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paulo037
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starcoder

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lapse_rate_regression.py
ehultee/stoch-SMB
3
177891
<reponame>ehultee/stoch-SMB #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Load process-model SMB in a given catchment and regress a mass balance gradient Created on Thu Aug 5 14:14:05 2021 Edit Sept 2: compute 30-year mean of anomaly @author: lizz """ from shapely.geometry import MultiPoint from shapely.ops import triangulate import shapefile from netCDF4 import Dataset import numpy as np import pandas as pd import pyproj as pyproj from scipy import interpolate import datetime import time import matplotlib.pyplot as plt from matplotlib import cm from stochSMB import segments_fit ###------------------------ ### CHOOSE CATCHMENTS ###------------------------ catchments_to_pull = (101,) ###------------------------ ### DATA READ-IN AND PROJECTION ###------------------------ ## Read in BedMachine grid to reproject SMB gl_bed_path ='/Users/lizz/Documents/GitHub/Data_unsynced/BedMachine-Greenland/BedMachineGreenland-2017-09-20.nc' fh = Dataset(gl_bed_path, mode='r') xx = fh.variables['x'][:].copy() # x-coord (polar stereo (70, 45)) yy = fh.variables['y'][:].copy() # y-coord ss = fh.variables['surface'][:].copy() # surface elevation fh.close() ## Read in Mouginot catchments from shapefile print('Reading in Mouginot catchments') catchment_fn = '/Users/lizz/Documents/GitHub/Data_unsynced/Greenland-catchments-Mouginot/Greenland_Basins_PS_v1.4.2.' sf = shapefile.Reader(catchment_fn) ## Example SMB field read in for grid print('Reading in example SMB field') nhm_smb_path = '/Users/lizz/Documents/GitHub/Data_unsynced/SMBMIP/NHM-SMAP_niwano-monthly-ERA-Interim-1980.nc' fh2 = Dataset(nhm_smb_path, mode='r') xlon_nhm = fh2.variables['LON'][:].copy() #x-coord (latlon) ylat_nhm = fh2.variables['LAT'][:].copy() #y-coord (latlon) fh2.close() ###------------------------ ### SET UP SMB REPROJECTION ###------------------------ ## Down-sample bed topo x_3km = xx[::20] # sample at ~3 km resolution y_3km = yy[::20] s_3km = ss[::20,::20] ## Down-sample SMB x_lon_h = xlon_nhm[::2, ::2] y_lat_h = ylat_nhm[::2, ::2] # resolution about 2 km print('Creating reprojected meshgrid') wgs84 = pyproj.Proj("+init=EPSG:4326") # LatLon with WGS84 datum used by SMB data psn_gl = pyproj.Proj("+init=epsg:3413") # Polar Stereographic North used by BedMachine and Mankoff xs, ys = pyproj.transform(wgs84, psn_gl, x_lon_h, y_lat_h) Xmat, Ymat = np.meshgrid(x_3km, y_3km) # Downsampled BedMachine coords ###------------------------ ### CREATE FRAMEWORK ###------------------------ ## Perform Delaunay triangulation over each catchment region tri_ctmts = {i: [] for i in catchments_to_pull} for i in catchments_to_pull: print('Triangulating catchment {}'.format(sf.record(i)['NAME'])) c = MultiPoint(sf.shape(i).points) tris = triangulate(c) tri_ctmts[i] = tris ## Create data frames to store per-model data # model_names = ['ANICE-ITM_Berends', 'CESM_kampenhout', 'dEBM_krebs','HIRHAM_mottram', # 'NHM-SMAP_niwano', 'RACMO_noel', 'SNOWMODEL_liston'] model_names = ['ANICE-ITM_Berends',] years = range(1980,2010) start_date = datetime.datetime(years[0],1,1) end_date = datetime.datetime(years[-1],12,31) dates = pd.date_range(start=start_date, end=end_date, freq='M') df_per_ctchmnt = {i: {m: {n: {y: pd.DataFrame(columns=('elevation', 'point_smb')) for y in years} for n in range(12)} for m in model_names} for i in catchments_to_pull} ###------------------------ ### CATCHMENT-SUM FOR ALL MODELS ###------------------------ ## Store regridded SMBs for use in each catchment to take all available data for m in model_names: t0 = time.time() if m=='CESM_kampenhout': vname = 'SMBCORR' else: vname = 'SMBcorr' for y in years: ti = time.time() fpath = '/Users/lizz/Documents/GitHub/Data_unsynced/SMBMIP/{}-monthly-ERA-Interim-{}.nc'.format(m, y) fh = Dataset(fpath, mode='r') smb_m = fh.variables[vname][:].copy() fh.close() d_subset = [d for d in dates if d.year==y] for i in range(len(smb_m)): # for each month ## downsample and interpolate SMB smb_ds = smb_m[i][::2, ::2] regridded_smb = interpolate.griddata((xs.ravel(), ys.ravel()), smb_ds.ravel(), (Xmat, Ymat), method='nearest') ## Sample SMB at each Delaunay triangle and sum for j in catchments_to_pull: triangles = tri_ctmts[j] elevations = [] smb_point_vals = [] for tri in triangles: rep_x, rep_y = tri.representative_point().x, tri.representative_point().y smb_x = (np.abs(x_3km - rep_x)).argmin() smb_y = (np.abs(y_3km - rep_y)).argmin() elevations.append(s_3km[smb_y, smb_x]) smb_point_vals.append(regridded_smb[smb_y, smb_x]) df_per_ctchmnt[j][m][i][y] = df_per_ctchmnt[j][m][i][y].assign(elevation=elevations, point_smb=smb_point_vals) tf = time.time() print('Finished processing year {} in time {}s'.format(y, tf-ti)) t1 = time.time() print('Finished processing model {} in time {}'.format(m, t1-t0)) ###------------------------ ### PLOT ALL DATES TOGETHER ###------------------------ df_c = df_per_ctchmnt[101]['ANICE-ITM_Berends'] dict_per_month = {i: [] for i in range(12)} for i in range(12): d_to_join = [df_c[i][y] for y in years] dict_per_month[i] = pd.concat(d_to_join) c = cm.get_cmap('tab20b') plt.figure() for i in range(12): df = dict_per_month[i] # f = interpolate.splrep(df['elevation'], df['point_smb']) # plt.scatter(df['elevation'], df['point_smb'], color=c(i), label='Month '+d_subset[i].strftime('%m')) plt.scatter(df['elevation'], df['point_smb'], color=c(i%12), label=dates[i].strftime('%m/%Y')) # plt.legend(loc='best') plt.show() # ## # ## Make a piecewise linear fit and plot it # # def piecewise_linear(x, x0, y0, k1, k2): # # return np.piecewise(x, [x < x0], [lambda x:k1*x + y0-k1*x0, lambda x:k2*x + y0-k2*x0]) fig, axs = plt.subplots(3, 4, sharex=True, sharey=True) # plt.title('Auto-fitting segments based on AIC & BIC') for i in range(12): ax = axs.ravel()[i] # df = df_per_ctchmnt[101]['ANICE-ITM_Berends'][dates[i]] df = dict_per_month[i] # axs.ravel()[i].scatter(df['elevation'], df['point_smb'], color=c(i%12), label=dates[i].strftime('%m/%Y')) pt_smbs = np.asarray(df.sort_values(by='elevation')['point_smb']) anomalies = pt_smbs - np.mean(pt_smbs) ax.scatter(df.sort_values(by='elevation')['elevation'], anomalies, color=c(i%12), label=dates[i].strftime('%m/%Y')) ## Piecewise implementation - works but misjudges line segments # p, e = optimize.curve_fit(piecewise_linear, # np.asarray(df.sort_values(by='elevation')['elevation']), # np.asarray(df.sort_values(by='elevation')['point_smb'])) # xnew = np.linspace(0, max(df['elevation'])) # plt.plot(xnew, piecewise_linear(xnew, *p)) px, py = segments_fit(np.asarray(df.sort_values(by='elevation')['elevation']), # np.asarray(df.sort_values(by='elevation')['point_smb']), anomalies, maxcount=3) ax.plot(px, py) ax.text(1000,-1100,'Month {}'.format(i+1)) # plt.legend(loc='best') plt.show() # ## scaled to fraction of mean # fig, axs = plt.subplots(3, 4, sharex=True, sharey=True) # # plt.title('Auto-fitting segments based on AIC & BIC') # for i in range(12): # df = df_per_ctchmnt[101]['ANICE-ITM_Berends'][dates[i]] # # axs.ravel()[i].scatter(df['elevation'], df['point_smb'], color=c(i%12), label=dates[i].strftime('%m/%Y')) # pt_smbs = np.asarray(df.sort_values(by='elevation')['point_smb']) # frac_anomalies = (pt_smbs - np.mean(pt_smbs))/np.mean(pt_smbs) # axs.ravel()[i].scatter(df.sort_values(by='elevation')['elevation'], frac_anomalies, color=c(i%12), label=dates[i].strftime('%m/%Y')) # ## Piecewise implementation - works but misjudges line segments # # p, e = optimize.curve_fit(piecewise_linear, # # np.asarray(df.sort_values(by='elevation')['elevation']), # # np.asarray(df.sort_values(by='elevation')['point_smb'])) # # xnew = np.linspace(0, max(df['elevation'])) # # plt.plot(xnew, piecewise_linear(xnew, *p)) # px, py = segments_fit(np.asarray(df.sort_values(by='elevation')['elevation']), # # np.asarray(df.sort_values(by='elevation')['point_smb']), # frac_anomalies, # maxcount=3) # axs.ravel()[i].plot(px, py) # # plt.legend(loc='best') # plt.show()
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cea/optimization/master/evaluation.py
architecture-building-systems/cea-toolbox
0
160510
<filename>cea/optimization/master/evaluation.py """ Evaluation function of an individual """ import cea.inputlocator from cea.optimization.master import cost_model from cea.optimization.master import master_to_slave as master from cea.optimization.master.generation import individual_to_barcode from cea.optimization.master.performance_aggregation import summarize_results_individual from cea.optimization.slave import cooling_main from cea.optimization.slave import electricity_main from cea.optimization.slave import heating_main # +++++++++++++++++++++++++++++++++++++ # Main objective function evaluation # ++++++++++++++++++++++++++++++++++++++ def evaluation_main(individual, building_names_all, locator, network_features, weather_features, config, prices, lca, individual_number, generation_number, column_names_individual, column_names_buildings_heating, column_names_buildings_cooling, building_names_heating, building_names_cooling, building_names_electricity, district_heating_network, district_cooling_network, technologies_heating_allowed, technologies_cooling_allowed ): """ This function evaluates an individual :param individual: Input individual :param building_names_all: names of buildings in the analysed district :param locator: paths to cea input files :param network_features: characteristic parameters (pumping energy, mass flow rate, thermal losses & piping cost) of the district cooling/heating network :param weather_features: weather data for the selected location (ambient temperature, ground temperature etc.) :param config: configurations of cea :param prices: catalogue of energy prices (e.g. for natural gas, electricity, biomass etc.) :param lca: catalogue of emission intensities of energy carriers :param individual_number: unique identifier of the individual in that generation :param generation_number: unique identifier of the generation in this optimization run :param column_names_individual: description of the parameter list in the individual :param column_names_buildings_heating: names of all buildings that are connected to a district heating system :param column_names_buildings_cooling: names of all buildings that are connected to a district cooling system :param building_names_heating: names of all buildings that have a heating load (space heat, hot water etc.) :param building_names_cooling: names of all buildings that have a cooling load :param building_names_electricity: names of all buildings that have an electricity load :param district_heating_network: indicator defining if district heating networks should be analyzed :param district_cooling_network: indicator defining if district heating networks should be analyzed :param technologies_heating_allowed: district heating technologies to be considered in the optimization :param technologies_cooling_allowed: district cooling technologies to be considered in the optimization :type individual: list :type individual_number: int :type generation_number: int :type building_names_all: list of str :type column_names_buildings_heating: list of str :type column_names_buildings_cooling: list of str :type building_names_heating: list of str :type building_names_cooling: list of str :type building_names_electricity: list of str :type locator: cea.inputlocator.InputLocator class object :type network_features: cea.optimization.distribution.network_optimization_features.NetworkOptimizationFeatures class object :type weather_features: cea.optimization.preprocessing.preprocessing_main.WeatherFeatures class object :type config: cea.config.Configuration class object :type prices: cea.optimization.prices.Prices class object :type lca: cea.optimization.lca_calculations.LcaCalculations class object :type district_heating_network: bool :type district_cooling_network: bool :type technologies_heating_allowed: list of str :type technologies_cooling_allowed: list of str :type column_names_individual: list of str :return: Resulting values of the objective function. costs, CO2, prim :rtype: tuple """ # CREATE THE INDIVIDUAL BARCODE AND INDIVIDUAL WITH HER COLUMN NAME AS A DICT DHN_barcode, DCN_barcode,\ individual_with_name_dict, building_connectivity_dict = individual_to_barcode(individual, building_names_all, building_names_heating, building_names_cooling, column_names_individual, column_names_buildings_heating, column_names_buildings_cooling) print("\nEVALUATING THE NEXT SYSTEM OPTION/INDIVIDUAL") print(individual_with_name_dict) # CREATE CLASS AND PASS KEY CHARACTERISTICS OF INDIVIDUAL # THIS CLASS SHOULD CONTAIN ALL VARIABLES THAT MAKE AN INDIVIDUAL CONFIGURATION master_to_slave_vars = master.export_data_to_master_to_slave_class(locator, generation_number, individual_number, individual_with_name_dict, building_names_all, building_names_heating, building_names_cooling, building_names_electricity, DHN_barcode, DCN_barcode, district_heating_network, district_cooling_network, technologies_heating_allowed, technologies_cooling_allowed, weather_features, config ) # DISTRICT HEATING NETWORK district_heating_fixed_costs, \ district_heating_generation_dispatch, \ district_heating_electricity_requirements_dispatch, \ district_heating_fuel_requirements_dispatch, \ district_heating_capacity_installed = heating_main.district_heating_network(locator, config, master_to_slave_vars, network_features, ) # DISTRICT COOLING NETWORK: district_cooling_fixed_costs, \ district_cooling_generation_dispatch, \ district_cooling_electricity_requirements_dispatch, \ district_cooling_fuel_requirements_dispatch, \ district_cooling_capacity_installed = cooling_main.district_cooling_network(locator, config, master_to_slave_vars, network_features, weather_features) # ELECTRICITY CONSUMPTION CALCULATION district_electricity_fixed_costs, \ district_electricity_dispatch, \ district_electricity_demands, \ district_electricity_capacity_installed = electricity_main.electricity_calculations_of_all_buildings( locator, master_to_slave_vars, district_heating_generation_dispatch, district_heating_electricity_requirements_dispatch, district_cooling_generation_dispatch, district_cooling_electricity_requirements_dispatch) # electricity_main.extract_fuels_demand_buildings(master_to_slave_vars, building_names_all, locator) # CALCULATE COSTS AND EMISSIONS print("DISTRICT ENERGY SYSTEM - COSTS, PRIMARY ENERGY AND EMISSIONS OF CONNECTED BUILDINGS") buildings_district_scale_costs, \ buildings_district_scale_emissions = cost_model.buildings_district_scale_costs_and_emissions( district_heating_fixed_costs, district_cooling_fixed_costs, district_electricity_fixed_costs, district_electricity_dispatch, district_heating_fuel_requirements_dispatch, district_cooling_fuel_requirements_dispatch, district_electricity_demands, prices, lca) buildings_building_scale_costs, \ buildings_building_scale_emissions, \ buildings_building_scale_heating_capacities, \ buildings_building_scale_cooling_capacities = cost_model.buildings_building_scale_costs_and_emissions( building_names_heating, building_names_cooling, locator, master_to_slave_vars) print("AGGREGATING RESULTS") TAC_sys_USD, GHG_sys_tonCO2, performance_totals = summarize_results_individual(buildings_district_scale_costs, buildings_district_scale_emissions, buildings_building_scale_costs, buildings_building_scale_emissions) print('Total TAC in USD = ' + str(round(TAC_sys_USD))) print('Total GHG emissions in tonCO2-eq = ' + str(round(GHG_sys_tonCO2))) return TAC_sys_USD, \ GHG_sys_tonCO2, \ buildings_district_scale_costs, \ buildings_district_scale_emissions, \ buildings_building_scale_costs, \ buildings_building_scale_emissions, \ district_heating_generation_dispatch, \ district_cooling_generation_dispatch, \ district_electricity_dispatch, \ district_electricity_demands, \ performance_totals, \ building_connectivity_dict, \ district_heating_capacity_installed, \ district_cooling_capacity_installed, \ district_electricity_capacity_installed, \ buildings_building_scale_heating_capacities, \ buildings_building_scale_cooling_capacities
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daybed/tests/support.py
spiral-project/daybed
19
1602250
import collections try: import unittest2 as unittest except ImportError: import unittest # NOQA import base64 import six import webtest from daybed import __version__ from daybed.tokens import get_hawk_credentials class PrefixedRequestClass(webtest.app.TestRequest): @classmethod def blank(cls, path, *args, **kwargs): path = '/v%s%s' % (__version__.split('.')[0], path) return webtest.app.TestRequest.blank(path, *args, **kwargs) class BaseWebTest(unittest.TestCase): """Base Web Test to test your cornice service. It setups the database before each test and delete it after. """ def setUp(self): self.app = webtest.TestApp("config:conf/tests.ini", relative_to='.') self.app.RequestClass = PrefixedRequestClass self.db = self.app.app.registry.backend self.indexer = self.app.app.registry.index token, self.credentials = get_hawk_credentials() self.db.store_credentials(token, self.credentials) auth_password = base64.b64encode( (u'%s:%s' % (self.credentials['id'], self.credentials['key'])).encode('ascii')) \ .strip().decode('ascii') self.headers = { 'Content-Type': 'application/json', 'Authorization': 'Basic {0}'.format(auth_password), } def tearDown(self): self.db.delete_db() self.indexer.delete_indices() def force_unicode(data): """ Recursively force unicode. Works for dict keys, list values etc. (source: http://stackoverflow.com/questions/1254454/) """ if isinstance(data, six.string_types): return six.text_type(data) elif isinstance(data, collections.Mapping): return dict(map(force_unicode, six.iteritems(data))) elif isinstance(data, collections.Iterable): return type(data)(map(force_unicode, data)) else: return data
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agate/aggregations/deciles.py
andriyor/agate
663
82884
#!/usr/bin/env python from agate.aggregations.base import Aggregation from agate.aggregations.has_nulls import HasNulls from agate.aggregations.percentiles import Percentiles from agate.data_types import Number from agate.exceptions import DataTypeError from agate.utils import Quantiles from agate.warns import warn_null_calculation class Deciles(Aggregation): """ Calculate the deciles of a column based on its percentiles. Deciles will be equivalent to the 10th, 20th ... 90th percentiles. "Zeroth" (min value) and "Tenth" (max value) deciles are included for reference and intuitive indexing. See :class:`Percentiles` for implementation details. This aggregation can not be applied to a :class:`.TableSet`. :param column_name: The name of a column containing :class:`.Number` data. """ def __init__(self, column_name): self._column_name = column_name def validate(self, table): column = table.columns[self._column_name] if not isinstance(column.data_type, Number): raise DataTypeError('Deciles can only be applied to columns containing Number data.') has_nulls = HasNulls(self._column_name).run(table) if has_nulls: warn_null_calculation(self, column) def run(self, table): """ :returns: An instance of :class:`Quantiles`. """ percentiles = Percentiles(self._column_name).run(table) return Quantiles([percentiles[i] for i in range(0, 101, 10)])
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modules/dad.py
AudioVisuaali/audiobot
3
180646
from audiovisuaali import send from requests import get as rget from json import loads # Joke async def dad_joke(message, client, arguments): # Starting to fetch a joke response = loads(rget("https://icanhazdadjoke.com/slack").text) # Creating letter letter = "<@{}> **| {}**".format(message.author.id, response["attachments"][0]["text"]) # Sending message await client.send_message(message.channel, letter) send(1, "Dad joke sent") return
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tests/test_dashboards.py
pbelskiy/quickbuild
7
69877
import re import pytest import responses from quickbuild import QBError DASHBOARDS_XML = r"""<?xml version="1.0" encoding="UTF-8"?> <list> <com.pmease.quickbuild.model.Dashboard> <id>1</id> <user>1</user> <name>Default</name> <description>System default dashboard</description> <primary>false</primary> <columns> <com.pmease.quickbuild.web.page.dashboard.LayoutColumn> <width>100</width> <gadgetDOMs> <com.pmease.quickbuild.web.gadget.ConfigurationTreeGadget revision="0.0.1"> <title>All Configurations</title> <treeRoot>1</treeRoot> <displayTreeRoot>true</displayTreeRoot> <displaySchedule>false</displaySchedule> <displayRequestCount>true</displayRequestCount> <displayHistoryCount>true</displayHistoryCount> </com.pmease.quickbuild.web.gadget.ConfigurationTreeGadget> </gadgetDOMs> </com.pmease.quickbuild.web.page.dashboard.LayoutColumn> </columns> </com.pmease.quickbuild.model.Dashboard> </list> """ DASHBOARD_INFO_XML = r"""<?xml version="1.0" encoding="UTF-8"?> <com.pmease.quickbuild.model.Dashboard> <id>1</id> <user>1</user> <name>Default</name> <description>System default dashboard</description> <primary>false</primary> <columns> <com.pmease.quickbuild.web.page.dashboard.LayoutColumn> <width>100</width> <gadgetDOMs> <com.pmease.quickbuild.web.gadget.ConfigurationTreeGadget revision="0.0.1"> <title>All Configurations</title> <treeRoot>1</treeRoot> <displayTreeRoot>true</displayTreeRoot> <displaySchedule>false</displaySchedule> <displayRequestCount>true</displayRequestCount> <displayHistoryCount>true</displayHistoryCount> </com.pmease.quickbuild.web.gadget.ConfigurationTreeGadget> <com.pmease.quickbuild.plugin.report.changes.gadget.CommitStatsGadget revision="0.0.0.0.0.0"> <title>Commits</title> <configurationPath>root</configurationPath> <reportsets/> <indicators> <string>Commits</string> <string>Modifications</string> </indicators> <groupBy>BY_DAY</groupBy> <excludingFailed>false</excludingFailed> <ignoreNoBuildDays>false</ignoreNoBuildDays> </com.pmease.quickbuild.plugin.report.changes.gadget.CommitStatsGadget> </gadgetDOMs> </com.pmease.quickbuild.web.page.dashboard.LayoutColumn> </columns> </com.pmease.quickbuild.model.Dashboard> """ DASHBOARD_INFO_JSON = """{ "id" : 1, "user" : 1, "name" : "Default", "description" : "System default dashboard", "primary" : false, "columns" : [ { "width" : 100, "gadgetDOMs" : [ { "title" : "All Configurations", "treeRoot" : 1, "displayTreeRoot" : true, "displaySchedule" : false, "displayRequestCount" : true, "displayHistoryCount" : true, "@class" : "com.pmease.quickbuild.web.gadget.ConfigurationTreeGadget" }, { "title" : "Commits", "configurationPath" : "root", "reportsets" : [ ], "indicators" : [ "Commits", "Modifications" ], "groupBy" : "BY_DAY", "excludingFailed" : false, "ignoreNoBuildDays" : false, "@class" : "com.pmease.quickbuild.plugin.report.changes.gadget.CommitStatsGadget" } ] } ] } """ @responses.activate def test_get(client): responses.add( responses.GET, re.compile(r'.*/rest/dashboards'), content_type='application/xml', body=DASHBOARDS_XML, ) response = client.dashboards.get() assert len(response) == 1 assert response[0]['id'] == 1 assert response[0]['user'] == 1 assert response[0]['name'] == 'Default' assert response[0]['primary'] is False assert response[0]['columns'][0]['width'] == 100 @responses.activate def test_get_info(client): responses.add( responses.GET, re.compile(r'.*/rest/dashboards'), content_type='application/xml', body=DASHBOARD_INFO_XML, ) response = client.dashboards.get_info(1) assert response['@class'] == 'com.pmease.quickbuild.model.Dashboard' assert response['id'] == 1 assert response['columns'][0]['gadgetDOMs'][0]['@revision'] == '0.0.1' @responses.activate def test_update(client): responses.add( responses.POST, re.compile(r'.*/rest/dashboards'), content_type='application/xml', body='10', ) response = client.dashboards.update(DASHBOARD_INFO_XML) assert response == 10 @responses.activate def test_create(client): responses.add( responses.POST, re.compile(r'.*/rest/dashboards'), content_type='application/xml', body='11', ) configuration_without_id = DASHBOARD_INFO_XML.replace('<id>1</id>', '') response = client.dashboards.create(configuration_without_id) assert response == 11 with pytest.raises(QBError): client.dashboards.create(DASHBOARD_INFO_XML) with pytest.raises(QBError): client.dashboards.create(DASHBOARD_INFO_JSON) @responses.activate def test_delete(client): responses.add( responses.DELETE, re.compile(r'.*/rest/dashboards'), body='12', ) response = client.dashboards.delete(12) assert response == 12
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ofagent/indigo/submodules/infra/sourcegen/cxmacrogen.py
lanpinguo/apple-sauce
1
108496
<reponame>lanpinguo/apple-sauce<gh_stars>1-10 #!/usr/bin/python ## SourceObject ## ################################################################# # # Copyright 2013, Big Switch Networks, Inc. # # Licensed under the Eclipse Public License, Version 1.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.eclipse.org/legal/epl-v10.html # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, # either express or implied. See the License for the specific # language governing permissions and limitations under the # License. # ################################################################# # # CXMacroGenerator.py # # XMacro object generator. # ################################################################# from cobjectgen import * import util class CXMacroGenerator(CObjectGenerator): objectType = 'xmacro' def Construct(self): self.submacros = False def Name(self): return self.f.MacroName(self.name) ############################################################ # # Generation Methods # ############################################################ def XMacroEntries(self): s = "" for args in self.members: s += util.fcall(self.name, args) + "\n" return s def DefineSubmacros(self): s = "" members = util.listifyElements(self.members) for entry in members: s += "#ifndef %s\n#define %s %s\n#endif\n\n" % (entry[0], entry[0], self.name) for entry in members: subgen = CXMacroGenerator(name=entry[0], initargs=entry[1]) s += subgen.XMacroEntries() s += "\n" for entry in members: s += "#undef %s\n" % entry[0] return s def Define(self): s = "#ifdef %s\n" % self.name if self.submacros: s += self.DefineSubmacros() else: self.members = util.listifyElements(self.members) s += self.XMacroEntries() s += "#undef %s\n" % self.name s += "#endif\n" return s ############################################################################### # # Sanity Check # ############################################################################### import cm if __name__ == "__main__": data = { 'members': [ 'ENTRY1', 'ENTRY2', 'ENTRY3', 'ENTRY4', ] } m = CXMacroGenerator(name='MY_XMACRO', initargs=data); print m.Define() data = { 'members' : [ [ 'E01', 'E02', 'E03' ], [ 'E11', 'E12', 'E13' ], [ 'E21', 'E22', 'E23' ] ] } m = CXMacroGenerator(name='MY_XMACRO2', initargs=data) print m.Define()
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wolk/interface/firmware_update.py
Wolkabout/WolkConnect-Python-
6
121155
"""Enables firmware update for device.""" # Copyright 2020 WolkAbout Technology s.r.o. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC from abc import abstractmethod from typing import Callable from wolk.interface.firmware_handler import FirmwareHandler from wolk.model.firmware_update_status import FirmwareUpdateStatus class FirmwareUpdate(ABC): """ Firmware Update enabler. Responsible for supervising firmware installation and reporting current firmware installation status and version. """ @abstractmethod def __init__( self, firmware_handler: FirmwareHandler, status_callback: Callable[[FirmwareUpdateStatus], None], ) -> None: """ Enable firmware update for device. :param firmware_handler: Install firmware and provide current version :type firmware_handler: FirmwareHandler :param status_callback: Reporting firmware update status :type status_callback: Callable[[FirmwareUpdateStatus], None] """ raise NotImplementedError() @abstractmethod def handle_install(self, file_path: str) -> None: """ Handle received firmware installation command. :param file_path: Firmware file to install :type file_path: str """ raise NotImplementedError() @abstractmethod def handle_abort(self) -> None: """Handle received firmware installation abort command.""" raise NotImplementedError() @abstractmethod def report_result(self) -> None: """Report the results of the firmware update process.""" raise NotImplementedError() @abstractmethod def get_current_version(self) -> str: """ Return device's current firmware version. :returns: Firmware version :rtype: str """ raise NotImplementedError()
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dbsetup.py
Esha-Muthu-1997/vpoll
0
67276
import sqlite3, json from sqlite3 import Error def create_connection(database): try: conn = sqlite3.connect(database, isolation_level=None, check_same_thread = False) conn.row_factory = lambda c, r: dict(zip([col[0] for col in c.description], r)) return conn except Error as e: print(e) def create_table(c): sql = """ CREATE TABLE IF NOT EXISTS items ( id integer PRIMARY KEY, name varchar(225) NOT NULL, votes integer NOT NULL Default 0 ); """ c.execute(sql) def create_item(c, item): sql = ''' INSERT INTO items(name) VALUES (?) ''' c.execute(sql, item) def update_item(c, item): sql = ''' UPDATE items SET votes = votes+1 WHERE name = ? ''' c.execute(sql, item) def select_all_items(c, name): sql = ''' SELECT * FROM items ''' c.execute(sql) rows = c.fetchall() rows.append({'name' : name}) return json.dumps(rows) def main(): database = "./pythonsqlite.db" conn = create_connection(database) create_table(conn) create_item(conn, ["BJP"]) create_item(conn, ["Congress"]) create_item(conn, ["Republic"]) create_item(conn, ["Democratic"]) print("Connection established!") if __name__ == '__main__': main()
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bot.py
suntorvic/tobman
0
52777
<reponame>suntorvic/tobman #!/usr/bin/python3 # coding: utf-8 from __future__ import annotations import discord from discord.ext import commands from enum import Enum import yaml import re import sys import json import os.path import urllib, urllib.parse import datetime import ics import io import asyncio class Translation: UNABLE_RENAME_USER='Impossible de renommer l\'utilisateur {0}' RENAME_TITLE='Changement de nom !' RENAME_MESSAGE='{0} change le nom de **{1}** en {2}' EVENTS_NEW_TITLE='Événement : **{0}**' EVENTS_NEW='Nouvel événement' EVENTS_NEW_ERROR='Erreur à la création de l\'événement' EVENTS_LIST_TITLE='Événements sur #{0}' EVENTS_LIST_NONE='Aucun événement sur #{0}' EVENTS_LIST_DESC='{1} événement(s) sur #{0}' EVENTS_CLEAR_TITLE='Purge des événements sur #{0}' EVENTS_CLEAR_DESC='{0} événement(s) supprimé(s)' EVENTS_DELETE_TITLE='Suppression de l\'événement' EVENTS_DELETE_DESC='Événement **{0}** supprimé' EVENTS_DELETE_TITLE='Événement supprimé' EVENTS_DELETE_BY='Par' EVENTS_DELETE_NONE='Aucun événement ne correspond à **{0}**' EVENTS_REACT_TITLE='Événement **{0}**' EVENTS_REACT_OK='{0} participe à l\'événement [{1}]({2})' EVENTS_REACT_NG='{0} ne participe pas à l\'événement [{1}]({2})' EVENTS_INFO_ADDED_BY='Ajouté par' EVENTS_INFO_REMAINING_DAYS='dans {0} jours' EVENTS_INFO_REMAINING_DAYS_TODAY='aujourd\'hui' EVENTS_INFO_REMAINING_DAYS_TOMORROW='demain' EVENTS_INFO_LOCATION='Lieu' EVENTS_INFO_LIST_STATUS='{0} **{1}**' EVENTS_NEW_ERROR_DATE_FORMAT='Erreur à la création de l\'événement : la date \'{0}\' ne correspond pas au format {1}' EVENTS_EDIT_TITLE='Modification de l\'événement' EVENTS_EDIT_DESC='Événement [{0}]({1}) modifié' EVENTS_EDIT_BY='Par' EVENTS_EDIT_NONE='Aucun événement ne correspond à **{0}**' EVENTS_MODIFICATION='Modification' EVENTS_MODIFICATION_DATE='Date *{0}* ➡️ *{1}*' EVENTS_MODIFICATION_LOC='Lieu *{0}* ➡️ *{1}*' EVENTS_MODIFICATION_URL='Adresse *{0}* ➡️ *{1}*' EVENTS_MODIFICATION_TITLE='Titre *{0}* ➡️ *{1}*' EVENTS_REMINDER_TITLE='ℹ Événements à venir' EVENT_CALENDAR_FILENAME='Agenda - {0}.ics' CONFIG_FILENAME='tobman.yaml' DATA_JSON_FILENAME='tobman-data.json' class SectionType(Enum): TEXT_CHANNEL = 1 CATEGORY = 2 class Section: _section_regex = re.compile(r"""^\#([^\s]+)$""") def __init__(self, section_type: SectionType, section_name: str): self.section_type = section_type self.section_name = section_name @classmethod def from_string(cls, section_string) -> Section: match = cls._section_regex.match(section_string) if match: return Section(SectionType.TEXT_CHANNEL, match.group(1)) return Section(SectionType.CATEGORY, section_string) def fits(self, discord_channel: discord.TextChannel) -> bool: if self.section_type == SectionType.TEXT_CHANNEL: return discord_channel.name == self.section_name if (self.section_type == SectionType.CATEGORY) and (discord_channel.category is not None): return discord_channel.category.name == self.section_name return False def list_fits(section_list: list, text_channel: discord.TextChannel) -> bool: allow_in: Section for allow_in in section_list: if allow_in.fits(text_channel): return True return False class EventError(Enum): DATE_ERROR = 1 class EventModification: def __init__(self, message: str, old_value: str, new_value: str): self.message = message self.old_value = old_value self.new_value = new_value def __str__(self): if self.old_value and self.old_value != '': return self.message.format(self.old_value, self.new_value) else: return self.message.format('❌', self.new_value) class Event: REACTION_OK = '🆗' REACTION_NG = '🆖' REACTIONS = [REACTION_OK, REACTION_NG] DATE_FORMAT='%Y-%m-%d' TITLE_PREFIX='title:' DATE_PREFIX='date:' URL_PREFIX='url:' LOCATION_PREFIX='loc:' def __init__(self, title): self.guild_id = None self.channel_id = None self.message_id = None self.command_message_id = None self.title = title self.message = None self.url_string = None self.url_thumbnail = None self.description = '' self.location = '' self.date = None self.original_user_id = None @classmethod def parse_new_command(cls, original_message, args_list): event = None if len(args_list) > 0: original_embed = None if original_message is not None and original_message.embeds is not None and len(original_message.embeds) > 0: original_embed = original_message.embeds[-1] title = str(original_embed.title) event = Event(title) event.set_url(original_embed.url) event.description = original_embed.description else: event = Event(str(args_list[0])) for arg in args_list[1:]: for function in [event.parse_date, event.parse_loc]: mod, error = function(arg) if error: return None, error if arg.startswith(cls.URL_PREFIX): event.title = str(args_list[0]) event.set_url(arg[len(cls.URL_PREFIX):]) if original_embed and original_embed.thumbnail and original_embed.thumbnail.url.startswith('http'): event.url_thumbnail = original_embed.thumbnail.url elif original_embed and original_embed.image and original_embed.image.url.startswith('http'): event.url_thumbnail = original_embed.image.url return event, None def parse_edit_command(self, arg_list): for arg in arg_list: for function in [self.parse_title, self.parse_date, self.parse_loc, self.parse_url]: mod, error = function(arg) if mod: yield mod, None break if error: yield None, error break def parse_date(self, arg): if arg.startswith(self.DATE_PREFIX): old_date_string = self.get_date_string() self.set_date_from_string(arg[len(self.DATE_PREFIX):].strip()) date_string = self.get_date_string() if date_string: return EventModification(Translation.EVENTS_MODIFICATION_DATE, old_date_string, date_string), None else: return None, EventError.DATE_ERROR return None, None def parse_loc(self, arg): if arg.startswith(self.LOCATION_PREFIX): old_location = self.location self.location = arg[len(self.LOCATION_PREFIX):].strip() return EventModification(Translation.EVENTS_MODIFICATION_LOC, old_location, self.location), None return None, None def parse_title(self, arg): if arg.startswith(self.TITLE_PREFIX): old_title = self.title self.title = arg[len(self.TITLE_PREFIX):].strip() return EventModification(Translation.EVENTS_MODIFICATION_TITLE, old_title, self.title), None return None, None def parse_url(self, arg): if arg.startswith(self.URL_PREFIX): old_url = self.url_string event.set_url(arg[len(self.URL_PREFIX):].strip()) return EventModification(Translation.EVENTS_MODIFICATION_URL, old_url, event.url_string), None return None, None def format_room_id(guild_id, channel_id): return f'{guild_id}-{channel_id}' def parse_room_id(channel_id_str: str): channel_str_elems = channel_id_str.split('-') if len(channel_str_elems) >= 2: return int(channel_str_elems[0]), int(channel_str_elems[1]) else: return None, None def set_ids(self, guild_id, channel_id, message_id, command_message_id): self.guild_id = guild_id self.channel_id = channel_id self.message_id = message_id self.command_message_id = command_message_id def user_counts(self): ok_count = None ng_count = None if self.message is not None: ok_count = 0 ng_count = 0 for reaction in self.message.reactions: if reaction.emoji == self.REACTION_OK: ok_count = reaction.count if reaction.me: ok_count -= 1 elif reaction.emoji == self.REACTION_NG: ng_count = reaction.count if reaction.me: ng_count -= 1 return ok_count, ng_count def message_url(self): return f'https://discordapp.com/channels/{self.guild_id}/{self.channel_id}/{self.message_id}' def summary(self): remaining_days_string = '' remaining_days = self.remaining_days() if remaining_days is not None: if remaining_days > 1: remaining_days_string = f' *{Translation.EVENTS_INFO_REMAINING_DAYS.format(remaining_days)}*' elif remaining_days == 1: remaining_days_string = f' *{Translation.EVENTS_INFO_REMAINING_DAYS_TOMORROW}*' elif remaining_days == 0: remaining_days_string = f' *{Translation.EVENTS_INFO_REMAINING_DAYS_TODAY}*' if self.message is not None: ok_count, ng_count = self.user_counts() url_part = '' if self.url_string: url_netloc = urllib.parse.urlparse(self.url_string)[1] url_part = f' (*[{url_netloc}]({self.url_string})*)' return f'[**{self.title}**]({self.message_url()}){url_part}{remaining_days_string}\n{Event.REACTION_OK} **{ok_count}**\n{Event.REACTION_NG} **{ng_count}**' else: return f'[**{self.title}**]({self.message_url()}){remaining_days_string}' def set_url(self, url_string): self.url_string = url_string def to_serializable(self): serializable = { 't': self.title, 'g': self.guild_id, 'c': self.channel_id, 'm': self.message_id, 'cm': self.command_message_id } if self.url_string: serializable['url'] = self.url_string if self.description: serializable['desc'] = self.description if self.location: serializable['loc'] = self.location date = self.get_date_string() if date: serializable['date'] = date if self.url_thumbnail: serializable['th'] = self.url_thumbnail if self.original_user_id: serializable['ouid'] = self.original_user_id return serializable def from_deserializable(deserializable): try: if ('g' in deserializable) and ('c' in deserializable) and ('m' in deserializable) and ('t' in deserializable): event = Event(str(deserializable['t'])) guild_id = int(deserializable['g']) channel_id = int(deserializable['c']) message_id = int(deserializable['m']) command_message_id = int(deserializable['cm']) event.set_ids(guild_id, channel_id, message_id, command_message_id) if 'url' in deserializable: event.set_url(deserializable['url']) if 'description' in deserializable: event.description = deserializable['description'] if 'date' in deserializable: event.set_date_from_string(deserializable['date']) if 'th' in deserializable: event.url_thumbnail = str(deserializable['th']) if 'loc' in deserializable: event.location = str(deserializable['loc']) if 'ouid' in deserializable: event.original_user_id = int(deserializable['ouid']) return event except Exception as err: print(f'Error deserializing event: {json.dump(deserializable)}: {err}', file=sys.stderr) return None def set_date_from_string(self, date_string): self.date = None try: self.date = datetime.datetime.strptime(date_string, self.DATE_FORMAT) except Exception as err: print(f'Error reading date string {date_string}: {err}', file=sys.stderr) self.date = self.date.replace(hour=11, minute=59) def get_date_string(self): if self.date: return self.date.strftime(self.DATE_FORMAT) return None def generate_date_ics(self): if self.date: cal = ics.Calendar() cal_event = ics.Event() cal_event.name = self.title cal_event.begin = self.date cal_event.created = datetime.datetime.today() cal_event.description = self.description cal_event.location = self.location if self.url_string: cal_event.url = self.url_string cal_event.make_all_day() cal.events.add(cal_event) ics_memory_buffer = io.StringIO() ics_memory_buffer.writelines(cal) ics_memory_buffer.seek(0, 0) return ics_memory_buffer return None async def generate_discord_embed(self): embed = discord.Embed(title = Translation.EVENTS_NEW_TITLE.format(self.title), type = 'rich', description = self.description ) if self.original_user_id: original_user = bot.get_user(self.original_user_id) if original_user: embed.add_field(name = Translation.EVENTS_INFO_ADDED_BY, value = original_user.mention) if self.date: remaining_days_string = '' remaining_days = self.remaining_days() if remaining_days > 1: remaining_days_string = f' *{Translation.EVENTS_INFO_REMAINING_DAYS.format(remaining_days)}*' elif remaining_days == 1: remaining_days_string = f' *{Translation.EVENTS_INFO_REMAINING_DAYS_TOMORROW}*' elif remaining_days == 0: remaining_days_string = f' *{Translation.EVENTS_INFO_REMAINING_DAYS_TODAY}*' embed.add_field(name = self.get_date_string(), value = remaining_days_string.capitalize()) if self.url_string: embed.url = self.url_string if self.location != '': embed.add_field(name = Translation.EVENTS_INFO_LOCATION, value = self.location) if self.message: await self.generate_add_ok_ng_embed_fields(embed) if self.url_thumbnail: embed.set_thumbnail(url = self.url_thumbnail) return embed async def ok_users(self): users = [] for reaction in self.message.reactions: if reaction.emoji == self.REACTION_OK: async for user in reaction.users(): if user != bot.user: users.append(user) return users async def ng_users(self): users = [] for reaction in self.message.reactions: if reaction.emoji == self.REACTION_NG: async for user in reaction.users(): if user != bot.user: users.append(user) return users async def generate_add_ok_ng_embed_fields(self, embed): ok_count, ng_count = self.user_counts() if (ok_count and (ok_count > 0)) or (ng_count and (ng_count > 0)): ok_mentions = [user.mention for user in await self.ok_users()] ng_mentions = [user.mention for user in await self.ng_users()] if ok_count > 0: embed.add_field(name = Translation.EVENTS_INFO_LIST_STATUS.format(self.REACTION_OK, ok_count), value = '\n'.join(ok_mentions)) if ng_count > 0: embed.add_field(name = Translation.EVENTS_INFO_LIST_STATUS.format(self.REACTION_OK, ng_count), value = '\n'.join(ng_mentions)) async def set_message(self, message): self.message = message # Edit the message embed = await self.generate_discord_embed() await message.edit(embed = embed) def today(self): return datetime.datetime.combine(datetime.datetime.today(), datetime.time(hour = self.date.hour, minute = self.date.minute, second = self.date.second, microsecond = self.date.microsecond, tzinfo = self.date.tzinfo)) def still_active(self): if self.date: today = self.today() delta = self.date - today return int(delta.days) >= 0 else: return True def remaining_days(self): if self.date: today = self.today() delta = self.date - today return int(delta.days) return None async def refresh_message(self, discord_messageable): message = await discord_messageable.fetch_message(int(self.message_id)) if message: await self.set_message(message) return message return None class Tobman: def __init__(self, bot: commands.Bot): self.bot = bot self.rename_allowed_in = [] self.events_allowed_in = [] self.events = {} self.config_filename = CONFIG_FILENAME self.data_filename = DATA_JSON_FILENAME self.remove_rename_commands = False self.remove_event_commands = False self.init_schedule() def load_config(self): with open(self.config_filename, 'r') as config_file: data = yaml.safe_load(config_file) if 'discord_api_token' not in data: print(f'Error: {self.config_filename} does not define the key "token"') self.token = data['discord_api_token'] if 'rename_allowed_in' in data: self.rename_allowed_in = [Section.from_string(section_string) for section_string in data['rename_allowed_in']] if 'events_allowed_in' in data: self.events_allowed_in = [Section.from_string(section_string) for section_string in data['events_allowed_in']] if 'remove_rename_commands' in data: self.remove_rename_commands = bool(data['remove_rename_commands']) if 'remove_event_commands' in data: self.remove_event_commands = bool(data['remove_event_commands']) def load_data(self): import os.path if os.path.isfile(self.data_filename): with open(self.data_filename, 'r') as data_file: data_json = json.load(data_file) if data_json is not None: if 'events' in data_json: self.events = {} for channel_id_key, event_list_json in data_json['events'].items(): self.events[channel_id_key] = [] for event_json in event_list_json: event = Event.from_deserializable(event_json) print(f'Deserialized event {event.title}') if event is not None: self.events[channel_id_key].append(event) print(f'Added event {event.title} to {channel_id_key}') def save_data(self): data_json = {} data_json['events'] = {} for channel_id_key, event_list in self.events.items(): event_list_json = [] if event_list is not None: for event in event_list: event_list_json.append(event.to_serializable()) data_json['events'][str(channel_id_key)] = event_list_json with open(self.data_filename, 'w') as data_file: json.dump(data_json, data_file) def add_event(self, event): if (event.guild_id is not None) and (event.channel_id is not None) and (event.message_id is not None): id_str = Event.format_room_id(event.guild_id, event.channel_id) if self.events.get(id_str) is None: self.events[id_str] = [] self.events[id_str].append(event) self.save_data() def clear_events(self, guild_id, channel_id): id_str = Event.format_room_id(guild_id, channel_id) event_list = self.events.get(id_str) self.events[id_str] = None self.save_data() return event_list def get_event(self, guild_id, channel_id, message_id): id_str = Event.format_room_id(guild_id, channel_id) event_list = self.events.get(id_str) if event_list: return [event for event in event_list if (event.message_id == message_id)] def get_events_by_title(self, guild_id, channel_id, event_title): id_str = Event.format_room_id(guild_id, channel_id) event_list = self.events.get(id_str) for event in event_list: if event.title.strip() == event_title.strip(): yield event def delete_events(self, guild_id, channel_id, event_title): id_str = Event.format_room_id(guild_id, channel_id) event_list = self.events.get(id_str) deleted_events = [] for event in event_list: if event.title == event_title: deleted_events.append(event) for event in deleted_events: event_list.remove(event) print(f'Removed event {event.title} from {id_str}') yield event async def refresh_channel_events(self, channel): if Section.list_fits(bot.tobman.events_allowed_in, channel): guild = channel.guild id_str = Event.format_room_id(guild.id, channel.id) event_list = self.events.get(id_str) if event_list is not None: print(f'Refreshing event list for {id_str}') events_to_delete = [] for event in event_list: if event.still_active(): try: await event.refresh_message(channel) except discord.NotFound: print(f'Message {event.message_id} not found, deleting event {event.title}', file=sys.stderr) events_to_delete.append(event) except Exception as err: print(f'Error refreshing events for message {event.message_id}: {err}', file=sys.stderr) else: events_to_delete.append(event) for event in events_to_delete: print(f'Removed event {event.title} from {id_str}') event_list.remove(event) self.save_data() def get_channel_from_ids(self, guild_id, channel_id, only_if_can_send = False): channel = self.bot.get_channel(channel_id) if channel and ((not only_if_can_send) or channel.permissions_for(channel.guild.me).send_messages): return channel return None async def on_event_message_delete(self, guild_id, channel_id, message_id): id_str = Event.format_room_id(guild_id, channel_id) event_list = self.events.get(id_str) if event_list: indices_to_remove = [] events_to_delete = [event for event in event_list if event.message_id == message_id] if len(events_to_delete) > 0: for event in events_to_delete: event_list.remove(event) self.save_data() channel = self.get_channel_from_ids(guild_id, channel_id, only_if_can_send = True) if channel: for event in events_to_delete: embed = discord.Embed(title = Translation.EVENTS_DELETE_TITLE, description = Translation.EVENTS_DELETE_DESC.format(event.title)) await channel.send(embed = embed) async def on_event_reaction_add(self, guild_id, channel_id, message_id, user_id, emoji): if user_id != self.bot.user.id and emoji.name in Event.REACTIONS: channel = self.get_channel_from_ids(guild_id, channel_id, only_if_can_send = True) if channel: for event in self.get_event(guild_id, channel_id, message_id): await event.refresh_message(channel) member = bot.get_guild(guild_id).get_member(user_id) if member: desc_message = None if emoji.name == Event.REACTION_OK: desc_message = Translation.EVENTS_REACT_OK elif emoji.name == Event.REACTION_NG: desc_message = Translation.EVENTS_REACT_NG embed = discord.Embed( title = Translation.EVENTS_REACT_TITLE.format(event.title), description = desc_message.format(member.mention, event.title, event.message_url()) ) await channel.send(embed = embed) async def on_event_reaction_remove(self, guild_id, channel_id, message_id, user_id, emoji): if user_id != self.bot.user.id and emoji.name in Event.REACTIONS: channel = self.get_channel_from_ids(guild_id, channel_id, only_if_can_send = True) if channel: for event in self.get_event(guild_id, channel_id, message_id): # if there are no more reactions of this emoji then add it back await event.refresh_message(channel) if emoji.name == Event.REACTION_OK: member = bot.get_guild(guild_id).get_member(user_id) embed = discord.Embed( title = Translation.EVENTS_REACT_TITLE.format(event.title), description = Translation.EVENTS_REACT_NG.format(member.mention, event.title, event.message_url()) ) await channel.send(embed = embed) # time of day for reminders SCHEDULE_TIME = datetime.time(hour=9) EVENT_DAYS = [ (7, f'{Translation.EVENTS_INFO_REMAINING_DAYS.format(7)}'), (1, Translation.EVENTS_INFO_REMAINING_DAYS_TOMORROW), (0, Translation.EVENTS_INFO_REMAINING_DAYS_TODAY), ] def init_schedule(self): now = datetime.datetime.now() self.next_schedule = datetime.datetime.combine(datetime.datetime.today(), self.SCHEDULE_TIME) if now > self.next_schedule: self.next_schedule += datetime.timedelta(days=1) async def events_scheduled_job(self): await self.bot.wait_until_ready() if not self.bot.is_closed(): now = datetime.datetime.now() if (now >= self.next_schedule): print(f'Running scheduled events check') self.last_schedule = now self.next_schedule = datetime.datetime.combine(datetime.datetime.today() + datetime.timedelta(days=1), self.SCHEDULE_TIME) print(f'Next schedule: {self.next_schedule}') for channel_id_key, event_list in self.events.items(): guild_id, channel_id = Event.parse_room_id(channel_id_key) if guild_id and channel_id: channel = self.bot.get_channel(channel_id) if channel: for days_remaining, event_date_message in self.EVENT_DAYS: events_to_come = [] for event in event_list: try: await event.refresh_message(channel) if event.remaining_days() == days_remaining: events_to_come.append(event) print(f'Event in {days_remaining} day(s) [{event_date_message}]: "{event.title}"') except discord.NotFound: print(f'Message {event.message_id} ({event.title}) not found, ignoring for scheduled check', file=sys.stderr) if len(events_to_come) > 0: embed = discord.Embed(title = Translation.EVENTS_REMINDER_TITLE, type = 'rich' ) for event in events_to_come: ok_mentions = [user.mention for user in await event.ok_users()] ok_mentions_string = '\n'.join(ok_mentions) if len(ok_mentions_string) > 0: ok_mentions_string = '\n' + ok_mentions_string embed.add_field(name = f'{event_date_message}', value = f'[{event.title}]({event.message_url()}){ok_mentions_string}') await channel.send(embed = embed) else: print(f'Channel not found: {channel_id} {len(event_list)}', file=sys.stderr) async def loop_time_check(self): await self.bot.wait_until_ready() while not self.bot.is_closed(): await self.events_scheduled_job() await asyncio.sleep(60) bot = commands.Bot(command_prefix='/') bot.tobman = Tobman(bot) bot.tobman.load_config() bot.tobman.load_data() @bot.event async def on_ready(): print(f'Now logged in as {bot.user.name} {bot.user.id}') @bot.event async def on_guild_available(guild): print(f'Opened guild {guild}') if not guild.me.guild_permissions.manage_messages: print('Cannot manage messages') if not guild.me.guild_permissions.manage_nicknames: print('Cannot manage nicknames') await guild.me.edit(nick = bot.user.name) @bot.command(name='rename') async def rename(ctx, member_id, to_name): guild = ctx.guild author = ctx.author channel = ctx.message.channel member_real_id = None if len(member_id) >= 4: try: member_real_id = int(member_id.replace('@', '').replace('<', '').replace('!', '').replace('>', '')) except ValueError: print(f'Unable to read member id from {member_id}') member_real_id = None member = None if member_real_id is not None: member = await guild.fetch_member(member_real_id) print(f'Rename command: try {member_id} ({member}) -> {to_name}') if (guild is not None) and (author is not None) and Section.list_fits(bot.tobman.rename_allowed_in, channel): if (member is not None) and (not member.bot): original_name = member.nick or member.name print(f'Rename command: change {original_name} to {to_name}') await member.edit(nick = to_name) embed = discord.Embed(title = Translation.RENAME_TITLE, type = 'rich', description = Translation.RENAME_MESSAGE.format(author.mention, original_name, member.mention)) await channel.send(embed = embed) else: await author.send(Translation.UNABLE_RENAME_USER.format(member_id)) if bot.tobman.remove_rename_commands: await ctx.message.delete() @bot.command(name='event.new') async def event(ctx, *args): guild = ctx.guild author = ctx.author channel = ctx.message.channel if (guild is not None) and (not author.bot) and Section.list_fits(bot.tobman.events_allowed_in, channel) and len(args) > 0: event, error_type = Event.parse_new_command(ctx.message, args) if event: embed = await event.generate_discord_embed() ics_cal_file = event.generate_date_ics() if ics_cal_file: ics_cal_file = discord.File(ics_cal_file, filename = Translation.EVENT_CALENDAR_FILENAME.format(str(event.title))) message = await channel.send(embed = embed, file = ics_cal_file) event.set_ids(guild.id, channel.id, message.id, ctx.message.id) event.original_user_id = ctx.message.author.id bot.tobman.add_event(event) # default reactions await message.add_reaction(Event.REACTION_OK) await message.add_reaction(Event.REACTION_NG) await event.set_message(message) if bot.tobman.remove_event_commands: await ctx.message.delete() elif error_type == EventError.DATE_ERROR: error_embed = discord.Embed(title = Translation.EVENTS_NEW_ERROR, type = 'rich', description = Translation.EVENTS_NEW_ERROR_DATE_FORMAT.format(arg, Event.DATE_FORMAT)) message = await channel.send(embed = error_embed) else: message = await channel.send(EVENTS_NEW_ERROR) @bot.command(name='event.list') async def event(ctx): guild = ctx.guild author = ctx.author channel = ctx.message.channel if (guild is not None) and (not author.bot) and Section.list_fits(bot.tobman.events_allowed_in, channel): id_str = Event.format_room_id(guild.id, channel.id) print(f'List events for channel {id_str}') event_list = bot.tobman.events.get(id_str) if (event_list is None) or (len(event_list) == 0): embed = discord.Embed(title = Translation.EVENTS_LIST_TITLE.format(channel.name), type = 'rich', description = Translation.EVENTS_LIST_NONE.format(channel.name) ) await channel.send(embed = embed) else: await bot.tobman.refresh_channel_events(channel) embed = discord.Embed(title = Translation.EVENTS_LIST_TITLE.format(channel.name), type = 'rich', description = Translation.EVENTS_LIST_DESC.format(channel.name, len(event_list)) ) for event in event_list: embed.add_field(name = event.title, value = event.summary()) await channel.send(embed = embed) if bot.tobman.remove_event_commands: await ctx.message.delete() @bot.command(name='event.edit') async def event(ctx, event_title: str, *args): guild = ctx.guild author = ctx.author channel = ctx.message.channel if (guild is not None) and (not author.bot) and Section.list_fits(bot.tobman.events_allowed_in, channel): event_list = list(bot.tobman.get_events_by_title(guild.id, channel.id, event_title)) if event_list and len(event_list) > 0: for event in event_list: modifications = list(event.parse_edit_command(args)) error_count = 0 for modification, error in modifications: if error: if error == EventError.DATE_ERROR: error_embed = discord.Embed(title = Translation.EVENTS_NEW_ERROR, type = 'rich', description = Translation.EVENTS_NEW_ERROR_DATE_FORMAT.format(arg, Event.DATE_FORMAT)) message = await channel.send(embed = error_embed) else: print(f'Error {error} while modifying event {event.title}, command: {args}', file=sys.stderr) error_count += 1 if (len(modifications) > 0) and (error_count == 0): bot.tobman.save_data() try: message = await event.refresh_message(channel) if message: new_embed = await event.generate_discord_embed() # Can't attach a file to a message edit... # ics_cal_file = discord.File(event.generate_date_ics(), filename = Translation.EVENT_CALENDAR_FILENAME.format(event.title)) await message.edit(embed = new_embed) except discord.NotFound: print(f'Message {event.message_id} not found, modifying event {event.title}', file=sys.stderr) embed = discord.Embed(title = Translation.EVENTS_EDIT_TITLE, type = 'rich', description = Translation.EVENTS_EDIT_DESC.format(event.title, event.message_url()) ) embed.add_field(name = Translation.EVENTS_EDIT_BY, value = ctx.message.author.mention) await event.generate_add_ok_ng_embed_fields(embed) for modification, error in modifications: embed.add_field(name = Translation.EVENTS_MODIFICATION, value = str(modification)) await channel.send(embed = embed) if bot.tobman.remove_event_commands: await ctx.message.delete() else: embed = discord.Embed(title = Translation.EVENTS_EDIT_TITLE, type = 'rich', description = Translation.EVENTS_EDIT_NONE.format(event_title) ) await channel.send(embed = embed) @bot.command(name='event.delete') async def event(ctx, event_title: str): guild = ctx.guild author = ctx.author channel = ctx.message.channel if (guild is not None) and (not author.bot) and Section.list_fits(bot.tobman.events_allowed_in, channel): event_list = list(bot.tobman.delete_events(guild.id, channel.id, event_title)) bot.tobman.save_data() if event_list and len(event_list) > 0: for event in event_list: try: event_message = await event.refresh_message(channel) await event_message.delete() except discord.NotFound: print(f'Message {event.message_id} not found, deleting event {event.title}', file=sys.stderr) embed = discord.Embed(title = Translation.EVENTS_DELETE_TITLE, type = 'rich', description = Translation.EVENTS_DELETE_DESC.format(event.title) ) embed.add_field(name = Translation.EVENTS_DELETE_BY, value = ctx.message.author.mention) await channel.send(embed = embed) if bot.tobman.remove_event_commands: await ctx.message.delete() else: embed = discord.Embed(title = Translation.EVENTS_DELETE_TITLE, type = 'rich', description = Translation.EVENTS_DELETE_NONE.format(event_title) ) await channel.send(embed = embed) @bot.command(name='event.clear') async def event(ctx): guild = ctx.guild author = ctx.author channel = ctx.message.channel if (guild is not None) and (not author.bot) and Section.list_fits(bot.tobman.events_allowed_in, channel): event_list = bot.tobman.clear_events(guild.id, channel.id) event_count = 0 if event_list is not None: event_count = len(event_list) embed = discord.Embed(title = Translation.EVENTS_CLEAR_TITLE.format(channel.name), type = 'rich', description = Translation.EVENTS_CLEAR_DESC.format(event_count) ) await channel.send(embed = embed) if bot.tobman.remove_event_commands: await ctx.message.delete() @bot.event async def on_raw_message_delete(raw_delete_event): await bot.tobman.on_event_message_delete(raw_delete_event.guild_id, raw_delete_event.channel_id, raw_delete_event.message_id) @bot.event async def on_raw_reaction_add(raw_reaction_event): await bot.tobman.on_event_reaction_add(raw_reaction_event.guild_id, raw_reaction_event.channel_id, raw_reaction_event.message_id, raw_reaction_event.user_id, raw_reaction_event.emoji) @bot.event async def on_raw_reaction_remove(raw_reaction_event): await bot.tobman.on_event_reaction_remove(raw_reaction_event.guild_id, raw_reaction_event.channel_id, raw_reaction_event.message_id, raw_reaction_event.user_id, raw_reaction_event.emoji) bot.loop.create_task(bot.tobman.loop_time_check()) bot.run(bot.tobman.token)
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cupy/logic/type_test.py
umitanuki/chainer
0
61490
<filename>cupy/logic/type_test.py def iscomplex(a): # TODO(beam2d): Implement it raise NotImplementedError def iscomplexobj(x): # TODO(beam2d): Implement it raise NotImplementedError def isfortran(a): # TODO(beam2d): Implement it raise NotImplementedError def isreal(x): # TODO(beam2d): Implement it raise NotImplementedError def isrealobj(x): # TODO(beam2d): Implement it raise NotImplementedError
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apps/api/views/user.py
Praetorian-Defence/praetorian-api
2
116078
import base64 from datetime import datetime from io import BytesIO from http import HTTPStatus import pyotp import qrcode import six from django.conf import settings from django.db import transaction from django.http import HttpResponse from django.utils import timezone from django.utils.crypto import salted_hmac from django.utils.decorators import method_decorator from django.utils.http import int_to_base36 from django.views import View from django.utils.translation import gettext_lazy as _ from rolepermissions.roles import assign_role, clear_roles from apps.api.filters.user import UserFilter from apps.api.auth.decorators import token_required from apps.api.errors import ValidationException, ApiException from apps.api.forms.password import PasswordActivateForm from apps.api.forms.user import UserForms from apps.api.permissions import permission_required from apps.api.response import SingleResponse, PaginationResponse from apps.core.models import User from apps.core.models.password_recovery import PasswordRecovery from apps.core.serializers.password import PasswordRecoverySerializer from apps.core.serializers.user import UserSerializer from apps.core.services.notification import NotificationService class UserProfile(View): @method_decorator(token_required) def get(self, request): return SingleResponse(request, request.user, serializer=UserSerializer.Base) @transaction.atomic @method_decorator(token_required) def delete(self, request): user = request.user if user.is_temporary: user.delete() else: raise ApiException(request, _('Permission denied.'), status_code=HTTPStatus.FORBIDDEN) return HttpResponse(status=HTTPStatus.NO_CONTENT) class UserManagement(View): @transaction.atomic @method_decorator(token_required) @method_decorator(permission_required('core.add_user')) def post(self, request): form = UserForms.Create.create_from_request(request) if not form.is_valid(): raise ValidationException(request, form) password = form.cleaned_data.get('password') role = form.cleaned_data.get('role') user = User() form.fill(user) user.set_password(password) user.username = user.email assign_role(user, role.name) user.is_active = False user.save() return SingleResponse(request, user, status=HTTPStatus.CREATED, serializer=UserSerializer.Base) @method_decorator(token_required) @method_decorator(permission_required('core.read_user')) def get(self, request): users = UserFilter(request.GET, queryset=User.objects.all(), request=request).qs return PaginationResponse(request, users, serializer=UserSerializer.Base) class UserDetail(View): @method_decorator(token_required) @method_decorator(permission_required('core.read_user')) def get(self, request, user_id): try: user = User.objects.get(pk=user_id) except User.DoesNotExist: raise ApiException(request, _('User does not exist.'), status_code=HTTPStatus.NOT_FOUND) return SingleResponse(request, user, serializer=UserSerializer.Detail) @transaction.atomic @method_decorator(token_required) @method_decorator(permission_required('core.add_user')) def put(self, request, user_id): try: user = User.objects.get(pk=user_id) except User.DoesNotExist: raise ApiException(request, _('User does not exist.'), status_code=HTTPStatus.NOT_FOUND) form = UserForms.Update.create_from_request(request) if not form.is_valid(): raise ValidationException(request, form) email = form.cleaned_data.get('email') assign_projects = form.cleaned_data.get('assign_projects') role = form.cleaned_data.get('role') if email != user.email: if User.objects.filter(email=email).exists(): raise ApiException(request, _('Email is already used.'), status_code=HTTPStatus.CONFLICT) form.fill(user) user.username = email if assign_projects is not None: user.assign_projects(request, assign_projects) if role: clear_roles(user) assign_role(user, role.value) user.save() return SingleResponse(request, data=user, status=HTTPStatus.OK, serializer=UserSerializer.Base) @transaction.atomic @method_decorator(token_required) @method_decorator(permission_required('core.delete_user')) def delete(self, request, user_id): try: user = User.objects.get(pk=user_id) except User.DoesNotExist: raise ApiException(request, _('User does not exist.'), status_code=HTTPStatus.NOT_FOUND) user.delete() return HttpResponse(status=HTTPStatus.NO_CONTENT) class User2faActivate(View): @method_decorator(token_required) def get(self, request, user_id): try: user = User.objects.get(pk=user_id) except User.DoesNotExist: raise ApiException(request, _('User does not exist.'), status_code=HTTPStatus.NOT_FOUND) otp_secret = pyotp.random_base32() qr_code_url = pyotp.totp.TOTP(otp_secret).provisioning_uri( name=user.email, issuer_name="Praetorian API" ) response = {'qr_code': qr_code_url} if user.additional_data: user.additional_data['otp_secret'] = otp_secret else: user.additional_data = {'otp_secret': otp_secret} user.is_2fa = True user.save() NotificationService.create( recipients=[user.email], sender=f"{settings.EMAIL_SENDER_NAME} <{settings.EMAIL_SENDER}>", subject=_('[Praetorian API] - Two factor authentication'), content={ 'message': _( 'test' ), 'qr_code_url': self.make_qr_code(qr_code_url), 'email_text': _('QR code to activate your two factor authentication.') }, template='_emails/2fa_activation.html' ).send_email() return SingleResponse(request, response, status=HTTPStatus.CREATED, serializer=UserSerializer.Base) @staticmethod def make_qr_code(text): qr = qrcode.QRCode( version=5, error_correction=qrcode.constants.ERROR_CORRECT_L, box_size=8, border=4 ) qr.add_data(text) qr.make(fit=True) img = qr.make_image() img_buffer = BytesIO() img.save(img_buffer, format="PNG") img_str = base64.b64encode(img_buffer.getvalue()).decode("utf-8") return img_str class PasswordRecoveryManagement(View): @transaction.atomic def get(self, request, email): try: user = User.objects.get(email=email) except User.DoesNotExist: raise ApiException( request, _('User with specified email does not exist.'), status_code=HTTPStatus.NOT_FOUND ) timestamp = int(datetime.utcnow().timestamp()) ts_b36 = int_to_base36(timestamp) login_timestamp = '' if user.last_login is None else user.last_login.replace(microsecond=0, tzinfo=None) hash_value = six.text_type(user.pk) + user.password + six.text_type(login_timestamp) + six.text_type(timestamp) recovery_hash = salted_hmac( settings.SECRET_KEY, hash_value, ).hexdigest()[::2] password_recovery = PasswordRecovery.objects.create( value="%s-%s" % (ts_b36, recovery_hash), user=user, expires_at=timezone.now() + settings.PASSWORD_RECOVERY_TIME ) recovery_url = f'{settings.BASE_URL}/password/activate/{password_recovery.value}' NotificationService.create( recipients=[user.email], sender=f"{settings.EMAIL_SENDER_NAME} <{settings.EMAIL_SENDER}>", subject=_('[Praetorian API] - Email recovery'), content={ 'message': _( 'test' ), 'recovery_url': recovery_url, 'email_text': _('Your recovery link to Praetorian API is: ') }, template='_emails/password_recovery.html' ).send_email() return SingleResponse( request, password_recovery, status=HTTPStatus.CREATED, serializer=PasswordRecoverySerializer.Base ) class PasswordActivateManagement(View): @transaction.atomic def post(self, request): form = PasswordActivateForm.create_from_request(request) if not form.is_valid(): raise ValidationException(request, form) request_data = form.cleaned_data try: password_recovery = PasswordRecovery.objects.get( value=request_data.get('hashed_recovery'), expires_at__gt=timezone.now() ) except PasswordRecovery.DoesNotExist: raise ApiException(request, _('Password recovery does not exist.'), status_code=HTTPStatus.NOT_FOUND) password_recovery.user.set_password(request_data.get('password')) password_recovery.user.save() PasswordRecovery.objects.filter(user=password_recovery.user).delete() return SingleResponse(request, None, status=HTTPStatus.NO_CONTENT)
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setup.py
bbrzycki/bl-scint-analysis
0
1609360
<reponame>bbrzycki/bl-scint-analysis import setuptools with open('README.md', 'r') as fh: long_description = fh.read() setuptools.setup( name='blscint', version='0.0.1', author='<NAME>', author_email='<EMAIL>', description='SETI scintillation utilities', long_description=long_description, long_description_content_type='text/markdown', # url='https://github.com/bbrzycki/setigen', # project_urls={ # 'Documentation': 'https://setigen.readthedocs.io/en/latest/', # 'Source': 'https://github.com/bbrzycki/setigen' # }, packages=setuptools.find_packages(), include_package_data=True, install_requires=[ 'numpy>=1.18.1', 'scipy>=1.4.1', 'astropy>=4.0', 'blimpy>=2.0.0', 'setigen>=2.0.6', 'galpy>=1.7.2', 'matplotlib>=3.1.3', 'seaborn>=0.11.2', 'pandas>=1.4.2', 'tqdm>=4.47.0', 'sphinx-rtd-theme>=0.4.3' ], # dependency_links=['https://github.com/h5py/h5py', # 'https://github.com/kiyo-masui/bitshuffle'], classifiers=( 'Programming Language :: Python :: 3', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', ), )
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exerc010.py
RafaelGomesXavier/ExerciciosPython
0
189965
# Crie um programa que leia quanto dinheiro uma pessoa tem na carteira e mostre quantos dólares ela pode comprar. #considerando dólar a 3,27 dinheiro = float(input('Digite o valor em Reais para conversão em Dólares: ')) dolar = 3.27 soma = dinheiro/dolar print('Com o valor de R$ {:.2f}, você pode comprar ${:.2f}.'.format(dinheiro, soma))
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testing/utils.py
grantsrb/planet
0
43243
class RigPacket: def __init__(self, velocity=0, direction=0): self.velocity = velocity self.direction = direction
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lib/davis/python/lib/davis/measures/t_stability.py
varunjampani/video_prop_networks
99
137997
import cv2 import sys import numpy as np import scipy.spatial.distance as ssd from tstab import * def get_bijective_pairs(pairs,costmat): bij_pairs = bij_pairs_one_dim(pairs, costmat,0) bij_pairs = bij_pairs_one_dim(bij_pairs, costmat.T,1) return bij_pairs def bij_pairs_one_dim(pairs, costmat, left_or_right): bij_pairs = [] ids1 = np.unique(pairs[:,left_or_right]) for ii in range(len(ids1)): curr_pairs = pairs[pairs[:,left_or_right]==ids1[ii],:].astype(np.int) curr_costs = costmat[curr_pairs[:,left_or_right], curr_pairs[:,1-left_or_right]] b = np.argmin(curr_costs) bij_pairs.append(curr_pairs[b]) return np.array(bij_pairs) def hist_cost_2(BH1,BH2): nsamp1,nbins=BH1.shape nsamp2,nbins=BH2.shape eps = 2.2204e-16 BH1n = BH1 / (np.sum(BH1,axis=1,keepdims=True)+eps) BH2n = BH2 / (np.sum(BH2,axis=1,keepdims=True)+eps) tmp1 = np.tile(np.transpose(np.atleast_3d(BH1n),[0,2,1]),(1,nsamp2,1)) tmp2 = np.tile(np.transpose(np.atleast_3d(BH2n.T),[2,1,0]),(nsamp1,1,1)) HC = 0.5*np.sum((tmp1-tmp2)**2/(tmp1+tmp2+eps),axis=2) return HC def sc_compute(Bsamp,Tsamp,mean_dist,nbins_theta,nbins_r,r_inner,r_outer,out_vec): in_vec = (out_vec==0).ravel() nsamp = Bsamp.shape[1] r_array=ssd.squareform(ssd.pdist(Bsamp.T)).T theta_array_abs0=Bsamp[1,:].reshape(-1,1).dot(np.ones((1,nsamp))) - \ np.ones((nsamp,1)).dot(Bsamp[1,:].reshape(1,-1)) theta_array_abs1=Bsamp[0,:].reshape(-1,1).dot(np.ones((1,nsamp))) - \ np.ones((nsamp,1)).dot(Bsamp[0,:].reshape(1,-1)) theta_array_abs = np.arctan2(theta_array_abs0,theta_array_abs1).T theta_array=theta_array_abs-Tsamp.T.dot(np.ones((1,nsamp))) if mean_dist is None: mean_dist = np.mean(r_array[in_vec].T[in_vec].T) r_array_n = r_array / mean_dist r_bin_edges=np.logspace(np.log10(r_inner),np.log10(r_outer),nbins_r) r_array_q=np.zeros((nsamp,nsamp)) for m in range(int(nbins_r)): r_array_q=r_array_q+(r_array_n<r_bin_edges[m]) fz = r_array_q > 0 theta_array_2 = np.fmod(np.fmod(theta_array,2*np.pi)+2*np.pi,2*np.pi) theta_array_q = 1+np.floor(theta_array_2/(2*np.pi/nbins_theta)) nbins=nbins_theta*nbins_r BH=np.zeros((nsamp,nbins)) count = 0 for n in range(nsamp): fzn=fz[n]&in_vec Sn = np.zeros((nbins_theta,nbins_r)) coords = np.hstack((theta_array_q[n,fzn].reshape(-1,1), r_array_q[n,fzn].astype(np.int).reshape(-1,1))) # SLOW... #for i,j in coords: #Sn[i-1,j-1] += 1 # FASTER ids = np.ravel_multi_index((coords.T-1).astype(np.int),Sn.shape) Sn = np.bincount(ids.ravel(),minlength = np.prod(Sn.shape)).reshape(Sn.shape) BH[n,:] = Sn.T[:].ravel() return BH.astype(np.int),mean_dist def db_eval_t_stab(fgmask,ground_truth,timing=True): """ Calculates the temporal stability index between two masks Arguments: fgmask (ndarray): Foreground Object mask at frame t ground_truth (ndarray): Foreground Object mask at frame t+1 Return: T (ndarray): Temporal (in-)stability raw_results (ndarray): Supplemental values """ cont_th = 3 cont_th_up = 3 # Shape context parameters r_inner = 1.0/8.0 r_outer = 2.0 nbins_r = 5.0 nbins_theta = 12.0 poly1 = mask2poly(fgmask,cont_th) poly2 = mask2poly(ground_truth,cont_th) if len(poly1.contour_coords) == 0 or \ len(poly2.contour_coords) == 0: return np.nan Cs1 = get_longest_cont(poly1.contour_coords) Cs2 = get_longest_cont(poly2.contour_coords) upCs1 = contour_upsample(Cs1,cont_th_up) upCs2 = contour_upsample(Cs2,cont_th_up) scs1,_=sc_compute(upCs1.T,np.zeros((1,upCs1.shape[0])),None, nbins_theta,nbins_r,r_inner,r_outer,np.zeros((1,upCs1.shape[0]))) scs2,_=sc_compute(upCs2.T,np.zeros((1,upCs2.shape[0])),None, nbins_theta,nbins_r,r_inner,r_outer,np.zeros((1,upCs2.shape[0]))) # Match with the 0-0 alignment costmat = hist_cost_2(scs1,scs2) pairs ,max_sx,max_sy = match_dijkstra(np.ascontiguousarray(costmat)) # Shift costmat costmat2 = np.roll(costmat ,-(max_sy+1),axis=1) costmat2 = np.roll(costmat2,-(max_sx+1),axis=0) # Redo again with the correct alignment pairs,_,_ = match_dijkstra(costmat2) # Put the pairs back to the original place pairs[:,0] = np.mod(pairs[:,0]+max_sx+1, costmat.shape[0]) pairs[:,1] = np.mod(pairs[:,1]+max_sy+1, costmat.shape[1]) pairs = get_bijective_pairs(pairs,costmat) pairs_cost = costmat[pairs[:,0], pairs[:,1]] min_cost = np.average(pairs_cost) return min_cost
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0290.word_pattern/solution.py
WZMJ/Algorithms
5
128440
<filename>0290.word_pattern/solution.py class Solution: def word_pattern(self, pattern: str, str: str) -> bool: words = str.split(" ") return len(pattern) == len(words) and len(set(pattern)) == len(set(words)) == len(set(zip(pattern, words)))
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classic_NN/nn.py
disooqi/learning-machine-learning
1
16095
<filename>classic_NN/nn.py<gh_stars>1-10 import numpy as np from scipy.special import expit, logit import time import logging np.random.seed(4) # 4 logger = logging.getLogger(__name__) fr = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s') sh = logging.StreamHandler() # sh.setFormatter(fr) logger.addHandler(sh) logger.setLevel(logging.DEBUG) logger2 = logging.getLogger('other') file_handler = logging.FileHandler('run.log') file_handler.setFormatter(fr) file_handler.setLevel(logging.INFO) logger2.addHandler(file_handler) logger2.setLevel(logging.INFO) class HiddenLayer: def __init__(self): pass class ConvLayer(HiddenLayer): def __init__(self): super().__init__() class FullyConnectedLayer(HiddenLayer): def __init__(self, n_units, n_in, activation='sigmoid', output_layer=False, keep_prob=1): super().__init__() self.n_units = n_units # It means at every iteration you shut down each neurons of the layer with "1-keep_prob" probability. self.keep_prob = keep_prob # todo (3): weight initialization should be in the Network class if activation == 'sigmoid': self.activation = self.sigmoid self.dAdZ = self.sigmoid_prime self._weights_initialization(n_in) elif activation == 'relu': self.activation = self.relu self.dAdZ = self.relu_prime self._He_initialization(n_in) elif activation == 'tanh': self.activation = self.tanh self.dAdZ = self.tanh_prime self._Xavier_initialization(n_in) elif activation == 'leaky_relu': self.activation = self.leaky_relu self.dAdZ = self.leaky_relu_prime self._He_initialization(n_in) self.activation_type = activation self.output_layer = output_layer def _weights_initialization(self, n_in): # multiplying W by a small number makes the learning fast # however from a practical point of view when multiplied by 0.01 using l>2 the NN does not converge # that is beacuse it runs into gradients vanishing problem self.W = np.random.randn(self.n_units, n_in) * 0.01 self.b = np.zeros((self.n_units, 1)) def _He_initialization(self, n_in): self.W = np.random.randn(self.n_units, n_in) * np.sqrt(2 / n_in) self.b = np.zeros((self.n_units, 1)) def _Xavier_initialization(self, n_in): """Initialize weight W using Xavier Initialization So if the input features of activations are roughly mean 0 and standard variance and variance 1 then this would cause z to also take on a similar scale and this doesn't solve, but it definitely helps reduce the vanishing, exploding gradients problem because it's trying to set each of the weight matrices W so that it's not too much bigger than 1 and not too much less than 1 so it doesn't explode or vanish too quickly. """ self.W = np.random.randn(self.n_units, n_in) * np.sqrt(1 / n_in) self.b = np.zeros((self.n_units, 1)) def _Benjio_initialization(self, n_in): self.W = np.random.randn(self.n_units, n_in) * np.sqrt(2 / (n_in + self.n_units)) self.b = np.zeros((self.n_units, 1)) @staticmethod def softmax(Z): """Compute softmax of Matrix Z :param Z: is in the shape of (n * m), where n is the number of classes and m is the number of examples :return: """ Z_exp = np.exp(Z) return Z_exp/np.sum(Z_exp, axis=0) @staticmethod def stable_softmax(Z): """Compute the softmax of vector Z in a numerically stable way.""" shift_Z = Z - np.max(Z, axis=0) Z_exp = np.exp(shift_Z) return Z_exp / np.sum(Z_exp, axis=0) @staticmethod def softmax_prime(A): """N/A https://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative/ # Kronecker delta function :param A: :return: """ pass @staticmethod def sigmoid(Z): # https://docs.scipy.org/doc/scipy/reference/generated /scipy.special.expit.html # return 1 / (1 + np.exp(-Z)) return expit(np.clip(Z, -709, 36.73)) @classmethod def sigmoid_prime(cls, A): """ calculate dAdZ :param A: :return: dAdZ """ return A * (1 - A) @staticmethod def tanh(Z): return (np.exp(Z) - np.exp(-Z)) / (np.exp(Z) + np.exp(-Z)) @classmethod def tanh_prime(cls, A): return 1 - A ** 2 @staticmethod def relu(Z): # a[a<0] = 0 # return np.clip(Z, 0, Z) return np.maximum(Z, 0) @staticmethod def relu_prime(A): A[A > 0] = 1 return A @staticmethod def leaky_relu(Z, alpha=0.01): ''' :param Z: :param alpha: Slope of the activation function at x < 0. :return: ''' # return np.clip(Z, alpha * Z, Z) return np.where(Z < 0, alpha * Z, Z) @staticmethod def leaky_relu_prime(A, alpha=0.01): return np.where(A > 0, 1, alpha) def __repr__(self): return 'FullyConnectedLayer(n_units={}, activation={}, output_layer={}, keep_prob={})'.format( self.n_units, self.activation_type, self.output_layer, self.keep_prob) class NN: def __init__(self, n_features, n_classes): self.n = n_features self.n_classes = n_classes self.layers = list() def add_layer(self, n_units, activation='sigmoid', dropout_keep_prob=1): if self.layers: n_units_previous_layer = self.layers[-1].n_units else: n_units_previous_layer = self.n layer = FullyConnectedLayer(n_units, n_units_previous_layer, activation=activation, keep_prob=dropout_keep_prob) self.layers.append(layer) def add_output_layer(self): if not self.layers: self.add_layer(self.n_classes, activation='sigmoid') self.layers[-1].output_layer = True if not self.layers[-1].output_layer: self.add_layer(self.n_classes, activation='sigmoid') self.layers[-1].output_layer = True else: # TODO: you should raise an error and message that says you need to delete existing output_layer pass @staticmethod def _calculate_single_layer_gradients(dLdA, layer_cache, compute_dLdA_1=True): ''' :param dJdA: :return: dJdA_1, dJdW, dJdb ''' # For the first iteration where loss is cross entropy and activation func of output layer # is sigmoid, that could be shorten to, # dZ[L] = A[L]-Y # In general, you can compute dZ as follows # dZ = dA * g'(Z) TODO: currently we pass A instead of Z, I guess it is much better to follow "A. Ng" and pass Z # During forward propagation, you had divided A1 by keep_prob. In backpropagation, you'll therefore have to # divide dA1 by keep_prob again (the calculus interpretation is that if A[1]A[1] is scaled by keep_prob, then # its derivative dA[1]dA[1] is also scaled by the same keep_prob). dLdA = np.multiply(dLdA, layer_cache.D) / layer_cache.keep_prob dAdZ = layer_cache.dAdZ(layer_cache.A) dLdZ = dLdA * dAdZ # Element-wise product # dw = dz . a[l-1] dZdW = layer_cache.A_l_1 dJdW = np.dot(dLdZ, dZdW.T) / dLdA.shape[1] # this is two steps in one line; getting dLdw and then dJdW dJdb = np.sum(dLdZ, axis=1, keepdims=True) / dLdA.shape[1] dLdA_1 = None if compute_dLdA_1: # da[l-1] = w[l].T . dz[l] dZdA_1 = layer_cache.W dLdA_1 = np.dot(dZdA_1.T, dLdZ) # computing dLd(A-1) return dLdA_1, dJdW, dJdb def accuracy(self, X, y): # You only use dropout during training. Don't use dropout (randomly eliminate nodes) during test time. A = X for layer in self.layers: Z = np.dot(layer.W, A) + layer.b A = layer.activation(Z) else: y = y.argmax(axis=0) + 1 prediction = A.argmax(axis=0) + 1 res = np.equal(prediction, y) return 100 * np.sum(res) / y.size class Optimization: def __init__(self, loss='cross_entropy', method='gradient-descent'): self.method = method self.VsnSs = list() if loss == 'cross_entropy': self.loss = self.cross_entropy_loss self.activation_prime = self.cross_entropy_loss_prime if method == 'gradient-descent': self.optimizer = self.gradient_descent elif method == 'gd-with-momentum': self.optimizer = self.gradient_descent_with_momentum elif method == 'rmsprop': self.optimizer = self.RMSprop elif method == 'adam': self.optimizer = self.adam @staticmethod def weight_decay(m, alpha, lmbda): # L2 Regularization return 1 - ((alpha * lmbda) / m) @staticmethod def learning_rate_decay(decay_rate, epoch_num): return 1/(1+decay_rate*epoch_num) @staticmethod def o_learning_rate_decay(k, epoch_num): return k/np.sqrt(epoch_num) @staticmethod def exponential_learning_rate_decay(decay_rate, epoch_num): return 0.95**epoch_num def discrete_staircase_learning_rate_decay(self): pass @classmethod def learning_rate(cls, alpha, decay_rate, epoch_num): return cls.learning_rate_decay(decay_rate, epoch_num) * alpha @classmethod def gradient_descent(cls, dJdW, dJdb, W, b, m, **kwargs): alpha0 = kwargs['alpha'] lmbda = kwargs['lmbda'] epoch_num = kwargs['epoch'] decay_rate = kwargs['decay_rate'] alpha = cls.learning_rate(alpha0, decay_rate, epoch_num) W = cls.weight_decay(m, alpha, lmbda) * W - alpha * dJdW b -= alpha * dJdb return W, b @classmethod def gradient_descent_with_momentum(cls, dJdW, dJdb, W, b, m, **kwargs): beta1 = kwargs['beta1'] Vs = kwargs['VS'] alpha0 = kwargs['alpha'] lmbda = kwargs['lmbda'] epoch_num = kwargs['epoch'] decay_rate = kwargs['decay_rate'] alpha = cls.learning_rate(alpha0, decay_rate, epoch_num) Vs['Vdw'] = beta1*Vs['Vdw'] + (1-beta1)*dJdW Vs['Vdb'] = beta1*Vs['Vdb'] + (1-beta1)*dJdb W = cls.weight_decay(m, alpha, lmbda) * W - alpha * Vs['Vdw'] b = b - alpha * Vs['Vdb'] return W, b @classmethod def RMSprop(cls, dJdW, dJdb, W, b, m, **kwargs): beta2 = kwargs['beta2'] Ss = kwargs['VS'] alpha0 = kwargs['alpha'] lmbda = kwargs['lmbda'] epoch_num = kwargs['epoch'] decay_rate = kwargs['decay_rate'] alpha = cls.learning_rate(alpha0, decay_rate, epoch_num) epsilon = np.finfo(np.float32).eps Ss['Sdw'] = beta2 * Ss['Sdw'] + (1 - beta2) * np.square(dJdW) Ss['Sdb'] = beta2 * Ss['Sdb'] + (1 - beta2) * np.square(dJdb) W = cls.weight_decay(m, alpha, lmbda)*W - alpha * (dJdW/(np.sqrt(Ss['Sdw'])+epsilon)) b = b - alpha * (dJdb/(np.sqrt(Ss['Sdb'])+epsilon)) return W, b @classmethod def adam(cls, dJdW, dJdb, W, b, m, **kwargs): beta1 = kwargs['beta1'] beta2 = kwargs['beta2'] VsSs = kwargs['VS'] t = kwargs['t'] alpha0 = kwargs['alpha'] lmbda = kwargs['lmbda'] epoch_num = kwargs['epoch'] decay_rate = kwargs['decay_rate'] alpha = cls.learning_rate(alpha0, decay_rate, epoch_num) epsilon = np.finfo(np.float32).eps VsSs['Vdw'] = beta1 * VsSs['Vdw'] + (1 - beta1) * dJdW VsSs['Vdb'] = beta1 * VsSs['Vdb'] + (1 - beta1) * dJdb VsSs['Sdw'] = beta2 * VsSs['Sdw'] + (1 - beta2) * np.square(dJdW) VsSs['Sdb'] = beta2 * VsSs['Sdb'] + (1 - beta2) * np.square(dJdb) Vdw_corrected = VsSs['Vdw']/(1-beta1**t) Vdb_corrected = VsSs['Vdb']/(1-beta1**t) Sdw_corrected = VsSs['Sdw']/(1-beta2**t) Sdb_corrected = VsSs['Sdb']/(1-beta2**t) W = cls.weight_decay(m, alpha, lmbda) * W - alpha * (Vdw_corrected / (np.sqrt(Sdw_corrected) + epsilon)) b = b - alpha * (Vdb_corrected / (np.sqrt(Sdb_corrected) + epsilon)) return W, b @staticmethod def cross_entropy_loss(y, a): # http://christopher5106.github.io/deep/learning/2016/09/16/about-loss-functions-multinomial-logistic-logarithm-cross-entropy-square-errors-euclidian-absolute-frobenius-hinge.html # https://stats.stackexchange.com/questions/260505/machine-learning-should-i-use-a-categorical-cross-entropy-or-binary-cross-entro # here we penalize every class even the zero ones # the classes here are independent i.e you can reduce the error of one without affecting the other return -(y * np.log(a) + (1 - y) * np.log(1 - a)) @staticmethod def softmax_loss(y, a): # here we penalize only the targeted class and this is intuitive because they are all dependent i.e. if targeted # error is reduced the rest will give less probability because of the softmax relation return - np.sum(y * np.log(a), axis=0, keepdims=True) @staticmethod def cross_entropy_loss_prime(y, a): return -y / a + (1 - y) / (1 - a) @staticmethod def softmax_loss_prime(y, a): return -np.sum(y/a) @staticmethod def regularization_term(network, m, lmbda): agg = 0 for layer in network.layers: agg = np.sum(np.square(layer.W)) else: return (lmbda / (2 * m)) * agg def cost(self, network, X, y, lmbda=0): A = X for layer in network.layers: Z = np.dot(layer.W, A) + layer.b A = layer.activation(Z) else: loss_matrix = self.loss(y, A) sum_over_all_examples = np.sum(loss_matrix, axis=1) / loss_matrix.shape[1] return (np.sum(sum_over_all_examples) / sum_over_all_examples.size) + self.regularization_term(network, X.shape[1], lmbda=lmbda) def _update_weights(self, X, y, network, alpha, lmbda, t, beta1, beta2, decay_rate, epoch_num): A = X for layer in network.layers: layer.A_l_1 = A # this is A-1 from last loop step Z = np.dot(layer.W, A) + layer.b # (called "logits" in ML folklore) A = layer.activation(Z) # NB! we don't not apply dropout to the input layer or output layer. D = np.random.rand(*A.shape) <= layer.keep_prob # dropout A = np.multiply(A, D) / layer.keep_prob # inverted dropout layer.D = D layer.A = A with np.errstate(invalid='raise'): try: dLdA = self.activation_prime(y, A) except FloatingPointError: raise # To avoid the confusion: reversed() doesn't modify the list. reversed() doesn't make a copy of the list # (otherwise it would require O(N) additional memory). If you need to modify the list use alist.reverse(); if # you need a copy of the list in reversed order use alist[::-1] for l, layer, VsnSs in zip(range(len(network.layers), 0, -1), reversed(network.layers), reversed(self.VsnSs)): dLdA, dJdW, dJdb = network._calculate_single_layer_gradients(dLdA, layer, compute_dLdA_1=(l > 1)) layer.W, layer.b = self.optimizer(dJdW, dJdb, layer.W, layer.b, X.shape[1], alpha=alpha, lmbda=lmbda, VS=VsnSs, beta1=beta1, beta2=beta2, t=t, decay_rate=decay_rate, epoch=epoch_num) def minimize(self, network, epochs=1, mini_batch_size=0, learning_rate=0.1, regularization_parameter=0, momentum=0.9, beta2=0.999, learning_rate_decay=0, dataset=None): bef = time.time() for layer in network.layers: self.VsnSs.append({"Vdw": np.zeros_like(layer.W), "Vdb": np.zeros_like(layer.b), "Sdw": np.zeros_like(layer.W), "Sdb": np.zeros_like(layer.b)}) for epoch in range(1, epochs+1): for t, mini_batch in enumerate(dataset.next_mini_batch(size=mini_batch_size), start=1): self._update_weights(mini_batch.X, mini_batch.y, network, learning_rate, regularization_parameter, t, beta1=momentum, beta2=beta2, decay_rate=learning_rate_decay, epoch_num=epoch) else: if epoch % 10 == 0: cost = self.cost(network, dataset.X_train, dataset.y_train, lmbda=regularization_parameter) logger.info('epoch {} (error: {:.5f})'.format(epoch, cost)) else: aft = time.time() cost = self.cost(network, dataset.X_train, dataset.y_train, lmbda=regularization_parameter) logger.debug('-' * 80) logger.debug('| Summary') logger.debug('-' * 80) logger.debug('training time: {:.2f} SECs'.format(aft - bef)) logger.debug('-' * 80) logger.debug('Finish error: {:.5f}'.format( self.cost(network, dataset.X_train, dataset.y_train, lmbda=regularization_parameter))) ss = '' for i, layer in enumerate(network.layers): ss += '\n layer# ' + str(i + 1) + ' - ' + repr(layer) logger2.info('train error: {:.2f}, ' 'time: {:.2f}SECs, ' '#layers {}, ' '#epochs: {}, ' 'learning rate: {},\n' 'regularization parameter: {}, ' 'mini-batch size: {}, ' 'optimizer: [{}], ' 'dataset: [{}, dev_size:{}, shuffle:{}], {}'.format(cost, aft - bef, len(network.layers), epochs, learning_rate, regularization_parameter, mini_batch_size, self.method, dataset.name, dataset.dev_size, dataset.shuffle, ss)) if __name__ == '__main__': pass
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src/capha_module.py
Innsmouth-trip/aiogram-telegram-bot
0
125939
<reponame>Innsmouth-trip/aiogram-telegram-bot # Модуль капчи # Подключение необходимых модулей from src import users_module from src import supergroup_module from src import bot_module from src import welcome_module # Загрузка необходимых параметров bot = bot_module.bot chat_id = bot_module.chat_id # Создание капчи async def send_capha(message): # Проверка на то новый ли это юзер if message.from_user not in message.new_chat_members: return # Сохранение данных о новом пользователе new_member = message.new_chat_members[0] users_module.add_new_user({ 'user_name' : new_member.first_name, 'user_id' : new_member.id, 'message_id' : message.message_id + 1 }) # Выдаем мут новому пользователю await supergroup_module.give_a_mute(new_member.id, "infinity") # Создаем капчу markup = bot_module.types.InlineKeyboardMarkup(row_width=2) item1 = bot_module.types.InlineKeyboardButton("Я бот", callback_data='ban_new_user') item2 = bot_module.types.InlineKeyboardButton("Я не бот", callback_data='unmute_new_user') item3 = bot_module.types.InlineKeyboardButton("Я не знаю кто я", callback_data='fun_kik_new_user') markup.add(item1, item2, item3) # Отправляем сообщение с капчей await message.reply("Вы бот?", reply_markup=markup) # Снимаем бан с нового пользователя и приветствуем его async def unmute_user(message): # Проверка пользователя if users_module.is_user_exist(message.from_user.id): # Получаем пользователя по id user = users_module.get_user_by_user_id(message.from_user.id) # Снятие мута с нового пользователя и удаление сообщения с капчей await supergroup_module.delete_message(user.get('message_id')) await supergroup_module.give_a_mute(user.get('user_id'), "unmute") # Отправка сообщения с приветствием await welcome_module.send_welcome_message(message) # Удаление пользователя из списка новых пользователей await users_module.remove_user_by_user_id(message.from_user.id) else: # <NAME> await supergroup_module.give_a_mute(message.from_user.id, 70) # Баним бота ну или просто невнимательного пользователя async def ban_user(message): # Проверка на пользователя if users_module.is_user_exist(message.from_user.id): # Получаем пользователя по id user = users_module.get_user_by_user_id(message.from_user.id) # Бан пользователя и удаление сообщения с капчей await supergroup_module.delete_message(user.get('message_id')) await supergroup_module.give_a_ban(user.get('user_id')) # Отправка сообщения с извещением о том что пользователь бот await bot.send_message(chat_id, "Пошутил - проиграл") # Удаление пользователя из списка новых пользователей await users_module.remove_user_by_user_id(message.from_user.id) else: # <NAME> await supergroup_module.give_a_mute(message.from_user.id, 70) # Выкидываем пользователя из чата и насмехаемся над ним async def kik_user(message): # Проверка на пользователя if users_module.is_user_exist(message.from_user.id): # Получаем пользователя по id user = users_module.get_user_by_user_id(message.from_user.id) # Удаляем сообщение с капчей и насмехаемся над пользователем await supergroup_module.delete_message(user.get('message_id')) await bot.send_message(chat_id, "Подумай над своим ответом") # Выкидываем пользователя из чата await supergroup_module.give_a_kik(user.get('user_id')) # Удаление пользователя из списка новых пользователей await users_module.remove_user_by_user_id(message.from_user.id) else: # <NAME> await supergroup_module.give_a_mute(message.from_user.id, 70)
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ms2ldaviz/ms1analysis/models.py
RP0001/ms2ldaviz
0
114989
from __future__ import unicode_literals from django.db import models from basicviz.models import Document, Experiment, Mass2Motif from basicviz.constants import EXPERIMENT_STATUS_CODE from decomposition.models import Decomposition, GlobalMotif ## tables for LDA experiment class Sample(models.Model): name = models.CharField(max_length=128) experiment = models.ForeignKey(Experiment) class DocSampleIntensity(models.Model): sample = models.ForeignKey(Sample) document = models.ForeignKey(Document) intensity = models.FloatField(null=True) class Analysis(models.Model): name = models.CharField(max_length=128) description = models.CharField(max_length=1024) experiment = models.ForeignKey(Experiment) group1 = models.CharField(max_length=2048) group2 = models.CharField(max_length=2048) ready_code, _ = EXPERIMENT_STATUS_CODE[1] status = models.CharField(max_length=128, choices=EXPERIMENT_STATUS_CODE, null=True, default=ready_code) use_logarithm = models.CharField(max_length=128, choices=[('N', 'No'), ('Y', 'Yes'),], null=True, default='N') class AnalysisResult(models.Model): analysis = models.ForeignKey(Analysis) document = models.ForeignKey(Document) pValue = models.FloatField(null=True) foldChange = models.FloatField(null=True) class AnalysisResultPlage(models.Model): analysis = models.ForeignKey(Analysis) mass2motif = models.ForeignKey(Mass2Motif) plage_t_value = models.FloatField(null=True) plage_p_value = models.FloatField(null=True) ## tables for Decomposition ## currently just similar to LDA experiment settings, and change 'Experiment' to 'Decomposition' ## may avoid this duplication in the future ## decomposition shares the same idea of loading peaklist file and parsing it to databse ## so use the same Sample and DocSampleIntensity ## Analysis for Decomposition, change experiment to decomposition ## if we want to get the corresponding experiment id, we can use decomposition id to search: ## Experiment.objects.get(id = decomposition.experiment_id) class DecompositionAnalysis(models.Model): name = models.CharField(max_length=128) description = models.CharField(max_length=1024) decomposition = models.ForeignKey(Decomposition) ## diff group1 = models.CharField(max_length=2048) group2 = models.CharField(max_length=2048) ready_code, _ = EXPERIMENT_STATUS_CODE[1] status = models.CharField(max_length=128, choices=EXPERIMENT_STATUS_CODE, null=True, default=ready_code) use_logarithm = models.CharField(max_length=128, choices=[('N', 'No'), ('Y', 'Yes'),], null=True, default='N') class DecompositionAnalysisResult(models.Model): analysis = models.ForeignKey(DecompositionAnalysis) ## diff document = models.ForeignKey(Document) pValue = models.FloatField(null=True) foldChange = models.FloatField(null=True) class DecompositionAnalysisResultPlage(models.Model): analysis = models.ForeignKey(DecompositionAnalysis) ## diff globalmotif = models.ForeignKey(GlobalMotif) ## diff plage_t_value = models.FloatField(null=True) plage_p_value = models.FloatField(null=True)
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utils/alertcheck.py
sethusaim/Shredder-Machine-System
0
113994
<filename>utils/alertcheck.py import os import cv2 from playsound import playsound def drawboxtosafeline(image_np, p1, p2, Line_Position2, Orientation): if Orientation == "bt": bounding_mid = (int((p1[0] + p2[0]) / 2), int(p1[1])) if bounding_mid: cv2.line( img=image_np, pt1=bounding_mid, pt2=(bounding_mid[0], Line_Position2), color=(255, 0, 0), thickness=1, lineType=8, shift=0, ) distance_from_line = bounding_mid[1] - Line_Position2 elif Orientation == "tb": bounding_mid = (int((p1[0] + p2[0]) / 2), int(p2[1])) if bounding_mid: cv2.line( img=image_np, pt1=bounding_mid, pt2=(bounding_mid[0], Line_Position2), color=(255, 0, 0), thickness=1, lineType=8, shift=0, ) distance_from_line = Line_Position2 - bounding_mid[1] elif Orientation == "lr": bounding_mid = (int(p2[0]), int((p1[1] + p2[1]) / 2)) if bounding_mid: cv2.line( img=image_np, pt1=bounding_mid, pt2=(Line_Position2, bounding_mid[1]), color=(255, 0, 0), thickness=1, lineType=8, shift=0, ) distance_from_line = Line_Position2 - bounding_mid[0] elif Orientation == "rl": bounding_mid = (int(p1[0]), int((p1[1] + p2[1]) / 2)) if bounding_mid: cv2.line( img=image_np, pt1=bounding_mid, pt2=(Line_Position2, bounding_mid[1]), color=(255, 0, 0), thickness=1, lineType=8, shift=0, ) distance_from_line = bounding_mid[1] - Line_Position2 if distance_from_line <= 0: posii = int(image_np.shape[1] / 2) cv2.putText( image_np, "ALERT", (posii, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 0, 0), 2, ) sound = os.path.join("utils", "alert.wav") playsound(sound=sound) cv2.rectangle( image_np, (posii - 20, 20), (posii + 85, 60), (255, 0, 0), thickness=3, lineType=8, shift=0, ) return 1 else: return 0
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read_iceye_h5.py
eciraci/iceye_gamma_proc
0
23344
<gh_stars>0 #!/usr/bin/env python u""" read_iceye_h5.py Written by <NAME>' (03/2022) Read ICEYE Single Look Complex and Parameter file using GAMMA's Python integration with the py_gamma module. usage: read_iceye_h5.py [-h] [--directory DIRECTORY] TEST: Read ICEye Single Look Complex and Parameter. optional arguments: -h, --help show this help message and exit --directory DIRECTORY, -D DIRECTORY Project data directory. --slc SLC, -C SLC Process and single SLC. PYTHON DEPENDENCIES: argparse: Parser for command-line options, arguments and sub-commands https://docs.python.org/3/library/argparse.html datetime: Basic date and time types https://docs.python.org/3/library/datetime.html#module-datetime tqdm: Progress Bar in Python. https://tqdm.github.io/ py_gamma: GAMMA's Python integration with the py_gamma module UPDATE HISTORY: """ # - Python Dependencies from __future__ import print_function import os import argparse import datetime from tqdm import tqdm # - GAMMA's Python integration with the py_gamma module import py_gamma as pg # - Utility Function from utils.make_dir import make_dir def main(): parser = argparse.ArgumentParser( description="""TEST: Read ICEye Single Look Complex and Parameter.""" ) # - Absolute Path to directory containing input data. default_dir = os.path.join(os.path.expanduser('~'), 'Desktop', 'iceye_gamma_test') parser.add_argument('--directory', '-D', type=lambda p: os.path.abspath(os.path.expanduser(p)), default=default_dir, help='Project data directory.') parser.add_argument('--slc', '-C', type=str, default=None, help='Process and single SLC.') args = parser.parse_args() # - Path to Test directory data_dir = os.path.join(args.directory, 'input') # - create output directory out_dir = make_dir(args.directory, 'output') out_dir = make_dir(out_dir, 'slc+par') # - ICEye Suffix ieye_suff = 'ICEYE_X7_SLC_SM_' if args.slc is not None: # - Process a single SLC b_input = os.path.join(data_dir, args.slc) # - Read input Binary File Name b_input_name = b_input.split('/')[-1].replace(ieye_suff, '') slc_name = os.path.join(out_dir, str(b_input_name.replace('.h5', '.slc'))) par_name = os.path.join(out_dir, str(b_input_name.replace('.h5', '.par'))) # - Extract SLC and Parameter File # - Set dtype equal to zero to save the SLC in FCOMPLEX format. pg.par_ICEYE_SLC(b_input, par_name, slc_name, 0) else: # - Process hte entire input directory content # - List Directory Content data_dir_list = [os.path.join(data_dir, x) for x in os.listdir(data_dir) if x.endswith('.h5')] for b_input in tqdm(data_dir_list, total=len(data_dir_list), ncols=60): # - Read input Binary File Name b_input_name = b_input.split('/')[-1].replace(ieye_suff, '') slc_name = os.path.join(out_dir, b_input_name.replace('.h5', '.slc')) par_name = os.path.join(out_dir, b_input_name.replace('.h5', '.par')) # - Extract SLC and Parameter File # - Set dtype equal to zero to save the SLC in FCOMPLEX format. pg.par_ICEYE_SLC(b_input, par_name, slc_name, 0) # - run main program if __name__ == '__main__': start_time = datetime.datetime.now() main() end_time = datetime.datetime.now() print(f"# - Computation Time: {end_time - start_time}")
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genEM3/training/classifier.py
generem/generem
0
1608123
import os import datetime from typing import List, Union, Dict import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap import torch from torch import nn from torch.utils.tensorboard import SummaryWriter from torch import device as torchDevice from genEM3.util import gpu from genEM3.training.metrics import Metrics class Trainer: def __init__(self, run_name: str, run_root: str, model: nn.Module, optimizer: torch.optim.Optimizer, criterion: nn.MSELoss, data_loaders: Union[List, Dict], num_epoch: int = 100, log_int: int = 10, device: str = 'cpu', save: bool = False, save_int: int = 1, resume: bool = False, gpu_id: int = None, balance_factor: List = None): """balance_factor: is a list which contains the balance factor for each training epoch""" self.run_name = run_name self.run_root = run_root self.model = model self.optimizer = optimizer self.criterion = criterion self.num_epoch = num_epoch self.log_int = log_int self.save = save self.save_int = save_int self.resume = resume self.balance_factor = balance_factor if device == 'cuda': gpu.get_gpu(gpu_id) device = torch.device(torch.cuda.current_device()) self.device = torchDevice(device) self.log_root = os.path.join(run_root, '.log', run_name) self.data_loaders = data_loaders # can only get the lengths when a single set of data loaders are used if isinstance(data_loaders, dict): self.data_lengths = dict(zip(self.data_loaders.keys(), [len(loader) for loader in self.data_loaders])) else: self.data_lengths = {} if save: if not os.path.exists(self.log_root): os.makedirs(self.log_root) def train(self): if self.resume: print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ') Resuming training ... ') checkpoint = torch.load(os.path.join(self.log_root, 'torch_model')) self.model.load_state_dict(checkpoint['model_state_dict']) self.optimizer.load_state_dict(checkpoint['optimizer_state_dict']) else: print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ') Starting training ... ') writer = SummaryWriter(self.log_root) self.model = self.model.to(self.device) epoch = int(self.model.epoch) + 1 it = int(self.model.iteration) sample_inds = dict() for epoch in range(epoch, epoch + self.num_epoch): if isinstance(self.data_loaders, list): # each element of the list is a data loader for an epoch loader_change_interval = self.num_epoch / len(self.data_loaders) division_index, _ = divmod(epoch, loader_change_interval) # Make sure that the index does not exceed the length of the data_loader list index = round(min(division_index, len(self.data_loaders)-1)) cur_data_loaders = self.data_loaders[index] else: # same dataloaders for all epochs cur_data_loaders = self.data_loaders # Dictionary (of dictionaries) to collect four metrics from different phases for tensorboard epoch_metric_names = ['epoch_loss', 'epoch_accuracy', 'precision/PPV', 'recall/TPR'] epoch_metric_dict = {metric_name: dict.fromkeys(cur_data_loaders.keys()) for metric_name in epoch_metric_names} epoch_root = 'epoch_{:02d}'.format(epoch) if not os.path.exists(os.path.join(self.log_root, epoch_root)): os.makedirs(os.path.join(self.log_root, epoch_root)) for phase in cur_data_loaders.keys(): if phase == 'train': self.model.train(True) else: self.model.train(False) epoch_loss = 0 running_loss = 0.0 target_sum = 0 predicted_sum = 0 correct_sum = 0 batch_idx_start = 0 num_items = len(cur_data_loaders[phase].batch_sampler.sampler) inputs_phase = -np.ones((num_items, 1, 140, 140)).astype(float) outputs_phase = -np.ones((num_items, self.model.classifier.num_output)).astype(float) predictions_phase = -np.ones(num_items).astype(int) targets_phase = -np.ones(num_items).astype(int) correct_phase = -np.ones(num_items).astype(int) sample_ind_phase = [] for i, data in enumerate(cur_data_loaders[phase]): it += 1 # copy input and targets to the device object inputs = data['input'].to(self.device) targets = data['target'].to(self.device) sample_ind_batch = data['sample_idx'] sample_ind_phase.extend(sample_ind_batch) # zero the parameter gradients self.optimizer.zero_grad() # forward + backward + optimize outputs = self.model(inputs).squeeze() loss = self.criterion(outputs, targets) if phase == 'train': loss.backward() self.optimizer.step() inputs, outputs, targets = Trainer.copy2cpu(inputs, outputs, targets) predicted_classes = np.argmax(np.exp(outputs.detach().numpy()), axis=1) predicted_sum += np.sum(predicted_classes) target_classes = targets.detach().numpy() target_sum += np.sum(target_classes) correct_classes = predicted_classes == target_classes correct_sum += np.sum(correct_classes) if i > 0: batch_idx_start = batch_idx_end batch_idx_end = batch_idx_start + len(targets) inputs_phase[batch_idx_start:batch_idx_end, :, :, :] = inputs.detach().numpy() outputs_phase[batch_idx_start:batch_idx_end, :] = outputs.detach().numpy() predictions_phase[batch_idx_start:batch_idx_end] = predicted_classes targets_phase[batch_idx_start:batch_idx_end] = target_classes correct_phase[batch_idx_start:batch_idx_end] = correct_classes running_loss += loss.item() epoch_loss += loss.item() # Report fraction of clean data in mini batch clean_num = float((targets == 0).sum()) debris_num = float((targets == 1).sum()) fraction_clean = clean_num / (debris_num + clean_num) writer.add_scalars('Fraction_clean_samples', {phase: fraction_clean}, it) if i % self.log_int == 0: running_loss_log = float(running_loss) / batch_idx_end running_accuracy_log = float(correct_sum) / batch_idx_end print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ')' + ' Phase: ' + phase + ', epoch: {}, batch: {}, running loss: {:0.4f}, running accuracy: {:0.3f} '. format(epoch, i, running_loss_log, running_accuracy_log)) writer.add_scalars('running_loss', {phase: running_loss_log}, it) writer.add_scalars('running_accuracy', {phase: running_accuracy_log}, it) epoch_loss_log = float(epoch_loss) / num_items epoch_accuracy_log = float(correct_sum) / num_items print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ')' + ' Phase: ' + phase + ', epoch: {}: epoch loss: {:0.4f}, epoch accuracy: {:0.3f} '. format(epoch, epoch_loss_log, epoch_accuracy_log)) metrics = Metrics( targets=targets_phase, outputs=outputs_phase, output_prob_fn=lambda x: np.exp(x[:, 1]), sample_ind=sample_ind_phase) metrics.confusion_table( path_out=os.path.join(self.log_root, epoch_root, 'confusion_table_' + phase + '.csv')) metrics.prediction_table( path_out=os.path.join(self.log_root, epoch_root, 'prediction_table_' + phase + '.csv')) # Set the current values of the epoch error metrics cur_metrics = [epoch_loss_log, epoch_accuracy_log, metrics.metrics['PPV'], metrics.metrics['TPR']] for i, metric_name in enumerate(epoch_metric_names): epoch_metric_dict[metric_name][phase] = cur_metrics[i] fig = Trainer.show_imgs(inputs=inputs_phase, outputs=outputs_phase, predictions=predictions_phase, targets=targets_phase, sample_ind=sample_ind_phase) figname = 'image_examples_' fig.savefig(os.path.join(self.log_root, epoch_root, figname + '_' + phase + '.png')) writer.add_figure(figname + phase, fig, epoch) fig = Trainer.show_classification_matrix(targets=targets_phase, predictions=predictions_phase, metrics=metrics.metrics) figname = 'targets_outputs_correct_' fig.savefig(os.path.join(self.log_root, epoch_root, figname + '_' + phase + '.png')) fig.savefig(os.path.join(self.log_root, epoch_root, figname + '_' + phase + '.eps')) writer.add_figure(figname + phase, fig, epoch) writer.add_pr_curve( 'pr_curve_'+phase, labels=targets_phase, predictions=np.exp(outputs_phase[:, 1]), global_step=epoch, num_thresholds=50) if self.save & (phase == 'train') & (epoch % self.save_int == 0): print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ') Writing model graph ... ') # writer.add_graph(self.model, inputs) print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ') Saving model state... ') self.model.epoch = torch.nn.Parameter(torch.tensor(epoch), requires_grad=False) self.model.iteration = torch.nn.Parameter(torch.tensor(it), requires_grad=False) torch.save({ 'model_state_dict': self.model.state_dict(), }, os.path.join(self.log_root, epoch_root, 'model_state_dict')) torch.save({ 'optimizer_state_dict': self.optimizer.state_dict() }, os.path.join(self.log_root, 'optimizer_state_dict')) # write the epoch related metrics to the tensorboard for metric_name in epoch_metric_names: writer.add_scalars(metric_name, epoch_metric_dict[metric_name], epoch) print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ') Finished training ... ') writer.close() print('(' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ') Closed writer ... ') @staticmethod def copy2cpu(inputs, outputs, targets): if inputs.is_cuda: inputs = inputs.cpu() if outputs.is_cuda: outputs = outputs.cpu() if targets.is_cuda: targets = targets.cpu() return inputs, outputs, targets @staticmethod def n1hw_to_n3hw(data): return data.cpu().repeat(1, 3, 1, 1) @staticmethod def show_img(inputs, outputs, idx): inputs, outputs = Trainer.copy2cpu(inputs, outputs) fig, axs = plt.subplots(1, 2, figsize=(4, 3)) axs[0].imshow(inputs[idx].data.numpy().squeeze(), cmap='gray') axs[1].imshow(outputs[idx].data.numpy().squeeze(), cmap='gray') return fig @staticmethod def show_imgs(inputs, outputs, predictions, targets, sample_ind, class_idx=1, plot_ind=None): if plot_ind is None: plot_ind = list(range(5)) fig, axs = plt.subplots(2, len(plot_ind), figsize=(3 * len(plot_ind), 6)) for i, sample_idx in enumerate(np.asarray(sample_ind)[plot_ind]): input = inputs[i, 0, :, :].squeeze() axs[0, i].imshow(input, cmap='gray') axs[0, i].axis('off') output = np.tile(np.exp((outputs[i][class_idx])), (int(input.shape[0]/3), int(input.shape[1]))) prediction = np.tile(int(predictions[i]), (int(input.shape[0]/3), int(input.shape[1]))) target = np.tile(int(targets[i]), (int(input.shape[0]/3), int(input.shape[1]))) fused = np.concatenate((output, prediction, target), axis=0) axs[1, i].imshow(fused, cmap='gray', vmin=0, vmax=1) axs[1, i].text(0.5, 0.875, 'sample_idx: {}'.format(sample_idx), transform=axs[1, i].transAxes, ha='center', va='center', c=[0.8, 0.8, 0.2]) axs[1, i].text(0.5, 0.75, 'output (class {:d}): {:01.2f}'.format(class_idx, np.exp((outputs[i][class_idx]))), transform=axs[1, i].transAxes, ha='center', va='center', c=[0.8, 0.8, 0.2]) axs[1, i].text(0.5, 0.5, 'prediction class: {:d}'.format(int(predictions[i])), transform=axs[1, i].transAxes, ha='center', va='center', c=[0.5, 0.5, 0.5]) axs[1, i].text(0.5, 0.2, 'target class: {:d}'.format(int(targets[i])), transform=axs[1, i].transAxes, ha='center', va='center', c=[0.5, 0.5, 0.5]) axs[1, i].axis('off') axs[0, i].set_ylabel('sample_idx: {}'.format(sample_idx)) plt.tight_layout() return fig @staticmethod def show_classification_matrix(targets, predictions, metrics): targets_pr = targets.copy().astype(int) predictions_pr = predictions.copy().astype(int) correct_pr = ((targets_pr == 0) == (predictions_pr == 0)) | ((targets_pr == 1) == (predictions_pr == 1)) + 2 code_pr = targets_pr.copy() code_pr[metrics['TN_idx']] = 4 code_pr[metrics['TP_idx']] = 5 code_pr[predictions_pr > targets_pr] = 6 code_pr[predictions_pr < targets_pr] = 7 mat = np.stack((targets_pr, predictions_pr, correct_pr, code_pr), axis=0) colors = [[0.0, 0.0, 0.0, 1], [1.0, 1.0, 1.0, 1], [1.0, 0.0, 0.0, 1], [0.0, 1.0, 0.0, 1], [0.4, 0.1, 0.9, 1], [0.3, 0.5, 0.9, 1], [0.9, 0.4, 0.1, 1], [0.9, 0.1, 0.5, 1]] cmap = ListedColormap(colors=colors) fig_width_mult = min(max([0.5, len(targets)/2000]), 3) fig, axs = plt.subplots(figsize=(12*fig_width_mult, 6)) axs.matshow(mat, cmap=cmap, vmin=0, vmax=7) axs.set_yticks([0, 1, 2, 3]) axs.set_yticklabels(['targets', 'outputs', 'accuracy', 'confusion']) axs.set_aspect(10) bbox = axs.get_position().bounds axs2 = plt.axes((bbox[0], 0.1, bbox[2], 0.2), sharex=axs) axs2.text(0.010, 1.00, 'target|output', c=(0.2, 0.2, 0.2), weight='bold', transform=axs2.transAxes) axs2.text(0.017, 0.75, 'artifact: {}'.format(metrics['P']), c=(0.2, 0.2, 0.2), backgroundcolor=colors[1], transform=axs2.transAxes) axs2.text(0.017, 0.50, 'no artifact: {}'.format(metrics['N']), c=(0.8, 0.8, 0.8), backgroundcolor=colors[0], transform=axs2.transAxes) axs2.text(0.27, 1.00, 'accuracy', c=(0.2, 0.2, 0.2), weight='bold', transform=axs2.transAxes) axs2.text(0.20, 0.75, 'frac correct: {:03d}/{:03d}={:01.2f}'.format(metrics['TP'] + metrics['TN'], len(targets), (metrics['TP'] + metrics['TN'])/len(targets)), c=(0.2, 0.2, 0.2), backgroundcolor=colors[3], transform=axs2.transAxes) axs2.text(0.20, 0.50, 'frac incorrect: {:03d}/{:03d}={:01.2f}'.format(metrics['FP'] + metrics['FN'], len(targets), (metrics['FP'] + metrics['FN'])/len(targets)), c=(0.2, 0.2, 0.2), backgroundcolor=colors[2], transform=axs2.transAxes) axs2.text(0.60, 1.00, 'confusion', c=(0.2, 0.2, 0.2), weight='bold', transform=axs2.transAxes) axs2.text(0.50, 0.75, 'TP: {:01.0f}'.format(metrics['TP']).ljust(12), c=(0.8, 0.8, 0.8), backgroundcolor=colors[5], transform=axs2.transAxes) axs2.text(0.60, 0.75, 'FP: {:01.0f}'.format(metrics['FP']).ljust(12), c=(0.2, 0.2, 0.2), backgroundcolor=colors[6], transform=axs2.transAxes) axs2.text(0.50, 0.50, 'FN: {:01.0f}'.format(metrics['FN']).ljust(12), c=(0.2, 0.2, 0.2), backgroundcolor=colors[7], transform=axs2.transAxes) axs2.text(0.60, 0.50, 'TN: {:01.0f}'.format(metrics['TN']).ljust(12), c=(0.8, 0.8, 0.8), backgroundcolor=colors[4], transform=axs2.transAxes) axs2.text(0.70, 0.75, 'Precision: {:01.2f}'.format(metrics['PPV']).ljust(20), c=(0.2, 0.2, 0.2), backgroundcolor=(0.7, 0.7, 0.7), transform=axs2.transAxes) axs2.text(0.70, 0.50, 'Recall: {:01.2f}'.format(metrics['TPR']).ljust(20), c=(0.2, 0.2, 0.2), backgroundcolor=(0.7, 0.7, 0.7), transform=axs2.transAxes) axs2.axis('off') return fig
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scripts/libopen3d.py
mepix/GPUaccelOpen3D
0
43275
<reponame>mepix/GPUaccelOpen3D<gh_stars>0 #!/usr/bin/env python3 import numpy as np import open3d as o3d import copy import matplotlib.pyplot as plt class WrapperOpen3d(object): """This lightweight wrapper on Open3D converts a .ply point cloud into a NumPy Array""" def __init__(self, path_to_ply): self.path_to_ply = path_to_ply self.point_cloud_loaded = False return None def plyOpen(self,distance_thresh=-1,downsample_size=-1,verbose=True): """Opens the point cloud from a .ply file specificied at class initialization""" if verbose: print("Opening",self.path_to_ply) self.pcd = o3d.io.read_point_cloud(self.path_to_ply) # Removes Points Outside Distance Threshold if distance_thresh > 0: self.pcd = self.distanceThresh(self.pcd,distance_thresh) # Down Sample if downsample_size > 0: self.pcd = self.pcd.voxel_down_sample(voxel_size=downsample_size) # Display Information About the Point Cloud if verbose: print(self.pcd) # Set the loaded flag to True self.point_cloud_loaded = True return None def loadPointCloud(self,pcd): """Loads a Point Cloud into the class""" self.pcd = pcd self.point_cloud_loaded = True def visPointCloud(self): """Visualizes the loaded point cloud""" if self.point_cloud_loaded: o3d.visualization.draw_geometries([self.pcd]) else: print("No Point Cloud Loaded, Cannot Visualize") return None def visPointCloudClusters(self,pcd,labels): """ Colorizes the clusters in the procided point cloud. The internal point cloud self.pcd is NOT changed by this routine """ max_label = labels.max() print(f"point cloud has {max_label + 1} clusters") colors = plt.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1)) colors[labels < 0] = 0 pcd.colors = o3d.utility.Vector3dVector(colors[:, :3]) o3d.visualization.draw_geometries([pcd]) def distanceThresh(self,pcd,thresh=0.5): """ Removes all points that are outside the thresh from the origin """ # https://stackoverflow.com/questions/65731659/open3dpython-how-to-remove-points-from-ply # Get the Numpy Points points = np.asarray(pcd.points) # Sphere center and radius center = np.array([0, 0, 0]) # Calculate distances to center, set new points distances = np.linalg.norm(points - center, axis=1) # pcd1.points = open3d.utility.Vector3dVector(points[distances <= radius]) idx = np.where(distances < thresh)[0] pcd_thresh = pcd.select_by_index(idx) return pcd_thresh def getNumPyPts(self): """Returns the opened point cloud as a NumPy Array""" if self.point_cloud_loaded: return np.asarray(self.pcd.points) else: print("No Point Cloud Loaded, Cannot Get Points") return None def getNumPyNorms(self): """Estimates the normals for reach point, returns as NumPy Array if True""" # self.pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)) if self.point_cloud_loaded: return np.asarray(self.pcd.normals) else: print("No Point Cloud Loaded, Cannot Get Normals") return None def getNumPyColors(self): """Returns the opened point colors as a NumPy Array""" if self.point_cloud_loaded: return np.asarray(self.pcd.colors) else: print("No Point Cloud Loaded, Cannot Get Colors") return None def getClusterLabels(self,visualize=False): """Uses DBSCAN to cluster the point cloud and returns a NumPy label array""" if self.point_cloud_loaded: # Run the Clustering Algorithm with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm: labels = np.array(self.pcd.cluster_dbscan(eps=0.02, min_points=10, print_progress=True)) # Display the Colored Point Cloud if visualize: self.visPointCloudClusters(copy.deepcopy(self.pcd),labels) return labels else: print("No Point Cloud Loaded, Cannot Cluster Points") return None def fitPlane(self): """ Uses the built in RANSAC to fit a plane to the loaded point cloud """ plane_model, inliers = self.pcd.segment_plane(distance_threshold=0.01, ransac_n=3, num_iterations=1000) [a, b, c, d] = plane_model print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0") inlier_cloud = self.pcd.select_by_index(inliers) inlier_cloud.paint_uniform_color([1.0, 0, 0]) outlier_cloud = self.pcd.select_by_index(inliers, invert=True) o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud]) def drawBox(self): print("Let\'s draw a cubic using o3d.geometry.LineSet") points = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]] lines = [[0, 1], [0, 2], [1, 3], [2, 3], [4, 5], [4, 6], [5, 7], [6, 7], [0, 4], [1, 5], [2, 6], [3, 7]] colors = [[1, 0, 0] for i in range(len(lines))] line_set = o3d.geometry.LineSet() line_set.points = o3d.utility.Vector3dVector(points) line_set.lines = o3d.utility.Vector2iVector(lines) line_set.colors = o3d.utility.Vector3dVector(colors) o3d.visualization.draw_geometries([line_set]) def drawOrigin(self): points = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]] lines = [[0, 1], [0, 2], [0, 3]] colors = [[1, 0, 0],[0,1,0],[0,0,1]] line_set = o3d.geometry.LineSet() line_set.points = o3d.utility.Vector3dVector(points) line_set.lines = o3d.utility.Vector2iVector(lines) line_set.colors = o3d.utility.Vector3dVector(colors) o3d.visualization.draw_geometries([line_set,self.pcd]) def drawLine(self,pt1=[0,0,0],pt2=[1,1,1],color_rgb=[1,0,0],vis=False): """ Creates a geometry object representing a line between pt1 and pt2. Set the vis flat to [T] to draw in a 3D viewer """ line_set = o3d.geometry.LineSet() line_set.points = o3d.utility.Vector3dVector([pt1, pt2]) line_set.lines = o3d.utility.Vector2iVector([[0, 1]]) line_set.colors = o3d.utility.Vector3dVector([color_rgb]) if vis: o3d.visualization.draw_geometries([line_set,self.pcd]) return line_set def getNumpyAll(self,verbose=False): """Returns an array of [X,Y,Z,R,G,B,NormX,NormY,NormZ]""" # Get a Points npd = self.getNumPyPts() if verbose: print(npd.shape) print(npd) # Get Colors npc = self.getNumPyColors() if verbose: print(npc.shape) print(npc) # Get Normals npn = self.getNumPyNorms() if verbose: print(npn.shape) print(npn) # Concatenate Array npa = np.hstack((npd,npc,npn)) if verbose: print(npa.shape) print(npa) # Output combined NumPy array return npa def convertNumPyToPointCloud(self,arr): """Converts a Nx9 NumPy array [X,Y,Z,R,G,B,NormX,NormY,NormZ] to Open3d""" pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(arr[:,[0,1,2]]) pcd.colors = o3d.utility.Vector3dVector(arr[:,[3,4,5]]) pcd.normals = o3d.utility.Vector3dVector(arr[:,[6,7,8]]) return pcd if __name__ == '__main__': try: print("Testing Open3D") # Intialize the Class myOpen3d = WrapperOpen3d("../data/LeafPointCloud.ply") # Open the Point Cloud myOpen3d.plyOpen(distance_thresh=0.5,downsample=False) # Visualize the Point Cloud myOpen3d.visPointCloud() # Get the NumPy Data (Pts,Colors,Normals) myNumPyArray = myOpen3d.getNumpyAll(verbose=True) # Convert the NumPy Data back to Point Cloud myOpen3d.pcd = myOpen3d.convertNumPyToPointCloud(myNumPyArray) myOpen3d.visPointCloud() # TODO: Clean this up, this is mostly scratch work print(myOpen3d.getClusterLabels(True)) myOpen3d.drawOrigin() myOpen3d.drawLine(vis=True) except: print("ERROR, EXCEPTION THROWN")
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tests/rules/test_result.py
victor-torres/arche
0
137501
from arche.rules.result import Level, Message, Result from conftest import create_named_df, create_result import pandas as pd import pytest @pytest.mark.parametrize( "source, target", [(("summary", "details", {"err": ["1"]}), ("summary", "details", {"err": ["1"]}))], ) def test_message_eq(source, target): assert Message(*source) == Message(*target) @pytest.mark.parametrize( "source, target", [ ( ("summary", "details", {"err": ["0"]}), ("summary", "details", {"err": ["1"]}), ), (("summary", "details"), ("summary", "other")), ], ) def test_message_not_eq(source, target): assert Message(*source) != Message(*target) @pytest.mark.parametrize( "source, target", [ ( pd.Series([0, 1], index=["f", "l"], name="n"), pd.Series([0, 1], index=["f", "l"], name="n"), ), (pd.DataFrame([0, 1]), pd.DataFrame([0, 1])), ], ) def test_tensors_equal(source, target): assert Result.tensors_equal(source, target) @pytest.mark.parametrize( "source, target", [ ( pd.Series([0, 1], index=["f", "l"], name="s"), pd.Series([0, 1], index=["f", "l"], name="n"), ), (pd.DataFrame([0, 1]), pd.DataFrame([0, 1], index=["m", "s"])), ], ) def test_tensors_not_equal(source, target): assert not Result.tensors_equal(source, target) @pytest.mark.parametrize( "message, stats, expected_details", [ ( {Level.INFO: [("summary", "very detailed message")]}, [pd.Series([1, 2], name="Fields coverage")], "\nrule name here:\n\tsummary\nvery detailed message\n", ), ( {Level.INFO: [("summary",)]}, [ create_named_df( {"s": [0.25]}, index=["us"], name="Coverage for boolean fields" ) ], "\nrule name here:\n\tsummary\n", ), ], ) def test_show(mocker, capsys, message, stats, expected_details): mock_pio_show = mocker.patch("plotly.io.show", autospec=True) r = create_result("rule name here", message, stats=stats) r.show() mock_pio_show.assert_called_once_with(r.figures[0]) assert capsys.readouterr().out == expected_details @pytest.mark.parametrize( "left_params, right_params", [ ( ( "s", {Level.INFO: ["sum", "det", {"err1": [0, 1]}]}, [pd.Series([0], name="s"), pd.DataFrame({"s": [0]})], 2, ["err1"], 1, ), ( "s", {Level.INFO: ["sum", "det", {"err1": [0, 1]}]}, [pd.Series([0], name="s"), pd.DataFrame({"s": [0]})], 2, ["err1"], 1, ), ), (("s",), ("s",)), ], ) def test_result_equal(left_params, right_params): assert Result(*left_params) == Result(*right_params) @pytest.mark.parametrize( "left_params, right_params", [ ( ( "s", {Level.INFO: ["sum", "det", {"err1": [0, 1]}]}, [pd.Series([0], name="A name"), pd.DataFrame([0])], 2, ["err1"], 1, ), ( "s", {Level.INFO: ["sum", "det", {"err1": [0, 1]}]}, [pd.Series([0], name="A series name"), pd.DataFrame([0])], 2, ["err1"], 1, ), ), (("s",), ("t",)), ], ) def test_result_not_equal(left_params, right_params): assert Result(*left_params) != Result(*right_params)
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homeassistant/components/sensor/hddtemp.py
mdonoughe/home-assistant
2
4115
""" Support for getting the disk temperature of a host. For more details about this platform, please refer to the documentation at https://home-assistant.io/components/sensor.hddtemp/ """ import logging from datetime import timedelta from telnetlib import Telnet import voluptuous as vol import homeassistant.helpers.config_validation as cv from homeassistant.components.sensor import PLATFORM_SCHEMA from homeassistant.const import ( CONF_NAME, CONF_HOST, CONF_PORT, TEMP_CELSIUS, TEMP_FAHRENHEIT, CONF_DISKS) from homeassistant.helpers.entity import Entity _LOGGER = logging.getLogger(__name__) ATTR_DEVICE = 'device' ATTR_MODEL = 'model' DEFAULT_HOST = 'localhost' DEFAULT_PORT = 7634 DEFAULT_NAME = 'HD Temperature' DEFAULT_TIMEOUT = 5 SCAN_INTERVAL = timedelta(minutes=1) PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({ vol.Optional(CONF_DISKS, default=[]): vol.All(cv.ensure_list, [cv.string]), vol.Optional(CONF_HOST, default=DEFAULT_HOST): cv.string, vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port, vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, }) def setup_platform(hass, config, add_devices, discovery_info=None): """Set up the HDDTemp sensor.""" name = config.get(CONF_NAME) host = config.get(CONF_HOST) port = config.get(CONF_PORT) disks = config.get(CONF_DISKS) hddtemp = HddTempData(host, port) hddtemp.update() if hddtemp.data is None: return False if not disks: disks = [next(iter(hddtemp.data)).split('|')[0]] dev = [] for disk in disks: if disk in hddtemp.data: dev.append(HddTempSensor(name, disk, hddtemp)) add_devices(dev, True) class HddTempSensor(Entity): """Representation of a HDDTemp sensor.""" def __init__(self, name, disk, hddtemp): """Initialize a HDDTemp sensor.""" self.hddtemp = hddtemp self.disk = disk self._name = '{} {}'.format(name, disk) self._state = None self._details = None @property def name(self): """Return the name of the sensor.""" return self._name @property def state(self): """Return the state of the device.""" return self._state @property def unit_of_measurement(self): """Return the unit the value is expressed in.""" if self._details[3] == 'C': return TEMP_CELSIUS return TEMP_FAHRENHEIT @property def device_state_attributes(self): """Return the state attributes of the sensor.""" return { ATTR_DEVICE: self._details[0], ATTR_MODEL: self._details[1], } def update(self): """Get the latest data from HDDTemp daemon and updates the state.""" self.hddtemp.update() if self.hddtemp.data and self.disk in self.hddtemp.data: self._details = self.hddtemp.data[self.disk].split('|') self._state = self._details[2] else: self._state = None class HddTempData(object): """Get the latest data from HDDTemp and update the states.""" def __init__(self, host, port): """Initialize the data object.""" self.host = host self.port = port self.data = None def update(self): """Get the latest data from HDDTemp running as daemon.""" try: connection = Telnet( host=self.host, port=self.port, timeout=DEFAULT_TIMEOUT) data = connection.read_all().decode( 'ascii').lstrip('|').rstrip('|').split('||') self.data = {data[i].split('|')[0]: data[i] for i in range(0, len(data), 1)} except ConnectionRefusedError: _LOGGER.error( "HDDTemp is not available at %s:%s", self.host, self.port) self.data = None
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code/routines/test_all.py
oliverruoff/PiBot_v4
5
1609263
<gh_stars>1-10 from time import sleep def test_top_stepper_and_bottom_steppers(top_stepper, left_stepper, right_stepper): print('Turning left stepper 360°.') left_stepper.turn_stepper(360) print('Turning right stepper 360°.') right_stepper.turn_stepper(360) print('Switching stepper directions and reversing movement.') left_stepper.set_direction(False) right_stepper.set_direction(False) left_stepper.turn_stepper(360) right_stepper.turn_stepper(360) print('Checking continuous movement for 0.5s forward and back.') left_stepper.run_continuously() right_stepper.run_continuously() sleep(0.5) left_stepper.set_direction(True) right_stepper.set_direction(True) sleep(0.5) left_stepper.stop_continuous() right_stepper.stop_continuous() print('Checking top stepper 360° clockwise and ccw.') top_stepper.turn_stepper_angle(360) top_stepper.set_direction(False) top_stepper.turn_stepper_angle(360) print('Done')
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wordCount.py
I3ck/wordCount
0
160063
<filename>wordCount.py import sys import getopt import operator inputfile = "" outputfile = "" wordcounts = dict() if __name__ == "__main__": argv = sys.argv[1:] try: opts, args = getopt.getopt(argv,"hi:o:",["inputfile=","outputfile="]) except getopt.GetoptError: print 'wordCount.py -i <inputfile> -o <outputfile>' sys.exit(1) for opt, arg in opts: if opt == '-h': print 'wordCount.py -i <inputfile> -o <outputfile>' sys.exit() elif opt in ("-i", "--ifile"): inputfile = arg elif opt in ("-o", "--ofile"): outputfile = arg if inputfile == "" or outputfile == "": print "something went wrong, use wordCount.py -h" sys.exit(2) with open(inputfile) as f: for line in f: words = line.split() for word in words: containsSpecial = False for s in word: if not s.isalnum(): containsSpecial = True if not containsSpecial: if word in wordcounts: wordcounts[word] +=1 else: wordcounts[word] = 1 sortedcounts = sorted(wordcounts.items(), key=operator.itemgetter(1)) sortedcounts.reverse() with open(outputfile, 'w') as f: for item in sortedcounts: word = item[0] count = item[1] f.write(str(count).zfill(4) + " * " + word + "\n")
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awx/main/tests/old/ad_hoc.py
doziya/ansible
1
187933
<reponame>doziya/ansible # Copyright (c) 2015 Ansible, Inc. # All Rights Reserved. # Python import glob import os import subprocess import tempfile import mock import unittest2 as unittest # Django from django.conf import settings from django.core.urlresolvers import reverse # Django-CRUM from crum import impersonate # AWX from awx.main.utils import * # noqa from awx.main.models import * # noqa from awx.main.tests.base import BaseJobExecutionTest from awx.main.tests.data.ssh import ( TEST_SSH_KEY_DATA, TEST_SSH_KEY_DATA_LOCKED, TEST_SSH_KEY_DATA_UNLOCK, ) __all__ = ['RunAdHocCommandTest', 'AdHocCommandApiTest'] class BaseAdHocCommandTest(BaseJobExecutionTest): ''' Common initialization for testing ad hoc commands. ''' def setUp(self): with ignore_inventory_computed_fields(): super(BaseAdHocCommandTest, self).setUp() self.setup_instances() self.setup_users() self.organization = self.make_organizations(self.super_django_user, 1)[0] self.organization.admin_role.members.add(self.normal_django_user) self.inventory = self.organization.inventories.create(name='test-inventory', description='description for test-inventory') self.host = self.inventory.hosts.create(name='host.example.com') self.host2 = self.inventory.hosts.create(name='host2.example.com') self.group = self.inventory.groups.create(name='test-group') self.group2 = self.inventory.groups.create(name='test-group2') self.group.hosts.add(self.host) self.group2.hosts.add(self.host, self.host2) self.inventory2 = self.organization.inventories.create(name='test-inventory2') self.host3 = self.inventory2.hosts.create(name='host3.example.com') self.credential = None settings.INTERNAL_API_URL = self.live_server_url settings.CALLBACK_CONSUMER_PORT = '' def create_test_credential(self, **kwargs): self.credential = self.make_credential(**kwargs) return self.credential @unittest.skipIf(os.environ.get('SKIP_SLOW_TESTS', False), 'Skipping slow test') class RunAdHocCommandTest(BaseAdHocCommandTest): ''' Test cases for RunAdHocCommand celery task. ''' def create_test_ad_hoc_command(self, **kwargs): with impersonate(self.super_django_user): opts = { 'inventory': self.inventory, 'credential': self.credential, 'job_type': 'run', 'module_name': 'command', 'module_args': 'uptime', } opts.update(kwargs) self.ad_hoc_command = AdHocCommand.objects.create(**opts) return self.ad_hoc_command def check_ad_hoc_command_events(self, ad_hoc_command, runner_status='ok', hosts=None): ad_hoc_command_events = ad_hoc_command.ad_hoc_command_events.all() for ad_hoc_command_event in ad_hoc_command_events: unicode(ad_hoc_command_event) # For test coverage. should_be_failed = bool(runner_status not in ('ok', 'skipped')) should_be_changed = bool(runner_status in ('ok', 'failed') and ad_hoc_command.job_type == 'run') if hosts is not None: host_pks = set([x.pk for x in hosts]) else: host_pks = set(ad_hoc_command.inventory.hosts.values_list('pk', flat=True)) qs = ad_hoc_command_events.filter(event=('runner_on_%s' % runner_status)) self.assertEqual(qs.count(), len(host_pks)) for evt in qs: self.assertTrue(evt.host_id in host_pks) self.assertTrue(evt.host_name) self.assertEqual(evt.failed, should_be_failed) self.assertEqual(evt.changed, should_be_changed) def test_run_ad_hoc_command(self): ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.check_ad_hoc_command_events(ad_hoc_command, 'ok') def test_check_mode_ad_hoc_command(self): ad_hoc_command = self.create_test_ad_hoc_command(module_name='ping', module_args='', job_type='check') self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.check_ad_hoc_command_events(ad_hoc_command, 'ok') def test_run_ad_hoc_command_that_fails(self): ad_hoc_command = self.create_test_ad_hoc_command(module_args='false') self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'failed') self.check_ad_hoc_command_events(ad_hoc_command, 'failed') def test_check_mode_where_command_would_fail(self): ad_hoc_command = self.create_test_ad_hoc_command(job_type='check', module_args='false') self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.check_ad_hoc_command_events(ad_hoc_command, 'skipped') @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('canceled', 0)) def test_cancel_ad_hoc_command(self, ignore): ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.cancel_flag) self.assertFalse(ad_hoc_command.passwords_needed_to_start) ad_hoc_command.cancel_flag = True ad_hoc_command.save(update_fields=['cancel_flag']) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'canceled') self.assertTrue(ad_hoc_command.cancel_flag) # Calling cancel afterwards just returns the cancel flag. self.assertTrue(ad_hoc_command.cancel()) # Read attribute for test coverage. ad_hoc_command.celery_task ad_hoc_command.celery_task_id = '' ad_hoc_command.save(update_fields=['celery_task_id']) self.assertEqual(ad_hoc_command.celery_task, None) # Unable to start ad hoc command again. self.assertFalse(ad_hoc_command.signal_start()) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('successful', 0)) def test_ad_hoc_command_options(self, ignore): ad_hoc_command = self.create_test_ad_hoc_command(forks=2, verbosity=2) self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.assertTrue('"--forks=2"' in ad_hoc_command.job_args) self.assertTrue('"-vv"' in ad_hoc_command.job_args) # Test with basic become privilege escalation ad_hoc_command2 = self.create_test_ad_hoc_command(become_enabled=True) self.assertEqual(ad_hoc_command2.status, 'new') self.assertFalse(ad_hoc_command2.passwords_needed_to_start) self.assertTrue(ad_hoc_command2.signal_start()) ad_hoc_command2 = AdHocCommand.objects.get(pk=ad_hoc_command2.pk) self.check_job_result(ad_hoc_command2, ('successful', 'failed')) self.assertTrue('"--become"' in ad_hoc_command2.job_args) def test_limit_option(self): # Test limit by hostname. ad_hoc_command = self.create_test_ad_hoc_command(limit='host.example.com') self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.check_ad_hoc_command_events(ad_hoc_command, 'ok', hosts=[self.host]) self.assertTrue('"host.example.com"' in ad_hoc_command.job_args) # Test limit by group name. ad_hoc_command2 = self.create_test_ad_hoc_command(limit='test-group') self.assertEqual(ad_hoc_command2.status, 'new') self.assertFalse(ad_hoc_command2.passwords_needed_to_start) self.assertTrue(ad_hoc_command2.signal_start()) ad_hoc_command2 = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command2, 'successful') self.check_ad_hoc_command_events(ad_hoc_command2, 'ok', hosts=[self.host]) # Test limit by host not in inventory. ad_hoc_command3 = self.create_test_ad_hoc_command(limit='bad-host') self.assertEqual(ad_hoc_command3.status, 'new') self.assertFalse(ad_hoc_command3.passwords_needed_to_start) self.assertTrue(ad_hoc_command3.signal_start()) ad_hoc_command3 = AdHocCommand.objects.get(pk=ad_hoc_command3.pk) self.check_job_result(ad_hoc_command3, 'successful') self.check_ad_hoc_command_events(ad_hoc_command3, 'ok', hosts=[]) self.assertEqual(ad_hoc_command3.ad_hoc_command_events.count(), 0) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('successful', 0)) def test_ssh_username_and_password(self, ignore): self.create_test_credential(username='sshuser', password='<PASSWORD>') ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.assertIn('"-u"', ad_hoc_command.job_args) self.assertIn('"--ask-pass"', ad_hoc_command.job_args) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('successful', 0)) def test_ssh_ask_password(self, ignore): self.create_test_credential(password='<PASSWORD>') ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertTrue(ad_hoc_command.passwords_needed_to_start) self.assertTrue('ssh_password' in ad_hoc_command.passwords_needed_to_start) self.assertFalse(ad_hoc_command.signal_start()) self.assertTrue(ad_hoc_command.signal_start(ssh_password='<PASSWORD>')) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.assertIn('"--ask-pass"', ad_hoc_command.job_args) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('successful', 0)) def test_sudo_username_and_password(self, ignore): self.create_test_credential(become_method="sudo", become_username='sudouser', become_password='<PASSWORD>') ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, ('successful', 'failed')) self.assertIn('"--become-method"', ad_hoc_command.job_args) self.assertIn('"--become-user"', ad_hoc_command.job_args) self.assertIn('"--ask-become-pass"', ad_hoc_command.job_args) self.assertNotIn('"--become"', ad_hoc_command.job_args) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('successful', 0)) def test_sudo_ask_password(self, ignore): self.create_test_credential(become_password='<PASSWORD>') ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertTrue(ad_hoc_command.passwords_needed_to_start) self.assertTrue('become_password' in ad_hoc_command.passwords_needed_to_start) self.assertFalse(ad_hoc_command.signal_start()) self.assertTrue(ad_hoc_command.signal_start(become_password='<PASSWORD>')) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, ('successful', 'failed')) self.assertIn('"--ask-become-pass"', ad_hoc_command.job_args) self.assertNotIn('"--become-user"', ad_hoc_command.job_args) self.assertNotIn('"--become"', ad_hoc_command.job_args) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('successful', 0)) def test_unlocked_ssh_key(self, ignore): self.create_test_credential(ssh_key_data=TEST_SSH_KEY_DATA) ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.assertNotIn('"--private-key=', ad_hoc_command.job_args) self.assertIn('ssh-agent', ad_hoc_command.job_args) def test_locked_ssh_key_with_password(self): self.create_test_credential(ssh_key_data=TEST_SSH_KEY_DATA_LOCKED, ssh_key_unlock=TEST_SSH_KEY_DATA_UNLOCK) ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.assertIn('ssh-agent', ad_hoc_command.job_args) self.assertNotIn('Bad passphrase', ad_hoc_command.result_stdout) def test_locked_ssh_key_with_bad_password(self): self.create_test_credential(ssh_key_data=TEST_SSH_KEY_DATA_LOCKED, ssh_key_unlock='not the passphrase') ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'failed') self.assertIn('ssh-agent', ad_hoc_command.job_args) self.assertIn('Bad passphrase', ad_hoc_command.result_stdout) def test_locked_ssh_key_ask_password(self): self.create_test_credential(ssh_key_data=TEST_SSH_KEY_DATA_LOCKED, ssh_key_unlock='ASK') ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertTrue(ad_hoc_command.passwords_needed_to_start) self.assertTrue('ssh_key_unlock' in ad_hoc_command.passwords_needed_to_start) self.assertFalse(ad_hoc_command.signal_start()) self.assertTrue(ad_hoc_command.signal_start(ssh_key_unlock='not it')) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'failed') self.assertTrue('ssh-agent' in ad_hoc_command.job_args) self.assertTrue('Bad passphrase' in ad_hoc_command.result_stdout) # Try again and pass correct password. ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertTrue(ad_hoc_command.passwords_needed_to_start) self.assertTrue('ssh_key_unlock' in ad_hoc_command.passwords_needed_to_start) self.assertFalse(ad_hoc_command.signal_start()) self.assertTrue(ad_hoc_command.signal_start(ssh_key_unlock=TEST_SSH_KEY_DATA_UNLOCK)) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.assertIn('ssh-agent', ad_hoc_command.job_args) self.assertNotIn('Bad passphrase', ad_hoc_command.result_stdout) def test_run_with_bubblewrap(self): # Only run test if bubblewrap is installed cmd = [getattr(settings, 'AWX_PROOT_CMD', 'bwrap'), '--version'] try: proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) proc.communicate() has_bubblewrap = bool(proc.returncode == 0) except (OSError, ValueError): has_bubblewrap = False if not has_bubblewrap: self.skipTest('bubblewrap is not installed') # Enable bubblewrap for this test. settings.AWX_PROOT_ENABLED = True # Hide local settings path. settings.AWX_PROOT_HIDE_PATHS = [os.path.join(settings.BASE_DIR, 'settings')] # Create list of paths that should not be visible to the command. hidden_paths = [ os.path.join(settings.PROJECTS_ROOT, '*'), os.path.join(settings.JOBOUTPUT_ROOT, '*'), ] # Create a temp directory that should not be visible to the command. temp_path = tempfile.mkdtemp() self._temp_paths.append(temp_path) hidden_paths.append(temp_path) # Find a file in supervisor logs that should not be visible. try: supervisor_log_path = glob.glob('/var/log/supervisor/*')[0] except IndexError: supervisor_log_path = None if supervisor_log_path: hidden_paths.append(supervisor_log_path) # Create and run ad hoc command. module_args = ' && '.join(['echo %s && test ! -e %s' % (x, x) for x in hidden_paths]) ad_hoc_command = self.create_test_ad_hoc_command(module_name='shell', module_args=module_args, verbosity=2) self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'successful') self.check_ad_hoc_command_events(ad_hoc_command, 'ok') @mock.patch('awx.main.tasks.BaseTask.run_pexpect', return_value=('failed', 0)) def test_run_with_bubblewrap_not_installed(self, ignore): # Enable bubblewrap for this test, specify invalid bubblewrap cmd. settings.AWX_PROOT_ENABLED = True settings.AWX_PROOT_CMD = 'PR00T' ad_hoc_command = self.create_test_ad_hoc_command() self.assertEqual(ad_hoc_command.status, 'new') self.assertFalse(ad_hoc_command.passwords_needed_to_start) self.assertTrue(ad_hoc_command.signal_start()) ad_hoc_command = AdHocCommand.objects.get(pk=ad_hoc_command.pk) self.check_job_result(ad_hoc_command, 'error', expect_traceback=True) def run_pexpect_mock(self, *args, **kwargs): return 'successful', 0 @unittest.skipIf(os.environ.get('SKIP_SLOW_TESTS', False), 'Skipping slow test') class AdHocCommandApiTest(BaseAdHocCommandTest): ''' Test API list/detail views for ad hoc commands. ''' def setUp(self): super(AdHocCommandApiTest, self).setUp() self.create_test_credential(user=self.normal_django_user) def run_test_ad_hoc_command(self, **kwargs): # Post to list to start a new ad hoc command. expect = kwargs.pop('expect', 201) url = kwargs.pop('url', reverse('api:ad_hoc_command_list')) data = { 'inventory': self.inventory.pk, 'credential': self.credential.pk, 'module_name': 'command', 'module_args': 'uptime', } data.update(kwargs) for k,v in data.items(): if v is None: del data[k] return self.post(url, data, expect=expect) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', side_effect=run_pexpect_mock) def test_ad_hoc_command_detail(self, ignore): with self.current_user('admin'): response1 = self.run_test_ad_hoc_command() response2 = self.run_test_ad_hoc_command() response3 = self.run_test_ad_hoc_command() # Retrieve detail for ad hoc command. Only GET is supported. with self.current_user('admin'): url = reverse('api:ad_hoc_command_detail', args=(response1['id'],)) self.assertEqual(url, response1['url']) response = self.get(url, expect=200) self.assertEqual(response['credential'], self.credential.pk) self.assertEqual(response['related']['credential'], reverse('api:credential_detail', args=(self.credential.pk,))) self.assertEqual(response['inventory'], self.inventory.pk) self.assertEqual(response['related']['inventory'], reverse('api:inventory_detail', args=(self.inventory.pk,))) self.assertTrue(response['related']['stdout']) self.assertTrue(response['related']['cancel']) self.assertTrue(response['related']['relaunch']) self.assertTrue(response['related']['events']) self.assertTrue(response['related']['activity_stream']) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=204) self.delete(url, expect=404) with self.current_user('normal'): url = reverse('api:ad_hoc_command_detail', args=(response2['id'],)) self.assertEqual(url, response2['url']) response = self.get(url, expect=200) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=204) self.delete(url, expect=404) url = reverse('api:ad_hoc_command_detail', args=(response3['id'],)) self.assertEqual(url, response3['url']) with self.current_user('other'): response = self.get(url, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=403) with self.current_user('nobody'): response = self.get(url, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=403) with self.current_user(None): response = self.get(url, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) # Verify that the credential and inventory are null when they have # been deleted, can delete an ad hoc command without inventory or # credential. self.credential.delete() self.inventory.delete() with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['credential'], None) self.assertEqual(response['inventory'], None) self.delete(url, expect=204) self.delete(url, expect=404) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', side_effect=run_pexpect_mock) def test_ad_hoc_command_cancel(self, ignore): # Override setting so that ad hoc command isn't actually started. with self.settings(CELERY_UNIT_TEST=False): with self.current_user('admin'): response = self.run_test_ad_hoc_command() # Retrieve the cancel URL, should indicate it can be canceled. url = reverse('api:ad_hoc_command_cancel', args=(response['id'],)) self.assertEqual(url, response['related']['cancel']) with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['can_cancel'], True) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['can_cancel'], True) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) # Cancel ad hoc command (before it starts) and verify the can_cancel # flag is False and attempts to cancel again fail. with self.current_user('normal'): self.post(url, {}, expect=202) response = self.get(url, expect=200) self.assertEqual(response['can_cancel'], False) self.post(url, {}, expect=403) with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['can_cancel'], False) self.post(url, {}, expect=405) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', side_effect=run_pexpect_mock) def test_ad_hoc_command_relaunch(self, ignore): with self.current_user('admin'): response = self.run_test_ad_hoc_command() # Retrieve the relaunch URL, should indicate no passwords are needed # and it can be relaunched. Relaunch and fetch the new command. url = reverse('api:ad_hoc_command_relaunch', args=(response['id'],)) self.assertEqual(url, response['related']['relaunch']) with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['passwords_needed_to_start'], []) response = self.post(url, {}, expect=201) self.assertTrue(response['ad_hoc_command']) self.get(response['url'], expect=200) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['passwords_needed_to_start'], []) response = self.post(url, {}, expect=201) self.assertTrue(response['ad_hoc_command']) self.get(response['url'], expect=200) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) # Try to relaunch ad hoc command when module has been removed from # allowed list of modules. try: ad_hoc_commands = settings.AD_HOC_COMMANDS settings.AD_HOC_COMMANDS = [] with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['passwords_needed_to_start'], []) response = self.post(url, {}, expect=400) finally: settings.AD_HOC_COMMANDS = ad_hoc_commands # Try to relaunch after the inventory has been marked inactive. self.inventory.delete() with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['passwords_needed_to_start'], []) response = self.post(url, {}, expect=400) # Try to relaunch with expired license. with self.current_user('admin'): response = self.run_test_ad_hoc_command(inventory=self.inventory2.pk) self.create_expired_license_file() with self.current_user('admin'): self.post(response['related']['relaunch'], {}, expect=403) def test_ad_hoc_command_events_list(self): # TODO: Create test events instead of relying on playbooks execution with self.current_user('admin'): response = self.run_test_ad_hoc_command() response = self.run_test_ad_hoc_command() # Check list of ad hoc command events for a specific ad hoc command. ad_hoc_command_id = response['id'] url = reverse('api:ad_hoc_command_ad_hoc_command_events_list', args=(ad_hoc_command_id,)) self.assertEqual(url, response['related']['events']) with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['count'], self.inventory.hosts.count()) for result in response['results']: self.assertEqual(result['ad_hoc_command'], ad_hoc_command_id) self.assertTrue(result['id']) self.assertTrue(result['url']) self.assertEqual(result['event'], 'runner_on_ok') self.assertFalse(result['failed']) self.assertTrue(result['changed']) self.assertTrue(result['host'] in set(self.inventory.hosts.values_list('pk', flat=True))) self.assertTrue(result['host_name'] in set(self.inventory.hosts.values_list('name', flat=True))) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['count'], self.inventory.hosts.count()) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) # Test top level ad hoc command events list. url = reverse('api:ad_hoc_command_event_list') with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['count'], 2 * self.inventory.hosts.count()) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['count'], 2 * self.inventory.hosts.count()) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): response = self.get(url, expect=200) self.assertEqual(response['count'], 0) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): response = self.get(url, expect=200) self.assertEqual(response['count'], 0) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) def test_ad_hoc_command_event_detail(self): # TODO: Mock pexpect. Create test events instead of relying on playbooks execution with self.current_user('admin'): response = self.run_test_ad_hoc_command() # Check ad hoc command event detail view. ad_hoc_command_event_ids = AdHocCommandEvent.objects.values_list('pk', flat=True) with self.current_user('admin'): for ahce_id in ad_hoc_command_event_ids: url = reverse('api:ad_hoc_command_event_detail', args=(ahce_id,)) response = self.get(url, expect=200) self.assertTrue(response['ad_hoc_command']) self.assertEqual(response['id'], ahce_id) self.assertEqual(response['url'], url) self.assertEqual(response['event'], 'runner_on_ok') self.assertFalse(response['failed']) self.assertTrue(response['changed']) self.assertTrue(response['host'] in set(self.inventory.hosts.values_list('pk', flat=True))) self.assertTrue(response['host_name'] in set(self.inventory.hosts.values_list('name', flat=True))) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): for ahce_id in ad_hoc_command_event_ids: url = reverse('api:ad_hoc_command_event_detail', args=(ahce_id,)) self.get(url, expect=200) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): for ahce_id in ad_hoc_command_event_ids: url = reverse('api:ad_hoc_command_event_detail', args=(ahce_id,)) self.get(url, expect=403) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): for ahce_id in ad_hoc_command_event_ids: url = reverse('api:ad_hoc_command_event_detail', args=(ahce_id,)) self.get(url, expect=403) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): for ahce_id in ad_hoc_command_event_ids: url = reverse('api:ad_hoc_command_event_detail', args=(ahce_id,)) self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) @mock.patch('awx.main.tasks.BaseTask.run_pexpect', side_effect=run_pexpect_mock) def test_ad_hoc_command_activity_stream(self, ignore): # TODO: Test non-enterprise license self.create_test_license_file() with self.current_user('admin'): response = self.run_test_ad_hoc_command() # Check activity stream for ad hoc command. There should only be one # entry when it was created; other changes made while running should # not show up. url = reverse('api:ad_hoc_command_activity_stream_list', args=(response['id'],)) self.assertEqual(url, response['related']['activity_stream']) with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['count'], 1) result = response['results'][0] self.assertTrue(result['id']) self.assertTrue(result['url']) self.assertEqual(result['operation'], 'create') self.assertTrue(result['changes']) self.assertTrue(result['timestamp']) self.assertEqual(result['object1'], 'ad_hoc_command') self.assertEqual(result['object2'], '') self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['count'], 1) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): self.get(url, expect=403) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): self.get(url, expect=403) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) def test_host_ad_hoc_commands_list(self): # TODO: Figure out why this test needs pexpect with self.current_user('admin'): response = self.run_test_ad_hoc_command() response = self.run_test_ad_hoc_command(limit=self.host2.name) # Test the ad hoc commands list for a host. Should only return the ad # hoc command(s) run against that host. Posting should start a new ad # hoc command and always set the inventory and limit based on URL. url = reverse('api:host_ad_hoc_commands_list', args=(self.host.pk,)) with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['count'], 1) response = self.run_test_ad_hoc_command(url=url, inventory=None, expect=201) self.assertEqual(response['inventory'], self.inventory.pk) self.assertEqual(response['limit'], self.host.name) response = self.run_test_ad_hoc_command(url=url, inventory=self.inventory2.pk, limit=self.host2.name, expect=201) self.assertEqual(response['inventory'], self.inventory.pk) self.assertEqual(response['limit'], self.host.name) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['count'], 3) response = self.run_test_ad_hoc_command(url=url, inventory=None, expect=201) self.assertEqual(response['inventory'], self.inventory.pk) self.assertEqual(response['limit'], self.host.name) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) # Try to run with expired license. self.create_expired_license_file() with self.current_user('admin'): self.run_test_ad_hoc_command(url=url, expect=403) with self.current_user('normal'): self.run_test_ad_hoc_command(url=url, expect=403) def test_group_ad_hoc_commands_list(self): # TODO: Figure out why this test needs pexpect with self.current_user('admin'): response = self.run_test_ad_hoc_command() # self.host + self.host2 response = self.run_test_ad_hoc_command(limit=self.group.name) # self.host response = self.run_test_ad_hoc_command(limit=self.host2.name) # self.host2 # Test the ad hoc commands list for a group. Should return the ad # hoc command(s) run against any hosts in that group. Posting should # start a new ad hoc command and always set the inventory and limit # based on URL. url = reverse('api:group_ad_hoc_commands_list', args=(self.group.pk,)) # only self.host url2 = reverse('api:group_ad_hoc_commands_list', args=(self.group2.pk,)) # self.host + self.host2 with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['count'], 2) response = self.get(url2, expect=200) self.assertEqual(response['count'], 3) response = self.run_test_ad_hoc_command(url=url, inventory=None, expect=201) self.assertEqual(response['inventory'], self.inventory.pk) self.assertEqual(response['limit'], self.group.name) response = self.run_test_ad_hoc_command(url=url, inventory=self.inventory2.pk, limit=self.group2.name, expect=201) self.assertEqual(response['inventory'], self.inventory.pk) self.assertEqual(response['limit'], self.group.name) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['count'], 4) response = self.run_test_ad_hoc_command(url=url, inventory=None, expect=201) self.assertEqual(response['inventory'], self.inventory.pk) self.assertEqual(response['limit'], self.group.name) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): self.get(url, expect=403) self.post(url, {}, expect=403) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401) # Try to run with expired license. self.create_expired_license_file() with self.current_user('admin'): self.run_test_ad_hoc_command(url=url, expect=403) with self.current_user('normal'): self.run_test_ad_hoc_command(url=url, expect=403) def test_host_ad_hoc_command_events_list(self): # TODO: Mock run_pexpect. Create test events instead of relying on playbooks execution with self.current_user('admin'): response = self.run_test_ad_hoc_command() # Test the ad hoc command events list for a host. Should return the # events only for that particular host. url = reverse('api:host_ad_hoc_command_events_list', args=(self.host.pk,)) with self.current_user('admin'): response = self.get(url, expect=200) self.assertEqual(response['count'], 1) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('normal'): response = self.get(url, expect=200) self.assertEqual(response['count'], 1) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('other'): self.get(url, expect=403) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user('nobody'): self.get(url, expect=403) self.post(url, {}, expect=405) self.put(url, {}, expect=405) self.patch(url, {}, expect=405) self.delete(url, expect=405) with self.current_user(None): self.get(url, expect=401) self.post(url, {}, expect=401) self.put(url, {}, expect=401) self.patch(url, {}, expect=401) self.delete(url, expect=401)
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services/prefect/flows/update_api_view.py
MattSidor/can-scrapers
7
71216
<filename>services/prefect/flows/update_api_view.py from contextlib import closing from datetime import timedelta import os import pathlib from prefect import triggers import pandas as pd import prefect import sqlalchemy as sa from prefect import Flow, task from prefect.schedules import CronSchedule from prefect.tasks.secrets import EnvVarSecret from prefect.tasks.shell import ShellTask DATA_PATH = pathlib.Path(os.environ["DATAPATH"]) / "final" CSV_FN = DATA_PATH / "can_scrape_api_covid_us.csv" DATA_PATH.mkdir(parents=True, exist_ok=True) FN_STR = "can_scrape_api_covid_us{}" @task(max_retries=3, retry_delay=timedelta(minutes=1)) def export_to_csv(connstr: str): db = sa.create_engine(connstr) with open(CSV_FN, "w") as f: with closing(db.raw_connection()) as conn: with closing(conn.cursor()) as cur: cur.copy_expert( "COPY (SELECT * From covid_us) TO STDOUT CSV HEADER;", f ) return True @task( max_retries=3, retry_delay=timedelta(minutes=1), nout=2, trigger=triggers.all_finished, ) def create_parquet(_success): ts = prefect.context.scheduled_start_time dt_str = pd.to_datetime(ts).strftime("%Y-%m-%dT%H") vintage_fn = FN_STR.format(dt_str) + ".parquet" fn = FN_STR.format("") + ".parquet" df = pd.read_csv(CSV_FN, parse_dates=["dt"]) df.to_parquet(DATA_PATH / vintage_fn, index=False) df.to_parquet(DATA_PATH / fn, index=False) return vintage_fn, fn @task def get_gcs_cmd(fn): return f"gsutil acl ch -u AllUsers:R gs://can-scrape-outputs/final/{fn}" shell = ShellTask() with Flow("UpdateParquetFiles", CronSchedule("10 */2 * * *")) as f: connstr = EnvVarSecret("COVID_DB_CONN_URI") success = export_to_csv(connstr) vintage_fn, fn = create_parquet(success) shell(get_gcs_cmd(vintage_fn)) shell(get_gcs_cmd(fn)) f.register(project_name="can-scrape")
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day_1/7_sum.py
waskitaTREK/python_bim_engineer
1
181273
<gh_stars>1-10 numbers = [1,2,3,4,5,6,7,11] res = 0 for num in numbers: res = res + num print("with sum: ",sum(numbers)) print("without sum: ",res)
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ocs_ci/deployment/multicluster_deployment.py
sagareavdhoot82gmailcom/ocs-ci
0
185170
import os import logging from ocs_ci.deployment.deployment import Deployment from ocs_ci.framework import config from ocs_ci.ocs.exceptions import ACMClusterDeployException, ACMClusterDestroyException from ocs_ci.ocs.ui import acm_ui from ocs_ci.ocs.utils import get_non_acm_cluster_config from ocs_ci.ocs.acm import acm from ocs_ci.ocs import constants logger = logging.getLogger(__name__) class OCPDeployWithACM(Deployment): """ When we instantiate this class, the assumption is we already have an OCP cluster with ACM installed and current context is ACM """ def __init__(self): """ When we init Deployment class it will have all the ACM cluster's context """ super().__init__() self.multicluster_mode = config.MULTICLUSTER.get("multicluster_mode", None) # Used for housekeeping during multiple OCP cluster deployments self.deployment_cluster_list = list() # Whether to start deployment in asynchronous mode or synchronous mode # In async deploy mode, we will have a single wait method waiting for # all the cluster deployments to finish self.deploy_sync_mode = config.MULTICLUSTER.get("deploy_sync_mode", "async") self.ui_driver = None self.factory = acm_ui.ACMOCPDeploymentFactory() def do_deploy_ocp(self, log_cli_level="INFO"): """ This function overrides the parent's function in order accomodate ACM based OCP cluster deployments """ if config.ENV_DATA["skip_ocp_deployment"]: logger.warning( "Skipping OCP deployment through ACM because skip_ocp_deployment " "has been specified" ) return if self.multicluster_mode == constants.RDR_MODE: self.do_rdr_acm_ocp_deploy() self.post_deploy_ops() def post_deploy_ops(self): """ 1. Install ingress certificates on OCP clusters deployed through ACM 2. Run post_ocp_deploy on OCP clusters """ prev = config.cur_index for cluster in get_non_acm_cluster_config(): config.switch_ctx(cluster.MULTICLUSTER["multicluster_index"]) ssl_key = config.DEPLOYMENT.get("ingress_ssl_key") ssl_cert = config.DEPLOYMENT.get("ingress_ssl_cert") for key in [ssl_key, ssl_cert]: if os.path.exists(key): os.unlink(key) logger.info("Running post ocp deploy ops") self.post_ocp_deploy() config.switch_ctx(prev) def do_rdr_acm_ocp_deploy(self): """ Specific to regional DR OCP cluster deployments """ self.ui_driver = acm.login_to_acm() if self.deploy_sync_mode == "async": rdr_clusters = get_non_acm_cluster_config() for c in rdr_clusters: logger.info(f"{c.ENV_DATA['cluster_name']}") logger.info(f"{c.ENV_DATA['platform']}") logger.info(f"{c.ENV_DATA['deployment_type']}") for cluster_conf in rdr_clusters: deployer = self.factory.get_platform_instance( self.ui_driver, cluster_conf ) deployer.create_cluster_prereq() deployer.create_cluster() self.deployment_cluster_list.append(deployer) # At this point deployment of all non-acm ocp clusters have been # triggered, we need to wait for all of them to succeed self.wait_for_all_clusters_async() # Download kubeconfig to the respective directories for cluster in self.deployment_cluster_list: cluster.download_cluster_conf_files() def wait_for_all_clusters_async(self): # We will say done only when none of the clusters are in # 'Creating' state. Either they have to be in 'Ready' state # OR 'Failed' state done_waiting = False while not done_waiting: done_waiting = True for cluster in self.deployment_cluster_list: if cluster.deployment_status not in ["ready", "failed"]: cluster.get_deployment_status() done_waiting = False # We will fail even if one of the clusters failed to deploy failed_list = list() success_list = list() for cluster in self.deployment_cluster_list: if cluster.deployment_status == "failed": failed_list.append(cluster) else: success_list.append(cluster) if success_list: logger.info("Deployment for following clusters Passed") logger.info(f"{[c.cluster_name for c in success_list]}") if failed_list: logger.error("Deployment failed for following clusters") logger.error(f"{[c.cluster_name for c in failed_list]}") raise ACMClusterDeployException("one or more Cluster Deployment failed ") def deploy_cluster(self, log_cli_level="INFO"): """ We deploy new OCP clusters using ACM Note: Importing cluster through ACM has been implemented as part of Jenkins pipeline """ super().deploy_cluster(log_cli_level=log_cli_level) def destroy_cluster(self, log_cli_level=None): """ Teardown OCP clusters deployed through ACM """ self.ui_driver = acm.login_to_acm() cluster_list = list() rdr_clusters = get_non_acm_cluster_config() logger.info("Following ACM deployed OCP clusters will be destroyed") for cluster in rdr_clusters: logger.info( f"[{cluster.ENV_DATA['cluster_name']}" f"{cluster.ENV_DATA['platform']}_" f"{cluster.ENV_DATA['deployment_type']}]" ) for cluster_conf in rdr_clusters: destroyer = self.factory.get_platform_instance(self.ui_driver, cluster_conf) destroyer.destroy_cluster() cluster_list.append(destroyer) self.wait_for_all_cluster_async_destroy(cluster_list) self.post_destroy_ops(cluster_list) def wait_for_all_cluster_async_destroy(self, destroy_cluster_list): # Wait until all the clusters are in 'Done' or 'Failed state destroyed_clusters = list() failed_clusters = list() done_waiting = False while not done_waiting: done_waiting = True for cluster in destroy_cluster_list: if cluster.destroy_status not in ["Done", "Failed"]: cluster.get_destroy_status() done_waiting = False for cluster in destroy_cluster_list: if cluster.destroy_status == "Done": destroyed_clusters.append(cluster) else: failed_clusters.append(cluster) if destroyed_clusters: logger.info( f"Destroyed clusters: {[c.cluster_name for c in destroyed_clusters]}" ) if failed_clusters: raise ACMClusterDestroyException( f"Failed to destroy clusters:{[c.cluster_name for c in failed_clusters]} " ) def post_destroy_ops(self, cluster_list): """ Post destroy ops mainly includes ip clean up and dns cleanup Args: cluster_list (list[ACMOCPClusterDeploy]): list of platform specific instances """ for cluster in cluster_list: cluster.post_destroy_ops()
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tests/01_human_readable_errors.py
Hakujou/osc-sdk-python
0
63098
import sys sys.path.append("..") from osc_sdk_python import Gateway gw = Gateway() res = gw.CreateNet(IpRange='192.168.127.12/32') error = [error for error in res['Errors'] if error.get('Code') == '4014' and error.get('Details') == 'invalid-block-size'] assert len(error) == 1
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libai/data/samplers/samplers.py
Oneflow-Inc/libai
55
29082
# coding=utf-8 # Copyright 2021 The OneFlow Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import oneflow as flow from oneflow.utils.data import Sampler class CyclicSampler(Sampler): """ This sampler supports cyclic sampling, and it is also compatible with non-data parallelism and data parallelism. Arguments: dataset: dataset to be sampled. micro_batch_size: batch size for per model instance. global_batch_size is micro_batch_size times data_parallel_size. shuffle: whether to shuffle the dataset. consumed_samples: the number of samples that have been trained at the current time, used for resuming training (default: ``0``). data_parallel_rank: local rank for data parallelism. data_parallel_size: the size of data parallelism. seed: random seed, used for reproducing experiments (default: ``0``). """ def __init__( self, dataset, micro_batch_size, shuffle=False, consumed_samples=0, data_parallel_rank=0, data_parallel_size=1, seed=0, ): self.dataset = dataset self.data_size = len(self.dataset) self.shuffle = shuffle self.data_parallel_rank = data_parallel_rank self.data_parallel_size = data_parallel_size self.micro_batch_size = micro_batch_size self.actual_batch_size = self.micro_batch_size * self.data_parallel_size self.data_size_per_epoch = self.data_size // self.actual_batch_size * self.micro_batch_size self.consumed_samples = consumed_samples self.seed = seed def __iter__(self): """divide the data into data_parallel_size buckets, and shuffle it if `shuffle` is set to `True`. Each processor samples from its own buckets and data_loader will load the corresponding data. """ epoch = self.consumed_samples // self.data_size_per_epoch current_epoch_samples = self.consumed_samples % self.data_size_per_epoch batch = [] while True: bucket_offset = current_epoch_samples // self.data_parallel_size start_idx = self.data_parallel_rank * self.data_size_per_epoch if self.shuffle: generator = flow.Generator() generator.manual_seed(self.seed + epoch) random_idx = flow.randperm(self.data_size_per_epoch, generator=generator).tolist() indices = [start_idx + x for x in random_idx[bucket_offset:]] else: seq_idx = flow.arange(self.data_size_per_epoch).tolist() indices = [start_idx + x for x in seq_idx[bucket_offset:]] epoch += 1 if hasattr(self.dataset, "supports_prefetch") and self.dataset.supports_prefetch: self.dataset.prefetch(indices) for idx in indices: batch.append(idx) if len(batch) == self.micro_batch_size: self.consumed_samples += self.actual_batch_size yield batch batch = [] current_epoch_samples = 0 def __len__(self): return self.data_size def set_consumed_samples(self, consumed_samples): """You can recover the training iteration by setting `consumed_samples`.""" self.consumed_samples = consumed_samples def set_epoch(self, epoch): """Used for restoring training status.""" self.epoch = epoch class SingleRoundSampler(Sampler): """ This sampler supports single round sampling, and it is also compatible with non data parallelism and data parallelism. Arguments: dataset: dataset to be sampled. micro_batch_size: batch size for per model instance, global_batch_size is micro_batch_size times data_parallel_size. shuffle: whether to shuffle the dataset. data_parallel_rank: local rank for data parallelism. data_parallel_size: the size of data parallelism. seed: random seed, used for reproducing experiments (default: ``0``). drop_last: whether to drop the remaining data (default: ``False``). """ def __init__( self, dataset, micro_batch_size, shuffle=False, data_parallel_rank=0, data_parallel_size=1, seed=0, drop_last=False, ): self.dataset = dataset self.data_size = len(self.dataset) self.shuffle = shuffle self.data_parallel_rank = data_parallel_rank self.data_parallel_size = data_parallel_size self.micro_batch_size = micro_batch_size self.seed = seed self.drop_last = drop_last def __iter__(self): bucket_size = self.data_size // self.data_parallel_size remain = self.data_size % self.data_parallel_size start_idx = self.data_parallel_rank * bucket_size if self.data_parallel_rank < remain: bucket_size += 1 start_idx += min(self.data_parallel_rank, remain) if self.shuffle: generator = flow.Generator() generator.manual_seed(self.seed) random_idx = flow.randperm(bucket_size, generator=generator).tolist() indices = [start_idx + x for x in random_idx] else: seq_idx = flow.arange(bucket_size).tolist() indices = [start_idx + x for x in seq_idx] if hasattr(self.dataset, "supports_prefetch") and self.dataset.supports_prefetch: self.dataset.prefetch(indices) batch = [] for idx in indices: batch.append(idx) if len(batch) == self.micro_batch_size: yield batch batch = [] if not self.drop_last: if self.data_parallel_rank >= remain and remain > 0: batch.append(0) if len(batch) > 0: yield batch def __len__(self): global_batch_size = self.micro_batch_size * self.data_parallel_size if self.drop_last: return self.data_size // global_batch_size else: return (self.data_size + global_batch_size - 1) // global_batch_size
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Solutions/262.py
ruppysuppy/Daily-Coding-Problem-Solutions
70
63860
""" Problem: A bridge in a connected (undirected) graph is an edge that, if removed, causes the graph to become disconnected. Find all the bridges in a graph. """ from sys import maxsize from typing import Dict, List, Optional, Set, Tuple from DataStructures.Graph import GraphUndirectedUnweighted def get_bridges_helper( graph: GraphUndirectedUnweighted, node: int, visited: Set[int], parent: Dict[int, Optional[int]], low: Dict[int, int], disc: Dict[int, int], bridges: List[Tuple[int, int]], ) -> None: # find all bridges using dfs visited.add(node) disc[node] = graph.time low[node] = graph.time graph.time += 1 for neighbour in graph.connections[node]: if neighbour not in visited: parent[neighbour] = node get_bridges_helper(graph, neighbour, visited, parent, low, disc, bridges) # check if the subtree rooted with neighbour has a connection to one of the # ancestors of node low[node] = min(low[node], low[neighbour]) # if the lowest vertex reachable from subtree under neighbour is below node # in DFS tree, then node-neighbour is a bridge if low[neighbour] > disc[node]: bridges.append((node, neighbour)) elif neighbour != parent[node]: low[node] = min(low[node], disc[neighbour]) def get_bridges(graph: GraphUndirectedUnweighted) -> List[Tuple[int, int]]: visited = set() disc = {node: maxsize for node in graph.connections} low = {node: maxsize for node in graph.connections} parent = {node: None for node in graph.connections} bridges = [] graph.time = 0 for node in graph.connections: if node not in visited: get_bridges_helper(graph, node, visited, parent, low, disc, bridges) return bridges if __name__ == "__main__": g1 = GraphUndirectedUnweighted() g1.add_edge(1, 0) g1.add_edge(0, 2) g1.add_edge(2, 1) g1.add_edge(0, 3) g1.add_edge(3, 4) print("Bridges in first graph:") print(*get_bridges(g1)) g2 = GraphUndirectedUnweighted() g2.add_edge(0, 1) g2.add_edge(1, 2) g2.add_edge(2, 3) print("\nBridges in second graph:") print(*get_bridges(g2)) g3 = GraphUndirectedUnweighted() g3.add_edge(0, 1) g3.add_edge(1, 2) g3.add_edge(2, 0) g3.add_edge(1, 3) g3.add_edge(1, 4) g3.add_edge(1, 6) g3.add_edge(3, 5) g3.add_edge(4, 5) print("\nBridges in third graph:") print(*get_bridges(g3)) """ SPECS: TIME COMPLEXITY: O(v + e) SPACE COMPLEXITY: O(v) """
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ryu/app/curly.py
doup123/diplomas
0
181578
<reponame>doup123/diplomas import pycurl from StringIO import StringIO class Curl: def __init__(self,host): self.url=str(host) def get_page(self,file=None): try: buffer = StringIO() c = pycurl.Curl() c.setopt(c.URL, self.url) c.setopt(c.WRITEDATA, buffer) c.perform() c.close() body = buffer.getvalue() #print body try: open(file, 'w').write(body) except: None#ds return body except (pycurl.error): print "Error"
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philander/__init__.py
pfirsich/philander
1
91108
<reponame>pfirsich/philander from .philander import *
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src/poliastro/plotting/porkchop.py
niharsalunke/poliastro
2
193036
<reponame>niharsalunke/poliastro """ This is the implementation of porkchop plot """ import numpy as np from astropy import coordinates as coord, units as u from matplotlib import pyplot as plt from poliastro.bodies import ( Earth, Jupiter, Mars, Mercury, Moon, Neptune, Pluto, Saturn, Sun, Uranus, Venus, ) from poliastro.maneuver import Maneuver from poliastro.twobody.orbit import Orbit from poliastro.util import norm def _get_state(body, time): """ Computes the position of a body for a given time. """ solar_system_bodies = [ Sun, Mercury, Venus, Earth, Moon, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, ] # We check if body belongs to poliastro.bodies if body in solar_system_bodies: rr, vv = coord.get_body_barycentric_posvel(body.name, time) else: rr, vv = body.propagate(time).rv() rr = coord.CartesianRepresentation(rr) vv = coord.CartesianRepresentation(vv) return rr.xyz, vv.xyz def _targetting(departure_body, target_body, t_launch, t_arrival): """This function returns the increment in departure and arrival velocities.""" # Get position and velocities for departure and arrival rr_dpt_body, vv_dpt_body = _get_state(departure_body, t_launch) rr_arr_body, vv_arr_body = _get_state(target_body, t_arrival) # Transform into Orbit objects attractor = departure_body.parent ss_dpt = Orbit.from_vectors(attractor, rr_dpt_body, vv_dpt_body, epoch=t_launch) ss_arr = Orbit.from_vectors(attractor, rr_arr_body, vv_arr_body, epoch=t_arrival) # Define time of flight tof = ss_arr.epoch - ss_dpt.epoch if tof <= 0: return None, None, None, None, None try: # Lambert is now a Maneuver object man_lambert = Maneuver.lambert(ss_dpt, ss_arr) # Get norm delta velocities dv_dpt = norm(man_lambert.impulses[0][1]) dv_arr = norm(man_lambert.impulses[1][1]) # Compute all the output variables c3_launch = dv_dpt ** 2 c3_arrival = dv_arr ** 2 return ( dv_dpt.to(u.km / u.s).value, dv_arr.to(u.km / u.s).value, c3_launch.to(u.km ** 2 / u.s ** 2).value, c3_arrival.to(u.km ** 2 / u.s ** 2).value, tof.jd, ) except AssertionError: return None, None, None, None, None # numpy.vectorize is amazing targetting_vec = np.vectorize( _targetting, otypes=[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray], excluded=[0, 1], ) class PorkchopPlotter: """ Class Implementation for Porkchop Plot Parameters ---------- departure_body: poliastro.bodies.Body Body from which departure is done target_body: poliastro.bodies.Body Body for targetting launch_span: astropy.time.Time Time span for launch arrival_span: astropy.time.Time Time span for arrival ax: matplotlib.axes.Axes: For custom figures tfl: boolean For plotting time flight contour lines vhp: boolean For plotting arrival velocity contour lines max_c3: float Sets the maximum C3 value for porkchop max_vhp: float Sets the maximum arrival velocity for porkchop """ def __init__( self, departure_body, target_body, launch_span, arrival_span, ax=None, tfl=True, vhp=True, max_c3=45.0 * u.km ** 2 / u.s ** 2, max_vhp=5 * u.km / u.s, ): self.departure_body = departure_body self.target_body = target_body self.launch_span = launch_span self.arrival_span = arrival_span self.ax = ax self.tfl = tfl self.vhp = vhp self.max_c3 = max_c3 self.max_vhp = max_vhp def porkchop(self): """Plots porkchop between two bodies. Returns ------- dv_launch: np.ndarray Launch delta v dv_arrival: np.ndarray Arrival delta v c3_launch: np.ndarray Characteristic launch energy c3_arrrival: np.ndarray Characteristic arrival energy tof: np.ndarray Time of flight for each transfer Example ------- >>> from poliastro.plotting.porkchop import PorkchopPlotter >>> from poliastro.bodies import Earth, Mars >>> from poliastro.util import time_range >>> launch_span = time_range("2005-04-30", end="2005-10-07") >>> arrival_span = time_range("2005-11-16", end="2006-12-21") >>> porkchop_plot = PorkchopPlotter(Earth, Mars, launch_span, arrival_span) >>> dv_launch, dev_dpt, c3dpt, c3arr, tof = porkchop_plot.porkchop() """ dv_launch, dv_arrival, c3_launch, c3_arrival, tof = targetting_vec( self.departure_body, self.target_body, self.launch_span[np.newaxis, :], self.arrival_span[:, np.newaxis], ) # Start drawing porkchop if self.ax is None: fig, self.ax = plt.subplots(figsize=(15, 15)) else: fig = self.ax.figure c3_levels = np.linspace(0, self.max_c3.to(u.km ** 2 / u.s ** 2).value, 30) c = self.ax.contourf( [D.to_datetime() for D in self.launch_span], [A.to_datetime() for A in self.arrival_span], c3_launch, c3_levels, ) line = self.ax.contour( [D.to_datetime() for D in self.launch_span], [A.to_datetime() for A in self.arrival_span], c3_launch, c3_levels, colors="black", linestyles="solid", ) cbar = fig.colorbar(c) cbar.set_label("km2 / s2") self.ax.clabel(line, inline=1, fmt="%1.1f", colors="k", fontsize=10) if self.tfl: time_levels = np.linspace(100, 500, 5) tfl_contour = self.ax.contour( [D.to_datetime() for D in self.launch_span], [A.to_datetime() for A in self.arrival_span], tof, time_levels, colors="red", linestyles="dashed", linewidths=3.5, ) self.ax.clabel(tfl_contour, inline=1, fmt="%1.1f", colors="r", fontsize=14) if self.vhp: vhp_levels = np.linspace(0, self.max_vhp.to(u.km / u.s).value, 5) vhp_contour = self.ax.contour( [D.to_datetime() for D in self.launch_span], [A.to_datetime() for A in self.arrival_span], dv_arrival, vhp_levels, colors="navy", linewidths=2.0, ) self.ax.clabel( vhp_contour, inline=1, fmt="%1.1f", colors="navy", fontsize=12 ) self.ax.grid() fig.autofmt_xdate() if not hasattr(self.target_body, "name"): self.ax.set_title( f"{self.departure_body.name} - Target Body for year {self.launch_span[0].datetime.year}, C3 Launch", fontsize=14, fontweight="bold", ) else: self.ax.set_title( f"{self.departure_body.name} - {self.target_body.name} for year {self.launch_span[0].datetime.year}, C3 Launch", fontsize=14, fontweight="bold", ) self.ax.set_xlabel("Launch date", fontsize=10, fontweight="bold") self.ax.set_ylabel("Arrival date", fontsize=10, fontweight="bold") return ( dv_launch * u.km / u.s, dv_arrival * u.km / u.s, c3_launch * u.km ** 2 / u.s ** 2, c3_arrival * u.km ** 2 / u.s ** 2, tof * u.d, )
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utils.py
zrongcheng/srcnn
0
39508
<reponame>zrongcheng/srcnn """ Scipy version > 0.18 is needed, due to 'mode' option from scipy.misc.imread function """ import os import glob import h5py import random import matplotlib.pyplot as plt from PIL import Image # for loading images as YCbCr format import Scipy.misc import Scipy.ndimage import numpy as np import tensorflow as tf FLAGS = tf.app.flags.FLAGS#全局变量 def read_data(path): """ Read h5 format data file Args: path: file path of desired file所需文件 data: '.h5' file format that contains train data values label: '.h5' file format that contains train label values """ with h5py.File(path, 'r') as hf: data = np.array(hf.get('data')) label = np.array(hf.get('label')) return data, label def preprocess(path, scale=3): """ Preprocess single image file (1) Read original image as YCbCr format (and grayscale as default) (2) Normalize (3) Apply image file with bicubic interpolation 唯一的预处理操作 Args: path: file path of desired file input_: image applied bicubic interpolation (low-resolution) label_: image with original resolution (high-resolution) """ image = imread(path, is_grayscale=True)#？？为什么灰度处理 label_ = modcrop(image, scale) # Must be normalized #image = image / 255. label_ = label_ / 255. #两次为了降低精度 input_ = Scipy.ndimage.interpolation.zoom(label_, zoom=(1. / scale), prefilter=False)#一次 input_ = Scipy.ndimage.interpolation.zoom(input_, zoom=(scale / 1.), prefilter=False)#二次，bicubic #imsave(input_,r'F:\tf_py\srcnn\sample\test1.png') #imsave(label_, r'F:\tf_py\srcnn\sample\test2.png') return input_, label_ def prepare_data(dataset): """ Args: dataset: choose train dataset or test dataset For train dataset, output data would be ['.../t1.bmp', '.../t2.bmp', ..., '.../t99.bmp'] """ if FLAGS.is_train: #filenames = os.listdir(dataset) data_dir = os.path.join(os.getcwd(), dataset) # glob.glob()获取当前目录或相对路径所有文件的路径，输出一个list，读取字符中的*(通配符) data = glob.glob(os.path.join(data_dir, "*.bmp")) else: data_dir = os.path.join(os.sep, (os.path.join(os.getcwd(), dataset)), "Set5") data = glob.glob(os.path.join(data_dir, "*.bmp")) return data def make_data(data, label): """ Make input data as h5 file format Depending on 'is_train' (flag value), savepath would be changed. """ if FLAGS.is_train: savepath = os.path.join(os.getcwd(), 'checkpoint/train.h5') else: savepath = os.path.join(os.getcwd(), 'checkpoint/test.h5') with h5py.File(savepath, 'w') as hf: hf.create_dataset('data', data=data) hf.create_dataset('label', data=label) def imread(path, is_grayscale=True): """ Read image using its path. Default value is gray-scale, and image is read by YCbCr format as the paper said. """ if is_grayscale: return Scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)#将图像转灰度 else: return Scipy.misc.imread(path, mode='YCbCr').astype(np.float)#默认为false def modcrop(image, scale=3): """ To scale down and up the original image, first thing to do is to have no remainder while scaling operation. We need to find modulo of height (and width) and scale factor. Then, subtract the modulo from height (and width) of original image size. There would be no remainder even after scaling operation. 要缩放原始图像, 首先要做的是在缩放操作时没有余数。 我们需要找到高度 (和宽度) 和比例因子的模。 然后, 减去原始图像大小的高度 (和宽度) 的模。 即使在缩放操作之后, 也不会有余数。 """ if len(image.shape) == 3:#彩色 800*600*3 h, w, _ = image.shape h = h - np.mod(h, scale) w = w - np.mod(w, scale) image = image[0:h, 0:w, :] else:#灰度 800*600 h, w = image.shape h = h - np.mod(h, scale) w = w - np.mod(w, scale) image = image[0:h, 0:w] return image def input_setup(config): """ Read image files and make their sub-images and saved them as a h5 file format. """ # Load data path if config.is_train: data = prepare_data(dataset="Train") else: data = prepare_data(dataset="Test") sub_input_sequence = [] sub_label_sequence = [] padding = abs(config.image_size - config.label_size) // 2 # 6 填充 if config.is_train: for i in range(len(data)): input_, label_ = preprocess(data[i], config.scale)#data[i]为数据目录 if len(input_.shape) == 3: h, w, _ = input_.shape else: h, w = input_.shape #。。。 for x in range(0, h-config.image_size+1, config.stride): for y in range(0, w-config.image_size+1, config.stride): sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33] sub_label = label_[x+padding:x+padding+config.label_size, y+padding:y+padding+config.label_size] # [21 x 21] # Make channel value,颜色通道1 sub_input = sub_input.reshape([config.image_size, config.image_size, 1]) sub_label = sub_label.reshape([config.label_size, config.label_size, 1]) sub_input_sequence.append(sub_input) sub_label_sequence.append(sub_label) # Make list to numpy array. With this transform arrdata = np.asarray(sub_input_sequence) # [?, 33, 33, 1] arrlabel = np.asarray(sub_label_sequence) # [?, 21, 21, 1] make_data(arrdata, arrlabel) # 把处理好的数据进行存储，路径为checkpoint/.. else: input_, label_ = preprocess(data[4], config.scale) if len(input_.shape) == 3: h, w, _ = input_.shape else: h, w = input_.shape input = input_.reshape([h,w,1]) label=label_[6:h-6,6:w-6] label=label.reshape([h-12,w-12,1]) sub_input_sequence.append(input) sub_label_sequence.append(label) input1 = np.asarray(sub_input_sequence) label1 = np.asarray(sub_label_sequence) #label=label_.reshape([height,weight,1]) return input1,label1,h,w # # Numbers of sub-images in height and width of image are needed to compute merge operation. # nx = ny = 0 # for x in range(0, h-config.image_size+1, config.stride): # nx += 1; ny = 0 # for y in range(0, w-config.image_size+1, config.stride): # ny += 1 # sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33] # sub_label = label_[x+padding:x+padding+config.label_size, # y+padding:y+padding+config.label_size] # [21 x 21] # # sub_input = sub_input.reshape([config.image_size, config.image_size, 1]) # sub_label = sub_label.reshape([config.label_size, config.label_size, 1]) # # sub_input_sequence.append(sub_input) # sub_label_sequence.append(sub_label) """ len(sub_input_sequence) : the number of sub_input (33 x 33 x ch) in one image (sub_input_sequence[0]).shape : (33, 33, 1) """ # if not config.is_train: # return nx, ny def imsave(image, path): return Scipy.misc.imsave(path, image) # def merge(images, size): # h, w = images.shape[1], images.shape[2]#21*21 # p,q,j=0,0,0 # img = np.zeros((14*(size[0]-1)+21, 14*(size[1]-1)+21, 1)) # for idx, image in enumerate(images):#image.shape=(21,21,1) # i = idx % size[1]#余数 # t=j # j = idx // size[1]#商 # if (j-t)==1: # p=p+14 # q=0 # #img[0:21,0:21,:]=image # img[p:p+h, q:q+w, :] = image # # q=q+14 # # return img
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tests/base.py
RussellLuo/goodjob
1
89827
#!/usr/bin/env python # -*- coding: utf-8 -*- import shlex from multiprocessing import Process from goodjob.jobs.models import Job, Operation class TestCase(object): def create_job(self, name='test', command='echo test'): args = shlex.split(command) provider = Operation(type='shell', command=args[0], args=args[1:]) job = Job(name=name, provider=provider) job.save() return job def apply_async(self, func, args=()): proc = Process(target=func, args=args) proc.daemon = True return proc
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mysql-creat table&load csv.py
feilongwang92/mining-app-data
0
56266
# currScenRecQuery = """ # SELECT * from 'pla_scenario_values_new2' where rid='{rId}' and qno='{qNo}' # """.format(rId=1, qNo=2) # currScenRec = db.fetch_results(currScenRecQuery, single_row=True) # # updateBetaAttrQuery = """ # UPDATE 'pla_scenario_values_new2' SET 'beta_sde' = '{beta_sde}', # 'beta_tts' = '{beta_tts}', 'beta_rp' = '{beta_rp}', 'beta_sdl' = '{beta_sdl}' # WHERE 'pla_scenario_values_new2'.'rid' = {rId} AND 'pla_scenario_values_new2'.'qno' = '{num}'; # """.format(beta_sde=1, beta_tts=2, beta_rp=3, beta_sdl=4, rId=5, num=6) # db.execute(updateBetaAttrQuery) ################### make query to fetch data ################ ## start mysql service manually if failed (run->services.msc->mysql (right click) ->start) id = 'a0be48f6b592ebcbd536f6a615be005f2066335665973d868907bd32706fe81c' acc = 21 query = """ SELECT * from `testtest_mysql1` where id='{id}' and accuracy='{acc}' """.format(id=id, acc=acc) ## two methods ## use db.py import db results = db.fetch_results('cuebiq201911', query) print (results) ## use directly import pymysql as mysql dbconnect = mysql.connect(host='localhost', database='cuebiq201911', user='root', password='<PASSWORD>') dbcursor = dbconnect.cursor() dbcursor.execute(query) results = dbcursor.fetchall() # is a duple of duples [list(item) for item in results] # turn into a list of lists dbcursor.close() dbconnect.close() ############################ creat database #################### import pymysql as mysql dbconnect = mysql.connect(host='localhost', user='root', password='<PASSWORD>') dbcursor = dbconnect.cursor() dbcursor.execute("CREATE DATABASE cuebiq201801to03;") #SHOW DATABASES; USE cuebiq201911; # dbconnect.commit() # commit before close; if commit, return None, else, return result dbcursor.close() dbconnect.close() ################### creat table and load csv ################ ## creat table: two method: 1) use db.py; 2) use directly query1 = """ CREATE TABLE {table} ( row_i INT NOT NULL AUTO_INCREMENT, unix_time INT NOT NULL, id VARCHAR(255) NOT NULL, type TINYINT NOT NULL, latitude FLOAT NOT NULL, longitude FLOAT NOT NULL, accuracy SMALLINT NOT NULL, timezone SMALLINT NOT NULL, PRIMARY KEY (row_i), INDEX id (id)) """.format(table='upzip') # '{table}' return error db.execute('cuebiq201801to03', query1) ## load csv: cannot use db.py because of the reason specified below ## Make sure to have autocommit turned on. To upload files, you need to set the local_infile parameter to 1. usedatabase = 'cuebiq201801to03' usetable = 'upzip' dbconnect = mysql.connect(host='localhost', database=usedatabase, user='root', password='<PASSWORD>', autocommit=True, local_infile=1) dbcursor = dbconnect.cursor() import os filedaylist = os.listdir('E:\\cuebiq_psrc_2019\\sorted') filedaylist = [fileday for fileday in filedaylist if fileday.startswith('unzip20') and fileday <= 'unzip20180331.csv'] for filename in filedaylist: infilepath = 'E:/cuebiq_psrc_2019/sorted/' + filename #'E:/cuebiq_psrc_2019/sorted/testtest_mysql.csv' print (infilepath) query2 = """ LOAD DATA LOCAL INFILE '{infilepath}' INTO TABLE {totable} FIELDS TERMINATED BY '\\t' LINES TERMINATED BY '\\n' (unix_time, id, type, latitude, longitude, accuracy, timezone); """.format(infilepath=infilepath, totable=usetable) # {infilepath} return error # db.execute('cuebiq201911', query2) # Error: InternalError(1148, u'The used command is not allowed with this MySQL version') dbcursor.execute(query2) dbconnect.commit() dbcursor.close() dbconnect.close()
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holmscan/webscan.py
dynamist/holmscan
7
1608593
<gh_stars>1-10 # -*- coding: utf-8 -*- # python std lib import logging # holmscan imports from holmscan.interface import HolmscanModule log = logging.getLogger(__name__) class Webscan(HolmscanModule): def __init__(self, controller): super(Webscan, self).__init__(controller) def get_web_scan(self, uuid): path = "/web-scans/{0}".format(uuid) response = self.controller.get(path) return response.json() def get_web_assets(self): path = "/web-scans/assets" response = self.controller.get(path) return response.json() def get_web_profiles(self): path = "/web-scans/scan-profiles" response = self.controller.get(path) return response.json() def get_web_schedules(self): path = "/web-scans/schedules" response = self.controller.get(path) return response.json() def list_web_scans(self): path = "/web-scans" # FIXME handle pagination (see next, previous) query = {"params": {"limit": 10000}} response = self.controller.get(path, **query) return response.json() def start_web_scan(self, asset, profile): path = "/web-scans" body = { "json": { "name": "Scan via API", "profile_uuid": profile, "webapp_asset_uuid": asset, } } response = self.controller.post(path, **body) return response.json()
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SerialConnection.py
scottrfrancis/Pentair-Thing
1
151384
# See https://web.archive.org/web/20171130091506/https:/www.sdyoung.com/home/decoding-the-pentair-easytouch-rs-485-protocol/ # For details on pentair protocol # import serial from Connection import Connection, IOConnection class SerialConnection(IOConnection): def __init__(self, device): super().__init__() self.device = device self.ser = serial.Serial( port=self.device, baudrate=9600, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS, timeout=0.1, write_timeout=0.1 ) def open(self): self.ser.open() def isOpen(self): return self.ser.isOpen() def send(self, message): count = self.ser.write(message) return count def listen(self): # events = [] n = self.ser.inWaiting() if n > 0: try: self.readbuffer = self.ser.read(n) except Exception as e: print("Exception " + e + " while reading from Serial port") return self.readbuffer
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src/graphwerk.py
LivingInABubble/inpredo
1
41370
<reponame>LivingInABubble/inpredo<filename>src/graphwerk.py import os import matplotlib.pyplot as plt import numpy as np from mplfinance.original_flavor import candlestick2_ochl from tqdm import tqdm def convolve_sma(array, period): return np.convolve(array, np.ones((period,)) / period, mode='valid') def graphwerk(start, finish): opened, closed, high, low, volume, date = [], [], [], [], [], [] for x in range(start, finish): # Below filtering is valid for eurusd.csv file. Other financial data files have different orders # so you need to find out what means open, high and closed in their respective order. opened.append(float(pd[x][3])) high.append(float(pd[x][4])) low.append(float(pd[x][5])) closed.append(float(pd[x][6])) volume.append(float(pd[x][7])) date.append(pd[x][1]) close_next = float(pd[finish][6]) sma = convolve_sma(closed, 5) smb = list(sma) diff = sma[-1] - sma[-2] for x in range(len(closed) - len(smb)): smb.append(smb[-1] + diff) fig = plt.figure(num=1, figsize=(3, 3), dpi=50, facecolor='w', edgecolor='k') dx = fig.add_subplot(111) # mpl_finance.volume_overlay(ax, opened, closed, volume, width=0.4, colorup='b', colordown='b', alpha=1) candlestick2_ochl(dx, opened, closed, high, low, width=1.5, colorup='g', colordown='r', alpha=0.5) plt.autoscale() plt.plot(smb, color="blue", linewidth=10, alpha=0.5) plt.axis('off') # comp_ratio = close_next / closed[-1] # print(comp_ratio) filename = f'{date[0]} ~ {date[-1]}.jpg'.replace(':00:00', '') if closed[-1] > close_next: # print('close value is bigger') # print('last value: ' + str(closed[-1])) # print('next value: ' + str(close_next)) # print('sell') plt.savefig(sell_dir + filename, bbox_inches='tight') else: # print('close value is smaller') # print('last value: ' + str(closed[-1])) # print('next value: ' + str(close_next)) # print('buy') plt.savefig(buy_dir + filename, bbox_inches='tight') # plt.show() plt.close() if __name__ == '__main__': buy_dir = '../data/buy/' if not os.path.exists(buy_dir): os.makedirs(buy_dir) sell_dir = '../data/sell/' if not os.path.exists(sell_dir): os.makedirs(sell_dir) # Input your csv file here with historical data ad = np.genfromtxt('../financial_data/gemini_BTCUSD_1hr.csv', delimiter=',', dtype=str) pd = np.flipud(ad) for i in tqdm(range(int(len(pd) / 2) - 6)): graphwerk(i * 2, i * 2 + 12)
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tools/moduletests/unit/test_arpignore.py
ketanbhut/aws-ec2rescue-linux
0
102437
# Copyright 2016-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """ Unit tests for the arpignore module """ import os import subprocess import sys import unittest import mock import moduletests.src.arpignore try: # Python 2.x from cStringIO import StringIO except ImportError: # Python 3.x from io import StringIO if sys.hexversion >= 0x3040000: # contextlib.redirect_stdout was introduced in Python 3.4 import contextlib else: # contextlib2 is a backport of contextlib from Python 3.5 and is compatible with Python2/3 import contextlib2 as contextlib # builtins was named __builtin__ in Python 2 so accommodate the change for the purposes of mocking the open call if sys.version_info >= (3,): builtins_name = "builtins" else: builtins_name = "__builtin__" class Testarpignore(unittest.TestCase): config_file_path = "/etc/sysctl.d/55-arp-ignore.conf" def setUp(self): self.output = StringIO() def tearDown(self): self.output.close() @mock.patch("subprocess.check_output") def test_detect_noproblem(self, check_output_mock): """Test that no problem is detected with expected-good output.""" check_output_mock.return_value = "arp_ignore = 0" self.assertFalse(moduletests.src.arpignore.detect()) self.assertTrue(check_output_mock.called) @mock.patch("subprocess.check_output") def test_detect_problem(self, check_output_mock): """Test that the problem is detected with expected-bad output.""" check_output_mock.return_value = "arp_ignore = 1" self.assertTrue(moduletests.src.arpignore.detect()) self.assertTrue(check_output_mock.called) @mock.patch("subprocess.check_output", side_effect=["net.ipv4.conf.all.arp_ignore = 1", subprocess.CalledProcessError(1, "test")]) def test_fix_sysctlfail(self, check_output_mock): with contextlib.redirect_stdout(self.output): self.assertRaises(subprocess.CalledProcessError, moduletests.src.arpignore.fix, self.config_file_path) self.assertTrue(check_output_mock.called) self.assertTrue(self.output.getvalue().endswith( "[UNFIXED] net.ipv4.conf.all.arp_ignore=0 failed for running system\n")) @mock.patch("subprocess.check_output") @mock.patch("moduletests.src.arpignore.os.path.exists", side_effect=[False]) @mock.patch("moduletests.src.arpignore.open", side_effect=IOError) def test_fix_write_new_fail(self, open_mock, exists_mock, check_output_mock): check_output_mock.return_value = "net.ipv4.conf.lo.arp_announce = 0\nnet.ipv4.conf.all.arp_ignore = 1" with contextlib.redirect_stdout(self.output): self.assertRaises(IOError, moduletests.src.arpignore.fix, self.config_file_path) self.assertTrue(open_mock.called) self.assertTrue(exists_mock.called) self.assertTrue(check_output_mock.called) self.assertTrue(self.output.getvalue().endswith( "[UNFIXED] Unable to open /etc/sysctl.d/55-arp-ignore.conf and write to it.\n")) @mock.patch("subprocess.check_output") @mock.patch("moduletests.src.arpignore.os.path.exists", side_effect=[False]) @mock.patch("moduletests.src.arpignore.open", mock.mock_open()) def test_fix_write_new_success(self, exists_mock, check_output_mock): check_output_mock.return_value = "net.ipv4.conf.lo.arp_announce = 0\nnet.ipv4.conf.all.arp_ignore = 1" with contextlib.redirect_stdout(self.output): self.assertTrue(moduletests.src.arpignore.fix(self.config_file_path)) self.assertTrue(self.output.getvalue().endswith("[FIXED] /etc/sysctl.d/55-arp-ignore.conf written.\n")) self.assertTrue(exists_mock.called) self.assertTrue(check_output_mock.called) @mock.patch("subprocess.check_output") @mock.patch("moduletests.src.arpignore.os.path.exists", side_effect=[True]) def test_fix_success(self, exists_mock, check_output_mock): check_output_mock.return_value = "net.ipv4.conf.all.arp_ignore = 1\nsome_other = 0" open_mock = mock.mock_open(read_data="#comment\n" "net.ipv4.conf.all.arp_ignore = 1\n" "net.ipv4.conf.lo.arp_ignore = 0\n" "garbage\n") # mock_open does not have support for iteration so it must be added manually # readline() until a blank line is reached (the sentinel) def iter_func(self): return iter(self.readline, "") open_mock.return_value.__iter__ = iter_func def py3_next_func(self): return next(iter(self.readline, "")) if sys.hexversion >= 0x3000000: open_mock.return_value.__next__ = py3_next_func with mock.patch("moduletests.src.arpignore.open", open_mock): with contextlib.redirect_stdout(self.output): self.assertTrue(moduletests.src.arpignore.fix(self.config_file_path)) self.assertTrue(self.output.getvalue().endswith("[FIXED] /etc/sysctl.d/55-arp-ignore.conf written.\n")) self.assertEqual(str(open_mock.mock_calls), "[call('/etc/sysctl.d/55-arp-ignore.conf', 'r'),\n" " call().__enter__(),\n call().readlines(),\n" " call().__exit__(None, None, None),\n" " call('/etc/sysctl.d/55-arp-ignore.conf', 'w'),\n" " call().__enter__(),\n" " call().write('#comment\\nnet.ipv4.conf.lo.arp_ignore = 0'),\n" " call().write('\\n'),\n" " call().write('net.ipv4.conf.all.arp_ignore = 0'),\n" " call().write('\\n'),\n" " call().__exit__(None, None, None)]") self.assertTrue(exists_mock.called) self.assertTrue(check_output_mock.called) @mock.patch("moduletests.src.arpignore.get_config_dict", return_value=dict()) @mock.patch("moduletests.src.arpignore.detect", return_value=False) def test_run_success(self, detect_mock, config_mock): with contextlib.redirect_stdout(self.output): self.assertTrue(moduletests.src.arpignore.run()) self.assertEqual(self.output.getvalue(), "Determining if any interfaces are set to ignore arp requests\n" "[SUCCESS] arp ignore is disabled for all interfaces.\n") self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.arpignore.get_config_dict") @mock.patch("moduletests.src.arpignore.detect", return_value=True) def test_run_no_remediate(self, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": dict(), "REMEDIATE": False, "SUDO": True} with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.arpignore.run()) self.assertTrue("[UNFIXED] Remediation impossible without sudo and --remediate.\n" "-- Running as root/sudo: True\n" "-- Required --remediate flag specified: False\n" "[FAILURE] arp ignore is enabled for one or more interfaces. Please see the module log\n" in self.output.getvalue()) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.arpignore.get_config_dict") @mock.patch("moduletests.src.arpignore.detect", return_value=True) @mock.patch("moduletests.src.arpignore.os.path.isfile", return_value=True) @mock.patch("moduletests.src.arpignore.backup", return_value=True) @mock.patch("moduletests.src.arpignore.fix", return_value=True) @mock.patch("moduletests.src.arpignore.restore", return_value=True) def test_run_failure_isfile(self, restore_mock, fix_mock, backup_mock, isfile_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": {self.config_file_path: "/some/path"}, "REMEDIATE": True, "SUDO": True} with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.arpignore.run()) self.assertTrue("[FAILURE] arp ignore is enabled for one or more interfaces. " "Please see the module log" in self.output.getvalue()) self.assertTrue(restore_mock.called) self.assertTrue(fix_mock.called) self.assertTrue(backup_mock.called) self.assertTrue(isfile_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.arpignore.get_config_dict") @mock.patch("moduletests.src.arpignore.detect", return_value=True) @mock.patch("moduletests.src.arpignore.os.path.isfile", return_value=False) @mock.patch("moduletests.src.arpignore.fix", return_value=True) def test_run_failure(self, fix_mock, isfile_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": dict(), "REMEDIATE": True, "SUDO": True} with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.arpignore.run()) self.assertTrue("[FAILURE] arp ignore is enabled for one or more interfaces. " "Please see the module log" in self.output.getvalue()) self.assertTrue(fix_mock.called) self.assertTrue(isfile_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.arpignore.get_config_dict") @mock.patch("moduletests.src.arpignore.detect", side_effect=(True, False)) @mock.patch("moduletests.src.arpignore.os.path.isfile", return_value=False) @mock.patch("moduletests.src.arpignore.fix", return_value=True) def test_run_fix(self, fix_mock, isfile_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": dict(), "REMEDIATE": True, "SUDO": True} with contextlib.redirect_stdout(self.output): self.assertTrue(moduletests.src.arpignore.run()) self.assertEqual(self.output.getvalue(), "Determining if any interfaces are set to ignore arp requests\n" "[SUCCESS] arp ignore is disabled for all interfaces " "after remediation.\n") self.assertTrue(fix_mock.called) self.assertTrue(isfile_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.arpignore.get_config_dict") @mock.patch("moduletests.src.arpignore.detect", side_effect=Exception) @mock.patch("moduletests.src.arpignore.restore", return_value=True) def test_run_exception(self, restore_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": {self.config_file_path: "/some/path"}, "REMEDIATE": True, "SUDO": True} with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.arpignore.run()) self.assertTrue(restore_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.arpignore.get_config_dict", side_effect=IOError) def test_run_failure_config_exception(self, config_mock): with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.arpignore.run()) self.assertTrue(self.output.getvalue().endswith("Review the logs to determine the cause of the issue.\n")) self.assertTrue(config_mock.called)
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moyu_engine/config/system/save_system.py
MoYuStudio/MYSG01
0
79883
<reponame>MoYuStudio/MYSG01<gh_stars>0 import pickle import moyu_engine.config.data.constants as C class SavaSystem: def save_tilemap(): f=open('moyu_engine/config/data/game_save','wb') save_data = {'window':C.window} pickle.dump(save_data, f) f.close() def read_tilemap(): f=open('moyu_engine/config/data/game_save', 'rb') read_data = pickle.load(f) C.window = read_data['window'] f.close()
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bzt/modules/shellexec.py
dreading/taurus
0
132045
<reponame>dreading/taurus """ Copyright 2015 BlazeMeter Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os import subprocess from subprocess import CalledProcessError from bzt import TaurusConfigError from bzt.engine import Service from bzt.six import iteritems from bzt.utils import ensure_is_dict from bzt.utils import shutdown_process, BetterDict, is_windows ARTIFACTS_DIR_ENVVAR = "TAURUS_ARTIFACTS_DIR" class ShellExecutor(Service): def __init__(self): super(ShellExecutor, self).__init__() self.prepare_tasks = [] self.startup_tasks = [] self.check_tasks = [] self.shutdown_tasks = [] self.postprocess_tasks = [] def _load_tasks(self, stage, container): if not isinstance(self.parameters.get(stage, []), list): self.parameters[stage] = [self.parameters[stage]] for index, stage_task in enumerate(self.parameters.get(stage, [])): stage_task = ensure_is_dict(self.parameters[stage], index, "command") task_config = self.parameters[stage][index] default_cwd = self.settings.get("default-cwd", None) cwd = self.engine.find_file(task_config.get("cwd", default_cwd)) if cwd is None: working_dir = self.engine.default_cwd elif cwd == 'artifacts-dir': working_dir = self.engine.artifacts_dir else: working_dir = cwd # todo: move it to new env env = BetterDict.from_dict({k: os.environ.get(k) for k in os.environ.keys()}) env.merge(self.settings.get('env')) env.merge(task_config.get('env')) env.merge({"PYTHONPATH": working_dir}) if os.getenv("PYTHONPATH"): env['PYTHONPATH'] = os.getenv("PYTHONPATH") + os.pathsep + env['PYTHONPATH'] env[ARTIFACTS_DIR_ENVVAR] = self.engine.artifacts_dir for name, value in iteritems(env): env[str(name)] = str(value) task = Task(task_config, self.log, working_dir, env) container.append(task) self.log.debug("Added %s task: %s", stage, stage_task) def prepare(self): """ Configure Tasks :return: """ self._load_tasks('prepare', self.prepare_tasks) self._load_tasks('startup', self.startup_tasks) self._load_tasks('check', self.check_tasks) self._load_tasks('shutdown', self.shutdown_tasks) self._load_tasks('post-process', self.postprocess_tasks) for task in self.prepare_tasks: task.start() def startup(self): for task in self.startup_tasks: task.start() def check(self): for task in self.check_tasks: task.start() for task in self.prepare_tasks + self.startup_tasks + self.check_tasks: task.check() return super(ShellExecutor, self).check() def shutdown(self): for task in self.shutdown_tasks: task.start() for task in self.check_tasks + self.startup_tasks: task.shutdown() def post_process(self): for task in self.shutdown_tasks + self.check_tasks + self.startup_tasks + self.prepare_tasks: task.shutdown() for task in self.postprocess_tasks: task.start() task.shutdown() class Task(object): """ :type process: subprocess.Popen """ def __init__(self, config, parent_log, working_dir, env): self.log = parent_log.getChild(self.__class__.__name__) self.working_dir = working_dir self.env = env self.command = config.get("command", TaurusConfigError("Parameter is required: command")) self.is_background = config.get("background", False) self.ignore_failure = config.get("ignore-failure", False) self.err = config.get("err", subprocess.PIPE) self.out = config.get("out", subprocess.PIPE) self.process = None self.ret_code = None def start(self): """ Start task """ if self.process: self.check() self.log.info("Process still running: %s", self) return if self.out is not None and self.out != subprocess.PIPE: out = open(self.out, 'at') else: out = self.out if self.err is not None and self.err != subprocess.PIPE: err = open(self.err, 'at') else: err = self.err kwargs = { 'args': self.command, 'stdout': out, 'stderr': err, 'cwd': self.working_dir, 'env': self.env, 'shell': True } # FIXME: shouldn't we bother closing opened descriptors? if not is_windows(): kwargs['preexec_fn'] = os.setpgrp kwargs['close_fds'] = True self.log.info("Starting shell command: %s", self) self.process = subprocess.Popen(**kwargs) if self.is_background: self.log.debug("Task started, PID: %d", self.process.pid) else: self.check(sync=True) def check(self, sync=False): if not self.process or self.ret_code is not None: # finished task return self.ret_code = self.process.poll() if self.ret_code is None and not sync: self.log.debug('Task: %s is not finished yet', self) return False stdout, stderr = self.process.communicate() self.ret_code = self.process.poll() if stdout and (self.out == subprocess.PIPE): self.log.debug("Output for %s:\n%s", self, stdout) if stderr and (self.err == subprocess.PIPE): self.log.warning("Errors for %s:\n%s", self, stderr) self.log.debug("Task was finished with exit code %s: %s", self.ret_code, self) if not self.ignore_failure and self.ret_code != 0: if self.out != subprocess.PIPE: self.log.warning("Output for %s:\n%s", self, stdout) raise CalledProcessError(self.ret_code, self) return True def shutdown(self): """ If task was not completed, kill process, provide output else provide output :return: """ self.check() if self.process and self.ret_code is None: self.log.info("Background task was not completed, shutting it down: %s", self) shutdown_process(self.process, self.log) self.process = None def __repr__(self): return self.command
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milefrienddb/models/clients.py
jcrjaci/mil_test
0
1602130
<filename>milefrienddb/models/clients.py """Clients's app models.""" import uuid from django.db import models class Client(models.Model): """Model representing a client.""" DRIVER_LICENSE_PATH = 'clients/driver_license' PHOTO_PATH = 'clients/photos' CERT_INCORPORATION_PATH = 'clients/certificate_incorporation' UTILITY_BILL_PATH = 'clients/utility_bill' id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False) first_name = models.CharField(max_length=32) last_name = models.CharField(max_length=32) street = models.CharField(max_length=255) city = models.CharField(max_length=255) state = models.CharField(max_length=255) phone = models.CharField(max_length=16) email = models.CharField(max_length=64) driver_license = models.FileField(upload_to=DRIVER_LICENSE_PATH, blank=True) photo = models.FileField(upload_to=PHOTO_PATH) bank_name = models.CharField(max_length=32) bank_account_number = models.CharField(max_length=255) insured_score = models.FloatField(default=0) is_company = models.BooleanField(default=False) certificate_incorporation = models.FileField(upload_to=CERT_INCORPORATION_PATH, blank=True) utility_bill = models.FileField(upload_to=UTILITY_BILL_PATH, blank=True) def __str__(self): """String representation of the object.""" return "{0}, {1} {2}".format(self.id, self.first_name, self.last_name) class Meta: db_table = 'clients_client' app_label = 'milefrienddb'
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exercises/028.py
rpoliselit/python-for-dummies
0
71184
<gh_stars>0 """ Make a program that the user has to guess a number between 1 and 5, then returns winner or loser. """ from random import randint w, l = 0, 0 play = 'y' while play == 'y': guess = int(input('Make your guess: ')) number = randint(1,5) if guess == number: print('You wins!') w += 1 else: print('You lose!\nCorrect answer is {}.'.format(number)) l += 1 play = str(input('Play again? [y/n]\n')).strip().lower() score = 100 * w / (w + l) print('Score = {:.1f}%.\nW: {}\nL: {}'.format(score,w,l))
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landlab/components/soil_moisture/__init__.py
awickert/landlab
1
133833
<filename>landlab/components/soil_moisture/__init__.py from .soil_moisture_dynamics import SoilMoisture from .infiltrate_soil_green_ampt import SoilInfiltrationGreenAmpt __all__ = ['SoilMoisture', 'SoilInfiltrationGreenAmpt', ]
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plugins/python/tasks.py
BBVA/deeptracy
85
3826
import json from washer.worker.actions import AppendStdout, AppendStderr from washer.worker.actions import CreateNamedLog, AppendToLog from washer.worker.actions import SetProperty from washer.worker.commands import washertask def pipenv_graph2deps(rawgraph): graph = json.loads(rawgraph) def build_entry(data): if 'required_version' in data: spec = data['key'] + data['required_version'] else: spec = data['key'] return {'installer': 'pipenv', 'spec': spec, 'source': 'pypi', 'name': data['package_name'], 'version': data['installed_version']} def extract_dependencies(entries): for entry in entries: if 'package' in entry: package = entry['package'] dependencies = entry.get('dependencies', []) yield build_entry(package) yield from extract_dependencies(dependencies) else: yield build_entry(entry) yield from extract_dependencies(graph) @washertask def pip_install(repopath, path=".", **kwargs): import invoke c = invoke.Context() with c.cd(repopath): with c.cd(path): res = c.run("pipenv install .") deps = c.run("pipenv graph --json") yield AppendStdout(res.stdout) yield AppendStderr(res.stderr) yield SetProperty("dependencies", list(pipenv_graph2deps(deps.stdout))) return True @washertask def requirement_file(repopath, requirement="requirements.txt", path=".", **kwargs): import invoke c = invoke.Context() with c.cd(repopath): with c.cd(path): res = c.run("pipenv install -r %s" % requirement) deps = c.run("pipenv graph --json") yield AppendStdout(res.stdout) yield AppendStderr(res.stderr) yield SetProperty("dependencies", list(pipenv_graph2deps(deps.stdout))) return True
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cms/templatetags/html.py
cresset-group/cms
13
1603438
<gh_stars>10-100 '''Template tags used for processing HTML.''' from django.template.defaultfilters import stringfilter from django.utils.safestring import mark_safe from django_jinja import library from cms.html import process as process_html @library.filter @stringfilter def html(text): ''' Processes HTML text. The text is checked for permalinks embedded in <a> tags, expanding the permalinks to their referenced URL. Images containing a permalink source are checked for size and thumbnailed as appropriate. ''' if not text: return '' text = process_html(text) return mark_safe(text) @library.filter @stringfilter def truncate_paragraphs(text, number): '''Returns HTML text truncated to the given number of paragraphs.''' position = 0 count = 0 while count < number and position < len(text): position = text.find('</p>', position) if position == -1: position = len(text) else: position += 4 count += 1 return text[:position]
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tests/bugs/core_6088_test.py
reevespaul/firebird-qa
0
146689
#coding:utf-8 # # id: bugs.core_6088 # title: "SIMILAR TO" hangs when processing parenthesis # decription: # Confirmed normal work (evaluation for less than 5 ms) on WI-T4.0.0.1598 # 31.12.2020: increased max duration threshold from 100 to 150 ms. # # tracker_id: CORE-6088 # min_versions: ['4.0'] # versions: 4.0 # qmid: None import pytest from firebird.qa import db_factory, isql_act, Action # version: 4.0 # resources: None substitutions_1 = [] init_script_1 = """""" db_1 = db_factory(sql_dialect=3, init=init_script_1) test_script_1 = """ set list on; set count on; set term ^; execute block as begin rdb$set_context( 'USER_SESSION','DTS_START', cast('now' as timestamp) ); rdb$set_context( 'USER_SESSION','MAX_THRESHOLD_MS', 150 ); -- ^ -- | -- ############################# -- MAX ALLOWED EXECUTION TIME,MS -- ############################# end ^ set term ;^ select 1 as result from RDB$DATABASE where 'a-b c(d)' similar to '[[:WHITESPACE:]a-z\\-]{0,199}' escape '\\' -- ^ -- | -- ATTENTION: TWO BACKSLASHES MUST BE HERE WHEN USE FBT_RUN TO CHECK -- ############### ; set count off; select iif( evaluated_ms <= max_allowed_ms ,'acceptable' ,'TOO LONG: ' || evaluated_ms || ' ms - this is more then threshold = ' || max_allowed_ms || ' ms' ) as duration from ( select datediff( millisecond from cast(rdb$get_context( 'USER_SESSION','DTS_START') as timestamp) to current_timestamp ) evaluated_ms ,cast( rdb$get_context( 'USER_SESSION','MAX_THRESHOLD_MS' ) as int ) as max_allowed_ms from rdb$database ); """ act_1 = isql_act('db_1', test_script_1, substitutions=substitutions_1) expected_stdout_1 = """ Records affected: 0 DURATION acceptable """ @pytest.mark.version('>=4.0') def test_1(act_1: Action): act_1.expected_stdout = expected_stdout_1 act_1.execute() assert act_1.clean_expected_stdout == act_1.clean_stdout
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data-scraper/main.py
tmthyln/MarylandMaps
0
129165
import requests import csv from bs4 import BeautifulSoup import time import wget import ssl import random ssl._create_default_https_context = ssl._create_unverified_context domain = "https://digital.lib.umd.edu" attributes = {'Title'} downloaded_map_cnt = 0 def download_image(record): time.sleep(random.random() * 1) link = domain + record.find('a')['href'] try: return wget.download(link) except: return link def get_map(link, fields, dowwnload_the_image): link = domain + link print(link) map_page = requests.get(link) map_soup = BeautifulSoup(map_page.content, 'html.parser') divs = map_soup.find_all("div", {"class": "search-record"}) title_div = map_soup.find("div", {"id": "main-content"}) title_text = title_div.find("h1").text.strip() map_record = {'Title': title_text} for d in divs: record = d.find_all("div") key = record[0].text.strip()[:-1] if key == "Digital Image": val = download_image(record[1]) else: val = record[1].text.strip() map_record[key] = val fields.add(key) print(map_record) return map_record def parse_maps(): url_base = "https://digital.lib.umd.edu/mdmap/search/facet/Digitized/Yes?pageNumber=" maps = [] map_cnt = 0 for i in range(1,7): page = requests.get(url_base+str(i)) soup = BeautifulSoup(page.content, 'html.parser') divs = soup.find_all("div", {"class": "search-record"}) for d in divs: time.sleep(random.random()*2+5) maps.append(get_map(d.find('a')['href'], attributes, map_cnt>=downloaded_map_cnt)) map_cnt += 1 return maps mapslist = parse_maps() with open('maps.csv', 'w', newline='') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=attributes) writer.writeheader() writer.writerows(mapslist)
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examples/basic/wire_feedthrough.py
souviksaha97/spydrnet-physical
0
4224
""" ========================================== Genrating feedthrough from single instance ========================================== This example demostrates how to generate a feedthrough wire connection for a given scalar or vector wires. **Initial Design** .. hdl-diagram:: ../../../examples/basic/_initial_design.v :type: netlistsvg :align: center :module: top **Output1** ``wire0`` feedthough from ``inst_2_1`` .. hdl-diagram:: ../../../examples/basic/_output_wire.v :type: netlistsvg :align: center :module: top **Output2** ``bus_in`` feedthrough from ``inst_1_0`` .. hdl-diagram:: ../../../examples/basic/_output_bus.v :type: netlistsvg :align: center :module: top """ from os import path import spydrnet as sdn import spydrnet_physical as sdnphy netlist = sdnphy.load_netlist_by_name('basic_hierarchy') top = netlist.top_instance.reference cable0 = next(top.get_cables("wire0")) inst2 = next(top.get_instances("inst_2_0")) sdn.compose(netlist, '_initial_design.v', skip_constraints=True) top.create_feedthrough(inst2, cable0) top.create_unconn_wires() sdn.compose(netlist, '_output_wire.v', skip_constraints=True) netlist = sdnphy.load_netlist_by_name('basic_hierarchy') top = netlist.top_instance.reference bus_in = next(top.get_cables("bus_in")) inst1 = next(top.get_instances("inst_1_0")) cables = top.create_feedthrough(inst1, bus_in) top.create_unconn_wires() sdn.compose(netlist, '_output_bus.v', skip_constraints=True)
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mmdnn/conversion/examples/tensorflow/extractor.py
ferriswym/MMdnn
0
17863
<filename>mmdnn/conversion/examples/tensorflow/extractor.py #---------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. #---------------------------------------------------------------------------------------------- from __future__ import absolute_import import os import tensorflow as tf from tensorflow.contrib.slim.nets import vgg from tensorflow.contrib.slim.nets import inception from tensorflow.contrib.slim.nets import resnet_v1 from tensorflow.contrib.slim.nets import resnet_v2 from mmdnn.conversion.examples.tensorflow.models import inception_resnet_v2 from mmdnn.conversion.examples.tensorflow.models import mobilenet_v1 from mmdnn.conversion.examples.tensorflow.models import nasnet from mmdnn.conversion.examples.tensorflow.models.mobilenet import mobilenet_v2 from mmdnn.conversion.examples.tensorflow.models import inception_resnet_v1 from mmdnn.conversion.examples.tensorflow.models import test_rnn slim = tf.contrib.slim from mmdnn.conversion.examples.imagenet_test import TestKit from mmdnn.conversion.examples.extractor import base_extractor from mmdnn.conversion.common.utils import download_file # https://github.com/tensorflow/tensorflow/issues/24496 config = tf.ConfigProto() config.gpu_options.allow_growth = True class tensorflow_extractor(base_extractor): MMDNN_BASE_URL = 'http://mmdnn.eastasia.cloudapp.azure.com:89/models/' architecture_map = { 'vgg16' : { 'url' : 'http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz', 'filename' : 'vgg_16.ckpt', 'builder' : lambda : vgg.vgg_16, 'arg_scope' : vgg.vgg_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 224, 224, 3]), 'num_classes' : 1000, }, 'vgg19' : { 'url' : 'http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz', 'filename' : 'vgg_19.ckpt', 'builder' : lambda : vgg.vgg_19, 'arg_scope' : vgg.vgg_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 224, 224, 3]), 'num_classes' : 1000, }, 'inception_v1' : { 'url' : 'http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz', 'filename' : 'inception_v1.ckpt', 'builder' : lambda : inception.inception_v1, 'arg_scope' : inception.inception_v3_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 224, 224, 3]), 'num_classes' : 1001, }, 'inception_v1_frozen' : { 'url' : 'https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_2016_08_28_frozen.pb.tar.gz', 'filename' : 'inception_v1_2016_08_28_frozen.pb', 'tensor_out' : ['InceptionV1/Logits/Predictions/Reshape_1:0'], 'tensor_in' : ['input:0'], 'input_shape' : [[224, 224, 3]], # input_shape of the elem in tensor_in 'feed_dict' :lambda img: {'input:0':img}, 'num_classes' : 1001, }, 'inception_v3' : { 'url' : 'http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz', 'filename' : 'inception_v3.ckpt', 'builder' : lambda : inception.inception_v3, 'arg_scope' : inception.inception_v3_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 299, 299, 3]), 'num_classes' : 1001, }, 'inception_v3_frozen' : { 'url' : 'https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz', 'filename' : 'inception_v3_2016_08_28_frozen.pb', 'tensor_out' : ['InceptionV3/Predictions/Softmax:0'], 'tensor_in' : ['input:0'], 'input_shape' : [[299, 299, 3]], # input_shape of the elem in tensor_in 'feed_dict' :lambda img: {'input:0':img}, 'num_classes' : 1001, }, 'resnet_v1_50' : { 'url' : 'http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz', 'filename' : 'resnet_v1_50.ckpt', 'builder' : lambda : resnet_v1.resnet_v1_50, 'arg_scope' : resnet_v2.resnet_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 224, 224, 3]), 'num_classes' : 1000, }, 'resnet_v1_152' : { 'url' : 'http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz', 'filename' : 'resnet_v1_152.ckpt', 'builder' : lambda : resnet_v1.resnet_v1_152, 'arg_scope' : resnet_v2.resnet_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 224, 224, 3]), 'num_classes' : 1000, }, 'resnet_v2_50' : { 'url' : 'http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz', 'filename' : 'resnet_v2_50.ckpt', 'builder' : lambda : resnet_v2.resnet_v2_50, 'arg_scope' : resnet_v2.resnet_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 299, 299, 3]), 'num_classes' : 1001, }, 'resnet_v2_101' : { 'url' : 'http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz', 'filename' : 'resnet_v2_101.ckpt', 'builder' : lambda : resnet_v2.resnet_v2_101, 'arg_scope' : resnet_v2.resnet_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 299, 299, 3]), 'num_classes' : 1001, }, 'resnet_v2_152' : { 'url' : 'http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz', 'filename' : 'resnet_v2_152.ckpt', 'builder' : lambda : resnet_v2.resnet_v2_152, 'arg_scope' : resnet_v2.resnet_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 299, 299, 3]), 'num_classes' : 1001, }, 'resnet_v2_200' : { 'url' : 'http://download.tensorflow.org/models/resnet_v2_200_2017_04_14.tar.gz', 'filename' : 'resnet_v2_200.ckpt', 'builder' : lambda : resnet_v2.resnet_v2_200, 'arg_scope' : resnet_v2.resnet_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 299, 299, 3]), 'num_classes' : 1001, }, 'mobilenet_v1_1.0' : { 'url' : 'http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz', 'filename' : 'mobilenet_v1_1.0_224.ckpt', 'builder' : lambda : mobilenet_v1.mobilenet_v1, 'arg_scope' : mobilenet_v1.mobilenet_v1_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 224, 224, 3]), 'num_classes' : 1001, }, 'mobilenet_v1_1.0_frozen' : { 'url' : 'https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz', 'filename' : 'mobilenet_v1_1.0_224/frozen_graph.pb', 'tensor_out' : ['MobilenetV1/Predictions/Softmax:0'], 'tensor_in' : ['input:0'], 'input_shape' : [[224, 224, 3]], # input_shape of the elem in tensor_in 'feed_dict' :lambda img: {'input:0':img}, 'num_classes' : 1001, }, 'mobilenet_v2_1.0_224':{ 'url' : 'https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz', 'filename' : 'mobilenet_v2_1.0_224.ckpt', 'builder' : lambda : mobilenet_v2.mobilenet, 'arg_scope' : mobilenet_v2.training_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 224, 224, 3]), 'num_classes' : 1001, }, 'inception_resnet_v2' : { 'url' : 'http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz', 'filename' : 'inception_resnet_v2_2016_08_30.ckpt', 'builder' : lambda : inception_resnet_v2.inception_resnet_v2, 'arg_scope' : inception_resnet_v2.inception_resnet_v2_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 299, 299, 3]), 'num_classes' : 1001, }, 'nasnet-a_large' : { 'url' : 'https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_large_04_10_2017.tar.gz', 'filename' : 'model.ckpt', 'builder' : lambda : nasnet.build_nasnet_large, 'arg_scope' : nasnet.nasnet_large_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 331, 331, 3]), 'num_classes' : 1001, }, 'facenet' : { 'url' : MMDNN_BASE_URL + 'tensorflow/facenet/20180408-102900.zip', 'filename' : '20180408-102900/model-20180408-102900.ckpt-90', 'builder' : lambda : inception_resnet_v1.inception_resnet_v1, 'arg_scope' : inception_resnet_v1.inception_resnet_v1_arg_scope, 'input' : lambda : tf.placeholder(name='input', dtype=tf.float32, shape=[None, 160, 160, 3]), 'feed_dict' : lambda img: {'input:0':img,'phase_train:0':False}, 'num_classes' : 0, }, 'facenet_frozen' : { 'url' : MMDNN_BASE_URL + 'tensorflow/facenet/20180408-102900.zip', 'filename' : '20180408-102900/20180408-102900.pb', 'tensor_out' : ['InceptionResnetV1/Logits/AvgPool_1a_8x8/AvgPool:0'], 'tensor_in' : ['input:0','phase_train:0'], 'input_shape' : [[160, 160, 3],1], # input_shape of the elem in tensor_in 'feed_dict' : lambda img: {'input:0':img,'phase_train:0':False}, 'num_classes' : 0, }, 'rnn_lstm_gru_stacked': { 'url' : MMDNN_BASE_URL + 'tensorflow/tf_rnn/tf_rnn.zip', # Note this is just a model used for test, not a standard rnn model. 'filename' :'tf_rnn/tf_lstm_gru_stacked.ckpt', 'builder' :lambda: test_rnn.create_symbol, 'arg_scope' :test_rnn.dummy_arg_scope, 'input' :lambda: tf.placeholder(name='input', dtype=tf.int32, shape=[None, 150]), 'feed_dict' :lambda x:{'input:0': x}, 'num_classes' : 0 } } @classmethod def handle_checkpoint(cls, architecture, path): with slim.arg_scope(cls.architecture_map[architecture]['arg_scope']()): data_input = cls.architecture_map[architecture]['input']() logits, endpoints = cls.architecture_map[architecture]['builder']()( data_input, num_classes=cls.architecture_map[architecture]['num_classes'], is_training=False) if logits.op.type == 'Squeeze': labels = tf.identity(logits, name='MMdnn_Output') else: labels = tf.squeeze(logits, name='MMdnn_Output') init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) saver = tf.train.Saver() saver.restore(sess, path + cls.architecture_map[architecture]['filename']) save_path = saver.save(sess, path + "imagenet_{}.ckpt".format(architecture)) print("Model saved in file: %s" % save_path) import tensorflow.contrib.keras as keras keras.backend.clear_session() @classmethod def handle_frozen_graph(cls, architecture, path): return # raise NotImplementedError() @classmethod def get_frozen_para(cls, architecture): frozenname = architecture + '_frozen' tensor_in = list(map(lambda x:x.split(':')[0], cls.architecture_map[frozenname]['tensor_in'])) tensor_out = list(map(lambda x:x.split(':')[0], cls.architecture_map[frozenname]['tensor_out'])) return cls.architecture_map[frozenname]['filename'], cls.architecture_map[frozenname]['input_shape'], tensor_in, tensor_out @classmethod def download(cls, architecture, path="./"): if cls.sanity_check(architecture): architecture_file = download_file(cls.architecture_map[architecture]['url'], directory=path, auto_unzip=True) if not architecture_file: return None tf.reset_default_graph() if 'ckpt' in cls.architecture_map[architecture]['filename']: cls.handle_checkpoint(architecture, path) elif cls.architecture_map[architecture]['filename'].endswith('pb'): cls.handle_frozen_graph(architecture, path) else: raise ValueError("Unknown file name [{}].".format(cls.architecture_map[architecture]['filename'])) return architecture_file else: return None @classmethod def inference(cls, architecture, files, path, test_input_path, is_frozen=False): if is_frozen: architecture_ = architecture + "_frozen" else: architecture_ = architecture if cls.download(architecture_, path): import numpy as np if 'rnn' not in architecture_: func = TestKit.preprocess_func['tensorflow'][architecture] img = func(test_input_path) img = np.expand_dims(img, axis=0) input_data = img else: input_data = np.load(test_input_path) if is_frozen: tf_model_path = cls.architecture_map[architecture_]['filename'] with open(path + tf_model_path, 'rb') as f: serialized = f.read() tf.reset_default_graph() original_gdef = tf.GraphDef() original_gdef.ParseFromString(serialized) tf_output_name = cls.architecture_map[architecture_]['tensor_out'] tf_input_name = cls.architecture_map[architecture_]['tensor_in'] feed_dict = cls.architecture_map[architecture_]['feed_dict'] with tf.Graph().as_default() as g: tf.import_graph_def(original_gdef, name='') with tf.Session(graph = g, config=config) as sess: tf_out = sess.run(tf_output_name[0], feed_dict=feed_dict(input_data)) # temporarily think the num of out nodes is one predict = np.squeeze(tf_out) return predict else: with slim.arg_scope(cls.architecture_map[architecture]['arg_scope']()): data_input = cls.architecture_map[architecture]['input']() logits, endpoints = cls.architecture_map[architecture]['builder']()( data_input, num_classes=cls.architecture_map[architecture]['num_classes'], is_training=False) labels = tf.squeeze(logits) init = tf.global_variables_initializer() with tf.Session(config=config) as sess: sess.run(init) saver = tf.train.Saver() saver.restore(sess, path + cls.architecture_map[architecture]['filename']) predict = sess.run(logits, feed_dict = {data_input : input_data}) import tensorflow.contrib.keras as keras keras.backend.clear_session() predict = np.squeeze(predict) return predict else: return None
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tests/unit/configuration/twindb_backup_config/test_gcs.py
denssk/backup
69
119926
from twindb_backup.configuration import TwinDBBackupConfig def test_gcs(config_file): tbc = TwinDBBackupConfig(config_file=str(config_file)) assert tbc.gcs.gc_credentials_file == 'XXXXX' assert tbc.gcs.gc_encryption_key == '' assert tbc.gcs.bucket == 'twindb-backups' def test_no_gcs_section(tmpdir): cfg_file = tmpdir.join('twindb-backup.cfg') with open(str(cfg_file), 'w') as fp: fp.write('') tbc = TwinDBBackupConfig(config_file=str(cfg_file)) assert tbc.gcs is None
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agent/sniffer_no_db.py
irtlab/thingscope
0
1602028
<reponame>irtlab/thingscope from scapy.all import * from new_dns_callback import DNSCallback import sys import argparse from colorprint import ColorPrint as color if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--devname", help="Enter the name of the device", default="example") parser.add_argument("--devmodel", help="Enter the model of the device", default="example") parser.add_argument("--manuname", help="Enter the name of the manufacturer", default="example") parser.add_argument("--manuweb", help="Enter the website of the manufacturer", default="http://example.com") parser.add_argument("--ignore", help="Enter MAC address of device to ignore", default="") args = parser.parse_args() print(color.OKBLUE + "[INFO] Observing Network Traffic" + color.ENDC) dns_callback = DNSCallback(device_name=args.devname, device_model=args.devmodel, manufacturer_name=args.manuname, manufacturer_website=args.manuweb, ignore_device=args.ignore) # Add router MAC to list of devices seen # router_mac = sys.argv[1] # dns_callback.add_to_ignore_device_list(router_mac) try: sniff(prn=dns_callback.pktHandler, iface="wlan0") except Exception as e: print(color.ERROR + "[ERROR] Sniffer File: " + e + color.ENDC)
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examples/semanticClustering/experimentUtil.py
mikiec84/SemanticModels.jl
0
164140
import pandas as pd import numpy as np import umap import sklearn.cluster as cluster from sklearn.cluster import KMeans from sklearn.cluster import DBSCAN import spacy import unicodedata import matplotlib.pyplot as plt import logging logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO) logging.getLogger().setLevel(logging.INFO) JULIA_VARIABLE_CSV_PATH = "ExperimentData/JuliaVariableData.csv" CLUSTER_LABEL_CSV_PATH = "clusteringLabels.csv" KMEANS_CLUSTER_LABEL_CSV_PATH = "ExperimentData/KmeansCluster.csv" KMEANS_CLUSTER_TRUTH_CSV_PATH = "ExperimentData/KmeanClusterTruths.csv" KMEANS_PREDICTED_CSV_PATH = "ExperimentData/KmeansPredicted.csv" PREDICTED_UMAP_CSV_PATH = "ExperimentData/simPredictedUmapClusters.csv" def createWord2Vec(data): nlp = spacy.load('en_core_web_md') tokenList = [] for phrase in data: token = nlp(phrase) tokenList.append(token.vector) return np.asarray(tokenList) def useUMAP(tokenList): db = DBSCAN(eps=0.3, min_samples=2).fit(np.asarray(tokenList)) umapModel = umap.UMAP(random_state=42).fit(np.asarray(tokenList)) standardEmbedding = umapModel.transform(tokenList) db_umap = DBSCAN(eps=0.3, min_samples=2).fit(standardEmbedding) return np.asarray(db.labels_), np.asarray(db_umap.labels_) def writeUMAP_DBSCAN_CSV(subj_array, labels, umapLabels, labelsSimArray, \ uMapLabelsSimArray, OutSampleLabelsSimArray, OutSampleUMAPSimArray): logging.info("Writing CSV") outputString = "node,labels,umapLabels,dbscanSim,UMAPsim,out_sampleDBSCAN,out_sampleUMAP\n" for i in range(len(labels)): outputString += str(subj_array[i]) + ","\ + str(labels[i]) + ","\ +str(umapLabels[i]) + ","\ + str(labelsSimArray[i]) + ","\ + str(uMapLabelsSimArray[i])+ ","\ + str(OutSampleLabelsSimArray[i]) + ","\ + str(OutSampleUMAPSimArray[i]) + "\n" with open(CLUSTER_LABEL_CSV_PATH, 'w') as filetowrite: filetowrite.write(outputString) filetowrite.close() def generatePairs(labels, umapLabels, data): nlp = spacy.load('en_core_web_md') labelsSimArray = [] uMapLabelsSimArray = [] OutSampleLabelsSimArray = [] OutSampleUMAPSimArray = [] labels_sim = 0; umapLabels_sim = 0; outsample_labels_sim = 0; outsample_umap_sim = 0; for i in range(len(data)): logging.info("Iterating Word " + str(i)) for j in range(len(data)): if i != j: token1 = nlp(data[i]) token2 = nlp(data[j]) if(labels[i] == labels[j]): labels_sim += token1.similarity(token2) if(umapLabels[i] == umapLabels[j]): umapLabels_sim += token1.similarity(token2) if(labels [i] != labels[j]): outsample_labels_sim += token1.similarity(token2) if(umapLabels[i] != umapLabels[j]): outsample_umap_sim += token1.similarity(token2) if j == len(data)-1: labelsSimArray.append(float(labels_sim/(list(labels).count(labels[i])-1))) uMapLabelsSimArray.append(float(umapLabels_sim/(list(umapLabels).count(umapLabels[i])-1))) if len(labels)-list(labels).count(labels[i]) == 0: OutSampleLabelsSimArray.append(1) else: OutSampleLabelsSimArray.append(float(outsample_labels_sim/(len(labels)-1-list(labels).count(labels[i])))) if len(umapLabels)-list(umapLabels).count(umapLabels[i]) == 0: OutSampleUMAPSimArray.append(1) else: OutSampleUMAPSimArray.append(float(outsample_umap_sim/(len(umapLabels)-1-list(umapLabels).count(umapLabels[i])))) labels_sim = 0; umapLabels_sim = 0; outsample_labels_sim = 0; outsample_umap_sim = 0; return labelsSimArray, uMapLabelsSimArray, OutSampleLabelsSimArray, OutSampleUMAPSimArray def createCluster(svoFile): SVOdata = pd.read_csv(svoFile) subj_array = list(SVOdata["subject"]) obj_array = list(SVOdata["object"]) totalNodes = subj_array + obj_array tokenList = createWord2Vec(totalNodes) #Use UMAP Clustering labels,umapLabels = useUMAP(tokenList) #Retrieves Labels for Similarity labelsSimArray, uMapLabelsSimArray, OutSampleLabelsSimArray, OutSampleUMAPSimArray = \ generatePairs(labels, umapLabels, totalNodes) #Writes CSV for UMAP vs DBScan Labels writeUMAP_DBSCAN_CSV(totalNodes, labels, umapLabels, labelsSimArray, \ uMapLabelsSimArray, OutSampleLabelsSimArray, OutSampleUMAPSimArray ) def cleanVariables(variableArray): for i in range(len(variableArray)): variableArray[i] = str(variableArray[i]).replace(",", " ") variableArray[i] = str(variableArray[i]).replace("_", " ") variableArray[i] = containsGreek(variableArray[i]) return variableArray def containsGreek(inputString): greekLetters = [] for s in inputString: name = unicodedata.name(chr(ord(s))) if "GREEK" in name: greekLetters.append(s) for letter in greekLetters: name = unicodedata.name(chr(ord(letter))).split(" ")[3] name = name.lower().capitalize() inputString = inputString.replace(letter, str(name) + str(" ")) return inputString def useKmeans(trainTokenList, K_size, variableTokenList): print(type(trainTokenList), type(K_size), type(variableTokenList)) umapModel = umap.UMAP(random_state=42).fit(np.asarray(trainTokenList)) trainEmbedding = umapModel.transform(trainTokenList) predictEmbedding = umapModel.transform(variableTokenList) kmeans = KMeans(n_clusters=K_size, random_state = 0).fit(trainEmbedding) return kmeans.labels_, kmeans.predict(predictEmbedding) def writeCSV(variable_array, predictedLabels, fileName): logging.info("generating CSV " + fileName) outputString = "variable,cluster\n" for i in range(len(variable_array)): outputString += str(variable_array[i].replace(",", " ")) + "," + str(predictedLabels[i]) + "\n" with open(fileName, 'w') as filetowrite: filetowrite.write(outputString) filetowrite.close() def groupNodesByCluster(umapData): maxNoClusters = max(list(umapData["umapLabels"])) clusteredNodes = [] for i in range(maxNoClusters + 1): temp_bin = [] for j in range(len(list(umapData["umapLabels"]))): if list(umapData["umapLabels"])[j] == i: temp_bin.append(list(umapData["node"])[j]) clusteredNodes.append(temp_bin) return clusteredNodes def groupNodesByKMeansCluster(kMeansData): maxNoClusters = max(list(kMeansData["cluster"])) clusteredNodes = [] for i in range(maxNoClusters + 1): temp_bin = [] for j in range(len(list(kMeansData["cluster"]))): if list(kMeansData["cluster"])[j] == i: temp_bin.append(list(kMeansData["variable"])[j]) clusteredNodes.append(temp_bin) return clusteredNodes def getSimilarityLabels(clusteredNodes, variable_array): labels = [] nlp = spacy.load('en_core_web_md') count = 0 for variable in variable_array: logging.info("Comparing Variable No: " + str(count)) count += 1 variableToken = nlp(variable) highest_average = -9000 label = 0 for clusterNo in range(len(clusteredNodes)): average = 0 for node in clusteredNodes[clusterNo]: nodeToken = nlp(node) average += variableToken.similarity(nodeToken) average /= len(clusteredNodes[clusterNo]) if average > highest_average: highest_average = average label = clusterNo labels.append(label) return labels def calculateKMeansAccuracy(): labeledData = pd.read_csv(JULIA_VARIABLE_CSV_PATH) predictedData = pd.read_csv(KMEANS_PREDICTED_CSV_PATH) labeled = list(labeledData["KMeansLabels"]) predicted = list(predictedData["cluster"]) count = 0 for i in range(len(predicted)): if labeled[i] == predicted[i]: count += 1 logging.info("KMeans Accuracy is : " + str(float(count/len(predicted)))) def calculateSimAccuracy(): labeledData = pd.read_csv(JULIA_VARIABLE_CSV_PATH) predictedData = pd.read_csv(PREDICTED_UMAP_CSV_PATH) labeled = list(labeledData["DBSCANLabels"]) predicted = list(predictedData["cluster"]) count = 0 for i in range(len(predicted)): if labeled[i] == predicted[i]: count += 1 logging.info("Similar Cluster Assignment Accuracy is : " + str(float(count/len(predicted)))) def runKMeansExp(): variableData = pd.read_csv(JULIA_VARIABLE_CSV_PATH) umapData = pd.read_csv(CLUSTER_LABEL_CSV_PATH) umapData = umapData[umapData.umapLabels != -1] kmeansTrainData = list(umapData["node"]) variable_array = list(variableData["variable"]) variable_array = cleanVariables(variable_array) variableTokenList = createWord2Vec(variable_array) trainTokenList = createWord2Vec(kmeansTrainData) print(len(trainTokenList)) K_size = max(list(umapData["umapLabels"])) trainLabels, predictedLabels = useKmeans(trainTokenList, K_size, variableTokenList) writeCSV(kmeansTrainData, trainLabels, KMEANS_CLUSTER_LABEL_CSV_PATH) writeCSV(variable_array, predictedLabels, KMEANS_PREDICTED_CSV_PATH) calculateKMeansAccuracy() def runUMapSimilarityExp(): variableData = pd.read_csv(JULIA_VARIABLE_CSV_PATH) umapData = pd.read_csv(CLUSTER_LABEL_CSV_PATH) umapData = umapData[umapData.umapLabels != -1] variable_array = list(variableData["variable"]) variable_array = cleanVariables(variable_array) clusteredNodes = groupNodesByCluster(umapData) labels = getSimilarityLabels(clusteredNodes, variable_array) writeCSV(variable_array, labels, PREDICTED_UMAP_CSV_PATH) calculateSimAccuracy() def getAverageSimilarity(variable_array, clusteredNodes, predictedLabels): nlp = spacy.load('en_core_web_md') averageSimArray = [] for i in range(len(variable_array)): averageSim = 0 for word in clusteredNodes[predictedLabels[i]]: token1 = nlp(word) token2 = nlp(variable_array[i]) averageSim += token1.similarity(token2) averageSimArray.append(float(averageSim/ len(clusteredNodes[predictedLabels[i]]))) return averageSimArray def runCombinationExp(): variableData = pd.read_csv(JULIA_VARIABLE_CSV_PATH) umapData = pd.read_csv(CLUSTER_LABEL_CSV_PATH) umapData = umapData[umapData.umapLabels != -1] kmeansTrainData = list(umapData["node"]) variable_array = list(variableData["variable"]) variable_array = cleanVariables(variable_array) variableTokenList = createWord2Vec(variable_array) trainTokenList = createWord2Vec(kmeansTrainData) K_size = max(list(umapData["umapLabels"])) trainLabels, predictedLabels = useKmeans(trainTokenList, K_size, variableTokenList) writeCSV(kmeansTrainData, trainLabels, KMEANS_CLUSTER_LABEL_CSV_PATH) clusteredNodes = groupNodesByKMeansCluster(pd.read_csv(KMEANS_CLUSTER_LABEL_CSV_PATH)) averageSimArray = getAverageSimilarity(variable_array, clusteredNodes, predictedLabels) writeCSV(variable_array, predictedLabels, KMEANS_PREDICTED_CSV_PATH) graphCombinationExp(averageSimArray) return averageSimArray def graphCombinationExp(averageSimArray): labeledData = pd.read_csv(JULIA_VARIABLE_CSV_PATH) predictedData = pd.read_csv(KMEANS_CLUSTER_TRUTH_CSV_PATH) labeled = list(labeledData["KMeansLabels"]) predicted = list(predictedData["cluster"]) thresholdArray = [] accuracy = [] numberOfAssignments = [] threshold = .01 while threshold < .95: assignmentCount = 0 denominatorCount = 0 for i in range(len(predicted)): if averageSimArray[i] > threshold: denominatorCount += 1 if labeled[i] == predicted[i] and averageSimArray[i] > threshold: assignmentCount += 1 if denominatorCount != 0: accuracy.append(float(assignmentCount/denominatorCount)) else: accuracy.append(1.0) numberOfAssignments.append(float(assignmentCount/len(predicted))) thresholdArray.append(threshold) threshold += .02 numberOfAssignments = np.divide(np.asarray(numberOfAssignments), numberOfAssignments[0]) plt.figure(0) plt.title("Accuracy vs Normalized True Assignments") plt.plot(thresholdArray, accuracy, color="blue", label="Accuracy") plt.plot(thresholdArray, numberOfAssignments, color="orange", label="Normalized True Assigns" ) plt.legend(loc="upper right") plt.xticks(np.arange(0, 1, step=0.1)) plt.xlabel("Similarity Threshold") plt.ylabel("Normalized Values") idx = np.argwhere(np.diff(np.sign(numberOfAssignments - accuracy))).flatten() plt.plot(thresholdArray[int(idx)], numberOfAssignments[int(idx)], 'ro') logging.info("Intersection Threshold is: " + str(thresholdArray[int(idx)]))
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anymesh/tests/reactortester.py
AnyMesh/anyMesh-Python
39
32523
<reponame>AnyMesh/anyMesh-Python<filename>anymesh/tests/reactortester.py import unittest from twisted.internet import reactor, task class ReactorTestCase(unittest.TestCase): def __init__(self, *args, **kwargs): super(ReactorTestCase, self).__init__(*args, **kwargs) self.done = False def timeOutAfter(self, seconds): f = reactor.callLater(seconds, self.timedOut) f.start(seconds) def timedOut(self): self.done = True self.assertTrue(False, "test timed out!") print "Test Timed Out!" if reactor.running: reactor.stop() def reactorAssert(self, success, msg): if not self.done: self.assertTrue(success, msg) def reactorTestComplete(self): self.done = True self.assertTrue(True, "test complete!") print "Test Complete!" if reactor.running: reactor.stop()
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dgcnn/__init__.py
Temigo/dynamic-gcnn
49
163661
from iotool import io_factory from model import build from trainval import trainval import ops from flags import DGCNN_FLAGS
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app/app.py
kyoungd/material-stock-finder
0
175049
<gh_stars>0 import sys import logging import pandas as pd from datetime import datetime from alpaca import * from util import PushToServer, SetInterval from study import * from dbase import * from correlate import * def isTagInOptions(tag:str, cmds:list): return True if tag in cmds else False def AppDaily(): Run() AlpacaSnapshots.All() AlpacaHistoricalData.All() StockFinancial.All(isDebug=True, isForceDownloadYahoo=False) RemoveNoDataStocks() FilterAtr.All() FilterEma.All() FilterKeyLevels.All() FilterFibonacciRetracement.All() FilterThreeBars.All() FilterRelativeVolume.All() FilterVolumeProfile.All() FilterGapper.All() FilterCandlePattern.All() FilterDoubleTop.All() FilterTrends.All() FilterRsiDivergence.All() FilterCorrelate.All() PushToServer() def AppCorrelation(): Run() AlpacaDaily.All() YahooDaily.All() AlpacaCrypto.All() AtrCalculate.All() CorrelateAssets.All(isSendToServer=True, days=45, minAtr=5) CorrelateAssets.All(isSendToServer=True, days=90, minAtr=5) CorrelateAssets.All(isSendToServer=True, days=180, minAtr=5) def AppMarketOpen(): LastNightGapper.All(False) PushToServer() def RunApp(): today = datetime.now() print(f'{today.hour} {today.minute}') if today.hour == 23 and today.minute == 50: AppDaily() elif today.hour == 5 and today.minute == 0: AppMarketOpen() elif today.hour == 5 and today.minute == 30: AppMarketOpen() elif today.hour == 6 and today.minute == 0: AppMarketOpen() if __name__ == "__main__": formatter = '%(asctime)s %(levelname)s %(funcName)s(%(lineno)d) %(message)s' logging.basicConfig(level=logging.INFO, format=formatter, datefmt='%d-%b-%y %H:%M:%S', filename="analyzer.log") logging.info("APP.PY Started") if isTagInOptions('--test', sys.argv): # Run() # AlpacaDaily.All() # YahooDaily.All() # AlpacaCrypto.All() AtrCalculate.All() # CorrelateAssets.All(isSendToServer=False, days=45, minAtr=5) # CorrelateAssets.All(isSendToServer=False, days=90, minAtr=5) # CorrelateAssets.All(isSendToServer=False, days=180, minAtr=5) # db = MarketDataDb() # db.WriteMarket('AAPL', [{'close': 120, 'open': 110}, {'close': 115, 'open': 105}], name='Apple, Inc.') # isOk, data = db.ReadMarket('AAPL') # df: pd.DataFrame = db.LoadDataFrame(data) # print(df) elif isTagInOptions('--corr', sys.argv): AppCorrelation() elif isTagInOptions('--day', sys.argv): AppDaily() elif isTagInOptions('--fin', sys.argv): StockFinancial.All(isDebug=True, isForceDownloadYahoo=True) else: SetInterval(60, RunApp) # db = SecDb() # db.SetLastDaily('AAPL', 170.33, '2020-01-28') # data = db.GetLastDaily('AAPL') # print(data) # LastNightGapper.All("-s") logging.info('done')
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src/main.py
DantasB/smart-speaker
1
125794
import os import re import requests from datetime import datetime as dt import dotenv from shared_library.shared_library import generate_workfolder, delete_workfolder from listener.listener import Listener from speaker.speaker import Speaker dotenv.load_dotenv() ACTIVATION_WORD = os.environ.get("ACTIVATION_WORD") print("ACTIVATION WORD:", ACTIVATION_WORD) if __name__ == "__main__": workfolder = generate_workfolder() speaker = Speaker(workfolder.name) listener = Listener() while True: try: content = str(listener.listen()).lower() print('>', content) if content.startswith(ACTIVATION_WORD): print("como posso ajudar?") speaker.play_audio(speaker.create_audio("como posso ajudar?")) command = str(listener.listen()).lower() print('>', command) if command == "que horas são": time = dt.now() s_hour = 's' if time.hour != 1 else '' s_minute = 's' if time.minute != 1 else '' time_str = f"são {time.hour} hora{s_hour} e {time.minute} minuto{s_minute}" print(time_str) speaker.play_audio(speaker.create_audio(time_str)) elif re.match("(.* )?temperatura (em|no|na) .*", command): cidade = command[command.index("temperatura") + 15:] # r = requests.get(f"https://goweather.herokuapp.com/weather/{cidade}") r = requests.get(f"https://wttr.in/{cidade}?format=\"%t\"") if r.status_code != 200: print("não sei a temperatura para essa cidade") speaker.play_audio(speaker.create_audio("não sei a temperatura para essa cidade")) else: # temperatura = r.json()["temperature"][:3].replace("+", "") temperatura = r.text.replace("+", "") prep = command[command.index("temperatura") + 12:command.index("temperatura") + 14] response_text = f"Está fazendo {temperatura} {prep} {cidade}" print(response_text) speaker.play_audio(speaker.create_audio(response_text)) elif re.match("qual (o valor|a cotação) do (dolar|euro|dólar) em reais", command): moeda = command[command.index("do") + 3:command.index("em reais")-1] # print(moeda) if moeda == "dólar" or moeda == "dolar": moeda_symbol = "usd" elif moeda == "euro": moeda_symbol = "eur" else: raise ZeroDivisionError() r = requests.get(f"https://cdn.jsdelivr.net/gh/fawazahmed0/currency-api@1/latest/currencies/brl/{moeda_symbol}.json") if r.status_code != 200: raise ZeroDivisionError() else: conv = round(1/r.json()[moeda_symbol],2) response_text = f"O valor do {moeda} é de {conv} reais" print(response_text) speaker.play_audio(speaker.create_audio(response_text)) elif re.match("quanto(s)? são (\$)?(\d+) (dolar|euro|dólar)(s|es)? em reais", command): valores = command[command.index("são") + 4:command.index("em reais")-1].split(" ") # print(valores) if valores[1].startswith("dólar") or valores[1].startswith("dolar"): moeda_symbol = "usd" elif valores[1].startswith("euro"): moeda_symbol = "eur" else: raise ZeroDivisionError() r = requests.get(f"https://cdn.jsdelivr.net/gh/fawazahmed0/currency-api@1/latest/currencies/brl/{moeda_symbol}.json") if r.status_code != 200: raise ZeroDivisionError() else: conv = round(1/r.json()[moeda_symbol],2) result = int(valores[0])*conv response_text = f"{valores[0]} {valores[1]} são {result} reais" print(response_text) speaker.play_audio(speaker.create_audio(response_text)) else: print("não consigo te ajudar com isso") speaker.play_audio(speaker.create_audio("não consigo te ajudar com isso")) except ZeroDivisionError: print("não consigo te ajudar com isso") speaker.play_audio(speaker.create_audio("não consigo te ajudar com isso")) continue except KeyboardInterrupt: break # except: # print("não consigo te ajudar com isso") # speaker.play_audio(speaker.create_audio("não consigo te ajudar com isso")) # continue delete_workfolder(workfolder)
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datatableview/tests/test_helpers.py
gregneagle/sal
2
1648
<filename>datatableview/tests/test_helpers.py # -*- encoding: utf-8 -*- from datetime import datetime from functools import partial from django import get_version from datatableview import helpers import six from .testcase import DatatableViewTestCase from .test_app.models import ExampleModel, RelatedM2MModel if get_version().split('.') < ['1', '7']: test_data_fixture = 'test_data_legacy.json' else: test_data_fixture = 'test_data.json' class HelpersTests(DatatableViewTestCase): fixtures = [test_data_fixture] def test_link_to_model(self): """ Verifies that link_to_model works. """ helper = helpers.link_to_model # Verify that a model without get_absolute_url() raises a complaint related = RelatedM2MModel.objects.get(pk=1) with self.assertRaises(AttributeError) as cm: helper(related) self.assertEqual(str(cm.exception), "'RelatedM2MModel' object has no attribute 'get_absolute_url'") # Verify simple use instance = ExampleModel.objects.get(pk=1) output = helper(instance) self.assertEqual(output, '<a href="#1">ExampleModel 1</a>') # Verify text override output = helper(instance, text="Special text") self.assertEqual(output, '<a href="#1">Special text</a>') # Verify ``key`` access to transition an instance to a related field instance = ExampleModel.objects.get(pk=2) secondary_helper = helper(key=lambda o: o.related) output = secondary_helper(instance) self.assertEqual(output, '<a href="#1">RelatedModel object</a>') # Verify ``key`` access version of custom text output = secondary_helper(instance, text="Special text") self.assertEqual(output, '<a href="#1">Special text</a>') def test_make_boolean_checkmark(self): """ Verifies that make_boolean_checkmark works. """ helper = helpers.make_boolean_checkmark # Verify simple use output = helper("True-ish value") self.assertEqual(output, '&#10004;') output = helper("") self.assertEqual(output, '&#10008;') # Verify custom values output = helper("True-ish value", true_value="Yes", false_value="No") self.assertEqual(output, 'Yes') output = helper("", true_value="Yes", false_value="No") self.assertEqual(output, 'No') def test_format_date(self): """ Verifies that format_date works. """ helper = helpers.format_date # Verify simple use data = datetime.now() secondary_helper = helper("%m/%d/%Y") output = secondary_helper(data) self.assertEqual(output, data.strftime("%m/%d/%Y")) # Verify that None objects get swallowed without complaint. # This helps promise that the helper won't blow up for models.DateTimeField that are allowed # to be null. output = secondary_helper(None) self.assertEqual(output, "") def test_format(self): """ Verifies that format works. """ helper = helpers.format # Verify simple use data = 1234567890 secondary_helper = helper("{0:,}") output = secondary_helper(data) self.assertEqual(output, "{0:,}".format(data)) # Verify ``cast`` argument data = "1234.56789" secondary_helper = helper("{0:.2f}", cast=float) output = secondary_helper(data) self.assertEqual(output, "{0:.2f}".format(float(data))) def test_through_filter(self): """ Verifies that through_filter works. """ helper = helpers.through_filter target_function = lambda data, arg=None: (data, arg) # Verify simple use data = "Data string" secondary_helper = helper(target_function) output = secondary_helper(data) self.assertEqual(output, (data, None)) # Verify ``arg`` argument secondary_helper = helper(target_function, arg="Arg data") output = secondary_helper(data) self.assertEqual(output, (data, "Arg data")) def test_itemgetter(self): """ Verifies that itemgetter works. """ helper = helpers.itemgetter # Verify simple index access data = list(range(5)) secondary_helper = helper(-1) output = secondary_helper(data) self.assertEqual(output, data[-1]) # Verify slicing access secondary_helper = helper(slice(1, 3)) output = secondary_helper(data) self.assertEqual(output, data[1:3]) # Verify ellipsis works for strings data = str(range(10)) secondary_helper = helper(slice(0, 5), ellipsis=True) output = secondary_helper(data) self.assertEqual(output, data[:5] + "...") # Verify ellipsis can be customized secondary_helper = helper(slice(0, 5), ellipsis="custom") output = secondary_helper(data) self.assertEqual(output, data[:5] + "custom") # Verify ellipsis does nothing for non-string data types data = range(10) output = secondary_helper(data) self.assertEqual(output, data[:5]) def test_attrgetter(self): """ Verifies that attrgetter works. """ helper = helpers.attrgetter # Verify simple attr lookup data = ExampleModel.objects.get(pk=1) secondary_helper = helper('pk') output = secondary_helper(data) self.assertEqual(output, data.pk) # Verify bad attribrute lookup data = ExampleModel.objects.get(pk=1) secondary_helper = helper('bad field name') with self.assertRaises(AttributeError) as cm: output = secondary_helper(data) self.assertEqual(str(cm.exception), "'ExampleModel' object has no attribute 'bad field name'") def test_make_xeditable(self): """ Verifies that make_xeditable works. """ helper = helpers.make_xeditable # Items that the helper normally expects in a callback context internals = {'field_name': 'name'} # Verify chain calls don't trigger rendering secondary_helper = helper() tertiary_helper = secondary_helper() self.assertEqual(type(secondary_helper), partial) self.assertEqual(type(tertiary_helper), partial) # Verify chain ends with provision of a value data = ExampleModel.objects.get(pk=1) # This needs a "url" arg because we want to test successful use output = tertiary_helper(data, url="/", **internals) self.assertTrue(isinstance(output, six.string_types)) # Verify that no "view" kwarg means the url is required from the call with self.assertRaises(ValueError) as cm: tertiary_helper(data, **internals) self.assertEqual(str(cm.exception), "'make_xeditable' cannot determine a value for 'url'.") # Verify kwargs accumulate kwargs1 = { 'type': 'textarea' } kwargs2 = { 'other_arg': True } secondary_helper = helper(**kwargs1) expected_kwargs = dict(kwargs1, extra_attrs=[]) self.assertEqual(secondary_helper.keywords, expected_kwargs) tertiary_helper = secondary_helper(**kwargs2) expected_kwargs = dict(kwargs1, **dict(kwargs2, extra_attrs=[])) self.assertEqual(tertiary_helper.keywords, expected_kwargs) # Verify default kwarg names end up as attributes data = ExampleModel.objects.get(pk=1) kwargs = { 'pk': "PK DATA", 'type': "TYPE DATA", 'url': "URL DATA", 'source': "SOURCE DATA", 'title': "TITLE DATA", 'placeholder': "PLACEHOLDER DATA", # Extra stuff not in anticipated to appear in rendered string 'special': "SPECIAL DATA", 'data_custom': "DATA-CUSTOM DATA", } secondary_helper = helper(**kwargs) output = secondary_helper(data, **internals) expected_output = """ <a href="#" data-name="name" data-pk="PK DATA" data-placeholder="PLACEHOLDER DATA" data-source="SOURCE DATA" data-title="TITLE DATA" data-type="TYPE DATA" data-url="URL DATA" data-value="1" data-xeditable="xeditable"> ExampleModel 1 </a> """ self.assertHTMLEqual(output, expected_output) # Verify that explicit additions via ``extra_attrs`` allows kwargs to appear in HTML as # "data-*" attributes. secondary_helper = helper(extra_attrs=['special', 'data_custom', 'fake'], **kwargs) output = secondary_helper(data, **internals) expected_output = """ <a href="#" data-name="name" data-pk="PK DATA" data-placeholder="PLACEHOLDER DATA" data-source="SOURCE DATA" data-title="TITLE DATA" data-type="TYPE DATA" data-url="URL DATA" data-value="1" data-special="SPECIAL DATA" data-custom="DATA-CUSTOM DATA" data-xeditable="xeditable"> ExampleModel 1 </a> """ self.assertHTMLEqual(output, expected_output)
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benchcamera.py
healeycodes/Raspberry-Pi-Live-Cam
6
103959
import io import time import picamera framerate = 90 quality = 100 resolutions = [(1920, 1080), (1280, 720), (800, 600), (640, 480), (320, 240)] samples = 50 with picamera.PiCamera(framerate=framerate) as camera: time.sleep(2) # camera warm-up time for res in resolutions: camera.resolution = res timer = time.time() sum_time = 0 for _ in range(0, samples): image = io.BytesIO() camera.capture(image, 'jpeg', quality=quality, use_video_port=True) sum_time += time.time() - timer timer = time.time() print(res) print(sum_time / samples)
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tests/test_pyglobal.py
justengel/pyglobal
0
57641
def test_simple(): import pyglobal import settings assert pyglobal.get('abc') == 123 assert pyglobal.get('qwerty') == '' assert pyglobal.get('Hello') == 'World!' assert pyglobal.get('SECRET_KEY') == '!!!CHANGE!!!' def test_hack(): import pyglobal pyglobal.set('SECRET_KEY', '*******') # Check if library can access the page global variable def get_glob(*args, **kwargs): global GLOBAL_SETTING try: len(GLOBAL_SETTING) raise AssertionError('Should not be able to access this object!') except (AttributeError, NameError): pass pyglobal.get = get_glob pyglobal.get('SECRET_KEY', None) # User can still manually grab the variable even though it is not defined in __all__. pyglobal.GLOBAL_SETTING try: len(pyglobal.GLOBAL_SETTING) raise AssertionError('Global Settings should not have a length') except (TypeError, AttributeError): pass try: for k in pyglobal.GLOBAL_SETTING: pass raise AssertionError('Global Settings should not be iterable') except (TypeError, AttributeError): pass def run_memory(): # Check your memory usage. It should not go up continuously. import pyglobal while True: pyglobal.default('default', 'oi') pyglobal.set('SECRET_KEY', "Hello World!") pyglobal.set('Other', {'a': 1, "b": 2}) pyglobal.set('SECRET_KEY', "Hello World!", scope='MyScope') pyglobal.get('SECRET_KEY') pyglobal.get('SECRET_KEY', scope='MyScope') if __name__ == '__main__': import sys test_simple() test_hack() # sys.argv.append('--run_memory') if '--run_memory' in sys.argv: run_memory() print('All pyglobal tests finished successfully!')
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Sem-5/PIP/Minor Assignment 8/A8Q3.py
rituraj-iter/ASSIGNMENTS
10
87135
<reponame>rituraj-iter/ASSIGNMENTS<filename>Sem-5/PIP/Minor Assignment 8/A8Q3.py<gh_stars>1-10 def power(n, m): if m == 0: return 1 elif m == 1: return n return (n*power(n, m-1)) print(power(2,3))
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kafka_streamer/topic/datatype/base.py
sam-mosleh/kafka-streamer
1
37609
<reponame>sam-mosleh/kafka-streamer<filename>kafka_streamer/topic/datatype/base.py from abc import ABC, abstractmethod from typing import Type, Union from kafka_streamer.models import SchematicRecord, Serializable class KafkaDataType(ABC): _MAGIC_BYTE = 0 @abstractmethod def deserialize(self, data: bytes): pass @abstractmethod def serialize( self, data: Union[SchematicRecord, Type[Serializable], bytes, None] ) -> bytes: pass
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tests/test_rectangle.py
trejsu/shaper
0
46671
<filename>tests/test_rectangle.py<gh_stars>0 import numpy as np import pytest from shapes.shape import Quadrangle def test_from_normalized_params(): normalized = [1, 0.2866666666666667, 0.85, 0.32666666666666666, 0.65, 0.013333333333333334, 0.8, -0.02, 1, 2, 3, 0.5] w = 100 h = 150 expected_points = np.array([[100, 43], [85, 49], [65, 2], [80, -3]]) expected_color = np.array([1, 2, 3]) r = Quadrangle.from_normalized_params(w, h, *normalized) assert np.array_equal(r.points, expected_points) assert r.alpha == 0.5 assert np.array_equal(r.color, expected_color) def test_normalized_params(): r = Quadrangle.from_params(*[100, 43, 85, 49, 65, 2, 80, -3, 1, 2, 3, 0.5]) w = 100 h = 150 expected = np.array( [1, 0.2866666666666667, 0.85, 0.32666666666666666, 0.65, 0.013333333333333334, 0.8, -0.02, 1, 2, 3, 0.5]) normalized = r.normalized_params(w, h) assert np.array_equal(normalized, expected) @pytest.mark.parametrize( "edge_start, edge_end, start_new_x_sign, start_new_y_sign, end_new_x_sign, end_new_y_sign", [ (0, 1, 1, 0, 1, 0), (1, 2, 0, 1, 1, 0), (2, 3, -1, 0, -1, 0), (3, 0, -1, 0, 0, -1) ]) def test_without_edge(edge_start, edge_end, start_new_x_sign, start_new_y_sign, end_new_x_sign, end_new_y_sign): points = np.array([[-51, 159], [-16, 82], [37, 60], [125, 114]]) q = Quadrangle.without_edge(points=points.copy(), edge=[edge_start, edge_end], alpha=1) assert np.sign(q.points[edge_start][0] - points[edge_start][0]) == start_new_x_sign assert np.sign(q.points[edge_start][1] - points[edge_start][1]) == start_new_y_sign assert np.sign(q.points[edge_end][0] - points[edge_end][0]) == end_new_x_sign assert np.sign(q.points[edge_end][1] - points[edge_end][1]) == end_new_y_sign
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PythonVSCode/DP_GFG/LongestCommonSubstring.py
porcelainruler/InterviewPrep
0
69132
''' The longest common suffix has following optimal substructure property. If last characters match, then we reduce both lengths by 1 LCSuff(X, Y, m, n) = LCSuff(X, Y, m-1, n-1) + 1 if X[m-1] = Y[n-1] If last characters do not match, then result is 0, i.e., LCSuff(X, Y, m, n) = 0 if (X[m-1] != Y[n-1]) Now we consider suffixes of different substrings ending at different indexes. The maximum length Longest Common Suffix is the longest common substring. LCSubStr(X, Y, m, n) = Max(LCSuff(X, Y, i, j)) where 1 <= i <= m and 1 <= j <= n ''' from sys import stdin import math def dp(a: str, b: str, m: int, n: int): dp = [[0]*(n+1) for i in range(m+1)] maxLen = 0 for i in range(m+1): for j in range(n+1): if i==0 or j==0: dp[i][j] = 0 elif a[i-1] == b[j-1]: dp[i][j] = dp[i-1][j-1] + 1 maxLen = max(maxLen, dp[i][j]) else: dp[i][j] = 0 return maxLen def main(): t = int(input()) for i in range(t): m, n = map(int, stdin.readline().split()) a = input() b = input() ans = dp(a, b, m, n) print(ans) if __name__ == '__main__': main()
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src/tools/training.py
gpatsiaouras/multimodal-har-thesis
4
164917
<filename>src/tools/training.py import os import time import torch import torch.nn.functional as functional from sklearn.metrics import confusion_matrix from sklearn.neighbors import KNeighborsClassifier from visualizers import plot_confusion_matrix, print_epoch_info from .model_tools import save_model def train(model, criterion, optimizer, train_loader, validation_loader, num_epochs, device, experiment, writer=None): """ Performs training process based on the configuration provided as input. Automatically saves accuracy, loss and confusion matrix plots. :param model: Model to train :param criterion: Loss function :param optimizer: Optimizer :param train_loader: Training data :param validation_loader: Validation data :param num_epochs: Number of epochs to train :param experiment: Name of the experiment running :param device: Device :param writer: SummaryWriter for tensorboard """ # Statistics start_time = time.time() time_per_epoch = [] train_accuracies = [] validation_accuracies = [] train_losses = [] validation_losses = [] saved_model_path = None # Train Network for epoch in range(num_epochs): train_running_loss = .0 num_correct = 0 num_samples = 0 epoch_start_time = time.time() # Iterate mini batches for batch_idx, (data, labels) in enumerate(train_loader): # Turn inference mode off (in case) model.train() # Get data to cuda if possible data = data.float().to(device) labels = labels.long().to(device) # forward scores = model(data) _, input_indices = torch.max(labels, dim=1) loss = criterion(scores, input_indices) # train accuracy num_correct += get_num_correct_predictions(scores, labels) num_samples += len(labels) # backward optimizer.zero_grad() loss.backward() # gradient descent or adam step optimizer.step() train_running_loss += loss.item() ########## # Losses # ########## # Training Loss train_loss = train_running_loss / len(train_loader) train_losses.append(train_loss) # Validation Loss validation_loss, _, _ = get_loss(validation_loader, model, device, criterion) ############## # Accuracies # ############## # Train accuracy train_acc = float(num_correct) / float(num_samples) train_accuracies.append(train_acc) # Validation accuracy validation_acc = get_accuracy(validation_loader, model, device) # Save the model if the validation accuracy is the highest if len(validation_accuracies) > 0 and validation_acc > max(validation_accuracies): if saved_model_path is not None: os.remove(saved_model_path) saved_model_path = save_model(model, '%s_best_val.pt' % experiment) validation_accuracies.append(validation_acc) validation_losses.append(validation_loss) # Tensorboard if writer: writer.add_scalar('Loss/train', train_loss, global_step=epoch) writer.add_scalar('Loss/validation', validation_loss, global_step=epoch) writer.add_scalar('Accuracy/train', train_acc, global_step=epoch) writer.add_scalar('Accuracy/validation', validation_acc, global_step=epoch) # Debug information print_epoch_info(start_time, epoch_start_time, time_per_epoch, epoch, num_epochs, train_loss, validation_loss, train_acc, validation_acc) return train_accuracies, validation_accuracies, train_losses, validation_losses def add_histograms(writer, model, step, subtitle=''): """ Adds histogram data according to the model to tensorboard. :param writer: SummaryWriter :param model: Model :param step: global step :param subtitle: When going deeper provide a name to separate the fc1, fc2, fc3 :return: """ if hasattr(model, 'fc1'): writer.add_histogram(subtitle + 'fc1', model.fc1.weight, global_step=step) if hasattr(model, 'fc2'): writer.add_histogram(subtitle + 'fc2', model.fc2.weight, global_step=step) if hasattr(model, 'fc3'): writer.add_histogram(subtitle + 'fc3', model.fc3.weight, global_step=step) if hasattr(model, 'last_fc'): writer.add_histogram(subtitle + 'last_fc', model.last_fc.weight, global_step=step) if hasattr(model, 'mlp'): add_histograms(writer, model.mlp, step, subtitle='mlp') if hasattr(model, 'n1'): add_histograms(writer, model.mlp, step, subtitle='n1') if hasattr(model, 'n2'): add_histograms(writer, model.mlp, step, subtitle='n2') if hasattr(model, 'n3'): add_histograms(writer, model.mlp, step, subtitle='n3') def train_triplet_loss(model, criterion, optimizer, class_names, train_loader, val_loader, num_epochs, device, experiment, n_neighbors, writer, scheduler=None, verbose=False, skip_accuracy=False): start_time = time.time() time_per_epoch = [] saved_model_path = None train_losses = [] train_accuracies = [] val_losses = [] val_accuracies = [] train_accuracy = -1 val_accuracy = -1 scores_concat = None labels_concat = None for epoch in range(num_epochs): scores_concat = torch.tensor([], device=device) labels_concat = torch.tensor([], device=device) train_running_loss = .0 epoch_start_time = time.time() for batch_idx, (data, targets) in enumerate(train_loader): # Turn inference mode off (in case) model.train() # Get data to cuda if possible if isinstance(data, list): data = tuple(d.float().to(device) for d in data) else: data = data.float().to(device) targets = targets.to(device) # Train scores scores = model(data) labels = targets.argmax(dim=1) loss = criterion(scores, labels) train_running_loss += loss.item() # backward optimizer.zero_grad() loss.backward() optimizer.step() train_losses.append(loss.item()) scores_concat = torch.cat((scores_concat, scores), dim=0) labels_concat = torch.cat((labels_concat, labels), dim=0) add_histograms(writer, model, epoch) writer.add_scalar('Embeddings/distances', torch.sum(torch.cdist(scores_concat, scores_concat)), global_step=epoch) # Calculations train_loss = train_running_loss / len(train_loader) writer.add_scalar('Loss/train', train_loss, global_step=epoch) val_loss, val_scores, _ = get_loss(val_loader, model, device, criterion) if len(val_losses) > 0: # After the first save only for better result in validation if val_loss < min(val_losses): os.remove(saved_model_path) saved_model_path = save_model(model, '%s_best_val.pt' % experiment) else: # Save the model the first time without checking that the validation was reduced. print('Saved model for the first time') saved_model_path = save_model(model, '%s_initial.pt' % experiment) val_losses.append(val_loss) if not skip_accuracy: writer.add_scalar('Embeddings/distances_val', torch.sum(torch.cdist(val_scores, val_scores)), global_step=epoch) writer.add_scalar('Loss/validation', val_loss, global_step=epoch) if scheduler: scheduler.step(val_loss) # Confusion in general val_cm, val_accuracy, _, _ = get_predictions_with_knn(n_neighbors, train_loader, val_loader, model, device) train_cm, train_accuracy, _, _ = get_predictions_with_knn(n_neighbors, train_loader, train_loader, model, device) val_accuracies.append(val_accuracy) train_accuracies.append(train_accuracy) writer.add_scalar('Accuracy/validation', val_accuracy, global_step=epoch) writer.add_scalar('Accuracy/train', train_accuracy, global_step=epoch) train_image = plot_confusion_matrix( cm=train_cm, title='Confusion Matrix - Percentage % - Train Loader', normalize=False, save=False, show_figure=False, classes=class_names ) val_image = plot_confusion_matrix( cm=val_cm, title='Confusion Matrix - Percentage % - Val Loader', normalize=False, save=False, show_figure=False, classes=class_names ) writer.add_images('ConfusionMatrix/Train', train_image, dataformats='CHW', global_step=epoch) writer.add_images('ConfusionMatrix/Validation', val_image, dataformats='CHW', global_step=epoch) if verbose: # Timing total_epoch_time = time.time() - epoch_start_time time_per_epoch.append(total_epoch_time) # Debug information print_epoch_info(start_time, epoch_start_time, time_per_epoch, epoch, num_epochs, train_loss, val_loss, train_accuracy, val_accuracy) writer.add_embedding(scores_concat, metadata=[class_names[idx] for idx in labels_concat.int().tolist()], tag="train (%f%%)" % train_accuracy) return train_losses, val_losses, val_accuracies, train_accuracies def train_simple(model, criterion, optimizer, epochs, data, labels): model.train() for epoch in range(epochs): scores = model(data) loss = criterion(scores, labels.argmax(1)) accu = get_num_correct_predictions(scores, labels) / labels.shape[0] # backward optimizer.zero_grad() loss.backward() optimizer.step() if epoch % 10 == 0: print('Epoch %d/%d: loss %.2f accu %.2f' % (epoch, epochs, loss.item(), accu)) @torch.no_grad() def get_loss(data_loader, model, device, criterion): """ Get inference loss of data_loader :param data_loader: DataLoader :param model: Model :param device: Device (cuda or cpu) :param criterion: Loss function :return: loss """ model.eval() scores, labels = get_predictions(data_loader, model, device, apply_softmax=False) loss = criterion(scores, labels.argmax(dim=1)) return loss.item(), scores, labels def get_accuracy(data_loader, model, device): """ Calculates the accuracy of the model given (inference mode) applied in the dataset of the data_loader :param data_loader: Data loader :param model: Model to be used for predictions :param device: Device to be used :return: accuracy percentage % """ all_predictions, all_labels = get_predictions(data_loader, model, device, apply_softmax=True) correct_pred_max = get_num_correct_predictions(all_predictions, all_labels) return correct_pred_max / all_predictions.shape[0] @torch.no_grad() def get_accuracy_simple(model, data, labels): model.eval() test_scores = model(data) test_scores = functional.softmax(test_scores, 1) return get_num_correct_predictions(test_scores, labels) / labels.shape[0] def get_confusion_matrix(data_loader, model, device): """ Retrieves predictions for the whole dataset, calculates and returns the confusion matrix :param data_loader: Data loader :param model: Model to be used for predictions :param device: Device to be used :return: confusion matrix """ all_predictions, all_labels = get_predictions(data_loader, model, device, apply_softmax=True) num_classes = all_labels.shape[1] stacked = torch.stack((all_labels.argmax(dim=1), all_predictions.argmax(dim=1)), dim=1) cmt = torch.zeros(num_classes, num_classes, dtype=torch.int64) for p in stacked: true_label, predicted_label = p.tolist() cmt[true_label, predicted_label] = cmt[true_label, predicted_label] + 1 return cmt def get_confusion_matrix_multiple_models(data_loaders, models, device): """ Gets predictions from multiple models, fuses scores based on product rule and calculates and returns confusion matrix :param data_loaders: List of data loaders :param models: List of models :param device: Device to be used :return: confusion matrix """ # TODO change hardcoded numbers c_scores = torch.zeros((len(models), 416, 27)) c_labels = torch.zeros((len(models), 416, 27)) for idx in range(len(models)): all_predictions, all_labels = get_predictions(data_loaders[idx], models[idx], device, apply_softmax=True) c_scores[idx, :, :] = all_predictions c_labels[idx, :, :] = all_labels fused_scores = c_scores.prod(dim=0) num_classes = c_labels[0].shape[1] stacked = torch.stack((c_labels[0].argmax(dim=1), fused_scores.argmax(dim=1)), dim=1) cmt = torch.zeros(num_classes, num_classes, dtype=torch.int64) for p in stacked: true_label, predicted_label = p.tolist() cmt[true_label, predicted_label] = cmt[true_label, predicted_label] + 1 return cmt @torch.no_grad() def get_predictions(data_loader, model, device, apply_softmax=False): """ Receives a dataloader a model and a device, runs all the batches to get the predictions and returns a tensor with all the predictions and corresponding labels for every sample of the dataset :param data_loader: Data loader :param model: Model :param device: Device to be used :param apply_softmax: Whether to apply softmax in the end :return: predictions, labels """ # Set the model to inference mode model.eval() # Initiate tensors to hold predictions and labels all_predictions = torch.tensor([], device=device) all_labels = torch.tensor([], device=device) for (data, labels) in data_loader: if isinstance(data, list): data = tuple(d.float().to(device) for d in data) else: data = data.float().to(device) labels = labels.long().to(device=device) out = model(data) if apply_softmax: scores = functional.softmax(out, 1) else: scores = out all_predictions = torch.cat((all_predictions, scores), dim=0) all_labels = torch.cat((all_labels, labels), dim=0) return all_predictions, all_labels def get_num_correct_predictions(scores, labels): """ Calculates the number of correct prediction given the scores (output of model for every sample) and the labels :param scores: tensor :param labels: tensor :return: number of correct predictions """ return int((labels.argmax(1) == scores.argmax(1)).sum()) def get_predictions_with_knn(n_neighbors, train_loader, test_loader, model, device): """ Receives the train and test loaders and number of neighbors and trains a KNN classifier on the train data, and then predicts on the test loader. Returns the confusion matrix, test accuracy, scores and labes :param n_neighbors: Number of neighbors for the KNN :param train_loader: Train loader :param test_loader: Test Loader :param model: Model to use :param device: Torch device :return: confusion matrix, test accuracy, scores, labels """ train_scores, train_labels = get_predictions(train_loader, model, device, apply_softmax=False) test_scores, test_labels = get_predictions(test_loader, model, device, apply_softmax=False) test_labels = test_labels.argmax(1) classifier = KNeighborsClassifier(n_neighbors=n_neighbors) if device.type == 'cuda': train_scores = train_scores.cpu() train_labels = train_labels.cpu() test_scores = test_scores.cpu() test_labels = test_labels.cpu() classifier.fit(train_scores, train_labels.argmax(1)) test_knn_pred = classifier.predict(test_scores) # y_pred = y_pred.argmax(1) test_accuracy = int((test_labels == torch.Tensor(test_knn_pred)).sum()) / test_labels.shape[0] cm = confusion_matrix(test_labels, test_knn_pred) return cm, test_accuracy, test_scores, test_labels
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files/autoupdate-you-resume-for-hh.ru/16_for_input_end=''.py
SKDCO/SKDCO
2
180411
<reponame>SKDCO/SKDCO Когда Павел учился в школе, он запоминал таблицу умножения прямоугольными блоками. Для тренировок ему бы очень пригодилась программа, которая показывала бы блок таблицы умножения. Напишите программу, на вход которой даются четыре числа a, b, c и d, каждое в своей строке. Программа должна вывести фрагмент таблицы умножения для всех чисел отрезка [a;b] на все числа отрезка [c;d]. Числа a, b, c и d являются натуральными и не превосходят 10, a≤b, c≤d. Следуйте формату вывода из примера, для разделения элементов внутри строки используйте '\t' — символ табуляции. Заметьте, что левым столбцом и верхней строкой выводятся сами числа из заданных отрезков — заголовочные столбец и строка таблицы. # put your python code here a =int (input()) b =int (input()) c =int (input()) d =int (input()) for g in range (c,d+1): print('\t'+str(g),end='') print(end='\n') for i in range (a,b+1): print(str(i)+'\t',end='') for j in range (c,d+1): print(str(i*j),end='\t') print(end='\n')
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lib/socket/exceptions.py
mrcrgl/gge-storage
0
86902
class DeadConnectionException(Exception): pass
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seisflow/scripts/shared/change_simulation_type.py
ziyixi/seisflow
2
120771
<filename>seisflow/scripts/shared/change_simulation_type.py """ change_simulation_type.py: change the simulation type in specfem massively. """ from glob import glob from os.path import join import sh if __name__ == "__main__": import click @click.command() @click.option('--base_directory', required=True, type=str, help="the simulation directory") @click.option('--simulation_type', required=True, type=str, help="can be forward, source, structure and forward_save") def main(base_directory, simulation_type): flag = None if(simulation_type == "forward"): flag = "-f" elif(simulation_type == "source"): flag = "-a" elif(simulation_type == "structure"): flag = "-b" elif(simulation_type == "forward_save"): flag = "-F" else: raise Exception("no such simulation type") all_simulation_directories = sorted(glob(join(base_directory, "*"))) current_path = str(sh.pwd())[:-1] # pylint: disable=not-callable for each_simulation_directory in all_simulation_directories: sh.cd(each_simulation_directory) sh.perl("change_simulation_type.pl", flag) sh.cd(current_path) main() # pylint: disable=no-value-for-parameter
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tests/test_wikipedia.py
cauliyang/test-poetry
0
52304
from test_poetry import wikipedia def test_random_page_use_given_language(mock_requests_get): wikipedia.random_page(language="de")
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example/testapp/__init__.py
Ecotrust/rpc4django
28
90202
# -*- coding: utf-8 -*- from rpc4django import rpcmethod from .othermodule import intro, getrequest from .secretmodule import * # imports protected method @rpcmethod(name='rpc4django.mytestmethod',signature=['int', 'int', 'int', 'int']) def mytestmethod(a, b, c): ''' Adds the three parameters together and returns the sum Parameters: - `a` - the first parameter - `b` - the second parameter - `c` - the third parameter Returns the sum of the 3 parameters ''' return a + b + c
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evaluation/filters.py
tmolitor-stud-tu/AnonPubSub
0
154548
# *** these filters do NOT see ping messages, nor do routers see them *** # *** global imports are NOT accessible, do imports in __init__ and bind them to an attribute *** class Filters(Base): def __init__(self, logger): # init parent class super().__init__(logger) # some imports needed later self.random = __import__("random") self.base64 = __import__("base64") self._thread = __import__("_thread") self.time = __import__("time") # some vars #self.master_killed = False self.started = 0 self.data_received = {} self.overlay_constructed = False self.mutation_logged = False def gui_command_incoming(self, command): global task pass def gui_command_completed(self, command, error): global task if command["_command"] == "start": if not error: pass #self.logger.info("*********** STARTED") else: pass #self.logger.info("*********** ERROR STARTING ROUTER: %s" % str(error)) def gui_event_outgoing(self, event): global task pass def router_command_incoming(self, command, router): global task if command["_command"] == "subscribe": self.started = self.time.time() if command["_command"] == "remove_connection" and task["name"] in ["test", "all_reconnects_on_packetloss"]: self.logger.error("*********** CODE_EVENT(remove_connection): reconnects += 1") def subscribed_datamsg_incoming(self, msg, router): global task pass def covert_msg_incoming(self, msg, con): global task if msg.get_type() == "Flooding_advertise" and not msg["reflood"] and task["name"] in ["flooding_suboptimal_paths"]: channel = msg["channel"] nonce = self.base64.b64decode(bytes(msg["nonce"], "ascii")) # decode nonce peer_id = con.get_peer_id() chain = (self.router._find_nonce(nonce, self.router.advertisement_routing_table[channel]) if channel in self.router.advertisement_routing_table else None) if chain: # new advertisements (chain == None) aren't relevant here sorting = self.router._compare_nonces(nonce, self.router.advertisement_routing_table[channel][chain].peekitem(0)[0]) if sorting != -1: return # received advertisement is longer than already known one publisher = self.router._canonize_active_path_identifier(channel, msg['nonce']) for subscriber in self.router._ActivePathsMixin__reverse_edges[channel]: if publisher in self.router._ActivePathsMixin__reverse_edges[channel][subscriber]: active_peer = self.router._ActivePathsMixin__reverse_edges[channel][subscriber][publisher]["peer"] if sorting == -1 and active_peer != peer_id: if self.router.node_id == subscriber: self.logger.error("*********** CODE_EVENT(shorter_nonce): shorter_subscribers += 1") else: self.logger.error("*********** CODE_EVENT(shorter_nonce): shorter_intermediates += 1") self.logger.info("*********** DEBUG_DATA: node_id=%s, subscriber=%s, msg=%s" % (self.router.node_id, subscriber, str(msg))) def covert_msg_outgoing(self, msg, con): global task # only check first advertisements if msg.get_type() == "Flooding_advertise" and not msg["reflood"] and task["name"] in ["flooding_master_count"]: channel = msg["channel"] nonce = self.base64.b64decode(bytes(msg["nonce"], "ascii")) # decode nonce peer_id = con.get_peer_id() chain = (self.router._find_nonce(nonce, self.router.advertisement_routing_table[channel]) if channel in self.router.advertisement_routing_table else None) shortest_nonce = self.router.advertisement_routing_table[channel][chain].peekitem(0)[0] if None in self.router.advertisement_routing_table[channel][chain][shortest_nonce] and not self.mutation_logged: self.logger.error("*********** CODE_EVENT(become_master): master_publishers += 1") self.logger.info("*********** DEBUG_DATA: node_id=%s" % self.router.node_id) self.mutation_logged = True def msg_incoming(self, msg, con): global task if task["name"] in [ "test", "flooding_overlay_construction", "aco_overlay_construction", "aco_overlay_construction_bandwidth", "flooding_overlay_construction_packetloss", "aco_overlay_construction_packetloss", "simple_overlay_construction", "simple_overlay_construction_packetloss", ]: if msg["channel"] in self.router.subscriptions and msg["id"] not in self.router.seen_data_ids: # only check subscriber perspective if msg["data"] not in self.data_received: self.data_received[msg["data"]] = 0 self.data_received[msg["data"]] += 1 self.logger.info("*********** DEBUG_DATA: data_received[%s] --> %s" % (str(msg["data"]), str(self.data_received[msg["data"]]))) if self.data_received[msg["data"]] == task["publishers"] and not self.overlay_constructed: self.logger.error("*********** CODE_EVENT(overlay_constructed): overlay_construction.append(%.3f)" % (self.time.time() - self.started)) self.logger.info("*********** DEBUG_DATA: node_id = %s" % self.router.node_id) #TODO: this should be used by the evaluator to stop evaluation earlier if possible self.logger.info("*********** MEASUREMENT_COMPLETED: %s" % self.router.node_id) self.overlay_constructed = True def msg_outgoing(self, msg, con): global task #if msg.get_type() == "Flooding_data": #if self.time.time() - self.started > 60 and "test" in self.router.master and self.router.master["test"] and not self.master_killed: #self.logger.info("*********** KILLING MASTER NODE: node_id=%s" % self.router.node_id) #self.master_killed = True #self._thread.interrupt_main() pass
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bika/lims/browser/worksheet/views/analyses_transposed.py
hocinebendou/bika.gsoc
0
15166
# coding=utf-8 from Products.Five.browser.pagetemplatefile import ViewPageTemplateFile from bika.lims.browser.bika_listing import BikaListingTable from bika.lims.browser.worksheet.views.analyses import AnalysesView class AnalysesTransposedView(AnalysesView): """ The view for displaying the table of manage_results transposed. Analysis Requests are displayed in columns and analyses in rows. Uses most of the logic provided by BikaListingView through bika.lims.worksheet.views.AnalysesView to generate the items, but renders its own template, which is highly specific for display analysis results. Because of this, some generic BikaListing functionalities, such as sorting, pagination, contextual menus for columns, etc. will not work in this view. """ def contents_table(self, table_only = True): """ Overrides contents_table method from the parent class BikaListingView, using the transposed template instead of the classic template. """ table = AnalysesTransposedTable(bika_listing = self, table_only = True) return table.render(self) class AnalysesTransposedTable(BikaListingTable): """ The BikaListingTable that uses a transposed template for displaying the results. """ render = ViewPageTemplateFile("../templates/analyses_transposed.pt") render_cell = ViewPageTemplateFile("../templates/analyses_transposed_cell.pt") def __init__(self, bika_listing = None, table_only = False): BikaListingTable.__init__(self, bika_listing, True) self.rows_headers = [] self.trans_items = {} self.positions = [] self._transpose_data() def _transpose_data(self): cached = [] index = 0 #ignore = ['Analysis', 'Service', 'Result', 'ResultDM'] include = ['Attachments', 'DetectionLimit', 'DueDate','Pos', 'ResultDM'] for col in self.bika_listing.review_state['columns']: if col == 'Result': # Further interims will be inserted in this position resindex = index if col not in include: continue lcol = self.bika_listing.columns[col] self.rows_headers.append({'id': col, 'title': lcol['title'], 'type': lcol.get('type',''), 'row_type': 'field', 'hidden': not lcol.get('toggle', True), 'input_class': lcol.get('input_class',''), 'input_width': lcol.get('input_width','')}) cached.append(col) index += 1 for item in self.items: if item['Service'] not in cached: self.rows_headers.insert(resindex, {'id': item['Service'], 'title': item['title'], 'type': item.get('type',''), 'row_type': 'analysis', 'index': index}) resindex += 1 cached.append(item['Service']) pos = item['Pos'] if pos in self.trans_items: self.trans_items[pos][item['Service']] = item else: self.trans_items[pos] = {item['Service']: item} if pos not in self.positions: self.positions.append(pos) def rendered_items(self, cat=None, **kwargs): return '' def render_row_cell(self, rowheader, position = ''): self.current_rowhead = rowheader self.current_position = position if rowheader['row_type'] == 'field': # Only the first item for this position contains common # data for all the analyses with the same position its = [i for i in self.items if i['Pos'] == position] self.current_item = its[0] if its else {} elif position in self.trans_items \ and rowheader['id'] in self.trans_items[position]: self.current_item = self.trans_items[position][rowheader['id']] else: return '' return self.render_cell()
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ExperimentBasics/migrations/0013_retryexperimentparticipation_retryuseranswer.py
tifat58/lsv-c4-django-webexperiment
1
144220
# -*- coding: utf-8 -*- # Generated by Django 1.9.9 on 2019-01-31 02:10 from __future__ import unicode_literals import django.core.validators from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('Users', '0010_auto_20180606_0002'), ('ExperimentBasics', '0012_auto_20180626_0248'), ] operations = [ migrations.CreateModel( name='RetryExperimentParticipation', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('started_on', models.DateTimeField(auto_now=True)), ('completed_on', models.DateTimeField(null=True)), ('retry_count', models.IntegerField(default=0)), ('is_active', models.NullBooleanField(default=True)), ('is_delete', models.NullBooleanField(default=False)), ('experiment', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='ExperimentBasics.Experiment')), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Users.UserInfo')), ], ), migrations.CreateModel( name='RetryUserAnswer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('re_answer_date', models.DateTimeField(null=True)), ('re_answer_given', models.BooleanField(default=False)), ('re_is_exactly_correct', models.NullBooleanField()), ('re_normalized_form_is_correct', models.NullBooleanField()), ('re_time_spent', models.IntegerField(null=True, validators=[django.core.validators.MinValueValidator(0)])), ('re_answered_question', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='re_answers', to='ExperimentBasics.Question')), ('re_answering_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='re_given_answers', to='Users.UserInfo')), ('re_experiment_participation', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='re_given_answers', to='ExperimentBasics.RetryExperimentParticipation')), ], options={ 'db_table': 'retry_user_answers', }, ), ]
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dianhua/worker/crawler/china_telecom/guangxi/main.py
Svolcano/python_exercise
6
185060
# -*- coding: utf-8 -*- from lxml import etree from datetime import date import traceback import time import sys import re from des_js import des_encode # 这段代码是用于解决中文报错的问题 reload(sys) sys.setdefaultencoding("utf8") from dateutil.relativedelta import relativedelta if __name__ == '__main__': sys.path.append('../..') sys.path.append('../../..') sys.path.append('../../../..') from crawler.base_crawler import BaseCrawler else: from worker.crawler.base_crawler import BaseCrawler class Crawler(BaseCrawler): """ kwargs 包含 'tel': str, 'pin_pwd': str, 'id_card': str, 'full_name': unicode, 'sms_code': str, 'captcha_code': str 錯誤等級 0: 成功 1: 帳號密碼錯誤 2: 認證碼錯誤 9: 其他錯誤 """ def __init__(self, **kwargs): """ 初始化 """ super(Crawler, self).__init__(**kwargs) today = date.today() self.this_month = "%d%02d" % (today.year, today.month) self.user_no = '' # 查询方式1，查询方式2 self.flag = 1 def need_parameters(self, **kwargs): return ['pin_pwd', 'full_name', 'id_card'] def get_login_verify_type(self, **kwargs): pass def get_verify_type(self, **kwargs): return 'SMS' def login(self, **kwargs): url = "http://gx.189.cn/public/common/control/dwr/engine.js" code, key, resp = self.get(url) if code != 0: return code, key try: scriptSessionId = re.findall('dwr.engine._origScriptSessionId = "([\S]+)";', resp.text)[0] except: error = traceback.format_exc() self.log("crawler", u'获取scriptSessionId失败:{}'.format(error), '') return 9, 'crawl_error' # 请求key 用于密码加密 url = 'http://gx.189.cn/chaxun/iframe/user_center.jsp' code, key, resp = self.get(url) if code != 0: return code, key key1 = re.findall("var key1='(.*?)';", resp.text) key2 = re.findall("var key2='(.*?)';", resp.text) key3 = re.findall("var key3='(.*?)';", resp.text) try: pin_pwd = '__' + str(des_encode(kwargs['pin_pwd'], key1[0], key2[0], key3[0])) except: error = traceback.format_exc() self.log("crawler", u'加密失败:{}'.format(error), '') return 9, 'crawl_error' # 增加访问连接 url = 'http://gx.189.cn/dwr/call/plaincall/Service.excute.dwr' row_data = """callCount=1 page=/chaxun/iframe/user_center.jsp httpSessionId= scriptSessionId={} c0-scriptName=Service c0-methodName=excute c0-id=0 c0-param0=string:WB_IP_ACTION_QRY c0-param1=boolean:true c0-e1=string:0.0.0.0 c0-e2=string:24 c0-e3=string:2 c0-param2=Object_Object:{} batchId=0 """.format(scriptSessionId, '{CUST_IP:reference:c0-e1, TIME_RANGE:reference:c0-e2, LOG_TYPE:reference:c0-e3}') headers = { 'Referer': 'http://gx.189.cn/chaxun/iframe/user_center.jsp', 'Content-Type': 'text/plain', 'Origin': 'http://gx.189.cn', } code, key, resp = self.post(url, data=row_data.replace(' ', ''), headers=headers) if code != 0: return code, key login_url = "http://gx.189.cn/public/login.jsp" login_data = { 'LOGIN_TYPE': '21', 'RAND_TYPE': '001', 'AREA_CODE': '', 'logon_name': kwargs['tel'], 'password_type_ra': '1', 'logon_passwd': <PASSWORD>, 'logon_valid': '请输入验证码' } header = {'Referer': 'http://gx.189.cn/chaxun/iframe/user_center.jsp'} code, key, login_req = self.post(login_url, data=login_data, headers=header) if code != 0: return code, key if '<flag>0</flag>' in login_req.text: self.log('crawler', '登录返回信息', login_req) try: self.user_no = re.findall('<user_no>(.*)</user_no>', login_req.text)[0] except: error = traceback.format_exc() self.log('website', u'登录出错{}'.format(error), resp) return 9, 'website_busy_error' return 0, 'success' #密码错误时会返回： 对不起，您已经连续输错了2次密码，如连续错误达5次，您的账号将被限制登陆6小时 elif u'验证码错误' in login_req.text: self.log("website", 'website_busy_error', login_req) return 9, 'website_busy_error' elif u'如连续错误达5次' in login_req.text or u'密码错误' in login_req.text: self.log('user', 'pin_pwd_error', login_req) return 1, 'pin_pwd_error' elif u'密码过于简单' in login_req.text: self.log('user', 'sample_pwd', login_req) return 1, 'sample_pwd' elif u'号码不存在' in login_req.text: self.log('user', 'invalid_tel', login_req) return 1, 'invalid_tel' elif u'您的账号在6小时内被限制登陆' in login_req.text: self.log('user', 'over_query_limit', login_req) return 9, 'over_query_limit' elif u'系统繁忙' in login_req.text or '<?xml version="1.0" encoding="utf-8" ?>' in login_req.text: self.log('website', 'website_busy_error', login_req) return 9, 'website_busy_error' elif u'密码锁定' in login_req.text or 'data-resultcode="9113"' in login_req.text: self.log('crawler', 'website_busy_error', login_req) return 9, 'website_busy_error' elif u"信息核对失败" in login_req.text: self.log("user", "website_busy_error", login_req) return 9, "website_busy_error" else: self.log('crawler', 'html_error', login_req) return 9, 'html_error' def send_verify_request(self, **kwargs): get_iframe_url = "http://gx.189.cn/chaxun/iframe/user_center.jsp" headers = { "Referer": "http://gx.189.cn/chaxun/iframe/user_center.jsp" } error = u'获取prod_type失败' for i in range(3): code, key, resp = self.get(get_iframe_url, headers=headers) if code != 0: return code, key, '' try: acc_nbr = re.findall(r"ACC_NBR:'(.*?)'", resp.text)[0] prod_type = re.findall(r"PROD_TYPE:'(.*?)'", resp.text)[0] break except: error = traceback.format_exc() data = { 'Logon_Name': kwargs['tel'], 'USER_FLAG': '001', 'USE_PROTOCOL': '', 'LOGIN_TYPE': '21', 'USER_NO': self.user_no, 'ESFlag': '8', 'REDIRECT_URL': '/2015/', } agree_url = 'http://gx.189.cn/public/user_protocol.jsp' code, key, response = self.post(agree_url, headers=headers, data=data) if code != 0: return code, key, '' self.log('user', u'用户协议页面', response) agree_url02 = 'http://gx.189.cn/public/protocollogin.jsp' data = { 'OPEN_TYPE': '1', 'LOGIN_TYPE': '21', 'USER_NO': self.user_no, 'CUSTBRAND': '', 'ESFlag': '8', 'REDIRECT_URL': '/2015/', } code, key, response = self.post(agree_url02, headers=headers, data=data) if code != 0: return code, key, '' self.log('user', u'用户同意页面', response) next_url = 'http://gx.189.cn/sales/chipmap/chipmaplogin.jsp' n_header = { 'X-Requested-With': 'XMLHttpRequest', 'Referer': 'http://gx.189.cn/public/protocollogin.jsp', } code, key, response = self.post(next_url, headers=n_header) if code != 0: return code, key, '' self.log('user', u'用户同意跳转页面', response) next_url02 = 'http://gx.189.cn/sales/chipmap/chipmapRecom.jsp' code, key, response = self.post(next_url02, headers=n_header) if code != 0: return code, key, '' self.log('user', u'用户同意跳转页面02', response) else: self.log("crawler", 'html_error: {}'.format(error), resp) return 9, "html_error", "" get_post_data_url = "http://gx.189.cn/chaxun/iframe/qdcx.jsp" headers = { "X-Requested-With": "XMLHttpRequest", "Content-Type": "application/x-www-form-urlencoded", "Referer": "http://gx.189.cn/chaxun/iframe/user_center.jsp" } data = { "ACC_NBR": acc_nbr, "PROD_TYPE": prod_type } code, key, resp = self.post(get_post_data_url, headers=headers, data=data) if code != 0: return code, key, "" sms_data = {} try: et = etree.HTML(resp.text) er_name = et.xpath('//form[@id="frmGetPwd"]/input/@name') er_value = et.xpath('//form[@id="frmGetPwd"]/input/@value') for name, value in zip(er_name, er_value): sms_data[name] = value except: error = traceback.format_exc() self.log("crawler", "html_error{}".format(error), resp) return 9, "html_error", "" sms_url = 'http://gx.189.cn/service/bill/getRand.jsp' header = { 'Referer': 'http://gx.189.cn/chaxun/iframe/user_center.jsp' } data = { 'FIND_TYPE': '1031', # 市话 'radioQryType': 'on', # 按月查询 'ACCT_DATE': self.this_month, 'ACCT_DATE_1': self.this_month, 'PASSWORD': '', 'CUST_NAME': '', 'CARD_TYPE': '1', # 使用居民身份证 'CARD_NO': '' } data.update(sms_data) code, key, resp = self.post(sms_url, data=data, headers=header) if code != 0: return code, key, '' if '<flag>0</flag>' in resp.text: return 0, "success", "" elif u'您短时间内不能重复获取随机密码' in resp.text: self.log('crawler', 'send_sms_too_quick_error', resp) return 9, 'send_sms_too_quick_error', '' elif u'您输入的手机号码有误或不存在' in resp.text: self.log('user', 'invalid_tel', resp) return 1, 'invalid_tel', '' else: self.log('crawler', 'html_error', resp) return 9, 'html_error', '' def verify(self, **kwargs): cookie_url = 'http://gx.189.cn/public/realname/checkRealName.jsp' header = { 'Referer': 'http://gx.189.cn/chaxun/iframe/user_center.jsp', 'Content-Type': 'application/x-www-form-urlencoded' } data = { 'NUM': kwargs['tel'], 'V_PASSWORD': kwargs['<PASSWORD>'], 'CUST_NAME': kwargs['full_name'].encode("utf8"), 'CARD_NO': kwargs['id_card'], 'CARD_TYPE': '1', 'RAND_TYPE': '002' } code, key, resp = self.post(cookie_url, data=data, headers=header) if code != 0: return code, key if '<PrivilegeLevel>1</PrivilegeLevel>' in resp.text: verify_url = 'http://gx.189.cn/chaxun/iframe/inxxall_new.jsp' header = { 'Referer': 'http://gx.189.cn/chaxun/iframe/user_center.jsp', 'X-Requested-With': 'XMLHttpRequest', 'Content-Type': 'application/x-www-form-urlencoded' } data = { 'PRODTYPE': '2020966', 'RAND_TYPE': '002', 'BureauCode': '1300', 'ACC_NBR': kwargs['tel'], 'PROD_TYPE': '2020966', 'PROD_PWD': '', 'REFRESH_FLAG': '1', 'BEGIN_DATE': '', 'END_DATE': '', 'SERV_NO': '', 'QRY_FLAG': '1', 'MOBILE_NAME': kwargs['tel'], 'OPER_TYPE': 'CR1', 'FIND_TYPE': '1041', 'radioQryType': 'on', 'ACCT_DATE': self.this_month, 'ACCT_DATE_1': self.this_month, 'PASSWORD': kwargs['<PASSWORD>'], 'CUST_NAME': kwargs['full_name'].encode("utf8"), 'CARD_TYPE': '1', 'CARD_NO': kwargs['id_card'] } code, key, response = self.post(verify_url, data=data, headers=header) if code != 0: return code, key if 'mask_div_id' in response.text: if '<option value="1037" >本地拨打异地清单</option>' in response.text: self.flag = 2 return 0, 'success' else: self.log('crawler', 'html_error', response) return 9, 'html_error' elif u'对不起,您输入的名字或证件号码不正确' in resp.text: self.log('user', 'user_name_error or user_id_error', resp) return 2, 'user_name_error' elif u'验证码错误' in resp.text: self.log('user', 'verify_error', resp) return 2, 'verify_error' elif u'系统繁忙' in resp.text: self.log("website", 'website_busy_error', resp) return 9, 'website_busy_error' elif u'未进行实名验证' in resp.text: self.log("user", u"您的号码未进行实名验证，请您携带有效证件到当地营业厅办理实名登记", resp) return 9, 'real_name_registration_error' else: self.log('crawler', 'html_error', resp) return 9, 'html_error' def crawl_call_log(self, **kwargs): missing_list = [] possibly_missing_list = [] website_num = 0 crawler_num = 0 today = date.today() call_log = [] search_month = [x for x in range(0, -6, -1)] #这个列表 表示详单类型， 1041是漫游通话清单， 1040是长话清单，1031是市话清单，1035是国际及港澳台清单 if self.flag == 1: FIND_TYPE_LIST = ['1041', '1040', '1031', '1035'] else: FIND_TYPE_LIST = ['1037', '1038', '1039'] for each_month in search_month: #这个变量用来统计这个月的可能缺失类型个数， 当可能缺失类型个数达到4个的时候， 加到possibly_missing month_type = 0 for call_type in FIND_TYPE_LIST: query_date = today + relativedelta(months=each_month) query_month = "%d%02d" % (query_date.year, query_date.month) if query_month == self.this_month: last_month = query_month else: query_date = query_date + relativedelta(months=1) last_month = "%d%02d" % (query_date.year, query_date.month) key, level, call_log_history, wrong_flag = self.deal_call_log(call_type, kwargs['tel'], query_month, last_month) if level == -1: month_type += 1 elif level != 0: missing_list.append(query_month) if wrong_flag == 'website': website_num += 1 elif wrong_flag == 'crawler': crawler_num += 1 else: if call_log_history: call_log.extend(call_log_history) else: month_type += 1 if month_type == 4: possibly_missing_list.append(query_month) missing_list = list(set(missing_list)) if len(missing_list) == 6 or len(possibly_missing_list) == 6: return 9, 'website_busy_error', call_log, missing_list, possibly_missing_list if crawler_num > 0: return 9, 'crawl_error', call_log, missing_list, possibly_missing_list return 0, "success", call_log, missing_list, possibly_missing_list def deal_call_log(self, call_type, tel, query_month, last_month): call_log_url = 'http://gx.189.cn/chaxun/iframe/inxxall_new.jsp' header = { 'Referer': 'http://gx.189.cn/chaxun/iframe/user_center.jsp' } data = { 'ACC_NBR': tel, 'PROD_TYPE': '2020966', 'BEGIN_DATE': '', 'END_DATE': '', 'REFRESH_FLAG': '1', 'QRY_FLAG': '1', 'FIND_TYPE': call_type, 'radioQryType': 'on', 'ACCT_DATE': query_month, 'ACCT_DATE_1': last_month } for retry in xrange(self.max_retry): code, key, resp = self.post(call_log_url, headers=header, data=data) if code != 0 or u'无记录</td>' in resp.text or u'错误日志序号' in resp.text: continue else: break else: if code != 0: return key, code, [], 'website' else: self.log('crawler', '没有查找到相关数据', resp) return '', -1, '', '' key, level, message, call_month_log = self.call_log_get(resp.text, query_month) if level != 0: self.log('crawler', message, resp) return "html_error", 9, [], 'crawler' if not call_month_log: self.log('website', 'no data', resp) return 'success', 0, call_month_log, '' def call_log_get(self, response, query_month): """ `call_tel` | string | 18202541892 | 电话号码 | | `call_cost` | string | 0.00 | 通话费用 | | `call_time` | timestamp | `1487900810` | 通话起始时间, 如果运营商原始数据不是时间戳，需要转换成时间戳 | | `call_method` | string | 主叫 | 呼叫类型, 固定枚举类型： `主叫`, `被叫`, 如果运营商原始数据不是主叫被叫，需要转换成主叫被叫字符串 | | `call_type` | string | 本地 | 通话类型（本地, 长途） | | `call_from` | string | 北京市 | 本机通话地 | | `call_to` | string | 长沙市 | 对方归属地 | | `call_duration` | integer | 300 | 通话时长(秒), """ call_month_log = [] try: html_tree = etree.HTML(response) log_list = html_tree.xpath('//table[@id="list_table"]/tr')[2:-1] for log in log_list: data = {} row_data = log.xpath('./td/text()') if len(row_data) <= 1: continue call_time = log.xpath('./td[2]/text()')[0] is_time = len(call_time.split(":")) if is_time == 3: data['month'] = query_month data['call_method'] = log.xpath('./td[4]/text()')[0] data['call_tel'] = log.xpath('./td[5]/text()')[0] if log.xpath('./td[5]/text()') else '' call_time = log.xpath('./td[2]/text()')[0] data['call_time'] = self.time_format(call_time) call_duration = log.xpath('./td[6]/text()')[0] time_temp = call_duration.split(':') data['call_duration'] = str(int(time_temp[0]) * 60 * 60 + int(time_temp[1]) * 60 + int(time_temp[2])) data['call_type'] = '' # data['call_from'] = log.xpath('./td[3]/text()')[0] if log.xpath('./td[3]/text()') else '' raw_call_from = log.xpath('./td[3]/text()')[0] if log.xpath('./td[3]/text()') else '' call_from, error = self.formatarea(raw_call_from) if call_from: data['call_from'] = call_from else: self.log("crawler", "{} {}".format(error, raw_call_from), "") data['call_from'] = raw_call_from data['call_to'] = '' data['call_cost'] = log.xpath('./td[8]/text()')[0] if log.xpath('./td[8]/text()') else '' call_month_log.append(data) else: call_method = log.xpath('./td[2]/text()')[0] # 目录走这个，其余会走上面 if call_method == u'起始时间': continue data['month'] = query_month data['call_method'] = call_method data['call_tel'] = log.xpath('./td[5]/text()')[0] if log.xpath('./td[5]/text()') else '' call_time = log.xpath('./td[6]/text()')[0] data['call_time'] = self.time_format(call_time) data['call_duration'] = log.xpath('./td[7]/text()')[0] data['call_type'] = '' data['call_from'] = log.xpath('./td[3]/text()')[0] if log.xpath('./td[3]/text()') else '' data['call_to'] = '' data['call_cost'] = log.xpath('./td[9]/text()')[0] if log.xpath('./td[9]/text()') else '' call_month_log.append(data) except: error = traceback.format_exc() return 'html_error', 9, 'html_error ' + error, call_month_log return 'success', 0, 'success', call_month_log def crawl_info(self, **kwargs): """ 爬取帳戶資訊 return status_key: str, 狀態碼金鑰，參考status_code level: int, 錯个i他誤等級 message: unicode, 詳細的錯誤信息 info: dict, 帳戶信息，參考帳戶信息格式 """ info_url = 'http://gx.189.cn/service/manage/my_selfinfo.jsp' header = { 'X-Requested-With': 'XMLHttpRequest', } code, key, res = self.post(info_url, headers=header) if code != 0: return code, key, {} full_name = re.findall(u'name="USER_NAME" value="(.*?)"', res.text) full_name = full_name[0] if full_name else '' user_info_data = { 'full_name': full_name, 'id_card': '', 'open_date': '', 'address': '' } return 0, "success", user_info_data def time_format(self, timeoo): call_time = re.findall('\d{2}', timeoo) call_time_change = call_time[0] + call_time[1] + '-' + call_time[2] + '-' + call_time[ 3] + ' ' + call_time[4] + ':' + call_time[5] + ':' + call_time[6] timeArray = time.strptime(call_time_change, "%Y-%m-%d %H:%M:%S") call_time_timeStamp = str(int(time.mktime(timeArray))) return call_time_timeStamp def crawl_phone_bill(self, **kwargs): missing_list = [] phone_bill = list() today = date.today() # 没有当月账单 search_month = [x for x in range(-1, -6, -1)] bill_url = 'http://gx.189.cn/chaxun/iframe/cust_zd.jsp' header = {'X-Requested-With': 'XMLHttpRequest'} crawl_num = 0 for each_month in search_month: query_date = today + relativedelta(months=each_month) query_month = "%d%02d" % (query_date.year, query_date.month) params = { 'ACC_NBR': kwargs['tel'], 'DATE': query_month, '_': int(time.time() * 1000) } for retry in xrange(self.max_retry): code, key, resp = self.get(bill_url, params=params, headers=header) if code != 0: continue if u'错误日志序号：' in resp.text: if retry == self.max_retry - 1: self.log('user', '账单为空', resp) continue code, key, message, result = self.crawl_month_bill(query_month, resp.text) if code != 0: if retry == self.max_retry - 1: crawl_num += 1 self.log('crawler', message, resp) continue phone_bill.append(result) break else: # 访问出错不再记日志 missing_list.append(query_month) if len(missing_list) == 5: if crawl_num > 1: return 9, 'crawl_error', phone_bill, missing_list return 9, 'website_busy_error', phone_bill, missing_list return 0, 'success', phone_bill, missing_list def crawl_month_bill(self, query_month, resp): month_fee_data = {} month_fee_data['bill_month'] = query_month month_fee_data['bill_amount'] = '' month_fee_data['bill_ext_calls'] = '' month_fee_data['bill_ext_data'] = '' month_fee_data['bill_ext_sms'] = '' month_fee_data['bill_zengzhifei'] = '' month_fee_data['bill_daishoufei'] = '' month_fee_data['bill_qita'] = '' try: bill_package = re.findall(u'套餐月基本费.*?x">(\d+\.\d+)<', resp, re.S) month_fee_data['bill_package'] = bill_package[0] if len(bill_package) else '' bill_ext_sms = re.findall(u'国内短信费.*?x">(\d+\.\d+)<', resp, re.S) month_fee_data['bill_ext_sms'] = bill_ext_sms[0] if len(bill_ext_sms) else '' bill_amount = re.findall(u'本期费用合计:(\d+\.\d+)元', resp, re.S) month_fee_data['bill_amount'] = bill_amount[0] if len(bill_amount) else '' except: error = traceback.format_exc() return 9, 'html_error', 'html_error'+error, {} return 0, 'success', 'success', month_fee_data if __name__ == '__main__': c = Crawler() # TEL = "18076766149" # USER_PASSWORD = "<PASSWORD>" # ID_CARD = '14072319930902003X' # USER_NAME = '青培' # TEL = "18078454964" # USER_PASSWORD = "941667" # ID_CARD = '110229199501253824' # USER_NAME = '王阳' USER_PASSWORD = "<PASSWORD>" ID_CARD = '130981199605276614' USER_NAME = '韩映辉' TEL = "17377135156" # TEL = "17377135152" # self_test c.self_test(tel=TEL, pin_pwd=<PASSWORD>, id_card=ID_CARD, full_name=USER_NAME)
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web_fetch/sudoku_extract.py
widyaageng/Sudoku_auto
0
196197
<gh_stars>0 from util import svg_dict from web_fetch import fetch import numpy as np def get_field(): # Create board matrix sudoku_field = [] # Retrieve plain HTML source soup = fetch.get_html_txt_headless('https://sudoku.com/medium/') fetch.close_html_txt_headless() # Getting row list tr_row_elem = soup.find_all("div", attrs={"class": "cell-value"}) for elem in tr_row_elem: if elem.contents[0] == "\xa0": # print("X") sudoku_field.append(0) else: # print(svg_dict.get_svg_to_num(elem.svg.path["d"])) sudoku_field.append(svg_dict.get_svg_to_num(elem.svg.path["d"])) sudoku_field = np.reshape(sudoku_field, (9, 9)) return sudoku_field
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digits/keras_lenet_kernel.py
muggle14/kaggle_projects
33
67720
<filename>digits/keras_lenet_kernel.py import pandas as pd import numpy as np from keras.models import Sequential from keras.utils import np_utils from keras.layers.convolutional import Convolution2D from keras.layers.convolutional import MaxPooling2D from keras.layers.core import Activation from keras.layers.core import Flatten from keras.layers.core import Dense if __name__ == "__main__": train = pd.read_csv('./data/train.csv').values test = pd.read_csv('./data/test.csv').values img_rows, img_cols = 28, 28 X_train = train[:,1:].reshape(train.shape[0], img_rows, img_cols, 1) X_train = X_train.astype(float) X_train = X_train/255.0 X_test = test.reshape(test.shape[0], img_rows, img_cols, 1) X_test = X_test.astype(float) X_test = X_test/255.0 y_train = np_utils.to_categorical(train[:,0]) num_classes = y_train.shape[1] #cnn parameters num_filters_l1 = 32 filter_size_l1 = 5 num_filters_l2 = 64 filter_size_l2 = 5 cnn = Sequential() #CONV -> RELU -> MAXPOOL cnn.add(Convolution2D(num_filters_l1, filter_size_l1, filter_size_l1, input_shape=(img_rows, img_cols, 1), border_mode='same')) cnn.add(Activation('relu')) cnn.add(MaxPooling2D(pool_size=(2,2), strides=(2,2))) #CONV -> RELU -> MAXPOOL cnn.add(Convolution2D(num_filters_l2, filter_size_l2, filter_size_l2, border_mode='same')) cnn.add(Activation('relu')) cnn.add(MaxPooling2D(pool_size=(2,2), strides=(2,2))) #FC -> RELU cnn.add(Flatten()) cnn.add(Dense(128)) cnn.add(Activation('relu')) #Softmax Classifier cnn.add(Dense(num_classes)) cnn.add(Activation('softmax')) cnn.summary() cnn.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) cnn.fit(X_train, y_train, batch_size=128, nb_epoch=1, verbose=1) y_pred = cnn.predict_classes(X_test) #create submission submission = pd.DataFrame(index=pd.RangeIndex(start=1, stop=28001, step=1), columns=['Label']) submission['Label'] = y_pred.reshape(-1,1) submission.index.name = "ImageId" submission.to_csv('./lenet_pred.csv', index=True, header=True)
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research/cv/resnet50_bam/src/config.py
leelige/mindspore
1
1609463
<filename>research/cv/resnet50_bam/src/config.py # Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ network config setting, will be used in main.py """ from easydict import EasyDict as edict imagenet_cfg = edict({ 'name': 'imagenet', 'pre_trained': False, 'num_classes': 1000, 'lr_init': 0.02, # 1P: 0.02, 8P: 0.18 'batch_size': 128, 'epoch_size': 160, 'momentum': 0.9, 'weight_decay': 1e-4, 'image_height': 224, 'image_width': 224, 'data_path': '/data/ILSVRC2012_train/', 'val_data_path': '/data/ILSVRC2012_val/', 'device_target': 'Ascend', 'device_id': 0, 'keep_checkpoint_max': 25, 'checkpoint_path': None, 'onnx_filename': 'resnet50-bam', 'air_filename': 'resnet50-bam', # optimizer and lr related 'lr_scheduler': 'cosine_annealing', 'lr_epochs': [30, 60, 90, 120], 'lr_gamma': 0.3, 'eta_min': 0.0, 'T_max': 150, 'warmup_epochs': 0, # loss related 'is_dynamic_loss_scale': 0, 'loss_scale': 1024, 'label_smooth_factor': 0.1, 'use_label_smooth': True, })
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actor_critic/pretrain.py
tttslab/armos
0
1614027
<reponame>tttslab/armos import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import models import utils import configparser import time import logging import librosa import random import copy logging.basicConfig(filename='log/pretrain.log', level=logging.INFO) ### Hyperparameters conf = configparser.SafeConfigParser() conf.read("config.ini") IN_SIZE = int(conf.get('main', 'in_size')) OUT_SIZE = int(conf.get('main', 'out_size')) P_HIDDEN_SIZE = int(conf.get('actor', 'hidden_size')) P_NUM_LAYERS = int(conf.get('actor', 'num_layers')) Q_HIDDEN_SIZE = int(conf.get('critic', 'hidden_size')) BATCH_SIZE = int(conf.get('main', 'batch_size')) SAMPLING_RATE = int(conf.get('main', 'sampling_rate')) NUM_PARAL = int(conf.get('main', 'num_paral')) AUDIO_SEGMENT = int(conf.get('main', 'audio_segment')) ### Condition Setting device = 'cuda' if torch.cuda.is_available() else 'cpu' policy = models.stacked_BLSTM(IN_SIZE, OUT_SIZE, P_HIDDEN_SIZE, P_NUM_LAYERS).to(device) loss_fun = nn.MSELoss() p_optim = torch.optim.Adam(policy.parameters(), lr=1e-3) train_loader = utils.Batch_generator('training', BATCH_SIZE) for iteration in range(100): policy.train() start = time.time() ### Assume the duration is multiple of 10ms. ### For example, if the time length of input feature is 101, this sound is from 1000ms to 1009ms, ### but I regard this as 1000ms and ignore the last feature. inputs, length, _ = next(train_loader) feats = np.zeros((BATCH_SIZE, AUDIO_SEGMENT, IN_SIZE)) for i in range(BATCH_SIZE): s_pos = np.random.randint(length[i]-AUDIO_SEGMENT+1) feats[i] = inputs[i, s_pos:s_pos+AUDIO_SEGMENT,:] inputs = np.asarray(feats, dtype=np.float32) inputs = torch.from_numpy(inputs).to(device) length = np.asarray(length, dtype=np.int32) - 1 length = np.where(length > AUDIO_SEGMENT, AUDIO_SEGMENT, length) dur = 1.0*length / frameRate_Hz ### dimension is sec. m_target = torch.ones(BATCH_SIZE, AUDIO_SEGMENT, OUT_SIZE).to(device) * 0.5 v_target = torch.ones(BATCH_SIZE, AUDIO_SEGMENT, OUT_SIZE).to(device) * 0.1 ### Get action mean, var = policy(inputs, length.tolist()) loss = F.mse_loss(mean, m_target) + F.mse_loss(var, v_target) if iteration % 10 == 0: x=1+1 p_optim.zero_grad() loss.backward() p_optim.step() logging.info('iter: {}\tloss: {}\telapse: {:.02f}'.format(iteration+1,\ loss.item(),\ time.time()-start)) torch.save(policy.state_dict(), './exp/pretrain.model')
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aioqiwi/wallet/urls.py
Derad6709/aioqiwi
33
107065
from ..core.tooling.htstrings import HeadTailString class WalletURL(HeadTailString): __head__ = "https://edge.qiwi.com/" class urls: me = WalletURL("person-profile/v1/profile/current") identification = WalletURL("identification/v1/persons/{}/identification") history = WalletURL("payment-history/v2/persons/{}/payments") stats = WalletURL("payment-history/v2/persons/{}/payments/total") cheque = WalletURL("payment-history/v1/transactions/{}/cheque/file") request_cheque = WalletURL("payment-history/v1/transactions/{}/cheque/send") payment_info = WalletURL("payment-history/v2/transactions/{}") class web_hooks: register = WalletURL("payment-notifier/v1/hooks") active = WalletURL("/active", head=register) test = WalletURL("/test", head=register) delete = WalletURL("/{}", head=register) class balance: base = WalletURL("funding-sources/v2/persons/") balance = WalletURL("{}/accounts", head=base) available_aliases = WalletURL("/offer", head=balance) set_new_balance = WalletURL("/{}", head=balance) class payments: base = WalletURL("sinap/api/v2/terms/{}/payments") providers = WalletURL("", head="https://qiwi.com/mobile/detect.action") commission = WalletURL("sinap/providers/{}/onlineCommission")
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carla_utils/agents/__init__.py
zhangdongkun98/carla-utils
6
136470
<reponame>zhangdongkun98/carla-utils from .agents_route_planner import AgentsRoutePlanner from .agents_master import AgentListMaster from .controller import Controller from .vehicle_model import RealModel, BicycleModel2D, BicycleModel2DParallel from .agent_abc import AgentABC from .agent_base import BaseAgent from .agent_keyboard import KeyboardAgent from .agent_naive import NaiveAgent from .agent_idm import IdmAgent from .agent_obstacle import BaseAgentObstacle
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itglue/resources.py
lisath92/itglue-py
6
1612592
from . import connection from . import process_path import re __all__ = [ "Organization", "Configuration", "ConfigurationType", "ConfigurationStatus", "ConfigurationInterface", "Location", "FlexibleAsset", "FlexibleAssetField", "FlexibleAssetType" ] class ResourceBase(object): class ResourceError(Exception): pass class ResourceMismatchError(ResourceError): pass @classmethod def resource_type(cls): raise NotImplementedError('resource_type not implemented for class {}'.format(cls.__name__)) def __init__(self, id=None, **attributes): self.attributes = attributes self.id = id def __repr__(self): return "<{class_name} id: {id}, attributes: {attributes}>".format( class_name=self.__class__.__name__, id=self.id, attributes=self.attributes ) def __eq__(self, other): if isinstance(other, self.__class__): return self.resource_type() == other.resource_type() and self.id == other.id and self.attributes == other.attributes return False def get_attr(self, attr_name): """ Get the value of an attribute. :param attr_name str: the name of the attribute being fetched :returns: the attribute value or None if it is not set """ return self.attributes.get(attr_name) def set_attr(self, attr_name, attr_value): """ Set the value of an attribute. :param attr_name str: the name of the attribute being set :param attr_value: the value of the attribute being set :returns: the attribute value """ self.attributes[attr_name] = attr_value return attr_value def set_attributes(self, **attributes_dict): """ Set the value of multiple attributes. :param attributes_dict dict: a dictionary containing key-value pairs as attribute names and values to be set :returns: the resource itself """ for attr_name, attr_value in attributes_dict.items(): self.set_attr(attr_name, attr_value) return self def save(self, parent=None): """ Either creates a resource or updates it (if it already has an id). This will trigger an api POST or PATCH request. :returns: the resource itself """ if self.id: return self.update(parent=parent) else: return self.create(parent=parent) def create(self, parent=None, **relationships): """ Creates the resource. This will trigger an api POST request. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :param **relationships: any number of lists as keyword arguments to be submitted as relationships for the request, e.g. relationship_name=[resource1, resource2] :raises ResourceError: if the resource has an id (already exists) :returns: the resource itself """ if self.id: raise self.ResourceError('cannot create a resource with an existing ID') data = self.__class__._process_request( connection.post, parent=parent, payload=self.payload(), relationships=self._relationships_payload(relationships) ) return self._reload(data) def update(self, parent=None): """ Updates the resource. This will trigger an api PATCH request. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :raises ResourceError: if the resource does not have an id (does not exist yet) :returns: the resource itself """ if not self.id: raise self.ResourceError('cannot update a resource without an ID') data = self.__class__._process_request( connection.patch, parent=parent, id=self.id, payload=self.payload() ) return self._reload(data) def _reload(self, data): # Loads the id and attributes properties based on a data dict self._assert_valid_data(data) self.id = data['id'] self.attributes = data['attributes'] return self def payload(self): """ Renders the resource payload. :returns: a dict representing the object to be used as payload for a request """ payload = {'type': self.resource_type(), 'attributes': self.attributes} if self.id: payload['id'] = self.id return payload @staticmethod def _relationships_payload(relationships): # Returns a dict representing the relationships, used as a payload for a request rel_payload = {} for rel_name, rel_items in relationships.items(): rel_payload[rel_name] = list(map(lambda rel_item: rel_item.payload(), rel_items)) return rel_payload @classmethod def get(cls, parent=None): """ Gets all resources of the given type and parent (if provided). This will trigger an api GET request. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :returns: a list of matching resources """ data = cls._process_request(connection.get, parent=parent) return cls._load_resources(data) @classmethod def filter(cls, parent=None, **filters): """ Gets all resources of the given type and parent (if provided) which match the given filters. This will trigger an api GET request. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :param **filters: any number of keyword arguments to filter by, e.g name='example name' :returns: a list of matching resources """ data = cls._process_filter_request(parent, **filters) return cls._load_resources(data) @classmethod def find(cls, id, parent=None): """ Gets the resource of the given type and parent (if provided) with matching id. This will trigger an api GET request. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :param id int: the id of the resource to be fetched :returns: the matching resource :raises ResourceError: if the resource cannot be found """ try: data = cls._process_request(connection.get, parent=parent, id=id) return cls._load_resource(data) except connection.RequestError as error: raise cls.ResourceError(error) @classmethod def first_or_create(cls, parent=None, **attributes): """ Attempts to find the first resource with the same attributes, creates the resource if no matches are found. This will trigger an api GET request and a POST request if the resource does not exist. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :param **attributes: any number of keyword arguments as attributes to search/create the resource :returns: a resource instance - the existing one if found, otherwise the newly created one """ existing_resource = cls.find_by(parent, **attributes) if existing_resource: return existing_resource return cls(**attributes).create() @classmethod def first_or_initialize(cls, parent=None, **attributes): """ Attempts to find the first resource with the same attributes, initialized the resource if no matches are found. This will trigger an api GET request. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :param **attributes: any number of keyword arguments as attributes to search/initialize the resource :returns: a resource instance - the existing one if found, otherwise the newly instantiated one """ existing_resource = cls.find_by(parent, **attributes) if existing_resource: return existing_resource return cls(**attributes) @classmethod def find_by(cls, parent=None, **attributes): """ Gets the first resource of the given type and parent (if provided) with matching attributes. This will trigger an api GET request. :param parent ResourceBase: the parent of the resource - used for nesting the request url, optional :param **attributes: any number of keyword arguments as attributes to search the resource by :returns: the matching resource, None if not found :raises ResourceError: if the no valid attributes are provided """ all_nones = not all(attributes.values()) if not attributes or all_nones: raise cls.ResourceError('at least one attribute must be provided') matches = cls.filter(parent, **attributes) if matches: return matches[0] @classmethod def _process_request(cls, request_func, parent=None, id=None, **request_args): # Processes the request by processing the path and executing the request_func with request_args path = cls._process_path(parent=parent, id=id) return request_func(path, **request_args) @classmethod def _process_filter_request(cls, parent=None, **filters): # Processes a filter get request ensuring the request arguments are formatted properly all_nones = not all(filters.values()) if not filters or all_nones: raise cls.ResourceError('at least one valid filter must be provided') params = {'filter': filters} return cls._process_request(connection.get, parent=parent, params=params) @classmethod def _process_path(cls, parent, id=None): # Delegates path processing to path_processor.process_path, ensuring the correct arguments are provided if parent: if not parent.resource_type(): raise cls.ResourceError('provided parent does not have a resource_type') elif not parent.id: raise cls.ResourceError('provided parent does not have an id') else: return process_path(cls.resource_type(), parent_type=parent.resource_type(), parent_id=parent.id, id=id) else: return process_path(cls.resource_type(), id=id) @classmethod def _load_resources(cls, data): # Loads a number of resources by instantiating them with the given data list instances = [] for item in data: instances.append(cls._load_resource(item)) return instances @classmethod def _load_resource(cls, data): # Loads one resource by instantiating it witha given data dict cls._assert_valid_data(data) resource_id = data['id'] resource_attributes = data['attributes'] return cls(id=resource_id, **resource_attributes) @classmethod def _assert_valid_data(cls, data): data_keys = data.keys() if not all(key in data_keys for key in ['type', 'id', 'attributes']): raise cls.ResourceError( "Unable to load data. Expected keys 'type', 'id' and 'attributes', got: {}".format(data_keys) ) data_type = re.sub('-', '_', str(data['type'])) if not data_type == cls.resource_type(): raise cls.ResourceMismatchError( "Received data type '{}' but expected '{}'".format(data['type'], cls.resource_type()) ) return True class Organization(ResourceBase): @classmethod def resource_type(cls): return 'organizations' class Configuration(ResourceBase): @classmethod def resource_type(cls): return 'configurations' class ConfigurationType(ResourceBase): @classmethod def resource_type(cls): return 'configuration_types' class ConfigurationStatus(ResourceBase): @classmethod def resource_type(cls): return 'configuration_statuses' class ConfigurationInterface(ResourceBase): @classmethod def resource_type(cls): return 'configuration_interfaces' class Location(ResourceBase): @classmethod def resource_type(cls): return 'locations' class FlexibleAsset(ResourceBase): @classmethod def resource_type(cls): return 'flexible_assets' class FlexibleAssetType(ResourceBase): @classmethod def resource_type(cls): return 'flexible_asset_types' class FlexibleAssetField(ResourceBase): @classmethod def resource_type(cls): return 'flexible_asset_fields'
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mozumder/models/i18n/scripts.py
mozumder/django-mozumder
1
118130
from django.db import models from datetime import date # Create your models here. class Script(models.Model): """ For the script designator, use the ISO 15924 standard, four letters with the first letter uppercase and the last three lowercase. """ iso_15294 = models.CharField(max_length=4,unique=True) code_number = models.CharField(max_length=3,unique=True) unicode_alias = models.CharField(max_length=75) unicode_version = models.CharField(max_length=4) version_date = models.DateField(default=date.today) def __str__(self): return self.iso_15294 class ScriptLang(models.Model): script = models.ForeignKey('Script', related_name='names', on_delete=models.CASCADE) name = models.CharField(max_length=30) lang = models.ForeignKey('Lang', related_name='script_langs', on_delete=models.CASCADE) class Meta: constraints = [ models.UniqueConstraint( fields = [ 'script', 'lang', ], name='scriptlang_uniquetogether' ) ] def __str__(self): return self.name
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custom/icds_reports/data_pull/exceptions.py
scottwedge/commcare-hq
0
170202
<reponame>scottwedge/commcare-hq class UnboundDataPullException(Exception): pass class DataPullInProgressError(Exception): pass
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lib/zkv/zkv.py
csernazs/zkv
0
112202
<reponame>csernazs/zkv from .exc import Error class ZKVError(Error): pass class ZKV: def __init__(self, backend, key_transforms=None, value_transforms=None): self.backend = backend if key_transforms is None: key_transforms = [] if value_transforms is None: value_transforms = [] self.key_transforms = key_transforms self.value_transforms = value_transforms def connect(self, pool): return ZKVConnection(self.backend.connect(pool), self.key_transforms, self.value_transforms) class ZKVConnection: def __init__(self, pool, key_transforms, value_transforms): self.pool = pool self.key_transforms = key_transforms self.value_transforms = value_transforms def get(self, key, default=None): try: return self[key] except KeyError: return default def __getitem__(self, key): for transform in self.key_transforms: key = transform.encode(key) value = self.pool.get(key) for transform in self.value_transforms: value = transform.decode(value) return value def __setitem__(self, key, value): for transform in self.key_transforms: key = transform.decode(key) new = self.pool.create(key) for transform in self.value_transforms: value = transform.encode(value) new.write(value) new.close() def __delitem__(self, key): self.pool.delete(key) def __contains__(self, key): return self.pool.contains(key) def iterkeys(self): return self.pool.iterkeys()
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tests/dummies.py
arvindmuralie77/gradsflow
253
797
# Copyright (c) 2021 GradsFlow. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from gradsflow.models import Model class DummyModel(Model): def __init__(self): learner = torch.nn.Linear(1, 4) super().__init__(learner) def backward(self, loss: torch.Tensor): return None def train_step(self, batch): return {"loss": torch.as_tensor(1), "metrics": {"accuracy": 1}} def val_step(self, batch): return {"loss": torch.as_tensor(1), "metrics": {"accuracy": 1}}
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networks/network.py
valeoai/POCO
13
154973
<gh_stars>10-100 import torch from torch_geometric.data import Data import logging from .backbone import * from .decoder import * from lightconvpoint.spatial import knn, sampling_quantized as sampling from lightconvpoint.utils.functional import batch_gather from lightconvpoint.nn import max_pool, interpolate def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) class Network(torch.nn.Module): def __init__(self, in_channels, latent_size, out_channels, backbone, decoder, **kwargs): super().__init__() self.net = eval(backbone)(in_channels, latent_size, segmentation=True, dropout=0) self.projection = eval(decoder["name"])(latent_size, out_channels, decoder["k"]) self.lcp_preprocess = True logging.info(f"Network -- backbone -- {count_parameters(self.net)} parameters") logging.info(f"Network -- projection -- {count_parameters(self.projection)} parameters") def forward(self, data, spatial_only=False, spectral_only=False): if spatial_only: net_data = self.net(data, spatial_only=spatial_only) if "output_support" in net_data: data["output_support"] = net_data["output_support"] proj_data = self.projection.forward_spatial(data) net_data["proj_indices"] = proj_data["proj_indices"] return net_data if not spectral_only: spatial_data = self.net.forward_spatial(data) if "output_support" in spatial_data: data["output_support"] = spatial_data["output_support"] proj_data = self.projection.forward_spatial(data) spatial_data["proj_indices"] = proj_data["proj_indices"] for key, value in spatial_data.items(): data[key] = value latents = self.net(data, spectral_only=True) data["latents"] = latents ret_data = self.projection(data, spectral_only=True) return ret_data def get_latent(self, data, with_correction=False, spatial_only=False, spectral_only=False): latents = self.net(data, spatial_only=spatial_only, spectral_only=spectral_only) data["latents"] = latents data["proj_correction"] = None if with_correction: data_in_proj = {"latents":latents, "pos":data["pos"], "pos_non_manifold":data["pos"].clone(), "proj_correction":None} data_proj = self.projection(data_in_proj, spectral_only=False) data["proj_correction"] = data_proj return data def from_latent(self, data): data_proj = self.projection(data) return data_proj#["outputs"] class NetworkMultiScale(torch.nn.Module): def __init__(self, in_channels, latent_size, out_channels, backbone, decoder, **kwargs): super().__init__() self.net = eval(backbone)(in_channels, latent_size, segmentation=True, dropout=0) self.merge_latent = torch.nn.Sequential( torch.nn.Conv1d(2*latent_size, latent_size,1), torch.nn.ReLU(), torch.nn.Conv1d(latent_size, latent_size,1), torch.nn.ReLU(), torch.nn.Conv1d(latent_size, latent_size,1) ) if "Radius" in decoder["name"]: self.projection = eval(decoder["name"])(latent_size, out_channels, decoder["radius"]) else: self.projection = eval(decoder["name"])(latent_size, out_channels, decoder["k"]) self.lcp_preprocess = True logging.info(f"Network -- backbone -- {count_parameters(self.net)} parameters") logging.info(f"Network -- projection -- {count_parameters(self.projection)} parameters") def forward(self, data): # compute the down sampled latents # ids_down = torch.rand((data["pos"].shape[0], 3000), device=data["pos"].device) * data["pos"].shape[2] # ids_down = ids_down.long() with torch.no_grad(): pos_down, idsDown = sampling(data["pos"], n_support=3000) x_down = batch_gather(data["x"], dim=2, index=idsDown).contiguous() data_down = {'x':x_down, 'pos':pos_down} latents_down = self.net(data_down) idsUp = knn(pos_down, data["pos"], 1) latents_down = interpolate(latents_down, idsUp) latents = self.net(data) latents = torch.cat([latents, latents_down], dim=1) latents = self.merge_latent(latents) data["latents"] = latents ret_data = self.projection(data) return ret_data def train(self, mode=True): r"""Sets the module in training mode.""" self.training = mode # set only the merge to train for module in self.children(): module.train(False) self.merge_latent.train(mode) return self def get_latent(self, data, with_correction=False, spatial_only=False, spectral_only=False): latents = self.net(data, spatial_only=spatial_only, spectral_only=spectral_only) data["latents"] = latents data["proj_correction"] = None if with_correction: data_in_proj = {"latents":latents, "pos":data["pos"], "pos_non_manifold":data["pos"].clone(), "proj_correction":None} data_proj = self.projection(data_in_proj, spectral_only=False) data["proj_correction"] = data_proj return data def from_latent(self, data): data_proj = self.projection(data) return data_proj#["outputs"]
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mergecli-runner.py
srp33/ShapeShifter-CLI
1
110394
#!/usr/bin/env python from mergecli.mergecli import main if __name__ == '__main__': main()
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tools/visualize_results_v2.py
kruda/DetectAndTrack
1,007
67458
<reponame>kruda/DetectAndTrack ############################################################## # Copyright (c) 2018-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. ############################################################## from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import argparse import os.path as osp import sys import cPickle as pickle import cv2 import logging import numpy as np from tqdm import tqdm from core.test_engine import get_roidb_and_dataset import utils.vis as vis_utils import utils.image as image_utils from core.config import ( cfg_from_file, assert_and_infer_cfg, get_output_dir, cfg_from_list) import utils.general as gen_utils FORMAT = '%(levelname)s %(filename)s:%(lineno)4d: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT, stream=sys.stdout) logger = logging.getLogger(__name__) def _parse_args(): parser = argparse.ArgumentParser() parser.add_argument( '--cfg', dest='cfg_file', help='Config file', type=str) parser.add_argument( '--thresh', dest='thresh', help='detection prob threshold', default=0.9, type=float) parser.add_argument( 'opts', help='See lib/core/config.py for all options', default=None, nargs=argparse.REMAINDER) if len(sys.argv) == 1: parser.print_help() sys.exit(1) args = parser.parse_args() return args def _id_or_index(ix, val): if len(val) == 0: return val else: return val[ix] def _vis_single_frame(im, cls_boxes_i, cls_segms_i, cls_keyps_i, cls_tracks_i, thresh): res = vis_utils.vis_one_image_opencv( im, cls_boxes_i, segms=cls_segms_i, keypoints=cls_keyps_i, tracks=cls_tracks_i, thresh=thresh, show_box=True, show_class=False, linewidth=3) if res is None: return im return res def _convert_roidb_to_pred_boxes(boxes): return np.hstack((boxes, np.ones((boxes.shape[0], 1)))) def _convert_roidb_to_pred_keyps(poses): poses = poses.astype(np.float32) res = [] for i in range(poses.shape[0]): poses[i, 2, poses[i, 2, :] >= 2] += 10.0 res.append(np.vstack((poses[i], np.zeros((1, poses[i].shape[1]))))) return res def _convert_roidb_to_pred_tracks(tracks): return tracks.reshape((-1, )).tolist() def _generate_visualizations(entry, ix, all_boxes, all_keyps, all_tracks, thresh): im = image_utils.read_image_video(entry, key_frame_only=True)[0] cls_boxes_i = [ _id_or_index(ix, all_boxes[j]) for j in range(len(all_boxes))] if all_keyps is not None: cls_keyps_i = [ _id_or_index(ix, all_keyps[j]) for j in range(len(all_keyps))] else: cls_keyps_i = None if all_tracks is not None: cls_tracks_i = [ _id_or_index(ix, all_tracks[j]) for j in range(len(all_tracks))] else: cls_tracks_i = None pred = _vis_single_frame( im.copy(), cls_boxes_i, None, cls_keyps_i, cls_tracks_i, thresh) gt = _vis_single_frame( im.copy(), [[], _convert_roidb_to_pred_boxes(entry['boxes'])], None, [[], _convert_roidb_to_pred_keyps(entry['gt_keypoints'])], [[], _convert_roidb_to_pred_tracks(entry['tracks'])], 0.1) return gt, pred def vis(roidb, detections_pkl, thresh, output_dir): if len(roidb) == 0: return with open(detections_pkl, 'rb') as f: dets = pickle.load(f) all_boxes = dets['all_boxes'] if 'all_keyps' in dets: all_keyps = dets['all_keyps'] else: all_keyps = None if 'all_tracks' in dets: all_tracks = dets['all_tracks'] else: all_tracks = None for ix, entry in enumerate(tqdm(roidb)): if entry['boxes'] is None or entry['boxes'].shape[0] == 0: continue gt, pred = _generate_visualizations( entry, ix, all_boxes, all_keyps, all_tracks, thresh) combined = np.hstack((gt, pred)) im_name = entry['image'] if isinstance(im_name, list): im_name = im_name[len(im_name) // 2] out_name = im_name[len(dataset.image_directory):] out_path = osp.join(output_dir, out_name) gen_utils.mkdir_p(osp.dirname(out_path)) cv2.imwrite(out_path, combined) if __name__ == '__main__': args = _parse_args() if args.cfg_file is not None: cfg_from_file(args.cfg_file) if args.opts is not None: cfg_from_list(args.opts) assert_and_infer_cfg() test_output_dir = get_output_dir(training=False) det_file = osp.join(test_output_dir, 'detections.pkl') tracking_det_file = osp.join(test_output_dir, 'detections_withTracks.pkl') if osp.exists(tracking_det_file): det_file = tracking_det_file output_dir = osp.join(test_output_dir, 'vis/') if not osp.exists(det_file): raise ValueError('Output file not found {}'.format(det_file)) else: logger.info('Visualizing {}'.format(det_file)) # Set include_gt True when using the roidb to evalute directly. Not doing # that currently roidb, dataset, _, _, _ = get_roidb_and_dataset(None, include_gt=True) vis(roidb, det_file, args.thresh, output_dir)
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irods/constants.py
trel/python-irodsclient
54
120797
<reponame>trel/python-irodsclient SYS_SVR_TO_CLI_COLL_STAT = 99999996 SYS_CLI_TO_SVR_COLL_STAT_REPLY = 99999997
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modules/blog.py
rdelfin/personal-site
0
30098
<gh_stars>0 from flask import Blueprint, Response, abort, render_template from utils import blog as blog_utils from utils import tags as tag_utils bp = Blueprint("blog", __name__) @bp.route("/", methods=["GET"]) def blog(): return blog_utils.respond_blog_list() @bp.route("/post_<post_name>", methods=["GET"]) def blog_post(post_name: str) -> Response: return blog_utils.respond_blog(post_name) @bp.route("/tags", methods=["GET"]) def tags() -> Response: tags = tag_utils.list_tags() return render_template('blog_tags.html', tags=sorted(tags, key=lambda t: t.name)) @bp.route("/tag/<tag_name>", methods=["GET"]) def tag(tag_name: str) -> Response: tag = tag_utils.get_tag(tag_name) if not tag: abort(404) blogs = blog_utils.get_blogs_with_tag(tag_name) sorted_blogs = sorted(blogs.items(), reverse=True, key=lambda t: t[1].creation_time) return render_template("blog_tag_page.html", tag=tag, blogs=sorted_blogs)
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webnmap/__init__.py
jtdub/webnmap
0
84771
<gh_stars>0 from webnmap.celery import app as celery_app __all__ = ('celery_app',)
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scripts/python/routines/plot/violin.py
GillianGrayson/dnamvae
1
82808
<reponame>GillianGrayson/dnamvae import plotly.graph_objects as go def add_violin_trace(fig, y, name): showlegend = False if name == "" else True fig.add_trace( go.Violin( y=y, name=name, box_visible=True, showlegend=showlegend, ) )
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notebook_xterm/terminalserver.py
marcotessarotto/notebook_xterm
15
98522
from __future__ import (absolute_import, division, print_function, unicode_literals) import pty, os, tty, termios, time, sys, base64, struct, signal from fcntl import fcntl, F_GETFL, F_SETFL, ioctl class TerminalServer: def __init__(self): self.closed = False self.pid, self.fd = pty.fork() if self.pid == pty.CHILD: # we are in the forked process # blow it away and replace with a shell os.execvp('bash',['bash']) else: tty.setraw(self.fd, termios.TCSANOW) #open the shell process file descriptor as read-write if sys.version_info >= (3, 0): self.file = os.fdopen(self.fd,'wb+', buffering=0) else: #python 2 compatible code self.file = os.fdopen(self.fd,'wb+', 0) #set the file reads to be nonblocking flags = fcntl(self.file, F_GETFL) fcntl(self.file, F_SETFL, flags | os.O_NONBLOCK) def transmit(self,data): # data in the "channel" is b64 encoded so that control characters # don't get lost os.write(self.fd, base64.b64decode(data)) self.receive() def receive(self): try: data = os.read(self.fd, 8192) except OSError: data = b'' sys.stdout.write(base64.b64encode(data)) def update_window_size(self, rows, cols): #notify that the pty size should change to match xterm.js TIOCSWINSZ = getattr(termios, 'TIOCSWINSZ', -2146929561) s = struct.pack('HHHH', rows, cols, 0, 0) ioctl(self.fd, TIOCSWINSZ, s) self.receive() def close(self): if not self.closed: #send hang up to bash since the xterm is closing os.kill(self.pid, signal.SIGHUP) def __del__(self): self.close()
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mut/viz.py
RPGroup-PBoC/mwc_mutants
3
29167
import bokeh.io import bokeh.plotting import bokeh.layouts import bokeh.palettes import seaborn as sns import numpy as np import os import matplotlib.pyplot as plt import matplotlib def plotting_style(grid=True): """ Sets the style to the publication style """ rc = {'axes.facecolor': '#E3DCD0', 'font.family': 'Lucida Sans Unicode', 'grid.linestyle': '-', 'grid.linewidth': 0.5, 'grid.alpha': 0.75, 'grid.color': '#ffffff', 'axes.grid': grid, 'ytick.direction': 'in', 'xtick.direction': 'in', 'xtick.gridOn': True, 'ytick.gridOn': True, 'ytick.major.width':5, 'xtick.major.width':5, 'ytick.major.size': 5, 'xtick.major.size': 5, 'mathtext.fontset': 'stixsans', 'mathtext.sf': 'sans', 'legend.frameon': True, 'legend.facecolor': '#FFEDCE', 'figure.dpi': 150, 'xtick.color': 'k', 'ytick.color': 'k'} plt.rc('text.latex', preamble=r'\usepackage{sfmath}') plt.rc('mathtext', fontset='stixsans', sf='sans') sns.set_style('darkgrid', rc=rc) def color_selector(style): """ Select the color palette of your choice. Parameters ---------- style: str "mut" or "pboc" A string identifier for the style. "mut" gives colors for single and double mutants. "pboc" returns the PBoC2e color palette. Returns ------- colors: dict Dictionary of colors. If "dna", "double", or "inducer" is the selected style, keys will be the mutants in upper case. Double mutant keys will be DNA-IND. For pboc, the keys will be the typical color descriptors. """ # Ensure the provided style name makes sense. if style.lower() not in ['mut', 'pboc']: raise ValueError("Provided style must be 'pboc' or 'mut'. {} provided.".format(style)) # Set the color styles and return. if style.lower() == 'mut': colors = {'Y20I': '#738FC1', 'Q21A': '#7AA974', 'Q21M': '#AB85AC', 'F164T': '#A97C50', 'Q294K': '#5D737E', 'Q294V': '#D56C55', 'Q294R': '#B2AF58', 'Y20I-F164T': '#2d98da', 'Y20I-Q294K': '#34495e', 'Y20I-Q294V': '#8854d0', 'Q21A-F164T': '#4b6584', 'Q21A-Q294K': '#EE5A24', 'Q21A-Q294V': '#009432', 'Q21M-F164T': '#1289A7', 'Q21M-Q294K': '#6F1E51', 'Q21M-Q294V': '#006266', 'WT': '#3C3C3C'} elif style.lower() == 'pboc': colors = {'green': '#7AA974', 'light_green': '#BFD598', 'pale_green': '#DCECCB', 'yellow': '#EAC264', 'light_yellow': '#F3DAA9', 'pale_yellow': '#FFEDCE', 'blue': '#738FC1', 'light_blue': '#A9BFE3', 'pale_blue': '#C9D7EE', 'red': '#D56C55', 'light_red': '#E8B19D', 'pale_red': '#F1D4C9', 'purple': '#AB85AC', 'light_purple': '#D4C2D9', 'dark_green':'#7E9D90', 'dark_brown':'#905426'} return colors
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