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annoviko/pyclustering
pyclustering/container/cftree.py
https://github.com/annoviko/pyclustering/blob/98aa0dd89fd36f701668fb1eb29c8fb5662bf7d0/pyclustering/container/cftree.py#L1153-L1183
def __split_nonleaf_node(self, node): """! @brief Performs splitting of the specified non-leaf node. @param[in] node (non_leaf_node): Non-leaf node that should be splitted. @return (list) New pair of non-leaf nodes [non_leaf_node1, non_leaf_node2]. """ [farthest_node1, farthest_node2] = node.get_farthest_successors(self.__type_measurement); # create new non-leaf nodes new_node1 = non_leaf_node(farthest_node1.feature, node.parent, [ farthest_node1 ], None); new_node2 = non_leaf_node(farthest_node2.feature, node.parent, [ farthest_node2 ], None); farthest_node1.parent = new_node1; farthest_node2.parent = new_node2; # re-insert other successors for successor in node.successors: if ( (successor is not farthest_node1) and (successor is not farthest_node2) ): distance1 = new_node1.get_distance(successor, self.__type_measurement); distance2 = new_node2.get_distance(successor, self.__type_measurement); if (distance1 < distance2): new_node1.insert_successor(successor); else: new_node2.insert_successor(successor); return [new_node1, new_node2];
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! @brief Performs splitting of the specified non-leaf node. @param[in] node (non_leaf_node): Non-leaf node that should be splitted. @return (list) New pair of non-leaf nodes [non_leaf_node1, non_leaf_node2].
[ "!" ]
python
valid
markovmodel/msmtools
msmtools/analysis/dense/decomposition.py
https://github.com/markovmodel/msmtools/blob/54dc76dd2113a0e8f3d15d5316abab41402941be/msmtools/analysis/dense/decomposition.py#L258-L317
def rdl_decomposition(T, k=None, reversible=False, norm='standard', mu=None): r"""Compute the decomposition into left and right eigenvectors. Parameters ---------- T : (M, M) ndarray Transition matrix k : int (optional) Number of eigenvector/eigenvalue pairs norm: {'standard', 'reversible', 'auto'} standard: (L'R) = Id, L[:,0] is a probability distribution, the stationary distribution mu of T. Right eigenvectors R have a 2-norm of 1. reversible: R and L are related via L=L[:,0]*R. auto: will be reversible if T is reversible, otherwise standard reversible : bool, optional Indicate that transition matrix is reversible mu : (d,) ndarray, optional Stationary distribution of T Returns ------- R : (M, M) ndarray The normalized (with respect to L) right eigenvectors, such that the column R[:,i] is the right eigenvector corresponding to the eigenvalue w[i], dot(T,R[:,i])=w[i]*R[:,i] D : (M, M) ndarray A diagonal matrix containing the eigenvalues, each repeated according to its multiplicity L : (M, M) ndarray The normalized (with respect to `R`) left eigenvectors, such that the row ``L[i, :]`` is the left eigenvector corresponding to the eigenvalue ``w[i]``, ``dot(L[i, :], T)``=``w[i]*L[i, :]`` Notes ----- If reversible=True the the eigenvalues and eigenvectors of the similar symmetric matrix `\sqrt(\mu_i / \mu_j) p_{ij}` will be used to compute the eigenvalues and eigenvectors of T. The precomputed stationary distribution will only be used if reversible=True. """ # auto-set norm if norm == 'auto': if is_reversible(T): norm = 'reversible' else: norm = 'standard' if reversible: R, D, L = rdl_decomposition_rev(T, norm=norm, mu=mu) else: R, D, L = rdl_decomposition_nrev(T, norm=norm) if k is None: return R, D, L else: return R[:, 0:k], D[0:k, 0:k], L[0:k, :]
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r"""Compute the decomposition into left and right eigenvectors. Parameters ---------- T : (M, M) ndarray Transition matrix k : int (optional) Number of eigenvector/eigenvalue pairs norm: {'standard', 'reversible', 'auto'} standard: (L'R) = Id, L[:,0] is a probability distribution, the stationary distribution mu of T. Right eigenvectors R have a 2-norm of 1. reversible: R and L are related via L=L[:,0]*R. auto: will be reversible if T is reversible, otherwise standard reversible : bool, optional Indicate that transition matrix is reversible mu : (d,) ndarray, optional Stationary distribution of T Returns ------- R : (M, M) ndarray The normalized (with respect to L) right eigenvectors, such that the column R[:,i] is the right eigenvector corresponding to the eigenvalue w[i], dot(T,R[:,i])=w[i]*R[:,i] D : (M, M) ndarray A diagonal matrix containing the eigenvalues, each repeated according to its multiplicity L : (M, M) ndarray The normalized (with respect to `R`) left eigenvectors, such that the row ``L[i, :]`` is the left eigenvector corresponding to the eigenvalue ``w[i]``, ``dot(L[i, :], T)``=``w[i]*L[i, :]`` Notes ----- If reversible=True the the eigenvalues and eigenvectors of the similar symmetric matrix `\sqrt(\mu_i / \mu_j) p_{ij}` will be used to compute the eigenvalues and eigenvectors of T. The precomputed stationary distribution will only be used if reversible=True.
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python
train
dmlc/xgboost
python-package/xgboost/sklearn.py
https://github.com/dmlc/xgboost/blob/253fdd8a42d5ec6b819788199584d27bf9ea6253/python-package/xgboost/sklearn.py#L803-L839
def predict_proba(self, data, ntree_limit=None, validate_features=True): """ Predict the probability of each `data` example being of a given class. .. note:: This function is not thread safe For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies of model object and then call predict Parameters ---------- data : DMatrix The dmatrix storing the input. ntree_limit : int Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees). validate_features : bool When this is True, validate that the Booster's and data's feature_names are identical. Otherwise, it is assumed that the feature_names are the same. Returns ------- prediction : numpy array a numpy array with the probability of each data example being of a given class. """ test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs) if ntree_limit is None: ntree_limit = getattr(self, "best_ntree_limit", 0) class_probs = self.get_booster().predict(test_dmatrix, ntree_limit=ntree_limit, validate_features=validate_features) if self.objective == "multi:softprob": return class_probs classone_probs = class_probs classzero_probs = 1.0 - classone_probs return np.vstack((classzero_probs, classone_probs)).transpose()
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Predict the probability of each `data` example being of a given class. .. note:: This function is not thread safe For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies of model object and then call predict Parameters ---------- data : DMatrix The dmatrix storing the input. ntree_limit : int Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees). validate_features : bool When this is True, validate that the Booster's and data's feature_names are identical. Otherwise, it is assumed that the feature_names are the same. Returns ------- prediction : numpy array a numpy array with the probability of each data example being of a given class.
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python
train
MostAwesomeDude/gentleman
gentleman/base.py
https://github.com/MostAwesomeDude/gentleman/blob/17fb8ffb922aa4af9d8bcab85e452c9311d41805/gentleman/base.py#L991-L1007
def PowercycleNode(r, node, force=False): """ Powercycles a node. @type node: string @param node: Node name @type force: bool @param force: Whether to force the operation @rtype: string @return: job id """ query = { "force": force, } return r.request("post", "/2/nodes/%s/powercycle" % node, query=query)
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Powercycles a node. @type node: string @param node: Node name @type force: bool @param force: Whether to force the operation @rtype: string @return: job id
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python
train
Legobot/Legobot
Legobot/Connectors/Discord.py
https://github.com/Legobot/Legobot/blob/d13da172960a149681cb5151ce34b2f3a58ad32b/Legobot/Connectors/Discord.py#L179-L192
def on_message(self, message): """ Runs on a create_message event from websocket connection Args: message (dict): Full message from Discord websocket connection" """ if 'content' in message['d']: metadata = self._parse_metadata(message) message = Message(text=message['d']['content'], metadata=metadata).__dict__ logger.debug(message) self.baseplate.tell(message)
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Runs on a create_message event from websocket connection Args: message (dict): Full message from Discord websocket connection"
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python
train
Gandi/gandi.cli
gandi/cli/core/utils/__init__.py
https://github.com/Gandi/gandi.cli/blob/6ee5b8fc8ec44b0a6c232043ca610606ad8f693d/gandi/cli/core/utils/__init__.py#L157-L166
def output_metric(gandi, metrics, key, justify=10): """ Helper to output metrics.""" for metric in metrics: key_name = metric[key].pop() values = [point.get('value', 0) for point in metric['points']] graph = sparks(values) if max(values) else '' # need to encode in utf-8 to work in python2.X if sys.version_info < (2, 8): graph = graph.encode('utf-8') output_line(gandi, key_name, graph, justify)
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Helper to output metrics.
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python
train
SeleniumHQ/selenium
py/selenium/webdriver/firefox/firefox_binary.py
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/firefox/firefox_binary.py#L210-L217
def which(self, fname): """Returns the fully qualified path by searching Path of the given name""" for pe in os.environ['PATH'].split(os.pathsep): checkname = os.path.join(pe, fname) if os.access(checkname, os.X_OK) and not os.path.isdir(checkname): return checkname return None
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Returns the fully qualified path by searching Path of the given name
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python
train
coleifer/irc
botnet/worker.py
https://github.com/coleifer/irc/blob/f9d2bd6369aafe6cb0916c9406270ca8ecea2080/botnet/worker.py#L110-L121
def command_patterns(self): """\ Actual messages listened for by the worker bot - note that worker-execute actually dispatches again by adding the command to the task queue, from which it is pulled then matched against self.task_patterns """ return ( ('!register-success (?P<cmd_channel>.+)', self.require_boss(self.register_success)), ('!worker-execute (?:\((?P<workers>.+?)\) )?(?P<task_id>\d+):(?P<command>.+)', self.require_boss(self.worker_execute)), ('!worker-ping', self.require_boss(self.worker_ping_handler)), ('!worker-stop', self.require_boss(self.worker_stop)), )
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\ Actual messages listened for by the worker bot - note that worker-execute actually dispatches again by adding the command to the task queue, from which it is pulled then matched against self.task_patterns
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python
test
bigchaindb/bigchaindb-driver
bigchaindb_driver/driver.py
https://github.com/bigchaindb/bigchaindb-driver/blob/c294a535f0696bd19483ae11a4882b74e6fc061e/bigchaindb_driver/driver.py#L403-L435
def get(self, public_key, spent=None, headers=None): """Get transaction outputs by public key. The public_key parameter must be a base58 encoded ed25519 public key associated with transaction output ownership. Args: public_key (str): Public key for which unfulfilled conditions are sought. spent (bool): Indicate if the result set should include only spent or only unspent outputs. If not specified (``None``) the result includes all the outputs (both spent and unspent) associated with the public key. headers (dict): Optional headers to pass to the request. Returns: :obj:`list` of :obj:`str`: List of unfulfilled conditions. Example: Given a transaction with `id` ``da1b64a907ba54`` having an `ed25519` condition (at index ``0``) with alice's public key:: >>> bdb = BigchainDB() >>> bdb.outputs.get(alice_pubkey) ... ['../transactions/da1b64a907ba54/conditions/0'] """ return self.transport.forward_request( method='GET', path=self.path, params={'public_key': public_key, 'spent': spent}, headers=headers, )
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Get transaction outputs by public key. The public_key parameter must be a base58 encoded ed25519 public key associated with transaction output ownership. Args: public_key (str): Public key for which unfulfilled conditions are sought. spent (bool): Indicate if the result set should include only spent or only unspent outputs. If not specified (``None``) the result includes all the outputs (both spent and unspent) associated with the public key. headers (dict): Optional headers to pass to the request. Returns: :obj:`list` of :obj:`str`: List of unfulfilled conditions. Example: Given a transaction with `id` ``da1b64a907ba54`` having an `ed25519` condition (at index ``0``) with alice's public key:: >>> bdb = BigchainDB() >>> bdb.outputs.get(alice_pubkey) ... ['../transactions/da1b64a907ba54/conditions/0']
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python
train
cloudnull/cloudlib
cloudlib/http.py
https://github.com/cloudnull/cloudlib/blob/5038111ce02521caa2558117e3bae9e1e806d315/cloudlib/http.py#L257-L271
def option(self, url, headers=None, kwargs=None): """Make a OPTION request. To make a OPTION request pass, ``url`` :param url: ``str`` :param headers: ``dict`` :param kwargs: ``dict`` """ return self._request( method='option', url=url, headers=headers, kwargs=kwargs )
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python
train
watson-developer-cloud/python-sdk
ibm_watson/language_translator_v3.py
https://github.com/watson-developer-cloud/python-sdk/blob/4c2c9df4466fcde88975da9ecd834e6ba95eb353/ibm_watson/language_translator_v3.py#L454-L459
def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status return _dict
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Return a json dictionary representing this model.
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python
train
cole/aiosmtplib
src/aiosmtplib/connection.py
https://github.com/cole/aiosmtplib/blob/0cd00e5059005371cbdfca995feff9183a16a51f/src/aiosmtplib/connection.py#L329-L340
def _raise_error_if_disconnected(self) -> None: """ See if we're still connected, and if not, raise ``SMTPServerDisconnected``. """ if ( self.transport is None or self.protocol is None or self.transport.is_closing() ): self.close() raise SMTPServerDisconnected("Disconnected from SMTP server")
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See if we're still connected, and if not, raise ``SMTPServerDisconnected``.
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python
train
maybelinot/df2gspread
df2gspread/gfiles.py
https://github.com/maybelinot/df2gspread/blob/f4cef3800704aceff2ed08a623a594b558d44898/df2gspread/gfiles.py#L19-L66
def get_file_id(credentials, gfile, write_access=False): """ Get file ID by provided path. If file does not exist and `write_access` is true, it will create whole path for you. :param credentials: provide own credentials :param gfile: path to Google Spreadsheet :param write_access: allows to create full path if file does not exist :type credentials: class 'oauth2client.client.OAuth2Credentials' :type gfile: str :type write_access: boolean :returns: file ID :rtype: str :Example: >>> from df2gspread.gfiles import get_file_id >>> from df2gspread.utils import get_credentials >>> gfile = '/some/folder/with/file' >>> credentials = get_credentials() >>> get_file_id(credentials=credentials, gfile=gfile, write_access=True) u'78asbcsSND8sdSACNsa7ggcasca8shscaSACVD' """ # auth for apiclient http = credentials.authorize(Http()) service = discovery.build('drive', 'v3', http=http, cache_discovery=False) file_id = service.files().get(fileId='root', fields='id').execute().get('id') # folder/folder/folder/spreadsheet pathway = gfile.strip('/').split('/') for idx, name in enumerate(pathway): files = service.files().list( q="name = '{}' and trashed = false and '{}' in parents".format(name, file_id)).execute()['files'] if len(files) > 0: # Why do you ever need to use several folders with the same name?! file_id = files[0].get('id') elif write_access == True: body = { 'mimeType': 'application/vnd.google-apps.' + ('spreadsheet' if idx == len(pathway)-1 else 'folder'), 'name': name, 'parents': [file_id] } file_id = service.files().create(body=body, fields='id').execute().get('id') else: return None return file_id
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Get file ID by provided path. If file does not exist and `write_access` is true, it will create whole path for you. :param credentials: provide own credentials :param gfile: path to Google Spreadsheet :param write_access: allows to create full path if file does not exist :type credentials: class 'oauth2client.client.OAuth2Credentials' :type gfile: str :type write_access: boolean :returns: file ID :rtype: str :Example: >>> from df2gspread.gfiles import get_file_id >>> from df2gspread.utils import get_credentials >>> gfile = '/some/folder/with/file' >>> credentials = get_credentials() >>> get_file_id(credentials=credentials, gfile=gfile, write_access=True) u'78asbcsSND8sdSACNsa7ggcasca8shscaSACVD'
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python
train
ARMmbed/yotta
yotta/lib/access_common.py
https://github.com/ARMmbed/yotta/blob/56bc1e56c602fa20307b23fe27518e9cd6c11af1/yotta/lib/access_common.py#L208-L240
def unpackFromCache(cache_key, to_directory): ''' If the specified cache key exists, unpack the tarball into the specified directory, otherwise raise NotInCache (a KeyError subclass). ''' if cache_key is None: raise NotInCache('"None" is never in cache') cache_key = _encodeCacheKey(cache_key) cache_dir = folders.cacheDirectory() fsutils.mkDirP(cache_dir) path = os.path.join(cache_dir, cache_key) logger.debug('attempt to unpack from cache %s -> %s', path, to_directory) try: unpackFrom(path, to_directory) try: shutil.copy(path + '.json', os.path.join(to_directory, '.yotta_origin.json')) except IOError as e: if e.errno == errno.ENOENT: pass else: raise cache_logger.debug('unpacked %s from cache into %s', cache_key, to_directory) return except IOError as e: if e.errno == errno.ENOENT: cache_logger.debug('%s not in cache', cache_key) raise NotInCache('not in cache') except OSError as e: if e.errno == errno.ENOTEMPTY: logger.error('directory %s was not empty: probably simultaneous invocation of yotta! It is likely that downloaded sources are corrupted.') else: raise
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If the specified cache key exists, unpack the tarball into the specified directory, otherwise raise NotInCache (a KeyError subclass).
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python
valid
nepalicalendar/nepalicalendar-py
nepalicalendar/nepcal.py
https://github.com/nepalicalendar/nepalicalendar-py/blob/a589c28b8e085049f30a7287753476b59eca6f50/nepalicalendar/nepcal.py#L29-L32
def monthrange(cls, year, month): """Returns the number of days in a month""" functions.check_valid_bs_range(NepDate(year, month, 1)) return values.NEPALI_MONTH_DAY_DATA[year][month - 1]
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Returns the number of days in a month
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python
train
tanghaibao/jcvi
jcvi/formats/chain.py
https://github.com/tanghaibao/jcvi/blob/d2e31a77b6ade7f41f3b321febc2b4744d1cdeca/jcvi/formats/chain.py#L229-L280
def frompsl(args): """ %prog frompsl old.new.psl old.fasta new.fasta Generate chain file from psl file. The pipeline is describe in: <http://genomewiki.ucsc.edu/index.php/Minimal_Steps_For_LiftOver> """ from jcvi.formats.sizes import Sizes p = OptionParser(frompsl.__doc__) opts, args = p.parse_args(args) if len(args) != 3: sys.exit(not p.print_help()) pslfile, oldfasta, newfasta = args pf = oldfasta.split(".")[0] # Chain together alignments from using axtChain chainfile = pf + ".chain" twobitfiles = [] for fastafile in (oldfasta, newfasta): tbfile = faToTwoBit(fastafile) twobitfiles.append(tbfile) oldtwobit, newtwobit = twobitfiles if need_update(pslfile, chainfile): cmd = "axtChain -linearGap=medium -psl {0}".format(pslfile) cmd += " {0} {1} {2}".format(oldtwobit, newtwobit, chainfile) sh(cmd) # Sort chain files sortedchain = chainfile.rsplit(".", 1)[0] + ".sorted.chain" if need_update(chainfile, sortedchain): cmd = "chainSort {0} {1}".format(chainfile, sortedchain) sh(cmd) # Make alignment nets from chains netfile = pf + ".net" oldsizes = Sizes(oldfasta).filename newsizes = Sizes(newfasta).filename if need_update((sortedchain, oldsizes, newsizes), netfile): cmd = "chainNet {0} {1} {2}".format(sortedchain, oldsizes, newsizes) cmd += " {0} /dev/null".format(netfile) sh(cmd) # Create liftOver chain file liftoverfile = pf + ".liftover.chain" if need_update((netfile, sortedchain), liftoverfile): cmd = "netChainSubset {0} {1} {2}".\ format(netfile, sortedchain, liftoverfile) sh(cmd)
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%prog frompsl old.new.psl old.fasta new.fasta Generate chain file from psl file. The pipeline is describe in: <http://genomewiki.ucsc.edu/index.php/Minimal_Steps_For_LiftOver>
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python
train
tino/pyFirmata
pyfirmata/pyfirmata.py
https://github.com/tino/pyFirmata/blob/05881909c4d7c4e808e9ed457144670b2136706e/pyfirmata/pyfirmata.py#L402-L410
def enable_reporting(self): """Enable reporting of values for the whole port.""" self.reporting = True msg = bytearray([REPORT_DIGITAL + self.port_number, 1]) self.board.sp.write(msg) for pin in self.pins: if pin.mode == INPUT: pin.reporting = True
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Enable reporting of values for the whole port.
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python
train
StagPython/StagPy
stagpy/stagyyparsers.py
https://github.com/StagPython/StagPy/blob/18c4416cc4a1011db2fd736ee8b0ec29aa6e4fd4/stagpy/stagyyparsers.py#L688-L741
def read_field_h5(xdmf_file, fieldname, snapshot, header=None): """Extract field data from hdf5 files. Args: xdmf_file (:class:`pathlib.Path`): path of the xdmf file. fieldname (str): name of field to extract. snapshot (int): snapshot number. header (dict): geometry information. Returns: (dict, numpy.array): geometry information and field data. None is returned if data is unavailable. """ if header is None: header, xdmf_root = read_geom_h5(xdmf_file, snapshot) else: xdmf_root = xmlET.parse(str(xdmf_file)).getroot() npc = header['nts'] // header['ncs'] # number of grid point per node flds = np.zeros(_flds_shape(fieldname, header)) data_found = False for elt_subdomain in xdmf_root[0][0][snapshot].findall('Grid'): ibk = int(elt_subdomain.get('Name').startswith('meshYang')) for data_attr in elt_subdomain.findall('Attribute'): if data_attr.get('Name') != fieldname: continue icore, fld = _get_field(xdmf_file, data_attr.find('DataItem')) # for some reason, the field is transposed fld = fld.T shp = fld.shape if shp[-1] == 1 and header['nts'][0] == 1: # YZ fld = fld.reshape((shp[0], 1, shp[1], shp[2])) if header['rcmb'] < 0: fld = fld[(2, 0, 1), ...] elif shp[-1] == 1: # XZ fld = fld.reshape((shp[0], shp[1], 1, shp[2])) if header['rcmb'] < 0: fld = fld[(0, 2, 1), ...] elif header['nts'][1] == 1: # cart XZ fld = fld.reshape((1, shp[0], 1, shp[1])) ifs = [icore // np.prod(header['ncs'][:i]) % header['ncs'][i] * npc[i] for i in range(3)] if header['zp']: # remove top row fld = fld[:, :, :, :-1] flds[:, ifs[0]:ifs[0] + npc[0] + header['xp'], ifs[1]:ifs[1] + npc[1] + header['yp'], ifs[2]:ifs[2] + npc[2], ibk] = fld data_found = True flds = _post_read_flds(flds, header) return (header, flds) if data_found else None
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Extract field data from hdf5 files. Args: xdmf_file (:class:`pathlib.Path`): path of the xdmf file. fieldname (str): name of field to extract. snapshot (int): snapshot number. header (dict): geometry information. Returns: (dict, numpy.array): geometry information and field data. None is returned if data is unavailable.
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python
train
openstack/networking-cisco
networking_cisco/ml2_drivers/nexus/nexus_restapi_network_driver.py
https://github.com/openstack/networking-cisco/blob/aa58a30aec25b86f9aa5952b0863045975debfa9/networking_cisco/ml2_drivers/nexus/nexus_restapi_network_driver.py#L468-L535
def initialize_baremetal_switch_interfaces(self, interfaces): """Initialize Nexus interfaces and for initial baremetal event. This get/create port channel number, applies channel-group to ethernet interface, and initializes trunking on interface. :param interfaces: Receive a list of interfaces containing: nexus_host: IP address of Nexus switch intf_type: String which specifies interface type. example: ethernet interface: String indicating which interface. example: 1/19 is_native: Whether native vlan must be configured. ch_grp: May replace port channel to each entry. channel number is 0 if none """ if not interfaces: return max_ifs = len(interfaces) starttime = time.time() learned, nexus_ip_list = self._build_host_list_and_verify_chgrp( interfaces) if not nexus_ip_list: return if max_ifs > 1: # update vpc db with learned vpcid or get new one. if learned: ch_grp = interfaces[0][-1] self._configure_learned_port_channel( nexus_ip_list, ch_grp) else: ch_grp = self._get_new_baremetal_portchannel_id(nexus_ip_list) else: ch_grp = 0 for i, (nexus_host, intf_type, nexus_port, is_native, ch_grp_saved) in enumerate(interfaces): if max_ifs > 1: if learned: ch_grp = ch_grp_saved else: self._config_new_baremetal_portchannel( ch_grp, nexus_host, intf_type, nexus_port) self._replace_interface_ch_grp(interfaces, i, ch_grp) # init port-channel instead of the provided ethernet intf_type = 'port-channel' nexus_port = str(ch_grp) else: self._replace_interface_ch_grp(interfaces, i, ch_grp) trunk_mode_present, vlan_present = ( self._get_interface_switch_trunk_present( nexus_host, intf_type, nexus_port)) if not vlan_present: self.send_enable_vlan_on_trunk_int( nexus_host, "", intf_type, nexus_port, False, not trunk_mode_present) elif not trunk_mode_present: LOG.warning(TRUNK_MODE_NOT_FOUND, nexus_host, nexus_help.format_interface_name( intf_type, nexus_port)) self.capture_and_print_timeshot( starttime, "init_bmif", switch=nexus_host)
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Initialize Nexus interfaces and for initial baremetal event. This get/create port channel number, applies channel-group to ethernet interface, and initializes trunking on interface. :param interfaces: Receive a list of interfaces containing: nexus_host: IP address of Nexus switch intf_type: String which specifies interface type. example: ethernet interface: String indicating which interface. example: 1/19 is_native: Whether native vlan must be configured. ch_grp: May replace port channel to each entry. channel number is 0 if none
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python
train
haikuginger/beekeeper
beekeeper/variables.py
https://github.com/haikuginger/beekeeper/blob/b647d3add0b407ec5dc3a2a39c4f6dac31243b18/beekeeper/variables.py#L223-L229
def fill_kwargs(self, **kwargs): """ Fill empty variable objects by name with the values from any present keyword arguments. """ for var, val in kwargs.items(): self.setval(var, val)
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Fill empty variable objects by name with the values from any present keyword arguments.
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python
train
skelsec/minidump
minidump/minidumpreader.py
https://github.com/skelsec/minidump/blob/0c4dcabe6f11d7a403440919ffa9e3c9889c5212/minidump/minidumpreader.py#L118-L139
def read(self, size = -1): """ Returns data bytes of size size from the current segment. If size is -1 it returns all the remaining data bytes from memory segment """ if size < -1: raise Exception('You shouldnt be doing this') if size == -1: t = self.current_segment.remaining_len(self.current_position) if not t: return None old_new_pos = self.current_position self.current_position = self.current_segment.end_address return self.current_segment.data[old_new_pos - self.current_segment.start_address:] t = self.current_position + size if not self.current_segment.inrange(t): raise Exception('Would read over segment boundaries!') old_new_pos = self.current_position self.current_position = t return self.current_segment.data[old_new_pos - self.current_segment.start_address :t - self.current_segment.start_address]
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Returns data bytes of size size from the current segment. If size is -1 it returns all the remaining data bytes from memory segment
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python
train
IndicoDataSolutions/Passage
passage/models.py
https://github.com/IndicoDataSolutions/Passage/blob/af6e100804dfe332c88bd2cd192e93a807377887/passage/models.py#L62-L108
def fit(self, trX, trY, batch_size=64, n_epochs=1, len_filter=LenFilter(), snapshot_freq=1, path=None): """Train model on given training examples and return the list of costs after each minibatch is processed. Args: trX (list) -- Inputs trY (list) -- Outputs batch_size (int, optional) -- number of examples in a minibatch (default 64) n_epochs (int, optional) -- number of epochs to train for (default 1) len_filter (object, optional) -- object to filter training example by length (default LenFilter()) snapshot_freq (int, optional) -- number of epochs between saving model snapshots (default 1) path (str, optional) -- prefix of path where model snapshots are saved. If None, no snapshots are saved (default None) Returns: list -- costs of model after processing each minibatch """ if len_filter is not None: trX, trY = len_filter.filter(trX, trY) trY = standardize_targets(trY, cost=self.cost) n = 0. t = time() costs = [] for e in range(n_epochs): epoch_costs = [] for xmb, ymb in self.iterator.iterXY(trX, trY): c = self._train(xmb, ymb) epoch_costs.append(c) n += len(ymb) if self.verbose >= 2: n_per_sec = n / (time() - t) n_left = len(trY) - n % len(trY) time_left = n_left/n_per_sec sys.stdout.write("\rEpoch %d Seen %d samples Avg cost %0.4f Time left %d seconds" % (e, n, np.mean(epoch_costs[-250:]), time_left)) sys.stdout.flush() costs.extend(epoch_costs) status = "Epoch %d Seen %d samples Avg cost %0.4f Time elapsed %d seconds" % (e, n, np.mean(epoch_costs[-250:]), time() - t) if self.verbose >= 2: sys.stdout.write("\r"+status) sys.stdout.flush() sys.stdout.write("\n") elif self.verbose == 1: print(status) if path and e % snapshot_freq == 0: save(self, "{0}.{1}".format(path, e)) return costs
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python
valid
Ex-Mente/auxi.0
auxi/modelling/process/materials/thermo.py
https://github.com/Ex-Mente/auxi.0/blob/2dcdae74154f136f8ca58289fe5b20772f215046/auxi/modelling/process/materials/thermo.py#L582-L599
def _calculate_H(self, T): """ Calculate the enthalpy of the package at the specified temperature. :param T: Temperature. [°C] :returns: Enthalpy. [kWh] """ if self.isCoal: return self._calculate_Hfr_coal(T) H = 0.0 for compound in self.material.compounds: index = self.material.get_compound_index(compound) dH = thermo.H(compound, T, self._compound_masses[index]) H = H + dH return H
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Calculate the enthalpy of the package at the specified temperature. :param T: Temperature. [°C] :returns: Enthalpy. [kWh]
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python
valid
apache/incubator-superset
superset/tasks/schedules.py
https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/tasks/schedules.py#L190-L204
def destroy_webdriver(driver): """ Destroy a driver """ # This is some very flaky code in selenium. Hence the retries # and catch-all exceptions try: retry_call(driver.close, tries=2) except Exception: pass try: driver.quit() except Exception: pass
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Destroy a driver
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python
train
google/tangent
tangent/reverse_ad.py
https://github.com/google/tangent/blob/6533e83af09de7345d1b438512679992f080dcc9/tangent/reverse_ad.py#L684-L792
def visit_Call(self, node): """Create adjoint for call. We don't allow unpacking of parameters, so we know that each argument gets passed in explicitly, allowing us to create partials for each. However, templates might perform parameter unpacking (for cases where the number of arguments is variable) and express their gradient as a tuple. In this case, we have to unpack this tuple of partials. """ # Find the function we are differentiating func = anno.getanno(node, 'func') if func in non_differentiable.NON_DIFFERENTIABLE: return node, [] if func == tracing.Traceable: return self.primal_and_adjoint_for_tracing(node) if func in grads.UNIMPLEMENTED_ADJOINTS: raise errors.ReverseNotImplementedError(func) # If we don't have an adjoint, we will have to step into the called # function and differentiate it if func not in grads.adjoints: active_args = tuple(i for i, arg in enumerate(node.args) if arg.id in self.active_variables) already_counted = False for f, a in self.required: if f.__name__ == func.__name__ and set(a) == set(active_args): already_counted = True break if not already_counted: self.required.append((func, active_args)) pri_name = naming.primal_name(func, active_args) pri_call = gast.Call( func=gast.Name(id=pri_name, ctx=gast.Load(), annotation=None), args=[self.substack] + node.args, keywords=node.keywords) anno.setanno(pri_call, 'pri_call', True) dy = create.create_grad(self.target, self.namer) dy.ctx = gast.Load() dx = create.create_grad(node.args[0], self.namer) dx.ctx = gast.Store() adj_name = naming.adjoint_name(func, active_args) adj_call = gast.Call( func=gast.Name(id=adj_name, ctx=gast.Load(), annotation=None), args=[self.substack, dy] + node.args, keywords=node.keywords) anno.setanno(adj_call, 'adj_call', True) adjoint = [template.replace('dxs = dfx', namer=self.namer, dfx=adj_call)] for j, i in enumerate(active_args): adjoint.append(template.replace('d[x] = dxs[i]', namer=self.namer, x=node.args[i].id, i=gast.Num(n=j))) return pri_call, adjoint # We have a template for the gradient that we need to fill in template_ = grads.adjoints[func] # Match the function call to the template sig = funcsigs.signature(template_) sig = sig.replace(parameters=list(sig.parameters.values())[1:]) kwargs = dict((keyword.arg, keyword.value) for keyword in node.keywords) bound_args = sig.bind(*node.args, **kwargs) # Fill in any missing kwargs with the defaults from the template args = quoting.parse_function(template_).body[0].args kwargs = dict(zip(*map(reversed, [args.args, args.defaults]))) kwargs.update(dict(zip(args.kwonlyargs, args.kw_defaults))) for arg, val in kwargs.items(): if arg.id not in bound_args.arguments: bound_args.arguments[arg.id] = val # Let's fill in the template. The first argument is the output, which # was stored in a temporary variable output_name = six.get_function_code(template_).co_varnames[0] arg_replacements = {output_name: ast_.copy_node(self.target)} arg_replacements.update(bound_args.arguments) # If the template uses *args, then we pack the corresponding inputs packing = [] flags = six.get_function_code(template_).co_flags if flags & inspect.CO_VARARGS: to_pack = node.args[six.get_function_code(template_).co_argcount - 1:] vararg_name = six.get_function_code(template_).co_varnames[-1] target = gast.Name(annotation=None, id=vararg_name, ctx=gast.Store()) value = gast.Tuple(elts=to_pack, ctx=gast.Load()) packing = [gast.Assign(targets=[target], value=value)] # And we fill in the packed tuple into the template arg_replacements[six.get_function_code( template_).co_varnames[-1]] = target adjoint = template.replace(template_, namer=self.namer, **arg_replacements) unpacking = [] if flags & inspect.CO_VARARGS: # If the template packs arguments, then we have to unpack the # derivatives afterwards # We also have to update the replacements tuple then dto_pack = [create.create_temp_grad(arg, self.namer) for arg in to_pack] value = create.create_grad(target, self.namer) target = gast.Tuple(elts=dto_pack, ctx=gast.Store()) unpacking = [gast.Assign(targets=[target], value=value)] return node, packing + adjoint + unpacking
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Create adjoint for call. We don't allow unpacking of parameters, so we know that each argument gets passed in explicitly, allowing us to create partials for each. However, templates might perform parameter unpacking (for cases where the number of arguments is variable) and express their gradient as a tuple. In this case, we have to unpack this tuple of partials.
[ "Create", "adjoint", "for", "call", "." ]
python
train
pybel/pybel
src/pybel/dsl/namespaces.py
https://github.com/pybel/pybel/blob/c8a7a1bdae4c475fa2a8c77f3a9a5f6d79556ca0/src/pybel/dsl/namespaces.py#L18-L20
def hgnc(name=None, identifier=None) -> Protein: """Build an HGNC protein node.""" return Protein(namespace='HGNC', name=name, identifier=identifier)
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Build an HGNC protein node.
[ "Build", "an", "HGNC", "protein", "node", "." ]
python
train
spacetelescope/drizzlepac
drizzlepac/updatenpol.py
https://github.com/spacetelescope/drizzlepac/blob/15bec3c929a6a869d9e71b9398ced43ede0620f1/drizzlepac/updatenpol.py#L257-L265
def find_d2ifile(flist,detector): """ Search a list of files for one that matches the detector specified. """ d2ifile = None for f in flist: fdet = fits.getval(f, 'detector', memmap=False) if fdet == detector: d2ifile = f return d2ifile
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Search a list of files for one that matches the detector specified.
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python
train
tango-controls/pytango
tango/databaseds/database.py
https://github.com/tango-controls/pytango/blob/9cf78c517c9cdc1081ff6d080a9646a740cc1d36/tango/databaseds/database.py#L809-L824
def DbDeleteDevice(self, argin): """ Delete a devcie from database :param argin: device name :type: tango.DevString :return: :rtype: tango.DevVoid """ self._log.debug("In DbDeleteDevice()") ret, dev_name, dfm = check_device_name(argin) if not ret: self.warn_stream("DataBase::db_delete_device(): device name " + argin + " incorrect ") th_exc(DB_IncorrectDeviceName, "failed to delete device, device name incorrect", "DataBase::DeleteDevice()") self.db.delete_device(dev_name)
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Delete a devcie from database :param argin: device name :type: tango.DevString :return: :rtype: tango.DevVoid
[ "Delete", "a", "devcie", "from", "database" ]
python
train
waqasbhatti/astrobase
astrobase/checkplot/pkl_utils.py
https://github.com/waqasbhatti/astrobase/blob/2922a14619d183fb28005fa7d02027ac436f2265/astrobase/checkplot/pkl_utils.py#L120-L1203
def _pkl_finder_objectinfo( objectinfo, varinfo, findercmap, finderconvolve, sigclip, normto, normmingap, deredden_object=True, custom_bandpasses=None, lclistpkl=None, nbrradiusarcsec=30.0, maxnumneighbors=5, plotdpi=100, findercachedir='~/.astrobase/stamp-cache', verbose=True, gaia_submit_timeout=10.0, gaia_submit_tries=3, gaia_max_timeout=180.0, gaia_mirror=None, fast_mode=False, complete_query_later=True ): '''This returns the finder chart and object information as a dict. Parameters ---------- objectinfo : dict or None If provided, this is a dict containing information on the object whose light curve is being processed. This function will then be able to look up and download a finder chart for this object and write that to the output checkplotdict. External services such as GAIA, SIMBAD, TIC@MAST, etc. will also be used to look up this object by its coordinates, and will add in information available from those services. The `objectinfo` dict must be of the form and contain at least the keys described below:: {'objectid': the name of the object, 'ra': the right ascension of the object in decimal degrees, 'decl': the declination of the object in decimal degrees, 'ndet': the number of observations of this object} You can also provide magnitudes and proper motions of the object using the following keys and the appropriate values in the `objectinfo` dict. These will be used to calculate colors, total and reduced proper motion, etc. and display these in the output checkplot PNG:: 'pmra' -> the proper motion in mas/yr in right ascension, 'pmdecl' -> the proper motion in mas/yr in declination, 'umag' -> U mag -> colors: U-B, U-V, U-g 'bmag' -> B mag -> colors: U-B, B-V 'vmag' -> V mag -> colors: U-V, B-V, V-R, V-I, V-K 'rmag' -> R mag -> colors: V-R, R-I 'imag' -> I mag -> colors: g-I, V-I, R-I, B-I 'jmag' -> 2MASS J mag -> colors: J-H, J-K, g-J, i-J 'hmag' -> 2MASS H mag -> colors: J-H, H-K 'kmag' -> 2MASS Ks mag -> colors: g-Ks, H-Ks, J-Ks, V-Ks 'sdssu' -> SDSS u mag -> colors: u-g, u-V 'sdssg' -> SDSS g mag -> colors: g-r, g-i, g-K, u-g, U-g, g-J 'sdssr' -> SDSS r mag -> colors: r-i, g-r 'sdssi' -> SDSS i mag -> colors: r-i, i-z, g-i, i-J, i-W1 'sdssz' -> SDSS z mag -> colors: i-z, z-W2, g-z 'ujmag' -> UKIRT J mag -> colors: J-H, H-K, J-K, g-J, i-J 'uhmag' -> UKIRT H mag -> colors: J-H, H-K 'ukmag' -> UKIRT K mag -> colors: g-K, H-K, J-K, V-K 'irac1' -> Spitzer IRAC1 mag -> colors: i-I1, I1-I2 'irac2' -> Spitzer IRAC2 mag -> colors: I1-I2, I2-I3 'irac3' -> Spitzer IRAC3 mag -> colors: I2-I3 'irac4' -> Spitzer IRAC4 mag -> colors: I3-I4 'wise1' -> WISE W1 mag -> colors: i-W1, W1-W2 'wise2' -> WISE W2 mag -> colors: W1-W2, W2-W3 'wise3' -> WISE W3 mag -> colors: W2-W3 'wise4' -> WISE W4 mag -> colors: W3-W4 If you have magnitude measurements in other bands, use the `custom_bandpasses` kwarg to pass these in. If this is None, no object information will be incorporated into the checkplot (kind of making it effectively useless for anything other than glancing at the phased light curves at various 'best' periods from the period-finder results). varinfo : dict or None If this is None, a blank dict of the form below will be added to the checkplotdict:: {'objectisvar': None -> variability flag (None indicates unset), 'vartags': CSV str containing variability type tags from review, 'varisperiodic': None -> periodic variability flag (None -> unset), 'varperiod': the period associated with the periodic variability, 'varepoch': the epoch associated with the periodic variability} If you provide a dict matching this format in this kwarg, this will be passed unchanged to the output checkplotdict produced. findercmap : str or matplotlib.cm.ColorMap object The Colormap object to use for the finder chart image. finderconvolve : astropy.convolution.Kernel object or None If not None, the Kernel object to use for convolving the finder image. sigclip : float or int or sequence of two floats/ints or None If a single float or int, a symmetric sigma-clip will be performed using the number provided as the sigma-multiplier to cut out from the input time-series. If a list of two ints/floats is provided, the function will perform an 'asymmetric' sigma-clip. The first element in this list is the sigma value to use for fainter flux/mag values; the second element in this list is the sigma value to use for brighter flux/mag values. For example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma dimmings and greater than 3-sigma brightenings. Here the meaning of "dimming" and "brightening" is set by *physics* (not the magnitude system), which is why the `magsarefluxes` kwarg must be correctly set. If `sigclip` is None, no sigma-clipping will be performed, and the time-series (with non-finite elems removed) will be passed through to the output. normto : {'globalmedian', 'zero'} or a float This is specified as below:: 'globalmedian' -> norms each mag to global median of the LC column 'zero' -> norms each mag to zero a float -> norms each mag to this specified float value. normmingap : float This defines how much the difference between consecutive measurements is allowed to be to consider them as parts of different timegroups. By default it is set to 4.0 days. deredden_object : bool If this is True, will use the 2MASS DUST service to get extinction coefficients in various bands, and then try to deredden the magnitudes and colors of the object already present in the checkplot's objectinfo dict. custom_bandpasses : dict This is a dict used to provide custom bandpass definitions for any magnitude measurements in the objectinfo dict that are not automatically recognized by :py:func:`astrobase.varclass.starfeatures.color_features`. lclistpkl : dict or str If this is provided, must be a dict resulting from reading a catalog produced by the `lcproc.catalogs.make_lclist` function or a str path pointing to the pickle file produced by that function. This catalog is used to find neighbors of the current object in the current light curve collection. Looking at neighbors of the object within the radius specified by `nbrradiusarcsec` is useful for light curves produced by instruments that have a large pixel scale, so are susceptible to blending of variability and potential confusion of neighbor variability with that of the actual object being looked at. If this is None, no neighbor lookups will be performed. nbrradiusarcsec : float The radius in arcseconds to use for a search conducted around the coordinates of this object to look for any potential confusion and blending of variability amplitude caused by their proximity. maxnumneighbors : int The maximum number of neighbors that will have their light curves and magnitudes noted in this checkplot as potential blends with the target object. plotdpi : int The resolution in DPI of the plots to generate in this function (e.g. the finder chart, etc.) findercachedir : str The path to the astrobase cache directory for finder chart downloads from the NASA SkyView service. verbose : bool If True, will indicate progress and warn about potential problems. gaia_submit_timeout : float Sets the timeout in seconds to use when submitting a request to look up the object's information to the GAIA service. Note that if `fast_mode` is set, this is ignored. gaia_submit_tries : int Sets the maximum number of times the GAIA services will be contacted to obtain this object's information. If `fast_mode` is set, this is ignored, and the services will be contacted only once (meaning that a failure to respond will be silently ignored and no GAIA data will be added to the checkplot's objectinfo dict). gaia_max_timeout : float Sets the timeout in seconds to use when waiting for the GAIA service to respond to our request for the object's information. Note that if `fast_mode` is set, this is ignored. gaia_mirror : str This sets the GAIA mirror to use. This is a key in the `services.gaia.GAIA_URLS` dict which defines the URLs to hit for each mirror. fast_mode : bool or float This runs the external catalog operations in a "fast" mode, with short timeouts and not trying to hit external catalogs that take a long time to respond. If this is set to True, the default settings for the external requests will then become:: skyview_lookup = False skyview_timeout = 10.0 skyview_retry_failed = False dust_timeout = 10.0 gaia_submit_timeout = 7.0 gaia_max_timeout = 10.0 gaia_submit_tries = 2 complete_query_later = False search_simbad = False If this is a float, will run in "fast" mode with the provided timeout value in seconds and the following settings:: skyview_lookup = True skyview_timeout = fast_mode skyview_retry_failed = False dust_timeout = fast_mode gaia_submit_timeout = 0.66*fast_mode gaia_max_timeout = fast_mode gaia_submit_tries = 2 complete_query_later = False search_simbad = False complete_query_later : bool If this is True, saves the state of GAIA queries that are not yet complete when `gaia_max_timeout` is reached while waiting for the GAIA service to respond to our request. A later call for GAIA info on the same object will attempt to pick up the results from the existing query if it's completed. If `fast_mode` is True, this is ignored. Returns ------- dict A checkplotdict is returned containing the objectinfo and varinfo dicts, ready to use with the functions below to add in light curve plots, phased LC plots, xmatch info, etc. ''' # optional mode to hit external services and fail fast if they timeout if fast_mode is True: skyview_lookup = False skyview_timeout = 10.0 skyview_retry_failed = False dust_timeout = 10.0 gaia_submit_timeout = 7.0 gaia_max_timeout = 10.0 gaia_submit_tries = 2 complete_query_later = False search_simbad = False elif isinstance(fast_mode, (int, float)) and fast_mode > 0.0: skyview_lookup = True skyview_timeout = fast_mode skyview_retry_failed = False dust_timeout = fast_mode gaia_submit_timeout = 0.66*fast_mode gaia_max_timeout = fast_mode gaia_submit_tries = 2 complete_query_later = False search_simbad = False else: skyview_lookup = True skyview_timeout = 10.0 skyview_retry_failed = True dust_timeout = 10.0 search_simbad = True if (isinstance(objectinfo, dict) and ('objectid' in objectinfo or 'hatid' in objectinfo) and 'ra' in objectinfo and 'decl' in objectinfo and objectinfo['ra'] and objectinfo['decl']): if 'objectid' not in objectinfo: objectid = objectinfo['hatid'] else: objectid = objectinfo['objectid'] if verbose and skyview_lookup: LOGINFO('adding in object information and ' 'finder chart for %s at RA: %.3f, DEC: %.3f' % (objectid, objectinfo['ra'], objectinfo['decl'])) elif verbose and not skyview_lookup: LOGINFO('adding in object information ' 'for %s at RA: %.3f, DEC: %.3f. ' 'skipping finder chart because skyview_lookup = False' % (objectid, objectinfo['ra'], objectinfo['decl'])) # get the finder chart try: if skyview_lookup: try: # generate the finder chart finder, finderheader = skyview_stamp( objectinfo['ra'], objectinfo['decl'], convolvewith=finderconvolve, verbose=verbose, flip=False, cachedir=findercachedir, timeout=skyview_timeout, retry_failed=skyview_retry_failed, ) except OSError as e: if not fast_mode: LOGERROR( 'finder image appears to be corrupt, retrying...' ) # generate the finder chart finder, finderheader = skyview_stamp( objectinfo['ra'], objectinfo['decl'], convolvewith=finderconvolve, verbose=verbose, flip=False, cachedir=findercachedir, forcefetch=True, timeout=skyview_timeout, retry_failed=False # do not start an infinite loop ) finderfig = plt.figure(figsize=(3,3),dpi=plotdpi) # initialize the finder WCS finderwcs = WCS(finderheader) # use the WCS transform for the plot ax = finderfig.add_subplot(111, frameon=False) ax.imshow(finder, cmap=findercmap, origin='lower') else: finder, finderheader, finderfig, finderwcs = ( None, None, None, None ) # skip down to after nbr stuff for the rest of the finderchart... # search around the target's location and get its neighbors if # lclistpkl is provided and it exists if (lclistpkl is not None and nbrradiusarcsec is not None and nbrradiusarcsec > 0.0): # if lclistpkl is a string, open it as a pickle if isinstance(lclistpkl, str) and os.path.exists(lclistpkl): if lclistpkl.endswith('.gz'): infd = gzip.open(lclistpkl,'rb') else: infd = open(lclistpkl,'rb') lclist = pickle.load(infd) infd.close() # otherwise, if it's a dict, we get it directly elif isinstance(lclistpkl, dict): lclist = lclistpkl # finally, if it's nothing we recognize, ignore it else: LOGERROR('could not understand lclistpkl kwarg, ' 'not getting neighbor info') lclist = dict() # check if we have a KDTree to use # if we don't, skip neighbor stuff if 'kdtree' not in lclist: LOGERROR('neighbors within %.1f arcsec for %s could ' 'not be found, no kdtree in lclistpkl: %s' % (objectid, lclistpkl)) neighbors = None kdt = None # otherwise, do neighbor processing else: kdt = lclist['kdtree'] obj_cosdecl = np.cos(np.radians(objectinfo['decl'])) obj_sindecl = np.sin(np.radians(objectinfo['decl'])) obj_cosra = np.cos(np.radians(objectinfo['ra'])) obj_sinra = np.sin(np.radians(objectinfo['ra'])) obj_xyz = np.column_stack((obj_cosra*obj_cosdecl, obj_sinra*obj_cosdecl, obj_sindecl)) match_xyzdist = ( 2.0 * np.sin(np.radians(nbrradiusarcsec/3600.0)/2.0) ) matchdists, matchinds = kdt.query( obj_xyz, k=maxnumneighbors+1, # get maxnumneighbors + tgt distance_upper_bound=match_xyzdist ) # sort by matchdist mdsorted = np.argsort(matchdists[0]) matchdists = matchdists[0][mdsorted] matchinds = matchinds[0][mdsorted] # luckily, the indices to the kdtree are the same as that # for the objects (I think) neighbors = [] nbrind = 0 for md, mi in zip(matchdists, matchinds): if np.isfinite(md) and md > 0.0: if skyview_lookup: # generate the xy for the finder we'll use a # HTML5 canvas and these pixcoords to highlight # each neighbor when we mouse over its row in # the neighbors tab # we use coord origin = 0 here and not the usual # 1 because we're annotating a numpy array pixcoords = finderwcs.all_world2pix( np.array([[lclist['objects']['ra'][mi], lclist['objects']['decl'][mi]]]), 0 ) # each elem is {'objectid', # 'ra','decl', # 'xpix','ypix', # 'dist','lcfpath'} thisnbr = { 'objectid':( lclist['objects']['objectid'][mi] ), 'ra':lclist['objects']['ra'][mi], 'decl':lclist['objects']['decl'][mi], 'xpix':pixcoords[0,0], 'ypix':300.0 - pixcoords[0,1], 'dist':_xyzdist_to_distarcsec(md), 'lcfpath': lclist['objects']['lcfname'][mi] } neighbors.append(thisnbr) nbrind = nbrind+1 # put in a nice marker for this neighbor into # the overall finder chart annotatex = pixcoords[0,0] annotatey = pixcoords[0,1] if ((300.0 - annotatex) > 50.0): offx = annotatex + 30.0 xha = 'center' else: offx = annotatex - 30.0 xha = 'center' if ((300.0 - annotatey) > 50.0): offy = annotatey - 30.0 yha = 'center' else: offy = annotatey + 30.0 yha = 'center' ax.annotate('N%s' % nbrind, (annotatex, annotatey), xytext=(offx, offy), arrowprops={'facecolor':'blue', 'edgecolor':'blue', 'width':1.0, 'headwidth':1.0, 'headlength':0.1, 'shrink':0.0}, color='blue', horizontalalignment=xha, verticalalignment=yha) else: thisnbr = { 'objectid':( lclist['objects']['objectid'][mi] ), 'ra':lclist['objects']['ra'][mi], 'decl':lclist['objects']['decl'][mi], 'xpix':0.0, 'ypix':0.0, 'dist':_xyzdist_to_distarcsec(md), 'lcfpath': lclist['objects']['lcfname'][mi] } neighbors.append(thisnbr) nbrind = nbrind+1 # if there are no neighbors, set the 'neighbors' key to None else: neighbors = None kdt = None if skyview_lookup: # # finish up the finder chart after neighbors are processed # ax.set_xticks([]) ax.set_yticks([]) # add a reticle pointing to the object's coordinates # we use coord origin = 0 here and not the usual # 1 because we're annotating a numpy array object_pixcoords = finderwcs.all_world2pix( [[objectinfo['ra'], objectinfo['decl']]], 0 ) ax.axvline( # x=150.0, x=object_pixcoords[0,0], ymin=0.375, ymax=0.45, linewidth=1, color='b' ) ax.axhline( # y=150.0, y=object_pixcoords[0,1], xmin=0.375, xmax=0.45, linewidth=1, color='b' ) ax.set_frame_on(False) # this is the output instance finderpng = StrIO() finderfig.savefig(finderpng, bbox_inches='tight', pad_inches=0.0, format='png') plt.close() # encode the finderpng instance to base64 finderpng.seek(0) finderb64 = base64.b64encode(finderpng.read()) # close the stringio buffer finderpng.close() else: finderb64 = None except Exception as e: LOGEXCEPTION('could not fetch a DSS stamp for this ' 'object %s using coords (%.3f,%.3f)' % (objectid, objectinfo['ra'], objectinfo['decl'])) finderb64 = None neighbors = None kdt = None # if we don't have ra, dec info, then everything is none up to this point else: finderb64 = None neighbors = None kdt = None # # end of finder chart operations # # now that we have the finder chart, get the rest of the object # information # get the rest of the features, these don't necessarily rely on ra, dec and # should degrade gracefully if these aren't provided if isinstance(objectinfo, dict): if 'objectid' not in objectinfo and 'hatid' in objectinfo: objectid = objectinfo['hatid'] objectinfo['objectid'] = objectid elif 'objectid' in objectinfo: objectid = objectinfo['objectid'] else: objectid = os.urandom(12).hex()[:7] objectinfo['objectid'] = objectid LOGWARNING('no objectid found in objectinfo dict, ' 'making up a random one: %s') # get the neighbor features and GAIA info nbrfeat = neighbor_gaia_features( objectinfo, kdt, nbrradiusarcsec, verbose=False, gaia_submit_timeout=gaia_submit_timeout, gaia_submit_tries=gaia_submit_tries, gaia_max_timeout=gaia_max_timeout, gaia_mirror=gaia_mirror, complete_query_later=complete_query_later, search_simbad=search_simbad ) objectinfo.update(nbrfeat) # see if the objectinfo dict has pmra/pmdecl entries. if it doesn't, # then we'll see if the nbrfeat dict has pmra/pmdecl from GAIA. we'll # set the appropriate provenance keys as well so we know where the PM # came from if ( ('pmra' not in objectinfo) or ( ('pmra' in objectinfo) and ( (objectinfo['pmra'] is None) or (not np.isfinite(objectinfo['pmra'])) ) ) ): if 'ok' in nbrfeat['gaia_status']: objectinfo['pmra'] = nbrfeat['gaia_pmras'][0] objectinfo['pmra_err'] = nbrfeat['gaia_pmra_errs'][0] objectinfo['pmra_source'] = 'gaia' if verbose: LOGWARNING('pmRA not found in provided objectinfo dict, ' 'using value from GAIA') else: objectinfo['pmra_source'] = 'light curve' if ( ('pmdecl' not in objectinfo) or ( ('pmdecl' in objectinfo) and ( (objectinfo['pmdecl'] is None) or (not np.isfinite(objectinfo['pmdecl'])) ) ) ): if 'ok' in nbrfeat['gaia_status']: objectinfo['pmdecl'] = nbrfeat['gaia_pmdecls'][0] objectinfo['pmdecl_err'] = nbrfeat['gaia_pmdecl_errs'][0] objectinfo['pmdecl_source'] = 'gaia' if verbose: LOGWARNING('pmDEC not found in provided objectinfo dict, ' 'using value from GAIA') else: objectinfo['pmdecl_source'] = 'light curve' # # update GAIA info so it's available at the first level # if 'ok' in objectinfo['gaia_status']: objectinfo['gaiaid'] = objectinfo['gaia_ids'][0] objectinfo['gaiamag'] = objectinfo['gaia_mags'][0] objectinfo['gaia_absmag'] = objectinfo['gaia_absolute_mags'][0] objectinfo['gaia_parallax'] = objectinfo['gaia_parallaxes'][0] objectinfo['gaia_parallax_err'] = ( objectinfo['gaia_parallax_errs'][0] ) objectinfo['gaia_pmra'] = objectinfo['gaia_pmras'][0] objectinfo['gaia_pmra_err'] = objectinfo['gaia_pmra_errs'][0] objectinfo['gaia_pmdecl'] = objectinfo['gaia_pmdecls'][0] objectinfo['gaia_pmdecl_err'] = objectinfo['gaia_pmdecl_errs'][0] else: objectinfo['gaiaid'] = None objectinfo['gaiamag'] = np.nan objectinfo['gaia_absmag'] = np.nan objectinfo['gaia_parallax'] = np.nan objectinfo['gaia_parallax_err'] = np.nan objectinfo['gaia_pmra'] = np.nan objectinfo['gaia_pmra_err'] = np.nan objectinfo['gaia_pmdecl'] = np.nan objectinfo['gaia_pmdecl_err'] = np.nan # # get the object's TIC information # if ('ra' in objectinfo and objectinfo['ra'] is not None and np.isfinite(objectinfo['ra']) and 'decl' in objectinfo and objectinfo['decl'] is not None and np.isfinite(objectinfo['decl'])): try: ticres = tic_conesearch(objectinfo['ra'], objectinfo['decl'], radius_arcmin=5.0/60.0, verbose=verbose, timeout=gaia_max_timeout, maxtries=gaia_submit_tries) if ticres is not None: with open(ticres['cachefname'],'r') as infd: ticinfo = json.load(infd) if ('data' in ticinfo and len(ticinfo['data']) > 0 and isinstance(ticinfo['data'][0], dict)): objectinfo['ticid'] = str(ticinfo['data'][0]['ID']) objectinfo['tessmag'] = ticinfo['data'][0]['Tmag'] objectinfo['tic_version'] = ( ticinfo['data'][0]['version'] ) objectinfo['tic_distarcsec'] = ( ticinfo['data'][0]['dstArcSec'] ) objectinfo['tessmag_origin'] = ( ticinfo['data'][0]['TESSflag'] ) objectinfo['tic_starprop_origin'] = ( ticinfo['data'][0]['SPFlag'] ) objectinfo['tic_lumclass'] = ( ticinfo['data'][0]['lumclass'] ) objectinfo['tic_teff'] = ( ticinfo['data'][0]['Teff'] ) objectinfo['tic_teff_err'] = ( ticinfo['data'][0]['e_Teff'] ) objectinfo['tic_logg'] = ( ticinfo['data'][0]['logg'] ) objectinfo['tic_logg_err'] = ( ticinfo['data'][0]['e_logg'] ) objectinfo['tic_mh'] = ( ticinfo['data'][0]['MH'] ) objectinfo['tic_mh_err'] = ( ticinfo['data'][0]['e_MH'] ) objectinfo['tic_radius'] = ( ticinfo['data'][0]['rad'] ) objectinfo['tic_radius_err'] = ( ticinfo['data'][0]['e_rad'] ) objectinfo['tic_mass'] = ( ticinfo['data'][0]['mass'] ) objectinfo['tic_mass_err'] = ( ticinfo['data'][0]['e_mass'] ) objectinfo['tic_density'] = ( ticinfo['data'][0]['rho'] ) objectinfo['tic_density_err'] = ( ticinfo['data'][0]['e_rho'] ) objectinfo['tic_luminosity'] = ( ticinfo['data'][0]['lum'] ) objectinfo['tic_luminosity_err'] = ( ticinfo['data'][0]['e_lum'] ) objectinfo['tic_distancepc'] = ( ticinfo['data'][0]['d'] ) objectinfo['tic_distancepc_err'] = ( ticinfo['data'][0]['e_d'] ) # # fill in any missing info using the TIC entry # if ('gaiaid' not in objectinfo or ('gaiaid' in objectinfo and (objectinfo['gaiaid'] is None))): objectinfo['gaiaid'] = ticinfo['data'][0]['GAIA'] if ('gaiamag' not in objectinfo or ('gaiamag' in objectinfo and (objectinfo['gaiamag'] is None or not np.isfinite(objectinfo['gaiamag'])))): objectinfo['gaiamag'] = ( ticinfo['data'][0]['GAIAmag'] ) objectinfo['gaiamag_err'] = ( ticinfo['data'][0]['e_GAIAmag'] ) if ('gaia_parallax' not in objectinfo or ('gaia_parallax' in objectinfo and (objectinfo['gaia_parallax'] is None or not np.isfinite(objectinfo['gaia_parallax'])))): objectinfo['gaia_parallax'] = ( ticinfo['data'][0]['plx'] ) objectinfo['gaia_parallax_err'] = ( ticinfo['data'][0]['e_plx'] ) if (objectinfo['gaiamag'] is not None and np.isfinite(objectinfo['gaiamag']) and objectinfo['gaia_parallax'] is not None and np.isfinite(objectinfo['gaia_parallax'])): objectinfo['gaia_absmag'] = ( magnitudes.absolute_gaia_magnitude( objectinfo['gaiamag'], objectinfo['gaia_parallax'] ) ) if ('pmra' not in objectinfo or ('pmra' in objectinfo and (objectinfo['pmra'] is None or not np.isfinite(objectinfo['pmra'])))): objectinfo['pmra'] = ticinfo['data'][0]['pmRA'] objectinfo['pmra_err'] = ( ticinfo['data'][0]['e_pmRA'] ) objectinfo['pmra_source'] = 'TIC' if ('pmdecl' not in objectinfo or ('pmdecl' in objectinfo and (objectinfo['pmdecl'] is None or not np.isfinite(objectinfo['pmdecl'])))): objectinfo['pmdecl'] = ticinfo['data'][0]['pmDEC'] objectinfo['pmdecl_err'] = ( ticinfo['data'][0]['e_pmDEC'] ) objectinfo['pmdecl_source'] = 'TIC' if ('bmag' not in objectinfo or ('bmag' in objectinfo and (objectinfo['bmag'] is None or not np.isfinite(objectinfo['bmag'])))): objectinfo['bmag'] = ticinfo['data'][0]['Bmag'] objectinfo['bmag_err'] = ( ticinfo['data'][0]['e_Bmag'] ) if ('vmag' not in objectinfo or ('vmag' in objectinfo and (objectinfo['vmag'] is None or not np.isfinite(objectinfo['vmag'])))): objectinfo['vmag'] = ticinfo['data'][0]['Vmag'] objectinfo['vmag_err'] = ( ticinfo['data'][0]['e_Vmag'] ) if ('sdssu' not in objectinfo or ('sdssu' in objectinfo and (objectinfo['sdssu'] is None or not np.isfinite(objectinfo['sdssu'])))): objectinfo['sdssu'] = ticinfo['data'][0]['umag'] objectinfo['sdssu_err'] = ( ticinfo['data'][0]['e_umag'] ) if ('sdssg' not in objectinfo or ('sdssg' in objectinfo and (objectinfo['sdssg'] is None or not np.isfinite(objectinfo['sdssg'])))): objectinfo['sdssg'] = ticinfo['data'][0]['gmag'] objectinfo['sdssg_err'] = ( ticinfo['data'][0]['e_gmag'] ) if ('sdssr' not in objectinfo or ('sdssr' in objectinfo and (objectinfo['sdssr'] is None or not np.isfinite(objectinfo['sdssr'])))): objectinfo['sdssr'] = ticinfo['data'][0]['rmag'] objectinfo['sdssr_err'] = ( ticinfo['data'][0]['e_rmag'] ) if ('sdssi' not in objectinfo or ('sdssi' in objectinfo and (objectinfo['sdssi'] is None or not np.isfinite(objectinfo['sdssi'])))): objectinfo['sdssi'] = ticinfo['data'][0]['imag'] objectinfo['sdssi_err'] = ( ticinfo['data'][0]['e_imag'] ) if ('sdssz' not in objectinfo or ('sdssz' in objectinfo and (objectinfo['sdssz'] is None or not np.isfinite(objectinfo['sdssz'])))): objectinfo['sdssz'] = ticinfo['data'][0]['zmag'] objectinfo['sdssz_err'] = ( ticinfo['data'][0]['e_zmag'] ) if ('jmag' not in objectinfo or ('jmag' in objectinfo and (objectinfo['jmag'] is None or not np.isfinite(objectinfo['jmag'])))): objectinfo['jmag'] = ticinfo['data'][0]['Jmag'] objectinfo['jmag_err'] = ( ticinfo['data'][0]['e_Jmag'] ) if ('hmag' not in objectinfo or ('hmag' in objectinfo and (objectinfo['hmag'] is None or not np.isfinite(objectinfo['hmag'])))): objectinfo['hmag'] = ticinfo['data'][0]['Hmag'] objectinfo['hmag_err'] = ( ticinfo['data'][0]['e_Hmag'] ) if ('kmag' not in objectinfo or ('kmag' in objectinfo and (objectinfo['kmag'] is None or not np.isfinite(objectinfo['kmag'])))): objectinfo['kmag'] = ticinfo['data'][0]['Kmag'] objectinfo['kmag_err'] = ( ticinfo['data'][0]['e_Kmag'] ) if ('wise1' not in objectinfo or ('wise1' in objectinfo and (objectinfo['wise1'] is None or not np.isfinite(objectinfo['wise1'])))): objectinfo['wise1'] = ticinfo['data'][0]['w1mag'] objectinfo['wise1_err'] = ( ticinfo['data'][0]['e_w1mag'] ) if ('wise2' not in objectinfo or ('wise2' in objectinfo and (objectinfo['wise2'] is None or not np.isfinite(objectinfo['wise2'])))): objectinfo['wise2'] = ticinfo['data'][0]['w2mag'] objectinfo['wise2_err'] = ( ticinfo['data'][0]['e_w2mag'] ) if ('wise3' not in objectinfo or ('wise3' in objectinfo and (objectinfo['wise3'] is None or not np.isfinite(objectinfo['wise3'])))): objectinfo['wise3'] = ticinfo['data'][0]['w3mag'] objectinfo['wise3_err'] = ( ticinfo['data'][0]['e_w3mag'] ) if ('wise4' not in objectinfo or ('wise4' in objectinfo and (objectinfo['wise4'] is None or not np.isfinite(objectinfo['wise4'])))): objectinfo['wise4'] = ticinfo['data'][0]['w4mag'] objectinfo['wise4_err'] = ( ticinfo['data'][0]['e_w4mag'] ) else: LOGERROR('could not look up TIC ' 'information for object: %s ' 'at (%.3f, %.3f)' % (objectinfo['objectid'], objectinfo['ra'], objectinfo['decl'])) except Exception as e: LOGEXCEPTION('could not look up TIC ' 'information for object: %s ' 'at (%.3f, %.3f)' % (objectinfo['objectid'], objectinfo['ra'], objectinfo['decl'])) # try to get the object's coord features coordfeat = coord_features(objectinfo) # get the color features colorfeat = color_features(objectinfo, deredden=deredden_object, custom_bandpasses=custom_bandpasses, dust_timeout=dust_timeout) # get the object's color classification colorclass = color_classification(colorfeat, coordfeat) # update the objectinfo dict with everything objectinfo.update(colorfeat) objectinfo.update(coordfeat) objectinfo.update(colorclass) # put together the initial checkplot pickle dictionary # this will be updated by the functions below as appropriate # and will written out as a gzipped pickle at the end of processing checkplotdict = {'objectid':objectid, 'neighbors':neighbors, 'objectinfo':objectinfo, 'finderchart':finderb64, 'sigclip':sigclip, 'normto':normto, 'normmingap':normmingap} # add the objecttags key to objectinfo checkplotdict['objectinfo']['objecttags'] = None # if there's no objectinfo, we can't do anything. else: # empty objectinfo dict checkplotdict = {'objectid':None, 'neighbors':None, 'objectinfo':{ 'available_bands':[], 'available_band_labels':[], 'available_dereddened_bands':[], 'available_dereddened_band_labels':[], 'available_colors':[], 'available_color_labels':[], 'bmag':None, 'bmag-vmag':None, 'decl':None, 'hatid':None, 'hmag':None, 'imag-jmag':None, 'jmag-kmag':None, 'jmag':None, 'kmag':None, 'ndet':None, 'network':None, 'objecttags':None, 'pmdecl':None, 'pmdecl_err':None, 'pmra':None, 'pmra_err':None, 'propermotion':None, 'ra':None, 'rpmj':None, 'sdssg':None, 'sdssi':None, 'sdssr':None, 'stations':None, 'twomassid':None, 'ucac4id':None, 'vmag':None }, 'finderchart':None, 'sigclip':sigclip, 'normto':normto, 'normmingap':normmingap} # end of objectinfo processing # add the varinfo dict if isinstance(varinfo, dict): checkplotdict['varinfo'] = varinfo else: checkplotdict['varinfo'] = { 'objectisvar':None, 'vartags':None, 'varisperiodic':None, 'varperiod':None, 'varepoch':None, } return checkplotdict
[ "def", "_pkl_finder_objectinfo", "(", "objectinfo", ",", "varinfo", ",", "findercmap", ",", "finderconvolve", ",", "sigclip", ",", "normto", ",", "normmingap", ",", "deredden_object", "=", "True", ",", "custom_bandpasses", "=", "None", ",", "lclistpkl", "=", "None", ",", "nbrradiusarcsec", "=", "30.0", ",", "maxnumneighbors", "=", "5", ",", "plotdpi", "=", "100", ",", "findercachedir", "=", "'~/.astrobase/stamp-cache'", ",", "verbose", "=", "True", ",", "gaia_submit_timeout", "=", "10.0", ",", "gaia_submit_tries", "=", "3", ",", "gaia_max_timeout", "=", "180.0", ",", "gaia_mirror", "=", "None", ",", "fast_mode", "=", "False", ",", "complete_query_later", "=", "True", ")", ":", "# optional mode to hit external services and fail fast if they timeout", "if", "fast_mode", "is", "True", ":", "skyview_lookup", "=", "False", "skyview_timeout", "=", "10.0", "skyview_retry_failed", "=", "False", "dust_timeout", "=", "10.0", "gaia_submit_timeout", "=", "7.0", "gaia_max_timeout", "=", "10.0", "gaia_submit_tries", "=", "2", "complete_query_later", "=", "False", "search_simbad", "=", "False", "elif", "isinstance", "(", "fast_mode", ",", "(", "int", ",", "float", ")", ")", "and", "fast_mode", ">", "0.0", ":", "skyview_lookup", "=", "True", "skyview_timeout", "=", "fast_mode", "skyview_retry_failed", "=", "False", "dust_timeout", "=", "fast_mode", "gaia_submit_timeout", "=", "0.66", "*", "fast_mode", "gaia_max_timeout", "=", "fast_mode", "gaia_submit_tries", "=", "2", "complete_query_later", "=", "False", "search_simbad", "=", "False", "else", ":", "skyview_lookup", "=", "True", "skyview_timeout", "=", "10.0", "skyview_retry_failed", "=", "True", "dust_timeout", "=", "10.0", "search_simbad", "=", "True", "if", "(", "isinstance", "(", "objectinfo", ",", "dict", ")", "and", "(", "'objectid'", "in", "objectinfo", "or", "'hatid'", "in", "objectinfo", ")", "and", "'ra'", "in", "objectinfo", "and", "'decl'", "in", "objectinfo", "and", "objectinfo", "[", "'ra'", "]", "and", "objectinfo", "[", "'decl'", "]", ")", ":", "if", "'objectid'", "not", "in", "objectinfo", ":", "objectid", "=", "objectinfo", "[", "'hatid'", "]", "else", ":", "objectid", "=", "objectinfo", "[", "'objectid'", "]", "if", "verbose", "and", "skyview_lookup", ":", "LOGINFO", "(", "'adding in object information and '", "'finder chart for %s at RA: %.3f, DEC: %.3f'", "%", "(", "objectid", ",", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ")", ")", "elif", "verbose", "and", "not", "skyview_lookup", ":", "LOGINFO", "(", "'adding in object information '", "'for %s at RA: %.3f, DEC: %.3f. '", "'skipping finder chart because skyview_lookup = False'", "%", "(", "objectid", ",", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ")", ")", "# get the finder chart", "try", ":", "if", "skyview_lookup", ":", "try", ":", "# generate the finder chart", "finder", ",", "finderheader", "=", "skyview_stamp", "(", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ",", "convolvewith", "=", "finderconvolve", ",", "verbose", "=", "verbose", ",", "flip", "=", "False", ",", "cachedir", "=", "findercachedir", ",", "timeout", "=", "skyview_timeout", ",", "retry_failed", "=", "skyview_retry_failed", ",", ")", "except", "OSError", "as", "e", ":", "if", "not", "fast_mode", ":", "LOGERROR", "(", "'finder image appears to be corrupt, retrying...'", ")", "# generate the finder chart", "finder", ",", "finderheader", "=", "skyview_stamp", "(", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ",", "convolvewith", "=", "finderconvolve", ",", "verbose", "=", "verbose", ",", "flip", "=", "False", ",", "cachedir", "=", "findercachedir", ",", "forcefetch", "=", "True", ",", "timeout", "=", "skyview_timeout", ",", "retry_failed", "=", "False", "# do not start an infinite loop", ")", "finderfig", "=", "plt", ".", "figure", "(", "figsize", "=", "(", "3", ",", "3", ")", ",", "dpi", "=", "plotdpi", ")", "# initialize the finder WCS", "finderwcs", "=", "WCS", "(", "finderheader", ")", "# use the WCS transform for the plot", "ax", "=", "finderfig", ".", "add_subplot", "(", "111", ",", "frameon", "=", "False", ")", "ax", ".", "imshow", "(", "finder", ",", "cmap", "=", "findercmap", ",", "origin", "=", "'lower'", ")", "else", ":", "finder", ",", "finderheader", ",", "finderfig", ",", "finderwcs", "=", "(", "None", ",", "None", ",", "None", ",", "None", ")", "# skip down to after nbr stuff for the rest of the finderchart...", "# search around the target's location and get its neighbors if", "# lclistpkl is provided and it exists", "if", "(", "lclistpkl", "is", "not", "None", "and", "nbrradiusarcsec", "is", "not", "None", "and", "nbrradiusarcsec", ">", "0.0", ")", ":", "# if lclistpkl is a string, open it as a pickle", "if", "isinstance", "(", "lclistpkl", ",", "str", ")", "and", "os", ".", "path", ".", "exists", "(", "lclistpkl", ")", ":", "if", "lclistpkl", ".", "endswith", "(", "'.gz'", ")", ":", "infd", "=", "gzip", ".", "open", "(", "lclistpkl", ",", "'rb'", ")", "else", ":", "infd", "=", "open", "(", "lclistpkl", ",", "'rb'", ")", "lclist", "=", "pickle", ".", "load", "(", "infd", ")", "infd", ".", "close", "(", ")", "# otherwise, if it's a dict, we get it directly", "elif", "isinstance", "(", "lclistpkl", ",", "dict", ")", ":", "lclist", "=", "lclistpkl", "# finally, if it's nothing we recognize, ignore it", "else", ":", "LOGERROR", "(", "'could not understand lclistpkl kwarg, '", "'not getting neighbor info'", ")", "lclist", "=", "dict", "(", ")", "# check if we have a KDTree to use", "# if we don't, skip neighbor stuff", "if", "'kdtree'", "not", "in", "lclist", ":", "LOGERROR", "(", "'neighbors within %.1f arcsec for %s could '", "'not be found, no kdtree in lclistpkl: %s'", "%", "(", "objectid", ",", "lclistpkl", ")", ")", "neighbors", "=", "None", "kdt", "=", "None", "# otherwise, do neighbor processing", "else", ":", "kdt", "=", "lclist", "[", "'kdtree'", "]", "obj_cosdecl", "=", "np", ".", "cos", "(", "np", ".", "radians", "(", "objectinfo", "[", "'decl'", "]", ")", ")", "obj_sindecl", "=", "np", ".", "sin", "(", "np", ".", "radians", "(", "objectinfo", "[", "'decl'", "]", ")", ")", "obj_cosra", "=", "np", ".", "cos", "(", "np", ".", "radians", "(", "objectinfo", "[", "'ra'", "]", ")", ")", "obj_sinra", "=", "np", ".", "sin", "(", "np", ".", "radians", "(", "objectinfo", "[", "'ra'", "]", ")", ")", "obj_xyz", "=", "np", ".", "column_stack", "(", "(", "obj_cosra", "*", "obj_cosdecl", ",", "obj_sinra", "*", "obj_cosdecl", ",", "obj_sindecl", ")", ")", "match_xyzdist", "=", "(", "2.0", "*", "np", ".", "sin", "(", "np", ".", "radians", "(", "nbrradiusarcsec", "/", "3600.0", ")", "/", "2.0", ")", ")", "matchdists", ",", "matchinds", "=", "kdt", ".", "query", "(", "obj_xyz", ",", "k", "=", "maxnumneighbors", "+", "1", ",", "# get maxnumneighbors + tgt", "distance_upper_bound", "=", "match_xyzdist", ")", "# sort by matchdist", "mdsorted", "=", "np", ".", "argsort", "(", "matchdists", "[", "0", "]", ")", "matchdists", "=", "matchdists", "[", "0", "]", "[", "mdsorted", "]", "matchinds", "=", "matchinds", "[", "0", "]", "[", "mdsorted", "]", "# luckily, the indices to the kdtree are the same as that", "# for the objects (I think)", "neighbors", "=", "[", "]", "nbrind", "=", "0", "for", "md", ",", "mi", "in", "zip", "(", "matchdists", ",", "matchinds", ")", ":", "if", "np", ".", "isfinite", "(", "md", ")", "and", "md", ">", "0.0", ":", "if", "skyview_lookup", ":", "# generate the xy for the finder we'll use a", "# HTML5 canvas and these pixcoords to highlight", "# each neighbor when we mouse over its row in", "# the neighbors tab", "# we use coord origin = 0 here and not the usual", "# 1 because we're annotating a numpy array", "pixcoords", "=", "finderwcs", ".", "all_world2pix", "(", "np", ".", "array", "(", "[", "[", "lclist", "[", "'objects'", "]", "[", "'ra'", "]", "[", "mi", "]", ",", "lclist", "[", "'objects'", "]", "[", "'decl'", "]", "[", "mi", "]", "]", "]", ")", ",", "0", ")", "# each elem is {'objectid',", "# 'ra','decl',", "# 'xpix','ypix',", "# 'dist','lcfpath'}", "thisnbr", "=", "{", "'objectid'", ":", "(", "lclist", "[", "'objects'", "]", "[", "'objectid'", "]", "[", "mi", "]", ")", ",", "'ra'", ":", "lclist", "[", "'objects'", "]", "[", "'ra'", "]", "[", "mi", "]", ",", "'decl'", ":", "lclist", "[", "'objects'", "]", "[", "'decl'", "]", "[", "mi", "]", ",", "'xpix'", ":", "pixcoords", "[", "0", ",", "0", "]", ",", "'ypix'", ":", "300.0", "-", "pixcoords", "[", "0", ",", "1", "]", ",", "'dist'", ":", "_xyzdist_to_distarcsec", "(", "md", ")", ",", "'lcfpath'", ":", "lclist", "[", "'objects'", "]", "[", "'lcfname'", "]", "[", "mi", "]", "}", "neighbors", ".", "append", "(", "thisnbr", ")", "nbrind", "=", "nbrind", "+", "1", "# put in a nice marker for this neighbor into", "# the overall finder chart", "annotatex", "=", "pixcoords", "[", "0", ",", "0", "]", "annotatey", "=", "pixcoords", "[", "0", ",", "1", "]", "if", "(", "(", "300.0", "-", "annotatex", ")", ">", "50.0", ")", ":", "offx", "=", "annotatex", "+", "30.0", "xha", "=", "'center'", "else", ":", "offx", "=", "annotatex", "-", "30.0", "xha", "=", "'center'", "if", "(", "(", "300.0", "-", "annotatey", ")", ">", "50.0", ")", ":", "offy", "=", "annotatey", "-", "30.0", "yha", "=", "'center'", "else", ":", "offy", "=", "annotatey", "+", "30.0", "yha", "=", "'center'", "ax", ".", "annotate", "(", "'N%s'", "%", "nbrind", ",", "(", "annotatex", ",", "annotatey", ")", ",", "xytext", "=", "(", "offx", ",", "offy", ")", ",", "arrowprops", "=", "{", "'facecolor'", ":", "'blue'", ",", "'edgecolor'", ":", "'blue'", ",", "'width'", ":", "1.0", ",", "'headwidth'", ":", "1.0", ",", "'headlength'", ":", "0.1", ",", "'shrink'", ":", "0.0", "}", ",", "color", "=", "'blue'", ",", "horizontalalignment", "=", "xha", ",", "verticalalignment", "=", "yha", ")", "else", ":", "thisnbr", "=", "{", "'objectid'", ":", "(", "lclist", "[", "'objects'", "]", "[", "'objectid'", "]", "[", "mi", "]", ")", ",", "'ra'", ":", "lclist", "[", "'objects'", "]", "[", "'ra'", "]", "[", "mi", "]", ",", "'decl'", ":", "lclist", "[", "'objects'", "]", "[", "'decl'", "]", "[", "mi", "]", ",", "'xpix'", ":", "0.0", ",", "'ypix'", ":", "0.0", ",", "'dist'", ":", "_xyzdist_to_distarcsec", "(", "md", ")", ",", "'lcfpath'", ":", "lclist", "[", "'objects'", "]", "[", "'lcfname'", "]", "[", "mi", "]", "}", "neighbors", ".", "append", "(", "thisnbr", ")", "nbrind", "=", "nbrind", "+", "1", "# if there are no neighbors, set the 'neighbors' key to None", "else", ":", "neighbors", "=", "None", "kdt", "=", "None", "if", "skyview_lookup", ":", "#", "# finish up the finder chart after neighbors are processed", "#", "ax", ".", "set_xticks", "(", "[", "]", ")", "ax", ".", "set_yticks", "(", "[", "]", ")", "# add a reticle pointing to the object's coordinates", "# we use coord origin = 0 here and not the usual", "# 1 because we're annotating a numpy array", "object_pixcoords", "=", "finderwcs", ".", "all_world2pix", "(", "[", "[", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", "]", "]", ",", "0", ")", "ax", ".", "axvline", "(", "# x=150.0,", "x", "=", "object_pixcoords", "[", "0", ",", "0", "]", ",", "ymin", "=", "0.375", ",", "ymax", "=", "0.45", ",", "linewidth", "=", "1", ",", "color", "=", "'b'", ")", "ax", ".", "axhline", "(", "# y=150.0,", "y", "=", "object_pixcoords", "[", "0", ",", "1", "]", ",", "xmin", "=", "0.375", ",", "xmax", "=", "0.45", ",", "linewidth", "=", "1", ",", "color", "=", "'b'", ")", "ax", ".", "set_frame_on", "(", "False", ")", "# this is the output instance", "finderpng", "=", "StrIO", "(", ")", "finderfig", ".", "savefig", "(", "finderpng", ",", "bbox_inches", "=", "'tight'", ",", "pad_inches", "=", "0.0", ",", "format", "=", "'png'", ")", "plt", ".", "close", "(", ")", "# encode the finderpng instance to base64", "finderpng", ".", "seek", "(", "0", ")", "finderb64", "=", "base64", ".", "b64encode", "(", "finderpng", ".", "read", "(", ")", ")", "# close the stringio buffer", "finderpng", ".", "close", "(", ")", "else", ":", "finderb64", "=", "None", "except", "Exception", "as", "e", ":", "LOGEXCEPTION", "(", "'could not fetch a DSS stamp for this '", "'object %s using coords (%.3f,%.3f)'", "%", "(", "objectid", ",", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ")", ")", "finderb64", "=", "None", "neighbors", "=", "None", "kdt", "=", "None", "# if we don't have ra, dec info, then everything is none up to this point", "else", ":", "finderb64", "=", "None", "neighbors", "=", "None", "kdt", "=", "None", "#", "# end of finder chart operations", "#", "# now that we have the finder chart, get the rest of the object", "# information", "# get the rest of the features, these don't necessarily rely on ra, dec and", "# should degrade gracefully if these aren't provided", "if", "isinstance", "(", "objectinfo", ",", "dict", ")", ":", "if", "'objectid'", "not", "in", "objectinfo", "and", "'hatid'", "in", "objectinfo", ":", "objectid", "=", "objectinfo", "[", "'hatid'", "]", "objectinfo", "[", "'objectid'", "]", "=", "objectid", "elif", "'objectid'", "in", "objectinfo", ":", "objectid", "=", "objectinfo", "[", "'objectid'", "]", "else", ":", "objectid", "=", "os", ".", "urandom", "(", "12", ")", ".", "hex", "(", ")", "[", ":", "7", "]", "objectinfo", "[", "'objectid'", "]", "=", "objectid", "LOGWARNING", "(", "'no objectid found in objectinfo dict, '", "'making up a random one: %s'", ")", "# get the neighbor features and GAIA info", "nbrfeat", "=", "neighbor_gaia_features", "(", "objectinfo", ",", "kdt", ",", "nbrradiusarcsec", ",", "verbose", "=", "False", ",", "gaia_submit_timeout", "=", "gaia_submit_timeout", ",", "gaia_submit_tries", "=", "gaia_submit_tries", ",", "gaia_max_timeout", "=", "gaia_max_timeout", ",", "gaia_mirror", "=", "gaia_mirror", ",", "complete_query_later", "=", "complete_query_later", ",", "search_simbad", "=", "search_simbad", ")", "objectinfo", ".", "update", "(", "nbrfeat", ")", "# see if the objectinfo dict has pmra/pmdecl entries. if it doesn't,", "# then we'll see if the nbrfeat dict has pmra/pmdecl from GAIA. we'll", "# set the appropriate provenance keys as well so we know where the PM", "# came from", "if", "(", "(", "'pmra'", "not", "in", "objectinfo", ")", "or", "(", "(", "'pmra'", "in", "objectinfo", ")", "and", "(", "(", "objectinfo", "[", "'pmra'", "]", "is", "None", ")", "or", "(", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'pmra'", "]", ")", ")", ")", ")", ")", ":", "if", "'ok'", "in", "nbrfeat", "[", "'gaia_status'", "]", ":", "objectinfo", "[", "'pmra'", "]", "=", "nbrfeat", "[", "'gaia_pmras'", "]", "[", "0", "]", "objectinfo", "[", "'pmra_err'", "]", "=", "nbrfeat", "[", "'gaia_pmra_errs'", "]", "[", "0", "]", "objectinfo", "[", "'pmra_source'", "]", "=", "'gaia'", "if", "verbose", ":", "LOGWARNING", "(", "'pmRA not found in provided objectinfo dict, '", "'using value from GAIA'", ")", "else", ":", "objectinfo", "[", "'pmra_source'", "]", "=", "'light curve'", "if", "(", "(", "'pmdecl'", "not", "in", "objectinfo", ")", "or", "(", "(", "'pmdecl'", "in", "objectinfo", ")", "and", "(", "(", "objectinfo", "[", "'pmdecl'", "]", "is", "None", ")", "or", "(", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'pmdecl'", "]", ")", ")", ")", ")", ")", ":", "if", "'ok'", "in", "nbrfeat", "[", "'gaia_status'", "]", ":", "objectinfo", "[", "'pmdecl'", "]", "=", "nbrfeat", "[", "'gaia_pmdecls'", "]", "[", "0", "]", "objectinfo", "[", "'pmdecl_err'", "]", "=", "nbrfeat", "[", "'gaia_pmdecl_errs'", "]", "[", "0", "]", "objectinfo", "[", "'pmdecl_source'", "]", "=", "'gaia'", "if", "verbose", ":", "LOGWARNING", "(", "'pmDEC not found in provided objectinfo dict, '", "'using value from GAIA'", ")", "else", ":", "objectinfo", "[", "'pmdecl_source'", "]", "=", "'light curve'", "#", "# update GAIA info so it's available at the first level", "#", "if", "'ok'", "in", "objectinfo", "[", "'gaia_status'", "]", ":", "objectinfo", "[", "'gaiaid'", "]", "=", "objectinfo", "[", "'gaia_ids'", "]", "[", "0", "]", "objectinfo", "[", "'gaiamag'", "]", "=", "objectinfo", "[", "'gaia_mags'", "]", "[", "0", "]", "objectinfo", "[", "'gaia_absmag'", "]", "=", "objectinfo", "[", "'gaia_absolute_mags'", "]", "[", "0", "]", "objectinfo", "[", "'gaia_parallax'", "]", "=", "objectinfo", "[", "'gaia_parallaxes'", "]", "[", "0", "]", "objectinfo", "[", "'gaia_parallax_err'", "]", "=", "(", "objectinfo", "[", "'gaia_parallax_errs'", "]", "[", "0", "]", ")", "objectinfo", "[", "'gaia_pmra'", "]", "=", "objectinfo", "[", "'gaia_pmras'", "]", "[", "0", "]", "objectinfo", "[", "'gaia_pmra_err'", "]", "=", "objectinfo", "[", "'gaia_pmra_errs'", "]", "[", "0", "]", "objectinfo", "[", "'gaia_pmdecl'", "]", "=", "objectinfo", "[", "'gaia_pmdecls'", "]", "[", "0", "]", "objectinfo", "[", "'gaia_pmdecl_err'", "]", "=", "objectinfo", "[", "'gaia_pmdecl_errs'", "]", "[", "0", "]", "else", ":", "objectinfo", "[", "'gaiaid'", "]", "=", "None", "objectinfo", "[", "'gaiamag'", "]", "=", "np", ".", "nan", "objectinfo", "[", "'gaia_absmag'", "]", "=", "np", ".", "nan", "objectinfo", "[", "'gaia_parallax'", "]", "=", "np", ".", "nan", "objectinfo", "[", "'gaia_parallax_err'", "]", "=", "np", ".", "nan", "objectinfo", "[", "'gaia_pmra'", "]", "=", "np", ".", "nan", "objectinfo", "[", "'gaia_pmra_err'", "]", "=", "np", ".", "nan", "objectinfo", "[", "'gaia_pmdecl'", "]", "=", "np", ".", "nan", "objectinfo", "[", "'gaia_pmdecl_err'", "]", "=", "np", ".", "nan", "#", "# get the object's TIC information", "#", "if", "(", "'ra'", "in", "objectinfo", "and", "objectinfo", "[", "'ra'", "]", "is", "not", "None", "and", "np", ".", "isfinite", "(", "objectinfo", "[", "'ra'", "]", ")", "and", "'decl'", "in", "objectinfo", "and", "objectinfo", "[", "'decl'", "]", "is", "not", "None", "and", "np", ".", "isfinite", "(", "objectinfo", "[", "'decl'", "]", ")", ")", ":", "try", ":", "ticres", "=", "tic_conesearch", "(", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ",", "radius_arcmin", "=", "5.0", "/", "60.0", ",", "verbose", "=", "verbose", ",", "timeout", "=", "gaia_max_timeout", ",", "maxtries", "=", "gaia_submit_tries", ")", "if", "ticres", "is", "not", "None", ":", "with", "open", "(", "ticres", "[", "'cachefname'", "]", ",", "'r'", ")", "as", "infd", ":", "ticinfo", "=", "json", ".", "load", "(", "infd", ")", "if", "(", "'data'", "in", "ticinfo", "and", "len", "(", "ticinfo", "[", "'data'", "]", ")", ">", "0", "and", "isinstance", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", ",", "dict", ")", ")", ":", "objectinfo", "[", "'ticid'", "]", "=", "str", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'ID'", "]", ")", "objectinfo", "[", "'tessmag'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'Tmag'", "]", "objectinfo", "[", "'tic_version'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'version'", "]", ")", "objectinfo", "[", "'tic_distarcsec'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'dstArcSec'", "]", ")", "objectinfo", "[", "'tessmag_origin'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'TESSflag'", "]", ")", "objectinfo", "[", "'tic_starprop_origin'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'SPFlag'", "]", ")", "objectinfo", "[", "'tic_lumclass'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'lumclass'", "]", ")", "objectinfo", "[", "'tic_teff'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'Teff'", "]", ")", "objectinfo", "[", "'tic_teff_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_Teff'", "]", ")", "objectinfo", "[", "'tic_logg'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'logg'", "]", ")", "objectinfo", "[", "'tic_logg_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_logg'", "]", ")", "objectinfo", "[", "'tic_mh'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'MH'", "]", ")", "objectinfo", "[", "'tic_mh_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_MH'", "]", ")", "objectinfo", "[", "'tic_radius'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'rad'", "]", ")", "objectinfo", "[", "'tic_radius_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_rad'", "]", ")", "objectinfo", "[", "'tic_mass'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'mass'", "]", ")", "objectinfo", "[", "'tic_mass_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_mass'", "]", ")", "objectinfo", "[", "'tic_density'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'rho'", "]", ")", "objectinfo", "[", "'tic_density_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_rho'", "]", ")", "objectinfo", "[", "'tic_luminosity'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'lum'", "]", ")", "objectinfo", "[", "'tic_luminosity_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_lum'", "]", ")", "objectinfo", "[", "'tic_distancepc'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'d'", "]", ")", "objectinfo", "[", "'tic_distancepc_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_d'", "]", ")", "#", "# fill in any missing info using the TIC entry", "#", "if", "(", "'gaiaid'", "not", "in", "objectinfo", "or", "(", "'gaiaid'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'gaiaid'", "]", "is", "None", ")", ")", ")", ":", "objectinfo", "[", "'gaiaid'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'GAIA'", "]", "if", "(", "'gaiamag'", "not", "in", "objectinfo", "or", "(", "'gaiamag'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'gaiamag'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'gaiamag'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'gaiamag'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'GAIAmag'", "]", ")", "objectinfo", "[", "'gaiamag_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_GAIAmag'", "]", ")", "if", "(", "'gaia_parallax'", "not", "in", "objectinfo", "or", "(", "'gaia_parallax'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'gaia_parallax'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'gaia_parallax'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'gaia_parallax'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'plx'", "]", ")", "objectinfo", "[", "'gaia_parallax_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_plx'", "]", ")", "if", "(", "objectinfo", "[", "'gaiamag'", "]", "is", "not", "None", "and", "np", ".", "isfinite", "(", "objectinfo", "[", "'gaiamag'", "]", ")", "and", "objectinfo", "[", "'gaia_parallax'", "]", "is", "not", "None", "and", "np", ".", "isfinite", "(", "objectinfo", "[", "'gaia_parallax'", "]", ")", ")", ":", "objectinfo", "[", "'gaia_absmag'", "]", "=", "(", "magnitudes", ".", "absolute_gaia_magnitude", "(", "objectinfo", "[", "'gaiamag'", "]", ",", "objectinfo", "[", "'gaia_parallax'", "]", ")", ")", "if", "(", "'pmra'", "not", "in", "objectinfo", "or", "(", "'pmra'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'pmra'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'pmra'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'pmra'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'pmRA'", "]", "objectinfo", "[", "'pmra_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_pmRA'", "]", ")", "objectinfo", "[", "'pmra_source'", "]", "=", "'TIC'", "if", "(", "'pmdecl'", "not", "in", "objectinfo", "or", "(", "'pmdecl'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'pmdecl'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'pmdecl'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'pmdecl'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'pmDEC'", "]", "objectinfo", "[", "'pmdecl_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_pmDEC'", "]", ")", "objectinfo", "[", "'pmdecl_source'", "]", "=", "'TIC'", "if", "(", "'bmag'", "not", "in", "objectinfo", "or", "(", "'bmag'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'bmag'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'bmag'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'bmag'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'Bmag'", "]", "objectinfo", "[", "'bmag_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_Bmag'", "]", ")", "if", "(", "'vmag'", "not", "in", "objectinfo", "or", "(", "'vmag'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'vmag'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'vmag'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'vmag'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'Vmag'", "]", "objectinfo", "[", "'vmag_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_Vmag'", "]", ")", "if", "(", "'sdssu'", "not", "in", "objectinfo", "or", "(", "'sdssu'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'sdssu'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'sdssu'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'sdssu'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'umag'", "]", "objectinfo", "[", "'sdssu_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_umag'", "]", ")", "if", "(", "'sdssg'", "not", "in", "objectinfo", "or", "(", "'sdssg'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'sdssg'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'sdssg'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'sdssg'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'gmag'", "]", "objectinfo", "[", "'sdssg_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_gmag'", "]", ")", "if", "(", "'sdssr'", "not", "in", "objectinfo", "or", "(", "'sdssr'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'sdssr'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'sdssr'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'sdssr'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'rmag'", "]", "objectinfo", "[", "'sdssr_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_rmag'", "]", ")", "if", "(", "'sdssi'", "not", "in", "objectinfo", "or", "(", "'sdssi'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'sdssi'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'sdssi'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'sdssi'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'imag'", "]", "objectinfo", "[", "'sdssi_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_imag'", "]", ")", "if", "(", "'sdssz'", "not", "in", "objectinfo", "or", "(", "'sdssz'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'sdssz'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'sdssz'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'sdssz'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'zmag'", "]", "objectinfo", "[", "'sdssz_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_zmag'", "]", ")", "if", "(", "'jmag'", "not", "in", "objectinfo", "or", "(", "'jmag'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'jmag'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'jmag'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'jmag'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'Jmag'", "]", "objectinfo", "[", "'jmag_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_Jmag'", "]", ")", "if", "(", "'hmag'", "not", "in", "objectinfo", "or", "(", "'hmag'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'hmag'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'hmag'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'hmag'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'Hmag'", "]", "objectinfo", "[", "'hmag_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_Hmag'", "]", ")", "if", "(", "'kmag'", "not", "in", "objectinfo", "or", "(", "'kmag'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'kmag'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'kmag'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'kmag'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'Kmag'", "]", "objectinfo", "[", "'kmag_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_Kmag'", "]", ")", "if", "(", "'wise1'", "not", "in", "objectinfo", "or", "(", "'wise1'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'wise1'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'wise1'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'wise1'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'w1mag'", "]", "objectinfo", "[", "'wise1_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_w1mag'", "]", ")", "if", "(", "'wise2'", "not", "in", "objectinfo", "or", "(", "'wise2'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'wise2'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'wise2'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'wise2'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'w2mag'", "]", "objectinfo", "[", "'wise2_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_w2mag'", "]", ")", "if", "(", "'wise3'", "not", "in", "objectinfo", "or", "(", "'wise3'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'wise3'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'wise3'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'wise3'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'w3mag'", "]", "objectinfo", "[", "'wise3_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_w3mag'", "]", ")", "if", "(", "'wise4'", "not", "in", "objectinfo", "or", "(", "'wise4'", "in", "objectinfo", "and", "(", "objectinfo", "[", "'wise4'", "]", "is", "None", "or", "not", "np", ".", "isfinite", "(", "objectinfo", "[", "'wise4'", "]", ")", ")", ")", ")", ":", "objectinfo", "[", "'wise4'", "]", "=", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'w4mag'", "]", "objectinfo", "[", "'wise4_err'", "]", "=", "(", "ticinfo", "[", "'data'", "]", "[", "0", "]", "[", "'e_w4mag'", "]", ")", "else", ":", "LOGERROR", "(", "'could not look up TIC '", "'information for object: %s '", "'at (%.3f, %.3f)'", "%", "(", "objectinfo", "[", "'objectid'", "]", ",", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ")", ")", "except", "Exception", "as", "e", ":", "LOGEXCEPTION", "(", "'could not look up TIC '", "'information for object: %s '", "'at (%.3f, %.3f)'", "%", "(", "objectinfo", "[", "'objectid'", "]", ",", "objectinfo", "[", "'ra'", "]", ",", "objectinfo", "[", "'decl'", "]", ")", ")", "# try to get the object's coord features", "coordfeat", "=", "coord_features", "(", "objectinfo", ")", "# get the color features", "colorfeat", "=", "color_features", "(", "objectinfo", ",", "deredden", "=", "deredden_object", ",", "custom_bandpasses", "=", "custom_bandpasses", ",", "dust_timeout", "=", "dust_timeout", ")", "# get the object's color classification", "colorclass", "=", "color_classification", "(", "colorfeat", ",", "coordfeat", ")", "# update the objectinfo dict with everything", "objectinfo", ".", "update", "(", "colorfeat", ")", "objectinfo", ".", "update", "(", "coordfeat", ")", "objectinfo", ".", "update", "(", "colorclass", ")", "# put together the initial checkplot pickle dictionary", "# this will be updated by the functions below as appropriate", "# and will written out as a gzipped pickle at the end of processing", "checkplotdict", "=", "{", "'objectid'", ":", "objectid", ",", "'neighbors'", ":", "neighbors", ",", "'objectinfo'", ":", "objectinfo", ",", "'finderchart'", ":", "finderb64", ",", "'sigclip'", ":", "sigclip", ",", "'normto'", ":", "normto", ",", "'normmingap'", ":", "normmingap", "}", "# add the objecttags key to objectinfo", "checkplotdict", "[", "'objectinfo'", "]", "[", "'objecttags'", "]", "=", "None", "# if there's no objectinfo, we can't do anything.", "else", ":", "# empty objectinfo dict", "checkplotdict", "=", "{", "'objectid'", ":", "None", ",", "'neighbors'", ":", "None", ",", "'objectinfo'", ":", "{", "'available_bands'", ":", "[", "]", ",", "'available_band_labels'", ":", "[", "]", ",", "'available_dereddened_bands'", ":", "[", "]", ",", "'available_dereddened_band_labels'", ":", "[", "]", ",", "'available_colors'", ":", "[", "]", ",", "'available_color_labels'", ":", "[", "]", ",", "'bmag'", ":", "None", ",", "'bmag-vmag'", ":", "None", ",", "'decl'", ":", "None", ",", "'hatid'", ":", "None", ",", "'hmag'", ":", "None", ",", "'imag-jmag'", ":", "None", ",", "'jmag-kmag'", ":", "None", ",", "'jmag'", ":", "None", ",", "'kmag'", ":", "None", ",", "'ndet'", ":", "None", ",", "'network'", ":", "None", ",", "'objecttags'", ":", "None", ",", "'pmdecl'", ":", "None", ",", "'pmdecl_err'", ":", "None", ",", "'pmra'", ":", "None", ",", "'pmra_err'", ":", "None", ",", "'propermotion'", ":", "None", ",", "'ra'", ":", "None", ",", "'rpmj'", ":", "None", ",", "'sdssg'", ":", "None", ",", "'sdssi'", ":", "None", ",", "'sdssr'", ":", "None", ",", "'stations'", ":", "None", ",", "'twomassid'", ":", "None", ",", "'ucac4id'", ":", "None", ",", "'vmag'", ":", "None", "}", ",", "'finderchart'", ":", "None", ",", "'sigclip'", ":", "sigclip", ",", "'normto'", ":", "normto", ",", "'normmingap'", ":", "normmingap", "}", "# end of objectinfo processing", "# add the varinfo dict", "if", "isinstance", "(", "varinfo", ",", "dict", ")", ":", "checkplotdict", "[", "'varinfo'", "]", "=", "varinfo", "else", ":", "checkplotdict", "[", "'varinfo'", "]", "=", "{", "'objectisvar'", ":", "None", ",", "'vartags'", ":", "None", ",", "'varisperiodic'", ":", "None", ",", "'varperiod'", ":", "None", ",", "'varepoch'", ":", "None", ",", "}", "return", "checkplotdict" ]
This returns the finder chart and object information as a dict. Parameters ---------- objectinfo : dict or None If provided, this is a dict containing information on the object whose light curve is being processed. This function will then be able to look up and download a finder chart for this object and write that to the output checkplotdict. External services such as GAIA, SIMBAD, TIC@MAST, etc. will also be used to look up this object by its coordinates, and will add in information available from those services. The `objectinfo` dict must be of the form and contain at least the keys described below:: {'objectid': the name of the object, 'ra': the right ascension of the object in decimal degrees, 'decl': the declination of the object in decimal degrees, 'ndet': the number of observations of this object} You can also provide magnitudes and proper motions of the object using the following keys and the appropriate values in the `objectinfo` dict. These will be used to calculate colors, total and reduced proper motion, etc. and display these in the output checkplot PNG:: 'pmra' -> the proper motion in mas/yr in right ascension, 'pmdecl' -> the proper motion in mas/yr in declination, 'umag' -> U mag -> colors: U-B, U-V, U-g 'bmag' -> B mag -> colors: U-B, B-V 'vmag' -> V mag -> colors: U-V, B-V, V-R, V-I, V-K 'rmag' -> R mag -> colors: V-R, R-I 'imag' -> I mag -> colors: g-I, V-I, R-I, B-I 'jmag' -> 2MASS J mag -> colors: J-H, J-K, g-J, i-J 'hmag' -> 2MASS H mag -> colors: J-H, H-K 'kmag' -> 2MASS Ks mag -> colors: g-Ks, H-Ks, J-Ks, V-Ks 'sdssu' -> SDSS u mag -> colors: u-g, u-V 'sdssg' -> SDSS g mag -> colors: g-r, g-i, g-K, u-g, U-g, g-J 'sdssr' -> SDSS r mag -> colors: r-i, g-r 'sdssi' -> SDSS i mag -> colors: r-i, i-z, g-i, i-J, i-W1 'sdssz' -> SDSS z mag -> colors: i-z, z-W2, g-z 'ujmag' -> UKIRT J mag -> colors: J-H, H-K, J-K, g-J, i-J 'uhmag' -> UKIRT H mag -> colors: J-H, H-K 'ukmag' -> UKIRT K mag -> colors: g-K, H-K, J-K, V-K 'irac1' -> Spitzer IRAC1 mag -> colors: i-I1, I1-I2 'irac2' -> Spitzer IRAC2 mag -> colors: I1-I2, I2-I3 'irac3' -> Spitzer IRAC3 mag -> colors: I2-I3 'irac4' -> Spitzer IRAC4 mag -> colors: I3-I4 'wise1' -> WISE W1 mag -> colors: i-W1, W1-W2 'wise2' -> WISE W2 mag -> colors: W1-W2, W2-W3 'wise3' -> WISE W3 mag -> colors: W2-W3 'wise4' -> WISE W4 mag -> colors: W3-W4 If you have magnitude measurements in other bands, use the `custom_bandpasses` kwarg to pass these in. If this is None, no object information will be incorporated into the checkplot (kind of making it effectively useless for anything other than glancing at the phased light curves at various 'best' periods from the period-finder results). varinfo : dict or None If this is None, a blank dict of the form below will be added to the checkplotdict:: {'objectisvar': None -> variability flag (None indicates unset), 'vartags': CSV str containing variability type tags from review, 'varisperiodic': None -> periodic variability flag (None -> unset), 'varperiod': the period associated with the periodic variability, 'varepoch': the epoch associated with the periodic variability} If you provide a dict matching this format in this kwarg, this will be passed unchanged to the output checkplotdict produced. findercmap : str or matplotlib.cm.ColorMap object The Colormap object to use for the finder chart image. finderconvolve : astropy.convolution.Kernel object or None If not None, the Kernel object to use for convolving the finder image. sigclip : float or int or sequence of two floats/ints or None If a single float or int, a symmetric sigma-clip will be performed using the number provided as the sigma-multiplier to cut out from the input time-series. If a list of two ints/floats is provided, the function will perform an 'asymmetric' sigma-clip. The first element in this list is the sigma value to use for fainter flux/mag values; the second element in this list is the sigma value to use for brighter flux/mag values. For example, `sigclip=[10., 3.]`, will sigclip out greater than 10-sigma dimmings and greater than 3-sigma brightenings. Here the meaning of "dimming" and "brightening" is set by *physics* (not the magnitude system), which is why the `magsarefluxes` kwarg must be correctly set. If `sigclip` is None, no sigma-clipping will be performed, and the time-series (with non-finite elems removed) will be passed through to the output. normto : {'globalmedian', 'zero'} or a float This is specified as below:: 'globalmedian' -> norms each mag to global median of the LC column 'zero' -> norms each mag to zero a float -> norms each mag to this specified float value. normmingap : float This defines how much the difference between consecutive measurements is allowed to be to consider them as parts of different timegroups. By default it is set to 4.0 days. deredden_object : bool If this is True, will use the 2MASS DUST service to get extinction coefficients in various bands, and then try to deredden the magnitudes and colors of the object already present in the checkplot's objectinfo dict. custom_bandpasses : dict This is a dict used to provide custom bandpass definitions for any magnitude measurements in the objectinfo dict that are not automatically recognized by :py:func:`astrobase.varclass.starfeatures.color_features`. lclistpkl : dict or str If this is provided, must be a dict resulting from reading a catalog produced by the `lcproc.catalogs.make_lclist` function or a str path pointing to the pickle file produced by that function. This catalog is used to find neighbors of the current object in the current light curve collection. Looking at neighbors of the object within the radius specified by `nbrradiusarcsec` is useful for light curves produced by instruments that have a large pixel scale, so are susceptible to blending of variability and potential confusion of neighbor variability with that of the actual object being looked at. If this is None, no neighbor lookups will be performed. nbrradiusarcsec : float The radius in arcseconds to use for a search conducted around the coordinates of this object to look for any potential confusion and blending of variability amplitude caused by their proximity. maxnumneighbors : int The maximum number of neighbors that will have their light curves and magnitudes noted in this checkplot as potential blends with the target object. plotdpi : int The resolution in DPI of the plots to generate in this function (e.g. the finder chart, etc.) findercachedir : str The path to the astrobase cache directory for finder chart downloads from the NASA SkyView service. verbose : bool If True, will indicate progress and warn about potential problems. gaia_submit_timeout : float Sets the timeout in seconds to use when submitting a request to look up the object's information to the GAIA service. Note that if `fast_mode` is set, this is ignored. gaia_submit_tries : int Sets the maximum number of times the GAIA services will be contacted to obtain this object's information. If `fast_mode` is set, this is ignored, and the services will be contacted only once (meaning that a failure to respond will be silently ignored and no GAIA data will be added to the checkplot's objectinfo dict). gaia_max_timeout : float Sets the timeout in seconds to use when waiting for the GAIA service to respond to our request for the object's information. Note that if `fast_mode` is set, this is ignored. gaia_mirror : str This sets the GAIA mirror to use. This is a key in the `services.gaia.GAIA_URLS` dict which defines the URLs to hit for each mirror. fast_mode : bool or float This runs the external catalog operations in a "fast" mode, with short timeouts and not trying to hit external catalogs that take a long time to respond. If this is set to True, the default settings for the external requests will then become:: skyview_lookup = False skyview_timeout = 10.0 skyview_retry_failed = False dust_timeout = 10.0 gaia_submit_timeout = 7.0 gaia_max_timeout = 10.0 gaia_submit_tries = 2 complete_query_later = False search_simbad = False If this is a float, will run in "fast" mode with the provided timeout value in seconds and the following settings:: skyview_lookup = True skyview_timeout = fast_mode skyview_retry_failed = False dust_timeout = fast_mode gaia_submit_timeout = 0.66*fast_mode gaia_max_timeout = fast_mode gaia_submit_tries = 2 complete_query_later = False search_simbad = False complete_query_later : bool If this is True, saves the state of GAIA queries that are not yet complete when `gaia_max_timeout` is reached while waiting for the GAIA service to respond to our request. A later call for GAIA info on the same object will attempt to pick up the results from the existing query if it's completed. If `fast_mode` is True, this is ignored. Returns ------- dict A checkplotdict is returned containing the objectinfo and varinfo dicts, ready to use with the functions below to add in light curve plots, phased LC plots, xmatch info, etc.
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python
valid
Cadair/jupyter_environment_kernels
environment_kernels/envs_common.py
https://github.com/Cadair/jupyter_environment_kernels/blob/3da304550b511bda7d5d39280379b5ca39bb31bc/environment_kernels/envs_common.py#L124-L138
def find_exe(env_dir, name): """Finds a exe with that name in the environment path""" if platform.system() == "Windows": name = name + ".exe" # find the binary exe_name = os.path.join(env_dir, name) if not os.path.exists(exe_name): exe_name = os.path.join(env_dir, "bin", name) if not os.path.exists(exe_name): exe_name = os.path.join(env_dir, "Scripts", name) if not os.path.exists(exe_name): return None return exe_name
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Finds a exe with that name in the environment path
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python
train
theirc/rapidsms-multitenancy
multitenancy/admin.py
https://github.com/theirc/rapidsms-multitenancy/blob/121bd0a628e691a88aade2e10045cba43af2dfcb/multitenancy/admin.py#L67-L73
def get_queryset(self, request): """Limit to TenantGroups that this user can access.""" qs = super(TenantGroupAdmin, self).get_queryset(request) if not request.user.is_superuser: qs = qs.filter(tenantrole__user=request.user, tenantrole__role=TenantRole.ROLE_GROUP_MANAGER) return qs
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Limit to TenantGroups that this user can access.
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python
train
hazelcast/hazelcast-python-client
hazelcast/util.py
https://github.com/hazelcast/hazelcast-python-client/blob/3f6639443c23d6d036aa343f8e094f052250d2c1/hazelcast/util.py#L119-L127
def validate_serializer(serializer, _type): """ Validates the serializer for given type. :param serializer: (Serializer), the serializer to be validated. :param _type: (Type), type to be used for serializer validation. """ if not issubclass(serializer, _type): raise ValueError("Serializer should be an instance of {}".format(_type.__name__))
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Validates the serializer for given type. :param serializer: (Serializer), the serializer to be validated. :param _type: (Type), type to be used for serializer validation.
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python
train
theolind/pymysensors
mysensors/__init__.py
https://github.com/theolind/pymysensors/blob/a139ab6e2f6b71ebaf37282f69bfd0f7fe6193b6/mysensors/__init__.py#L98-L104
def add_sensor(self, sensorid=None): """Add a sensor to the gateway.""" if sensorid is None: sensorid = self._get_next_id() if sensorid is not None and sensorid not in self.sensors: self.sensors[sensorid] = Sensor(sensorid) return sensorid if sensorid in self.sensors else None
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Add a sensor to the gateway.
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python
train
tcalmant/ipopo
pelix/ipopo/core.py
https://github.com/tcalmant/ipopo/blob/2f9ae0c44cd9c34ef1a9d50837b3254e75678eb1/pelix/ipopo/core.py#L353-L410
def __try_instantiate(self, component_context, instance): # type: (ComponentContext, object) -> bool """ Instantiates a component, if all of its handlers are there. Returns False if a handler is missing. :param component_context: A ComponentContext bean :param instance: The component instance :return: True if the component has started, False if a handler is missing """ with self.__instances_lock: # Extract information about the component factory_context = component_context.factory_context handlers_ids = factory_context.get_handlers_ids() name = component_context.name factory_name = factory_context.name try: # Get handlers handler_factories = self.__get_handler_factories(handlers_ids) except KeyError: # A handler is missing, stop here return False # Instantiate the handlers all_handlers = set() # type: Set[Any] for handler_factory in handler_factories: handlers = handler_factory.get_handlers( component_context, instance ) if handlers: all_handlers.update(handlers) # Prepare the stored instance stored_instance = StoredInstance( self, component_context, instance, all_handlers ) # Manipulate the properties for handler in all_handlers: handler.manipulate(stored_instance, instance) # Store the instance self.__instances[name] = stored_instance # Start the manager stored_instance.start() # Notify listeners now that every thing is ready to run self._fire_ipopo_event( constants.IPopoEvent.INSTANTIATED, factory_name, name ) # Try to validate it stored_instance.update_bindings() stored_instance.check_lifecycle() return True
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Instantiates a component, if all of its handlers are there. Returns False if a handler is missing. :param component_context: A ComponentContext bean :param instance: The component instance :return: True if the component has started, False if a handler is missing
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python
train
PyMLGame/pymlgame
pymlgame/__init__.py
https://github.com/PyMLGame/pymlgame/blob/450fe77d35f9a26c107586d6954f69c3895bf504/pymlgame/__init__.py#L40-L60
def get_events(maximum=10): """ Get all events since the last time you asked for them. You can define a maximum which is 10 by default. :param maximum: Maximum number of events :type maximum: int :return: List of events :rtype: list """ events = [] for ev in range(0, maximum): try: if CONTROLLER.queue.empty(): break else: events.append(CONTROLLER.queue.get_nowait()) except NameError: print('PyMLGame is not initialized correctly. Use pymlgame.init() first.') events = False break return events
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Get all events since the last time you asked for them. You can define a maximum which is 10 by default. :param maximum: Maximum number of events :type maximum: int :return: List of events :rtype: list
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python
train
rytilahti/python-eq3bt
eq3bt/eq3cli.py
https://github.com/rytilahti/python-eq3bt/blob/595459d9885920cf13b7059a1edd2cf38cede1f0/eq3bt/eq3cli.py#L55-L60
def mode(dev, target): """ Gets or sets the active mode. """ click.echo("Current mode: %s" % dev.mode_readable) if target: click.echo("Setting mode: %s" % target) dev.mode = target
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Gets or sets the active mode.
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python
train
sammchardy/python-binance
examples/save_historical_data.py
https://github.com/sammchardy/python-binance/blob/31c0d0a32f9edd528c6c2c1dd3044d9a34ce43cc/examples/save_historical_data.py#L60-L136
def get_historical_klines(symbol, interval, start_str, end_str=None): """Get Historical Klines from Binance See dateparse docs for valid start and end string formats http://dateparser.readthedocs.io/en/latest/ If using offset strings for dates add "UTC" to date string e.g. "now UTC", "11 hours ago UTC" :param symbol: Name of symbol pair e.g BNBBTC :type symbol: str :param interval: Biannce Kline interval :type interval: str :param start_str: Start date string in UTC format :type start_str: str :param end_str: optional - end date string in UTC format :type end_str: str :return: list of OHLCV values """ # create the Binance client, no need for api key client = Client("", "") # init our list output_data = [] # setup the max limit limit = 500 # convert interval to useful value in seconds timeframe = interval_to_milliseconds(interval) # convert our date strings to milliseconds start_ts = date_to_milliseconds(start_str) # if an end time was passed convert it end_ts = None if end_str: end_ts = date_to_milliseconds(end_str) idx = 0 # it can be difficult to know when a symbol was listed on Binance so allow start time to be before list date symbol_existed = False while True: # fetch the klines from start_ts up to max 500 entries or the end_ts if set temp_data = client.get_klines( symbol=symbol, interval=interval, limit=limit, startTime=start_ts, endTime=end_ts ) # handle the case where our start date is before the symbol pair listed on Binance if not symbol_existed and len(temp_data): symbol_existed = True if symbol_existed: # append this loops data to our output data output_data += temp_data # update our start timestamp using the last value in the array and add the interval timeframe start_ts = temp_data[len(temp_data) - 1][0] + timeframe else: # it wasn't listed yet, increment our start date start_ts += timeframe idx += 1 # check if we received less than the required limit and exit the loop if len(temp_data) < limit: # exit the while loop break # sleep after every 3rd call to be kind to the API if idx % 3 == 0: time.sleep(1) return output_data
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python
train
blockchain/api-v1-client-python
blockchain/wallet.py
https://github.com/blockchain/api-v1-client-python/blob/52ea562f824f04303e75239364e06722bec8620f/blockchain/wallet.py#L84-L95
def get_balance(self): """Fetch the wallet balance. Includes unconfirmed transactions and possibly double spends. :return: wallet balance in satoshi """ response = util.call_api("merchant/{0}/balance".format(self.identifier), self.build_basic_request(), base_url=self.service_url) json_response = json.loads(response) self.parse_error(json_response) return json_response.get('balance')
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Fetch the wallet balance. Includes unconfirmed transactions and possibly double spends. :return: wallet balance in satoshi
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python
train
timothyb0912/pylogit
pylogit/base_multinomial_cm_v2.py
https://github.com/timothyb0912/pylogit/blob/f83b0fd6debaa7358d87c3828428f6d4ead71357/pylogit/base_multinomial_cm_v2.py#L1073-L1115
def _create_fit_summary(self): """ Create and store a pandas series that will display to users the various statistics/values that indicate how well the estimated model fit the given dataset. Returns ------- None. """ # Make sure we have all attributes needed to create the results summary needed_attributes = ["df_model", "nobs", "null_log_likelihood", "log_likelihood", "rho_squared", "rho_bar_squared", "estimation_message"] try: assert all([hasattr(self, attr) for attr in needed_attributes]) assert all([getattr(self, attr) is not None for attr in needed_attributes]) except AssertionError: msg = "Call this function only after setting/calculating all other" msg_2 = " estimation results attributes" raise NotImplementedError(msg + msg_2) self.fit_summary = pd.Series([self.df_model, self.nobs, self.null_log_likelihood, self.log_likelihood, self.rho_squared, self.rho_bar_squared, self.estimation_message], index=["Number of Parameters", "Number of Observations", "Null Log-Likelihood", "Fitted Log-Likelihood", "Rho-Squared", "Rho-Bar-Squared", "Estimation Message"]) return None
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Create and store a pandas series that will display to users the various statistics/values that indicate how well the estimated model fit the given dataset. Returns ------- None.
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python
train
uber/rides-python-sdk
uber_rides/request.py
https://github.com/uber/rides-python-sdk/blob/76ecd75ab5235d792ec1010e36eca679ba285127/uber_rides/request.py#L124-L137
def _send(self, prepared_request): """Send a PreparedRequest to the server. Parameters prepared_request (requests.PreparedRequest) Returns (Response) A Response object, whichcontains a server's response to an HTTP request. """ session = Session() response = session.send(prepared_request) return Response(response)
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Send a PreparedRequest to the server. Parameters prepared_request (requests.PreparedRequest) Returns (Response) A Response object, whichcontains a server's response to an HTTP request.
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python
train
ethereum/py-evm
eth/vm/logic/arithmetic.py
https://github.com/ethereum/py-evm/blob/58346848f076116381d3274bbcea96b9e2cfcbdf/eth/vm/logic/arithmetic.py#L167-L183
def signextend(computation: BaseComputation) -> None: """ Signed Extend """ bits, value = computation.stack_pop(num_items=2, type_hint=constants.UINT256) if bits <= 31: testbit = bits * 8 + 7 sign_bit = (1 << testbit) if value & sign_bit: result = value | (constants.UINT_256_CEILING - sign_bit) else: result = value & (sign_bit - 1) else: result = value computation.stack_push(result)
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Signed Extend
[ "Signed", "Extend" ]
python
train
kstaniek/condoor
condoor/protocols/ssh.py
https://github.com/kstaniek/condoor/blob/77c054b29d4e286c1d7aca2c74dff86b805e1fae/condoor/protocols/ssh.py#L54-L83
def connect(self, driver): """Connect using the SSH protocol specific FSM.""" # 0 1 2 events = [driver.password_re, self.device.prompt_re, driver.unable_to_connect_re, # 3 4 5 6 7 NEWSSHKEY, KNOWN_HOSTS, HOST_KEY_FAILED, MODULUS_TOO_SMALL, PROTOCOL_DIFFER, # 8 9 10 driver.timeout_re, pexpect.TIMEOUT, driver.syntax_error_re] transitions = [ (driver.password_re, [0, 1, 4, 5], -1, partial(a_save_last_pattern, self), 0), (driver.syntax_error_re, [0], -1, CommandSyntaxError("Command syntax error"), 0), (self.device.prompt_re, [0], -1, partial(a_save_last_pattern, self), 0), # cover all messages indicating that connection was not set up (driver.unable_to_connect_re, [0], -1, a_unable_to_connect, 0), (NEWSSHKEY, [0], 1, partial(a_send_line, "yes"), 10), (KNOWN_HOSTS, [0, 1], 0, None, 0), (HOST_KEY_FAILED, [0], -1, ConnectionError("Host key failed", self.hostname), 0), (MODULUS_TOO_SMALL, [0], 0, self.fallback_to_sshv1, 0), (PROTOCOL_DIFFER, [0], 4, self.fallback_to_sshv1, 0), (PROTOCOL_DIFFER, [4], -1, ConnectionError("Protocol version differs", self.hostname), 0), (pexpect.TIMEOUT, [0], 5, partial(a_send, "\r\n"), 10), (pexpect.TIMEOUT, [5], -1, ConnectionTimeoutError("Connection timeout", self.hostname), 0), (driver.timeout_re, [0], -1, ConnectionTimeoutError("Connection timeout", self.hostname), 0), ] self.log("EXPECTED_PROMPT={}".format(pattern_to_str(self.device.prompt_re))) fsm = FSM("SSH-CONNECT", self.device, events, transitions, timeout=_C['connect_timeout'], searchwindowsize=160) return fsm.run()
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Connect using the SSH protocol specific FSM.
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python
train
Pytwitcher/pytwitcherapi
src/pytwitcherapi/models.py
https://github.com/Pytwitcher/pytwitcherapi/blob/d53ac5ad5ca113ecb7da542e8cdcbbf8c762b336/src/pytwitcherapi/models.py#L283-L295
def wrap_get_stream(cls, response): """Wrap the response from getting a stream into an instance and return it :param response: The response from getting a stream :type response: :class:`requests.Response` :returns: the new stream instance :rtype: :class:`list` of :class:`stream` :raises: None """ json = response.json() s = cls.wrap_json(json['stream']) return s
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Wrap the response from getting a stream into an instance and return it :param response: The response from getting a stream :type response: :class:`requests.Response` :returns: the new stream instance :rtype: :class:`list` of :class:`stream` :raises: None
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python
train
broadinstitute/fiss
firecloud/fiss.py
https://github.com/broadinstitute/fiss/blob/dddf91547479506dbbafb69ec84d44dcc4a94ab4/firecloud/fiss.py#L1672-L1680
def _nonempty_project(string): """ Argparse validator for ensuring a workspace is provided """ value = str(string) if len(value) == 0: msg = "No project provided and no default project configured" raise argparse.ArgumentTypeError(msg) return value
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Argparse validator for ensuring a workspace is provided
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python
train
edeposit/edeposit.amqp.antivirus
src/edeposit/amqp/antivirus/__init__.py
https://github.com/edeposit/edeposit.amqp.antivirus/blob/011b38bbe920819fab99a5891b1e70732321a598/src/edeposit/amqp/antivirus/__init__.py#L24-L58
def reactToAMQPMessage(message, send_back): """ React to given (AMQP) message. `message` is expected to be :py:func:`collections.namedtuple` structure from :mod:`.structures` filled with all necessary data. Args: message (object): One of the request objects defined in :mod:`.structures`. send_back (fn reference): Reference to function for responding. This is useful for progress monitoring for example. Function takes one parameter, which may be response structure/namedtuple, or string or whatever would be normally returned. Returns: object: Response class from :mod:`structures`. Raises: ValueError: if bad type of `message` structure is given. """ if _instanceof(message, structures.ScanFile): result = antivirus.save_and_scan( message.filename, message.b64_data ) return structures.ScanResult(message.filename, result) elif _instanceof(message, structures.UpdateDatabase): return structures.DatabaseUpdated( antivirus.update_database() ) raise ValueError( "Unknown type of request: '" + str(type(message)) + "'!" )
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React to given (AMQP) message. `message` is expected to be :py:func:`collections.namedtuple` structure from :mod:`.structures` filled with all necessary data. Args: message (object): One of the request objects defined in :mod:`.structures`. send_back (fn reference): Reference to function for responding. This is useful for progress monitoring for example. Function takes one parameter, which may be response structure/namedtuple, or string or whatever would be normally returned. Returns: object: Response class from :mod:`structures`. Raises: ValueError: if bad type of `message` structure is given.
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python
train
pandeylab/pythomics
pythomics/proteomics/structures.py
https://github.com/pandeylab/pythomics/blob/ab0a5651a2e02a25def4d277b35fa09d1631bfcb/pythomics/proteomics/structures.py#L84-L107
def addModification(self, aa,position, modMass, modType): """ !!!!MODIFICATION POSITION IS 0 BASED!!!!!! Modifications are stored internally as a tuple with this format: (amino acid modified, index in peptide of amino acid, modification type, modification mass) ie (M, 7, Oxidation, 15.9...) such as: M35(o) for an oxidized methionine at residue 35 """ #clean up xtandem if not modType: #try to figure out what it is tmass = abs(modMass) smass = str(tmass) prec = len(str(tmass-int(tmass)))-2 precFormat = '%'+'0.%df'%prec # modType = "" # masses = config.MODIFICATION_MASSES # for i in masses: # if tmass in masses[i] or smass == precFormat%masses[i][0]: # #found it # modType = i # if not modType: # sys.stderr.write('mod not found %s\n'%modMass) self.mods.add((aa,str(position),str(modMass),str(modType)))
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!!!!MODIFICATION POSITION IS 0 BASED!!!!!! Modifications are stored internally as a tuple with this format: (amino acid modified, index in peptide of amino acid, modification type, modification mass) ie (M, 7, Oxidation, 15.9...) such as: M35(o) for an oxidized methionine at residue 35
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python
train
opencobra/cobrapy
cobra/flux_analysis/variability.py
https://github.com/opencobra/cobrapy/blob/9d1987cdb3a395cf4125a3439c3b002ff2be2009/cobra/flux_analysis/variability.py#L304-L335
def find_essential_reactions(model, threshold=None, processes=None): """Return a set of essential reactions. A reaction is considered essential if restricting its flux to zero causes the objective, e.g., the growth rate, to also be zero, below the threshold, or infeasible. Parameters ---------- model : cobra.Model The model to find the essential reactions for. threshold : float, optional Minimal objective flux to be considered viable. By default this is 1% of the maximal objective. processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not explicitly passed, it will be set from the global configuration singleton. Returns ------- set Set of essential reactions """ if threshold is None: threshold = model.slim_optimize(error_value=None) * 1E-02 deletions = single_reaction_deletion( model, method='fba', processes=processes) essential = deletions.loc[deletions['growth'].isna() | (deletions['growth'] < threshold), :].index return {model.reactions.get_by_id(r) for ids in essential for r in ids}
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Return a set of essential reactions. A reaction is considered essential if restricting its flux to zero causes the objective, e.g., the growth rate, to also be zero, below the threshold, or infeasible. Parameters ---------- model : cobra.Model The model to find the essential reactions for. threshold : float, optional Minimal objective flux to be considered viable. By default this is 1% of the maximal objective. processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not explicitly passed, it will be set from the global configuration singleton. Returns ------- set Set of essential reactions
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python
valid
maxalbert/tohu
tohu/v6/utils.py
https://github.com/maxalbert/tohu/blob/43380162fadec99cdd5c5c3152dd6b7d3a9d39a8/tohu/v6/utils.py#L113-L128
def make_dummy_tuples(chars='abcde'): """ Helper function to create a list of namedtuples which are useful for testing and debugging (especially of custom generators). Example ------- >>> make_dummy_tuples(chars='abcd') [Quux(x='AA', y='aa'), Quux(x='BB', y='bb'), Quux(x='CC', y='cc'), Quux(x='DD', y='dd')] """ Quux = namedtuple('Quux', ['x', 'y']) some_tuples = [Quux((c*2).upper(), c*2) for c in chars] return some_tuples
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Helper function to create a list of namedtuples which are useful for testing and debugging (especially of custom generators). Example ------- >>> make_dummy_tuples(chars='abcd') [Quux(x='AA', y='aa'), Quux(x='BB', y='bb'), Quux(x='CC', y='cc'), Quux(x='DD', y='dd')]
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python
train
Sheeprider/BitBucket-api
bitbucket/ssh.py
https://github.com/Sheeprider/BitBucket-api/blob/be45515d506d87f14807a676f3c2f20d79674b75/bitbucket/ssh.py#L24-L28
def get(self, key_id=None): """ Get one of the ssh keys associated with your account. """ url = self.bitbucket.url('GET_SSH_KEY', key_id=key_id) return self.bitbucket.dispatch('GET', url, auth=self.bitbucket.auth)
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Get one of the ssh keys associated with your account.
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python
train
marcelm/xopen
src/xopen/__init__.py
https://github.com/marcelm/xopen/blob/891ca71fb9b8b2b599de74caa4ed92206e5719f2/src/xopen/__init__.py#L290-L343
def xopen(filename, mode='r', compresslevel=6, threads=None): """ A replacement for the "open" function that can also open files that have been compressed with gzip, bzip2 or xz. If the filename is '-', standard output (mode 'w') or input (mode 'r') is returned. The file type is determined based on the filename: .gz is gzip, .bz2 is bzip2 and .xz is xz/lzma. When writing a gzip-compressed file, the following methods are tried in order to get the best speed 1) using a pigz (parallel gzip) subprocess; 2) using a gzip subprocess; 3) gzip.open. A single gzip subprocess can be faster than gzip.open because it runs in a separate process. Uncompressed files are opened with the regular open(). mode can be: 'rt', 'rb', 'at', 'ab', 'wt', or 'wb'. Also, the 't' can be omitted, so instead of 'rt', 'wt' and 'at', the abbreviations 'r', 'w' and 'a' can be used. In Python 2, the 't' and 'b' characters are ignored. Append mode ('a', 'at', 'ab') is unavailable with BZ2 compression and will raise an error. compresslevel is the gzip compression level. It is not used for bz2 and xz. threads is the number of threads for pigz. If None, then the pigz default is used. """ if mode in ('r', 'w', 'a'): mode += 't' if mode not in ('rt', 'rb', 'wt', 'wb', 'at', 'ab'): raise ValueError("mode '{0}' not supported".format(mode)) if not _PY3: mode = mode[0] filename = fspath(filename) if not isinstance(filename, basestring): raise ValueError("the filename must be a string") if compresslevel not in range(1, 10): raise ValueError("compresslevel must be between 1 and 9") if filename == '-': return _open_stdin_or_out(mode) elif filename.endswith('.bz2'): return _open_bz2(filename, mode) elif filename.endswith('.xz'): return _open_xz(filename, mode) elif filename.endswith('.gz'): return _open_gz(filename, mode, compresslevel, threads) else: # Python 2.6 and 2.7 have io.open, which we could use to make the returned # object consistent with the one returned in Python 3, but reading a file # with io.open() is 100 times slower (!) on Python 2.6, and still about # three times slower on Python 2.7 (tested with "for _ in io.open(path): pass") return open(filename, mode)
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A replacement for the "open" function that can also open files that have been compressed with gzip, bzip2 or xz. If the filename is '-', standard output (mode 'w') or input (mode 'r') is returned. The file type is determined based on the filename: .gz is gzip, .bz2 is bzip2 and .xz is xz/lzma. When writing a gzip-compressed file, the following methods are tried in order to get the best speed 1) using a pigz (parallel gzip) subprocess; 2) using a gzip subprocess; 3) gzip.open. A single gzip subprocess can be faster than gzip.open because it runs in a separate process. Uncompressed files are opened with the regular open(). mode can be: 'rt', 'rb', 'at', 'ab', 'wt', or 'wb'. Also, the 't' can be omitted, so instead of 'rt', 'wt' and 'at', the abbreviations 'r', 'w' and 'a' can be used. In Python 2, the 't' and 'b' characters are ignored. Append mode ('a', 'at', 'ab') is unavailable with BZ2 compression and will raise an error. compresslevel is the gzip compression level. It is not used for bz2 and xz. threads is the number of threads for pigz. If None, then the pigz default is used.
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python
train
alorence/django-modern-rpc
modernrpc/core.py
https://github.com/alorence/django-modern-rpc/blob/6dc42857d35764b24e2c09334f4b578629a75f9e/modernrpc/core.py#L162-L178
def html_doc(self): """Methods docstring, as HTML""" if not self.raw_docstring: result = '' elif settings.MODERNRPC_DOC_FORMAT.lower() in ('rst', 'restructred', 'restructuredtext'): from docutils.core import publish_parts result = publish_parts(self.raw_docstring, writer_name='html')['body'] elif settings.MODERNRPC_DOC_FORMAT.lower() in ('md', 'markdown'): import markdown result = markdown.markdown(self.raw_docstring) else: result = "<p>{}</p>".format(self.raw_docstring.replace('\n\n', '</p><p>').replace('\n', ' ')) return result
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Methods docstring, as HTML
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python
train
apple/turicreate
src/unity/python/turicreate/toolkits/_mxnet/_mxnet_utils.py
https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/_mxnet/_mxnet_utils.py#L100-L108
def get_gpus_in_use(max_devices=None): """ Like get_num_gpus_in_use, but returns a list of dictionaries with just queried GPU information. """ from turicreate.util import _get_cuda_gpus gpu_indices = get_gpu_ids_in_use(max_devices=max_devices) gpus = _get_cuda_gpus() return [gpus[index] for index in gpu_indices]
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Like get_num_gpus_in_use, but returns a list of dictionaries with just queried GPU information.
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python
train
etal/biofrills
biofrills/alnutils.py
https://github.com/etal/biofrills/blob/36684bb6c7632f96215e8b2b4ebc86640f331bcd/biofrills/alnutils.py#L95-L100
def col_frequencies(col, weights=None, gap_chars='-.'): """Frequencies of each residue type (totaling 1.0) in a single column.""" counts = col_counts(col, weights, gap_chars) # Reduce to frequencies scale = 1.0 / sum(counts.values()) return dict((aa, cnt * scale) for aa, cnt in counts.iteritems())
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Frequencies of each residue type (totaling 1.0) in a single column.
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python
train
sijis/sumologic-python
src/sumologic/collectors.py
https://github.com/sijis/sumologic-python/blob/b50200907837f0d452d14ead5e647b8e24e2e9e5/src/sumologic/collectors.py#L51-L64
def find(self, name): """Returns a dict of collector's details if found. Args: name (str): name of collector searching for """ collectors = self.get_collectors() for collector in collectors: if name.lower() == collector['name'].lower(): self.collector_id = collector['id'] return collector return {'status': 'No results found.'}
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Returns a dict of collector's details if found. Args: name (str): name of collector searching for
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python
train
geophysics-ubonn/reda
lib/reda/utils/norrec.py
https://github.com/geophysics-ubonn/reda/blob/46a939729e40c7c4723315c03679c40761152e9e/lib/reda/utils/norrec.py#L12-L45
def average_repetitions(df, keys_mean): """average duplicate measurements. This requires that IDs and norrec labels were assigned using the *assign_norrec_to_df* function. Parameters ---------- df DataFrame keys_mean: list list of keys to average. For all other keys the first entry will be used. """ if 'norrec' not in df.columns: raise Exception( 'The "norrec" column is required for this function to work!' ) # Get column order to restore later cols = list(df.columns.values) keys_keep = list(set(df.columns.tolist()) - set(keys_mean)) agg_dict = {x: _first for x in keys_keep} agg_dict.update({x: np.mean for x in keys_mean}) for key in ('id', 'timestep', 'frequency', 'norrec'): if key in agg_dict: del(agg_dict[key]) # print(agg_dict) # average over duplicate measurements extra_dimensions_raw = ['id', 'norrec', 'frequency', 'timestep'] extra_dimensions = [x for x in extra_dimensions_raw if x in df.columns] df = df.groupby(extra_dimensions).agg(agg_dict) df.reset_index(inplace=True) return df[cols]
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average duplicate measurements. This requires that IDs and norrec labels were assigned using the *assign_norrec_to_df* function. Parameters ---------- df DataFrame keys_mean: list list of keys to average. For all other keys the first entry will be used.
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python
train
evandempsey/fp-growth
pyfpgrowth/pyfpgrowth.py
https://github.com/evandempsey/fp-growth/blob/6bf4503024e86c5bbea8a05560594f2f7f061c15/pyfpgrowth/pyfpgrowth.py#L39-L45
def add_child(self, value): """ Add a node as a child node. """ child = FPNode(value, 1, self) self.children.append(child) return child
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Add a node as a child node.
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python
train
ARMmbed/mbed-cloud-sdk-python
src/mbed_cloud/_backends/device_directory/apis/default_api.py
https://github.com/ARMmbed/mbed-cloud-sdk-python/blob/c0af86fb2cdd4dc7ed26f236139241067d293509/src/mbed_cloud/_backends/device_directory/apis/default_api.py#L535-L559
def device_log_list(self, **kwargs): # noqa: E501 """DEPRECATED: List all device events. # noqa: E501 DEPRECATED: List all device events. Use `/v3/device-events/` instead. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass asynchronous=True >>> thread = api.device_log_list(asynchronous=True) >>> result = thread.get() :param asynchronous bool :param int limit: How many objects to retrieve in the page. :param str order: The order of the records based on creation time, `ASC` or `DESC`; by default `ASC`. :param str after: The ID of The item after which to retrieve the next page. :param str include: Comma-separated list of data fields to return. Currently supported: `total_count`. :param str filter: URL encoded query string parameter to filter returned data. ##### Filtering ```?filter={URL encoded query string}``` The query string is made up of key/value pairs separated by ampersands. So for a query of ```key1=value1&key2=value2&key3=value3``` this would be encoded as follows: ```?filter=key1%3Dvalue1%26key2%3Dvalue2%26key3%3Dvalue3``` ###### Filterable fields: The below table lists all the fields that can be filtered on with certain filters: <table> <thead> <tr> <th>Field</th> <th>= / __eq / __neq</th> <th>__in / __nin</th> <th>__lte / __gte</th> <tr> <thead> <tbody> <tr> <td>date_time</td> <td>✓</td> <td>✓</td> <td>✓</td> </tr> <tr> <td>description</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>id</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>device_id</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>event_type</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>state_change</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> </tbody> </table> &nbsp; The examples below show the queries in *unencoded* form. ###### By id: ```id={id}``` ###### By state change: ```state_change=[True|False]``` ###### By event type: ```event_type={value}``` ###### On date-time fields: Date-time fields should be specified in UTC RFC3339 format ```YYYY-MM-DDThh:mm:ss.msZ```. There are three permitted variations: * UTC RFC3339 with milliseconds e.g. 2016-11-30T16:25:12.1234Z * UTC RFC3339 without milliseconds e.g. 2016-11-30T16:25:12Z * UTC RFC3339 shortened - without milliseconds and punctuation e.g. 20161130T162512Z Date-time filtering supports three operators: * equality * greater than or equal to &ndash; field name suffixed with ```__gte``` * less than or equal to &ndash; field name suffixed with ```__lte``` Lower and upper limits to a date-time range may be specified by including both the ```__gte``` and ```__lte``` forms in the filter. ```{field name}[|__lte|__gte]={UTC RFC3339 date-time}``` ##### Multi-field example ```id=0158d38771f70000000000010010038c&state_change=True&date_time__gte=2016-11-30T16:25:12.1234Z``` Encoded: ```?filter=id%3D0158d38771f70000000000010010038c%26state_change%3DTrue%26date_time__gte%3D2016-11-30T16%3A25%3A12.1234Z``` ##### Filtering with filter operators String field filtering supports the following operators: * equality: `__eq` * non-equality: `__neq` * in : `__in` * not in: `__nin` For `__in` and `__nin` filters list of parameters must be comma-separated: `event_type__in=update.device.device-created,update.device.device-updated` :return: DeviceEventPage If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('asynchronous'): return self.device_log_list_with_http_info(**kwargs) # noqa: E501 else: (data) = self.device_log_list_with_http_info(**kwargs) # noqa: E501 return data
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DEPRECATED: List all device events. # noqa: E501 DEPRECATED: List all device events. Use `/v3/device-events/` instead. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass asynchronous=True >>> thread = api.device_log_list(asynchronous=True) >>> result = thread.get() :param asynchronous bool :param int limit: How many objects to retrieve in the page. :param str order: The order of the records based on creation time, `ASC` or `DESC`; by default `ASC`. :param str after: The ID of The item after which to retrieve the next page. :param str include: Comma-separated list of data fields to return. Currently supported: `total_count`. :param str filter: URL encoded query string parameter to filter returned data. ##### Filtering ```?filter={URL encoded query string}``` The query string is made up of key/value pairs separated by ampersands. So for a query of ```key1=value1&key2=value2&key3=value3``` this would be encoded as follows: ```?filter=key1%3Dvalue1%26key2%3Dvalue2%26key3%3Dvalue3``` ###### Filterable fields: The below table lists all the fields that can be filtered on with certain filters: <table> <thead> <tr> <th>Field</th> <th>= / __eq / __neq</th> <th>__in / __nin</th> <th>__lte / __gte</th> <tr> <thead> <tbody> <tr> <td>date_time</td> <td>✓</td> <td>✓</td> <td>✓</td> </tr> <tr> <td>description</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>id</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>device_id</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>event_type</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> <tr> <td>state_change</td> <td>✓</td> <td>✓</td> <td>&nbsp;</td> </tr> </tbody> </table> &nbsp; The examples below show the queries in *unencoded* form. ###### By id: ```id={id}``` ###### By state change: ```state_change=[True|False]``` ###### By event type: ```event_type={value}``` ###### On date-time fields: Date-time fields should be specified in UTC RFC3339 format ```YYYY-MM-DDThh:mm:ss.msZ```. There are three permitted variations: * UTC RFC3339 with milliseconds e.g. 2016-11-30T16:25:12.1234Z * UTC RFC3339 without milliseconds e.g. 2016-11-30T16:25:12Z * UTC RFC3339 shortened - without milliseconds and punctuation e.g. 20161130T162512Z Date-time filtering supports three operators: * equality * greater than or equal to &ndash; field name suffixed with ```__gte``` * less than or equal to &ndash; field name suffixed with ```__lte``` Lower and upper limits to a date-time range may be specified by including both the ```__gte``` and ```__lte``` forms in the filter. ```{field name}[|__lte|__gte]={UTC RFC3339 date-time}``` ##### Multi-field example ```id=0158d38771f70000000000010010038c&state_change=True&date_time__gte=2016-11-30T16:25:12.1234Z``` Encoded: ```?filter=id%3D0158d38771f70000000000010010038c%26state_change%3DTrue%26date_time__gte%3D2016-11-30T16%3A25%3A12.1234Z``` ##### Filtering with filter operators String field filtering supports the following operators: * equality: `__eq` * non-equality: `__neq` * in : `__in` * not in: `__nin` For `__in` and `__nin` filters list of parameters must be comma-separated: `event_type__in=update.device.device-created,update.device.device-updated` :return: DeviceEventPage If the method is called asynchronously, returns the request thread.
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python
train
saltstack/salt
salt/client/mixins.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/client/mixins.py#L279-L444
def low(self, fun, low, print_event=True, full_return=False): ''' Execute a function from low data Low data includes: required: - fun: the name of the function to run optional: - arg: a list of args to pass to fun - kwarg: kwargs for fun - __user__: user who is running the command - __jid__: jid to run under - __tag__: tag to run under ''' # fire the mminion loading (if not already done) here # this is not to clutter the output with the module loading # if we have a high debug level. self.mminion # pylint: disable=W0104 jid = low.get('__jid__', salt.utils.jid.gen_jid(self.opts)) tag = low.get('__tag__', salt.utils.event.tagify(jid, prefix=self.tag_prefix)) data = {'fun': '{0}.{1}'.format(self.client, fun), 'jid': jid, 'user': low.get('__user__', 'UNKNOWN'), } event = salt.utils.event.get_event( 'master', self.opts['sock_dir'], self.opts['transport'], opts=self.opts, listen=False) if print_event: print_func = self.print_async_event \ if hasattr(self, 'print_async_event') \ else None else: # Suppress printing of return event (this keeps us from printing # runner/wheel output during orchestration). print_func = None namespaced_event = salt.utils.event.NamespacedEvent( event, tag, print_func=print_func ) # TODO: test that they exist # TODO: Other things to inject?? func_globals = {'__jid__': jid, '__user__': data['user'], '__tag__': tag, # weak ref to avoid the Exception in interpreter # teardown of event '__jid_event__': weakref.proxy(namespaced_event), } try: self_functions = pycopy.copy(self.functions) salt.utils.lazy.verify_fun(self_functions, fun) # Inject some useful globals to *all* the function's global # namespace only once per module-- not per func completed_funcs = [] for mod_name in six.iterkeys(self_functions): if '.' not in mod_name: continue mod, _ = mod_name.split('.', 1) if mod in completed_funcs: continue completed_funcs.append(mod) for global_key, value in six.iteritems(func_globals): self.functions[mod_name].__globals__[global_key] = value # There are some discrepancies of what a "low" structure is in the # publisher world it is a dict including stuff such as jid, fun, # arg (a list of args, with kwargs packed in). Historically this # particular one has had no "arg" and just has had all the kwargs # packed into the top level object. The plan is to move away from # that since the caller knows what is an arg vs a kwarg, but while # we make the transition we will load "kwargs" using format_call if # there are no kwargs in the low object passed in. if 'arg' in low and 'kwarg' in low: args = low['arg'] kwargs = low['kwarg'] else: f_call = salt.utils.args.format_call( self.functions[fun], low, expected_extra_kws=CLIENT_INTERNAL_KEYWORDS ) args = f_call.get('args', ()) kwargs = f_call.get('kwargs', {}) # Update the event data with loaded args and kwargs data['fun_args'] = list(args) + ([kwargs] if kwargs else []) func_globals['__jid_event__'].fire_event(data, 'new') # Track the job locally so we know what is running on the master serial = salt.payload.Serial(self.opts) jid_proc_file = os.path.join(*[self.opts['cachedir'], 'proc', jid]) data['pid'] = os.getpid() with salt.utils.files.fopen(jid_proc_file, 'w+b') as fp_: fp_.write(serial.dumps(data)) del data['pid'] # Initialize a context for executing the method. with tornado.stack_context.StackContext(self.functions.context_dict.clone): func = self.functions[fun] try: data['return'] = func(*args, **kwargs) except TypeError as exc: data['return'] = salt.utils.text.cli_info('Error: {exc}\nUsage:\n{doc}'.format( exc=exc, doc=func.__doc__), 'Passed invalid arguments') except Exception as exc: data['return'] = salt.utils.text.cli_info(six.text_type(exc), 'General error occurred') try: data['success'] = self.context.get('retcode', 0) == 0 except AttributeError: # Assume a True result if no context attribute data['success'] = True if isinstance(data['return'], dict) and 'data' in data['return']: # some functions can return boolean values data['success'] = salt.utils.state.check_result(data['return']['data']) except (Exception, SystemExit) as ex: if isinstance(ex, salt.exceptions.NotImplemented): data['return'] = six.text_type(ex) else: data['return'] = 'Exception occurred in {client} {fun}: {tb}'.format( client=self.client, fun=fun, tb=traceback.format_exc()) data['success'] = False finally: # Job has finished or issue found, so let's clean up after ourselves try: os.remove(jid_proc_file) except OSError as err: log.error("Error attempting to remove master job tracker: %s", err) if self.store_job: try: salt.utils.job.store_job( self.opts, { 'id': self.opts['id'], 'tgt': self.opts['id'], 'jid': data['jid'], 'return': data, }, event=None, mminion=self.mminion, ) except salt.exceptions.SaltCacheError: log.error('Could not store job cache info. ' 'Job details for this run may be unavailable.') # Outputters _can_ mutate data so write to the job cache first! namespaced_event.fire_event(data, 'ret') # if we fired an event, make sure to delete the event object. # This will ensure that we call destroy, which will do the 0MQ linger log.info('Runner completed: %s', data['jid']) del event del namespaced_event return data if full_return else data['return']
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'", "'Job details for this run may be unavailable.'", ")", "# Outputters _can_ mutate data so write to the job cache first!", "namespaced_event", ".", "fire_event", "(", "data", ",", "'ret'", ")", "# if we fired an event, make sure to delete the event object.", "# This will ensure that we call destroy, which will do the 0MQ linger", "log", ".", "info", "(", "'Runner completed: %s'", ",", "data", "[", "'jid'", "]", ")", "del", "event", "del", "namespaced_event", "return", "data", "if", "full_return", "else", "data", "[", "'return'", "]" ]
Execute a function from low data Low data includes: required: - fun: the name of the function to run optional: - arg: a list of args to pass to fun - kwarg: kwargs for fun - __user__: user who is running the command - __jid__: jid to run under - __tag__: tag to run under
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python
train
mabuchilab/QNET
src/qnet/convert/to_sympy_matrix.py
https://github.com/mabuchilab/QNET/blob/cc20d26dad78691d34c67173e5cd67dcac94208a/src/qnet/convert/to_sympy_matrix.py#L27-L33
def SympyCreate(n): """Creation operator for a Hilbert space of dimension `n`, as an instance of `sympy.Matrix`""" a = sympy.zeros(n) for i in range(1, n): a += sympy.sqrt(i) * basis_state(i, n) * basis_state(i-1, n).H return a
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Creation operator for a Hilbert space of dimension `n`, as an instance of `sympy.Matrix`
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python
train
tensorpack/tensorpack
examples/FasterRCNN/model_frcnn.py
https://github.com/tensorpack/tensorpack/blob/d7a13cb74c9066bc791d7aafc3b744b60ee79a9f/examples/FasterRCNN/model_frcnn.py#L128-L172
def fastrcnn_losses(labels, label_logits, fg_boxes, fg_box_logits): """ Args: labels: n, label_logits: nxC fg_boxes: nfgx4, encoded fg_box_logits: nfgxCx4 or nfgx1x4 if class agnostic Returns: label_loss, box_loss """ label_loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=label_logits) label_loss = tf.reduce_mean(label_loss, name='label_loss') fg_inds = tf.where(labels > 0)[:, 0] fg_labels = tf.gather(labels, fg_inds) num_fg = tf.size(fg_inds, out_type=tf.int64) empty_fg = tf.equal(num_fg, 0) if int(fg_box_logits.shape[1]) > 1: indices = tf.stack( [tf.range(num_fg), fg_labels], axis=1) # #fgx2 fg_box_logits = tf.gather_nd(fg_box_logits, indices) else: fg_box_logits = tf.reshape(fg_box_logits, [-1, 4]) with tf.name_scope('label_metrics'), tf.device('/cpu:0'): prediction = tf.argmax(label_logits, axis=1, name='label_prediction') correct = tf.cast(tf.equal(prediction, labels), tf.float32) # boolean/integer gather is unavailable on GPU accuracy = tf.reduce_mean(correct, name='accuracy') fg_label_pred = tf.argmax(tf.gather(label_logits, fg_inds), axis=1) num_zero = tf.reduce_sum(tf.cast(tf.equal(fg_label_pred, 0), tf.int64), name='num_zero') false_negative = tf.where( empty_fg, 0., tf.cast(tf.truediv(num_zero, num_fg), tf.float32), name='false_negative') fg_accuracy = tf.where( empty_fg, 0., tf.reduce_mean(tf.gather(correct, fg_inds)), name='fg_accuracy') box_loss = tf.losses.huber_loss( fg_boxes, fg_box_logits, reduction=tf.losses.Reduction.SUM) box_loss = tf.truediv( box_loss, tf.cast(tf.shape(labels)[0], tf.float32), name='box_loss') add_moving_summary(label_loss, box_loss, accuracy, fg_accuracy, false_negative, tf.cast(num_fg, tf.float32, name='num_fg_label')) return [label_loss, box_loss]
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Args: labels: n, label_logits: nxC fg_boxes: nfgx4, encoded fg_box_logits: nfgxCx4 or nfgx1x4 if class agnostic Returns: label_loss, box_loss
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python
train
apache/spark
python/pyspark/sql/udf.py
https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/udf.py#L232-L341
def register(self, name, f, returnType=None): """Register a Python function (including lambda function) or a user-defined function as a SQL function. :param name: name of the user-defined function in SQL statements. :param f: a Python function, or a user-defined function. The user-defined function can be either row-at-a-time or vectorized. See :meth:`pyspark.sql.functions.udf` and :meth:`pyspark.sql.functions.pandas_udf`. :param returnType: the return type of the registered user-defined function. The value can be either a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string. :return: a user-defined function. To register a nondeterministic Python function, users need to first build a nondeterministic user-defined function for the Python function and then register it as a SQL function. `returnType` can be optionally specified when `f` is a Python function but not when `f` is a user-defined function. Please see below. 1. When `f` is a Python function: `returnType` defaults to string type and can be optionally specified. The produced object must match the specified type. In this case, this API works as if `register(name, f, returnType=StringType())`. >>> strlen = spark.udf.register("stringLengthString", lambda x: len(x)) >>> spark.sql("SELECT stringLengthString('test')").collect() [Row(stringLengthString(test)=u'4')] >>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect() [Row(stringLengthString(text)=u'3')] >>> from pyspark.sql.types import IntegerType >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) >>> spark.sql("SELECT stringLengthInt('test')").collect() [Row(stringLengthInt(test)=4)] >>> from pyspark.sql.types import IntegerType >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) >>> spark.sql("SELECT stringLengthInt('test')").collect() [Row(stringLengthInt(test)=4)] 2. When `f` is a user-defined function: Spark uses the return type of the given user-defined function as the return type of the registered user-defined function. `returnType` should not be specified. In this case, this API works as if `register(name, f)`. >>> from pyspark.sql.types import IntegerType >>> from pyspark.sql.functions import udf >>> slen = udf(lambda s: len(s), IntegerType()) >>> _ = spark.udf.register("slen", slen) >>> spark.sql("SELECT slen('test')").collect() [Row(slen(test)=4)] >>> import random >>> from pyspark.sql.functions import udf >>> from pyspark.sql.types import IntegerType >>> random_udf = udf(lambda: random.randint(0, 100), IntegerType()).asNondeterministic() >>> new_random_udf = spark.udf.register("random_udf", random_udf) >>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP [Row(random_udf()=82)] >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP ... def add_one(x): ... return x + 1 ... >>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP >>> spark.sql("SELECT add_one(id) FROM range(3)").collect() # doctest: +SKIP [Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)] >>> @pandas_udf("integer", PandasUDFType.GROUPED_AGG) # doctest: +SKIP ... def sum_udf(v): ... return v.sum() ... >>> _ = spark.udf.register("sum_udf", sum_udf) # doctest: +SKIP >>> q = "SELECT sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2" >>> spark.sql(q).collect() # doctest: +SKIP [Row(sum_udf(v1)=1), Row(sum_udf(v1)=5)] .. note:: Registration for a user-defined function (case 2.) was added from Spark 2.3.0. """ # This is to check whether the input function is from a user-defined function or # Python function. if hasattr(f, 'asNondeterministic'): if returnType is not None: raise TypeError( "Invalid returnType: data type can not be specified when f is" "a user-defined function, but got %s." % returnType) if f.evalType not in [PythonEvalType.SQL_BATCHED_UDF, PythonEvalType.SQL_SCALAR_PANDAS_UDF, PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF]: raise ValueError( "Invalid f: f must be SQL_BATCHED_UDF, SQL_SCALAR_PANDAS_UDF or " "SQL_GROUPED_AGG_PANDAS_UDF") register_udf = UserDefinedFunction(f.func, returnType=f.returnType, name=name, evalType=f.evalType, deterministic=f.deterministic) return_udf = f else: if returnType is None: returnType = StringType() register_udf = UserDefinedFunction(f, returnType=returnType, name=name, evalType=PythonEvalType.SQL_BATCHED_UDF) return_udf = register_udf._wrapped() self.sparkSession._jsparkSession.udf().registerPython(name, register_udf._judf) return return_udf
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Register a Python function (including lambda function) or a user-defined function as a SQL function. :param name: name of the user-defined function in SQL statements. :param f: a Python function, or a user-defined function. The user-defined function can be either row-at-a-time or vectorized. See :meth:`pyspark.sql.functions.udf` and :meth:`pyspark.sql.functions.pandas_udf`. :param returnType: the return type of the registered user-defined function. The value can be either a :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string. :return: a user-defined function. To register a nondeterministic Python function, users need to first build a nondeterministic user-defined function for the Python function and then register it as a SQL function. `returnType` can be optionally specified when `f` is a Python function but not when `f` is a user-defined function. Please see below. 1. When `f` is a Python function: `returnType` defaults to string type and can be optionally specified. The produced object must match the specified type. In this case, this API works as if `register(name, f, returnType=StringType())`. >>> strlen = spark.udf.register("stringLengthString", lambda x: len(x)) >>> spark.sql("SELECT stringLengthString('test')").collect() [Row(stringLengthString(test)=u'4')] >>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect() [Row(stringLengthString(text)=u'3')] >>> from pyspark.sql.types import IntegerType >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) >>> spark.sql("SELECT stringLengthInt('test')").collect() [Row(stringLengthInt(test)=4)] >>> from pyspark.sql.types import IntegerType >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) >>> spark.sql("SELECT stringLengthInt('test')").collect() [Row(stringLengthInt(test)=4)] 2. When `f` is a user-defined function: Spark uses the return type of the given user-defined function as the return type of the registered user-defined function. `returnType` should not be specified. In this case, this API works as if `register(name, f)`. >>> from pyspark.sql.types import IntegerType >>> from pyspark.sql.functions import udf >>> slen = udf(lambda s: len(s), IntegerType()) >>> _ = spark.udf.register("slen", slen) >>> spark.sql("SELECT slen('test')").collect() [Row(slen(test)=4)] >>> import random >>> from pyspark.sql.functions import udf >>> from pyspark.sql.types import IntegerType >>> random_udf = udf(lambda: random.randint(0, 100), IntegerType()).asNondeterministic() >>> new_random_udf = spark.udf.register("random_udf", random_udf) >>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP [Row(random_udf()=82)] >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP ... def add_one(x): ... return x + 1 ... >>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP >>> spark.sql("SELECT add_one(id) FROM range(3)").collect() # doctest: +SKIP [Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)] >>> @pandas_udf("integer", PandasUDFType.GROUPED_AGG) # doctest: +SKIP ... def sum_udf(v): ... return v.sum() ... >>> _ = spark.udf.register("sum_udf", sum_udf) # doctest: +SKIP >>> q = "SELECT sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2" >>> spark.sql(q).collect() # doctest: +SKIP [Row(sum_udf(v1)=1), Row(sum_udf(v1)=5)] .. note:: Registration for a user-defined function (case 2.) was added from Spark 2.3.0.
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python
train
hydpy-dev/hydpy
hydpy/models/lland/lland_control.py
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/lland/lland_control.py#L661-L679
def trim(self, lower=None, upper=None): """Trim upper values in accordance with :math:`EQI1 \\leq EQB`. >>> from hydpy.models.lland import * >>> parameterstep('1d') >>> eqi1.value = 2.0 >>> eqb(1.0) >>> eqb eqb(2.0) >>> eqb(2.0) >>> eqb eqb(2.0) >>> eqb(3.0) >>> eqb eqb(3.0) """ if lower is None: lower = getattr(self.subpars.eqi1, 'value', None) super().trim(lower, upper)
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Trim upper values in accordance with :math:`EQI1 \\leq EQB`. >>> from hydpy.models.lland import * >>> parameterstep('1d') >>> eqi1.value = 2.0 >>> eqb(1.0) >>> eqb eqb(2.0) >>> eqb(2.0) >>> eqb eqb(2.0) >>> eqb(3.0) >>> eqb eqb(3.0)
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python
train
aewallin/allantools
examples/gradev-demo.py
https://github.com/aewallin/allantools/blob/b5c695a5af4379fcea4d4ce93a066cb902e7ee0a/examples/gradev-demo.py#L34-L52
def example2(): """ Compute the GRADEV of a nonstationary white phase noise. """ N=1000 # number of samples f = 1 # data samples per second s=1+5/N*np.arange(0,N) y=s*np.random.randn(1,N)[0,:] x = [xx for xx in np.linspace(1,len(y),len(y))] x_ax, y_ax, (err_l, err_h) , ns = allan.gradev(y,data_type='phase',rate=f,taus=x) plt.loglog(x_ax, y_ax,'b.',label="No gaps") y[int(0.4*N):int(0.6*N,)] = np.NaN # Simulate missing data x_ax, y_ax, (err_l, err_h), ns = allan.gradev(y,data_type='phase',rate=f,taus=x) plt.loglog(x_ax, y_ax,'g.',label="With gaps") plt.grid() plt.legend() plt.xlabel('Tau / s') plt.ylabel('Overlapping Allan deviation') plt.show()
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Compute the GRADEV of a nonstationary white phase noise.
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python
train
limix/limix-core
limix_core/util/preprocess.py
https://github.com/limix/limix-core/blob/5c590b4d351409f83ca320844b4897ce92203814/limix_core/util/preprocess.py#L5-L20
def standardize(Y,in_place=False): """ standardize Y in a way that is robust to missing values in_place: create a copy or carry out inplace opreations? """ if in_place: YY = Y else: YY = Y.copy() for i in range(YY.shape[1]): Iok = ~SP.isnan(YY[:,i]) Ym = YY[Iok,i].mean() YY[:,i]-=Ym Ys = YY[Iok,i].std() YY[:,i]/=Ys return YY
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standardize Y in a way that is robust to missing values in_place: create a copy or carry out inplace opreations?
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python
train
saltstack/salt
salt/states/mount.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/mount.py#L731-L827
def swap(name, persist=True, config='/etc/fstab'): ''' Activates a swap device .. code-block:: yaml /root/swapfile: mount.swap .. note:: ``swap`` does not currently support LABEL ''' ret = {'name': name, 'changes': {}, 'result': True, 'comment': ''} on_ = __salt__['mount.swaps']() if __salt__['file.is_link'](name): real_swap_device = __salt__['file.readlink'](name) if not real_swap_device.startswith('/'): real_swap_device = '/dev/{0}'.format(os.path.basename(real_swap_device)) else: real_swap_device = real_swap_device else: real_swap_device = name if real_swap_device in on_: ret['comment'] = 'Swap {0} already active'.format(name) elif __opts__['test']: ret['result'] = None ret['comment'] = 'Swap {0} is set to be activated'.format(name) else: __salt__['mount.swapon'](real_swap_device) on_ = __salt__['mount.swaps']() if real_swap_device in on_: ret['comment'] = 'Swap {0} activated'.format(name) ret['changes'] = on_[real_swap_device] else: ret['comment'] = 'Swap {0} failed to activate'.format(name) ret['result'] = False if persist: device_key_name = 'device' if 'AIX' in __grains__['os']: device_key_name = 'dev' if '/etc/fstab' == config: # Override default for AIX config = "/etc/filesystems" fstab_data = __salt__['mount.filesystems'](config) else: fstab_data = __salt__['mount.fstab'](config) if __opts__['test']: if name not in fstab_data and name not in [fstab_data[item]['device'] for item in fstab_data]: ret['result'] = None if name in on_: ret['comment'] = ('Swap {0} is set to be added to the ' 'fstab and to be activated').format(name) return ret if 'none' in fstab_data: if fstab_data['none'][device_key_name] == name and \ fstab_data['none']['fstype'] != 'swap': return ret if 'AIX' in __grains__['os']: out = None ret['result'] = False ret['comment'] += '. swap not present in /etc/filesystems on AIX.' return ret else: # present, new, change, bad config # Make sure the entry is in the fstab out = __salt__['mount.set_fstab']('none', name, 'swap', ['defaults'], 0, 0, config) if out == 'present': return ret if out == 'new': ret['changes']['persist'] = 'new' ret['comment'] += '. Added new entry to the fstab.' return ret if out == 'change': ret['changes']['persist'] = 'update' ret['comment'] += '. Updated the entry in the fstab.' return ret if out == 'bad config': ret['result'] = False ret['comment'] += '. However, the fstab was not found.' return ret return ret
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Activates a swap device .. code-block:: yaml /root/swapfile: mount.swap .. note:: ``swap`` does not currently support LABEL
[ "Activates", "a", "swap", "device" ]
python
train
OpenHumans/open-humans-api
ohapi/projects.py
https://github.com/OpenHumans/open-humans-api/blob/ca2a28cf5d55cfdae13dd222ba58c25565bdb86e/ohapi/projects.py#L94-L137
def download_member_shared(cls, member_data, target_member_dir, source=None, max_size=MAX_SIZE_DEFAULT, id_filename=False): """ Download files to sync a local dir to match OH member shared data. Files are downloaded to match their "basename" on Open Humans. If there are multiple files with the same name, the most recent is downloaded. :param member_data: This field is data related to member in a project. :param target_member_dir: This field is the target directory where data will be downloaded. :param source: This field is the source from which to download data. :param max_size: This field is the maximum file size. It's default value is 128m. """ logging.debug('Download member shared data...') sources_shared = member_data['sources_shared'] file_data = cls._get_member_file_data(member_data, id_filename=id_filename) logging.info('Downloading member data to {}'.format(target_member_dir)) for basename in file_data: # If not in sources shared, it's the project's own data. Skip. if file_data[basename]['source'] not in sources_shared: continue # Filter source if specified. Determine target directory for file. if source: if source == file_data[basename]['source']: target_filepath = os.path.join(target_member_dir, basename) else: continue else: source_data_dir = os.path.join(target_member_dir, file_data[basename]['source']) if not os.path.exists(source_data_dir): os.mkdir(source_data_dir) target_filepath = os.path.join(source_data_dir, basename) download_file(download_url=file_data[basename]['download_url'], target_filepath=target_filepath, max_bytes=parse_size(max_size))
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Download files to sync a local dir to match OH member shared data. Files are downloaded to match their "basename" on Open Humans. If there are multiple files with the same name, the most recent is downloaded. :param member_data: This field is data related to member in a project. :param target_member_dir: This field is the target directory where data will be downloaded. :param source: This field is the source from which to download data. :param max_size: This field is the maximum file size. It's default value is 128m.
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python
train
quodlibet/mutagen
mutagen/id3/_tags.py
https://github.com/quodlibet/mutagen/blob/e393df5971ba41ba5a50de9c2c9e7e5484d82c4e/mutagen/id3/_tags.py#L286-L326
def _add(self, frame, strict): """Add a frame. Args: frame (Frame): the frame to add strict (bool): if this should raise in case it can't be added and frames shouldn't be merged. """ if not isinstance(frame, Frame): raise TypeError("%r not a Frame instance" % frame) orig_frame = frame frame = frame._upgrade_frame() if frame is None: if not strict: return raise TypeError( "Can't upgrade %r frame" % type(orig_frame).__name__) hash_key = frame.HashKey if strict or hash_key not in self: self[hash_key] = frame return # Try to merge frames, or change the new one. Since changing # the new one can lead to new conflicts, try until everything is # either merged or added. while True: old_frame = self[hash_key] new_frame = old_frame._merge_frame(frame) new_hash = new_frame.HashKey if new_hash == hash_key: self[hash_key] = new_frame break else: assert new_frame is frame if new_hash not in self: self[new_hash] = new_frame break hash_key = new_hash
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Add a frame. Args: frame (Frame): the frame to add strict (bool): if this should raise in case it can't be added and frames shouldn't be merged.
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python
train
ranaroussi/qtpylib
qtpylib/instrument.py
https://github.com/ranaroussi/qtpylib/blob/0dbbc465fafd9cb9b0f4d10e1e07fae4e15032dd/qtpylib/instrument.py#L536-L560
def get_margin_requirement(self): """ Get margin requirements for intrument (futures only) :Retruns: margin : dict margin requirements for instrument (all values are ``None`` for non-futures instruments) """ contract = self.get_contract() if contract.m_secType == "FUT": return futures.get_ib_futures(contract.m_symbol, contract.m_exchange) # else... return { "exchange": None, "symbol": None, "description": None, "class": None, "intraday_initial": None, "intraday_maintenance": None, "overnight_initial": None, "overnight_maintenance": None, "currency": None, }
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Get margin requirements for intrument (futures only) :Retruns: margin : dict margin requirements for instrument (all values are ``None`` for non-futures instruments)
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python
train
splunk/splunk-sdk-python
splunklib/client.py
https://github.com/splunk/splunk-sdk-python/blob/a245a4eeb93b3621730418008e31715912bcdcd8/splunklib/client.py#L2920-L2938
def create(self, query, **kwargs): """ Creates a search using a search query and any additional parameters you provide. :param query: The search query. :type query: ``string`` :param kwargs: Additiona parameters (optional). For a list of available parameters, see `Search job parameters <http://dev.splunk.com/view/SP-CAAAEE5#searchjobparams>`_ on Splunk Developer Portal. :type kwargs: ``dict`` :return: The :class:`Job`. """ if kwargs.get("exec_mode", None) == "oneshot": raise TypeError("Cannot specify exec_mode=oneshot; use the oneshot method instead.") response = self.post(search=query, **kwargs) sid = _load_sid(response) return Job(self.service, sid)
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Creates a search using a search query and any additional parameters you provide. :param query: The search query. :type query: ``string`` :param kwargs: Additiona parameters (optional). For a list of available parameters, see `Search job parameters <http://dev.splunk.com/view/SP-CAAAEE5#searchjobparams>`_ on Splunk Developer Portal. :type kwargs: ``dict`` :return: The :class:`Job`.
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python
train
adamzap/landslide
landslide/rst.py
https://github.com/adamzap/landslide/blob/59b0403d7a7cca4b8ff6ba7fb76efb9748b3f832/landslide/rst.py#L82-L100
def html_body(input_string, source_path=None, destination_path=None, input_encoding='unicode', doctitle=1, initial_header_level=1): """ Given an input string, returns an HTML fragment as a string. The return value is the contents of the <body> element. Parameters (see `html_parts()` for the remainder): - `output_encoding`: The desired encoding of the output. If a Unicode string is desired, use the default value of "unicode" . """ parts = html_parts( input_string=input_string, source_path=source_path, destination_path=destination_path, input_encoding=input_encoding, doctitle=doctitle, initial_header_level=initial_header_level) fragment = parts['html_body'] return fragment
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Given an input string, returns an HTML fragment as a string. The return value is the contents of the <body> element. Parameters (see `html_parts()` for the remainder): - `output_encoding`: The desired encoding of the output. If a Unicode string is desired, use the default value of "unicode" .
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python
train
ianmiell/shutit
shutit_pexpect.py
https://github.com/ianmiell/shutit/blob/19cd64cdfb23515b106b40213dccff4101617076/shutit_pexpect.py#L1907-L1923
def whoarewe(self, note=None, loglevel=logging.DEBUG): """Returns the current group. @param note: See send() @return: the first group found @rtype: string """ shutit = self.shutit shutit.handle_note(note) res = self.send_and_get_output(' command id -n -g', echo=False, loglevel=loglevel).strip() shutit.handle_note_after(note=note) return res
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Returns the current group. @param note: See send() @return: the first group found @rtype: string
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python
train
MacHu-GWU/pymongo_mate-project
pymongo_mate/pkg/pandas_mate/csv_io.py
https://github.com/MacHu-GWU/pymongo_mate-project/blob/be53170c2db54cb705b9e548d32ef26c773ff7f3/pymongo_mate/pkg/pandas_mate/csv_io.py#L56-L113
def index_row_dict_from_csv(path, index_col=None, iterator=False, chunksize=None, skiprows=None, nrows=None, use_ordered_dict=True, **kwargs): """Read the csv into a dictionary. The key is it's index, the value is the dictionary form of the row. :param path: csv file path. :param index_col: None or str, the column that used as index. :param iterator: :param chunksize: :param skiprows: :param nrows: :param use_ordered_dict: :returns: {index_1: row1, index2: row2, ...} **中文文档** 读取csv, 选择一值完全不重复, 可作为index的列作为index, 生成一个字典 数据结构, 使得可以通过index直接访问row。 """ _kwargs = dict(list(kwargs.items())) _kwargs["iterator"] = None _kwargs["chunksize"] = None _kwargs["skiprows"] = 0 _kwargs["nrows"] = 1 df = pd.read_csv(path, index_col=index_col, **_kwargs) columns = df.columns if index_col is None: raise Exception("please give index_col!") if use_ordered_dict: table = OrderedDict() else: table = dict() kwargs["iterator"] = iterator kwargs["chunksize"] = chunksize kwargs["skiprows"] = skiprows kwargs["nrows"] = nrows if iterator is True: for df in pd.read_csv(path, index_col=index_col, **kwargs): for ind, tp in zip(df.index, itertuple(df)): table[ind] = dict(zip(columns, tp)) else: df = pd.read_csv(path, index_col=index_col, **kwargs) for ind, tp in zip(df.index, itertuple(df)): table[ind] = dict(zip(columns, tp)) return table
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Read the csv into a dictionary. The key is it's index, the value is the dictionary form of the row. :param path: csv file path. :param index_col: None or str, the column that used as index. :param iterator: :param chunksize: :param skiprows: :param nrows: :param use_ordered_dict: :returns: {index_1: row1, index2: row2, ...} **中文文档** 读取csv, 选择一值完全不重复, 可作为index的列作为index, 生成一个字典 数据结构, 使得可以通过index直接访问row。
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python
train
graphistry/pygraphistry
graphistry/plotter.py
https://github.com/graphistry/pygraphistry/blob/3dfc50e60232c6f5fedd6e5fa9d3048b606944b8/graphistry/plotter.py#L68-L170
def bind(self, source=None, destination=None, node=None, edge_title=None, edge_label=None, edge_color=None, edge_weight=None, point_title=None, point_label=None, point_color=None, point_size=None): """Relate data attributes to graph structure and visual representation. To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs. :param source: Attribute containing an edge's source ID :type source: String. :param destination: Attribute containing an edge's destination ID :type destination: String. :param node: Attribute containing a node's ID :type node: String. :param edge_title: Attribute overriding edge's minimized label text. By default, the edge source and destination is used. :type edge_title: HtmlString. :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings. :type edge_label: HtmlString. :param edge_color: Attribute overriding edge's color. `See palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer. :type edge_color: String. :param edge_weight: Attribute overriding edge weight. Default is 1. Advanced layout controls will relayout edges based on this value. :type edge_weight: String. :param point_title: Attribute overriding node's minimized label text. By default, the node ID is used. :type point_title: HtmlString. :param point_label: Attribute overriding node's expanded label text. By default, scrollable list of attribute/value mappings. :type point_label: HtmlString. :param point_color: Attribute overriding node's color. `See palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer. :type point_color: Integer. :param point_size: Attribute overriding node's size. By default, uses the node degree. The visualization will normalize point sizes and adjust dynamically using semantic zoom. :type point_size: HtmlString. :returns: Plotter. :rtype: Plotter. **Example: Minimal** :: import graphistry g = graphistry.bind() g = g.bind(source='src', destination='dst') **Example: Node colors** :: import graphistry g = graphistry.bind() g = g.bind(source='src', destination='dst', node='id', point_color='color') **Example: Chaining** :: import graphistry g = graphistry.bind(source='src', destination='dst', node='id') g1 = g.bind(point_color='color1', point_size='size1') g.bind(point_color='color1b') g2a = g1.bind(point_color='color2a') g2b = g1.bind(point_color='color2b', point_size='size2b') g3a = g2a.bind(point_size='size3a') g3b = g2b.bind(point_size='size3b') In the above **Chaining** example, all bindings use src/dst/id. Colors and sizes bind to: :: g: default/default g1: color1/size1 g2a: color2a/size1 g2b: color2b/size2b g3a: color2a/size3a g3b: color2b/size3b """ res = copy.copy(self) res._source = source or self._source res._destination = destination or self._destination res._node = node or self._node res._edge_title = edge_title or self._edge_title res._edge_label = edge_label or self._edge_label res._edge_color = edge_color or self._edge_color res._edge_weight = edge_weight or self._edge_weight res._point_title = point_title or self._point_title res._point_label = point_label or self._point_label res._point_color = point_color or self._point_color res._point_size = point_size or self._point_size return res
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Relate data attributes to graph structure and visual representation. To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs. :param source: Attribute containing an edge's source ID :type source: String. :param destination: Attribute containing an edge's destination ID :type destination: String. :param node: Attribute containing a node's ID :type node: String. :param edge_title: Attribute overriding edge's minimized label text. By default, the edge source and destination is used. :type edge_title: HtmlString. :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings. :type edge_label: HtmlString. :param edge_color: Attribute overriding edge's color. `See palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer. :type edge_color: String. :param edge_weight: Attribute overriding edge weight. Default is 1. Advanced layout controls will relayout edges based on this value. :type edge_weight: String. :param point_title: Attribute overriding node's minimized label text. By default, the node ID is used. :type point_title: HtmlString. :param point_label: Attribute overriding node's expanded label text. By default, scrollable list of attribute/value mappings. :type point_label: HtmlString. :param point_color: Attribute overriding node's color. `See palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer. :type point_color: Integer. :param point_size: Attribute overriding node's size. By default, uses the node degree. The visualization will normalize point sizes and adjust dynamically using semantic zoom. :type point_size: HtmlString. :returns: Plotter. :rtype: Plotter. **Example: Minimal** :: import graphistry g = graphistry.bind() g = g.bind(source='src', destination='dst') **Example: Node colors** :: import graphistry g = graphistry.bind() g = g.bind(source='src', destination='dst', node='id', point_color='color') **Example: Chaining** :: import graphistry g = graphistry.bind(source='src', destination='dst', node='id') g1 = g.bind(point_color='color1', point_size='size1') g.bind(point_color='color1b') g2a = g1.bind(point_color='color2a') g2b = g1.bind(point_color='color2b', point_size='size2b') g3a = g2a.bind(point_size='size3a') g3b = g2b.bind(point_size='size3b') In the above **Chaining** example, all bindings use src/dst/id. Colors and sizes bind to: :: g: default/default g1: color1/size1 g2a: color2a/size1 g2b: color2b/size2b g3a: color2a/size3a g3b: color2b/size3b
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python
train
lrq3000/pyFileFixity
pyFileFixity/lib/brownanrs/rs.py
https://github.com/lrq3000/pyFileFixity/blob/fd5ef23bb13835faf1e3baa773619b86a1cc9bdf/pyFileFixity/lib/brownanrs/rs.py#L890-L918
def _old_forney(self, omega, X, k=None): '''Computes the error magnitudes (only works with errors or erasures under t = floor((n-k)/2), not with erasures above (n-k)//2)''' # XXX Is floor division okay here? Should this be ceiling? if not k: k = self.k t = (self.n - k) // 2 Y = [] for l, Xl in enumerate(X): # Compute the sequence product and multiply its inverse in prod = GF2int(1) # just to init the product (1 is the neutral term for multiplication) Xl_inv = Xl.inverse() for ji in _range(t): # do not change to _range(len(X)) as can be seen in some papers, it won't give the correct result! (sometimes yes, but not always) if ji == l: continue if ji < len(X): Xj = X[ji] else: # if above the maximum degree of the polynomial, then all coefficients above are just 0 (that's logical...) Xj = GF2int(0) prod = prod * (Xl - Xj) #if (ji != l): # prod = prod * (GF2int(1) - X[ji]*(Xl.inverse())) # Compute Yl Yl = Xl**t * omega.evaluate(Xl_inv) * Xl_inv * prod.inverse() Y.append(Yl) return Y
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Computes the error magnitudes (only works with errors or erasures under t = floor((n-k)/2), not with erasures above (n-k)//2)
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python
train
readbeyond/aeneas
aeneas/globalfunctions.py
https://github.com/readbeyond/aeneas/blob/9d95535ad63eef4a98530cfdff033b8c35315ee1/aeneas/globalfunctions.py#L1119-L1133
def human_readable_number(number, suffix=""): """ Format the given number into a human-readable string. Code adapted from http://stackoverflow.com/a/1094933 :param variant number: the number (int or float) :param string suffix: the unit of the number :rtype: string """ for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: if abs(number) < 1024.0: return "%3.1f%s%s" % (number, unit, suffix) number /= 1024.0 return "%.1f%s%s" % (number, "Y", suffix)
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Format the given number into a human-readable string. Code adapted from http://stackoverflow.com/a/1094933 :param variant number: the number (int or float) :param string suffix: the unit of the number :rtype: string
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python
train
acorg/dark-matter
dark/blast/alignments.py
https://github.com/acorg/dark-matter/blob/c78a1bf262667fa5db3548fa7066c4ec14d0551d/dark/blast/alignments.py#L96-L143
def iter(self): """ Extract BLAST records and yield C{ReadAlignments} instances. For each file except the first, check that the BLAST parameters are compatible with those found (above, in __init__) in the first file. @return: A generator that yields C{ReadAlignments} instances. """ # Note that self._reader is already initialized (in __init__) for # the first input file. This is less clean than it could be, but it # makes testing easier, since open() is then only called once for # each input file. count = 0 reader = self._reader reads = iter(self.reads) first = True for blastFilename in self.blastFilenames: if first: # No need to check params in the first file. We already read # them in and stored them in __init__. first = False else: reader = self._getReader(blastFilename, self.scoreClass) differences = checkCompatibleParams( self.params.applicationParams, reader.params) if differences: raise ValueError( 'Incompatible BLAST parameters found. The parameters ' 'in %s differ from those originally found in %s. %s' % (blastFilename, self.blastFilenames[0], differences)) for readAlignments in reader.readAlignments(reads): count += 1 yield readAlignments # Make sure all reads were used. try: read = next(reads) except StopIteration: pass else: raise ValueError( 'Reads iterator contained more reads than the number of BLAST ' 'records found (%d). First unknown read id is %r.' % (count, read.id))
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Extract BLAST records and yield C{ReadAlignments} instances. For each file except the first, check that the BLAST parameters are compatible with those found (above, in __init__) in the first file. @return: A generator that yields C{ReadAlignments} instances.
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python
train
python-astrodynamics/spacetrack
spacetrack/aio.py
https://github.com/python-astrodynamics/spacetrack/blob/18f63b7de989a31b983d140a11418e01bd6fd398/spacetrack/aio.py#L242-L256
async def _download_predicate_data(self, class_, controller): """Get raw predicate information for given request class, and cache for subsequent calls. """ await self.authenticate() url = ('{0}{1}/modeldef/class/{2}' .format(self.base_url, controller, class_)) resp = await self._ratelimited_get(url) await _raise_for_status(resp) resp_json = await resp.json() return resp_json['data']
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Get raw predicate information for given request class, and cache for subsequent calls.
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python
train
Rockhopper-Technologies/enlighten
enlighten/_manager.py
https://github.com/Rockhopper-Technologies/enlighten/blob/857855f940e6c1bb84d0be849b999a18fff5bf5a/enlighten/_manager.py#L169-L209
def _resize_handler(self, *args, **kwarg): # pylint: disable=unused-argument """ Called when a window resize signal is detected Resets the scroll window """ # Make sure only one resize handler is running try: assert self.resize_lock except AssertionError: self.resize_lock = True term = self.term term.clear_cache() newHeight = term.height newWidth = term.width lastHeight = lastWidth = 0 while newHeight != lastHeight or newWidth != lastWidth: lastHeight = newHeight lastWidth = newWidth time.sleep(.2) term.clear_cache() newHeight = term.height newWidth = term.width if newWidth < self.width: offset = (self.scroll_offset - 1) * (1 + self.width // newWidth) term.move_to(0, max(0, newHeight - offset)) self.stream.write(term.clear_eos) self.width = newWidth self._set_scroll_area(force=True) for cter in self.counters: cter.refresh(flush=False) self.stream.flush() self.resize_lock = False
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Called when a window resize signal is detected Resets the scroll window
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python
train
abourget/gevent-socketio
socketio/namespace.py
https://github.com/abourget/gevent-socketio/blob/1cdb1594a315326987a17ce0924ea448a82fab01/socketio/namespace.py#L227-L240
def call_method_with_acl(self, method_name, packet, *args): """You should always use this function to call the methods, as it checks if the user is allowed according to the ACLs. If you override :meth:`process_packet` or :meth:`process_event`, you should definitely want to use this instead of ``getattr(self, 'my_method')()`` """ if not self.is_method_allowed(method_name): self.error('method_access_denied', 'You do not have access to method "%s"' % method_name) return return self.call_method(method_name, packet, *args)
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You should always use this function to call the methods, as it checks if the user is allowed according to the ACLs. If you override :meth:`process_packet` or :meth:`process_event`, you should definitely want to use this instead of ``getattr(self, 'my_method')()``
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python
valid
d0c-s4vage/pfp
pfp/interp.py
https://github.com/d0c-s4vage/pfp/blob/32f2d34fdec1c70019fa83c7006d5e3be0f92fcd/pfp/interp.py#L1301-L1336
def _handle_struct_ref(self, node, scope, ctxt, stream): """TODO: Docstring for _handle_struct_ref. :node: TODO :scope: TODO :ctxt: TODO :stream: TODO :returns: TODO """ self._dlog("handling struct ref") # name # field struct = self._handle_node(node.name, scope, ctxt, stream) try: sub_field = getattr(struct, node.field.name) except AttributeError as e: # should be able to access implicit array items by index OR # access the last one's members directly without index # # E.g.: # # local int total_length = 0; # while(!FEof()) { # HEADER header; # total_length += header.length; # } if isinstance(struct, fields.Array) and struct.implicit: last_item = struct[-1] sub_field = getattr(last_item, node.field.name) else: raise return sub_field
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TODO: Docstring for _handle_struct_ref. :node: TODO :scope: TODO :ctxt: TODO :stream: TODO :returns: TODO
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python
train
cmap/cmapPy
cmapPy/pandasGEXpress/concat.py
https://github.com/cmap/cmapPy/blob/59d833b64fd2c3a494cdf67fe1eb11fc8008bf76/cmapPy/pandasGEXpress/concat.py#L106-L155
def concat_main(args): """ Separate method from main() in order to make testing easier and to enable command-line access. """ # Get files directly if args.input_filepaths is not None: files = args.input_filepaths # Or find them else: files = get_file_list(args.file_wildcard) # No files found if len(files) == 0: msg = "No files were found. args.file_wildcard: {}".format(args.file_wildcard) logger.error(msg) raise Exception(msg) # Only 1 file found if len(files) == 1: logger.warning("Only 1 file found. No concatenation needs to be done, exiting") return # More than 1 file found else: # Parse each file and append to a list gctoos = [] for f in files: gctoos.append(parse.parse(f)) # Create concatenated gctoo object if args.concat_direction == "horiz": out_gctoo = hstack(gctoos, args.remove_all_metadata_fields, args.error_report_output_file, args.fields_to_remove, args.reset_ids) elif args.concat_direction == "vert": out_gctoo = vstack(gctoos, args.remove_all_metadata_fields, args.error_report_output_file, args.fields_to_remove, args.reset_ids) # Write out_gctoo to file logger.info("Writing to output file args.out_name: {}".format(args.out_name)) if args.out_type == "gctx": write_gctx.write(out_gctoo, args.out_name) elif args.out_type == "gct": write_gct.write(out_gctoo, args.out_name, filler_null=args.filler_null, metadata_null=args.metadata_null, data_null=args.data_null)
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Separate method from main() in order to make testing easier and to enable command-line access.
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python
train
monarch-initiative/dipper
dipper/models/Pathway.py
https://github.com/monarch-initiative/dipper/blob/24cc80db355bbe15776edc5c7b41e0886959ba41/dipper/models/Pathway.py#L73-L85
def addComponentToPathway(self, component_id, pathway_id): """ This can be used directly when the component is directly involved in the pathway. If a transforming event is performed on the component first, then the addGeneToPathway should be used instead. :param pathway_id: :param component_id: :return: """ self.graph.addTriple(component_id, self.globaltt['involved in'], pathway_id) return
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This can be used directly when the component is directly involved in the pathway. If a transforming event is performed on the component first, then the addGeneToPathway should be used instead. :param pathway_id: :param component_id: :return:
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python
train
Julian/jsonschema
jsonschema/_utils.py
https://github.com/Julian/jsonschema/blob/a72332004cdc3ba456de7918bc32059822b2f69a/jsonschema/_utils.py#L122-L139
def types_msg(instance, types): """ Create an error message for a failure to match the given types. If the ``instance`` is an object and contains a ``name`` property, it will be considered to be a description of that object and used as its type. Otherwise the message is simply the reprs of the given ``types``. """ reprs = [] for type in types: try: reprs.append(repr(type["name"])) except Exception: reprs.append(repr(type)) return "%r is not of type %s" % (instance, ", ".join(reprs))
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Create an error message for a failure to match the given types. If the ``instance`` is an object and contains a ``name`` property, it will be considered to be a description of that object and used as its type. Otherwise the message is simply the reprs of the given ``types``.
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python
train
dropbox/stone
stone/frontend/ir_generator.py
https://github.com/dropbox/stone/blob/2e95cbcd1c48e05cca68c919fd8d24adec6b0f58/stone/frontend/ir_generator.py#L916-L955
def _create_struct_field(self, env, stone_field): """ This function resolves symbols to objects that we've instantiated in the current environment. For example, a field with data type named "String" is pointed to a String() object. The caller needs to ensure that this stone_field is for a Struct and not for a Union. Returns: stone.data_type.StructField: A field of a struct. """ if isinstance(stone_field, AstVoidField): raise InvalidSpec( 'Struct field %s cannot have a Void type.' % quote(stone_field.name), stone_field.lineno, stone_field.path) data_type = self._resolve_type(env, stone_field.type_ref) annotations = [self._resolve_annotation_type(env, annotation) for annotation in stone_field.annotations] if isinstance(data_type, Void): raise InvalidSpec( 'Struct field %s cannot have a Void type.' % quote(stone_field.name), stone_field.lineno, stone_field.path) elif isinstance(data_type, Nullable) and stone_field.has_default: raise InvalidSpec('Field %s cannot be a nullable ' 'type and have a default specified.' % quote(stone_field.name), stone_field.lineno, stone_field.path) api_type_field = StructField( name=stone_field.name, data_type=data_type, doc=stone_field.doc, ast_node=stone_field, ) api_type_field.set_annotations(annotations) return api_type_field
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This function resolves symbols to objects that we've instantiated in the current environment. For example, a field with data type named "String" is pointed to a String() object. The caller needs to ensure that this stone_field is for a Struct and not for a Union. Returns: stone.data_type.StructField: A field of a struct.
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python
train
WalletGuild/desw
desw/server.py
https://github.com/WalletGuild/desw/blob/f966c612e675961d9dbd8268749e349ba10a47c2/desw/server.py#L336-L370
def network_info(network): """ Get information about the transaction network indicated. Returned info is: enabled/disabled, available hot wallet balance, & the transaction fee. --- description: Get information about the transaction network indicated. operationId: getinfo produces: - application/json parameters: - name: network in: path type: string required: true description: The network name i.e. Bitcoin, Dash responses: '200': description: the network information schema: $ref: '#/definitions/NetworkInfo' default: description: an error schema: $ref: '#/definitions/errorModel' """ lnet = network.lower() isenabled = lnet in ps fee = float(CFG.get(lnet, 'FEE')) roughAvail = str(int(ps[lnet].get_balance()['available'].to_double())) available = float(10 ** (len(roughAvail) - 1)) response = json.dumps({'isenabled': isenabled, 'fee': fee, 'available': available}) ses.close() return response
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Get information about the transaction network indicated. Returned info is: enabled/disabled, available hot wallet balance, & the transaction fee. --- description: Get information about the transaction network indicated. operationId: getinfo produces: - application/json parameters: - name: network in: path type: string required: true description: The network name i.e. Bitcoin, Dash responses: '200': description: the network information schema: $ref: '#/definitions/NetworkInfo' default: description: an error schema: $ref: '#/definitions/errorModel'
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python
train
geronimp/graftM
graftm/hmmsearcher.py
https://github.com/geronimp/graftM/blob/c82576517290167f605fd0bc4facd009cee29f48/graftm/hmmsearcher.py#L25-L69
def hmmsearch(self, input_pipe, hmms, output_files): r"""Run HMMsearch with all the HMMs, generating output files Parameters ---------- input_pipe: String A string which is a partial command line. When this command is run is outputs to STDOUT fasta formatted protein sequences, which hmmsearch runs on. hmms: list of paths A list of (string) paths to HMM files which are used to search with. output_files: list of paths A list of (string) paths to output CSV files to be generated by the HMM searching Returns ------- N/A May raise an exception if hmmsearching went amiss""" # Check input and output paths are the same length if len(hmms) != len(output_files): raise Exception("Programming error: number of supplied HMMs differs from the number of supplied output files") # Create queue data structure queue = [] for i, hmm in enumerate(hmms): queue.append( [hmm, output_files[i]] ) # While there are more things left in the queue while len(queue) > 0: pairs_to_run = self._munch_off_batch(queue) # Run hmmsearches with each of the pairs cmd = self._hmm_command(input_pipe, pairs_to_run) logging.debug("Running command: %s" % cmd) try: extern.run(cmd) except extern.ExternCalledProcessError, e: if e.stderr == '\nError: Sequence file - is empty or misformatted\n\n': raise NoInputSequencesException(cmd) else: raise e
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r"""Run HMMsearch with all the HMMs, generating output files Parameters ---------- input_pipe: String A string which is a partial command line. When this command is run is outputs to STDOUT fasta formatted protein sequences, which hmmsearch runs on. hmms: list of paths A list of (string) paths to HMM files which are used to search with. output_files: list of paths A list of (string) paths to output CSV files to be generated by the HMM searching Returns ------- N/A May raise an exception if hmmsearching went amiss
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python
train
suurjaak/InputScope
inputscope/webui.py
https://github.com/suurjaak/InputScope/blob/245ff045163a1995e8cd5ac558d0a93024eb86eb/inputscope/webui.py#L52-L64
def keyboard(table, day=None): """Handler for showing the keyboard statistics page.""" cols, group = "realkey AS key, COUNT(*) AS count", "realkey" where = (("day", day),) if day else () counts_display = counts = db.fetch(table, cols, where, group, "count DESC") if "combos" == table: counts_display = db.fetch(table, "key, COUNT(*) AS count", where, "key", "count DESC") events = db.fetch(table, where=where, order="stamp") for e in events: e["dt"] = datetime.datetime.fromtimestamp(e["stamp"]) stats, collatedevents = stats_keyboard(events, table) days, input = db.fetch("counts", order="day", type=table), "keyboard" return bottle.template("heatmap.tpl", locals(), conf=conf)
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Handler for showing the keyboard statistics page.
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python
train
shtalinberg/django-actions-logger
actionslog/registry.py
https://github.com/shtalinberg/django-actions-logger/blob/2a7200bfb277ace47464a77b57aa475a9710271a/actionslog/registry.py#L27-L45
def register(self, model, include_fields=[], exclude_fields=[]): """ Register a model with actionslog. Actionslog will then track mutations on this model's instances. :param model: The model to register. :type model: Model :param include_fields: The fields to include. Implicitly excludes all other fields. :type include_fields: list :param exclude_fields: The fields to exclude. Overrides the fields to include. :type exclude_fields: list """ if issubclass(model, Model): self._registry[model] = { 'include_fields': include_fields, 'exclude_fields': exclude_fields, } self._connect_signals(model) else: raise TypeError("Supplied model is not a valid model.")
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Register a model with actionslog. Actionslog will then track mutations on this model's instances. :param model: The model to register. :type model: Model :param include_fields: The fields to include. Implicitly excludes all other fields. :type include_fields: list :param exclude_fields: The fields to exclude. Overrides the fields to include. :type exclude_fields: list
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python
train
materialsproject/pymatgen
pymatgen/analysis/local_env.py
https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/analysis/local_env.py#L1553-L1588
def solid_angle(center, coords): """ Helper method to calculate the solid angle of a set of coords from the center. Args: center (3x1 array): Center to measure solid angle from. coords (Nx3 array): List of coords to determine solid angle. Returns: The solid angle. """ # Compute the displacement from the center r = [np.subtract(c, center) for c in coords] # Compute the magnitude of each vector r_norm = [np.linalg.norm(i) for i in r] # Compute the solid angle for each tetrahedron that makes up the facet # Following: https://en.wikipedia.org/wiki/Solid_angle#Tetrahedron angle = 0 for i in range(1, len(r) - 1): j = i + 1 tp = np.abs(np.dot(r[0], np.cross(r[i], r[j]))) de = r_norm[0] * r_norm[i] * r_norm[j] + \ r_norm[j] * np.dot(r[0], r[i]) + \ r_norm[i] * np.dot(r[0], r[j]) + \ r_norm[0] * np.dot(r[i], r[j]) if de == 0: my_angle = 0.5 * pi if tp > 0 else -0.5 * pi else: my_angle = np.arctan(tp / de) angle += (my_angle if my_angle > 0 else my_angle + np.pi) * 2 return angle
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Helper method to calculate the solid angle of a set of coords from the center. Args: center (3x1 array): Center to measure solid angle from. coords (Nx3 array): List of coords to determine solid angle. Returns: The solid angle.
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python
train
rocky/python-filecache
pyficache/main.py
https://github.com/rocky/python-filecache/blob/60709ccd837ef5df001faf3cb02d4979ba342a23/pyficache/main.py#L436-L443
def remove_remap_file(filename): """Remove any mapping for *filename* and return that if it exists""" global file2file_remap if filename in file2file_remap: retval = file2file_remap[filename] del file2file_remap[filename] return retval return None
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Remove any mapping for *filename* and return that if it exists
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python
train
ciena/afkak
afkak/_util.py
https://github.com/ciena/afkak/blob/6f5e05ba6f135ea3c29cdb80efda009f7845569a/afkak/_util.py#L27-L44
def _coerce_topic(topic): """ Ensure that the topic name is text string of a valid length. :param topic: Kafka topic name. Valid characters are in the set ``[a-zA-Z0-9._-]``. :raises ValueError: when the topic name exceeds 249 bytes :raises TypeError: when the topic is not :class:`unicode` or :class:`str` """ if not isinstance(topic, string_types): raise TypeError('topic={!r} must be text'.format(topic)) if not isinstance(topic, text_type): topic = topic.decode('ascii') if len(topic) < 1: raise ValueError('invalid empty topic name') if len(topic) > 249: raise ValueError('topic={!r} name is too long: {} > 249'.format( topic, len(topic))) return topic
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Ensure that the topic name is text string of a valid length. :param topic: Kafka topic name. Valid characters are in the set ``[a-zA-Z0-9._-]``. :raises ValueError: when the topic name exceeds 249 bytes :raises TypeError: when the topic is not :class:`unicode` or :class:`str`
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python
train
datajoint/datajoint-python
datajoint/table.py
https://github.com/datajoint/datajoint-python/blob/4f29bb154a7ed2b8b64b4d3a9c8be4c16b39621c/datajoint/table.py#L421-L440
def drop(self): """ Drop the table and all tables that reference it, recursively. User is prompted for confirmation if config['safemode'] is set to True. """ if self.restriction: raise DataJointError('A relation with an applied restriction condition cannot be dropped.' ' Call drop() on the unrestricted Table.') self.connection.dependencies.load() do_drop = True tables = [table for table in self.connection.dependencies.descendants(self.full_table_name) if not table.isdigit()] if config['safemode']: for table in tables: print(table, '(%d tuples)' % len(FreeTable(self.connection, table))) do_drop = user_choice("Proceed?", default='no') == 'yes' if do_drop: for table in reversed(tables): FreeTable(self.connection, table).drop_quick() print('Tables dropped. Restart kernel.')
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Drop the table and all tables that reference it, recursively. User is prompted for confirmation if config['safemode'] is set to True.
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python
train
n1analytics/python-paillier
examples/federated_learning_with_encryption.py
https://github.com/n1analytics/python-paillier/blob/955f8c0bfa9623be15b75462b121d28acf70f04b/examples/federated_learning_with_encryption.py#L160-L164
def fit(self, n_iter, eta=0.01): """Linear regression for n_iter""" for _ in range(n_iter): gradient = self.compute_gradient() self.gradient_step(gradient, eta)
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Linear regression for n_iter
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python
train
mitsei/dlkit
dlkit/records/assessment/basic/drag_and_drop_records.py
https://github.com/mitsei/dlkit/blob/445f968a175d61c8d92c0f617a3c17dc1dc7c584/dlkit/records/assessment/basic/drag_and_drop_records.py#L756-L775
def add_droppable(self, droppable_text, name='', reuse=1, drop_behavior_type=None): """stub""" if not isinstance(droppable_text, DisplayText): raise InvalidArgument('droppable_text is not a DisplayText object') if not isinstance(reuse, int): raise InvalidArgument('reuse must be an integer') if reuse < 0: raise InvalidArgument('reuse must be >= 0') if not isinstance(name, DisplayText): # if default '' name = self._str_display_text(name) droppable = { 'id': str(ObjectId()), 'texts': [self._dict_display_text(droppable_text)], 'names': [self._dict_display_text(name)], 'reuse': reuse, 'dropBehaviorType': drop_behavior_type } self.my_osid_object_form._my_map['droppables'].append(droppable) return droppable
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stub
[ "stub" ]
python
train
iotile/coretools
iotilegateway/iotilegateway/supervisor/client.py
https://github.com/iotile/coretools/blob/2d794f5f1346b841b0dcd16c9d284e9bf2f3c6ec/iotilegateway/iotilegateway/supervisor/client.py#L235-L250
async def service_info(self, name): """Pull descriptive info of a service by name. Information returned includes the service's user friendly name and whether it was preregistered or added dynamically. Returns: dict: A dictionary of service information with the following keys set: long_name (string): The user friendly name of the service preregistered (bool): Whether the service was explicitly called out as a preregistered service. """ return await self.send_command(OPERATIONS.CMD_QUERY_INFO, {'name': name}, MESSAGES.QueryInfoResponse, timeout=5.0)
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Pull descriptive info of a service by name. Information returned includes the service's user friendly name and whether it was preregistered or added dynamically. Returns: dict: A dictionary of service information with the following keys set: long_name (string): The user friendly name of the service preregistered (bool): Whether the service was explicitly called out as a preregistered service.
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python
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L12507-L12552
def spkw05(handle, body, center, inframe, first, last, segid, gm, n, states, epochs): # see libspice args for solution to array[][N] problem """ Write an SPK segment of type 5 given a time-ordered set of discrete states and epochs, and the gravitational parameter of a central body. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/spkw05_c.html :param handle: Handle of an SPK file open for writing. :type handle: int :param body: Body code for ephemeris object. :type body: int :param center: Body code for the center of motion of the body. :type center: int :param inframe: The reference frame of the states. :type inframe: str :param first: First valid time for which states can be computed. :type first: float :param last: Last valid time for which states can be computed. :type last: float :param segid: Segment identifier. :type segid: str :param gm: Gravitational parameter of central body. :type gm: float :param n: Number of states and epochs. :type n: int :param states: States. :type states: Nx6-Element Array of floats :param epochs: Epochs. :type epochs: Array of floats """ handle = ctypes.c_int(handle) body = ctypes.c_int(body) center = ctypes.c_int(center) inframe = stypes.stringToCharP(inframe) first = ctypes.c_double(first) last = ctypes.c_double(last) segid = stypes.stringToCharP(segid) gm = ctypes.c_double(gm) n = ctypes.c_int(n) states = stypes.toDoubleMatrix(states) epochs = stypes.toDoubleVector(epochs) libspice.spkw05_c(handle, body, center, inframe, first, last, segid, gm, n, states, epochs)
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python
train
yamcs/yamcs-python
yamcs-client/yamcs/mdb/client.py
https://github.com/yamcs/yamcs-python/blob/1082fee8a299010cc44416bbb7518fac0ef08b48/yamcs-client/yamcs/mdb/client.py#L89-L109
def list_containers(self, page_size=None): """ Lists the containers visible to this client. Containers are returned in lexicographical order. :rtype: :class:`.Container` iterator """ params = {} if page_size is not None: params['limit'] = page_size return pagination.Iterator( client=self._client, path='/mdb/{}/containers'.format(self._instance), params=params, response_class=mdb_pb2.ListContainersResponse, items_key='container', item_mapper=Container, )
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Lists the containers visible to this client. Containers are returned in lexicographical order. :rtype: :class:`.Container` iterator
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python
train
myaooo/pysbrl
pysbrl/utils.py
https://github.com/myaooo/pysbrl/blob/74bba8c6913a7f82e32313108f8c3e025b89d9c7/pysbrl/utils.py#L12-L20
def before_save(file_or_dir): """ make sure that the dedicated path exists (create if not exist) :param file_or_dir: :return: None """ dir_name = os.path.dirname(os.path.abspath(file_or_dir)) if not os.path.exists(dir_name): os.makedirs(dir_name)
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make sure that the dedicated path exists (create if not exist) :param file_or_dir: :return: None
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python
train
ninapavlich/django-imagekit-cropper
imagekit_cropper/utils.py
https://github.com/ninapavlich/django-imagekit-cropper/blob/c1c2dc5c3c4724492052e5d244e9de1cc362dbcc/imagekit_cropper/utils.py#L32-L50
def instance_ik_model_receiver(fn): """ A method decorator that filters out sign_original_specals coming from models that don't have fields that function as ImageFieldSourceGroup sources. """ @wraps(fn) def receiver(self, sender, **kwargs): # print 'inspect.isclass(sender? %s'%(inspect.isclass(sender)) if not inspect.isclass(sender): return for src in self._source_groups: if issubclass(sender, src.model_class): fn(self, sender=sender, **kwargs) # If we find a match, return. We don't want to handle the signal # more than once. return return receiver
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A method decorator that filters out sign_original_specals coming from models that don't have fields that function as ImageFieldSourceGroup sources.
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python
train
joedborg/CoPing
CoPing/ping.py
https://github.com/joedborg/CoPing/blob/2239729ee4107b999c1cba696d94f7d48ab73d36/CoPing/ping.py#L157-L192
def do(self): """ Send one ICMP ECHO_REQUEST and receive the response until self.timeout. """ try: # One could use UDP here, but it's obscure current_socket = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.getprotobyname("icmp")) except socket.error as (errno, msg): if errno == 1: # Operation not permitted - Add more information to traceback etype, evalue, etb = sys.exc_info() evalue = etype( "%s - Note that ICMP messages can only be send from processes running as root." % evalue ) raise etype, evalue, etb raise # raise the original error send_time = self.send_one_ping(current_socket) if send_time == None: return self.send_count += 1 receive_time, packet_size, ip, ip_header, icmp_header = self.receive_one_ping(current_socket) current_socket.close() if receive_time: self.receive_count += 1 delay = (receive_time - send_time) * 1000.0 self.total_time += delay if self.min_time > delay: self.min_time = delay if self.max_time < delay: self.max_time = delay return PingSuccess(delay, ip, packet_size, ip_header, icmp_header) else: return PingTimeout(self.destination)
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Send one ICMP ECHO_REQUEST and receive the response until self.timeout.
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python
train