
},
{
"docid": "d767db9cf7334383a8d7456127fc741b",
"score": "0.6209032",
"text": "def get_all_files(dir, ext):\n extension_suffix = f\".{ext}\"\n files = []\n for file in os.listdir(dir):\n if file.endswith(extension_suffix):\n files.append(os.path.join(dir, file))\n return files",
"title": ""
},
{
"docid": "e47ab99363540050623a47b24d81fc9a",
"score": "0.62019986",
"text": "def load_images_without_extention(path):\n images = []\n valid_images = [\".jpg\", \".png\", \".jpeg\"]\n for f in os.listdir(path):\n file, ext = os.path.splitext(f)\n if ext.lower() not in valid_images:\n continue\n images.append(file)\n return images",
"title": ""
},
{
"docid": "7eb177ef54ee82e41b2fb88a46dfe5bf",
"score": "0.6201061",
"text": "def read_directory(extensions):\n for file in os.listdir('.'):\n filenames = file.split('.')\n if os.path.isdir(file):\n continue\n if filenames[1] not in extensions:\n extensions.append(filenames[1])\n print(extensions)",
"title": ""
},
{
"docid": "8755751c0c0eede4b772f14be8436f93",
"score": "0.6200504",
"text": "def get_filenames(format: str = 'json') -> List[str]:\n filenames = []\n for type in get_filetypes(format):\n filenames.extend(glob.glob(f'*.{type}'))\n return filenames",
"title": ""
},
{
"docid": "2cd5d5d20c08c7a82fb7c096ac33b951",
"score": "0.6196302",
"text": "def get_files(directory):\n files = []\n for item in os.listdir(directory):\n item = os.path.join(directory, item)\n if not os.path.isfile(item):\n continue\n parts = os.path.splitext(item)\n if parts and len(parts) > 1 and parts[1] == '.py'\\\n and parts[0].lower().find(\"test\") == -1:\n files.append(item)\n return files",
"title": ""
},
{
"docid": "fceba3ab8b472f6765e4facaea1c5bee",
"score": "0.61914957",
"text": "def _collect_files(self, path, extensions):\n path_list = []\n for dirpath, _, filenames in os.walk(path):\n path_list.extend(\n [\n os.path.join(dirpath, filename)\n for filename in filenames\n if os.path.splitext(filename)[1].lower() in extensions\n ]\n )\n return path_list",
"title": ""
},
{
"docid": "feecc2c670c46c78eacd50b912e03f26",
"score": "0.61866087",
"text": "def filter_files(pattern, file_names):\n p = re.compile(pattern)\n return [p.match(f) for f in file_names if p.match(f) is not None]",
"title": ""
},
{
"docid": "f73372929bb991939b97752d0da98eda",
"score": "0.6159966",
"text": "def get_files(dir, suffix):\n files = []\n for file in os.listdir(dir):\n if file.endswith(suffix):\n files.append(file)\n return files",
"title": ""
},
{
"docid": "f73372929bb991939b97752d0da98eda",
"score": "0.6159966",
"text": "def get_files(dir, suffix):\n files = []\n for file in os.listdir(dir):\n if file.endswith(suffix):\n files.append(file)\n return files",
"title": ""
},
{
"docid": "ddf7989ea21b31799113ffb7259206cc",
"score": "0.6147999",
"text": "def stringsWithImageFileExtensions(listOfStrings):\r\n\r\n #todo: string comparison should ignore case\r\n result = []\r\n for s in listOfStrings:\r\n if (s.find('.tif') != -1) or\\\r\n (s.find('.bmp') != -1) or\\\r\n (s.find('.pgm') != -1) or\\\r\n (s.find('.gif') != -1) or\\\r\n (s.find('.png') != -1):\r\n result.append(s)\r\n return result",
"title": ""
},
{
"docid": "9ea904a64b978053bd910323a78d3c9e",
"score": "0.6144758",
"text": "def _get_c_files(srcfiles, extensions=False):\n files_out = []\n for srcfile in srcfiles:\n ext = os.path.splitext(srcfile)[1]\n if ext.lower() in [\".c\", \".cpp\"]:\n if extensions:\n if ext not in files_out:\n files_out.append(ext)\n else:\n files_out.append(srcfile)\n if len(files_out) < 1:\n files_out = None\n return files_out",
"title": ""
},
{
"docid": "00a19e8793e3621beb428a0710c1a5af",
"score": "0.6140286",
"text": "def get_file_list(path, extensions):\n if not os.path.isdir(path):\n log_error_exit('Directory invalid.')\n else:\n files_list = []\n # The FITS files are named in numerically ascending order with an optional\n for file in os.listdir(path):\n if file.split('.')[-1] in extensions:\n files_list.append(os.path.join(path, file))\n if len(files_list) == 0:\n log_error_exit('No files with given extensins found !')\n return sorted(files_list)",
"title": ""
},
{
"docid": "c7430a703d144531116f0ef4a00b716f",
"score": "0.6116649",
"text": "def get_files_with_suffix(dir_name, suffixes):\n import os\n found_filenames = []\n for localpath, directories, filenames in os.walk(dir_name):\n for filename in filenames:\n for suffix in suffixes:\n if filename.endswith(suffix):\n found_filenames.append(os.path.join(localpath, filename))\n found_filenames.sort()\n return found_filenames",
"title": ""
},
{
"docid": "8227919c4a92055b6e0f0aae097b9067",
"score": "0.6115884",
"text": "def _splitext(fname):\n dir, filename = os.path.split(fname)\n for special_ext in ['.nii.gz', '.tar.gz']:\n if filename.endswith(special_ext):\n stem, ext = filename[:-len(special_ext)], special_ext\n return os.path.join(dir, stem), ext\n # If no special case, behaves like the regular splitext\n stem, ext = os.path.splitext(filename)\n return os.path.join(dir, stem), ext",
"title": ""
},
{
"docid": "c98eb8adabba7af1cf0a28c40a232498",
"score": "0.6114838",
"text": "def listfiles(mypath, mytype):\n result = []\n # Match Extension.\n for foundfile in Path(mypath).glob(\"**/*.*\"):\n filetype = str(foundfile).split(\".\")[-1]\n if filetype in mytype:\n # Force string type was Posix(path)\n result.append(str(foundfile))\n return result",
"title": ""
},
{
"docid": "73d0dc0b8cca82a9ec101f45f6470ac3",
"score": "0.61128",
"text": "def remove_all_file_extensions(filepath: Path, extensions: list = None):\n\n def _remove_next_suffix(f):\n if extensions is not None and len(extensions) > 0:\n return f.suffix in extensions\n return len(f.suffix) > 0\n\n while _remove_next_suffix(filepath):\n filepath = filepath.with_suffix(\"\")\n\n return filepath",
"title": ""
},
{
"docid": "81288495451141c1b16e070d8a7535db",
"score": "0.61059415",
"text": "def list_all_extension_files(directory_path, extension='.txt'):\n files_paths = []\n for r, d, f in os.walk(directory_path):\n f = [os.path.join(r,file) for file in f if file.find(extension) != -1]\n files_paths.append(f)\n\n files_paths = list(np.hstack(files_paths))\n files_paths = [tuple((in_file, in_file.replace(\"text_volumes\",\"speeches\").replace(\".txt\",\".csv\"))) for in_file in files_paths]\n\n files_paths = [tuple((in_file,out_file)) for in_file, out_file in files_paths if not os.path.exists(out_file)]\n return files_paths",
"title": ""
}
]
dfc4c0b6369e884b99877afc55bfd359
Build a Cypher query based on given parameters.
[
{
"docid": "d75bc1828ce83ede1c9f954653f24192",
"score": "0.5487266",
"text": "def _build_dependencies_query(\n cls, team_id, topic, label, node, filter_on_config=False, impacted=False\n ):\n where = \"\"\n order = \"(n)<-[r]-(m)\" if impacted else \"(n)-[r]->(m)\"\n query = (\n \"MATCH(n:{topic}_{label}{{name: '{name}'}}) \"\n \"OPTIONAL MATCH {order} {where}\"\n \"RETURN n,r,m ORDER BY m.name LIMIT 10\"\n )\n\n # Filter the dependencies using the labels declared in the configuration\n if filter_on_config:\n label_config = ConfigController.get_label_config(team_id, label)\n regex = r\"^.*(\\[[A-Za-z]+(, [A-Za-z]+)*?\\])$\"\n\n match = re.search(regex, label_config[\"qos\"])\n if match:\n deps = match.group(1)[1:-1].split(\", \")\n where += \"WHERE '{}_{}' IN LABELS(m) \".format(topic, deps[0])\n\n for dep in deps[1:]:\n where += \"OR '{}_{}' IN LABELS(m) \".format(topic, dep)\n\n return query.format(\n where=where, order=order, topic=topic, label=label, name=node\n )",
"title": ""
}
]
[
{
"docid": "72ebf75d0424beb0d9398dd9dcc5f821",
"score": "0.7439297",
"text": "def build_query(self, params):\n pass",
"title": ""
},
{
"docid": "a95e410d68282d18b99170eb9c871fae",
"score": "0.70802385",
"text": "def _build_query(self, params, boolean='AND'):\n query = []\n for p in params:\n if isinstance(p, str):\n # direct query string\n query.append(p)\n\n elif isinstance(p, tuple) and len(p) == 3:\n # <field> <op> <value> triplet\n (field, op, value) = p\n op_ = '-' if op == '!=' else ''\n\n if isinstance(value, list):\n value_ = '(\"' + '\",\"'.join(value) + '\")'\n elif isinstance(value, tuple):\n value_ = '[%s TO %s]' % value\n else:\n quote = '\"' if not '*' in str(value) else ''\n value_ = quote + str(value) + quote\n\n query.append('%s%s:%s' % (op_, field, value_))\n\n elif isinstance(p, tuple) and len(p) == 2:\n # group/subquery with boolean operator\n (op, subquery) = p\n query.append('(' + self._build_query(subquery, op) + ')')\n\n return (' '+boolean+' ').join(query)",
"title": ""
},
{
"docid": "2c96f74b26acea483825aadcbb969353",
"score": "0.6558855",
"text": "def build_query(self, params):\n phrase = params[\"phrase\"]\n slop = params[\"slop\"]\n\n query = {}\n query[\"query\"] = {\"match\":{}}\n\n infl_form = {}\n infl_form[\"type\"] = \"phrase\"\n infl_form[\"query\"] = phrase\n infl_form[\"slop\"] = slop\n query[\"query\"][\"match\"][\"inflected_form\"] = infl_form\n\n query = json.dumps(query)\n return query",
"title": ""
},
{
"docid": "2a8397a2a8e9edc0608094386c96404c",
"score": "0.653392",
"text": "def _build_query_string(cls, parameters: dict) -> str:\n\t\tquery_string = '?'\n\n\t\t# Builds a list of tuples for easy iteration like:\n\t\t# [('channel', 'channel-1'), ('channel', 'channel-2'), ('assetId', 'asset-1'), ...]\n\t\tparameter_tuples = [(parameter_name, parameter_value) \n\t\t\tfor parameter_name, parameter_values in parameters.items()\n\t\t\t\tfor parameter_value in parameter_values]\n\n\t\tfor index, parameter_tuple in enumerate(parameter_tuples):\n\t\t\tname, value = parameter_tuple\n\t\t\tvalue = quote(value.encode()) if type(value) == str else value\n\n\t\t\tif name == 'orderBy':\n\t\t\t\tvalue = cls._convert_order_by(value)\n\n\t\t\tquery_string += f'{name}={value}' if index == 0 else f'&{name}={value}'\n\n\t\treturn query_string",
"title": ""
},
{
"docid": "0a18169ab89094b2a6bab17605534b4f",
"score": "0.6477496",
"text": "def _build_query(self, *args, **kwargs):\n raise NotImplementedError",
"title": ""
},
{
"docid": "76313a0e15cad414416780ca9e335fb0",
"score": "0.6379244",
"text": "def _build_search(query, params):\n #PLACEHOLDER -- only searches the objects table and does the simplest\n #search possible for now\n query_terms = params.copy() if( params and params != {} ) else _translate_query(query)\n arguments = _args()\n keywords = \"\"\n query_string = \"\"\n\n if 'query_string' in query_terms:\n query_string = query_terms['query_string']\n arguments.update({'query_string':query_string})\n del query_terms['query_string']\n if 'keywords' in query_terms:\n arguments.update({'keywords':query_terms['keywords']})\n del query_terms['keywords']\n # TODO: need to do all the query string processing here \n # but, for now just a simple search assumption will work by default\n\n if query_string != \"\":\n keywords += query_string\n \n return \"ca_objects?q=\"+keywords, arguments",
"title": ""
},
{
"docid": "c157292ed4cc40e351d63d9d0af82576",
"score": "0.6359702",
"text": "def generate_query(self):\n return ''.join(['SELECT ',\n ', '.join(self.columns),\n ' ',\n 'FROM molecule ',\n ' '.join(self.joins),\n ' ',\n 'WHERE ',\n '(', ') AND ('.join(self.wheres), ')'])",
"title": ""
},
{
"docid": "cba63cfec2c1ae532de6e05ad75c5ffe",
"score": "0.6332386",
"text": "def _set_sql_query(self, query_params):\n query = f'SELECT * FROM {CATS_TABLE}'\n # attributes in query string\n if query_params.get('attribute'):\n query += ' ORDER BY '\n # may be 1>= attrs\n for attr in query_params['attribute']:\n query += f'{attr}, '\n query = query.rstrip(', ')\n\n # order in query string\n if query_params.get('order'):\n order = query_params['order'][0].upper()\n query += f' {order}'\n\n # offset in query string\n if query_params.get('offset'):\n offset = query_params['offset'][0]\n query += f' OFFSET {offset}'\n\n # limit in query string\n if query_params.get('limit'):\n limit = query_params['limit'][0]\n query += f' LIMIT {limit}'\n\n return query",
"title": ""
},
{
"docid": "6aafa03146dc86896e8b4203a577f391",
"score": "0.6295957",
"text": "def make_query(self, **kw):\n query = kw.pop(\"query\", {})\n\n query.update(self.get_request_query())\n query.update(self.get_custom_query())\n query.update(self.get_keyword_query(**kw))\n\n sort_on, sort_order = self.get_sort_spec()\n if sort_on and \"sort_on\" not in query:\n query.update({\"sort_on\": sort_on})\n if sort_order and \"sort_order\" not in query:\n query.update({\"sort_order\": sort_order})\n\n logger.info(\"make_query:: query={} | catalog={}\".format(\n query, self.catalog))\n\n return query",
"title": ""
},
{
"docid": "1216cd9552ca67c038d89c1a33795cb6",
"score": "0.6221052",
"text": "def build_query(self):\n\n # initalize config\n self.config.update_config()\n\n # get the base query from the config\n query = self.config.get_base_query()\n return query",
"title": ""
},
{
"docid": "d11b2046780debe8f975dcb0277e234c",
"score": "0.62166256",
"text": "def buildQuery(self):\n if not self.Valid:\n print(\"Invalid usage: cannot build.\")\n return False\n if not self.needsBuilt:\n return self.queryString\n\n filterDict = dict()\n theFilters = dict()\n if len(self._andFilters) > 0:\n theFilters[\"and\"] = self._andFilters\n if len(self._orFilters) > 0:\n theFilters[\"or\"] = self._orFilters\n filterDict[\"filter\"] = theFilters\n queryString = json.dumps(filterDict)\n\n self.queryString = queryString\n return queryString",
"title": ""
},
{
"docid": "bb15855e6fd47d6e9d3a76d89a8b5ace",
"score": "0.6071394",
"text": "def _build_query(symbol: str, start_date: str, end_date: str, order_by: str):\n query = \"select * from stock_price where symbol = '{symbol}'\".format(symbol=symbol)\n if start_date:\n query += \" and time >= '{}'\".format(start_date)\n if end_date:\n query += \" and time <= '{}'\".format(end_date)\n\n if order_by == 'ASC':\n # Default\n pass\n elif order_by == 'DESC':\n query += ' order by time desc'\n\n return query",
"title": ""
},
{
"docid": "d46b3852c7f93eb9f1037b46961e8edb",
"score": "0.60509795",
"text": "def query_generator(self, kwargs):\n\n query_addition = \"\"\n\n if \"quarter\" in kwargs:\n query_addition += f\" & qtr == {kwargs['quarter']}\"\n if \"down\" in kwargs:\n query_addition += f\" & down == {kwargs['down']}\"\n if \"half\" in kwargs:\n query_addition += f\" & game_half == 'Half{kwargs['half']}'\"\n if \"close\" in kwargs:\n if kwargs['close'] is True:\n query_addition += f\" & score_differential >= -12 & score_differential <= 12\"\n else:\n query_addition += f\" & (score_differential < -12 | score_differential > 12)\"\n if \"rz\" in kwargs:\n if kwargs['rz'] is True:\n query_addition += f\" & yardline_100 <= 25\"\n else:\n query_addition += f\" & yardline_100 > 25\"\n if \"where\" in kwargs:\n if kwargs['where'] is 'home':\n query_addition += f\" & home_team == '{self.team}'\"\n else:\n query_addition += f\" & away_team == '{self.team}'\"\n if \"play\" in kwargs:\n if kwargs['play'] is 'run':\n query_addition += f\" & rush_attempt == 1\"\n else:\n query_addition += f\" & pass_attempt == 1\"\n if \"since\" in kwargs:\n if kwargs['since'] is 'sep':\n query_addition += f\" & (game_date.str.slice(5, 7) == '09' | game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n elif kwargs['since'] is 'oct':\n query_addition += f\" & (game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n elif kwargs['since'] is 'nov':\n query_addition += f\" & (game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n else:\n query_addition += f\" & game_date.str.slice(5, 7) == '12'\"\n\n return query_addition",
"title": ""
},
{
"docid": "c570052933c609ad72ebabe6e69a8bfd",
"score": "0.598974",
"text": "def data_query(self, parents=None):\n filter_q = self.filter_query(parents=parents)\n q = db.session.query()\n stmt, q = self._add_statement(q)\n\n filter_sq = filter_q.subquery()\n q = q.filter(stmt.subject == filter_sq.c.subject)\n q = q.filter(stmt._attribute.in_(self.project()))\n\n q = q.add_column(stmt.subject.label('id'))\n q = q.add_column(stmt._attribute.label('attribute'))\n q = q.add_column(stmt._value.label('value'))\n\n if parents is not None and self.node.attribute:\n parent_stmt, q = self._add_statement(q)\n q = q.filter(stmt.subject == parent_stmt._value)\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\n q = q.add_column(parent_stmt.subject.label('parent_id'))\n\n q = q.order_by(filter_sq.c.subject.desc())\n q = q.order_by(stmt.created_at.asc())\n return q",
"title": ""
},
{
"docid": "dd1bf23e219921f97adc765b4e477518",
"score": "0.5976437",
"text": "def make_query(columns, filter):\n if columns is not None:\n # the user only wants to report a subset of the columns\n query = \"SELECT \" + columns + \" FROM variants\"\n# elif cancers != 'none':\n# query = \"SELECT \" + columns + \",civic_gene_abbreviations,cgi_gene_abbreviations FROM variants\"\n else:\n # report the kitchen sink\n query = \"SELECT * FROM variants\"\n if filter is not None:\n # add any non-genotype column limits to the where clause\n query += \" WHERE \" + filter\n return query",
"title": ""
},
{
"docid": "3662e9f0259cba0673eaafff569ccaaa",
"score": "0.59676003",
"text": "def make_query(request, **kw):\n\n # build the catalog query\n query = {\n \"sort_limit\": get_sort_limit(request),\n \"sort_on\": get_sort_on(request),\n \"sort_order\": get_sort_order(request),\n \"SearchableText\": get_query(request),\n }\n\n # inject keyword args\n query.update(kw)\n\n # inject the creator if given\n if get_creator(request):\n query[\"Creator\"] = get_creator(request)\n\n logger.info(\"Catalog Query --> %r\", query)\n return query",
"title": ""
},
{
"docid": "a0196bcfee0829e1a676506cb8f483df",
"score": "0.59151345",
"text": "def query_from_params(self, params):\n q = {}\n start, rows = 0, 10\n if \"start\" in params:\n start = int(params[\"start\"])\n if \"rows\" in params:\n rows = int(params[\"rows\"])\n\n if \"lat\" in params and \"lon\" in params:\n lat = float(params[\"lat\"])\n lon = float(params[\"lon\"])\n q[\"loc\"] = {\n \"$near\": [lat, lon]\n }\n for atr in [\"city\", \"state\", \"country\"]:\n if atr in params:\n q[atr] = params[atr]\n return self.query(start=start, rows=rows, **q)",
"title": ""
},
{
"docid": "324d683b973caa2910387cddae4780e5",
"score": "0.5909418",
"text": "def _create_query_params(self, kwargs):\n if None == kwargs or 0 == len(kwargs):\n return (\"\", [])\n\n if not isinstance(kwargs, basestring):\n pairs = kwargs.items()\n stmt = \" WHERE \" + \\\n \" AND \".join([ x[0] + (None is x[1] and \" IS NULL\" or \" = ?\")\n for x in pairs ])\n\n args = [x[1] for x in filter(lambda x: None is not x[1], pairs)]\n else:\n stmt = \" WHERE \" + kwargs\n args = []\n return (stmt, args)",
"title": ""
},
{
"docid": "49409b5c49174457e063fc4a1f2caf74",
"score": "0.5909326",
"text": "def build_query(cols=None, tables=None, group_by=None, order_by=None, **kwargs):\n assert len(tables)\n\n # Create SELECT line\n cols_str = \", \".join(cols) if cols else \"*\"\n query = f'SELECT {cols_str} FROM '\n\n # Create FROM line\n # Automatically add bracelets around strings with space inside them. Useful for nested queries.\n query += ' NATURAL JOIN '.join(f'({table})' if ' ' in table else table for table in tables)\n\n # Apply all filters in the kwargs dict\n constraints = []\n for col_name, value in kwargs.items():\n # Empty filters don't need to be applied\n if value not in (None, ''):\n # Use \"LIKE\" for strings, and \"==\" otherwise\n if isinstance(value, str):\n constraints.append(f'{col_name} LIKE \"{value}\"')\n else:\n constraints.append(f'{col_name} == {value}')\n\n # Create WHERE line if there are constraints\n if constraints:\n query += ' WHERE ' + ' AND '.join(constraints)\n\n # Create GROUP BY line if needed\n if group_by:\n query += ' GROUP BY ' + group_by\n\n # Create ORDER BY line if needed\n if order_by:\n query += ' ORDER BY ' + order_by\n\n return query",
"title": ""
},
{
"docid": "1fb4cc87b5249fb99610ec14d3f62cf8",
"score": "0.58879614",
"text": "def GenerateQuery(unused_ref, args, request):\n customer_id = ConvertOrgIdToObfuscatedCustomerId(args.organization)\n labels = FilterLabels(args.labels)\n labels_str = ','.join(labels)\n request.query = 'parent==\\\"customerId/{0}\\\" && \\\"{1}\\\" in labels'.format(\n customer_id, labels_str)\n\n return request",
"title": ""
},
{
"docid": "ff8ba2a0ee0c22e8f3b0dc0581901893",
"score": "0.5856376",
"text": "def make_query(self, ns):\n if issubclass(self.model_class, db.Model):\n query = db.Query(self.model_class, namespace=ns)\n for f in self.filters:\n query.filter(\"%s %s\" % (f[0], f[1]), f[2])\n else:\n query = self.model_class.query(namespace=ns)\n for f in self.filters:\n query = query.filter(ndb.FilterNode(*f))\n return query",
"title": ""
},
{
"docid": "a5aeac92141db6643f5dae341690653e",
"score": "0.5830022",
"text": "def build_query(\n self,\n regions=None,\n genes=None,\n effect_types=None,\n family_ids=None,\n person_ids=None,\n inheritance=None,\n roles=None,\n sexes=None,\n variant_type=None,\n real_attr_filter=None,\n ultra_rare=None,\n frequency_filter=None,\n return_reference=None,\n return_unknown=None,\n limit=None,\n pedigree_fields=None):\n # pylint: disable=too-many-arguments\n self.query_builder.reset_product()\n\n self.query_builder.build_select()\n\n self.query_builder.build_from()\n\n self.query_builder.build_join()\n\n self.query_builder.build_where(\n regions=regions,\n genes=genes,\n effect_types=effect_types,\n family_ids=family_ids,\n person_ids=person_ids,\n inheritance=inheritance,\n roles=roles,\n sexes=sexes,\n variant_type=variant_type,\n real_attr_filter=real_attr_filter,\n ultra_rare=ultra_rare,\n frequency_filter=frequency_filter,\n return_reference=return_reference,\n return_unknown=return_unknown,\n pedigree_fields=pedigree_fields,\n )\n\n self.query_builder.build_group_by()\n self.query_builder.build_limit(limit)",
"title": ""
},
{
"docid": "9bf3e320668377af9f499baa5b818919",
"score": "0.5816653",
"text": "def make_query(config):\n\n query = {}\n\n items = ['object', 'radius', 'max_out', 'source', 'output', 'mime', 'sort']\n\n for item in items\n query[item] = str(config[item])\n\n return query",
"title": ""
},
{
"docid": "2159f3098a64c4db73cdb716ccac6fd2",
"score": "0.57821774",
"text": "def make_where_statement(self, table_name, params):\n if not params:\n return (\" 1 \", ())\n column_names = [row[1] for row in self.get_rows(\"PRAGMA table_info(%s)\" % table_name)]\n for column in params.keys():\n if column not in column_names:\n raise KeyError(\"Error: Database column %s not in our database\" % column)\n # Construct our SQL statement\n where_statement = ' AND '.join(['\"%s\" = ?' % column for (column, value) in sorted(params.items())])\n where_values = tuple([value for (column, value) in sorted(params.items())])\n return (where_statement, where_values)",
"title": ""
},
{
"docid": "dd14224ced22e5c08b3414302d16b36f",
"score": "0.5775194",
"text": "def build_query(self) -> Query:\n logger.info(\n {\n \"message\": f\"Start building query for resource {self.analysis.definition_id}\",\n \"resource_id\": self.analysis.resource_id,\n },\n )\n\n # We don't need to have columns duplicated in the dataframe\n # so we keep the one we've already seen here.\n # Note that the primary key column is added at the query initialization.\n self._cur_query_columns = {self.analysis.primary_key_column}\n # To avoid duplicated joins, we keep them here\n self._cur_query_join_tables = {}\n\n query = self.session.query(self.get_column(self.analysis.primary_key_column, self._sqlalchemy_pk_table))\n\n # Add attributes to query\n for attribute in self.analysis.attributes:\n query = self.add_attribute_to_query(query, attribute)\n\n # Add filters to query\n query = self.apply_filters(query)\n\n logger.info(\n {\n \"message\": f\"Built query for resource {self.analysis.definition_id}: {query.statement}\",\n \"resource_id\": self.analysis.resource_id,\n },\n )\n return query",
"title": ""
},
{
"docid": "deb662581d7042718da96ca7baef1b0e",
"score": "0.5773945",
"text": "def query(self):\n if 'order' in self.config['database']:\n order = 'ORDER BY {order}'.format(**self.config['database'])\n else:\n order = ''\n\n if isinstance(self.cols, tuple):\n cols = self.cols\n else:\n cols = (self.cols,)\n\n where, params = parse_queries(self.selection, self.mapping)\n if where:\n where = 'WHERE {conditions}'.format(conditions=' AND '.join(where))\n else:\n where = ''\n limit = (self.slice[0].stop or sys.maxint) - (self.slice[0].start or 0)\n\n sql = \"\"\"\n SELECT {cols} FROM {table}\n {where}\n {order}\n LIMIT {limit}\n OFFSET {offset}\n \"\"\".format(\n cols=', '.join(self.config[key]['col'] for key in cols),\n table=self.config['database']['table'],\n where=where, order=order, limit=limit,\n offset=self.slice[0].start or 0)\n\n return sql, params",
"title": ""
},
{
"docid": "86f87622ad6a714500a7b6ccb0df8944",
"score": "0.5773749",
"text": "def sql_with_params(self):\r\n return self.get_compiler(DEFAULT_DB_ALIAS).as_sql()",
"title": ""
},
{
"docid": "a12abdcbed66b2452ab47d4061b8ce7d",
"score": "0.5761608",
"text": "def build_query(self, query_dict):\n\n # Loop over keys in request dictionary, match them up to database fields\n # Build query accounting for the exceptions\n qry_sci_name = ''\n qry_common_name = ''\n qry_group = ''\n qry_native = ''\n qry_pollut_tol = ''\n qry_rarity = ''\n qry_range = ''\n qry_max_age = ''\n qry_mean_weight = ''\n qry_mean_length = ''\n qry_max_length = ''\n qry_habitat = ''\n qry_ubiquity = ''\n qry_extent = ''\n qry_tolerance = ''\n qry_robustness = ''\n\n #Used in a couple of places to remove a trailing 'or '\n trailing_or = ' or'\n trailing_and = ' and'\n\n #This will be used later for habitat related fields that contain 1 for true. E.g. caves=1\n set_keys = set(query_dict.keys())\n\n for req_key, req_val in query_dict.items():\n # Is request param a valid query param\n if req_key in self.attrib:\n\n if (req_key.lower() == 'scientific_name'):\n words = req_val.split('_')\n if len(words) == 1:\n qry_sci_name = (\"( genus LIKE INITCAP('%%{0}%%') or species LIKE LOWER('%%{0}%%'))\")\n qry_sci_name = str.format(qry_sci_name, words[0])\n elif len(words) == 2:\n qry_sci_name = (\" ((genus LIKE INITCAP('%%{0}%%') and species LIKE LOWER('%%{1}%%')) or (species LIKE LOWER('%%{0}%%') and genus LIKE INITCAP('%%{1}%%')))\")\n qry_sci_name = str.format(qry_sci_name, words[0], words[1])\n\n if req_key.lower() == 'common_name':\n words = req_val.split('_')\n if len(words) == 1:\n qry_common_name = (\" commonname LIKE INITCAP('%%{0}%%')\")\n qry_common_name = str.format(qry_common_name, words[0])\n elif len(words) == 2:\n qry_common_name = (\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%'))\")\n qry_common_name = str.format(qry_common_name, words[0], words[1])\n elif len(words) == 3:\n qry_common_name = (\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%') and commonname LIKE INITCAP('%%{2}%%'))\")\n qry_common_name = str.format(qry_common_name, words[0], words[1], words[2])\n\n\n # Can be multiple groups\n #e.g. grp='Black Bass' or grp='Mullet'\n if (req_key.lower() == 'group'):\n words = req_val.lower()\n words = words.replace('_', ' ')\n words = words.split(',')\n for idx, grp in enumerate(words):\n if idx != 0:\n qry_group += \" or \"\n qry_group += str.format(\" lower(grp) = '{0}'\", grp.lower())\n continue\n\n if (req_key.lower() == 'native'):\n qry_native = str.format(\" native='{0}'\",req_val)\n continue\n\n # pollution tolerance can be: \"I\", \"T\", \"M\", or \"U\"\n # if (req_key.lower() == 'pollut_tol'):\n # if (req_key.lower() == 'tolerance'):\n # words = req_val.lower()\n # words = words.split(',')\n # for idx, pollut in enumerate(words):\n # if idx != 0:\n # qry_pollut_tol += \" or \"\n # qry_pollut_tol += str.format(\" lower(pollut_tol) = '{0}'\", pollut)\n # continue\n\n if (req_key.lower() == 'rarity'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_rarity = str.format(\" rarity>={0} and rarity<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'max_age'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_max_age = str.format(\" max_age>={0} and max_age<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'mean_weight'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_mean_weight = str.format(\" mean_weight>={0} and mean_weight<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'mean_length'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_mean_length = str.format(\" mean_length>={0} and mean_length<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'max_length'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_max_length = str.format(\" max_length>={0} and max_length<={1}\", range[0], range[1])\n continue\n\n ubiquity_range = [0, 75]\n if (req_key.lower() == 'ubiquity'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != ubiquity_range[0] or int(range[1]) != ubiquity_range[1]):\n qry_ubiquity = str.format(\" ubiquity>={0} and ubiquity<={1}\", range[0], range[1])\n continue\n\n extent_range = [0, 77]\n if (req_key.lower() == 'extent'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != extent_range[0] or int(range[1]) != extent_range[1]):\n qry_extent = str.format(\" extent>={0} and extent<={1}\", range[0], range[1])\n continue\n\n tolerance_range = [0, 94]\n if (req_key.lower() == 'tolerance'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != tolerance_range[0] or int(range[1]) != tolerance_range[1]):\n qry_tolerance = str.format(\" tolerance>={0} and tolerance<={1}\", range[0], range[1])\n continue\n\n robustness_range = [0, 47]\n if (req_key.lower() == 'robustness'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != robustness_range[0] or int(range[1]) != robustness_range[1]):\n qry_robustness = str.format(\" robustness>={0} and robustness<={1}\", range[0], range[1])\n continue\n\n if qry_habitat.endswith(trailing_and):\n qry_habitat = qry_habitat[:-len(trailing_and)]\n\n # The rest of the query parameters are are used if value == 1\n # e.g. caves=1, springs=1, headwaters=1\n for cat in self.categories:\n intersect = set_keys & self.categories[cat]\n if len(intersect) > 0:\n print(cat)\n print(intersect)\n for param in intersect:\n val = query_dict[param]\n if (val == '1'):\n self.query_categories[cat].append(param)\n\n qry_habitat = ''\n query_cat = ''\n for category in self.query_categories:\n query_cat = ''\n for val in self.query_categories[category]:\n query_cat += str.format(\" {0}='1' or\", val)\n\n if query_cat.endswith(trailing_or):\n query_cat = query_cat[:-len(trailing_or)]\n\n if len(query_cat) > 0:\n qry_habitat += ' (' + query_cat + ') and'\n print(qry_habitat)\n\n if qry_habitat.endswith(trailing_and):\n qry_habitat = qry_habitat[:-len(trailing_and)]\n\n query = \"select * from fishproperties where\"\n first_condition = True\n\n if qry_sci_name != '':\n query += qry_sci_name\n first_condition = False\n\n if qry_common_name != '':\n if not first_condition:\n query += ' and'\n query += qry_common_name\n first_condition = False\n\n if qry_group != '':\n if not first_condition:\n query += ' and'\n query += qry_group\n first_condition = False\n\n if qry_native != '':\n if not first_condition:\n query += ' and'\n query += qry_native\n first_condition = False\n\n if qry_pollut_tol != '':\n if not first_condition:\n query += ' and'\n query += qry_pollut_tol\n first_condition = False\n\n if qry_rarity != '':\n if not first_condition:\n query += ' and'\n query += qry_rarity\n first_condition = False\n\n if qry_range != '':\n if not first_condition:\n query += ' and'\n query += qry_range\n first_condition = False\n\n if qry_max_age != '':\n if not first_condition:\n query += ' and'\n query += qry_max_age\n first_condition = False\n\n if qry_mean_weight != '':\n if not first_condition:\n query += ' and'\n query += qry_mean_weight\n first_condition = False\n\n if qry_mean_length != '':\n if not first_condition:\n query += ' and'\n query += qry_mean_length\n first_condition = False\n\n if qry_max_length != '':\n if not first_condition:\n query += ' and'\n query += qry_max_length\n first_condition = False\n\n if qry_ubiquity != '':\n if not first_condition:\n query += ' and'\n query += qry_ubiquity\n first_condition = False\n\n if qry_extent != '':\n if not first_condition:\n query += ' and'\n query += qry_extent\n first_condition = False\n\n if qry_tolerance != '':\n if not first_condition:\n query += ' and'\n query += qry_tolerance\n first_condition = False\n\n if qry_robustness != '':\n if not first_condition:\n query += ' and'\n query += qry_robustness\n first_condition = False\n\n if qry_habitat != '':\n if not first_condition:\n query += ' and'\n query += qry_habitat\n first_condition = False\n\n print(query)\n\n return query",
"title": ""
},
{
"docid": "c45b3d40d8a26bb7193088e942d5a655",
"score": "0.5736782",
"text": "def _construct_query(self, query_chain):\n # here we take the query_chain and convert to a real sql sentence\n res_dict = query_chain.pop(0)\n\n for q in query_chain:\n if q[\"action\"] == \"_db__where\":\n if res_dict[\"action\"] == \"_db__select_all\":\n res_dict.update({\"action\": \"_db__select\"})\n\n condition = res_dict.get(\"condition\", \"\")\n if condition:\n condition = \" AND \".join([condition, q[\"condition\"]])\n else:\n condition = q[\"condition\"]\n\n res_dict.update({\"condition\": condition})\n elif q[\"action\"] == \"_db__select_related\":\n for model_join in q[\"fields\"]:\n join_const = getattr(self, q[\"action\"]).format(**model_join)\n res_dict[\"join\"] += join_const\n\n select = res_dict[\"select\"][:]\n\n if select == \"COUNT(*)\":\n pass\n elif select == \"*\":\n select = select.replace(\n \"*\",\n \"{left_table}.*, {f_formatter}\".format(\n left_table=model_join[\"left_table\"], f_formatter=model_join[\"fields_formatter\"]\n ),\n )\n res_dict[\"select\"] = select\n else:\n res_dict[\"select\"] += \", \" + model_join[\"fields_formatter\"]\n\n # if we are not counting, then we can assign ordering\n operations = [\"COUNT\", \"MAX\", \"MIN\", \"SUM\", \"AVG\", \"STDDEV\"]\n\n # we must get error, if we set only one field and update instance\n if res_dict[\"action\"] == \"_db__update\" and len(res_dict[\"field_values\"]) == 1:\n res_dict.update({\"action\": \"_db__update_single_field\"})\n\n if res_dict.get(\"select\", \"\").split(\"(\")[0] not in operations:\n res_dict[\"ordering\"] = self._ordering_syntax(res_dict.get(\"ordering\", []))\n else:\n res_dict[\"ordering\"] = \"\"\n query = getattr(self, res_dict[\"action\"]).format(**res_dict)\n query = self._query_clean(query)\n\n logger.debug(\"QUERY: {}, VALUES: {}\".format(query, res_dict.get(\"field_values\")))\n return query, res_dict.get(\"field_values\")",
"title": ""
},
{
"docid": "0c13ae92a74d4f3943d1cd2abdd2d58f",
"score": "0.572707",
"text": "def _build_query(env, start, end, certname=None):\n query = ExtractOperator()\n query.add_field(FunctionOperator('count'))\n query.add_field('status')\n subquery = AndOperator()\n subquery.add(GreaterEqualOperator('producer_timestamp', start))\n subquery.add(LessOperator('producer_timestamp', end))\n if certname is not None:\n subquery.add(EqualsOperator('certname', certname))\n if env != '*':\n subquery.add(EqualsOperator('environment', env))\n query.add_query(subquery)\n query.add_group_by(\"status\")\n return query",
"title": ""
},
{
"docid": "6a1dd0e6509804a1d36f57e0e6c5750a",
"score": "0.5720286",
"text": "def query() -> str:\n return \"\"\"\n MATCH (doc:Publication)<-[:located_in]-(subject)-[association:chemical_to_disease_or_phenotypic_feature_association]->(obj)-[:located_in]->(sec_doc:Publication)\n WHERE association.relation <> 'null' and obj.name <> 'null' and subject.name <> 'null' and doc.pmid in {pmids} and association.relation <> 'null' and type(association) <> 'could_refer_to' and type(association) <> 'located_in' and sec_doc.pmid = doc.pmid\n RETURN DISTINCT subject.name as subject, subject.synonym as subj_syn, association.relation as predicate, obj.name as obj, obj.synonym as disease_synonym, type(association) as rel_type, doc.pmid as pmid, doc.abstract as abstract\n \"\"\"",
"title": ""
},
{
"docid": "6002db82a2bae451e4ed335385a5f2a2",
"score": "0.57197946",
"text": "def build_where_clause(args: dict, table_args_dict: dict) -> str:\n where_clause: str = ''\n for key in args.keys():\n if key in table_args_dict.keys():\n if key == 'query':\n # if query arg is supplied than we just need to parse it and only it\n return args[key].strip()\n else:\n values_list: list = argToList(args[key])\n for raw_value in values_list:\n for field in table_args_dict[key]:\n value = raw_value\n if key == 'url':\n value = f'*{raw_value}*'\n if not where_clause:\n # the beginning of the where part should start without OR\n where_clause += f\"{field}'{value}'\"\n else:\n where_clause += f\" OR {field}'{value}'\"\n return where_clause",
"title": ""
},
{
"docid": "fd48cb6a7ac2b019d07866613d70b519",
"score": "0.5693227",
"text": "def generate_where_clause(state=None, product=None,\n issue=None, min_complaints=None):\n where_inner = []\n where_outer = []\n\n if state and not state == \"All\":\n where_inner.append(\"ccdb.state = '{}'\".format(state))\n\n if product and not product == \"All\":\n where_inner.append(\"ccdb.product = '{}'\".format(product))\n\n if issue and not issue == \"All\":\n where_inner.append(\"ccdb.issue = '{}'\".format(issue))\n\n if min_complaints:\n where_outer.append(\"complaint_count > {}\".format(min_complaints))\n\n if len(where_inner) > 0:\n where_inner = \"WHERE \" + \" AND \".join(where_inner)\n else:\n where_inner = \"\"\n\n if len(where_outer) > 0:\n where_outer = \"WHERE \" + \" AND \".join(where_outer)\n else:\n where_outer = \"\"\n\n return where_inner, where_outer",
"title": ""
},
{
"docid": "a7e6e63e44f78e69cf88a741ac17d2fb",
"score": "0.5682573",
"text": "def build_query(self, params):\n term = params[\"term\"]\n size = params[\"size\"]\n page = params[\"page\"]\n\n query = {}\n query[\"query\"] = {\"match\":{}}\n query[\"query\"][\"match\"][\"inflected_form\"] = term\n\n query[\"sort\"] = []\n query[\"sort\"].append({\"_score\": \"desc\"})\n query[\"sort\"].append({\"word_count\": \"asc\"})\n\n query[\"from\"] = (page - 1) * size\n query[\"size\"] = size\n\n query = json.dumps(query)\n return query",
"title": ""
},
{
"docid": "930b18d9b4e9205bd3e078001ad05e29",
"score": "0.5674045",
"text": "def generate(self):\n result = self.query\n for param in self.params:\n result = result.replace(param.raw, param.sql_value())\n\n return result",
"title": ""
},
{
"docid": "2aa19aae92a018fc0f26673cb0f78bfb",
"score": "0.56575936",
"text": "def filter_query(self, parents=None):\n q = db.session.query()\n stmt, q = self._add_statement(q)\n q = q.add_column(stmt.subject)\n\n if parents is not None and self.node.attribute:\n parent_stmt, q = self._add_statement(q)\n q = q.filter(stmt.subject == parent_stmt._value)\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\n q = q.filter(parent_stmt.subject.in_(parents))\n\n q = self.filter(q, stmt)\n q = q.group_by(stmt.subject)\n q = q.order_by(stmt.subject.asc())\n\n if self.node.root:\n q = q.limit(self.node.limit)\n q = q.offset(self.node.offset)\n\n return q",
"title": ""
},
{
"docid": "ddd52219e9c4b5adb4afd6a139da0d56",
"score": "0.56358266",
"text": "def derived(self, statement, params=(), replace=False):\n return Query(\n db=self.db,\n sql=(statement,) if replace else self.sql + (statement,),\n params=self.params + params,\n )",
"title": ""
},
{
"docid": "2cd8fe36d01097c20060583de2e8bc83",
"score": "0.562555",
"text": "def make_query(self):",
"title": ""
},
{
"docid": "9fe5061d0a503021cd5b78f56d96ffed",
"score": "0.5612804",
"text": "def build_query(media_type=None, tags=None, sort=None, max_duration=None, published_after=None, categories=None,\n region=None, random=False, limit=constants.DEFAULT_QUERY_LIMIT,\n offset=constants.DEFAULT_QUERY_OFFSET):\n # NOTE: query time skyrockets with added OPTIONAL patterns, see:\n # https://stackoverflow.com/questions/25609691/alternative-for-optional-keyword-in-sparql-queries\n #\n # NOTE: query assumes ordering of group concat operations are same across tag and source - have eyeballed and\n # looks good\n if published_after is not None:\n raise NotImplementedError('The parameter `publishedAfter` is not yet implemented')\n if region is not None:\n raise NotImplementedError('The parameter `region` is not yet implemented')\n if sort is not None and random:\n raise InvalidInputParameterCombination('Cannot specify both `sort` and `random`.')\n\n regular_fields = '?title ?image ?version ?programme ?pid ?media ?duration ?publicationDate ?masterBrand'\n query_string = f\"\"\"\n SELECT\n {regular_fields}\n {build_tags_select_statement()}\n {build_genres_select_statement()}\n\n WHERE {{\n {build_regular_fields_pattern_statement()}\n {build_tags_pattern_statement()}\n {build_genres_pattern_statement()}\n\n {build_sparql_anding_statement(\n object_list=tags,\n predicate_str='datalab:tag/datalab:tagValue',\n object_binding='programme'\n )}\n {build_values_oring_statement('media', media_type)}\n {build_filter_statement('duration', '<', max_duration)}\n {build_filter_statement('published_date', '>', published_after)}\n {build_sparql_anding_statement(\n object_list=categories,\n predicate_str='po:genre/datalab:genreKey',\n object_binding='programme'\n )}\n }}\n GROUP BY {regular_fields}\n {'ORDER BY RAND()' if random else 'ORDER BY ' + build_sort_statement(sort) if sort is not None else ''}\n LIMIT {limit}\n OFFSET {offset}\n \"\"\"\n\n return query_string",
"title": ""
},
{
"docid": "864d460b0beb79116a83d3be73eff240",
"score": "0.5609568",
"text": "def compiledQuery(self, columns='', where='', order_by='',\n distinct='', limit='', offset='',\n group_by='', having='', for_update=False,\n relationDict=None,\n bagFields=False,\n count=False, excludeLogicalDeleted=True,excludeDraft=True,\n ignorePartition=False,ignoreTableOrderBy=False,\n addPkeyColumn=True):\n # get the SqlCompiledQuery: an object that mantains all the informations to build the sql text\n self.cpl = SqlCompiledQuery(self.tblobj.sqlfullname,relationDict=relationDict,maintable_as=self.aliasCode(0))\n distinct = distinct or '' # distinct is a text to be inserted in the sql query string\n \n # aggregate: test if the result will aggregate db rows\n aggregate = bool(distinct or group_by)\n \n # group_by == '*': if all columns are aggregate functions, there will be no GROUP BY columns, \n # but SqlQueryCompiler need to know that result will aggregate db rows\n if group_by == '*':\n group_by = None\n\n if not ignoreTableOrderBy and not aggregate:\n order_by = order_by or self.tblobj.attributes.get('order_by')\n self.init()\n if not 'pkey' in self.cpl.relationDict:\n self.cpl.relationDict['pkey'] = self.tblobj.pkey\n \n # normalize the columns string\n columns = columns or ''\n columns = columns.replace(' ', ' ')\n columns = columns.replace('\\n', '')\n columns = columns.replace(' as ', ' AS ')\n columns = columns.replace(' ,', ',')\n if columns and not columns.endswith(','):\n columns = columns + ','\n \n # expand * and *filters: see self.expandMultipleColumns\n if '*' in columns:\n col_list = [col for col in gnrstring.split(columns, ',') if col]\n new_col_list = []\n for col in col_list:\n col = col.strip()\n if col.startswith('*'):\n new_col_list = new_col_list + self.expandMultipleColumns(col[1:], bagFields)\n else:\n new_col_list.append(col)\n columns = ','.join(new_col_list)\n \n # translate @relname.fldname in $_relname_fldname and add them to the relationDict\n if where:\n where = PERIODFINDER.sub(self.expandPeriod, where)\n \n currentEnv = self.db.currentEnv\n env_conditions = dictExtract(currentEnv,'env_%s_condition_' %self.tblobj.fullname.replace('.','_'))\n if env_conditions:\n wherelist = [where] if where else []\n for condition in env_conditions.values():\n wherelist.append('( %s )' %condition)\n where = ' AND '.join(wherelist)\n\n partition_kwargs = dictExtract(self.tblobj.attributes,'partition_')\n\n if not ignorePartition and partition_kwargs:\n wherelist = [where] if where else []\n for k,v in partition_kwargs.items():\n if currentEnv.get('current_%s' %v):\n wherelist.append('( $%s=:env_current_%s )' % (k,v))\n where = ' AND '.join(wherelist)\n columns = self.updateFieldDict(columns)\n where = self.updateFieldDict(where or '')\n order_by = self.updateFieldDict(order_by or '')\n group_by = self.updateFieldDict(group_by or '')\n having = self.updateFieldDict(having or '')\n \n col_list = uniquify([col for col in gnrstring.split(columns, ',') if col])\n new_col_list = []\n for col in col_list:\n col = col.strip()\n if col.startswith('SUM'):\n aggregate = True\n if not ' AS ' in col:\n if col.startswith('$') and col[1:].replace('_', '').isalnum():\n as_ = col[1:]\n else:\n # replace non word char with _ and check for numbers\n as_ = self.db.colToAs(col)\n col = '%s AS \"%s\"' % (col, as_)\n else:\n colbody, as_ = col.split(' AS ', 1)\n # leave the col as is, but save the AS name to recover the db column original name from selection result\n self.cpl.aliasDict[as_.strip()] = colbody.strip()\n new_col_list.append(col)\n \n # build the clean and complete sql string for the columns, but still all fields are expressed as $fieldname\n columns = ',\\n'.join(new_col_list)\n \n # translate all fields and related fields from $fldname to t0.fldname, t1.fldname... and prepare the JOINs\n colPars = {}\n for key, value in self.cpl.relationDict.items():\n # self._currColKey manage exploding columns in recursive getFieldAlias without add too much parameters\n self._currColKey = key\n colPars[key] = self.getFieldAlias(value)\n if count: # if the query is executed in count mode...\n order_by = '' # sort has no meaning\n if group_by: # the number of rows is defined only from GROUP BY cols, so clean aggregate functions from columns.\n columns = group_by # was 't0.%s' % self.tblobj.pkey # ???? \n elif distinct:\n pass # leave columns as is to calculate distinct values\n else:\n columns = 'count(*) AS gnr_row_count' # use the sql count function istead of load all data\n elif addPkeyColumn and self.tblobj.pkey and not aggregate:\n columns = columns + ',\\n' + '%s.%s AS pkey' % (self.aliasCode(0),self.tblobj.pkey) # when possible add pkey to all selections\n columns = columns.lstrip(',') # if columns was '', now it starts with ','\n else:\n columns = columns.strip('\\n').strip(',')\n \n # replace $fldname with tn.fldname: finally the real SQL columns!\n columns = gnrstring.templateReplace(columns, colPars, safeMode=True)\n \n # replace $fldname with tn.fldname: finally the real SQL where!\n \n where = gnrstring.templateReplace(where, colPars)\n #if excludeLogicalDeleted==True we have additional conditions in the where clause\n logicalDeletionField = self.tblobj.logicalDeletionField\n draftField = self.tblobj.draftField\n if logicalDeletionField:\n if excludeLogicalDeleted is True:\n extracnd = '%s.%s IS NULL' % (self.aliasCode(0),logicalDeletionField)\n if where:\n where = '%s AND %s' % (extracnd, where)\n else:\n where = extracnd\n elif excludeLogicalDeleted == 'mark':\n if not (aggregate or count):\n columns = '%s, %s.%s AS _isdeleted' % (columns, self.aliasCode(0),logicalDeletionField) #add logicalDeletionField\n if draftField:\n if excludeDraft is True:\n extracnd = '%s.%s IS NOT TRUE' %(self.aliasCode(0),draftField)\n if where:\n where = '%s AND %s' % (extracnd, where)\n else:\n where = extracnd\n # add a special joinCondition for the main selection, not for JOINs\n if self.joinConditions:\n extracnd, one_one = self.getJoinCondition('*', '*', self.aliasCode(0))\n if extracnd:\n if where:\n where = '(%s) AND (%s)' % (where, extracnd)\n else:\n where = extracnd\n order_by = gnrstring.templateReplace(order_by, colPars)\n having = gnrstring.templateReplace(having, colPars)\n group_by = gnrstring.templateReplace(group_by, colPars)\n \n if distinct:\n distinct = 'DISTINCT '\n elif distinct is None or distinct == '':\n if self._explodingRows:\n if not aggregate: # if there is not yet a group_by\n distinct = 'DISTINCT ' # add a DISTINCT to remove unusefull rows: eg. a JOIN used only for a where, not for columns\n if order_by:\n xorderby=(('%s '%order_by.lower()).replace(' ascending ','').replace(' descending ','').replace(' asc ','').replace(' desc','')).split(',')\n lowercol=columns.lower()\n for xrd in xorderby:\n if not xrd.strip() in lowercol:\n columns = '%s, \\n%s' % (columns, xrd)\n #order_by=None\n if count:\n columns = '%s.%s' % (self.aliasCode(0),self.tblobj.pkey)\n # Count the DISTINCT maintable pkeys, instead of count(*) which will give the number of JOIN rows.\n # That gives the count of rows on the main table: the result is different from the actual number\n # of rows returned by the query, but it is correct in terms of main table records.\n # It is the right behaviour ???? Yes in some cases: see SqlSelection._aggregateRows\n \n self.cpl.distinct = distinct\n self.cpl.columns = columns\n self.cpl.where = where\n self.cpl.group_by = group_by\n self.cpl.having = having\n self.cpl.order_by = order_by\n self.cpl.limit = limit\n self.cpl.offset = offset\n self.cpl.for_update = for_update\n #raise str(self.cpl.get_sqltext(self.db)) # uncomment it for hard debug\n return self.cpl",
"title": ""
},
{
"docid": "3f2a3634474b6bca3382e5a869663d66",
"score": "0.5586637",
"text": "def rebuild_query(self, comments=True, pretty=True):\n if not self.tree:\n self._logger.error('El arbol no ha sido generado todavia. Por tanto, todavia no hay ninguna query que '\n 'reconstruir. Por favor ejecuta la funcion parse_query() para generar el arbol')\n raise LookupError\n\n if not self.__queries:\n self._logger.error('El arbol no ha sido procesado todavia. Por favor ejecuta la funcion rename_tree() para '\n 'procesar el arbol.')\n raise LookupError\n\n query = ' '.join(self.tree.leaves())\n query = (self._untokenize(query)\n .replace(' , ', ', ')\n .replace(' ; ', ';')\n .replace(' ( ', ' (')\n .replace(' ) ', ') ')\n #.replace(' = ', '=')\n .replace(' . ', '.')\n )\n\n if self.udfs:\n for point, scape in zip(self.udfs, self._udfs_norm):\n pattern = re.compile(scape, re.IGNORECASE)\n query = pattern.sub(point, query)\n\n if pretty:\n if comments:\n final = '\\n'.join(self.__comments) + '\\n' + sqlparse.format(query, reindent=True, keyword_case='upper')\n self.__comments = []\n return final\n else:\n return sqlparse.format(query, reindent=True, keyword_case='upper')\n else:\n return query",
"title": ""
},
{
"docid": "4df307799efca220bbeb0a0c969810c8",
"score": "0.55771285",
"text": "def construct_query(*args, dialect='MSSS', allow_coalesce=True):\n n = len(args)\n assert all([isinstance(o, UserOption) for o in args]), \"Not all args are UserOptions\"\n invalids = [not o.validate() for o in args]\n assert not any(invalids), \"{:d}/{:d} options have not been validated\".format(\n sum(invalids), n)\n assert all([args[0].context == args[i+1].context for i in range(n-1)]), \"Different\" +\\\n \"contexts associated with the User Opts. Ensure these are the same.\"\n stmt = Statement(args[0].context)\n\n # ==== GET ALL TABLES AND FORM BASIC JOIN SUBTREE ============\n context = args[0].context\n nodes = [o.get_table() for o in args]\n unique_nodes = list(set(nodes))\n join_tree = minimum_subtree(unique_nodes)\n\n # The primary table is considered the \"root node\" of the tree -- edges are undirected.\n primary = [o for o in args if not o.is_secondary]\n assert len(primary) == 1, f'Expecting one primary field. Got {len(primary)}.'\n primary = primary[0]\n primary_tbl = primary.get_table()\n\n # topological sort\n edges = [(k, v[0][0]) for (i,(k,v)) in enumerate(join_tree.items()) if i > 0]\n G = graph.Graph(edges, directed=False)\n sorted_nodes = G.topological_sort(leave_until_last=primary_tbl)\n # (Recreate G since the topological_sort mutates G (ikr - should fix this!))\n G = graph.Graph(edges, directed=False)\n\n # ======= CREATE CTEs WHENEVER WE FIND A NESTED AGGREGATION ============\n field_tbl_lkp = defaultdict(list)\n for arg in args:\n field_tbl_lkp[arg.get_table()].append(arg)\n all_fields = dict()\n\n # Traverse the join tree of the selected tables, making CTEs wherever\n # there exists secondary aggregation (i.e. not of the primary variable).\n for i, v in enumerate(sorted_nodes[:-1]):\n v_fields = field_tbl_lkp[v]\n any_v_has_agg = any([arg.has_aggregation for arg in v_fields])\n\n # Get 'child tables' and 'parent tables' (wrt topological sort)\n child_tbls = G._graph[v] - set(sorted_nodes[i:])\n parent_tbl = join_tree[v][0][0]\n parent_fks, v_pks = join_tree[v][0][1], join_tree[v][1][1]\n\n # The current table contains an aggregation, so create a CTE.\n if any_v_has_agg:\n # Get all arguments for table which are not aggregated\n f_non_agg = [arg for arg in v_fields if not arg.has_aggregation]\n f_non_agg += flatten([all_fields[w] for w in child_tbls])\n\n # Add in table PKs if not already included in `f_non_agg`.\n f_non_agg_names = [f.sql_fieldname for f in f_non_agg]\n pk_names_to_add = [f for f in v_pks if f not in f_non_agg_names]\n pks_to_add = [construct_simple_field(f, v, context) for f in pk_names_to_add]\n\n # Make a copy of all non-aggregated AND aggregated fields.\n f_non_agg_cte = [f.copy() for f in f_non_agg]\n f_agg_cte = [arg for arg in v_fields if arg.has_aggregation]\n f_agg = [f.copy() for f in f_agg_cte]\n\n # consolidate CTE fields and add primary designator to (any) field in root\n cte_fields = pks_to_add + f_non_agg_cte + f_agg_cte\n for field in cte_fields:\n if field.sql_fieldname in v_pks:\n field.is_secondary = False\n break\n\n # Defer lookups until the final query\n for field in cte_fields:\n field.perform_lkp = False\n\n # Construct CTE\n cte = CTENode([parent_tbl], # parent\n v_pks, # pk\n cte_fields) # cte_fields\n stmt.ctes.append(cte)\n\n # Point the [references to the aggregations] outside the CTE to the CTE field\n for f in f_agg:\n f.field_alias = f.field_alias # this is *not* a noop! (@property...)\n f.sql_item = '{alias}' + f.field_alias\n f.table = cte\n f.set_aggregation(None, force=True)\n f.set_transform(None, force=True)\n if allow_coalesce:\n f.coalesce = 0 # assumes that aggregation is numeric\n\n for f in f_non_agg:\n f.table = cte\n f.sql_item = '{alias}'+f._field_alias_logic(will_perform_lkp=False)\n\n # Push these pointers to within the CTE up to the parent (although not PKs)\n all_fields[v] = f_non_agg + f_agg\n\n # # overwrite the node `v` in the subtree with `cte`\n # join_cond = tree_final[v]\n # join_cond = (join_cond[0], (cte, join_cond[1][1]))\n # tree_final = replace_in_ordered_dict(tree_final, v, cte, join_cond)\n else:\n all_fields[v] = v_fields + flatten([all_fields[w] for w in child_tbls])\n\n # Set args to the args propagated up to the root node\n primary_children = G._graph[primary_tbl]\n args = field_tbl_lkp[primary_tbl] + flatten([all_fields[c] for c in primary_children])\n # Place the resulting tree into the FROM clause of the Statement object.\n\n nodes = [o.get_table() for o in args]\n unique_nodes = list(set(nodes))\n tree_final = minimum_subtree(unique_nodes)\n\n stmt._from.update(tree_final)\n\n # Add dimension tables (if requested to joins)\n lkp_joins = []\n for o in args:\n if o.perform_lkp:\n dtbl = o.dimension_table\n assert not dtbl.is_cte, \"Currently unable to support CTEs for dimension \" + \\\n \"tables. (But it only requires thinking about how \" + \\\n \"to avoid multiple copies.)\"\n fk = o.sql_fieldname\n lkp_joins.append(((o.table, fk), (dtbl, dtbl.pk[0])))\n\n # Generate statement in order to populate aliases dict\n stmt._from.generate_basic_statement(force_alias=(len(lkp_joins) > 0))\n stmt._from.add_lookups_to_statement(lkp_joins)\n\n # === CONSTRUCT SELECT / WHERE ===========\n has_agg = any([arg.has_aggregation for arg in args])\n\n for o in args:\n # Get table alias for field, depending on whether has lookup table or not\n if not o.perform_lkp:\n alias = stmt.aliases[o.get_table()]\n # coalesce is usually None anyway\n coalesce = o.coalesce if allow_coalesce else None\n else:\n dtbl = o.dimension_table\n fk = rm_alias_placeholder(o.sql_item)\n alias = stmt.lkp_aliases[((o.table, fk), (dtbl, dtbl.pk[0]))]\n coalesce = context.coalesce_default if allow_coalesce else None\n\n sel, where = o.sql_transform(alias=alias, dialect=dialect, coalesce=coalesce)\n # SELECT\n stmt.select.append(sel)\n # WHERE\n if len(where) > 0:\n stmt.where.extend(where)\n # GROUP BY\n if has_agg and not o.has_aggregation:\n gby = re.sub('AS [a-zA-Z0-9_]+$', '', sel).strip()\n stmt.groupby.append(gby)\n\n return stmt.generate_statement(dialect=dialect)",
"title": ""
},
{
"docid": "97b15f5e2a70c1eb7088c3897a2bfef2",
"score": "0.5570735",
"text": "def _build_query(self, metric, node, ts_from, ts_to):\n resolution = ts_to - ts_from\n if resolution > PROMETHEUS_TS_LIMIT:\n ts_from = ts_to - PROMETHEUS_TS_LIMIT\n\n query_head = \"http://{}:{}/api/v1/query_range?query=\".format(\n self.tsdb_ip, self.tsdb_port)\n query_times = \"&start={}&end={}&step=1s\".format(ts_from, ts_to)\n\n query_selectors = self._get_query_selectors(metric, node)\n query_selector = '{}{{{}}}'.format(metric, query_selectors)\n\n query = \"{}{}{}\".format(query_head, query_selector, query_times)\n return query",
"title": ""
},
{
"docid": "923405ac27fc863e4ecf89c859843768",
"score": "0.55545264",
"text": "def get_clean_query(self, additional_params={}):\n # NOTE: right now this makes us pretty vulnerable\n # to SQL Injection. We should make this safer at some point.\n # Graphs likely have the same problem.\n reg = re.compile('(\\{\\{.*?\\}\\})')\n query = self.query\n matches = reg.findall(query)\n if matches:\n for match in matches:\n attr = match[2:len(match)-2]\n if attr in additional_params:\n query = query.replace(match, additional_params[attr])\n else:\n query = query.replace(match, \"\")\n return query",
"title": ""
},
{
"docid": "374044813c297baa727f456ec5a2a772",
"score": "0.5546569",
"text": "def buildQueryFromFilters(date, rainfall, temperature, month, year):\n\t# Main part of the query\n\tquery = sql.SQL(\"SELECT * FROM climate\")\n\n\tconditions = []\n\tvalues = []\n\n\t# Check if each filter is actually applied, \n\t# if so, its respective part of the query is appended to the main part\n\tif date != None:\n\t\tclause = sql.SQL(\"date = to_date(%s, 'DD-MM-YYYY')\")\n\t\tconditions.append(clause)\n\t\tvalues.append(date)\n\tif temperature != None:\n\t\tclause = sql.SQL(\"temperature = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(temperature)\n\tif rainfall != None:\n\t\tclause = sql.SQL(\"rainfall = real '%s'\")\n\t\tconditions.append(clause)\n\t\tvalues.append(rainfall)\n\tif month != None:\n\t\tclause = sql.SQL(\"EXTRACT(MONTH FROM date) = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(month)\n\tif year != None:\n\t\tclause = sql.SQL(\"EXTRACT(YEAR FROM date) = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(year)\n\n\t# Put all together\n\tif len(conditions) > 0:\n\t\tquery = sql.SQL(\" \").join([query, sql.SQL(\"WHERE\")])\n\n\t\tconj = sql.SQL(\" AND \").join(conditions)\n\n\t\tquery = sql.SQL(\" \").join([query, conj])\n\n\t\n\treturn query, values",
"title": ""
},
{
"docid": "e9b14da9066124004791a46561a7d775",
"score": "0.55110025",
"text": "def query(**kw):",
"title": ""
},
{
"docid": "dfaf1e939ed62d271c7e53d6dfd18c69",
"score": "0.54942393",
"text": "def create_query(vaccine=None, age_group=\"adult\", without_registration=None, self_payer=False, monday=None,\n tuesday=None, wednesday=None, thursday=None, friday=None, saturday=None, sunday=None):\n\n open_hours = {'monday': monday, 'tuesday': tuesday, 'wednesday': wednesday, 'thursday': thursday, 'friday': friday,\n 'saturday': saturday, 'sunday': sunday}\n\n query = \"\"\"\n SELECT loc.vacc_id, loc.latitude, loc.longitude\n FROM vacc_center_location as loc\n INNER JOIN vacc_center_type as typ USING(vacc_id)\n \"\"\"\n if age_group not in AGE_GROUP_SET: raise Exception('age_group argument can be only: adult, teenage, child')\n if int(self_payer) not in [0, 1]: raise Exception('self_payer argument can be only: True, False')\n query_where = f\"\"\"WHERE typ.{age_group} = 1\n AND typ.self_payers = {int(self_payer)}\n \"\"\"\n if without_registration is not None:\n if int(without_registration) not in [0, 1]: raise Exception('without_registration argument can be only: True, False, None')\n query_where += f\"AND typ.without_registration = {int(without_registration)} \\n\"\n\n if vaccine is not None:\n if vaccine.lower() not in VACCINES_SET:\n raise Exception('vaccine argument can be only: COMIRNATY, SPIKEVAX, JANSSEN, Vaxzevria, None')\n query += \"INNER JOIN vacc_center_vaccines AS vacc USING(vacc_id) \\n\"\n query_where += f\"AND vacc.{vaccine.lower()} = 1 \\n\"\n\n open_hours = {day: hour for (day, hour) in open_hours.items() if hour is not None}\n if len(open_hours) != 0:\n query += \"INNER JOIN vacc_center_hours as hour USING(vacc_id) \\n\"\n list_hours = []\n for (day, hour) in open_hours.items():\n if not ((hour >= 0) and (hour <= 24)): raise Exception('days arguments can be only set to None or number between 0 to 24')\n list_hours.append(f\"(hour.{day}_open <= {hour} AND hour.{day}_closed >= {hour})\")\n\n query_hours = \"AND (\"\n n = len(list_hours)\n for i in range(n):\n query_hours += list_hours[i]\n if (i+1) == n:\n query_hours += \")\"\n else:\n query_hours += \" OR \"\n query_where += query_hours\n return query + query_where",
"title": ""
},
{
"docid": "3f83fb856d12b26ca2684c9d9c9dfb40",
"score": "0.5437405",
"text": "def build_goods_query(\n good_ids: List[str],\n currency_id: str,\n is_searching_for_sellers: bool,\n is_search_query: bool,\n) -> Query:\n if is_search_query:\n # the OEF does not accept attribute names consisting of integers only\n good_ids = [PREFIX + good_id for good_id in good_ids]\n\n data_model = _build_goods_datamodel(\n good_ids=good_ids, is_supply=is_searching_for_sellers\n )\n constraints = [Constraint(good_id, ConstraintType(\">=\", 1)) for good_id in good_ids]\n constraints.append(Constraint(\"currency_id\", ConstraintType(\"==\", currency_id)))\n constraint_expr = cast(List[ConstraintExpr], constraints)\n\n if len(good_ids) > 1:\n constraint_expr = [Or(constraint_expr)]\n\n query = Query(constraint_expr, model=data_model)\n return query",
"title": ""
},
{
"docid": "3716fc1f19951155d518f81b705b75c9",
"score": "0.54263324",
"text": "def cypher(self, query, params=None):\r\n path = cypher_path\r\n params = dict(query=query,params=params)\r\n resp = self.request.post(path, params)\r\n\r\n # Cypher data hack\r\n resp.total_size = len(resp.results.data)\r\n resp.results = (Neo4jResult(result[0], self.config) for result in resp.results.data)\r\n return resp",
"title": ""
},
{
"docid": "800590f0ff9faf64788181eb91302965",
"score": "0.5422505",
"text": "def buildQuery (self, obj_type, ids=None, params=None, attributes=None):\n\n if type(obj_type) is type(''):\n wrapper = KNOWN_WRAPPERS.get(obj_type.lower(), None)\n if wrapper is None:\n raise KeyError(\"obj_type of %s not supported by getOjbects(). E.g. use 'Image' etc\" % obj_type)\n else:\n raise AttributeError(\"getObjects uses a string to define obj_type, E.g. 'Image'\")\n\n q = self.getQueryService()\n if params is None:\n params = omero.sys.Parameters()\n if params.map is None:\n params.map = {}\n\n # get the base query from the instantiated object itself. E.g \"select obj Project as obj\"\n query = wrapper()._getQueryString()\n\n clauses = []\n # getting object by ids\n if ids != None:\n clauses.append(\"obj.id in (:ids)\")\n params.map[\"ids\"] = rlist([rlong(a) for a in ids])\n\n # support filtering by owner (not for some object types)\n if params.theFilter and params.theFilter.ownerId and obj_type.lower() not in [\"experimentergroup\", \"experimenter\"]:\n clauses.append(\"owner.id = (:eid)\")\n params.map[\"eid\"] = params.theFilter.ownerId\n\n # finding by attributes\n if attributes != None:\n for k,v in attributes.items():\n clauses.append('obj.%s=:%s' % (k, k) )\n params.map[k] = omero_type(v)\n\n if clauses:\n query += \" where \" + (\" and \".join(clauses))\n\n return (query, params, wrapper)",
"title": ""
},
{
"docid": "3193232c6e9b86e2cb3be30784d0c07c",
"score": "0.5413109",
"text": "def make_query(store, query, prefix=\"\"):\r\n query = common.parse_query(query)\r\n q = Query()\r\n q.prefix = prefix\r\n q.offset = common.safeint(query.pop('offset', None), 0)\r\n q.limit = common.safeint(query.pop('limit', 20), 20)\r\n if q.limit > 1000:\r\n q.limit = 1000\r\n sort = query.pop('sort', None)\r\n \r\n nested = (prefix != \"\")\r\n \r\n for k, v in query.items():\r\n # key foo can also be written as label:foo\r\n k = k.split(':')[-1]\r\n if v is None:\r\n q.requested[k] = v\r\n elif isinstance(v, dict):\r\n # make sure op is ==\r\n v = dict((k + '.' + key, value) for key, value in v.items())\r\n q2 = make_query(store, v, prefix=prefix + k + \".\")\r\n #@@ Anand: Quick-fix\r\n # dbstore.things looks for key to find whether type is required or not. \r\n q2.key = k \r\n if q2.conditions:\r\n q.conditions.append(q2)\r\n else:\r\n q.requested[k] = q2\r\n else:\r\n k, op = parse_key(k)\r\n q.add_condition(k, op, None, v)\r\n \r\n if not nested:\r\n q.assert_type_required()\r\n \r\n type = get_thing(store, q.get_type())\r\n #assert type is not None, 'Not found: ' + q.get_type()\r\n for c in q.conditions:\r\n if not isinstance(c, Query):\r\n c.datatype = find_datatype(type, c.key, c.value)\r\n \r\n if sort:\r\n parse_key(sort) # to validate key\r\n q.sort = web.storage(key=sort, datatype=find_datatype(type, sort, None))\r\n else:\r\n q.sort = None\r\n \r\n return q",
"title": ""
},
{
"docid": "bac716271d29523c3ec034478d633e21",
"score": "0.54028887",
"text": "def create_query(neo4j_entities, entity_value):\n\n def get_value(index):\n return SINONIMOS_NEO4J[entity_value[index]] if SINONIMOS_NEO4J.get(entity_value[index])\\\n else entity_value[index]\n\n get_query_part = {\n \"espacio\": lambda x: \"(n:Room {{ type: '{}' }})\".format(get_value(x)),\n \"sensor\": lambda x: (\"(d:Device)\", f\" '{get_value(x)}' in d.type\"),\n \"orientacion\": lambda x: \" n.orientacion CONTAINS '{}'\".format(get_value(x)),\n \"edificio\": lambda x: (\"(b:Building)\", \" b.name CONTAINS '{}'\".format(get_value(x))),\n \"nombre\": lambda x: (\"(b:Building)\", \" b.name CONTAINS '{}'\".format(get_value(x))),\n \"lugares\": lambda x: \" n.name CONTAINS '{}'\".format(get_value(x))\n }\n\n\n dic = {}\n for entity in neo4j_entities:\n key = entity[1]\n if entity[1] == \"nombre\":\n key = \"edificio\"\n dic[key] = get_query_part[entity[1]](entity[0])\n\n espacio = dic[\"espacio\"] if dic.get(\"espacio\") else \"\"\n sensor = dic[\"sensor\"] if dic.get(\"sensor\") else (\"\", \"\")\n orientacion = dic[\"orientacion\"] if dic.get(\"orientacion\") else \"\"\n edificio = dic[\"edificio\"] if dic.get(\"edificio\") else (\"\", \"\")\n lugares = dic[\"lugares\"] if dic.get(\"lugares\") else \"\"\n ret = []\n\n # crear relaciones que son opcionales en la consulta\n if espacio is not \"\" and sensor != (\"\", \"\"):\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n if espacio is not \"\" and edificio != (\"\", \"\"):\n espacio += \"<-[:HAS]-(f:Floor)<-[:HAS]-\"\n\n #Añadir información a la consulta que no se infica pero que es necesaria para poder recorrer las relaciones en Neo4j.\n \n # Si se pregunta por sensor y lugar pero no por espacio.\n if espacio is \"\" and sensor != (\"\", \"\") and lugares is not \"\":\n espaciocio = \"(n:Room)\"\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n # Si se pregunta por nombre u orientación pero no por espacio.\n if espacio is \"\" and (lugares is not \"\" or orientacion is not \"\"):\n espacio = \"(n:Room)\"\n # Si se pregunta por sensores en un edificio sin especificar un espacio.\n if espacio is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n espacio = \"(n:Room)<-[:HAS]-(f:Floor)<-[:HAS]-\"\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n # Si hay 4\n # orientacion lugares edificio sensor\n # X X X X\n if orientacion is not \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n edificio = (edificio[0], \"AND \" + edificio[1])\n sensor = (sensor[0], \"AND \" + sensor[1])\n\n # Si hay 3\n # orientacion lugares edificio sensor\n # X X X -\n # X - X X\n # - X X X\n # X - X X\n if orientacion is not \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n edificio = (edificio[0], \"AND \" + edificio[1])\n if orientacion is not \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n sensor = (sensor[0], \"AND \" + sensor[1])\n if orientacion is \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"WHERE \" + lugares\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"AND \" + edificio[1])\n if orientacion is not \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n orientacion = \"WHERE \" + orientacion\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"AND \" + edificio[1])\n\n # Si hay 2\n # orientacion lugares edificio sensor\n # X X - -\n # X - X -\n # X - - X\n # - X X -\n # - X - X\n # - - X X\n\n if orientacion is not \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n if orientacion is not \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"AND \" + edificio[1])\n orientacion = \"WHERE \" + orientacion\n if orientacion is not \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n orientacion = \"WHERE \" + orientacion\n if orientacion is \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"AND \" + edificio[1])\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"WHERE \" + edificio[1])\n # Si hay 1\n # orientacion lugares edificio sensor\n # X - - -\n # - X - -\n # - - X -\n # - - - X\n if orientacion is not \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n orientacion = \"WHERE \" + orientacion\n if orientacion is \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"WHERE \" + edificio[1])\n if orientacion is \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"WHERE \" + sensor[1])\n\n\n\n # Preparar los atributos de salida\n if espacio is not \"\":\n ret.append(\"n.name, n.id\")\n if sensor != (\"\", \"\"):\n ret.append(\"d.id, d.type\")\n if orientacion is not \"\":\n ret.append(\"n.orientacion\")\n\n return TEMPLATE_NEO.format(sensor[0], espacio, edificio[0], orientacion,\\\n lugares, edificio[1], sensor[1], \", \".join(ret))",
"title": ""
},
{
"docid": "4e23579f229cdea216279b1841dfff82",
"score": "0.53795767",
"text": "def generate_entrez_query(self):\n ending = ') AND ' + self.gene + '[Gene Name] ' + \\\n 'AND 1000:8000[Sequence Length]'\n types = []\n for hpv_type in itertools.chain(self.harmfull, self.safe):\n types.append('\"Human papillomavirus type %d\"[Organism]' % hpv_type)\n self._query = '(' + ' OR '.join(types) + ending\n return self._query",
"title": ""
},
{
"docid": "68cd306f78e9a696fa45ccc17d112056",
"score": "0.5368679",
"text": "def sql_query_str(program):\n node_type, node_children = program[0], program[1:]\n assert node_type in sql_entry_tokens and syntax_valid(program, sql_cfg)\n\n if node_type == \"SELECT_AGG\":\n select_exprs_node, joins_node, group_by_exprs_node = node_children\n where_node = None\n elif node_type == \"SELECT_AGG_WHERE\":\n (select_exprs_node,\n joins_node,\n group_by_exprs_node,\n where_node) = node_children\n else:\n raise RuntimeError(\n \"Missing node-type decoding logic for generating SQL query from AST.\"\n )\n\n return query_str(\n select_exprs=list(sql_select_exprs(select_exprs_node)),\n joins=list(sql_joins(joins_node)),\n where_exprs=list(sql_and_exprs_strs(where_node)) if where_node else [],\n group_by_exprs=list(sql_group_by_exprs(group_by_exprs_node)),\n order_by_exprs=[],\n limit_exprs=[],\n )",
"title": ""
},
{
"docid": "f43c43ecaf245aaab63b127c15a173ed",
"score": "0.53679913",
"text": "def _getQueryString(self):\n query = \"select obj from Plate as obj \" \\\n \"join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent \"\\\n \"left outer join fetch obj.screenLinks spl \" \\\n \"left outer join fetch spl.parent sc\"\n return query",
"title": ""
},
{
"docid": "51fd823b07a49d65be55acc10cb16971",
"score": "0.53457093",
"text": "def _create_qualifiedname_query(self, type_name, *values):\n query = {\n 'typeName': type_name,\n 'excludeDeletedEntities': True,\n 'limit': 10000\n }\n entity_filter = {\n 'condition': 'AND'\n }\n criterion = []\n n = 0\n for v in values:\n criteria = {\n 'attributeName': 'qualifiedName',\n 'operator': 'STARTSWITH' if n == 0 else 'CONTAINS',\n 'attributeValue': v\n }\n n += 1\n criterion.append(criteria)\n if type_name == 'hive_table':\n # Ignore temporary tables.\n criterion.append({'operator': '=', 'attributeName': 'temporary', 'attributeValue': False})\n entity_filter['criterion'] = criterion\n query['entityFilters'] = entity_filter\n return query",
"title": ""
},
{
"docid": "44970fb2e787db5c9720c86d5e16d3f3",
"score": "0.53439456",
"text": "def create_query(\n self, query_text, locale=None, language=None, time_zone=None, timestamp=None\n ):\n if not query_text:\n query_text = \"\"\n if isinstance(query_text, (list, tuple)):\n return self._process_list(\n query_text,\n \"create_query\",\n locale=locale,\n language=language,\n time_zone=time_zone,\n timestamp=timestamp,\n )\n return self.resource_loader.query_factory.create_query(\n query_text,\n language=language,\n locale=locale,\n time_zone=time_zone,\n timestamp=timestamp,\n )",
"title": ""
},
{
"docid": "8b4dabb7c03fa99fdfdd73c857a0b88d",
"score": "0.5328662",
"text": "def sql_with_where(self):\n e = get_executor(SqlRule)\n where_clause = \"WHERE \"\n where_time_filter = e.compose_where_time_filter(self)\n where_condition = e.compose_where_condition(self)\n if where_time_filter == \"\" and where_condition == \"\":\n where_clause = \"\"\n elif where_time_filter != \"\" and where_condition != \"\":\n where_clause = f\"{where_clause} {where_time_filter} AND {where_condition}\"\n else:\n where_clause = f\"{where_clause} {where_time_filter} {where_condition}\"\n final_sql = f\"{self.sql} {where_clause}\"\n return self.render_sql(final_sql)",
"title": ""
},
{
"docid": "dae062c702cf1a20a574ddd68d4b28aa",
"score": "0.532052",
"text": "def construct_url(self\n , query_params: Dict\n , ) -> str:\n url_const = self.faers_ep\n initialized = False\n for param_q, param_val in query_params.items():\n # Need to add '&' to 2nd,3rd,... query_params\n if initialized:\n url_const += '&'\n else:\n initialized = True\n if type(param_val) == str and len(query_params) > 0 \\\n or type(param_val) == int:\n url_const += f'{param_q}={param_val}'\n elif type(param_val) == dict and len(param_val) > 0:\n url_const += f'{param_q}=' + \"+AND+\".join(\n [f'{sub_p_k}:{sub_p_v}' for sub_p_k, sub_p_v in param_val.items()])\n elif type(param_val) == list and len(param_val) > 0:\n url_const += f'{param_q}=' + \"+AND+\".join(param_val)\n else:\n raise TypeError\n return url_const",
"title": ""
},
{
"docid": "f60d6e0b78286e4e35b88227402bb392",
"score": "0.5318269",
"text": "def get_custom_query(self):\n query = {}\n\n # searchable text queries\n q = req.get_query()\n if q:\n query[\"SearchableText\"] = q\n\n # physical path queries\n path = req.get_path()\n if path:\n query[\"path\"] = {'query': path, 'depth': req.get_depth()}\n\n # special handling for recent created/modified\n recent_created = req.get_recent_created()\n if recent_created:\n date = api.calculate_delta_date(recent_created)\n query[\"created\"] = {'query': date, 'range': 'min'}\n\n recent_modified = req.get_recent_modified()\n if recent_modified:\n date = api.calculate_delta_date(recent_modified)\n query[\"modified\"] = {'query': date, 'range': 'min'}\n\n return query",
"title": ""
},
{
"docid": "88503be9fbc27ea6562b4928b4a8d6c0",
"score": "0.530554",
"text": "def get_where_clause(columns_to_query_lst):\n where_str = \"where \"\n equals_str =[]\n for row in columns_to_query_lst:\n temp_str = \"c.\" + row + \" = t.\" + row\n equals_str.append(temp_str)\n joined_str = \" AND \".join(equals_str)\n where_str = where_str + joined_str\n return where_str",
"title": ""
},
{
"docid": "746bf5ea5425e576e9ff1f9bce37f702",
"score": "0.52996916",
"text": "def create_query(processed_request):\n query = {\n \"variant\": {\n \"referenceBases\": \"\" if not processed_request.get(\"referenceBases\") else processed_request.get(\"referenceBases\"),\n \"alternateBases\": \"\" if not processed_request.get(\"alternateBases\") else processed_request.get(\"alternateBases\"),\n \"referenceName\": \"\" if not processed_request.get(\"referenceName\") else processed_request.get(\"referenceName\"),\n \"start\": None if not processed_request.get(\"start\") else processed_request.get(\"start\"),\n \"end\": None if not processed_request.get(\"end\") else processed_request.get(\"end\"),\n \"assemblyId\": \"\" if not processed_request.get(\"assemblyId\") else processed_request.get(\"assemblyId\")\n },\n \"datasets\": {\n \"datasetIds\": None if not processed_request.get(\"datasetIds\") else processed_request.get(\"datasetIds\"),\n \"includeDatasetResponses\": \"ALL\" if not processed_request.get(\"includeDatasetResponses\") else processed_request.get(\"includeDatasetResponses\")\n },\n \"filters\": None if not processed_request.get(\"filters\") else processed_request.get(\"filters\"),\n \"customFilters\": None if not processed_request.get(\"customFilters\") else processed_request.get(\"customFilters\"),\n }\n\n return query",
"title": ""
},
{
"docid": "e8e6478863c1ea4a7a2b784bc3c49cd9",
"score": "0.529957",
"text": "def build_where_stmt(self, ident, filters, q_filters=None, source_class=None):\n if q_filters is not None:\n stmts = self._parse_q_filters(ident, q_filters, source_class)\n if stmts:\n self._ast.where.append(stmts)\n else:\n stmts = []\n for row in filters:\n negate = False\n\n # pre-process NOT cases as they are nested dicts\n if \"__NOT__\" in row and len(row) == 1:\n negate = True\n row = row[\"__NOT__\"]\n\n for prop, operator_and_val in row.items():\n operator, val = operator_and_val\n if operator in _UNARY_OPERATORS:\n # unary operators do not have a parameter\n statement = (\n f\"{'NOT' if negate else ''} {ident}.{prop} {operator}\"\n )\n else:\n place_holder = self._register_place_holder(ident + \"_\" + prop)\n statement = f\"{'NOT' if negate else ''} {ident}.{prop} {operator} ${place_holder}\"\n self._query_params[place_holder] = val\n stmts.append(statement)\n\n self._ast.where.append(\" AND \".join(stmts))",
"title": ""
},
{
"docid": "3c5f867e7aef319a9323809c18719262",
"score": "0.5287673",
"text": "def __init__ (self, database, var, conditions = (), echo = False):\n\n self.session = buildSession(\n database=database,\n echo=echo)\n if var == \"ncommits\":\n self.query = self.session.query().select_nscmlog([\"commits\",])\n elif var == \"listcommits\":\n self.query = self.session.query().select_listcommits()\n elif var == \"nauthors\":\n self.query = self.session.query().select_nscmlog([\"authors\",])\n elif var == \"listauthors\":\n self.query = self.session.query().select_listauthors()\n for condition in conditions:\n self.query = condition.filter(self.query)",
"title": ""
},
{
"docid": "d298399d7e7d292b986fa091d5abed50",
"score": "0.5285017",
"text": "def full_query_pattern(self, include_conclusion: bool = False) -> str:\n query_pattern = \"\"\n for literal in self.premise:\n query_pattern += literal.sparql_patterns()\n\n if include_conclusion:\n query_pattern += self.conclusion.sparql_patterns()\n\n if \"?b\" not in query_pattern and \"?a\" not in query_pattern:\n query_projection = \"ask \"\n else:\n # insert the selectors for subject and object into the select query if they exist in the query pattern\n query_projection = \"select where \"\n\n # the resulting query would look like \"select ?a ?b ...\" if both cases are true\n if \"?b\" in query_pattern:\n query_projection = query_projection.replace(\"select \", \"select ?b \")\n if \"?a\" in query_pattern:\n query_projection = query_projection.replace(\"select \", \"select ?a \")\n\n # build remaining part of the query and execute it\n query_pattern = \"{\" + query_pattern + \"}\"\n return query_projection + query_pattern",
"title": ""
},
{
"docid": "3f90b7aa24950c089ec7f416c7f71163",
"score": "0.5283903",
"text": "def _getQueryString(self):\n return \"select obj from %s obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\" % self.OMERO_CLASS",
"title": ""
},
{
"docid": "edec057b919f8d0df46ebd8515dfc0e2",
"score": "0.5279631",
"text": "def _getQueryString(self):\n return \"select obj from BooleanAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\"",
"title": ""
},
{
"docid": "5238770c84b4b535af51b45d8d738378",
"score": "0.52578133",
"text": "def query(self):\n\n if not hasattr(self, \"_query\"):\n\n # Set up some convenience aliases.\n Query = db.Query\n Condition = Query.Condition\n Or = Query.Or\n\n # Create a query object.\n columns = \"d.id\", \"d.title\"\n self._query = Query(\"document d\", *columns).order(\"d.title\")\n\n # Add the conditions: one for each field with a value.\n conditions = []\n have_doctype = False\n for field in self.fields:\n\n # See if we got a value for this field.\n value = field.var.strip() if field.var else None\n if not value:\n continue\n\n # Remember the criterion for display to the user.\n self.criteria.append(value)\n\n # If 'selectors' is a string, it's a column in `document`.\n value_op = getQueryOp(value)\n if isinstance(field.selectors, str):\n column = f\"d.{field.selectors}\"\n conditions.append(Condition(column, value, value_op))\n continue\n\n # Build up a subquery for the field.\n have_doctype = True\n subquery = Query(\"query_term\", \"doc_id\").unique()\n\n # These are ORd together.\n selector_conditions = []\n for path in field.selectors:\n path_op = \"LIKE\" if \"%\" in path else \"=\"\n\n # Simple case: test for a string stored in the doc.\n if not path.endswith(\"/@cdr:ref[int_val]\"):\n path_test = Condition(\"path\", path, path_op)\n value_test = Condition(\"value\", value, value_op)\n selector_conditions.append((path_test, value_test))\n\n # Trickier case: find values in linked documents.\n else:\n path = path.replace(\"[int_val]\", \"\")\n title_query = Query(\"document\", \"id\").unique()\n args = \"title\", value, value_op\n title_query.where(Condition(*args))\n args = \"int_val\", title_query, \"IN\"\n title_test = Condition(*args)\n path_test = Condition(\"path\", path, path_op)\n selector_conditions.append((path_test, title_test))\n\n # Add the conditions for this field's selectors to the mix.\n subquery.where(Or(*selector_conditions))\n conditions.append(Condition(\"d.id\", subquery, \"IN\"))\n\n # Sanity check.\n if not conditions:\n raise Exception(\"No search conditions specified\")\n\n # If all the selectors are from the document table (or,\n # view, to be precise), then we still need to narrow\n # the search to this document type.\n if not have_doctype:\n self._query.join(\"doc_type t\", \"t.id = d.doc_type\")\n self._query.where(Condition(\"t.name\", self.doctype))\n # Plug the top-level conditions into the query.\n if self.match_all:\n for condition in conditions:\n self._query.where(condition)\n else:\n self._query.where(Or(*conditions))\n\n # All the fields with values have been folded into the query.\n return self._query",
"title": ""
},
{
"docid": "bc3e93874b247c4bb5a6d828028febe0",
"score": "0.5255788",
"text": "def query(self):\n self._check_mandatory_inputs()\n return self._gen_query()",
"title": ""
},
{
"docid": "74e698e0dc148ed3565a45d92b4dec8c",
"score": "0.5251471",
"text": "def _get_clause(self):\r\n params = [(primary_key, sql.bindparam(None, type_=primary_key.type))\r\n for primary_key in self.primary_key]\r\n return sql.and_(*[k == v for (k, v) in params]), \\\r\n util.column_dict(params)",
"title": ""
},
{
"docid": "bb58117d3fda3c2db7fe7688c4120b96",
"score": "0.5243088",
"text": "def build_query_text_request(\n **kwargs # type: Any\n):\n # type: (...) -> HttpRequest\n\n content_type = kwargs.pop('content_type', None) # type: Optional[str]\n\n api_version = \"2021-05-01-preview\"\n accept = \"application/json\"\n # Construct URL\n url = kwargs.pop(\"template_url\", '/:query-text')\n\n # Construct parameters\n query_parameters = kwargs.pop(\"params\", {}) # type: Dict[str, Any]\n query_parameters['api-version'] = _SERIALIZER.query(\"api_version\", api_version, 'str')\n\n # Construct headers\n header_parameters = kwargs.pop(\"headers\", {}) # type: Dict[str, Any]\n if content_type is not None:\n header_parameters['Content-Type'] = _SERIALIZER.header(\"content_type\", content_type, 'str')\n header_parameters['Accept'] = _SERIALIZER.header(\"accept\", accept, 'str')\n\n return HttpRequest(\n method=\"POST\",\n url=url,\n params=query_parameters,\n headers=header_parameters,\n **kwargs\n )",
"title": ""
},
{
"docid": "5e0b0884588b76f9e715cfc138ae0212",
"score": "0.5239496",
"text": "def create_complex_query(self):\n columns = copy(self.columns)\n shuffle(columns)\n query = \"\"\"\\\n SELECT\n {col}\n FROM\n datadog_test.dbo.high_cardinality AS hc1\n JOIN (\n SELECT\n id,\n COUNT(*) col12_float\n FROM\n datadog_test.dbo.high_cardinality AS hc2\n WHERE\n hc2.col1_txt LIKE '%-%'\n AND hc2.col14_int > (\n SELECT\n AVG(hc3.col15_int)\n FROM\n datadog_test.dbo.high_cardinality AS hc3)\n GROUP BY\n hc2.id) AS hc4 ON hc4.id = hc1.id\n JOIN datadog_test.dbo.high_cardinality AS hc5 ON hc5.id = hc1.id\n WHERE\n CAST(hc5.col17_date AS VARCHAR)\n IN('2003-04-23', '2043-09-10', '1996-08-08')\n AND hc5.col13_float IS NOT NULL\n AND hc5.col17_date NOT LIKE '21%';\n \"\"\"\n # Select a range of random columns and prefix to match our alias\n return query.format(col=','.join(['hc1.' + col for col in columns[: randint(1, len(columns) - 1)]]))",
"title": ""
},
{
"docid": "23ac357cf50e246b557978088edbcf6b",
"score": "0.5237348",
"text": "def __gencondition(self, querylist):\n conditions = []\n for key in self.PROPERTY_SEARCH_LIST:\n if querylist.get(key):\n conditions.append(self.PROPERTY_SEARCH_LIST[key][1] % (querylist.get(key)))\n return \" AND \".join(conditions)",
"title": ""
},
{
"docid": "b90ad42bba7570be5827a9b27873f0c5",
"score": "0.52357996",
"text": "def build_sql(self, table, row, known_fkeys={}):\n # Initialize the builder properties\n self.row = row\n self.known_fkeys = known_fkeys\n return self.build_table_sql(table)",
"title": ""
},
{
"docid": "a69e3debc69789bbd12bb2ee0e29ccfd",
"score": "0.5234567",
"text": "def __build_where_clause(where_columns_dictionary):\n return ' AND '.join(key + \" = ? \"\n if value is not None\n else key + \" IS NULL \"\n for key, value\n in where_columns_dictionary.items())",
"title": ""
},
{
"docid": "df322a9786ad715a3d5849f5ed411c2d",
"score": "0.5219735",
"text": "def concat_conds(*args):\n stmt = \" WHERE 1\"\n for cond in args:\n stmt = stmt + \" AND \" + cond\n return stmt",
"title": ""
},
{
"docid": "b443f879e890682442787a51400a4a67",
"score": "0.5218033",
"text": "def build_query(cls, request, model):\n # print(request.data)\n name = request.data.get(\"search_phrase\")\n criteria = request.data.get(\"search_criteria\")\n frequency = request.data.get(\"frequency\")\n age_distribution = request.data.get(\"age_distribution\")\n letters_range = request.data.get(\"letters_range\")\n\n resp = model.objects.filter()\n\n if frequency is not None and len(frequency) > 0:\n resp = model.objects.filter(frequency__in=frequency).defer(\"double_name\", \"number_of_letters\")\n\n double_name = True in letters_range\n\n # resp = resp.filter(double_name=True)\n\n if letters_range is not None and len(letters_range) > 0:\n if double_name:\n letters_range.remove(True)\n resp = resp.filter(Q(number_of_letters__in=letters_range) | Q(double_name=True))\n else:\n resp = resp.filter(double_name=False)\n resp = resp.filter(number_of_letters__in=letters_range)\n\n if age_distribution is not None and len(age_distribution) > 0:\n mask = \"age_distribution_{}__gt\"\n\n # AND age_distribution way\n # dd = {}\n #\n # for d in age_distribution:\n # key = mask.format(d)\n # dd[key] = 0\n # resp = resp.filter(**dd)\n\n # OR age_distribution way\n or_query_set = Q()\n for d in age_distribution:\n tmp = Q(**{mask.format(d): 0})\n or_query_set |= tmp\n\n resp = resp.filter(or_query_set)\n\n if criteria is not None and len(name) > 0:\n if criteria == \"start\":\n resp = resp.filter(name__lower__istartswith=name.lower())\n elif criteria == \"middle\":\n resp = resp.filter(name__lower__icontains=name.lower())\n elif criteria == \"end\":\n resp = resp.filter(name__lower__iendswith=name.lower())\n else:\n resp = resp.filter(name__lower=name)\n\n return resp",
"title": ""
},
{
"docid": "9a699f49d4c0ba175277a65d66a2562a",
"score": "0.52174175",
"text": "def query_to_clojure(obj, params):\n # Serialize the query, sorted keys for deterministic substitution, no pretty printing\n data = json.dumps(obj, sort_keys=True)\n # Split the JSON into parts using 'PARAM' as seperator\n parts = data.split(\"PARAM\")\n # Escape each JSON part by serializing as a JSON list and removing the qruare brackets\n escaped_parts = [json.dumps([part])[1:-1] for part in parts]\n # The amount of parts should be one more than the amount ot parameters\n if len(parts) != len(params) + 1:\n raise RuntimeError(\"Param count mismatch: \" +\n str(len(params)) +\n \" given \" +\n str(len(parts) -1) +\n \" needed ; \" +\n data)\n # Interleave the escaped JSON parts and the parameters and put them in a (str ) clojure expression\n rval = \" \".join(list(itertools.chain(*zip([\"(query (str \"] + params, escaped_parts))) + [\") )\"])\n return rval",
"title": ""
},
{
"docid": "c47f0f449170335a3dd6dfeb971f17fa",
"score": "0.52149814",
"text": "def process(self):\n q = \"?q=\"\n lst = []\n for field in self.filter_dict:\n for keyword in self.filter_dict[field]:\n for criteria in self.filter_dict[field][keyword]:\n lst.append(\"{0} {1} '{2}'\".format(field, kw_map[keyword], criteria))\n\n q += \" and \".join(lst)\n\n if self.count:\n q +=\"&count=True\"\n self.query = q\n\n return self",
"title": ""
},
{
"docid": "35ea0ee25067279790679c78c36e66c2",
"score": "0.52111125",
"text": "def run_sql_statement(self, request, params=()):\n\n # simple string formatting RISK OF SQL INJECTION, DO NOT USE IN REAL DATABASE\n output = request % tuple([\"'\" + p + \"'\" for p in params])\n\n self.output_sql += output + \"\\n\"",
"title": ""
},
{
"docid": "c29abca87c3ec6ed8e7655886a9e9437",
"score": "0.52013975",
"text": "def prepare_sql(self, **kwargs):\n source = kwargs.get(\"source\", self.source)\n if not source:\n raise ValueError(\"No source table or view specified.\")\n\n cutoff = kwargs.get(\"cutoff\", self.cutoff)\n start_time = \"start_time_local\" if self.local else \"start_time\"\n end_time = \"end_time_local\" if self.local else \"end_time\"\n\n predicates = kwargs.get(\"predicates\", [])\n predicates = [predicates] if not isinstance(predicates, list) else predicates\n\n provider_name = kwargs.get(\"provider_name\", self.provider_name)\n\n if provider_name:\n predicates.append(f\"lower(provider_name) = lower('{provider_name}')\")\n\n vts = \"'::vehicle_types,'\"\n vehicle_types = kwargs.get(\"vehicle_types\", self.vehicle_types)\n if vehicle_types:\n if not isinstance(vehicle_types, list):\n vehicle_types = [vehicle_types]\n predicates.append(f\"vehicle_type IN ('{vts.join(vehicle_types)}'::vehicle_types)\")\n\n if len(predicates) > 0:\n predicates = \" AND \".join(predicates) + \" AND \"\n else:\n predicates = \"\"\n\n timeranges = [\n f\"({start_time} <= %(start)s AND {end_time} > %(start)s)\",\n f\"({start_time} < %(end)s AND {end_time} >= %(end)s)\",\n f\"({start_time} >= %(start)s AND {end_time} <= %(end)s)\"\n ]\n\n if cutoff > 0:\n where = f\"\"\"\n {predicates}\n ({end_time} IS NULL AND {start_time} between %(start)s - '{cutoff} days'::interval and %(end)s OR\n (date_part('day', {end_time} - {start_time}) <= {cutoff} AND\n ({\" OR \".join(timeranges)})))\n \"\"\"\n else:\n timeranges.append(f\"({start_time} < %(end)s AND {end_time} IS NULL)\")\n where = f\"\"\"\n {predicates}\n ({\" OR \".join(timeranges)})\n \"\"\"\n\n order_by = kwargs.get(\"order_by\", self.order_by)\n if order_by:\n if not isinstance(order_by, list):\n order_by = [order_by]\n order_by = \",\".join(order_by)\n order_by = f\"ORDER BY {order_by}\"\n\n return f\"\"\"\n SELECT\n *\n FROM\n {source}\n WHERE\n {where}\n {order_by};\n \"\"\"",
"title": ""
},
{
"docid": "37809edf146a1f0665c82f677f3754e7",
"score": "0.51989144",
"text": "def generate_select_request(self, conditions=None):\n\n if conditions is None:\n return self._SELECT_REQUEST_STRING\n else:\n request = self._SELECT_REQUEST_STRING + \" WHERE {}={}\"\n request = request.format(*conditions.split(\"=\"))\n return request",
"title": ""
},
{
"docid": "d39ef560bb5a4fecbc1c6cd66834ec85",
"score": "0.5198576",
"text": "def _getQueryString(self):\n return \"select obj from CommentAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\"",
"title": ""
},
{
"docid": "f80c750afc8c0dee265f318d3f44d2f7",
"score": "0.51965994",
"text": "def generate_clause(vars, operator):\n return operator(*vars, evaluate=False)",
"title": ""
},
{
"docid": "84e77ba0a35e57f35fb497a8cc5137a0",
"score": "0.5192215",
"text": "def subquery(self, withs, filters=None, optional=False):\n input_with = ', '.join(withs)\n new_withs = withs - {self.collection_alias}\n output_with = ', '.join(new_withs) + ', ' + self.with_statement()\n\n where_string = ''\n if filters is not None:\n relevant = []\n for c in filters:\n if c.involves(self):\n relevant.append(c.for_cypher())\n if relevant:\n where_string = 'WHERE ' + '\\nAND '.join(relevant)\n for_match = self.subquery_match_template.format(anchor_alias=self.anchor_node.alias,\n collection_alias=self.collection_alias)\n kwargs = {'for_match': for_match,\n 'optional': '',\n 'collection_alias': self.collection_alias,\n 'output_with_string': output_with}\n if optional:\n kwargs['optional'] = 'OPTIONAL '\n return self.subquery_template.format(**kwargs)",
"title": ""
},
{
"docid": "0c91288d0f92106eb67c80c495661bb2",
"score": "0.5183175",
"text": "def build_query(self):\n # initalize config\n query = super(GlobalTaskTableSource, self).build_query()\n query = query.avoid_duplicates()\n return query",
"title": ""
},
{
"docid": "f4c4035fb5ab40bfe3985d56797e9590",
"score": "0.51793414",
"text": "def prepareQuery(queryString, initNs={}, base=None):\n ret = translateQuery(parseQuery(queryString), base, initNs)\n ret._original_args = (queryString, initNs, base)\n return ret",
"title": ""
},
{
"docid": "06ac992c3bdb35350862ce9f31ab0d1b",
"score": "0.51777846",
"text": "def buildQuery(self, formInfo, user, query, languages):\r\n return query.options(subqueryload('concepts'))\\\r\n .join(concept_list_concepts)\\\r\n .join(Concept)\\\r\n .join(formInfo.formModel, (formInfo.formModel.language_id == languages.foreign.id) & (formInfo.formModel.concept_id == Concept.id))\\\r\n .outerjoin(Mastery, (Mastery.user_id==user.id) & (getattr(Mastery, formInfo.masteryFieldName) == formInfo.formModel.id))\\\r\n .outerjoin(StalenessPeriod)\\\r\n .order_by(formInfo.listModel.averageRatingFor(user, languages.foreign))",
"title": ""
},
{
"docid": "d1c27b14b20da118bc686538eb54c03e",
"score": "0.51636916",
"text": "def concat_query_make(query_ends):\n\n\tquery = result_queries.START\n\tstart = True\n\n\tif query_ends:\n\t\tfor end in query_ends:\n\t\t\tif start:\n\t\t\t\tquery += end\n\t\t\t\t#end is the varying return value to an otherwise identical query\n\n\n\t\t\tif not start:\n\t\t\t\tquery += \" || \\\"DELIM\\\" || \"\n\t\t\t\tquery += end\n\t\n\t\t\tstart = False\n\t\t\t\n\telse:\n\t\tquery = \"(no query selected)\"\n\t\n\treturn query",
"title": ""
},
{
"docid": "0ba8011bc739cf5b088eb97c87937d2b",
"score": "0.51636404",
"text": "def evaluate_node_sql(node):\n if 'condition' in node:\n # Node is a condition, get the values of the sub-clauses\n sub_pairs = \\\n [evaluate_node_sql(x) for x in node['rules']]\n\n if not sub_pairs:\n # Nothing has been returned, so it is an empty query\n return '', []\n\n # Now combine\n if node['condition'] == 'AND':\n result = '((' + \\\n ') AND ('.join([x for x, __ in sub_pairs]) + '))'\n else:\n result = '((' + \\\n ') OR ('.join([x for x, __ in sub_pairs]) + '))'\n result_fields = \\\n list(itertools.chain.from_iterable([x for __, x in sub_pairs]))\n\n if node.get('not', False):\n result = '(NOT (' + result + '))'\n\n return result, result_fields\n\n # Get the variable name and duplicate the symbol % in case it is part of\n # the variable name (escape needed for SQL processing)\n varname = fix_pctg_in_name(node['field'])\n\n # Get the operator\n operator = node['operator']\n\n # If the operator is between or not_between, there is a special case,\n # the constant cannot be computed because the node['value'] is a pair\n constant = None\n if node['value'] is not None and not isinstance(node['value'], list):\n # Calculate the constant value depending on the type\n if node['type'] == 'integer':\n constant = int(node['value'])\n elif node['type'] == 'double':\n constant = float(node['value'])\n elif node['type'] == 'boolean':\n constant = node['value'] == '1'\n elif node['type'] == 'string':\n constant = node['value']\n elif node['type'] == 'datetime':\n constant = node['value']\n else:\n raise Exception(\n _('No function to translate type {0}').format(node['type'])\n )\n\n # Terminal Node\n result_fields = []\n if operator == 'equal':\n result = '(\"{0}\"'.format(varname) + \\\n ' = %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [str(constant)]\n\n elif operator == 'not_equal':\n result = '(\"{0}\"'.format(varname) + \\\n '!= %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [str(constant)]\n\n elif operator == 'begins_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [node['value'] + \"%\"]\n\n elif operator == 'not_begins_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [node['value'] + \"%\"]\n\n elif operator == 'contains' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [\"%\" + node['value'] + \"%\"]\n\n elif operator == 'not_contains' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [\"%\" + node['value'] + \"%\"]\n\n elif operator == 'ends_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [\"%\" + node['value']]\n\n elif operator == 'not_ends_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [\"%\" + node['value']]\n\n elif operator == 'is_empty' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n \" = '') OR (\\\"{0}\\\" is null)\".format(varname)\n\n elif operator == 'is_empty' and node['type'] != 'string':\n result = \"(\\\"{0}\\\" is null)\".format(varname)\n\n elif operator == 'is_not_empty' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n \" != '') AND (\\\"{0}\\\" is not null)\".format(varname)\n\n elif operator == 'is_not_empty' and node['type'] != 'string':\n result = \"(\\\"{0}\\\" is not null)\".format(varname)\n\n elif operator == 'less' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' < %s'\n result_fields = [str(constant)]\n\n elif operator == 'less_or_equal' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' <= %s'\n result_fields = [str(constant)]\n\n elif operator == 'greater' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' > %s'\n result_fields = [str(constant)]\n\n elif operator == 'greater_or_equal' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' >= %s'\n result_fields = [str(constant)]\n\n elif operator == 'between' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' BETWEEN %s AND %s'\n result_fields = [str(node['value'][0]), str(node['value'][1])]\n\n elif operator == 'not_between' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' NOT BETWEEN %s AND %s'\n result_fields = [str(node['value'][0]), str(node['value'][1])]\n\n else:\n raise Exception(\n _('Type, operator, field {0}, {1}, {2} not supported yet').format(\n node['type'], operator, varname\n )\n )\n\n if node.get('not', False):\n raise Exception(_('Negation found in unexpected location'))\n\n return result, result_fields",
"title": ""
},
{
"docid": "0dd8285bd0816627e7f7b40210481df8",
"score": "0.5158893",
"text": "def query(\n self, strOrQuery, initBindings={},\n initNs={}, base=None, DEBUG=False):\n\n if not isinstance(strOrQuery, Query):\n parsetree = parseQuery(strOrQuery)\n query = translateQuery(parsetree, base, initNs)\n else:\n query = strOrQuery\n\n return evalQuery(self.graph, query, initBindings, base)",
"title": ""
},
{
"docid": "911fc88c4f06f7a15ccb80b7985bca8c",
"score": "0.51363057",
"text": "def execute(self, neo4j_session: Session):\n if self.query == \"\":\n raise ValueError(\n \"The query must be generated before this method is called.\"\n )\n try:\n return neo4j_session.write_transaction(lambda tx: tx.run(self.query))\n except:\n print(\"Query not defined error\")\n return None",
"title": ""
},
{
"docid": "5af7e59578232b5286f33142426d48b4",
"score": "0.51362425",
"text": "def buildConnectionString(params):\n return \";\".join([\"%s=%s\" % (k, v) for k, v in params.items()])",
"title": ""
},
{
"docid": "250b21f2993d378d79cd2b0b0096170a",
"score": "0.5131796",
"text": "def _row_to_query_statement_string(self, row):\n start_node = 'MERGE (start:{state_lab} {{code:\"{start_state}\"}})'.format(\n state_lab=self.labels.state, start_state=row[self.start_state_col]\n )\n\n end_node = 'MERGE (end:{state_lab} {{code:\"{end_state}\"}})'.format(\n state_lab=self.labels.state, end_state=row[self.end_state_col]\n )\n\n transition = \"MERGE (start)<-[:SOURCE]-(trans:{trans_lab})-[:TARGET]->(end)\".format(\n trans_lab=self.labels.transition\n )\n\n def _conditions_str(row, start_state_col, end_state_col):\n \"\"\"Build the string used to express transition conditions.\n \n This is the part in curly braces specifying the Condition node's\n properties.\n \"\"\"\n row = row.drop([start_state_col, end_state_col])\n count = 0\n s = \"\"\n for i, val in row.iteritems():\n s += i + \":\"\n if isinstance(val, six.string_types):\n s += '\"' + val + '\"'\n elif isinstance(val, bool):\n s += str(val).lower()\n else:\n s += str(val)\n if count < len(row) - 1:\n s += \", \"\n count += 1\n\n return \"{\" + s + \"}\"\n\n condition = \"MERGE (cond:{cond_lab} {cond_str})-[:CAUSES]->(trans)\".format(\n cond_lab=self.labels.condition,\n cond_str=_conditions_str(row, self.start_state_col, self.end_state_col),\n )\n\n query_str = (\n start_node + \" \" + end_node + \" \" + transition + \" \" + condition + \";\"\n )\n\n if self.global_params:\n query_str = self._add_global_params_to_query_string(\n query_str, self.global_params\n )\n\n return query_str",
"title": ""
},
{
"docid": "54c7e0913a92b7dfb4e7cc3e5cb349f6",
"score": "0.5125673",
"text": "def build_query(url: str, *args, **kwargs) -> str:\n url = url.strip(\"/\")\n args = map(lambda arg: arg.strip(\"/\"), args)\n kwargs = dict(filter(lambda kv: kv[1] is not None, kwargs.items()))\n url = \"/\".join([url, *args]) + \"?\"\n url += \"&\".join([f\"{key}={value}\" for key, value in kwargs.items()])\n return url",
"title": ""
},
{
"docid": "976a7fa212ec09e76082f81572c24b87",
"score": "0.51216996",
"text": "def build_query(field, fields, fuzziness=\"AUTO\"):\n val = fields[field]\n if \"*\" in val:\n qtype = \"wildcard\"\n fval = val\n else:\n qtype = \"match\"\n fval = dict(query=val, fuzziness=fuzziness)\n return Q(qtype, **{field: fval})",
"title": ""
},
{
"docid": "27d86e7c41d5fb86b9a53f995bbc51a9",
"score": "0.5117474",
"text": "def q(*args: Any, **kwargs: Any) -> \"Query\":\n return Query(*args, **kwargs)",
"title": ""
},
{
"docid": "f0fd368bbe1ee92e7081900909296ef4",
"score": "0.51087725",
"text": "def query_on_params(self,\\\n **kwargs):\n if self.check_empty_query(**kwargs):\n return []\n \n elif 'username' in kwargs and len(kwargs) == 1:\n name = kwargs['username'][0]\n return self.query_on_name(\\\n name,\\\n self.all()) \n\n username_s = kwargs['username'][0]\n fullname_s = kwargs['fullname'][0]\n faculty_s = kwargs['faculty'][0]\n program_s = kwargs['program'][0]\n query_set_l = self.all()\n if username_s != '':\n query_set_l = query_set_l.filter(\\\n username__icontains = username_s)\n if fullname_s != '':\n query_set_l = self.query_on_name(\\\n fullname_s,\\\n query_set_l)\n if faculty_s != '':\n query_set_l = query_set_l.filter(\\\n faculty__exact = faculty_s)\n if program_s != '':\n query_set_l = query_set_l.filter(\\\n program__exact = program_s)\n return query_set_l",
"title": ""
},
{
"docid": "a40795caa1ea72b2cdda71b5e0413200",
"score": "0.51069057",
"text": "def sql_condition(\n self,\n conditions=tuple(),\n all_inner=True,\n all_outer=True,\n match_inner=True,\n match_outer=True,\n ):\n args = locals()\n # put the conditions into a dict\n conds = {\n pref: args.get(pref)\n for pref in (\n \"all_inner\",\n \"all_outer\",\n \"match_inner\",\n \"match_outer\",\n )\n }\n conds = { # properly cycle all settings\n pref: itertools.cycle(val if hasattr(val, \"__iter__\") else (val,))\n for pref, val in conds.items()\n }\n conds[\"values\"] = (\n (conditions,) if hasattr(conditions, \"items\") else conditions\n )\n sql_conditions, sql_params = [], []\n for cond in listdict_to_dictlist(conds):\n sanitized_values = {\n self.sanitize(k, brutally=True): self.sanitize(\n v, brutally=False\n )\n if isinstance(v, str)\n else v\n for k, v in cond[\"values\"].items()\n if k in self.tableheader\n }\n inner_sql_cond = (\" AND \" if cond[\"all_inner\"] else \" OR \").join(\n map(\n \"{} {} ?\".format,\n sanitized_values.keys(),\n itertools.cycle(\n (\"IS\" if cond[\"match_inner\"] else \"IS NOT\",)\n ),\n )\n ) or \"1=1\"\n sql_conditions.append(\n \"{}({})\".format(\n \"\" if cond[\"match_outer\"] else \"NOT \", inner_sql_cond\n )\n )\n sql_params.extend(tuple(sanitized_values.values()))\n sql_condition = (\n functools.reduce(\n lambda x, y: (\n \" AND \" if next(conds[\"all_outer\"]) else \" OR \"\n ).join((x, y)),\n sql_conditions,\n )\n if sql_conditions\n else \"1=1\"\n )\n return sql_condition, tuple(sql_params)",
"title": ""
},
{
"docid": "c126e83185b3f095e5bc3f7c32cda876",
"score": "0.51048845",
"text": "def post(self, statement, parameters=None):\n payload = {\"query\": statement}\n if parameters:\n payload[\"params\"] = {}\n for key, value in parameters.items():\n if isinstance(value, (Node, Rel, Relationship)):\n value = value._id\n payload[\"params\"][key] = value\n log.info(\"execute %r %r\", payload[\"query\"], payload.get(\"params\", {}))\n return self.resource.post(payload)",
"title": ""
}
]
152c3edbb3aa0b05d956c5dcb6acb205
Initialize the Decomposable OperatorValued Kernel.
[
{
"docid": "215366153e758085c8bc79a022ff8b6c",
"score": "0.6252689",
"text": "def calculate(self):\n item_number = self.fields.curselection()\n polynomial = self.json_data[\"functions\"][int(item_number[0])]\n result_floating = None\n result_interval = None\n if self.json_data[\"method\"] == \"regula_falsi\":\n try:\n result_floating = regula_falsi(polynomial[\"a\"],\n polynomial[\"b\"],\n function_from_list,\n polynomial[\"coeff\"]\n )\n except MyError as error:\n result_floating = error.value\n except ZeroDivisionError:\n result_floating = \"Unfortunately, division by 0 happened\"\n try:\n result_interval = regula_falsi_interval(polynomial[\"a\"],\n polynomial[\"b\"],\n function_from_list,\n polynomial[\"coeff\"]\n )\n result_interval = result_interval.format('%.20E')[9:]\n result_interval = result_interval[:-1]\n except MyError as error:\n result_interval = error.value\n elif self.json_data[\"method\"] == \"newton\":\n try:\n result_floating = newton(polynomial[\"x\"],\n polynomial[\"epsilon\"],\n polynomial[\"max_iterations\"],\n function_from_list,\n polynomial[\"coeff\"],\n polynomial[\"derivative_coeff\"]\n )\n except ZeroDivisionError:\n result_floating = \"Unfortunately, division by zero happened\"\n except MyError as error:\n result_floating = error.value\n try:\n result_interval = newton_interval(polynomial[\"x\"],\n polynomial[\"epsilon\"],\n polynomial[\"max_iterations\"],\n function_from_list,\n polynomial[\"coeff\"],\n polynomial[\"derivative_coeff\"]\n )\n result_interval = result_interval.format('%.20E')[9:]\n result_interval = result_interval[:-1]\n except MyError as error:\n result_interval = error.value\n else:\n result_interval = \"You propably\"\n result_floating = \"gave me wrong JSON\"\n self.show_result_floating.config(text=result_interval)\n self.show_result_interval.config(text=result_floating)",
"title": ""
},
{
"docid": "e0830761115955abace478bf63277abd",
"score": "0.6215401",
"text": "def calculate_results():\n\n print('Calculating results..')",
"title": ""
},
{
"docid": "d07116c7437a3ffc65ec7ced8caca88f",
"score": "0.6053697",
"text": "def _calculate(self):\n ...",
"title": ""
},
{
"docid": "977d47a74d67da7d19a1b22a8e36d483",
"score": "0.59952414",
"text": "def do_calculation():\n dT = config.getfloat(\"thermodynamics\", \"Tmin\")\n try:\n nmax = config.getint(\"thermodynamics\", \"nmax\")\n except ValueError:\n nmax = config.get(\"thermodynamics\", \"nmax\")\n if not nmax == \"automatic\":\n print(\"nmax has to be given as integer or \\\"automatic\\\"\") \n try:\n analytic_n0 = config.get(\"geometry\", \"analytic_n0\")\n except ValueError:\n print(\"analytic_n0 is not given correctly: \", analytic_n0)\n analytic_n0 = True\n if type(geom.r1) == list or type(geom.r2) == list:\n analytic_n0 = False\n Tmax = config.getfloat(\"thermodynamics\", \"Tmax\")\n f = thermodynamics.finiteT(dT, nmax, Tmax, geom, analytic_n0)\n quantity = config.get(\"thermodynamics\", \"quantity\")\n result = eval(\"f.%s\" %quantity)\n return result, f",
"title": ""
},
{
"docid": "f05dd3eeb2320b01c30e115c6928fb5f",
"score": "0.5965643",
"text": "def error_calculation():",
"title": ""
},
{
"docid": "69c8ad689e17b9cd1efc84e5ba7563bd",
"score": "0.59115",
"text": "def calc(self, *args):\n pass",
"title": ""
},
{
"docid": "387f4fedb43cb0287db06ecf37b13dc3",
"score": "0.5901406",
"text": "def calc():\r\n nonlocal func\r\n func = e.get()\r\n func = func.replace(\" \", \"\")\r\n func = toReversePolish(check(fixmult(findNeg(tokenizer(func)))))\r\n result.set(calculate(func, float(e2.get())))",
"title": ""
},
{
"docid": "b3ba20e0c2fecb5806fb08b0ca839ef9",
"score": "0.58904105",
"text": "def calculate(**kwargs):\n return None",
"title": ""
},
{
"docid": "29a6f09fde14e1f16e33b66e2b65226c",
"score": "0.58634573",
"text": "def _postCalculate(self,retList):\n\t\tpass",
"title": ""
},
{
"docid": "3eccf3447683a2dc735f5799fbff98c3",
"score": "0.58459675",
"text": "def calculate(self, *args, **kwargs) -> Any:",
"title": ""
},
{
"docid": "f37e62a56b03972b4b59b80389e4d31d",
"score": "0.5809612",
"text": "def compute(self):\n\n self.setInitialValue()\n self.partialResult = self.initialValue\n self.elementResult = None\n\n self.updateFunctionPort()\n\n self.set_output('Result', self.partialResult)",
"title": ""
},
{
"docid": "81d0a1c04bf6c68b3a921b158c6c03fb",
"score": "0.57289326",
"text": "def OnCalcDone(self, event):\n if event.data is None:\n self.log(\"No result from calculation!\")\n self.PSF_HDUlist = None\n else:\n self.PSF_HDUlist = event.data\n for w in ['Display PSF', 'Display profiles', 'Save PSF As...']:\n self.widgets[w].Enable(True)\n\n self._refresh_window()\n self.log(\"PSF calculation complete\")\n # In either event, the worker is done\n self.calcthread = None",
"title": ""
},
{
"docid": "3ae7ea9f7af562970e90b832e81e037a",
"score": "0.5720635",
"text": "def calculate(self):\n pass",
"title": ""
},
{
"docid": "fd18a5ecb864880f5b28ca9d88e14303",
"score": "0.56965977",
"text": "def on_evaluate(self, interp):\n return 0.000000",
"title": ""
},
{
"docid": "b8afeea6242d6a4492b1855f0c649af5",
"score": "0.5681769",
"text": "def Calculate():\r\n values = GetValues()\r\n values = SolveEqn(values)\r\n DisplayValues(values)",
"title": ""
},
{
"docid": "b38fe131032ab41802c0adfc8618129c",
"score": "0.5660559",
"text": "def _run_calculation(self):\n # collect input\n input_vs = OrderedDict()\n for intf in self.input_intfs:\n if intf.sibling_intf_allowed:\n input_vs.setdefault(intf.family_name, []).append(intf.get_calculated_value())\n else:\n input_vs[intf] = intf.get_calculated_value()\n\n # run actual function defined by user\n try:\n results = self.calculate(*input_vs.values())\n except Exception as e:\n # set all outputs NULL\n results = [NullValue(f\"calculation fail of {self}\")] * len(self.output_intfs)\n # record and print error status\n self._calculation_status = e\n self.print_status()\n raise\n else:\n # wrap singular result and match length\n results = [results] if not isinstance(results, (list, tuple)) else list(results)\n results += [NullValue(\"calculation didn't return enough values\")] * (len(self.output_intfs) - len(results))\n self._calculation_status = ''\n finally:\n # push calculation result down to outputs\n for result, intf in zip(results, self.output_intfs):\n intf.set_provoke_value(result)",
"title": ""
},
{
"docid": "e3918df179bdebeaa8326fa3d94277d7",
"score": "0.5617621",
"text": "def compute_value(self):\n result = 0.00000000\n if self.value1 and self.tasa:\n result = (self.value1 * 1) / self.tasa \n self.result = result\n if not self.flag1:\n self.id_record = self.env.context.get('active_id')\n self.act_model = self.env.context.get('active_model')\n self.flag1 = True\n return {\n 'view_mode': 'form',\n 'res_model': 'compute.crypto.value',\n 'res_id': self.id,\n 'view_id': False,\n 'type': 'ir.actions.act_window',\n 'target': 'new',\n 'context': {\"default_act_model\":self.act_model, \"default_result\": result, \"default_id_record\": self.id_record,},\n }\n else:\n raise UserError(\"You need set all values first.\")",
"title": ""
},
{
"docid": "e43cd1561f63a67897cd240b00229f59",
"score": "0.55982625",
"text": "def process_final(self, *args):\n return self._sum / self._count",
"title": ""
},
{
"docid": "aad11d6d48fea29f5ab6cfd496c2674f",
"score": "0.5591634",
"text": "def doCalculation()->JSON_RESPONSE: \n\n # Get JSON data from client\n first:int = int(request.json['first'])\n second:int = int(request.json['second'])\n oper:str = request.json['operation']\n\n rst:int = performCalc(first, second, oper)\n\n # Prepare the return structure\n result:dict[str,int] = {\"result\":rst}\n\n # Send JSON back to the client\n return json.dumps(result)",
"title": ""
},
{
"docid": "37453e9332d7d6b5fbee48eb2fe7abf2",
"score": "0.55907995",
"text": "def handle_calculate(self, value):\n #try:\n\n result = self.valid_number_check() * 1.6093\n self.root.ids.output_label.text = str(result)\n print(result)\n #except: ValueError",
"title": ""
},
{
"docid": "11725e0d4aa23fdfc0173c1231d04274",
"score": "0.5529947",
"text": "def _compute(self):",
"title": ""
},
{
"docid": "85e680e952960f28dbfe0efb0c4bc700",
"score": "0.5507777",
"text": "def calc(self):\n raise Exception(\"Workflow calculation not implemented yet\")",
"title": ""
},
{
"docid": "43ddd32b2c49f7c13ef977e81a00da9d",
"score": "0.54745024",
"text": "def calc_results():\n global num_solutions\n subt = 100*get_subt(0) + 10*get_subt(1) + get_subt(2)\n diff = 100*get_diff(0) + 10*get_diff(1) + get_diff(2)\n minu = 1000*get_minu(0) + 100*get_minu(1) + 10*get_minu(2) + get_minu(3)\n if (subt + diff == minu) and (minu >= 1000):\n print minu, \"-\", subt, \"==\", diff\n num_solutions += 1",
"title": ""
},
{
"docid": "4fb90820e644680bc6d5e502aead6fde",
"score": "0.5453361",
"text": "def calculate(self, structure, **kwargs):",
"title": ""
},
{
"docid": "70fffa82abf3ab29e63d6e09fafe4f39",
"score": "0.54355496",
"text": "def _postCalculate(self,retList,time):\n\t\tif len(retList) > 0: \n\t\t\tret = pandas.concat(retList,ignore_index=True)\n\t\t\tret = self.calculateThresholdPolygon(ret,time)\n\t\t\tret['percentEffected'] = 1.\n\t\telse: \n\t\t\tret = None\n\t\treturn ret",
"title": ""
},
{
"docid": "a94e16cfebaa89484f51c5df781ba26c",
"score": "0.5427222",
"text": "def __check_calc(self):\n # nothing done\n return",
"title": ""
},
{
"docid": "166673f4de6c078d348a3759ee698ae2",
"score": "0.54081076",
"text": "def calculate(self, var_list):\n # Run dummy calculation.\n index = var_list[1].split(' ')[0]\n submit = call('cp files/pw-scf_{}.out pw-scf.out'.format(index),\n shell=True)\n if submit != 0:\n raise AssertionError('Could not submit job.')\n energy = self.scrape_output(self.output_file)\n\n print('iteration {0} energy {1}'.format(index, energy))\n\n return energy",
"title": ""
},
{
"docid": "b8ed09a292242295d9bd0a9dc162cc28",
"score": "0.54075795",
"text": "def calificacion_final(par1, par2, par3):\n calfinal = par1 + par2 + par3\n promedio = calfinal / 3\n return promedio",
"title": ""
},
{
"docid": "d8124e8fa74cc5964d34246b27cb46bf",
"score": "0.5406774",
"text": "def run_interp():\n func = str(input('Enter string or trigonometric function: '))\n print('Value l')\n l_a = float_input(positive=True)\n print('Value u')\n u_b = float_input(positive=True)\n print('Value n')\n n_c = integer_input()\n print('Your intermediate results: {}'.format(interp(func, l_a, u_b, n_c)))",
"title": ""
},
{
"docid": "36a407754c15e9a2256c75e9d606681f",
"score": "0.53978",
"text": "def __calc(self):\n timezone = self.timezone # in hours, east is positive\n longitude= self.long # in decimal degrees, east is positive\n latitude = self.lat # in decimal degrees, north is positive\n\n time = self.time # percentage past midnight, i.e. noon is 0.5\n day = self.day # daynumber 1=1/1/1900\n \n Jday =day+2415018.5+time-timezone/24 # Julian day\n Jcent =(Jday-2451545)/36525 # Julian century\n\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\n Mobliq = 23+(26+((21.448-Jcent*(46.815+Jcent*(0.00059-Jcent*0.001813))))/60)/60\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent))+sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+sin(rad(3*Manom))*0.000289\n Struelong= Mlong+Seqcent\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\n \n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))-0.5*vary*vary*sin(4*rad(Mlong))-1.25*Eccent*Eccent*sin(2*rad(Manom)))\n\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))*cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\n\n self.solarnoon_t=(720-4*longitude-eqtime+timezone*60)/1440\n self.sunrise_t =self.solarnoon_t-hourangle*4/1440\n self.sunset_t =self.solarnoon_t+hourangle*4/1440",
"title": ""
},
{
"docid": "9393ef12dcb8b1ff273f5733e0539ddb",
"score": "0.53911626",
"text": "def compute(self) -> float:\n raise NotImplementedError",
"title": ""
},
{
"docid": "b254264ee7ae0c135148c1ce91717f2d",
"score": "0.5388854",
"text": "def Exemples_calculs():\n while True :\n (xA,yA)=input_coord(\"A\")\n while True :\n (xB,yB)=input_coord(\"B\")\n if xB!=xA or yB!=yA :\n break\n else :\n display(\"Il faut deux points distincts pour faire une droite ! Recommencez !\")\n\n intro=Latex(\"\"\"Nous allons calculer le coefficient directeur de la droite (AB) avec\n A( {} ; {} ) et B( {} ; {} )\"\"\".format(xA,yA,xB,yB))\n if xA==xB :\n\n calcul=Latex(\"\"\"Les droites parallèles à l'axe des ordonnées n'ont pas de coefficient directeur !\n Il faut donc que vos points n'aient pas la même abscisse :\"\"\")\n else :\n frac=fractions.Fraction((yB-yA),(xB-xA))\n if frac==int(frac):\n frac=str(int(frac))\n else :\n frac=\"\\dfrac{{{numerator}}}{{{denominator}}}\".format(numerator=frac.numerator,denominator=frac.denominator)\n calcul=Math(\"m= \\dfrac{{y_B-y_A}}{{x_B-x_A}}= \\dfrac{{{yB}-{yA}}}{{{xB}-{xA}}}= \\dfrac{{{yByA}}}{{{xBxA}}}={frac}\"\n .format(xA=xA,yA=yA,xB=xB,yB=yB,yByA=yB-yA,xBxA=xB-xA,frac=frac))\n Trace_droite(xA,yA,xB,yB)\n display(intro,calcul)\n s=input(\"Voulez-vous recommencer ?(o/n)\")\n if s.lower() in ['n','non','no'] :\n break\n else :\n clear_output()",
"title": ""
},
{
"docid": "5dd7780b29a79b6fd8d3b696267d256c",
"score": "0.53845376",
"text": "def processAlgorithm(self, parameters, context, feedback):\n\n # Load present raster\n present_raster = self.parameterAsRasterLayer(parameters, self.INPUT1, context)\n ds_present = gdal.Open(present_raster.dataProvider().dataSourceUri())\n arr_present = ds_present.GetRasterBand(1).ReadAsArray()\n # Clean negative values\n arr_present[arr_present<0] = 0\n\n # Load future raster\n future_raster = self.parameterAsRasterLayer(parameters, self.INPUT2, context)\n ds_future = gdal.Open(future_raster.dataProvider().dataSourceUri())\n arr_future = ds_future.GetRasterBand(1).ReadAsArray()\n # Clean negative values\n arr_future[arr_future<0] = 0\n\n print(arr_future)\n print(np.unique(arr_future))\n # Parameters\n V = self.parameterAsDouble(parameters, self.INPUT5, context)\n\n r = 0\n\n c = 3\n\n area_pixel = self.parameterAsInt(parameters, self.PIXEL_RES, context) * self.parameterAsInt(\n parameters, self.PIXEL_RES, context)\n # Years\n present = self.parameterAsInt(parameters, self.INPUT3, context)\n future = self.parameterAsInt(parameters, self.INPUT4, context)\n\n # Calculate coeff sequestration\n arr_diff = arr_future - arr_present\n arr_diff_norm = arr_diff / float((future - present))\n arr_years = np.array(range(0, future - present))\n coeff = sum(1 / ((1 + r / 100) ** arr_years * ((1 + c / 100) ** arr_years)))\n carbon_sequestration_value = V * arr_diff_norm * coeff\n carbon_sequestration_difference = arr_future - arr_present\n # Initialize and write on output raster\n path_output = self.parameterAsString(parameters, self.OUTPUT, context)\n file_output = path_output + '/SE_01_carbon_sequestration_delta_euro.tiff'\n driver = gdal.GetDriverByName(\"GTiff\")\n [cols, rows] = carbon_sequestration_value.shape\n carbon_sequestration_difference_area = np.sum(carbon_sequestration_difference) / (cols * rows * area_pixel)\n outdata = driver.Create(file_output, rows, cols, 1, gdal.GDT_Float64)\n outdata.SetGeoTransform(ds_present.GetGeoTransform())##sets same geotransform as input\n outdata.SetProjection(ds_present.GetProjection())##sets same projection as input\n outdata.GetRasterBand(1).WriteArray(carbon_sequestration_value)\n print(np.max(outdata.GetRasterBand(1).ReadAsArray()))\n outdata.FlushCache() ##saves to disk!!\n outdata = None\n band = None\n ds = None\n print(np.sum(arr_present))\n report_output = path_output + '/SE_sequestro_carbonio.txt'\n f = open(report_output, \"w+\")\n today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n f.write(\"Sommario dell'analisi del sequestro di carbonio\\n\")\n f.write(\"Data: \" + today +\"\\n\\n\\n\")\n f.write(\"Analisi stato di fatto\\n\\n\")\n f.write(\"Anno corrente: %i \\n\" % (present))\n f.write(\"Sequestro carbonio Stato attuale (ton Corg): %f \\n\" % (np.sum(arr_present)))\n f.write(\"Valore medio del carbonio sequestrato per unità di superficie - Stato attuale (ton Corg/ha): : %f \\n\" % (\n np.sum(arr_present) / (cols * rows * area_pixel) * 10000))\n f.write(\"Valore totale del sequestro di carbonio (€): %f \\n\\n\\n\" % ((np.sum(arr_present)*V*coeff)/ float((future - present))))\n f.write(\"Analisi stato di progetto\\n\\n\")\n f.write(\"Anno progetto: %i \\n\" % (future))\n f.write(\"Sequestro carbonio Stato di progetto (ton Corg): %f \\n\" % (np.sum(arr_future)))\n f.write(\"Valore medio del carbonio sequestrato per unità di superficie - Stato di progetto (ton Corg/ha): %f \\n\" % (\n np.sum(arr_future) / (cols * rows * area_pixel) * 10000))\n f.write(\"Valore totale del sequestro di carbonio (€): %f \\n\\n\\n\" % ((np.sum(arr_future)*V*coeff) / float((future - present))))\n f.write(\"Differenze tra stato di progetto e stato attuale\\n\\n\")\n f.write(\"Periodo di analisi: %i - %i\\n\" % (present, future))\n f.write(\"Differenza di sequestro carbonio (ton Corg): %f \\n\" % (np.sum(arr_diff)))\n f.write(\"Differenza carbonio sequestrato per unità di superficie (ton Corg/ha): %f \\n\" % (\n carbon_sequestration_difference_area * 10000))\n f.write(\"Differenza in termini economici del SE di sequestro di carbonio (stato di progetto – stato attuale) (€):%d \\n\" % (\n np.sum(carbon_sequestration_value)))\n return {self.OUTPUT: carbon_sequestration_value}\n\n \n # ----------------------------------------------------------------------------------- \n # Copyright (c) 2021 Città di Torino.\n # \n # This material is free software: you can redistribute it and/or modify\n # it under the terms of the GNU General Public License as published by\n # the Free Software Foundation, either version 2 of the License, or\n # (at your option) any later version.\n # \n # This program is distributed in the hope that it will be useful,\n # but WITHOUT ANY WARRANTY; without even the implied warranty of\n # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n # GNU General Public License for more details.\n # \n # You should have received a copy of the GNU General Public License\n # along with this program. If not, see http://www.gnu.org/licenses.\n # ----------------------------------------------------------------------------------- ",
"title": ""
},
{
"docid": "ddba8492bd02da00237d8e1464e5a126",
"score": "0.5371956",
"text": "def _run(self):\n self._solve()\n return self._ret",
"title": ""
},
{
"docid": "11080b93adcf2859b7f8662c9f7e6fff",
"score": "0.53654003",
"text": "def _compute(y, t, N, b, k):\n S, I, R = y\n #print(t)\n '''The Susceptible Equation'''\n dSdt = -b[int(t)] * S * I / N\n \n '''The Infected Equation = -The Susceptible Equation - The Recovered Equation'''\n dIdt = b[int(t)] * S * I / N - k * I\n \n '''The Recovered Equation'''\n dRdt = k * I\n return dSdt, dIdt, dRdt",
"title": ""
},
{
"docid": "fd122062e4d6256eaf6d4aa2bbf05994",
"score": "0.53594244",
"text": "def integral(self, time1, time2):",
"title": ""
},
{
"docid": "1fb010d6f5ea79ce4b09974b0bc0d8c9",
"score": "0.5352363",
"text": "def bulk_calc(self):\n self.calc_csa()\n self.calc_CCC()\n self.calc_impedence()\n self.calc_runVd()",
"title": ""
},
{
"docid": "7ca2393dd1b3cb44fb5ee38d774cc7de",
"score": "0.5340337",
"text": "def compute_values(self):\n pass",
"title": ""
},
{
"docid": "7ca2393dd1b3cb44fb5ee38d774cc7de",
"score": "0.5340337",
"text": "def compute_values(self):\n pass",
"title": ""
},
{
"docid": "c8e6b33eeb34f158ca8b2ddbd6257563",
"score": "0.5340099",
"text": "def run_calculation(self):\n\n if w.ignore_history_false.isChecked():\n\n fp = w.input_file_line_edit.text()\n\n w.df, w.cache = read_from_file(fp, raw_mt=True)\n # TODO: Check if the filter index is still necessary when matching string indices in match_target_mt below.\n filter_index = w.df.query('status == \"mt\"').index\n w.df = w.df.loc[filter_index]\n\n # Create boolean filter with all repetitions except for first occurrences set to True\n filter_index = w.df.query('stype == \"source\"').duplicated('text')\n w.df = w.df[-filter_index]\n\n else:\n w.df = new_translation(w.df, w.cache, w.sample_object)\n\n df_mt = match_target_mt(w.df)\n\n w.cache = pe_density(df_mt, w.cache)\n w.actionSave_as.setEnabled(True)\n w.textOutput.setText(str('Your Post-Edit Density score is {:.3f}\\n'.format(w.cache['ped'])))\n w.statistics()\n\n w.autosave()",
"title": ""
},
{
"docid": "db551929b95c9e61285499b44233dd40",
"score": "0.5333397",
"text": "def calc(self, x):\n pass",
"title": ""
},
{
"docid": "9ab45ce819ad5f27ffcf041865253bdf",
"score": "0.53273827",
"text": "def post(self, start_value, end_value):\n start_value, end_value = int(start_value), int(end_value)\n engine_name = get_engine_name()\n if os.path.exists(engine_name):\n self.__clean_hashes()\n\n if not os.path.exists(json_filename):\n create_minion_json()\n engine_db_name = 'sqlite:///' + engine_name\n try:\n p = Process(target=calculate_range, args=(start_value, end_value, engine_db_name, m_id))\n p.start()\n processes.append(p)\n except:\n print('Error: failed to run calculation on minion ' + str(m_id) + '.')\n return {'status': 'error', 'message': 'calculation failed'}\n update_status('busy')\n return {'status': 'busy', 'message': 'calculation started'}",
"title": ""
},
{
"docid": "81f4e7059b0fd4d4a86a03694951504c",
"score": "0.5327031",
"text": "def compute():\n message1[\"text\"] = \"\" #reset messages\n message2[\"text\"] = \"\"\n message4[\"text\"] = \"\"\n message5[\"text\"] = \"\" \n message6[\"text\"] = \"\" \n message7[\"text\"] = \"\" \n \n smux=str(smux_entry.get()) #return the smux value in the tkinter entry \n try:\n volts_min=float(volt_min_entry.get()) # min voltage \n volts_max=float(volt_max_entry.get()) # max voltage \n R=float(resistance_entry.get()) # protection resistance \n if abs(volts_max)>20 or abs(volts_min)>20:\n texte5=\"abs(volt) <=10\"\n message5[\"text\"] = texte5 \n print(texte5) \n else: \n nb=int(point_number_entry.get()) #measures nb\n if nb<1:\n texte4=\"nb>0\"\n message4[\"text\"] = texte4 \n print(texte4) \n else:\n # delay=float(delay_entry.get()) #not needed\n # print(smux,volts_min,volts_max,nb,delay) #used to debug\n try:\n complete_measure(volts_min,volts_max,nb,smux=smux,R=R,compliance=compliance)\n texte7=\"Measures done\"\n message7[\"text\"] = texte7 \n print(texte7) \n except Exception as ex:\n texte6=\"Error from measurement detected: %s\" % str(ex) #display the error name\n message6[\"text\"] = texte6 \n print(texte6) \n reset() \n except:\n texte2=\"floats\"\n message2[\"text\"] = texte2 \n print(texte2)",
"title": ""
},
{
"docid": "1594a7502a7cae5142f313f0066993f3",
"score": "0.53201216",
"text": "def func(progress_remaining: float) -> float:\n return initial_value",
"title": ""
},
{
"docid": "c176dcfdf4a68d52eaa145558ca732f5",
"score": "0.53149915",
"text": "def calc(self, pvalues, lamb):\n\t\traise NotImplementedError",
"title": ""
},
{
"docid": "2c01c7f7208d17f565623b9efa9f2829",
"score": "0.5313737",
"text": "def _evaluate_function(self, inputs, aliases):\n # TODO how to handle aliases????\n method = self._sub_name\n request = inputs.pop('request', None) # sometimes just the inputs will be used without the request, like in elasticity curves\n result = self._obj.evaluate(method, request, inputs)\n balance = result[0]\n meta = result[1]\n return balance, meta",
"title": ""
},
{
"docid": "0b7bf3ab90c18dae7b8553fba4cb5d3e",
"score": "0.53023535",
"text": "def calculate(self, mol: Molecule) -> float:",
"title": ""
},
{
"docid": "034d41aa8d8f3385ce7f1536f5f8b7b5",
"score": "0.52879363",
"text": "def __calc(self):\n timezone = self.timezone # in hours, east is positive\n longitude= self.long # in decimal degrees, east is positive\n latitude = self.lat # in decimal degrees, north is positive\n\n time = self.time # percentage past midnight, i.e. noon is 0.5\n day = self.day # daynumber 1=1/1/1900\n \n # NOAA Spreadsheet\n Jday = day+2415018.5+time-timezone/24. # Julian day\n Jcent = (Jday-2451545)/36525 # Julian century\n\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\n Mobliq = 23+(26+((21.448-Jcent*(46.815+ \\\n Jcent*(0.00059-Jcent*0.001813))))/60)/60\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent)) \\\n +sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+ \\\n sin(rad(3*Manom))*0.000289\n Struelong= Mlong+Seqcent\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\n \n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+ \\\n 4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))- \\\n 0.5*vary*vary*sin(4*rad(Mlong))- \\\n 1.25*Eccent*Eccent*sin(2*rad(Manom)))\n\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))* \\\n cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\n\n self.solarnoon_t = (720-4*longitude-eqtime+timezone*60)/1440.\n self.sunrise_t = self.solarnoon_t-hourangle*4/1440.\n self.sunset_t = self.solarnoon_t+hourangle*4/1440.",
"title": ""
},
{
"docid": "ee1d7a33c192af97967f2c8a2e3c713b",
"score": "0.5286321",
"text": "def execute_strategy(self):\n data = self.results.copy().dropna()\n data['position'] = np.sign(data['returns'].rolling(self.mom).mean())\n data['strategy'] = data['position'].shift(1) * data['returns']\n # determine when trades take place\n # trades = data['position'].diff().fillna(0) != 0\n # subtract transaction costs from return where trades take place\n # data['strategy'][trades] -= self.tc\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)",
"title": ""
},
{
"docid": "7b38b01232a3f553a7ee77b5f648b7ec",
"score": "0.5280257",
"text": "def calc(self):\n sorted_pop = self.sortpop()\n initial_route = self.population[sorted_pop[0][0]]\n distance = 1 / sorted_pop[0][1]\n progress = [ distance ]\n if callable(self.animate):\n self.plot = True\n individual = Individual(initial_route)\n stop_animation = Event()\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\n else:\n self.plot = False\n if self.prn:\n print(f\"Initial distance: {distance}\")\n try:\n if self.plot:\n for i in range(self.generations):\n population = self.next_gen()\n sorted_pop = self.sortpop()\n distance = 1 / sorted_pop[0][1]\n progress.append(distance)\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n route = population[sorted_pop[0][0]]\n individual.update(route)\n else:\n for i in range(self.generations):\n population = self.next_gen()\n distance = 1 / self.sortpop()[0][1]\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n \n \n except KeyboardInterrupt:\n pass\n try:\n stop_animation.set()\n except NameError:\n pass\n final_route_index = self.sortpop()[0][0]\n final_route = population[final_route_index]\n if self.prn:\n print(\"Final route:\", final_route)\n\n return initial_route, final_route, distance",
"title": ""
},
{
"docid": "7b38b01232a3f553a7ee77b5f648b7ec",
"score": "0.5280257",
"text": "def calc(self):\n sorted_pop = self.sortpop()\n initial_route = self.population[sorted_pop[0][0]]\n distance = 1 / sorted_pop[0][1]\n progress = [ distance ]\n if callable(self.animate):\n self.plot = True\n individual = Individual(initial_route)\n stop_animation = Event()\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\n else:\n self.plot = False\n if self.prn:\n print(f\"Initial distance: {distance}\")\n try:\n if self.plot:\n for i in range(self.generations):\n population = self.next_gen()\n sorted_pop = self.sortpop()\n distance = 1 / sorted_pop[0][1]\n progress.append(distance)\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n route = population[sorted_pop[0][0]]\n individual.update(route)\n else:\n for i in range(self.generations):\n population = self.next_gen()\n distance = 1 / self.sortpop()[0][1]\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n \n \n except KeyboardInterrupt:\n pass\n try:\n stop_animation.set()\n except NameError:\n pass\n final_route_index = self.sortpop()[0][0]\n final_route = population[final_route_index]\n if self.prn:\n print(\"Final route:\", final_route)\n\n return initial_route, final_route, distance",
"title": ""
},
{
"docid": "75cc49ea82b117f6b2d47af646f819d0",
"score": "0.527877",
"text": "def _evaluate(data, context):\n # print \"Received : \", data, \" Sequence Number : \", context[\"data_sequence_no\"]\n score = 0\n secondary_score = 0\n\n for k in range(100):\n time.sleep(random.randint(0,100)/1000.0)\n percent_complete = k*1.0/100 * 100\n update_progress(context, percent_complete, \"\")\n # print \"Context Response Channel ::: \", context['response_channel']\n # if k%20==0:\n # # print \"Update : \", percent_complete\n score += random.randint(1,100)*1.0/0.7 / 100\n secondary_score += random.randint(1,100)*1.0/0.7 / 100\n\n _result_object = {\n \"score\" : score,\n \"secondary_score\" : secondary_score,\n }\n return _result_object",
"title": ""
},
{
"docid": "e0178d47f659aae4e12fbcd69db2468c",
"score": "0.52750695",
"text": "def on_pushButton_calc_clicked(self):\r\n # TODO: not implemented yet\r\n if self.str1 == '':\r\n a = 0\r\n else:\r\n a = float(self.str1)\r\n if self.str2 == '':\r\n b = 0\r\n else:\r\n b = float(self.str2)\r\n res = 0\r\n if self.op == '+':\r\n res = a + b\r\n elif self.op == '-':\r\n res = a - b\r\n elif self.op == '*':\r\n res = a * b\r\n elif self.op == '/':\r\n if b == 0:\r\n res = 0\r\n else:\r\n res = a / b\r\n\r\n self.lineEdit_result.setText(str(res))\r\n self.flag = 2\r\n\r\n # raise NotImplementedError\r",
"title": ""
},
{
"docid": "320ea0ff97999dcee761fcc123dd114c",
"score": "0.52461845",
"text": "def calculate(self):\n self.correct_extinction()\n self.rdistance = calculate_radial_distance(self.position, self.center,\n self.distance)\n self.r23 = calculate_r23(self.fluxes)\n self.calculate_OH()\n self.SFR = calculate_sfr(self.distance, self.fluxes['halpha'])",
"title": ""
},
{
"docid": "23dd86b596bed8c6aac3dcd00301e5c7",
"score": "0.5244022",
"text": "def compute(self, today, assets, out, opens, closes):\n out[:] = (opens[-1] - closes[0]) / closes[0]",
"title": ""
},
{
"docid": "23dd86b596bed8c6aac3dcd00301e5c7",
"score": "0.5244022",
"text": "def compute(self, today, assets, out, opens, closes):\n out[:] = (opens[-1] - closes[0]) / closes[0]",
"title": ""
},
{
"docid": "a33836659fccc659301fd2466ce5d572",
"score": "0.5242563",
"text": "def run(self):\n self.evaluate()\n self.accumulate()\n self.summarize()",
"title": ""
},
{
"docid": "675188c454c830f80a91cf94327c7e3e",
"score": "0.52329695",
"text": "def process_final(self, *args):\n mean = self._sum / self._count\n mean = mean ** 2\n variance = (self._sum_sq / self._count) - mean\n if variance < 0:\n return 0\n else: \n return variance ** 0.5",
"title": ""
},
{
"docid": "598dc322c12f3f2638ec51cc2bcf894c",
"score": "0.52291757",
"text": "def func(progress_remaining: float):\n return progress_remaining * initial_value",
"title": ""
},
{
"docid": "14aa6575bdb29a5d01bfab980c63bd12",
"score": "0.5228993",
"text": "def linear_schedule(progress):\n return 1 - progress",
"title": ""
},
{
"docid": "c27142e62aa568661d8bbdf43d69f8b6",
"score": "0.5228111",
"text": "def computation(self, results, *parameters):\n\n return self.add_computation(computation(results, *parameters))",
"title": ""
},
{
"docid": "7768d4b0f72db48a4418dd627fdd9c73",
"score": "0.52274144",
"text": "def _compute(self):\n pass",
"title": ""
},
{
"docid": "6abf202e5afb80f54132416bc47cb3f1",
"score": "0.52269894",
"text": "def inspect_calculation(self):\n converged_mc = True\n self.ctx.restart_calc = self.ctx.calculation['retrieved']\n if converged_mc:\n self.report(\"Calculation converged, terminating the workflow\")\n self.ctx.done = True",
"title": ""
},
{
"docid": "28e2904de8eda761267f0b480736bd0d",
"score": "0.52208334",
"text": "def thread_start_calculating(self, distance_value):\n print(\"Breakpoint Distance value:{}\".format(distance_value))\n logger.info('Slot:thread_start_calculating :', \n extra={'sim_index': self._data_store.get_simulation_index()})\n\n if distance_value == 0:\n logger.info('Slot:thread_start_calculating SINGLE STEP NOT IMPLEMENTED:', \n extra={'sim_index': self._data_store.get_simulation_index()})\n #TODO - finish this breakpoint case\n self.simulationComputing = False\n\n elif distance_value == -1:\n logger.info('Slot:thread_start_calculating RUN TO COMPLETION :', \n extra={'sim_index': self._data_store.get_simulation_index()})\n # set the breakpoint to be a very large number to indicate run to completion\n self.breakpointDistance = 9999999\n self.simulationComputing = True\n else:\n # run to the distance value point in the track\n sim_index = self._data_store.get_simulation_index()\n if distance_value > self._data_store.get_distance_at_index(sim_index) :\n logger.info('Slot:thread_start_calculating RUN TO DISTANCE :', \n extra={'sim_index': sim_index})\n # requested breakpoint is further down the track\n self.breakpointDistance = distance_value\n # Start computing and acknowledge to MainWindow by sending a signal back \n self.simulationThreadSignal.emit(\"Calculating...\")\n # \"state\" variable indicating thread should be calculating\n self.simulationComputing = True\n else:\n logger.info('Slot:thread_start_calculating PAST REQUESTED DISTANCE :', \n extra={'sim_index': sim_index})\n # simulation has already past this point in the track, don't proceed\n self.simulationComputing = False",
"title": ""
},
{
"docid": "4213b442ff6408444f7a99b113d82637",
"score": "0.52148896",
"text": "def _calculate_avg():\n while (True):\n _avg_num(_q_uwb_a, q_to_a, \"to_a\", 8)\n _avg_num(_q_uwb_b, q_to_b, \"to_b\", 8)\n time.sleep(0.015)",
"title": ""
},
{
"docid": "6a06c78d7eb16247bf403e1389083f14",
"score": "0.52093",
"text": "def integrate(self, t):",
"title": ""
},
{
"docid": "4ee388bb9a884aa4cea1bef1a52b88fa",
"score": "0.5208004",
"text": "def __call__(self, *args, **kwargs) -> Any:\n return self.calculate(*args, **kwargs)",
"title": ""
},
{
"docid": "a7a19b61964963525e9d4ad7f5b8669f",
"score": "0.5207855",
"text": "def func(progress_remaining: float) -> float:\n return progress_remaining * initial_value",
"title": ""
},
{
"docid": "a7a19b61964963525e9d4ad7f5b8669f",
"score": "0.5207855",
"text": "def func(progress_remaining: float) -> float:\n return progress_remaining * initial_value",
"title": ""
},
{
"docid": "54047f7afc5fafe5f39587ee486749fd",
"score": "0.5203203",
"text": "def __cost_function(self, sense='single'):\n\n self.SnappyHexMeshrun()\n self.Optimisationrun()\n if sense==\"single\":\n t = self.RunUtilities()\n return p\n elif sense==\"multi\":\n t, p = self.RunUtilities(sense=sense)\n return t, p\n else:\n print(\"Invalid input for sense: \", sense)\n print(\"Available options are: 'single' or 'multi'\")\n return None",
"title": ""
},
{
"docid": "30447d2a14ea9b45e16c20607615711e",
"score": "0.5196032",
"text": "def calculate_derivative(param):",
"title": ""
},
{
"docid": "e1106def928c6e8ab53fb563e3bff385",
"score": "0.51902646",
"text": "def evaluate(self) -> None:\n\n self._generate_paths()\n self._compute_payoff_fn()\n self._is_evaluated = True",
"title": ""
},
{
"docid": "f1b28047d6734cbcd08da68de38b381b",
"score": "0.51892775",
"text": "def done(self, result):\n output_file = result['output']\n GLF = result['GLF']\n Qt.QApplication.restoreOverrideCursor()\n self.statusBar().showMessage('GLF = {:0.1f}%'.format(GLF*100))\n # Show a MessageBox.\n QtWidgets.QMessageBox.information(self, 'Ausgabe erzeugt',\n str(output_file),\n QtWidgets.QMessageBox.Ok)\n self.start_button.setEnabled(True)\n\n if self.plot_timeseries:\n try: # Create a line plot of the time series in a new window\n self.plot_window = PlotWindow()\n self.plot_window.update_lineplot(result['df_sum'])\n self.plot_window.show()\n except Exception as e:\n logger.exception(e)\n QtWidgets.QMessageBox.critical(self, 'Fehler', str(e),\n QtWidgets.QMessageBox.Ok)",
"title": ""
},
{
"docid": "daceaadc42197c7d5f2b3753e3192aee",
"score": "0.5189256",
"text": "def when_computation_done(self, out, param):\r\n tkMessageBox.showinfo('Computation is done', out)\r\n self.set_state('normal')\r\n\r\n if self.plugin_type == \"Chimera\":\r\n self.profile_wd = os.path.realpath(\r\n os.path.normcase(self.main_parameters[1] + \"/chimera/\"))\r\n\r\n tunnels_script = os.path.join(self.profile_wd, 'complex.py')\r\n paths_script = os.path.join(self.wd, 'paths.py')\r\n pores_script = os.path.join(self.wd, 'pores.py')\r\n autopores_script = os.path.join(self.wd, 'autopores.py')\r\n dom = parse(os.path.join(self.xml_wd, 'tunnels.xml'))\r\n\r\n for e in dom.getElementsByTagName('Exception'):\r\n tkMessageBox.showinfo('Exception', e.getAttribute('Text'))\r\n\r\n if self.plugin_type == \"PyMOL\":\r\n\r\n if param == \"pores\":\r\n self.group_objects(tunnels_script, 'Pores', \"MOLE_tunnels\")\r\n else:\r\n self.group_objects(tunnels_script, 'Tunnels', \"MOLE_pores\")\r\n\r\n self.group_objects(paths_script, 'Paths', 'MOLE_Paths')\r\n self.group_objects(pores_script, 'Pores', 'MOLE_Pores')\r\n self.group_objects(autopores_script, 'AutoPores', 'MOLE_AutoPores')\r\n plugin().show(\"cgo\", \"Interior*\")\r\n plugin().set_view(self.original_view)\r\n\r\n else:\r\n plugin.run(tunnels_script)\r\n\r\n self.set_state('normal')",
"title": ""
},
{
"docid": "8bc4859850d902bbbabda8a6f64c2238",
"score": "0.51880544",
"text": "def render(self, request):\n self._calculatePi(request).addCallback(self._gotResult, request)\n return server.NOT_DONE_YET",
"title": ""
},
{
"docid": "7174f03ecc0b1631771bcd650b043bf5",
"score": "0.5187847",
"text": "def _postCalculate(self,retList,time):\n\t\tmodList = []\n\t\tfor vals in retList: \n\t\t\tvals[\"percentEffected\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\n\t\t\tvals = self.calculateThresholdPolygon(vals,time)\n\t\t\tmodList.append(vals)\n\n\t\treturn pandas.concat(modList,ignore_index=True)",
"title": ""
},
{
"docid": "7174f03ecc0b1631771bcd650b043bf5",
"score": "0.5187847",
"text": "def _postCalculate(self,retList,time):\n\t\tmodList = []\n\t\tfor vals in retList: \n\t\t\tvals[\"percentEffected\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\n\t\t\tvals = self.calculateThresholdPolygon(vals,time)\n\t\t\tmodList.append(vals)\n\n\t\treturn pandas.concat(modList,ignore_index=True)",
"title": ""
},
{
"docid": "36b0c8b9bdb177f85aa5d45a556a0e22",
"score": "0.5185649",
"text": "def Calculate(self):\n pass",
"title": ""
},
{
"docid": "b214cdc4438aebfa3d63f75c8e78c910",
"score": "0.5171598",
"text": "def compute_pi_main(workers, accuracy, batchsize):\n n_total = 0\n s_total = 0\n tasks_queue = mp.Queue() # each task (or batch) is represented by the batchsize\n done_queue = mp.Queue() # result of each task is the number of successes in that batch\n processes = mp.Pool(workers, initializer=compute_pi_worker, initargs=(tasks_queue, done_queue))\n\n for _ in range(workers):\n tasks_queue.put(batchsize)\n\n while True:\n s = done_queue.get(block=True)\n s_total += s\n n_total += batchsize\n pi_est = (4.0 * s_total) / n_total\n error = abs(math.pi - pi_est)\n\n if error > accuracy:\n tasks_queue.put(batchsize)\n else:\n processes.terminate()\n processes.join()\n break\n\n print(\"Estimate:\", pi_est)\n print(\"Error:\", error)\n print(\"Steps:\", n_total)\n return pi_est, error, n_total",
"title": ""
},
{
"docid": "67da759295be1801c868c36af3dd3052",
"score": "0.5168196",
"text": "def calculate_float(self, default: int):\n self.total_float = self.latest_finish - self.earliest_finish\n\n if not self.successors:\n self.free_float = default - self.earliest_finish\n else:\n self.free_float = min(s.earliest_start for s in self.successors) - self.earliest_finish",
"title": ""
},
{
"docid": "ce99a8d17f1502a9ede8378469d864af",
"score": "0.5166104",
"text": "def calculate(s):",
"title": ""
},
{
"docid": "fff35d5e25b8885f6ecc9ff6d4200e5b",
"score": "0.5155409",
"text": "def handler(event, context):\n try:\n parsed_event = CalculatorEvent(**event)\n except ValidationError as exp:\n raise Exception(json.dumps(f\"[BadRequest] {exp}\"))\n\n if parsed_event.year not in crypto_prices[parsed_event.query]['old_prices']:\n raise Exception(json.dumps(f\"[BadRequest] Data for requested year not available\"))\n\n\n current_value = (parsed_event.amount/crypto_prices[parsed_event.query]['old_prices'][parsed_event.year]) * crypto_prices[parsed_event.query]['current_price']\n \n return round(current_value,2)",
"title": ""
},
{
"docid": "a28c312c92b4217aed7e3c0932a1bfe0",
"score": "0.5150363",
"text": "def execute_strategy(self):\n data = self.results.copy().dropna()\n data['position'] = np.where(data['SMA1'] > data['SMA2'], 1, 0)\n data['strategy'] = data['position'].shift(1) * data['returns']\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)",
"title": ""
},
{
"docid": "da3563d9154880d9234b4a9a1fead992",
"score": "0.5143086",
"text": "def evaluate_metric(self, assignments):\n sleep_seconds = 10\n print('{0} - Sleeping for {1} seconds to simulate expensive computation...'.format(threading.current_thread(), sleep_seconds))\n time.sleep(sleep_seconds)\n return sigopt.examples.franke_function(assignments['x1'], assignments['x2'])",
"title": ""
},
{
"docid": "0647838977b755e73ac53acb14e7ed14",
"score": "0.5141489",
"text": "def execute_strategy(self):\n data = self.results.copy().dropna()\n data['sma'] = data['returns'].rolling(self.sma).mean()\n data['distance'] = data['close'] - data['sma']\n # sell signals\n data['position'] = np.where(data['distance'] > self.threshold, -1, np.nan)\n # buy signals\n data['position'] = np.where(data['distance'] < -self.threshold, 1, data['position'])\n # cross of current price and SMA (zero distance)\n data['position'] = np.where(data['distance'] * data['distance'].shift(1) < 0, 0, data['position'])\n data['position'] = data['position'].ffill().fillna(0)\n data['strategy'] = data['position'].shift(1) * data['returns']\n # determine when trades take place\n # trades = data['position'].diff().fillna(0) != 0\n # subtract transaction costs from return where trades take place\n # data['strategy'][trades] -= self.tc\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)",
"title": ""
},
{
"docid": "ab977317ec5b5501427a8c5d5683e375",
"score": "0.51393765",
"text": "def calculate(self):\n super().calculate()\n\n\n tr_key = Constants.get_key(\"TR\")\n trn_key = Constants.get_key(\"TRn\")\n\n dm_plus_key = Constants.get_key(\"DMplus\")\n dm_plus_n_key = Constants.get_key(\"DMplusN\")\n\n dm_minus_key = Constants.get_key(\"DMminus\")\n dm_minus_n_key = Constants.get_key(\"DMminusN\")\n di_plus_n_key = Constants.get_key(\"DIplusN\")\n di_minus_n_key = Constants.get_key(\"DIminusN\")\n di_diff_key = Constants.get_key(\"DIdiff\")\n di_sum_key = Constants.get_key(\"DIsum\")\n dx_key = Constants.get_key(\"DX\")\n\n df_result = []\n\n for ticker in self.tickers:\n\n df_data_atr = self.df[[ticker]].copy()\n atr_ind = ATR(df=df_data_atr, n=14)\n\n df_data = self.df[ticker].copy()\n # the period parameter of ATR function does not matter because period does not influence TR calculation\n df_data[tr_key] = atr_ind.calculate()[ticker][[tr_key]]\n\n df_data[dm_plus_key] = \\\n np.where(\n (df_data[self.high_key] - df_data[self.high_key].shift(1)) >\n (df_data[self.low_key].shift(1) - df_data[self.low_key]),\n df_data[self.high_key] - df_data[self.high_key].shift(1),\n 0)\n\n df_data[dm_plus_key] = \\\n np.where(\n df_data[dm_plus_key] < 0,\n 0,\n df_data[dm_plus_key])\n\n df_data[dm_minus_key] = \\\n np.where((df_data[self.low_key].shift(1) - df_data[self.low_key]) >\n (df_data[self.high_key] - df_data[self.high_key].shift(1)),\n df_data[self.low_key].shift(1) - df_data[self.low_key],\n 0)\n\n df_data[dm_minus_key] = np.where(df_data[dm_minus_key] < 0, 0, df_data[dm_minus_key])\n\n TRn = []\n DMplusN = []\n DMminusN = []\n TR = df_data[tr_key].tolist()\n DMplus = df_data[dm_plus_key].tolist()\n DMminus = df_data[dm_minus_key].tolist()\n\n for i in range(len(df_data)):\n if i < self.n:\n TRn.append(np.NaN)\n DMplusN.append(np.NaN)\n DMminusN.append(np.NaN)\n elif i == self.n:\n TRn.append(df_data[tr_key].rolling(self.n).sum().tolist()[self.n])\n DMplusN.append(df_data[dm_plus_key].rolling(self.n).sum().tolist()[self.n])\n DMminusN.append(df_data[dm_minus_key].rolling(self.n).sum().tolist()[self.n])\n elif i > self.n:\n TRn.append(TRn[i - 1] - (TRn[i - 1] / 14) + TR[i])\n DMplusN.append(DMplusN[i - 1] - (DMplusN[i - 1] / 14) + DMplus[i])\n DMminusN.append(DMminusN[i - 1] - (DMminusN[i - 1] / 14) + DMminus[i])\n\n df_data[trn_key] = np.array(TRn)\n df_data[dm_plus_n_key] = np.array(DMplusN)\n df_data[dm_minus_n_key] = np.array(DMminusN)\n df_data[di_plus_n_key] = 100 * (df_data[dm_plus_n_key] / df_data[trn_key])\n df_data[di_minus_n_key] = 100 * (df_data[dm_minus_n_key] / df_data[trn_key])\n df_data[di_diff_key] = abs(df_data[di_plus_n_key] - df_data[di_minus_n_key])\n df_data[di_sum_key] = df_data[di_plus_n_key] + df_data[di_minus_n_key]\n df_data[dx_key] = 100 * (df_data[di_diff_key] / df_data[di_sum_key])\n ADX = []\n DX = df_data[dx_key].tolist()\n for j in range(len(df_data)):\n if j < 2 * self.n - 1:\n ADX.append(np.NaN)\n elif j == 2 * self.n - 1:\n ADX.append(df_data[dx_key][j - self.n + 1:j + 1].mean())\n elif j > 2 * self.n - 1:\n ADX.append(((self.n - 1) * ADX[j - 1] + DX[j]) / self.n)\n df_data[self.indicator_key] = np.array(ADX)\n\n df_result.append(df_data.loc[:, [self.indicator_key]])\n\n self.df = pd.concat(df_result, axis=1, keys=self.tickers)\n\n return self.df",
"title": ""
},
{
"docid": "eafd971da2d043a48e336831a0a68557",
"score": "0.5138379",
"text": "def process_queue_fringes(self):\n\n try:\n result = self.queue.get(0)\n\n if result == \"ABORT\":\n self.addlog(\"Mission aborted.\", self.warningcolor2)\n self.abortmission.set(0)\n self.totaltime = 0\n self.removeprogressbar()\n return\n\n # Show result of the task if needed\n self.entry_23.delete(0, END)\n self.entry_23.insert(0, '{0:.2f}'.format(result[0]))\n self.entry_24.delete(0, END)\n self.entry_24.insert(0, '{0:.2f}'.format(result[1]))\n\n self.setoffsets()\n\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list = [], [], [], []\n\n for i in range(1, self.layernumber + 1):\n self.layertype_list.append(getattr(self, \"layertypevar{}\".format(i)).get())\n self.entry_x_list.append(float(getattr(self, \"entry_x_{}\".format(i)).get()))\n self.entry_d_list.append(float(getattr(self, \"entry_d_{}\".format(i)).get()))\n self.checklayer_list.append(int(getattr(self, \"checklayer{}\".format(i)).get()))\n\n fitobject = FIT_FTIR(self.Temp, self.wavenumbers_cut, self.trans_cut, self.entry_d_0.get(),\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list,\n float(self.entry_21.get()), self.angle,\n float(self.entry_23.get()), float(self.entry_24.get()), self.subtype, 2,\n self.listbox, self.progress_var, self.wn_beingcalculated,\n self.FTIRplot, self.absorptionplot, self.canvas, self.blindcal, self.abortmission)\n self.peakvalues_fit = fitobject.returnpeakvalues()\n\n self.addlog('Fitting fringes complete. Total time: {:.1f}s. MSE={}'.format(self.totaltime, result[2]))\n\n self.totaltime = 0\n\n self.try_remove_fitline(self.fitline_trans)\n self.fitline_trans = self.plot_and_show(self.FTIRplot, self.fitline_trans, 0, self.wavenumbers_cut,\n self.peakvalues_fit, 'r', '', 'Transmission (%)', 0, 'upper right')\n\n self.removeprogressbar()\n\n except queue.Empty:\n self.totaltime += 0.1\n self.after(100, self.process_queue_fringes)",
"title": ""
},
{
"docid": "5820bfa6b2e06e705d2fad4ec1847994",
"score": "0.5132884",
"text": "def compute(\n self,\n blade_cost,\n blade_mass,\n hub_system_cost,\n hub_system_mass,\n nacelle_mass,\n nacelle_cost,\n tower_cost,\n tower_mass,\n blade_number=3,\n offshore=True,\n ):\n\n # Variables\n self.blade_cost = (\n blade_cost # Float(0.0, units='USD', iotype='in', desc='cost for a single wind turbine blade')\n )\n self.blade_mass = blade_mass # Float(0.0, units='kg', iotype='in', desc='mass for a single wind turbine blade')\n self.hub_system_cost = hub_system_cost # Float(0.0, units='USD', iotype='in', desc='hub system cost')\n self.hub_system_mass = hub_system_mass # Float(0.0, units='kg', iotype='in', desc='hub system mass')\n self.nacelle_mass = nacelle_mass # Float(0.0, units='kg', iotype='in', desc='nacelle mass')\n self.nacelle_cost = nacelle_cost # Float(0.0, units='USD', iotype='in', desc='nacelle cost')\n self.tower_cost = tower_cost # Float(0.0, units='USD', iotype='in', desc='cost for a tower')\n self.tower_mass = tower_mass # Float(0.0, units='kg', iotype='in', desc='mass for a turbine tower')\n\n # Parameters (and ignored inputs)\n self.blade_number = blade_number # Int(3, iotype='in', desc = 'number of rotor blades')\n self.offshore = offshore # Bool(False, iotype='in', desc= 'boolean for offshore')\n\n # high level output assignment\n self.rotor_mass = self.blade_mass * self.blade_number + self.hub_system_mass\n self.rotor_cost = self.blade_cost * self.blade_number + self.hub_system_cost\n self.turbine_mass = self.rotor_mass + self.nacelle_mass + self.tower_mass\n self.turbine_cost = self.rotor_cost + self.nacelle_cost + self.tower_cost\n\n if self.offshore:\n self.turbine_cost *= 1.1\n\n # derivatives\n self.d_mass_d_blade_mass = self.blade_number\n self.d_mass_d_hub_mass = 1.0\n self.d_mass_d_nacelle_mass = 1.0\n self.d_mass_d_tower_mass = 1.0\n\n if self.offshore:\n self.d_cost_d_blade_cost = 1.1 * self.blade_number\n self.d_cost_d_hub_cost = 1.1\n self.d_cost_d_nacelle_cost = 1.1\n self.d_cost_d_tower_cost = 1.1\n else:\n self.d_cost_d_blade_cost = self.blade_number\n self.d_cost_d_hub_cost = 1.0\n self.d_cost_d_nacelle_cost = 1.0\n self.d_cost_d_tower_cost = 1.0",
"title": ""
},
{
"docid": "a2818a75b1740c04b7c7c9442d420e04",
"score": "0.5128805",
"text": "def getval(self):\n \n if self.averagingAttemps > 1:\n result = 0.0\n readings = 0.0\n readingsc = 0.0\n\t readings_array = {}\n for i in range(0, self.averagingAttemps):\n reading = self.getReading()\n if (reading != 0 and reading != 1023):\n if self.averagingMethod == \"max\":\n if reading > readings:\n\t\t\t \"\"\"print(str(reading) + \" higher as \" + str(readings))\"\"\"\n readings = reading\n else:\n readings = readings + reading\n readingsc = readingsc + 1\n\t\t if reading in readings_array:\n\t\t readings_array[reading] = readings_array[reading] + 1\n\t\t else:\n\t\t\treadings_array[reading] = 1\n time.sleep(self.averagingTimeout)\n if readingsc == 0:\n result = self.getReading()\n else:\n\t\tif self.averagingMethod == \"most\":\n\t\t sorted_x = sorted(readings_array.items(), key=operator.itemgetter(1), reverse=True)\n\t\t result = sorted_x[0][0]\n elif self.averagingMethod == \"max\":\n result = readings\n else:\n result = readings / readingsc\n else:\n result = self.getReading()\n \n if result == 0:\n msg = \"Error: Check wiring for the \" + self.sensorname\n msg += \" measurement, no voltage detected on ADC input \"\n msg += str(self.adcpin)\n print(msg)\n return None\n if result == 1023:\n if self.sensorname == \"LDR\":\n # Carrying on with 1023 gives divide by zero error below\n result = 1022\n else:\n msg = \"Error: Check wiring for the \" + self.sensorname\n msg += \" measurement, full voltage detected on ADC input \"\n msg += str(self.adcpin)\n print(msg)\n return None\n\n\tif self.sensorname == \"WindDirection\":\n\t return result\n\n\t\"\"\"print(self.sensorname + \" \" + str(self.sensorvoltage))\"\"\"\n\n vout = float(result)/1023 * self.sensorvoltage\n if self.pulldown != None:\n resout = (self.pulldown * self.sensorvoltage) / vout - self.pulldown\n elif self.pullup != None:\n resout = self.pullup / ((self.sensorvoltage / vout) - 1)\n else:\n resout = vout * 1000\n return resout",
"title": ""
},
{
"docid": "621c70cbb28fa1e4a9c190a799b7b1e6",
"score": "0.51253504",
"text": "def compute(self): # pylint: disable=E0202\n pass",
"title": ""
},
{
"docid": "621c70cbb28fa1e4a9c190a799b7b1e6",
"score": "0.51253504",
"text": "def compute(self): # pylint: disable=E0202\n pass",
"title": ""
},
{
"docid": "01998f20124e1512ef226959005fc520",
"score": "0.512176",
"text": "def calculate_callback(self):\n\n # Create the interpreter\n from asteval import Interpreter\n aeval = Interpreter()\n\n # Grab the calculation from the text box which the user wants to do\n calculation = str(self.calculation_text.text())\n\n lhs = calculation.split('=')[0].strip()\n\n # Use the package asteval to do the calculation, we are going to\n # assume here that the lhs of the equals sign is going to be the output named variable\n\n try:\n if lhs in self.data_components:\n raise KeyError('{} is already in the data components, use a different variable on the left hand side.'.format(lhs))\n\n # Pull in the required data and run the calculation\n for dc in self.data_components:\n if dc in calculation:\n aeval.symtable[dc] = self.data[dc]\n aeval(calculation)\n\n # Pull out the output data and add to the proper drop-downs\n out_data = aeval.symtable[lhs]\n self.data.add_component(out_data, lhs)\n\n # Add the new data to the list of available data for arithemitic operations\n self.data_components.append(lhs)\n\n self.close()\n\n except KeyError as e:\n self.calculation_text.setStyleSheet(\"background-color: rgba(255, 0, 0, 128);\")\n\n # Display the error in the Qt popup\n if aeval.error_msg:\n self.error_label_text.setText('{}'.format(aeval.error_msg))\n else:\n self.error_label_text.setText('{}'.format(e))\n\n self.error_label_text.setStyleSheet(\"color: rgba(255, 0, 0, 128)\")",
"title": ""
},
{
"docid": "9ad61c57a35376b206230052921c1336",
"score": "0.512145",
"text": "def update(self,current_value):\n\n self.error = self.set_point - current_value\n\n self.P_value = self.Kp * self.error\n if (self.last_value >= current_value):\n change = self.error - self.last_error\n else:\n change = 0.0\n\n if self.error > 0.0:\n self.I_value = self.Integrator * self.Ki\n else:\n self.I_value = (self.Integrator * self.Ki)\n\n\n #self.D_value = self.Kd * ( self.error - self.Derivator)\n self.D_value = self.Kd * change\n self.Derivator = self.error\n\n self.Integrator = self.Integrator + self.error\n\n if self.Integrator > self.Integrator_max:\n self.Integrator = self.Integrator_max\n elif self.Integrator < self.Integrator_min:\n self.Integrator = self.Integrator_min\n\n self.last_error = self.error\n self.last_value = current_value\n\n PID = self.P_value + self.I_value + self.D_value\n\n self._z_data[\"data\"][\"P\"] = self.P_value\n self._z_data[\"data\"][\"I\"] = self.I_value\n self._z_data[\"data\"][\"D\"] = self.D_value\n self._z_data[\"data\"][\"E\"] = self.error\n self._z_data[\"data\"][\"SP\"] = self.set_point\n self._z_data[\"data\"][\"OUT\"] = PID\n\n if self._zmq:\n try:\n self._zmq.send_json(self._z_data, zmq.NOBLOCK)\n except zmq.error.Again:\n pass\n\n return PID",
"title": ""
},
{
"docid": "472ea9d79a812c81d9dbbb43bbe45dc9",
"score": "0.50997347",
"text": "def _calc_result(self):\n import mcmc\n\n g=zeros(2)\n gcov=zeros((2,2))\n gsens = zeros(2)\n\n if self.eta:\n eta1vals,eta2vals=self.trials[:,2], self.trials[:,3]\n g1vals,g2vals=self._get_gvals_from_eta(eta1vals,eta2vals)\n else:\n g1vals=self.trials[:,2]\n g2vals=self.trials[:,3]\n\n prior = self.gprior(g1vals,g2vals)\n dpri_by_g1 = self.gprior.dbyg1(g1vals,g2vals)\n dpri_by_g2 = self.gprior.dbyg2(g1vals,g2vals)\n\n psum = prior.sum()\n\n g0=None\n gcov0=None\n if self.when_prior=='after':\n # this will be eta not g if using that parametrization\n g0,gcov0 = mcmc.extract_stats(self.trials[:,2:4])\n\n pars, pcov = mcmc.extract_stats(self.trials,weights=prior)\n # we need to multiply each by the prior\n \"\"\"\n g[0] = (g1vals*prior).sum()/psum\n g[1] = (g2vals*prior).sum()/psum\n\n\n gcov[0,0] = (g1diff**2*prior).sum()/psum\n gcov[0,1] = (g1diff*g2diff*prior).sum()/psum\n gcov[1,0] = gcov[0,1]\n gcov[1,1] = (g2diff**2*prior).sum()/psum\n \"\"\"\n\n g[:] = pars[2:4]\n gcov[:,:] = pcov[2:4, 2:4]\n\n # now the sensitivity is \n # sum( (<g>-g) L*dP/dg )\n # ----------------------\n # sum(L*P)\n #\n # the likelihood is already in the points\n\n g1diff = g[0]-g1vals\n g2diff = g[1]-g2vals\n gsens[0] = 1. - (g1diff*dpri_by_g1).sum()/psum\n gsens[1] = 1. - (g2diff*dpri_by_g2).sum()/psum\n else:\n # prior is already in the distribution of\n # points. This is simpler for most things but\n # for sensitivity we need a factor of (1/P)dP/de\n\n pars,pcov = mcmc.extract_stats(self.trials)\n #g, gcov = mcmc.extract_stats(self.trials[:,2:2+2])\n\n g[:] = pars[2:4]\n gcov[:,:] = pcov[2:4, 2:4]\n\n g1diff = g[0]-g1vals\n g2diff = g[1]-g2vals\n\n w,=where(prior > 0)\n if w.size == 0:\n raise ValueError(\"no prior values > 0!\")\n\n gsens[0]= 1.-(g1diff[w]*dpri_by_g1[w]/prior[w]).mean()\n gsens[1]= 1.-(g2diff[w]*dpri_by_g2[w]/prior[w]).mean()\n\n \n arates = self._emcee_sampler.acceptance_fraction\n arate = arates.mean()\n #print 'acceptance rate:',w.size/float(self.trials.size)\n\n # weighted s/n based on the most likely point\n s2n,loglike,chi2per,dof,prob=self._calculate_maxlike_stats()\n if self.eta:\n g0name='eta'\n else:\n g0name='g'\n\n Tmean=pars[4]\n Terr=sqrt(pcov[4,4])\n Ts2n=pars[4]/sqrt(pcov[4,4])\n #print 'T s/n:',Ts2n\n self._result={'model':self.model,\n 'g':g,\n 'gcov':gcov,\n 'gsens':gsens,\n g0name+'0':g0,\n g0name+'cov0':gcov0,\n 'pars':pars,\n 'pcov':pcov,\n 'Tmean':Tmean,\n 'Terr':Terr,\n 'Ts2n':Ts2n,\n 'arate':arate,\n 's2n_w':s2n,\n 'loglike':loglike,\n 'chi2per':chi2per,\n 'dof':dof,\n 'fit_prob':prob}\n #wlog(\"arate:\",self._result['arate'])",
"title": ""
},
{
"docid": "81e1d43e967bd2ad66fe32eeb4f512d8",
"score": "0.5096479",
"text": "def interpolate(self, double: float) -> float:\n ...",
"title": ""
},
{
"docid": "f9ccb6ccc97f671fb95181dc45fb44f8",
"score": "0.5088823",
"text": "def postsolve(self, lbfgs_result):\n raise NotImplementedError",
"title": ""
},
{
"docid": "7a7d46d8d9ffadc367fe21aad2f8c9bb",
"score": "0.50876844",
"text": "def result_relative(self, result1, result2):\n value1 = self.objective.get_mean(result1)\n value2 = self.objective.get_mean(result2)\n if value2 == 0:\n return float('inf') * value1\n return value1 / value2",
"title": ""
},
{
"docid": "0bc1f58636b2ddf43f909825f55e8d1e",
"score": "0.50867045",
"text": "def calcul_throughput(self, spectrumrangeairmass):\n \"\"\"\n data_mag = spectrumrangeairmass.data_mag.T\n data_mag -= np.log(self.data_calspec)\n spectrumrangeairmass.data_mag = data_mag.T\n\n data_order2 = spectrumrangeairmass.order2.T\n data_order2 /= self.data_calspec\n spectrumrangeairmass.order2 = data_order2.T\n #print(self.data_calspec)\n #print(self.data_calspec.T)\n #print(self.data_calspec.T @ self.data_calspec)\n MULT = np.zeros((len(self.data_calspec),len(self.data_calspec)))\n for i in range(len(self.data_calspec)):\n for j in range(len(self.data_calspec)):\n MULT[i][j], MULT[j][i] = self.data_calspec[i] * self.data_calspec[j], self.data_calspec[i] * self.data_calspec[j]\n\n spectrumrangeairmass.INVCOV *= MULT\n spectrumrangeairmass.cov /= MULT\n \"\"\"\n\n self.slope, self.ord, self.err_slope, self.err_ord = spectrumrangeairmass.bouguer_line()\n disp = np.loadtxt(self.rep_disp_name)\n Data_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1]/100, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n tel = np.loadtxt(self.rep_tel_name)\n Data_tel = sp.interpolate.interp1d(tel.T[0], tel.T[1], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err_tel = sp.interpolate.interp1d(tel.T[0], tel.T[2], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n \"\"\"\n data_disp_new_lambda = Data_disp(self.new_lambda)\n err_disp_new_lambda = Err_disp(self.new_lambda)\n data_tel_new_lambda = Data_tel(self.new_lambda)\n err_tel_new_lambda = Err_tel(self.new_lambda)\n \"\"\"\n self.data_bouguer = np.exp(self.ord)\n err_bouguer = self.err_ord * self.data_bouguer\n self.data = self.data_bouguer / self.data_calspec\n self.lambdas = self.new_lambda\n self.err = err_bouguer / self.data_calspec\n\n # self.data = filter_detect_lines(self.lambdas, self.data, self.plot_filt, self.save_filter)\n\n Data = sp.interpolate.interp1d(self.lambdas, self.data, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err = sp.interpolate.interp1d(self.lambdas, self.err, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n self.lambdas = self.new_lambda\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\n\n self.data_tel = Data_tel(self.lambdas)\n self.data_tel_err = Err_tel(self.lambdas)\n self.data_disp = Data_disp(self.lambdas)\n self.data_disp_err = Err_disp(self.lambdas)\n self.data = Data(self.lambdas)\n self.err = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n # self.data = sp.signal.savgol_filter(self.data, 111, 2)\n\n #spectrumrangeairmass.archi_mega_fit()\n if self.order2 and not self.mega_fit:\n self.slope2, self.ord2, self.err_slope2, self.err_ord2, self.A2, self.A2_err = spectrumrangeairmass.bouguer_line_order2()\n self.data_bouguer = np.exp(self.ord2)\n err_bouguer = self.err_ord2 * self.data_bouguer\n self.data_order2 = self.data_bouguer / self.data_calspec\n self.lambdas = self.new_lambda\n self.err_order2 = err_bouguer / self.data_calspec\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n self.lambdas = self.new_lambda\n self.data_order2 = Data(self.lambdas)\n self.err_order2 = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n # self.data_order2 = sp.signal.savgol_filter(self.data_order2, 111, 2)\n\n if self.mega_fit:\n self.ord2, self.err_order2 = spectrumrangeairmass.megafit_emcee()\n print(self.ord2)\n print(len(self.ord2))\n self.data_order2 = self.ord2 / self.data_calspec\n self.err_order2 = self.err_order2 / self.data_calspec\n self.lambdas = self.new_lambda\n\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.ord2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n self.lambdas = self.new_lambda\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\n self.data_order2 = Data(self.lambdas)\n self.err_order2 = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n self.data_order2[21:-21] = sp.signal.savgol_filter(self.data_order2[21:-21], 11, 2)",
"title": ""
},
{
"docid": "83954746e4599a297a386e46edd8832a",
"score": "0.5083051",
"text": "def solve(self, show_progress=True):\n dt = self.dt\n old_dt = self.dt\n\n if self.in_parallel:\n show = False\n else:\n show = show_progress\n bar = FloatPBar(self.t, self.tf, show=show)\n\n # Initial solution\n self.dump_output()\n self.barrier() # everybody waits for this to complete\n\n # initial solution damping time\n tdamp = self.tdamp\n\n # Compute the accelerations once for the predictor corrector\n # integrator to work correctly at the first time step.\n self.acceleration_eval.compute(self.t, dt)\n\n # solution output times\n output_at_times = numpy.array( self.output_at_times )\n\n while self.t < self.tf:\n\n # perform any pre step functions\n for func in self.pre_step_functions:\n func.eval(self)\n\n if self.rank == 0:\n logger.debug(\n \"Iteration=%d, time=%f, timestep=%f\" % \\\n (self.count, self.t, dt)\n )\n # perform the integration and update the time.\n #print 'Solver Iteration', self.count, dt, self.t, tdamp\n self.integrator.step(self.t, dt)\n\n # perform any post step functions\n for func in self.post_step_functions:\n func.eval(self)\n\n # update time and iteration counters if successfully\n # integrated\n self.t += dt\n self.count += 1\n\n # dump output if the iteration number is a multiple of the\n # printing frequency\n if self.count % self.pfreq == 0:\n self.dump_output()\n self.barrier()\n\n # dump output if forced\n if self.force_output:\n self.dump_output()\n self.barrier()\n\n self.force_output = False\n\n # re-set the time-step to the old time step before it\n # was adjusted\n dt = old_dt\n\n # update progress bar\n bar.update(self.t)\n\n # update the time for all arrays\n self.update_particle_time()\n\n # compute the new time step across all processors\n if self.adaptive_timestep:\n\n # locally stable time step\n dt = self.integrator.compute_time_step(\n self.dt, self.cfl)\n\n # damp the initial solution\n if self.t < tdamp:\n dt *= 0.5 * (numpy.sin(numpy.pi*(-0.5+self.t/tdamp)) + 1.0)\n\n # set the globally stable time step\n if self.in_parallel:\n dt = self.pm.update_time_steps(dt)\n\n # adjust dt to land on final time\n if self.t + dt > self.tf:\n dt = self.tf - self.t\n\n # adjust dt to land on specific output times\n if output_at_times.size > 0:\n tdiff = output_at_times - self.t\n condition = (tdiff > 0) & (tdiff < dt)\n\n if numpy.any( condition ):\n output_time = output_at_times[ numpy.where(condition) ]\n\n # save the old time-step and compute the new\n # time-step to fall on the specified output time\n # instant\n old_dt = dt\n dt = float( output_time - self.t )\n\n self.force_output = True\n\n # set the adjusted time-step for the solver\n self.dt = dt\n\n if self.execute_commands is not None:\n if self.count % self.command_interval == 0:\n self.execute_commands(self)\n\n # close the progress bar\n bar.finish()\n\n # final output save\n self.dump_output()",
"title": ""
},
{
"docid": "a0a145553849371012e1201d8c01f8d3",
"score": "0.50810856",
"text": "def evaluate(solutions, results, rollouts=100): # pipaek : rollout 100 -> 10 , originally 100\n index_min = np.argmin(results)\n best_guess = solutions[index_min]\n restimates = []\n\n for s_id in range(rollouts):\n print('p_queue.put(), s_id=%d' % s_id)\n p_queue.put((s_id, best_guess))\n print('>>>rollout_routine!!')\n rollout_routine() # pipaek : 여기서도 p_queue.put 하자마자 바로 처리..\n\n print(\">>>Evaluating...\")\n for _ in tqdm(range(rollouts)):\n #while r_queue.empty():\n # sleep(.1) # pipaek : multi-process가 아니므로\n if not r_queue.empty(): # pipaek : 20180718 r_queue.get()에서 stuck되어 있는 것을 방지하기 위해 체크!!\n #print('r_queue.get()')\n #restimates.append(r_queue.get()[1])\n r_s_id, r = r_queue.get()\n print('in evaluate r_queue.get() r_s_id=%d, r_queue remain=%d' % (r_s_id, r_queue.qsize()))\n restimates.append(r)\n else:\n print('r_queue.empty() -> break!!')\n break\n\n return best_guess, np.mean(restimates), np.std(restimates)",
"title": ""
},
{
"docid": "5ce81706280fd507142c3a9781b2eaa7",
"score": "0.5079482",
"text": "def test_calc_final_slope():\n calc_final_slope(gridv1.data)\n assert True",
"title": ""
}
]
3510ac42e4ef7ae99ff81d34dfc62eaa
Get List of field names when a container name is given. Container names if repeated in the snapshot metadata, will always contain the same internal metadata, so the parent of the container doesn't matter. container_field_name The container of whose List of field names are required List of field names
[
{
"docid": "19a19c2a810e3f9fd8aa2036cfc9c0ba",
"score": "0.8049855",
"text": "def data_field_container_field_name_list_get(self, container_field_name):\n def dfs_helper(dict_to_find_in, container_field_name, ret_list):\n if dict_to_find_in is None:\n return\n if container_field_name in dict_to_find_in:\n for field_name, field in list(dict_to_find_in[container_field_name].container_dict.items()):\n ret_list.append(field.name)\n return\n else:\n for field_name, field in list(dict_to_find_in.items()):\n return dfs_helper(field.container_dict, container_field_name, ret_list)\n ret_list = []\n dfs_helper(self.data_dict, container_field_name, ret_list)\n return ret_list",
"title": ""
}
]
[
{
"docid": "30c975d7087e5406555f5a29ddfc5d2f",
"score": "0.61451197",
"text": "def _get_list(container, name):\n\n try:\n return container[name]\n except KeyError:\n return []",
"title": ""
},
{
"docid": "f10c4c60092e3146611d5e905294f06d",
"score": "0.5811185",
"text": "def _field_names_(self):\n ret = []\n\n def _merge(f, rename):\n if not f:\n return\n\n if not rename:\n ret.extend([n for n, _ in f])\n else:\n for n, _ in f:\n new_n = rename.get(n, None)\n ret.append(new_n) if new_n else ret.append(n)\n\n for b in self.__class__.__mro__:\n _merge(\n getattr(b, '__swagger_fields__', None),\n getattr(b, '__swagger_rename__', None)\n )\n\n return ret",
"title": ""
},
{
"docid": "437d5d3800f0ce0776ef979ed27f1d87",
"score": "0.5712169",
"text": "def fields_container(self):\n return self._fields_container",
"title": ""
},
{
"docid": "437d5d3800f0ce0776ef979ed27f1d87",
"score": "0.5712169",
"text": "def fields_container(self):\n return self._fields_container",
"title": ""
},
{
"docid": "437d5d3800f0ce0776ef979ed27f1d87",
"score": "0.5712169",
"text": "def fields_container(self):\n return self._fields_container",
"title": ""
},
{
"docid": "b1441683baa86cccd7332f66af646866",
"score": "0.5601177",
"text": "def get_container_entries(self, container, prefix):\n try:\n headers = self._headers()\n swift_objects = self.conn.get_container(container,\n prefix=prefix,\n full_listing=True,\n headers=headers)[1]\n except socket.error as err:\n raise exception.SwiftConnectionFailed(reason=err)\n swift_object_names = [swift_obj['name'] for swift_obj in swift_objects]\n return swift_object_names",
"title": ""
},
{
"docid": "4d8c066c5cc911b046911ca8ae57b598",
"score": "0.5588793",
"text": "def GetContainerFields(request, layer):\n icon = request.get('icon')\n icon = util.GetInstance(model.Resource, icon, layer, required=False)\n if icon and icon.type != 'icon':\n raise util.BadRequest('Invalid (non-icon) resource specified.')\n\n folder = request.get('folder')\n folder = util.GetInstance(model.Folder, folder, layer, required=False)\n folder_index = request.get('folder_index', None)\n if folder_index is not None: folder_index = int(folder_index)\n\n region = request.get('region')\n region = util.GetInstance(model.Region, region, layer, required=False)\n\n return {\n 'icon': icon,\n 'folder': folder,\n 'folder_index': folder_index,\n 'region': region,\n 'name': request.get('name', None),\n 'description': request.get('description', None),\n 'item_type': request.get('item_type', None),\n 'custom_kml': request.get('custom_kml', None)\n }",
"title": ""
},
{
"docid": "9876c3a1dd2807b45e9acca543a94186",
"score": "0.551232",
"text": "def list_containers(self):\r\n if self._container_names is None:\r\n visited = set([id(self)])\r\n if hasattr(self, '_parent'): # fix for weird unpickling bug\r\n visited.add(id(self.parent))\r\n names = []\r\n for n, v in self.__dict__.items():\r\n if is_instance(v, Container) and id(v) not in visited:\r\n visited.add(id(v))\r\n names.append(n)\r\n self._container_names = names\r\n return self._container_names",
"title": ""
},
{
"docid": "b619bd1f3f17cba660356f07ab0102bd",
"score": "0.55013055",
"text": "def field_names(self) -> List[str]:\n return [x.name for x in self.fields.all()]",
"title": ""
},
{
"docid": "49731fc3f52b6436791ae50e39483390",
"score": "0.5474507",
"text": "def get_list_of_fields_for_node(node_name):\n return _value_to_node_fields[node_name]",
"title": ""
},
{
"docid": "1f462a5031e0d1a0da6991690b93c2ef",
"score": "0.5405363",
"text": "def flatten_container(self, container):\n for names in ARG_MAP.values():\n if names[TransformationTypes.CHRONOS.value]['name'] and \\\n '.' in names[TransformationTypes.CHRONOS.value]['name']:\n chronos_dotted_name = names[TransformationTypes.CHRONOS.value]['name']\n parts = chronos_dotted_name.split('.')\n\n if parts[-2] == 'parameters':\n # Special lookup for docker parameters\n common_type = names[TransformationTypes.CHRONOS.value].get('type')\n result = self._lookup_parameter(container, parts[-1], common_type)\n if result:\n container[chronos_dotted_name] = result\n else:\n result = lookup_nested_dict(container, *parts)\n if result:\n container[chronos_dotted_name] = result\n return container",
"title": ""
},
{
"docid": "8d7120d4d10f2d84545c761e0ae1cb6e",
"score": "0.53995526",
"text": "def data_field_name_list_get(self, action_name=None):\n ret_field_name_list = []\n if action_name is not None:\n ret_field_name_list=[field_name_ for field_name_, data_ in \n list(self.action_dict[self.action_dict_allname[action_name]].data_dict.items())]\n for data_name_, data_ in list(self.data_dict.items()):\n ret_field_name_list.append(data_name_)\n return ret_field_name_list",
"title": ""
},
{
"docid": "613b688dbcd887d59bea4ed78d7433a4",
"score": "0.5380753",
"text": "def get_field_names(self):\n\n field_names = set()\n\n # Add field names from each component.\n for component in self.components:\n field_names.update(component.get_field_names())\n\n return list(field_names)",
"title": ""
},
{
"docid": "5390ba244ba96352a1d6c9f1db4879ab",
"score": "0.53681356",
"text": "def get_field_names(self, format_string):\n return self.name_pattern.findall(format_string)",
"title": ""
},
{
"docid": "d862b976f871cbcee5188ed6f6cb4e95",
"score": "0.536005",
"text": "def get_container_list(self, source):\n storage_client = None\n container = None\n\n if 'fileGroup' in source:\n # Input data stored in auto-storage\n storage_client = self.resolve_storage_account()\n container = _get_container_name(source['fileGroup'])\n elif 'containerUrl' in source:\n uri = urlsplit(source['containerUrl'])\n if not uri.query:\n raise ValueError('Invalid container url.')\n storage_account_name = uri.netloc.split('.')[0]\n sas_token = uri.query\n storage_client = BlockBlobService(account_name=storage_account_name,\n sas_token=sas_token)\n container = uri.pathname.split('/')[1]\n else:\n raise ValueError('Unknown source.')\n\n return self.list_container_contents(source, container, storage_client)",
"title": ""
},
{
"docid": "a0dfb7fb0ea38f44ad645e2780ee0cca",
"score": "0.535109",
"text": "def getAllFields(layer):\n fields = layer.fields()\n fieldLists = []\n for field in fields:\n fieldLists.append(field.name())\n return fieldLists",
"title": ""
},
{
"docid": "7d0822d0c9a73ae8026a352e77623b96",
"score": "0.5347515",
"text": "def get_field_names(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "cb33f81c7565c3cc3e972a6b30431442",
"score": "0.52762234",
"text": "def get_field_names(self):\n\n field_names = set(sch_field_id_to_name.values())\n for component in self.components:\n field_names.update(component.get_field_names())\n return list(field_names)",
"title": ""
},
{
"docid": "70dc8549c75e78f1846fe87ad0c2d70a",
"score": "0.5217515",
"text": "def getFieldNames(self):\n return self.fieldNames",
"title": ""
},
{
"docid": "5ee8470c4b9687c8fe862ec39f66c2a4",
"score": "0.5198622",
"text": "def get_field_names(\n project_type,\n table_name,\n):\n # sql = \"SELECT * FROM [{}]\".format(table_name)\n sql = \"PRAGMA table_info( [{}] );\".format(table_name)\n\n data = run_query(sql, project_type)\n if data:\n return [x.get(\"name\") for x in data]\n else:\n return []",
"title": ""
},
{
"docid": "b674a72881c343ca3e65f83343c954a6",
"score": "0.51946247",
"text": "def data_field_name_list_get(self, *unused):\n return [data_name_ for data_name_, data_ in list(self.data_dict.items())]",
"title": ""
},
{
"docid": "b2434dc23b4245c69caa615e0fd07d5d",
"score": "0.5171875",
"text": "def get_containers_names(client, prefix=DOCK_CONTAINER_NAME_PREFIX_BTC):\n return [container.name for container in client.containers.list(\"all\") if prefix in container.name]",
"title": ""
},
{
"docid": "7b77ae64070e8055affd29d40113c872",
"score": "0.5169079",
"text": "def _get_list_field_names(self):\n\n all_fields = ()\n if not self.list_fields:\n all_fields = self._get_default_list_fields()\n else:\n all_fields = list(self.list_fields)\n if self._is_list_pk_required():\n self._inject_primary_key(all_fields)\n\n all_fields = self._extract_field_names(all_fields, allow_string=True)\n if self.list_indexed is True:\n index_name = self.list_index_name\n if index_name in (None, ''):\n index_name = config_services.get('api', 'schema', 'index_name')\n\n all_fields = list(all_fields)\n all_fields.insert(0, index_name)\n all_fields = tuple(all_fields)\n\n duplicates = misc_utils.get_duplicates(all_fields)\n if duplicates:\n raise DuplicateListFieldNamesError('There are some duplicate field names '\n 'in \"list_fields\" of [{admin}] class. '\n 'this will make the find result incorrect. '\n 'please remove duplicate fields or set '\n 'unique labels for them: {duplicates}'\n .format(admin=self, duplicates=duplicates))\n\n return all_fields",
"title": ""
},
{
"docid": "c48e94f111a0111011a30f30aff4c88a",
"score": "0.5166085",
"text": "def _field_names(self):\n names = []\n self._read_token('{')\n if self._is_field_name():\n names.append(self._read_token().value)\n while self._next_token().kind == ',':\n self._read_token(',')\n if self._is_field_name():\n name == self._read_token().value\n if name in names:\n raise Exception(\"Repeated field name: %s\" % name)\n names.append(name)\n else:\n raise Exception(\"field name expected, found %s\" %\n self._next_token().value)\n self._read_token('}')\n return names",
"title": ""
},
{
"docid": "55843e0a9f4a7fe56c2087de43caa3c4",
"score": "0.5160062",
"text": "def field_names(self):\r\n return self._names",
"title": ""
},
{
"docid": "25d221ff14221b5be396cb2fd14b695f",
"score": "0.51470417",
"text": "def get_field_names(self):\n\n field_names = set()\n for f in self.fields:\n try:\n field_names.add(unquote(f[\"name\"]))\n except KeyError:\n pass\n field_names.discard(\"\")\n return field_names",
"title": ""
},
{
"docid": "2c14e57dbd84db2ec879f1e1adf15d5f",
"score": "0.5139172",
"text": "def list(self) -> list:\n labels = []\n for k, v in self._labels.items():\n labels.append(\"{}={}\".format(k, v))\n\n #TODO: return names\n containers = self.client.containers.list(filters={\"label\": labels})\n return list(map(lambda s: s.name, containers))",
"title": ""
},
{
"docid": "b6c2df00f269215d05e90947cd79bb3f",
"score": "0.51312125",
"text": "def export_field_names(self) -> List[str]:\n return [f.name for f in self.fields.all()]",
"title": ""
},
{
"docid": "b7aae64056ae801052747746a5423abd",
"score": "0.5128673",
"text": "def fields(self):\r\n try:\r\n self._field_name_cache\r\n except AttributeError:\r\n self._fill_fields_cache()\r\n return self._field_name_cache",
"title": ""
},
{
"docid": "e7d6dc8c87f29a148679a18694ce2684",
"score": "0.51274216",
"text": "def get_field_names(self):\n\n return {f[\"name\"] for f in self.fields}",
"title": ""
},
{
"docid": "3a797639a31c6fabc6d35cc0ef6e6484",
"score": "0.5123486",
"text": "def get_versioned_field_names(self, version_number):\n return []",
"title": ""
},
{
"docid": "7b77852ac9831875ec06d5fe55dde409",
"score": "0.5094264",
"text": "def get_field_names(self):\n field_names = []\n for field in self.data_fields.all():\n field_names.append(field.name)\n return field_names",
"title": ""
},
{
"docid": "aa3d8bbaa16f7920fd4f9fcc1da8b28c",
"score": "0.50611734",
"text": "def get_column_names_list(record_info_in: sdk.RecordInfo) -> List[str]:\n return [field.name for field in record_info_in]",
"title": ""
},
{
"docid": "704297f96eac44cd01a88ca465d5b805",
"score": "0.5060997",
"text": "def field_names(self):\n return frozenset(self._children.keys())",
"title": ""
},
{
"docid": "f64563f859bbb1a7450773db6425e8bd",
"score": "0.5054078",
"text": "def list(self, container_name: str) -> None:\n try:\n res = requests.get(self.url + 'list', json={'container_name': container_name})\n if res.status_code == 200:\n self.logger.info(f\"Listing files of container {container_name}\")\n return json.loads(res.content)\n else:\n return None\n except ConnectionError as ce:\n raise ce",
"title": ""
},
{
"docid": "d3dfb9b4a47eed06de1d9963222288d2",
"score": "0.5046736",
"text": "def get_field_names(self):\n return self.fields.values()",
"title": ""
},
{
"docid": "f23b83640bca611ba5973965e550f962",
"score": "0.50379735",
"text": "def get_container_name(self, container_id):\n if not os.path.isdir(self.containersdir):\n return []\n link_list = []\n for fname in os.listdir(self.containersdir):\n container = self.containersdir + \"/\" + fname\n if os.path.islink(container):\n real_container = os.readlink(container)\n if os.path.basename(real_container) == container_id:\n link_list.append(fname)\n return link_list",
"title": ""
},
{
"docid": "4636b4e0dcd5bfaf02de8b0ee0c445e3",
"score": "0.5035241",
"text": "def fieldsNames_get(self,recordset):\r\n\t\tif recordset.upper().startswith('SELECT'):\r\n\t\t\trs=self.__open_recordset(self.conn, recordset, maxrecords=1)\r\n\t\telse:\r\n\t\t\trs=self.__open_recordset(self.conn, '['+recordset+']', maxrecords=1)\r\n\r\n\t\tif rs.Fields.Count>0:\r\n\t\t\tfields=[rs.Fields.Item(item).Name for item in range(rs.Fields.Count)]\r\n\t\telse:\r\n\t\t\tfields=[]\r\n\t\treturn fields",
"title": ""
},
{
"docid": "50f25ecf9257d7170423361e610a667f",
"score": "0.50169486",
"text": "def get_fields(self, field_name):\r\n if field_name not in self.fields:\r\n raise OGRException('invalid field name: %s' % field_name)\r\n return [feat.get(field_name) for feat in self]",
"title": ""
},
{
"docid": "7d34c0f7d6e4e777f87562f000824e1f",
"score": "0.4993697",
"text": "def get_fieldnames_from_cursor(cursor: Cursor) -> List[str]:\n return [i[0] for i in cursor.description]",
"title": ""
},
{
"docid": "4d3d15cdef20f82411536b5fcbf3cacc",
"score": "0.49833426",
"text": "def fields(self):\r\n return [force_text(capi.get_field_name(capi.get_field_defn(self._ldefn, i)),\r\n self._ds.encoding, strings_only=True)\r\n for i in xrange(self.num_fields)]",
"title": ""
},
{
"docid": "cc2836b21ba3be6d217c40e9a0be9a63",
"score": "0.49688077",
"text": "def _extract_field_names(self, fields, allow_string=True):\n\n names = []\n for item in fields:\n if self._is_valid_field(item):\n names.append(item.key)\n elif allow_string is True and isinstance(item, str) \\\n and self._is_valid_method(item):\n names.append(item)\n else:\n message = 'Provided field [{field}] is not a valid value. ' \\\n 'it must be a column attribute{sign} ' \\\n 'expression level hybrid property{end}'\n\n if allow_string is True:\n message = message.format(\n field=str(item), sign=',',\n end=' or a string representing a method name of [{admin}] class.')\n else:\n message = message.format(field=str(item), sign=' or',\n end='.')\n\n raise InvalidListFieldError(message.format(admin=self))\n\n return tuple(names)",
"title": ""
},
{
"docid": "f24c7585e363e44d0434d30c329dbd1e",
"score": "0.49683803",
"text": "def field_list(cls) -> List[str]:\n return list(attr.fields_dict(cls).keys())",
"title": ""
},
{
"docid": "4f6299e85d77d37267f451d0f8648195",
"score": "0.495515",
"text": "def _to_fields(d: DictAny) -> List[str]:\n cs: List[DictAny] = d[\"meta\"][\"view\"][\"columns\"]\n\n # written this way to get type annotations to work\n def f(x: DictAny) -> str:\n sx: str = x['name']\n return sx\n\n return list(map(f, cs))",
"title": ""
},
{
"docid": "574c75a4ad74382c72ae9e5f91447525",
"score": "0.49546686",
"text": "def container_image_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"container_image_names\")",
"title": ""
},
{
"docid": "8a079a0ce17f9ad49ef4125b8aff75ab",
"score": "0.49508816",
"text": "def stored_field_names(self):\r\n \r\n bn = self._by_name\r\n return [name for name in self._names if bn[name].stored]",
"title": ""
},
{
"docid": "f72b6899c69e686da1f2003d0a10b4cc",
"score": "0.49477538",
"text": "def key_field_name_list_get(self):\n return list(self.key_dict.keys())",
"title": ""
},
{
"docid": "e9f44857f2954aef14200b01f3bdce23",
"score": "0.49375585",
"text": "def get_names(collection):\n return [element.name for element in collection]",
"title": ""
},
{
"docid": "f1ea66213ccf40c7c8ec94541b1078fb",
"score": "0.49365887",
"text": "def fields(self):\n return [self._fields[name] for name in self.fieldNames]",
"title": ""
},
{
"docid": "8638226e6ca75fe8bb4d4eb091341832",
"score": "0.49363112",
"text": "def field_names(self, record_type=\"\"):\n return [f[\"name\"] for f in self.record_types[record_type][\"fields\"]]",
"title": ""
},
{
"docid": "06107a2cdf60711c2ff205daad75e3a5",
"score": "0.49352884",
"text": "def fieldnames(self):\n return self._fieldnames",
"title": ""
},
{
"docid": "0fc69c4893af6b3663f692a42e03452b",
"score": "0.49313736",
"text": "def get_canonical_container_name(container):\n return sorted(container['Names'], key=lambda name: len(name))[0][1:]",
"title": ""
},
{
"docid": "5ca31ef9e8cbb6928657eed4cbea6060",
"score": "0.48984218",
"text": "def containers(self, module_name):\n\t\tbase, name = split_module_name(module_name)\n\n\t\t# the printer modules does not have the same name scheme\n\t\tif module_name == 'shares/print':\n\t\t\tbase = 'printers'\n\n\t\tself._read_directories()\n\n\t\tif self.directory is None:\n\t\t\treturn []\n\t\treturn map(lambda x: {'id': x, 'label': ldap_dn2path(x)}, self.directory.info.get(base, []))",
"title": ""
},
{
"docid": "bec166b06411e731bed78a486883042f",
"score": "0.48848704",
"text": "def get_field_list(self):\n return list(self.table_format_dict)",
"title": ""
},
{
"docid": "1918484db22ccaff5f158a9c670452d1",
"score": "0.48847437",
"text": "def fields(self):\r\n try:\r\n return self._fields[self.tname]\r\n except KeyError:\r\n # introspect the field names from the database\r\n # by relying on the DB API 2.0\r\n # NB: we cannot trust the ordering in the Django model\r\n curs = connections[self.alias].cursor()\r\n curs.execute('select * from %s where 1=0' % self.tname)\r\n names = self._fields[self.tname] = [\r\n r[0] for r in curs.description if r[0] != 'id']\r\n return names",
"title": ""
},
{
"docid": "4e066b142abbdaa5a587ebabadc21915",
"score": "0.48470128",
"text": "def get_field_names(self):\n key_fields = [ d[0] for d in self.fpkey.fields ]\n data_fields = [ d[0] for d in self.fpdata.fields ]\n return key_fields + data_fields",
"title": ""
},
{
"docid": "c44ff81bc789d7aee7edef6b560353f0",
"score": "0.48232073",
"text": "def get_field_names(table):\n return [field['name'] for field in get_db().execute(f'SELECT name FROM {FIELDS + get_table_id(table)}').fetchall()]",
"title": ""
},
{
"docid": "899305a9f751ae70d7a33b5600f4746a",
"score": "0.47798315",
"text": "def list_fields(self):\n _require_metadata(gnats.MINIMAL_METADATA)\n return list(self.ordered_fields)",
"title": ""
},
{
"docid": "da861e81945237457eaed0a3ff7571d6",
"score": "0.47780028",
"text": "def get_field_names(self):\n return sorted(self.column_name_describer.keys())",
"title": ""
},
{
"docid": "92c4c389c1b8081ba8e278620cc39858",
"score": "0.47687584",
"text": "def fields(cls) -> List[str]:\n fields = attr.fields(cls)\n field_types = [\n (f.name, innermost_type(f.type)) for f in fields if f.name != \"gid\"\n ]\n field_names = [\n [f\"{name}.{f}\" for f in typ.fields()]\n if issubclass(typ, _HasFields)\n else [name]\n for (name, typ) in field_types\n ]\n return [name for names in field_names for name in names]",
"title": ""
},
{
"docid": "72667bc449108aded84cbcafc14597fe",
"score": "0.47650254",
"text": "def _list_field_names(self, field_type):\n fields = self._properties.get('indexedField', {})\n return fields.get(field_type)",
"title": ""
},
{
"docid": "b09a9db0023271ff3cce904fceae0e09",
"score": "0.47543573",
"text": "def fields(self):\n return self.field_list",
"title": ""
},
{
"docid": "f3a8bf83e5d8353fb269facfffe1d9f9",
"score": "0.4749665",
"text": "def fields(self) -> t.List[str]:\n return []",
"title": ""
},
{
"docid": "06b4eb3cb3fd1ecb3f1e60574510dad1",
"score": "0.47387376",
"text": "def list_collection_fields(self, environment_id, collection_id, **kwargs):\n\n if environment_id is None:\n raise ValueError('environment_id must be provided')\n if collection_id is None:\n raise ValueError('collection_id must be provided')\n\n headers = {}\n if 'headers' in kwargs:\n headers.update(kwargs.get('headers'))\n sdk_headers = get_sdk_headers('discovery', 'V1',\n 'list_collection_fields')\n headers.update(sdk_headers)\n\n params = {'version': self.version}\n\n url = '/v1/environments/{0}/collections/{1}/fields'.format(\n *self._encode_path_vars(environment_id, collection_id))\n response = self.request(\n method='GET',\n url=url,\n headers=headers,\n params=params,\n accept_json=True)\n return response",
"title": ""
},
{
"docid": "c323dd4c958bbb31f175a4ea32a98404",
"score": "0.4738311",
"text": "def fields(self):\n\n if hasattr(self, '__parameters__'):\n return self.__parameters__\n else:\n return [f'f{i}' for i in self.keys()]",
"title": ""
},
{
"docid": "3975d8dac6769204bb26cdba3ace7719",
"score": "0.47211117",
"text": "def get_network_container_hostnames(name):\n for network in get_networks():\n if network[\"Name\"] == name:\n return [get_container_hostname(container) for container in network[\"Containers\"]]",
"title": ""
},
{
"docid": "499acedfa5db0e6d0724a7a7620e9479",
"score": "0.47182256",
"text": "def get_parameter_names(diagnostic_fields: List[List[str]]) -> List[List[str]]:\n parameter_names = []\n for condition in diagnostic_fields:\n if isinstance(condition, list):\n parameter_names.append(get_parameter_names(condition))\n elif is_variable(condition):\n parameter_names.append(condition)\n return parameter_names",
"title": ""
},
{
"docid": "e40fba1fc77f5cea111df8942e072ae9",
"score": "0.47160146",
"text": "def _string_helper(self, field_name): # pylint: disable=g-ambiguous-str-annotation\n result = [\"{} {}\".format(str(self.node), str(field_name))]\n for k, v in self._children.items():\n recursive = v._string_helper(k) # pylint: disable=protected-access\n result.extend([\" {}\".format(x) for x in recursive])\n return result",
"title": ""
},
{
"docid": "2d12959f5bd0d6a8af770d946d3c9bcb",
"score": "0.47127327",
"text": "def _get_dates_from_fields(self):\n\n if not self._fields_container:\n return []\n\n dates = []\n # Widgets are arranged in the reverse order of their addition.\n for field in reversed(self._fields_container.children):\n try:\n date = datetime.datetime.strptime(field.text, \"%d/%m/%Y\").date()\n except ValueError:\n date = None\n dates.append(date)\n\n return dates",
"title": ""
},
{
"docid": "1eca8a0bdc4cc6aa80b7a640dd671a0c",
"score": "0.47007495",
"text": "def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\n from .planner_container_type import PlannerContainerType\n\n from .planner_container_type import PlannerContainerType\n\n fields: Dict[str, Callable[[Any], None]] = {\n \"containerId\": lambda n : setattr(self, 'container_id', n.get_str_value()),\n \"@odata.type\": lambda n : setattr(self, 'odata_type', n.get_str_value()),\n \"type\": lambda n : setattr(self, 'type', n.get_enum_value(PlannerContainerType)),\n \"url\": lambda n : setattr(self, 'url', n.get_str_value()),\n }\n return fields",
"title": ""
},
{
"docid": "340c5011c1971fe81abd7d8ab3651ba7",
"score": "0.4667802",
"text": "def fieldnames(self):\n fieldnames = {} # sets are unordered\n for row in self.rows:\n for field in row:\n fieldnames[field] = True\n return list(fieldnames.keys())",
"title": ""
},
{
"docid": "6ca6b7bfdfdbc0d80d69b191611a1481",
"score": "0.46639836",
"text": "def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:",
"title": ""
},
{
"docid": "13a3989ca8336b4ac5811f4c12b9894c",
"score": "0.46563387",
"text": "def get_containers_list(self, dir_only=True):\n containers_list = []\n if not os.path.isdir(self.containersdir):\n return []\n for fname in os.listdir(self.containersdir):\n container_dir = self.containersdir + '/' + fname\n if os.path.isdir(container_dir):\n try:\n filep = open(container_dir + \"/imagerepo.name\", 'r')\n except (IOError, OSError):\n reponame = \"\"\n else:\n reponame = filep.read()\n filep.close()\n if dir_only:\n containers_list.append(container_dir)\n elif not os.path.islink(container_dir):\n names = self.get_container_name(fname)\n if not names:\n names = \"\"\n containers_list.append((fname, reponame, str(names)))\n return containers_list",
"title": ""
},
{
"docid": "f5f79f4914c26655b3a3e72b6b5129e7",
"score": "0.46369",
"text": "def get_split_names(self):\n try:\n return [split.name for split in self.get_all()]\n except Exception:\n _LOGGER.error('Error getting split names from storage')\n _LOGGER.debug('Error: ', exc_info=True)\n return None",
"title": ""
},
{
"docid": "da11509e1da3a03ab4800f720bc7a9de",
"score": "0.4634459",
"text": "def name_get(self):\n result = []\n for document in self:\n if document.version:\n name = '[%s] %s'%(document.version, document.name)\n else: \n name = document.name\n \n result.append((document.id, name))\n \n return result",
"title": ""
},
{
"docid": "49c2e57520127645e0075b6b0029a0b4",
"score": "0.46329495",
"text": "def _get_field_names(reader: IO) -> Sequence[str]:\n csv_reader = csv.DictReader(reader)\n next(csv_reader)\n reader.seek(0)\n return csv_reader.fieldnames or []",
"title": ""
},
{
"docid": "3660cc3d64c393055d4ac42e3b877e8f",
"score": "0.4630384",
"text": "def get_fields(ms_name):\n logger.debug(\"Getting antenna names\")\n\n subname = \"::\".join((ms_name, \"FIELD\"))\n field_subtab = list(xm.xds_from_table(subname))\n field_subtab = field_subtab[0]\n field_names = field_subtab.NAME\n\n logger.debug(f\"Fields found: {', '.join(field_names.values)}\")\n return field_names",
"title": ""
},
{
"docid": "7e2bf9854325734e655c6fe8dad3c739",
"score": "0.46236715",
"text": "def dependencies(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:",
"title": ""
},
{
"docid": "b8ca2acf5377bdb047a8b663c874b75c",
"score": "0.46212012",
"text": "def _get_user_field_names(self):\n field_names = []\n internal_fields = (\"left\", \"right\", \"tree_id\", \"level\")\n for field in self._meta.fields:\n if (field.name not in internal_fields) and (not isinstance(field, AutoField)) and (not field.primary_key):\n field_names.append(field.name)\n return field_names",
"title": ""
},
{
"docid": "0ab96239137f7598c22ea2ed46b559b2",
"score": "0.46098047",
"text": "def list_fields(table):\r\n if not table:\r\n return []\r\n cols = table.__table__.columns if hasattr(table, '__table__') else table.columns\r\n return [f.name for f in cols]",
"title": ""
},
{
"docid": "5cb28afef9eb9d2da7ce3083fc7e4c48",
"score": "0.46097603",
"text": "def field_names(cls):\n return [field.name for field in cls()]",
"title": ""
},
{
"docid": "0e676c72e977e1d12726b12347010650",
"score": "0.46061674",
"text": "def _GetToListFields(self, get_fields=None):\n if get_fields:\n list_fields = set()\n for field in get_fields:\n list_fields.add('items/' + field)\n return list_fields",
"title": ""
},
{
"docid": "adf73010cff289c4920cac47f74bb3a2",
"score": "0.4604988",
"text": "def container_name(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"containerName\"),\n )",
"title": ""
},
{
"docid": "e1fe3030e55145e0e75c85e0b470c3a5",
"score": "0.46005386",
"text": "def __init__(self, field_list, field_container, *args, **kwargs):\r\n super(DynamicForm, self).__init__(*args, **kwargs)\r\n\r\n self.field_structure = []\r\n\r\n for field_name in field_list:\r\n field_data = getattr(field_container, field_name)[1]\r\n\r\n field_object = getattr(forms.fields, field_data['type'])(**field_data['kwargs'])\r\n self.fields[field_name] = field_object\r\n\r\n field_data['field_name'] = field_name\r\n self.field_structure.append(field_data)",
"title": ""
},
{
"docid": "dc90e0b264c2830b135d06667b3634bc",
"score": "0.45973927",
"text": "def fld_lst(type):\n lst = []\n if isinstance(type, dict):\n return list(type.keys())\n elif isinstance(type, list):\n for i in range(1, len(type) + 1):\n lst.append(\"element \" + str(i))\n return lst",
"title": ""
},
{
"docid": "1d47eaac5c428fd666c6b79881ef1b66",
"score": "0.45942882",
"text": "def get_signature_fields(interview_metadata_dict):\r\n signature_fields = []\r\n for field_dict in (interview_metadata_dict.get('built_in_fields_used',[]) + interview_metadata_dict.get('fields',[])):\r\n field = map_names(field_dict.get('variable',''))\r\n if field.endswith('.signature'):\r\n signature_fields.append(field)\r\n return signature_fields",
"title": ""
},
{
"docid": "6cd0478bf7c2cea9e76455ff40a8f67f",
"score": "0.4587273",
"text": "def flatten_container_info(self, inner_ip):\n def iter_container(tg):\n for g in tg:\n for d in g['data']['containerStatuses']:\n c = {'name': d['name'],\n 'image': d['image'],\n 'status': d['status'].lower(),\n 'container_id': d['containerID']}\n yield c\n taskgroups = self.get_unit_info(inner_ip, fields='data,namespace').get('data') or []\n containers = sorted([i for i in iter_container(taskgroups)],\n key=lambda x: constants.DockerStatusOrdering.get(\n x['status'], constants.DockerStatusDefaultOrder))\n return containers",
"title": ""
},
{
"docid": "e1dee9b7b5498c9877ec8ba5dbdf9fc9",
"score": "0.45822173",
"text": "def get_field_name_order(field_option_list):\n\n field_name_list = [None] * len(field_option_list)\n while not all(field_name_list):\n for idx, field_option in enumerate(field_option_list):\n if len(field_option) == 1:\n found_name = field_option.pop()\n field_name_list[idx] = found_name\n for field_set in field_option_list:\n field_set.discard(found_name)\n break\n\n return field_name_list",
"title": ""
},
{
"docid": "335a0e40733d20c568f2211893f67242",
"score": "0.4575407",
"text": "def get_all_field_names(self):\r\n try:\r\n cache = self._name_map\r\n except AttributeError:\r\n cache = self.init_name_map()\r\n names = sorted(cache.keys())\r\n # Internal-only names end with \"+\" (symmetrical m2m related names being\r\n # the main example). Trim them.\r\n return [val for val in names if not val.endswith('+')]",
"title": ""
},
{
"docid": "e70dd224a19862e060ab3b2ef1410db9",
"score": "0.4568496",
"text": "def test_schema_sql_field_list(self):\n sql_fields = BQTestSchema.get_sql_field_names()\n self.assertEqual('descr, timestamp, nested', sql_fields)\n\n sql_fields = BQTestSchema.get_sql_field_names(exclude_fields=['timestamp'])\n self.assertEqual('descr, nested', sql_fields)",
"title": ""
},
{
"docid": "212711c27e5a6607162d499c7949a51a",
"score": "0.45502537",
"text": "def get_fields(self, field_name: str) -> Optional[PropertyValues]:\n for p in self.properties:\n if p.name == field_name:\n return p.values\n return None",
"title": ""
},
{
"docid": "95b9ae9153f14cf90dae683977ade3e8",
"score": "0.45465776",
"text": "def list_containers():",
"title": ""
},
{
"docid": "6d778c5d87a68079760c520f0f65fa8c",
"score": "0.45192477",
"text": "def list_names(self):\n return self.child_names",
"title": ""
},
{
"docid": "9ff30b92abf478bba51988c5f5f550f4",
"score": "0.45170188",
"text": "def get_list_fields(self):\n return self.fields",
"title": ""
},
{
"docid": "7386c2c08d3ec1ca914444f07634040d",
"score": "0.45169434",
"text": "def get_field_ids_by_name(self, name_regex):\n d = {k: v for k, v in self._get_id_to_name_dict().items() if name_regex.match(v) is not None}\n return d",
"title": ""
},
{
"docid": "417fcc89d4af8adfbb5d16facc1a5543",
"score": "0.45124117",
"text": "def getContainerName(ctx, containerFlavor):\n return getattr(ctx.cf.ocp.containers, containerFlavor).name",
"title": ""
},
{
"docid": "ce030cf1a66b95ea5ba9759c1b2a34f5",
"score": "0.45090052",
"text": "def _string_helper(self, field_name: path.Step) -> Sequence[str]:\n result = [\"{} {}\".format(str(self.node), str(field_name))]\n for k, v in self._children.items():\n recursive = v._string_helper(k) # pylint: disable=protected-access\n result.extend([\" {}\".format(x) for x in recursive])\n return result",
"title": ""
},
{
"docid": "0762fbc266f3c0da9136fea60d69961c",
"score": "0.44986236",
"text": "def _list_containers(self):\n response = self._session.get(\"http+unix://%2Fvar%2Frun%2Fdocker.sock/containers/json?all=1\")\n if not response.ok:\n raise Exception(\"Couldn't get Containers list. Check if your docker daemon alive or not\")\n\n strio = self.stringIO(response.text)\n json_response = self.json.load(strio)\n lst_containers = [str(container[\"Names\"])[3:-2] for container in json_response]\n return lst_containers",
"title": ""
},
{
"docid": "85b097ce3af3f1fcd437792f757f3e58",
"score": "0.44954124",
"text": "def get_list(self):\n\n if self.mode == 'history':\n container_list = self.dopq.history\n elif self.mode == 'enqueued':\n container_list = self.dopq.container_list\n else:\n raise ValueError('invalid mode of operation: {}'.format(self.mode))\n\n if len(container_list) > self.max_containers:\n container_list = container_list[:self.max_containers]\n\n return container_list",
"title": ""
},
{
"docid": "9a2e65a55f6d8a915330cf30b44d59e3",
"score": "0.44941473",
"text": "def getFields(context, field_names=None):\n pairs = []\n if context.Schema(): # is archetype\n schema = context.Schema()\n fields = schema.fields()\n if field_names:\n for field_name in field_names:\n val = None\n pairs.append(schema[field_name].getName())\n val = schema[field_name].get(context)\n if isinstance(val, tuple) or isinstance(val, list):\n val = iterToTags(val)\n elif str(val).find('\\n') != -1:\n val = newlinesToTags(val)\n pairs.append(val)\n else:\n for field in fields:\n val = None\n pairs.append(field.getName())\n val = str(field.get(context))\n if val.find('\\n') != -1:\n val = newlinesToTags(val)\n pairs.append(val)\n return pairs",
"title": ""
}
]
3d86c1af870937aabf2ff3595bf01c6e
Return package metadata information.
[
{
"docid": "5c54becc64e16a019dde1941f01c5892",
"score": "0.0",
"text": "def get_classifiers():\n return [\n 'Private :: Do Not Upload',\n 'Development Status :: 4 - Beta',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3.7',\n 'Intended Audience :: System Administrators',\n 'License :: OSI Approved :: The MIT License (MIT)',\n ]",
"title": ""
}
]
[
{
"docid": "42d07c0d20fbaab7a3ce210e4325d89b",
"score": "0.7362344",
"text": "def hook_events(self):\n for cmd in self.commands:\n self.core.add_command(cmd, self)\n for event in self.hooks:\n self.core.add_callback(event, self)\n for cmd in self.rawhooks:\n self.core.add_raw(cmd, self)",
"title": ""
},
{
"docid": "5450bb7786ed53fe294cc35583a4b091",
"score": "0.7053781",
"text": "def set_game_hooks(hooks: GameHooks) -> None:\n global _game_hooks\n _game_hooks = hooks",
"title": ""
},
{
"docid": "1d717911c38f804986b4721041fd10b5",
"score": "0.69996136",
"text": "def register_hooks(self, hooks):\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)",
"title": ""
},
{
"docid": "1d717911c38f804986b4721041fd10b5",
"score": "0.69996136",
"text": "def register_hooks(self, hooks):\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)",
"title": ""
},
{
"docid": "8a249d756642ea01dd9ad4457bff3712",
"score": "0.6859483",
"text": "def enable_hooks(self) -> None:\n self.hooks_enabled = True",
"title": ""
},
{
"docid": "11a1126a6ab2f0b85fc66e57b16e028e",
"score": "0.6821246",
"text": "def putInHooks ():\n\n # Need to modify Leo's Kernel first\n # Overwrite existing Leo methods.\n g.funcToMethod(replaceTargetFileIfDifferent,leoAtFile.atFile)\n g.funcToMethod(massageComment,leoImport.leoImportCommands)\n\n # Add new methods used by this plugin to various classes.\n g.funcToMethod(openForRead,leoAtFile.atFile)\n g.funcToMethod(openForWrite,leoAtFile.atFile)\n g.funcToMethod(gotoLineNumberOpen,leoCommands.Commands)\n g.funcToMethod(applyLineNumberMappingIfAny, leoCommands.Commands)",
"title": ""
},
{
"docid": "88d6493b3885f6278f8212ed6423e014",
"score": "0.67838776",
"text": "def prepare_hooks(self, hooks):\r\n for event in hooks:\r\n self.register_hook(event, hooks[event])",
"title": ""
},
{
"docid": "88d6493b3885f6278f8212ed6423e014",
"score": "0.67838776",
"text": "def prepare_hooks(self, hooks):\r\n for event in hooks:\r\n self.register_hook(event, hooks[event])",
"title": ""
},
{
"docid": "cc16bf846e0425e5913d8b44001ce900",
"score": "0.6763753",
"text": "def configure_hooks_feature(self):\n hooks_folder = getattr(config, \"HOOKS_FOLDER\", \"./hooks\")\n if not os.path.exists(hooks_folder):\n self.logger.info(\"Hooks folder '%s' doesn't exist, creating it\" % hooks_folder)\n os.makedirs(hooks_folder)\n self.hook_files = glob.glob(os.path.join(hooks_folder, \"*.py\"))",
"title": ""
},
{
"docid": "8e754d61588c30b277f496a6f5fe68f2",
"score": "0.6674685",
"text": "def _init_hooks(self):\n self._hooks = Hooks()\n self._hooks.ready_to_run = self._init_hexrays_hooks\n self._hooks.hook()",
"title": ""
},
{
"docid": "8e754d61588c30b277f496a6f5fe68f2",
"score": "0.6674685",
"text": "def _init_hooks(self):\n self._hooks = Hooks()\n self._hooks.ready_to_run = self._init_hexrays_hooks\n self._hooks.hook()",
"title": ""
},
{
"docid": "0489a542c08e02331af5086e2981ff82",
"score": "0.66713774",
"text": "def hook(self):\n\n\t\tself.db.begin()\n\n\t\tret = self.db.get_hook()\n\t\texisting = [dict(_)['name'] for _ in ret]\n\n\t\t# Add hook modules\n\t\tif len(self.args.hook):\n\t\t\tfor h in self.args.hook:\n\t\t\t\th = h.strip()\n\t\t\t\tif h in existing:\n\t\t\t\t\tprint(\"Hook module '%s' already in hook list\" % h)\n\t\t\t\telse:\n\t\t\t\t\tself.db.add_hook(h)\n\t\t\t\t\tself.db.commit()\n\t\t\t\t\tprint(\"Added: %s\" % h)\n\n\t\t# List all hooks\n\t\telse:\n\t\t\tif len(existing):\n\t\t\t\tprint(\"Existing hooks:\")\n\t\t\t\texisting = sorted(existing)\n\n\t\t\t\tfor h in existing:\n\t\t\t\t\tprint(\"\\t%s\" % h)\n\n\t\t\telse:\n\t\t\t\tprint(\"No existing hooks\")",
"title": ""
},
{
"docid": "b303d72b01b79b91713af6bf520ec161",
"score": "0.66632324",
"text": "def go_hook_all():\n global hooks\n priority = ''\n version = weechat.info_get('version_number', '') or 0\n # use high priority for hook to prevent conflict with other plugins/scripts\n # (WeeChat >= 0.3.4 only)\n if int(version) >= 0x00030400:\n priority = '2000|'\n for hook, value in HOOK_COMMAND_RUN.items():\n if hook not in hooks:\n hooks[hook] = weechat.hook_command_run(\n '%s%s' % (priority, value[0]),\n value[1], '')\n if 'modifier' not in hooks:\n hooks['modifier'] = weechat.hook_modifier(\n 'input_text_display_with_cursor', 'go_input_modifier', '')",
"title": ""
},
{
"docid": "b14f7b51831e081ee793e34d442312a4",
"score": "0.6640559",
"text": "def register_hooks(self, hooks: List[Hook]):\n if not hasattr(self, '_hooks'):\n self._hooks: List[Hook] = []\n for hook in hooks:\n hook.register()\n self._hooks.append(hook)",
"title": ""
},
{
"docid": "c29c6b427d21004ac609bfb2d25391cc",
"score": "0.6605009",
"text": "def hook_all():\n global hook_command_run, hooks\n priority = \"\"\n version = weechat.info_get(\"version_number\", \"\") or 0\n # use high priority for hook to prevent conflict with other plugins/scripts\n # (WeeChat >= 0.3.4 only)\n if int(version) >= 0x00030400:\n priority = \"2000|\"\n for hook, value in hook_command_run.items():\n if hook not in hooks:\n hooks[hook] = weechat.hook_command_run(\"%s%s\" % (priority, value[0]),\n value[1], \"\")\n if \"modifier\" not in hooks:\n hooks[\"modifier\"] = weechat.hook_modifier(\n \"input_text_display_with_cursor\", \"input_modifier\", \"\")",
"title": ""
},
{
"docid": "28d97868f2cac67cd0e8cc6006ca1d2c",
"score": "0.65615565",
"text": "def add_hook(self, hook):\n if hook not in [type(me) for me in self.hooks]:\n self.__hooks += [hook()]",
"title": ""
},
{
"docid": "0cb8b3979e2bf906d4dc8356ad14ebb7",
"score": "0.6503424",
"text": "def _setup_hooks(self): # pragma no cover\n pass",
"title": ""
},
{
"docid": "fa0b1cadc33681909a5166075eac3202",
"score": "0.6501873",
"text": "def addhook(self, name, uname, function):\n self.server.addhook(name, \"%s:%s\" % (self.index, uname), function)\n self.hooks.append((name, \"%s:%s\" % (self.index, uname)))",
"title": ""
},
{
"docid": "c5c7e9824dc521cf94e916cb68430537",
"score": "0.64930224",
"text": "def hooks_initializer(initializer):\n # Register default build hooks\n initializer.register_hook(hooks.add_versions_chooser)\n initializer.register_hook(hooks.add_warning)",
"title": ""
},
{
"docid": "4344c7329eb0f2cf2e62da3a45bb6d00",
"score": "0.6403244",
"text": "def _register_required_hooks(self):\n\n packaging_services.register_hook(PackagingHook())",
"title": ""
},
{
"docid": "1f736d2001be5931c63ce218fa4d8bb9",
"score": "0.6390347",
"text": "def register_hooks(self):\n\n self.on_pre_GET += on_list\n self.on_delete_item += on_delete\n self.on_insert += init_document_with_acl\n self.on_update += on_update",
"title": ""
},
{
"docid": "fb89b8f5d17bf4cb987b08cf0ec8efdc",
"score": "0.63517725",
"text": "def pytest_addhooks(pluginmanager) -> None:\n from . import newhooks\n\n pluginmanager.add_hookspecs(newhooks)",
"title": ""
},
{
"docid": "08edef69582cf0ea9d879b20c97d29c6",
"score": "0.63485664",
"text": "def init_hooks():\n raise NotImplementedError()",
"title": ""
},
{
"docid": "988c259581ed00239a5e4b0bb9dfd80f",
"score": "0.6284471",
"text": "def run_hooks(self, config):\n for hook in self.setup_hooks:\n hook(config)",
"title": ""
},
{
"docid": "ddfac105d4a5917c847228d02b558d0c",
"score": "0.6270114",
"text": "def register_hook(self, hook):\n self._hooks.append(hook)",
"title": ""
},
{
"docid": "63d9dee33b36016b6d95e7ff7079755f",
"score": "0.6270062",
"text": "def pytest_addhooks(pluginmanager):\n from seekret.apitest.pytest_plugin import newhooks\n pluginmanager.add_hookspecs(newhooks)",
"title": ""
},
{
"docid": "8b98798e5110a7ae6e8ac8286d29d7c3",
"score": "0.6247726",
"text": "def add_events(self):\n self.bot.event(self.on_ready)",
"title": ""
},
{
"docid": "fed26d9ec1952a689b322a0a269bff07",
"score": "0.6242092",
"text": "def _addAEHooks():\n # Realflow uses the AEshapeHooks global variable as a convention for sharing AEshapeTemplate overrides,\n # so we will too, unless a more popular convention is found.\n pm.melGlobals.initVar('string[]', 'AEshapeHooks')\n hooks = list(pm.melGlobals['AEshapeHooks'])\n import mtoa.ui.ae.templates\n procName = utils.pyToMelProc(mtoa.ui.ae.templates.loadArnoldTemplate, [('string', 'nodeName')], useName=True)\n hooks.append(procName)\n pm.melGlobals['AEshapeHooks'] = hooks",
"title": ""
},
{
"docid": "afbcf0e45ef1bcc1330e7d298d50d166",
"score": "0.62211084",
"text": "def add_hook(self, command, method):\n self.hooks[command] = method",
"title": ""
},
{
"docid": "b24452a621b7bae3da58941d871f16db",
"score": "0.6214256",
"text": "def install_hooks(self, pipe):\n assert len(self.hooks) == 0\n text_encoder_targets = self._get_target_modules(pipe.text_encoder, \"lora_te\", [\"CLIPAttention\", \"CLIPMLP\"])\n unet_targets = self._get_target_modules(pipe.unet, \"lora_unet\", [\"Transformer2DModel\", \"Attention\"])\n for name, target_module in text_encoder_targets + unet_targets:\n hook = LoRAHook()\n hook.install(target_module)\n self.hooks[name] = hook\n # print(name)\n\n self.device = pipe.device\n self.dtype = pipe.unet.dtype",
"title": ""
},
{
"docid": "f5be4d905626ef49be5a636ab0ea8ee5",
"score": "0.6170191",
"text": "def game_hooks() -> GameHooks:\n return _game_hooks",
"title": ""
},
{
"docid": "1484d616169295d164f73b32366494ad",
"score": "0.6154442",
"text": "def run(self, hooks):\n if settings['disable_hooks']:\n return\n\n queue = []\n for value in hooks.values():\n value = seedlib.apply_template(value, self.values)\n queue.append(value)\n \n for command in queue:\n print ('info: running hook \"%s\"' % command)\n seedlib.run_pipe(command)",
"title": ""
},
{
"docid": "482eb32d67492d3848f3b2001ca25120",
"score": "0.61518055",
"text": "def setup(bot):\n bot.add_cog(Events(bot))",
"title": ""
},
{
"docid": "e40ea95a597c036314c0ac0b5a4bb00d",
"score": "0.6124972",
"text": "def startHook(self):\n # Empty ",
"title": ""
},
{
"docid": "e5b43f0224a2a54a6a30b1fdb6d5b8f1",
"score": "0.6113805",
"text": "def set_bottle_hooks(self, app, shutdown, rollback):\n if hasattr(app, 'hook'):\n app.hook('after_request')(shutdown)",
"title": ""
},
{
"docid": "925a2a709e21bed37d6e591bc6029891",
"score": "0.611031",
"text": "def install_hooks(config):\n config.on_create.extend([copy_environ, hide_cookie])\n config.on_create.insert(0, update_report)",
"title": ""
},
{
"docid": "f6c60de55b36b553993bc477dfb188a7",
"score": "0.607913",
"text": "def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n parse_source=parse_source,\n )\n return hook",
"title": ""
},
{
"docid": "b44b466b97d7a6660a00d78c539037c5",
"score": "0.6075598",
"text": "def add_hook(self, hook):\n hook_addr = hook.identifier\n \n if self.hooks.has_key(hook_addr):\n self.hooks[hook_addr].append(hook)\n logging.debug(\"Adding another hook for addr 0x%08x (name: %s)\" % \\\n (hook_addr,\n hook.name))\n else:\n self.hooks[hook_addr] = [hook]",
"title": ""
},
{
"docid": "a8255d4c578bc301629d6436a109124a",
"score": "0.60621554",
"text": "async def initialize_code_hook_functions(self):\n for ability in await self.data_svc.locate('abilities'):\n for executor in ability.executors:\n if executor.code and executor.language:\n executor.HOOKS[executor.language] = self.generate_ability_execution_method",
"title": ""
},
{
"docid": "8b80ce9f5a3f87e9bb9c1642bdcfb4a3",
"score": "0.60540694",
"text": "def setup(bot):\n bot.add_cog(EventLoops(bot))",
"title": ""
},
{
"docid": "0c45dce46acba5ba5710436ac8ac26b6",
"score": "0.6000275",
"text": "def setup(bot):\n bot.add_cog(Logs(bot))",
"title": ""
},
{
"docid": "36d5b30cf031b019fc2c3b8fed1a6dee",
"score": "0.5998982",
"text": "def setup(bot: Bot) -> None:\n bot.add_cog(Tags(bot))",
"title": ""
},
{
"docid": "1944791d46eca2a6bbb32d2297613235",
"score": "0.59902436",
"text": "def assign_standard_hooks(obj, get, conf):\n hook_names = ['step_hook', 'post_process_hook',\n 'post_process_hook_final', 'pre_process_hook']\n for hook_name in hook_names:\n setattr(obj, hook_name, conf.get_function(get(hook_name, None)))",
"title": ""
},
{
"docid": "c773fd5a7366f71b8bc48cf16391dd3a",
"score": "0.59853834",
"text": "def _register_hook(self, hook_name, func):\r\n self.hooks.setdefault(hook_name, []).append(func)",
"title": ""
},
{
"docid": "a5a3262430aa75a2dc4230898591f488",
"score": "0.59784955",
"text": "def add_hook(self, level, location, hook, position=-1):\n\n if location not in self.levels[level].hooks:\n self.levels[level].hooks[location] = []\n\n if position == -1:\n position = len(self.levels[level].hooks[location])\n\n self.levels[level].hooks[location].insert(position, hook)",
"title": ""
},
{
"docid": "cc99595a1169c0fa58e110dd5336a50f",
"score": "0.59615684",
"text": "def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n source_init=source_init,\n transform_source=transform_source,\n )\n return hook",
"title": ""
},
{
"docid": "cc99595a1169c0fa58e110dd5336a50f",
"score": "0.59615684",
"text": "def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n source_init=source_init,\n transform_source=transform_source,\n )\n return hook",
"title": ""
},
{
"docid": "42af3093cb3fc5de769600b2d9bcde98",
"score": "0.5953135",
"text": "def run_hooks(hooks, data):\n for hook_key, hook_class in hooks.items():\n hook = hook_class(data)\n log.info('Running {} hook'.format(hook_key))\n hook.run()",
"title": ""
},
{
"docid": "b0a8b923b37eba8f732792eb53cb6b71",
"score": "0.5936176",
"text": "async def run_init_hooks(self) -> None:\n self.logger.info(\"Running init hooks\")\n for hook in self.init_hooks:\n self.logger.info(f\"Running init hook for plugin {hook.plugin}\")\n await hook.func() if inspect.iscoroutinefunction(hook.func) else hook.func()",
"title": ""
},
{
"docid": "d65c171195ea46e096e3c1c55ae1c291",
"score": "0.5925443",
"text": "def on(self, hook):\n\n def decorator(handler):\n self.registry[hook].append(handler)\n\n return handler\n\n return decorator",
"title": ""
},
{
"docid": "792e20dbb3796793cf5123be2589cedc",
"score": "0.59254354",
"text": "def register_hook(self, func):\r\n\r\n if func not in self.hooks:\r\n self.hooks.append(func)",
"title": ""
},
{
"docid": "992e660201db15b027d78bbf73909ecd",
"score": "0.5917185",
"text": "def _before_application_run(self):\n\n for hook in self._get_hooks():\n hook.before_application_run()",
"title": ""
},
{
"docid": "4098a25602a66288543c0be8a1720c0a",
"score": "0.59140986",
"text": "def setup(bot):\n bot.add_cog(Dictionary())",
"title": ""
},
{
"docid": "128feab0e9e690fd164055f3dc6224ce",
"score": "0.5912825",
"text": "def _after_application_loaded(self):\n\n for hook in self._get_hooks():\n hook.after_application_loaded()",
"title": ""
},
{
"docid": "7a3b2b4a6f0402b41ae4eff4cf4fc845",
"score": "0.5884704",
"text": "def on_start(self):\n self.add_bot(ScoutManager(bot_player=self))\n self.add_bot(BuildManager(bot_player=self))\n self.add_bot(GatherManager(bot_player=self))\n self.add_bot(DefenseManager(bot_player=self))\n self.init_request_board()",
"title": ""
},
{
"docid": "2a582bbb4bf0a67821652519bd94f5ec",
"score": "0.588175",
"text": "def add_hook(cls, hook_type, hook_func):\r\n if hook_type not in cls._hooks_map:\r\n cls._hooks_map[hook_type] = []\r\n\r\n cls._hooks_map[hook_type].append(hook_func)",
"title": ""
},
{
"docid": "200dd6ea332f6486cfb958496dd7eafe",
"score": "0.5878965",
"text": "async def setup(bot) -> None:\n await bot.add_cog(HotReload(bot))",
"title": ""
},
{
"docid": "61cbfc3f4a2d622fbd0030e22de81d12",
"score": "0.58777094",
"text": "def setup(bot: Bot):\n bot.add_cog(EventoryCog(bot))\n log.info(\"loaded Eventory extension!\")",
"title": ""
},
{
"docid": "269b00d45f4e69768306379771ce086d",
"score": "0.5848767",
"text": "def setup(bot: Bot) -> None:\n bot.add_cog(Utilities(bot))",
"title": ""
},
{
"docid": "69a5ab4cf1d8372d05b8687ae7d4fdc2",
"score": "0.58468145",
"text": "def setup(bot):\n bot.add_cog(Tickets(bot))",
"title": ""
},
{
"docid": "bae9f0da4d2e05b9bdea26aa011ab59a",
"score": "0.58353835",
"text": "def setup(bot: Bot) -> None:\n bot.add_cog(LemonStuff(bot))\n log.info(\"Cog loaded: LemonStuff\")",
"title": ""
},
{
"docid": "9ca07bcea77b5998babc1f8f475dda35",
"score": "0.5832757",
"text": "def hooks(self):\n return self.__hooks",
"title": ""
},
{
"docid": "b6d22ce1884a7faa025d158e62aa578e",
"score": "0.5821258",
"text": "def hook_dispatch(self) -> None:\n if sys.argv[2].strip().lower() == \"add\":\n self.add_hook(\n hook_state = sys.argv[3],\n cmd = \" \".join(sys.argv[4:]),\n )\n\n elif sys.argv[2].strip().lower() == \"del\":\n self.del_hook(\n hook_state = sys.argv[3],\n )",
"title": ""
},
{
"docid": "e666c8c599a3a0a56b13e57e58b45c50",
"score": "0.58076733",
"text": "def _add_trachook(self, req, hookfile, current):\n base_url = self.config.get('trac', 'base_url')\n # try to append hook\n try:\n fp = open(hookfile, 'a')\n fp.writelines(os.linesep)\n if 'REPOS=\"$1\"' not in current:\n fp.writelines('REPOS=\"$1\"' + os.linesep)\n if 'REV=\"$2\"' not in current:\n fp.writelines('REV=\"$2\"' + os.linesep)\n if 'LOG=' not in current:\n fp.writelines('LOG=`/usr/bin/svnlook log -r $REV $REPOS`' \\\n + os.linesep)\n if 'AUTHOR=' not in current:\n fp.writelines('AUTHOR=`/usr/bin/svnlook author -r $REV ' + \\\n '$REPOS`' + os.linesep)\n if 'TRAC_ENV=' not in current:\n fp.writelines('TRAC_ENV=\"'+self.env.path+'\"' + os.linesep)\n if 'TRAC_URL=' not in current:\n fp.writelines('TRAC_URL=\"'+base_url+'\"' + os.linesep)\n fp.writelines('/usr/bin/python /usr/share/trac/contrib/' + \\\n 'trac-post-commit-hook \\\\' + os.linesep)\n fp.writelines('-p \"$TRAC_ENV\" \\\\' + os.linesep)\n fp.writelines('-r \"$REV\" \\\\' + os.linesep)\n fp.writelines('-u \"$AUTHOR\" \\\\' + os.linesep)\n fp.writelines('-m \"$LOG\" \\\\' + os.linesep)\n fp.writelines('-s \"$TRAC_URL\"' + os.linesep)\n fp.writelines(os.linesep)\n fp.close()\n except Exception:\n raise TracError(\"Can't write repository hook %s\" % hookfile)",
"title": ""
},
{
"docid": "b1a651ece69c23f871ae87944408d516",
"score": "0.5799101",
"text": "def run_hooks(cls, hook_type, *args, **kwargs):\r\n hook_funcs = cls._hooks_map.get(hook_type) or []\r\n for hook_func in hook_funcs:\r\n hook_func(*args, **kwargs)",
"title": ""
},
{
"docid": "4fcf31c68e866cf847c0a5373217c2b3",
"score": "0.5785056",
"text": "def register_default_hooks(self, *hooks: Hook, as_group: bool = True):\n self.hooks.extend(hooks)\n\n if as_group and self.group is not None:\n for g in get_group_by_name(self.group):\n if g is not self:\n g.register_hooks(*hooks, as_group=False)",
"title": ""
},
{
"docid": "396772b6a60c6762461d747ec1f4b8dc",
"score": "0.5783806",
"text": "async def setup(bot) -> None:\n await bot.add_cog(Gambling(bot))",
"title": ""
},
{
"docid": "f50c868844e933e05acf6e0224d81642",
"score": "0.57818186",
"text": "def install_custom_hook(self, hook_name):\n self._install_custom_hook(hook_name)",
"title": ""
},
{
"docid": "e677a9f8d0ec6ba94293c47993d275df",
"score": "0.5769153",
"text": "def add_hook(self,\n hook_state: str,\n cmd: str,\n ) -> None:\n state = hook_state.strip().lower()\n\n if state not in self.valid_hook_states:\n print(f\"Invalid hook state: {hook_state}. Valid states are: {self.valid_hook_states}\")\n return\n\n # At this point we simply overwrite any existing command\n # and store it to the data file\n data = self.load()\n data[\"hooks\"][state] = cmd\n self.save(data)\n\n # If cmd is None this function as been called from within del_hook()\n if cmd is None:\n print(f\"Deleted hook command for state {state}\")\n else:\n print(f\"Added hook command for state {state}: {cmd}\")",
"title": ""
},
{
"docid": "b8c18e6f43da52ca2d550b82ddba2845",
"score": "0.5764374",
"text": "def install_api_hooks(dbg, dll_api_hooks):\n # _pt_log(\"remain system dlls to install api hook: %d\" % (len(dll_api_hooks)))\n\n new_sys_dll = dbg.system_dlls[-1]\n dll_name = new_sys_dll.name.lower()\n\n for (dll_name, api_ctrl_list) in dll_api_hooks.items():\n if dbg.check_has_system_dll(dll_name):\n for api_ctrl in api_ctrl_list:\n dbg_set_api_bp(dbg, api_ctrl)\n del dll_api_hooks[dll_name]",
"title": ""
},
{
"docid": "e63584e75c449d989e5334871a61d146",
"score": "0.574739",
"text": "def setup(bot):\n bot.add_cog(Messages(bot))",
"title": ""
},
{
"docid": "ba6688fa7d65bc9590cd3596cc6eead6",
"score": "0.5747233",
"text": "def InstallHook(self):\r\n pass",
"title": ""
},
{
"docid": "e17e625fb36c2298dbac9f5647e71522",
"score": "0.5714983",
"text": "def setup(bot: Bot) -> None:\n bot.add_cog(MessageLogCog(bot))",
"title": ""
},
{
"docid": "efb0f516bd6a1e7fe91485da5245ead6",
"score": "0.5709663",
"text": "def registerhooks(entry=[], exit=[], *args, **kwargs):\n def wrap(f):\n _kwargs = dict(kwargs)\n _kwargs.update({'entry': entry, 'exit': exit})\n def wrapped_f(*args, **kwargs):\n return f(*args, **kwargs)\n wrapped_f.__hooks = _kwargs\n return wrapped_f\n return wrap",
"title": ""
},
{
"docid": "044a2ad7b8e51eec0d32c615365ec6bc",
"score": "0.570686",
"text": "def setup(bot):\n bt.INFO('Loading Misc.py')\n bot.add_cog(Misc(bot))",
"title": ""
},
{
"docid": "71fa5b3abe2be9c9c55ea45b9c82dd43",
"score": "0.56982493",
"text": "async def setup(self) -> None:\n if self.playing_title is not None:\n await self.change_presence(\n activity=discord.Game(name=self.playing_title),\n )\n\n self.tree.clear_commands(guild=None)\n for guild in self.guilds:\n self.tree.clear_commands(guild=guild)\n\n commands = registered_app_commands()\n for command in commands:\n self.tree.add_command(command)\n logger.info(f'registered {len(commands)} app commands')\n\n listeners = registered_listeners()\n for listener in listeners:\n self.add_listener(listener.func, listener.event)\n logger.info(f'registered {len(listeners)} listeners')\n\n if self._cmd_group_exts is not None:\n for ext in self._cmd_group_exts:\n self.tree.add_command(ext)\n await ext.post_init(self)\n logger.info(f'registered {ext.name} command group')\n\n await self.tree.sync()\n for guild in self.guilds:\n await self.tree.sync(guild=guild)",
"title": ""
},
{
"docid": "fccb4dede5aa12489b6365d7dac5f30b",
"score": "0.5684656",
"text": "def setup(bot):\n bot.add_cog(LoadHelp(bot))",
"title": ""
},
{
"docid": "59ace20514f392a89f07428e9134c5bc",
"score": "0.5670448",
"text": "def list_hooks(self) -> None:\n data = self.load()\n hooks = data.get(\"hooks\", {})\n\n print(\"Current hook command configuration:\")\n for state in self.valid_hook_states:\n cmd = hooks.get(state, None)\n print(f\" {state:6s} {cmd}\")",
"title": ""
},
{
"docid": "c13e1b3cbfa89944d2a510fb35f6ed58",
"score": "0.5668042",
"text": "def on_bot_startup(bot):\n pass",
"title": ""
},
{
"docid": "5a8ba5a4c77b793ba734b50ced6e7aaf",
"score": "0.56633395",
"text": "def register(name):\r\n global SUPPORTED_HOOKS\r\n if not name in SUPPORTED_HOOKS:\r\n SUPPORTED_HOOKS.append(name)",
"title": ""
},
{
"docid": "5ad29e41de5a20f7deb3ed694f518194",
"score": "0.5659891",
"text": "def add_permissions_hook(self, hook: commands.CheckPredicate) -> None:\n self._permissions_hooks.append(hook)",
"title": ""
},
{
"docid": "85d8761c3de9dfeea1cd5f99b7ec825c",
"score": "0.56558925",
"text": "def register_hook(self, event, hook):\r\n\r\n if isinstance(hook, collections.Callable):\r\n self.hooks[event].append(hook)\r\n elif hasattr(hook, '__iter__'):\r\n self.hooks[event].extend(h for h in hook if isinstance(h, collections.Callable))",
"title": ""
},
{
"docid": "3258ecf3f6e2c20cf1fd7f7cecca9cc6",
"score": "0.5652265",
"text": "def get_hooks():\n tensor_to_log = {'loss': loss}\n # tensor_to_log = {}\n logging_tensor_hook = tf.train.LoggingTensorHook(\n tensors=tensor_to_log,\n every_n_iter=2\n )\n return [logging_tensor_hook]",
"title": ""
},
{
"docid": "55c7449a2267ac9fd64c1310ad51e382",
"score": "0.56491655",
"text": "def apply_hooks():\n for line in fileinput.input():\n std_input = line.split(\" \")\n oldrev, newrev, refname = [item.strip() for item in std_input]\n # Check for zero commit, check branch deletions\n # also, avoid new package additions\n if ZERO_COMMIT in (oldrev, newrev):\n continue\n # insert new hooks here\n package_start_build(oldrev, newrev, refname)\n # Pass in 'length' as an optional argument\n # default 'length' of feed is 499\n write_rss_feed(oldrev, newrev, refname, length=499)\n return",
"title": ""
},
{
"docid": "f70c1c6821cc9de1cb8cab705ac6ee85",
"score": "0.5649146",
"text": "def setup(bot: Bot) -> None:\n bot.add_cog(Internal(bot))",
"title": ""
},
{
"docid": "0f6a737d4f0384ad0b93c99010ebf635",
"score": "0.5637375",
"text": "def hook(self, server=None):\n\n if server is not None:\n logging.debug('Adding hook route to web server')\n server.add_route(\n Wrapper(callable=self.get_hook(),\n route=self.context.get('server.hook', '/hook')))\n\n if (self.context.get('server.binding') is not None\n and self.context.get('server.url') is not None):\n\n self.space.register(\n hook_url=self.context.get('server.url')\n + self.context.get('server.hook', '/hook'))",
"title": ""
},
{
"docid": "1b50dc58fd7d07d03898d7d1205095a7",
"score": "0.56359833",
"text": "def setup(bot):\n\n bot.add_cog(Eval(bot))",
"title": ""
},
{
"docid": "b683c47d5bdae345391754b51c5bac6f",
"score": "0.5632748",
"text": "def add_callback(self, hook, function, pre=False):\n if hook not in self._hooks:\n raise Exception('This program has no hook named \"%s\"' % hook)\n hook_def = self._hook_defs.get(hook, None)\n\n if (hook_def is None or not isinstance(hook_def, FunctionChain)):\n raise TypeError(\"Cannot add callback to hook '%s'; not a \"\n \"FunctionChain. (%s)\" % (hook, type(hook_def)))\n\n if pre:\n hook_def.insert(0, function)\n else:\n hook_def.append(function)\n\n # TODO: remove or resurrect\n #self._install_dep_callbacks(function)\n self._need_build = True",
"title": ""
},
{
"docid": "d4499e1842dac2feaba1f51e52a73f48",
"score": "0.56314707",
"text": "def run_hooks():\n\n hookdir = '/usr/lib/oem-config/post-install'\n\n if os.path.isdir(hookdir):\n # Exclude hooks containing '.', so that *.dpkg-* et al are avoided.\n hooks = filter(lambda entry: '.' not in entry, os.listdir(hookdir))\n child_env = dict(os.environ)\n child_env['DEBIAN_FRONTEND'] = 'noninteractive'\n if 'DEBIAN_HAS_FRONTEND' in child_env:\n del child_env['DEBIAN_HAS_FRONTEND']\n for hookentry in hooks:\n hook = os.path.join(hookdir, hookentry)\n if os.access(hook, os.X_OK):\n # Errors are ignored at present, although this may change.\n subprocess.call([hook], env=child_env)",
"title": ""
},
{
"docid": "1b864916beac1e7a3522c10bae5c0f74",
"score": "0.56313306",
"text": "def run_hook(db, hook_name, **kwargs):\n\n\t# Get all hooks in the DB\n\thooks = db.get_hook()\n\thooks = [dict(_)['name'] for _ in hooks]\n\n\t# Iteratue through the hooks\n\tfor hook in hooks:\n\t\t# Try importing the hook module and finding any functions that have used the ydl.hook decorate\n\t\t# which means it has a _hooks attribute indicating which hooks it accepts\n\t\ttry:\n\t\t\tz = importlib.import_module(hook)\n\t\t\tfor x in dir(z):\n\t\t\t\to = getattr(z, x)\n\t\t\t\tif hasattr(o, '_hooks') and hook_name in o._hooks:\n\t\t\t\t\to(hook_name, db, **kwargs)\n\t\t\t\tdel o\n\t\t\tdel z\n\t\texcept Exception as e:\n\t\t\ttraceback.print_exc()\n\t\t\t# Move along",
"title": ""
},
{
"docid": "2cfa6dc8d943c488df97857dd75522ee",
"score": "0.5624743",
"text": "def setup(bot: Bot) -> None:\n bot.add_cog(Reminder(bot))\n log.info(\"Cog loaded: reminder\")",
"title": ""
},
{
"docid": "e7df076bc40f988cd86f24cb3e5db89e",
"score": "0.56241655",
"text": "def setup(bot: Bot) -> None:\n bot.add_cog(SpookyEightBall())",
"title": ""
},
{
"docid": "53fa260f6f97a677c52fabbf44b86b38",
"score": "0.5622936",
"text": "def add_commands(self, *args):\n for command in args:\n self._script += command + '\\n'",
"title": ""
},
{
"docid": "7496c40c7d9e4b36c3df9cb62d2c5ad4",
"score": "0.55818033",
"text": "def run(self, hook, *args, **kwargs):\n\n assert hook in self._callbacks, f\"hook '{hook}' not found in callbacks {self._callbacks}\"\n\n for logger in self._callbacks[hook]:\n logger['callback'](*args, **kwargs)",
"title": ""
},
{
"docid": "9499c2213ce0272e0a32ffd4439f8e08",
"score": "0.5580782",
"text": "def _init_airflow_core_hooks(self):\n core_dummy_hooks = {\n \"generic\": \"Generic\",\n \"email\": \"Email\",\n \"mesos_framework-id\": \"Mesos Framework ID\",\n }\n for key, display in core_dummy_hooks.items():\n self._hooks_lazy_dict[key] = HookInfo(\n hook_class_name=None,\n connection_id_attribute_name=None,\n package_name=None,\n hook_name=display,\n connection_type=None,\n connection_testable=False,\n )\n for cls in [FSHook, PackageIndexHook]:\n package_name = cls.__module__\n hook_class_name = f\"{cls.__module__}.{cls.__name__}\"\n hook_info = self._import_hook(\n connection_type=None,\n provider_info=None,\n hook_class_name=hook_class_name,\n package_name=package_name,\n )\n self._hook_provider_dict[hook_info.connection_type] = HookClassProvider(\n hook_class_name=hook_class_name, package_name=package_name\n )\n self._hooks_lazy_dict[hook_info.connection_type] = hook_info",
"title": ""
},
{
"docid": "4fd2b1eb01258336636e0bd8adc0178b",
"score": "0.5579576",
"text": "def _trigger_hooks(self, hook_dict, hook_name, **kwargs):\n for attached_callback in hook_dict[hook_name].attached_callbacks:\n try:\n if attached_callback.callback(**kwargs):\n if attached_callback.action == MMHookAct.LOAD_MODULE:\n # load the module!\n self.logger.debug('some hook returned true, '\n 'loading module {}'\n .format(attached_callback.argument))\n # module must accept same kwargs, this is mandatory\n # with this discovery event\n try:\n cb_arg = attached_callback.argument\n module_name = cb_arg.get_module_desc().arg_name\n self.load_module(module_name,\n **kwargs)\n except Exception as ex:\n self.logger.error('loading of module of class '\n '\"{}\" failed with: {}'\n .format(cb_arg.__name__,\n str(ex)))\n elif attached_callback.action == MMHookAct.UNLOAD_MODULE:\n # unload the attached module\n self.logger.debug('a hook required module '\n '{} to be unloaded'\n .format(attached_callback.argument))\n self.unload_module(attached_callback.argument)\n except Exception as ex:\n self.logger.error('failed to call function '\n '{} attached to \"{}\" with: {}'\n .format(attached_callback,\n hook_name,\n str(ex)))",
"title": ""
},
{
"docid": "b19e4deca1d85266e102d958ffa41ff0",
"score": "0.55784094",
"text": "def setup(bot):\n bot.add_cog(ReminderCog(bot))",
"title": ""
},
{
"docid": "8e2d79bb76fc224f12b869daf1739030",
"score": "0.55750185",
"text": "def runHook(self):\n self.run()",
"title": ""
},
{
"docid": "a78408bd292126b7640c238bfb0c8ec8",
"score": "0.55676615",
"text": "async def setup(bot: Bot) -> None:\n await bot.add_cog(Patreon(bot))",
"title": ""
},
{
"docid": "05f45412830a1b3a2b2c9b7bdb0d1cbb",
"score": "0.556712",
"text": "def hooks(self) -> MutableMapping[str, HookInfo | None]:\n self.initialize_providers_hooks()\n # When we return hooks here it will only be used to retrieve hook information\n return self._hooks_lazy_dict",
"title": ""
}
]
0678ed29cb4ed04785fd661416546558
Check is a path is a broken symlink.
[
{
"docid": "202729acc69cd2b9313649493f56ef9f",
"score": "0.870235",
"text": "def is_symlink_broken(path):\n if os.path.islink(path):\n return not os.path.exists(os.readlink(path))\n else:\n raise TypeError('path={!r} is not a symbolic link'.format(path))",
"title": ""
}
]
[
{
"docid": "71564a56fc163c8e7d254c575b623e30",
"score": "0.7800918",
"text": "def checklink(path):\n # mktemp is not racy because symlink creation will fail if the\n # file already exists\n name = tempfile.mktemp(dir=path)\n try:\n os.symlink(\".\", name)\n os.unlink(name)\n return True\n except (OSError, AttributeError):\n return False",
"title": ""
},
{
"docid": "1301b1cb5da838eaea975a866ca6bb1f",
"score": "0.7800738",
"text": "def test_is_symlink(path: Path) -> None:\n assert (path / \"link\").is_symlink()",
"title": ""
},
{
"docid": "b1c304a94d85d6653cded13e445a0b19",
"score": "0.7560013",
"text": "def is_symlink(self):\n try:\n return S_ISLNK(self.lstat().st_mode)\n except OSError as e:\n if e.errno not in (ENOENT, ENOTDIR):\n raise\n # Path doesn't exist\n return False",
"title": ""
},
{
"docid": "45e1ac62ad49004dab7dc483330692cb",
"score": "0.75018257",
"text": "def is_symlink(self):\n return False",
"title": ""
},
{
"docid": "896891d9194d8ad8e3c7b93d9f4735d6",
"score": "0.7391739",
"text": "def test_symlink_on_unknown_file(self):\n root = URI(self.baseurl)\n notexisting_path = root / 'ma' / 'moo'\n tee_path = root / 'helloworld'\n notexisting_path.symlink(tee_path)\n\n self.assertTrue(tee_path.islink())\n self.assertEqual(tee_path.readlink(), notexisting_path)\n self.assertTrue(not notexisting_path.exists())",
"title": ""
},
{
"docid": "9543b5f7076123e979ef23a52736cca5",
"score": "0.7297981",
"text": "def test_symlink_missing_src(file, source):\n target = source.parent / \"symlink.lnk\"\n missing_source = source.parent / \"missing.txt\"\n try:\n file.symlink(str(missing_source), str(target))\n assert salt.utils.path.islink(str(target))\n finally:\n target.unlink()",
"title": ""
},
{
"docid": "faaf23de79bf29e8868d7acf350b1324",
"score": "0.72364473",
"text": "def islink(path):\r\n return False",
"title": ""
},
{
"docid": "117fd290d3a937d8daa06de391ab888b",
"score": "0.7161739",
"text": "def islink(self, path):\n path = ftputil.tool.as_unicode(path)\n try:\n lstat_result = self._host.lstat(\n path, _exception_for_missing_path=False)\n except ftputil.error.RootDirError:\n return False\n else:\n if lstat_result is None:\n # Non-existent path\n return False\n else:\n return stat.S_ISLNK(lstat_result.st_mode)",
"title": ""
},
{
"docid": "7e466325a1eb6f6c43507803db038e87",
"score": "0.7131687",
"text": "def islink(path):\r\n try:\r\n st = os.lstat(path)\r\n except (os.error, AttributeError):\r\n return False\r\n return stat.S_ISLNK(st.st_mode)",
"title": ""
},
{
"docid": "e1f68b0566a7c14c2badd11901266889",
"score": "0.71089053",
"text": "def is_symlink(self):\n if os.name == \"posix\": return os.path.islink(self.path)\n if os.name == \"nt\":\n import win32api\n import win32con\n num = win32con.FILE_ATTRIBUTE_REPARSE_POINT\n return bool(win32api.GetFileAttributes(self.path) & num)",
"title": ""
},
{
"docid": "dac7484c1ede678b11d426cfd0dff2ef",
"score": "0.71058583",
"text": "def islink(path):\n try:\n st = os.lstat(path)\n except (OSError, AttributeError):\n return False\n return stat.S_ISLNK(st.st_mode)",
"title": ""
},
{
"docid": "dac7484c1ede678b11d426cfd0dff2ef",
"score": "0.71058583",
"text": "def islink(path):\n try:\n st = os.lstat(path)\n except (OSError, AttributeError):\n return False\n return stat.S_ISLNK(st.st_mode)",
"title": ""
},
{
"docid": "4c1aec9e1a0da95eb624cbc8cdd68e79",
"score": "0.70764434",
"text": "def test_symlink_exists_different(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n with pytest.raises(CommandExecutionError) as exc:\n file.symlink(str(source), str(target))\n assert \"Found existing symlink:\" in exc.value.message\n finally:\n target.unlink()",
"title": ""
},
{
"docid": "8816e12f05b8fce70a4dc0a21726d931",
"score": "0.70211464",
"text": "def test_symlink_exists_different_force(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n file.symlink(str(source), str(target), force=True)\n assert salt.utils.path.readlink(str(target)) == str(source)\n finally:\n target.unlink()",
"title": ""
},
{
"docid": "bb95c61abb8f5354e7e9d4d715e4ef7a",
"score": "0.6982841",
"text": "def test_readlink(path: Path) -> None:\n assert PurePath(\"file\") == (path / \"link\").readlink()",
"title": ""
},
{
"docid": "f6c1e77dc34b5c401947222e0bbf911a",
"score": "0.6982368",
"text": "def _is_link(self, path):\n return os.path.islink(os.path.expanduser(path))",
"title": ""
},
{
"docid": "8627bffa0d7e759f5d4710ef759afa77",
"score": "0.69228035",
"text": "def is_symlink(self):\n if self._is_symlink is None:\n self._is_symlink = os.path.islink(self.storage_url.object_name)\n return self._is_symlink",
"title": ""
},
{
"docid": "fe21b7cecc2c9973a253737963706133",
"score": "0.68756634",
"text": "def test_constrain_symlinks_to_filesystem() -> None:\n path = root(\n {\n \"file\": \"text\",\n \"dir\": {\"link\": PurePath(\"..\", \"file\")},\n },\n innerpath=PurePath(\"dir\"),\n )\n # XXX Should we rewrite the error message to hide the 'dir/' prefix?\n with pytest.raises(FileNotFoundError, match=\"file not found: dir/file\"):\n assert (path / \"link\").read_text()",
"title": ""
},
{
"docid": "923a01647ae572518a7cb698b01d2789",
"score": "0.6867287",
"text": "def test_symlink_exists_file(file, source):\n with pytest.helpers.temp_file(\"symlink.txt\", contents=\"Source content\") as target:\n with pytest.raises(CommandExecutionError) as exc:\n file.symlink(str(source), str(target))\n assert \"Existing path is not a symlink:\" in exc.value.message",
"title": ""
},
{
"docid": "41bff0c324b7c71a826535c2a5479b5c",
"score": "0.6856679",
"text": "def test_symlink(file, source):\n target = source.parent / \"symlink.lnk\"\n try:\n file.symlink(str(source), str(target))\n assert salt.utils.path.islink(str(target))\n finally:\n target.unlink()",
"title": ""
},
{
"docid": "1d7a46dd208c245d602979b6f8c24590",
"score": "0.68321455",
"text": "def test_symlink_exists_same(file, source):\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(source)\n try:\n before_time = os.stat(str(target)).st_mtime\n ret = file.symlink(str(source), str(target))\n after_time = os.stat(str(target)).st_mtime\n assert before_time == after_time\n assert ret is True\n finally:\n target.unlink()",
"title": ""
},
{
"docid": "73c3309bca6d764f2d429a94c01e2867",
"score": "0.68014234",
"text": "def is_symlink(\n linkfile: T.Union[str, pathlib.Path],\n src: T.Union[None, str, pathlib.Path] = None,\n) -> bool:\n linkfile = pathlib.Path(linkfile)\n if not linkfile.is_symlink():\n return False\n\n if src is None:\n return True\n\n return linkfile.resolve() == pathlib.Path(src).resolve()",
"title": ""
},
{
"docid": "6a5673d6915d6847708663b71cf2eab7",
"score": "0.67836535",
"text": "def check_if_relative_link(link):\n\n if not link.startswith(\"http\"):\n if not link.startswith(\"/\"):\n return True\n return False",
"title": ""
},
{
"docid": "1067f2b5ebb747c4d55bca1975463a6a",
"score": "0.6782696",
"text": "def _valid_link(self, link, fail_on_disk_check=True):\n\n if not link in self._links:\n raise Exception('link [{0}] not found in timeline!'.format(link))\n\n if not os.path.islink(self._links[link]['path']):\n msg = 'link [{0}] not found!'.format(self._links[link]['path'])\n if fail_on_disk_check:\n raise Exception(msg)\n self.logger.warning(msg)\n return False\n\n if not os.path.exists(self._links[link]['path']):\n msg = 'link [{0}] is broken!'.format(self._links[link]['path'])\n if fail_on_disk_check:\n raise Exception(msg)\n self.logger.warning(msg)\n return False\n\n return True",
"title": ""
},
{
"docid": "968ba0654b4a586ad577e6ad741ee0ed",
"score": "0.6718646",
"text": "def is_symlink(self):\n return self.type == self.Type.SYMLINK",
"title": ""
},
{
"docid": "f7331b2df6b96881496979b7a913c461",
"score": "0.6693001",
"text": "def is_symlink(self):\n if (\n not self.custom_fields\n or resource_util.SYMLINK_METADATA_KEY not in self.custom_fields\n ):\n return False\n return (\n self.custom_fields[resource_util.SYMLINK_METADATA_KEY].lower() == 'true'\n )",
"title": ""
},
{
"docid": "81d65ef687eac4cb308d24311371b04b",
"score": "0.66920704",
"text": "def islink(self, path: AnyStr) -> bool:\n return self.filesystem.islink(path)",
"title": ""
},
{
"docid": "0a1435142fcb2dce5b3f51f29d763b77",
"score": "0.66706246",
"text": "def islink (self) :\n try :\n return stat.S_ISLNK (self.mode)\n except OSError :\n return 0",
"title": ""
},
{
"docid": "318e97be28a1d07940b9a69de3c8b1f6",
"score": "0.6630757",
"text": "def copy_if_symlink_fails(\n src: Union[str, Path],\n dst: Union[str, Path],\n backend_args: Optional[dict] = None,\n) -> bool:\n backend = get_file_backend(\n src, backend_args=backend_args, enable_singleton=True)\n return backend.copy_if_symlink_fails(src, dst)",
"title": ""
},
{
"docid": "8c3050fd54816a14c87c867d2e9a3748",
"score": "0.6584976",
"text": "def test_through_symlink(self) -> None:\n link1 = os.path.join(self.tmp_dir, \"link1\")\n os.symlink(self.mount, link1)\n\n info1 = json.loads(self.eden.run_cmd(\"info\", link1))\n self.assertEqual(self.mount, info1[\"mount\"])\n\n # Create a non-normalized symlink pointing to the parent directory\n # of the mount\n link2 = os.path.join(self.tmp_dir, \"mounts_link\")\n os.symlink(self.mount + \"//..\", link2)\n mount_through_link2 = os.path.join(link2, self.repo_name)\n\n info2 = json.loads(self.eden.run_cmd(\"info\", mount_through_link2))\n self.assertEqual(self.mount, info2[\"mount\"])",
"title": ""
},
{
"docid": "fc10782d2de274eef803bde25fbaf972",
"score": "0.65710294",
"text": "def test_symlink_exists_different_atomic(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n file.symlink(str(source), str(target), atomic=True)\n assert salt.utils.path.readlink(str(target)) == str(source)\n finally:\n target.unlink()",
"title": ""
},
{
"docid": "741e573a30028ce18a81c363e3d5a5e9",
"score": "0.6550941",
"text": "def islink(s):\n\n try:\n import Carbon.File\n return Carbon.File.ResolveAliasFile(s, 0)[2]\n except:\n return False",
"title": ""
},
{
"docid": "3b04b3104b1dd15775e4fb15a939ca49",
"score": "0.6517931",
"text": "def islink(pathname):\n from os import listdir\n from os.path import isdir, isfile\n import os.path\n pathname = pathname.rstrip(\"/\")\n if os.path.islink(pathname): return True\n elif isfile(pathname) and ishidden(pathname): return True\n elif isdir(pathname):\n entries = listdir(pathname)\n for entry in entries:\n if not islink(pathname+\"/\"+entry): return False\n return True\n else: return False",
"title": ""
},
{
"docid": "2c17a63c3f070a9ace82b381e0997881",
"score": "0.6510741",
"text": "def check_file(filename,path):\n chksumX = chksum_file(\"%s/%s\" % (path,filename))\n (pathY,chksumY) = find_file(filename,path,chksumX)\n if chksumX == chksumY:\n\thardlink(filename,path,pathY)",
"title": ""
},
{
"docid": "6924dff27d54d6cab5d242f412cc20a7",
"score": "0.6508725",
"text": "def silent_remove_symlink(path): # TODO: test\n result = {'error': ''}\n if symlink_exists(path):\n try:\n os.unlink(path)\n except OSError as e:\n result['error'] = str(e)\n return result",
"title": ""
},
{
"docid": "595ab9271ea617ec1ff274f308897593",
"score": "0.64662755",
"text": "def has_valid_link(dotfile, dotfiles_dir):\n homefolder_path = get_homefolder_path(dotfile, dotfiles_dir)\n if os.path.islink(homefolder_path) and os.readlink(homefolder_path) == dotfile:\n return True\n\n return False",
"title": ""
},
{
"docid": "b8b6fb9618f82b8429612697328e30ae",
"score": "0.6455614",
"text": "def is_valid_file(abs_path):\n if os.path.exists(abs_path) and os.path.isfile(abs_path) \\\n and not os.path.islink(abs_path):\n return True\n\n else: return False",
"title": ""
},
{
"docid": "8a6353f544288001a6c15f9a0558223e",
"score": "0.6414611",
"text": "def ismount_raw(path):\r\n try:\r\n s1 = os.lstat(path)\r\n except os.error as err:\r\n if err.errno == errno.ENOENT:\r\n # It doesn't exist -- so not a mount point :-)\r\n return False\r\n raise\r\n\r\n if stat.S_ISLNK(s1.st_mode):\r\n # A symlink can never be a mount point\r\n return False\r\n\r\n s2 = os.lstat(os.path.join(path, '..'))\r\n dev1 = s1.st_dev\r\n dev2 = s2.st_dev\r\n if dev1 != dev2:\r\n # path/.. on a different device as path\r\n return True\r\n\r\n ino1 = s1.st_ino\r\n ino2 = s2.st_ino\r\n if ino1 == ino2:\r\n # path/.. is the same i-node as path\r\n return True\r\n\r\n return False",
"title": ""
},
{
"docid": "8d851ba210920164362039bc52ea42fd",
"score": "0.6394149",
"text": "def is_linked(self) -> bool:\n return self.parent_path.is_symlink() \\\n and (not self.child_path.is_symlink()) \\\n and self.child_path.samefile(self.parent_path)",
"title": ""
},
{
"docid": "6b635c87ed41938fbf0245d78d827c98",
"score": "0.63874704",
"text": "def symlinks_supported():\n with tempfile.TemporaryDirectory() as temp_dir:\n original_path = os.path.join(temp_dir, 'original')\n symlink_path = os.path.join(temp_dir, 'symlink')\n os.makedirs(original_path)\n try:\n os.symlink(original_path, symlink_path)\n supported = True\n except (OSError, NotImplementedError):\n supported = False\n return supported",
"title": ""
},
{
"docid": "89bd6c13a93b82e506573bc794a24693",
"score": "0.6330474",
"text": "def check_path(data_pointer, log, msg):\n if not os.path.exists(data_pointer):\n log.debug(msg)\n return False\n else:\n return data_pointer",
"title": ""
},
{
"docid": "13fb5713db8da8497f3a87fe901a9745",
"score": "0.6315982",
"text": "def test_symlink_target_relative_path(file, source):\n target = \"..{}symlink.lnk\".format(os.path.sep)\n with pytest.raises(SaltInvocationError) as exc:\n file.symlink(str(source), str(target))\n assert \"Link path must be absolute\" in exc.value.message",
"title": ""
},
{
"docid": "0f87c41104cb5341d0b2c9ad58a4970e",
"score": "0.6286066",
"text": "def _resolve_symlinks_and_relative_paths(path):\n # type: pathlib.Path -> pathlib.Path\n try:\n path = path.resolve()\n except WindowsError: # TODO: check for more specific error\n pass\n except OSError as e:\n if e.errno == 2:\n pass\n else:\n raise\n\n return path",
"title": ""
},
{
"docid": "d75a5874ba22708116a98d08093a24ae",
"score": "0.6252006",
"text": "def check_path(path):\n if(os.path.exists(path) == False):\n print_error_and_exit(\"Cannot find %s\" % path)",
"title": ""
},
{
"docid": "3d007389a31ebd7fb16f28910d95422f",
"score": "0.62242776",
"text": "def symlink(self, target, source):\n if self.debug == True:\n logging.debug(\"in symlink\")\n raise FuseOSError(ENOENT)",
"title": ""
},
{
"docid": "90387b542107e8c28fba24a7fd822dad",
"score": "0.62149453",
"text": "def path_ok(match_tuple: MatchTuple) -> bool:\n relative_path = match_tuple.link.split(\"#\")[0]\n full_path = os.path.join(os.path.dirname(str(match_tuple.source)), relative_path)\n return os.path.exists(full_path)",
"title": ""
},
{
"docid": "c41d676430289b18beaa7f4c28d60dc6",
"score": "0.6214474",
"text": "def test_clear_broken_symlink(self):\n nonexistent_file_path = os.path.join(settings.STATIC_ROOT, 'nonexistent.txt')\n broken_symlink_path = os.path.join(settings.STATIC_ROOT, 'symlink.txt')\n os.symlink(nonexistent_file_path, broken_symlink_path)\n self.run_collectstatic(clear=True)\n self.assertFalse(os.path.lexists(broken_symlink_path))",
"title": ""
},
{
"docid": "7db9930863d4dcb3dd9b4a03dbd9b6d5",
"score": "0.6211496",
"text": "def test_symlinks():\n\n config_file = \"study/profit_symlink.yaml\"\n config = BaseConfig.from_file(config_file)\n base_file = \"./study/run_000/mockup.in\"\n link_file = \"./study/run_000/some_subdir/symlink_link.txt\"\n try:\n run(f\"profit run {config_file}\", shell=True, timeout=TIMEOUT)\n with open(link_file, \"r\") as link:\n with open(base_file, \"r\") as base:\n link_data = link.read()\n base_data = base.read()\n assert link_data == base_data and not link_data.startswith(\"{\")\n finally:\n clean(config)\n run(f\"profit clean --all {config_file}\", shell=True, timeout=CLEAN_TIMEOUT)",
"title": ""
},
{
"docid": "de9e9a27b97a6b79b9f1382fe5c6addc",
"score": "0.6205026",
"text": "def do_ismount(path):\n try:\n s1 = os.lstat(path)\n except os.error as err:\n if err.errno == errno.ENOENT:\n # It doesn't exist -- so not a mount point :-)\n return False\n else:\n raise GlusterFileSystemOSError(\n err.errno, '%s, os.lstat(\"%s\")' % (err.strerror, path))\n\n if stat.S_ISLNK(s1.st_mode):\n # A symlink can never be a mount point\n return False\n\n try:\n s2 = os.lstat(os.path.join(path, '..'))\n except os.error as err:\n raise GlusterFileSystemOSError(\n err.errno, '%s, os.lstat(\"%s\")' % (err.strerror,\n os.path.join(path, '..')))\n\n dev1 = s1.st_dev\n dev2 = s2.st_dev\n if dev1 != dev2:\n # path/.. on a different device as path\n return True\n\n ino1 = s1.st_ino\n ino2 = s2.st_ino\n if ino1 == ino2:\n # path/.. is the same i-node as path\n return True\n\n return False",
"title": ""
},
{
"docid": "d97181c311338ad47abd8855991b9b9a",
"score": "0.6189933",
"text": "def test_broken_symlink(self):\n path = os.path.join(settings.STATIC_ROOT, 'test.txt')\n os.unlink(path)\n self.run_collectstatic()\n self.assertTrue(os.path.islink(path))",
"title": ""
},
{
"docid": "a4525da4084481db3ff2dc0510d16515",
"score": "0.6175588",
"text": "def lexists(self, path: AnyStr) -> bool:\n return self.filesystem.exists(path, check_link=True)",
"title": ""
},
{
"docid": "e869be7867b9731154d811f96ce03792",
"score": "0.6169737",
"text": "def check_if_link_in_use(filepath, link):\n\n if not \"previous\" in link:\n if not in_use_links.get(link):\n new_file_name = remove_extra_from_path(filepath)\n\n if new_file_name.startswith(\"\\\\\"):\n new_file_name = new_file_name.replace(\"\\\\\", \"/\")\n\n if new_file_name != link:\n in_use_links[link] = True",
"title": ""
},
{
"docid": "e3a6e146797833743267c59619036c22",
"score": "0.61650556",
"text": "def ismount(path):\n try:\n s1 = os.lstat(path)\n except OSError:\n # It doesn't exist -- so not a mount point. :-)\n return False\n else:\n # A symlink can never be a mount point\n if stat.S_ISLNK(s1.st_mode):\n return False\n\n if isinstance(path, bytes):\n parent = join(path, b'..')\n else:\n parent = join(path, '..')\n try:\n s2 = os.lstat(parent)\n except OSError:\n return False\n\n dev1 = s1.st_dev\n dev2 = s2.st_dev\n if dev1 != dev2:\n return True # path/.. on a different device as path\n ino1 = s1.st_ino\n ino2 = s2.st_ino\n if ino1 == ino2:\n return True # path/.. is the same i-node as path\n return False",
"title": ""
},
{
"docid": "2e371c24edcbab6afb4eab8ea6a95def",
"score": "0.61448544",
"text": "def check_path(path):\r\n if os.path.exists(path):\r\n return True\r\n return False",
"title": ""
},
{
"docid": "6bfbb920868b15f86a5bd492a0366588",
"score": "0.6144068",
"text": "def test_check_permissions_and_broken_symlinks(self, filesystem_world_writable_whitelist, filesystem_ignore_patterns):\n stack = [] # Used for storing directories to parse\n starting_dir = \"/\"\n failed = False\n\n # Helper functions\n is_dir = lambda x: stat.S_ISDIR( x.st_mode)\n is_lnk = lambda x: stat.S_ISLNK(x.st_mode)\n is_sock = lambda x: stat.S_ISSOCK(x.st_mode)\n is_chr = lambda x: stat.S_ISCHR(x.st_mode)\n is_blk = lambda x: stat.S_ISBLK(x.st_mode)\n is_reg = lambda x: stat.S_ISREG(x.st_mode)\n is_world_writable = lambda x: stat.S_IWOTH & x.st_mode\n\n # Start it up\n stack.append(starting_dir)\n while len(stack) > 0:\n # Get actual directory\n directory = stack.pop()\n # Parse through it\n for entry in os.listdir(directory):\n # Stat it\n path = \"%s/%s\" % (directory, entry)\n if path.startswith(\"//\"): # Strip beginning // because of root dir\n path = path[1:]\n\n # Skip this file/dir, has the wrong pattern\n if any([fnmatch.fnmatch(path, p) for p in filesystem_ignore_patterns]):\n sys.stdout.write(\"[ignoring] %s\\n\" % (path,))\n continue\n\n info = os.lstat(path)\n if is_lnk(info):\n # Check if it's broken or not\n if not Test.Shell.exists_in_path(os.readlink(path), os.path.abspath(directory)):\n sys.stderr.write(\"[broken-symlink] %s -> %s\\n\" % (path, os.readlink(path)))\n failed = True\n else:\n if is_dir(info):\n stack.append(path)\n\n # FIXME - determine whether file is truly writable (parent dir\n # may not be readable)\n # Check its permissions, if wrong, append it\n if is_world_writable(info):\n # If the path isn't on the whitelist ... we found a failure\n if not any([fnmatch.fnmatch(path, p) for p in filesystem_world_writable_whitelist]):\n sys.stderr.write(\"[world-writable] %s %s\\n\" % (self.fmt_mode_str(info.st_mode), path))\n failed = True\n if failed:\n Test.Fail(msg=\"Test failed\")",
"title": ""
},
{
"docid": "8ffe407236011d5ddfea62710e51f2d8",
"score": "0.6135178",
"text": "def is_device_link_fail(self):\n return self._tag == 'device_link_fail'",
"title": ""
},
{
"docid": "3d9a697a78f12fdb87286c0b5c3558e3",
"score": "0.6132719",
"text": "def test_symlink_file5(self):\n src = os.path.join(self.rootdir,\"non-existing-file\") \n target = os.path.join(self.targetdir,os.path.basename(src))\n with self.assertRaises(transfer.TransferError):\n transfer.SymlinkAgent(src,target).transfer()",
"title": ""
},
{
"docid": "771b570903a84e5ab0839ea57aff428e",
"score": "0.6085972",
"text": "def silent_symlink(src, dst):\n try:\n os.symlink(src, dst)\n except OSError:\n pass",
"title": ""
},
{
"docid": "b346d5a2d9646b6ff0a8844a820c0b6e",
"score": "0.60828763",
"text": "def readlink(path):\n if not path:\n return path\n\n if path[0] == \"~\":\n path = os.path.expanduser(path)\n\n return run_bash_cmd([\"readlink\", \"-f\", path]) or path",
"title": ""
},
{
"docid": "bdc8a241fd5bbbd50d166cdb6622d9a4",
"score": "0.6063484",
"text": "def _is_valid_path(path):\r\n try:\r\n urlresolvers.resolve(path)\r\n return True\r\n except urlresolvers.Resolver404:\r\n return False",
"title": ""
},
{
"docid": "a8bae97837e4ff75dd6ca0ac4d853a0f",
"score": "0.6037345",
"text": "def exists(path, followlinks=True):\n if path is None:\n raise Tools.exceptions.Value(\"Path is not passed in system.fs.exists\")\n found = False\n try:\n st = os.lstat(path)\n found = True\n except (OSError, AttributeError):\n pass\n if found and followlinks and stat.S_ISLNK(st.st_mode):\n if MyEnv.debug:\n Tools.log(\"path %s exists\" % str(path.encode(\"utf-8\")))\n linkpath = os.readlink(path)\n if linkpath[0] != \"/\":\n linkpath = os.path.join(Tools.path_parent(path), linkpath)\n return Tools.exists(linkpath)\n if found:\n return True\n # Tools.log('path %s does not exist' % str(path.encode(\"utf-8\")))\n return False",
"title": ""
},
{
"docid": "dd3c3cb6566686327f09cc836e98684d",
"score": "0.60363925",
"text": "def check_path(p):\n if not os.path.exists(p):\n msg = 'Error: Path \"{}\" does not exist.'.format(p)\n print_help(msg)",
"title": ""
},
{
"docid": "b6931c31cb4ea78fdd2dcb5957104c1b",
"score": "0.6035318",
"text": "def check_path(path):\n if (not os.path.isfile(path)):\n raise Exception('The path specified does not point to a file')",
"title": ""
},
{
"docid": "bfdbda55742febd9b0ba06e3c8969c13",
"score": "0.6016378",
"text": "def symlinks_test(dirname):\n\n links = []\n\n for (path, dirs, files) in os.walk(dirname):\n if path.find('/.git') > -1:\n continue\n\n # search for directory links\n for d in dirs:\n dname = os.path.join(path, d)\n if os.path.islink(dname):\n links.append(os.path.relpath(dname, dirname)+'/')\n\n # search for file links\n for f in files:\n fname = os.path.join(path, f)\n if os.path.islink(fname):\n links.append(os.path.relpath(fname, dirname))\n\n if links:\n return (FAIL, '\\n'.join(links))\n else:\n return (PASS, \"None\")",
"title": ""
},
{
"docid": "a97ceeb3e2a1ef2d4f8add3a4d41507d",
"score": "0.60025454",
"text": "def test_force_symlink_overwrite(tmpdir):\n src = join(str(tmpdir), \"src\")\n dst = join(str(tmpdir), \"dst\")\n\n with open(src, \"w\") as f:\n f.write(\"Correct.\")\n\n with open(dst, \"w\") as f:\n f.write(\"Wrong.\")\n\n utils.force_symlink(src, dst)\n assert os.path.islink(dst)\n\n with open(dst, \"r\") as f:\n assert \"Correct\" in f.read()",
"title": ""
},
{
"docid": "5fbf806aa189307c157cd564dbcd1a8d",
"score": "0.59952956",
"text": "def fix_all_git_symlinked(topdir):\n # Determine whether or not symlinks need fixing (they don’t if installing\n # from a .tar.gz file)\n with io.open(topdir + r'\\nikola\\data\\symlink-test-link.txt', 'r', encoding='utf-8-sig') as f:\n text = f.read()\n if text.startswith(\"NIKOLA_SYMLINKS=OK\"):\n return -1\n with io.open(topdir + r'\\nikola\\data\\symlinked.txt', 'r', encoding='utf-8-sig') as f:\n text = f.read()\n # expect each line a relpath from git or zip root,\n # smoke test relpaths are relative to git root\n if text.startswith('.'):\n raise Exception(r'Bad data in \\nikola\\data\\symlinked.txt')\n relnames = text.split('\\n')\n relnames = [name.strip().replace('/', '\\\\') for name in relnames]\n relnames = [name for name in relnames if name]\n\n failures = 0\n for name in relnames:\n # build dst path and do some basic validation\n dst = os.path.join(topdir, name)\n # don't access files outside topdir\n if not is_file_into_dir(dst, topdir):\n continue\n if os.path.isdir(dst):\n # assume the file was de-symlinked\n continue\n\n # build src path and do some basic validation\n with io.open(os.path.join(topdir, dst), 'r', encoding='utf-8-sig') as f:\n text = f.read()\n dst_dir = os.path.dirname(dst)\n try:\n src = os.path.normpath(os.path.join(dst_dir, text))\n if not os.path.exists(src):\n # assume the file was de-symlinked before\n continue\n # don't access files outside topdir\n if not is_file_into_dir(src, topdir):\n continue\n except Exception:\n # assume the file was de-symlinked before\n continue\n\n # copy src to dst\n try:\n if os.path.isdir(src):\n os.unlink(dst)\n shutil.copytree(src, dst)\n else:\n shutil.copy2(src, dst)\n except Exception:\n failures += 1\n print(\"*** copy failed for\")\n print(\"\\t src:\", src)\n print(\"\\t dst:\", dst)\n\n return failures",
"title": ""
},
{
"docid": "727d2ea9be0ae6be316b42e299d3ad3b",
"score": "0.599236",
"text": "def test_hardlink(self):\n # Prevent errors from being caught\n self.tar.errorlevel = 1\n\n self.tar.extract(\"0-REGTYPE\", dirname())\n try:\n # Extract 1-LNKTYPE which is a hardlink to 0-REGTYPE\n self.tar.extract(\"1-LNKTYPE\", dirname())\n except EnvironmentError, e:\n import errno\n if e.errno == errno.ENOENT:\n self.fail(\"hardlink not extracted properly\")",
"title": ""
},
{
"docid": "e9745941b98c710c4c4ec6d21f02e9e1",
"score": "0.59648645",
"text": "def test_symlink_folder7(self):\n src = self.rootdir\n target = os.path.join(self.targetdir,os.path.basename(src))\n with mock.patch.object(\n transfer.os,\n 'symlink',\n side_effect=OSError(\"Mocked error\")):\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()",
"title": ""
},
{
"docid": "01c8991464103ff5c306a31023d7fce3",
"score": "0.5945459",
"text": "def can_symlink_to(self, outfile_name, symlink_candidate):\n if symlink_candidate == outfile_name:\n # Symlinking to ourself is *not* ok.\n return False\n\n if symlink_candidate in self._db.transaction_keys():\n # Don't try to symlink if our target file is being updated\n # itself; we can't tell whether we're 'equivalent' to it\n # or not if it's currently being modified!\n return False\n\n symlink_mtime_map = self._db.get(symlink_candidate)\n if symlink_mtime_map is None:\n # We don't have up-to-date-ness information on the candidate.\n return False\n\n # outfile_name is currently in a transaction (changed_files()\n # returned false), so we have to get its mtime info that way.\n outfile_mtime_map = self._db.get_transaction(outfile_name)\n\n # Get a copy of mtime_maps that do not include the output file.\n pruned_outfile_mtime_map = outfile_mtime_map.copy()\n pruned_outfile_mtime_map.pop(outfile_name)\n pruned_symlink_mtime_map = symlink_mtime_map.copy()\n pruned_symlink_mtime_map.pop(symlink_candidate)\n\n if (frozenset(pruned_outfile_mtime_map.keys()) !=\n frozenset(pruned_symlink_mtime_map.keys())):\n # This means symlink_candidate has different deps that we\n # do (common case).\n return False\n\n for (k, v) in pruned_outfile_mtime_map.iteritems():\n # This means symlink_candidate has the same deps as us,\n # but those deps aren't up to date.\n # This holds because infile_map has the *current* mtimes of\n # the input files, and if the (db-based) values of\n # symlink_candidate don't match, that means it's out of date.\n symlink_v = pruned_symlink_mtime_map.get(k, (None, None, None))\n if not file_info_equal(v, symlink_v):\n return False\n\n if not file_info_equal(get_file_info(symlink_candidate),\n symlink_mtime_map[symlink_candidate]):\n # This finishes off the 'other_outfile is up-to-date' check.\n # We tested all the input files are up-to-date above, now we\n # need to test the output file too. Note we never bother to\n # do the crc check here; we assume that as a generated file,\n # other_outfile will not suffer from the\n # 'git-changed-my-mtime' problem.\n return False\n\n return True",
"title": ""
},
{
"docid": "1e9b2fc97f39ea65d0aff639dfc352fb",
"score": "0.5944636",
"text": "def verify_path(self, path):\n if Path(path).exists():\n return True\n return False",
"title": ""
},
{
"docid": "28f897359724e00998034cf686012bf2",
"score": "0.59438217",
"text": "def check_path(path):\n assert isinstance(path, basestring), \"Expected string or unicode, found %s.\" % type(path)\n try:\n open(path, \"r\")\n except IOError:\n raise IOError('File does not exist: %s' % path)\n return path",
"title": ""
},
{
"docid": "1d2dcd5fddfe2a01bb5a15519baa67b1",
"score": "0.59393996",
"text": "def test_symlink_folder8(self):\n src = self.rootdir\n target = self.targetdir\n with mock.patch.object(\n transfer.os.path,\n 'exists',\n side_effect=Exception(\"Mocked error\")):\n with self.assertRaises(Exception):\n transfer.SymlinkAgent(src,target).transfer()",
"title": ""
},
{
"docid": "a04cd212041a2c05af0b7a22f90a585d",
"score": "0.5926641",
"text": "def is_wsl2_path(path: p.PurePath) -> bool:\n return re.match(r\"^/mnt/[a-z]/\", path.as_posix()) is not None",
"title": ""
},
{
"docid": "72eaf06e2133216050eaec901507e2aa",
"score": "0.59235525",
"text": "def S_ISLNK(mode):\n return S_IFMT(mode) == S_IFLNK",
"title": ""
},
{
"docid": "2b55905af587ce30f9e7fd3e568d64b0",
"score": "0.59146184",
"text": "def is_malformed_path(self):\n return self._tag == 'malformed_path'",
"title": ""
},
{
"docid": "b32a7f3b0b52be5499eccabc3f8c8143",
"score": "0.59071994",
"text": "def is_link_ok(link: str) -> bool:\n return not any([re.search(exc, link) for exc in excludes])",
"title": ""
},
{
"docid": "e1dfb6e5ef886289956a028ef3bd3d34",
"score": "0.5889858",
"text": "def is_safe_path(basedir, path, follow_symlinks=True):\n if basedir is None:\n return True\n if follow_symlinks:\n return os.path.realpath(path).startswith(basedir.encode('utf-8'))\n return os.path.abspath(path).startswith(basedir.encode('utf-8'))",
"title": ""
},
{
"docid": "d5ffdb8f73ffd102df61f9778afce65d",
"score": "0.5889097",
"text": "def valid_permlink(permlink, allow_empty=False):\n if not permlink:\n assert allow_empty, 'permlink cannot be blank'\n return \"\"\n assert isinstance(permlink, str), 'permlink must be string'\n assert len(permlink) <= 256, \"invalid permlink length\"\n return permlink",
"title": ""
},
{
"docid": "d5ffdb8f73ffd102df61f9778afce65d",
"score": "0.5889097",
"text": "def valid_permlink(permlink, allow_empty=False):\n if not permlink:\n assert allow_empty, 'permlink cannot be blank'\n return \"\"\n assert isinstance(permlink, str), 'permlink must be string'\n assert len(permlink) <= 256, \"invalid permlink length\"\n return permlink",
"title": ""
},
{
"docid": "7413b59212368171f2e2be09dfcd62d8",
"score": "0.58867145",
"text": "def check_links(params):\n\n\tfor s in _file_links:\n\t\t#print \"check:\", s, cfg.out_dir\n\t\tout_file = os.path.join(cfg.out_dir, s)\n\t\tif not os.path.exists(out_file):\n\t \t\twarning(\"%s: invalid link to '%s'\" % (_file_links[s], s))",
"title": ""
},
{
"docid": "e62c55844b430153e153df0db262b290",
"score": "0.588417",
"text": "def _resolve_link(path):\r\n paths_seen = []\r\n while islink(path):\r\n if path in paths_seen:\r\n # Already seen this path, so we must have a symlink loop\r\n return None\r\n paths_seen.append(path)\r\n # Resolve where the link points to\r\n resolved = os.readlink(path)\r\n if not isabs(resolved):\r\n dir = dirname(path)\r\n path = normpath(join(dir, resolved))\r\n else:\r\n path = normpath(resolved)\r\n return path",
"title": ""
},
{
"docid": "c8b85174b6188a7a19df4eff61790b94",
"score": "0.58817095",
"text": "def hardlink(source, link_name):\n try:\n os.link(source, link_name)\n except FileExistsError:\n # It's possible that the user created a different file with the same name as the\n # one we're trying to download. Thus we need to check the if the inode is different\n # and raise an error in this case.\n source_stat = os.stat(source)\n dest_stat = os.stat(link_name)\n # Check device first because different drives can have the same inode number\n if source_stat.st_dev != dest_stat.st_dev or source_stat.st_ino != dest_stat.st_ino:\n raise",
"title": ""
},
{
"docid": "962858e7a2194d210b9dfedd9b981e87",
"score": "0.5866979",
"text": "def _force_symlink(\n root: Path, target: Union[str, PurePath], link_to: Union[str, Path]\n) -> None:\n current_symlink = root.joinpath(target)\n try:\n current_symlink.unlink()\n except OSError:\n pass\n try:\n current_symlink.symlink_to(link_to)\n except Exception:\n pass",
"title": ""
},
{
"docid": "adf4744cb862d5219816f94cf0ef9f77",
"score": "0.58613175",
"text": "def _process_symlink(self, target, path): #aka target_path, dotfile_path\n\n if not os.path.lexists(target):\n self._error = True\n print('\\tmissing target: {}'.format(target))\n return ('DOTFILE NOT FOUND', None)\n\n if not os.path.lexists(path):\n self._changed = True\n if self.args.test:\n return ('missing', None)\n else:\n create_dotfile_link(target, path)\n return ('missing', 'synced')\n\n existing_realpath = os.path.realpath(path)\n if existing_realpath != os.path.realpath(target):\n if self.args.debug:\n print('\\tCurrent target path: {}'.format(existing_realpath))\n status = 'unmanaged' if os.path.lexists(existing_realpath) else 'broken'\n\n if self.args.verbose:\n pass # XXX: TODO: ...\n\n if status != 'broken' and not self.args.force:\n return (status, 'skipping')\n self._changed = True\n if self.args.test:\n return (status, None)\n\n #import pdb; pdb.set_trace()\n remove_any(path)\n create_dotfile_link(target, path)\n return (status, 'removed; synced')\n\n return ('ok', None)",
"title": ""
},
{
"docid": "0f124a12bcf057e1e114461e076db3da",
"score": "0.58529246",
"text": "def _checkLink(link):\n try:\n link = link.attrs['href']\n except KeyError:\n return -1\n if link[0] == '/':\n link = '://'.join(list(urllib.parse.urlparse(self.address)[0:2])) + link\n elif re.match(r'^mailto|^#', link):\n return None\n good = ['200', '206', '301', '302']\n try:\n status, response = self.http.request(link)\n if status['status'] not in good:\n self.badlinks.append((status['status'], link))\n else:\n self.goodlinks.append(link)\n except httplib2.ServerNotFoundError:\n self.other.append((\"httplib2.ServerNotFoundError\", link))\n except httplib2.ssl.SSLError:\n self.other.append((\"httplib2.ssl.SSLError\", link))\n except ConnectionResetError:\n self.other.append((\"ConnectionResetError\", link))\n except UnicodeError:\n self.other.append((\"UnicodeError\", link))",
"title": ""
},
{
"docid": "af98442fe78a61c211571253d3a076e5",
"score": "0.58304495",
"text": "def internal_link_checker(filepath, html_str):\n\n # Flag to determine if internal link is broken\n internal_link_error = False\n\n # \n\n problems = []\n relative_links = []\n\n # find all links\n for prefix in [\"href\", \"src\"]:\n\n links = re.findall(\n f\"{prefix}\\s?=\\s?[\\\"']([{allowed_in_link}]+)[\\\"']\", html_str)\n\n # check if link has a dest\n for link in links:\n\n # Check if link is relative path\n is_relative = check_if_relative_link(link)\n\n # Add to relative links list if relative\n if is_relative:\n if not link in relative_links:\n relative_links.append(link)\n\n # Get correct path\n link = get_correct_link(link)\n\n # Check if link is in use\n check_if_link_in_use(filepath, link)\n\n if link in links_list:\n if links_list[link]:\n if link not in problems:\n problems.append(link) \n else:\n if internal_link_test(link):\n problems.append(link)\n links_list[link] = True\n\n if not internal_link_error:\n internal_link_error = True\n else:\n links_list[link] = False\n\n return problems, relative_links, internal_link_error",
"title": ""
},
{
"docid": "8a8c415ec1777ae102d937832bd39339",
"score": "0.5820183",
"text": "def symlink (self, src, dst):\n if self.system ('/bin/ln -sf %s %s'%(src, dst)):\n raise OSError, 'can\\'t symlink [%s] as [%s]' % (src, dst)",
"title": ""
},
{
"docid": "bf5255caff8fa0c01af5e9a0e3b0a7b8",
"score": "0.58178663",
"text": "def test_symlink_file6(self):\n src = self.rootdir\n target = os.path.join(self.targetdir,\"target-file\")\n open(target,'w').close()\n with mock.patch.object(\n transfer.os,\n 'unlink',\n side_effect=OSError(\"Mocked error\")):\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()",
"title": ""
},
{
"docid": "9b019bad4cd351d9787fe08c2bc3f228",
"score": "0.57966596",
"text": "def symlink(self, req, link, parent, name):\n self.reply_err(req, EROFS)",
"title": ""
},
{
"docid": "b9684816cb8583b1d44b66d7cd04ca40",
"score": "0.5782404",
"text": "def test_force_link(tmpdir):\n src = join(str(tmpdir), \"src\")\n dst = join(str(tmpdir), \"dst\")\n\n with open(src, \"w\") as f:\n f.write(\"Not empty.\")\n\n utils.force_link(src, dst)\n assert os.path.isfile(dst)\n assert not os.path.islink(dst)",
"title": ""
},
{
"docid": "6419b2fcb183ad206a852387f3bc8d86",
"score": "0.57806474",
"text": "def test_scan_files(self):\n self.assertFalse(fdf.valid_path('some thing wrong'))\n result = fdf.scan_files(join(getcwd(), 'test'))\n self.assertNotIn(join(getcwd(), 'test/symlink/symlink'), result)",
"title": ""
},
{
"docid": "e2fcb55acc8c1275ebfe5f75e72876a0",
"score": "0.57789207",
"text": "def test_non_html_links_are_path_sensitive(self):\n validator = self._create_validator(\n \"\"\"Use [this CSV](data.csv) for the exercise.\"\"\")\n self.assertFalse(validator._validate_links())",
"title": ""
},
{
"docid": "169daa1a343f03131a9d00db91a8b596",
"score": "0.5773865",
"text": "def test_symlink_folder4(self):\n src = os.path.join(self.rootdir) \n target = os.path.join(self.targetdir)\n with mock.patch('taca.utils.transfer.os.path') as mockobj:\n mockobj.ismount.return_value = False\n mockobj.isfile.return_value = False\n mockobj.islink.return_value = False\n mockobj.isdir.return_value = False\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()",
"title": ""
},
{
"docid": "7a3cd10319fcfb6990ceba408de225a7",
"score": "0.5773567",
"text": "def is_file_link(self, link):\n return link.startswith(\"###file\") or link.startswith('/repository') or \\\n (link.startswith('../') and '/files/' in link)",
"title": ""
},
{
"docid": "78c2fa09a69a0c70db6567fbff768f2d",
"score": "0.57712406",
"text": "def provide_link(dest, src):\n if not os.path.exists(dest):\n # strip trailing /'s just in case it exists\n os.symlink(src, dest.rstrip(\"/\"))",
"title": ""
},
{
"docid": "f5141a7e126b929ce6f8259580dbeb5c",
"score": "0.5764898",
"text": "def filepath_validator(path):\n if isinstance(path, str):\n path = Path(path)\n return path.exists() and path.isfile()",
"title": ""
},
{
"docid": "308f791745e59acf2880a0ec1831b152",
"score": "0.5761583",
"text": "def test_symlink_lock(self):\n\n # Get an image object and tests its manual lock mechanism.\n api_obj = self.get_img_api_obj()\n img = api_obj.img\n\n # Verify a lock file is created.\n img.lock()\n lfpath = os.path.join(img.imgdir, \"lock\")\n self.assertTrue(os.path.exists(lfpath))\n img.unlock()\n os.remove(lfpath)\n\n # Make lock file a symlink by pointing it to a random file .\n tmp_file = os.path.join(img.imgdir, \"test_symlink\")\n fo = open(tmp_file, 'wb+')\n fo.close()\n os.symlink(tmp_file, lfpath)\n\n # Verify that both pkg install and refresh generate an error\n # if the lock file is a symlink.\n self.pkg(\"install foo\", su_wrap=True, exit=1)\n self.assertTrue(\"contains a symlink\" in self.errout)\n\n self.pkg(\"refresh --full\", su_wrap=True, exit=1)\n self.assertTrue(\"contains a symlink\" in self.errout)",
"title": ""
},
{
"docid": "18658f8e04412069e3a03d80b360b5b3",
"score": "0.57601094",
"text": "def check_path_existence(fpath: str) -> bool:\n return os.path.exists(fpath)",
"title": ""
},
{
"docid": "31411b5398f85370a62fa13f46f581a5",
"score": "0.5740772",
"text": "def validate_path(self, path):\n if not os.path.exists(path):\n raise IOError('Path {} does not exist'.format(path))\n return True",
"title": ""
},
{
"docid": "042985e4ccc6c626626e50eb20ef3c8a",
"score": "0.5739684",
"text": "def ismount(path):\r\n try:\r\n s1 = os.lstat(path)\r\n s2 = os.lstat(join(path, '..'))\r\n except os.error:\r\n return False # It doesn't exist -- so not a mount point :-)\r\n dev1 = s1.st_dev\r\n dev2 = s2.st_dev\r\n if dev1 != dev2:\r\n return True # path/.. on a different device as path\r\n ino1 = s1.st_ino\r\n ino2 = s2.st_ino\r\n if ino1 == ino2:\r\n return True # path/.. is the same i-node as path\r\n return False",
"title": ""
}
]
54c87c218016afc006e423f7687dcaee
Returns a boolean indicating whether this type is an 'interface' type that is implemented in Gecko. At the moment, this returns true for all interface types that are not types from the TypedArray spec.
[
{
"docid": "50186e06c1db50f28ee3b3f7d75a25e9",
"score": "0.700691",
"text": "def isGeckoInterface(self):\n return self.isInterface() and not self.isSpiderMonkeyInterface()",
"title": ""
}
]
[
{
"docid": "757ffa3fd3a6bd9841075b459360f26d",
"score": "0.49190748",
"text": "def is_enactable(self, as_of):\n return \\\n not self.disabled and \\\n not self.archived and \\\n not self._is_finished(as_of) and \\\n self._is_ready(as_of) and \\\n not self._is_billing_complete()",
"title": ""
}
]
[
{
"docid": "4c04ec54a498200fddebe61bfad145c7",
"score": "0.6974328",
"text": "def enact(self, billing_cycle, disable_if_done=True):\n as_of = billing_cycle.date_range.lower\n if not self.is_enactable(as_of):\n raise CannotEnactUnenactableRecurringCostError(\n \"RecurringCost {} is unenactable.\".format(self.uuid)\n )\n\n if self.has_enacted(billing_cycle):\n raise RecurringCostAlreadyEnactedForBillingCycle(\n 'RecurringCost cost {} already enacted for {}'.format(self, billing_cycle)\n )\n\n with db_transaction.atomic():\n recurred_cost = RecurredCost(\n recurring_cost=self,\n billing_cycle=billing_cycle,\n )\n recurred_cost.make_transaction()\n recurred_cost.save()\n\n if disable_if_done:\n self.disable_if_done(billing_cycle)",
"title": ""
},
{
"docid": "b7445a2491181d4ec5c6d4f004d143bf",
"score": "0.6717582",
"text": "def has_enacted(self, billing_cycle):\n return RecurredCost.objects.filter(\n recurring_cost=self,\n billing_cycle=billing_cycle,\n ).exists()",
"title": ""
},
{
"docid": "a365ef9fd5b0a002ccf6bc7bdc0e6573",
"score": "0.62935936",
"text": "def make_eligible(self):\n pass",
"title": ""
},
{
"docid": "d8dff91131c125b350fd2012d9790602",
"score": "0.5993728",
"text": "def charge_recurring(self, grace_period=None):\n pass",
"title": ""
},
{
"docid": "9039e5f6aa07f9bcc62aac117fff3c64",
"score": "0.5735424",
"text": "def confirmed(self, cr, uid, ids, context=None):\n\tallow_archive_line_obj = self.pool.get('services.contracts.allowances.lines')\n for record in self.browse(cr, uid, ids, context=context):\n\t\tif not record.allowances_lines_before :\n \traise osv.except_osv(_('Partner Lines !'), _('Sorry no partner Lines!'))\n\n\t \tlines_ids = [line.id for line in record.allowances_lines_after]\n \tallow_archive_line_obj.unlink(cr,uid,lines_ids,context=context)\n\n\t\tfor lines in record.allowances_lines_before:\n\t\t\tif lines.percentage_rating < 0 or lines.percentage_rating > 100 :\n \t\traise osv.except_osv(_('Rate Error !'), _('Sorry you insert wrong rate ... rate is between (0,100)!'))\n \t\tamount_after_rate_id = allow_archive_line_obj.create(cr, uid, {\n \t\t\t\t'cost_of_rent':lines.cost_of_rent,\n \t\t\t\t'amount_untaxed':round (lines.amount_untaxed*lines.percentage_rating/100,2),\n \t\t\t\t'amount_tax':round(lines.amount_tax*lines.percentage_rating/100,2),\n \t\t\t\t'amount_total':round(lines.amount_total*lines.percentage_rating/100,2),\n \t\t\t\t'deduct_days':lines.deduct_days,\n \t\t\t\t'deduct_amount':lines.deduct_amount,\n \t\t\t\t'contract_id':lines.contract_id.id,\n\t\t\t\t\t'env_allow_id_after_rate':record.id,\n\t\t\t\t\t'type': 'after',\n 'category_id':lines.category_id.id,\n\t\t\t\t\t'percentage_rating':lines.percentage_rating,\n\n })\n\t\t\n \n self.write(cr, uid, ids, {'state':'confirmed'})\n return True",
"title": ""
},
{
"docid": "e06878f1f73ebe95cde824d74b51178a",
"score": "0.57182837",
"text": "def credit_deliverer():\n return True",
"title": ""
},
{
"docid": "072e3adb9a2b8b733955c2d0470f5cfd",
"score": "0.57024777",
"text": "def disable_costs():\n RecurringCost.objects.all().disable_if_done()",
"title": ""
},
{
"docid": "729f7ea408343845dae0e20a444735a6",
"score": "0.5669726",
"text": "def awaiting_payment(self):",
"title": ""
},
{
"docid": "46b89439cb1fb4ae0622c0559693f5b3",
"score": "0.56223905",
"text": "def test_add_recurring_schedule(self):\n pass",
"title": ""
},
{
"docid": "43c2e807bcff1aea5874de05585dc1b9",
"score": "0.55890375",
"text": "def test_renew_user_pending_cancel(self):\n self.braintree_customer.active = True\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.subscription_id = \"ValidSubscriptionID\"\n\n result = SubscriptionManager.renew(self.braintree_customer)\n self.assertEqual(\"ValidSubscriptionID\",result)\n self.assertFalse(self.braintree_customer.pending_cancel)",
"title": ""
},
{
"docid": "c4473da0917973c1c85d7b653ba0f05d",
"score": "0.5505976",
"text": "def _check_cost(self, cr, uid, ids, context=None):\n for enrich in self.browse(cr, uid, ids, context=context):\n if enrich.amount <= 0:\n raise osv.except_osv(_('ValidateError'), _('The Cost Must Be Greater Than Zero!'))\n return True",
"title": ""
},
{
"docid": "2e2212377ef5be3f290b7c7bf4d129b1",
"score": "0.5493613",
"text": "def test_subscribe_already_subscribed(self):\n self.braintree_customer.active = True\n self.braintree_customer.save()\n with self.assertRaises(BraintreeError):\n SubscriptionManager.subscribe(self.braintree_customer)\n\n # Check state not altered\n self.assertTrue(self.braintree_customer.active)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertIsNone(self.braintree_customer.expiry_date)",
"title": ""
},
{
"docid": "0eff043c23370b9890345712648bb3b8",
"score": "0.54642403",
"text": "def test_renew_attempt_on_active_subscription(self):\n self.braintree_customer.pending_cancel = False\n self.braintree_customer.active = True\n\n with self.assertRaises(BraintreeError):\n SubscriptionManager.renew(self.braintree_customer)",
"title": ""
},
{
"docid": "2878f031423ce10692b6087cd1179530",
"score": "0.5447805",
"text": "def make_transaction(self):\n if self.pk:\n raise CannotRecreateTransactionOnRecurredCost(\n 'The transaction for this recurred cost has already been created. You cannot create it again.'\n )\n\n amount = self.recurring_cost.get_amount(self.billing_cycle)\n\n # It is quite possible that there will be nothing to bill, in which\n # case we cannot create a transaction with no legs, nor can we create\n # legs with zero values. Therefore we don't create any transaction.\n if not amount:\n return None\n\n self.transaction = Transaction.objects.create(\n description='Created by recurring cost',\n date=self.billing_cycle.date_range.lower\n )\n\n # Use the SplitManager's custom queryset's split() method to get the\n # amount to be billed for each split\n splits = self.recurring_cost.splits.all().split(amount)\n\n # Create the transaction leg for the outbound funds\n # (normally to an expense account)\n self.transaction.legs.add(Leg.objects.create(\n transaction=self.transaction,\n amount=Money(amount, self.recurring_cost.currency),\n account=self.recurring_cost.to_account,\n ))\n\n for split, split_amount in splits:\n # Create the transaction legs for the inbound funds\n # (from housemate accounts)\n if split_amount:\n self.transaction.legs.add(Leg.objects.create(\n transaction=self.transaction,\n amount=Money(split_amount * -1, self.recurring_cost.currency),\n account=split.from_account,\n ))\n\n return self.transaction",
"title": ""
},
{
"docid": "ba47d041e96bc2b902f7e951534eb141",
"score": "0.5434562",
"text": "def _cost(self, action):\n raise NotImplementedError",
"title": ""
},
{
"docid": "0e3085b8493d861cc1b7d398c776e29a",
"score": "0.54062164",
"text": "def is_recurring(self):\n return self.__is_recurring",
"title": ""
},
{
"docid": "c624f7dcb4ff52e7b73c58f67c9e6318",
"score": "0.5402047",
"text": "def test_request_cancel_already_pending_cancel(self):\n self.braintree_customer.subscription_id = \"1234\"\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.save()\n\n self.assertFalse(SubscriptionManager.request_cancel(self.braintree_customer))\n self.assertTrue(self.braintree_customer.pending_cancel)",
"title": ""
},
{
"docid": "33fbd06b632f15c7255f0e18c5897445",
"score": "0.53812355",
"text": "def is_invited_pending_activation(self):\n if self.registration_method == self.INVITED \\\n and self.is_pending_activation():\n return True\n else:\n return False",
"title": ""
},
{
"docid": "ceb71fff2f7605e3d2c71500830dcbfa",
"score": "0.53572863",
"text": "def give_raise(self):\r\n self.hourly_pay = 12.00",
"title": ""
},
{
"docid": "a1633c21b982abbaba97996b7c87877d",
"score": "0.5347127",
"text": "def charge(self, other):\n if self.flag:\n self.credit += other\n return \"{} Tomans has been added to your card credit and now the credit of your card is {} Tomans\".format(\n other, self.credit)\n else:\n return \"Sorry, your card has expired.\"",
"title": ""
},
{
"docid": "8a0bca7e754388a34640f20c91afa68b",
"score": "0.534086",
"text": "def payment_approval(self, house_cost: (int, float)):\n if self.money_available >= house_cost: # Person has enough available money to make a deal with Realtor\n self.money_available -= house_cost\n print(f'Payment from {self.name} was approved')\n return True\n print(f'{self.name} doesn\\'t have enough money to buy this house')\n return False",
"title": ""
},
{
"docid": "a34fc2ccd08cf094015a4a7a71287468",
"score": "0.53131855",
"text": "def test_request_cancel_active_subscription(self):\n self.braintree_customer.subscription_id = \"1234\"\n self.braintree_customer.pending_cancel = False\n self.braintree_customer.save()\n self.assertTrue(SubscriptionManager.request_cancel(self.braintree_customer))\n self.assertTrue(self.braintree_customer.pending_cancel)",
"title": ""
},
{
"docid": "3e8c9b966e3614e15ccd1ab4a2563ef9",
"score": "0.5306632",
"text": "def test_overlap(self):\r\n t = Expense(name = \"fake lunch\",\r\n amount = 1.,\r\n on = (WeeklyRecurring(FR,\r\n fromdt = self.fromdt,\r\n todt = self.todt),\r\n DailyRecurring(fromdt = self.fromdt, \r\n todt = self.todt)))\r\n\r\n self.m.addTransaction(t)\r\n self.assertEqual(self.m.totalSaved(self.fromdt, self.todt), -365.)",
"title": ""
},
{
"docid": "fa3965aee40434d2d49b4ec667e75657",
"score": "0.5294787",
"text": "def reports_editable(self):\n end_plus_time = self.datetime_end + datetime.timedelta(days=CCR_DELTA)\n return timezone.now() < end_plus_time",
"title": ""
},
{
"docid": "bc118ce9f8f76044d0d8341c976cc671",
"score": "0.5265665",
"text": "def make_payment(self, cost):\n self.process_coins()\n if self.money_received >= cost:\n change = round(self.money_received - cost, 2)\n print(f\"Here is {self.CURRENCY}{change} in change.\")\n self.profit += cost\n self.money_received = 0\n return True\n else:\n print(\"Sorry that's not enough money. Money refunded.\")\n self.money_received = 0\n return False",
"title": ""
},
{
"docid": "58a11d04b563ecf30411d522d7a9987d",
"score": "0.5261785",
"text": "def test_create_sugar_cpn_exp_task(self):\n offset_days = 2\n coupon = Coupon.objects.get(id=416)\n coupon.expiration_date = (datetime.date.today() + \n datetime.timedelta(days=offset_days))\n coupon.save()\n self.assertEqual(create_sugar_coupon_expire_task(MockSugar(), \n offset_days=offset_days), None)",
"title": ""
},
{
"docid": "27b0305fb2f568ecd0568b90978b95e3",
"score": "0.52602494",
"text": "async def resign(self, ctx):\n currency = await bank.get_currency_name(ctx.guild)\n await self.config.user(ctx.author).gameRole.set(\"User\")\n await ctx.send(\n f\"{ctx.author} has spent 10,000 {currency}- to resign from their current job.\"\n )",
"title": ""
},
{
"docid": "8491f0b61b51e158a8d7c69100f4d319",
"score": "0.5213568",
"text": "def test_subscription_transaction_declined_suspend(self):\n\n # We'll create a plan that starts here\n start_date = dt.date(2019, 1, 1)\n\n # And the trial date ends here too\n trial_end_date = dt.date(2019, 1, 1)\n\n # The customer will use some metered features here\n metered_usage_on = dt.date(2019, 1, 10)\n\n # Docs will be generated to bill here.\n prev_billing_date = dt.date(2019, 1, 3)\n\n # So, the customer grace period ends here.\n # First billing interval: dt.date(2019, 2, 1)\n # \n billing_grace_exp = dt.date(2019, 2, 3)\n\n # The next billing check should discover that the subscription\n # is unpaid.\n # Billing due date is: dt.date(2019, 2, 6)\n # With the grace period: dt.date(2019, 2, 9)\n # \n billing_post_grace_check = dt.date(2019, 2, 10)\n\n # Create a customer\n #\n customer = CustomerFactory.create(sales_tax_percent=Decimal('0.00'),\n payment_due_days=3)\n PaymentMethodFactory.create(\n payment_processor=triggered_processor, customer=customer,\n canceled=False,\n verified=True,\n )\n\n # Create a metered feature\n #\n mf_price = Decimal('2.5')\n metered_feature = MeteredFeatureFactory(\n included_units_during_trial=Decimal('0.00'),\n price_per_unit=mf_price)\n currency = 'USD'\n\n # Crate a plan with metered features. Generate the invoice after\n # the 5 day trial period, the plan will be billed every 30 days.\n # \n generate_after = 5\n plan = PlanFactory.create(interval=Plan.INTERVALS.DAY,\n interval_count=30,\n generate_after=generate_after,\n enabled=True,\n amount=Decimal('20.00'),\n trial_period_days=1,\n metered_features=[metered_feature],\n currency=currency)\n\n # Subscribe the customer\n #\n subscription = SubscriptionFactory.create(\n plan=plan, start_date=start_date, customer=customer)\n subscription.activate()\n subscription.save()\n\n # Log some metered usage\n consumed_1 = Decimal('5.00')\n consumed_2 = Decimal('5.00')\n mf_log = MeteredFeatureUnitsLogFactory.create(\n subscription=subscription,\n metered_feature=metered_feature,\n start_date=metered_usage_on,\n end_date=subscription.trial_end,\n consumed_units=consumed_1)\n\n # Generate the docs\n call_command('generate_docs',\n billing_date=prev_billing_date,\n stdout=self.output)\n\n proforma = Proforma.objects.first()\n\n assert proforma.proforma_entries.count() != 0\n assert Subscription.objects.all().count() == 1\n assert Invoice.objects.all().count() == 0\n assert Proforma.objects.all()[0].total > Decimal('0.00')\n\n # Consume more units\n mf_log.consumed_units += consumed_2\n mf_log.save()\n\n call_command('generate_docs',\n billing_date=billing_grace_exp,\n stdout=self.output)\n\n assert Proforma.objects.all().count() != 0\n assert Invoice.objects.all().count() == 0\n\n for pf in Proforma.objects.all():\n # # Issue the proforma to generate transactions\n # proforma = Proforma.objects.all()[1]\n pf.issue()\n pf.save()\n\n self.assertEqual(pf.state, Proforma.STATES.ISSUED)\n # Fail the transaction\n for tx in pf.transactions:\n # tx = proforma.transactions[0]\n tx.fail()\n tx.save()\n self.assertEqual(tx.state, Transaction.States.Failed)\n\n assert Transaction.objects.all().count() != 0\n\n call_command('check_subscriptions',\n billing_date=billing_post_grace_check,\n stdout=self.output)\n\n subscr = Subscription.objects.first()\n\n # Scan for subscriptions with unpaid documents\n logging.debug(\"subscr %s\" % subscr)\n self.assertEqual(subscr.state, Subscription.STATES.CANCELED)",
"title": ""
},
{
"docid": "5417e376b07167245209d7488eceae5c",
"score": "0.5208941",
"text": "def test_get_active_coupon(self):\n coupon = COUPON_FACTORY.create_coupon()\n slot = SLOT_FACTORY.create_slot(coupon=coupon)\n future_date = datetime.date.today() + datetime.timedelta(weeks=1)\n coupon.expiration_date = future_date\n coupon.save()\n self.assertEqual(slot.get_active_coupon(), coupon)",
"title": ""
},
{
"docid": "257ba26e9929795ad5f5632d4cddacf1",
"score": "0.52071846",
"text": "def test_create_sugar_cc_exp_task(self):\n current_date = datetime.datetime.now()\n order = Order.objects.create(billing_record_id=114)\n credit_card = CreditCard.objects.get(id=500)\n credit_card.exp_month = int(current_date.strftime(\"%m\"))\n credit_card.exp_year = int(current_date.strftime(\"%y\"))\n credit_card.encrypt_cc('4111111111111111')\n credit_card.clean()\n credit_card.save()\n Payment.objects.create(order=order, credit_card=credit_card,\n amount='50.00', method='C', status='p')\n self.assertEqual(create_sugar_cc_expire_task(MockSugar(), \n test_mode=True), None)",
"title": ""
},
{
"docid": "eb78960a18fbea5f420521a73cc9d91e",
"score": "0.5201274",
"text": "def save(self, *args, **kwargs):\n if not self.pk:\n self.start_time_rent = datetime.date.today()\n self.end_time_rent = self.start_time_rent + datetime.timedelta(days=7)\n self.reservation.isrented = True\n self.reservation.save()\n return super(Rental, self).save(*args, **kwargs)",
"title": ""
},
{
"docid": "f499e3c7140c536233b59aa5018b00e0",
"score": "0.5198995",
"text": "def life_insurance_to_recive_total(self):\n pass",
"title": ""
},
{
"docid": "93e3ba91aae2b3005b29c825e9df6617",
"score": "0.51950544",
"text": "def pay_costs(self):\r\n cost = self.cost\r\n if cost:\r\n self.owner.player.char_ob.pay_money(cost)\r\n self.owner.player.msg(\"You pay %s coins for the event.\" % cost)",
"title": ""
},
{
"docid": "4a554eae4772018c64970255353249ec",
"score": "0.5190392",
"text": "def accepting(self):\n quote = self.get_quotes('labor')[0]\n quantity = max(0, quote.price - 14)\n self.create('labor', quantity)\n self.accept_quote_partial(quote, quantity)",
"title": ""
},
{
"docid": "da7b38cc190f388ab6d0549317e33158",
"score": "0.51902777",
"text": "def recap(self, request, pk=None):\n retreat = self.get_object()\n # This is a hard-coded limitation to allow anonymous users to call\n # the function.\n time_limit = retreat.end_time - timedelta(days=1)\n if timezone.now() < time_limit:\n response_data = {\n 'detail': \"Retreat ends in more than 1 day.\"\n }\n return Response(response_data, status=status.HTTP_200_OK)\n\n # Notify a user for every reserved seat\n emails = []\n for reservation in retreat.reservations.filter(\n is_active=True,\n post_event_send=False):\n send_post_retreat_email(reservation.user, retreat)\n reservation.post_event_send = True\n reservation.save()\n emails.append(reservation.user.email)\n\n response_data = {\n 'stop': True,\n 'emails': emails\n }\n return Response(response_data, status=status.HTTP_200_OK)",
"title": ""
},
{
"docid": "40e1634797064f5c392aeeff54879a26",
"score": "0.5186644",
"text": "def check_costs(self):\r\n if self.cost > self.owner.player.char_ob.currency:\r\n self.add_error(\r\n \"celebration_tier\",\r\n \"You cannot afford to pay the cost of %s.\" % self.cost,\r\n )",
"title": ""
},
{
"docid": "d10a00c4c4117a81dd34a039b5016bec",
"score": "0.51843166",
"text": "def test_no_credit_change_for_same_subscription(self):\n credits = 20\n\n current_plan = Subscription.get_plan_by_id('pro')\n new_plan = Subscription.get_plan_by_id('pro')\n\n may_29_2015 = datetime.datetime(2015, 5, 29, 0, 0, 0)\n may_29_2015 = pytz.utc.localize(may_29_2015)\n\n credits = add_subscription_credits(credits, current_plan, new_plan,\n may_29_2015)\n\n assert credits == 20",
"title": ""
},
{
"docid": "f6cfe667dbc270d1f0777d11ac37720c",
"score": "0.51762897",
"text": "def test_remove_recurring_schedule(self):\n pass",
"title": ""
},
{
"docid": "ffd0405d5d1dcce4d6e9af01483ebde3",
"score": "0.5167032",
"text": "def confirmed(self):",
"title": ""
},
{
"docid": "eb1bb3838c74ae2e2fc33261a8bcad5d",
"score": "0.51638985",
"text": "def test_retire_rate_plan(self):\n pass",
"title": ""
},
{
"docid": "acd5fc643f60a16857bf0dbbe97c12f5",
"score": "0.5128787",
"text": "def add_to_excess(self) -> None:\n if self.msg.value <= 0:\n revert(\"No amount added to excess\")\n self._treasury_balance.set(self.icx.get_balance(self.address))\n self.FundReceived(self.msg.sender, self.msg.value, f\"{self.msg.value} added to excess\")",
"title": ""
},
{
"docid": "e170852adf1c091d578eada0184631b9",
"score": "0.51178837",
"text": "async def admin_credit(self, ctx, target: discord.Member, sum: int = 100):\n if is_registered(target.id):\n \n inventories = get_file(\"inventories\")\n inventories[str(target.id)][\"balance\"] += sum\n update_file(\"inventories\", inventories)\n\n embed = discord.Embed(color=admin_color)\n embed.set_author(name=\"🛠️ Admin\")\n embed.add_field(name=\"💰 Credit\",\n value=f\"{ctx.author.mention}, {target.mention} a été crédité de `{sum}` PO (pièces d'or)\")\n embed = set_footer(embed, ctx)\n await ctx.send(embed=embed)",
"title": ""
},
{
"docid": "41e253144f1d44f70f72725a3cdfd381",
"score": "0.5104723",
"text": "def test_plan_autorenew_at_expire(self):\n up = baker.make(\"UserPlan\", expire=date(2020, 1, 1))\n self.assertEqual(up.plan_autorenew_at(), date(2020, 1, 1))",
"title": ""
},
{
"docid": "6ba629d8534d92877f8ce279d15aafa1",
"score": "0.5101618",
"text": "def trigger_expiration_notices(at_time=None, nb_days=15, dry_run=False):\n\n def _handle_organization_notices(organization):\n if organization.processor_card_key:\n card = organization.retrieve_card()\n try:\n exp_month, exp_year = card['exp_date'].split('/')\n exp_date = datetime(year=int(exp_year),\n month=int(exp_month), day=1, tzinfo=at_time.tzinfo)\n if lower >= exp_date:\n LOGGER.info(\"payment method expires soon for %s\",\n organization)\n if not dry_run:\n signals.card_expires_soon.send(\n sender=__name__, organization=organization,\n nb_days=nb_days)\n except (KeyError, ValueError):\n # exp info is missing or the format is incorrect\n pass\n else:\n LOGGER.info(\"%s doesn't have a payment method attached\",\n organization)\n if not dry_run:\n signals.payment_method_absent.send(sender=__name__,\n organization=organization)\n\n at_time = datetime_or_now(at_time)\n lower = at_time + relativedelta(days=nb_days)\n upper = at_time + relativedelta(days=nb_days + 1)\n LOGGER.info(\n \"trigger notifications for subscription expiring within [%s,%s[ ...\",\n lower, upper)\n prev_organization = None\n subscription = None\n for subscription in Subscription.objects.valid_for(ends_at__gte=lower,\n ends_at__lt=upper).order_by('organization'):\n org = subscription.organization\n plan = subscription.plan\n\n try:\n if subscription.auto_renew:\n if plan.renewal_type == plan.AUTO_RENEW:\n if org.id != prev_organization:\n _handle_organization_notices(org)\n\n prev_organization = org.id\n else:\n if plan.renewal_type == plan.ONE_TIME:\n LOGGER.info(\"trigger upgrade soon for %s\", subscription)\n if not dry_run:\n signals.subscription_upgrade.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n elif plan.renewal_type == plan.REPEAT:\n LOGGER.info(\"trigger expires soon for %s\", subscription)\n if not dry_run:\n signals.expires_soon.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n except Exception as err: #pylint:disable=broad-except\n # We use `Exception` because the email server might be\n # unavailable but ConnectionRefusedError is not a subclass\n # of RuntimeError.\n LOGGER.exception(\"error: %s\", err)\n\n # flushing the last organization\n if subscription and subscription.organization.id != prev_organization:\n if subscription.auto_renew:\n if subscription.plan.renewal_type == subscription.plan.AUTO_RENEW:\n _handle_organization_notices(subscription.organization)",
"title": ""
},
{
"docid": "0c66ea0657cc0bf9e28af5aaeb6647c4",
"score": "0.5100977",
"text": "def redeem(self, instance, customer, save=True):\n start = timezone.now().date()\n end = start + relativedelta(months=self.duration)\n discount = Discount(instance=instance,\n coupon=self,\n start=start,\n end=end,\n customer=customer)\n discount.full_clean()\n if save:\n discount.save()\n return discount",
"title": ""
},
{
"docid": "be729754fa4102aa1c8b61477d0cdb82",
"score": "0.50970876",
"text": "def customer_wants_condiments(self):\n return True",
"title": ""
},
{
"docid": "58052ca7885a95388e8a943ad79fc9ee",
"score": "0.50891936",
"text": "def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 11, 10, tzinfo=dt_timezone.utc\n )\n self.ticketed_event.ticket_cost = 0\n self.ticketed_event.save()\n self.assertFalse(self.unpaid.cancelled)\n self.assertFalse(self.paid.cancelled)\n\n management.call_command('cancel_unpaid_ticket_bookings')\n # emails are sent to user per cancelled booking and studio once for all\n # cancelled bookings\n self.unpaid.refresh_from_db()\n self.paid.refresh_from_db()\n self.assertEqual(len(mail.outbox), 0)\n self.assertFalse(self.unpaid.cancelled)\n self.assertFalse(self.paid.cancelled)",
"title": ""
},
{
"docid": "c02baa7b4f7d3c8e78826cc17010a89a",
"score": "0.5078882",
"text": "def register_withdraw(self, withdraw_intent): \n if withdraw_intent > 0:\n self.teo.register_withdraw(self, withdraw_intent)",
"title": ""
},
{
"docid": "87a75db34bf124af171ddcacdf0705d9",
"score": "0.5062819",
"text": "def reckon(self):",
"title": ""
},
{
"docid": "3ef1115a68c9a4958c538af0517b7e7f",
"score": "0.5059867",
"text": "def reimport_eligible(self) -> bool:\n return pulumi.get(self, \"reimport_eligible\")",
"title": ""
},
{
"docid": "cabd4c0d5435e6616abe559534230b62",
"score": "0.5054001",
"text": "def test_add_taxation_strategy_to_rate_plan(self):\n pass",
"title": ""
},
{
"docid": "e3d7067817879f0fa2eb4466e3f35086",
"score": "0.50444573",
"text": "def can_act(self) -> bool:\n return self.cooldown < 1",
"title": ""
},
{
"docid": "e3d7067817879f0fa2eb4466e3f35086",
"score": "0.50444573",
"text": "def can_act(self) -> bool:\n return self.cooldown < 1",
"title": ""
},
{
"docid": "57a314d8c47829fe7aea4b854dab54cb",
"score": "0.5037591",
"text": "def restock(self):\n self.money = 9999",
"title": ""
},
{
"docid": "5b99cdf09d4409ff6cacc373e96ffd86",
"score": "0.5028068",
"text": "def may_send_escalation(self, source_type: str, escalation_reminder_cadence: timedelta) -> bool:\n with self.session.begin() as session:\n last_escalated = (\n session.query(EventRecord.escalated_at)\n .filter(EventRecord.source_type == source_type)\n .order_by(EventRecord.escalated_at.desc())\n .limit(1)\n .one_or_none()\n )\n\n if not last_escalated[0]:\n return True\n\n return last_escalated[0] <= datetime.utcnow() - escalation_reminder_cadence",
"title": ""
},
{
"docid": "846e2e0abaea6b50e2948f1d6060a39c",
"score": "0.50160515",
"text": "def filter(self, event, *args, **kwargs):\n return event[\"expiration_datetime\"] <= datetime.now()",
"title": ""
},
{
"docid": "201aa010ced8cf71f548ecf6d393575b",
"score": "0.5011571",
"text": "def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 10, 10, tzinfo=dt_timezone.utc\n )\n self.event.cost = 0\n self.event.save()\n self.assertEqual(\n self.unpaid.status, 'OPEN', self.unpaid.status\n )\n self.assertEqual(\n self.paid.status, 'OPEN', self.paid.status\n )\n management.call_command('cancel_unpaid_bookings')\n # emails are sent to user per cancelled booking and studio once for all\n # cancelled bookings\n unpaid_booking = Booking.objects.get(id=self.unpaid.id)\n paid_booking = Booking.objects.get(id=self.paid.id)\n self.assertEqual(len(mail.outbox), 0)\n self.assertEqual(\n unpaid_booking.status, 'OPEN', unpaid_booking.status\n )\n self.assertEqual(\n paid_booking.status, 'OPEN', paid_booking.status\n )\n\n # auto_cancelled set to True only on cancelled bookings\n self.assertFalse(unpaid_booking.auto_cancelled)\n self.assertFalse(paid_booking.auto_cancelled)",
"title": ""
},
{
"docid": "820d1f3307e066ab24821548eb04bf4c",
"score": "0.50115657",
"text": "def isOnSaleSoon(ticket):\n return ticket.start_time > timezone.now()",
"title": ""
},
{
"docid": "99f1843e58d485cff565bd73b3ba806c",
"score": "0.500794",
"text": "def _claim_iscore(self) -> None:\n iscore_details_dict = self._system.queryIScore(self.address)\n if iscore_details_dict['estimatedICX'] != 0:\n amount = iscore_details_dict[\"estimatedICX\"]\n self._system.claimIScore()\n self._daily_reward.set(amount)\n self._rate.set(self.getRate())\n self._total_lifetime_reward.set(self.getLifetimeReward() + amount)\n self._distributing.set(True)",
"title": ""
},
{
"docid": "800b7a21c89f88bfe6327f7bb4361f22",
"score": "0.5006686",
"text": "def should_pay_attention(self):\n return random.randint(1,100) > self.wander",
"title": ""
},
{
"docid": "d3c1c2ca303c52e34683b990d4edc7da",
"score": "0.50048196",
"text": "def test_authorize_pending_payment(self):\n pass",
"title": ""
},
{
"docid": "e9eeb30737a0056e7591f58ee9369f45",
"score": "0.5003403",
"text": "def acknowledge_visitor(visitor):\n visitor.acknowledged = True\n visitor.acknowledged_time = datetime.now()\n visitor.save()",
"title": ""
},
{
"docid": "a4fcf332d5161239a6433b289ae158ed",
"score": "0.50032485",
"text": "def resent(self):\n return 'Resent-Date' in self",
"title": ""
},
{
"docid": "42ef7fe9adf9b416d5c880d173273e84",
"score": "0.49881572",
"text": "def pre_approve(self, cr, uid, ids, context={}):\n \tfor voucher in self.browse(cr, uid, ids, context=context):\n \t if not voucher.department_id.analytic_account_id:\n \t raise osv.except_osv(_('Configration Check!'), _(\"Please add cost center for your department!\"))\n \t periods = self.pool.get('account.period').search(cr, uid, [('date_start','<=',voucher.date),('date_stop','>=',voucher.date),('company_id','=',voucher.company_id.id)], context=context)\n\n\n res=0.0\n if voucher.purpose:\n if not voucher.purpose.account_id: raise osv.except_osv(_('Warning!'), _('Please configure account for this purpose!')) \n voucher_line = {\n \t\t'voucher_id': voucher.id,\n \t\t'partner_id': voucher.partner_id.id,\n \t\t'untax_amount': voucher.amount,\n \t\t'amount': voucher.amount,\n 'name': voucher.narration,\n \t\t'type': 'dr',\n \t\t'account_analytic_id': voucher.department_id.analytic_account_id and voucher.department_id.analytic_account_id.id,\n 'account_id': voucher.purpose.account_id.id,\n \t }\n new_amount = res and res or voucher.amount \n voucher_line.update({'amount':new_amount,'untax_amount':new_amount})\n \t if voucher.line_ids :\n for line in voucher.line_ids:\n \t\t self.pool.get('account.voucher.line').write(cr, uid, line.id, {\n \t\t'voucher_id': voucher.id,\n \t\t'partner_id': voucher.partner_id.id,\n \t\t'untax_amount': res or line.amount,\n \t\t'amount': line.amount,\n 'name': voucher.narration,\n \t\t'type': 'dr',\n \t\t'account_analytic_id': line.account_analytic_id and line.account_analytic_id.id or voucher.department_id.analytic_account_id.id,\n 'account_id': voucher.purpose.account_id.id or line.account_id.id,\n \t }, context=context)\n \t else:\n\n \t\t new_voucher_line = self.pool.get('account.voucher.line').create(cr, uid, voucher_line, context=context)\n context.update({'purchase':True})\n self.create_budget_confirmation(cr, uid, [voucher.id], context)\n \tself.write(cr, uid, ids,{'state': 'preapprove','type':'purchase','ratification':True}, context=context)\n #cxt = context.copy()\n #cxt.update({'type':'ratification'})\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\n self.write(cr, uid, ids, {'state': 'approved'}, context=context)\n\n \t'''self.write(cr, uid, ids, {'state': 'preapprove'})\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\n self.write(cr, uid, ids, {'state': 'approve','type':'purchase','ratification':True}, context=context)'''\n return True",
"title": ""
},
{
"docid": "9bcdcd6a69d116b1f4b370a2e026f1a4",
"score": "0.49817756",
"text": "def test_update_rate_plan(self):\n pass",
"title": ""
},
{
"docid": "4acf948659222da3835bbf26fc3a9017",
"score": "0.49740785",
"text": "def test_carryover(self):\n carryover = Money(100)\n available = {Decimal(0.5): carryover}\n self.forecast(available)\n self.assertEqual(\n self.forecast.carryover,\n carryover)",
"title": ""
},
{
"docid": "3c2504305e30cfc1efa4189640cffd33",
"score": "0.4970885",
"text": "def recurring(self):\n return self.filter(total_billing_cycles__isnull=True)",
"title": ""
},
{
"docid": "29d0e95adc90beb142a910e5cbf8e54c",
"score": "0.4959831",
"text": "def activate_card(**kwargs):\n for card in ClientClubCard.objects.filter(\n date_begin__isnull=True, is_paid_activate=False):\n # period for activate\n days = card.club_card.period_activation\n if card.date_start + timedelta(days - 1) < date.today():\n card.activate()",
"title": ""
},
{
"docid": "12d51add250d35f7917a5213649c725c",
"score": "0.49592566",
"text": "def aspiration_criteria_satisfied(self,index):\n\t\tnewEnergy = self.currentEnergy + self._diffEi[index]#self.count_energy(state)\n\t\t# newTax = self._taxes[index]#self.count_tax(state=state)\n\t\ttmpState = copy(self.currentState)\n\t\ttmpState[index] = 1 - tmpState[index]\n\t\tnewTax = self.count_tax(state=tmpState)\n\t\toutput(\"Checking aspiration criteria: oldEnergy={}, current tax = {}; new energy = {}, new tax = {}\"\\\n\t\t\t\t\t \t\t\t\t.format(self.currentEnergy,self.currentTax,newEnergy,newTax),isDebug=True,tabsNum=1)\n\t\tif newEnergy < self.localMinimumEnergy or newTax < self.localMinimumTax:\n\t\t\treturn True\n\t\treturn False",
"title": ""
},
{
"docid": "b5cc8f4771dd58297a6e3b452182daef",
"score": "0.4951388",
"text": "def agree_to_tos(self, regr):\n return self.update_registration(\n regr.update(\n body=regr.body.update(\n agreement=regr.terms_of_service)))",
"title": ""
},
{
"docid": "75ab2bff6b751ae042892e7659f71902",
"score": "0.49507055",
"text": "def test_deny_pending_payment(self):\n pass",
"title": ""
},
{
"docid": "3edd57c4e43f7b72baf285f6459805fb",
"score": "0.49433905",
"text": "def test_4(self):\n c1 = Store.Customer(\"harold\", \"qcf\", False)\n self.assertFalse(c1.is_premium_member(), \"IS premium member\")",
"title": ""
},
{
"docid": "e9b6b7553ae5fcebc14a9b407f801a55",
"score": "0.4942659",
"text": "def test_rate_cost_type_valid(self):\n self.ocp_data[\"rates\"][0][\"tiered_rates\"] = [\n {\n \"unit\": \"USD\",\n \"value\": 0.22,\n \"usage\": {\"usage_start\": None, \"usage_end\": None},\n \"cost_type\": \"Infrastructure\",\n }\n ]\n\n with tenant_context(self.tenant):\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\n if serializer.is_valid(raise_exception=True):\n serializer.save()\n\n self.ocp_data[\"rates\"][0][\"tiered_rates\"] = [\n {\n \"unit\": \"USD\",\n \"value\": 0.22,\n \"usage\": {\"usage_start\": None, \"usage_end\": None},\n \"cost_type\": \"Supplementary\",\n }\n ]\n\n with tenant_context(self.tenant):\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\n if serializer.is_valid(raise_exception=True):\n serializer.save()",
"title": ""
},
{
"docid": "89995bce1c766439b816fae14b3a212a",
"score": "0.49375176",
"text": "def test_cancel_on_braintree_success(self):\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.active = True\n self.braintree_customer.subscription_id = self.cancellable_id\n\n SubscriptionManager.cancel_on_braintree(self.braintree_customer)\n\n self.assertEqual(\"\",self.braintree_customer.subscription_id)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertFalse(self.braintree_customer.active)",
"title": ""
},
{
"docid": "ceb541e8c03f9188a6ad804fc06af67f",
"score": "0.49262586",
"text": "def test_create_new_order_VIES_fault(self):\n rup = baker.make(\n \"RecurringUserPlan\",\n user_plan__user__billinginfo__country=\"CZ\",\n user_plan__user__billinginfo__tax_number=\"CZ0123\",\n amount=10,\n tax=11,\n )\n with no_connection():\n order = rup.create_renew_order()\n self.assertEqual(order.tax, 11)",
"title": ""
},
{
"docid": "b1d65a3322432bdf78bb8926583c4261",
"score": "0.49252683",
"text": "def test_charge_correct_for_regular_after_close_1_day(self):\n rental = create_test_rental(\n book=self.book3,\n customer=self.user1,\n date_borrowed=\"2019-05-24 00:00:00.400952+00:00\",\n )\n close_rental_url = reverse(\"close_rental\", kwargs={\"pk\": rental.pk})\n\n data = {\"date_returned\": \"2019-05-25 13:46:57.249145+03:00\"}\n response = self.client.put(close_rental_url, data=data, format=\"json\")\n\n self.assertEqual(response.data[\"amount_charged\"], \"2.00\")\n self.assertEqual(response.data[\"rental_status\"], \"Closed\")\n self.assertEqual(response.data[\"currency\"], CURRENCY)",
"title": ""
},
{
"docid": "01d505b0decf38a647955a28599ddb50",
"score": "0.4904093",
"text": "def modify_expiration(self, new_expiration):\n if self.is_cancellable:\n if self.expiration!=new_expiration:\n log.info(\"bo#%s: modify expiration of pending order\" % self.ticket)\n dt = get_datetime()\n new_expiration = dt_td_to_dt(new_expiration, dt)\n not_implemented_error(\"Can't modify modify expiration of pending order\")\n else:\n return",
"title": ""
},
{
"docid": "8dbfc818a7ffbd6d8f09400f973bae44",
"score": "0.49022606",
"text": "def contribute(k):\n global amount\n global _alarm\n amount = amount + k\n # remove the alarm\n if amount >= initial_amount * 0.3:\n _alarm = False",
"title": ""
},
{
"docid": "d2a8259def53576441e2c3cbf731cff5",
"score": "0.48961106",
"text": "def test_3(self):\n c1 = Store.Customer(\"harold\", \"qcf\", True)\n self.assertTrue(c1.is_premium_member(), \"not premium member\")",
"title": ""
},
{
"docid": "ed609928d752e18a160ececd58722048",
"score": "0.48943877",
"text": "def done(self,cr,uid,ids,context=None):\n for record in self.browse(cr, uid, ids, context=context):\n search_result = self.pool.get('payment.enrich').browse(cr, uid,record.enrich_id.id)\n if record.cost < 1 :\n raise osv.except_osv(_('Invalid Action Error'), _('The Entered Cost Is Wrong!'))\n if record.cost > search_result.residual_amount :\n raise osv.except_osv(_('Invalid Action Error'), _('Your Residual Balance Is Less Than Your Cost!'))\n if context:\n if 'owner' in context and 'model_id' in context:\n owner = context['owner']\n owner = int(owner)\n model_id = context['model_id']\n if str(model_id) == 'fleet.vehicle.log.contract':\n model_obj = self.pool.get(model_id)\n model = model_obj.browse(cr, uid, owner, context=context)\n model.write({'state':'open'})\n return self.write(cr, uid, ids, {'state':'done'},context=context)",
"title": ""
},
{
"docid": "69a162a79de7a75785f9e6b2d9f07588",
"score": "0.48916712",
"text": "def use(self):\n if self.flag:\n if self.credit < self.price_of_trip:\n return \"Your credit is not enough, please increase your credit\"\n else:\n self.credit -= self.price_of_trip\n return \"Done\"\n else:\n return \"Sorry, your card has expired.\"",
"title": ""
},
{
"docid": "8f42aed0cc21bdb3278ce55b720546c9",
"score": "0.48887736",
"text": "def save(self, *args, **kwargs): #pylint: disable=W0221\n created = not self.uuid\n if created:\n existing_balance = self.account.calculated_balance\n else:\n all_other_active_transactions = [x for x in self.account.transactions.all() if x != self and x.active]\n existing_balance = sum(x.amount for x in all_other_active_transactions)\n\n if not self.active:\n pass\n elif (existing_balance + self.amount) < 0:\n raise AccountBalanceError(\n 'Balance of account {} would be brought below 0'.format(self.account)\n )\n\n instance = super().save(*args, **kwargs)\n self.account.update_balance()\n return instance",
"title": ""
},
{
"docid": "b363d7bccb6a832fb10e43ce7a25224e",
"score": "0.48865578",
"text": "def test_check_cost():",
"title": ""
},
{
"docid": "5414a8c5839816743bf0cbd8ac9e56b2",
"score": "0.4879177",
"text": "def test_subscribe_no_previous_subscription(self):\n self.braintree_customer.payment_method_token = 'valid_payment_token'\n SubscriptionManager.subscribe(self.braintree_customer)\n\n # fetch customer again\n self.braintree_customer = BraintreeUser.objects.get(user=self.user)\n self.assertTrue(self.braintree_customer.active)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertEqual(self.braintree_customer.expiry_date, timezone.make_aware(datetime.datetime(2017,7,25,0,0),pytz.utc))",
"title": ""
},
{
"docid": "8ca9f8a29d701d9d48f8e45102180e20",
"score": "0.48787516",
"text": "def use(self):\n if self.credit < self.price_of_trip:\n print(\"Your credit is not enough, please increase your credit\")\n else:\n self.credit -= self.price_of_trip\n print(\"Done\")",
"title": ""
},
{
"docid": "9cd45ad080c1c56c8cbfc9207b5b5102",
"score": "0.48765975",
"text": "def user_added_credit(self):\n return (self.user.Credit > 0)",
"title": ""
},
{
"docid": "062c77ab3749342bb1564bcd2adac432",
"score": "0.48746642",
"text": "def can_accept_credit(self, value):\n return value >= 0",
"title": ""
},
{
"docid": "8122483a63fd15da82f48566f50c3229",
"score": "0.48686194",
"text": "def can_reschedule(self) -> bool:\n return pulumi.get(self, \"can_reschedule\")",
"title": ""
},
{
"docid": "16f6380e73fd49f8b86458e7fc9e0b33",
"score": "0.48665464",
"text": "def evaluate_cost(self, msg):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "6f89aace960b6791c1f3e1ff7d144a06",
"score": "0.4865596",
"text": "def event_charge(order_type):\r\n if order_type == \"Promo event\":\r\n return 400\r\n\r\n else:\r\n return 0\r\n # promo event adds $400\r",
"title": ""
},
{
"docid": "47d5396dbeb716ea766bf4f1d486f78b",
"score": "0.48641136",
"text": "def accept_venue_transferring(venue_transferring_id, notice_id):\n result = False\n try:\n venue_transferring = VenueAccountTransferring.objects.get(pk=venue_transferring_id)\n except VenueAccountTransferring.DoesNotExist:\n venue_transferring = None\n\n if venue_transferring and venue_transferring.target:\n venue_account = venue_transferring.venue_account\n from_user = venue_transferring.venue_account.account.user\n target = venue_transferring.target.user\n\n venue_account.account = venue_transferring.target\n venue_account.save()\n\n venue_account.venue.user = venue_transferring.target.user\n venue_account.venue.save()\n\n Event.events.filter(venue_account_owner_id=venue_account.id).update(owner=venue_transferring.target.user.id)\n\n venue_transferring.delete()\n try:\n notice = Notice.objects.get(pk=notice_id)\n except Notice.DoesNotExist:\n notice = None\n\n if notice:\n notice_data = json.loads(notice.log)\n notice_data['state'] = 'Accepted'\n notice.log = json.dumps(notice_data)\n notice.read = True\n notice.save()\n\n target_name = target.username\n target_link = reverse('userena_profile_detail', kwargs={'username': target.username})\n notice_service.create_notice('venue_transferring_accepting', from_user, {\n 'subject': 'Cityfusion: transferring of your venue has been accepted.',\n 'user': from_user,\n 'venue_account': venue_account,\n 'target_name': target_name,\n 'target_link': target_link,\n }, {\n 'venue_name': venue_account.venue.name,\n 'venue_link': reverse('public_venue_account', kwargs={'slug': venue_account.slug}),\n 'target_name': target_name,\n 'target_link': target_link,\n 'date': datetime.datetime.now().strftime('%A, %b. %d, %I:%M %p'),\n })\n\n result = True\n return result",
"title": ""
},
{
"docid": "49498e9ce2c5bce44185577c91b23796",
"score": "0.48574036",
"text": "def test_calendar_query_expanded_recurring(self):\n raise SkipTest(\"test unimplemented\")",
"title": ""
},
{
"docid": "c6324c28d862c00ffdc2ae0dc5e70e6b",
"score": "0.48518384",
"text": "def canAct(self) -> bool:\n return self.cooldown < 1",
"title": ""
},
{
"docid": "f45779ffbce56bfee3e2be30e2f8f871",
"score": "0.48489207",
"text": "def can_act(self, **kwargs):\n return True",
"title": ""
},
{
"docid": "93a9c89b27742aba82795030c9f364b2",
"score": "0.48466724",
"text": "def allowed(self, user, amount):\n return True",
"title": ""
},
{
"docid": "03ed1b16924abba4e92036f3818dbe89",
"score": "0.48446918",
"text": "def accept_transfer(self):\n self.is_accepted = True\n self.date_time_accepted = models.DateTimeField(auto_now=True)",
"title": ""
},
{
"docid": "1840fd1a32ea102c2e1a3f9b81b39e3c",
"score": "0.4842639",
"text": "def reservation_mark_entrance(user: User, reservation: Reservation):\n owns_restaurant = reservation.restaurant.operator == user\n if owns_restaurant and reservation.status is ReservationState.ACCEPTED and reservation.reservation_time <= datetime.datetime.now():\n #Might want to add user notification\n reservation.entrance_time = datetime.datetime.now()\n reservation.status = ReservationState.SEATED\n db.session.commit()\n return True\n\n return False",
"title": ""
},
{
"docid": "feb54b24ff87cfae41c9d6f70d27ac2f",
"score": "0.48392415",
"text": "def is_pending_approval(self):\n if self.registration_method == self.REQUESTED \\\n and self.is_pending_activation():\n return True\n else:\n return False",
"title": ""
},
{
"docid": "19cfca091b1463122f1d72df8cd26df0",
"score": "0.48392242",
"text": "def save(self, *args, **kwargs):\n self.expire_date = self.sent_date + timedelta(days=self.period)\n super(Referral, self).save(*args, **kwargs)",
"title": ""
},
{
"docid": "a0cc51ab46ff66425dd3db7cba10015c",
"score": "0.48387",
"text": "def test_no_credit_change_for_subscription_downgrade(self):\n credits = 20\n\n current_plan = Subscription.get_plan_by_id('pro')\n new_plan = Subscription.get_plan_by_id('standard')\n\n credits = add_subscription_credits(credits, current_plan, new_plan, None)\n\n assert credits == 20",
"title": ""
}
]
ff858a38afa1ebd09c9cc6d02fb8e698
r""" Computes the reduced density matrix representation of the state. May be numerical or symbolic.
[
{
"docid": "5571e02f616db6f7dcabfb10903524cd",
"score": "0.6066919",
"text": "def reduced_dm(self, modes, **kwargs):\n if modes == list(range(self.num_modes)):\n # reduced state is full state\n return self.dm(**kwargs)\n\n if isinstance(modes, int):\n modes = [modes]\n if modes != sorted(modes):\n raise ValueError(\"The specified modes cannot be duplicated.\")\n if len(modes) > self.num_modes:\n raise ValueError(\n \"The number of specified modes cannot \" \"be larger than the number of subsystems.\"\n )\n\n reduced = self.dm(**kwargs)\n reduced = reduced_density_matrix(reduced, modes, False, batched=self.batched)\n\n s = tf.identity(reduced, name=\"reduced_density_matrix\")\n return s",
"title": ""
}
]
[
{
"docid": "364f54a74c07f748ea0080ffc36d9b1d",
"score": "0.6417854",
"text": "def subsystem_reduced_density_matrix(self, bit_labels):\n bit_labels_inds = [i-1 for i in bit_labels]\n other_labels_inds = list(set(range(self.system_size)) - set(bit_labels_inds))\n state_reshape = np.reshape(self.state, tuple([2]*self.system_size))\n state_reshape = np.transpose(state_reshape, bit_labels_inds + other_labels_inds)\n state_reshape = np.reshape(state_reshape, (2**len(bit_labels_inds), 2**len(other_labels_inds)))\n state_reshape = np.asmatrix(state_reshape)\n rho = state_reshape*state_reshape.H\n return rho",
"title": ""
},
{
"docid": "f06c3a022b5422d6e7db9221dbeeb3a6",
"score": "0.6406758",
"text": "def purity(state):\n mat = np.reshape(state, (2, 2))\n reduced_rho = np.dot(mat, np.conj(mat.T))\n return np.trace(np.dot(reduced_rho, reduced_rho))",
"title": ""
},
{
"docid": "06ee8f9e55a76e8517b4a14e76094cf4",
"score": "0.6384309",
"text": "def reduced_density_matrix(self, left_bits, right_bits, which='left'):\n reshaped_state = np.reshape(self.state, (2**left_bits,\n 2**right_bits)) \n reshaped_state = np.asmatrix(reshaped_state)\n if which == 'left':\n rho = reshaped_state*reshaped_state.H\n return rho",
"title": ""
},
{
"docid": "85162c067d7fa099823f5501dad6eedc",
"score": "0.57764155",
"text": "def _derivatives(self, state):\n ad_state = ad.adnumber(state)\n state_deriv = self.force_model(0, ad_state)\n a_matrix = jacobian(state_deriv, ad_state)\n # remove ad number from all state values before returning\n state_deriv = [state.real for state in state_deriv]\n\n return state_deriv, a_matrix",
"title": ""
},
{
"docid": "79cf5a3c466bbd9f31a3da7ef43a8265",
"score": "0.5732339",
"text": "def get_state_realspace_density(self, b, k, s, dim=None):\n\n\t\tself.check_c_projectors()\n\t\tif dim is not None:\n\t\t\tself.update_dim(np.array(dim)//2)\n\t\treturn self._get_realspace_state_density(b, k, s)",
"title": ""
},
{
"docid": "e4bb90809375f1c7d4c2865bd2ccec94",
"score": "0.558464",
"text": "def get_density(self):\n return nx.density(self.G)",
"title": ""
},
{
"docid": "05d106b220b1ee6e4f25244506b6018c",
"score": "0.55698353",
"text": "def quantum_density(self):\n d12 = self._D12.sum()\n\n rho = np.array(\n [[self._D11.sum(), d12], [d12.conjugate(), self._D22.sum()]]\n )\n\n rho *= self.dqdp\n\n # For the following consistency checks, get eigenvalues\n p = np.linalg.eigvalsh(rho)\n\n assert np.allclose(p[p < 0], 0), \"Quantum density matrix must be non-negative\"\n\n assert np.allclose(p.sum(), 1), \"Trace of quantum density matrix must be one\"\n\n return rho",
"title": ""
},
{
"docid": "1c698a987e20025f57dbbd45c3e3d824",
"score": "0.5514214",
"text": "def NE2001_dens():\n pass",
"title": ""
},
{
"docid": "954fe278d25fb5a651c45cd128595f60",
"score": "0.5507463",
"text": "def _density(self):\n prof_copy = self.profile.copy()\n ptype = prof_copy.pop('type', \"NFW\")\n return density_factory(ptype, **prof_copy)",
"title": ""
},
{
"docid": "1dc786523a52221b43a05731b29580a9",
"score": "0.5466165",
"text": "def stateDensity(self, Eps):\n return self._stateDensity(Eps)\n '''\n # Alternative calculaltion without interpolation\n if not isinstance(Eps, (collections.Sequence, np.ndarray)):\n return 16.*np.pi**2 * quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0]\n return 16.*np.pi**2 *np.array([quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0] for Eps in Eps])\n '''",
"title": ""
},
{
"docid": "1e7e001fdb35f9e2b0b2b37b56ddebaa",
"score": "0.54642904",
"text": "def __call__(self, state: tf.Tensor, is_density_matrix: bool = False\n ) -> tf.Tensor:\n if self._nqubits is None:\n if is_density_matrix:\n self.nqubits = len(tuple(state.shape)) // 2\n else:\n self.nqubits = len(tuple(state.shape))\n\n if self.is_controlled_by:\n return self._controlled_by_call(state, is_density_matrix)\n\n if is_density_matrix:\n state = self.einsum(self.calculation_cache.right, state,\n tf.math.conj(self.matrix))\n state = self.einsum(self.calculation_cache.left, state, self.matrix)\n return state\n\n return self.einsum(self.calculation_cache.vector, state, self.matrix)",
"title": ""
},
{
"docid": "73ac4e6dcdd8dfcf436fc9926b6cf18a",
"score": "0.54595035",
"text": "def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)\n self.mode.fourier = None; self.mode.energies = None\n densStates = self.mode.getDensityOfStates(Elist)",
"title": ""
},
{
"docid": "f6cec97371d2389325ad11ed52f664f0",
"score": "0.54263574",
"text": "def density(self, state: c_void_p, temperature: float) -> float:\n return self.api.glycolDensity(state, self.instance, temperature)",
"title": ""
},
{
"docid": "b5a522d307701bceef7662e3d7225273",
"score": "0.53983563",
"text": "def mass_density(self):\n return self._get_data('density') * pq.g / pq.cm**3",
"title": ""
},
{
"docid": "efcf24d459728bfefcbcfc4a9558562f",
"score": "0.53568625",
"text": "def calcStatePopulationDensity(self) -> None:\n\t\t# selecting rows that correspond with the state\n\t\tsumPopArea = self.stateCensusTractGDF['DP0010001'].sum(axis=0)\n\t\tsumLandArea = self.stateCensusTractGDF['ALAND10'].sum(axis=0)\n\t\t# sums_population_area= self.state.sum(axis=0)\n\t\t# pop_density_calclulated= sums_population_area.DP001001/sums_population_area.LAND_AREA\n\t\tpopDensityCalculated = sumPopArea / sumLandArea\n\t\t# adding new col\n\t\t# self.nationalCensusTractsGDF['Population Density']=None\n\t\tself.nationalCensusTractsGDF['statePopDensity'] = popDensityCalculated",
"title": ""
},
{
"docid": "fc4ed56fbc646f9391728c57ddf0c7cb",
"score": "0.53507805",
"text": "def W_red(W):\n assert issparse(W), \"Matrix needs to be sparse\"\n D_inv = degree_matrix(W, squared=False, indegree=False) # Assumes undireced graph, thus in-degree = out-degree.\n D_inv.data[:] = np.power(D_inv.data, -0.5) # D^(-1/2)\n return D_inv.dot(W).dot(D_inv)",
"title": ""
},
{
"docid": "24ad33e776fd43339cbc1a24518cc4b1",
"score": "0.5348118",
"text": "def saturated_density(self, state: c_void_p, temperature: float, quality: float) -> float:\n return self.api.refrigerantSaturatedDensity(state, self.instance, temperature, quality)",
"title": ""
},
{
"docid": "166bdca3b0bc2ee9a22ccd5d071e23ae",
"score": "0.5335536",
"text": "def cc_dense(self):\n cc_dense = self.iweights[[\"pid\", \"STATE\", \"iweight_state\"]].copy()\n # Use PID to match initial weights with weighted targets\n cc_dense = cc_dense.merge(self.puf_2017_filter, on=\"pid\")\n for var in self.targ_list:\n state_var = var + \"_targ\"\n cc_dense[state_var] = cc_dense[var] * cc_dense[\"iweight_state\"]\n cc_dense = cc_dense.drop(labels=self.var_list, axis=1)\n cc_dense = cc_dense.drop(labels=[\"s006\", \"AGI_STUB\"], axis=1)\n # j is an index for x, the variable we will solve for\n cc_dense[\"j\"] = np.arange(len(cc_dense)) + 1\n return cc_dense",
"title": ""
},
{
"docid": "89e3b690833477628c973f2500d5ee5c",
"score": "0.5307281",
"text": "def density(self):\n return DensityModel(a=14.1 * units.gram /\n units.cm**3, model='constant')",
"title": ""
},
{
"docid": "614c386f70be9342154c0d4d615222e9",
"score": "0.530172",
"text": "def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)",
"title": ""
},
{
"docid": "614c386f70be9342154c0d4d615222e9",
"score": "0.530172",
"text": "def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)",
"title": ""
},
{
"docid": "614c386f70be9342154c0d4d615222e9",
"score": "0.530172",
"text": "def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)",
"title": ""
},
{
"docid": "e30eb831acf5f41270a6137d9ea4fed5",
"score": "0.5297523",
"text": "def reduced_state(self, wires):\n if len(wires) == self.num_wires:\n # reduced state is full state\n return self._state\n\n # translate to wire labels used by device\n device_wires = self.map_wires(wires)\n\n # reduce rho down to specified subsystems\n ind = np.concatenate([device_wires.toarray(), device_wires.toarray() + self.num_wires])\n rows = ind.reshape(-1, 1)\n cols = ind.reshape(1, -1)\n\n return self._state[0][rows, cols], self._state[1][ind]",
"title": ""
},
{
"docid": "956672841395e0ef4f499aa106f010f1",
"score": "0.52795523",
"text": "def density_matrix(psi):\n return np.dot(psi[:, np.newaxis], psi[np.newaxis, :])",
"title": ""
},
{
"docid": "825591400c59d579f1da6146edd1c41e",
"score": "0.52540666",
"text": "def compute_density(self):\n return density(self.coef_)",
"title": ""
},
{
"docid": "825591400c59d579f1da6146edd1c41e",
"score": "0.52540666",
"text": "def compute_density(self):\n return density(self.coef_)",
"title": ""
},
{
"docid": "84dfdc84274e9b5827a102a50f6fb572",
"score": "0.52432853",
"text": "def _discrete_state(self):\n return np.array(int(round(self.S * self.N)))",
"title": ""
},
{
"docid": "72b2749f4888f4104463f042150bf700",
"score": "0.5217364",
"text": "def DO(self):\n # statevector.py and operators.py have circular import\n from . import operators\n return operators.DensityOperator(self ^ self.conj(), self.basis)",
"title": ""
},
{
"docid": "fe31db5ab488dc0f4a5f72a53c34cf52",
"score": "0.520201",
"text": "def deq_mm(ssa):\n return 1e3 * 6 / (917 * np.asarray(ssa))",
"title": ""
},
{
"docid": "03b2c137509c41dff51bf722abe37482",
"score": "0.5191413",
"text": "def reactive_density(\n assignments, sinks, source=None, all_reactive=False, verbose=False):\n if not all_reactive:\n pathways = reactive_pathways(assignments, sinks, verbose=verbose)\n else:\n pathways = assignments\n\n state_counts = np.array(\n np.bincount(np.concatenate(pathways)))\n densities = state_counts / np.sum(state_counts)\n\n return densities",
"title": ""
},
{
"docid": "c10bea322cdeeb893df7b384935f3487",
"score": "0.5177796",
"text": "def __getDressedMatrixElements(self,UNmat):\n UNmatdimension = len(UNmat.toarray())\n #print(UNmat.toarray())\n n0 = self.n\n j0 = self.j\n l0 = self.l\n m0 = self.m1\n state_main = [self.nn,self.ll,self.jj,self.m2]\n d0 = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\n Omega_array = []\n Omg0 = self.Omega0 \n for i in range(UNmatdimension):\n d = 0 \n if (self.basisStates[i][:4] == state_main) or (self.basisStates[i][4:] == state_main):\n d = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\n Omega_array = np.append(Omega_array,0.5*Omg0*d/d0)\n row = np.zeros(UNmatdimension) + 1\n col = np.arange(UNmatdimension)\n mat = csr_matrix((Omega_array, (row, col)), shape=(2, UNmatdimension))\n UNmat = vstack([mat,UNmat])\n row = np.arange(UNmatdimension+2)\n row = np.concatenate((np.array([1]), row))\n col = np.zeros(UNmatdimension+2) + 1\n col = np.concatenate((np.array([0]), col))\n Omega_array = np.concatenate((np.array([Omg0*0.5,Omg0*0.5,self.Delta0]), Omega_array))\n mat = csr_matrix((Omega_array, (row, col)), shape=(UNmatdimension+2, 2))\n UNmat = hstack([mat,UNmat])\n UNmat = csr_matrix(UNmat)\n #print(UNmat.toarray())\n return UNmat",
"title": ""
},
{
"docid": "c93bea98cc7212c6e4a8d5a3c7305310",
"score": "0.51725036",
"text": "def dm(self, **kwargs):\n if self.is_pure:\n if self.batched:\n batch_index = indices[0]\n free_indices = indices[1:]\n else:\n batch_index = \"\"\n free_indices = indices\n ket = self.data\n left_str = [batch_index] + [free_indices[i] for i in range(0, 2 * self.num_modes, 2)]\n right_str = [batch_index] + [free_indices[i] for i in range(1, 2 * self.num_modes, 2)]\n out_str = [batch_index] + [free_indices[: 2 * self.num_modes]]\n einstr = \"\".join(left_str + [\",\"] + right_str + [\"->\"] + out_str)\n s = tf.einsum(einstr, ket, tf.math.conj(ket))\n else:\n s = tf.identity(self.data, name=\"density_matrix\")\n\n return s",
"title": ""
},
{
"docid": "9c48980a00fc054b50cebc53e52fd805",
"score": "0.515427",
"text": "def to_valid_density_matrix(\n density_matrix_rep: Union[int, np.ndarray],\n num_qubits: Optional[int] = None,\n *, # Force keyword arguments\n qid_shape: Optional[Tuple[int, ...]] = None,\n dtype: Type[np.number] = np.complex64,\n atol: float = 1e-7) -> np.ndarray:\n qid_shape = _qid_shape_from_args(num_qubits, qid_shape)\n if (isinstance(density_matrix_rep, np.ndarray) and\n density_matrix_rep.ndim == 2):\n if density_matrix_rep.shape != (np.prod(qid_shape, dtype=int),) * 2:\n raise ValueError(\n 'Density matrix was not square and of size 2 ** num_qubit, '\n 'instead was {}'.format(density_matrix_rep.shape))\n if not np.allclose(density_matrix_rep,\n np.transpose(np.conj(density_matrix_rep)),\n atol=atol):\n raise ValueError('The density matrix is not hermitian.')\n if not np.isclose(np.trace(density_matrix_rep), 1.0, atol=atol):\n raise ValueError(\n 'Density matrix did not have trace 1 but instead {}'.format(\n np.trace(density_matrix_rep)))\n if density_matrix_rep.dtype != dtype:\n raise ValueError(\n 'Density matrix had dtype {} but expected {}'.format(\n density_matrix_rep.dtype, dtype))\n if not np.all(np.linalg.eigvalsh(density_matrix_rep) > -atol):\n raise ValueError('The density matrix is not positive semidefinite.')\n return density_matrix_rep\n\n state_vector = to_valid_state_vector(density_matrix_rep,\n len(qid_shape),\n qid_shape=qid_shape,\n dtype=dtype)\n return np.outer(state_vector, np.conj(state_vector))",
"title": ""
},
{
"docid": "63f5bd7522ce30ae82d10f2d485eea53",
"score": "0.5153912",
"text": "def density(self):\n return(self.mass() / ((4.0/3.0)*scipy.pi*(self.radius()**3)))",
"title": ""
},
{
"docid": "9ce4558b5432a7aad8b52addf8a14f14",
"score": "0.51530224",
"text": "def density_matrix_from_state_vector(\n state_vector: Sequence,\n indices: Optional[Iterable[int]] = None,\n qid_shape: Optional[Tuple[int, ...]] = None,\n) -> np.ndarray:\n shape = validate_qid_shape(state_vector, qid_shape)\n n_qubits = len(shape)\n\n if indices is None:\n return np.outer(state_vector, np.conj(state_vector))\n\n indices = list(indices)\n validate_indices(n_qubits, indices)\n\n state_vector = np.asarray(state_vector).reshape(shape)\n\n sum_inds = np.array(range(n_qubits))\n sum_inds[indices] += n_qubits\n\n rho = np.einsum(state_vector, list(range(n_qubits)), np.conj(state_vector),\n sum_inds.tolist(), indices + sum_inds[indices].tolist())\n new_shape = np.prod([shape[i] for i in indices], dtype=int)\n\n return rho.reshape((new_shape, new_shape))",
"title": ""
},
{
"docid": "6a3b6f070d782e552643309a0e086854",
"score": "0.51415163",
"text": "def density_model(self) -> BaseModel1D:\n return self._dens_model",
"title": ""
},
{
"docid": "134a3fd7dc38484a5d225c4c8a41acc5",
"score": "0.5096904",
"text": "def spd_decomposition(S):\n\tP = force_symmetric_to_symmetric_positive_definite(S)\n\tM = symmetric_positive_definite_decomposition(P)\n\treturn M",
"title": ""
},
{
"docid": "0771801c8c7fff1b3ad82e4b2a1da3ff",
"score": "0.5084259",
"text": "def diagonalize(reduced_density_matrix):\n try:\n eigenvals, eigenvecs = np.linalg.eigh(reduced_density_matrix)\t\n except LinAlgError:\n\traise DMRGException(\"Error diagonalizing the reduced DM\")\n\n return (eigenvals, eigenvecs)",
"title": ""
},
{
"docid": "7ef40d865d68440d621ba2b4df6efb3b",
"score": "0.50785637",
"text": "def deco_symmatrix ( m ) :\n\n if m in LinAlg.decorated_matrices : return m \n\n LinAlg.deco_matrix ( m )\n \n R = m.rep_type\n \n m.__str__ = LinAlg.MS_STR\n m.__repr__ = LinAlg.MS_STR\n m.table = LinAlg.MS_STR\n\n m.correlations = LinAlg.MS_CORR\n \n m.Sim = LinAlg.SIM\n m.sim = LinAlg.SIM\n m.SimT = LinAlg.SIMT\n m.simT = LinAlg.SIMT\n \n m.eigenValues = LinAlg.MS_EIGENVALUES \n m.eigenVectors = LinAlg.MS_EIGENVECTORS\n m.eigen_values = LinAlg.MS_EIGENVALUES \n m.eigen_vectors = LinAlg.MS_EIGENVECTORS\n\n m.__reduce__ = LinAlg.MS_REDUCE \n m.rep_size = classgetter ( lambda cls : cls.rep_type.kSize ) \n \n return m",
"title": ""
},
{
"docid": "d02996822e121457dbadc79e77833684",
"score": "0.50629157",
"text": "def normalized(self):\n # swap cols and rows and row reduce\n A = sp.Matrix(self.mat).transpose().rref()[0]\n # ensure all entries are integral by multiplying by the LCD\n A *= reduce(lcm, map(lambda x: x.as_numer_denom()[1], # get denominators\n sp.utilities.iterables.flatten(A)))\n # see https://groups.google.com/forum/#!topic/sympy/e8hcF4QAldc\n A = np.array(sp.lambdify((), A, 'numpy')()) # convert to numpy array\n A = A[np.any(A,1),:].transpose() # remove 0 rows, swap rows and columns\n return HashableMatrix(np.matrix(A))",
"title": ""
},
{
"docid": "e9d2948fdd0278c0960eac13394ff777",
"score": "0.50607157",
"text": "def test_dephasing(self):\n\n # Check for identity on |11> state\n test_density_matrix = (dephasing_channel(self.density_matrix, 1, 1))\n self.assertAlmostEqual(norm(self.density_matrix - test_density_matrix),\n 0.0)\n\n test_density_matrix = (dephasing_channel(self.density_matrix,\n 1,\n 1,\n transpose=True))\n\n correct_matrix = np.array([[0., 0., 0., 0.], [0., 0., 0., 0.],\n [0., 0., 0.5, -0.5], [0., 0., -0.5, 1.]])\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)\n\n # Check for correct action on cat state\n # With probability = 0\n test_density_matrix = (dephasing_channel(self.cat_matrix, 0, 1))\n self.assertAlmostEqual(norm(self.cat_matrix - test_density_matrix), 0.0)\n # With probability = 1\n\n correct_matrix = np.array([[0.50, 0.25, 0.00, 0.00],\n [0.25, 0.25, 0.00, -0.25],\n [0.00, 0.00, 0.00, 0.00],\n [0.00, -0.25, 0.00, 0.50]])\n test_density_matrix = (dephasing_channel(self.cat_matrix, 1, 1))\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)",
"title": ""
},
{
"docid": "3fc538d60bed88cd11599c3c6959f4b5",
"score": "0.5043117",
"text": "def density(self):\r\n graph = self.__graph_dict.copy()\r\n vertexes = len(graph.keys())\r\n edges = len(self.edges())\r\n\r\n return 2.0 * edges / (vertexes *(vertexes - 1))",
"title": ""
},
{
"docid": "e5f9f9e4020c8f8330863b342a455e0c",
"score": "0.50127727",
"text": "def density(compound, state, value_index=0):\n table = density_table(compound)\n state_idx = state_index(state)\n try:\n return list(table.loc[(table['state'] == state_idx), 'value'])[value_index]\n except IndexError:\n print('Invalid state or compound')",
"title": ""
},
{
"docid": "3f9417401b3ef118e225d0562a146075",
"score": "0.50119233",
"text": "def dd(self, mu: int, nu: int):\n\t\tif self.evaluated[mu,nu]: return self.Ric[mu,nu]\n\t\tRic = 0\n\t\tfor rho in range(self.dim):\n\t\t\tRic += self.R.uddd(rho,mu,rho,nu)\n\t\tself.Ric[mu,nu] = Ric.simplify()\n\t\tself.evaluated[mu,nu] = True\n\t\treturn self.Ric[mu,nu]",
"title": ""
},
{
"docid": "ef79b4384b1a00538764f5c98445ce4d",
"score": "0.50119007",
"text": "def density(self):\n return sum(np.array(mass(self.symbols))) / self.volume",
"title": ""
},
{
"docid": "b87cc4a601fab0d64ed28e35a4405a96",
"score": "0.50103897",
"text": "def density(self, state: c_void_p, barometric_pressure: float, dry_bulb_temp: float, humidity_ratio: float) -> float:\n return self.api.psyRhoFnPbTdbW(state, barometric_pressure, dry_bulb_temp, humidity_ratio)",
"title": ""
},
{
"docid": "fe0da1191fb5e7d644d2e8a32e1b3aed",
"score": "0.5010093",
"text": "def classical_density(self):\n rho = self._D11 + self._D22\n\n # Check for the normalization condition\n assert np.allclose(rho.sum() * self.dqdp, 1), \"Classical density must be normalized.\"\n\n # Check for the positivity of the density\n assert np.allclose(rho[rho < 0], 0), \"Classical density mus be nonnegative\"\n\n assert not np.isnan(rho).any(), \"Classical density contains NaNs.\"\n\n # Make sure all values are real\n assert isinstance(rho[0, 0], np.float), \"Classical density must be real\"\n\n return rho",
"title": ""
},
{
"docid": "2fe6a799ba948be43589b718b1442f2a",
"score": "0.5002014",
"text": "def get_reversible_rate_matrix(nstates):\n M = np.exp(np.random.randn(nstates, nstates))\n pre_Q = 0.5 * (M + M.T)\n v = sample_distn(nstates)\n v_sqrt = np.sqrt(v)\n pre_Q = (pre_Q.T / v_sqrt).T * v_sqrt\n Q = pre_Q - np.diag(np.sum(pre_Q, axis=1))\n assert_reversibility(Q, v)\n return Q, v",
"title": ""
},
{
"docid": "080916809178ad11c99fcca08377dc79",
"score": "0.49931666",
"text": "def density(self):\n return self._density",
"title": ""
},
{
"docid": "ea7bff0064c282b34ff4c47214f0f4ed",
"score": "0.49819955",
"text": "def evaluate_density_vec(self, x):\r\n return self._evaluate_scalar_quantity(x[:,0], x[:,1], x[:,2], MNnModel.mn_density)",
"title": ""
},
{
"docid": "8fe41ebade879197650c9af2761a6dfc",
"score": "0.4976917",
"text": "def get_density(self,wavefunction): \n if wavefunction is None:\n return self.density\n \n density=np.zeros(self.shape,dtype=np.float)\n for k in range(self.NK):\n for n in range(self.nbands):\n for m in range(self.nbands):\n density+=2*self.wk[k]*self.f[k,n]*np.abs(wavefunction[k,m,n]*self.ukn[k,m])**2\n return density",
"title": ""
},
{
"docid": "5d65c01ac6a9758392a6b3882f17e447",
"score": "0.49748877",
"text": "def get_density(self, x):\n \n return self.get_logdensity(x).exp()",
"title": ""
},
{
"docid": "c272faaf51fe765b66950a3fcb109cce",
"score": "0.4974089",
"text": "def generate_reduced_sim_mat(self):\n self.reduced_sim_mat = np.zeros((self.k, self.k), dtype='float32')\n\n for r in range(2, self.k + 1):\n for s in (range(1, r) if not self.drop_edges_between_irregular_pairs else self.regularity_list[r - 2]):\n if self.is_weighted:\n cl_pair = WeightedClassesPair(self.sim_mat, self.adj_mat, self.classes, r, s, self.epsilon)\n else:\n cl_pair = ClassesPair(self.adj_mat, self.classes, r, s, self.epsilon)\n self.reduced_sim_mat[r - 1, s - 1] = self.reduced_sim_mat[s - 1, r - 1] = cl_pair.bip_density",
"title": ""
},
{
"docid": "30bb90332888e55a02603a36f21e30ef",
"score": "0.4962091",
"text": "def initialize_density(x,dx,normalization=1):\n rho=exp(-x**2)\n A=simps(rho,x)\n return normalization/A*rho",
"title": ""
},
{
"docid": "c9ef96029d241edd7a4d21334f51ce44",
"score": "0.4958901",
"text": "def density(self, r):\n pass",
"title": ""
},
{
"docid": "1ebd7cc640cd6a72cce91981a2d5ea69",
"score": "0.49578607",
"text": "def mass_matrix(self):\r\n if not self._fr or not self._frstar:\r\n raise ValueError('Need to compute Fr, Fr* first.')\r\n return Matrix([self._k_d, self._k_dnh])",
"title": ""
},
{
"docid": "5af34f7f073abed1d0aba606c4e3d4c5",
"score": "0.49560684",
"text": "def _matrix(self):\n n, d = self._x.shape\n if n < d:\n return np.dot(self._x, self._x.T) / float(n - 1)\n return np.dot(self._x.T, self._x) / float(n - 1)",
"title": ""
},
{
"docid": "b72faf3dd76737395397e71108fe09c0",
"score": "0.49559936",
"text": "def depurify(self, eps):\n probs = np.random.random(self.num_rows)\n probs = probs/np.sum(probs)\n arr = (self.arr + eps*np.diag(probs)) / (self.trace() + eps)\n return DenMat(self.num_rows, self.row_shape, arr)",
"title": ""
},
{
"docid": "a5db345c5c728b77651f7354563b650c",
"score": "0.49555916",
"text": "def irreducible(self):\r\n\t\treturn farq((self.num/MCD(self.den,self.num)),(self.den/MCD(self.den,self.num)))",
"title": ""
},
{
"docid": "0acd9a2c48d8dba88077ac1ef6e038df",
"score": "0.4938685",
"text": "def compute_diagonalizing_gates(\n basis_state, wires\n ): # pylint: disable=arguments-differ,unused-argument\n return []",
"title": ""
},
{
"docid": "22cbc58a6fc66c8903026332ba3d82ec",
"score": "0.49319252",
"text": "def distribution_matrix(mat):\n return np.diag(stationary(mat))",
"title": ""
},
{
"docid": "b9ae00ca62821f82771a40d1b404a03f",
"score": "0.49295992",
"text": "def normalize_adj(adj):\n # adj: dense adj\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1)) # D\n d_inv_sqrt = np.power(rowsum, -0.5).flatten() # D^-0.5\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D^-0.5\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() # D^-0.5AD^0.5",
"title": ""
},
{
"docid": "a94ce23a0f9d4ffeea5706804d3a5d4d",
"score": "0.49190745",
"text": "def densityFactorDry(weather):\n temperature = extractTemperature(weather, useKelvin=True)\n waterVaporPressure = humidityToPressure(weather)\n airPressure = weather.getAirPressure()*units.pascal\n dryPressure = airPressure - waterVaporPressure\n eqn = dryPressure.to_value(cds.mbar)*(57.90E-8 - 9.3250E-4/temperature.to_value(units.Kelvin)\n + 0.25844/temperature.to_value(units.Kelvin)**2.)\n densityFactor = (1. + eqn)*dryPressure.to_value(cds.mbar)/temperature.to_value(units.Kelvin)\n return densityFactor",
"title": ""
},
{
"docid": "8fd36f9555dc547e0a2e2a0b67384f63",
"score": "0.49190095",
"text": "def strain_displacement_matrix(self):\n L = np.zeros((3,4)) # matrix used for mapping shape function derivatives into strains\n L[0,0] = 1; L[1,3] = 1; L[2,1:3] = 1;\n xi = 0; eta = 0; # evaluation point\n _,_,dNmat = self.mesh.eval_shape_func(xi,eta) # differentiated shape functions evaluated in (xi,eta)\n Jac = np.array([[0.5,0],[0,0.5]]) # mapping back to global coordinates\n detJ = np.linalg.det(Jac)\n invJ = 1/detJ*np.array([[Jac[1,1], -Jac[0,1]], [-Jac[1,0], Jac[0,0]]])\n G = np.zeros((4,4)) # mapping matrix between rectangular element space coordinates and global coordinates\n G[:2,:2] = invJ; G[-2:,-2:] = invJ\n B0 = np.matmul(np.matmul(L,G),dNmat) # strain displacement matrix\n\n return B0",
"title": ""
},
{
"docid": "7acb1f9b47bb5ec27070f96bdb361bc2",
"score": "0.491127",
"text": "def evaluate_density(self, x, y, z):\r\n return self._evaluate_scalar_quantity(x, y, z, MNnModel.mn_density)",
"title": ""
},
{
"docid": "9e7e01cd867d1edacc06d2d11684496c",
"score": "0.48791188",
"text": "def _densityfunction(\n r: float,\n core_wavenumber: floatcomplex,\n shell_wavenumber: floatcomplex,\n core_width: float,\n shell_width: float,\n) -> float:\n return (\n abs(\n _wavefunction(r, core_wavenumber, shell_wavenumber, core_width, shell_width)\n )\n ** 2\n )",
"title": ""
},
{
"docid": "7ef32ca4bb9d73ed6bf0304dda863126",
"score": "0.4866826",
"text": "def cost_down(states):\n return np.linalg.norm(states)",
"title": ""
},
{
"docid": "7873db806f156abc30c0f468865452ee",
"score": "0.48629946",
"text": "def getDensity(self):\n return self.__density",
"title": ""
},
{
"docid": "9936c5f50045750f35b6449fe5ae7d59",
"score": "0.4831887",
"text": "def _d_sigma(self):\n try:\n dd = self.d_derivs\n except ValueError:\n return None\n den = self.density\n dv = np.matrix(np.zeros((len(den.deriv_params))))\n for i, pname in enumerate(den.deriv_params):\n dv[0, i] = dd[pname].angularIntegral(self.psi_max)[0]\n return np.sqrt((dv * self.density.covar * dv.T)[0, 0])",
"title": ""
},
{
"docid": "0743541340bf045e3787e5333449e532",
"score": "0.48263004",
"text": "def renormalise(self):\n\t\trn = self.compute_partition_function_fast()\n\t\tfor prod in self.productions:\n\t\t\tif rn[prod[0]] == 0:\n\t\t\t\tself.parameters[prod] = 0\n\t\t\telse:\n\t\t\t\tif len(prod) == 3:\n\t\t\t\t\ta,b,c = prod\n\t\t\t\t\tself.parameters[prod] *= (rn[b] * rn[c])/rn[a] \n\t\t\t\telse:\n\t\t\t\t\tself.parameters[prod] *= 1.0/rn[prod[0]]\n\t\tself.trim_zeros()\n\t\tself.set_log_parameters()\n\t\treturn self",
"title": ""
},
{
"docid": "0e562d84de910866b7f2e9ce09c62f04",
"score": "0.48175946",
"text": "def densFunc(z):\t\n\t\treturn rho00 + 0.5 * (rho01-rho00) * (1 + np.tanh((z-ze)/we))",
"title": ""
},
{
"docid": "12b2038a3047cfa21d8adad453bad6a9",
"score": "0.4816128",
"text": "def build_distribution_normalization_matrix(self):\n vector = np.array((self.distribution_matrix * self.xtable_matrix).sum(axis=1)).T\n mask = vector > 0\n vector[mask] = 1 / vector[mask]\n return diags(vector, [0], format=\"csr\", dtype=np.float32)",
"title": ""
},
{
"docid": "a495785d1aad265edf5f3d89225b4319",
"score": "0.4811534",
"text": "def d2matrix(dx):\n N, Nngb, dim = dx.shape\n N_derivs = 2*dim + comb(dim, 2, exact=True)\n A = np.empty((N, Nngb, N_derivs), dtype=np.float64) \n for k in range(N):\n for i in range(Nngb):\n for j in range(N_derivs):\n if j < dim:\n A[k,i,j] = dx[k,i,j] \n elif j < 2*dim:\n A[k,i,j] = dx[k,i,j-dim] * dx[k,i,j-dim] / 2\n else:\n A[k,i,j] = dx[k,i,(j+1)%dim] * dx[k,i,(j+2)%dim] # this does the cross-terms, e.g. xy, xz, yz\n return A",
"title": ""
},
{
"docid": "170bba8658ede3ab821a07eb00ffc8e7",
"score": "0.48088142",
"text": "def d(self):\n\n # Calculate and return the local displacement vector\n return matmul(self.T, self.D())",
"title": ""
},
{
"docid": "b836ed4e795f661348350e710cb2bb65",
"score": "0.48069096",
"text": "def density(self, q, t=0.0):\n q = self._remove_units_prepare_shape(q)\n orig_shape, q = self._get_c_valid_arr(q)\n t = self._validate_prepare_time(t, q)\n ret_unit = self.units[\"mass\"] / self.units[\"length\"] ** 3\n return (self._density(q, t=t).T * ret_unit).to(self.units[\"mass density\"])",
"title": ""
},
{
"docid": "8cc5445f9fee2382cf934dd683ea59bf",
"score": "0.48019257",
"text": "def calculate_density(self):\n wavelet_density = len(self.phi_matrices[0].nonzero()[0])/(self.graph.number_of_nodes()**2)\n wavelet_density = str(round(100*wavelet_density, 2))\n inverse_wavelet_density = len(self.phi_matrices[1].nonzero()[0])/(self.graph.number_of_nodes()**2)\n inverse_wavelet_density = str(round(100*inverse_wavelet_density, 2))\n print(\"Density of wavelets: \"+wavelet_density+\"%.\")\n print(\"Density of inverse wavelets: \"+inverse_wavelet_density+\"%.\\n\")",
"title": ""
},
{
"docid": "a22dc3d06834558bc2cdfe95c4af1565",
"score": "0.48018953",
"text": "def DesignMatrix(states):\n N = np.size(states, 0)\n size3 = (N, L ** 2)\n X = np.zeros(size3)\n\n for i in range(0, N):\n X[i] = np.outer(states[i, :], states[i, :]).reshape(1, -1) # .ravel()\n return X",
"title": ""
},
{
"docid": "92a80ae3e4b8a7df27f667e195552e8e",
"score": "0.4795498",
"text": "def _normalize_adj_diag_enhance_dense(adj, diag_lambda:float, improved=False):\n fill_value = 1 if not improved else 2\n \n adj = add_self_loop_dense(adj, fill_value=fill_value)\n degree = torch.sum(adj, dim=-1)\n degree_inv = 1 / degree\n degree_inv[degree_inv == float(\"inf\")] = 0\n adj = adj * degree_inv.reshape(-1, 1)\n\n adj += (diag_lambda * adj.diag()).diag()\n\n return adj",
"title": ""
},
{
"docid": "b0377a23d2b9b5ab5ed62edfd4e05488",
"score": "0.4789015",
"text": "def _dcorss_dm(self):\n\n dcdm = np.empty((self.num_mog_comp, self.num_latent_proc, self.num_inducing))\n for j in range(self.num_latent_proc):\n dcdm[:, j, :] = -cho_solve((self.chol[j, :, :], True), self.MoG.m[:, j, :].T).T * self.MoG.pi[:, np.newaxis]\n return dcdm",
"title": ""
},
{
"docid": "eb66346d8ffa513bf4e6829ee29cc39d",
"score": "0.478672",
"text": "def FssInvDiag(self, recompute = True):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "bc864306e26f90c471f48a40ac16a0ec",
"score": "0.4782814",
"text": "def rh_density(self, init_dens, gamma, mach):\n\n dens_ratio = ((gamma+1)*mach**2)/(2+(gamma-1)*mach**2)\n\n final_dens = dens_ratio*init_dens\n\n return final_dens",
"title": ""
},
{
"docid": "039502c9e7c7e8d4ad0fd52010a922ed",
"score": "0.47798538",
"text": "def evalulate_function(self, disk_density: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) -> torch.Tensor:\n rmat, zmat = torch.meshgrid(self.rmid, self.zmid)\n return disk_density(rmat, zmat)",
"title": ""
},
{
"docid": "9564f033e5d7d266c571edf867268ed7",
"score": "0.4779669",
"text": "def _dcross_ds(self):\n\n dc_ds = np.empty((self.num_mog_comp, self.num_latent_proc, self.MoG.get_sjk_size()))\n for j in range(self.num_latent_proc):\n dc_ds[:, j] = -1. / 2 * np.array(\n [self.MoG.dAinvS_dS(self.chol[j, :, :], k, j) * self.MoG.pi[k] for k in range(self.num_mog_comp)])\n return dc_ds",
"title": ""
},
{
"docid": "43d44e17c8cab7dfd74d9dfe4bbc03ce",
"score": "0.47766522",
"text": "def compute_dci_d(importances):\n\n # normalize across the state_variable dimension, so we get the importance of\n # each state variable for a slot\n slot_normalized_importances = importances / importances.sum(axis=0)\n sni = slot_normalized_importances\n\n # each entropy computed is the entropy for a slot over all the state variables\n slot_entropy = entropy(sni, base=importances.shape[0])\n dci_ds = 1 - slot_entropy\n dci_d = dci_ds.mean()\n\n return dci_d",
"title": ""
},
{
"docid": "63108ffb70e437a436de9e0fe5091598",
"score": "0.47744197",
"text": "def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)",
"title": ""
},
{
"docid": "63108ffb70e437a436de9e0fe5091598",
"score": "0.47744197",
"text": "def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)",
"title": ""
},
{
"docid": "63108ffb70e437a436de9e0fe5091598",
"score": "0.47744197",
"text": "def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)",
"title": ""
},
{
"docid": "a8697247f45a6c2c24dd1e3ea7b3ffc0",
"score": "0.47690842",
"text": "def stiffness_matrix_assembly(self,rho,sparse=True):\n rho = rho.flatten(order='F')\n if sparse is False:\n self.stiffness_matrix = np.zeros((self.mesh.ndof,self.mesh.ndof))\n mat_idx = np.indices((8,8))\n I = mat_idx[0].flatten()\n J = mat_idx[1].flatten()\n # assemble stiffness matrix\n for ele in range(len(self.mesh.edofMat)):\n edof = self.mesh.edofMat[ele]\n self.stiffness_matrix[edof[I],edof[J]] += (self.Emin+(rho[ele])**self.penal*(self.Emax-self.Emin))*self.KE[I,J]\n elif sparse is True:\n iK = np.kron(self.mesh.edofMat,np.ones((8,1))).flatten()\n jK = np.kron(self.mesh.edofMat,np.ones((1,8))).flatten()\n sK=((self.KE.flatten()[np.newaxis]).T*(self.Emin+(rho)**self.penal*(self.Emax-self.Emin))).flatten(order='F')\n self.stiffness_matrix = sp.coo_matrix((sK,(iK,jK)),shape=(self.mesh.ndof,self.mesh.ndof)).tocsc()\n\n return self.stiffness_matrix",
"title": ""
},
{
"docid": "af13d60edf934fd69ff211074ce62cb7",
"score": "0.47683695",
"text": "def simplify(self):\r\n fact = greatest_common_divisor(self.__numerator, self.__denominator)\r\n self.__numerator = self.__numerator//fact\r\n self.__denominator = self.__denominator//fact",
"title": ""
},
{
"docid": "2e023b8c609745368bc2f84375d6a06c",
"score": "0.47601324",
"text": "def simplify_state(state: SDFGState, remove_views: bool = False) -> MultiDiGraph:\n\n sdfg = state.parent\n\n # Copy the whole state\n G = MultiDiGraph()\n for n in state.nodes():\n G.add_node(n)\n for n in state.nodes():\n for e in state.all_edges(n):\n G.add_edge(e.src, e.dst)\n # Collapse all mappings and their scopes into one node\n scope_children = state.scope_children()\n for n in scope_children[None]:\n if isinstance(n, nodes.EntryNode):\n G.add_edges_from([(n, x) for (y, x) in G.out_edges(state.exit_node(n))])\n G.remove_nodes_from(scope_children[n])\n # Remove all nodes that are not AccessNodes or have incoming\n # wcr edges and connect their predecessors and successors\n for n in state.nodes():\n if n in G.nodes():\n if (not isinstance(n, nodes.AccessNode) or (remove_views and isinstance(sdfg.arrays[n.data], data.View))):\n for p in G.predecessors(n):\n for c in G.successors(n):\n G.add_edge(p, c)\n G.remove_node(n)\n else:\n for e in state.all_edges(n):\n if e.data.wcr is not None:\n for p in G.predecessors(n):\n for s in G.successors(n):\n G.add_edge(p, s)\n G.remove_node(n)\n break\n\n return G",
"title": ""
},
{
"docid": "5852be77521a54263f39fb8e44ef4dce",
"score": "0.47589034",
"text": "def ddx(n):\n return sp.spdiags((np.ones((n + 1, 1)) * [-1, 1]).T, [0, 1], n, n + 1, format=\"csr\")",
"title": ""
},
{
"docid": "1305563ab5d730662b91cba0c765426e",
"score": "0.4755412",
"text": "def compute(self):\n m, n = self.D.shape\n\n # Allocate memory.\n # We need +2 because we use indices starting from 1\n # and to deal with edge cases in the backward recursion.\n self.R_ = np.zeros((m+2, n+2), dtype=np.float64)\n\n _soft_dtw(self.D, self.R_, gamma=self.gamma)\n\n return self.R_[m, n]",
"title": ""
},
{
"docid": "2ff928f269c5ac8d7b3d2ae000cd42a5",
"score": "0.47552603",
"text": "def density(self, r):\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**3, 0.)",
"title": ""
},
{
"docid": "779305611eb50458e49f536acc482e7f",
"score": "0.47546223",
"text": "def to_dense(self):\n if self.size is None:\n size = max(self.d.keys()) + 1\n else:\n size = self.size\n dense = np.zeros(size)\n for k,v in self.d.items():\n dense[k] = v\n return dense",
"title": ""
},
{
"docid": "1db22c46cf4d3e4a22ca0797871bab30",
"score": "0.4754565",
"text": "def density(self, r):\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**2, 0.)",
"title": ""
},
{
"docid": "be8eb30b81846b14a296dec1b1e7d2ea",
"score": "0.47464868",
"text": "def reduced_formula(self):\n if self.structure is not None:\n return self.composition.reduced_formula\n return \"Unkonwn\"",
"title": ""
},
{
"docid": "efbd2abf113aed04471eebea96b78baf",
"score": "0.47410783",
"text": "def s_d(self) -> float:\n return self.s0 + 1 / (self.w * self.k_x) * self.v_t",
"title": ""
},
{
"docid": "f15bc534a6a908e854b492720e967cf5",
"score": "0.47379398",
"text": "def diagonalize_del_dm_in_sbasis_in_degenerate_spaces(self):\n classes = ut.get_equiv_classes(list(self.dm0_eigen_sys[0]))\n umat = np.eye(self.del_dm.num_rows, dtype=complex)\n # eq = equivalence\n for eq_class in classes:\n dim = len(eq_class)\n if dim > 1:\n if self.verbose:\n print(\"non-trivial eq class\", eq_class)\n eq_class = sorted(eq_class)\n arr = np.empty((dim, dim), dtype=complex)\n for k1, kk1 in enumerate(eq_class):\n for k2, kk2 in enumerate(eq_class):\n arr[k1, k2] = self.del_dm_in_sbasis[kk1, kk2]\n norm_arr = np.linalg.norm(arr)\n # print('norm arr', norm_arr)\n if norm_arr > 1e-4:\n _, evec_cols = np.linalg.eigh(arr)\n assert ut.is_unitary_arr(evec_cols)\n # print('bbbbbbbb', np.around(np.dot(np.dot(\n # evec_cols.conj().T,\n # arr), evec_cols).real,4))\n for k1, kk1 in enumerate(eq_class):\n for k2, kk2 in enumerate(eq_class):\n umat[kk1, kk2] = evec_cols[k1, k2]\n assert ut.is_unitary_arr(umat)\n # print('ccccccccnorm of rotated_del_dm', np.linalg.norm(\n # self.del_dm_in_sbasis.arr))\n rotated_del_dm = np.dot(\n np.dot(umat.conj().T, self.del_dm_in_sbasis.arr), umat)\n # print('norm of rotated_del_dm', np.linalg.norm(rotated_del_dm))\n self.del_dm_in_sbasis.arr = rotated_del_dm",
"title": ""
},
{
"docid": "b666a78a3c4f8253b314a53a530136dd",
"score": "0.4735516",
"text": "def pgradient(self):\n d = {}\n\n # Det coeff\n curr_val = self.value()\n nonzero = curr_val[0] != 0.0\n\n # dets[spin][ (phase,log), configuration, determinant]\n dets = (\n self._dets[0][:, :, self._det_map[0]],\n self._dets[1][:, :, self._det_map[1]],\n )\n\n d[\"det_coeff\"] = gpu.cp.zeros(dets[0].shape[1:], dtype=dets[0].dtype)\n d[\"det_coeff\"][nonzero, :] = (\n dets[0][0, nonzero, :]\n * dets[1][0, nonzero, :]\n * gpu.cp.exp(\n dets[0][1, nonzero, :]\n + dets[1][1, nonzero, :]\n - gpu.cp.array(curr_val[1][nonzero, np.newaxis])\n )\n / gpu.cp.array(curr_val[0][nonzero, np.newaxis])\n )\n\n for s, parm in zip([0, 1], [\"mo_coeff_alpha\", \"mo_coeff_beta\"]):\n ao = self._aovals[\n :, :, s * self._nelec[0] : self._nelec[s] + s * self._nelec[0], :\n ]\n\n split, aos = self.orbitals.pgradient(ao, s)\n mos = gpu.cp.split(gpu.cp.arange(split[-1]), gpu.asnumpy(split).astype(int))\n # Compute dj Diu/Diu\n nao = aos[0].shape[-1]\n nconf = aos[0].shape[0]\n nmo = int(split[-1])\n deriv = gpu.cp.zeros(\n (len(self._det_occup[s]), nconf, nao, nmo), dtype=curr_val[0].dtype\n )\n for det, occ in enumerate(self._det_occup[s]):\n for ao, mo in zip(aos, mos):\n for i in mo:\n if i in occ:\n col = occ.index(i)\n deriv[det, :, :, i] = self._testcol(det, col, s, ao)\n\n # now we reduce over determinants\n d[parm] = gpu.cp.zeros(deriv.shape[1:], dtype=curr_val[0].dtype)\n for di, coeff in enumerate(self.parameters[\"det_coeff\"]):\n whichdet = self._det_map[s][di]\n d[parm] += (\n deriv[whichdet]\n * coeff\n * d[\"det_coeff\"][:, di, np.newaxis, np.newaxis]\n )\n\n for k, v in d.items():\n d[k] = gpu.asnumpy(v)\n\n for k in list(d.keys()):\n if np.prod(d[k].shape) == 0:\n del d[k]\n return d",
"title": ""
},
{
"docid": "5b62944f2f5f14daf76514400d4af117",
"score": "0.4733377",
"text": "def __repr__(self):\n stats = self.population.getStats(self.truncFrac)\n outStr = (\"\"\"DeMin state:\n Generation: %s\n best trial solution's cost value: %s\n best trial solution: %s\\n\n worst trial solution's cost value: %s\n worst trial solution: %s\\n\n mean cost value: %s\n standard deviation of cost values: %s \n fractional tolerance: %s\n chi-square: %s\n ndf: %s\n np_trunc: %s\\n\n Parameter mean values:\\n%s\\n\n Covariance Matrix for np_trunc members:\\n%s\n \"\"\" % (self.population.generation,\n stats[0],\n self.population[self.population.ibest].x,\n stats[1],\n self.population[self.population.iworst].x,\n stats[2],\n stats[3],\n stats[4],\n stats[5],\n stats[6],\n self.population.getNpTrunc(self.truncFrac),\n stats[7],\n self.population.getCovMatRepr(stats[8])))\n return outStr",
"title": ""
}
]
