PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

a4d4a478f55b6f61c291806eae0647097756825f

13,399

py

Python

PhysicsTools/NanoAOD/python/custom_jme_cff.py

scodella/cmssw

974479b50b19bd795cb5f91eac872857fd6290f6

[ "Apache-2.0" ]

1

2021-04-13T13:26:16.000Z

PhysicsTools/NanoAOD/python/custom_jme_cff.py

scodella/cmssw

974479b50b19bd795cb5f91eac872857fd6290f6

[ "Apache-2.0" ]

2

2020-11-03T19:02:02.000Z

2020-11-10T16:44:22.000Z

PhysicsTools/NanoAOD/python/custom_jme_cff.py

scodella/cmssw

974479b50b19bd795cb5f91eac872857fd6290f6

[ "Apache-2.0" ]

1

2020-04-01T15:30:45.000Z

import FWCore.ParameterSet.Config as cms from Configuration.Eras.Modifier_run2_miniAOD_80XLegacy_cff import run2_miniAOD_80XLegacy from Configuration.Eras.Modifier_run2_nanoAOD_94X2016_cff import run2_nanoAOD_94X2016 from PhysicsTools.NanoAOD.common_cff import Var, P4Vars from PhysicsTools.NanoAOD.jets_cff import jetTable from PhysicsTools.PatAlgos.tools.jetCollectionTools import GenJetAdder, RecoJetAdder import copy # # By default, these collections are saved in NanoAODs: # - ak4gen (GenJet in NanoAOD) # - ak8gen (GenJetAK8 in NanoAOD) # Below is a list of genjets that we can save in NanoAOD. Set # "enabled" to true if you want to store the jet collection config_genjets = [ { "jet" : "ak5gen", "enabled" : False, }, { "jet" : "ak6gen", "enabled" : False, }, { "jet" : "ak7gen", "enabled" : False, }, { "jet" : "ak9gen", "enabled" : False, }, { "jet" : "ak10gen", "enabled" : False, }, ] config_genjets = list(filter(lambda k: k['enabled'], config_genjets)) # # GenJets info in NanoAOD # nanoInfo_genjets = { "ak5gen" : { "name" : "GenJetAK5", "doc" : "AK5 jets", }, "ak6gen" : { "name" : "GenJetAK6", "doc" : "AK6 jets", }, "ak7gen" : { "name" : "GenJetAK7", "doc" : "AK9 jets", }, "ak9gen" : { "name" : "GenJetAK9", "doc" : "AK9 jets", }, "ak10gen" : { "name" : "GenJetAK10", "doc" : "AK10 jets", }, } # # By default, these collections are saved in NanoAODs: # - ak4pfchs (Jet in NanoAOD) # - ak8pfpuppi (FatJet in NanoAOD) # By default, the ak4pfchs (Jet) and ak8pfpuppi (FatJet) collections # are saved in NanoAODs. # Below is a list of recojets that we can save in NanoAOD. Set "enabled" # to true if you want to store the recojet collection. # config_recojets = [ { "jet" : "ak4pfpuppi", "enabled" : True, "inputCollection" : "slimmedJetsPuppi", #Exist in MiniAOD "genJetsCollection": "slimmedGenJets", }, { "jet" : "ak4calo", "enabled" : True, "inputCollection" : "slimmedCaloJets", #Exist in MiniAOD "genJetsCollection": "slimmedGenJets", }, { "jet" : "ak4pf", "enabled" : True, "inputCollection" : "", "genJetsCollection": "slimmedGenJets", }, { "jet" : "ak8pf", "enabled" : True, "inputCollection" : "", "genJetsCollection": "slimmedGenJetsAK8", }, { "jet" : "ak8pfchs", "enabled" : True, "inputCollection" : "", "genJetsCollection": "slimmedGenJetsAK8", }, { "jet" : "ak6pf", "enabled" : False, "inputCollection" : "", "genJetsCollection": "AK6GenJetsNoNu", }, { "jet" : "ak10pf", "enabled" : False, "inputCollection" : "", "genJetsCollection": "AK10GenJetsNoNu", }, ] config_recojets = list(filter(lambda k: k['enabled'], config_recojets)) # # RecoJets info in NanoAOD # nanoInfo_recojets = { "ak4pfpuppi" : { "name" : "JetPUPPI", "doc" : "AK4PFPUPPI jets", }, "ak4calo" : { "name": "JetCalo", "doc" : "AK4Calo jets", }, "ak4pf" : { "name": "JetPF", "doc" : "AK4PF jets", }, "ak8pf" : { "name": "FatJetPF", "doc" : "AK8PF jets", }, "ak8pfchs" : { "name" : "FatJetCHS", "doc" : "AK8PFCHS jets", }, "ak6pf" : { "name": "JetAK6PF", "doc" : "AK6PF jets", }, "ak10pf" : { "name" : "FatJetAK10PF", "doc" : "AK10PF jets", }, } # # The reco jet names already exists # in NanoAOD. # recojetNameInNano = [ "Jet", "FatJet" ] # # The gen jet names already exists # in NanoAOD. # genjetNameInNano = [ "GenJet", "GenJetAK8" ] JETVARS = cms.PSet(P4Vars, HFHEF = Var("HFHadronEnergyFraction()", float, doc = "energy fraction in forward hadronic calorimeter", precision = 6), HFEMEF = Var("HFEMEnergyFraction()", float, doc = "energy fraction in forward EM calorimeter", precision = 6), area = jetTable.variables.area, chHEF = jetTable.variables.chHEF, neHEF = jetTable.variables.neHEF, chEmEF = jetTable.variables.chEmEF, neEmEF = jetTable.variables.neEmEF, muEF = jetTable.variables.muEF, rawFactor = jetTable.variables.rawFactor, jetId = jetTable.variables.jetId, jercCHPUF = jetTable.variables.jercCHPUF, jercCHF = jetTable.variables.jercCHF, ) for modifier in run2_miniAOD_80XLegacy, run2_nanoAOD_94X2016: modifier.toModify(JETVARS, jetId = Var("userInt('tightId')*2+userInt('looseId')", int, doc = "Jet ID flags bit1 is loose, bit2 is tight") ) #============================================ # # TableGenJetAdder # #============================================ class TableGenJetAdder(object): """ Tool to store gen jet variables in NanoAOD for customized gen jet collections. """ def __init__(self): self.main = [] def getSequence(self, proc): """ Tool to add """ tasks = self.main resultSequence = cms.Sequence() for idx, task in enumerate(tasks): if idx == 0: resultSequence = cms.Sequence(getattr(proc, task)) else: resultSequence.insert(idx, getattr(proc, task)) return resultSequence def addTable(self, proc, genJetInfo): currentTasks = [] print("custom_jme_cff::TableGenJetAdder::addTable: Adding Table for GenJet Collection: {}".format(genJetInfo.jet)) name = nanoInfo_genjets[genJetInfo.jet]["name"] doc = nanoInfo_genjets[genJetInfo.jet]["doc"] if name in genjetNameInNano: raise RuntimeError('GenJet collection name (%s) taken in NanoAOD for %s' %(name, genJetInfo.jet)) # # GenJet Table # table = "{}Table".format(genJetInfo.jetTagName) genJetsCollection = "{}{}{}".format(genJetInfo.jetAlgo.upper(), genJetInfo.jetSize, 'GenJetsNoNu') setattr(proc, table, cms.EDProducer("SimpleCandidateFlatTableProducer", src = cms.InputTag(genJetsCollection), cut = cms.string(""), name = cms.string(name), doc = cms.string('{} (generator level)'.format(doc)), singleton = cms.bool(False), extension = cms.bool(False), variables = cms.PSet(P4Vars, area = jetTable.variables.area, ), ) ) currentTasks.append(table) # # GenJet Flavour Table # genFlavour = "{}Flavour".format(genJetInfo.jetTagName) genFlavourTable = "{}Table".format(genFlavour) if genFlavourTable in self.main: raise ValueError("Step '%s' already implemented" % genFlavourTable) setattr(proc, genFlavourTable, cms.EDProducer("GenJetFlavourTableProducer", name = cms.string(name), src = cms.InputTag(genJetsCollection), cut = cms.string(""), deltaR = cms.double(0.1), jetFlavourInfos = cms.InputTag(genFlavour), ) ) currentTasks.append(genFlavourTable) self.main.extend(currentTasks) #============================================ # # TableRecoJetAdder # #============================================ class TableRecoJetAdder(object): """ Tool to store reco jet variables in NanoAOD for customized reco jet collections. """ def __init__(self): self.main = [] def getSequence(self, proc): tasks = self.main resultSequence = cms.Sequence() for idx, task in enumerate(tasks): if idx == 0: resultSequence = cms.Sequence(getattr(proc, task)) else: resultSequence.insert(idx, getattr(proc, task)) return resultSequence def addTable(self, proc, recoJetInfo): currentTasks = [] print("custom_jme_cff::TableRecoJetAdder::addTable: Adding Table for Reco Jet Collection: {}".format(recoJetInfo.jet)) name = nanoInfo_recojets[recoJetInfo.jet]["name"] doc = nanoInfo_recojets[recoJetInfo.jet]["doc"] if name in recojetNameInNano: raise RuntimeError('RecoJet collection name (%s) taken in NanoAOD for %s' %(name, recoJetInfo.jet)) table = "{}Table".format(recoJetInfo.jetTagName) if recoJetInfo.skipUserData: if recoJetInfo.doCalo: tableContents = cms.PSet( P4Vars, area = jetTable.variables.area, rawFactor = jetTable.variables.rawFactor, emf = Var("emEnergyFraction()", float, doc = "electromagnetic energy fraction", precision = 10), ) else: tableContents = cms.PSet( P4Vars, area = jetTable.variables.area, rawFactor = jetTable.variables.rawFactor, ) else: tableContents = JETVARS.clone() updatedJets = "updatedJets{}".format(recoJetInfo.jetTagName) setattr(proc, table, cms.EDProducer("SimpleCandidateFlatTableProducer", src = cms.InputTag(updatedJets), cut = cms.string(""), name = cms.string(name), doc = cms.string(doc), singleton = cms.bool(False), extension = cms.bool(False), variables = tableContents, ) ) currentTasks.append(table) tightJetIdLepVeto = "tightJetIdLepVeto{}".format(recoJetInfo.jetTagName) if not recoJetInfo.skipUserData: altTasks = copy.deepcopy(currentTasks) for idx, task in enumerate(altTasks): if task == tightJetIdLepVeto: altTasks[idx] = looseJetId for modifier in run2_miniAOD_80XLegacy, run2_nanoAOD_94X2016: modifier.toReplaceWith(currentTasks, altTasks) self.main.extend(currentTasks) def PrepJMECustomNanoAOD(process): # # Additional variables to AK4GenJets # process.genJetTable.variables.area = JETVARS.area # # Additional variables to AK8GenJets # process.genJetAK8Table.variables.area = JETVARS.area # # Additional variables for AK4PFCHS # process.jetTable.variables.HFHEF = JETVARS.HFHEF process.jetTable.variables.HFEMEF = JETVARS.HFEMEF # # Additional variables to AK8PFPUPPI # # These variables are not stored for AK8PFCHS (slimmedJetsAK8) # in MiniAOD if their pt < 170 GeV. Hence the conditional fill. # process.fatJetTable.variables.chHEF = Var("?isPFJet()?chargedHadronEnergyFraction():-1", float, doc="charged Hadron Energy Fraction", precision = 6) process.fatJetTable.variables.neHEF = Var("?isPFJet()?neutralHadronEnergyFraction():-1", float, doc="neutral Hadron Energy Fraction", precision = 6) process.fatJetTable.variables.chEmEF = Var("?isPFJet()?chargedEmEnergyFraction():-1", float, doc="charged Electromagnetic Energy Fraction", precision = 6) process.fatJetTable.variables.neEmEF = Var("?isPFJet()?neutralEmEnergyFraction():-1", float, doc="neutral Electromagnetic Energy Fraction", precision = 6) process.fatJetTable.variables.muEF = Var("?isPFJet()?muonEnergyFraction():-1", float, doc="muon Energy Fraction", precision = 6) process.fatJetTable.variables.HFHEF = Var("?isPFJet()?HFHadronEnergyFraction():-1", float, doc="energy fraction in forward hadronic calorimeter", precision = 6) process.fatJetTable.variables.HFEMEF = Var("?isPFJet()?HFEMEnergyFraction():-1", float, doc="energy fraction in forward EM calorimeter", precision = 6) # # # process.jercVarsFatJet = process.jercVars.clone( srcJet = "updatedJetsAK8", maxDR = 0.8, ) process.jetSequence.insert(process.jetSequence.index(process.updatedJetsAK8WithUserData), process.jercVarsFatJet) process.updatedJetsAK8WithUserData.userFloats.jercCHPUF = cms.InputTag( "%s:chargedHadronPUEnergyFraction" % process.jercVarsFatJet.label() ) process.updatedJetsAK8WithUserData.userFloats.jercCHF = cms.InputTag( "%s:chargedHadronCHSEnergyFraction" % process.jercVarsFatJet.label() ) process.fatJetTable.variables.jercCHPUF = JETVARS.jercCHPUF process.fatJetTable.variables.jercCHF = JETVARS.jercCHF # # Remove any pT cuts. # process.finalJets.cut = "" # 15 -> 10 process.finalJetsAK8.cut = "" # 170 -> 170 process.genJetTable.cut = "" # 10 -> 8 process.genJetFlavourTable.cut = "" # 10 -> 8 process.genJetAK8Table.cut = "" # 100 -> 80 process.genJetAK8FlavourTable.cut = "" # 100 -> 80 ###################################################################################################################### # # Add GenJets to NanoAOD # genJA = GenJetAdder() tableGenJA = TableGenJetAdder() for jetConfig in config_genjets: cfg = { k : v for k, v in jetConfig.items() if k != "enabled" } genJetInfo = genJA.addGenJetCollection(process, **cfg) tableGenJA.addTable(process, genJetInfo) process.nanoSequenceMC += genJA.getSequence(process) process.nanoSequenceMC += tableGenJA.getSequence(process) # # Add RecoJets to NanoAOD # recoJA = RecoJetAdder() tableRecoJA = TableRecoJetAdder() for jetConfig in config_recojets: cfg = { k : v for k, v in jetConfig.items() if k != "enabled" } recoJetInfo = recoJA.addRecoJetCollection(process, **cfg) tableRecoJA.addTable(process, recoJetInfo) process.nanoSequenceMC += recoJA.getSequence(process) process.nanoSequenceMC += tableRecoJA.getSequence(process)

31.676123

168

0.623106

e3479d8c737706c04c13f9035e2ba3982c1f87ff

948

py

Python

idact/detail/config/validation/validate_non_negative_int.py

intdata-bsc/idact

54cb65a711c145351e205970c27c83e6393cccf5

[ "MIT" ]

5

2018-12-06T15:40:34.000Z

2019-06-19T11:22:58.000Z

idact/detail/config/validation/validate_non_negative_int.py

garstka/idact

b9c8405c94db362c4a51d6bfdf418b14f06f0da1

[ "MIT" ]

9

2018-12-06T16:35:26.000Z

2019-04-28T19:01:40.000Z

idact/detail/config/validation/validate_non_negative_int.py

intdata-bsc/idact

54cb65a711c145351e205970c27c83e6393cccf5

[ "MIT" ]

2

2019-04-28T19:18:58.000Z

2019-06-17T06:56:28.000Z

"""This module contains a function for validating a non-negative int.""" from idact.detail.config.validation.validation_error_message import \ validation_error_message VALID_NON_NEGATIVE_INT = "Non-negative int." def validate_non_negative_int(value, label: str) -> int: """Returns the parameter if it's a non negative int, otherwise raises an exception. :param value: Object to validate. :param label: Object label for error message. :raises TypeError: On wrong type. :raises ValueError: When integer is out of range. """ if not isinstance(value, int): raise TypeError(validation_error_message( label=label, value=value, expected=VALID_NON_NEGATIVE_INT)) if value < 0: raise ValueError(validation_error_message( label=label, value=value, expected=VALID_NON_NEGATIVE_INT)) return value

27.085714

73

0.665612

b9b158838cb7a3d19b1216deb866e9d440b9b18e

5,749

py

Python

gemini/severe_impact.py

raonyguimaraes/gemini

a8d03f5c5eefc6ef725e667130f141dbb63916b3

[ "MIT" ]

null

gemini/severe_impact.py

raonyguimaraes/gemini

a8d03f5c5eefc6ef725e667130f141dbb63916b3

[ "MIT" ]

null

gemini/severe_impact.py

raonyguimaraes/gemini

a8d03f5c5eefc6ef725e667130f141dbb63916b3

[ "MIT" ]

null

import sys import snpEff import vep def interpret_severe_impact(args, var): """ Interpret the report from SnpEff or VEP to determine the impact of the variant. SnpEff examples: 0 NON_SYNONYMOUS_CODING(MODERATE|MISSENSE|Aca/Gca|T/A|OR4F5|protein_coding|CODING|ENST00000335137|exon_1_69091_70008), 1 NON_SYNONYMOUS_CODING(MODERATE|MISSENSE|Aca/Gca|T/A|OR4F5|protein_coding|CODING|ENST00000534990|exon_1_69037_69829) VEP examples CSQ: Consequence|Codons|Amino_acids|Gene|hgnc|Feature|EXON|polyphen|sift non_synonymous_codon|gaT/gaG|D/E|ENSG00000116254|CHD5|ENST00000378006|18/25|benign(0.011)|tolerated(0.3) nc_transcript_variant|||ENSG00000116254|CHD5|ENST00000491020|5/6|| """ effect_strings_str = "" effect_strings = impact_all = string = [] impact_details = impact_features = None max_severity = 9 # initialize to a value greater than the largest value in impact info priority code counter = 0 # initialize counter for anno_id if args.anno_type == "snpEff": try: effect_strings_str = var.INFO["EFF"] effect_strings = effect_strings_str.split(",") except KeyError: if "SNPEFF_EFFECT" in var.INFO: impact_details = snpEff.gatk_effect_details(var.INFO) else: sys.stderr.write("WARNING: The input VCF has no snpEFF annotations. " "Variant impact will be set to unknown\n") for effect_string in effect_strings: counter += 1 eff_pieces = snpEff.eff_search.findall(effect_string) for piece in eff_pieces: impact_string = piece[0] # the predicted inpact, which is outside the () impact_detail = piece[1] # all the other information, which is inside the () impact_info = snpEff.effect_map[impact_string] # update the impact stored if a higher or an equal severity transcript # is encountered if impact_info.priority_code <= max_severity: impact_details = snpEff.EffectDetails(impact_string, impact_info.priority, impact_detail, counter, args.maj_version) impact_all.append(impact_details) # store the current "winning" severity for the next iteration max_severity = impact_info.priority_code # This would store the highest priority for the next outer loop top_severity = impact_info.priority # prioritizing biotype: initialize flags to a high value set_flag = flag = 4 for idx, impact in enumerate(impact_all): string = str(impact).split("\t") if string[1] == str(top_severity) and string[7] == "protein_coding": set_flag = 0 elif string[1] == str(top_severity) and string[7] != "protein_coding": set_flag = 1 if string[1] == str(top_severity) and set_flag < flag: flag = set_flag impact_features = impact return impact_features elif args.anno_type == "VEP": try: effect_strings_str = var.INFO["CSQ"] effect_strings = effect_strings_str.split(",") except KeyError: sys.stderr.write("WARNING: The input VCF has no VEP annotations. \ Variant impact will be set to unknown\n") for effect_string in effect_strings: # nc_transcript_variant&intron_variant|||ENSG00000243485|MIR1302-11|ENST00000 each_string = effect_string.split("|") if "&" in each_string[0]: impact_strings = each_string[0].split("&") # impact_strings will be [nc_transcript_variant, # intron_variant] for impact_string in impact_strings: counter += 1 try: impact_info = vep.effect_map[impact_string] # update the impact stored only if a higher severity # transcript is encountered if impact_info.priority_code < max_severity: impact_details = vep.EffectDetails( impact_string, impact_info.priority, effect_string, counter) max_severity = impact_info.priority_code # store the current "winning" severity for the next iteration. except KeyError: pass # we expect VEP to produce a valid impact label for each_string[0] elif "&" not in each_string[0]: counter += 1 impact_string = each_string[0] impact_info = vep.effect_map.get(impact_string) if impact_info is None: pass elif impact_info.priority_code < max_severity: impact_details = vep.EffectDetails( impact_string, impact_info.priority, effect_string, counter) max_severity = impact_info.priority_code # initialize the max_severity to the former value of priority code else: # should not get here, as the valid -t options should be handled # in main() sys.exit("ERROR: Unsupported variant annotation type.\n") return impact_details

47.122951

132

0.574187

eb4d903516b55cff60acdc37962f7a5b0985124e

6,453

py

Python

mapproxy/test/unit/test_request_wmts.py

cunha17/mapproxy

31f646bbd0465f566c2ac2ea39f8e794bea78dfc

[ "ECL-2.0", "Apache-2.0" ]

347

2015-01-19T15:47:08.000Z

2022-03-23T20:33:17.000Z

mapproxy/test/unit/test_request_wmts.py

cunha17/mapproxy

31f646bbd0465f566c2ac2ea39f8e794bea78dfc

[ "ECL-2.0", "Apache-2.0" ]

374

2015-01-05T07:54:31.000Z

2022-03-24T08:23:10.000Z

mapproxy/test/unit/test_request_wmts.py

cunha17/mapproxy

31f646bbd0465f566c2ac2ea39f8e794bea78dfc

[ "ECL-2.0", "Apache-2.0" ]

188

2015-01-07T00:59:28.000Z

2022-03-15T09:05:57.000Z

# This file is part of the MapProxy project. # Copyright (C) 2011 Omniscale <http://omniscale.de> # # 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 mapproxy.exception import RequestError from mapproxy.request.wmts import wmts_request, WMTS100CapabilitiesRequest from mapproxy.request.wmts import ( URLTemplateConverter, FeatureInfoURLTemplateConverter, InvalidWMTSTemplate, make_wmts_rest_request_parser, WMTS100RestTileRequest, WMTS100RestFeatureInfoRequest, ) from mapproxy.request.base import url_decode import pytest def dummy_req(args): return DummyRequest(url_decode(args.replace("\n", ""))) def dummy_rest_req(path): return DummyRequest(args="", path=path) class DummyRequest(object): def __init__(self, args, path="", url=""): self.args = args self.path = path self.base_url = url def test_tile_request(): args = """requeST=GetTile&service=wmts&tileMatrixset=EPSG900913& tilematrix=2&tileROW=4&TILECOL=2&FORMAT=image/png&Style=&layer=Foo&version=1.0.0""" req = wmts_request(dummy_req(args)) assert req.params.coord == (2, 4, "2") assert req.params.layer == "Foo" assert req.params.format == "png" assert req.params.tilematrixset == "EPSG900913" def test_featureinfo_request(): args = """requeST=GetFeatureInfo&service=wmts&tileMatrixset=EPSG900913& tilematrix=2&tileROW=4&TILECOL=2&FORMAT=image/png&Style=&layer=Foo&version=1.0.0& i=5&j=10&infoformat=application/json""" req = wmts_request(dummy_req(args)) assert req.params.coord == (2, 4, "2") assert req.params.layer == "Foo" assert req.params.format == "png" assert req.params.tilematrixset == "EPSG900913" assert req.params.pos == (5, 10) assert req.params.infoformat == "application/json" def test_capabilities_request(): args = """requeST=GetCapabilities&service=wmts""" req = wmts_request(dummy_req(args)) assert isinstance(req, WMTS100CapabilitiesRequest) def test_template_converter(): regexp = URLTemplateConverter( "/{Layer}/{Style}/{TileMatrixSet}-{TileMatrix}-{TileCol}-{TileRow}/tile" ).regexp() match = regexp.match("/wmts/test/bar/foo-EPSG4326-4-12-99/tile") assert match assert match.groupdict()["Layer"] == "test" assert match.groupdict()["TileMatrixSet"] == "foo-EPSG4326" assert match.groupdict()["TileMatrix"] == "4" assert match.groupdict()["TileCol"] == "12" assert match.groupdict()["TileRow"] == "99" assert match.groupdict()["Style"] == "bar" def test_template_converter_deprecated_format(): # old format that doesn't match the WMTS spec, now deprecated regexp = URLTemplateConverter( "/{{Layer}}/{{Style}}/{{TileMatrixSet}}-{{TileMatrix}}-{{TileCol}}-{{TileRow}}/tile" ).regexp() match = regexp.match("/wmts/test/bar/foo-EPSG4326-4-12-99/tile") assert match assert match.groupdict()["Layer"] == "test" assert match.groupdict()["TileMatrixSet"] == "foo-EPSG4326" assert match.groupdict()["TileMatrix"] == "4" assert match.groupdict()["TileCol"] == "12" assert match.groupdict()["TileRow"] == "99" assert match.groupdict()["Style"] == "bar" def test_template_converter_missing_vars(): with pytest.raises(InvalidWMTSTemplate): URLTemplateConverter("/wmts/{Style}/{TileMatrixSet}/{TileCol}.png").regexp() def test_template_converter_dimensions(): converter = URLTemplateConverter( "/{Layer}/{Dim1}/{Dim2}/{TileMatrixSet}-{TileMatrix}-{TileCol}-{TileRow}/tile" ) assert converter.dimensions == ["Dim1", "Dim2"] class TestRestRequestParser(object): @pytest.fixture def parser(self): return make_wmts_rest_request_parser( URLTemplateConverter( "/{Layer}/{TileMatrixSet}/{TileMatrix}/{TileCol}/{TileRow}.{Format}" ), FeatureInfoURLTemplateConverter( "/{Layer}/{TileMatrixSet}/{TileMatrix}/{TileCol}/{TileRow}/{I}/{J}.{InfoFormat}" ), ) @pytest.mark.parametrize( "url,tile,pos", [ ["/wmts/roads/webmercator/09/201/123/10/30.json", (201, 123, 9), (10, 30)], ["/wmts/roads/webmercator/10/201/1/0/999.json", (201, 1, 10), (0, 999)], ["/wmts/roads/webmercator/09/201/123/10/30json", None, None], ["/wmts/roads/webmercator/09/201/123/10.json", None, None], ["/wmts/roads-webmercator/09/201/123/10/30.json", None, None], ["/roads/webmercator/09/201/123/10/30.json", None, None], ], ) def test_featureinfo(self, parser, url, tile, pos): if tile is None: with pytest.raises(RequestError): parser(dummy_rest_req(url)) else: req = parser(dummy_rest_req(url)) assert isinstance(req, WMTS100RestFeatureInfoRequest) req.make_request() assert req.pos == pos assert req.tile == tile assert req.infoformat == "json" assert req.tilematrixset == "webmercator" @pytest.mark.parametrize( "url,tile", [ ["/wmts/roads/webmercator/09/201/123.png", (201, 123, 9)], ["/wmts/roads/webmercator/10/201/123.png", (201, 123, 10)], ["/wmts/roads/webmercator/10/201/123a.png", None], ["/wmts/roads/webmercator/10/201/123png", None], ["/wmts/roads/webmercator/10/2013.png", None], ["/wmts/roads-webmercator/10/201/123.png", None], ], ) def test_tile(self, parser, url, tile): if tile is None: with pytest.raises(RequestError): parser(dummy_rest_req(url)) else: req = parser(dummy_rest_req(url)) assert isinstance(req, WMTS100RestTileRequest) req.make_request() assert req.tile == tile assert req.format == "png" assert req.tilematrixset == "webmercator"

36.050279

96

0.648071

766983ddce7481139bf7afc0780dd13a6f4199ba

3,181

py

Python

main_app/coreapp/views.py

KiranGangadhar01/FlipkartScrapping

ea3fe1a316fb1ff591a46ec7b94fd05ac5c40819

[ "MIT" ]

null

main_app/coreapp/views.py

KiranGangadhar01/FlipkartScrapping

ea3fe1a316fb1ff591a46ec7b94fd05ac5c40819

[ "MIT" ]

null

main_app/coreapp/views.py

KiranGangadhar01/FlipkartScrapping

ea3fe1a316fb1ff591a46ec7b94fd05ac5c40819

[ "MIT" ]

null

from django.shortcuts import render from bs4 import BeautifulSoup as soup import requests import re from .models import Information def basicScrap(request): def main(): url_list = ["https://www.flipkart.com/signoraware-best-steel-lunch-box"\ "-blue-500ml-350ml-200ml-tumbler-370ml-3-containers-box/p/itm"\ "fedq3gcpfutwv?pid=LBXFED4W8HSF9SPD&otracker=wishlist&lid=LST"\ "LBXFED4W8HSF9SPD8EFHDU&fm=organic&iid=24db7969-8ff2-4574-8550"\ "-dfed03f05549.LBXFED4W8HSF9SPD.PRODUCTSUMMARY&ppt=hp&ppn=hp&s"\ "sid=hm69tsuogg0000001569056937359"] url_list.append("https://www.flipkart.com/himalaya-anti-hair-fall-sham"\ "poo/p/itmf3xfxdmywkwhs?pid=SMPEUHHFZYBXXHGB&lid=LSTSMPEUHHFZY"\ "BXXHGBNMGARX&marketplace=FLIPKART&sattr[]=quantity&st=quantity") url_list.append("https://www.flipkart.com/sparx-sfg-529/p/itmfafydk6kw"\ "wurs?pid=SFFE5VWZMMGKQDGE&lid=LSTSFFE5VWZMMGKQDGEGDB3LS&marketpl"\ "ace=FLIPKART&sattr[]=color&sattr[]=size&st=size") for my_url in url_list: """Iterating through each url""" uClient = requests.get(my_url) if uClient.status_code == 200: page_html = uClient.text page_soup = soup(page_html, "html.parser") containers = page_soup.find("div", {"class": "_1HmYoV _35HD7C col-8-12"}) name = containers.find("span", {"class": "_35KyD6"}) pName = name.text print(pName) price = containers.find("div", {"class": "_1vC4OE _3qQ9m1"}) boxPrice = price.text.strip() pPrice = int(''.join(filter(str.isdigit, boxPrice))) print(pPrice) minPrice = pPrice get_name = Information.objects.filter(productName=pName) """If data not in the record then it'll create new object otherwise it'll update the existing object with new price""" if get_name.count()==0: information_instance = Information.objects.create( productName=pName, productPrice=pPrice, minPrice=minPrice, maxP=pPrice) else: minPrice = get_name[0].minPrice maxP = get_name[0].maxP if minPrice > pPrice: minPrice=pPrice if maxP < pPrice: maxP=pPrice information_instance = Information.objects.filter(productName=pName).update( productPrice=pPrice, minPrice=minPrice, maxP=maxP) print(minPrice) else: print('Not Found.') main() details = Information.objects.all() detailsDict = {'detailsDict' : details} return render(request, 'coreapp/index.html', context=detailsDict)

45.442857

101

0.546055

3d060c0d217dfb19c9a8c86e0947fe4f6bf1a97e

24,244

py

Python

sdv/metadata/table.py

argo12/SDV

7f90bb3a3f9991b34f4c4363edd91e3fa448fd17

[ "MIT" ]

1

2021-03-07T21:30:40.000Z

sdv/metadata/table.py

ppeddada97/SDV

6fbddedcd1ed02adcd7a0ec3698f280a9a86e01c

[ "MIT" ]

null

sdv/metadata/table.py

ppeddada97/SDV

6fbddedcd1ed02adcd7a0ec3698f280a9a86e01c

[ "MIT" ]

null

"""Metadata for a single table.""" import copy import json import logging import numpy as np import pandas as pd import rdt from faker import Faker from sdv.constraints.base import Constraint from sdv.metadata.errors import MetadataError, MetadataNotFittedError from sdv.metadata.utils import strings_from_regex LOGGER = logging.getLogger(__name__) class Table: """Table Metadata. The Metadata class provides a unified layer of abstraction over the metadata of a single Table, which includes all the necessary details to handle the table of this data, including the data types, the fields with pii information and the constraints that affect this data. Args: name (str): Name of this table. Optional. field_names (list[str]): List of names of the fields that need to be modeled and included in the generated output data. Any additional fields found in the data will be ignored and will not be included in the generated output. If ``None``, all the fields found in the data are used. field_types (dict[str, dict]): Dictinary specifying the data types and subtypes of the fields that will be modeled. Field types and subtypes combinations must be compatible with the SDV Metadata Schema. field_transformers (dict[str, str]): Dictinary specifying which transformers to use for each field. Available transformers are: * ``integer``: Uses a ``NumericalTransformer`` of dtype ``int``. * ``float``: Uses a ``NumericalTransformer`` of dtype ``float``. * ``categorical``: Uses a ``CategoricalTransformer`` without gaussian noise. * ``categorical_fuzzy``: Uses a ``CategoricalTransformer`` adding gaussian noise. * ``one_hot_encoding``: Uses a ``OneHotEncodingTransformer``. * ``label_encoding``: Uses a ``LabelEncodingTransformer``. * ``boolean``: Uses a ``BooleanTransformer``. * ``datetime``: Uses a ``DatetimeTransformer``. anonymize_fields (dict[str, str]): Dict specifying which fields to anonymize and what faker category they belong to. primary_key (str): Name of the field which is the primary key of the table. constraints (list[Constraint, dict]): List of Constraint objects or dicts. dtype_transformers (dict): Dictionary of transformer templates to be used for the different data types. The keys must be any of the `dtype.kind` values, `i`, `f`, `O`, `b` or `M`, and the values must be either RDT Transformer classes or RDT Transformer instances. model_kwargs (dict): Dictionary specifiying the kwargs that need to be used in each tabular model when working on this table. This dictionary contains as keys the name of the TabularModel class and as values a dictionary containing the keyword arguments to use. This argument exists mostly to ensure that the models are fitted using the same arguments when the same Table is used to fit different model instances on different slices of the same table. sequence_index (str): Name of the column that acts as the order index of each sequence. The sequence index column can be of any type that can be sorted, such as integer values or datetimes. entity_columns (list[str]): Names of the columns which identify different time series sequences. These will be used to group the data in separated training examples. context_columns (list[str]): The columns in the dataframe which are constant within each group/entity. These columns will be provided at sampling time (i.e. the samples will be conditioned on the context variables). """ _hyper_transformer = None _fakers = None _constraint_instances = None _fields_metadata = None fitted = False _ANONYMIZATION_MAPPINGS = dict() _TRANSFORMER_TEMPLATES = { 'integer': rdt.transformers.NumericalTransformer(dtype=int), 'float': rdt.transformers.NumericalTransformer(dtype=float), 'categorical': rdt.transformers.CategoricalTransformer, 'categorical_fuzzy': rdt.transformers.CategoricalTransformer(fuzzy=True), 'one_hot_encoding': rdt.transformers.OneHotEncodingTransformer, 'label_encoding': rdt.transformers.LabelEncodingTransformer, 'boolean': rdt.transformers.BooleanTransformer, 'datetime': rdt.transformers.DatetimeTransformer(strip_constant=True), } _DTYPE_TRANSFORMERS = { 'i': 'integer', 'f': 'float', 'O': 'one_hot_encoding', 'b': 'boolean', 'M': 'datetime', } _DTYPES_TO_TYPES = { 'i': { 'type': 'numerical', 'subtype': 'integer', }, 'f': { 'type': 'numerical', 'subtype': 'float', }, 'O': { 'type': 'categorical', }, 'b': { 'type': 'boolean', }, 'M': { 'type': 'datetime', } } _TYPES_TO_DTYPES = { ('categorical', None): 'object', ('boolean', None): 'bool', ('numerical', None): 'float', ('numerical', 'float'): 'float', ('numerical', 'integer'): 'int', ('datetime', None): 'datetime64', ('id', None): 'int', ('id', 'integer'): 'int', ('id', 'string'): 'str' } def _get_faker(self, category): """Return the faker object to anonymize data. Args: category (str or tuple): Fake category to use. If a tuple is passed, the first element is the category and the rest are additional arguments for the Faker. Returns: function: Faker function to generate new fake data instances. Raises: ValueError: A ``ValueError`` is raised if the faker category we want don't exist. """ if isinstance(category, (tuple, list)): category, *args = category else: args = tuple() try: faker_method = getattr(Faker(), category) if not args: return faker_method def faker(): return faker_method(*args) return faker except AttributeError: raise ValueError('Category "{}" couldn\'t be found on faker'.format(category)) def __init__(self, name=None, field_names=None, field_types=None, field_transformers=None, anonymize_fields=None, primary_key=None, constraints=None, dtype_transformers=None, model_kwargs=None, sequence_index=None, entity_columns=None, context_columns=None): self.name = name self._field_names = field_names self._field_types = field_types or {} self._field_transformers = field_transformers or {} self._anonymize_fields = anonymize_fields or {} self._model_kwargs = model_kwargs or {} self._primary_key = primary_key self._sequence_index = sequence_index self._entity_columns = entity_columns or [] self._context_columns = context_columns or [] self._constraints = constraints or [] self._dtype_transformers = self._DTYPE_TRANSFORMERS.copy() if dtype_transformers: self._dtype_transformers.update(dtype_transformers) def __repr__(self): return 'Table(name={}, field_names={})'.format(self.name, self._field_names) def get_model_kwargs(self, model_name): """Return the required model kwargs for the indicated model. Args: model_name (str): Qualified Name of the model for which model kwargs are needed. Returns: dict: Keyword arguments to use on the indicated model. """ return copy.deepcopy(self._model_kwargs.get(model_name)) def set_model_kwargs(self, model_name, model_kwargs): """Set the model kwargs used for the indicated model.""" self._model_kwargs[model_name] = model_kwargs def _get_field_dtype(self, field_name, field_metadata): field_type = field_metadata['type'] field_subtype = field_metadata.get('subtype') dtype = self._TYPES_TO_DTYPES.get((field_type, field_subtype)) if not dtype: raise MetadataError( 'Invalid type and subtype combination for field {}: ({}, {})'.format( field_name, field_type, field_subtype) ) return dtype def get_fields(self): """Get fields metadata. Returns: dict: Dictionary of fields metadata for this table. """ return copy.deepcopy(self._fields_metadata) def get_dtypes(self, ids=False): """Get a ``dict`` with the ``dtypes`` for each field of the table. Args: ids (bool): Whether or not to include the id fields. Defaults to ``False``. Returns: dict: Dictionary that contains the field names and data types. """ dtypes = dict() for name, field_meta in self._fields_metadata.items(): field_type = field_meta['type'] if ids or (field_type != 'id'): dtypes[name] = self._get_field_dtype(name, field_meta) return dtypes def _build_fields_metadata(self, data): """Build all the fields metadata. Args: data (pandas.DataFrame): Data to be analyzed. Returns: dict: Dict of valid fields. Raises: ValueError: If a column from the data analyzed is an unsupported data type """ fields_metadata = dict() for field_name in self._field_names: if field_name not in data: raise ValueError('Field {} not found in given data'.format(field_name)) field_meta = self._field_types.get(field_name) if field_meta: dtype = self._get_field_dtype(field_name, field_meta) else: dtype = data[field_name].dtype field_template = self._DTYPES_TO_TYPES.get(dtype.kind) if field_template is None: msg = 'Unsupported dtype {} in column {}'.format(dtype, field_name) raise ValueError(msg) field_meta = copy.deepcopy(field_template) field_transformer = self._field_transformers.get(field_name) if field_transformer: field_meta['transformer'] = field_transformer else: field_meta['transformer'] = self._dtype_transformers.get(np.dtype(dtype).kind) anonymize_category = self._anonymize_fields.get(field_name) if anonymize_category: field_meta['pii'] = True field_meta['pii_category'] = anonymize_category fields_metadata[field_name] = field_meta return fields_metadata def _get_transformers(self, dtypes): """Create the transformer instances needed to process the given dtypes. Args: dtypes (dict): mapping of field names and dtypes. Returns: dict: mapping of field names and transformer instances. """ transformers = dict() for name, dtype in dtypes.items(): field_metadata = self._fields_metadata.get(name, {}) transformer_template = field_metadata.get('transformer') if transformer_template is None: transformer_template = self._dtype_transformers[np.dtype(dtype).kind] if transformer_template is None: # Skip this dtype continue field_metadata['transformer'] = transformer_template if isinstance(transformer_template, str): transformer_template = self._TRANSFORMER_TEMPLATES[transformer_template] if isinstance(transformer_template, type): transformer = transformer_template() else: transformer = copy.deepcopy(transformer_template) LOGGER.debug('Loading transformer %s for field %s', transformer.__class__.__name__, name) transformers[name] = transformer return transformers def _fit_transform_constraints(self, data): for idx, constraint in enumerate(self._constraints): if isinstance(constraint, type): constraint = constraint().to_dict() elif isinstance(constraint, Constraint): constraint = constraint.to_dict() constraint = Constraint.from_dict(constraint) self._constraints[idx] = constraint data = constraint.fit_transform(data) return data def _fit_hyper_transformer(self, data, extra_columns): """Create and return a new ``rdt.HyperTransformer`` instance. First get the ``dtypes`` and then use them to build a transformer dictionary to be used by the ``HyperTransformer``. Args: data (pandas.DataFrame): Data to transform. extra_columns (set): Names of columns that are not in the metadata but that should also be transformed. In most cases, these are the fields that were added by previous transformations which the data underwent. Returns: rdt.HyperTransformer """ meta_dtypes = self.get_dtypes(ids=False) dtypes = {} for column in data.columns: if column in meta_dtypes: dtypes[column] = meta_dtypes[column] elif column in extra_columns: dtypes[column] = data[column].dtype.kind transformers_dict = self._get_transformers(dtypes) self._hyper_transformer = rdt.HyperTransformer(transformers=transformers_dict) self._hyper_transformer.fit(data[list(dtypes.keys())]) @staticmethod def _get_key_subtype(field_meta): """Get the appropriate key subtype.""" field_type = field_meta['type'] if field_type == 'categorical': field_subtype = 'string' elif field_type in ('numerical', 'id'): field_subtype = field_meta['subtype'] if field_subtype not in ('integer', 'string'): raise ValueError( 'Invalid field "subtype" for key field: "{}"'.format(field_subtype) ) else: raise ValueError( 'Invalid field "type" for key field: "{}"'.format(field_type) ) return field_subtype def set_primary_key(self, primary_key): """Set the primary key of this table. The field must exist and either be an integer or categorical field. Args: primary_key (str or list): Name of the field(s) to be used as the new primary key. Raises: ValueError: If the table or the field do not exist or if the field has an invalid type or subtype. """ if primary_key is not None: fields = primary_key if isinstance(primary_key, list) else [primary_key] for field_name in fields: if field_name not in self._fields_metadata: raise ValueError('Field "{}" does not exist in this table'.format(field_name)) field_metadata = self._fields_metadata[field_name] if field_metadata['type'] != 'id': field_subtype = self._get_key_subtype(field_metadata) field_metadata.update({ 'type': 'id', 'subtype': field_subtype }) self._primary_key = primary_key def _make_anonymization_mappings(self, data): mappings = {} for name, field_metadata in self._fields_metadata.items(): if field_metadata['type'] != 'id' and field_metadata.get('pii'): faker = self._get_faker(field_metadata['pii_category']) uniques = data[name].unique() fake_values = [faker() for _ in range(len(uniques))] mappings[name] = dict(zip(uniques, fake_values)) self._ANONYMIZATION_MAPPINGS[id(self)] = mappings return mappings def _anonymize(self, data): anonymization_mappings = self._ANONYMIZATION_MAPPINGS.get(id(self)) if anonymization_mappings: data = data.copy() for name, mapping in anonymization_mappings.items(): if name in data: data[name] = data[name].map(mapping) return data def fit(self, data): """Fit this metadata to the given data. Args: data (pandas.DataFrame): Table to be analyzed. """ LOGGER.info('Fitting table %s metadata', self.name) if not self._field_names: self._field_names = list(data.columns) elif isinstance(self._field_names, set): self._field_names = [field for field in data.columns if field in self._field_names] self._dtypes = data[self._field_names].dtypes if not self._fields_metadata: self._fields_metadata = self._build_fields_metadata(data) # Re-set the primary key to validate its name and type self.set_primary_key(self._primary_key) self._make_anonymization_mappings(data) LOGGER.info('Anonymizing table %s', self.name) data = self._anonymize(data) LOGGER.info('Fitting constraints for table %s', self.name) constrained = self._fit_transform_constraints(data) extra_columns = set(constrained.columns) - set(data.columns) LOGGER.info('Fitting HyperTransformer for table %s', self.name) self._fit_hyper_transformer(constrained, extra_columns) self.fitted = True def transform(self, data): """Transform the given data. Args: data (pandas.DataFrame): Table data. Returns: pandas.DataFrame: Transformed data. """ if not self.fitted: raise MetadataNotFittedError() fields = [field for field in self.get_dtypes(ids=False) if field in data.columns] LOGGER.debug('Anonymizing table %s', self.name) data = self._anonymize(data[fields]) LOGGER.debug('Transforming constraints for table %s', self.name) for constraint in self._constraints: data = constraint.transform(data) LOGGER.debug('Transforming table %s', self.name) return self._hyper_transformer.transform(data) @classmethod def _make_ids(cls, field_metadata, length): field_subtype = field_metadata.get('subtype', 'integer') if field_subtype == 'string': regex = field_metadata.get('regex', '[a-zA-Z]+') generator, max_size = strings_from_regex(regex) if max_size < length: raise ValueError(( 'Unable to generate {} unique values for regex {}, the ' 'maximum number of unique values is {}.' ).format(length, regex, max_size)) values = [next(generator) for _ in range(length)] return pd.Series(list(values)[:length]) else: return pd.Series(np.arange(length)) def reverse_transform(self, data): """Reverse the transformed data to the original format. Args: data (pandas.DataFrame): Data to be reverse transformed. Returns: pandas.DataFrame """ if not self.fitted: raise MetadataNotFittedError() reversed_data = self._hyper_transformer.reverse_transform(data) for constraint in self._constraints: reversed_data = constraint.reverse_transform(reversed_data) for name, field_metadata in self._fields_metadata.items(): field_type = field_metadata['type'] if field_type == 'id' and name not in reversed_data: field_data = self._make_ids(field_metadata, len(reversed_data)) elif field_metadata.get('pii', False): faker = self._get_faker(field_metadata['pii_category']) field_data = pd.Series([faker() for _ in range(len(reversed_data))]) else: field_data = reversed_data[name] reversed_data[name] = field_data[field_data.notnull()].astype(self._dtypes[name]) return reversed_data[self._field_names] def filter_valid(self, data): """Filter the data using the constraints and return only the valid rows. Args: data (pandas.DataFrame): Table data. Returns: pandas.DataFrame: Table containing only the valid rows. """ for constraint in self._constraints: data = constraint.filter_valid(data) return data # ###################### # # Metadata Serialization # # ###################### # def to_dict(self): """Get a dict representation of this metadata. Returns: dict: dict representation of this metadata. """ return { 'fields': copy.deepcopy(self._fields_metadata), 'constraints': [ constraint if isinstance(constraint, dict) else constraint.to_dict() for constraint in self._constraints ], 'model_kwargs': copy.deepcopy(self._model_kwargs), 'name': self.name, 'primary_key': self._primary_key, 'sequence_index': self._sequence_index, 'entity_columns': self._entity_columns, 'context_columns': self._context_columns, } def to_json(self, path): """Dump this metadata into a JSON file. Args: path (str): Path of the JSON file where this metadata will be stored. """ with open(path, 'w') as out_file: json.dump(self.to_dict(), out_file, indent=4) @classmethod def from_dict(cls, metadata_dict, dtype_transformers=None): """Load a Table from a metadata dict. Args: metadata_dict (dict): Dict metadata to load. dtype_transformers (dict): If passed, set the dtype_transformers on the new instance. """ metadata_dict = copy.deepcopy(metadata_dict) fields = metadata_dict['fields'] or {} instance = cls( name=metadata_dict.get('name'), field_names=set(fields.keys()), field_types=fields, constraints=metadata_dict.get('constraints') or [], model_kwargs=metadata_dict.get('model_kwargs') or {}, primary_key=metadata_dict.get('primary_key'), sequence_index=metadata_dict.get('sequence_index'), entity_columns=metadata_dict.get('entity_columns') or [], context_columns=metadata_dict.get('context_columns') or [], dtype_transformers=dtype_transformers, ) instance._fields_metadata = fields return instance @classmethod def from_json(cls, path): """Load a Table from a JSON. Args: path (str): Path of the JSON file to load """ with open(path, 'r') as in_file: return cls.from_dict(json.load(in_file))

36.789074

98

0.595611

f8103f5dda307ccbcd7d0201268753fe86102d35

1,756

py

Python

cnn.py

archit120/lingatagger

cb3d0e262900dba1fd1ead0a37fad531e37cff9f

[ "Apache-2.0" ]

1

2019-06-29T10:59:22.000Z

cnn.py

archit120/lingatagger

cb3d0e262900dba1fd1ead0a37fad531e37cff9f

[ "Apache-2.0" ]

null

cnn.py

archit120/lingatagger

cb3d0e262900dba1fd1ead0a37fad531e37cff9f

[ "Apache-2.0" ]

null

import pickle with open('X', 'rb') as fp: X_cnn = pickle.load(fp) with open('Y', 'rb') as fp: Y_cnn = pickle.load(fp) a = set(list(set("ऀँंःऄअआइईउऊऋऌऍऎएऐऑऒओऔकखगघङचछजझञटठडढणतथदधनऩपफबभमयरऱलळऴवशषसहऺऻ़ऽािीुूृॄॅॆेैॉॊोौ्ॎॏॐ॒॑॓॔ॕॖॗक़ख़ग़ज़ड़ढ़फ़य़ॠॡॢॣ।॥॰ॱॲॳॴॵॶॷॸॹॺॻॼॽॾॿ-"))) split1 = int(len(Y_cnn)*0.8) X_train = X_cnn[:split1] Y_train = Y_cnn[:split1] X_test = X_cnn[split1:] Y_test = Y_cnn[split1:] from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers import Embedding from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D max_features = len(a)+1 for loss_f in ['binary_crossentropy']: for opt in ['rmsprop','adam','nadam','sgd']: for cnn_len in [32,64,128]: for dropout in [0.4,0.45,0.5,0.55,0.6]: model = Sequential() model.add(Conv1D(cnn_len, 3, activation='relu', input_shape=(18, 120))) model.add(Conv1D(64, 3, activation='relu')) model.add(MaxPooling1D(3)) model.add(GlobalAveragePooling1D()) model.add(Dropout(dropout)) model.add(Dense(1, activation='sigmoid')) model.compile(loss=loss_f, optimizer=opt, metrics=['accuracy']) print("Training new model, loss:"+loss_f+", optimizer="+opt+", cnn_len="+str(cnn_len)+", dropoff="+str(dropout)) model.fit(X_train, Y_train, batch_size=512, validation_split = 0.2, epochs=20, verbose=2) score = model.evaluate(X_test, Y_test, batch_size=512, verbose = 0) print("") print("test score: " + str(score)) print("") print("")

40.837209

141

0.577449

8aa4e050e1b4b3a4d70feeb585f91d89b805bca3

7,242

py

Python

gym_molecule/envs/docking_simple.py

AITRICS/FREED

ff75e903808edc57f01ef6d0d198b44a56dcdf4b

[ "Apache-2.0" ]

29

2021-11-03T09:27:41.000Z

2022-03-29T00:49:00.000Z

gym_molecule/envs/docking_simple.py

doxxitxxyoung/FREED

f7fed79dd98c0ea2ea66ae9b03de63fc57083995

[ "Apache-2.0" ]

1

2022-01-11T08:02:58.000Z

2022-03-06T05:49:54.000Z

gym_molecule/envs/docking_simple.py

doxxitxxyoung/FREED

f7fed79dd98c0ea2ea66ae9b03de63fc57083995

[ "Apache-2.0" ]

7

2021-12-09T07:13:06.000Z

2022-02-15T11:15:47.000Z

#!/usr/bin/env python import sys import os from multiprocessing import Manager from multiprocessing import Process from multiprocessing import Queue import subprocess from openbabel import pybel # import pybel class DockingVina(object): """ python module for Vina """ def __init__(self, docking_params): """ Construction Docking object put parameters with dictionary docking_params['vina_program'] docking_params['receptor_file'] docking_params['box_parameter'] (self.box_center, self.box_size) = box_parameter docking_params['temp_dir'] docking_params['exhaustiveness'] docking_params['num_sub_proc'] docking_params['num_cpu_dock'] docking_params['num_modes'] docking_params['timeout_gen3d'] docking_params['timeout_dock'] """ super(DockingVina, self).__init__() self.vina_program = docking_params['vina_program'] self.receptor_file = docking_params['receptor_file'] box_parameter = docking_params['box_parameter'] (self.box_center, self.box_size) = box_parameter self.temp_dir = docking_params['temp_dir'] self.exhaustiveness = docking_params['exhaustiveness'] self.num_sub_proc = docking_params['num_sub_proc'] self.num_cpu_dock = docking_params['num_cpu_dock'] self.num_modes = docking_params['num_modes'] self.timeout_gen3d = docking_params['timeout_gen3d'] self.timeout_dock = docking_params['timeout_dock'] if not os.path.exists(self.temp_dir): os.makedirs(self.temp_dir) def gen_3d(self, smi, ligand_mol_file): """ generate initial 3d conformation from SMILES input : SMILES string ligand_mol_file (output file) """ run_line = 'obabel -:%s --gen3D -O %s' % (smi, ligand_mol_file) result = subprocess.check_output(run_line.split(), stderr=subprocess.STDOUT, timeout=self.timeout_gen3d, universal_newlines=True) def docking(self, receptor_file, ligand_mol_file, ligand_pdbqt_file, docking_pdbqt_file): """ run_docking program using subprocess input : receptor_file ligand_mol_file ligand_pdbqt_file docking_pdbqt_file output : affinity list for a input molecule """ ms = list(pybel.readfile("mol", ligand_mol_file)) m = ms[0] m.write("pdbqt", ligand_pdbqt_file, overwrite=True) run_line = '%s --receptor %s --ligand %s --out %s' % (self.vina_program, receptor_file, ligand_pdbqt_file, docking_pdbqt_file) run_line += ' --center_x %s --center_y %s --center_z %s' %(self.box_center) run_line += ' --size_x %s --size_y %s --size_z %s' %(self.box_size) run_line += ' --cpu %d' % (self.num_cpu_dock) run_line += ' --num_modes %d' % (self.num_modes) run_line += ' --exhaustiveness %d ' % (self.exhaustiveness) result = subprocess.check_output(run_line.split(), stderr=subprocess.STDOUT, timeout=self.timeout_dock, universal_newlines=True) result_lines = result.split('\n') check_result = False affinity_list = list() for result_line in result_lines: if result_line.startswith('-----+'): check_result = True continue if not check_result: continue if result_line.startswith('Writing output'): break if result_line.startswith('Refine time'): break lis = result_line.strip().split() if not lis[0].isdigit(): break # mode = int(lis[0]) affinity = float(lis[1]) affinity_list += [affinity] return affinity_list def creator(self, q, data, num_sub_proc): """ put data to queue input: queue data = [(idx1,smi1), (idx2,smi2), ...] num_sub_proc (for end signal) """ for d in data: idx = d[0] dd = d[1] q.put((idx, dd)) for i in range(0, num_sub_proc): q.put('DONE') def docking_subprocess(self, q, return_dict, sub_id=0): """ generate subprocess for docking input q (queue) return_dict sub_id: subprocess index for temp file """ while True: qqq = q.get() if qqq == 'DONE': break (idx, smi) = qqq # print(smi) receptor_file = self.receptor_file ligand_mol_file = '%s/ligand_%s.mol' % (self.temp_dir, sub_id) ligand_pdbqt_file = '%s/ligand_%s.pdbqt' % (self.temp_dir, sub_id) docking_pdbqt_file = '%s/dock_%s.pdbqt' % (self.temp_dir, sub_id) try: self.gen_3d(smi, ligand_mol_file) except Exception as e: print(e) print("gen_3d unexpected error:", sys.exc_info()) print("smiles: ", smi) return_dict[idx] = 99.9 continue try: affinity_list = self.docking(receptor_file, ligand_mol_file, ligand_pdbqt_file, docking_pdbqt_file) except Exception as e: print(e) print("docking unexpected error:", sys.exc_info()) print("smiles: ", smi) return_dict[idx] = 99.9 continue if len(affinity_list)==0: affinity_list.append(99.9) affinity = affinity_list[0] return_dict[idx] = affinity def predict(self, smiles_list): """ input SMILES list output affinity list corresponding to the SMILES list if docking is fail, docking score is 99.9 """ data = list(enumerate(smiles_list)) q1 = Queue() manager = Manager() return_dict = manager.dict() proc_master = Process(target=self.creator, args=(q1, data, self.num_sub_proc)) proc_master.start() # create slave process procs = [] for sub_id in range(0, self.num_sub_proc): proc = Process(target=self.docking_subprocess, args=(q1, return_dict, sub_id)) procs.append(proc) proc.start() q1.close() q1.join_thread() proc_master.join() for proc in procs: proc.join() keys = sorted(return_dict.keys()) affinity_list = list() for key in keys: affinity = return_dict[key] affinity_list += [affinity] return affinity_list

37.329897

115

0.540597

8586980b429d66292b83f2ca4ee18003af5f0695

5,318

py

Python

src/globus_cli/commands/logout.py

globusonline/globus-cli

696857baafac198141edc3c1c29c72215f217df1

[ "Apache-2.0" ]

null

src/globus_cli/commands/logout.py

globusonline/globus-cli

696857baafac198141edc3c1c29c72215f217df1

[ "Apache-2.0" ]

1

2016-04-09T17:26:05.000Z

2016-04-11T16:13:50.000Z

src/globus_cli/commands/logout.py

globusonline/globus-cli

696857baafac198141edc3c1c29c72215f217df1

[ "Apache-2.0" ]

null

import click import globus_sdk from globus_sdk import AuthAPIError from globus_cli.login_manager import ( LoginManager, delete_templated_client, internal_native_client, is_client_login, token_storage_adapter, ) from globus_cli.login_manager.auth_flows import _STORE_CONFIG_USERINFO from globus_cli.parsing import command def warnecho(msg): click.echo(click.style(msg, fg="yellow"), err=True) _LOGOUT_EPILOG = """\ You are now successfully logged out of the Globus CLI. You may also want to logout of any browser session you have with Globus: https://auth.globus.org/v2/web/logout Before attempting any further CLI commands, you will have to login again using globus login """ _CLIENT_LOGOUT_EPILOG = """ You have successfully revoked all CLI tokens for this client identity, however client identities are always considered logged in as they can request new tokens at will. You will need to unset the GLOBUS_CLI_CLIENT_ID and GLOBUS_CLI_CLIENT_SECRET environment variables if you wish to prevent further use of the Globus CLI with this client identity, or be able to run globus login for a normal login flow. """ @command( "logout", short_help="Logout of the Globus CLI", disable_options=["format", "map_http_status"], ) @click.confirmation_option( prompt="Are you sure you want to logout?", help='Automatically say "yes" to all prompts', ) @click.option( "--ignore-errors", help="Ignore any errors encountered during logout", is_flag=True, default=False, ) @LoginManager.requires_login() def logout_command(*, login_manager: LoginManager, ignore_errors): """ Logout of the Globus CLI This command both removes all tokens used for authenticating the user from local storage and revokes them so that they cannot be used anymore globally. If an expected token cannot be found in local storage a warning will be raised as it is possible the token still exists and needs to be manually rescinded at https://auth.globus.org/consents for security. If the GLOBUS_PROFILE environment variable is set, you will log out of the account for the profile it's set to, and may still be logged in on other profiles, including the default profile. See the docs for details: https://docs.globus.org/cli/environment_variables/#profile_switching_with_globus_profile """ # try to get the user's preferred username from userinfo # if an API error is raised, they probably are not logged in try: username = login_manager.get_auth_client().oauth2_userinfo()[ "preferred_username" ] except AuthAPIError: warnecho( "Unable to lookup username. You may not be logged in. " "Attempting logout anyway...\n" ) username = None if is_client_login(): click.echo(f"Revoking all CLI tokens for {username}") else: click.echo( "Logging out of Globus{}\n".format(" as " + username if username else "") ) # first, try to delete the templated credentialed client # ignore failure (maybe creds are already invalidated or the client was deleted) # client logins don't do this as they don't use a templated client if not is_client_login(): try: delete_templated_client() except AuthAPIError: if not ignore_errors: warnecho( "Failure while deleting internal client. " "Please try logging out again", ) click.get_current_context().exit(1) else: warnecho( "Warning: Failed to delete internal client. " "Continuing... (--ignore-errors)", ) # because the client was deleted above, the tokens should all be revoked # but it could have been the `--ignore-errors` case, so take a shot at revoking # tokens # use the native client for this purpose so that we definitely have a valid API # client in this case native_client = internal_native_client() adapter = token_storage_adapter() for rs, tokendata in adapter.get_by_resource_server().items(): for tok_key in ("access_token", "refresh_token"): token = tokendata[tok_key] try: native_client.oauth2_revoke_token(token) # if we network error, revocation failed -- print message and abort so # that the user can try again when the network is working except globus_sdk.NetworkError: if not ignore_errors: warnecho( "Failed to reach Globus to revoke tokens. " "Because we cannot revoke these tokens, cancelling logout", ) click.get_current_context().exit(1) else: warnecho( "Warning: Failed to reach Globus to revoke tokens. " "Continuing... (--ignore-errors)", ) adapter.remove_tokens_for_resource_server(rs) adapter.remove_config(_STORE_CONFIG_USERINFO) if is_client_login(): click.echo(_CLIENT_LOGOUT_EPILOG) else: click.echo(_LOGOUT_EPILOG)

34.986842

92

0.660211

5e16f6ebce5c8337418e9c16385a8d7280bd62b9

19,239

py

Python

tensorflow/python/kernel_tests/split_op_test.py

devsangwoo/tensor

066592c9f9cdf4acdd1b9b104766271133e9088e

[ "Apache-2.0" ]

1

2020-01-12T14:38:34.000Z

tensorflow/python/kernel_tests/split_op_test.py

devsangwoo/tensor

066592c9f9cdf4acdd1b9b104766271133e9088e

[ "Apache-2.0" ]

null

tensorflow/python/kernel_tests/split_op_test.py

devsangwoo/tensor

066592c9f9cdf4acdd1b9b104766271133e9088e

[ "Apache-2.0" ]

null

<<<<<<< HEAD # Copyright 2015 The TensorFlow 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. # ============================================================================== """Functional tests for Split Op.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.platform import test _TEST_DTYPES = (dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128) class SplitOpTest(test.TestCase): def _makeData(self, shape, dtype): data = np.random.rand(*shape).astype(dtype.as_numpy_dtype) if dtype.is_complex: data -= 1j * data return data @test_util.run_deprecated_v1 def testShapeInference(self): model_input = array_ops.placeholder(dtypes.float32, shape=(1, 10)) # check that we fail during static shape inference if sizes are known with self.assertRaises(ValueError): # pylint: disable=expression-not-assigned array_ops.split(model_input, [4], axis=1)[0] # pylint: enable=expression-not-assigned model_input = array_ops.placeholder(dtypes.float32) inp = np.zeros((1, 10)) # check that we still fail at runtime if the shapes were unknown with self.cached_session(use_gpu=True) as sess: with self.assertRaises(errors_impl.InvalidArgumentError): sess.run(array_ops.split(model_input, [4]), {model_input: inp}) # scalar Tensors are not permitted as num_splits for axis in [0, -2]: with self.cached_session(use_gpu=True) as sess: with self.assertRaises(ValueError): # pylint: disable=expression-not-assigned sess.run( array_ops.split( array_ops.ones([4, 4]), num_or_size_splits=constant_op.constant(2), axis=axis)) # pylint: enable=expression-not-assigned # test that none split dimensions remain, even if we don't know how # the split_dim will be split, but we do know the axis result = array_ops.split( array_ops.ones([5, 2]), array_ops.constant([2, 1, 2]) * 1, axis=0) self.assertEqual(result[0].shape[1], 2) self.assertEqual(result[1].shape[1], 2) self.assertEqual(result[2].shape[1], 2) model_input2 = array_ops.placeholder(dtypes.float32, shape=[None, 2]) result = array_ops.split(model_input2, [2, 2], axis=0)[0] with self.cached_session(use_gpu=True) as sess: sess.run(result, feed_dict={model_input2: np.ones([4, 2])}) @test_util.run_deprecated_v1 def testFailWithoutExplicitNum(self): size_splits = array_ops.placeholder(dtype=dtypes.int32, shape=[None]) value = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] with self.session(use_gpu=True) as sess: with self.assertRaises(ValueError) as context: sess.run(array_ops.split(value, size_splits), {size_splits: [2, 2, 6]}) self.assertTrue("Cannot infer num from shape" in str(context.exception)) @test_util.run_in_graph_and_eager_modes def testExplicitNum(self): size_splits = array_ops.constant([2, 2, 6], dtype=dtypes.int32) value = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # Eager and Graph modes raise different exceptions with self.assertRaises((errors_impl.InvalidArgumentError, ValueError)): array_ops.split(value, size_splits, num=4) r = self.evaluate(array_ops.split(value, size_splits, num=3)) self.assertAllEqual(r[0], value[0:2]) self.assertAllEqual(r[1], value[2:4]) self.assertAllEqual(r[2], value[4:]) @test_util.run_in_graph_and_eager_modes def testListOfScalarTensors(self): a = math_ops.cast(5, dtypes.int32) b = math_ops.cast(6, dtypes.int32) value = np.random.rand(11, 11) with test_util.device(use_gpu=True): result = self.evaluate(array_ops.split(value, [a, b])) self.assertAllEqual(result[0], value[0:5, :]) self.assertAllEqual(result[1], value[5:, :]) def _RunAndVerifyVariable(self, dtype, large_num_splits=False): # Random dims of rank 5 shape = np.random.randint(1, 5, size=5) split_dim = np.random.randint(-5, 5) if large_num_splits: num_split = np.random.randint(16, 25) else: num_split = np.random.randint(2, 8) size_splits = np.random.randint(2, 8, num_split, dtype=np.int32) shape[split_dim] = np.sum(size_splits) inp = self._makeData(shape, dtype) with test_util.device(use_gpu=True): result = self.evaluate(array_ops.split(inp, size_splits, split_dim)) slices = [slice(0, x) for x in shape] offset = 0 for i in range(num_split): slices[split_dim] = slice(offset, offset + size_splits[i]) offset += size_splits[i] self.assertAllEqual(result[i], inp[slices]) def _testSpecialCasesVariable(self): inp = np.random.rand(4, 4).astype("f") with test_util.device(use_gpu=True): result = self.evaluate(array_ops.split(inp, [4], 0)) self.assertAllEqual(result[0], inp) result = self.evaluate(array_ops.split(inp, [-1, 3], 0)) self.assertAllEqual(result[0], inp[0:1, :]) self.assertAllEqual(result[1], inp[1:4, :]) def _testHugeNumberOfTensorsVariable(self, dtype): num_split = 1000 size_splits = np.random.randint(1, 3, num_split, dtype=np.int32) shape = [3, np.sum(size_splits)] split_dim = 1 inp = self._makeData(shape, dtype) with test_util.device(use_gpu=True): result = self.evaluate(array_ops.split(inp, size_splits, split_dim)) slices = [slice(0, x) for x in shape] offset = 0 for i in range(num_split): slices[split_dim] = slice(offset, offset + size_splits[i]) offset += size_splits[i] self.assertAllEqual(result[i], inp[slices]) @test_util.run_in_graph_and_eager_modes def testSpecialCasesVariable(self): self._testSpecialCasesVariable() for dtype in _TEST_DTYPES: self._testHugeNumberOfTensorsVariable(dtype) @test_util.run_in_graph_and_eager_modes def testDegenerateVariable(self): inp = np.random.rand(4, 4).astype("f") with test_util.device(use_gpu=True): result = self.evaluate(array_ops.split(inp, [-1, 4], 0)) self.assertAllEqual(result[0], inp[0:0, :]) self.assertAllEqual(result[1], inp[0:4, :]) result = self.evaluate(array_ops.split(inp, [4, -1], 0)) self.assertAllEqual(result[0], inp[0:4, :]) self.assertAllEqual(result[1], inp[4:4, :]) result = self.evaluate(array_ops.split(inp, [-1, 4], 1)) self.assertAllEqual(result[0], inp[:, 0:0]) self.assertAllEqual(result[1], inp[:, 0:4]) result = self.evaluate(array_ops.split(inp, [4, -1], 1)) self.assertAllEqual(result[0], inp[:, 0:4]) self.assertAllEqual(result[1], inp[:, 4:4]) def _testGradientsSimpleVariable(self, dtype): inp = self._makeData((4, 4), dtype) with test_util.device(use_gpu=True): inp_tensor = ops.convert_to_tensor(inp) s = array_ops.split(inp_tensor, [1, 3], 1) inp_grads = [ self._makeData((4, 1), dtype), self._makeData((4, 3), dtype) ] grad_tensors = [constant_op.constant(x) for x in inp_grads] grad = gradients_impl.gradients(s, [inp_tensor], grad_tensors)[-1] result = self.evaluate(grad) self.assertAllEqual(result[:, 0:1], inp_grads[0]) self.assertAllEqual(result[:, 1:4], inp_grads[1]) @test_util.run_deprecated_v1 def testOutputShape(self): for axis in [1, -1]: with self.cached_session(use_gpu=True): tensor = array_ops.placeholder(dtypes.float32, shape=[None, 12]) size_splits = [3, 7, 2] outputs = array_ops.split(tensor, size_splits, axis) for i, output in enumerate(outputs): self.assertEqual(output.get_shape().as_list(), [None, size_splits[i]]) def _compare(self, x, dim, num): np_ans = np.split(x, num, dim) with test_util.device(use_gpu=True): tf_ans = array_ops.split(value=x, num_or_size_splits=num, axis=dim) out = self.evaluate(tf_ans) ======= """Functional tests for Split Op.""" import tensorflow.python.platform import numpy as np import tensorflow as tf class SplitOpTest(tf.test.TestCase): def _compare(self, x, dim, num, use_gpu): np_ans = np.split(x, num, dim) with self.test_session(use_gpu=use_gpu) as sess: tf_ans = tf.split(dim, num, x) out = sess.run(tf_ans) >>>>>>> f41959ccb2... TensorFlow: Initial commit of TensorFlow library. self.assertEqual(num, len(np_ans)) self.assertEqual(num, len(np_ans)) self.assertEqual(num, len(out)) for i in range(num): self.assertAllEqual(np_ans[i], out[i]) self.assertShapeEqual(np_ans[i], tf_ans[i]) <<<<<<< HEAD @test_util.run_in_graph_and_eager_modes def testSplitRows(self): for dtype in _TEST_DTYPES: inp = self._makeData((4, 4), dtype) self._compare(inp, 0, 4) @test_util.run_in_graph_and_eager_modes def testSplitCols(self): for dtype in _TEST_DTYPES: inp = self._makeData((4, 4), dtype) self._compare(inp, 1, 4) def _testEmpty(self, x, dim, num, expected_shape): with test_util.device(use_gpu=True): tf_ans = array_ops.split(value=x, num_or_size_splits=num, axis=dim) out = self.evaluate(tf_ans) ======= def _testSplitRows(self, use_gpu): inp = np.random.rand(4, 4).astype("f") self._compare(inp, 0, 4, use_gpu) def testSplitRowsAll(self): self._testSplitRows(use_gpu=False) self._testSplitRows(use_gpu=True) def _testSplitCols(self, use_gpu): inp = np.random.rand(4, 4).astype("f") self._compare(inp, 1, 4, use_gpu) def testSplitColsAll(self): self._testSplitRows(use_gpu=False) self._testSplitCols(use_gpu=True) def _testEmpty(self, x, dim, num, expected_shape): with self.test_session() as sess: tf_ans = tf.split(dim, num, x) out = sess.run(tf_ans) >>>>>>> f41959ccb2... TensorFlow: Initial commit of TensorFlow library. self.assertEqual(x.size, 0) self.assertEqual(len(out), num) for i in range(num): self.assertEqual(out[i].shape, expected_shape) self.assertEqual(expected_shape, tf_ans[i].get_shape()) <<<<<<< HEAD @test_util.run_in_graph_and_eager_modes def testEmpty(self): # Note: np.split returns a rank-0 empty ndarray # if the input ndarray is empty. for dtype in _TEST_DTYPES: inp = self._makeData((8, 0, 21), dtype) self._testEmpty(inp, 0, 2, (4, 0, 21)) self._testEmpty(inp, 0, 4, (2, 0, 21)) self._testEmpty(inp, 1, 4, (8, 0, 21)) self._testEmpty(inp, 2, 3, (8, 0, 7)) self._testEmpty(inp, 2, 7, (8, 0, 3)) @test_util.run_in_graph_and_eager_modes def testIdentity(self): for dtype in _TEST_DTYPES: inp = self._makeData((2, 2, 2), dtype) self._compare(inp, 0, 1) self._compare(inp, 1, 1) self._compare(inp, 2, 1) @test_util.run_in_graph_and_eager_modes def testSplitDim0(self): for dtype in _TEST_DTYPES: self._compare(self._makeData((6, 10, 18), dtype), 0, 3) self._compare(self._makeData((6, 7, 18), dtype), 0, 3) self._compare(self._makeData((6, 7, 9), dtype), 0, 3) def _RunAndVerify(self, dtype, large_num_splits=False): # Random dims of rank 5 shape = np.random.randint(0, 5, size=5) split_dim = np.random.randint(-5, 5) if large_num_splits: num_split = np.random.randint(9, 15) else: num_split = np.random.randint(2, 8) shape[split_dim] = np.random.randint(2, 5) * num_split inp = self._makeData(shape, dtype) with test_util.device(use_gpu=True): result = self.evaluate( array_ops.split( value=inp, num_or_size_splits=num_split, axis=split_dim)) slices = [slice(0, x) for x in shape] offset = 0 length = shape[split_dim] // num_split ======= def testEmpty(self): # Note: np.split returns a rank-0 empty ndarray # if the input ndarray is empty. inp = np.random.rand(8, 0, 21).astype("f") self._testEmpty(inp, 0, 2, (4, 0, 21)) self._testEmpty(inp, 0, 4, (2, 0, 21)) self._testEmpty(inp, 1, 4, (8, 0, 21)) self._testEmpty(inp, 2, 3, (8, 0, 7)) self._testEmpty(inp, 2, 7, (8, 0, 3)) def testIdentity(self): inp = np.random.rand(2, 2, 2).astype("f") for use_gpu in [False, True]: self._compare(inp, 0, 1, use_gpu) self._compare(inp, 1, 1, use_gpu) self._compare(inp, 2, 1, use_gpu) def testSplitDim0(self): for use_gpu in [False, True]: self._compare(np.random.rand(6, 10, 18).astype("f"), 0, 3, use_gpu) self._compare(np.random.rand(6, 7, 18).astype("f"), 0, 3, use_gpu) self._compare(np.random.rand(6, 7, 9).astype("f"), 0, 3, use_gpu) def _RunAndVerify(self, use_gpu): # Random dims of rank 5 shape = np.random.randint(0, 5, size=5) split_dim = np.random.randint(0, 5) num_split = np.random.randint(2, 8) shape[split_dim] = np.random.randint(2, 5) * num_split inp = np.random.rand(*shape).astype("f") with self.test_session(use_gpu=use_gpu) as sess: result = sess.run(tf.split(split_dim, num_split, inp)) slices = [slice(0, x) for x in shape] offset = 0 length = shape[split_dim] / num_split >>>>>>> f41959ccb2... TensorFlow: Initial commit of TensorFlow library. for i in range(num_split): slices[split_dim] = slice(offset, offset + length) offset += length self.assertAllEqual(result[i], inp[slices]) <<<<<<< HEAD @test_util.run_in_graph_and_eager_modes def testRandom(self): for dtype in _TEST_DTYPES: for _ in range(5): self._RunAndVerify(dtype) self._RunAndVerify(dtype, large_num_splits=True) self._RunAndVerifyVariable(dtype) self._RunAndVerifyVariable(dtype, large_num_splits=True) def _testGradientsSimple(self, dtype): inp = self._makeData((4, 4), dtype) with self.cached_session(use_gpu=True): inp_tensor = ops.convert_to_tensor(inp) s = array_ops.split(value=inp_tensor, num_or_size_splits=4, axis=1) inp_grads = [self._makeData((4, 1), dtype)for _ in range(4)] grad_tensors = [constant_op.constant(x) for x in inp_grads] grad = gradients_impl.gradients(s, [inp_tensor], grad_tensors)[0] result = self.evaluate(grad) for i in range(4): self.assertAllEqual(result[:, i:i + 1], inp_grads[i]) @test_util.run_deprecated_v1 def testGradientsAll(self): for dtype in _TEST_DTYPES: self._testGradientsSimple(dtype) self._testGradientsSimpleVariable(dtype) @test_util.run_deprecated_v1 def testShapeFunctionEdgeCases(self): # split_dim greater than rank of input. with self.assertRaises(ValueError): array_ops.split(value=[[0, 1], [2, 3]], num_or_size_splits=4, axis=2) # split dim less than -(rank of input) with self.assertRaises(ValueError): array_ops.split(value=[[0, 1], [2, 3]], num_or_size_splits=4, axis=-3) # num_split does not evenly divide the size in split_dim. with self.assertRaisesRegexp(ValueError, "should evenly divide"): array_ops.split(value=[0, 1, 2, 3], num_or_size_splits=3, axis=0) # Unknown split_dim. splits = array_ops.split( value=[[0, 1, 2, 3]], num_or_size_splits=4, axis=array_ops.placeholder(dtypes.int32)) ======= def testRandom(self): for _ in range(5): self._RunAndVerify(use_gpu=False) self._RunAndVerify(use_gpu=True) def _testGradientsSimple(self, use_gpu): inp = np.random.rand(4, 4).astype("f") with self.test_session(use_gpu=use_gpu): inp_tensor = tf.convert_to_tensor(inp) s = tf.split(1, 4, inp_tensor) inp_grads = [np.random.rand(4, 1).astype("f") for _ in range(4)] grad_tensors = [tf.constant(x) for x in inp_grads] grad = tf.gradients(s, [inp_tensor], grad_tensors)[0] result = grad.eval() for i in range(4): self.assertAllEqual(result[:, i:i+1], inp_grads[i]) def testGradientsAll(self): self._testGradientsSimple(use_gpu=False) self._testGradientsSimple(use_gpu=True) def testShapeFunctionEdgeCases(self): # split_dim greater than rank of input. with self.assertRaises(ValueError): tf.split(2, 4, [[0, 1], [2, 3]]) # num_split does not evenly divide the size in split_dim. with self.assertRaisesRegexp(ValueError, "should evenly divide"): tf.split(0, 3, [0, 1, 2, 3]) # Unknown split_dim. splits = tf.split(tf.placeholder(tf.int32), 4, [[0, 1, 2, 3]]) >>>>>>> f41959ccb2... TensorFlow: Initial commit of TensorFlow library. for s in splits: self.assertEqual([None, None], s.get_shape().as_list()) # Unknown split_dim and input shape. <<<<<<< HEAD splits = array_ops.split( value=array_ops.placeholder(dtypes.float32), num_or_size_splits=4, axis=array_ops.placeholder(dtypes.int32)) for s in splits: self.assertEqual(None, s.get_shape().ndims) @test_util.run_deprecated_v1 def testVariableShapeFunction(self): # size_splits too big with self.assertRaises(ValueError): array_ops.split([0, 1], [3, -1], axis=0) # Correct inference of variable dimension s0, s1 = array_ops.split([0, 1, 2], [2, -1], axis=0) assert s0.shape.as_list() == [2] assert s1.shape.as_list() == [1] @test_util.run_deprecated_v1 @test_util.disable_xla("b/123337890") # Error messages differ def testNonexistentDimTensor(self): x = array_ops.placeholder(dtypes.int32) values = np.zeros([5, 30]) splits = array_ops.placeholder(dtypes.int32) with self.assertRaisesRegexp(ValueError, "Cannot infer"): y = array_ops.split(values, splits, axis=x) splits = array_ops.placeholder(dtypes.int32, [3]) y = array_ops.split(values, splits, axis=x) with self.session(use_gpu=True) as sess: with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, "must have exactly one element"): sess.run(y, {x: np.array([], dtype=np.int32), splits: [4, 11, 15]}) if __name__ == "__main__": test.main() ======= splits = tf.split(tf.placeholder(tf.int32), 4, tf.placeholder(tf.float32)) for s in splits: self.assertEqual(None, s.get_shape().ndims) if __name__ == "__main__": tf.test.main() >>>>>>> f41959ccb2... TensorFlow: Initial commit of TensorFlow library.

37.069364

80

0.666979

2aa01b24453d5eb7f3a13f0ce05d7f98d34af083

12,660

py

Python

Project/GuiTest_portrait.py

quample/spatial_filter_GUI

e1490fc615f221009e0522c77e53392c009d7ca1

[ "MIT" ]

1

2019-10-20T01:18:21.000Z

Project/GuiTest_portrait.py

quample/spatial_filter_GUI

e1490fc615f221009e0522c77e53392c009d7ca1

[ "MIT" ]

null

Project/GuiTest_portrait.py

quample/spatial_filter_GUI

e1490fc615f221009e0522c77e53392c009d7ca1

[ "MIT" ]

null

from tkinter import * from tkinter import ttk from tkinter import filedialog from PIL import Image, ImageTk import cv2 import os #from folder_name i3mport python_name as shortcut from Filter import spatial_filtering_portrait as sf class Root(Tk): def __init__(self): super(Root, self).__init__() self.MAXSIZE = 1024 self.thumbnailSize = (300,300) #set the max thumbnail display size self.outputfilenameLabel = ttk.Label() #Label for After Processing Image self.afterLabel = ttk.Label() #Label for After Processing Text # Initalize filename to None/Null self.filename = None # Generates the window self.title("Spatial Filter") self.minsize(500, 400) self.wm_iconbitmap('icon.ico') # Input Frame region self.inputFrame = ttk.LabelFrame(self, text = "Input Image") self.inputFrame.grid(column=0, row=0, padx=20, pady=20, sticky="w") self.BrowseFileButton() # Operation Frame region self.operationTypeFrame = ttk.LabelFrame(self, text = "Operation") self.operationTypeFrame.grid(column=0, row=1, padx=20, pady=20, sticky="w") self.OperationTypeDropDown() # Filter Size region self.filterSizeFrame = ttk.LabelFrame(self, text = "Filter Size") self.filterSizeFrame.grid(column=0, row=2, padx=20, pady=20, sticky="w") self.FilterSizeSlider() # Process Button self.ProcessButton() #Portrait Button self.PortraitButton() # Creates the browse button to select Image def BrowseFileButton(self): self.BrowseButton = ttk.Button(self.inputFrame, text = "Browse", command=self.fileDialog) #Creates Button for Browse self.BrowseButton.grid(column = 0, row = 0, sticky="w") #Places Button in grid/frame # Command that executes when Browse button is clicked. # Open/Display JPEG or PNG files def fileDialog(self): # self.filename = filedialog.askopenfilename(initialdir = "/", title = "Select A File", filetype = (("jpeg", "*.jpg"), ("PNG", "*.png"), ("All Files", "*.*" ))) self.outputfilenameLabel.grid_forget() self.afterLabel.grid_forget() self.filename = filedialog.askopenfilename(initialdir =os.path.dirname(os.path.abspath(__file__)), title = "Select A File", filetype = (("PNG", "*.png"), ("JPEG", "*.jpg"))) if self.filename: self.image_thumbnail = Image.open(self.filename).convert('L') #Open input image self.image_thumbnail.thumbnail(self.thumbnailSize) #Creates a thumbnail that keeps the aspect ratio resized = self.image_thumbnail.resize(self.thumbnailSize, Image.ANTIALIAS) #this resizes the input image in the display out ONLY self.image_disp_tk = ImageTk.PhotoImage(resized) #Format image for Tkinter compatibility display self.filenameLabel = ttk.Label(self.inputFrame, image=self.image_disp_tk) #Display input image self.filenameLabel.grid(column=0, row =1) #Places thumbnail image in grid/frame self.beforeLabel = ttk.Label(self.inputFrame, text="Before processing") #Creates Label for input image self.beforeLabel.grid(column=0, row=2) #Places Label in grid/frame # Creates the Drop down to select the type of operation: Smoothing or Sharpening def OperationTypeDropDown(self): # Initialize Operation Type variable, create a label for it and place it in the grid self.operationType = StringVar() #Operation Variable: Smoothing OR Sharpening self.operationTypeLabel = ttk.Label(self.operationTypeFrame, text="Operation:") #Create Operation Label self.operationTypeLabel.grid(column=0, row=0) #Place Label in the grid/frame self.operationType.set("Smoothing") #Set the default value for the Operation Drop Down self.operationType.trace('w',self.updateFilterOptions) #Track when the Operation drop down gets changed. self.operationTypeOptions = OptionMenu(self.operationTypeFrame, self.operationType, 'Smoothing', 'Sharpening') #Populate the Operation Drop Down Option self.operationTypeOptions.grid(column = 0, row = 1) #Place the Drop Down in the grid/frame # Initialize Filter Type variable, create a lable for it and place it in the grid self.filterType = StringVar() #Filter Variable: Average/Gaussian OR Laplace/Unsharp self.filterTypeLabel = ttk.Label(self.operationTypeFrame, text="Filter:") #Create the Filter Label self.filterTypeLabel.grid(column=1, row=0) #Place the Label in the grid/frame # For Initial Start Up, will get replaced/updated by updateFilterOptions when the Operation Type gets changed self.filterType.set("Average Filter") #Set Default of variable self.filterTypeOptions = OptionMenu(self.operationTypeFrame, self.filterType, "Average Filter", "Gaussian Filter") self.filterTypeOptions.grid(column = 1, row = 1) # Unsharp Mask Initialization unsharpMaskValues = [0.2, 0.4, 0.6, 0.8, 1.0, 2.0, 5.0, 7.0] #Create a list for values self.unsharpMaskConstant = DoubleVar() #UnsharpMaskConstant Variable (will use the list above later) self.unsharpMaskLabel = ttk.Label(self.operationTypeFrame, text="UnSharp Mask Constant:") #Create a Label for Unsharp Mask Constant self.unsharpMaskConstant.trace('w', self.updateHighBoost) #Track when the UnsharpMaskConstant get changed self.unsharpMaskConstantOptions = OptionMenu(self.operationTypeFrame, self.unsharpMaskConstant, *unsharpMaskValues) #Populate the UnsharpMaskConstant Drop Down Option self.highBoostLabel = ttk.Label(self.operationTypeFrame, text="This is now High Boost Sharpening") #Create a Label for High Boost # Variance Initialization varianceValues = [1.0, 2.0, 4.0, 5.0, 10.0] self.varianceValue = DoubleVar() self.varianceValue.set(1.0) self.varianceLabel = ttk.Label(self.operationTypeFrame, text="Variance:") # self.varianceOptions = Scale(self.operationTypeFrame, from_=0.1, to=1.0, tickinterval=0.05, orient=HORIZONTAL) self.varianceOptions = OptionMenu(self.operationTypeFrame, self.varianceValue, *varianceValues) # First call to inialize the tracers self.updateFilterOptions() # Function that executes whenever the Filter Drop Down is changed def updateFilterOptions(self, *args): # When Operation selected is Smoothing, the Filter Drop Down gets set accordingly if(self.operationType.get() == 'Smoothing'): self.filterType.set("Average Filter") # self.filterType.trace('w', self.updateGaussianOptions) self.filterType.trace('w', self.updateGaussianOptions) self.filterTypeOptions = OptionMenu(self.operationTypeFrame, self.filterType, "Average Filter", "Gaussian Filter") elif (self.operationType.get() == 'Sharpening'): self.filterType.set("Laplacian Filter") self.filterType.trace('w', self.updateUnsharpOptions) self.filterTypeOptions = OptionMenu(self.operationTypeFrame, self.filterType, "Laplacian Filter", "Unsharp Mask") else: self.filterType.set("Average Filter") self.filterTypeOptions.grid(column = 1, row = 1) def updateGaussianOptions(self, *args): if self.filterType.get()=="Gaussian Filter": self.varianceLabel.grid(column=2, row=0) self.varianceOptions.grid(column=2, row=1) else: self.varianceOptions.grid_forget() self.varianceLabel.grid_forget() # Function that executes whenever the UnsharpMaskConstant Drop Down is changed def updateUnsharpOptions(self, *args): self.unsharpMaskConstant.set(0.2) #Reset value whenever the Drop Down is inialized # Shows/Hides the UnsharpMaskConstant Label and Drop Down option depending on the Filter selection if(self.filterType.get()=='Unsharp Mask'): self.unsharpMaskLabel.grid(column=2, row=0) self.unsharpMaskConstantOptions.grid(column=2, row =1) else: self.unsharpMaskConstantOptions.grid_forget() self.unsharpMaskLabel.grid_forget() # Function that executes whenever the UnsharpMaskConstant variable is under or over 1.0 def updateHighBoost(self, *args): # Shows/Hides the High Boost Label whenever the selection is under or over 1.0 if (self.unsharpMaskConstant.get()>1.0): self.highBoostLabel.grid(column = 4, row=1) else: self.highBoostLabel.grid_forget() # Creates the Filter Size Slider def FilterSizeSlider(self): filterSizeOptions = [3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31] self.filterSize = IntVar() self.filterSize.set(3) self.filterSizeOptions = OptionMenu(self.filterSizeFrame, self.filterSize, *filterSizeOptions) #creates dropdown for filter size self.filterSizeOptions.grid(column = 0, row = 0) # Creates the Process Button def ProcessButton(self): self.ProcessButton = ttk.Button(self, text = "Process Image", command=self.process) self.ProcessButton.grid(column = 0, row = 3, padx=20, pady=20, sticky="w") # Command that executes when the Process Button is clicked def process(self): # Calls the class of the imported python file for spatial filtering(sf) spatial_filter = sf.spatial_filtering() self.outputfilenameLabel.grid_forget() self.afterLabel.grid_forget() # if there's a valid filename if self.filename: if self.operationType.get()=='Smoothing': # Calls the smoothing function from the spatial_filter.py file and sets the outputfilename self.outputfilename = spatial_filter.smoothing(self.filename, self.filterType.get(), self.filterSize.get(), self.varianceValue.get()) elif self.operationType.get() == 'Sharpening': # Calls the sharpening function from the spatial_filter.py file and sets the outputfilename self.outputfilename = spatial_filter.sharpening(self.filename, self.filterType.get(), self.filterSize.get(), self.unsharpMaskConstant.get()) self.display_output() else: print("No image to process") # Function to display the filtered image def display_output(self): if self.outputfilename: print(self.outputfilename) self.output_image_thumbnail = Image.open(self.outputfilename) # put the output image side by side when done? self.output_image_thumbnail.thumbnail(self.thumbnailSize) #creates a thumbnail resized = self.output_image_thumbnail.resize(self.thumbnailSize, Image.ANTIALIAS) #this resizes the output image in the display out ONLY self.output_image_disp_tk = ImageTk.PhotoImage(resized) #Format image for Tkinter compatibility display self.outputfilenameLabel = ttk.Label(self.inputFrame, image=self.output_image_disp_tk) #Display input image self.outputfilenameLabel.grid(column=1, row =1) #places the image on the window self.afterLabel = ttk.Label(self.inputFrame, text="After processing") #create an "After Processing" label self.afterLabel.grid(column=1, row=2) #places the label on the window else: print("No OUTPUT IMAGE to display.") def PortraitButton(self): # Creates button and places in window for Portrait Mode self.PortraitButton = ttk.Button(self, text = "Portrait Mode", command=self.portrait) self.PortraitButton.grid(column = 1, row = 3, padx=20, pady=20, sticky="w") def portrait(self): # Calls the portrait function that blurs the background and sharpens the foreground self.outputfilenameLabel.grid_forget() self.afterLabel.grid_forget() spatial_filter = sf.spatial_filtering() if self.filename: self.outputfilename = spatial_filter.portrait(self.filename, self.filterType.get(), self.filterSize.get()) self.display_output() else: print("No image to process") if __name__== '__main__': root = Root() style = ttk.Style() style.theme_use("winnative") root.mainloop()

55.043478

181

0.674092

7968e3851d7c727f0343d757cfe0a21f4f1d2290

4,498

py

Python

zeeguu_api/api/accounts.py

TWEApol/Zeeguu-API

832f1cb2d0950bd879af72d9e606783698d78b57

[ "MIT" ]

null

zeeguu_api/api/accounts.py

TWEApol/Zeeguu-API

832f1cb2d0950bd879af72d9e606783698d78b57

[ "MIT" ]

null

zeeguu_api/api/accounts.py

TWEApol/Zeeguu-API

832f1cb2d0950bd879af72d9e606783698d78b57

[ "MIT" ]

null

import flask import sqlalchemy import zeeguu from flask import request from zeeguu.model import User, Cohort, Teacher from zeeguu.model.unique_code import UniqueCode from zeeguu_api.api.sessions import get_session, get_anon_session from zeeguu_api.api.utils.abort_handling import make_error from zeeguu_api.emailer.user_activity import send_new_user_account_email from zeeguu_api.emailer.password_reset import send_password_reset_email from .utils.route_wrappers import cross_domain from . import api, db_session @api.route("/add_user/<email>", methods=["POST"]) @cross_domain def add_user(email): """ Creates user, then redirects to the get_session endpoint. Returns a session """ def _valid_invite_code(invite_code: str): return invite_code in zeeguu.app.config.get("INVITATION_CODES") or Cohort.exists_with_invite_code(invite_code) password = request.form.get("password") username = request.form.get("username") invite_code = request.form.get("invite_code") cohort_name = '' if password is None or len(password) < 4: return make_error(400, "Password should be at least 4 characters long") if not (_valid_invite_code(invite_code)): return make_error(400, "Invitation code is not recognized. Please contact us.") try: cohort = Cohort.query.filter_by(inv_code=invite_code).first() if cohort: # if the invite code is from a cohort, then there has to be capacity if not cohort.cohort_still_has_capacity(): return make_error(400, "No more places in this class. Please contact us.") cohort_name = cohort.name new_user = User(email, username, password, invitation_code=invite_code, cohort=cohort) db_session.add(new_user) if cohort: if cohort.is_cohort_of_teachers: teacher = Teacher(new_user) db_session.add(teacher) db_session.commit() send_new_user_account_email(username, invite_code, cohort_name) except sqlalchemy.exc.IntegrityError: return make_error(401, "There is already an account for this email.") except ValueError: return make_error(400, "Invalid value") return get_session(email) @api.route("/add_anon_user", methods=["POST"]) @cross_domain def add_anon_user(): """ Creates anonymous user, then redirects to the get_session endpoint. Returns a session """ # These two are post parameters required by the method uuid = request.form.get("uuid", None) password = request.form.get("password", None) # These two are optional language_code = request.form.get("learned_language_code", None) native_code = request.form.get("native_language_code", None) try: new_user = User.create_anonymous(uuid, password, language_code, native_code) db_session.add(new_user) db_session.commit() except ValueError as e: flask.abort(flask.make_response("Could not create anon user.", 400)) except sqlalchemy.exc.IntegrityError as e: flask.abort(flask.make_response("Could not create anon user. Maybe uuid already exists?", 400)) return get_anon_session(uuid) @api.route("/send_code/<email>", methods=["POST"]) @cross_domain def send_code(email): """ This endpoint generates a unique code that will be used to allow the user to change his/her password. The unique code is send to the specified email address. """ code = UniqueCode(email) db_session.add(code) db_session.commit() send_password_reset_email(email, code) return "OK" @api.route("/reset_password/<email>", methods=["POST"]) @cross_domain def reset_password(email): """ This endpoint can be used to rest a users password. To do this a uniquecode is required. """ last_code = UniqueCode.last_code(email) code = request.form.get("code", None) if not (last_code == code): return make_error(400, "Invalid code") password = request.form.get("password", None) if len(password) < 4: return make_error(400, "Password should be at least 4 characters long") user = User.find(email) if user is None: return make_error(400, "Email unknown") user.update_password(password) db_session.commit() # Delete all the codes for this user for x in UniqueCode.all_codes_for(email): db_session.delete(x) db_session.commit() return "OK"

30.808219

118

0.696087

0da6056fce2d1dc474e46a849747974f7760b071

1,247

py

Python

src/bindings/python/tests/test_ngraph/test_ops_multioutput.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

2

2021-12-14T15:27:46.000Z

2021-12-14T15:34:16.000Z

src/bindings/python/tests/test_ngraph/test_ops_multioutput.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

33

2021-09-23T04:14:30.000Z

2022-01-24T13:21:32.000Z

src/bindings/python/tests/test_ngraph/test_ops_multioutput.py

ryanloney/openvino-1

4e0a740eb3ee31062ba0df88fcf438564f67edb7

[ "Apache-2.0" ]

11

2021-11-09T00:51:40.000Z

2021-11-10T12:04:16.000Z

# Copyright (C) 2018-2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import numpy as np import openvino.runtime.opset8 as ov from tests.runtime import get_runtime def test_split(): runtime = get_runtime() input_tensor = ov.constant(np.array([0, 1, 2, 3, 4, 5], dtype=np.int32)) axis = ov.constant(0, dtype=np.int64) splits = 3 split_node = ov.split(input_tensor, axis, splits) computation = runtime.computation(split_node) split_results = computation() expected_results = np.array([[0, 1], [2, 3], [4, 5]], dtype=np.int32) assert np.allclose(split_results, expected_results) def test_variadic_split(): runtime = get_runtime() input_tensor = ov.constant(np.array([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]], dtype=np.int32)) axis = ov.constant(1, dtype=np.int64) splits = ov.constant(np.array([2, 4], dtype=np.int64)) v_split_node = ov.variadic_split(input_tensor, axis, splits) computation = runtime.computation(v_split_node) results = computation() split0 = np.array([[0, 1], [6, 7]], dtype=np.int32) split1 = np.array([[2, 3, 4, 5], [8, 9, 10, 11]], dtype=np.int32) assert np.allclose(results[0], split0) assert np.allclose(results[1], split1)

33.702703

100

0.668805

18c3c0279f9fde696d746782e5db6ff8cd132415

9,970

py

Python

v0.5.0/google/research_v3.32/gnmt-tpuv3-32/code/gnmt/model/t2t/tensor2tensor/data_generators/gym_utils.py

myelintek/results

11c38436a158c453e3011f8684570f7a55c03330

[ "Apache-2.0" ]

44

2018-11-07T18:52:33.000Z

2019-07-06T12:48:18.000Z

v0.5.0/google/research_v3.32/gnmt-tpuv3-32/code/gnmt/model/t2t/tensor2tensor/data_generators/gym_utils.py

myelintek/results

11c38436a158c453e3011f8684570f7a55c03330

[ "Apache-2.0" ]

63

2017-12-19T20:29:10.000Z

2021-08-04T21:49:36.000Z

v0.5.0/google/research_v3.32/gnmt-tpuv3-32/code/gnmt/model/t2t/tensor2tensor/data_generators/gym_utils.py

myelintek/results

11c38436a158c453e3011f8684570f7a55c03330

[ "Apache-2.0" ]

44

2018-11-09T21:04:52.000Z

2019-06-24T07:40:28.000Z

# coding=utf-8 # Copyright 2018 The Tensor2Tensor Authors. # # 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. """Utilities for openai gym.""" from collections import deque import gym import numpy as np # pylint: disable=method-hidden class WarmupWrapper(gym.Wrapper): """Warmup wrapper.""" def __init__(self, env, warm_up_examples=0, warmup_action=0): gym.Wrapper.__init__(self, env) self.warm_up_examples = warm_up_examples self.warm_up_action = warmup_action self.observation_space = gym.spaces.Box( low=0, high=255, shape=(210, 160, 3), dtype=np.uint8) def get_starting_data(self, num_frames): self.reset() starting_observations, starting_actions, starting_rewards = [], [], [] for _ in range(num_frames): observation, rew, _, _ = self.env.step(self.warm_up_action) starting_observations.append(observation) starting_rewards.append(rew) starting_actions.append(self.warm_up_action) return starting_observations, starting_actions, starting_rewards def step(self, action): return self.env.step(action) def reset(self, **kwargs): del kwargs self.env.reset() observation = None for _ in range(self.warm_up_examples): observation, _, _, _ = self.env.step(self.warm_up_action) return observation class PongWrapper(WarmupWrapper): """Pong Wrapper.""" def __init__(self, env, warm_up_examples=0, action_space_reduction=False, reward_skip_steps=0, big_ball=False): super(PongWrapper, self).__init__(env, warm_up_examples=warm_up_examples) self.action_space_reduction = action_space_reduction if self.action_space_reduction: self.action_space = gym.spaces.Discrete(2) self.warm_up_examples = warm_up_examples self.observation_space = gym.spaces.Box( low=0, high=255, shape=(210, 160, 3), dtype=np.uint8) self.reward_skip_steps = reward_skip_steps self.big_ball = big_ball def step(self, action): if self.action_space_reduction: action = 2 if int(action) == 0 else 5 ob, rew, done, info = self.env.step(action) ob = self.process_observation(ob) if rew != 0 and self.reward_skip_steps != 0: for _ in range(self.reward_skip_steps): self.env.step(0) return ob, rew, done, info def reset(self, **kwargs): observation = super(PongWrapper, self).reset(**kwargs) observation = self.process_observation(observation) return observation def process_observation(self, obs): if self.big_ball: pos = PongWrapper.find_ball(obs) if pos is not None: x, y = pos obs[x-5:x+5, y-5:y+5, :] = 255 return obs @staticmethod def find_ball(obs, default=None): ball_area = obs[37:193, :, 0] res = np.argwhere(ball_area == 236) if not res: return default else: x, y = res[0] x += 37 return x, y def wrapped_pong_factory(warm_up_examples=0, action_space_reduction=False, reward_skip_steps=0, big_ball=False): """Wrapped pong games.""" env = gym.make("PongDeterministic-v4") env = env.env # Remove time_limit wrapper. env = PongWrapper(env, warm_up_examples=warm_up_examples, action_space_reduction=action_space_reduction, reward_skip_steps=reward_skip_steps, big_ball=big_ball) return env gym.envs.register(id="T2TPongWarmUp20RewSkip200Steps-v1", entry_point=lambda: wrapped_pong_factory( # pylint: disable=g-long-lambda warm_up_examples=20, reward_skip_steps=15), max_episode_steps=200) gym.envs.register(id="T2TPongWarmUp20RewSkipFull-v1", entry_point=lambda: wrapped_pong_factory( # pylint: disable=g-long-lambda warm_up_examples=20, reward_skip_steps=15)) class BreakoutWrapper(WarmupWrapper): """Breakout Wrapper.""" FIRE_ACTION = 1 def __init__(self, env, warm_up_examples=0, ball_down_skip=0, big_ball=False, include_direction_info=False, reward_clipping=True): super(BreakoutWrapper, self).__init__( env, warm_up_examples=warm_up_examples, warmup_action=BreakoutWrapper.FIRE_ACTION) self.warm_up_examples = warm_up_examples self.observation_space = gym.spaces.Box(low=0, high=255, shape=(210, 160, 3), dtype=np.uint8) self.ball_down_skip = ball_down_skip self.big_ball = big_ball self.reward_clipping = reward_clipping self.include_direction_info = include_direction_info self.direction_info = deque([], maxlen=2) self.points_gained = False msg = ("ball_down_skip should be bigger equal 9 for " "include_direction_info to work correctly") assert not self.include_direction_info or ball_down_skip >= 9, msg def step(self, action): ob, rew, done, info = self.env.step(action) if BreakoutWrapper.find_ball(ob) is None and self.ball_down_skip != 0: for _ in range(self.ball_down_skip): # We assume that nothing interesting happens during ball_down_skip # and discard all information. # We fire all the time to start new game ob, _, _, _ = self.env.step(BreakoutWrapper.FIRE_ACTION) self.direction_info.append(BreakoutWrapper.find_ball(ob)) ob = self.process_observation(ob) self.points_gained = self.points_gained or rew > 0 if self.reward_clipping: rew = np.sign(rew) return ob, rew, done, info def reset(self, **kwargs): observation = super(BreakoutWrapper, self).reset(**kwargs) self.env.step(BreakoutWrapper.FIRE_ACTION) self.direction_info = deque([], maxlen=2) observation = self.process_observation(observation) return observation @staticmethod def find_ball(ob, default=None): off_x = 63 clipped_ob = ob[off_x:-21, :, 0] pos = np.argwhere(clipped_ob == 200) if not pos.size: return default x = off_x + pos[0][0] y = 0 + pos[0][1] return x, y def process_observation(self, obs): if self.big_ball: pos = BreakoutWrapper.find_ball(obs) if pos is not None: x, y = pos obs[x-5:x+5, y-5:y+5, :] = 255 if self.include_direction_info: for point in list(self.direction_info): if point is not None: x, y = point obs[x-2:x+2, y-2:y+2, 1] = 255 return obs def wrapped_breakout_factory(warm_up_examples=0, ball_down_skip=0, big_ball=False, include_direction_info=False, reward_clipping=True): """Wrapped breakout games.""" env = gym.make("BreakoutDeterministic-v4") env = env.env # Remove time_limit wrapper. env = BreakoutWrapper(env, warm_up_examples=warm_up_examples, ball_down_skip=ball_down_skip, big_ball=big_ball, include_direction_info=include_direction_info, reward_clipping=reward_clipping) return env gym.envs.register(id="T2TBreakoutWarmUp20RewSkip500Steps-v1", entry_point=lambda: wrapped_breakout_factory( # pylint: disable=g-long-lambda warm_up_examples=1, ball_down_skip=9, big_ball=False, include_direction_info=True, reward_clipping=True ), max_episode_steps=500) class FreewayWrapper(WarmupWrapper): """Wrapper for Freeway.""" def __init__(self, env, warm_up_examples=0, reward_clipping=True, easy_freeway=False): super(FreewayWrapper, self).__init__(env, warm_up_examples) self.easy_freeway = easy_freeway self.half_way_reward = 1.0 # this is probably not needed, just in case self.reward_clipping = reward_clipping def chicken_height(self, image): raise NotImplementedError() def step(self, action): ob, rew, done, info = self.env.step(action) if self.easy_freeway: if rew > 0: self.half_way_reward = 1 chicken_height = self.chicken_height(ob) if chicken_height < 105: rew += self.half_way_reward self.half_way_reward = 0 if self.reward_clipping: rew = np.sign(rew) return ob, rew, done, info def reset(self, **kwargs): self.half_way_reward = 1.0 observation = super(FreewayWrapper, self).reset(**kwargs) return observation def wrapped_freeway_factory(warm_up_examples=0, reward_clipping=True, easy_freeway=False): """Wrapped freeway games.""" env = gym.make("FreewayDeterministic-v4") env = env.env # Remove time_limit wrapper. env = FreewayWrapper(env, warm_up_examples=warm_up_examples, reward_clipping=reward_clipping, easy_freeway=easy_freeway) return env gym.envs.register(id="T2TFreewayWarmUp20RewSkip500Steps-v1", entry_point=lambda: wrapped_freeway_factory( # pylint: disable=g-long-lambda warm_up_examples=1, reward_clipping=True, easy_freeway=False ), max_episode_steps=500)

32.90429

96

0.64674

b4725928f8386233ac89556d03d03a1a0d1bddbd

6,268

py

Python

wabisabi/kwarg_name_change.py

hmaarrfk/deprecation_factory

bc7921425d3136c429845620c8a163eef58d8c49

[ "BSD-3-Clause" ]

null

wabisabi/kwarg_name_change.py

hmaarrfk/deprecation_factory

bc7921425d3136c429845620c8a163eef58d8c49

[ "BSD-3-Clause" ]

14

2018-08-09T04:02:38.000Z

2018-09-06T23:55:03.000Z

wabisabi/kwarg_name_change.py

hmaarrfk/deprecation_factory

bc7921425d3136c429845620c8a163eef58d8c49

[ "BSD-3-Clause" ]

null

""" As this library is still in early development, the API might change. I suggest you vendor this so that you don't create conflicts if other libraries decide to use an older, incompatible version If you are shipping python source code, then I've included the license at the bottom of this file to make your life easier. """ from distutils.version import LooseVersion as Version from functools import wraps import inspect from warnings import warn from .merge_docstrings import merge_docstrings # Keep the order of params as is, # `previous_arg_order` and `keep_old_signature` # should be optional positional args. def kwarg_name_change(version, previous_kwarg_map=None, library_name=None, current_library_version=None): """A decorator factory for keyword arguments to be specified by their old names. If the current library version is smaller than version in which the new signature takes effect, this deprecator will: Issue a warnign pointing to the user's code if they call a function with too many positional arguments. Parameters ---------- version: version-like Version in which the new function signature takes full effect. previous_kwarg_map: dictionary of kwarg name maps This dictionary contains the keys (the old kwarg names) and the associated values of the new kwarg names. You may specifiy as many kwarg name pairs as you wish. library_name: str Friendly library name to include in warning messages current_library_version: version-like The current version of the shipped library (typically your module's ``__version__``). Examples -------- >>> from wabisabi import kwonly_change >>> import functools >>> >>> kwarg_name_change = functools.partial(kwarg_name_change, ... library_name='my super lib', ... current_library_version='0.14') >>> @kwarg_name_change('0.15', {'bar': 'foo'}) ... def foo_new(zap='zip', *, foo='hello', baz='world'): ... return zap + foo + baz """ if current_library_version is None: raise ValueError('Must provide a version for your library.') if library_name is None: raise ValueError('You must provide a user friendly name for your ' 'library.') def the_decorator(func): if Version(current_library_version) >= version: return func @wraps(func) def wrapper(*args, **kwargs): new_kwargs = {} for key, value in kwargs.items(): if key not in previous_kwarg_map: new_key = key else: new_key = previous_kwarg_map[key] warn("In version {version} of {library_name}, the " "keyword argument '{key}' will be replaced by " "'{new_key}'. " "To suppress this warning, specify the keyword " "argument with its new name." "".format( version=version, library_name=library_name, key=key, new_key=new_key), FutureWarning, stacklevel=2) if new_key in new_kwargs: # Search for the old key. for old_key, n_key in previous_kwarg_map.items(): if n_key == new_key: break raise TypeError( "'{old_key}' and '{new_key}' refer to the same " "parameter. Using both is not allowed." "".format(old_key=old_key, new_key=new_key)) new_kwargs[new_key] = value return func(*args, **new_kwargs) # only add a docstring if they had one already if wrapper.__doc__ is None: return wrapper warnings_string = """ Warns ----- FutureWarning In release {version} of {module}, the following keyword arguments can only be referred by their new names. For now, both names are accepted as valid: """.format(version=version, module=library_name) for key, new_key in previous_kwarg_map.items(): warnings_string += (""" '{new_key}' refers to the old keyword argument '{key}'""" "".format(new_key=new_key, key=key)) warnings_string += """ To avoid this warning, provide all the listed keyword arguments using their new names. """ wrapper.__doc__ = merge_docstrings(wrapper, warnings_string) return wrapper return the_decorator """ BSD 3-Clause License Copyright (c) 2019, Mark Harfouche All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """

38.453988

84

0.651723

9baffb1e020d9ab75f1fdabfd47795eb44734ec6

3,277

py

Python

snake.py

farhad-rezazadeh/snake

1200d9d627b81d3db22a491e7854ea6403988530

[ "MIT" ]

null

snake.py

farhad-rezazadeh/snake

1200d9d627b81d3db22a491e7854ea6403988530

[ "MIT" ]

null

snake.py

farhad-rezazadeh/snake

1200d9d627b81d3db22a491e7854ea6403988530

[ "MIT" ]

null

import consts class Snake: def __init__(self, keys, game, pos, color, direction): self.control_keys = keys self.cells = [pos] self.game_manager = game self.color = color self.game_manager.add_snake(self) self.direction = direction def get_head(self): return self.cells[-1] def next_move(self): temp = self.go(self.direction) temp = [((temp[0] - consts.sx) // consts.cell_size), ((temp[1] - consts.sy) // consts.cell_size)] situation = self.game_manager.check_cell(temp) if situation == "fruit": self.cells.append(temp) self.game_manager.cells_class_build[temp[0]][temp[1]].set_color(self.color) elif situation == "empty_cell": self.cells.append(temp) self.game_manager.cells_class_build[temp[0]][temp[1]].set_color(self.color) last_cell = self.cells.pop(0) self.game_manager.cells_class_build[last_cell[0]][last_cell[1]].set_color(consts.back_color) else: self.game_manager.kill(self) def handle(self, keys): for key in keys: if key in self.control_keys: key = self.control_keys[key] if key == "UP" and self.direction != "DOWN": self.direction = key break elif key == "DOWN" and self.direction != "UP": self.direction = key break elif key == "RIGHT" and self.direction != "LEFT": self.direction = key break elif key == "LEFT" and self.direction != "RIGHT": self.direction = key break # ---------------------------------------function_for_moving_cell---------------------------------------- def go_right(self): pos = [self.get_head()[0], self.get_head()[1]] if pos[0]+1 > consts.table_size-1: temp_pos = self.game_manager.cells[0][pos[1]] else: temp_pos = self.game_manager.cells[pos[0]+1][pos[1]] return temp_pos def go_left(self): pos = [self.get_head()[0], self.get_head()[1]] if pos[0]-1 < 0: temp_pos = self.game_manager.cells[consts.table_size-1][pos[1]] else: temp_pos = self.game_manager.cells[pos[0]-1][pos[1]] return temp_pos def go_up(self): pos = [self.get_head()[0], self.get_head()[1]] if pos[1]-1 < 0: temp_pos = self.game_manager.cells[pos[0]][consts.table_size-1] else: temp_pos = self.game_manager.cells[pos[0]][pos[1]-1] return temp_pos def go_down(self): pos = [self.get_head()[0], self.get_head()[1]] if pos[1]+1 > consts.table_size-1: temp_pos = self.game_manager.cells[pos[0]][0] else: temp_pos = self.game_manager.cells[pos[0]][pos[1]+1] return temp_pos def go(self, _dir): if _dir == "UP": return self.go_up() elif _dir == "DOWN": return self.go_down() elif _dir == "RIGHT": return self.go_right() elif _dir == "LEFT": return self.go_left()

35.619565

105

0.527617

bb3101fce035b824cfb5db11d70caee8ca8a809b

3,736

py

Python

data/stanfordindoor_dataset.py

aksh1501/DepthAware_CNN_edit

3f6a859ef3f3b7bba1201dc087c860a22d1cd258

[ "MIT" ]

null

data/stanfordindoor_dataset.py

aksh1501/DepthAware_CNN_edit

3f6a859ef3f3b7bba1201dc087c860a22d1cd258

[ "MIT" ]

null

data/stanfordindoor_dataset.py

aksh1501/DepthAware_CNN_edit

3f6a859ef3f3b7bba1201dc087c860a22d1cd258

[ "MIT" ]

null

import os.path import numpy as np import torchvision import torchvision.transforms as transforms import torch import h5py ### CHANGES - START import sys sys.path.insert(0, '/home/jarvis/DepthAwareCNN') ### CHANGES - END from data.base_dataset import * from PIL import Image import math, random import time def make_dataset_fromlst(listfilename): """ NYUlist format: imagepath seglabelpath depthpath HHApath """ images = [] segs = [] depths = [] with open(listfilename) as f: content = f.readlines() for x in content: imgname, segname, depthname = x.strip().split(' ') images += [imgname] segs += [segname] depths += [depthname] return {'images':images, 'segs':segs, 'depths':depths} class StanfordIndoorDataset(BaseDataset): def initialize(self, opt): self.opt = opt np.random.seed(int(time.time())) self.paths_dict = make_dataset_fromlst(opt.list) self.len = len(self.paths_dict['images']) # self.label_weight = torch.Tensor(label_weight) self.datafile = 'stanfordindoor_dataset.py' def __getitem__(self, index): img = np.asarray(Image.open(self.paths_dict['images'][index]))#.astype(np.uint8) depth = np.asarray(Image.open(self.paths_dict['depths'][index])).astype(np.float32)/120. # 1/10 * depth seg = np.asarray(Image.open(self.paths_dict['segs'][index]))-1 params = get_params_sunrgbd(self.opt, seg.shape,maxcrop=0.7, maxscale=1.1) depth_tensor_tranformed = transform(depth, params, normalize=False,istrain=self.opt.isTrain) seg_tensor_tranformed = transform(seg, params, normalize=False,method='nearest',istrain=self.opt.isTrain) if self.opt.inputmode == 'bgr-mean': img_tensor_tranformed = transform(img, params, normalize=False, istrain=self.opt.isTrain, option=1) else: img_tensor_tranformed = transform(img, params, istrain=self.opt.isTrain, option=1) return {'image':img_tensor_tranformed, 'depth':depth_tensor_tranformed, 'seg': seg_tensor_tranformed, 'imgpath': self.paths_dict['segs'][index]} def __len__(self): return self.len def name(self): return 'stanfordindoor_dataset' class StanfordIndoorDataset_val(BaseDataset): def initialize(self, opt): self.opt = opt np.random.seed(8964) self.paths_dict = make_dataset_fromlst(opt.vallist) self.len = len(self.paths_dict['images']) def __getitem__(self, index): img = np.asarray(Image.open(self.paths_dict['images'][index]))#.astype(np.uint8) depth = np.asarray(Image.open(self.paths_dict['depths'][index])).astype(np.float32)/120. # 1/10 * depth seg = np.asarray(Image.open(self.paths_dict['segs'][index]))-1 params = get_params_sunrgbd(self.opt, seg.shape, test=True) depth_tensor_tranformed = transform(depth, params, normalize=False,istrain=self.opt.isTrain) seg_tensor_tranformed = transform(seg, params, normalize=False,method='nearest',istrain=self.opt.isTrain) if self.opt.inputmode == 'bgr-mean': img_tensor_tranformed = transform(img, params, normalize=False, istrain=self.opt.isTrain, option=1) else: img_tensor_tranformed = transform(img, params, istrain=self.opt.isTrain, option=1) return {'image':img_tensor_tranformed, 'depth':depth_tensor_tranformed, 'seg': seg_tensor_tranformed, 'imgpath': self.paths_dict['segs'][index]} def __len__(self): return self.len def name(self): return 'stanfordindoor_dataset'

36.271845

113

0.65712

99363d36c2e610243a250558b9aace120d96c6e6

110,490

py

Python

campaign/controllers.py

nirvanabear/WeVoteServer

f4a6bceaffa1273cb215cc561831a4aaaff19018

[ "MIT" ]

1

2021-05-17T07:52:26.000Z

campaign/controllers.py

nirvanabear/WeVoteServer

f4a6bceaffa1273cb215cc561831a4aaaff19018

[ "MIT" ]

null

campaign/controllers.py

nirvanabear/WeVoteServer

f4a6bceaffa1273cb215cc561831a4aaaff19018

[ "MIT" ]

null

# campaign/controllers.py # Brought to you by We Vote. Be good. # -*- coding: UTF-8 -*- from .models import CampaignX, CampaignXListedByOrganization, CampaignXManager, CampaignXNewsItem, CampaignXOwner, \ CampaignXSupporter, FINAL_ELECTION_DATE_COOL_DOWN import base64 from image.controllers import cache_campaignx_image, create_resized_images import json from io import BytesIO from PIL import Image, ImageOps import re from activity.controllers import update_or_create_activity_notice_seed_for_campaignx_supporter_initial_response from voter.models import VoterManager import wevote_functions.admin from wevote_functions.functions import generate_date_as_integer, positive_value_exists logger = wevote_functions.admin.get_logger(__name__) # Search for in image/controllers.py as well CAMPAIGN_PHOTO_ORIGINAL_MAX_WIDTH = 1200 CAMPAIGN_PHOTO_ORIGINAL_MAX_HEIGHT = 628 CAMPAIGN_PHOTO_LARGE_MAX_WIDTH = 575 CAMPAIGN_PHOTO_LARGE_MAX_HEIGHT = 301 CAMPAIGN_PHOTO_MEDIUM_MAX_WIDTH = 224 CAMPAIGN_PHOTO_MEDIUM_MAX_HEIGHT = 117 CAMPAIGN_PHOTO_SMALL_MAX_WIDTH = 140 CAMPAIGN_PHOTO_SMALL_MAX_HEIGHT = 73 def campaignx_list_retrieve_for_api( # campaignListRetrieve request=None, voter_device_id='', hostname='', recommended_campaigns_for_campaignx_we_vote_id=''): """ :param request: :param voter_device_id: :param hostname: :param recommended_campaigns_for_campaignx_we_vote_id: :return: """ campaignx_display_list = [] status = "" promoted_campaignx_list_returned = True voter_can_vote_for_politicians_list_returned = True voter_owned_campaignx_list_returned = True voter_started_campaignx_list_returned = True voter_supported_campaignx_list_returned = True if positive_value_exists(recommended_campaigns_for_campaignx_we_vote_id): # Do not retrieve certain data if returning recommended campaigns promoted_campaignx_list_returned = False voter_can_vote_for_politicians_list_returned = False voter_owned_campaignx_list_returned = False voter_started_campaignx_list_returned = False voter_supported_campaignx_list_returned = False promoted_campaignx_we_vote_ids = [] voter_can_send_updates_campaignx_we_vote_ids = [] voter_owned_campaignx_we_vote_ids = [] voter_started_campaignx_we_vote_ids = [] voter_supported_campaignx_we_vote_ids = [] voter_manager = VoterManager() voter_results = voter_manager.retrieve_voter_from_voter_device_id(voter_device_id=voter_device_id, read_only=True) if not positive_value_exists(voter_results['voter_found']): status += 'VOTER_WE_VOTE_ID_COULD_NOT_BE_FETCHED ' json_data = { 'status': status, 'success': False, 'campaignx_list': [], } return json_data voter = voter_results['voter'] voter_signed_in_with_email = voter.signed_in_with_email() voter_we_vote_id = voter.we_vote_id from organization.controllers import site_configuration_retrieve_for_api results = site_configuration_retrieve_for_api(hostname) site_owner_organization_we_vote_id = results['organization_we_vote_id'] voter_can_vote_for_politician_we_vote_ids = [] # TODO: Creates excessive slow-down. We need a more optimized solution. # if voter_can_vote_for_politicians_list_returned: # # We need to know all of the politicians this voter can vote for so we can figure out # # if the voter can vote for any politicians in the election # from ballot.controllers import what_voter_can_vote_for # results = what_voter_can_vote_for(request=request, voter_device_id=voter_device_id) # voter_can_vote_for_politician_we_vote_ids = results['voter_can_vote_for_politician_we_vote_ids'] visible_on_this_site_campaignx_we_vote_id_list = [] campaignx_manager = CampaignXManager() if positive_value_exists(recommended_campaigns_for_campaignx_we_vote_id): results = retrieve_recommended_campaignx_list_for_campaignx_we_vote_id( request=request, voter_device_id=voter_device_id, voter_we_vote_id=voter_we_vote_id, campaignx_we_vote_id=recommended_campaigns_for_campaignx_we_vote_id, site_owner_organization_we_vote_id=site_owner_organization_we_vote_id) else: if positive_value_exists(site_owner_organization_we_vote_id): results = campaignx_manager.retrieve_campaignx_list_for_private_label( including_started_by_voter_we_vote_id=voter_we_vote_id, site_owner_organization_we_vote_id=site_owner_organization_we_vote_id) visible_on_this_site_campaignx_we_vote_id_list = results['visible_on_this_site_campaignx_we_vote_id_list'] else: results = campaignx_manager.retrieve_campaignx_list( including_started_by_voter_we_vote_id=voter_we_vote_id) success = results['success'] status += results['status'] campaignx_list = results['campaignx_list'] campaignx_list_found = results['campaignx_list_found'] if success: voter_owned_campaignx_we_vote_ids = campaignx_manager.retrieve_voter_owned_campaignx_we_vote_ids( voter_we_vote_id=voter_we_vote_id, ) voter_can_send_updates_campaignx_we_vote_ids = \ campaignx_manager.retrieve_voter_can_send_updates_campaignx_we_vote_ids( voter_we_vote_id=voter_we_vote_id, ) final_election_date_plus_cool_down = generate_date_as_integer() + FINAL_ELECTION_DATE_COOL_DOWN for campaignx in campaignx_list: viewer_is_owner = campaignx.we_vote_id in voter_owned_campaignx_we_vote_ids final_election_date_in_past = \ final_election_date_plus_cool_down >= campaignx.final_election_date_as_integer \ if positive_value_exists(campaignx.final_election_date_as_integer) else False # Should we promote this campaign on home page? if campaignx.is_still_active and campaignx.is_ok_to_promote_on_we_vote \ and not final_election_date_in_past and not campaignx.is_in_team_review_mode: if positive_value_exists(site_owner_organization_we_vote_id): if campaignx.we_vote_id in visible_on_this_site_campaignx_we_vote_id_list: promoted_campaignx_we_vote_ids.append(campaignx.we_vote_id) else: if campaignx.is_supporters_count_minimum_exceeded(): promoted_campaignx_we_vote_ids.append(campaignx.we_vote_id) campaignx_owner_list = [] campaignx_owner_object_list = campaignx_manager.retrieve_campaignx_owner_list( campaignx_we_vote_id=campaignx.we_vote_id, viewer_is_owner=viewer_is_owner) for campaignx_owner in campaignx_owner_object_list: campaign_owner_dict = { 'feature_this_profile_image': campaignx_owner.feature_this_profile_image, 'organization_name': campaignx_owner.organization_name, 'organization_we_vote_id': campaignx_owner.organization_we_vote_id, 'visible_to_public': campaignx_owner.visible_to_public, 'we_vote_hosted_profile_image_url_medium': campaignx_owner.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': campaignx_owner.we_vote_hosted_profile_image_url_tiny, } campaignx_owner_list.append(campaign_owner_dict) if campaignx.politician_starter_list_serialized: campaignx_politician_starter_list = json.loads(campaignx.politician_starter_list_serialized) else: campaignx_politician_starter_list = [] campaignx_politician_list_modified = [] campaignx_politician_list_exists = False campaignx_politician_list = campaignx_manager.retrieve_campaignx_politician_list( campaignx_we_vote_id=campaignx.we_vote_id, ) for campaignx_politician in campaignx_politician_list: campaignx_politician_list_exists = True campaignx_politician_dict = { 'campaignx_politician_id': campaignx_politician.id, 'politician_name': campaignx_politician.politician_name, 'politician_we_vote_id': campaignx_politician.politician_we_vote_id, 'state_code': campaignx_politician.state_code, 'we_vote_hosted_profile_image_url_large': campaignx_politician.we_vote_hosted_profile_image_url_large, 'we_vote_hosted_profile_image_url_medium': campaignx_politician.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': campaignx_politician.we_vote_hosted_profile_image_url_tiny, } campaignx_politician_list_modified.append(campaignx_politician_dict) # Get list of SEO Friendly Paths related to this campaignX. For most campaigns, there will only be one. seo_friendly_path_list = campaignx_manager.retrieve_seo_friendly_path_simple_list( campaignx_we_vote_id=campaignx.we_vote_id, ) voter_campaignx_supporter_dict = {} supporter_results = campaignx_manager.retrieve_campaignx_supporter( campaignx_we_vote_id=campaignx.we_vote_id, voter_we_vote_id=voter_we_vote_id, read_only=True) if supporter_results['success'] and supporter_results['campaignx_supporter_found']: campaignx_supporter = supporter_results['campaignx_supporter'] chip_in_total = 'none' date_last_changed_string = '' date_supported_string = '' try: date_last_changed_string = campaignx_supporter.date_last_changed.strftime('%Y-%m-%d %H:%M:%S') date_supported_string = campaignx_supporter.date_supported.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " try: from stripe_donations.models import StripeManager chip_in_total = StripeManager.retrieve_chip_in_total(voter_we_vote_id, campaignx.we_vote_id) except Exception as e: status += "LIST_RETRIEVE_CHIP_IN_TOTAL_ERROR: " + str(e) + " " voter_campaignx_supporter_dict = { 'campaign_supported': campaignx_supporter.campaign_supported, 'campaignx_we_vote_id': campaignx_supporter.campaignx_we_vote_id, 'chip_in_total': chip_in_total, 'date_last_changed': date_last_changed_string, 'date_supported': date_supported_string, 'id': campaignx_supporter.id, 'organization_we_vote_id': campaignx_supporter.organization_we_vote_id, 'supporter_endorsement': campaignx_supporter.supporter_endorsement, 'supporter_name': campaignx_supporter.supporter_name, 'visible_to_public': campaignx_supporter.visible_to_public, 'voter_we_vote_id': campaignx_supporter.voter_we_vote_id, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_medium': campaignx_supporter.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_photo_image_url_tiny': campaignx_supporter.we_vote_hosted_profile_image_url_tiny, } if hasattr(campaignx, 'visible_on_this_site'): visible_on_this_site = campaignx.visible_on_this_site else: visible_on_this_site = True # If smaller sizes weren't stored, use large image if campaignx.we_vote_hosted_campaign_photo_medium_url: we_vote_hosted_campaign_photo_medium_url = campaignx.we_vote_hosted_campaign_photo_medium_url else: we_vote_hosted_campaign_photo_medium_url = campaignx.we_vote_hosted_campaign_photo_large_url if campaignx.we_vote_hosted_campaign_photo_small_url: we_vote_hosted_campaign_photo_small_url = campaignx.we_vote_hosted_campaign_photo_small_url else: we_vote_hosted_campaign_photo_small_url = campaignx.we_vote_hosted_campaign_photo_large_url supporters_count_next_goal = campaignx_manager.fetch_supporters_count_next_goal( supporters_count=campaignx.supporters_count, supporters_count_victory_goal=campaignx.supporters_count_victory_goal) one_campaignx = { 'campaign_description': campaignx.campaign_description, 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': campaignx_politician_list_modified, 'campaignx_politician_list_exists': campaignx_politician_list_exists, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaign_title': campaignx.campaign_title, 'campaignx_we_vote_id': campaignx.we_vote_id, 'final_election_date_as_integer': campaignx.final_election_date_as_integer, 'final_election_date_in_past': final_election_date_in_past, 'in_draft_mode': campaignx.in_draft_mode, 'is_blocked_by_we_vote': campaignx.is_blocked_by_we_vote, 'is_blocked_by_we_vote_reason': campaignx.is_blocked_by_we_vote_reason, 'is_in_team_review_mode': campaignx.is_in_team_review_mode, 'is_supporters_count_minimum_exceeded': campaignx.is_supporters_count_minimum_exceeded(), 'seo_friendly_path': campaignx.seo_friendly_path, 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': campaignx.supporters_count, 'supporters_count_next_goal': supporters_count_next_goal, 'supporters_count_victory_goal': campaignx.supporters_count_victory_goal, 'visible_on_this_site': visible_on_this_site, 'voter_campaignx_supporter': voter_campaignx_supporter_dict, 'voter_can_send_updates_to_campaignx': campaignx.we_vote_id in voter_can_send_updates_campaignx_we_vote_ids, 'voter_is_campaignx_owner': viewer_is_owner, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': campaignx.we_vote_hosted_campaign_photo_large_url, 'we_vote_hosted_campaign_photo_medium_url': we_vote_hosted_campaign_photo_medium_url, 'we_vote_hosted_campaign_photo_small_url': we_vote_hosted_campaign_photo_small_url, } campaignx_display_list.append(one_campaignx) if success and voter_started_campaignx_list_returned: results = campaignx_manager.retrieve_campaignx_we_vote_id_list_started_by_voter( started_by_voter_we_vote_id=voter_we_vote_id) if not results['success']: voter_started_campaignx_list_returned = False else: voter_started_campaignx_we_vote_ids = results['campaignx_we_vote_id_list'] if success and voter_supported_campaignx_list_returned: supporter_list_results = campaignx_manager.retrieve_campaignx_supporter_list( voter_we_vote_id=voter_we_vote_id, limit=0, require_visible_to_public=False, read_only=True) if supporter_list_results['supporter_list_found']: supporter_list = supporter_list_results['supporter_list'] for one_supporter in supporter_list: voter_supported_campaignx_we_vote_ids.append(one_supporter.campaignx_we_vote_id) json_data = { 'status': status, 'success': success, 'campaignx_list': campaignx_display_list, 'campaignx_list_found': campaignx_list_found, } if promoted_campaignx_list_returned: json_data['promoted_campaignx_list_returned'] = True json_data['promoted_campaignx_we_vote_ids'] = promoted_campaignx_we_vote_ids if positive_value_exists(recommended_campaigns_for_campaignx_we_vote_id): json_data['recommended_campaigns_for_campaignx_we_vote_id'] = recommended_campaigns_for_campaignx_we_vote_id if voter_can_vote_for_politicians_list_returned: json_data['voter_can_vote_for_politicians_list_returned'] = True json_data['voter_can_vote_for_politician_we_vote_ids'] = voter_can_vote_for_politician_we_vote_ids if voter_owned_campaignx_list_returned: json_data['voter_owned_campaignx_list_returned'] = True json_data['voter_owned_campaignx_we_vote_ids'] = voter_owned_campaignx_we_vote_ids json_data['voter_can_send_updates_campaignx_we_vote_ids'] = voter_can_send_updates_campaignx_we_vote_ids if voter_started_campaignx_list_returned: json_data['voter_started_campaignx_list_returned'] = True json_data['voter_started_campaignx_we_vote_ids'] = voter_started_campaignx_we_vote_ids if voter_supported_campaignx_list_returned: json_data['voter_supported_campaignx_list_returned'] = True json_data['voter_supported_campaignx_we_vote_ids'] = voter_supported_campaignx_we_vote_ids return json_data def campaignx_news_item_save_for_api( # campaignNewsItemSave campaign_news_subject='', campaign_news_subject_changed=False, campaign_news_text='', campaign_news_text_changed=False, campaignx_news_item_we_vote_id='', campaignx_we_vote_id='', in_draft_mode=False, in_draft_mode_changed=False, send_now=False, visible_to_public=False, visible_to_public_changed=False, voter_device_id=''): status = '' success = True voter_manager = VoterManager() voter_results = voter_manager.retrieve_voter_from_voter_device_id(voter_device_id) if voter_results['voter_found']: voter = voter_results['voter'] voter_we_vote_id = voter.we_vote_id linked_organization_we_vote_id = voter.linked_organization_we_vote_id else: status += "VALID_VOTER_ID_MISSING " results = { 'status': status, 'success': False, 'campaign_news_subject': '', 'campaign_news_text': '', 'campaignx_news_item_we_vote_id': '', 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_posted': '', 'date_sent_to_email': '', 'in_draft_mode': True, 'organization_we_vote_id': '', 'speaker_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results if not positive_value_exists(campaignx_we_vote_id): status += "CAMPAIGNX_WE_VOTE_ID_REQUIRED " results = { 'status': status, 'success': False, 'campaign_news_subject': '', 'campaign_news_text': '', 'campaignx_news_item_we_vote_id': '', 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_posted': '', 'date_sent_to_email': '', 'in_draft_mode': True, 'organization_we_vote_id': '', 'speaker_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results campaignx_manager = CampaignXManager() voter_is_campaignx_owner = campaignx_manager.is_voter_campaignx_owner( campaignx_we_vote_id=campaignx_we_vote_id, voter_we_vote_id=voter_we_vote_id, ) if not positive_value_exists(voter_is_campaignx_owner): status += "VOTER_DOES_NOT_HAVE_RIGHT_TO_CREATE_NEWS_ITEM " results = { 'status': status, 'success': False, 'campaign_news_subject': '', 'campaign_news_text': '', 'campaignx_news_item_we_vote_id': '', 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_posted': '', 'date_sent_to_email': '', 'in_draft_mode': True, 'organization_we_vote_id': '', 'speaker_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results update_values = { 'campaign_news_subject': campaign_news_subject, 'campaign_news_subject_changed': campaign_news_subject_changed, 'campaign_news_text': campaign_news_text, 'campaign_news_text_changed': campaign_news_text_changed, 'in_draft_mode': in_draft_mode, 'in_draft_mode_changed': in_draft_mode_changed, 'visible_to_public': visible_to_public, 'visible_to_public_changed': visible_to_public_changed, } create_results = campaignx_manager.update_or_create_campaignx_news_item( campaignx_news_item_we_vote_id=campaignx_news_item_we_vote_id, campaignx_we_vote_id=campaignx_we_vote_id, organization_we_vote_id=linked_organization_we_vote_id, voter_we_vote_id=voter_we_vote_id, update_values=update_values, ) status += create_results['status'] campaignx_news_item = None campaignx_news_item_found = False date_sent_to_email_found = False if create_results['campaignx_news_item_found']: campaignx_news_item = create_results['campaignx_news_item'] date_sent_to_email_found = positive_value_exists(campaignx_news_item.date_sent_to_email) campaignx_news_item_found = True send_campaignx_news_item = positive_value_exists(send_now) if campaignx_news_item_found: results = campaignx_manager.retrieve_campaignx( campaignx_we_vote_id=campaignx_we_vote_id, read_only=True, ) if results['campaignx_found']: campaignx = results['campaignx'] from activity.controllers import update_or_create_activity_notice_seed_for_campaignx_news_item activity_results = update_or_create_activity_notice_seed_for_campaignx_news_item( campaignx_news_item_we_vote_id=campaignx_news_item.we_vote_id, campaignx_we_vote_id=campaignx.we_vote_id, send_campaignx_news_item=send_campaignx_news_item, speaker_name=campaignx_news_item.speaker_name, speaker_organization_we_vote_id=campaignx_news_item.organization_we_vote_id, speaker_voter_we_vote_id=campaignx_news_item.voter_we_vote_id, speaker_profile_image_url_medium=campaignx_news_item.we_vote_hosted_profile_image_url_medium, speaker_profile_image_url_tiny=campaignx_news_item.we_vote_hosted_profile_image_url_tiny, statement_subject=campaignx_news_item.campaign_news_subject, statement_text=campaignx_news_item.campaign_news_text) status += activity_results['status'] if activity_results['success'] and send_campaignx_news_item and not date_sent_to_email_found: if activity_results['activity_notice_seed_found']: activity_notice_seed = activity_results['activity_notice_seed'] campaignx_news_item.date_sent_to_email = activity_notice_seed.date_sent_to_email campaignx_news_item.save() if campaignx_news_item_found: date_last_changed_string = '' date_posted_string = '' date_sent_to_email_string = '' try: date_last_changed_string = campaignx_news_item.date_last_changed.strftime('%Y-%m-%d %H:%M:%S') date_posted_string = campaignx_news_item.date_posted.strftime('%Y-%m-%d %H:%M:%S') date_sent_to_email_string = campaignx_news_item.date_posted.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " results = { 'status': status, 'success': success, 'campaign_news_subject': campaignx_news_item.campaign_news_subject, 'campaign_news_text': campaignx_news_item.campaign_news_text, 'campaignx_news_item_we_vote_id': campaignx_news_item.we_vote_id, 'campaignx_we_vote_id': campaignx_news_item.campaignx_we_vote_id, 'date_last_changed': date_last_changed_string, 'date_posted': date_posted_string, 'date_sent_to_email': date_sent_to_email_string, 'in_draft_mode': campaignx_news_item.in_draft_mode, 'organization_we_vote_id': campaignx_news_item.organization_we_vote_id, 'speaker_name': campaignx_news_item.speaker_name, 'voter_we_vote_id': campaignx_news_item.voter_we_vote_id, 'we_vote_hosted_profile_photo_image_url_medium': campaignx_news_item.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_photo_image_url_tiny': campaignx_news_item.we_vote_hosted_profile_image_url_tiny, } return results else: status += "CAMPAIGNX_NEWS_ITEM_NOT_FOUND_ERROR " results = { 'status': status, 'success': False, 'campaign_news_subject': '', 'campaign_news_text': '', 'campaignx_news_item_we_vote_id': '', 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_posted': '', 'date_sent_to_email': '', 'in_draft_mode': True, 'organization_we_vote_id': '', 'speaker_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results def campaignx_retrieve_for_api( # campaignRetrieve & campaignRetrieveAsOwner (No CDN) request=None, voter_device_id='', campaignx_we_vote_id='', seo_friendly_path='', as_owner=False, hostname=''): status = '' campaignx_owner_list = [] campaignx_politician_starter_list = [] seo_friendly_path_list = [] voter_signed_in_with_email = False voter_we_vote_id = '' campaignx_manager = CampaignXManager() voter_manager = VoterManager() voter_results = voter_manager.retrieve_voter_from_voter_device_id(voter_device_id) if voter_results['voter_found']: voter = voter_results['voter'] voter_signed_in_with_email = voter.signed_in_with_email() voter_we_vote_id = voter.we_vote_id if positive_value_exists(as_owner): if not positive_value_exists(voter_we_vote_id): status += "VALID_VOTER_ID_MISSING " results = { 'status': status, 'success': False, 'campaign_description': '', 'campaign_title': '', 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': [], 'campaignx_politician_list_exists': False, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': '', 'final_election_date_as_integer': None, 'final_election_date_in_past': False, 'in_draft_mode': True, 'is_blocked_by_we_vote': False, 'is_blocked_by_we_vote_reason': '', 'is_supporters_count_minimum_exceeded': False, 'seo_friendly_path': '', 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': 0, 'supporters_count_next_goal': 0, 'supporters_count_victory_goal': 0, 'visible_on_this_site': False, 'voter_campaignx_supporter': {}, 'voter_can_send_updates_to_campaignx': False, 'voter_is_campaignx_owner': False, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': '', 'we_vote_hosted_campaign_photo_medium_url': '', 'we_vote_hosted_campaign_photo_small_url': '', } return results results = campaignx_manager.retrieve_campaignx_as_owner( campaignx_we_vote_id=campaignx_we_vote_id, seo_friendly_path=seo_friendly_path, voter_we_vote_id=voter_we_vote_id, read_only=True, ) voter_is_campaignx_owner = results['viewer_is_owner'] else: results = campaignx_manager.retrieve_campaignx( campaignx_we_vote_id=campaignx_we_vote_id, seo_friendly_path=seo_friendly_path, voter_we_vote_id=voter_we_vote_id, read_only=True, ) voter_is_campaignx_owner = results['viewer_is_owner'] status += results['status'] if not results['success']: status += "CAMPAIGNX_RETRIEVE_ERROR " results = { 'status': status, 'success': False, 'campaign_description': '', 'campaign_title': '', 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': [], 'campaignx_politician_list_exists': False, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': '', 'in_draft_mode': True, 'is_supporters_count_minimum_exceeded': False, 'seo_friendly_path': '', 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': 0, 'supporters_count_next_goal': 0, 'supporters_count_victory_goal': 0, 'visible_on_this_site': False, 'voter_campaignx_supporter': {}, 'voter_can_send_updates_to_campaignx': False, 'voter_is_campaignx_owner': False, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': '', 'we_vote_hosted_campaign_photo_medium_url': '', 'we_vote_hosted_campaign_photo_small_url': '', } return results elif not results['campaignx_found']: status += "CAMPAIGNX_NOT_FOUND: " status += results['status'] + " " results = { 'status': status, 'success': True, 'campaign_description': '', 'campaign_title': '', 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': [], 'campaignx_politician_list_exists': False, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': '', 'final_election_date_as_integer': None, 'final_election_date_in_past': False, 'in_draft_mode': True, 'is_blocked_by_we_vote': False, 'is_blocked_by_we_vote_reason': '', 'is_supporters_count_minimum_exceeded': False, 'seo_friendly_path': '', 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': 0, 'supporters_count_next_goal': 0, 'supporters_count_victory_goal': 0, 'visible_on_this_site': False, 'voter_campaignx_supporter': {}, 'voter_can_send_updates_to_campaignx': False, 'voter_is_campaignx_owner': False, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': '', 'we_vote_hosted_campaign_photo_medium_url': '', 'we_vote_hosted_campaign_photo_small_url': '', } return results campaignx = results['campaignx'] campaignx_owner_list = results['campaignx_owner_list'] seo_friendly_path_list = results['seo_friendly_path_list'] # Get campaignx news items / updates campaignx_news_item_list = [] news_item_list_results = campaignx_manager.retrieve_campaignx_news_item_list( campaignx_we_vote_id=campaignx.we_vote_id, read_only=True, voter_is_campaignx_owner=voter_is_campaignx_owner) if news_item_list_results['campaignx_news_item_list_found']: news_item_list = news_item_list_results['campaignx_news_item_list'] for news_item in news_item_list: date_last_changed_string = '' date_posted_string = '' date_sent_to_email_string = '' try: date_last_changed_string = news_item.date_last_changed.strftime('%Y-%m-%d %H:%M:%S') date_posted_string = news_item.date_posted.strftime('%Y-%m-%d %H:%M:%S') if positive_value_exists(news_item.date_sent_to_email): date_sent_to_email_string = news_item.date_sent_to_email.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " one_news_item_dict = { 'campaign_news_subject': news_item.campaign_news_subject, 'campaign_news_text': news_item.campaign_news_text, 'campaignx_news_item_we_vote_id': news_item.we_vote_id, 'campaignx_we_vote_id': news_item.campaignx_we_vote_id, 'date_last_changed': date_last_changed_string, 'date_posted': date_posted_string, 'date_sent_to_email': date_sent_to_email_string, 'in_draft_mode': news_item.in_draft_mode, 'organization_we_vote_id': news_item.organization_we_vote_id, 'speaker_name': news_item.speaker_name, 'visible_to_public': news_item.visible_to_public, 'voter_we_vote_id': news_item.voter_we_vote_id, 'we_vote_hosted_profile_image_url_medium': news_item.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': news_item.we_vote_hosted_profile_image_url_tiny, } campaignx_news_item_list.append(one_news_item_dict) from organization.controllers import site_configuration_retrieve_for_api site_results = site_configuration_retrieve_for_api(hostname) site_owner_organization_we_vote_id = site_results['organization_we_vote_id'] if positive_value_exists(site_owner_organization_we_vote_id): try: visible_on_this_site_campaignx_we_vote_id_list = \ campaignx_manager.retrieve_visible_on_this_site_campaignx_simple_list( site_owner_organization_we_vote_id=site_owner_organization_we_vote_id) if campaignx.we_vote_id in visible_on_this_site_campaignx_we_vote_id_list: campaignx.visible_on_this_site = True else: campaignx.visible_on_this_site = False except Exception as e: success = False status += "RETRIEVE_CAMPAIGNX_LIST_FOR_PRIVATE_LABEL_FAILED: " + str(e) + " " else: campaignx.visible_on_this_site = True voter_can_send_updates_campaignx_we_vote_ids = \ campaignx_manager.retrieve_voter_can_send_updates_campaignx_we_vote_ids( voter_we_vote_id=voter_we_vote_id, ) if campaignx.politician_starter_list_serialized: campaignx_politician_starter_list = json.loads(campaignx.politician_starter_list_serialized) else: campaignx_politician_starter_list = [] campaignx_politician_list_modified = [] campaignx_politician_list_exists = False campaignx_politician_list = campaignx_manager.retrieve_campaignx_politician_list( campaignx_we_vote_id=campaignx.we_vote_id, ) # We need to know all of the politicians this voter can vote for so we can figure out # if the voter can vote for any politicians in the election from ballot.controllers import what_voter_can_vote_for results = what_voter_can_vote_for(request=request, voter_device_id=voter_device_id) voter_can_vote_for_politician_we_vote_ids = results['voter_can_vote_for_politician_we_vote_ids'] for campaignx_politician in campaignx_politician_list: campaignx_politician_list_exists = True campaignx_politician_dict = { 'campaignx_politician_id': campaignx_politician.id, 'politician_name': campaignx_politician.politician_name, 'politician_we_vote_id': campaignx_politician.politician_we_vote_id, 'state_code': campaignx_politician.state_code, 'we_vote_hosted_profile_image_url_large': campaignx_politician.we_vote_hosted_profile_image_url_large, 'we_vote_hosted_profile_image_url_medium': campaignx_politician.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': campaignx_politician.we_vote_hosted_profile_image_url_tiny, } campaignx_politician_list_modified.append(campaignx_politician_dict) supporter_results = campaignx_manager.retrieve_campaignx_supporter( campaignx_we_vote_id=campaignx.we_vote_id, voter_we_vote_id=voter_we_vote_id, read_only=True) if supporter_results['success'] and supporter_results['campaignx_supporter_found']: campaignx_supporter = supporter_results['campaignx_supporter'] chip_in_total = 'none' date_last_changed_string = '' date_supported_string = '' try: date_last_changed_string = campaignx_supporter.date_last_changed.strftime('%Y-%m-%d %H:%M:%S') date_supported_string = campaignx_supporter.date_supported.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " try: from stripe_donations.models import StripeManager chip_in_total = StripeManager.retrieve_chip_in_total(voter_we_vote_id, campaignx.we_vote_id) except Exception as e: status += "RETRIEVE_CHIP_IN_TOTAL_ERROR: " + str(e) + " " voter_campaignx_supporter_dict = { 'campaign_supported': campaignx_supporter.campaign_supported, 'campaignx_we_vote_id': campaignx_supporter.campaignx_we_vote_id, 'chip_in_total': chip_in_total, 'date_last_changed': date_last_changed_string, 'date_supported': date_supported_string, 'id': campaignx_supporter.id, 'organization_we_vote_id': campaignx_supporter.organization_we_vote_id, 'supporter_endorsement': campaignx_supporter.supporter_endorsement, 'supporter_name': campaignx_supporter.supporter_name, 'visible_to_public': campaignx_supporter.visible_to_public, 'voter_we_vote_id': campaignx_supporter.voter_we_vote_id, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_image_url_medium': campaignx_supporter.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': campaignx_supporter.we_vote_hosted_profile_image_url_tiny, } else: voter_campaignx_supporter_dict = {} # Get most recent supporters latest_campaignx_supporter_list = [] supporter_list_results = campaignx_manager.retrieve_campaignx_supporter_list( campaignx_we_vote_id=campaignx.we_vote_id, limit=7, read_only=True, require_visible_to_public=True) if supporter_list_results['supporter_list_found']: supporter_list = supporter_list_results['supporter_list'] for campaignx_supporter in supporter_list: date_supported_string = '' try: date_supported_string = campaignx_supporter.date_supported.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " one_supporter_dict = { 'id': campaignx_supporter.id, 'campaign_supported': campaignx_supporter.campaign_supported, 'campaignx_we_vote_id': campaignx_supporter.campaignx_we_vote_id, 'date_supported': date_supported_string, 'organization_we_vote_id': campaignx_supporter.organization_we_vote_id, 'supporter_endorsement': campaignx_supporter.supporter_endorsement, 'supporter_name': campaignx_supporter.supporter_name, 'voter_we_vote_id': campaignx_supporter.voter_we_vote_id, 'we_vote_hosted_profile_image_url_medium': campaignx_supporter.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': campaignx_supporter.we_vote_hosted_profile_image_url_tiny, } latest_campaignx_supporter_list.append(one_supporter_dict) # Get most recent supporter_endorsements latest_campaignx_supporter_endorsement_list = [] supporter_list_results = campaignx_manager.retrieve_campaignx_supporter_list( campaignx_we_vote_id=campaignx.we_vote_id, limit=10, require_supporter_endorsement=True, read_only=True) if supporter_list_results['supporter_list_found']: supporter_list = supporter_list_results['supporter_list'] for campaignx_supporter in supporter_list: date_supported_string = '' try: date_supported_string = campaignx_supporter.date_supported.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " one_supporter_dict = { 'id': campaignx_supporter.id, 'campaign_supported': campaignx_supporter.campaign_supported, 'campaignx_we_vote_id': campaignx_supporter.campaignx_we_vote_id, 'date_supported': date_supported_string, 'organization_we_vote_id': campaignx_supporter.organization_we_vote_id, 'supporter_endorsement': campaignx_supporter.supporter_endorsement, 'supporter_name': campaignx_supporter.supporter_name, 'voter_we_vote_id': campaignx_supporter.voter_we_vote_id, 'we_vote_hosted_profile_image_url_medium': campaignx_supporter.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': campaignx_supporter.we_vote_hosted_profile_image_url_tiny, } latest_campaignx_supporter_endorsement_list.append(one_supporter_dict) # If smaller sizes weren't stored, use large image if campaignx.we_vote_hosted_campaign_photo_medium_url: we_vote_hosted_campaign_photo_medium_url = campaignx.we_vote_hosted_campaign_photo_medium_url else: we_vote_hosted_campaign_photo_medium_url = campaignx.we_vote_hosted_campaign_photo_large_url if campaignx.we_vote_hosted_campaign_photo_small_url: we_vote_hosted_campaign_photo_small_url = campaignx.we_vote_hosted_campaign_photo_small_url else: we_vote_hosted_campaign_photo_small_url = campaignx.we_vote_hosted_campaign_photo_large_url supporters_count_next_goal = campaignx_manager.fetch_supporters_count_next_goal( supporters_count=campaignx.supporters_count, supporters_count_victory_goal=campaignx.supporters_count_victory_goal) final_election_date_plus_cool_down = generate_date_as_integer() + FINAL_ELECTION_DATE_COOL_DOWN final_election_date_in_past = \ final_election_date_plus_cool_down >= campaignx.final_election_date_as_integer \ if positive_value_exists(campaignx.final_election_date_as_integer) else False results = { 'status': status, 'success': True, 'campaign_description': campaignx.campaign_description, 'campaign_title': campaignx.campaign_title, 'campaignx_news_item_list': campaignx_news_item_list, 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': campaignx_politician_list_modified, 'campaignx_politician_list_exists': campaignx_politician_list_exists, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': campaignx.we_vote_id, 'final_election_date_as_integer': campaignx.final_election_date_as_integer, 'final_election_date_in_past': final_election_date_in_past, 'in_draft_mode': campaignx.in_draft_mode, 'is_blocked_by_we_vote': campaignx.is_blocked_by_we_vote, 'is_blocked_by_we_vote_reason': campaignx.is_blocked_by_we_vote_reason, 'is_supporters_count_minimum_exceeded': campaignx.is_supporters_count_minimum_exceeded(), 'latest_campaignx_supporter_endorsement_list': latest_campaignx_supporter_endorsement_list, 'latest_campaignx_supporter_list': latest_campaignx_supporter_list, 'seo_friendly_path': campaignx.seo_friendly_path, 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': campaignx.supporters_count, 'supporters_count_next_goal': supporters_count_next_goal, 'supporters_count_victory_goal': campaignx.supporters_count_victory_goal, 'visible_on_this_site': campaignx.visible_on_this_site, 'voter_campaignx_supporter': voter_campaignx_supporter_dict, 'voter_can_send_updates_to_campaignx': campaignx.we_vote_id in voter_can_send_updates_campaignx_we_vote_ids, 'voter_can_vote_for_politician_we_vote_ids': voter_can_vote_for_politician_we_vote_ids, 'voter_is_campaignx_owner': voter_is_campaignx_owner, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': campaignx.we_vote_hosted_campaign_photo_large_url, 'we_vote_hosted_campaign_photo_medium_url': we_vote_hosted_campaign_photo_medium_url, 'we_vote_hosted_campaign_photo_small_url': we_vote_hosted_campaign_photo_small_url, } return results def campaignx_save_for_api( # campaignSave & campaignStartSave campaign_description='', campaign_description_changed=False, in_draft_mode=False, in_draft_mode_changed=False, campaign_photo_from_file_reader='', campaign_photo_changed=False, campaign_photo_delete=False, campaign_photo_delete_changed=False, campaign_title='', campaign_title_changed=False, campaignx_we_vote_id='', politician_delete_list_serialized='', politician_starter_list_serialized='', politician_starter_list_changed=False, voter_device_id=''): status = '' success = True campaignx_owner_list = [] campaignx_politician_starter_list = [] seo_friendly_path_list = [] voter_signed_in_with_email = False voter_manager = VoterManager() voter_results = voter_manager.retrieve_voter_from_voter_device_id(voter_device_id) if voter_results['voter_found']: voter = voter_results['voter'] voter_signed_in_with_email = voter.signed_in_with_email() voter_we_vote_id = voter.we_vote_id linked_organization_we_vote_id = voter.linked_organization_we_vote_id else: status += "VALID_VOTER_ID_MISSING " results = { 'status': status, 'success': False, 'campaign_description': '', 'campaign_title': '', 'final_election_date_as_integer': None, 'final_election_date_in_past': False, 'in_draft_mode': True, 'is_blocked_by_we_vote': False, 'is_blocked_by_we_vote_reason': '', 'is_supporters_count_minimum_exceeded': False, 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': [], 'campaignx_politician_list_exists': False, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': '', 'seo_friendly_path': '', 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': 0, 'supporters_count_next_goal': 0, 'supporters_count_victory_goal': 0, 'visible_on_this_site': False, 'voter_can_send_updates_to_campaignx': False, 'voter_is_campaignx_owner': False, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': '', 'we_vote_hosted_campaign_photo_medium_url': '', 'we_vote_hosted_campaign_photo_small_url': '', } return results if positive_value_exists(in_draft_mode_changed) and not positive_value_exists(in_draft_mode): # Make sure organization object has necessary images needed for CampaignXOwner and CampaignXSupporter from organization.models import OrganizationManager organization_manager = OrganizationManager() organization_results = \ organization_manager.retrieve_organization_from_we_vote_id(linked_organization_we_vote_id) organization_changed = False if organization_results['organization_found']: try: organization = organization_results['organization'] if not positive_value_exists(organization.we_vote_hosted_profile_image_url_tiny) and \ positive_value_exists(voter.we_vote_hosted_profile_image_url_tiny): organization.we_vote_hosted_profile_image_url_tiny = voter.we_vote_hosted_profile_image_url_tiny organization_changed = True if not positive_value_exists(organization.we_vote_hosted_profile_image_url_medium) and \ positive_value_exists(voter.we_vote_hosted_profile_image_url_medium): organization.we_vote_hosted_profile_image_url_medium = voter.we_vote_hosted_profile_image_url_medium organization_changed = True if not positive_value_exists(organization.we_vote_hosted_profile_image_url_large) and \ positive_value_exists(voter.we_vote_hosted_profile_image_url_large): organization.we_vote_hosted_profile_image_url_large = voter.we_vote_hosted_profile_image_url_large organization_changed = True if organization_changed: organization.save() except Exception as e: status += "COULD_NOT_UPDATE_ORGANIZATION_FROM_VOTER: " + str(e) + " " # To publish a campaign, voter must be signed in with an email address if not voter.signed_in_with_email(): status += "MUST_BE_SIGNED_IN_WITH_EMAIL " results = { 'status': status, 'success': False, 'campaign_description': '', 'campaign_title': '', 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': [], 'campaignx_politician_list_exists': False, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': '', 'final_election_date_as_integer': None, 'final_election_date_in_past': False, 'in_draft_mode': True, 'is_blocked_by_we_vote': False, 'is_blocked_by_we_vote_reason': '', 'is_supporters_count_minimum_exceeded': False, 'seo_friendly_path': '', 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': 0, 'supporters_count_next_goal': 0, 'supporters_count_victory_goal': 0, 'visible_on_this_site': False, 'voter_can_send_updates_to_campaignx': False, 'voter_is_campaignx_owner': False, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': '', 'we_vote_hosted_campaign_photo_medium_url': '', 'we_vote_hosted_campaign_photo_small_url': '', } return results campaignx_manager = CampaignXManager() viewer_is_owner = False if positive_value_exists(campaignx_we_vote_id): viewer_is_owner = campaignx_manager.is_voter_campaignx_owner( campaignx_we_vote_id=campaignx_we_vote_id, voter_we_vote_id=voter_we_vote_id) if not positive_value_exists(viewer_is_owner): status += "VOTER_IS_NOT_OWNER_OF_CAMPAIGNX " results = { 'status': status, 'success': False, 'campaign_description': '', 'campaign_title': '', 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': [], 'campaignx_politician_list_exists': False, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': '', 'final_election_date_as_integer': None, 'final_election_date_in_past': False, 'in_draft_mode': False, 'is_blocked_by_we_vote': False, 'is_blocked_by_we_vote_reason': '', 'is_supporters_count_minimum_exceeded': False, 'seo_friendly_path': '', 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': 0, 'supporters_count_next_goal': 0, 'supporters_count_victory_goal': 0, 'visible_on_this_site': False, 'voter_can_send_updates_to_campaignx': False, 'voter_is_campaignx_owner': False, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': '', 'we_vote_hosted_campaign_photo_medium_url': '', 'we_vote_hosted_campaign_photo_small_url': '', } return results # Save campaign_photo_from_file_reader and get back we_vote_hosted_campaign_photo_original_url we_vote_hosted_campaign_photo_large_url = None we_vote_hosted_campaign_photo_medium_url = None we_vote_hosted_campaign_photo_original_url = None we_vote_hosted_campaign_photo_small_url = None if campaign_photo_changed and campaign_photo_from_file_reader: photo_results = campaignx_save_photo_from_file_reader( campaignx_we_vote_id=campaignx_we_vote_id, campaign_photo_from_file_reader=campaign_photo_from_file_reader) if photo_results['we_vote_hosted_campaign_photo_original_url']: we_vote_hosted_campaign_photo_original_url = photo_results['we_vote_hosted_campaign_photo_original_url'] # Now we want to resize to a large version create_resized_image_results = create_resized_images( campaignx_we_vote_id=campaignx_we_vote_id, campaignx_photo_url_https=we_vote_hosted_campaign_photo_original_url) we_vote_hosted_campaign_photo_large_url = \ create_resized_image_results['cached_resized_image_url_large'] we_vote_hosted_campaign_photo_medium_url = \ create_resized_image_results['cached_resized_image_url_medium'] we_vote_hosted_campaign_photo_small_url = \ create_resized_image_results['cached_resized_image_url_tiny'] update_values = { 'campaign_description': campaign_description, 'campaign_description_changed': campaign_description_changed, 'in_draft_mode': in_draft_mode, 'in_draft_mode_changed': in_draft_mode_changed, 'campaign_photo_changed': campaign_photo_changed, 'campaign_photo_delete': campaign_photo_delete, 'campaign_photo_delete_changed': campaign_photo_delete_changed, 'campaign_title': campaign_title, 'campaign_title_changed': campaign_title_changed, 'politician_delete_list_serialized': politician_delete_list_serialized, 'politician_starter_list_changed': politician_starter_list_changed, 'politician_starter_list_serialized': politician_starter_list_serialized, 'we_vote_hosted_campaign_photo_large_url': we_vote_hosted_campaign_photo_large_url, 'we_vote_hosted_campaign_photo_medium_url': we_vote_hosted_campaign_photo_medium_url, 'we_vote_hosted_campaign_photo_small_url': we_vote_hosted_campaign_photo_small_url, 'we_vote_hosted_campaign_photo_original_url': we_vote_hosted_campaign_photo_original_url, } create_results = campaignx_manager.update_or_create_campaignx( campaignx_we_vote_id=campaignx_we_vote_id, voter_we_vote_id=voter_we_vote_id, organization_we_vote_id=linked_organization_we_vote_id, update_values=update_values, ) else: # If here, we are working with a draft # Save campaign_photo_from_file_reader and get back we_vote_hosted_campaign_photo_large_url # after initial update update_values = { 'campaign_description': campaign_description, 'campaign_description_changed': campaign_description_changed, 'in_draft_mode': in_draft_mode, 'in_draft_mode_changed': in_draft_mode_changed, 'campaign_photo_delete': campaign_photo_delete, 'campaign_photo_delete_changed': campaign_photo_delete_changed, 'campaign_title': campaign_title, 'campaign_title_changed': campaign_title_changed, 'politician_delete_list_serialized': politician_delete_list_serialized, 'politician_starter_list_changed': politician_starter_list_changed, 'politician_starter_list_serialized': politician_starter_list_serialized, } create_results = campaignx_manager.update_or_create_campaignx( voter_we_vote_id=voter_we_vote_id, organization_we_vote_id=linked_organization_we_vote_id, update_values=update_values, ) if create_results['campaignx_created']: # Campaign was just created, so save the voter as an owner campaignx_we_vote_id = create_results['campaignx_we_vote_id'] owner_results = campaignx_manager.update_or_create_campaignx_owner( campaignx_we_vote_id=campaignx_we_vote_id, organization_we_vote_id=linked_organization_we_vote_id, voter_we_vote_id=voter_we_vote_id) status += owner_results['status'] if create_results['campaignx_found'] and campaign_photo_changed: campaignx = create_results['campaignx'] campaignx_we_vote_id = create_results['campaignx_we_vote_id'] if campaign_photo_from_file_reader: photo_results = campaignx_save_photo_from_file_reader( campaignx_we_vote_id=campaignx_we_vote_id, campaign_photo_from_file_reader=campaign_photo_from_file_reader) if photo_results['we_vote_hosted_campaign_photo_original_url']: campaignx.we_vote_hosted_campaign_photo_original_url = \ photo_results['we_vote_hosted_campaign_photo_original_url'] # Now we want to resize to a large version create_resized_image_results = create_resized_images( campaignx_we_vote_id=campaignx_we_vote_id, campaignx_photo_url_https=campaignx.we_vote_hosted_campaign_photo_original_url) campaignx.we_vote_hosted_campaign_photo_large_url = \ create_resized_image_results['cached_resized_image_url_large'] campaignx.we_vote_hosted_campaign_photo_medium_url = \ create_resized_image_results['cached_resized_image_url_medium'] campaignx.we_vote_hosted_campaign_photo_small_url = \ create_resized_image_results['cached_resized_image_url_tiny'] campaignx.save() else: # Deleting image campaignx.we_vote_hosted_campaign_photo_large_url = None campaignx.we_vote_hosted_campaign_photo_medium_url = None campaignx.we_vote_hosted_campaign_photo_small_url = None campaignx.save() status += create_results['status'] if create_results['campaignx_found']: campaignx = create_results['campaignx'] campaignx_we_vote_id = campaignx.we_vote_id # Get owner_list results = campaignx_manager.retrieve_campaignx_as_owner( campaignx_we_vote_id=campaignx_we_vote_id, voter_we_vote_id=voter_we_vote_id, read_only=True, ) campaignx_owner_list = results['campaignx_owner_list'] voter_can_send_updates_campaignx_we_vote_ids = \ campaignx_manager.retrieve_voter_can_send_updates_campaignx_we_vote_ids( voter_we_vote_id=voter_we_vote_id, ) # Get politician_starter_list if campaignx.politician_starter_list_serialized: campaignx_politician_starter_list = json.loads(campaignx.politician_starter_list_serialized) else: campaignx_politician_starter_list = [] campaignx_politician_list_modified = [] campaignx_politician_list_exists = False # if positive_value_exists(politician_starter_list_changed): # results = campaignx_manager.update_or_create_campaignx_politicians_from_starter( # campaignx_we_vote_id=campaignx.we_vote_id, # politician_starter_list=campaignx_politician_starter_list, # ) # campaignx_politician_list_exists = results['campaignx_politician_list_found'] # campaignx_politician_list = results['campaignx_politician_list'] # else: campaignx_politician_list = campaignx_manager.retrieve_campaignx_politician_list( campaignx_we_vote_id=campaignx_we_vote_id, ) # Convert to json-friendly for campaignx_politician in campaignx_politician_list: campaignx_politician_list_exists = True campaignx_politician_dict = { 'campaignx_politician_id': campaignx_politician.id, 'politician_name': campaignx_politician.politician_name, 'politician_we_vote_id': campaignx_politician.politician_we_vote_id, 'state_code': campaignx_politician.state_code, 'we_vote_hosted_profile_image_url_large': campaignx_politician.we_vote_hosted_profile_image_url_large, 'we_vote_hosted_profile_image_url_medium': campaignx_politician.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_image_url_tiny': campaignx_politician.we_vote_hosted_profile_image_url_tiny, } campaignx_politician_list_modified.append(campaignx_politician_dict) # Get list of SEO Friendly Paths related to this campaignX. For most campaigns, there will only be one. seo_friendly_path_list = campaignx_manager.retrieve_seo_friendly_path_simple_list( campaignx_we_vote_id=campaignx_we_vote_id, ) if in_draft_mode_changed and not positive_value_exists(in_draft_mode): if voter.signed_in_with_email(): # Make sure the person creating the campaign has a campaignx_supporter entry IFF they are signed in update_values = { 'campaign_supported': True, 'campaign_supported_changed': True, 'supporter_endorsement': '', 'supporter_endorsement_changed': False, 'visible_to_public': True, 'visible_to_public_changed': True, } create_results = campaignx_manager.update_or_create_campaignx_supporter( campaignx_we_vote_id=campaignx_we_vote_id, voter_we_vote_id=voter_we_vote_id, organization_we_vote_id=linked_organization_we_vote_id, update_values=update_values, ) status += create_results['status'] # Make sure an owner entry exists owner_results = campaignx_manager.update_or_create_campaignx_owner( campaignx_we_vote_id=campaignx_we_vote_id, organization_we_vote_id=linked_organization_we_vote_id, voter_we_vote_id=voter_we_vote_id) status += owner_results['status'] if hasattr(campaignx, 'visible_on_this_site'): visible_on_this_site = campaignx.visible_on_this_site else: visible_on_this_site = True # If smaller sizes weren't stored, use large image if campaignx.we_vote_hosted_campaign_photo_medium_url: we_vote_hosted_campaign_photo_medium_url = campaignx.we_vote_hosted_campaign_photo_medium_url else: we_vote_hosted_campaign_photo_medium_url = campaignx.we_vote_hosted_campaign_photo_large_url if campaignx.we_vote_hosted_campaign_photo_small_url: we_vote_hosted_campaign_photo_small_url = campaignx.we_vote_hosted_campaign_photo_small_url else: we_vote_hosted_campaign_photo_small_url = campaignx.we_vote_hosted_campaign_photo_large_url supporters_count_next_goal = campaignx_manager.fetch_supporters_count_next_goal( supporters_count=campaignx.supporters_count, supporters_count_victory_goal=campaignx.supporters_count_victory_goal) final_election_date_plus_cool_down = generate_date_as_integer() + FINAL_ELECTION_DATE_COOL_DOWN final_election_date_in_past = \ final_election_date_plus_cool_down >= campaignx.final_election_date_as_integer \ if positive_value_exists(campaignx.final_election_date_as_integer) else False results = { 'status': status, 'success': success, 'campaign_description': campaignx.campaign_description, 'campaign_title': campaignx.campaign_title, 'campaignx_owner_list': campaignx_owner_list, 'campaignx_politician_list': campaignx_politician_list_modified, 'campaignx_politician_list_exists': campaignx_politician_list_exists, 'campaignx_politician_starter_list': campaignx_politician_starter_list, 'campaignx_we_vote_id': campaignx.we_vote_id, 'final_election_date_as_integer': campaignx.final_election_date_as_integer, 'final_election_date_in_past': final_election_date_in_past, 'in_draft_mode': campaignx.in_draft_mode, 'is_blocked_by_we_vote': campaignx.is_blocked_by_we_vote, 'is_blocked_by_we_vote_reason': campaignx.is_blocked_by_we_vote_reason, 'is_supporters_count_minimum_exceeded': campaignx.is_supporters_count_minimum_exceeded(), 'seo_friendly_path': campaignx.seo_friendly_path, 'seo_friendly_path_list': seo_friendly_path_list, 'supporters_count': campaignx.supporters_count, 'supporters_count_next_goal': supporters_count_next_goal, 'supporters_count_victory_goal': campaignx.supporters_count_victory_goal, 'visible_on_this_site': visible_on_this_site, 'voter_can_send_updates_to_campaignx': campaignx.we_vote_id in voter_can_send_updates_campaignx_we_vote_ids, 'voter_is_campaignx_owner': viewer_is_owner, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': campaignx.we_vote_hosted_campaign_photo_large_url, 'we_vote_hosted_campaign_photo_medium_url': we_vote_hosted_campaign_photo_medium_url, 'we_vote_hosted_campaign_photo_small_url': we_vote_hosted_campaign_photo_small_url, } return results else: status += "CAMPAIGNX_SAVE_ERROR " results = { 'status': status, 'success': False, 'campaign_description': '', 'campaign_title': '', 'campaignx_owner_list': [], 'campaignx_politician_list': [], 'campaignx_politician_list_exists': False, 'campaignx_politician_starter_list': [], 'campaignx_we_vote_id': '', 'in_draft_mode': True, 'seo_friendly_path': '', 'seo_friendly_path_list': [], 'supporters_count': 0, 'supporters_count_next_goal': 0, 'supporters_count_victory_goal': 0, 'visible_on_this_site': False, 'voter_can_send_updates_to_campaignx': False, 'voter_is_campaignx_owner': False, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_campaign_photo_large_url': '', 'we_vote_hosted_campaign_photo_medium_url': '', 'we_vote_hosted_campaign_photo_small_url': '', } return results def campaignx_save_photo_from_file_reader( campaignx_we_vote_id='', campaign_photo_from_file_reader=None): image_data_found = False python_image_library_image = None status = "" success = True we_vote_hosted_campaign_photo_original_url = '' if not positive_value_exists(campaignx_we_vote_id): status += "MISSING_CAMPAIGNX_WE_VOTE_ID " results = { 'status': status, 'success': success, 'we_vote_hosted_campaign_photo_original_url': we_vote_hosted_campaign_photo_original_url, } return results if not positive_value_exists(campaign_photo_from_file_reader): status += "MISSING_CAMPAIGNX_PHOTO_FROM_FILE_READER " results = { 'status': status, 'success': success, 'we_vote_hosted_campaign_photo_original_url': we_vote_hosted_campaign_photo_original_url, } return results img_dict = re.match("data:(?P<type>.*?);(?P<encoding>.*?),(?P<data>.*)", campaign_photo_from_file_reader).groupdict() if img_dict['encoding'] == 'base64': try: base64_data = img_dict['data'] byte_data = base64.b64decode(base64_data) image_data = BytesIO(byte_data) original_image = Image.open(image_data) format_to_cache = original_image.format python_image_library_image = ImageOps.exif_transpose(original_image) python_image_library_image.thumbnail( (CAMPAIGN_PHOTO_ORIGINAL_MAX_WIDTH, CAMPAIGN_PHOTO_ORIGINAL_MAX_HEIGHT), Image.ANTIALIAS) python_image_library_image.format = format_to_cache image_data_found = True except Exception as e: status += 'PROBLEM_DECODING_CAMPAIGN_PHOTO_LARGE: {error} [type: {error_type}] ' \ ''.format(error=e, error_type=type(e)) else: status += "INCOMING_CAMPAIGN_PHOTO_LARGE-BASE64_NOT_FOUND " if image_data_found: cache_results = cache_campaignx_image( python_image_library_image=python_image_library_image, campaignx_we_vote_id=campaignx_we_vote_id, kind_of_image_campaignx_photo=True, kind_of_image_original=True) status += cache_results['status'] if cache_results['success']: cached_master_we_vote_image = cache_results['we_vote_image'] try: we_vote_hosted_campaign_photo_original_url = cached_master_we_vote_image.we_vote_image_url except Exception as e: status += "FAILED_TO_CACHE_CAMPAIGNX_IMAGE: " + str(e) + ' ' success = False else: success = False results = { 'status': status, 'success': success, 'we_vote_hosted_campaign_photo_original_url': we_vote_hosted_campaign_photo_original_url, } return results def campaignx_supporter_retrieve_for_api( # campaignSupporterRetrieve voter_device_id='', campaignx_we_vote_id=''): status = '' voter_signed_in_with_email = False voter_manager = VoterManager() voter_results = voter_manager.retrieve_voter_from_voter_device_id(voter_device_id) if voter_results['voter_found']: voter = voter_results['voter'] voter_signed_in_with_email = voter.signed_in_with_email() voter_we_vote_id = voter.we_vote_id else: status += "VALID_VOTER_ID_MISSING " results = { 'status': status, 'success': False, 'campaign_supported': False, 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_supported': '', 'organization_we_vote_id': '', 'supporter_endorsement': '', 'supporter_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_medium': '', 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results campaignx_manager = CampaignXManager() results = campaignx_manager.retrieve_campaignx_supporter( campaignx_we_vote_id=campaignx_we_vote_id, voter_we_vote_id=voter_we_vote_id, read_only=True, ) status += results['status'] if not results['success']: status += "CAMPAIGNX_SUPPORTER_RETRIEVE_ERROR " results = { 'status': status, 'success': False, 'campaign_supported': False, 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_supported': '', 'organization_we_vote_id': '', 'supporter_endorsement': '', 'supporter_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results elif not results['campaignx_supporter_found']: status += "CAMPAIGNX_SUPPORTER_NOT_FOUND: " status += results['status'] + " " results = { 'status': status, 'success': False, 'campaign_supported': False, 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_supported': '', 'organization_we_vote_id': '', 'supporter_endorsement': '', 'supporter_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results campaignx_supporter = results['campaignx_supporter'] date_last_changed_string = '' date_supported_string = '' try: date_last_changed_string = campaignx_supporter.date_last_changed.strftime('%Y-%m-%d %H:%M:%S') date_supported_string = campaignx_supporter.date_supported.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " results = { 'status': status, 'success': True, 'campaign_supported': campaignx_supporter.campaign_supported, 'campaignx_we_vote_id': campaignx_supporter.campaignx_we_vote_id, 'date_last_changed': date_last_changed_string, 'date_supported': date_supported_string, 'organization_we_vote_id': campaignx_supporter.organization_we_vote_id, 'supporter_endorsement': campaignx_supporter.supporter_endorsement, 'supporter_name': campaignx_supporter.supporter_name, 'visible_to_public': campaignx_supporter.visible_to_public, 'voter_we_vote_id': campaignx_supporter.voter_we_vote_id, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_medium': campaignx_supporter.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_photo_image_url_tiny': campaignx_supporter.we_vote_hosted_profile_image_url_tiny, } return results def campaignx_supporter_save_for_api( # campaignSupporterSave campaignx_we_vote_id='', campaign_supported=False, campaign_supported_changed=False, supporter_endorsement='', supporter_endorsement_changed=False, visible_to_public=False, visible_to_public_changed=False, voter_device_id=''): status = '' success = True voter_signed_in_with_email = False voter_manager = VoterManager() voter_results = voter_manager.retrieve_voter_from_voter_device_id(voter_device_id) if voter_results['voter_found']: voter = voter_results['voter'] voter_signed_in_with_email = voter.signed_in_with_email() voter_we_vote_id = voter.we_vote_id linked_organization_we_vote_id = voter.linked_organization_we_vote_id else: status += "VALID_VOTER_ID_MISSING " results = { 'status': status, 'success': False, 'campaign_supported': False, 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_supported': '', 'id': '', 'organization_we_vote_id': '', 'supporter_endorsement': '', 'supporter_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results if positive_value_exists(campaign_supported): # To support a campaign, voter must be signed in with an email address if not voter.signed_in_with_email(): status += "MUST_BE_SIGNED_IN_WITH_EMAIL " results = { 'status': status, 'success': False, 'campaign_supported': False, 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_supported': '', 'id': '', 'organization_we_vote_id': '', 'supporter_endorsement': '', 'supporter_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results if not positive_value_exists(campaignx_we_vote_id): status += "CAMPAIGNX_WE_VOTE_ID_REQUIRED " results = { 'status': status, 'success': False, 'campaign_supported': False, 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_supported': '', 'id': '', 'organization_we_vote_id': '', 'supporter_endorsement': '', 'supporter_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results campaignx_manager = CampaignXManager() update_values = { 'campaign_supported': campaign_supported, 'campaign_supported_changed': campaign_supported_changed, 'supporter_endorsement': supporter_endorsement, 'supporter_endorsement_changed': supporter_endorsement_changed, 'visible_to_public': visible_to_public, 'visible_to_public_changed': visible_to_public_changed, } create_results = campaignx_manager.update_or_create_campaignx_supporter( campaignx_we_vote_id=campaignx_we_vote_id, voter_we_vote_id=voter_we_vote_id, organization_we_vote_id=linked_organization_we_vote_id, update_values=update_values, ) if create_results['campaignx_supporter_found']: campaignx_supporter = create_results['campaignx_supporter'] results = campaignx_manager.retrieve_campaignx( campaignx_we_vote_id=campaignx_we_vote_id, read_only=True, ) statement_text = '' if results['campaignx_found']: campaignx = results['campaignx'] statement_text = campaignx.campaign_title activity_results = update_or_create_activity_notice_seed_for_campaignx_supporter_initial_response( campaignx_we_vote_id=campaignx_supporter.campaignx_we_vote_id, visibility_is_public=campaignx_supporter.visible_to_public, speaker_name=campaignx_supporter.supporter_name, speaker_organization_we_vote_id=campaignx_supporter.organization_we_vote_id, speaker_voter_we_vote_id=campaignx_supporter.voter_we_vote_id, speaker_profile_image_url_medium=voter.we_vote_hosted_profile_image_url_medium, speaker_profile_image_url_tiny=voter.we_vote_hosted_profile_image_url_tiny, statement_text=statement_text) status += activity_results['status'] status += create_results['status'] if create_results['campaignx_supporter_found']: count_results = campaignx_manager.update_campaignx_supporters_count(campaignx_we_vote_id) if not count_results['success']: status += count_results['status'] campaignx_supporter = create_results['campaignx_supporter'] date_last_changed_string = '' date_supported_string = '' try: date_last_changed_string = campaignx_supporter.date_last_changed.strftime('%Y-%m-%d %H:%M:%S') date_supported_string = campaignx_supporter.date_supported.strftime('%Y-%m-%d %H:%M:%S') except Exception as e: status += "DATE_CONVERSION_ERROR: " + str(e) + " " results = { 'status': status, 'success': success, 'campaign_supported': campaignx_supporter.campaign_supported, 'campaignx_we_vote_id': campaignx_supporter.campaignx_we_vote_id, 'date_last_changed': date_last_changed_string, 'date_supported': date_supported_string, 'id': campaignx_supporter.id, 'organization_we_vote_id': campaignx_supporter.organization_we_vote_id, 'supporter_endorsement': campaignx_supporter.supporter_endorsement, 'supporter_name': campaignx_supporter.supporter_name, 'visible_to_public': campaignx_supporter.visible_to_public, 'voter_we_vote_id': campaignx_supporter.voter_we_vote_id, 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_medium': campaignx_supporter.we_vote_hosted_profile_image_url_medium, 'we_vote_hosted_profile_photo_image_url_tiny': campaignx_supporter.we_vote_hosted_profile_image_url_tiny, } return results else: status += "CAMPAIGNX_SUPPORTER_SAVE_ERROR " results = { 'status': status, 'success': False, 'campaign_supported': False, 'campaignx_we_vote_id': '', 'date_last_changed': '', 'date_supported': '', 'id': '', 'organization_we_vote_id': '', 'supporter_endorsement': '', 'supporter_name': '', 'visible_to_public': True, 'voter_we_vote_id': '', 'voter_signed_in_with_email': voter_signed_in_with_email, 'we_vote_hosted_profile_photo_image_url_tiny': '', } return results def fetch_sentence_string_from_politician_list(politician_list, max_number_of_list_items=200): """ Parallel to politicianListToSentenceString in Campaigns site :param politician_list: :param max_number_of_list_items: :return: """ sentence_string = '' if not politician_list or len(politician_list) == 0: return sentence_string if len(politician_list) == 1: sentence_string += ' ' + politician_list[0].politician_name return sentence_string number_of_politicians_in_list = len(politician_list) number_of_politicians_in_list_great_than_max_number_of_list_items = \ number_of_politicians_in_list > max_number_of_list_items if number_of_politicians_in_list_great_than_max_number_of_list_items: number_of_politicians_to_show = max_number_of_list_items else: number_of_politicians_to_show = number_of_politicians_in_list politician_number = 0 for politician in politician_list: politician_number += 1 if politician_number >= number_of_politicians_to_show: if number_of_politicians_in_list_great_than_max_number_of_list_items: sentence_string += ' and more' else: sentence_string += ' and ' + politician.politician_name else: comma_or_not = '' if politician_number == (number_of_politicians_to_show - 1) else ',' sentence_string += ' ' + politician.politician_name + comma_or_not return sentence_string def move_campaignx_to_another_organization( from_organization_we_vote_id, to_organization_we_vote_id, to_organization_name=None): status = '' success = True campaignx_entries_moved = 0 campaignx_listed_entries_moved = 0 campaignx_news_item_entries_moved = 0 campaignx_owner_entries_moved = 0 campaignx_supporter_entries_moved = 0 if not positive_value_exists(from_organization_we_vote_id) or not positive_value_exists(to_organization_we_vote_id): status += "MOVE_CAMPAIGNX_TO_ORG-MISSING_EITHER_FROM_OR_TO_ORG_WE_VOTE_ID " success = False results = { 'status': status, 'success': success, 'from_organization_we_vote_id': from_organization_we_vote_id, 'to_organization_we_vote_id': to_organization_we_vote_id, 'campaignx_entries_moved': campaignx_entries_moved, 'campaignx_owner_entries_moved': campaignx_owner_entries_moved, } return results if from_organization_we_vote_id == to_organization_we_vote_id: status += "MOVE_CAMPAIGNX_TO_ORG-FROM_AND_TO_ORG_WE_VOTE_IDS_IDENTICAL " success = False results = { 'status': status, 'success': success, 'from_organization_we_vote_id': from_organization_we_vote_id, 'to_organization_we_vote_id': to_organization_we_vote_id, 'campaignx_entries_moved': campaignx_entries_moved, 'campaignx_owner_entries_moved': campaignx_owner_entries_moved, } return results # ############################################# # Move based on organization_we_vote_id if positive_value_exists(to_organization_name): try: campaignx_owner_entries_moved += CampaignXOwner.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_name=to_organization_name, organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_TO_ORG_OWNER_UPDATE-FROM_ORG_WE_VOTE_ID-WITH_NAME: " + str(e) + " " try: campaignx_supporter_entries_moved += CampaignXSupporter.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(supporter_name=to_organization_name, organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_TO_ORG_SUPPORTER_UPDATE-FROM_ORG_WE_VOTE_ID-WITH_NAME: " + str(e) + " " try: campaignx_news_item_entries_moved += CampaignXNewsItem.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(speaker_name=to_organization_name, organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_NEWS_ITEM_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " else: try: campaignx_owner_entries_moved += CampaignXOwner.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_TO_ORG_OWNER_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " try: campaignx_supporter_entries_moved += CampaignXSupporter.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_TO_ORG_SUPPORTER_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " try: campaignx_news_item_entries_moved += CampaignXNewsItem.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_NEWS_ITEM_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " try: campaignx_listed_entries_moved += CampaignXListedByOrganization.objects \ .filter(site_owner_organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(site_owner_organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_LISTED_BY_ORG_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " results = { 'status': status, 'success': success, 'from_organization_we_vote_id': from_organization_we_vote_id, 'to_organization_we_vote_id': to_organization_we_vote_id, 'campaignx_entries_moved': campaignx_owner_entries_moved, 'campaignx_owner_entries_moved': campaignx_owner_entries_moved, } return results def move_campaignx_to_another_voter( from_voter_we_vote_id, to_voter_we_vote_id, from_organization_we_vote_id, to_organization_we_vote_id, to_organization_name=None): status = '' success = True campaignx_entries_moved = 0 campaignx_listed_entries_moved = 0 campaignx_news_item_entries_moved = 0 campaignx_owner_entries_moved = 0 campaignx_supporter_entries_moved = 0 if not positive_value_exists(from_voter_we_vote_id) or not positive_value_exists(to_voter_we_vote_id): status += "MOVE_CAMPAIGNX-MISSING_EITHER_FROM_OR_TO_VOTER_WE_VOTE_ID " success = False results = { 'status': status, 'success': success, 'from_voter_we_vote_id': from_voter_we_vote_id, 'to_voter_we_vote_id': to_voter_we_vote_id, 'campaignx_entries_moved': campaignx_entries_moved, 'campaignx_owner_entries_moved': campaignx_owner_entries_moved, } return results if from_voter_we_vote_id == to_voter_we_vote_id: status += "MOVE_CAMPAIGNX-FROM_AND_TO_VOTER_WE_VOTE_IDS_IDENTICAL " success = False results = { 'status': status, 'success': success, 'from_voter_we_vote_id': from_voter_we_vote_id, 'to_voter_we_vote_id': to_voter_we_vote_id, 'campaignx_entries_moved': campaignx_entries_moved, 'campaignx_owner_entries_moved': campaignx_owner_entries_moved, } return results # ###################### # Move based on started_by_voter_we_vote_id try: campaignx_entries_moved += CampaignX.objects\ .filter(started_by_voter_we_vote_id__iexact=from_voter_we_vote_id)\ .update(started_by_voter_we_vote_id=to_voter_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_UPDATE: " + str(e) + " " # ###################### # Move News Item based on voter_we_vote_id try: campaignx_news_item_entries_moved += CampaignXNewsItem.objects\ .filter(voter_we_vote_id__iexact=from_voter_we_vote_id)\ .update(voter_we_vote_id=to_voter_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_NEWS_ITEM_UPDATE-FROM_VOTER_WE_VOTE_ID: " + str(e) + " " # ###################### # Move owners based on voter_we_vote_id try: campaignx_owner_entries_moved += CampaignXOwner.objects\ .filter(voter_we_vote_id__iexact=from_voter_we_vote_id)\ .update(voter_we_vote_id=to_voter_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_OWNER_UPDATE-FROM_VOTER_WE_VOTE_ID: " + str(e) + " " # ###################### # Move supporters based on voter_we_vote_id try: campaignx_supporter_entries_moved += CampaignXSupporter.objects\ .filter(voter_we_vote_id__iexact=from_voter_we_vote_id)\ .update(voter_we_vote_id=to_voter_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_SUPPORTER_UPDATE-FROM_VOTER_WE_VOTE_ID: " + str(e) + " " # ############################################# # Move based on organization_we_vote_id if positive_value_exists(to_organization_name): try: campaignx_owner_entries_moved += CampaignXOwner.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_name=to_organization_name, organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_OWNER_UPDATE-FROM_ORG_WE_VOTE_ID-WITH_NAME: " + str(e) + " " try: campaignx_supporter_entries_moved += CampaignXSupporter.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(supporter_name=to_organization_name, organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_SUPPORTER_UPDATE-FROM_ORG_WE_VOTE_ID-WITH_NAME: " + str(e) + " " try: campaignx_news_item_entries_moved += CampaignXNewsItem.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(speaker_name=to_organization_name, organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_NEWS_ITEM_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " else: try: campaignx_owner_entries_moved += CampaignXOwner.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_OWNER_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " try: campaignx_supporter_entries_moved += CampaignXSupporter.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_SUPPORTER_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " try: campaignx_news_item_entries_moved += CampaignXNewsItem.objects \ .filter(organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_NEWS_ITEM_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " try: campaignx_listed_entries_moved += CampaignXListedByOrganization.objects \ .filter(site_owner_organization_we_vote_id__iexact=from_organization_we_vote_id) \ .update(site_owner_organization_we_vote_id=to_organization_we_vote_id) except Exception as e: status += "FAILED-CAMPAIGNX_LISTED_BY_ORG_UPDATE-FROM_ORG_WE_VOTE_ID: " + str(e) + " " results = { 'status': status, 'success': success, 'from_voter_we_vote_id': from_voter_we_vote_id, 'to_voter_we_vote_id': to_voter_we_vote_id, 'campaignx_entries_moved': campaignx_owner_entries_moved, 'campaignx_owner_entries_moved': campaignx_owner_entries_moved, } return results def retrieve_recommended_campaignx_list_for_campaignx_we_vote_id( request=None, voter_device_id='', campaignx_we_vote_id='', voter_we_vote_id='', site_owner_organization_we_vote_id='', minimum_number_of_campaignx_options=15, read_only=True): """ Regarding minimum_number_of_campaignx_options: If we ask voters to sign 5 more campaigns, we want to make sure we send 3x options so we have enough available on the front end so we can filter out campaigns with duplicate politicians (after the voter makes choices) and let the voter skip campaigns they aren't interested in :param request: :param voter_device_id: :param campaignx_we_vote_id: :param voter_we_vote_id: :param site_owner_organization_we_vote_id: :param minimum_number_of_campaignx_options: :param read_only: :return: """ campaignx_manager = CampaignXManager() campaignx_list = [] original_campaignx_we_vote_id_list = [campaignx_we_vote_id] success = True status = "" # Remove campaigns already supported by this voter supported_by_voter_campaignx_we_vote_id_list = [] if positive_value_exists(voter_we_vote_id): results = campaignx_manager.retrieve_campaignx_we_vote_id_list_supported_by_voter( voter_we_vote_id=voter_we_vote_id) if results['campaignx_we_vote_id_list_found']: supported_by_voter_campaignx_we_vote_id_list = results['campaignx_we_vote_id_list'] campaignx_we_vote_id_list_voter_can_vote_for = [] if positive_value_exists(voter_device_id): from ballot.controllers import what_voter_can_vote_for results = what_voter_can_vote_for(request=request, voter_device_id=voter_device_id) if len(results['voter_can_vote_for_politician_we_vote_ids']) > 0: voter_can_vote_for_politician_we_vote_ids = results['voter_can_vote_for_politician_we_vote_ids'] politician_results = campaignx_manager.retrieve_campaignx_we_vote_id_list_by_politician_we_vote_id( politician_we_vote_id_list=voter_can_vote_for_politician_we_vote_ids) if politician_results['campaignx_we_vote_id_list_found']: campaignx_we_vote_id_list_voter_can_vote_for = politician_results['campaignx_we_vote_id_list'] # Create pool of options # recommended_campaignx_we_vote_id_list = ['wv02camp4'] continue_searching_for_options = True if positive_value_exists(site_owner_organization_we_vote_id): # Retrieve all campaigns visible on this site visible_on_this_site_campaignx_we_vote_id_list = \ campaignx_manager.retrieve_visible_on_this_site_campaignx_simple_list( site_owner_organization_we_vote_id=site_owner_organization_we_vote_id) recommended_campaignx_we_vote_id_list = \ list(set(visible_on_this_site_campaignx_we_vote_id_list) - set(original_campaignx_we_vote_id_list)) if len(supported_by_voter_campaignx_we_vote_id_list) > 0: recommended_campaignx_we_vote_id_list = \ list(set(recommended_campaignx_we_vote_id_list) - set(supported_by_voter_campaignx_we_vote_id_list)) continue_searching_for_options = False else: recommended_campaignx_we_vote_id_list = campaignx_we_vote_id_list_voter_can_vote_for recommended_campaignx_we_vote_id_list = \ list(set(recommended_campaignx_we_vote_id_list) - set(original_campaignx_we_vote_id_list)) if len(supported_by_voter_campaignx_we_vote_id_list) > 0: recommended_campaignx_we_vote_id_list = \ list(set(recommended_campaignx_we_vote_id_list) - set(supported_by_voter_campaignx_we_vote_id_list)) if len(recommended_campaignx_we_vote_id_list) >= minimum_number_of_campaignx_options: # If we have the number we need, we can stop here continue_searching_for_options = False if continue_searching_for_options: number_of_options_already_found = len(recommended_campaignx_we_vote_id_list) number_to_find = minimum_number_of_campaignx_options - number_of_options_already_found if number_to_find > 0: campaignx_we_vote_id_list_to_exclude = \ list(set(recommended_campaignx_we_vote_id_list + supported_by_voter_campaignx_we_vote_id_list + original_campaignx_we_vote_id_list)) results = campaignx_manager.retrieve_campaignx_we_vote_id_list_filler_options( campaignx_we_vote_id_list_to_exclude=campaignx_we_vote_id_list_to_exclude, limit=number_to_find) if results['campaignx_we_vote_id_list_found']: campaignx_we_vote_id_list = results['campaignx_we_vote_id_list'] recommended_campaignx_we_vote_id_list = \ list(set(recommended_campaignx_we_vote_id_list + campaignx_we_vote_id_list)) results = campaignx_manager.retrieve_campaignx_list_by_campaignx_we_vote_id_list( campaignx_we_vote_id_list=recommended_campaignx_we_vote_id_list, read_only=read_only) campaignx_list_found = results['campaignx_list_found'] campaignx_list = results['campaignx_list'] status += results['status'] if campaignx_list_found: if len(campaignx_list) > minimum_number_of_campaignx_options: # Consider sorting this list and filtering out ones with lowest "score" pass results = { 'success': success, 'status': status, 'campaignx_list_found': campaignx_list_found, 'campaignx_list': campaignx_list, } return results

53.7926

120

0.644529

0f9129f2972743b93a81b88d5d6aa619f469e607

1,961

py

Python

function/python/brightics/function/transform/split_data.py

sharon1321/studio

c5ce7f6db5503f5020b2aa0c6f2e6acfc61c90c5

[ "Apache-2.0" ]

null

function/python/brightics/function/transform/split_data.py

sharon1321/studio

c5ce7f6db5503f5020b2aa0c6f2e6acfc61c90c5

[ "Apache-2.0" ]

null

function/python/brightics/function/transform/split_data.py

sharon1321/studio

c5ce7f6db5503f5020b2aa0c6f2e6acfc61c90c5

[ "Apache-2.0" ]

null

""" Copyright 2019 Samsung SDS 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 sklearn.model_selection import train_test_split as sktrain_test_split from brightics.common.validation import validate, greater_than from brightics.common.groupby import _function_by_group from brightics.common.utils import check_required_parameters from brightics.common.utils import get_default_from_parameters_if_required def split_data(table, group_by=None, **params): params = get_default_from_parameters_if_required(params,_split_data) param_validation_check = [greater_than(params, 0.0, 'train_ratio'), greater_than(params, 0.0, 'test_ratio')] validate(*param_validation_check) check_required_parameters(_split_data, params, ['table']) if group_by is not None: return _function_by_group(_split_data, table, group_by=group_by, **params) else: return _split_data(table, **params) def _split_data(table, train_ratio=7.0, test_ratio=3.0, random_state=None, shuffle=True, stratify=None): ratio = test_ratio / (train_ratio + test_ratio) out_table_train, out_table_test = sktrain_test_split(table, test_size=ratio, random_state=random_state, shuffle=shuffle, stratify=stratify) return {'train_table' : out_table_train.reset_index(drop=True), 'test_table' : out_table_test.reset_index(drop=True)}

44.568182

144

0.734319

7f420612d3d82bf85500b922499d1ade341d554d

763

py

Python

test_app/test_app/migrations/0001_initial.py

mkalioby/django-query-parser

f87acdf68dab2c78ddf6048b517a0dda194fd6ab

[ "MIT" ]

null

test_app/test_app/migrations/0001_initial.py

mkalioby/django-query-parser

f87acdf68dab2c78ddf6048b517a0dda194fd6ab

[ "MIT" ]

null

test_app/test_app/migrations/0001_initial.py

mkalioby/django-query-parser

f87acdf68dab2c78ddf6048b517a0dda194fd6ab

[ "MIT" ]

null

# Generated by Django 2.2.16 on 2020-10-26 14:23 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Order', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('order_date', models.DateTimeField()), ('status', models.CharField(max_length=15)), ('ordered_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

28.259259

124

0.625164

c99b37c6add646b664063a4f049252b9ae27feba

1,669

py

Python

.venv/lib/python3.7/site-packages/notebook/terminal/api_handlers.py

ITCRStevenLPZ/Proyecto2-Analisis-de-Algoritmos

4acdbc423428fb2e0068720add69e7870c87929a

[ "Apache-2.0" ]

11

2019-08-29T03:51:09.000Z

2021-07-13T16:44:08.000Z

.venv/lib/python3.7/site-packages/notebook/terminal/api_handlers.py

ITCRStevenLPZ/Proyecto2-Analisis-de-Algoritmos

4acdbc423428fb2e0068720add69e7870c87929a

[ "Apache-2.0" ]

24

2020-03-25T19:35:43.000Z

2022-02-10T11:46:50.000Z

.venv/lib/python3.7/site-packages/notebook/terminal/api_handlers.py

ITCRStevenLPZ/Proyecto2-Analisis-de-Algoritmos

4acdbc423428fb2e0068720add69e7870c87929a

[ "Apache-2.0" ]

11

2019-01-21T17:51:48.000Z

2021-08-10T07:04:33.000Z

import json from tornado import web, gen from ..base.handlers import APIHandler from ..prometheus.metrics import TERMINAL_CURRENTLY_RUNNING_TOTAL class TerminalRootHandler(APIHandler): @web.authenticated def get(self): tm = self.terminal_manager terms = [{'name': name} for name in tm.terminals] self.finish(json.dumps(terms)) # Update the metric below to the length of the list 'terms' TERMINAL_CURRENTLY_RUNNING_TOTAL.set( len(terms) ) @web.authenticated def post(self): """POST /terminals creates a new terminal and redirects to it""" name, _ = self.terminal_manager.new_named_terminal() self.finish(json.dumps({'name': name})) # Increase the metric by one because a new terminal was created TERMINAL_CURRENTLY_RUNNING_TOTAL.inc() class TerminalHandler(APIHandler): SUPPORTED_METHODS = ('GET', 'DELETE') @web.authenticated def get(self, name): tm = self.terminal_manager if name in tm.terminals: self.finish(json.dumps({'name': name})) else: raise web.HTTPError(404, "Terminal not found: %r" % name) @web.authenticated @gen.coroutine def delete(self, name): tm = self.terminal_manager if name in tm.terminals: yield tm.terminate(name, force=True) self.set_status(204) self.finish() # Decrease the metric below by one # because a terminal has been shutdown TERMINAL_CURRENTLY_RUNNING_TOTAL.dec() else: raise web.HTTPError(404, "Terminal not found: %r" % name)

30.345455

72

0.63571

286310f46df480ebe94bd5c746cdf3e4614aac01

4,635

py

Python

src/gdb/gdb-7.11/gdb/testsuite/gdb.python/py-completion.py

aps337/unum-sdk

2de3ae625e474c5064f6a88b720ec2ffdcdefad9

[ "Apache-2.0" ]

31

2018-08-01T21:25:24.000Z

2022-02-14T07:52:34.000Z

src/gdb/gdb-7.11/gdb/testsuite/gdb.python/py-completion.py

aps337/unum-sdk

2de3ae625e474c5064f6a88b720ec2ffdcdefad9

[ "Apache-2.0" ]

40

2018-12-03T19:48:52.000Z

2021-03-10T06:34:26.000Z

src/gdb/gdb-7.11/gdb/testsuite/gdb.python/py-completion.py

aps337/unum-sdk

2de3ae625e474c5064f6a88b720ec2ffdcdefad9

[ "Apache-2.0" ]

20

2018-11-16T21:19:22.000Z

2021-10-18T23:08:24.000Z

# Copyright (C) 2014-2016 Free Software Foundation, Inc. # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # This testcase tests PR python/16699 import gdb class CompleteFileInit(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completefileinit',gdb.COMMAND_USER,gdb.COMPLETE_FILENAME) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') class CompleteFileMethod(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completefilemethod',gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return gdb.COMPLETE_FILENAME class CompleteFileCommandCond(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completefilecommandcond',gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): # This is a test made to know if the command # completion still works fine. When the user asks to # complete something like "completefilecommandcond # /path/to/py-completion-t", it should not complete to # "/path/to/py-completion-test/", but instead just # wait for input. if "py-completion-t" in text: return gdb.COMPLETE_COMMAND else: return gdb.COMPLETE_FILENAME class CompleteLimit1(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completelimit1',gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return ["cl11", "cl12", "cl13"] class CompleteLimit2(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completelimit2', gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return ["cl21", "cl23", "cl25", "cl27", "cl29", "cl22", "cl24", "cl26", "cl28", "cl210"] class CompleteLimit3(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completelimit3', gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return ["cl31", "cl33", "cl35", "cl37", "cl39", "cl32", "cl34", "cl36", "cl38", "cl310"] class CompleteLimit4(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completelimit4', gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return ["cl41", "cl43", "cl45", "cl47", "cl49", "cl42", "cl44", "cl46", "cl48", "cl410"] class CompleteLimit5(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completelimit5', gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return ["cl51", "cl53", "cl55", "cl57", "cl59", "cl52", "cl54", "cl56", "cl58", "cl510"] class CompleteLimit6(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completelimit6', gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return ["cl61", "cl63", "cl65", "cl67", "cl69", "cl62", "cl64", "cl66", "cl68", "cl610"] class CompleteLimit7(gdb.Command): def __init__(self): gdb.Command.__init__(self,'completelimit7', gdb.COMMAND_USER) def invoke(self,argument,from_tty): raise gdb.GdbError('not implemented') def complete(self,text,word): return ["cl71", "cl73", "cl75", "cl77", "cl79", "cl72", "cl74", "cl76", "cl78", "cl710"] CompleteFileInit() CompleteFileMethod() CompleteFileCommandCond() CompleteLimit1() CompleteLimit2() CompleteLimit3() CompleteLimit4() CompleteLimit5() CompleteLimit6() CompleteLimit7()

31.317568

86

0.674865

7072c7e933063fe320cfeac2d21bacff9f579e0b

13,974

py

Python

tensorflow/python/data/ops/multi_device_iterator_ops.py

Sonata-Wang/tensorflow

8bbef0cd77879d05ed69bf30e76087847a8ca4a2

[ "Apache-2.0" ]

6

2019-02-05T22:36:51.000Z

2022-01-14T03:50:57.000Z

tensorflow/python/data/ops/multi_device_iterator_ops.py

YaoYaoZhi/tensorflow

83903c9dd9b5235996ec9158c30a1607fcfb4c73

[ "Apache-2.0" ]

1

2019-09-14T04:40:07.000Z

2020-11-18T18:16:17.000Z

tensorflow/python/data/ops/multi_device_iterator_ops.py

YaoYaoZhi/tensorflow

83903c9dd9b5235996ec9158c30a1607fcfb4c73

[ "Apache-2.0" ]

8

2016-01-14T13:12:56.000Z

2021-04-09T10:20:53.000Z

# Copyright 2017 The TensorFlow 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. # ============================================================================== """Python wrapper for prefetching_ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context from tensorflow.python.eager import function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import functional_ops from tensorflow.python.ops import gen_dataset_ops from tensorflow.python.ops import resource_variable_ops class _PerDeviceGenerator(dataset_ops.DatasetV2): """A `dummy` generator dataset.""" def __init__(self, shard_num, multi_device_iterator_resource, incarnation_id, source_device, element_structure): self._structure = element_structure multi_device_iterator_string_handle = ( gen_dataset_ops.multi_device_iterator_to_string_handle( multi_device_iterator_resource)) # TODO(b/124254153): Enable autograph once the overhead is low enough. @function.defun(autograph=False) # Pure graph code. def _init_func(): return multi_device_iterator_string_handle init_func_concrete = _init_func._get_concrete_function_internal() # pylint: disable=protected-access # TODO(b/124254153): Enable autograph once the overhead is low enough. @function.defun(autograph=False) # Pure graph code. def _remote_init_func(): return functional_ops.remote_call( target=source_device, args=init_func_concrete.captured_inputs, Tout=[dtypes.string], f=init_func_concrete) self._init_func = _remote_init_func._get_concrete_function_internal() # pylint: disable=protected-access self._init_captured_args = self._init_func.captured_inputs # TODO(b/124254153): Enable autograph once the overhead is low enough. @function.defun( input_signature=[tensor_spec.TensorSpec([], dtypes.string)], autograph=False) # Pure graph code. def _next_func(string_handle): # pylint: disable=protected-access multi_device_iterator = ( gen_dataset_ops.multi_device_iterator_from_string_handle( string_handle=string_handle, output_types=self._structure._flat_types, output_shapes=self._structure._flat_shapes)) return gen_dataset_ops.multi_device_iterator_get_next_from_shard( multi_device_iterator=multi_device_iterator, shard_num=shard_num, incarnation_id=incarnation_id, output_types=self._structure._flat_types, output_shapes=self._structure._flat_shapes) next_func_concrete = _next_func._get_concrete_function_internal() # pylint: disable=protected-access # TODO(b/124254153): Enable autograph once the overhead is low enough. @function.defun_with_attributes( input_signature=[tensor_spec.TensorSpec([], dtypes.string)], attributes={"experimental_ints_on_device": True}, autograph=False) # Pure graph code. def _remote_next_func(string_handle): return functional_ops.remote_call( target=source_device, args=[string_handle] + next_func_concrete.captured_inputs, Tout=self._structure._flat_types, # pylint: disable=protected-access f=next_func_concrete) self._next_func = _remote_next_func._get_concrete_function_internal() # pylint: disable=protected-access self._next_captured_args = self._next_func.captured_inputs self._incarnation_id_index = -1 for i, arg in enumerate(self._next_captured_args): if arg == incarnation_id: self._incarnation_id_index = i # TODO(b/124254153): Enable autograph once the overhead is low enough. @function.defun( input_signature=[tensor_spec.TensorSpec([], dtypes.string)], autograph=False) # Pure graph code. def _finalize_func(unused_string_handle): return array_ops.constant(0, dtypes.int64) finalize_func_concrete = _finalize_func._get_concrete_function_internal() # pylint: disable=protected-access # TODO(b/124254153): Enable autograph once the overhead is low enough. @function.defun( input_signature=[tensor_spec.TensorSpec([], dtypes.string)], autograph=False) # Pure graph code. def _remote_finalize_func(string_handle): return functional_ops.remote_call( target=source_device, args=[string_handle] + finalize_func_concrete.captured_inputs, Tout=[dtypes.int64], f=finalize_func_concrete) self._finalize_func = _remote_finalize_func._get_concrete_function_internal( # pylint: disable=protected-access ) self._finalize_captured_args = self._finalize_func.captured_inputs variant_tensor = gen_dataset_ops.generator_dataset( self._init_captured_args, self._next_captured_args, self._finalize_captured_args, init_func=self._init_func, next_func=self._next_func, finalize_func=self._finalize_func, **dataset_ops.flat_structure(self)) super(_PerDeviceGenerator, self).__init__(variant_tensor) def _inputs(self): # TODO(b/116506223): Determine which datasets should be used as inputs here. return [] @property def _element_structure(self): return self._structure class _ReincarnatedPerDeviceGenerator(dataset_ops.DatasetV2): """Creates a _PerDeviceGenerator-like dataset with a new incarnation_id. Re-uses the functions from the provided per_device_dataset and just switches out the function argument corresponding to the incarnation_id. """ def __init__(self, per_device_dataset, incarnation_id): # pylint: disable=protected-access self._structure = per_device_dataset._structure self._init_func = per_device_dataset._init_func self._init_captured_args = self._init_func.captured_inputs self._next_func = per_device_dataset._next_func self._next_captured_args = per_device_dataset._next_captured_args # The captured arguments to the next_func are string_handle, incarnation_id. # We update the incarnation id to the new one. self._next_captured_args[ per_device_dataset._incarnation_id_index] = incarnation_id self._finalize_func = per_device_dataset._finalize_func self._finalize_captured_args = per_device_dataset._finalize_captured_args variant_tensor = gen_dataset_ops.generator_dataset( self._init_captured_args, self._next_captured_args, self._finalize_captured_args, init_func=self._init_func, next_func=self._next_func, finalize_func=self._finalize_func, **dataset_ops.flat_structure(self)) super(_ReincarnatedPerDeviceGenerator, self).__init__(variant_tensor) def _inputs(self): # TODO(b/116506223): Determine which datasets should be used as inputs here. return [] @property def _element_structure(self): return self._structure class MultiDeviceIterator(object): """An iterator over multiple devices.""" def __init__(self, dataset, devices, max_buffer_size=1, prefetch_buffer_size=1, source_device="/cpu:0"): """Constructs a MultiDeviceIterator. Args: dataset: The input dataset to be iterated over. devices: The list of devices to fetch data to. max_buffer_size: Maximum size of the host side per device buffer to keep. prefetch_buffer_size: if > 1, then we setup a buffer on each device to prefetch into. source_device: The host device to place the `dataset` on. In order to prevent deadlocks, if the prefetch_buffer_size is greater than the max_buffer_size, we set the max_buffer_size to prefetch_buffer_size. Raises: RuntimeError: If run in Eager mode. """ self._dataset = dataset._apply_options() # pylint: disable=protected-access self._devices = devices self._source_device = source_device self._source_device_tensor = ops.convert_to_tensor(source_device) self._max_buffer_size = max_buffer_size self._prefetch_buffer_size = prefetch_buffer_size if self._prefetch_buffer_size > self._max_buffer_size: self._max_buffer_size = self._prefetch_buffer_size # Create the MultiDeviceIterator. with ops.device(self._source_device): # TODO(b/121378567): Get rid of this shared_name hack. shared_name = "" if context.executing_eagerly(): shared_name = context.shared_name() self._multi_device_iterator_resource = ( gen_dataset_ops.multi_device_iterator( devices=self._devices, shared_name=shared_name, container="", **dataset_ops.flat_structure(self._dataset))) if context.executing_eagerly(): # Delete the resource when this object is deleted self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._multi_device_iterator_resource, handle_device=self._source_device) # The incarnation ID is used to ensure consistency between the per-device # iterators and the multi-device iterator. self._incarnation_id = gen_dataset_ops.multi_device_iterator_init( self._dataset._variant_tensor, # pylint: disable=protected-access self._multi_device_iterator_resource, max_buffer_size=self._max_buffer_size) self._prototype_device_datasets = [] for i, device in enumerate(self._devices): with ops.device(device): ds = _PerDeviceGenerator( i, self._multi_device_iterator_resource, self._incarnation_id, self._source_device_tensor, self._dataset._element_structure) # pylint: disable=protected-access self._prototype_device_datasets.append(ds) # TODO(rohanj): Explore the possibility of the MultiDeviceIterator to # initialize the device side of the pipeline. This would allow the # MultiDeviceIterator to choose, for example, to move some transformations # into the device side from its input. It might be useful in rewriting. # Create the per device iterators. self._device_iterators = [] for i, device in enumerate(self._devices): with ops.device(device): ds = self._create_device_dataset(i) if context.executing_eagerly(): self._device_iterators.append(dataset_ops.make_one_shot_iterator(ds)) else: self._device_iterators.append( dataset_ops.make_initializable_iterator(ds)) if not context.executing_eagerly(): device_iterator_initializers = [ iterator.initializer for iterator in self._device_iterators ] self._initializer = control_flow_ops.group(*device_iterator_initializers) def _create_device_dataset(self, i): """Uses _prototype_device_datasets[i] to build a dataset for the device.""" ds = self._prototype_device_datasets[i] ds = _ReincarnatedPerDeviceGenerator(ds, self._incarnation_id) if self._prefetch_buffer_size > 0: ds = ds.prefetch(self._prefetch_buffer_size) # TODO(jsimsa): Enable auto-tuning and optimizations when supported for # non-CPU devices. options = dataset_ops.Options() options.experimental_autotune = False options.experimental_optimization.apply_default_optimizations = False ds = ds.with_options(options) return ds def get_next(self, device=None): """Returns the next element given a `device`, else returns all in a list.""" if device is not None: index = self._devices.index(device) return self._device_iterators[index].get_next() result = [] for i, device in enumerate(self._devices): with ops.device(device): result.append(self._device_iterators[i].get_next()) return result def get_next_as_optional(self): result = [] for i, device in enumerate(self._devices): with ops.device(device): result.append(iterator_ops.get_next_as_optional( self._device_iterators[i])) return result @property def initializer(self): if context.executing_eagerly(): return control_flow_ops.no_op() return self._initializer def _eager_reset(self): """Resets the MultiDeviceIterator in eager mode.""" if not context.executing_eagerly(): raise ValueError("Eager reset is only supported in eager mode.") # pylint: disable=protected-access self._incarnation_id = gen_dataset_ops.multi_device_iterator_init( self._dataset._variant_tensor, self._multi_device_iterator_resource, max_buffer_size=self._max_buffer_size) for i, device in enumerate(self._devices): with ops.device(device): ds = self._create_device_dataset(i) # Reset the device iterator resources with the new dataset. ds_variant = ds._variant_tensor gen_dataset_ops.make_iterator( ds_variant, self._device_iterators[i]._iterator_resource) @property def _element_structure(self): return dataset_ops.get_structure(self._dataset)

41.1

116

0.728138

d6ad2de6f92dc747b40b1864c3a145851723278f

3,648

py

Python

mianjing/google/increasing_paths.py

RENHANFEI/LintCode

d572dee248ba4c2a95b52cd737d76c7297f4e7b4

[ "CNRI-Python" ]

null

mianjing/google/increasing_paths.py

RENHANFEI/LintCode

d572dee248ba4c2a95b52cd737d76c7297f4e7b4

[ "CNRI-Python" ]

null

mianjing/google/increasing_paths.py

RENHANFEI/LintCode

d572dee248ba4c2a95b52cd737d76c7297f4e7b4

[ "CNRI-Python" ]

null

# increasing paths in an array def f1_3(array): # O(N^2) if len(array) <= 1: return ans = [] start_idx, idx = 0, 1 prenum = array[start_idx] while idx < len(array): if array[idx] >= prenum: for i in range(start_idx, idx): ans.append(array[i:idx + 1]) else: start_idx = idx prenum = array[idx] idx += 1 return ans def f1_2(array): # O(N^2) search from start to end if len(array) <= 1: return ans = [] # a list of increasing arrays for start_idx in range(len(array) - 1): prenum = array[start_idx] idx = start_idx + 1 subarray = [prenum] while idx < len(array) and array[idx] >= prenum: subarray.append(array[idx]) ans.append(subarray[:]) prenum = array[idx] idx += 1 return ans def f1(array): # O(N^2) search from end to start if len(array) <= 1: return ans = [] # list of increasing arrays for end_idx in range(1, len(array)): last_num = array[end_idx] subarray = [last_num] idx = end_idx - 1 while idx >= 0: num = array[idx] if num <= last_num: subarray.append(num) ans.append(list(reversed(subarray))) idx -= 1 last_num = num else: break return ans array = [1, 3, 4, 2, 6] print(f1(array)) print(f1_2(array)) print(f1_3(array)) # increasing paths in a tree class Node(object): def __init__(self, val, left=None, right=None): self.val = val self.left = left self.right = right def f2(root): if not root or (not root.left and not root.right): return ans = [] traverse(root, ans) return ans def traverse(root, ans): # O(N) valid_path(root, ans, [root.val]) left, right = root.left, root.right if left: traverse(left, ans) if right: traverse(right, ans) def valid_path(root, ans, path): # O(logN ^2) left, right = root.left, root.right if left and left.val >= root.val: ans.append(path + [left.val]) valid_path(left, ans, path + [left.val]) if right and right.val >= root.val: ans.append(path + [right.val]) valid_path(right, ans, path + [right.val]) def f2_2(root): if not root or (not root.left and not root.right): return ans = [] get_paths(root, [root.val], ans) return ans def get_paths(root, path, ans): left, right = root.left, root.right if left: if left.val >= path[-1]: valid_path_reversed = [left.val] for val in reversed(path): valid_path_reversed.append(val) ans.append(list(reversed(valid_path_reversed))) get_paths(left, path + [left.val], ans) else: get_paths(left, [left.val], ans) if right and right.val >= path[-1]: if right.val >= path[-1]: valid_path_reversed = [right.val] for val in reversed(path): valid_path_reversed.append(val) ans.append(list(reversed(valid_path_reversed))) get_paths(right, path + [right.val], ans) else: get_paths(right, [right.val], ans) node_4 = Node(4) node_5 = Node(5) node_9 = Node(9) node_3 = Node(3) node_6 = Node(6) node_8 = Node(8) node_10 = Node(10) node_4.left = node_5 node_4.right = node_9 node_5.left = node_3 node_5.right = node_6 node_9.left = node_8 node_9.right = node_10 print(f2(node_4)) print(f2_2(node_4))

23.688312

63

0.553728

5aa35d2e76daeb44471ba123d06a44b3a0f45076

4,982

py

Python

test/test_LensModel/test_Profiles/test_nie.py

lucateo/lenstronomy

3ab6cfd4adea2222f02d3f0f1a9cb5390c533aab

[ "MIT" ]

1

2020-07-31T07:55:17.000Z

test/test_LensModel/test_Profiles/test_nie.py

lucateo/lenstronomy

3ab6cfd4adea2222f02d3f0f1a9cb5390c533aab

[ "MIT" ]

null

test/test_LensModel/test_Profiles/test_nie.py

lucateo/lenstronomy

3ab6cfd4adea2222f02d3f0f1a9cb5390c533aab

[ "MIT" ]

2

2020-10-26T10:45:11.000Z

2021-03-04T12:25:19.000Z

__author__ = 'sibirrer' import numpy as np import numpy.testing as npt import pytest import lenstronomy.Util.param_util as param_util from lenstronomy.Util import util from lenstronomy.LensModel.Profiles.nie import NIE, NIEMajorAxis from lenstronomy.LensModel.Profiles.spemd import SPEMD from lenstronomy.LensModel.Profiles.sis import SIS try: import fastell4py bool_test = True except: bool_test = False print("Warning: fastell4py not available, tests will not crosscheck with fastell4py on your machine") class TestNIE(object): """ tests the Gaussian methods """ def setup(self): self.nie = NIE() self.spemd = SPEMD(suppress_fastell=True) self.sis = SIS() def test_function(self): y = np.array([1., 2]) x = np.array([0., 0.]) theta_E = 1. q = 0.9999 s = 0.00001 phi_G = 0 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) values = self.nie.function(x, y, theta_E, e1, e2, s_scale=s) delta_pot = values[1] - values[0] values_spemd = self.sis.function(x, y, theta_E) delta_pot_spemd = values_spemd[1] - values_spemd[0] npt.assert_almost_equal(delta_pot, delta_pot_spemd, decimal=4) if bool_test is True: q = 0.99 s = 0.000001 phi_G = 0 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) values = self.nie.function(x, y, theta_E, e1, e2, s_scale=s) delta_pot = values[1] - values[0] gamma = 2. values_spemd = self.spemd.function(x, y, theta_E, gamma, e1, e2, s_scale=s) delta_pot_spemd = values_spemd[1] - values_spemd[0] npt.assert_almost_equal(delta_pot, delta_pot_spemd, decimal=2) def test_derivatives(self): x = np.array([1]) y = np.array([2]) theta_E = 1. q = 0.99999 phi_G = 0 s = 0.0000001 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_x, f_y = self.nie.derivatives(x, y, theta_E, e1, e2, s_scale=s) f_x_spemd, f_y_spemd = self.sis.derivatives(x, y, theta_E) npt.assert_almost_equal(f_x, f_x_spemd, decimal=4) npt.assert_almost_equal(f_y, f_y_spemd, decimal=4) if bool_test is True: q = 0.99 s = 0.000001 phi_G = 0 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_x, f_y = self.nie.derivatives(x, y, theta_E, e1, e2, s_scale=s) gamma = 2. f_x_spemd, f_y_spemd = self.spemd.derivatives(x, y, theta_E, gamma, e1, e2, s_scale=s) print(f_x/f_x_spemd, 'ratio deflections') print(1+(1-q)/2) npt.assert_almost_equal(f_x, f_x_spemd, decimal=2) npt.assert_almost_equal(f_y, f_y_spemd, decimal=2) def test_hessian(self): x = np.array([1]) y = np.array([2]) theta_E = 1. q = 0.999999 phi_G = 0 s = 0.0000001 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_xx, f_yy, f_xy = self.nie.hessian(x, y, theta_E, e1, e2, s_scale=s) f_xx_spemd, f_yy_spemd, f_xy_spemd = self.sis.hessian(x, y, theta_E) npt.assert_almost_equal(f_xx, f_xx_spemd, decimal=4) npt.assert_almost_equal(f_yy, f_yy_spemd, decimal=4) npt.assert_almost_equal(f_xy, f_xy_spemd, decimal=4) def test_convergence2surface_brightness(self): from lenstronomy.LightModel.Profiles.nie import NIE as NIE_Light nie_light = NIE_Light() kwargs = {'e1': 0.3, 'e2': -0.05, 's_scale': 0.5} x, y = util.make_grid(numPix=10, deltapix=0.1) f_xx, f_yy, f_xy = self.nie.hessian(x, y, theta_E=1, **kwargs) kappa = 1/2. * (f_xx + f_yy) flux = nie_light.function(x, y, amp=1, **kwargs) npt.assert_almost_equal(kappa/np.sum(kappa), flux/np.sum(flux), decimal=5) def test_static(self): x, y = 1., 1. phi_G, q = 0.3, 0.8 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) kwargs_lens = {'theta_E': 1., 's_scale': .1, 'e1': e1, 'e2': e2} f_ = self.nie.function(x, y, **kwargs_lens) self.nie.set_static(**kwargs_lens) f_static = self.nie.function(x, y, **kwargs_lens) npt.assert_almost_equal(f_, f_static, decimal=8) self.nie.set_dynamic() kwargs_lens = {'theta_E': 2., 's_scale': .1, 'e1': e1, 'e2': e2} f_dyn = self.nie.function(x, y, **kwargs_lens) assert f_dyn != f_static class TestNIEMajorAxis(object): def setup(self): pass def test_kappa(self): nie = NIEMajorAxis() x, y = util.make_grid(numPix=10, deltapix=0.1) kwargs = {'b': 1, 's': 0.2, 'q': 0.3} f_xx, f_yy, f_xy = nie.hessian(x, y, **kwargs) kappa_num = 1./2 * (f_xx + f_yy) kappa = nie.kappa(x, y, **kwargs) npt.assert_almost_equal(kappa_num, kappa, decimal=5) if __name__ == '__main__': pytest.main()

35.841727

105

0.59996

7b70d318af47b2b3c8e9aaf3c684b6ab6d013a26

703

py

Python

wjordpress/tests/__init__.py

texastribune/wjordpress

c98759708867091e419fc2b2a8f77568104bac98

[ "Apache-2.0" ]

4

2015-06-12T18:36:00.000Z

2016-04-25T14:28:44.000Z

wjordpress/tests/__init__.py

texastribune/wjordpress

c98759708867091e419fc2b2a8f77568104bac98

[ "Apache-2.0" ]

null

wjordpress/tests/__init__.py

texastribune/wjordpress

c98759708867091e419fc2b2a8f77568104bac98

[ "Apache-2.0" ]

null

from django.db.models.signals import post_save from django.test import TestCase from ..models import WPPost try: from example_project.content.signals import sync_post DISCONNECT_SIGNALS = True except ImportError: DISCONNECT_SIGNALS = False class WPTestCase(TestCase): """ TestCase with common logic. The example project adds some evil stuff we don't care about. Make sure it's gone when we test. """ @classmethod def setUpClass(cls): if DISCONNECT_SIGNALS: post_save.disconnect(sync_post, sender=WPPost) @classmethod def tearDownClass(cls): if DISCONNECT_SIGNALS: post_save.connect(sync_post, sender=WPPost)

23.433333

75

0.70697

2735014b2f6c8e88f41d8728bbca2732f3584485

177

py

Python

edit_earthquake_data.py

swappy208/Artificial-Intelligence-Computational-Model

fbbbf13fe3912243645f22cc9724721ca3c23162

[ "Apache-2.0" ]

null

edit_earthquake_data.py

swappy208/Artificial-Intelligence-Computational-Model

fbbbf13fe3912243645f22cc9724721ca3c23162

[ "Apache-2.0" ]

null

edit_earthquake_data.py

swappy208/Artificial-Intelligence-Computational-Model

fbbbf13fe3912243645f22cc9724721ca3c23162

[ "Apache-2.0" ]

null

import pandas data = pandas.read_csv("data.csv") #replace missing ages with mean data["Age"] = data["Age"].apply(lambda x: "25_39" if x=="don_t_know" or x=="refused" else x)

22.125

92

0.689266

cf22407c7dbe0fd0a167b780b1f439be3535594d

2,676

py

Python

testscripts/autotest.py

schopin-pro/libyami-utils

c3d25b64b05aeb0c4eecc140aef617cfeced6b8e

[ "Apache-2.0" ]

13

2018-02-22T18:52:04.000Z

2021-06-30T03:28:40.000Z

testscripts/autotest.py

schopin-pro/libyami-utils

c3d25b64b05aeb0c4eecc140aef617cfeced6b8e

[ "Apache-2.0" ]

106

2016-06-24T01:12:04.000Z

2018-01-15T13:49:34.000Z

testscripts/autotest.py

schopin-pro/libyami-utils

c3d25b64b05aeb0c4eecc140aef617cfeced6b8e

[ "Apache-2.0" ]

32

2016-06-08T20:36:42.000Z

2017-11-20T08:23:06.000Z

#! /usr/bin/env python import os import sys import shutil from yamilib import libyami if __name__ == '__main__': if len(sys.argv) < 5: print('Pls autotest.py file|dirctory decode|encode|v4l2 outdir savemode') sys.exit(-1) if not os.path.exists(sys.argv[1]): print(sys.argv[1] + ' does not exist') sys.exit(-1) mediafiles = os.path.abspath(sys.argv[1]) testformat = sys.argv[2] testoutput = sys.argv[3] savemode = True if int(sys.argv[4])==1 else False autotestpath = os.path.dirname(os.path.abspath(sys.argv[0])) yamitestpath = os.path.join(autotestpath[0:autotestpath.rfind('/')],'tests') yamidecode = os.path.join(yamitestpath, 'yamidecode') yamiencode = os.path.join(yamitestpath, 'yamiencode') v4l2decode = os.path.join(yamitestpath, 'v4l2decode') v4l2encode = os.path.join(yamitestpath, 'v4l2encode') capidecode = os.path.join(yamitestpath, 'decodecapi') capiencode = os.path.join(yamitestpath, 'encodecapi') psnr = os.path.join(autotestpath, 'psnr') decodemodelist = ['m', '0', '-2'] encodemodelist = ['m', '0'] v4l2wmodelist = ['w', '0'] v4l2mmodelist = ['m', '0', '3', '4'] if testformat == 'decode': yamiobject = libyami(yamidecode, yamiencode, psnr) yamiobject.yamiplay(mediafiles, testoutput, 'D', decodemodelist, savemode, not savemode) yamiobject.writetologfile() elif testformat == 'encode': yamiobject = libyami(yamidecode, yamiencode, psnr) yamiobject.yamiplay(mediafiles, testoutput, 'E', encodemodelist, savemode, True) yamiobject.writetologfile() elif testformat == 'v4l2': yamiobject = libyami(yamidecode, yamiencode, psnr) yamiobject.yamiplay(mediafiles, testoutput, 'D', v4l2wmodelist, savemode, False) yamiobject.yamiplay(mediafiles, testoutput, 'D', v4l2mmodelist, savemode, False) yamiobject.yamiplay(mediafiles, testoutput, 'E', v4l2mmodelist, savemode, False) yamiobject.setdeencode(capidecode, capiencode) yamiobject.yamiplay(mediafiles, testoutput, 'D', v4l2wmodelist, savemode, False) yamiobject.yamiplay(mediafiles, testoutput, 'D', v4l2mmodelist, savemode, False) yamiobject.yamiplay(mediafiles, testoutput, 'E', v4l2mmodelist, savemode, False) yamiobject.setdeencode(v4l2decode, v4l2encode) yamiobject.yamiplay(mediafiles, testoutput, 'D', v4l2wmodelist, savemode, False) yamiobject.yamiplay(mediafiles, testoutput, 'D', v4l2mmodelist, savemode, False) yamiobject.yamiplay(mediafiles, testoutput, 'E', v4l2mmodelist, savemode, False) yamiobject.writetologfile()

50.490566

96

0.684604

4d0e389d23fdc7736f06581fe356199a36c89506

8,772

py

Python

2.1/faster_rcnn/utils/rpn/proposal_layer.py

waikato-datamining/cntk

1b626407ef750dfbd4ad66fe9aed28487f2a4441

[ "MIT" ]

null

2.1/faster_rcnn/utils/rpn/proposal_layer.py

waikato-datamining/cntk

1b626407ef750dfbd4ad66fe9aed28487f2a4441

[ "MIT" ]

null

2.1/faster_rcnn/utils/rpn/proposal_layer.py

waikato-datamining/cntk

1b626407ef750dfbd4ad66fe9aed28487f2a4441

[ "MIT" ]

null

# Copyright (c) Microsoft. All rights reserved. # Licensed under the MIT license. See LICENSE.md file in the project root # for full license information. # ============================================================================== from cntk import output_variable, FreeDimension from cntk.ops.functions import UserFunction import numpy as np import yaml from utils.rpn.generate_anchors import generate_anchors from utils.rpn.bbox_transform import bbox_transform_inv, clip_boxes from utils.nms.nms_wrapper import nms try: from config import cfg except ImportError: from utils.default_config import cfg DEBUG = False class ProposalLayer(UserFunction): ''' Outputs object detection proposals by applying estimated bounding-box transformations to a set of regular boxes (called "anchors"). ''' def __init__(self, arg1, arg2, arg3, name='ProposalLayer', param_str=None): super(ProposalLayer, self).__init__([arg1, arg2, arg3], name=name) self.param_str_ = param_str if param_str is not None else "'feat_stride': 16\n'scales':\n - 8 \n - 16 \n - 32" # parse the layer parameter string, which must be valid YAML layer_params = yaml.load(self.param_str_) self._feat_stride = layer_params['feat_stride'] anchor_scales = layer_params.get('scales', (8, 16, 32)) self._anchors = generate_anchors(scales=np.array(anchor_scales)) self._num_anchors = self._anchors.shape[0] if DEBUG: print ('feat_stride: {}'.format(self._feat_stride)) print ('anchors:') print (self._anchors) def infer_outputs(self): # rois blob: holds R regions of interest, each is a 5-tuple # (n, x1, y1, x2, y2) specifying an image batch index n and a # rectangle (x1, y1, x2, y2) # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python proposalShape = (FreeDimension, 4) return [output_variable(proposalShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_rois_raw", needs_gradient=False)] def forward(self, arguments, device=None, outputs_to_retain=None): # Algorithm: # # for each (H, W) location i # generate A anchor boxes centered on cell i # apply predicted bbox deltas at cell i to each of the A anchors # clip predicted boxes to image # remove predicted boxes with either height or width < threshold # sort all (proposal, score) pairs by score from highest to lowest # take top pre_nms_topN proposals before NMS # apply NMS with threshold 0.7 to remaining proposals # take after_nms_topN proposals after NMS # return the top proposals (-> RoIs top, scores top) # use potentially different number of proposals for training vs evaluation if len(outputs_to_retain) == 0: # print("EVAL") pre_nms_topN = cfg["TEST"].RPN_PRE_NMS_TOP_N post_nms_topN = cfg["TEST"].RPN_POST_NMS_TOP_N nms_thresh = cfg["TEST"].RPN_NMS_THRESH min_size = cfg["TEST"].RPN_MIN_SIZE else: pre_nms_topN = cfg["TRAIN"].RPN_PRE_NMS_TOP_N post_nms_topN = cfg["TRAIN"].RPN_POST_NMS_TOP_N nms_thresh = cfg["TRAIN"].RPN_NMS_THRESH min_size = cfg["TRAIN"].RPN_MIN_SIZE bottom = arguments assert bottom[0].shape[0] == 1, \ 'Only single item batches are supported' # the first set of _num_anchors channels are bg probs # the second set are the fg probs, which we want scores = bottom[0][:, self._num_anchors:, :, :] bbox_deltas = bottom[1] im_info = bottom[2][0] if DEBUG: # im_info = (pad_width, pad_height, scaled_image_width, scaled_image_height, orig_img_width, orig_img_height) # e.g.(1000, 1000, 1000, 600, 500, 300) for an original image of 600x300 that is scaled and padded to 1000x1000 print ('im_size: ({}, {})'.format(im_info[0], im_info[1])) print ('scaled im_size: ({}, {})'.format(im_info[2], im_info[3])) print ('original im_size: ({}, {})'.format(im_info[4], im_info[5])) # 1. Generate proposals from bbox deltas and shifted anchors height, width = scores.shape[-2:] if DEBUG: print ('score map size: {}'.format(scores.shape)) # Enumerate all shifts shift_x = np.arange(0, width) * self._feat_stride shift_y = np.arange(0, height) * self._feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y) shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() # Enumerate all shifted anchors: # # add A anchors (1, A, 4) to # cell K shifts (K, 1, 4) to get # shift anchors (K, A, 4) # reshape to (K*A, 4) shifted anchors A = self._num_anchors K = shifts.shape[0] anchors = self._anchors.reshape((1, A, 4)) + \ shifts.reshape((1, K, 4)).transpose((1, 0, 2)) anchors = anchors.reshape((K * A, 4)) # Transpose and reshape predicted bbox transformations to get them # into the same order as the anchors: # # bbox deltas will be (1, 4 * A, H, W) format # transpose to (1, H, W, 4 * A) # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a) # in slowest to fastest order bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4)) # Same story for the scores: # # scores are (1, A, H, W) format # transpose to (1, H, W, A) # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a) scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1)) # Convert anchors into proposals via bbox transformations proposals = bbox_transform_inv(anchors, bbox_deltas) # 2. clip predicted boxes to image proposals = clip_boxes(proposals, im_info) # 3. remove predicted boxes with either height or width < threshold # (NOTE: convert min_size to input image scale. Original size = im_info[4:6], scaled size = im_info[2:4]) cntk_image_scale = im_info[2] / im_info[4] keep = _filter_boxes(proposals, min_size * cntk_image_scale) proposals = proposals[keep, :] scores = scores[keep] # 4. sort all (proposal, score) pairs by score from highest to lowest # 5. take top pre_nms_topN (e.g. 6000) order = scores.ravel().argsort()[::-1] if pre_nms_topN > 0: order = order[:pre_nms_topN] proposals = proposals[order, :] scores = scores[order] # 6. apply nms (e.g. threshold = 0.7) # 7. take after_nms_topN (e.g. 300) # 8. return the top proposals (-> RoIs top) keep = nms(np.hstack((proposals, scores)), nms_thresh) if post_nms_topN > 0: keep = keep[:post_nms_topN] proposals = proposals[keep, :] scores = scores[keep] # pad with zeros if too few rois were found num_found_proposals = proposals.shape[0] if num_found_proposals < post_nms_topN: if DEBUG: print("Only {} proposals generated in ProposalLayer".format(num_found_proposals)) proposals_padded = np.zeros(((post_nms_topN,) + proposals.shape[1:]), dtype=np.float32) proposals_padded[:num_found_proposals, :] = proposals proposals = proposals_padded # Output rois blob # Our RPN implementation only supports a single input image, so all # batch inds are 0 # for CNTK: add batch axis to output shape proposals.shape = (1,) + proposals.shape return None, proposals def backward(self, state, root_gradients, variables): """This layer does not propagate gradients.""" pass def clone(self, cloned_inputs): return ProposalLayer(cloned_inputs[0], cloned_inputs[1], cloned_inputs[2], param_str=self.param_str_) def serialize(self): internal_state = {} internal_state['param_str'] = self.param_str_ return internal_state @staticmethod def deserialize(inputs, name, state): param_str = state['param_str'] return ProposalLayer(inputs[0], inputs[1], inputs[2], name=name, param_str=param_str) def _filter_boxes(boxes, min_size): """Remove all boxes with any side smaller than min_size.""" ws = boxes[:, 2] - boxes[:, 0] + 1 hs = boxes[:, 3] - boxes[:, 1] + 1 keep = np.where((ws >= min_size) & (hs >= min_size))[0] return keep

41.377358

123

0.616279

895c2540a4feb263539a3a90a7a5e4a9969c3619

3,713

py

Python

contrib/macdeploy/custom_dsstore.py

traficoin/traficoin

f6985559b8c9dfffa97faa77419b9fa672122b71

[ "MIT" ]

null

contrib/macdeploy/custom_dsstore.py

traficoin/traficoin

f6985559b8c9dfffa97faa77419b9fa672122b71

[ "MIT" ]

null

contrib/macdeploy/custom_dsstore.py

traficoin/traficoin

f6985559b8c9dfffa97faa77419b9fa672122b71

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2013-2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. import biplist from ds_store import DSStore from mac_alias import Alias import sys output_file = sys.argv[1] package_name_ns = sys.argv[2] ds = DSStore.open(output_file, 'w+') ds['.']['bwsp'] = { 'ShowStatusBar': False, 'WindowBounds': b'{{300, 280}, {500, 343}}', 'ContainerShowSidebar': False, 'SidebarWidth': 0, 'ShowTabView': False, 'PreviewPaneVisibility': False, 'ShowToolbar': False, 'ShowSidebar': False, 'ShowPathbar': True } icvp = { 'gridOffsetX': 0.0, 'textSize': 12.0, 'viewOptionsVersion': 1, 'backgroundImageAlias': b'\x00\x00\x00\x00\x02\x1e\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xd1\x94\\\xb0H+\x00\x05\x00\x00\x00\x98\x0fbackground.tiff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x99\xd19\xb0\xf8\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\r\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0b.background\x00\x00\x10\x00\x08\x00\x00\xd1\x94\\\xb0\x00\x00\x00\x11\x00\x08\x00\x00\xd19\xb0\xf8\x00\x00\x00\x01\x00\x04\x00\x00\x00\x98\x00\x0e\x00 \x00\x0f\x00b\x00a\x00c\x00k\x00g\x00r\x00o\x00u\x00n\x00d\x00.\x00t\x00i\x00f\x00f\x00\x0f\x00\x02\x00\x00\x00\x12\x00\x1c/.background/background.tiff\x00\x14\x01\x06\x00\x00\x00\x00\x01\x06\x00\x02\x00\x00\x0cMacintosh HD\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xce\x97\xab\xc3H+\x00\x00\x01\x88[\x88\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02u\xab\x8d\xd1\x94\\\xb0devrddsk\xff\xff\xff\xff\x00\x00\t \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x07bitcoin\x00\x00\x10\x00\x08\x00\x00\xce\x97\xab\xc3\x00\x00\x00\x11\x00\x08\x00\x00\xd1\x94\\\xb0\x00\x00\x00\x01\x00\x14\x01\x88[\x88\x00\x16\xa9\t\x00\x08\xfaR\x00\x08\xfaQ\x00\x02d\x8e\x00\x0e\x00\x02\x00\x00\x00\x0f\x00\x1a\x00\x0c\x00M\x00a\x00c\x00i\x00n\x00t\x00o\x00s\x00h\x00 \x00H\x00D\x00\x13\x00\x01/\x00\x00\x15\x00\x02\x00\x14\xff\xff\x00\x00\xff\xff\x00\x00', 'backgroundColorBlue': 1.0, 'iconSize': 96.0, 'backgroundColorGreen': 1.0, 'arrangeBy': 'none', 'showIconPreview': True, 'gridSpacing': 100.0, 'gridOffsetY': 0.0, 'showItemInfo': False, 'labelOnBottom': True, 'backgroundType': 2, 'backgroundColorRed': 1.0 } alias = Alias.from_bytes(icvp['backgroundImageAlias']) alias.volume.name = package_name_ns alias.volume.posix_path = '/Volumes/' + package_name_ns alias.volume.disk_image_alias.target.filename = package_name_ns + '.temp.dmg' alias.volume.disk_image_alias.target.carbon_path = 'Macintosh HD:Users:\x00bitcoinuser:\x00Documents:\x00bitcoin:\x00bitcoin:\x00' + package_name_ns + '.temp.dmg' alias.volume.disk_image_alias.target.posix_path = 'Users/bitcoinuser/Documents/bitcoin/bitcoin/' + package_name_ns + '.temp.dmg' alias.target.carbon_path = package_name_ns + ':.background:\x00background.tiff' icvp['backgroundImageAlias'] = biplist.Data(alias.to_bytes()) ds['.']['icvp'] = icvp ds['.']['vSrn'] = ('long', 1) ds['Applications']['Iloc'] = (370, 156) ds['TRAFICOIN-Qt.app']['Iloc'] = (128, 156) ds.flush() ds.close()

61.883333

1,817

0.724482

d360f8c171aa24d690f2edd4b1eb1426b5ad4d93

9,545

py

Python

courses/machine_learning/deepdive/08_image/flowersmodel/model.py

mikepope-google/training-data-analyst

fe04e605686f8281e7b2adacadf6adb16a1345e6

[ "Apache-2.0" ]

1

2019-06-06T15:16:30.000Z

courses/machine_learning/deepdive/08_image/flowersmodel/model.py

mikepope-google/training-data-analyst

fe04e605686f8281e7b2adacadf6adb16a1345e6

[ "Apache-2.0" ]

null

courses/machine_learning/deepdive/08_image/flowersmodel/model.py

mikepope-google/training-data-analyst

fe04e605686f8281e7b2adacadf6adb16a1345e6

[ "Apache-2.0" ]

1

2020-03-05T05:40:33.000Z

#!/usr/bin/env python # Copyright 2017 Google Inc. 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf tf.logging.set_verbosity(tf.logging.INFO) LIST_OF_LABELS = 'daisy,dandelion,roses,sunflowers,tulips'.split(',') HEIGHT = 299 WIDTH = 299 NUM_CHANNELS = 3 NCLASSES = 5 def linear_model(img, mode, hparams): X = tf.reshape(img,[-1,HEIGHT*WIDTH*NUM_CHANNELS]) #flatten ylogits = tf.layers.dense(X,NCLASSES,activation=None) return ylogits, NCLASSES def dnn_model(img, mode, hparams): X = tf.reshape(img, [-1, HEIGHT*WIDTH*NUM_CHANNELS]) #flatten h1 = tf.layers.dense(X, 300, activation=tf.nn.relu) h2 = tf.layers.dense(h1,100, activation=tf.nn.relu) h3 = tf.layers.dense(h2, 30, activation=tf.nn.relu) ylogits = tf.layers.dense(h3, NCLASSES, activation=None) return ylogits, NCLASSES def dnn_dropout_model(img, mode, hparams): dprob = hparams.get('dprob', 0.1) X = tf.reshape(img, [-1, HEIGHT*WIDTH*NUM_CHANNELS]) #flatten h1 = tf.layers.dense(X, 300, activation=tf.nn.relu) h2 = tf.layers.dense(h1,100, activation=tf.nn.relu) h3 = tf.layers.dense(h2, 30, activation=tf.nn.relu) h3d = tf.layers.dropout(h3, rate=dprob, training=( mode == tf.estimator.ModeKeys.TRAIN)) #only dropout when training ylogits = tf.layers.dense(h3d, NCLASSES, activation=None) return ylogits, NCLASSES def cnn_model(img, mode, hparams): ksize1 = hparams.get('ksize1', 5) ksize2 = hparams.get('ksize2', 5) nfil1 = hparams.get('nfil1', 10) nfil2 = hparams.get('nfil2', 20) dprob = hparams.get('dprob', 0.25) c1 = tf.layers.conv2d(img, filters=nfil1, kernel_size=ksize1, strides=1, padding='same', activation=tf.nn.relu) p1 = tf.layers.max_pooling2d(c1,pool_size=2, strides=2) c2 = tf.layers.conv2d(p1, filters=nfil2, kernel_size=ksize2, strides=1, padding='same', activation=tf.nn.relu) p2 = tf.layers.max_pooling2d(c2,pool_size=2, strides=2) outlen = p2.shape[1]*p2.shape[2]*p2.shape[3] p2flat = tf.reshape(p2, [-1, outlen]) # flattened #apply batch normalization if hparams['batch_norm']: h3 = tf.layers.dense(p2flat, 300, activation=None) h3 = tf.layers.batch_normalization( h3, training=(mode == tf.estimator.ModeKeys.TRAIN)) #only batchnorm when training h3 = tf.nn.relu(h3) else: h3 = tf.layers.dense(p2flat, 300, activation=tf.nn.relu) #apply dropout h3d = tf.layers.dropout(h3, rate=dprob, training=(mode == tf.estimator.ModeKeys.TRAIN)) ylogits = tf.layers.dense(h3d, NCLASSES, activation=None) #apply batch normalization once more if hparams['batch_norm']: ylogits = tf.layers.batch_normalization( ylogits, training=(mode == tf.estimator.ModeKeys.TRAIN)) return ylogits, NCLASSES def read_and_preprocess_with_augment(image_bytes, label=None): return read_and_preprocess(image_bytes, label, augment=True) def read_and_preprocess(image_bytes, label=None, augment=False): # decode the image # end up with pixel values that are in the -1, 1 range image = tf.image.decode_jpeg(image_bytes, channels=NUM_CHANNELS) image = tf.image.convert_image_dtype(image, dtype=tf.float32) # 0-1 image = tf.expand_dims(image, 0) # resize_bilinear needs batches if augment: image = tf.image.resize_bilinear( image, [HEIGHT+10, WIDTH+10], align_corners=False) image = tf.squeeze(image) #remove batch dimension image = tf.random_crop(image, [HEIGHT, WIDTH, NUM_CHANNELS]) image = tf.image.random_flip_left_right(image) image = tf.image.random_brightness(image, max_delta=63.0/255.0) image = tf.image.random_contrast(image, lower=0.2, upper=1.8) else: image = tf.image.resize_bilinear(image, [HEIGHT, WIDTH, NUM_CHANNELS], align_corners=False) image = tf.squeeze(image) #remove batch dimension #pixel values are in range [0,1], convert to [-1,1] image = tf.subtract(image, 0.5) image = tf.multiply(image, 2.0) return {'image':image}, label def serving_input_fn(): # Note: only handles one image at a time feature_placeholders = {'image_bytes': tf.placeholder(tf.string, shape=())} image, _ = read_and_preprocess( tf.squeeze(feature_placeholders['image_bytes'])) image['image'] = tf.expand_dims(image['image'],0) return tf.estimator.export.ServingInputReceiver(image, feature_placeholders) def make_input_fn(csv_of_filenames, batch_size, mode, augment=False): def _input_fn(): def decode_csv(csv_row): filename, label = tf.decode_csv( csv_row, record_defaults = [[''],['']]) image_bytes = tf.read_file(filename) return image_bytes, label # Create tf.data.dataset from filename dataset = tf.data.TextLineDataset(csv_of_filenames).map(decode_csv) if augment: dataset = dataset.map(read_and_preprocess_with_augment) else: dataset = dataset.map(read_and_preprocess) if mode == tf.estimator.ModeKeys.TRAIN: num_epochs = None # indefinitely dataset = dataset.shuffle(buffer_size = 10 * batch_size) else: num_epochs = 1 # end-of-input after this dataset = dataset.repeat(num_epochs).batch(batch_size) return dataset.make_one_shot_iterator().get_next() return _input_fn def image_classifier(features, labels, mode, params): model_functions = { 'linear':linear_model, 'dnn':dnn_model, 'dnn_dropout':dnn_dropout_model, 'cnn':cnn_model} model_function = model_functions[params['model']] ylogits, nclasses = model_function(features['image'], mode, params) probabilities = tf.nn.softmax(ylogits) class_int = tf.cast(tf.argmax(probabilities, 1), tf.uint8) class_str = tf.gather(LIST_OF_LABELS, tf.cast(class_int, tf.int32)) if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL: #convert string label to int labels_table = tf.contrib.lookup.index_table_from_tensor( tf.constant(LIST_OF_LABELS)) labels = labels_table.lookup(labels) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2( logits=ylogits, labels=tf.one_hot(labels, nclasses))) evalmetrics = {'accuracy': tf.metrics.accuracy(class_int, labels)} if mode == tf.estimator.ModeKeys.TRAIN: # this is needed for batch normalization, but has no effect otherwise update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = tf.contrib.layers.optimize_loss( loss, tf.train.get_global_step(), learning_rate=params['learning_rate'], optimizer="Adam") else: train_op = None else: loss = None train_op = None evalmetrics = None return tf.estimator.EstimatorSpec( mode=mode, predictions={"probabilities": probabilities, "classid": class_int, "class": class_str}, loss=loss, train_op=train_op, eval_metric_ops=evalmetrics, export_outputs={'classes': tf.estimator.export.PredictOutput( {"probabilities": probabilities, "classid": class_int, "class": class_str})} ) def train_and_evaluate(output_dir, hparams): EVAL_INTERVAL = 300 #every 5 minutes estimator = tf.estimator.Estimator(model_fn = image_classifier, params = hparams, config= tf.estimator.RunConfig( save_checkpoints_secs = EVAL_INTERVAL), model_dir = output_dir) train_spec = tf.estimator.TrainSpec(input_fn = make_input_fn( hparams['train_data_path'], hparams['batch_size'], mode = tf.estimator.ModeKeys.TRAIN, augment = hparams['augment']), max_steps = hparams['train_steps']) exporter = tf.estimator.LatestExporter('exporter', serving_input_fn) eval_spec = tf.estimator.EvalSpec(input_fn = make_input_fn( hparams['eval_data_path'], hparams['batch_size'], mode = tf.estimator.ModeKeys.EVAL), steps = None, exporters = exporter, start_delay_secs = EVAL_INTERVAL, throttle_secs = EVAL_INTERVAL) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

41.142241

98

0.644421

e704c7ee2fad793c8e6f0f7dda0ce27cf32ec098

30

py

Python

cookbook/control_plane/register_project.py

mayitbeegh/flytesnacks

35fe9db45f08fce3d94923b4245b1a9980a915ef

[ "Apache-2.0" ]

null

cookbook/control_plane/register_project.py

mayitbeegh/flytesnacks

35fe9db45f08fce3d94923b4245b1a9980a915ef

[ "Apache-2.0" ]

null

cookbook/control_plane/register_project.py

mayitbeegh/flytesnacks

35fe9db45f08fce3d94923b4245b1a9980a915ef

[ "Apache-2.0" ]

null

""" Register a New Project """

10

22

0.633333

c5e007316f8cd4cf3e97aa5d2fb5cb124936ca33

7,096

py

Python

main.py

osucookiezi/gulag

cfb5a4bfbfcd281ca1d572b83d799355b0a9b443

[ "MIT" ]

null

main.py

osucookiezi/gulag

cfb5a4bfbfcd281ca1d572b83d799355b0a9b443

[ "MIT" ]

null

main.py

osucookiezi/gulag

cfb5a4bfbfcd281ca1d572b83d799355b0a9b443

[ "MIT" ]

null

#!/usr/bin/python3.9 # -*- coding: utf-8 -*- # if you're interested in development, my test server is # usually up at 51.161.34.235. just switch the ip of any # switcher to the one above, toggle it off and on again, and # you should be connected. registration is done ingame with # osu!'s built-in registration. # certificate: https://akatsuki.pw/static/ca.crt import sys sys._excepthook = sys.excepthook # backup def _excepthook(type, value, traceback): if type is KeyboardInterrupt: print('\33[2K\r', end='Aborted startup.') return print('\x1b[0;31mgulag ran into an issue ' 'before starting up :(\x1b[0m') sys._excepthook(type, value, traceback) sys.excepthook = _excepthook import os from pathlib import Path import aiohttp import cmyui import datadog import orjson # go zoom from cmyui import Ansi from cmyui import log import bg_loops from constants.privileges import Privileges from objects import glob from objects.achievement import Achievement from objects.collections import PlayerList from objects.collections import MatchList from objects.collections import ChannelList from objects.collections import ClanList from objects.collections import MapPoolList from objects.player import Player from utils.misc import download_achievement_pngs from utils.updater import Updater __all__ = () # current version of gulag # NOTE: this is used internally for the updater, it may be # worth reading through it's code before playing with it. glob.version = cmyui.Version(3, 2, 4) async def setup_collections() -> None: """Setup & cache many global collections (mostly from sql).""" glob.players = PlayerList() # online players glob.matches = MatchList() # active multiplayer matches glob.channels = await ChannelList.prepare() # active channels glob.clans = await ClanList.prepare() # active clans glob.pools = await MapPoolList.prepare() # active mappools # create our bot & append it to the global player list. res = await glob.db.fetch('SELECT name FROM users WHERE id = 1') glob.bot = Player( id = 1, name = res['name'], priv = Privileges.Normal, login_time = float(0x7fffffff) # never auto-dc ) glob.players.append(glob.bot) # global achievements (sorted by vn gamemodes) glob.achievements = {0: [], 1: [], 2: [], 3: []} async for row in glob.db.iterall('SELECT * FROM achievements'): # NOTE: achievement conditions are stored as # stringified python expressions in the database # to allow for easy custom achievements. condition = eval(f'lambda score: {row.pop("cond")}') achievement = Achievement(**row, cond=condition) # NOTE: achievements are grouped by modes internally. glob.achievements[row['mode']].append(achievement) # static api keys glob.api_keys = { row['api_key']: row['id'] for row in await glob.db.fetchall( 'SELECT id, api_key FROM users ' 'WHERE api_key IS NOT NULL' ) } async def before_serving() -> None: """Called before the server begins serving connections.""" # retrieve a client session to use for http connections. glob.http = aiohttp.ClientSession(json_serialize=orjson.dumps) # retrieve a pool of connections to use for mysql interaction. glob.db = cmyui.AsyncSQLPool() await glob.db.connect(glob.config.mysql) # run the sql & submodule updater (uses http & db). updater = Updater(glob.version) await updater.run() await updater.log_startup() # cache many global collections/objects from sql, # such as channels, mappools, clans, bot, etc. await setup_collections() new_coros = [] # create a task for each donor expiring in 30d. new_coros.extend(await bg_loops.donor_expiry()) # setup a loop to kick inactive ghosted players. new_coros.append(bg_loops.disconnect_ghosts()) # if the surveillance webhook has a value, run # automatic (still very primitive) detections on # replays deemed by the server's configurable values. if glob.config.webhooks['surveillance']: new_coros.append(bg_loops.replay_detections()) # reroll the bot's random status every `interval` sec. new_coros.append(bg_loops.reroll_bot_status(interval=300)) for coro in new_coros: glob.app.add_pending_task(coro) async def after_serving() -> None: """Called after the server stops serving connections.""" await glob.http.close() if glob.db.pool is not None: await glob.db.close() if glob.datadog: glob.datadog.stop() # stop thread glob.datadog.flush() # flush any leftover if __name__ == '__main__': # set cwd to /gulag. os.chdir(os.path.dirname(os.path.realpath(__file__))) # create /.data and its subdirectories. data_path = Path.cwd() / '.data' data_path.mkdir(exist_ok=True) for sub_dir in ('avatars', 'logs', 'osu', 'osr', 'ss'): subdir = data_path / sub_dir subdir.mkdir(exist_ok=True) achievements_path = data_path / 'assets/medals/client' if not achievements_path.exists(): # create directory & download achievement pngs achievements_path.mkdir(parents=True) download_achievement_pngs(achievements_path) # make sure oppai-ng is built and ready. glob.oppai_built = (Path.cwd() / 'oppai-ng/oppai').exists() if not glob.oppai_built: log('No oppai-ng compiled binary found. PP for all ' 'std & taiko scores will be set to 0; instructions ' 'can be found in the README file.', Ansi.LRED) # create a server object, which serves as a map of domains. app = glob.app = cmyui.Server( name=f'gulag v{glob.version}', gzip=4, debug=glob.config.debug ) # add our endpoint's domains to the server; # each may potentially hold many individual endpoints. from domains.cho import domain as cho_domain # c[e4-6]?.ppy.sh from domains.osu import domain as osu_domain # osu.ppy.sh from domains.ava import domain as ava_domain # a.ppy.sh app.add_domains({cho_domain, osu_domain, ava_domain}) # enqueue tasks to run once the server # begins, and stops serving connections. # these make sure we set everything up # and take it down nice and graceful. app.before_serving = before_serving app.after_serving = after_serving # support for https://datadoghq.com if all(glob.config.datadog.values()): datadog.initialize(**glob.config.datadog) glob.datadog = datadog.ThreadStats() glob.datadog.start(flush_in_thread=True, flush_interval=15) # wipe any previous stats from the page. glob.datadog.gauge('gulag.online_players', 0) else: glob.datadog = None # start up the server; this starts # an event loop internally, using # uvloop if it's installed. app.run(glob.config.server_addr, handle_signals=True, # SIGHUP, SIGTERM, SIGINT sigusr1_restart=True) # use SIGUSR1 for restarts

34.784314

68

0.687148

6e4428416d964028b6c45bf49d98cc2ebd57bf74

1,810

py

Python

bounded_multi_processing.py

zinob/python_tutorials

a730160d13780360e4c0e407b87a8b090bb2042d

[ "MIT" ]

null

bounded_multi_processing.py

zinob/python_tutorials

a730160d13780360e4c0e407b87a8b090bb2042d

[ "MIT" ]

null

bounded_multi_processing.py

zinob/python_tutorials

a730160d13780360e4c0e407b87a8b090bb2042d

[ "MIT" ]

null

#!/usr/bin/python ## """ ## How do i parallelize a retriever function and a sender/saver function when they ## 1: require mpthreads ## 2: The dataammount is significant enough that I don't want the receiver to get to far ahead of the sender. ## """ ## This code uses a semaphore to bound the amount of in-flight data. ## The general rule of thumb is that you should use twice the number of threads that you have CPUs, ## so this one uses one pool for senders and one pool for receivers, each with equal workers as there are CPUs in the system. ## The semaphore size is set to the twice the number of possible in-flight sending requests in order to avoid thread starvation ## (if it is less than cpu*chunk it will stall, if its less than twice that performance might suffer) import multiprocessing from multiprocessing import Pool import time try: from tqdm import tqdm except: tqdm=lambda x,*_,**__:x import random def ini(y): global semaphore semaphore=y def receive(i): semaphore.acquire() time.sleep(1+random.randrange(-4,4)/10) return i+1 def send(x): time.sleep(2) semaphore.release() return(f"sent:{x}") ITERS=99 CHUNK=4 CPUs=multiprocessing.cpu_count() m = multiprocessing.Manager() s = m.BoundedSemaphore(CHUNK*CPUs*2) t=time.time() with Pool(CPUs,initializer=ini, initargs=[s] ) as receiver_p: with Pool(CPUs,initializer=ini, initargs=[s]) as sender_p: indata = range(ITERS) received=receiver_p.imap_unordered(receive, indata,chunksize=CHUNK) sent=sender_p.imap_unordered(send, tqdm(received, desc="received", total=ITERS,miniters=1),chunksize=CHUNK) for i in tqdm(sent,desc="sent",total=ITERS,miniters=1): pass time.sleep(1) print() print(time.time()-t) print(time.time()-t)

31.754386

127

0.709392

67111e3fcadda5601597bb03ba46740c6f0b602f

4,730

py

Python

ExcelHandler.py

btminzon/bandcombination

c2e869f3e31e1655b9c22982b7a90580e080dd1d

[ "BSD-2-Clause-FreeBSD" ]

9

2019-04-19T03:19:23.000Z

2022-02-22T22:08:10.000Z

ExcelHandler.py

btminzon/bandcombination

c2e869f3e31e1655b9c22982b7a90580e080dd1d

[ "BSD-2-Clause-FreeBSD" ]

null

ExcelHandler.py

btminzon/bandcombination

c2e869f3e31e1655b9c22982b7a90580e080dd1d

[ "BSD-2-Clause-FreeBSD" ]

4

2019-04-19T03:19:28.000Z

2021-01-26T22:53:44.000Z

import xlsxwriter def writeBandOnFile(workbook, bandsList, modulationList): # Add a worksheet to Excel file BandWorksheet = workbook.add_worksheet('Band Info') # Header BandCombinationHeader = ("Band", "256QAM in DL", "64QAM in UL") # used formats on this worksheet cellHeaderFormat = workbook.add_format({'align': 'center', 'valign': 'vcenter', 'border': 1, 'bold': 1, 'font_size': 14, }) cellFormat = workbook.add_format({'align': 'center', 'valign': 'vcenter', 'border': 1, 'bold': 0, 'font_size': 11, }) itemCellFormat = workbook.add_format({'align': 'center', 'valign': 'vcenter', 'border': 1, 'bold': 1, 'font_size': 11, }) row = 0 col = 0 # set 256QAM column size to 17 BandWorksheet.set_column(1, 1, 17) # set 64QAM column size to 16 BandWorksheet.set_column(2, 2, 16) for headerItem in (BandCombinationHeader): BandWorksheet.write(row, col, headerItem, cellHeaderFormat) col += 1 row = 1 col = 0 for band,modulation in zip(bandsList,modulationList): BandWorksheet.write(row, col, band, cellFormat) BandWorksheet.write(row, col+1, modulation['dl-256QAM-r12'], cellFormat) BandWorksheet.write(row, col+2, modulation['ul-64QAM-r12'], cellFormat) row += 1 def writeBandCombinationOnFile(workbook, bandCombinationList, layersList, bcsList): # No Carrier Aggregation Support if bandCombinationList == ["None"]: return # Add a worksheet to Excel file BandCombinationWorksheet = workbook.add_worksheet('Band Combination Info') # Header BandCombinationHeader = ("Item", "Band", "BW Class", "MIMO", "Layers", "Bandwidth Combination Set*") # used formats on this worksheet cellHeaderFormat = workbook.add_format({'align': 'center', 'valign': 'vcenter', 'border': 1, 'bold': 1, 'font_size': 14, }) cellFormat = workbook.add_format({'align': 'center', 'valign': 'vcenter', 'border': 1, 'bold': 0, 'font_size': 11, }) itemCellFormat = workbook.add_format({'align': 'center', 'valign': 'vcenter', 'border': 1, 'bold': 1, 'font_size': 11, }) noteFormat = workbook.add_format({'align': 'left', 'valign': 'vcenter', 'border': 1, 'bold': 1, 'font_size': 11, }) row = 0 col = 0 # set BW Class column size to 11 BandCombinationWorksheet.set_column(2,2,11) # set BCS column size to 28 BandCombinationWorksheet.set_column(5, 5, 44) for headerItem in BandCombinationHeader: BandCombinationWorksheet.write(row, col, headerItem, cellHeaderFormat) col += 1 row = 1 col = 0 notMerged = True ItemIndex = 0 for items in bandCombinationList: for bandCombinationObject in items: if len(items) > 1 and notMerged: BandCombinationWorksheet.merge_range(row, col, row+len(items)-1, col, bandCombinationObject['Item'], itemCellFormat) BandCombinationWorksheet.merge_range(row, col+4, row+len(items)-1, col+4, layersList[ItemIndex], cellFormat) BandCombinationWorksheet.merge_range(row, col+5, row+len(items)-1, col+5, "ALL" if bcsList[ItemIndex] == "" else bcsList[ItemIndex], cellFormat) notMerged = False elif len(items) is 1: BandCombinationWorksheet.write(row, col, bandCombinationObject['Item'], itemCellFormat) BandCombinationWorksheet.write(row, col+4, layersList[ItemIndex], cellFormat) BandCombinationWorksheet.write(row, col+5, "ALL" if bcsList[ItemIndex] == '' else bcsList[ItemIndex], cellFormat) BandCombinationWorksheet.write(row, col+1, bandCombinationObject['Band'], cellFormat) BandCombinationWorksheet.write(row, col+2, bandCombinationObject['BwClass'].upper(), cellFormat) BandCombinationWorksheet.write(row, col+3, "2x2" if bandCombinationObject['SupportedMIMOLayers'] == 'twoLayers' else "4x4", cellFormat) col = 0 row += 1 notMerged = True ItemIndex += 1 # Write a note about where to find BCS definition, in 3gpp BandCombinationWorksheet.merge_range(row, 0, row, col + 5, "* For BCS information, please check 36.301-Rel15 tables 5.6A.1-1 and 5.6A.1-2", noteFormat) def write2Excel(bandsList, bandCombinationList,layersList, bcsList, modulationList, file): # filename workbook = xlsxwriter.Workbook(file) # Write supported bands and related items writeBandOnFile(workbook, bandsList, modulationList) # Write supported band combination items and related features writeBandCombinationOnFile(workbook, bandCombinationList, layersList, bcsList) print("Created file {}".format(file)) # close file workbook.close()

43

160

0.670825

2dfb031a36e59666a51582deb13e88481abc0fda

6,325

py

Python

climateeconomics/tests/l1_test_gradient_oil_resource_discipline.py

os-climate/witness-core

3ef9a44d86804c5ad57deec3c9916348cb3bfbb8

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

1

2022-01-14T06:37:42.000Z

climateeconomics/tests/l1_test_gradient_oil_resource_discipline.py

os-climate/witness-core

3ef9a44d86804c5ad57deec3c9916348cb3bfbb8

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

null

climateeconomics/tests/l1_test_gradient_oil_resource_discipline.py

os-climate/witness-core

3ef9a44d86804c5ad57deec3c9916348cb3bfbb8

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

null

''' Copyright 2022 Airbus SAS 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 unittest from os.path import join, dirname import numpy as np from pandas import read_csv from sos_trades_core.execution_engine.execution_engine import ExecutionEngine from sos_trades_core.tests.core.abstract_jacobian_unit_test import AbstractJacobianUnittest class OilResourceJacobianDiscTest(AbstractJacobianUnittest): """ Oil resource jacobian test class """ #AbstractJacobianUnittest.DUMP_JACOBIAN = True def analytic_grad_entry(self): return [ self.test_oil_resource_analytic_grad, self.test_oil_resource_demand_variable_analytic_grad ] def setUp(self): ''' Initialize third data needed for testing ''' self.year_start = 2020 self.year_end = 2100 data_dir = join(dirname(__file__), 'data') self.energy_oil_demand_df = read_csv( join(data_dir, 'all_demand_from_energy_mix.csv'), usecols=['years','oil_resource']) self.energy_oil_variable_demand_df = read_csv( join(data_dir, 'all_demand_variable.csv'), usecols=['years','oil_resource']) # part to adapt lenght to the year range self.energy_oil_demand_df = self.energy_oil_demand_df.loc[self.energy_oil_demand_df['years'] >= self.year_start] self.energy_oil_demand_df = self.energy_oil_demand_df.loc[self.energy_oil_demand_df['years'] <= self.year_end] self.energy_oil_variable_demand_df = self.energy_oil_variable_demand_df.loc[self.energy_oil_variable_demand_df['years'] >= self.year_start] self.energy_oil_variable_demand_df = self.energy_oil_variable_demand_df.loc[self.energy_oil_variable_demand_df['years'] <= self.year_end] def test_oil_resource_analytic_grad(self): self.name = 'Test' self.ee = ExecutionEngine(self.name) self.model_name = 'oil_resource' ns_dict = {'ns_public': f'{self.name}', 'ns_witness': f'{self.name}.{self.model_name}', 'ns_functions': f'{self.name}.{self.model_name}', 'ns_oil_resource':f'{self.name}.{self.model_name}', 'ns_resource': f'{self.name}.{self.model_name}'} self.ee.ns_manager.add_ns_def(ns_dict) mod_path = 'climateeconomics.core.core_resources.models.oil_resource.oil_resource_disc.OilResourceDiscipline' builder = self.ee.factory.get_builder_from_module(self.model_name, mod_path) self.ee.factory.set_builders_to_coupling_builder(builder) self.ee.configure() self.ee.display_treeview_nodes() inputs_dict = {f'{self.name}.year_start': self.year_start, f'{self.name}.year_end': self.year_end, f'{self.name}.{self.model_name}.resources_demand': self.energy_oil_demand_df } self.ee.load_study_from_input_dict(inputs_dict) disc_techno = self.ee.root_process.sos_disciplines[0] self.check_jacobian(location=dirname(__file__), filename=f'jacobian_oil_discipline.pkl', discipline=disc_techno, step=1e-15, derr_approx='complex_step', inputs=[f'{self.name}.{self.model_name}.resources_demand'], outputs=[f'{self.name}.{self.model_name}.resource_stock', f'{self.name}.{self.model_name}.resource_price', f'{self.name}.{self.model_name}.use_stock', f'{self.name}.{self.model_name}.predictable_production']) def test_oil_resource_demand_variable_analytic_grad(self): self.name = 'Test' self.ee = ExecutionEngine(self.name) self.model_name = 'oil_resource' ns_dict = {'ns_public': f'{self.name}', 'ns_witness': f'{self.name}.{self.model_name}', 'ns_functions': f'{self.name}.{self.model_name}', 'ns_oil_resource':f'{self.name}.{self.model_name}', 'ns_resource': f'{self.name}.{self.model_name}'} self.ee.ns_manager.add_ns_def(ns_dict) mod_path = 'climateeconomics.core.core_resources.models.oil_resource.oil_resource_disc.OilResourceDiscipline' builder = self.ee.factory.get_builder_from_module(self.model_name, mod_path) self.ee.factory.set_builders_to_coupling_builder(builder) self.ee.configure() self.ee.display_treeview_nodes() inputs_dict = {f'{self.name}.year_start': self.year_start, f'{self.name}.year_end': self.year_end, f'{self.name}.{self.model_name}.resources_demand': self.energy_oil_variable_demand_df } self.ee.load_study_from_input_dict(inputs_dict) disc_techno = self.ee.root_process.sos_disciplines[0] self.check_jacobian(location=dirname(__file__), filename=f'jacobian_oil_demand_variable_discipline.pkl', discipline=disc_techno, step=1e-15, derr_approx='complex_step', inputs=[f'{self.name}.{self.model_name}.resources_demand'], outputs=[f'{self.name}.{self.model_name}.resource_stock', f'{self.name}.{self.model_name}.resource_price', f'{self.name}.{self.model_name}.use_stock', f'{self.name}.{self.model_name}.predictable_production'])

48.653846

127

0.625455

ebf3dbecca0a0aaff02e1679cecb774e09072831

6,869

py

Python

Cartwheel/lib/Python26/Lib/site-packages/OpenGL/raw/GL/ARB/uniform_buffer_object.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

Cartwheel/lib/Python26/Lib/site-packages/OpenGL/raw/GL/ARB/uniform_buffer_object.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

Cartwheel/lib/Python26/Lib/site-packages/OpenGL/raw/GL/ARB/uniform_buffer_object.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

'''OpenGL extension ARB.uniform_buffer_object The official definition of this extension is available here: http://oss.sgi.com/projects/ogl-sample/registry/ARB/uniform_buffer_object.txt Automatically generated by the get_gl_extensions script, do not edit! ''' from OpenGL import platform, constants, constant, arrays from OpenGL import extensions from OpenGL.GL import glget import ctypes EXTENSION_NAME = 'GL_ARB_uniform_buffer_object' GL_UNIFORM_BUFFER = constant.Constant( 'GL_UNIFORM_BUFFER', 0x8A11 ) GL_UNIFORM_BUFFER_BINDING = constant.Constant( 'GL_UNIFORM_BUFFER_BINDING', 0x8A28 ) GL_UNIFORM_BUFFER_START = constant.Constant( 'GL_UNIFORM_BUFFER_START', 0x8A29 ) GL_UNIFORM_BUFFER_SIZE = constant.Constant( 'GL_UNIFORM_BUFFER_SIZE', 0x8A2A ) GL_MAX_VERTEX_UNIFORM_BLOCKS = constant.Constant( 'GL_MAX_VERTEX_UNIFORM_BLOCKS', 0x8A2B ) GL_MAX_GEOMETRY_UNIFORM_BLOCKS = constant.Constant( 'GL_MAX_GEOMETRY_UNIFORM_BLOCKS', 0x8A2C ) GL_MAX_FRAGMENT_UNIFORM_BLOCKS = constant.Constant( 'GL_MAX_FRAGMENT_UNIFORM_BLOCKS', 0x8A2D ) GL_MAX_COMBINED_UNIFORM_BLOCKS = constant.Constant( 'GL_MAX_COMBINED_UNIFORM_BLOCKS', 0x8A2E ) GL_MAX_UNIFORM_BUFFER_BINDINGS = constant.Constant( 'GL_MAX_UNIFORM_BUFFER_BINDINGS', 0x8A2F ) GL_MAX_UNIFORM_BLOCK_SIZE = constant.Constant( 'GL_MAX_UNIFORM_BLOCK_SIZE', 0x8A30 ) GL_MAX_COMBINED_VERTEX_UNIFORM_COMPONENTS = constant.Constant( 'GL_MAX_COMBINED_VERTEX_UNIFORM_COMPONENTS', 0x8A31 ) GL_MAX_COMBINED_GEOMETRY_UNIFORM_COMPONENTS = constant.Constant( 'GL_MAX_COMBINED_GEOMETRY_UNIFORM_COMPONENTS', 0x8A32 ) GL_MAX_COMBINED_FRAGMENT_UNIFORM_COMPONENTS = constant.Constant( 'GL_MAX_COMBINED_FRAGMENT_UNIFORM_COMPONENTS', 0x8A33 ) GL_UNIFORM_BUFFER_OFFSET_ALIGNMENT = constant.Constant( 'GL_UNIFORM_BUFFER_OFFSET_ALIGNMENT', 0x8A34 ) GL_ACTIVE_UNIFORM_BLOCK_MAX_NAME_LENGTH = constant.Constant( 'GL_ACTIVE_UNIFORM_BLOCK_MAX_NAME_LENGTH', 0x8A35 ) GL_ACTIVE_UNIFORM_BLOCKS = constant.Constant( 'GL_ACTIVE_UNIFORM_BLOCKS', 0x8A36 ) GL_UNIFORM_TYPE = constant.Constant( 'GL_UNIFORM_TYPE', 0x8A37 ) GL_UNIFORM_SIZE = constant.Constant( 'GL_UNIFORM_SIZE', 0x8A38 ) GL_UNIFORM_NAME_LENGTH = constant.Constant( 'GL_UNIFORM_NAME_LENGTH', 0x8A39 ) GL_UNIFORM_BLOCK_INDEX = constant.Constant( 'GL_UNIFORM_BLOCK_INDEX', 0x8A3A ) GL_UNIFORM_OFFSET = constant.Constant( 'GL_UNIFORM_OFFSET', 0x8A3B ) GL_UNIFORM_ARRAY_STRIDE = constant.Constant( 'GL_UNIFORM_ARRAY_STRIDE', 0x8A3C ) GL_UNIFORM_MATRIX_STRIDE = constant.Constant( 'GL_UNIFORM_MATRIX_STRIDE', 0x8A3D ) GL_UNIFORM_IS_ROW_MAJOR = constant.Constant( 'GL_UNIFORM_IS_ROW_MAJOR', 0x8A3E ) GL_UNIFORM_BLOCK_BINDING = constant.Constant( 'GL_UNIFORM_BLOCK_BINDING', 0x8A3F ) GL_UNIFORM_BLOCK_DATA_SIZE = constant.Constant( 'GL_UNIFORM_BLOCK_DATA_SIZE', 0x8A40 ) GL_UNIFORM_BLOCK_NAME_LENGTH = constant.Constant( 'GL_UNIFORM_BLOCK_NAME_LENGTH', 0x8A41 ) GL_UNIFORM_BLOCK_ACTIVE_UNIFORMS = constant.Constant( 'GL_UNIFORM_BLOCK_ACTIVE_UNIFORMS', 0x8A42 ) GL_UNIFORM_BLOCK_ACTIVE_UNIFORM_INDICES = constant.Constant( 'GL_UNIFORM_BLOCK_ACTIVE_UNIFORM_INDICES', 0x8A43 ) GL_UNIFORM_BLOCK_REFERENCED_BY_VERTEX_SHADER = constant.Constant( 'GL_UNIFORM_BLOCK_REFERENCED_BY_VERTEX_SHADER', 0x8A44 ) GL_UNIFORM_BLOCK_REFERENCED_BY_GEOMETRY_SHADER = constant.Constant( 'GL_UNIFORM_BLOCK_REFERENCED_BY_GEOMETRY_SHADER', 0x8A45 ) GL_UNIFORM_BLOCK_REFERENCED_BY_FRAGMENT_SHADER = constant.Constant( 'GL_UNIFORM_BLOCK_REFERENCED_BY_FRAGMENT_SHADER', 0x8A46 ) GL_INVALID_INDEX_ARB = constant.Constant( 'GL_INVALID_INDEX_ARB', 0xFFFFFFFF ) glGetUniformIndices = platform.createExtensionFunction( 'glGetUniformIndices', dll=platform.GL, extension=EXTENSION_NAME, resultType=None, argTypes=(constants.GLuint, constants.GLsizei, ctypes.POINTER( ctypes.POINTER( constants.GLchar )), arrays.GLuintArray,), doc = 'glGetUniformIndices( GLuint(program), GLsizei(uniformCount), POINTER( ctypes.POINTER( constants.GLchar ))(uniformNames), GLuintArray(uniformIndices) ) -> None', argNames = ('program', 'uniformCount', 'uniformNames', 'uniformIndices',), ) glGetActiveUniformsiv = platform.createExtensionFunction( 'glGetActiveUniformsiv', dll=platform.GL, extension=EXTENSION_NAME, resultType=None, argTypes=(constants.GLuint, constants.GLsizei, arrays.GLuintArray, constants.GLenum, arrays.GLintArray,), doc = 'glGetActiveUniformsiv( GLuint(program), GLsizei(uniformCount), GLuintArray(uniformIndices), GLenum(pname), GLintArray(params) ) -> None', argNames = ('program', 'uniformCount', 'uniformIndices', 'pname', 'params',), ) glGetActiveUniformName = platform.createExtensionFunction( 'glGetActiveUniformName', dll=platform.GL, extension=EXTENSION_NAME, resultType=None, argTypes=(constants.GLuint, constants.GLuint, constants.GLsizei, arrays.GLsizeiArray, arrays.GLcharArray,), doc = 'glGetActiveUniformName( GLuint(program), GLuint(uniformIndex), GLsizei(bufSize), GLsizeiArray(length), GLcharArray(uniformName) ) -> None', argNames = ('program', 'uniformIndex', 'bufSize', 'length', 'uniformName',), ) glGetUniformBlockIndex = platform.createExtensionFunction( 'glGetUniformBlockIndex', dll=platform.GL, extension=EXTENSION_NAME, resultType=constants.GLuint, argTypes=(constants.GLuint, arrays.GLcharArray,), doc = 'glGetUniformBlockIndex( GLuint(program), GLcharArray(uniformBlockName) ) -> constants.GLuint', argNames = ('program', 'uniformBlockName',), ) glGetActiveUniformBlockiv = platform.createExtensionFunction( 'glGetActiveUniformBlockiv', dll=platform.GL, extension=EXTENSION_NAME, resultType=None, argTypes=(constants.GLuint, constants.GLuint, constants.GLenum, arrays.GLintArray,), doc = 'glGetActiveUniformBlockiv( GLuint(program), GLuint(uniformBlockIndex), GLenum(pname), GLintArray(params) ) -> None', argNames = ('program', 'uniformBlockIndex', 'pname', 'params',), ) glGetActiveUniformBlockName = platform.createExtensionFunction( 'glGetActiveUniformBlockName', dll=platform.GL, extension=EXTENSION_NAME, resultType=None, argTypes=(constants.GLuint, constants.GLuint, constants.GLsizei, arrays.GLsizeiArray, arrays.GLcharArray,), doc = 'glGetActiveUniformBlockName( GLuint(program), GLuint(uniformBlockIndex), GLsizei(bufSize), GLsizeiArray(length), GLcharArray(uniformBlockName) ) -> None', argNames = ('program', 'uniformBlockIndex', 'bufSize', 'length', 'uniformBlockName',), ) glUniformBlockBinding = platform.createExtensionFunction( 'glUniformBlockBinding', dll=platform.GL, extension=EXTENSION_NAME, resultType=None, argTypes=(constants.GLuint, constants.GLuint, constants.GLuint,), doc = 'glUniformBlockBinding( GLuint(program), GLuint(uniformBlockIndex), GLuint(uniformBlockBinding) ) -> None', argNames = ('program', 'uniformBlockIndex', 'uniformBlockBinding',), ) def glInitUniformBufferObjectARB(): '''Return boolean indicating whether this extension is available''' return extensions.hasGLExtension( EXTENSION_NAME )

60.787611

168

0.819624

78cd41b5c9bdd2df8aaa665b7c853b26ed3f2255

3,596

py

Python

robots/create_robots_resources.py

rrazor/wtop

a8c428d91ed841028d36455317485aaf1eb6dc3b

[ "BSD-3-Clause" ]

55

2015-01-05T17:11:06.000Z

2021-07-31T14:42:16.000Z

robots/create_robots_resources.py

rrazor/wtop

a8c428d91ed841028d36455317485aaf1eb6dc3b

[ "BSD-3-Clause" ]

4

2015-03-25T21:38:25.000Z

2018-06-18T19:33:20.000Z

robots/create_robots_resources.py

rrazor/wtop

a8c428d91ed841028d36455317485aaf1eb6dc3b

[ "BSD-3-Clause" ]

7

2015-09-15T10:57:15.000Z

2019-07-16T14:52:58.000Z

#!/usr/bin/env python # vim: set fileencoding=utf-8 : """Create an optimized regex for matching robot user agents based on the data compiled by http://user-agent-string.info/""" # Standard library import argparse import io import re import urllib from xml.dom.minidom import parse # Third-party import trie uasparser_xml_uri = "http://user-agent-string.info/rpc/get_data.php" uasparser_xml_query = "key=free&format=xml&download=y" download_xml_url = "%s?%s" % (uasparser_xml_uri, uasparser_xml_query) file_uasparser_xml = "uasparser.xml" file_robots_config = "robots_excerpt.ini" file_user_agents = "robot_user_agents.txt" others = ["curl", "gsa-crawler", "wget"] default_xml_file = "" re_trailing_version = re.compile(r""" (?P<robot>.{4}) # robot name [/ ][0-9][.][0-9].* # trailing version """, re.VERBOSE) def parser_setup(): """Instantiate, configure, and return an argarse instance.""" ap = argparse.ArgumentParser(description=__doc__) ap.add_argument("-f", "--file", help="File containing UASparser data in xml format (if " "omitted, the file will be downloaded)") args = ap.parse_args() return args def download_uasparser_xml(xml_url, xml_file): """Download "Data for UASparser" in XML format.""" urllib.urlretrieve(xml_url, xml_file) def add_others_to_robots(robots, others_list): """Add others to robots set.""" for other in others_list: robots.add(other) return robots def add_xml_names_to_robots(robots, xml_file): """Add robots from "Data for UASparser" in XML format to robots set.""" dom = parse(xml_file) user_agents = list() for robot in dom.getElementsByTagName("robot"): for node in robot.childNodes: if node.localName == "family": family = node.firstChild.data.strip() if node.localName == "name": name = node.firstChild.data.strip() if node.localName == "useragent": user_agents.append(node.firstChild.data.strip()) # Use the family title instead of the name, if it is long enough if len(family) > 3: name = family # Remove trailing versions (length protected in regex) name_new = re_trailing_version.search(name) if name_new: name = name_new.group("robot") if name.endswith(" b"): name = name.rstrip(" b") robots.add(name.strip()) return robots, sorted(user_agents) def create_optmized_robots_regex(robots): """Create an optimized regex from list of robots using a trie.""" optimized_robots = trie.Trie() for robot in sorted(robots): optimized_robots.add(robot) return optimized_robots.regexp() def main(): robots = set() # Command Line Options args = parser_setup() if args.file: xml_file = args.file else: download_uasparser_xml(download_xml_url, file_uasparser_xml) xml_file = file_uasparser_xml robots = add_others_to_robots(robots, others) robots, user_agents = add_xml_names_to_robots(robots, xml_file) optibots = create_optmized_robots_regex(robots) # write out robots config excerpt with io.open(file_robots_config, "w", encoding="utf8") as fh: fh.write(u"[patterns]\n") fh.write(u"robots=%s\n" % optibots) # write out robot users agents for testing with io.open(file_user_agents, "w", encoding="utf8") as fh: for agent in user_agents: fh.write("%s\n" % agent) if __name__ == "__main__": main()

32.107143

77

0.659066

f38ed06d537fa8c64e30e55fab4394cd27bb5540

7,495

py

Python

sublime/pymdownx/st3/pymdownx/arithmatex.py

brunobergher/dotfiles

2b3bfb9cf1b26441514b5565722fb187be2789cc

[ "MIT" ]

2

2018-04-24T10:02:26.000Z

2019-06-02T13:53:31.000Z

sublime/pymdownx/st3/pymdownx/arithmatex.py

brunobergher/dotfiles

2b3bfb9cf1b26441514b5565722fb187be2789cc

[ "MIT" ]

null

sublime/pymdownx/st3/pymdownx/arithmatex.py

brunobergher/dotfiles

2b3bfb9cf1b26441514b5565722fb187be2789cc

[ "MIT" ]

2

2019-04-11T04:13:02.000Z

2019-06-02T13:53:33.000Z

r""" Arithmatex. pymdownx.arithmatex Extension that preserves the following for MathJax use: $Equation$, $Equation$ $$ Display Equations $$ \[ Display Equations \] \begin{align} Display Equations \end{align} and $Inline MathJax Equations$ Inline equations are converted to the following form for HTML output by default: $Inline MathJax Equations$ While block/display equations are converted to the following form for HTML output by default: \[ Display Equations \] MIT license. Copyright (c) 2014 - 2017 Isaac Muse <isaacmuse@gmail.com> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from __future__ import unicode_literals from markdown import Extension from markdown.inlinepatterns import Pattern from markdown.blockprocessors import BlockProcessor from markdown import util as md_util from . import util import re RE_DOLLAR_INLINE = r'(?:(?<!\\)((?:\\{2})+)(?=\$)|(?<!\\)(\$)(?!\s)((?:\\.|[^\$])+?)(?<!\s)(?:\$))' RE_BRACKET_INLINE = r'(?:(?<!\\)((?:\\{2})+?)(?=\\$)|(?<!\$(\\$)((?:\\[^)]|[^\\])+?)(?:\\$))' class InlineArithmatexPattern(Pattern): """Arithmatex inline pattern handler.""" ESCAPED_BSLASH = '%s%s%s' % (md_util.STX, ord('\\'), md_util.ETX) def __init__(self, pattern, wrap, script, md): """Initialize.""" self.script = script self.wrap = wrap[0] + '%s' + wrap[1] Pattern.__init__(self, pattern) self.markdown = md def handleMatch(self, m): """Handle notations and switch them to something that will be more detectable in HTML.""" # Handle escapes escapes = m.group(2) if not escapes: escapes = m.group(5) if escapes: return escapes.replace('\\\\', self.ESCAPED_BSLASH) # Handle Tex math = m.group(4) if not math: math = m.group(7) if self.script: el = md_util.etree.Element('script', {'type': 'math/tex'}) el.text = md_util.AtomicString(math) else: el = md_util.etree.Element('span') el.text = md_util.AtomicString(self.wrap % math) return el class BlockArithmatexProcessor(BlockProcessor): """Mathjax block processor to find $$mathjax$$ content.""" RE_DOLLAR_BLOCK = r'(?P<dollar>[$]{2})(?P<math>.+?)(?P=dollar)' RE_TEX_BLOCK = r'(?P<math2>\\begin\{(?P<env>[a-z]+\*?)\}.+?\\end\{(?P=env)\})' RE_BRACKET_BLOCK = r'\\\[(?P<math3>(?:\\[^\]]|[^\\])+?)\\\]' def __init__(self, config, md): """Initialize.""" self.script = config.get('insert_as_script', False) wrap = config.get('tex_block_wrap', ['\\[', '\\]']) self.wrap = wrap[0] + '%s' + wrap[1] allowed_patterns = set(config.get('block_syntax', ['dollar', 'square', 'begin'])) pattern = [] if 'dollar' in allowed_patterns: pattern.append(self.RE_DOLLAR_BLOCK) if 'square' in allowed_patterns: pattern.append(self.RE_BRACKET_BLOCK) if 'begin' in allowed_patterns: pattern.append(self.RE_TEX_BLOCK) self.match = None if pattern: self.pattern = re.compile(r'(?s)^(?:%s)[ ]*$' % '|'.join(pattern)) else: self.pattern = None self.markdown = md BlockProcessor.__init__(self, md.parser) def test(self, parent, block): """Return 'True' for future Python Markdown block compatibility.""" self.match = self.pattern.match(block) if self.pattern is not None else None return self.match is not None def run(self, parent, blocks): """Find and handle block content.""" blocks.pop(0) math = self.match.group('math') if not math: math = self.match.group('math2') if not math: math = self.match.group('math3') if self.script: el = md_util.etree.SubElement(parent, 'script', {'type': 'math/tex; mode=display'}) el.text = md_util.AtomicString(math) else: el = md_util.etree.SubElement(parent, 'span') el.text = md_util.AtomicString(self.wrap % math) return True class ArithmatexExtension(Extension): """Adds delete extension to Markdown class.""" def __init__(self, *args, **kwargs): """Initialize.""" self.config = { 'tex_inline_wrap': [ ["\$", "\$"], "Wrap inline content with the provided text ['open', 'close'] - Default: ['', '']" ], 'tex_block_wrap': [ ["\\[", "\\]"], "Wrap blick content with the provided text ['open', 'close'] - Default: ['', '']" ], "block_syntax": [ ['dollar', 'square', 'begin'], 'Enable block syntax: "dollar" ($$...$$), "square" (\\[...\\]), and ' '"begin" (\\begin{env}...\\end{env}). - Default: ["dollar", "square", "begin"]' ], "inline_syntax": [ ['dollar', 'round'], 'Enable block syntax: "dollar" ($$...$$), "bracket" (\$...\$) ' ' - Default: ["dollar", "round"]' ], 'insert_as_script': [ False, "Insert the math Tex notation into a script tag. Overrides wrapping. - Default: False" ] } super(ArithmatexExtension, self).__init__(*args, **kwargs) def extendMarkdown(self, md, md_globals): """Extend the inline and block processor objects.""" md.registerExtension(self) util.escape_chars(md, ['$']) config = self.getConfigs() allowed_inline = set(config.get('inline_syntax', ['dollar', 'round'])) inline_patterns = [] if 'dollar' in allowed_inline: inline_patterns.append(RE_DOLLAR_INLINE) if 'round' in allowed_inline: inline_patterns.append(RE_BRACKET_INLINE) if inline_patterns: inline = InlineArithmatexPattern( '(?:%s)' % '|'.join(inline_patterns), config.get('tex_inline_wrap', ["\$", "\$"]), config.get('insert_as_script', False), md ) md.inlinePatterns.add("arithmatex-inline", inline, ">backtick") md.parser.blockprocessors.add('arithmatex-block', BlockArithmatexProcessor(config, md), "<code") def makeExtension(*args, **kwargs): """Return extension.""" return ArithmatexExtension(*args, **kwargs)

34.380734

111

0.591194

cffb59a2ae6306049d63c6be518aa4f3760ac276

12,000

py

Python

test/vanilla/legacy/Expected/AcceptanceTests/BodyComplex/bodycomplex/aio/operations/_basic_operations.py

changlong-liu/autorest.python

1f03e4c6a11934d385fab050dc44041f1e91e9ff

[ "MIT" ]

null

test/vanilla/legacy/Expected/AcceptanceTests/BodyComplex/bodycomplex/aio/operations/_basic_operations.py

changlong-liu/autorest.python

1f03e4c6a11934d385fab050dc44041f1e91e9ff

[ "MIT" ]

null

test/vanilla/legacy/Expected/AcceptanceTests/BodyComplex/bodycomplex/aio/operations/_basic_operations.py

changlong-liu/autorest.python

1f03e4c6a11934d385fab050dc44041f1e91e9ff

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import Any, Callable, Dict, Generic, Optional, TypeVar import warnings from azure.core.exceptions import ( ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error, ) from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator_async import distributed_trace_async from ... import models as _models from ..._vendor import _convert_request from ...operations._basic_operations import ( build_get_empty_request, build_get_invalid_request, build_get_not_provided_request, build_get_null_request, build_get_valid_request, build_put_valid_request, ) T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class BasicOperations: """BasicOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~bodycomplex.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace_async async def get_valid(self, **kwargs: Any) -> "_models.Basic": """Get complex type {id: 2, name: 'abc', color: 'YELLOW'}. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_valid_request( template_url=self.get_valid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_valid.metadata = {"url": "/complex/basic/valid"} # type: ignore @distributed_trace_async async def put_valid(self, complex_body: "_models.Basic", **kwargs: Any) -> None: """Please put {id: 2, name: 'abc', color: 'Magenta'}. :param complex_body: Please put {id: 2, name: 'abc', color: 'Magenta'}. :type complex_body: ~bodycomplex.models.Basic :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType[None] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) api_version = kwargs.pop("api_version", "2016-02-29") # type: str content_type = kwargs.pop("content_type", "application/json") # type: Optional[str] _json = self._serialize.body(complex_body, "Basic") request = build_put_valid_request( api_version=api_version, content_type=content_type, json=_json, template_url=self.put_valid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) if cls: return cls(pipeline_response, None, {}) put_valid.metadata = {"url": "/complex/basic/valid"} # type: ignore @distributed_trace_async async def get_invalid(self, **kwargs: Any) -> "_models.Basic": """Get a basic complex type that is invalid for the local strong type. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_invalid_request( template_url=self.get_invalid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_invalid.metadata = {"url": "/complex/basic/invalid"} # type: ignore @distributed_trace_async async def get_empty(self, **kwargs: Any) -> "_models.Basic": """Get a basic complex type that is empty. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_empty_request( template_url=self.get_empty.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_empty.metadata = {"url": "/complex/basic/empty"} # type: ignore @distributed_trace_async async def get_null(self, **kwargs: Any) -> "_models.Basic": """Get a basic complex type whose properties are null. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_null_request( template_url=self.get_null.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_null.metadata = {"url": "/complex/basic/null"} # type: ignore @distributed_trace_async async def get_not_provided(self, **kwargs: Any) -> "_models.Basic": """Get a basic complex type while the server doesn't provide a response payload. :keyword callable cls: A custom type or function that will be passed the direct response :return: Basic, or the result of cls(response) :rtype: ~bodycomplex.models.Basic :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Basic"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_not_provided_request( template_url=self.get_not_provided.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Basic", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_not_provided.metadata = {"url": "/complex/basic/notprovided"} # type: ignore

42.553191

106

0.68

a340d7fc12e348d484509c5f32662e368ca8046c

32,162

py

Python

toontown/shtiker/OptionsPage.py

drewc5131/epic-toontown

a02862fc325d70820a51fb83458195062f092c96

[ "BSD-3-Clause" ]

null

toontown/shtiker/OptionsPage.py

drewc5131/epic-toontown

a02862fc325d70820a51fb83458195062f092c96

[ "BSD-3-Clause" ]

null

toontown/shtiker/OptionsPage.py

drewc5131/epic-toontown

a02862fc325d70820a51fb83458195062f092c96

[ "BSD-3-Clause" ]

null

from pandac.PandaModules import * from libotp import * from . import ShtikerPage from toontown.toontowngui import TTDialog from direct.gui.DirectGui import * from pandac.PandaModules import * from toontown.toonbase import TTLocalizer from . import DisplaySettingsDialog from direct.task import Task from otp.speedchat import SpeedChat from otp.speedchat import SCColorScheme from otp.speedchat import SCStaticTextTerminal from direct.showbase import PythonUtil from direct.directnotify import DirectNotifyGlobal from toontown.toonbase import ToontownGlobals speedChatStyles = ((2000, (200 / 255.0, 60 / 255.0, 229 / 255.0), (200 / 255.0, 135 / 255.0, 255 / 255.0), (220 / 255.0, 195 / 255.0, 229 / 255.0)), (2001, (0 / 255.0, 0 / 255.0, 255 / 255.0), (140 / 255.0, 150 / 255.0, 235 / 255.0), (201 / 255.0, 215 / 255.0, 255 / 255.0)), (2002, (90 / 255.0, 175 / 255.0, 225 / 255.0), (120 / 255.0, 215 / 255.0, 255 / 255.0), (208 / 255.0, 230 / 255.0, 250 / 255.0)), (2003, (130 / 255.0, 235 / 255.0, 235 / 255.0), (120 / 255.0, 225 / 255.0, 225 / 255.0), (234 / 255.0, 255 / 255.0, 255 / 255.0)), (2004, (0 / 255.0, 200 / 255.0, 70 / 255.0), (0 / 255.0, 200 / 255.0, 80 / 255.0), (204 / 255.0, 255 / 255.0, 204 / 255.0)), (2005, (235 / 255.0, 230 / 255.0, 0 / 255.0), (255 / 255.0, 250 / 255.0, 100 / 255.0), (255 / 255.0, 250 / 255.0, 204 / 255.0)), (2006, (255 / 255.0, 153 / 255.0, 0 / 255.0), (229 / 255.0, 147 / 255.0, 0 / 255.0), (255 / 255.0, 234 / 255.0, 204 / 255.0)), (2007, (255 / 255.0, 0 / 255.0, 50 / 255.0), (229 / 255.0, 0 / 255.0, 50 / 255.0), (255 / 255.0, 204 / 255.0, 204 / 255.0)), (2008, (255 / 255.0, 153 / 255.0, 193 / 255.0), (240 / 255.0, 157 / 255.0, 192 / 255.0), (255 / 255.0, 215 / 255.0, 238 / 255.0)), (2009, (170 / 255.0, 120 / 255.0, 20 / 255.0), (165 / 255.0, 120 / 255.0, 50 / 255.0), (210 / 255.0, 200 / 255.0, 180 / 255.0))) PageMode = PythonUtil.Enum('Options, Codes') class OptionsPage(ShtikerPage.ShtikerPage): notify = DirectNotifyGlobal.directNotify.newCategory('OptionsPage') def __init__(self): ShtikerPage.ShtikerPage.__init__(self) def load(self): ShtikerPage.ShtikerPage.load(self) self.optionsTabPage = OptionsTabPage(self) self.optionsTabPage.hide() self.codesTabPage = CodesTabPage(self) self.codesTabPage.hide() titleHeight = 0.61 self.title = DirectLabel(parent=self, relief=None, text=TTLocalizer.OptionsPageTitle, text_scale=0.12, pos=(0, 0, titleHeight)) normalColor = (1, 1, 1, 1) clickColor = (0.8, 0.8, 0, 1) rolloverColor = (0.15, 0.82, 1.0, 1) diabledColor = (1.0, 0.98, 0.15, 1) gui = loader.loadModel('phase_3.5/models/gui/fishingBook') self.optionsTab = DirectButton(parent=self, relief=None, text=TTLocalizer.OptionsPageTitle, text_scale=TTLocalizer.OPoptionsTab, text_align=TextNode.ALeft, text_pos=(0.01, 0.0, 0.0), image=gui.find('**/tabs/polySurface1'), image_pos=(0.55, 1, -0.91), image_hpr=(0, 0, -90), image_scale=(0.033, 0.033, 0.035), image_color=normalColor, image1_color=clickColor, image2_color=rolloverColor, image3_color=diabledColor, text_fg=Vec4(0.2, 0.1, 0, 1), command=self.setMode, extraArgs=[PageMode.Options], pos=(-0.36, 0, 0.77)) self.codesTab = DirectButton(parent=self, relief=None, text=TTLocalizer.OptionsPageCodesTab, text_scale=TTLocalizer.OPoptionsTab, text_align=TextNode.ALeft, text_pos=(-0.035, 0.0, 0.0), image=gui.find('**/tabs/polySurface2'), image_pos=(0.12, 1, -0.91), image_hpr=(0, 0, -90), image_scale=(0.033, 0.033, 0.035), image_color=normalColor, image1_color=clickColor, image2_color=rolloverColor, image3_color=diabledColor, text_fg=Vec4(0.2, 0.1, 0, 1), command=self.setMode, extraArgs=[PageMode.Codes], pos=(0.11, 0, 0.77)) return def enter(self): self.setMode(PageMode.Options, updateAnyways=1) ShtikerPage.ShtikerPage.enter(self) def exit(self): self.optionsTabPage.exit() self.codesTabPage.exit() ShtikerPage.ShtikerPage.exit(self) def unload(self): self.optionsTabPage.unload() del self.title ShtikerPage.ShtikerPage.unload(self) def setMode(self, mode, updateAnyways = 0): messenger.send('wakeup') if not updateAnyways: if self.mode == mode: return else: self.mode = mode if mode == PageMode.Options: self.mode = PageMode.Options self.title['text'] = TTLocalizer.OptionsPageTitle self.optionsTab['state'] = DGG.DISABLED self.optionsTabPage.enter() self.codesTab['state'] = DGG.NORMAL self.codesTabPage.exit() elif mode == PageMode.Codes: self.mode = PageMode.Codes self.title['text'] = TTLocalizer.CdrPageTitle self.optionsTab['state'] = DGG.NORMAL self.optionsTabPage.exit() self.codesTab['state'] = DGG.DISABLED self.codesTabPage.enter() else: raise Exception('OptionsPage::setMode - Invalid Mode %s' % mode) class OptionsTabPage(DirectFrame): notify = DirectNotifyGlobal.directNotify.newCategory('OptionsTabPage') DisplaySettingsTaskName = 'save-display-settings' DisplaySettingsDelay = 60 ChangeDisplaySettings = base.config.GetBool('change-display-settings', 1) ChangeDisplayAPI = base.config.GetBool('change-display-api', 0) DisplaySettingsApiMap = {'OpenGL': Settings.GL, 'DirectX7': Settings.DX7, 'DirectX8': Settings.DX8} def __init__(self, parent = aspect2d): self._parent = parent self.currentSizeIndex = None DirectFrame.__init__(self, parent=self._parent, relief=None, pos=(0.0, 0.0, 0.0), scale=(1.0, 1.0, 1.0)) self.load() return def destroy(self): self._parent = None DirectFrame.destroy(self) return def load(self): self.displaySettings = None self.displaySettingsChanged = 0 self.displaySettingsSize = (None, None) self.displaySettingsFullscreen = None self.displaySettingsEmbedded = None self.displaySettingsApi = None self.displaySettingsApiChanged = 0 guiButton = loader.loadModel('phase_3/models/gui/quit_button') gui = loader.loadModel('phase_3.5/models/gui/friendslist_gui') titleHeight = 0.61 textStartHeight = 0.45 textRowHeight = 0.145 leftMargin = -0.72 buttonbase_xcoord = 0.35 buttonbase_ycoord = 0.45 button_image_scale = (0.7, 1, 1) button_textpos = (0, -0.02) options_text_scale = 0.052 disabled_arrow_color = Vec4(0.6, 0.6, 0.6, 1.0) self.speed_chat_scale = 0.055 self.Music_Label = DirectLabel(parent=self, relief=None, text='', text_align=TextNode.ALeft, text_scale=options_text_scale, pos=(leftMargin, 0, textStartHeight)) self.SoundFX_Label = DirectLabel(parent=self, relief=None, text='', text_align=TextNode.ALeft, text_scale=options_text_scale, text_wordwrap=16, pos=(leftMargin, 0, textStartHeight - textRowHeight)) self.Friends_Label = DirectLabel(parent=self, relief=None, text='', text_align=TextNode.ALeft, text_scale=options_text_scale, text_wordwrap=16, pos=(leftMargin, 0, textStartHeight - 3 * textRowHeight)) self.Whispers_Label = DirectLabel(parent=self, relief=None, text='', text_align=TextNode.ALeft, text_scale=options_text_scale, text_wordwrap=16, pos=(leftMargin, 0, textStartHeight - 4 * textRowHeight)) self.DisplaySettings_Label = DirectLabel(parent=self, relief=None, text='', text_align=TextNode.ALeft, text_scale=options_text_scale, text_wordwrap=10, pos=(leftMargin, 0, textStartHeight - 5 * textRowHeight)) self.SpeedChatStyle_Label = DirectLabel(parent=self, relief=None, text=TTLocalizer.OptionsPageSpeedChatStyleLabel, text_align=TextNode.ALeft, text_scale=options_text_scale, text_wordwrap=10, pos=(leftMargin, 0, textStartHeight - 6 * textRowHeight)) self.ToonChatSounds_Label = DirectLabel(parent=self, relief=None, text='', text_align=TextNode.ALeft, text_scale=options_text_scale, text_wordwrap=15, pos=(leftMargin, 0, textStartHeight - 2 * textRowHeight + 0.025)) self.ToonChatSounds_Label.setScale(0.9) self.Music_toggleButton = DirectButton(parent=self, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale=button_image_scale, text='', text_scale=options_text_scale, text_pos=button_textpos, pos=(buttonbase_xcoord, 0.0, buttonbase_ycoord), command=self.__doToggleMusic) self.SoundFX_toggleButton = DirectButton(parent=self, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale=button_image_scale, text='', text_scale=options_text_scale, text_pos=button_textpos, pos=(buttonbase_xcoord, 0.0, buttonbase_ycoord - textRowHeight), command=self.__doToggleSfx) self.Friends_toggleButton = DirectButton(parent=self, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale=button_image_scale, text='', text_scale=options_text_scale, text_pos=button_textpos, pos=(buttonbase_xcoord, 0.0, buttonbase_ycoord - textRowHeight * 3), command=self.__doToggleAcceptFriends) self.Whispers_toggleButton = DirectButton(parent=self, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale=button_image_scale, text='', text_scale=options_text_scale, text_pos=button_textpos, pos=(buttonbase_xcoord, 0.0, buttonbase_ycoord - textRowHeight * 4), command=self.__doToggleAcceptWhispers) self.DisplaySettingsButton = DirectButton(parent=self, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image3_color=Vec4(0.5, 0.5, 0.5, 0.5), image_scale=button_image_scale, text=TTLocalizer.OptionsPageChange, text3_fg=(0.5, 0.5, 0.5, 0.75), text_scale=options_text_scale, text_pos=button_textpos, pos=(buttonbase_xcoord, 0.0, buttonbase_ycoord - textRowHeight * 5), command=self.__doDisplaySettings) self.speedChatStyleLeftArrow = DirectButton(parent=self, relief=None, image=(gui.find('**/Horiz_Arrow_UP'), gui.find('**/Horiz_Arrow_DN'), gui.find('**/Horiz_Arrow_Rllvr'), gui.find('**/Horiz_Arrow_UP')), image3_color=Vec4(1, 1, 1, 0.5), scale=(-1.0, 1.0, 1.0), pos=(0.25, 0, buttonbase_ycoord - textRowHeight * 6), command=self.__doSpeedChatStyleLeft) self.speedChatStyleRightArrow = DirectButton(parent=self, relief=None, image=(gui.find('**/Horiz_Arrow_UP'), gui.find('**/Horiz_Arrow_DN'), gui.find('**/Horiz_Arrow_Rllvr'), gui.find('**/Horiz_Arrow_UP')), image3_color=Vec4(1, 1, 1, 0.5), pos=(0.65, 0, buttonbase_ycoord - textRowHeight * 6), command=self.__doSpeedChatStyleRight) self.ToonChatSounds_toggleButton = DirectButton(parent=self, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR'), guiButton.find('**/QuitBtn_UP')), image3_color=Vec4(0.5, 0.5, 0.5, 0.5), image_scale=button_image_scale, text='', text3_fg=(0.5, 0.5, 0.5, 0.75), text_scale=options_text_scale, text_pos=button_textpos, pos=(buttonbase_xcoord, 0.0, buttonbase_ycoord - textRowHeight * 2 + 0.025), command=self.__doToggleToonChatSounds) self.ToonChatSounds_toggleButton.setScale(0.8) self.speedChatStyleText = SpeedChat.SpeedChat(name='OptionsPageStyleText', structure=[2000], backgroundModelName='phase_3/models/gui/ChatPanel', guiModelName='phase_3.5/models/gui/speedChatGui') self.speedChatStyleText.setScale(self.speed_chat_scale) self.speedChatStyleText.setPos(0.37, 0, buttonbase_ycoord - textRowHeight * 6 + 0.03) self.speedChatStyleText.reparentTo(self, DGG.FOREGROUND_SORT_INDEX) self.exitButton = DirectButton(parent=self, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale=1.15, text=TTLocalizer.OptionsPageExitToontown, text_scale=options_text_scale, text_pos=button_textpos, textMayChange=0, pos=(0.45, 0, -0.6), command=self.__handleExitShowWithConfirm) guiButton.removeNode() gui.removeNode() return def enter(self): self.show() taskMgr.remove(self.DisplaySettingsTaskName) self.settingsChanged = 0 self.__setMusicButton() self.__setSoundFXButton() self.__setAcceptFriendsButton() self.__setAcceptWhispersButton() self.__setDisplaySettings() self.__setToonChatSoundsButton() self.speedChatStyleText.enter() self.speedChatStyleIndex = base.localAvatar.getSpeedChatStyleIndex() self.updateSpeedChatStyle() if self._parent.book.safeMode: self.exitButton.hide() else: self.exitButton.show() def exit(self): self.ignore('confirmDone') self.hide() if self.settingsChanged != 0: Settings.writeSettings() self.speedChatStyleText.exit() if self.displaySettingsChanged: taskMgr.doMethodLater(self.DisplaySettingsDelay, self.writeDisplaySettings, self.DisplaySettingsTaskName) def unload(self): self.writeDisplaySettings() taskMgr.remove(self.DisplaySettingsTaskName) if self.displaySettings != None: self.ignore(self.displaySettings.doneEvent) self.displaySettings.unload() self.displaySettings = None self.exitButton.destroy() self.Music_toggleButton.destroy() self.SoundFX_toggleButton.destroy() self.Friends_toggleButton.destroy() self.Whispers_toggleButton.destroy() self.DisplaySettingsButton.destroy() self.speedChatStyleLeftArrow.destroy() self.speedChatStyleRightArrow.destroy() del self.exitButton del self.SoundFX_Label del self.Music_Label del self.Friends_Label del self.Whispers_Label del self.SpeedChatStyle_Label del self.SoundFX_toggleButton del self.Music_toggleButton del self.Friends_toggleButton del self.Whispers_toggleButton del self.speedChatStyleLeftArrow del self.speedChatStyleRightArrow self.speedChatStyleText.exit() self.speedChatStyleText.destroy() del self.speedChatStyleText self.currentSizeIndex = None return def __doToggleMusic(self): messenger.send('wakeup') if base.musicActive: base.enableMusic(0) Settings.setMusic(0) else: base.enableMusic(1) Settings.setMusic(1) self.settingsChanged = 1 self.__setMusicButton() def __setMusicButton(self): if base.musicActive: self.Music_Label['text'] = TTLocalizer.OptionsPageMusicOnLabel self.Music_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.Music_Label['text'] = TTLocalizer.OptionsPageMusicOffLabel self.Music_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn def __doToggleSfx(self): messenger.send('wakeup') if base.sfxActive: base.enableSoundEffects(0) Settings.setSfx(0) else: base.enableSoundEffects(1) Settings.setSfx(1) self.settingsChanged = 1 self.__setSoundFXButton() def __doToggleToonChatSounds(self): messenger.send('wakeup') if base.toonChatSounds: base.toonChatSounds = 0 Settings.setToonChatSounds(0) else: base.toonChatSounds = 1 Settings.setToonChatSounds(1) self.settingsChanged = 1 self.__setToonChatSoundsButton() def __setSoundFXButton(self): if base.sfxActive: self.SoundFX_Label['text'] = TTLocalizer.OptionsPageSFXOnLabel self.SoundFX_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.SoundFX_Label['text'] = TTLocalizer.OptionsPageSFXOffLabel self.SoundFX_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn self.__setToonChatSoundsButton() def __setToonChatSoundsButton(self): if base.toonChatSounds: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOnLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOffLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn if base.sfxActive: self.ToonChatSounds_Label.setColorScale(1.0, 1.0, 1.0, 1.0) self.ToonChatSounds_toggleButton['state'] = DGG.NORMAL else: self.ToonChatSounds_Label.setColorScale(0.5, 0.5, 0.5, 0.5) self.ToonChatSounds_toggleButton['state'] = DGG.DISABLED def __doToggleAcceptFriends(self): messenger.send('wakeup') if base.localAvatar.acceptingNewFriends: base.localAvatar.acceptingNewFriends = 0 Settings.setAcceptingNewFriends(0) else: base.localAvatar.acceptingNewFriends = 1 Settings.setAcceptingNewFriends(1) self.settingsChanged = 1 self.__setAcceptFriendsButton() def __doToggleAcceptWhispers(self): messenger.send('wakeup') if base.localAvatar.acceptingNonFriendWhispers: base.localAvatar.acceptingNonFriendWhispers = 0 Settings.setAcceptingNonFriendWhispers(0) else: base.localAvatar.acceptingNonFriendWhispers = 1 Settings.setAcceptingNonFriendWhispers(1) self.settingsChanged = 1 self.__setAcceptWhispersButton() def __setAcceptFriendsButton(self): if base.localAvatar.acceptingNewFriends: self.Friends_Label['text'] = TTLocalizer.OptionsPageFriendsEnabledLabel self.Friends_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.Friends_Label['text'] = TTLocalizer.OptionsPageFriendsDisabledLabel self.Friends_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn def __setAcceptWhispersButton(self): if base.localAvatar.acceptingNonFriendWhispers: self.Whispers_Label['text'] = TTLocalizer.OptionsPageWhisperEnabledLabel self.Whispers_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.Whispers_Label['text'] = TTLocalizer.OptionsPageWhisperDisabledLabel self.Whispers_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn def __doDisplaySettings(self): if self.displaySettings == None: self.displaySettings = DisplaySettingsDialog.DisplaySettingsDialog() self.displaySettings.load() self.accept(self.displaySettings.doneEvent, self.__doneDisplaySettings) self.displaySettings.enter(self.ChangeDisplaySettings, self.ChangeDisplayAPI) return def __doneDisplaySettings(self, anyChanged, apiChanged): if anyChanged: self.__setDisplaySettings() properties = base.win.getProperties() self.displaySettingsChanged = 1 self.displaySettingsSize = (properties.getXSize(), properties.getYSize()) self.displaySettingsFullscreen = properties.getFullscreen() self.displaySettingsEmbedded = self.isPropertiesEmbedded(properties) self.displaySettingsApi = base.pipe.getInterfaceName() self.displaySettingsApiChanged = apiChanged def isPropertiesEmbedded(self, properties): result = False if properties.getParentWindow(): result = True return result def __setDisplaySettings(self): properties = base.win.getProperties() if properties.getFullscreen(): screensize = '%s x %s' % (properties.getXSize(), properties.getYSize()) else: screensize = TTLocalizer.OptionsPageDisplayWindowed isEmbedded = self.isPropertiesEmbedded(properties) if isEmbedded: screensize = TTLocalizer.OptionsPageDisplayEmbedded api = base.pipe.getInterfaceName() settings = {'screensize': screensize, 'api': api} if self.ChangeDisplayAPI: OptionsPage.notify.debug('change display settings...') text = TTLocalizer.OptionsPageDisplaySettings % settings else: OptionsPage.notify.debug('no change display settings...') text = TTLocalizer.OptionsPageDisplaySettingsNoApi % settings self.DisplaySettings_Label['text'] = text def __doSpeedChatStyleLeft(self): if self.speedChatStyleIndex > 0: self.speedChatStyleIndex = self.speedChatStyleIndex - 1 self.updateSpeedChatStyle() def __doSpeedChatStyleRight(self): if self.speedChatStyleIndex < len(speedChatStyles) - 1: self.speedChatStyleIndex = self.speedChatStyleIndex + 1 self.updateSpeedChatStyle() def updateSpeedChatStyle(self): nameKey, arrowColor, rolloverColor, frameColor = speedChatStyles[self.speedChatStyleIndex] newSCColorScheme = SCColorScheme.SCColorScheme(arrowColor=arrowColor, rolloverColor=rolloverColor, frameColor=frameColor) self.speedChatStyleText.setColorScheme(newSCColorScheme) self.speedChatStyleText.clearMenu() colorName = SCStaticTextTerminal.SCStaticTextTerminal(nameKey) self.speedChatStyleText.append(colorName) self.speedChatStyleText.finalize() self.speedChatStyleText.setPos(0.445 - self.speedChatStyleText.getWidth() * self.speed_chat_scale / 2, 0, self.speedChatStyleText.getPos()[2]) if self.speedChatStyleIndex > 0: self.speedChatStyleLeftArrow['state'] = DGG.NORMAL else: self.speedChatStyleLeftArrow['state'] = DGG.DISABLED if self.speedChatStyleIndex < len(speedChatStyles) - 1: self.speedChatStyleRightArrow['state'] = DGG.NORMAL else: self.speedChatStyleRightArrow['state'] = DGG.DISABLED base.localAvatar.b_setSpeedChatStyleIndex(self.speedChatStyleIndex) def writeDisplaySettings(self, task = None): if not self.displaySettingsChanged: return taskMgr.remove(self.DisplaySettingsTaskName) self.notify.info('writing new display settings %s, fullscreen %s, embedded %s, %s to SettingsFile.' % (self.displaySettingsSize, self.displaySettingsFullscreen, self.displaySettingsEmbedded, self.displaySettingsApi)) Settings.setResolutionDimensions(self.displaySettingsSize[0], self.displaySettingsSize[1]) Settings.setWindowedMode(not self.displaySettingsFullscreen) Settings.setEmbeddedMode(self.displaySettingsEmbedded) if self.displaySettingsApiChanged: api = self.DisplaySettingsApiMap.get(self.displaySettingsApi) if api == None: self.notify.warning('Cannot save unknown display API: %s' % self.displaySettingsApi) else: Settings.setDisplayDriver(api) Settings.writeSettings() self.displaySettingsChanged = 0 return Task.done def __handleExitShowWithConfirm(self): self.confirm = TTDialog.TTGlobalDialog(doneEvent='confirmDone', message=TTLocalizer.OptionsPageExitConfirm, style=TTDialog.TwoChoice) self.confirm.show() self._parent.doneStatus = {'mode': 'exit', 'exitTo': 'closeShard'} self.accept('confirmDone', self.__handleConfirm) def __handleConfirm(self): status = self.confirm.doneStatus self.ignore('confirmDone') self.confirm.cleanup() del self.confirm if status == 'ok': base.cr._userLoggingOut = True messenger.send(self._parent.doneEvent) class CodesTabPage(DirectFrame): notify = DirectNotifyGlobal.directNotify.newCategory('CodesTabPage') def __init__(self, parent = aspect2d): self._parent = parent DirectFrame.__init__(self, parent=self._parent, relief=None, pos=(0.0, 0.0, 0.0), scale=(1.0, 1.0, 1.0)) self.load() return def destroy(self): self._parent = None DirectFrame.destroy(self) return def load(self): cdrGui = loader.loadModel('phase_3.5/models/gui/tt_m_gui_sbk_codeRedemptionGui') instructionGui = cdrGui.find('**/tt_t_gui_sbk_cdrPresent') flippyGui = cdrGui.find('**/tt_t_gui_sbk_cdrFlippy') codeBoxGui = cdrGui.find('**/tt_t_gui_sbk_cdrCodeBox') self.resultPanelSuccessGui = cdrGui.find('**/tt_t_gui_sbk_cdrResultPanel_success') self.resultPanelFailureGui = cdrGui.find('**/tt_t_gui_sbk_cdrResultPanel_failure') self.resultPanelErrorGui = cdrGui.find('**/tt_t_gui_sbk_cdrResultPanel_error') self.successSfx = base.loader.loadSfx('phase_3.5/audio/sfx/tt_s_gui_sbk_cdrSuccess.ogg') self.failureSfx = base.loader.loadSfx('phase_3.5/audio/sfx/tt_s_gui_sbk_cdrFailure.ogg') self.instructionPanel = DirectFrame(parent=self, relief=None, image=instructionGui, image_scale=0.8, text=TTLocalizer.CdrInstructions, text_pos=TTLocalizer.OPCodesInstructionPanelTextPos, text_align=TextNode.ACenter, text_scale=TTLocalizer.OPCodesResultPanelTextScale, text_wordwrap=TTLocalizer.OPCodesInstructionPanelTextWordWrap, pos=(-0.429, 0, -0.05)) self.codeBox = DirectFrame(parent=self, relief=None, image=codeBoxGui, pos=(0.433, 0, 0.35)) self.flippyFrame = DirectFrame(parent=self, relief=None, image=flippyGui, pos=(0.44, 0, -0.353)) self.codeInput = DirectEntry(parent=self.codeBox, relief=DGG.GROOVE, scale=0.08, pos=(-0.33, 0, -0.006), borderWidth=(0.05, 0.05), frameColor=((1, 1, 1, 1), (1, 1, 1, 1), (0.5, 0.5, 0.5, 0.5)), state=DGG.NORMAL, text_align=TextNode.ALeft, text_scale=TTLocalizer.OPCodesInputTextScale, width=10.5, numLines=1, focus=1, backgroundFocus=0, cursorKeys=1, text_fg=(0, 0, 0, 1), suppressMouse=1, autoCapitalize=0, command=self.__submitCode) submitButtonGui = loader.loadModel('phase_3/models/gui/quit_button') self.submitButton = DirectButton(parent=self, relief=None, image=(submitButtonGui.find('**/QuitBtn_UP'), submitButtonGui.find('**/QuitBtn_DN'), submitButtonGui.find('**/QuitBtn_RLVR'), submitButtonGui.find('**/QuitBtn_UP')), image3_color=Vec4(0.5, 0.5, 0.5, 0.5), image_scale=1.15, state=DGG.NORMAL, text=TTLocalizer.NameShopSubmitButton, text_scale=TTLocalizer.OPCodesSubmitTextScale, text_align=TextNode.ACenter, text_pos=TTLocalizer.OPCodesSubmitTextPos, text3_fg=(0.5, 0.5, 0.5, 0.75), textMayChange=0, pos=(0.45, 0.0, 0.0896), command=self.__submitCode) self.resultPanel = DirectFrame(parent=self, relief=None, image=self.resultPanelSuccessGui, text='', text_pos=TTLocalizer.OPCodesResultPanelTextPos, text_align=TextNode.ACenter, text_scale=TTLocalizer.OPCodesResultPanelTextScale, text_wordwrap=TTLocalizer.OPCodesResultPanelTextWordWrap, pos=(-0.42, 0, -0.0567)) self.resultPanel.hide() closeButtonGui = loader.loadModel('phase_3/models/gui/dialog_box_buttons_gui') self.closeButton = DirectButton(parent=self.resultPanel, pos=(0.296, 0, -0.466), relief=None, state=DGG.NORMAL, image=(closeButtonGui.find('**/CloseBtn_UP'), closeButtonGui.find('**/CloseBtn_DN'), closeButtonGui.find('**/CloseBtn_Rllvr')), image_scale=(1, 1, 1), command=self.__hideResultPanel) closeButtonGui.removeNode() cdrGui.removeNode() submitButtonGui.removeNode() return def enter(self): self.show() localAvatar.chatMgr.fsm.request('otherDialog') self.codeInput['focus'] = 1 self.codeInput.enterText('') self.__enableCodeEntry() def exit(self): self.resultPanel.hide() self.hide() localAvatar.chatMgr.fsm.request('mainMenu') def unload(self): self.instructionPanel.destroy() self.instructionPanel = None self.codeBox.destroy() self.codeBox = None self.flippyFrame.destroy() self.flippyFrame = None self.codeInput.destroy() self.codeInput = None self.submitButton.destroy() self.submitButton = None self.resultPanel.destroy() self.resultPanel = None self.closeButton.destroy() self.closeButton = None del self.successSfx del self.failureSfx return def __submitCode(self, input = None): if input == None: input = self.codeInput.get() self.codeInput['focus'] = 1 if input == '': return messenger.send('wakeup') if hasattr(base, 'codeRedemptionMgr'): base.codeRedemptionMgr.redeemCode(input, self.__getCodeResult) self.codeInput.enterText('') self.__disableCodeEntry() return def __getCodeResult(self, result, awardMgrResult): self.notify.debug('result = %s' % result) self.notify.debug('awardMgrResult = %s' % awardMgrResult) self.__enableCodeEntry() if result == 0: self.resultPanel['image'] = self.resultPanelSuccessGui self.resultPanel['text'] = TTLocalizer.CdrResultSuccess elif result == 1 or result == 3: self.resultPanel['image'] = self.resultPanelFailureGui self.resultPanel['text'] = TTLocalizer.CdrResultInvalidCode elif result == 2: self.resultPanel['image'] = self.resultPanelFailureGui self.resultPanel['text'] = TTLocalizer.CdrResultExpiredCode elif result == 4: self.resultPanel['image'] = self.resultPanelErrorGui if awardMgrResult == 0: self.resultPanel['text'] = TTLocalizer.CdrResultSuccess elif awardMgrResult == 1 or awardMgrResult == 2 or awardMgrResult == 15 or awardMgrResult == 16: self.resultPanel['text'] = TTLocalizer.CdrResultUnknownError elif awardMgrResult == 3 or awardMgrResult == 4: self.resultPanel['text'] = TTLocalizer.CdrResultMailboxFull elif awardMgrResult == 5 or awardMgrResult == 10: self.resultPanel['text'] = TTLocalizer.CdrResultAlreadyInMailbox elif awardMgrResult == 6 or awardMgrResult == 7 or awardMgrResult == 11: self.resultPanel['text'] = TTLocalizer.CdrResultAlreadyInQueue elif awardMgrResult == 8: self.resultPanel['text'] = TTLocalizer.CdrResultAlreadyInCloset elif awardMgrResult == 9: self.resultPanel['text'] = TTLocalizer.CdrResultAlreadyBeingWorn elif awardMgrResult == 12 or awardMgrResult == 13 or awardMgrResult == 14: self.resultPanel['text'] = TTLocalizer.CdrResultAlreadyReceived elif result == 5: self.resultPanel['text'] = TTLocalizer.CdrResultTooManyFails self.__disableCodeEntry() elif result == 6: self.resultPanel['text'] = TTLocalizer.CdrResultServiceUnavailable self.__disableCodeEntry() if result == 0: self.successSfx.play() else: self.failureSfx.play() self.resultPanel.show() def __hideResultPanel(self): self.resultPanel.hide() def __disableCodeEntry(self): self.codeInput['state'] = DGG.DISABLED self.submitButton['state'] = DGG.DISABLED def __enableCodeEntry(self): self.codeInput['state'] = DGG.NORMAL self.codeInput['focus'] = 1 self.submitButton['state'] = DGG.NORMAL

52.552288

525

0.678503

89163c64fe3b118214c02476ffc34bf5b6a4b8ae

1,311

py

Python

Utils/ConsoleColors.py

chae03yb/DaeGal

7451bf372ec3a5a174fd7aede952f81127fa64a2

[ "MIT" ]

null

Utils/ConsoleColors.py

chae03yb/DaeGal

7451bf372ec3a5a174fd7aede952f81127fa64a2

[ "MIT" ]

null

Utils/ConsoleColors.py

chae03yb/DaeGal

7451bf372ec3a5a174fd7aede952f81127fa64a2

[ "MIT" ]

null

# For Linux class EFCT(): CLEAR = "\033[0m" BOLD = "\033[1m" ITALIC = "\033[3m" UNDERLINE = "\033[4m" STRIKEOUT = "\033[9m" class TEXT(): BLACK = "\033[30m" RED = "\033[31m" GREEN = "\033[32m" YELLOW = "\033[33" BLUE = "\033[34m" PURPLE = "\033[35m" CYAN = "\033[36m" WHITE = "\033[37m" Bright = "\033[90m" BrightRed = "\033[91m" BrightGreen = "\033[92m" BrightYellow = "\033[93m" BrightBlue = "\033[94m" BrightPurple = "\033[95m" BrightCyan = "\033[96m" BrightWhite = "\033[97m" class BG(): BLACK = "\033[40m" RED = "\033[41m" GREEN = "\033[42m" YELLOW = "\033[43m" BLUE = "\033[44m" PURPLE = "\033[45m" CYAN = "\033[46m" WHITE = "\033[47m" BrightBlack = "\033[100m" BrightRed = "\033[101m" BrightGreen = "\033[102m" BrightYellow = "\033[103m" BrightBlue = "\033[104m" BrightPurple = "\033[105m" BrightCyan = "\033[107m" BrightWhite = "\033[108m" def TEXT_8Bit(Num:int): return f"\033[38;5;{Num}m" def BG_8Bit(Num:int): return f"\033[48;5;{Num}m" def TEXT_RGB(R:int, G:int, B:int): return f"\033[38;2;{R};{G};{B}m" def BG_RGB(R:int, G:int, B:int): return f"\033[48;2;{R};{G};{B}m"

22.603448

36

0.517925

8e2665c5150a5aabe2cf4db3e1cd2d742dd86629

13,967

py

Python

pyts/plot/axform.py

JensGM/pyTurbSim

89357e94607e99e5a27e069975684fe8e6a89a9d

[ "Apache-2.0" ]

8

2017-07-24T23:01:28.000Z

2021-12-06T04:31:23.000Z

pyts/plot/axform.py

camilledub/pyTurbSim

69db0d7670afe5b2b386087aa0229a7511ef070d

[ "Apache-2.0" ]

9

2015-04-06T16:01:06.000Z

2020-06-16T16:51:49.000Z

pyts/plot/axform.py

camilledub/pyTurbSim

69db0d7670afe5b2b386087aa0229a7511ef070d

[ "Apache-2.0" ]

17

2015-04-22T15:27:28.000Z

2021-11-20T08:10:16.000Z

from ..main import tsdata, tsrun # used to identify object types import superaxes as supax import psd from base import indx, mpl import numpy as np class axesForm(object): """A base 'axesForm' class for for quickly plotting TurbSim data. Parameters ---------- xlim : iterable(2) (default: [None, None]) Specify the xlims of the axes of these plots. The default will simply select the limits automatically. ylim : iterable(2) (default: [None, None]) Specify the ylims of the axes of these plots. The default will simply select the limits automatically. xscale : ('log' or 'linear') specify whether the x-scale should be logarithmic or linear. yscale : ('log' or 'linear') specify whether the y-scale should be logarithmic or linear. """ method_map = {tsdata: '_calc_tsdata', tsrun: '_calc_tsrun'} hide_ylabels = False _lin_x_scale = 0 def __init__(self, xlim=[None, None], ylim=[None, None], xscale='linear', yscale='linear',): self.xlim = xlim self.ylim = ylim self.xscale = xscale self.yscale = yscale def finalize(self, axes): """ This function 'finishes' the `axes` according to the specifications in this axesForm. Parameters ---------- axes : A :class:`superaxes.axgroup` instance. """ axes.hide('xticklabels', ax=axes[-1]) if self.hide_ylabels: axes.hide('yticklabels') if not self.hide_ylabels and hasattr(self, '_ylabel'): for ax in axes: ax.set_ylabel(self._ylabel) if hasattr(self, '_xlabel'): axes[-1].set_xlabel(self._xlabel) if hasattr(self, '_title'): axes[0].set_title(self._title) if hasattr(self, '_grid_x'): for ax in axes: for val in self._grid_x: ax.axvline(val, linestyle=':', color='k', zorder=-10) if hasattr(self, '_labels'): for ax in axes: ax.annoteCorner(self._labels[ax.comp], pos='ul') if hasattr(self, 'xtick_n'): for ax in axes: ax.xaxis.set_major_locator( mpl.ticker.MaxNLocator(self.xtick_n)) for ax in axes: ax.set_xlim(self.xlim) for ax in axes: ax.set_ylim(self.ylim) def _calc(self, obj, comp): """ Call the appropriate 'calc' method depending on the object class. Parameters ---------- obj : object An object containing data/information to be plotted. comp : int,str The component (u,v,w or 0,1,2) to compute. Returns ------- x : array_like The x data to plot. y : array_like The y data to plot. Notes ----- This method is a parser for the individual '_calc_<obj>' methods. It utilizes the :attr:`method_map` to determine which method should be called for each `obj` object type. """ if comp.__class__ is str: comp = indx[comp] for cls, meth in self.method_map.iteritems(): if isinstance(obj, cls): if not hasattr(self, meth): return np.NaN, np.NaN return getattr(self, meth)(obj, comp) raise Exception('Object type %s not recognized for %s axes-type' % (obj.__class__, self.__class__)) def plot(self, obj, axes, **kwargs): """ Plot the data in `obj` to `axes`. Parameters ---------- obj : object An object containing data to be plotted. axes : :class:`superaxes.axgroup` instance The axes into which that data should be plotted. """ for ax in axes: x, y = self._calc(obj, ax.comp) # print x, y, self._lin_x_scale ax.plot(x / (10 ** self._lin_x_scale), y, **kwargs) ax.set_xscale(self.xscale) ax.set_yscale(self.yscale) @property def _xlabel(self,): if self._lin_x_scale == 0: return '$\mathrm{[m^2s^{-2}]}$' else: return '$\mathrm{[10^{%d}m^2s^{-2}]}$' % self._lin_x_scale class prof(axesForm): "A base axesForm for plotting vertical profiles." yax = 'z' _ylabel = '$z\,\mathrm{[m]}$' hrel = 0.6 xtick_n = 3 class velprof(prof): """A 'mean velocity profile' axesForm format. See also -------- :class:`prof` :class:`axesForm` """ xax = 'vel' _title = 'Mean Velocity' _xlabel = '$\mathrm{[m/s]}$' def _calc_tsdata(self, tsdat, comp, igrid=None): """ The function that calculates x,y values for plotting :class:`.tsdata` objects. """ return tsdat.uprof[comp, :, tsdat.ihub[1]], tsdat.z def _calc_tsrun(self, tsr, comp, igrid=None): """ The function that calculates x,y values for plotting :class:`.tsrun` objects. """ return tsr.prof[comp, :, tsr.grid.ihub[1]], tsr.grid.z class tkeprof(prof): """A 'tke profile' axesForm. See also -------- :class:`prof` :class:`axesForm` """ xax = 'tke' _title = 'tke' #_lin_x_scale = -2 # Units are 10^-2 def _calc_tsdata(self, tsdat, comp, igrid=None): return tsdat.tke[comp, :, tsdat.ihub[1]], tsdat.z def _calc_tsrun(self, tsr, comp, igrid=None): return tsr.spec.tke[comp, :, tsr.grid.ihub[1]], tsr.grid.z class Tiprof(prof): """A Turbulence intensity axesForm See also -------- :class:`prof` :class:`axesForm` """ xax = 'Ti' _title = 'Ti' _lin_x_scale = 0 _ylabel = '%' def _calc_tsdata(self, tsdat, comp, igrid=None): tmp = (100 * np.std(tsdat.uturb[comp, :, tsdat.ihub[1]], axis=-1) / tsdat.uprof[0, :, tsdat.ihub[1]]) return tmp, tsdat.z def _calc_tsrun(self, tsr, comp, igrid=None): return (100 * np.sqrt(tsr.spec.tke[comp, :, tsr.grid.ihub[1]]) / tsr.prof.u[:, tsr.grid.ihub[1]], tsr.grid.z) class stressprof(tkeprof): """A 'Reynold's stress profile' axesForm. See also -------- :class:`prof` :class:`axesForm` """ xax = 'tke' _title = 'stress' _lin_x_scale = -2 # Units are 10^-2 _grid_x = [0] _labels = {'u': r"$\overline{u'v'}$", 'v': r"$\overline{u'w'}$", 'w': r"$\overline{v'w'}$"} def _calc_tsdata(self, tsdat, comp, igrid=None): igrid = igrid or tsdat.ihub[1] return tsdat.stress[comp, :, igrid], tsdat.z def _calc_tsrun(self, tsr, comp, igrid=None): igrid = igrid or tsr.grid.ihub[1] return tsr.stress.array[comp, :, igrid], tsr.grid.z class spec(axesForm): """A 'spectral' axesForm. Parameters ---------- window_time_sec : float the length of the fft window (seconds). igrid : tuple,list (2), *optional* (default: i_hub) The spatial-index of the grid-point that should be plotted. Notes ----- This axesForm defaults to have 'log' x- and y-scales. See also -------- :class:`axesForm` """ hrel = 1 _title = 'spectrum' yax = 'spec' xax = 'freq' _xlabel = '$f\,\mathrm{[Hz]}$' _ylabel = '$\mathrm{[m^2s^{-2}/Hz]}$' def __init__(self, window_time_sec=600, igrid=None, xscale='log', yscale='log', **kwargs): axesForm.__init__(self, xscale=xscale, yscale=yscale, **kwargs) self.window_time = window_time_sec self.igrid = igrid def _calc_tsdata(self, tsdat, comp, igrid=None): nfft = int(self.window_time / tsdat.dt) nfft += np.mod(nfft, 2) igrid = igrid or self.igrid or tsdat.ihub tmp = psd.psd(tsdat.uturb[comp][igrid], 1. / tsdat.dt, nfft) # print tmp return tmp def _calc_tsrun(self, tsr, comp, igrid=None): igrid = igrid or self.igrid or tsr.grid.ihub return tsr.grid.f, tsr.spec[comp][igrid] class cohere(axesForm): """ A 'coherence' plotting format for showing coherence between two points. Parameters ---------- window_time_sec : float the length of the fft window (seconds). igrid0 : tuple,list (2), *optional* (default: i_hub) The first spatial-index from which to estimate+plot coherence. igrid1 : tuple,list (2), *optional* (default: (0,0)) The second spatial-index from which to estimate+plot coherence. Notes ----- This axesForm defaults to have a 'log' x-scale, and to have a linear y-axis with ylim=[0, 1]. See also -------- :class:`axesForm` """ hrel = 1 _title = 'coherence' xax = 'freq' yax = 'coh' _xlabel = '$f\,\mathrm{[Hz]}$' def __init__(self, window_time_sec=600, igrid0=None, igrid1=None, xscale='log', ylim=[0, 1], **kwargs): axesForm.__init__(self, xscale=xscale, ylim=ylim, **kwargs) self.window_time = 600 self.igrid0 = igrid0 self.igrid1 = igrid1 def _calc_tsdata(self, tsdat, comp, igrid0=None, igrid1=None): nfft = int(self.window_time / tsdat.dt) nfft += np.mod(nfft, 2) igrid0 = igrid0 or self.igrid0 or tsdat.ihub igrid1 = igrid1 or self.igrid1 or (0, 0) return psd.coh(tsdat.uturb[comp][igrid0], tsdat.uturb[comp][igrid1], 1. / tsdat.dt, nfft) def _calc_tsrun(self, tsr, comp, igrid0=None, igrid1=None): igrid0 = tsr.grid.sub2ind(igrid0 or self.igrid0 or tsr.grid.ihub) igrid1 = tsr.grid.sub2ind(igrid1 or self.igrid1 or (0, 0)) return tsr.grid.f, tsr.cohere.calcCoh(tsr.grid.f, comp, igrid0, igrid1) ** 2 class FigAxForm(supax.sfig): """The 'figure' class that uses and handles :class:`axesForm`. Parameters ---------- fignum : integer,string The figure number, or string, into which the data will be plotted. axforms : list of axForms (e.g. :class:`velprof`, :class:`tkeprof`) These are the axes formats that will be plotted. comp : list of velocity components (0,1,2 or 'u','v','w') axsize : float, tuple, list (2) The size of the axes. If 2 parameters are specified this sets they set the horizontal and vertical size of the axes. frame : tuple(4) ,list(4) This specifies the border around the axes (left, right, bottom, top) gap : tuple(2), list(2) This specifies the gap between axes in the horizontal and vertical directions. tightgap : float This specifies the horizontal spacing between axes that have the same type of y-axis (specified in the formats 'yax' attribute). Other inputs are passed directly to :meth:`superaxes.sfig.__init__`. Notes ----- This will create an NxM axes-grid, where N=len(comp), and M=len(axforms). The width of each axes is scaled by the 'hrel' attribute of each axform. """ def __init__(self, fignum, axforms=[], comp=['u', 'v', 'w'], axsize=2, frame=[.6, .3, 1, .3], gap=[.2, 1], tightgap=.2, **kwargs): if len(axforms) == 0: raise Exception('At least one axes-type \ instance must be provided.') sharex = np.tile(np.arange(len(axforms), dtype=np.uint8) + 1, (len(comp), 1)) sharey = np.tile(np.arange(len(axforms), dtype=np.uint8) + 1, (len(comp), 1)) hspacer = supax.simpleAxSpacer(len(axforms), axsize, gap[1], frame[2:]) vspacer = supax.simpleAxSpacer(len(comp), axsize, gap[0], frame[:2], vertical=True) last_yax = None for idx, axt in enumerate(axforms): if axt.yax == last_yax: sharey[:, idx] = sharey[:, idx - 1] hspacer.gap[idx] = tightgap axt.hide_ylabels = True last_yax = axt.yax if hasattr(axt, 'hrel'): hspacer.axsize[idx] *= axt.hrel axp = supax.axPlacer(vspacer, hspacer) supax.sfig.__init__(self, fignum, axp, sharex=sharex, sharey=sharey, **kwargs) self.comp = comp for idx, axt in enumerate(axforms): for c, ax in zip(comp, self.sax[:, idx]): ax.comp = c self.axforms = axforms def plot(self, obj, **kwargs): """ Plot the data in `obj` to the figure according to the plotting formats. Parameters ---------- obj : tsdata, tsrun, turbdata A data or turbsim object to plot. """ for idx, axt in enumerate(self.axforms): axt.plot(obj, self.sax[:, idx], **kwargs) def finalize(self,): """ Finalize the figure according to the plotting formats. """ for idx, axt in enumerate(self.axforms): axt.finalize(self.sax[:, idx]) for c, ax in zip(self.comp, self.ax[:, 0]): p = ax.get_position().ymax self.fig.text(0.02, p, '$' + c + '$', va='top', ha='left', size='x-large', backgroundcolor='w') def summfig(fignum=400, axforms=[velprof(), spec(600)], **kwargs): return FigAxForm(fignum, axforms=axforms, **kwargs)

31.316143

79

0.54235

7dbc7f9dce4983df8de7270a59ab2927b4522d6a

368

py

Python

users/models.py

Joabsonlg/ddm-rest-api

27d72f5e7dcb061f56b7fd504c76bc7746ecabe2

[ "MIT" ]

null

users/models.py

Joabsonlg/ddm-rest-api

27d72f5e7dcb061f56b7fd504c76bc7746ecabe2

[ "MIT" ]

null

users/models.py

Joabsonlg/ddm-rest-api

27d72f5e7dcb061f56b7fd504c76bc7746ecabe2

[ "MIT" ]

null

from django.contrib.auth.models import AbstractUser from django.db import models class User(AbstractUser): email = models.EmailField(max_length=255, unique=True) name = models.CharField(max_length=255) is_active = models.BooleanField(default=True) def save(self, *args, **kwargs): self.is_active = True super().save(*args, **kwargs)

28.307692

58

0.709239

4d3069aed98aff2ee71023c842ea1611c3c750cd

5,466

py

Python

apps/beat.py

TinlokLee/Django-Celery

320b36ee2091aa876ecf92003af6b8e11b3041ed

[ "Apache-2.0" ]

null

apps/beat.py

TinlokLee/Django-Celery

320b36ee2091aa876ecf92003af6b8e11b3041ed

[ "Apache-2.0" ]

null

apps/beat.py

TinlokLee/Django-Celery

320b36ee2091aa876ecf92003af6b8e11b3041ed

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """Beat command-line program. This module is the 'program-version' of :mod:`celery.beat`. It does everything necessary to run that module as an actual application, like installing signal handlers and so on. """ from __future__ import absolute_import, print_function, unicode_literals import numbers import socket import sys from datetime import datetime from celery import VERSION_BANNER, beat, platforms from celery.five import text_t from celery.utils.imports import qualname from celery.utils.log import LOG_LEVELS, get_logger from celery.utils.time import humanize_seconds __all__ = ('Beat',) STARTUP_INFO_FMT = """ LocalTime -> {timestamp} Configuration -> . broker -> {conninfo} . loader -> {loader} . scheduler -> {scheduler} {scheduler_info} . logfile -> {logfile}@%{loglevel} . maxinterval -> {hmax_interval} ({max_interval}s) """.strip() logger = get_logger('celery.beat') class Beat(object): """Beat as a service.""" Service = beat.Service app = None def __init__(self, max_interval=None, app=None, socket_timeout=30, pidfile=None, no_color=None, loglevel='WARN', logfile=None, schedule=None, scheduler=None, scheduler_cls=None, # XXX use scheduler redirect_stdouts=None, redirect_stdouts_level=None, **kwargs): self.app = app = app or self.app either = self.app.either self.loglevel = loglevel self.logfile = logfile self.schedule = either('beat_schedule_filename', schedule) self.scheduler_cls = either( 'beat_scheduler', scheduler, scheduler_cls) self.redirect_stdouts = either( 'worker_redirect_stdouts', redirect_stdouts) self.redirect_stdouts_level = either( 'worker_redirect_stdouts_level', redirect_stdouts_level) self.max_interval = max_interval self.socket_timeout = socket_timeout self.no_color = no_color self.colored = app.log.colored( self.logfile, enabled=not no_color if no_color is not None else no_color, ) self.pidfile = pidfile if not isinstance(self.loglevel, numbers.Integral): self.loglevel = LOG_LEVELS[self.loglevel.upper()] def run(self): print(str(self.colored.cyan( 'celery beat v{0} is starting.'.format(VERSION_BANNER)))) self.init_loader() self.set_process_title() self.start_scheduler() def setup_logging(self, colorize=None): if colorize is None and self.no_color is not None: colorize = not self.no_color self.app.log.setup(self.loglevel, self.logfile, self.redirect_stdouts, self.redirect_stdouts_level, colorize=colorize) def start_scheduler(self): if self.pidfile: platforms.create_pidlock(self.pidfile) service = self.Service( app=self.app, max_interval=self.max_interval, scheduler_cls=self.scheduler_cls, schedule_filename=self.schedule, ) print(self.banner(service)) self.setup_logging() if self.socket_timeout: logger.debug('Setting default socket timeout to %r', self.socket_timeout) socket.setdefaulttimeout(self.socket_timeout) try: self.install_sync_handler(service) service.start() except Exception as exc: logger.critical('beat raised exception %s: %r', exc.__class__, exc, exc_info=True) raise def banner(self, service): c = self.colored return text_t( # flake8: noqa c.blue('__ ', c.magenta('-'), c.blue(' ... __ '), c.magenta('-'), c.blue(' _\n'), c.reset(self.startup_info(service))), ) def init_loader(self): # Run the worker init handler. # (Usually imports task modules and such.) self.app.loader.init_worker() self.app.finalize() def startup_info(self, service): scheduler = service.get_scheduler(lazy=True) return STARTUP_INFO_FMT.format( conninfo=self.app.connection().as_uri(), timestamp=datetime.now().replace(microsecond=0), logfile=self.logfile or '[stderr]', loglevel=LOG_LEVELS[self.loglevel], loader=qualname(self.app.loader), scheduler=qualname(scheduler), scheduler_info=scheduler.info, hmax_interval=humanize_seconds(scheduler.max_interval), max_interval=scheduler.max_interval, ) def set_process_title(self): arg_start = 'manage' in sys.argv[0] and 2 or 1 platforms.set_process_title( 'celery beat', info=' '.join(sys.argv[arg_start:]), ) def install_sync_handler(self, service): """Install a `SIGTERM` + `SIGINT` handler saving the schedule.""" def _sync(signum, frame): service.sync() raise SystemExit() platforms.signals.update(SIGTERM=_sync, SIGINT=_sync)

34.815287

79

0.594768

ba440b0603c0827c61260ea7620830b3bb9f0ed3

2,265

py

Python

fn_incident_utils/fn_incident_utils/components/fn_incident_utils_close_incident.py

lmahoney1/resilient-community-apps

1f60fb100e6a697df7b901d7a4aad707fea3dfee

[ "MIT" ]

null

fn_incident_utils/fn_incident_utils/components/fn_incident_utils_close_incident.py

lmahoney1/resilient-community-apps

1f60fb100e6a697df7b901d7a4aad707fea3dfee

[ "MIT" ]

null

fn_incident_utils/fn_incident_utils/components/fn_incident_utils_close_incident.py

lmahoney1/resilient-community-apps

1f60fb100e6a697df7b901d7a4aad707fea3dfee

[ "MIT" ]

null

# (c) Copyright IBM Corp. 2010, 2020. All Rights Reserved. # -*- coding: utf-8 -*- # pragma pylint: disable=unused-argument, no-self-use """Function implementation""" import logging import json from resilient_circuits import ResilientComponent, function, handler, StatusMessage, FunctionResult, FunctionError from resilient_lib import close_incident, ResultPayload PACKAGE_NAME = "fn_incident_utils" class FunctionComponent(ResilientComponent): """Component that implements Resilient function 'incident_utils_close_incident''""" def __init__(self, opts): """constructor provides access to the configuration options""" super(FunctionComponent, self).__init__(opts) self.options = opts.get(PACKAGE_NAME, {}) @handler("reload") def _reload(self, event, opts): """Configuration options have changed, save new values""" self.options = opts.get(PACKAGE_NAME, {}) @function("incident_utils_close_incident") def _incident_utils_close_incident_function(self, event, *args, **kwargs): """Function: Function that takes a JSON String of field and value pairs to close an Incident.""" try: # Get the function parameters: incident_id = kwargs.get("incident_id") # number close_fields = kwargs.get("close_fields") # text # Check JSON string and convert it to dict if close_fields is None: close_fields = {} else: close_fields = json.loads(close_fields) log = logging.getLogger(__name__) log.info("incident_id: %s", incident_id) log.info("close_fields: %s", close_fields) rp = ResultPayload(PACKAGE_NAME, **kwargs) yield StatusMessage("starting...") # Instansiate new Resilient API object res_client = self.rest_client() # API call to Close an Incident response = close_incident(res_client, incident_id, close_fields) results = rp.done(True, response.json()) yield StatusMessage("done...") # Produce a FunctionResult with the results yield FunctionResult(results) except Exception: yield FunctionError()

36.532258

114

0.65298

9f2f7e94b40b0d663f204568f1a8c860ce45e38b

663

py

Python

_BACKUP/.gitbook/assets/dfs_paths_dict_recur.py

bgoonz/python-gitbook

d3b1321a42064e3cf46d1ac16ec1d7c67e4ae04f

[ "MIT" ]

2

2021-10-01T22:14:38.000Z

2021-11-29T00:32:52.000Z

_BACKUP/.gitbook/assets/dfs_paths_dict_recur.py

bgoonz/python-gitbook

d3b1321a42064e3cf46d1ac16ec1d7c67e4ae04f

[ "MIT" ]

14

2021-09-04T13:28:28.000Z

2021-09-22T04:06:35.000Z

_BACKUP/.gitbook/assets/dfs_paths_dict_recur.py

bgoonz/python-gitbook

d3b1321a42064e3cf46d1ac16ec1d7c67e4ae04f

[ "MIT" ]

1

2021-11-27T20:28:11.000Z

""" ID: 3df740e0-34f3-4852-add2-7076c8be47c3 """ from collections.abc import Iterable, Mapping from typing import Optional from src.typehints import Node def dfs_paths_dict_recur( graph: Mapping[Node, set[Node]], start: Node, goal: Node, path: Optional[list[Node]] = None, ) -> Iterable[list[Node]]: """Find all the paths from start to goal recursively on a dict.""" if path is None: path = [start] if start == goal: yield path else: for next_node in graph[start].difference(path): next_path = path + [next_node] yield from dfs_paths_dict_recur(graph, next_node, goal, next_path)

26.52

78

0.656109

2fb1d3760cfed5357bbf719f6cd2025092ad9b78

2,368

py

Python

cluster.py

ekrell/nir2watermap

5253f2cde142a62103eb06fb2931c9aed6431211

[ "MIT" ]

null

cluster.py

ekrell/nir2watermap

5253f2cde142a62103eb06fb2931c9aed6431211

[ "MIT" ]

null

cluster.py

ekrell/nir2watermap

5253f2cde142a62103eb06fb2931c9aed6431211

[ "MIT" ]

null

import rasterio from rasterio.mask import mask from rasterio.plot import reshape_as_raster, reshape_as_image, adjust_band, show import geopandas as gpd import numpy as np from sklearn.cluster import KMeans, DBSCAN import matplotlib.pyplot as plt from optparse import OptionParser from skimage.transform import resize def color_stretch(image, index): colors = image[:, :, index].astype(np.float64) for b in range(colors.shape[2]): colors[:, :, b] = rasterio.plot.adjust_band(colors[:, :, b]) return colors # Options parser = OptionParser() parser.add_option("-i", "--in_file", help = "File with raster of bands (red, green, blue, NIR, ???)", default = "naip18-nc-cir-60cm_2797141_20181210_occ.jp2") parser.add_option("-o", "--out_file", help = "File to store raster, sixth added band is cluster IDs", default = "naip18-nc-cir-60cm_2797141_20181210_occ_cluster.jp2") parser.add_option("-m", "--masked", action = "store_true", help = "Input raster's fifth band is occupancy grid", default = False) (options, args) = parser.parse_args() inFile = options.in_file outFile = options.out_file masked = options.masked # Load NAIP raster full_dataset = rasterio.open(inFile, driver = "JP2OpenJPEG") img_rows, img_cols = full_dataset.shape img_bands = full_dataset.count print(full_dataset.shape) print(full_dataset.count) # Read image img = full_dataset.read() #[:, 100:2600, 2000:14000] bands, rows, cols = img.shape print(bands, rows, cols) # If masked, apply it if masked: for b in range(bands): img[b, :, :][img[4, :, :] == 255] = 255 # Remove map band img = img[:4, :, :] #scaled_img = resize(img, (int(scale * float(rows)), int(scale * float(cols)))) # reshape into long 2d array (nrow * ncol, nband) for classification reshaped_img = reshape_as_image(img) # Kmeans k = 8 kmeans_predictions = KMeans(n_clusters=k, random_state=0, algorithm='full').fit(reshaped_img.reshape(-1, 4)) kmeans_predictions_2d = kmeans_predictions.labels_.reshape(rows, cols) # Visualize rgb = img[0:3] rgb_norm = adjust_band(rgb) # normalize bands to range between 1.0 to 0.0 rgb_reshaped = reshape_as_image(rgb_norm) # reshape to [rows, cols, bands] fig, axs = plt.subplots(2, 1, figsize=(10, 20)) show(rgb_norm, ax=axs[1]) axs[1].set_title("RGB") axs[0].imshow(kmeans_predictions_2d) axs[0].set_title("KMEANS") plt.show()

30.358974

108

0.718328

0b20465747723f7fd98fb793e4aafb2101e0e225

1,004

py

Python

examples/python/test.py

MartinNum/HNSW_OPT

c40943154d55bca4c0ebd6ca7bf719fa1e621aa2

[ "Apache-2.0" ]

1

2022-03-31T10:58:21.000Z

examples/python/test.py

MartinNum/HNSW_OPT

c40943154d55bca4c0ebd6ca7bf719fa1e621aa2

[ "Apache-2.0" ]

null

examples/python/test.py

MartinNum/HNSW_OPT

c40943154d55bca4c0ebd6ca7bf719fa1e621aa2

[ "Apache-2.0" ]

null

import numpy as np def ivecs_read(fname): a = np.fromfile(fname, dtype='int32') d = a[0] return a.reshape(-1, d + 1)[:, 1:].copy() def fvecs_read(fname): return ivecs_read(fname).view('float32') if __name__ == "__main__": source = fvecs_read("/home/martin/Project/experiment/data_set/sift1M_128/sift_base.fvecs") # labels = ivecs_read("/home/martin/Project/experiment/data_set/sift1M_128/siftsmall_groundtruth.ivecs") querys = fvecs_read("/home/martin/Project/experiment/data_set/sift1M_128/sift_query.fvecs") # size = len(source) # print(source[8000]) # for line in source: # print(line) # source = fvecs_read("/home/tdlab/dataset/sift1M/sift_base.fvecs") # print(len(source[0])) # print(labels[100]) print(len(source)) print(len(querys)) # for i in source[0]: # print(i+1000) # for i in range(len(source[0])): # print(i) # file = ivecs_read("/home/tdlab/test/quan_Result") # print(file)

23.904762

108

0.645418

9cc56eb0cf897582a07707ea11453eac157701a6

1,916

py

Python

venv/Lib/site-packages/pyrogram/raw/base/dialog.py

iamgeorgiy/heroku-userbot

5a92417d16f8ead949d88cb38da213fc2da5d3a4

[ "Apache-2.0" ]

null

venv/Lib/site-packages/pyrogram/raw/base/dialog.py

iamgeorgiy/heroku-userbot

5a92417d16f8ead949d88cb38da213fc2da5d3a4

[ "Apache-2.0" ]

null

venv/Lib/site-packages/pyrogram/raw/base/dialog.py

iamgeorgiy/heroku-userbot

5a92417d16f8ead949d88cb38da213fc2da5d3a4

[ "Apache-2.0" ]

null

# Pyrogram - Telegram MTProto API Client Library for Python # Copyright (C) 2017-2020 Dan <https://github.com/delivrance> # # This file is part of Pyrogram. # # Pyrogram is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published # by the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Pyrogram is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with Pyrogram. If not, see <http://www.gnu.org/licenses/>. # # # # # # # # # # # # # # # # # # # # # # # # # !!! WARNING !!! # # This is a generated file! # # All changes made in this file will be lost! # # # # # # # # # # # # # # # # # # # # # # # # # from typing import Union from pyrogram import raw from pyrogram.raw.core import TLObject Dialog = Union[raw.types.Dialog, raw.types.DialogFolder] # noinspection PyRedeclaration class Dialog: # type: ignore """This base type has 2 constructors available. Constructors: .. hlist:: :columns: 2 - :obj:`Dialog <pyrogram.raw.types.Dialog>` - :obj:`DialogFolder <pyrogram.raw.types.DialogFolder>` """ QUALNAME = "pyrogram.raw.base.Dialog" def __init__(self): raise TypeError("Base types can only be used for type checking purposes: " "you tried to use a base type instance as argument, " "but you need to instantiate one of its constructors instead. " "More info: https://docs.pyrogram.org/telegram/base/dialog")

37.568627

87

0.634656

92bb046084b17e9bf251efa0e24f6f31c2dee95c

3,030

py

Python

rustici_software_cloud_v2/models/message_schema.py

ryanhope2/scormcloud-api-v2-client-python

fcc392933218d32b70987f8bfb1711f891f31c06

[ "Apache-2.0" ]

null

rustici_software_cloud_v2/models/message_schema.py

ryanhope2/scormcloud-api-v2-client-python

fcc392933218d32b70987f8bfb1711f891f31c06

[ "Apache-2.0" ]

null

rustici_software_cloud_v2/models/message_schema.py

ryanhope2/scormcloud-api-v2-client-python

fcc392933218d32b70987f8bfb1711f891f31c06

[ "Apache-2.0" ]

null

# coding: utf-8 """ SCORM Cloud Rest API REST API used for SCORM Cloud integrations. OpenAPI spec version: 2.0 Contact: systems@rusticisoftware.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class MessageSchema(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, message=None): """ MessageSchema - a model defined in Swagger :param dict swaggerTypes: The key is attribute name and the value is attribute type. :param dict attributeMap: The key is attribute name and the value is json key in definition. """ self.swagger_types = { 'message': 'str' } self.attribute_map = { 'message': 'message' } self._message = message @property def message(self): """ Gets the message of this MessageSchema. :return: The message of this MessageSchema. :rtype: str """ return self._message @message.setter def message(self, message): """ Sets the message of this MessageSchema. :param message: The message of this MessageSchema. :type: str """ if message is None: raise ValueError("Invalid value for `message`, must not be `None`") self._message = message def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ if not isinstance(other, MessageSchema): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

25.677966

79

0.533003

3c8c62d6e5c2ccb0446d74f93f455faf1e3dae92

1,490

py

Python

app/user/serializers.py

hmoodallahma/recipe-app-api

13ea22aca5cc2dd1a4e6ed4860f937e46fd2939b

[ "MIT" ]

null

app/user/serializers.py

hmoodallahma/recipe-app-api

13ea22aca5cc2dd1a4e6ed4860f937e46fd2939b

[ "MIT" ]

null

app/user/serializers.py

hmoodallahma/recipe-app-api

13ea22aca5cc2dd1a4e6ed4860f937e46fd2939b

[ "MIT" ]

null

from django.contrib.auth import get_user_model, authenticate from rest_framework import serializers from django.utils.translation import ugettext_lazy as _ class UserSerializer(serializers.ModelSerializer): """Serializer for the users object""" class Meta: model = get_user_model() fields = ('email', 'password', 'name') extra_kwargs = {'password': {'write_only': True, 'min_length': 5}} def create(self, validated_data): """Create a new user with encrypted password and return it""" user = get_user_model().objects.create_user(**validated_data) user.set_password(validated_data['password']) user.save() return user class AuthTokenSerializer(serializers.Serializer): """Serializer for the user authentication object""" email = serializers.CharField() password = serializers.CharField( style={'input_type': 'password'}, trim_whitespace=False ) def validate(self, attrs): """Validate and authenticate the user""" email = attrs.get('email') password = attrs.get('password') user = authenticate( request=self.context.get("request"), username=email, password=password, ) if not user: msg = _('Cannot authenticate user with those credentials.') raise serializers.ValidationError(msg, code='authentication') attrs['user'] = user return attrs

31.041667

74

0.644295

7fd6b86e204af136883717eaec052af737ce9961

1,704

py

Python

setup.py

tylershunt/katana

1afbc755dddc3f88ee0414d0db59d0bf5928f6b3

[ "BSD-3-Clause" ]

null

setup.py

tylershunt/katana

1afbc755dddc3f88ee0414d0db59d0bf5928f6b3

[ "BSD-3-Clause" ]

null

setup.py

tylershunt/katana

1afbc755dddc3f88ee0414d0db59d0bf5928f6b3

[ "BSD-3-Clause" ]

null

import sys import os import setuptools from skbuild import setup # Require pytest-runner only when running tests pytest_runner = ( ["pytest-runner>=2.0,<3dev"] if any(arg in sys.argv for arg in ("pytest", "test")) else [] ) setup_requires = pytest_runner def find_files(root, suffix): """ Find files ending with a given suffix in root and its subdirectories and return their names relative to root. """ files = [] for dirpath, _, filenames in os.walk(root): for f in filenames: if not f.endswith(suffix): continue relpath = os.path.relpath(dirpath, root) files.append(os.path.join(relpath, f)) return files def package_setup(): with open("config/version.txt") as f: version = f.read().strip() pxd_files = find_files("python/katana", ".pxd") # "pip wheel --build-option=..." disables use of wheels for dependencies. # In order to support passing build arguments directly, accept arguments # via the environment. cmake_args = os.environ.get("KATANA_CMAKE_ARGS", "").split() cmake_args.append("-DBUILD_SHARED_LIBS=ON") cmake_args.append("-DBUILD_TESTING=OFF") # Following PEP-518, use pyproject.toml instead of setup(setup_requires=...) to # specify setup dependencies. setup( version=version, name="katana", packages=setuptools.find_packages("python"), package_data={"katana": pxd_files}, package_dir={"": "python"}, tests_require=["pytest"], setup_requires=setup_requires, cmake_args=cmake_args, cmake_source_dir="python", ) if __name__ == "__main__": package_setup()

27.934426

94

0.649648

9698135c91da53e0bf42587dfbdb3c397b0a5641

488

py

Python

tests/test_smoke/test_non_english.py

souvikg10/rasa-nlu-examples

553a6461949f57232601e778fccdff74a488e0cd

[ "Apache-2.0" ]

1

2021-04-11T09:17:11.000Z

tests/test_smoke/test_non_english.py

souvikg10/rasa-nlu-examples

553a6461949f57232601e778fccdff74a488e0cd

[ "Apache-2.0" ]

null

tests/test_smoke/test_non_english.py

souvikg10/rasa-nlu-examples

553a6461949f57232601e778fccdff74a488e0cd

[ "Apache-2.0" ]

2

2021-04-12T23:52:51.000Z

2021-04-13T00:30:55.000Z

import pytest from rasa.train import train_nlu # Take heed! Pytest fails if you use a function that starts with "test" from rasa.test import test_nlu as run_nlu non_english_files = [("thai-tokenizer-config.yml", "tests/data/nlu/th/nlu-th.md")] @pytest.mark.parametrize("fp,nlu", non_english_files) def test_run_train_test_command_non_english(fp, nlu): mod = train_nlu(nlu_data=nlu, config=f"tests/configs/{fp}", output="models") run_nlu(model=f"models/{mod}", nlu_data=nlu)

30.5

82

0.754098

4d24ddff69c9944420703282e1a260af369dc338

3,438

py

Python

lar1nd/run_sbnd.py

NuTufts/chroma_lartpc

ea6d1a62d22eeeaac069efdef1068a56be683fcc

[ "BSD-3-Clause" ]

null

lar1nd/run_sbnd.py

NuTufts/chroma_lartpc

ea6d1a62d22eeeaac069efdef1068a56be683fcc

[ "BSD-3-Clause" ]

null

lar1nd/run_sbnd.py

NuTufts/chroma_lartpc

ea6d1a62d22eeeaac069efdef1068a56be683fcc

[ "BSD-3-Clause" ]

null

import os,sys,time #os.environ['PYOPENCL_CTX']='0:0' #os.environ['PYOPENCL_COMPILER_OUTPUT'] = '0' #os.environ['CUDA_PROFILE'] = '1' import chroma.api as api #api.use_opencl() api.use_cuda() import numpy as np from pyqtgraph.Qt import QtCore, QtGui import pyqtgraph as pg import pyqtgraph.opengl as gl from chroma.display.pyqtdisplay import PyQtDisplay from chroma.sim import Simulation from chroma.event import Photons import chroma.event # LOAD CHROMA UBOONE nthreads_per_block = 1 from sbnd import SBND def gen_photons( nphotons ): dphi = np.random.uniform(0,2.0*np.pi, nphotons) dcos = np.random.uniform(-1.0, 1.0, nphotons) dir = np.array( zip( np.sqrt(1-dcos[:]*dcos[:])*np.cos(dphi[:]), np.sqrt(1-dcos[:]*dcos[:])*np.sin(dphi[:]), dcos[:] ), dtype=np.float32 ) pos = np.tile([0,0,0], (nphotons,1)).astype(np.float32) pol = np.zeros_like(pos) phi = np.random.uniform(0, 2*np.pi, nphotons).astype(np.float32) pol[:,0] = np.cos(phi) pol[:,1] = np.sin(phi) pol = np.cross( pol, dir ) for n,p in enumerate(pol): norm = np.sqrt( p[0]*p[0] + p[1]*p[1] + p[2]*p[2] ) p /= norm t = np.zeros(nphotons, dtype=np.float32) + 100.0 # Avoid negative photon times wavelengths = np.empty(nphotons, np.float32) wavelengths.fill(128.0) return Photons(pos=pos, dir=dir, pol=pol, t=t, wavelengths=wavelengths) if __name__ == "__main__": try: app = QtGui.QApplication([]) except: pass start = time.time() det = SBND() print "[ TIME ] Load detector data ",time.time()-start,"secs" try: display = PyQtDisplay( det ) except: pass print "[ Start Sim. ]" start = time.time() sim = Simulation(det, geant4_processes=0, nthreads_per_block=nthreads_per_block, max_blocks=1024) print "[ TIME ] push geometry data to GPU: ",time.time()-start,"secs" nphotons = 256*100 start = time.time() photons = gen_photons( nphotons ) print "[ TIME ] generate photons ",time.time()-start,"secs" start = time.time() events = sim.simulate( photons, keep_photons_end=True, max_steps=2000) print "[ TIME ] propagate photons ",time.time()-start,"secs" for ev in events: nhits = ev.channels.hit[ np.arange(0,36)[:] ] print "Channels with Hits: " print nhits print "Photoelectrons in each channel: " print ev.channels.q detected_photons = ev.photons_end.flags[:] & chroma.event.SURFACE_DETECT # bit-wise AND. if detected bit set, then value >0, otherwise 0. print "Detected photons: ",np.count_nonzero( detected_photons ) print "hit prep: ",len( ev.photons_end.last_hit_triangles ),len(det.solid_id_to_channel_index),len(det.solid_id) channelhit = np.zeros( len(detected_photons), dtype=np.int ) channelhit[:] = det.solid_id_to_channel_index[ det.solid_id[ ev.photons_end.last_hit_triangles[:] ] ] for n,f in enumerate(detected_photons): if f!=0: # by convention chroma starts event at t=100.0 print "HIT DETID=",channelhit[n]," POS=",ev.photons_end.pos[n,:]," TIME=",ev.photons_end.t[n]-100.0 try: display.plotEvent( ev ) except: pass try: if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): QtGui.QApplication.instance().exec_() except: pass

32.742857

147

0.636707

16e4f99b455ca4d7da1d4038c73c4b91f9aefa4f

1,946

py

Python

summary_workflows/differential_usage/diff.usage.barplot.py

chilampoon/APAeval

ed9f86bf76d760bb20b1484e5853de4d4a30d240

[ "MIT" ]

null

summary_workflows/differential_usage/diff.usage.barplot.py

chilampoon/APAeval

ed9f86bf76d760bb20b1484e5853de4d4a30d240

[ "MIT" ]

null

summary_workflows/differential_usage/diff.usage.barplot.py

chilampoon/APAeval

ed9f86bf76d760bb20b1484e5853de4d4a30d240

[ "MIT" ]

null

#!/usr/bin/env python import os, re, argparse import pandas as pd import matplotlib.pyplot as plt def bar_plot(files, plot_out): ''' Inputs: - files: a list of tsv files containing gene_id & usage changes (e.g. deltaPPAU in QAPA); NOTE that these genes have been filtered by FDR/q-value. File names are like 'AA_diffUTR_03.tsv' - [hardcoded] cutoff: the cutoff to determine if the change is big enough or not, e.g. in QAPA, deltaPPAU > 20 means lengthening - output: path to store the image ''' files = [f for f in files if '_03.tsv' in f] methods = {} for file in files: method = re.sub('_03.tsv$', '', os.path.basename(file)) method = re.sub('^.*_', '', method) # hardcode the cutoffs since each method has its own calculation cutoff = 0 if method == "QAPA": cutoff = 20 elif method == "diffUTR": cutoff = 0.5 #not sure, can ask the author # others to be added df = pd.read_csv(file, sep='\t', header=None, names=['gene','usage']) l = df[df.usage >= cutoff].shape[0] s = df[df.usage <= -cutoff].shape[0] nc = df.shape[0] - l - s methods[method] = [l,s,nc] df = pd.DataFrame(methods, index=['Lengthening', 'Shortening', 'NoChange']) ax = df.plot.bar(rot=0) fig = ax.get_figure() fig.savefig(plot_out) def main(args): files = args['tsvfile'] # tsv files out_path = args['outdir'][0] # directory to output image plot_out = os.path.join(out_path, 'diff.usages.pdf') #cutoff = float(args['cutoff'][0]) bar_plot(files, plot_out) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Differential usage plot') #parser.add_argument('--cutoff', required=True, nargs=1) parser.add_argument('--tsvfile', required=True, nargs='+') parser.add_argument('--outdir', required=True, nargs=1) args = vars(parser.parse_args()) main(args) # usage: ./diff.usage.barplot.py --tsvfile ../AA_QAPA_03.tsv ../BB_MISO_03.tsv ../AB_diffUTR_03.tsv --outdir .

30.888889

111

0.67112

6d7ad4f003d00b4e64eeca99232999280bbd4a67

1,288

py

Python

src/poetry/console/commands/shell.py

zEdS15B3GCwq/poetry

2afe9840533aacfe561d3fdf65c6fb2e790d89b1

[ "MIT" ]

7,258

2018-02-28T16:23:08.000Z

2019-12-11T18:27:58.000Z

src/poetry/console/commands/shell.py

zEdS15B3GCwq/poetry

2afe9840533aacfe561d3fdf65c6fb2e790d89b1

[ "MIT" ]

1,608

2018-02-28T15:31:35.000Z

2019-12-11T20:00:05.000Z

src/poetry/console/commands/shell.py

zEdS15B3GCwq/poetry

2afe9840533aacfe561d3fdf65c6fb2e790d89b1

[ "MIT" ]

597

2018-03-07T15:07:46.000Z

2019-12-11T16:36:22.000Z

from __future__ import annotations import sys from distutils.util import strtobool from os import environ from poetry.console.commands.env_command import EnvCommand class ShellCommand(EnvCommand): name = "shell" description = "Spawns a shell within the virtual environment." help = """The <info>shell</> command spawns a shell, according to the <comment>$SHELL</> environment variable, within the virtual environment. If one doesn't exist yet, it will be created. """ def handle(self) -> int: from poetry.utils.shell import Shell # Check if it's already activated or doesn't exist and won't be created venv_activated = strtobool(environ.get("POETRY_ACTIVE", "0")) or getattr( sys, "real_prefix", sys.prefix ) == str(self.env.path) if venv_activated: self.line( f"Virtual environment already activated: <info>{self.env.path}</>" ) return 0 self.line(f"Spawning shell within <info>{self.env.path}</>") # Setting this to avoid spawning unnecessary nested shells environ["POETRY_ACTIVE"] = "1" shell = Shell.get() shell.activate(self.env) # type: ignore[arg-type] environ.pop("POETRY_ACTIVE") return 0

29.272727

82

0.651398

5404a08097f242a1926bb3fea5b9c46fdeb50024

1,499

py

Python

python3/koans/about_none.py

willsheppard/python_koans

e843070518b62a26aae3b71620dd4dfba25d18f9

[ "MIT" ]

null

python3/koans/about_none.py

willsheppard/python_koans

e843070518b62a26aae3b71620dd4dfba25d18f9

[ "MIT" ]

null

python3/koans/about_none.py

willsheppard/python_koans

e843070518b62a26aae3b71620dd4dfba25d18f9

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Based on AboutNil in the Ruby Koans # from runner.koan import * class AboutNone(Koan): def test_none_is_an_object(self): "Unlike NULL in a lot of languages" self.assertEqual(True, isinstance(None, object)) def test_none_is_universal(self): "There is only one None" self.assertEqual(True, None is None) def test_what_exception_do_you_get_when_calling_nonexistent_methods(self): """ What is the Exception that is thrown when you call a method that does not exist? Hint: launch python command console and try the code in the block below. Don't worry about what 'try' and 'except' do, we'll talk about this later """ try: None.some_method_none_does_not_know_about() except Exception as ex: ex2 = ex # What exception has been caught? # # Need a recap on how to evaluate __class__ attributes? # # http://bit.ly/__class__ self.assertEqual(AttributeError, ex2.__class__) # What message was attached to the exception? # (HINT: replace __ with part of the error message.) self.assertRegex(ex2.args[0], 'has no attribute') def test_none_is_distinct(self): """ None is distinct from other things which are False. """ self.assertEqual(True, None is not 0) self.assertEqual(True, None is not False)

28.826923

81

0.632422

d3702ff2b84a3f8dc1b9b15098a575fb28dc4f01

4,661

py

Python

osf/utils/datetime_aware_jsonfield.py

alexschiller/osf.io

4122d4be152c6189142c2ebb19cfdee09c77035d

[ "Apache-2.0" ]

null

osf/utils/datetime_aware_jsonfield.py

alexschiller/osf.io

4122d4be152c6189142c2ebb19cfdee09c77035d

[ "Apache-2.0" ]

null

osf/utils/datetime_aware_jsonfield.py

alexschiller/osf.io

4122d4be152c6189142c2ebb19cfdee09c77035d

[ "Apache-2.0" ]

null

from __future__ import unicode_literals import datetime as dt import json import logging from decimal import Decimal from functools import partial import pytz from dateutil import parser from django.contrib.postgres import lookups from django.contrib.postgres.fields.jsonb import JSONField from django.core.serializers.json import DjangoJSONEncoder from osf.exceptions import NaiveDatetimeException, ValidationError from psycopg2.extras import Json logger = logging.getLogger(__name__) def coerce_nonnaive_datetimes(json_data): if isinstance(json_data, list): coerced_data = [coerce_nonnaive_datetimes(data) for data in json_data] elif isinstance(json_data, dict): coerced_data = dict() for key, value in json_data.iteritems(): coerced_data[key] = coerce_nonnaive_datetimes(value) elif isinstance(json_data, dt.datetime): try: worked = json_data.astimezone(pytz.utc) # aware object can be in any timezone # noqa except ValueError: # naive coerced_data = json_data.replace(tzinfo=pytz.utc) # json_data must be in UTC logger.warn('Coerced naive datetime to aware for {}'.format(json_data)) else: coerced_data = json_data # it's already aware else: coerced_data = json_data return coerced_data class DateTimeAwareJSONEncoder(DjangoJSONEncoder): def default(self, o): if isinstance(o, dt.datetime): if o.tzinfo is None or o.tzinfo.utcoffset(o) is None: raise NaiveDatetimeException('Tried to encode a naive datetime.') return dict(type='encoded_datetime', value=o.isoformat()) elif isinstance(o, dt.date): return dict(type='encoded_date', value=o.isoformat()) elif isinstance(o, dt.time): if o.tzinfo is None or o.tzinfo.utcoffset(o) is None: raise NaiveDatetimeException('Tried to encode a naive time.') return dict(type='encoded_time', value=o.isoformat()) elif isinstance(o, Decimal): return dict(type='encoded_decimal', value=str(o)) return super(DateTimeAwareJSONEncoder, self).default(o) def decode_datetime_objects(nested_value): if isinstance(nested_value, list): return [decode_datetime_objects(item) for item in nested_value] elif isinstance(nested_value, dict): for key, value in nested_value.iteritems(): if isinstance(value, dict) and 'type' in value.keys(): if value['type'] == 'encoded_datetime': nested_value[key] = parser.parse(value['value']) if value['type'] == 'encoded_date': nested_value[key] = parser.parse(value['value']).date() if value['type'] == 'encoded_time': nested_value[key] = parser.parse(value['value']).time() if value['type'] == 'encoded_decimal': nested_value[key] = Decimal(value['value']) elif isinstance(value, dict): nested_value[key] = decode_datetime_objects(value) elif isinstance(value, list): nested_value[key] = decode_datetime_objects(value) return nested_value return nested_value class DateTimeAwareJSONField(JSONField): def get_prep_value(self, value): if value is not None: return Json(value, dumps=partial(json.dumps, cls=DateTimeAwareJSONEncoder)) return value def from_db_value(self, value, expression, connection, context): if value is None: return None return super(DateTimeAwareJSONField, self).to_python(decode_datetime_objects(value)) def get_prep_lookup(self, lookup_type, value): if lookup_type in ('has_key', 'has_keys', 'has_any_keys'): return value if isinstance(value, (dict, list)): return Json(value, dumps=partial(json.dumps, cls=DateTimeAwareJSONEncoder)) return super(JSONField, self).get_prep_lookup(lookup_type, value) def validate(self, value, model_instance): super(JSONField, self).validate(value, model_instance) try: json.dumps(value, cls=DateTimeAwareJSONEncoder) except TypeError: raise ValidationError( self.error_messages['invalid'], code='invalid', params={'value': value}, ) JSONField.register_lookup(lookups.DataContains) JSONField.register_lookup(lookups.ContainedBy) JSONField.register_lookup(lookups.HasKey) JSONField.register_lookup(lookups.HasKeys) JSONField.register_lookup(lookups.HasAnyKeys)

40.885965

97

0.667024

af65fdeeed6c6cd5fd7ea66ed6079b367b457dea

11,517

py

Python

src/hardware/graph.py

yuezuegu/UBoostNAS

a3ddef3d4b2640eded5858f1a1dd24ec230503b6

[ "MIT" ]

null

src/hardware/graph.py

yuezuegu/UBoostNAS

a3ddef3d4b2640eded5858f1a1dd24ec230503b6

[ "MIT" ]

null

src/hardware/graph.py

yuezuegu/UBoostNAS

a3ddef3d4b2640eded5858f1a1dd24ec230503b6

[ "MIT" ]

null

from collections import OrderedDict from tensorflow.keras.utils import model_to_dot class Node: def __init__(self, layer_name, layer_type): self.layer_name = layer_name self.layer_type = layer_type self.layer_attr = {} self.src = [] self.dst = [] def add_src(self, node): if node is None: return if node not in self.src: self.src.append(node) if self not in node.dst: node.dst.append(self) def add_dst(self, node): if node is None: return if node not in self.dst: self.dst.append(node) if self not in node.src: node.src.append(self) def set_layer_attr(self, key, value): self.layer_attr[key] = value def copy(self, node): self.layer_name = node.layer_name self.layer_type = node.layer_type self.layer_attr = node.layer_attr.copy() self.src = node.src.copy() self.dst = node.dst.copy() def delete(self): for node in self.src: node.dst.remove(self) for node in self.dst: node.src.remove(self) def print_node(self): print("{} -> ".format(self.layer_name), end='') for d in self.dst: print("{}, ".format(d.layer_name), end='') class Graph: def __init__(self) -> None: self.root = None self.node_list = OrderedDict() def add_node(self, node) -> None: if self.root is None: self.root = node self.node_list[node.layer_name] = node def get_node(self, layer_name) -> Node: if layer_name in self.node_list.keys(): return self.node_list[layer_name] else: return None def copy(self, graph) -> None: if graph.root is None: self.root = None else: self.root.copy(graph.root) self.node_list = graph.node_list.copy() def get_layer_names(self) -> list: return list(self.node_list.keys()) def print_graph(self): print("\nPrinting graph...") for k in self.node_list: self.node_list[k].print_node() print() def conv2matrix(weight_tensor, output_tensor): input_size = [ output_tensor[0]*output_tensor[1]*output_tensor[2], weight_tensor[0]*weight_tensor[1]*weight_tensor[2] ] weight_size = [ weight_tensor[0]*weight_tensor[1]*weight_tensor[2], weight_tensor[3] ] return input_size, weight_size def dense2matrix(weight_tensor, output_tensor): assert len(weight_tensor) == 2, "Unconventional weight tensor" input_reuse = 1 for d in range(len(output_tensor)-1): input_reuse = input_reuse * output_tensor[d] input_size = [input_reuse, weight_tensor[0]] weight_size = [weight_tensor[0], weight_tensor[1]] return input_size, weight_size def get_layer_info(node_label): return node_label.split(': ') def convert_keras_to_graph(model): dot_graph = model_to_dot(model, show_shapes=False, show_layer_names=True, rankdir='TB', expand_nested=False, dpi=96) graph = Graph() for n in dot_graph.get_nodes(): label = n.get_label() if label is not None: layer_name, layer_type = get_layer_info(label) graph.add_node(Node(layer_name, layer_type)) for e in dot_graph.get_edges(): src = e.get_source() src_node = dot_graph.get_node(src)[0] src_label = src_node.get_label() if src_label is None: continue src_layer_name, _ = get_layer_info(src_label) dst = e.get_destination() dst_node = dot_graph.get_node(dst)[0] dst_label = dst_node.get_label() dst_layer_name, _ = get_layer_info(dst_label) graph.get_node(dst_layer_name).add_src(graph.get_node(src_layer_name)) graph_tmp = Graph() for layer_name in graph.get_layer_names(): node = graph.get_node(layer_name) layer = model.get_layer(layer_name) if node is None: continue if node.layer_type == "Conv2D": weight_tensor = layer.get_weights()[0].shape output_tensor = layer.output_shape input_size, weight_size = conv2matrix(weight_tensor, output_tensor) node.set_layer_attr("input_size", input_size) node.set_layer_attr("weight_size", weight_size) kernel_size = [layer.get_weights()[0].shape[0], layer.get_weights()[0].shape[1]] node.set_layer_attr("kernel_size", kernel_size) graph_tmp.add_node(node) elif node.layer_type == "DepthwiseConv2D": weight_tensor = layer.get_weights()[0].shape output_tensor = layer.output_shape in_channels = weight_tensor[2] out_channels = in_channels weight_tensor = (weight_tensor[0], weight_tensor[1], 1, weight_tensor[3]) output_tensor = (output_tensor[0], output_tensor[1], output_tensor[2], 1) input_size, weight_size = conv2matrix(weight_tensor, output_tensor) kernel_size = [layer.get_weights()[0].shape[0], layer.get_weights()[0].shape[1]] for ch in range(in_channels): node_single_ch = Node(layer_name+str(ch), node.layer_type) node_single_ch.set_layer_attr("input_size", input_size) node_single_ch.set_layer_attr("weight_size", weight_size) node_single_ch.set_layer_attr("kernel_size", kernel_size) for s in node.src: node_single_ch.add_src(s) for d in node.dst: node_single_ch.add_dst(d) graph_tmp.add_node(node_single_ch) node.delete() elif node.layer_type == "Dense": weight_tensor = layer.get_weights()[0].shape output_tensor = layer.output_shape input_size, weight_size = dense2matrix(weight_tensor, output_tensor) node.set_layer_attr("input_size", input_size) node.set_layer_attr("weight_size", weight_size) graph_tmp.add_node(node) elif node.layer_type == "FeedForward": node_dense1 = Node(layer_name+"1", "Dense") weight_tensor = layer.W1.get_shape().as_list() output_tensor = layer.output_shape input_size, weight_size = dense2matrix(weight_tensor, output_tensor) node_dense1.set_layer_attr("input_size", input_size) node_dense1.set_layer_attr("weight_size", weight_size) for s in node.src: node_dense1.add_src(s) node_dense2 = Node(layer_name+"2", "Dense") weight_tensor = layer.W2.get_shape().as_list() output_tensor = layer.output_shape input_size, weight_size = dense2matrix(weight_tensor, output_tensor) node_dense2.set_layer_attr("input_size", input_size) node_dense2.set_layer_attr("weight_size", weight_size) node_dense2.add_src(node_dense1) for d in node.dst: node_dense2.add_dst(d) graph_tmp.add_node(node_dense1) graph_tmp.add_node(node_dense2) node.delete() elif node.layer_type == "MultiHeadAttention": if isinstance(layer.input, list): #decoder query attention input_tensor = layer.input_shape[0] for i in range(1,len(layer.input_shape)): assert input_tensor == layer.input_shape[i] else: #self-attention input_tensor = layer.input_shape batch_size = input_tensor[0] sentence_len = input_tensor[1] d_model = input_tensor[2] node_K = Node(layer_name+"_K", "Dense") Wk = layer.Wk.get_shape().as_list() input_size, weight_size = [[batch_size*sentence_len, d_model],list(Wk)] node_K.set_layer_attr("input_size", input_size) node_K.set_layer_attr("weight_size", weight_size) for s in node.src: node_K.add_src(s) node_Q = Node(layer_name+"_Q", "Dense") Wq = layer.Wq.get_shape().as_list() input_size, weight_size = [[batch_size*sentence_len, d_model],list(Wq)] node_Q.set_layer_attr("input_size", input_size) node_Q.set_layer_attr("weight_size", weight_size) for s in node.src: node_Q.add_src(s) node_V = Node(layer_name+"_V", "Dense") Wv = layer.Wv.get_shape().as_list() input_size, weight_size = [[batch_size*sentence_len, d_model],list(Wv)] node_V.set_layer_attr("input_size", input_size) node_V.set_layer_attr("weight_size", weight_size) for s in node.src: node_V.add_src(s) d_h = int(d_model/layer.head_num) node_QK = [] for i in range(layer.head_num): n = Node(layer_name+"_QK"+str(i), "Dense") input_size, weight_size = [[input_tensor[0]*input_tensor[1], d_h], [d_h, input_tensor[0]*input_tensor[1]]] n.set_layer_attr("input_size", input_size) n.set_layer_attr("weight_size", weight_size) n.add_src(node_K) n.add_src(node_Q) n.add_src(node_V) node_QK.append(n) node_QK_concat = Node(layer_name+"_QK-Concat", "Concat") for i in range(layer.head_num): node_QK_concat.add_src(node_QK[i]) node_PV = [] for i in range(layer.head_num): n = Node(layer_name+"_PV"+str(i), "Dense") input_size, weight_size = [[input_tensor[0]*input_tensor[1], input_tensor[0]*input_tensor[1]], [input_tensor[0]*input_tensor[1], d_h]] n.set_layer_attr("input_size", input_size) n.set_layer_attr("weight_size", weight_size) n.add_src(node_QK_concat) node_PV.append(n) node_PV_concat = Node(layer_name+"_PV-Concat", "Concat") for i in range(layer.head_num): node_PV_concat.add_src(node_PV[i]) node_O = Node(layer_name+"_O", "Dense") Wo = layer.Wo.get_shape().as_list() input_size, weight_size = [[batch_size*sentence_len, d_model],list(Wo)] node_O.set_layer_attr("input_size", input_size) node_O.set_layer_attr("weight_size", weight_size) node_O.add_src(node_PV_concat) for d in node.dst: node_O.add_dst(d) graph_tmp.add_node(node_K) graph_tmp.add_node(node_Q) graph_tmp.add_node(node_V) for i in range(layer.head_num): graph_tmp.add_node(node_QK[i]) graph_tmp.add_node(node_QK_concat) for i in range(layer.head_num): graph_tmp.add_node(node_PV[i]) graph_tmp.add_node(node_PV_concat) graph_tmp.add_node(node_O) node.delete() else: graph_tmp.add_node(node) graph.copy(graph_tmp) # graph.print_graph() # from tensorflow.keras.utils import plot_model # plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True) return graph

34.585586

150

0.596075

ad0d7664779de98923f4a887930781c4ace6155b

1,467

py

Python

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QueryCustomerAddressListRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

null

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QueryCustomerAddressListRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

null

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QueryCustomerAddressListRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 aliyunsdkcore.request import RpcRequest from aliyunsdkbssopenapi.endpoint import endpoint_data class QueryCustomerAddressListRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'BssOpenApi', '2017-12-14', 'QueryCustomerAddressList') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_OwnerId(self): # Long return self.get_query_params().get('OwnerId') def set_OwnerId(self, OwnerId): # Long self.add_query_param('OwnerId', OwnerId)

37.615385

84

0.766871

4bc49efa19b29942b786c0ae1cfd9d264ccc664e

207

py

Python

appengine/tictactoe/routing.py

salendron/ai-kindergarten

02782730753b7d9b70fcff2dcfbff0f7a4ed6e0e

[ "MIT" ]

null

appengine/tictactoe/routing.py

salendron/ai-kindergarten

02782730753b7d9b70fcff2dcfbff0f7a4ed6e0e

[ "MIT" ]

null

appengine/tictactoe/routing.py

salendron/ai-kindergarten

02782730753b7d9b70fcff2dcfbff0f7a4ed6e0e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ TODO """ __author__ = 'Bruno Hautzenberger' import bottle from .webhandler import index def setup_routing(): bottle.route('/', ['GET', 'POST'], index)

15.923077

45

0.642512

63668934999a198e71f1a6d365063473a35449ae

782

py

Python

p00-python-fundamentals/hermes.py

rahulswarnkar/slides

6d90c5f1ff9c3c774580af8ac7c2f74b959eb2c2

[ "MIT" ]

null

p00-python-fundamentals/hermes.py

rahulswarnkar/slides

6d90c5f1ff9c3c774580af8ac7c2f74b959eb2c2

[ "MIT" ]

1

2022-02-10T19:15:46.000Z

p00-python-fundamentals/hermes.py

rahulswarnkar/slides

6d90c5f1ff9c3c774580af8ac7c2f74b959eb2c2

[ "MIT" ]

null

import scraper def collect_urls(products_url, prefix = ''): products_html = scraper.content(products_url) products_hrefs = scraper.get_attrs(products_html, 'div.product-item a', 'href') product_urls = [(prefix + href) for href in products_hrefs] return product_urls def collect_info(product_url): product_html = scraper.content(product_url) info = { 'name': scraper.get_text(product_html, 'div#variant-info h1').pop(), 'sku': scraper.get_text(product_html, 'div.commerce-product-sku span').pop(), 'price': scraper.get_text(product_html, 'p.field-type-commerce-price').pop() } return info urls = collect_urls('https://www.hermes.com/uk/en/men/shoes/', 'https://www.hermes.com/') for url in urls: print(collect_info(url))

39.1

89

0.699488

2ac762fc2b09524279f9639fbe4bb7802dc23833

685

py

Python

cookbook/c07/p7_lambda_value.py

RaiderTcT/python3-cookbook

4eb7715b9b9d3418897ec08276eabe401e5ce513

[ "Apache-2.0" ]

null

cookbook/c07/p7_lambda_value.py

RaiderTcT/python3-cookbook

4eb7715b9b9d3418897ec08276eabe401e5ce513

[ "Apache-2.0" ]

null

cookbook/c07/p7_lambda_value.py

RaiderTcT/python3-cookbook

4eb7715b9b9d3418897ec08276eabe401e5ce513

[ "Apache-2.0" ]

null

# @作者： Ulysses # @功能描述：在lambda中绑定变量的值 # @修改记录： 2019/2/14 16:27 创建 x = 10 a = lambda y: x + y x = 20 b = lambda y: x + y print(a(10), b(10)) # x是自由变量，在运行时绑定变量的值 # 30 30 funcs = [lambda x: x+n for n in range(5)] print(funcs) # 还没有执行 # [<function <listcomp>.<lambda> at 0x00000000029B1598>, <function <listcomp>.<lambda> at 0x00000000029B1620>, <function <listcomp>.<lambda> at 0x00000000029B16A8>, <function <listcomp>.<lambda> at 0x00000000029B1730>, <function <listcomp>.<lambda> at 0x00000000029B17B8>] for f in funcs: print(f(0), end=' ') # 4 4 4 4 4 print() funcs_1 = [lambda x, n=n: x+n for n in range(5)] for f in funcs_1: print(f(1), end=' ') # 1 2 3 4 5

31.136364

272

0.643796

83b631635747e4fe0a374c9c904060708a276aea

2,378

py

Python

test_model.py

Ahid-Naif/Breaking-Captchas-with-a-CNN

81987ce54d8756699812c315bd8725bd2219b9b2

[ "MIT" ]

3

2020-05-25T15:13:23.000Z

2022-02-26T21:30:20.000Z

test_model.py

Ahid-Naif/Breaking-Captchas-with-a-CNN

81987ce54d8756699812c315bd8725bd2219b9b2

[ "MIT" ]

null

test_model.py

Ahid-Naif/Breaking-Captchas-with-a-CNN

81987ce54d8756699812c315bd8725bd2219b9b2

[ "MIT" ]

5

2020-03-12T00:42:02.000Z

2020-06-08T16:24:19.000Z

from keras.preprocessing.image import img_to_array from keras.models import load_model from utils.captchahelper import preprocess from imutils import contours, paths import imutils import numpy as np import cv2 modelPath = "lenet.hdf5" imagesPath = "downloads" # load the pre-trained network print("[INFO] loading pre-trained network...") model = load_model(modelPath) # randomly sample a few of the input images imagesPaths = list(paths.list_images(imagesPath)) imagesPaths = np.random.choice(imagesPaths, size=(10,), replace=False) # loop over the image paths for imagePath in imagesPaths: # load the image and convert it to grayscale image = cv2.imread(imagePath) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # pad the image gray = cv2.copyMakeBorder(gray, 20, 20, 20, 20, cv2.BORDER_REPLICATE) # threshold the image to reveal the digits thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] # find contours in the image, keeping only the four largest ones, # then sort them from left-to-right cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:4] cnts = contours.sort_contours(cnts)[0] # initialize the output image as a "grayscale" image with 3 # channels along with the output predictions output = cv2.merge([gray] * 3) predictions = [] # loop over the contours for c in cnts: # compute the bounding box for the contour then extract the digit (x, y, w, h) = cv2.boundingRect(c) roi = gray[y - 5:y + h + 5, x - 5:x + w + 5] # pre-process the ROI and classify it then classify it roi = preprocess(roi, 28, 28) roi = np.expand_dims(img_to_array(roi), axis=0) pred = model.predict(roi).argmax(axis=1)[0] + 1 predictions.append(str(pred)) # draw the prediction on the output image cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4), (0, 255, 0), 1) cv2.putText(output, str(pred), (x - 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 2) # show the output image print("[INFO] captcha: {}".format("".join(predictions))) cv2.imshow("Output", output) cv2.waitKey()

36.030303

85

0.649706

39712fa63f8726101ca851612fb14afaa2194b17

2,382

py

Python

golden_fish_pb2_grpc.py

lablabla/GoldenFishServer

71eece8c7d340a70a86bc7595699c858cc7a9d30

[ "Apache-2.0" ]

null

golden_fish_pb2_grpc.py

lablabla/GoldenFishServer

71eece8c7d340a70a86bc7595699c858cc7a9d30

[ "Apache-2.0" ]

null

golden_fish_pb2_grpc.py

lablabla/GoldenFishServer

71eece8c7d340a70a86bc7595699c858cc7a9d30

[ "Apache-2.0" ]

null

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" import grpc import golden_fish_pb2 as golden__fish__pb2 class GoldenFishStub(object): """Interface exported by the server. """ def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.RegisterValve = channel.unary_unary( '/goldenfish.GoldenFish/RegisterValve', request_serializer=golden__fish__pb2.Valve.SerializeToString, response_deserializer=golden__fish__pb2.Status.FromString, ) class GoldenFishServicer(object): """Interface exported by the server. """ def RegisterValve(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_GoldenFishServicer_to_server(servicer, server): rpc_method_handlers = { 'RegisterValve': grpc.unary_unary_rpc_method_handler( servicer.RegisterValve, request_deserializer=golden__fish__pb2.Valve.FromString, response_serializer=golden__fish__pb2.Status.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'goldenfish.GoldenFish', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class GoldenFish(object): """Interface exported by the server. """ @staticmethod def RegisterValve(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/goldenfish.GoldenFish/RegisterValve', golden__fish__pb2.Valve.SerializeToString, golden__fish__pb2.Status.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

34.028571

101

0.663728

e6b40f613202ef1594b5da1025bf67c62a1de21b

5,526

py

Python

motion_analysis/gui/dialogs/select_scene_objects_dialog.py

gemlongman/motion_preprocessing_tool

0d46fc41ff951e7cc4788d1d9355b0346a9df306

[ "MIT" ]

null

motion_analysis/gui/dialogs/select_scene_objects_dialog.py

gemlongman/motion_preprocessing_tool

0d46fc41ff951e7cc4788d1d9355b0346a9df306

[ "MIT" ]

null

motion_analysis/gui/dialogs/select_scene_objects_dialog.py

gemlongman/motion_preprocessing_tool

0d46fc41ff951e7cc4788d1d9355b0346a9df306

[ "MIT" ]

null

#!/usr/bin/env python # # Copyright 2019 DFKI GmbH. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to permit # persons to whom the Software is furnished to do so, subject to the # following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN # NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE # USE OR OTHER DEALINGS IN THE SOFTWARE. from PySide2.QtWidgets import QDialog, QListWidgetItem, QHBoxLayout, QLabel, QCheckBox, QLineEdit, QPushButton from PySide2.QtCore import Qt from motion_analysis.gui.layout.select_scene_object_dialog_ui import Ui_Dialog from .utils import get_all_objects from functools import partial from .set_properties_dialog import SetPropertiesDialog class SelectSceneObjectsDialog(QDialog, Ui_Dialog): def __init__(self, scene, filter_function=get_all_objects, parent=None, name=None, properties=None): QDialog.__init__(self, parent) Ui_Dialog.setupUi(self, self) if name is not None: self.setWindowTitle(name) self._filter_function = filter_function self.selectButton.clicked.connect(self.slot_accept) self.cancelButton.clicked.connect(self.slot_reject) self.success = False self.selected_node_id = -1 self._fill_list_with_scene_objects(scene) self.properties = dict() if properties is not None: self.properties = properties for key in list(self.properties.keys()): self.add_line(key) def _fill_list_with_scene_objects(self, scene): for sceneObject in self._filter_function(scene): item = QListWidgetItem() item.setText(sceneObject.name) item.setData(Qt.UserRole, sceneObject.node_id) self.sceneObjectListWidget.addItem(item) def slot_accept(self): selected_item = self.sceneObjectListWidget.currentItem() node_id = selected_item.data(Qt.UserRole) self.selected_node_id = node_id print("selected item", selected_item.text(),self.selected_node_id) self.success = True self.close() def slot_reject(self): self.close() def add_line(self, key): layout = QHBoxLayout() label = QLabel(self) name =str(key) label.setText(name) layout.addWidget(label) value = self.properties[key] if isinstance(value, (bool)): checkbox = QCheckBox(self) checkbox.setChecked(bool(value)) layout.addWidget(checkbox) checkbox.stateChanged.connect(partial(self.update_value, key=key, type_func=bool)) #self.connect(checkbox, QtCore.SIGNAL('stateChanged(int)'), partial(self.update_value, key=key, type_func=bool)) elif isinstance(value, (int, float, complex)): value = str(value) line_edit = QLineEdit(self) line_edit.setText(value) layout.addWidget(line_edit) line_edit.textChanged.connect(partial(self.update_value, key=key, type_func=float)) #self.connect(line_edit, QtCore.SIGNAL('textChanged(QString)'), partial(self.update_value, key=key, type_func=float)) elif isinstance(value, (int)): value = str(value) line_edit = QLineEdit(self) line_edit.setText(value) layout.addWidget(line_edit) line_edit.textChanged.connect(partial(self.update_value, key=key, type_func=int)) #self.connect(line_edit, QtCore.SIGNAL('textChanged(QString)'), partial(self.update_value, key=key, type_func=int)) elif isinstance(value, (str)): value = str(value) line_edit = QLineEdit(self) line_edit.setText(value) layout.addWidget(line_edit) line_edit.textChanged.connect(partial(self.update_value, key=key, type_func=str)) #self.connect(line_edit, QtCore.SIGNAL('textChanged(QString)'), partial(self.update_value, key=key, type_func=str)) elif isinstance(value, dict): button = QPushButton(self) button.setText("Set Values") button.setObjectName(key) button.clicked.connect(self.update_dictionary) #self.connect(button, QtCore.SIGNAL("clicked()"), self.update_dictionary) layout.addWidget(button) self.settingsVerticalLayout.addLayout(layout) def update_value(self, value, key, type_func): self.properties[key] = type_func(value) print("value changed", key, self.properties[key]) def update_dictionary(self): key = str(self.sender().objectName()) print("sender", key) dialog = SetPropertiesDialog(self.properties[key], self) dialog.exec_() self.properties[key] = dialog.properties

45.669421

129

0.685306

8fe260419699d7296488d77507a9079c5cba983e

8,261

py

Python

linprog/matrixtools.py

Napam/JallaResearch

f0ed573f20f050ec0a653bff09068eb11a9c4128

[ "MIT" ]

1

2021-04-30T20:13:05.000Z

linprog/matrixtools.py

Napam/JallaResearch

f0ed573f20f050ec0a653bff09068eb11a9c4128

[ "MIT" ]

null

linprog/matrixtools.py

Napam/JallaResearch

f0ed573f20f050ec0a653bff09068eb11a9c4128

[ "MIT" ]

null

import numpy as np from typing import Callable, Sequence from numbers import Number from fractions import Fraction from numpy.core.fromnumeric import ravel from sympy.solvers.inequalities import solve_poly_inequality from sympy.polys import Poly import sympy as sp from itertools import combinations from pprint import pprint UTF = { 0: u'\u2080', 1: u'\u2081', 2: u'\u2082', 3: u'\u2083', 4: u'\u2084', 5: u'\u2085', 6: u'\u2086', 7: u'\u2087', 8: u'\u2088', 9: u'\u2089', 10: '₁₀', 11: '₁₁', 12: '₁₂', 'cal B': '𝓑', 'cal N': '𝓝', '_B': 'ᵦ', '_N': 'ₙ', 'inv': '⁻¹', 'T': 'ᵀ', 'zeta': 'ζ', 'geq': '≥', 'Delta': 'Δ', 'rarrow':'⇒', } def get_formatter(spacing: int) -> Callable: def formatter(x): a = str(Fraction(x).limit_denominator()) return f'{a:>{spacing}}' return formatter def print_lp_dict(DICp: Sequence[Sequence[Number]], basic: np.ndarray, nonbasic: np.ndarray): with np.printoptions(formatter={'float':get_formatter(4)}): strbasic = [f'x{UTF[i]}' for i in basic+1] strnonbasic = [f'x{UTF[i]}' for i in nonbasic+1] objstring = f'{UTF["zeta"]} = {DICp[0]}' print(f'x{UTF["_N"]} = ', ' , '.join(strnonbasic)) print(objstring) print(f'{"":_^{len(objstring)}}') for basicvar, row in zip(strbasic, DICp[1:]): print(f'{basicvar} = {row}') def get_dict_from_basis( A: Sequence[Sequence[Number]], b: Sequence[Number], c: Sequence[Number], basis: Sequence[int] ) -> None: A = np.array(A) b = np.array(b) c = np.array(c) basis = np.array(basis) m, n = A.shape A = np.c_[A, np.eye(m, dtype=int)] c = np.concatenate([c, np.zeros(m)]) basic = basis - 1 allvars = np.arange(m+n) nonbasic = np.setdiff1d(allvars, basic) c_basic = c[basic] c_nonbasic = c[nonbasic] print('Variables:', *['x'+UTF[v] for v in allvars+1]) print(UTF['cal B']+' =', basic+1) print(UTF['cal N']+' =', nonbasic+1) print('c'+UTF['_B'], c_basic) print('c'+UTF['_N'], c_nonbasic) B = A[:,basic] N = A[:,nonbasic] with np.printoptions(formatter={'float':get_formatter(4)}): print('\nB =') print(B) print('\nN =') print(N) print('\nb =') print(b) Binv = np.linalg.inv(B) print('\nB'+UTF['inv']+'N =') print(Binv@N) with np.printoptions(formatter={'float':get_formatter(4)}): Binvb = Binv@b print('\nB'+UTF['inv']+'b =') print(Binvb) cBb = c_basic@Binv@b print('\nc'+UTF['T']+UTF['_B']+'B'+UTF['inv']+'b =', cBb) with np.printoptions(formatter={'float':get_formatter(4)}): BinvN = Binv@N zn = (BinvN.T)@c_basic - c_nonbasic print(f'\n(B{UTF["inv"]}N){UTF["T"]}c{UTF["_B"]}-c{UTF["_N"]} =', zn) print('\nDictionary:') DICp = np.zeros((m+1, n+m-1)) DICp[0] = [cBb, *(-zn)] DICp[1:,0] = Binvb.ravel() DICp[1:,1:] = -BinvN print_lp_dict(DICp, basic, nonbasic) def ranging( DICp: Sequence[Sequence[Number]], c: Sequence[Number], dc: Sequence[Number], basic: Sequence[int], nonbasic: Sequence[int], ) -> None: DICp = np.array(DICp) c = np.array(c) dc = np.array(dc) basic = np.array(basic) nonbasic = np.array(nonbasic) zn = -DICp[0,1:].reshape(-1,1) basic = np.array(basic)-1 nonbasic = np.array(nonbasic)-1 dc_basic = dc[basic].reshape(-1,1) dc_nonbasic = dc[nonbasic].reshape(-1,1) BinvN = -DICp[1:,1:] BinvNTcb = BinvN.T@dc_basic dzn = BinvNTcb - dc_nonbasic print('Got dicitonary:') print_lp_dict(DICp, basic, nonbasic) print(f'\n-B{UTF["inv"]}N =') print(-BinvN) print() print(f'{UTF["Delta"]}c{UTF["T"]} = {dc.ravel()}') print(f'{UTF["Delta"]}c{UTF["_B"]}{UTF["T"]} = {dc_basic.ravel()}') print(f'{UTF["Delta"]}c{UTF["_N"]}{UTF["T"]} = {dc_nonbasic.ravel()}') print(f'\n(B{UTF["inv"]}N){UTF["T"]} =') print(BinvN.T) print() print(f'{UTF["Delta"]}z{UTF["_N"]}= ' f'(B{UTF["inv"]}N){UTF["T"]}{UTF["Delta"]}c{UTF["_B"]} - c{UTF["_N"]} =') print(f'{BinvNTcb.ravel()}{UTF["T"]} - {dc_nonbasic.ravel()}{UTF["T"]} = {dzn.ravel()}{UTF["T"]}') print(f'\nz*{UTF["_N"]} + t{UTF["Delta"]}z{UTF["_N"]} {UTF["geq"]} 0 {UTF["rarrow"]} ' f'{zn.ravel()}{UTF["T"]} + t{dzn.ravel()}{UTF["T"]} {UTF["geq"]} 0') print(UTF['rarrow']) for i, j in zip(zn.ravel(), dzn.ravel()): string = '' string += str(i) if j >= 0: string += ' +' elif j < 0: string += ' -' string += f' {abs(j)}t {UTF["geq"]} 0' print(string) t = sp.Symbol('t', real=True) polys = [(Poly(i + j*t), '>=') for i, j in zip(zn.ravel(), dzn.ravel()) if j != 0] result = sp.Intersection(*[s for p in polys for s in solve_poly_inequality(*p)]) # l for lower, u for upper l_bound, u_bound = result.left, result.right l_c = c + dc*l_bound u_c = c + dc*u_bound # Fix nans mask = l_c == sp.nan l_c[mask] = c[mask] mask = u_c == sp.nan u_c[mask] = c[mask] print() print(f'Respective to {UTF["Delta"]}c = {dc}') print(f'We see that we can vary c in:') print(l_c) print('to') print(u_c) def submatrix(A: Sequence[Sequence[Number]], b: Sequence[Sequence[Number]]) -> None: A = np.array(A) b = np.array(b) m, n = A.shape print(f'A = \n{A}') print(f'b = \n{b}\n') indices = np.arange(n) pairs = np.array(list(combinations(indices,2))) submatrices = np.zeros((len(pairs), m, m)) for i, pair in enumerate(pairs): mat = A[:,pair] submatrices[i] = (mat) for pair, mat in zip(pairs, submatrices): print(f'Basic vars: {pair} {UTF["rarrow"]}') print('B=') print(mat) det = np.linalg.det(mat) print(f'det = {det:.2f}') if np.linalg.det(mat) != 0: print('Is nonsingular') else: print('Is SINGULAR') print() inverses = np.zeros_like(submatrices) vertices = np.zeros((len(submatrices), m)) for i, mat in enumerate(submatrices): Binv = np.linalg.inv(mat) inverses[i] = Binv vertices[i] = (Binv@b).ravel() feasible_mask = (vertices < 0).sum(axis=1) == 0 print('\n\nBasis feasible') for isbasis, pair, mat in zip(feasible_mask, pairs, submatrices): if isbasis: print(pair) print(mat) if __name__ == '__main__': submatrix( A=[ [-1, 4, 1, 0], [ 2, 6, 0, 1] ], b=[ [-1], [ 6], ] ) # get_dict_from_basis( # A=[ # [-1,-1, 0, 0, 0, 0], # [ 1, 0,-1, 0,-1, 0], # [ 0, 1, 1,-1, 0, 0], # [ 0, 0, 0, 1, 0, 1], # [ 0, 0, 0, 0, 1,-1], # ], # b=[ # [ 2], # [ 2], # [-1], # [-4], # [ 1], # ], # c=[4,2,1,1,1,3], # basis=[1,3,4,6] # ) # Compulsory 2 # get_dict_from_basis( # A=[ # [ 1, 2, 3, 4, 5], # [ 0, 5,-3,-2,-1], # ], # b=[ # [ 2], # [ 3], # ], # c=[1,2,4,8,16], # basis=[1,5] # ) # H15 Ex2 # get_dict_from_basis( # A=[ # [ 1, 2, 3], # [-7, 5,-1], # ], # b=[ # [ 1], # [-3], # ], # c=[1,2,4], # basis=[1,3] # ) # ranging( # DICp=[ # [ 6,-1,-1,-2], # [ 3,-1,-1,-1], # [ 4, 2,-2, 3] # ], # c=[1,2,1,0,0], # dc=[0,1,0,0,0], # basic=[2,5], # nonbasic=[1,3,4], # ) # ranging( # DICp=[ # [ 3,-1,-0,-1], # [ 3,-1,-1,-1], # [10,-2,-4, 1] # ], # c=[1,0,1,0,0], # dc=[0,1,0,0,0], # basic=[1,5], # nonbasic=[2,3,4], # )

24.807808

102

0.468708

45edaa36251e2b2e1ab4693071a631211aad7277

19,886

py

Python

tests/components/apple_tv/test_config_flow.py

basicpail/core

5cc54618c5af3f75c08314bf2375cc7ac40d2b7e

[ "Apache-2.0" ]

11

2018-02-16T15:35:47.000Z

2020-01-14T15:20:00.000Z

tests/components/apple_tv/test_config_flow.py

basicpail/core

5cc54618c5af3f75c08314bf2375cc7ac40d2b7e

[ "Apache-2.0" ]

87

2020-07-06T22:22:54.000Z

2022-03-31T06:01:46.000Z

tests/components/apple_tv/test_config_flow.py

Vaarlion/core

f3de8b9f28de01abf72c0f5bb0b457eb1841f201

[ "Apache-2.0" ]

11

2020-12-16T13:48:14.000Z

2022-02-01T00:28:05.000Z

"""Test config flow.""" from unittest.mock import patch from pyatv import exceptions from pyatv.const import Protocol import pytest from homeassistant import config_entries, data_entry_flow from homeassistant.components.apple_tv.const import CONF_START_OFF, DOMAIN from tests.common import MockConfigEntry DMAP_SERVICE = { "type": "_touch-able._tcp.local.", "name": "dmapid.something", "properties": {"CtlN": "Apple TV"}, } @pytest.fixture(autouse=True) def mock_setup_entry(): """Mock setting up a config entry.""" with patch( "homeassistant.components.apple_tv.async_setup_entry", return_value=True ): yield # User Flows async def test_user_input_device_not_found(hass, mrp_device): """Test when user specifies a non-existing device.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["description_placeholders"] == {"devices": "`MRP Device (127.0.0.1)`"} result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "none"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["errors"] == {"base": "no_devices_found"} async def test_user_input_unexpected_error(hass, mock_scan): """Test that unexpected error yields an error message.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) mock_scan.side_effect = Exception result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "dummy"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["errors"] == {"base": "unknown"} async def test_user_adds_full_device(hass, full_device, pairing): """Test adding device with all services.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {} result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "MRP Device"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["description_placeholders"] == {"name": "MRP Device"} result3 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) assert result3["type"] == data_entry_flow.RESULT_TYPE_FORM assert result3["description_placeholders"] == {"protocol": "MRP"} result4 = await hass.config_entries.flow.async_configure( result["flow_id"], {"pin": 1111} ) assert result4["type"] == data_entry_flow.RESULT_TYPE_FORM assert result4["description_placeholders"] == {"protocol": "DMAP", "pin": 1111} result5 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) assert result5["type"] == data_entry_flow.RESULT_TYPE_FORM assert result5["description_placeholders"] == {"protocol": "AirPlay"} result6 = await hass.config_entries.flow.async_configure( result["flow_id"], {"pin": 1234} ) assert result6["type"] == "create_entry" assert result6["data"] == { "address": "127.0.0.1", "credentials": { Protocol.DMAP.value: "dmap_creds", Protocol.MRP.value: "mrp_creds", Protocol.AirPlay.value: "airplay_creds", }, "name": "MRP Device", "protocol": Protocol.MRP.value, } async def test_user_adds_dmap_device(hass, dmap_device, dmap_pin, pairing): """Test adding device with only DMAP service.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "DMAP Device"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["description_placeholders"] == {"name": "DMAP Device"} result3 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) assert result3["type"] == data_entry_flow.RESULT_TYPE_FORM assert result3["description_placeholders"] == {"pin": 1111, "protocol": "DMAP"} result6 = await hass.config_entries.flow.async_configure( result["flow_id"], {"pin": 1234} ) assert result6["type"] == "create_entry" assert result6["data"] == { "address": "127.0.0.1", "credentials": {Protocol.DMAP.value: "dmap_creds"}, "name": "DMAP Device", "protocol": Protocol.DMAP.value, } async def test_user_adds_dmap_device_failed(hass, dmap_device, dmap_pin, pairing): """Test adding DMAP device where remote device did not attempt to pair.""" pairing.always_fail = True result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "DMAP Device"}, ) await hass.config_entries.flow.async_configure(result["flow_id"], {}) result2 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) assert result2["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result2["reason"] == "device_did_not_pair" async def test_user_adds_device_with_credentials(hass, dmap_device_with_credentials): """Test adding DMAP device with existing credentials (home sharing).""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "DMAP Device"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["description_placeholders"] == {"name": "DMAP Device"} result3 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) assert result3["type"] == "create_entry" assert result3["data"] == { "address": "127.0.0.1", "credentials": {Protocol.DMAP.value: "dummy_creds"}, "name": "DMAP Device", "protocol": Protocol.DMAP.value, } async def test_user_adds_device_with_ip_filter( hass, dmap_device_with_credentials, mock_scan ): """Test add device filtering by IP.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "127.0.0.1"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["description_placeholders"] == {"name": "DMAP Device"} result3 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) assert result3["type"] == "create_entry" assert result3["data"] == { "address": "127.0.0.1", "credentials": {Protocol.DMAP.value: "dummy_creds"}, "name": "DMAP Device", "protocol": Protocol.DMAP.value, } async def test_user_adds_device_by_ip_uses_unicast_scan(hass, mock_scan): """Test add device by IP-address, verify unicast scan is used.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "127.0.0.1"}, ) assert str(mock_scan.hosts[0]) == "127.0.0.1" async def test_user_adds_existing_device(hass, mrp_device): """Test that it is not possible to add existing device.""" MockConfigEntry(domain="apple_tv", unique_id="mrpid").add_to_hass(hass) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "127.0.0.1"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["errors"] == {"base": "already_configured"} async def test_user_adds_unusable_device(hass, device_with_no_services): """Test that it is not possible to add device with no services.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "Invalid Device"}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["errors"] == {"base": "no_usable_service"} async def test_user_connection_failed(hass, mrp_device, pairing_mock): """Test error message when connection to device fails.""" pairing_mock.begin.side_effect = exceptions.ConnectionFailedError result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "MRP Device"}, ) await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result2["reason"] == "invalid_config" async def test_user_start_pair_error_failed(hass, mrp_device, pairing_mock): """Test initiating pairing fails.""" pairing_mock.begin.side_effect = exceptions.PairingError result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "MRP Device"}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result2["reason"] == "invalid_auth" async def test_user_pair_invalid_pin(hass, mrp_device, pairing_mock): """Test pairing with invalid pin.""" pairing_mock.finish.side_effect = exceptions.PairingError result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "MRP Device"}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"pin": 1111}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["errors"] == {"base": "invalid_auth"} async def test_user_pair_unexpected_error(hass, mrp_device, pairing_mock): """Test unexpected error when entering PIN code.""" pairing_mock.finish.side_effect = Exception result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "MRP Device"}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"pin": 1111}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["errors"] == {"base": "unknown"} async def test_user_pair_backoff_error(hass, mrp_device, pairing_mock): """Test that backoff error is displayed in case device requests it.""" pairing_mock.begin.side_effect = exceptions.BackOffError result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "MRP Device"}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result2["reason"] == "backoff" async def test_user_pair_begin_unexpected_error(hass, mrp_device, pairing_mock): """Test unexpected error during start of pairing.""" pairing_mock.begin.side_effect = Exception result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) await hass.config_entries.flow.async_configure( result["flow_id"], {"device_input": "MRP Device"}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result2["reason"] == "unknown" # Zeroconf async def test_zeroconf_unsupported_service_aborts(hass): """Test discovering unsupported zeroconf service.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data={ "type": "_dummy._tcp.local.", "properties": {}, }, ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "unknown" async def test_zeroconf_add_mrp_device(hass, mrp_device, pairing): """Test add MRP device discovered by zeroconf.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data={ "type": "_mediaremotetv._tcp.local.", "properties": {"UniqueIdentifier": "mrpid", "Name": "Kitchen"}, }, ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["description_placeholders"] == {"name": "MRP Device"} result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["description_placeholders"] == {"protocol": "MRP"} result3 = await hass.config_entries.flow.async_configure( result["flow_id"], {"pin": 1111} ) assert result3["type"] == "create_entry" assert result3["data"] == { "address": "127.0.0.1", "credentials": {Protocol.MRP.value: "mrp_creds"}, "name": "MRP Device", "protocol": Protocol.MRP.value, } async def test_zeroconf_add_dmap_device(hass, dmap_device, dmap_pin, pairing): """Test add DMAP device discovered by zeroconf.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data=DMAP_SERVICE ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["description_placeholders"] == {"name": "DMAP Device"} result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["description_placeholders"] == {"protocol": "DMAP", "pin": 1111} result3 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) assert result3["type"] == "create_entry" assert result3["data"] == { "address": "127.0.0.1", "credentials": {Protocol.DMAP.value: "dmap_creds"}, "name": "DMAP Device", "protocol": Protocol.DMAP.value, } async def test_zeroconf_add_existing_aborts(hass, dmap_device): """Test start new zeroconf flow while existing flow is active aborts.""" await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data=DMAP_SERVICE ) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data=DMAP_SERVICE ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "already_in_progress" async def test_zeroconf_add_but_device_not_found(hass, mock_scan): """Test add device which is not found with another scan.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data=DMAP_SERVICE ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "no_devices_found" async def test_zeroconf_add_existing_device(hass, dmap_device): """Test add already existing device from zeroconf.""" MockConfigEntry(domain="apple_tv", unique_id="dmapid").add_to_hass(hass) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data=DMAP_SERVICE ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "already_configured" async def test_zeroconf_unexpected_error(hass, mock_scan): """Test unexpected error aborts in zeroconf.""" mock_scan.side_effect = Exception result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_ZEROCONF}, data=DMAP_SERVICE ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "unknown" # Re-configuration async def test_reconfigure_update_credentials(hass, mrp_device, pairing): """Test that reconfigure flow updates config entry.""" config_entry = MockConfigEntry(domain="apple_tv", unique_id="mrpid") config_entry.add_to_hass(hass) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_REAUTH}, data={"identifier": "mrpid", "name": "apple tv"}, ) result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {}, ) assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM assert result2["description_placeholders"] == {"protocol": "MRP"} result3 = await hass.config_entries.flow.async_configure( result["flow_id"], {"pin": 1111} ) assert result3["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result3["reason"] == "already_configured" assert config_entry.data == { "address": "127.0.0.1", "protocol": Protocol.MRP.value, "name": "MRP Device", "credentials": {Protocol.MRP.value: "mrp_creds"}, } async def test_reconfigure_ongoing_aborts(hass, mrp_device): """Test start additional reconfigure flow aborts.""" data = { "identifier": "mrpid", "name": "Apple TV", } await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_REAUTH}, data=data ) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_REAUTH}, data=data ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "already_in_progress" # Options async def test_option_start_off(hass): """Test start off-option flag.""" config_entry = MockConfigEntry( domain=DOMAIN, unique_id="dmapid", options={"start_off": False} ) config_entry.add_to_hass(hass) result = await hass.config_entries.options.async_init(config_entry.entry_id) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM result2 = await hass.config_entries.options.async_configure( result["flow_id"], user_input={CONF_START_OFF: True} ) assert result2["type"] == "create_entry" assert config_entry.options[CONF_START_OFF]

34.05137

88

0.679272

41c6003f18f4f49f78cffbf6ba97cd797590b5a0

3,640

py

Python

pyllars/sklearn_transformers/multicolumn_label_encoder.py

bmmalone/pyllars

e37c2bd6fba91a587e54f0bc6a456cee050a6d00

[ "MIT" ]

3

2020-04-19T15:22:43.000Z

2022-01-05T16:06:50.000Z

pyllars/sklearn_transformers/multicolumn_label_encoder.py

bmmalone/pyllars

e37c2bd6fba91a587e54f0bc6a456cee050a6d00

[ "MIT" ]

19

2018-12-29T13:59:39.000Z

2021-07-22T11:53:23.000Z

pyllars/sklearn_transformers/multicolumn_label_encoder.py

bmmalone/pymisc-utils

a1000824623d7db4873836864c14b830cc81aeed

[ "MIT" ]

null

import sklearn.preprocessing import sklearn.base from pyllars.validation_utils import check_is_fitted import numpy as np import pandas as pd from pyllars.sklearn_transformers.nan_label_encoder import NaNLabelEncoder class MultiColumnLabelEncoder(sklearn.base.TransformerMixin): """ Encode multiple columns using independent label encoders Optionally, the columns to encode can be specified; if they are not given, then all columns in the matrix are encoded (independently). A number of similar implementations are available online; this implementation keeps the encoders around so that they can be used later, for example, on test data. It also provides an inverse_transform operation. It also attempts with both data frames (with named columns) and simpler np.arrays (which only have indices). Parameters ---------- columns: list-like of column identifiers, or None A list of the columns for imputation. These should be integer indices for np.arrays or string column names for pd.DataFrames. """ def __init__(self, columns=None): self.columns = columns def fit(self, X, *_, **__): """ Fit the encoders for all of the specified columns """ # first, check if we have an np array or a data frame if isinstance(X, np.ndarray): self.is_np_array_ = True elif isinstance(X, pd.DataFrame): self.is_np_array_ = False else: msg = ("[multicolumn_le]: attepting to encode an object which is " "neither an np.array nor a pd.DataFrame. type: {}".format( type(X))) raise ValueError(msg) # select the columns, if necessary if self.columns is None: if self.is_np_array_: self.columns = list(range(X.shape[1])) else: self.columns = X.columns # keep around the label encoders self.le_ = {} for c in self.columns: # make sure we actually grab a column if self.is_np_array_: y = X[:,c] else: y = X[c] label_encoder = NaNLabelEncoder() label_encoder.fit(y) self.le_[c] = label_encoder return self def transform(self, X, *_, **__): """ Encode the respective columns of X """ check_is_fitted(self, "le_") # make a copy to keep around everything we do not encode X = X.copy() for c in self.columns: le = self.le_[c] # make sure we actually grab a column if self.is_np_array_: # so np.array y = X[:,c] y = le.transform(y) X[:,c] = y else: # then pd.DataFrame y = X[c] y = le.transform(y) X[c] = y return X def inverse_transform(self, X, *_, **__): """ Transform labels back to the original encoding """ check_is_fitted(self, "le_") # make a copy to keep around everything we do not encode X = X.copy() for c in self.columns: le = self.le_[c] # make sure we actually grab a column if self.is_np_array_: # so np.array y = X[:,c] y = le.inverse_transform(y) X[:,c] = y else: # then pd.DataFrame y = X[c] y = le.inverse_transform(y) X[c] = y return X

29.12

79

0.557143

7487322543082c9081637fde6d509cafd61117b8

2,876

py

Python

vehicle/OVMS.V3/components/wolfssl/wrapper/python/wolfssl/src/wolfssl/exceptions.py

qtwre/Open-Vehicle-Monitoring-System-3

0ebd21bdff06190c0909c29b215ab63f5792e7d6

[ "MIT" ]

322

2017-06-12T16:56:49.000Z

2022-03-27T15:46:38.000Z

vehicle/OVMS.V3/components/wolfssl/wrapper/python/wolfssl/src/wolfssl/exceptions.py

qtwre/Open-Vehicle-Monitoring-System-3

0ebd21bdff06190c0909c29b215ab63f5792e7d6

[ "MIT" ]

426

2017-08-30T04:47:34.000Z

2022-03-25T21:01:11.000Z

vehicle/OVMS.V3/components/wolfssl/wrapper/python/wolfssl/src/wolfssl/exceptions.py

qtwre/Open-Vehicle-Monitoring-System-3

0ebd21bdff06190c0909c29b215ab63f5792e7d6

[ "MIT" ]

194

2017-07-03T23:34:08.000Z

2022-03-16T09:09:22.000Z

# -*- coding: utf-8 -*- # # exceptions.py # # Copyright (C) 2006-2020 wolfSSL Inc. # # This file is part of wolfSSL. # # wolfSSL is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # wolfSSL is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1335, USA #/ # pylint: disable=missing-docstring from socket import error as socket_error class SSLError(socket_error): """ Raised to signal an error from the wolfSSL's SSL/TLS library. This signifies some problem in the higher-level encryption and authentication layer that's superimposed on the underlying network connection. This error is a subtype of socket.error, which in turn is a subtype of IOError. The error code and message of SSLError instances are provided by the wolfSSL library. """ pass class SSLZeroReturnError(SSLError): """ A subclass of SSLError raised when trying to read or write and the SSL connection has been closed cleanly. Note that this doesn't mean that the underlying transport (read TCP) has been closed. """ pass class SSLWantReadError(SSLError): """ A subclass of SSLError raised by a non-blocking SSL socket when trying to read or write data, but more data needs to be received on the underlying TCP transport before the request can be fulfilled. """ pass class SSLWantWriteError(SSLError): """ A subclass of SSLError raised by a non-blocking SSL socket when trying to read or write data, but more data needs to be sent on the underlying TCP transport before the request can be fulfilled. """ pass class SSLSyscallError(SSLError): """ A subclass of SSLError raised when a system error was encountered while trying to fulfill an operation on a SSL socket. Unfortunately, there is no easy way to inspect the original errno number. """ pass class SSLEOFError(SSLError): """ A subclass of SSLError raised when the SSL connection has been terminated abruptly. Generally, you shouldn't try to reuse the underlying transport when this error is encountered. """ pass class CertificateError(ValueError): """ Raised to signal an error with a certificate (such as mismatching hostname). Certificate errors detected by wolfSSL, though, raise an SSLError. """ pass

31.955556

80

0.728442

32cb86fe55a49ea9d42e7f8ce75e7756a1326c47

2,250

py

Python

rnacentral_pipeline/databases/quickgo/parser.py

RNAcentral/rnacentral-import-pipeline

238e573440c72581a051b16c15f56fcd25bece74

[ "Apache-2.0" ]

1

2018-08-09T14:41:16.000Z

rnacentral_pipeline/databases/quickgo/parser.py

RNAcentral/rnacentral-import-pipeline

238e573440c72581a051b16c15f56fcd25bece74

[ "Apache-2.0" ]

60

2015-02-04T16:43:53.000Z

2022-01-27T10:28:43.000Z

rnacentral_pipeline/databases/quickgo/parser.py

RNAcentral/rnacentral-import-pipeline

238e573440c72581a051b16c15f56fcd25bece74

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Copyright [2009-2017] EMBL-European Bioinformatics Institute 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 operator as op import itertools as it import typing as ty from Bio.UniProt.GOA import gpa_iterator as raw_parser from rnacentral_pipeline.databases.data.go_annotations import GoTermAnnotation from rnacentral_pipeline.databases.quickgo import helpers def as_annotation(record: ty.Dict[str, ty.Any]) -> GoTermAnnotation: """ Turn a record into an annotation. """ return GoTermAnnotation( rna_id=helpers.rna_id(record), qualifier=helpers.qualifier(record), term_id=helpers.go_id(record), evidence_code=record["ECO_Evidence_code"], extensions=helpers.extensions(record), assigned_by=helpers.assigned_by(record), publications=helpers.publications(record), ) def parse(handle: ty.IO) -> ty.Iterable[GoTermAnnotation]: """ Parse the given file to produce an iterable of GoTerm objects to import. """ key = op.attrgetter( "rna_id", "qualifier", "term_id", "evidence_code", "assigned_by" ) records = raw_parser(handle) records = filter(lambda r: r["Assigned_by"] != "RNAcentral", records) records = filter(lambda r: r["DB:Reference"] != "GO_REF:0000115", records) annotations = list(map(as_annotation, records)) annotations.sort(key=key) for _, similar_iter in it.groupby(annotations, key): similar = list(similar_iter) if len(similar) == 1: yield similar[0] continue merged = similar.pop(0) for ann in similar: merged.publications.extend(ann.publications) merged.extensions.extend(ann.extensions) yield merged

32.608696

78

0.705333

a1de42bca41435ab1fcee6be72dc3c5270cfb068

6,178

py

Python

chemdataextractor/scrape/pub/springer.py

materialsintelligence/deprecated-cde-selfcontained-nodawg

f899d64c7e09be1b56cd1d90ba34b86b4270e78a

[ "MIT" ]

199

2016-10-07T06:55:23.000Z

2022-03-29T09:50:03.000Z

chemdataextractor/scrape/pub/springer.py

qingtong00/ChemDataExtractor

349a3bea965f2073141d62043b89319222e46af1

[ "MIT" ]

29

2016-10-04T08:56:05.000Z

2022-03-06T19:36:55.000Z

chemdataextractor/scrape/pub/springer.py

qingtong00/ChemDataExtractor

349a3bea965f2073141d62043b89319222e46af1

[ "MIT" ]

95

2016-10-10T14:24:27.000Z

2022-03-16T18:30:00.000Z

# -*- coding: utf-8 -*- """ chemdataextractor.scrape.springer ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Tools for scraping documents from Springer, Biomed Central and Chemistry Central XML files. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import logging from ...text.normalize import normalize from ...text.processors import Chain, LStrip, RStrip, Discard from ..clean import Cleaner from ..entity import Entity from ..fields import StringField, EntityField, UrlField, IntField log = logging.getLogger(__name__) #: XML stripper that also kills equations/formulas. strip_springer_xml = Cleaner(strip_xpath='.//*', kill_xpath='.//display-formula|.//inline-formula|.//m:math|.//abbrgrp', namespaces={'m': 'http://www.w3.org/1998/Math/MathML'}) #: XML stripper that also kills headings strip_springer_abstract_xml = Cleaner(strip_xpath='.//*', kill_xpath='.//display-formula|.//inline-formula|.//m:math|.//abbrgrp|.//st', namespaces={'m': 'http://www.w3.org/1998/Math/MathML'}) def tidy_springer_references(document): """Remove punctuation around references like brackets, commas, hyphens.""" def strip_preceding(text): stext = text.rstrip() if stext.endswith('[') or stext.endswith('('): #log.debug('%s -> %s' % (text, stext[:-1])) return stext[:-1] return text def strip_between(text): stext = text.strip() if stext in {',', '-', '\u2013'}: #log.debug('%s -> %s' % (text, '')) return '' return text def strip_following(text): stext = text.lstrip() if stext.startswith(']') or stext.startswith(')'): #log.debug('%s -> %s' % (text, stext[1:])) return stext[1:] return text for ref in document.xpath('.//abbrgrp'): parent = ref.getparent() previous = ref.getprevious() next = ref.getnext() if previous is None: parent.text = strip_preceding(parent.text or '') else: previous.tail = strip_preceding(previous.tail or '') if next is not None and next.tag == 'abbrgrp': ref.tail = strip_between(ref.tail or '') ref.tail = strip_following(ref.tail or '') return document class SpringerXmlAuthor(Entity): """Author information from a Springer XML file.""" firstname = StringField('./fnm', xpath=True, strip=True) middlename = StringField('./mnm|./mi', xpath=True, strip=True) lastname = StringField('./snm', xpath=True, strip=True) suffix = StringField('./suf', xpath=True, strip=True) email = StringField('./email', xpath=True, strip=True) process_email = Discard('') class SpringerXmlImage(Entity): """Figure information from a Springer XML file.""" label = StringField('./title', xpath=True, strip=True) caption = StringField('./text', xpath=True, strip=True) reference = StringField('@id', xpath=True, strip=True) clean_caption = strip_springer_xml process_caption = normalize class SpringerXmlTable(Entity): """Table information from a Springer XML file.""" label = StringField('./title', xpath=True, strip=True) caption = StringField('./caption', xpath=True, strip=True) reference = StringField('@id', xpath=True, strip=True) src = StringField('.', xpath=True, strip=True, raw=True) clean_caption = strip_springer_xml process_caption = normalize class SpringerXmlDocument(Entity): """Document information from a Springer XML file.""" ui = StringField('/art/ui/text()', xpath=True, strip=True) doi = StringField('/art/fm/bibl/xrefbib//pubid[@idtype="doi"]/text()', xpath=True, lower=True) title = StringField('/art/fm/bibl/title', xpath=True, strip=True) authors = EntityField(SpringerXmlAuthor, '/art/fm/bibl/aug/au', xpath=True, all=True) journal = StringField('/art/fm/bibl/source/text()', xpath=True, strip=True) firstpage = StringField('/art/fm/bibl/fpage/text()', xpath=True, strip=True) year = IntField('/art/fm/bibl/pubdate/text()', xpath=True) volume = StringField('/art/fm/bibl/volume/text()', xpath=True, strip=True) issue = StringField('/art/fm/bibl/issue/text()', xpath=True, strip=True) issn = StringField('/art/fm/bibl/issn/text()', xpath=True, strip=True) landing_url = UrlField('/art/fm/bibl/url/text()', xpath=True) abstract = StringField('/art/fm/abs/sec/p|/art/fm/abs', xpath=True, strip=True) published_year = IntField('/art/fm/history/pub/date/year/text()', xpath=True) published_month = IntField('/art/fm/history/pub/date/month/text()', xpath=True) published_day = IntField('/art/fm/history/pub/date/day/text()', xpath=True) accepted_year = IntField('/art/fm/history/acc/date/year/text()', xpath=True) accepted_month = IntField('/art/fm/history/acc/date/month/text()', xpath=True) accepted_day = IntField('/art/fm/history/acc/date/day/text()', xpath=True) received_year = IntField('/art/fm/history/rec/date/year/text()', xpath=True) received_month = IntField('/art/fm/history/rec/date/month/text()', xpath=True) received_day = IntField('/art/fm/history/rec/date/day/text()', xpath=True) license = UrlField('/art/fm/cpyrt/note/url/text()', xpath=True, strip=True) figures = EntityField(SpringerXmlImage, '/art/bdy//fig', xpath=True, all=True) schemes = EntityField(SpringerXmlImage, '/art/bdy//scheme', xpath=True, all=True) tables = EntityField(SpringerXmlTable, '/art/bdy//tbl|/art/bdy//table', xpath=True, all=True) headings = StringField('/art/bdy//st', xpath=True, strip=True, all=True) paragraphs = StringField('/art/bdy//sec/p', xpath=True, strip=True, all=True) clean_title = strip_springer_xml clean_abstract = Chain(tidy_springer_references, strip_springer_abstract_xml) clean_headings = strip_springer_xml clean_paragraphs = Chain(tidy_springer_references, strip_springer_xml) process_abstract = normalize process_headings = normalize process_paragraphs = Chain(normalize, Discard('')) process_license = Chain(LStrip('('), RStrip(')'))

43.202797

191

0.673357

542625c2cd04c7449ca14f4e6328695b96bcdd6a

33

py

Python

elichika/elichika/parser/config.py

kuenishi/chainer-compiler

942c3b09759d0857ddc7c6bb93b81117d011ff50

[ "MIT" ]

null

elichika/elichika/parser/config.py

kuenishi/chainer-compiler

942c3b09759d0857ddc7c6bb93b81117d011ff50

[ "MIT" ]

null

elichika/elichika/parser/config.py

kuenishi/chainer-compiler

942c3b09759d0857ddc7c6bb93b81117d011ff50

[ "MIT" ]

null

import sys show_warnings = True

8.25

20

0.787879

b9ca5a2834c64ac5f95960f021e2046243f97686

8,804

py

Python

examples/STRUMPACK/strumpack_MLA_KRR.py

kadircs/GPTune

4a6d35f1080a99e85f838ed3180c7221f4600175

[ "BSD-3-Clause-LBNL" ]

null

examples/STRUMPACK/strumpack_MLA_KRR.py

kadircs/GPTune

4a6d35f1080a99e85f838ed3180c7221f4600175

[ "BSD-3-Clause-LBNL" ]

null

examples/STRUMPACK/strumpack_MLA_KRR.py

kadircs/GPTune

4a6d35f1080a99e85f838ed3180c7221f4600175

[ "BSD-3-Clause-LBNL" ]

null

#! /usr/bin/env python3 # GPTune Copyright (c) 2019, The Regents of the University of California, # through Lawrence Berkeley National Laboratory (subject to receipt of any # required approvals from the U.S.Dept. of Energy) and the University of # California, Berkeley. All rights reserved. # # If you have questions about your rights to use or distribute this software, # please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov. # # NOTICE. This Software was developed under funding from the U.S. Department # of Energy and the U.S. Government consequently retains certain rights. # As such, the U.S. Government has been granted for itself and others acting # on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in # the Software to reproduce, distribute copies to the public, prepare # derivative works, and perform publicly and display publicly, and to permit # other to do so. # ################################################################################ """ Example of invocation of this script: mpirun -n 1 python ./strumpack_MLA_KRR.py -ntask 1 -nrun 20 -npernode 32 where: -npernode is the number of MPIs per node for launching the application code -ntask is the number of different matrix sizes that will be tuned -nrun is the number of calls per task """ ################################################################################ import sys import os import numpy as np import argparse import pickle import mpi4py from mpi4py import MPI from array import array import math sys.path.insert(0, os.path.abspath(__file__ + "/../../../GPTune/")) from gptune import * # import all from autotune.problem import * from autotune.space import * from autotune.search import * from callopentuner import OpenTuner from callhpbandster import HpBandSter import math ################################################################################ def objectives(point): # should always use this name for user-defined objective function ######################################### ##### constants defined in TuningProblem nodes = point['nodes'] cores = point['cores'] npernode = point['npernode'] ######################################### datafile = point['datafile'] h = 10**point['h'] Lambda = 10**point['Lambda'] nproc = nodes*npernode nthreads = int(cores / npernode) params = ['datafile', datafile, 'h', h,'Lambda', Lambda,'nthreads', nthreads, 'npernode', npernode, 'nproc',nproc] RUNDIR = os.path.abspath(__file__ + "/../STRUMPACK/examples") INPUTDIR = os.path.abspath(__file__ + "/../STRUMPACK/examples") TUNER_NAME = os.environ['TUNER_NAME'] """ pass some parameters through environment variables """ info = MPI.Info.Create() envstr= 'OMP_NUM_THREADS=%d\n' %(nthreads) print('OMP_NUM_THREADS=%d\n' %(nthreads)) info.Set('env',envstr) info.Set('npernode','%d'%(npernode)) # YL: npernode is deprecated in openmpi 4.0, but no other parameter (e.g. 'map-by') works # datafile = "data/susy_10Kn" # h = 0.1 # Lambda = 3.11 degree = 1 # only used when kernel=ANOVA (degree<=d) in KernelRegressionMPI.py """ use MPI spawn to call the executable, and pass the other parameters and inputs through command line """ # print('exec', "%s/testPoisson3dMPIDist"%(RUNDIR), 'args', ['%s'%(gridsize), '1', '--sp_reordering_method', '%s'%(sp_reordering_method),'--sp_matching', '0','--sp_compression', '%s'%(sp_compression1),'--sp_nd_param', '%s'%(sp_nd_param),'--sp_compression_min_sep_size', '%s'%(sp_compression_min_sep_size),'--sp_compression_min_front_size', '%s'%(sp_compression_min_front_size),'--sp_compression_leaf_size', '%s'%(sp_compression_leaf_size)]+extra_str, 'nproc', nproc, 'env', 'OMP_NUM_THREADS=%d' %(nthreads)) comm = MPI.COMM_SELF.Spawn("%s/KernelRegressionMPI.py"%(RUNDIR), args=['%s/%s'%(INPUTDIR,datafile), '%s'%(h),'%s'%(Lambda),'%s'%(degree)], maxprocs=nproc,info=info) """ gather the return value using the inter-communicator """ tmpdata = np.array([0],dtype=np.float64) comm.Reduce(sendbuf=None, recvbuf=[tmpdata,MPI.DOUBLE],op=MPI.MAX,root=mpi4py.MPI.ROOT) comm.Disconnect() retval = tmpdata[0] print(params, ' krr prediction accuracy (%): ', retval) return [-retval] def main(): # Parse command line arguments args = parse_args() # Extract arguments ntask = args.ntask npernode = args.npernode optimization = args.optimization nrun = args.nrun TUNER_NAME = args.optimization (machine, processor, nodes, cores) = GetMachineConfiguration() print ("machine: " + machine + " processor: " + processor + " num_nodes: " + str(nodes) + " num_cores: " + str(cores)) os.environ['MACHINE_NAME'] = machine os.environ['TUNER_NAME'] = TUNER_NAME datafiles = ["data/susy_10Kn"] # Task input parameters datafile = Categoricalnorm (datafiles, transform="onehot", name="datafile") # Tuning parameters h = Real(-10, 10, transform="normalize", name="h") Lambda = Real(-10, 10, transform="normalize", name="Lambda") # npernode = Integer (int(math.log2(nprocmin_pernode)), int(math.log2(cores)), transform="normalize", name="npernode") result = Real (0 , float("Inf"),name="r") IS = Space([datafile]) # PS = Space([h,Lambda,npernode]) PS = Space([h,Lambda]) OS = Space([result]) constraints = {} models = {} constants={"nodes":nodes,"cores":cores,"npernode":npernode} """ Print all input and parameter samples """ print(IS, PS, OS, constraints, models) problem = TuningProblem(IS, PS, OS, objectives, constraints, constants=constants) computer = Computer(nodes = nodes, cores = cores, hosts = None) """ Set and validate options """ options = Options() options['model_processes'] = 1 # options['model_threads'] = 1 options['model_restarts'] = 1 # options['search_multitask_processes'] = 1 # options['model_restart_processes'] = 1 options['distributed_memory_parallelism'] = False options['shared_memory_parallelism'] = False options['model_class '] = 'Model_LCM' # 'Model_GPy_LCM' options['verbose'] = False options.validate(computer = computer) # """ Building MLA with the given list of tasks """ giventask = [["data/susy_10Kn"]] data = Data(problem) if(TUNER_NAME=='GPTune'): gt = GPTune(problem, computer=computer, data=data, options=options, driverabspath=os.path.abspath(__file__)) NI = len(giventask) NS = nrun (data, model, stats) = gt.MLA(NS=NS, NI=NI, Igiven=giventask, NS1=max(NS//2, 1)) print("stats: ", stats) """ Print all input and parameter samples """ for tid in range(NI): print("tid: %d"%(tid)) print(" matrix:%s"%(data.I[tid][0])) print(" Ps ", data.P[tid]) print(" Os ", -data.O[tid]) print(' Popt ', data.P[tid][np.argmin(data.O[tid])], 'Oopt ', -min(data.O[tid])[0], 'nth ', np.argmin(data.O[tid])) if(TUNER_NAME=='opentuner'): NI = ntask NS = nrun (data,stats) = OpenTuner(T=giventask, NS=NS, tp=problem, computer=computer, run_id="OpenTuner", niter=1, technique=None) print("stats: ", stats) """ Print all input and parameter samples """ for tid in range(NI): print("tid: %d"%(tid)) print(" matrix:%s"%(data.I[tid][0])) print(" Ps ", data.P[tid]) print(" Os ", -data.O[tid]) print(' Popt ', data.P[tid][np.argmin(data.O[tid])], 'Oopt ', -min(data.O[tid])[0], 'nth ', np.argmin(data.O[tid])) if(TUNER_NAME=='hpbandster'): NI = ntask NS = nrun (data,stats)=HpBandSter(T=giventask, NS=NS, tp=problem, computer=computer, run_id="HpBandSter", niter=1) print("stats: ", stats) """ Print all input and parameter samples """ for tid in range(NI): print("tid: %d"%(tid)) print(" matrix:%s"%(data.I[tid][0])) print(" Ps ", data.P[tid]) print(" Os ", -data.O[tid]) print(' Popt ', data.P[tid][np.argmin(data.O[tid])], 'Oopt ', -min(data.O[tid])[0], 'nth ', np.argmin(data.O[tid])) def parse_args(): parser = argparse.ArgumentParser() # Problem related arguments # Machine related arguments parser.add_argument('-nodes', type=int, default=1, help='Number of machine nodes') parser.add_argument('-cores', type=int, default=1, help='Number of cores per machine node') parser.add_argument('-npernode', type=int, default=1,help='Minimum number of MPIs per machine node for the application code') parser.add_argument('-machine', type=str, help='Name of the computer (not hostname)') # Algorithm related arguments parser.add_argument('-optimization', type=str,default='GPTune',help='Optimization algorithm (opentuner, hpbandster, GPTune)') parser.add_argument('-ntask', type=int, default=-1, help='Number of tasks') parser.add_argument('-nrun', type=int, help='Number of runs per task') args = parser.parse_args() return args if __name__ == "__main__": main()

35.788618

508

0.661745

8bbc1b1838beb7829dc5b27804a61604d1bff5d7

10,343

py

Python

utils.py

lihenryhfl/projection_stg

4affabf6b5c4033a1b636073ed535facc887340c

[ "MIT" ]

null

utils.py

lihenryhfl/projection_stg

4affabf6b5c4033a1b636073ed535facc887340c

[ "MIT" ]

null

utils.py

lihenryhfl/projection_stg

4affabf6b5c4033a1b636073ed535facc887340c

[ "MIT" ]

null

# Taken from https://github.com/davebiagioni/pyomp import numpy as np from scipy.optimize import nnls class Result(object): """Result object for storing input and output data for omp. When called from `omp`, runtime parameters are passed as keyword arguments and stored in the `params` dictionary. Attributes: X: Predictor array after (optional) standardization. y: Response array after (optional) standarization. ypred: Predicted response. residual: Residual vector. coef: Solution coefficients. active: Indices of the active (non-zero) coefficient set. err: Relative error per iteration. params: Dictionary of runtime parameters passed as keyword args. """ def __init__(self, **kwargs): # to be computed self.X = None self.y = None self.ypred = None self.residual = None self.coef = None self.active = None self.err = None # runtime parameters self.params = {} for key, val in kwargs.items(): self.params[key] = val def update(self, coef, active, err, residual, ypred): """Update the solution attributes.""" self.coef = coef self.active = active self.err = err self.residual = residual self.ypred = ypred def omp( X, y, const, nonneg=True, ncoef=None, maxit=5000, tol=1e-5, ztol=1e-14, verbose=True ): """Compute sparse orthogonal matching pursuit solution with unconstrained or non-negative coefficients. Args: X: Dictionary array of size n_samples x n_features. y: Reponse array of size n_samples x 1. nonneg: Enforce non-negative coefficients. ncoef: Max number of coefficients. Set to n_features/2 by default. tol: Convergence tolerance. If relative error is less than tol * ||y||_2, exit. ztol: Residual covariance threshold. If all coefficients are less than ztol * ||y||_2, exit. verbose: Boolean, print some info at each iteration. Returns: result: Result object. See Result.__doc__ """ def norm2(x): return np.linalg.norm(x) / np.sqrt(len(x)) # initialize result object result = Result(nnoneg=nonneg, ncoef=ncoef, maxit=maxit, tol=tol, ztol=ztol) # if verbose: # print(result.params) # check types, try to make somewhat user friendly if type(X) is not np.ndarray: X = np.array(X) if type(y) is not np.ndarray: y = np.array(y) # check that n_samples match if X.shape[0] != len(y): # print('X and y must have same number of rows (samples)') return result # store arrays in result object result.y = y result.X = X # for rest of call, want y to have ndim=1 if np.ndim(y) > 1: y = np.reshape(y, (len(y),)) # by default set max number of coef to half of total possible if ncoef is None: ncoef = int(X.shape[1] / 2) # initialize things X_transpose = X.T # store for repeated use # active = np.array([], dtype=int) # initialize list of active set active = [] coef = np.zeros(X.shape[1], dtype=float) # solution vector residual = y # residual vector ypred = np.zeros(y.shape, dtype=float) ynorm = norm2(y) # store for computing relative err err = np.zeros(maxit, dtype=float) # relative err vector # Check if response has zero norm, because then we're done. This can happen # in the corner case where the response is constant and you normalize it. if ynorm < tol: # the same as ||residual|| < tol * ||residual|| # print('Norm of the response is less than convergence tolerance.') result.update(coef, active, err[0], residual, ypred) return result # convert tolerances to relative tol = tol * ynorm # convergence tolerance ztol = ztol * ynorm # threshold for residual covariance # if verbose: # print('\nIteration, relative error, number of non-zeros') # main iteration for it in range(maxit): # compute residual covariance vector and check threshold rcov = np.dot(X_transpose, residual) if nonneg: i = np.argmax(rcov) rc = rcov[i] else: # i = np.argmax(np.abs(rcov)) # print(rcov) norm_x = np.diag(np.matmul(X_transpose, X)) est = rcov / np.sqrt(norm_x) prob_exp = np.exp((0.5 * const) * est ** 2) prob_exp = prob_exp / np.sum(prob_exp) ind = np.arange(0, prob_exp.shape[0]) i = np.random.choice(ind, size=1, replace=True, p=prob_exp)[0] # print(l) rc = np.abs(rcov[i]) if rc < ztol: # print('All residual covariances are below threshold.') break # update active set if i not in active: # active = np.concatenate([active, [i]], axis=1) active.append(i) # solve for new coefficients on active set if nonneg: coefi, _ = nnls(X[:, active], y) else: coefi, _, _, _ = np.linalg.lstsq(X[:, active], y) coef[active] = coefi # update solution # update residual vector and error residual = y - np.dot(X[:, active], coefi) ypred = y - residual err[it] = norm2(residual) / ynorm # print status # if verbose: # print('{}, {}, {}'.format(it, err[it], len(active))) # check stopping criteria if err[it] < tol: # converged print("\nConverged.") break if len(active) >= ncoef: # hit max coefficients break # print('\nFound solution with max number of coefficients.') # break if it == maxit - 1: # max iterations print("\nHit max iterations.") result.update(coef, active, err[: (it + 1)], residual, ypred) return result def omp_orig( X, y, const, nonneg=True, ncoef=None, maxit=5000, tol=1e-5, ztol=1e-14, verbose=True ): """Compute sparse orthogonal matching pursuit solution with unconstrained or non-negative coefficients. Args: X: Dictionary array of size n_samples x n_features. y: Reponse array of size n_samples x 1. nonneg: Enforce non-negative coefficients. ncoef: Max number of coefficients. Set to n_features/2 by default. tol: Convergence tolerance. If relative error is less than tol * ||y||_2, exit. ztol: Residual covariance threshold. If all coefficients are less than ztol * ||y||_2, exit. verbose: Boolean, print some info at each iteration. Returns: result: Result object. See Result.__doc__ """ def norm2(x): return np.linalg.norm(x) / np.sqrt(len(x)) # initialize result object result = Result(nnoneg=nonneg, ncoef=ncoef, maxit=maxit, tol=tol, ztol=ztol) # if verbose: # print(result.params) # check types, try to make somewhat user friendly if type(X) is not np.ndarray: X = np.array(X) if type(y) is not np.ndarray: y = np.array(y) # check that n_samples match if X.shape[0] != len(y): # print('X and y must have same number of rows (samples)') return result # store arrays in result object result.y = y result.X = X # for rest of call, want y to have ndim=1 if np.ndim(y) > 1: y = np.reshape(y, (len(y),)) # by default set max number of coef to half of total possible if ncoef is None: ncoef = int(X.shape[1] / 2) # initialize things X_transpose = X.T # store for repeated use # active = np.array([], dtype=int) # initialize list of active set active = [] coef = np.zeros(X.shape[1], dtype=float) # solution vector residual = y # residual vector ypred = np.zeros(y.shape, dtype=float) ynorm = norm2(y) # store for computing relative err err = np.zeros(maxit, dtype=float) # relative err vector # Check if response has zero norm, because then we're done. This can happen # in the corner case where the response is constant and you normalize it. if ynorm < tol: # the same as ||residual|| < tol * ||residual|| # print('Norm of the response is less than convergence tolerance.') result.update(coef, active, err[0], residual, ypred) return result # convert tolerances to relative tol = tol * ynorm # convergence tolerance ztol = ztol * ynorm # threshold for residual covariance # if verbose: # print('\nIteration, relative error, number of non-zeros') # main iteration for it in range(maxit): # compute residual covariance vector and check threshold rcov = np.dot(X_transpose, residual) if nonneg: i = np.argmax(rcov) rc = rcov[i] else: i = np.argmax(np.abs(rcov)) # print(rcov) rc = np.abs(rcov[i]) if rc < ztol: # print('All residual covariances are below threshold.') break # update active set if i not in active: # active = np.concatenate([active, [i]], axis=1) active.append(i) # solve for new coefficients on active set if nonneg: coefi, _ = nnls(X[:, active], y) else: coefi, _, _, _ = np.linalg.lstsq(X[:, active], y) coef[active] = coefi # update solution # update residual vector and error residual = y - np.dot(X[:, active], coefi) ypred = y - residual err[it] = norm2(residual) / ynorm # print status # if verbose: # print('{}, {}, {}'.format(it, err[it], len(active))) # check stopping criteria if err[it] < tol: # converged print("\nConverged.") break if len(active) >= ncoef: # hit max coefficients break # print('\nFound solution with max number of coefficients.') # break if it == maxit - 1: # max iterations print("\nHit max iterations.") result.update(coef, active, err[: (it + 1)], residual, ypred) return result

33.472492

88

0.589577

0a42aa946eee7d245198517945624ed6ddd3d988

2,440

py

Python

tests/project/packages/test_install.py

drLis/brownie

bf1e1056df70b7696e298ce695b18291fb5f9b2f

[ "MIT" ]

null

tests/project/packages/test_install.py

drLis/brownie

bf1e1056df70b7696e298ce695b18291fb5f9b2f

[ "MIT" ]

null

tests/project/packages/test_install.py

drLis/brownie

bf1e1056df70b7696e298ce695b18291fb5f9b2f

[ "MIT" ]

null

import shutil import pytest import yaml from brownie._config import _get_data_folder from brownie.exceptions import InvalidPackage from brownie.project.main import install_package @pytest.fixture(autouse=True) def setup(package_test): yield path = _get_data_folder().joinpath("packages") shutil.rmtree(path) path.mkdir() @pytest.fixture def dependentproject(newproject): with newproject._path.joinpath("brownie-config.yaml").open() as fp: config = yaml.safe_load(fp) config["dependencies"] = ["brownie-mix/token-mix@1.0.0"] with newproject._path.joinpath("brownie-config.yaml").open("w") as fp: yaml.dump(config, fp) yield newproject def test_install_from_github(): install_package("brownie-mix/token-mix@1.0.0") def test_install_from_ethpm(ipfs_mock): install_package("ethpm://zeppelin.snakecharmers.eth:1/access@1.0.0") def test_github_already_installed(): path = _get_data_folder().joinpath("packages/brownie-mix") path.mkdir() path.joinpath("token-mix@1.0.0").mkdir() with pytest.raises(FileExistsError): install_package("brownie-mix/token-mix@1.0.0") def test_ethpm_already_installed(): path = _get_data_folder().joinpath("packages/zeppelin.snakecharmers.eth") path.mkdir() path.joinpath("access@1.0.0").mkdir() with pytest.raises(FileExistsError): install_package("ethpm://zeppelin.snakecharmers.eth:1/access@1.0.0") def test_unknown_version(): with pytest.raises(ValueError): install_package("brownie-mix/token-mix@1.0.1") def test_bad_project_id_version(): with pytest.raises(ValueError): install_package("brownie-mix/token-mix") def test_bad_project_id_repo_org(): with pytest.raises(ValueError): install_package("token-mix@1.0.0") def test_valid_repo_not_a_project(): with pytest.raises(InvalidPackage): install_package("iamdefinitelyahuman/eth-event@0.2.2") assert not _get_data_folder().joinpath("packages/iamdefinitelyahuman/eth-event@0.2.2").exists() def test_install_from_config_dependencies(dependentproject): package_folder = _get_data_folder().joinpath("packages/brownie-mix/token-mix@1.0.0") assert not package_folder.exists() dependentproject.load() assert package_folder.exists() def test_dependency_already_installed(dependentproject): install_package("brownie-mix/token-mix@1.0.0") dependentproject.load()

27.41573

99

0.736885

96e0378e5068bf415910697ab7e6559173b660d1

18,662

py

Python

synapse/storage/databases/main/state.py

Erethon/synapse

d13863d6eb4b3a1ce1ca4a702ae30d4f8808e51f

[ "Apache-2.0" ]

null

synapse/storage/databases/main/state.py

Erethon/synapse

d13863d6eb4b3a1ce1ca4a702ae30d4f8808e51f

[ "Apache-2.0" ]

null

synapse/storage/databases/main/state.py

Erethon/synapse

d13863d6eb4b3a1ce1ca4a702ae30d4f8808e51f

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2014-2016 OpenMarket Ltd # Copyright 2020 The Matrix.org Foundation C.I.C. # # 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 collections.abc import logging from collections import namedtuple from typing import Iterable, Optional, Set from synapse.api.constants import EventTypes, Membership from synapse.api.errors import NotFoundError, UnsupportedRoomVersionError from synapse.api.room_versions import KNOWN_ROOM_VERSIONS, RoomVersion from synapse.events import EventBase from synapse.storage._base import SQLBaseStore from synapse.storage.database import DatabasePool from synapse.storage.databases.main.events_worker import EventsWorkerStore from synapse.storage.databases.main.roommember import RoomMemberWorkerStore from synapse.storage.state import StateFilter from synapse.util.caches import intern_string from synapse.util.caches.descriptors import cached, cachedList logger = logging.getLogger(__name__) MAX_STATE_DELTA_HOPS = 100 class _GetStateGroupDelta( namedtuple("_GetStateGroupDelta", ("prev_group", "delta_ids")) ): """Return type of get_state_group_delta that implements __len__, which lets us use the itrable flag when caching """ __slots__ = [] def __len__(self): return len(self.delta_ids) if self.delta_ids else 0 # this inherits from EventsWorkerStore because it calls self.get_events class StateGroupWorkerStore(EventsWorkerStore, SQLBaseStore): """The parts of StateGroupStore that can be called from workers. """ def __init__(self, database: DatabasePool, db_conn, hs): super(StateGroupWorkerStore, self).__init__(database, db_conn, hs) async def get_room_version(self, room_id: str) -> RoomVersion: """Get the room_version of a given room Raises: NotFoundError: if the room is unknown UnsupportedRoomVersionError: if the room uses an unknown room version. Typically this happens if support for the room's version has been removed from Synapse. """ room_version_id = await self.get_room_version_id(room_id) v = KNOWN_ROOM_VERSIONS.get(room_version_id) if not v: raise UnsupportedRoomVersionError( "Room %s uses a room version %s which is no longer supported" % (room_id, room_version_id) ) return v @cached(max_entries=10000) async def get_room_version_id(self, room_id: str) -> str: """Get the room_version of a given room Raises: NotFoundError: if the room is unknown """ # First we try looking up room version from the database, but for old # rooms we might not have added the room version to it yet so we fall # back to previous behaviour and look in current state events. # We really should have an entry in the rooms table for every room we # care about, but let's be a bit paranoid (at least while the background # update is happening) to avoid breaking existing rooms. version = await self.db_pool.simple_select_one_onecol( table="rooms", keyvalues={"room_id": room_id}, retcol="room_version", desc="get_room_version", allow_none=True, ) if version is not None: return version # Retrieve the room's create event create_event = await self.get_create_event_for_room(room_id) return create_event.content.get("room_version", "1") async def get_room_predecessor(self, room_id: str) -> Optional[dict]: """Get the predecessor of an upgraded room if it exists. Otherwise return None. Args: room_id: The room ID. Returns: A dictionary containing the structure of the predecessor field from the room's create event. The structure is subject to other servers, but it is expected to be: * room_id (str): The room ID of the predecessor room * event_id (str): The ID of the tombstone event in the predecessor room None if a predecessor key is not found, or is not a dictionary. Raises: NotFoundError if the given room is unknown """ # Retrieve the room's create event create_event = await self.get_create_event_for_room(room_id) # Retrieve the predecessor key of the create event predecessor = create_event.content.get("predecessor", None) # Ensure the key is a dictionary if not isinstance(predecessor, collections.abc.Mapping): return None return predecessor async def get_create_event_for_room(self, room_id: str) -> EventBase: """Get the create state event for a room. Args: room_id: The room ID. Returns: The room creation event. Raises: NotFoundError if the room is unknown """ state_ids = await self.get_current_state_ids(room_id) create_id = state_ids.get((EventTypes.Create, "")) # If we can't find the create event, assume we've hit a dead end if not create_id: raise NotFoundError("Unknown room %s" % (room_id,)) # Retrieve the room's create event and return create_event = await self.get_event(create_id) return create_event @cached(max_entries=100000, iterable=True) def get_current_state_ids(self, room_id): """Get the current state event ids for a room based on the current_state_events table. Args: room_id (str) Returns: deferred: dict of (type, state_key) -> event_id """ def _get_current_state_ids_txn(txn): txn.execute( """SELECT type, state_key, event_id FROM current_state_events WHERE room_id = ? """, (room_id,), ) return {(intern_string(r[0]), intern_string(r[1])): r[2] for r in txn} return self.db_pool.runInteraction( "get_current_state_ids", _get_current_state_ids_txn ) # FIXME: how should this be cached? def get_filtered_current_state_ids( self, room_id: str, state_filter: StateFilter = StateFilter.all() ): """Get the current state event of a given type for a room based on the current_state_events table. This may not be as up-to-date as the result of doing a fresh state resolution as per state_handler.get_current_state Args: room_id state_filter: The state filter used to fetch state from the database. Returns: defer.Deferred[StateMap[str]]: Map from type/state_key to event ID. """ where_clause, where_args = state_filter.make_sql_filter_clause() if not where_clause: # We delegate to the cached version return self.get_current_state_ids(room_id) def _get_filtered_current_state_ids_txn(txn): results = {} sql = """ SELECT type, state_key, event_id FROM current_state_events WHERE room_id = ? """ if where_clause: sql += " AND (%s)" % (where_clause,) args = [room_id] args.extend(where_args) txn.execute(sql, args) for row in txn: typ, state_key, event_id = row key = (intern_string(typ), intern_string(state_key)) results[key] = event_id return results return self.db_pool.runInteraction( "get_filtered_current_state_ids", _get_filtered_current_state_ids_txn ) async def get_canonical_alias_for_room(self, room_id: str) -> Optional[str]: """Get canonical alias for room, if any Args: room_id: The room ID Returns: The canonical alias, if any """ state = await self.get_filtered_current_state_ids( room_id, StateFilter.from_types([(EventTypes.CanonicalAlias, "")]) ) event_id = state.get((EventTypes.CanonicalAlias, "")) if not event_id: return event = await self.get_event(event_id, allow_none=True) if not event: return return event.content.get("canonical_alias") @cached(max_entries=50000) def _get_state_group_for_event(self, event_id): return self.db_pool.simple_select_one_onecol( table="event_to_state_groups", keyvalues={"event_id": event_id}, retcol="state_group", allow_none=True, desc="_get_state_group_for_event", ) @cachedList( cached_method_name="_get_state_group_for_event", list_name="event_ids", num_args=1, inlineCallbacks=True, ) def _get_state_group_for_events(self, event_ids): """Returns mapping event_id -> state_group """ rows = yield self.db_pool.simple_select_many_batch( table="event_to_state_groups", column="event_id", iterable=event_ids, keyvalues={}, retcols=("event_id", "state_group"), desc="_get_state_group_for_events", ) return {row["event_id"]: row["state_group"] for row in rows} async def get_referenced_state_groups( self, state_groups: Iterable[int] ) -> Set[int]: """Check if the state groups are referenced by events. Args: state_groups Returns: The subset of state groups that are referenced. """ rows = await self.db_pool.simple_select_many_batch( table="event_to_state_groups", column="state_group", iterable=state_groups, keyvalues={}, retcols=("DISTINCT state_group",), desc="get_referenced_state_groups", ) return {row["state_group"] for row in rows} class MainStateBackgroundUpdateStore(RoomMemberWorkerStore): CURRENT_STATE_INDEX_UPDATE_NAME = "current_state_members_idx" EVENT_STATE_GROUP_INDEX_UPDATE_NAME = "event_to_state_groups_sg_index" DELETE_CURRENT_STATE_UPDATE_NAME = "delete_old_current_state_events" def __init__(self, database: DatabasePool, db_conn, hs): super(MainStateBackgroundUpdateStore, self).__init__(database, db_conn, hs) self.server_name = hs.hostname self.db_pool.updates.register_background_index_update( self.CURRENT_STATE_INDEX_UPDATE_NAME, index_name="current_state_events_member_index", table="current_state_events", columns=["state_key"], where_clause="type='m.room.member'", ) self.db_pool.updates.register_background_index_update( self.EVENT_STATE_GROUP_INDEX_UPDATE_NAME, index_name="event_to_state_groups_sg_index", table="event_to_state_groups", columns=["state_group"], ) self.db_pool.updates.register_background_update_handler( self.DELETE_CURRENT_STATE_UPDATE_NAME, self._background_remove_left_rooms, ) async def _background_remove_left_rooms(self, progress, batch_size): """Background update to delete rows from `current_state_events` and `event_forward_extremities` tables of rooms that the server is no longer joined to. """ last_room_id = progress.get("last_room_id", "") def _background_remove_left_rooms_txn(txn): # get a batch of room ids to consider sql = """ SELECT DISTINCT room_id FROM current_state_events WHERE room_id > ? ORDER BY room_id LIMIT ? """ txn.execute(sql, (last_room_id, batch_size)) room_ids = [row[0] for row in txn] if not room_ids: return True, set() ########################################################################### # # exclude rooms where we have active members sql = """ SELECT room_id FROM local_current_membership WHERE room_id > ? AND room_id <= ? AND membership = 'join' GROUP BY room_id """ txn.execute(sql, (last_room_id, room_ids[-1])) joined_room_ids = {row[0] for row in txn} to_delete = set(room_ids) - joined_room_ids ########################################################################### # # exclude rooms which we are in the process of constructing; these otherwise # qualify as "rooms with no local users", and would have their # forward extremities cleaned up. # the following query will return a list of rooms which have forward # extremities that are *not* also the create event in the room - ie # those that are not being created currently. sql = """ SELECT DISTINCT efe.room_id FROM event_forward_extremities efe LEFT JOIN current_state_events cse ON cse.event_id = efe.event_id AND cse.type = 'm.room.create' AND cse.state_key = '' WHERE cse.event_id IS NULL AND efe.room_id > ? AND efe.room_id <= ? """ txn.execute(sql, (last_room_id, room_ids[-1])) # build a set of those rooms within `to_delete` that do not appear in # the above, leaving us with the rooms in `to_delete` that *are* being # created. creating_rooms = to_delete.difference(row[0] for row in txn) logger.info("skipping rooms which are being created: %s", creating_rooms) # now remove the rooms being created from the list of those to delete. # # (we could have just taken the intersection of `to_delete` with the result # of the sql query, but it's useful to be able to log `creating_rooms`; and # having done so, it's quicker to remove the (few) creating rooms from # `to_delete` than it is to form the intersection with the (larger) list of # not-creating-rooms) to_delete -= creating_rooms ########################################################################### # # now clear the state for the rooms logger.info("Deleting current state left rooms: %r", to_delete) # First we get all users that we still think were joined to the # room. This is so that we can mark those device lists as # potentially stale, since there may have been a period where the # server didn't share a room with the remote user and therefore may # have missed any device updates. rows = self.db_pool.simple_select_many_txn( txn, table="current_state_events", column="room_id", iterable=to_delete, keyvalues={"type": EventTypes.Member, "membership": Membership.JOIN}, retcols=("state_key",), ) potentially_left_users = {row["state_key"] for row in rows} # Now lets actually delete the rooms from the DB. self.db_pool.simple_delete_many_txn( txn, table="current_state_events", column="room_id", iterable=to_delete, keyvalues={}, ) self.db_pool.simple_delete_many_txn( txn, table="event_forward_extremities", column="room_id", iterable=to_delete, keyvalues={}, ) self.db_pool.updates._background_update_progress_txn( txn, self.DELETE_CURRENT_STATE_UPDATE_NAME, {"last_room_id": room_ids[-1]}, ) return False, potentially_left_users finished, potentially_left_users = await self.db_pool.runInteraction( "_background_remove_left_rooms", _background_remove_left_rooms_txn ) if finished: await self.db_pool.updates._end_background_update( self.DELETE_CURRENT_STATE_UPDATE_NAME ) # Now go and check if we still share a room with the remote users in # the deleted rooms. If not mark their device lists as stale. joined_users = await self.get_users_server_still_shares_room_with( potentially_left_users ) for user_id in potentially_left_users - joined_users: await self.mark_remote_user_device_list_as_unsubscribed(user_id) return batch_size class StateStore(StateGroupWorkerStore, MainStateBackgroundUpdateStore): """ Keeps track of the state at a given event. This is done by the concept of `state groups`. Every event is a assigned a state group (identified by an arbitrary string), which references a collection of state events. The current state of an event is then the collection of state events referenced by the event's state group. Hence, every change in the current state causes a new state group to be generated. However, if no change happens (e.g., if we get a message event with only one parent it inherits the state group from its parent.) There are three tables: * `state_groups`: Stores group name, first event with in the group and room id. * `event_to_state_groups`: Maps events to state groups. * `state_groups_state`: Maps state group to state events. """ def __init__(self, database: DatabasePool, db_conn, hs): super(StateStore, self).__init__(database, db_conn, hs)

36.592157

90

0.618583

9f3fefedb6574803b8ccb5eb76419d5aa98b80e9

12,958

py

Python

csld/basic_lattice_model.py

jsyony37/csld

b0e6d5845d807174f24ca7b591bc164c608c99c8

[ "MIT" ]

16

2019-07-22T18:49:33.000Z

2022-03-04T10:22:05.000Z

csld/basic_lattice_model.py

jsyony37/csld

b0e6d5845d807174f24ca7b591bc164c608c99c8

[ "MIT" ]

8

2019-07-18T20:49:55.000Z

2021-09-09T00:47:04.000Z

csld/basic_lattice_model.py

jsyony37/csld

b0e6d5845d807174f24ca7b591bc164c608c99c8

[ "MIT" ]

8

2019-07-18T17:52:44.000Z

2020-09-04T11:35:04.000Z

#!/usr/bin/env python3 """ This module provides the basic lattice model as used in e.g. cluster expansion, lattice dynamics """ import numpy as np import logging import scipy.sparse from collections import Counter from .cluster import Cluster from .atomic_model import AtomicModel from .util.mathtool import relativePosition from .util.tool import pad_right, matrix2text from _c_util import get_structure_ordering logger = logging.getLogger(__name__) class BasicLatticeModel(AtomicModel): """ A generic model with variables on the lattice: """ def __init__(self, prim, raw_clusters, irange=None, **kwargs): """ :param prim: :param raw_clusters: :return: """ self.clusters = raw_clusters self.prim = prim self.maxorder=kwargs['maxorder'] self.proper_range=kwargs['prange'] self.cluster_filter = kwargs['filter'] self.orbits = None self.imp_range = irange @classmethod def from_file(cls, prim, clusf, **kwargs): raw = Cluster.read_cluster(prim, clusf) return cls(prim, raw, **kwargs) def save_clusters(self, fname): self.save_clusters_to_file(self.clusters, fname) def save_clusters_to_file(self, clusters, fname): scale = self.prim.lattice._scale elements= self.prim.elements sublat = self.prim.orbit_of_l out_s = [] out_ijkl = [] for cl in clusters: out_s.append("1\n%.8f\n%s\n%s\n" % (cl.diameter/scale, " ".join([str(i) for i in cl.orders] + ([str(i[0]) for i in cl.id] if hasattr(cl, 'id') else [])), matrix2text(pad_right( np.array(cl.frac_coords, dtype=object), (cl.order, 5))))) out_ijkl.append(", ".join(["%d %d %d %d %s%d"%tuple(i.tolist()+[elements[i[-1]],sublat[i[-1]]]) for i in cl._ijkls_np])) out_ijkl[-1]+= " frac: "+ matrix2text(cl.frac_coords, ' ') out_ijkl[-1]+= " cart: "+ matrix2text(cl.coords, ' ') open(fname, "w").write("\n".join(out_s)) open(fname+'_ijkl', "w").write("\n".join(out_ijkl)) @classmethod def generate_clusters(cls, prim, **kwargs): """ Generating PROPER clusters. Iteratively generation starting from empty and point clusters :param prim: Primitive cell :return: LDModel with certain clusters """ sites = kwargs['sites'] if 'sites' in kwargs.keys() else list(range(prim.num_sites)) proper_range = kwargs['prange'] maxord = max([ord for ord in proper_range.keys() if proper_range[ord]>0]) raw= [] clus_per_ord = [] for ord in range(maxord+1): clus_per_ord = cls.generate_n(prim, ord, proper_range[ord], clus_per_ord, sites) raw.extend(clus_per_ord) return cls(prim, raw, **kwargs) def generate_improper(self): """ :return: Improper clusters """ improp = [] assert self.imp_range is not None, ValueError('improper range not specified') for ord, cut in self.imp_range.items(): if ord <= 1: continue for clus in self.clusters: if (clus.diameter <= cut) and (clus.order< ord) and clus.order >0: # print("extending", ord, cut, clus) improp.extend(Cluster.improper_from_proper(clus, ord, self.prim)) print(" generated %d improper clusters" %(len(improp)), self.tally_clusters(improp, 2, self.maxorder)) self.clusters.extend(improp) def tally(self, minord=0): return self.tally_clusters(self.clusters, minord, self.maxorder+1) @staticmethod def tally_clusters(clusters, minord=0, maxord=0): return dict(Counter([c.order for c in clusters])) @staticmethod def generate_n(prim, n, cutoff, clus0, sites): """ Input: prim cell order n cutoff for diameter of clusters clus0 clusters at order n-1 :param sites: allowed sites in the primitive cell. Default all sites """ pts = [[0, 0, 0, l] for l in sites] if n == 0: uc = [Cluster([], prim)] elif n == 1: uc = [Cluster.from_ijkl([pt], prim) for pt in pts] uc = Cluster.remove_equivalent_clusters(uc) #assert len(uc) == prim.n_orb elif n >= 2: print(" generating order %d clusters..."% (n)) uc = [] for clus in clus0: if clus.diameter > cutoff: continue ijkl0 = clus.ijkls # each cluster is now sorted by the orbit (sub-lattice) type max_id = prim.orbit_of_l[ijkl0[-1][-1]] sumPts = prim.find_nb_cluster(np.array(clus.ijkls), cutoff) clus_new = [] for pt_n in sumPts: pt = pt_n.tolist() if not pt[-1] in sites: continue if prim.orbit_of_l[pt[-1]] < max_id: continue if pt in ijkl0: continue clusSum = clus.append_site(pt) if clusSum.diameter > cutoff: continue clus_new.append(clusSum) clus_new = Cluster.remove_equivalent_clusters(clus_new) uc.extend(clus_new) uc = Cluster.remove_equivalent_clusters(uc) return uc def __str__(self): if self.orbits is None: outs= [" No. ord uniq diam"] for i, c in enumerate(self.clusters): outs.append("%4d %4d %4d %9.5f" % (i, c.order, c.order_uniq, c.diameter)) return "\n".join(outs) else: outs= [" No. ord uniq mult diam"] for i, orb in enumerate(self.orbits): c = orb.cluster outs.append("%5d %4d %4d %4d %9.5f" % (i, c.order, c.order_uniq, len(orb.clusters), c.diameter)) return "\n".join(outs) def cleanup(self): """ 1. filter through clusters 2. move first atoms of each cluster to primitive cell 3. sort clusters :return: """ self.clusters= list(filter(self.cluster_filter, self.clusters)) for i in range(len(self.clusters)): self.clusters[i].move_to_prim() self.clusters.sort() def l_point_cls(self): return [orb.cluster.ijkls[0][-1] for orb in self.orbits if orb.cluster.order==1] def symmetrize(self): self.Cmat = scipy.sparse.identity(self.ncorr_full) @property def ncorr(self): return self.Cmat.shape[0] def prepare_index_full(self): # implementations should set the number of correlations for each cluster. # by default set to 1 per cluster for orb in self.orbits: if orb.ncorr_full<0: print("WARNING: ncorr_full NOT set for cluster and set to 1:",orb.cluster) orb.ncorr_full = 1 self.orb_idx_full=np.cumsum([0]+[orb.ncorr_full for orb in self.orbits]) self.ncorr_full= self.orb_idx_full[-1] def prepare_index(self): npara_ord = [0]*(self.maxorder+1) for orb in self.orbits: npara_ord[orb.cluster.order]+= orb.ncorr_full self.ord_idx=np.cumsum([0]+npara_ord) self.orb_idx=self.orb_idx_full def get_orbit_isotropy(self): """ 1. Find orbit of each cluster, modulus lattice translation 2. Find isotropy group of each representative (first) cluster of an orbit :return: list of [orbit, isotropy] orbit is a list of Clusters isotropy is list of [ig, relativePos] ig = symmop index relativePos indexing function under this symmop """ if self.orbits is None: self.orbits = Cluster.clusters_to_orbit_isotropy(self.prim, self.clusters) return self.orbits def translate_to_supercell(self, sc, orb_idx=None, clus_idx=None): """ :param sc: SupercellStructure :return: all clusters in the supercell. Each cluster is give as [list of the indices of the vertices, i.d. of orbit, i.d. of symop] orb_idx: default (None) to process all; explicit e.g. [0, 3, 5] to process selected few clus_idx: default (None) to process all; explicit e.g. [0] to process selected few """ # natom = self.prim.num_sites # def match_ordering(l1, l2): # """ # ordering such that l1[ordering] == l2 # """ # n= len(l1) # ordering = list(range(n)) orb_range = orb_idx if orb_idx is not None else range(len(self.orbits)) allclus = [] for iorb in orb_range: orbit = self.orbits[iorb] clus_range = clus_idx if clus_idx is not None else range(len(orbit.clusters)) for ic in clus_range: clus = orbit.clusters[ic] ig = orbit.clusters_ig[ic] allclus.extend([cx, iorb, ig, ic] for cx in self.translate_cluster_to_supercell(sc, clus)) return allclus @staticmethod def translate_cluster_to_supercell(sc, clus): """ :param sc: SupercellStructure :return: all clusters in the supercell """ if clus.order == 0: return [[]]*sc.n_cell coords= clus.frac_coords use_compiled_code = True if use_compiled_code: from f_util import f_util allclus= f_util.tr_cl_sc(sc._ijkl.T, sc.sc_mat.T, sc.inv_sc_mat.T, sc.sc_ref.T, sc.prim.frac_coords.T, clus._ijkls_np.T).T-1 else: allclus = [get_structure_ordering((coords+ijk[None,:]).dot(sc.inv_sc_mat), sc.frac_coords,0) for ijk in sc.ijk_ref] return allclus def identify_cluster(self, clus_in): """ Find if and which the input cluster matches the model clusters :param clus_try: :return: [matched cluster, iorb, ig, clus_sorted] """ for iorb, orb in enumerate(self.orbits): # Quick return if not match obviously *) if orb.cluster._must_differ(clus_in): continue # logger.debug(" proceed with %d"%(iorb)) for ic, clus_match in enumerate(orb.clusters): [flag, foundOrb] = clus_match.equivalent_by_lattranslation(clus_in.ijkls) if flag: ig = orb.clusters_ig[ic] # logger.debug("debug> sum "+repr(clus_in)+" == orbit %d cluster %d sym %d matching="%(iorb, ic, ig) +str(foundOrb)) # logger.debug(" calc FC Trans mem=")#, MemoryInUse()/1E6) return [True, iorb, ic, ig, relativePosition(clus_match.ijkls, foundOrb, dim=len(clus_in.orders))] return [False, -1, -1, None, None] def load_solution(self, sol_f): return np.loadtxt(sol_f, ndmin=2) def process_name_ord(self, name_ord, all_ord): if len(name_ord) == 0: name_ord = [['All', all_ord]] else: for i in name_ord: try: i[1]= list(map(int, i[1])) except: continue if i[0][-6:].lower()=='except': i[1] = [j for j in all_ord if j not in i[1]] else: i[1] = [j for j in i[1] if j in all_ord] return name_ord def get_submodels(self, name_ord, knownsol=None, **kwargs): """ :param name_ord: list of [name, fct order], e.g. pair 0,2 or [name, filename] where filename is a matrix :return: list of [name, matrix] defining the different fittings """ def down_select(o, sol0): if isinstance(o, list): return sum([list(range(self.ord_idx[i], self.ord_idx[i+1])) for i in o if i<=self.maxorder], []) elif o=='except-known': return np.where(sol0==0)[0] else: return np.loadtxt(o).reshape((-1)) all_ord=list(set([o.cluster.order for o in self.orbits])) name_ord = self.process_name_ord(name_ord, all_ord) print("No. of parameters", {o: self.ord_idx[o+1]-self.ord_idx[o] for o in range(self.maxorder+1)}) sol0 = np.zeros(self.ncorr) if knownsol: print(" Reading previous solution from %s"%(knownsol)) input_sol = self.load_solution(knownsol).reshape(-1) sol0[:min(sol0.size, input_sol.size)] = input_sol[:min(sol0.size, input_sol.size)] return [[nm, scipy.sparse.identity(self.ncorr).tocsr()[:, down_select(o, sol0)], sol0] for (nm, o) in name_ord]

36.708215

137

0.566677

3c6f22f1509518a9ecf30eeca268459e8c5c4c9e

226

py

Python

application/KitchenMagician/kitchen_magician/testing/views.py

AsuPaul19/Kitchen-Magician

8c22bc1a45942d22c183096f8e92f858983d351d

[ "MIT" ]

null

application/KitchenMagician/kitchen_magician/testing/views.py

AsuPaul19/Kitchen-Magician

8c22bc1a45942d22c183096f8e92f858983d351d

[ "MIT" ]

8

2021-04-08T20:50:25.000Z

2022-03-12T00:53:18.000Z

application/KitchenMagician/kitchen_magician/testing/views.py

AsuPaul19/Kitchen-Magician

8c22bc1a45942d22c183096f8e92f858983d351d

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def testing(request): context = { 'title': 'Testing', 'diets': 'Gluten Free' } return render(request, 'testing/testing.html', context)

22.6

59

0.641593

fe8f789a8bbf284e756dec4e6b84a57005d4db61

1,774

py

Python

third_party/blink/web_tests/wpt_internal/prerender/PRESUBMIT.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

14,668

2015-01-01T01:57:10.000Z

2022-03-31T23:33:32.000Z

third_party/blink/web_tests/wpt_internal/prerender/PRESUBMIT.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

86

2015-10-21T13:02:42.000Z

2022-03-14T07:50:50.000Z

third_party/blink/web_tests/wpt_internal/prerender/PRESUBMIT.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

5,941

2015-01-02T11:32:21.000Z

2022-03-31T16:35:46.000Z

# Copyright 2021 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Chromium presubmit script for prerender in Web Platform Tests. See http://dev.chromium.org/developers/how-tos/depottools/presubmit-scripts for more details about the presubmit API built into gcl. """ import os USE_PYTHON3 = True def _CheckFileTimeoutMetaTags(f): """Checks if the given file has timeout meta tags.""" new_contents = f.NewContents() for line in new_contents: if 'name="timeout" content="long"' in line: return True return False def _CheckTimeoutMetaTags(input_api, output_api): """ This function ensures that all WPTs for prerendering have meta tags to lengthen test timeout as some tests can possibly run out of time on windows platform. """ results = [] def file_filter(f): return (f.LocalPath().endswith(('html')) and (os.path.join('resources', '') not in f.LocalPath())) for f in input_api.AffectedFiles(include_deletes=False, file_filter=file_filter): if not _CheckFileTimeoutMetaTags(f): results.append( output_api.PresubmitError( ('Missing long timeout.' 'Add `<meta name="timeout" content="long">` to %s.') % f.LocalPath())) return results def CheckChangeOnUpload(input_api, output_api): results = [] results.extend(_CheckTimeoutMetaTags(input_api, output_api)) return results def CheckChangeOnCommit(input_api, output_api): results = [] results.extend(_CheckTimeoutMetaTags(input_api, output_api)) return results

31.122807

75

0.66009

a480363595f24f83ac682ff0d566a911850fe4ff

8,634

py

Python

conwhat/utils/readers.py

GriffithsLab/conwhat

282765211cd4da31d7a7ed48e95e29614899cd22

[ "BSD-3-Clause" ]

1

2021-01-19T22:26:44.000Z

conwhat/utils/readers.py

GriffithsLab/conwhat

282765211cd4da31d7a7ed48e95e29614899cd22

[ "BSD-3-Clause" ]

10

2021-01-19T21:24:07.000Z

2021-06-01T17:51:16.000Z

conwhat/utils/readers.py

GriffithsLab/ConWhAt

282765211cd4da31d7a7ed48e95e29614899cd22

[ "BSD-3-Clause" ]

null

""" Utils for reading ConWhAt and external data files """ # Author: John Griffiths # License: simplified BSD import os,sys,yaml,h5py import numpy as np,networkx as nx, pandas as pd import nibabel as nib, nilearn as nl from nibabel.affines import apply_affine from dipy.io import Dpy import indexed_gzip as igzip # Atlas base dir abd = os.path.split(__file__)[0] + '/../data' def load_vol_file_mappings(atlas_name=None,atlas_dir=None): print('loading file mapping') if atlas_dir == None: atlas_dir = os.path.join(abd,atlas_name) mappings = pd.read_csv(atlas_dir + '/mappings.txt', sep=',') return mappings,atlas_dir def load_vol_bboxes(atlas_name=None,atlas_dir=None): print('loading vol bbox') if atlas_dir == None: atlas_dir = os.path.join(abd,atlas_name) bbox = pd.read_csv(atlas_dir + '/bounding_boxes.txt', sep=',') return bbox def load_connectivity(atlas_name=None,atlas_dir=None,weights_name='weights'): print('loading connectivity') if not atlas_dir: atlas_dir = os.path.join(abd,atlas_name) # Mandatory files ws_file = '%s/%s.txt' %(atlas_dir,weights_name) rls_file = '%s/region_labels.txt' % atlas_dir ws = np.loadtxt(ws_file) rls = [l[:-1] for l in open(rls_file, 'r').readlines()] # Optional files tls_file = '%s/tract_lengths.txt' % atlas_dir rxyzs_file = '%s/region_xyzs.txt' % atlas_dir rnii_file = '%s/region_masks.nii.gz' % atlas_dir hs_file = '%s/hemispheres.txt' % atlas_dir ctx_file = '%s/cortex.txt' % atlas_dir rmfslh_file = '%s/region_mapping_fsav_lh.txt' % atlas_dir rmfsrh_file = '%s/region_mapping_fsav_rh.txt' % atlas_dir tls,rxyzs,rnii,ctxi,hs,rmfslh,rmfsrh = None,None,None,None,None,None,None if os.path.isfile(tls_file): itls = np.loadtxt(tls_file) if os.path.isfile(rxyzs_file): rxyzs = np.loadtxt(rxyzs_file) if os.path.isfile(rnii_file): rnii = nib.load(rnii_file) if os.path.isfile(hs_file): hs = np.loadtxt(hs_file) if os.path.isfile(ctx_file): ctx = np.loadtxt(ctx_file) if os.path.isfile(rmfslh_file): rmfslh =np.loadtxt(rmfslh_file) if os.path.isfile(rmfsrh_file): rmfsrh = np.loadtxt(rmfsrh_file) return ws,rls,tls,rxyzs,rnii,ctx,hs,rmfslh,rmfsrh def make_nx_graph(vfms,bboxes,weights,region_labels,hemis,cortex): G = nx.Graph() # add node info for node_it,node in enumerate(region_labels): rl = region_labels[node_it] hemi = hemis[node_it] ctx = cortex[node_it] G.add_node(node_it, **{'region_label': rl, 'hemisphere': hemi, 'cortex': ctx}) # add edge info for idx in vfms.index: vfm = vfms.iloc[idx] name = vfm['name'] # Allow either '33-55' or '33_to_55' naming conventions if '_to_' in name: roi1,roi2 = name.split('_to_') else: roi1,roi2 = name.split('-') roi1 = int(roi1); roi2 = int(roi2) ad = vfm.to_dict() ad.update(bboxes.iloc[idx]) ad['idx'] = idx ad['weight'] = weights[roi1,roi2] n1,n2 = G.nodes[roi1],G.nodes[roi2] # (ibid...) if '_to_' in name: fullname = n1['region_label'] + '_to_' + n2['region_label'] else: fullname = n1['region_label'] + '-' + n2['region_label'] ad['fullname'] = fullname G.add_edge(roi1,roi2,**ad) return G def load_stream_file_mappings(atlas_name=None,atlas_dir=None): print('loading streamline file mappings') if not atlas_dir: atlas_dir = os.path.join(abd,atlas_name) F = h5py.File(atlas_dir + '/mappings.h5', 'r') KVs = {k: v.value.astype(int) for k,v in F.items()} F.close() mappings = pd.DataFrame(np.array(KVs.values()), index=KVs.keys()) mappings.columns = ['idxlist'] mappings.index.names = ['name'] mappings = mappings.reset_index() return mappings,atlas_dir def load_stream_file_mappings_multifile(atlas_name=None,atlas_dir=None): print('loading mult-file streamline file mappings') if not atlas_dir: atlas_dir = os.path.join(abd,atlas_name) # Difference from above is that the keys are now (sub,cnxn), rather than cnxn F = h5py.File(atlas_dir + '/mappings_multifile.h5', 'r') KVs = {k: v.value for k,v in F.items()} F.close() mappings = pd.DataFrame(np.array(KVs.values()), index=KVs.keys()) mappings.columns = ['idxlist'] mappings.index.names = ['sub','name'] mappings = mappings.reset_index() return mappings,atlas_dir # (this is identical to load vox bboxes. Remove both # and replace with single func?) def load_stream_bboxes(atlas_name=None,atlas_dir=None): print('loading stream bbox') if not atlas_dir: atlas_dir = os.path.join(abd,atlas_name) bbox = pd.read_csv(atlas_dir + '/bounding_boxes.txt', sep=',', names=['xmin', 'xmax', 'ymin', 'ymax', 'zmin', 'zmax']) return bbox def make_streams_nx_graph(sfms,bboxes,weights,region_labels,hemis,cortex): # I THINK THIS CAN BE THE SAME FUNC FOR BOTH \ # VOLUMETRIC AND STREAMLINETRIC # ...just writing one for streamlines first to get clear... G = nx.Graph() # add node info for node_it,node in enumerate(region_labels): rl = region_labels[node_it] hemi = hemis[node_it] ctx = cortex[node_it] G.add_node(node_it, **{'region_label': rl, 'hemisphere': hemi, 'cortex': ctx}) # add edge info for idx in sfms.index: sfm = sfms.ix[idx] name = sfm['name'] # Allow either '33-55' or '33_to_55' naming conventions if '_to_' in name: roi1,roi2 = name.split('_to_') else: roi1,roi2 = name.split('-') roi1 = int(roi1); roi2 = int(roi2) ad = sfm.to_dict() ad.update(bboxes.ix[idx]) ad['idx'] = idx ad['weight'] = weights[roi1,roi2] n1,n2 = G.node[roi1],G.node[roi2] # (ibid...) if '_to_' in name: fullname = n1['region_label'] + '_to_' + n2['region_label'] else: fullname = n1['region_label'] + '-' + n2['region_label'] ad['fullname'] = fullname G.add_edge(roi1,roi2,**ad) def igzip4dnii(fname,inds3d, inds0d='all',inds1d='all',inds2d='all', atlas_name=None, atlas_dir=None): # If atlas dir given, file is assumed to be in there if atlas_dir: fname = '%s/%s' %(atlas_dir,fname) else: # If atlas dir not given but atlas name is given, assumes path is relative # to local conwhat atlas dir if atlas_name: fname = '%s/%s/%s' %(abd,atlas_name,fname) # Here we are usin 4MB spacing between # seek points, and using a larger read # buffer (than the default size of 16KB). fobj = igzip.IndexedGzipFile( filename=fname,#'big_image.nii.gz', spacing=4194304, readbuf_size=131072) # Create a nibabel image using # the existing file handle. fmap = nib.Nifti1Image.make_file_map() fmap['image'].fileobj = fobj image = nib.Nifti1Image.from_file_map(fmap) if inds3d == 'N/A' or np.isnan(inds3d): dims0,dims1,dims2 = image.shape dat = np.squeeze(image.dataobj[:,:,:]) else: # Use the image ArrayProxy to access the # data - the index will automatically be # built as data is accessed. dims0,dims1,dims2,dims3 = image.shape #if inds0d == 'all': inds0d = range(dims0) #if inds1d == 'all': inds1d = range(dims1) #if inds2d == 'all': inds2d = range(dims2) dat = np.squeeze(image.dataobj[:,:,:,int(inds3d)]) #dat = np.squeeze(image.dataobj[inds0d,inds1d,inds2d,int(inds3d)]) #if type(inds3d) == int: # len(inds3d) == 1: # dat = np.squeeze(image.dataobj[inds0d,inds1d,inds2d,int(inds3d)]) #else: # dat = np.array([(image.dataobj[inds0d,inds1d,inds2d,int(i3)]) for i3 in inds3d]) # dat = dat.reshape([dims[1],dims[2],dims[3],dims[0]]) return dat def dpy_to_trk(dpy_file,ref,outfile,inds='all'): if os.path.isfile(ref): ref_img = nib.load(ref) else: ref_img = ref # Make trackvis header hdr = nib.trackvis.empty_header() hdr['voxel_size'] = ref_img.get_header().get_zooms() hdr['dim'] = ref_img.shape hdr['voxel_order'] = "LAS"#"RAS" hdr['vox_to_ras'] = ref_img.Affine zooms = ref_img.header.get_zooms() # Load streamlines D = Dpy(dpy_file, 'r') if inds == 'all': dpy_streams = D.read_tracks() else: dpy_streams = D.read_tracksi(inds) D.close() # Convert to trackvis space + format [apply_affine(hdr['vox_to_ras'], s*zooms) for s in dpy_streams] trk_streams = [(s,None,None) for s in dpy_streams] nib.trackvis.write(outfile,trk_streams,hdr)

25.172012

87

0.645356

9d891bb512e52d9fd6c006686575c07959d44483

2,753

py

Python

corunner/runner.py

thepatrik/corunner

a68ed8e4f9f659b71a03c37833710544532c302d

[ "MIT" ]

null

corunner/runner.py

thepatrik/corunner

a68ed8e4f9f659b71a03c37833710544532c302d

[ "MIT" ]

null

corunner/runner.py

thepatrik/corunner

a68ed8e4f9f659b71a03c37833710544532c302d

[ "MIT" ]

null

import asyncio import contextlib import inspect import time from typing import Any, Awaitable, Callable, List import click_spinner from .exceptions import CancelledError from .report import Execution, Report def run(*groups, users: int = 5, duration: int = 0, logger=None, show_progress_bar: bool = False, halt_and_catch_fire: bool = False) -> Report: cm = contextlib.nullcontext() if show_progress_bar and not logger: cm = click_spinner.spinner() with cm: started = time.time() executions = [] if duration: while time.time() - started <= duration: res = _run(*groups, users=users, logger=logger, halt_and_catch_fire=halt_and_catch_fire) executions.extend(res) else: res = _run(*groups, users=users, logger=logger, halt_and_catch_fire=halt_and_catch_fire) executions.extend(res) return Report(executions) def _run( *groups, users: int, logger, halt_and_catch_fire: bool) -> List[Execution]: asyncio_semaphore = asyncio.Semaphore(users) coros = [] for c in [c for group in groups for c in group._coros]: i = 0 while i < c.count: coros.append(_run_coro(c.id, c.f, asyncio_semaphore, halt_and_catch_fire, logger)) i += 1 async def runner(coros) -> Awaitable: return await asyncio.gather(*coros) return _run_until_complete(runner(coros)) def _run_until_complete(f: Awaitable[Any], close: bool = False): loop = asyncio.get_event_loop() try: return loop.run_until_complete(f) except KeyboardInterrupt: raise CancelledError() finally: loop.run_until_complete(loop.shutdown_asyncgens()) for task in asyncio.all_tasks(loop=loop): task.cancel() if close: loop.close() async def _run_coro(id: str, f: Callable[..., Any], semaphore: asyncio.Semaphore, halt_and_catch_fire: bool, callback, *args, **kw): async with semaphore: error = None ts = time.time() try: await f(*args, **kw) except Exception as e: error = e te = time.time() execution = Execution(id=id, error=error, start_time=ts * 1000, end_time=te * 1000, latency=(te - ts) * 1000) if callback: if inspect.iscoroutinefunction(callback): await callback(execution) else: callback(execution) if error and halt_and_catch_fire: raise error return execution

29.287234

81

0.586996

901bc7cbba875fcb68fbeeeccac4773ef79bda17

5,463

py

Python

tests/test_types.py

saullocarvalho/angr

48a7297f70f9420f3a79882b48bf9a682f910a0c

[ "BSD-2-Clause" ]

null

tests/test_types.py

saullocarvalho/angr

48a7297f70f9420f3a79882b48bf9a682f910a0c

[ "BSD-2-Clause" ]

null

tests/test_types.py

saullocarvalho/angr

48a7297f70f9420f3a79882b48bf9a682f910a0c

[ "BSD-2-Clause" ]

null

import nose import claripy import angr from angr.sim_type import SimTypeFunction, SimTypeInt, SimTypePointer, SimTypeChar, SimStruct, SimTypeFloat, SimUnion, SimTypeDouble, SimTypeLongLong, SimTypeLong, SimTypeNum from angr.utils.library import convert_cproto_to_py def test_type_annotation(): my_ty = angr.sim_type.SimTypeTop() ptr = claripy.BVS('ptr', 32).annotate(angr.type_backend.TypeAnnotation(angr.sim_type.SimTypePointer(my_ty, label=[]))) ptroffset = ptr + 4 bt = angr.type_backend.TypeBackend() tv = bt.convert(ptroffset) nose.tools.assert_is(tv.ty.pts_to, my_ty) nose.tools.assert_true(claripy.is_true(tv.ty.offset == 4)) def test_cproto_conversion(): # A normal function declaration cproto_0 = "int main(int argc, char** argv);" pyproto_name, pyproto, the_str = convert_cproto_to_py(cproto_0) nose.tools.assert_equal(pyproto_name, "main") nose.tools.assert_is_instance(pyproto, SimTypeFunction) nose.tools.assert_is_instance(pyproto.args[0], SimTypeInt) nose.tools.assert_is_instance(pyproto.args[1], SimTypePointer) nose.tools.assert_is_instance(pyproto.args[1].pts_to.pts_to, SimTypeChar) nose.tools.assert_is_instance(pyproto.returnty, SimTypeInt) # Directly comparing the strings... how bad can I be? nose.tools.assert_equal(the_str, '# int main(int argc, char** argv);\n"main": SimTypeFunction([SimTypeInt(signed=True, label=None), SimTypePointer(SimTypePointer(SimTypeChar(label=None), label=None, offset=0), label=None, offset=0)], SimTypeInt(signed=True, label=None), label=None),') # A bad function declaration cproto_1 = "int bad(xxxxxxx);" pyproto_name, pyproto, the_str = convert_cproto_to_py(cproto_1) # pylint:disable=unused-variable nose.tools.assert_equal(pyproto_name, "bad") nose.tools.assert_is(pyproto, None) # A even worse function declaration # Special thanks to @schieb, see GitHub PR #958 cproto_2 = "__attribute__ ((something)) void foo(void);" pyproto_name, pyproto, the_str = convert_cproto_to_py(cproto_2) # pylint:disable=unused-variable nose.tools.assert_equal(pyproto_name, "foo") def test_struct_deduplication(): angr.types.define_struct('struct ahdr { int a ;}') angr.types.define_struct('struct bhdr { int b ;}') angr.types.define_struct('struct chdr { int c ;}') angr.types.register_types(angr.types.parse_types('typedef struct ahdr ahdr;')) angr.types.register_types(angr.types.parse_types('typedef struct bhdr bhdr;')) angr.types.register_types(angr.types.parse_types('typedef struct chdr chdr;')) dhdr = angr.types.define_struct('struct dhdr { struct ahdr a; struct bhdr b; struct chdr c;}') assert dhdr.fields['a'].fields def test_parse_type(): int_ptr = angr.types.parse_type('int *') nose.tools.assert_is_instance(int_ptr, SimTypePointer) nose.tools.assert_is_instance(int_ptr.pts_to, SimTypeInt) char_ptr = angr.types.parse_type('char *c') nose.tools.assert_is_instance(char_ptr, SimTypePointer) nose.tools.assert_is_instance(char_ptr.pts_to, SimTypeChar) struct_abcd = angr.types.parse_type('struct abcd { char c; float f; }') nose.tools.assert_is_instance(struct_abcd, SimStruct) nose.tools.assert_equal(struct_abcd.name, 'abcd') nose.tools.assert_true(len(struct_abcd.fields) == 2) nose.tools.assert_is_instance(struct_abcd.fields['c'], SimTypeChar) nose.tools.assert_is_instance(struct_abcd.fields['f'], SimTypeFloat) union_dcba = angr.types.parse_type('union dcba { double d; long long int lli; }') nose.tools.assert_is_instance(union_dcba, SimUnion) nose.tools.assert_equal(union_dcba.name, 'dcba') nose.tools.assert_true(len(union_dcba.members) == 2) nose.tools.assert_is_instance(union_dcba.members['d'], SimTypeDouble) nose.tools.assert_is_instance(union_dcba.members['lli'], SimTypeLongLong) struct_llist = angr.types.parse_type('struct llist { int data; struct llist * next; }') nose.tools.assert_is_instance(struct_llist, SimStruct) nose.tools.assert_equal(struct_llist.name, 'llist') nose.tools.assert_true(len(struct_llist.fields) == 2) nose.tools.assert_is_instance(struct_llist.fields['data'], SimTypeInt) nose.tools.assert_is_instance(struct_llist.fields['next'], SimTypePointer) nose.tools.assert_is_instance(struct_llist.fields['next'].pts_to, SimStruct) nose.tools.assert_equal(struct_llist.fields['next'].pts_to.name, 'llist') func_ptr = angr.types.parse_type('double (*) (int, float)') nose.tools.assert_is_instance(func_ptr, SimTypePointer) nose.tools.assert_is_instance(func_ptr.pts_to, SimTypeFunction) nose.tools.assert_is_instance(func_ptr.pts_to.returnty, SimTypeDouble) nose.tools.assert_true(len(func_ptr.pts_to.args) == 2) nose.tools.assert_is_instance(func_ptr.pts_to.args[0], SimTypeInt) nose.tools.assert_is_instance(func_ptr.pts_to.args[1], SimTypeFloat) def test_parse_type_no_basic_types(): time_t = angr.types.parse_type('time_t') nose.tools.assert_is_instance(time_t, SimTypeLong) byte = angr.types.parse_type('byte') nose.tools.assert_is_instance(byte, SimTypeNum) nose.tools.assert_true(byte.size, 8) nose.tools.assert_false(byte.signed) if __name__ == '__main__': test_type_annotation() test_cproto_conversion() test_struct_deduplication() test_parse_type() test_parse_type_no_basic_types()

45.907563

280

0.746842

417c98a6c2adee0d04f431a80fd90c5b2c372d0f

344

py

Python

example/data/rocketdataset/rocketdatasetresize.py

SeanMabli/aiinpy

bd332fce454c489e236878c9da91bb86ec6dda14

[ "MIT" ]

2

2021-04-19T21:49:34.000Z

2021-05-17T21:03:08.000Z

testing/data/rocketdataset/rocketdatasetresize.py

Sean-Mabli/aiinpy

827e4f85861436c0332046fa8aa84e24153513d6

[ "MIT" ]

null

testing/data/rocketdataset/rocketdatasetresize.py

Sean-Mabli/aiinpy

827e4f85861436c0332046fa8aa84e24153513d6

[ "MIT" ]

null

from PIL import Image numofelectronimages = 23 numoffalcon9images = 72 numofsoyuzimages = 59 numofspaceshuttleimages = 71 for i in range(72): im = Image.open('testing\\data\\rocketdataset\\960x540\\SoyuzImagesFormated\\Soyuz_' + format(i, "04") + '_Credit=Roscosmos.png') im.save('testing\\data\\rocketdataset\\soyuz\\' + str(i) + '.png')

34.4

131

0.732558

b9885dfec7242cbbbb808f31b466dabae833761f

2,013

py

Python

open/Inspur/code/common/lwis_harness.py

fenz-org/mlperf_inference_results_v0.7

2e38bec7f8df806283802a69db3d0038a37d026e

[ "Apache-2.0" ]

19

2020-10-26T17:37:22.000Z

2022-01-20T09:32:38.000Z

open/Inspur/code/common/lwis_harness.py

fenz-org/mlperf_inference_results_v0.7

2e38bec7f8df806283802a69db3d0038a37d026e

[ "Apache-2.0" ]

11

2020-10-21T19:18:48.000Z

2021-03-11T18:50:36.000Z

open/Inspur/code/common/lwis_harness.py

fenz-org/mlperf_inference_results_v0.7

2e38bec7f8df806283802a69db3d0038a37d026e

[ "Apache-2.0" ]

19

2020-10-21T19:15:17.000Z

2022-01-04T08:32:08.000Z

# Copyright (c) 2020, NVIDIA CORPORATION. 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 re import os, sys sys.path.insert(0, os.getcwd()) from code.common import BENCHMARKS, SCENARIOS, logging, args_to_string from code.common.harness import BaseBenchmarkHarness import code.common.arguments as common_args response_postprocess_map = { BENCHMARKS.SSDResNet34: "coco", BENCHMARKS.SSDMobileNet: "coco" } class LWISHarness(BaseBenchmarkHarness): def __init__(self, args, name=""): super().__init__(args, name=name) self.use_jemalloc = (self.scenario == SCENARIOS.Server) self.flag_builder_custom_args = common_args.LOADGEN_ARGS + common_args.LWIS_ARGS + common_args.SHARED_ARGS def _get_harness_executable(self): return "./build/bin/harness_default" def _build_custom_flags(self, flag_dict): if self.has_dla: flag_dict["dla_engines"] = self.dla_engine if self.has_gpu and self.has_dla: pass elif self.has_gpu: flag_dict["max_dlas"] = 0 elif self.has_dla: flag_dict["max_dlas"] = 1 else: raise ValueError("Cannot specify --no_gpu and --gpu_only at the same time") argstr = args_to_string(flag_dict) + " --scenario " + self.scenario + " --model " + self.name if self.name in response_postprocess_map: argstr += " --response_postprocess " + response_postprocess_map[self.name] return argstr

34.706897

114

0.701937

a7ac2e4e6627e3117ff2dce0a8397c8df4a4ed25

11,390

py

Python

run_simulation_yaml.py

larrycameron80/metta

4a15b790701fe84a6493e52aec95e5788fef1a04

[ "MIT" ]

940

2018-02-13T00:36:42.000Z

2022-03-30T20:15:52.000Z

run_simulation_yaml.py

larrycameron80/metta

4a15b790701fe84a6493e52aec95e5788fef1a04

[ "MIT" ]

24

2018-02-11T22:02:53.000Z

2020-10-08T16:32:14.000Z

run_simulation_yaml.py

larrycameron80/metta

4a15b790701fe84a6493e52aec95e5788fef1a04

[ "MIT" ]

140

2018-02-13T02:48:56.000Z

2022-03-09T13:35:18.000Z

# author Chris Gates - Uber # additions Maus - Uber # additions author Russ Nolen - Riot Games # adversarial simulation engine from __future__ import print_function import datetime import json import logging import os import subprocess import sys import time from argparse import ArgumentParser from random import randint import requests import yaml from reporting.log_to_file import * from workers.vagranttasks import * try: import configparser as ConfigParser # Python 3 except ImportError: import ConfigParser # Python 2 # slack hook URL hook = "" # vagrant variables that get populated below windows = " " osx = " " linux = " " kali = " " # banners for metta banner = ''' _____ __ __ / \ _____/ |__/ |______ / \ / \_/ __ \ __\ __\__ \ / Y \ ___/| | | | / __ \_ \____|__ /\___ >__| |__| (____ / \/ \/ \/ ''' banner2 = ''' __ __ _______ _______ _______ _______ | |_| || || || || _ | | || ___||_ _||_ _|| |_| | | || |___ | | | | | | | || ___| | | | | | | | ||_|| || |___ | | | | | _ | |_| |_||_______| |___| |___| |__| |__| ''' # module to post to slack if you set the webhook in config.ini def post_to_slack(hook, json): try: r = requests.post(hook, json=json) except Exception as e: print(e) def run_scenario(ioc_filename): try: print("### Running the Scenario ###") raw_iocs = yaml.load_all(open(ioc_filename, 'r').read()) timenow = datetime.datetime.utcnow() for raw_ioc in raw_iocs: scenario = raw_ioc.get('meta').get('scenario_actions') rule_name = raw_ioc.get('name') print("### {} ###".format(rule_name)) scenario_actions = [] # read the steps from purple_actions in yaml and load them into purple_actions for x in range(1, len(scenario)+1): scenario_actions.append(raw_ioc.get('meta').get('scenario_actions').get(x)) for uuid_file in scenario_actions: run_uuid(uuid_file) except Exception as e: print(e) def run_uuid(ioc_filename): try: print("\nRunning UUID actions inside:{}".format(ioc_filename)) raw_iocs = yaml.load_all(open(ioc_filename, 'r').read()) for raw_ioc in raw_iocs: rule_name = raw_ioc.get('name') rule_uuid = raw_ioc.get('uuid') rule_os = raw_ioc.get('os') mitre_phase = raw_ioc.get('meta').get('mitre_attack_phase') mitre_tech = raw_ioc.get('meta').get('mitre_attack_technique') purple = raw_ioc.get('meta').get('purple_actions') if not purple: print("No Purple Actions detected you've probably messed up your scenario.yml...") sys.exit(0) purple_actions = [] # read the steps from purple_actions in yaml and load them into purple_actions for x in range(1, len(purple)+1): purple_actions.append(raw_ioc.get('meta').get('purple_actions').get(x)) if rule_os == "windows": print("OS matched Windows...sending to the windows vagrant") for action in purple_actions: print("Running: {}".format(action)) timenow = datetime.datetime.utcnow() date = timenow.strftime('%Y-%m-%d') hourminsec = timenow.strftime('%H:%M:%S') time_to_log = date+" "+hourminsec try: vagrant = runcmd_nodb_win.delay(action, rule_name, rule_uuid, windows) data = json.dumps({'time': time_to_log, 'rule_name': rule_name, 'action': action, 'mitre_attack_phase': mitre_phase, 'mitre_attack_technique': mitre_tech, 'host': windows}) logging.info(data) write_row(time_to_log, rule_name, action, mitre_phase, mitre_tech, windows) ''' # if you want to post to slack uncomment this and set the slack hook above json = {'text': "Automated Purple Team --> Simulation: {} | Action: {} | Host: {} | Execution Time: {} UTC".format(rule_name,action,windows,datetime.datetime.utcnow())} post_to_slack(hook,json) ''' time.sleep(randint(2, 30)) except Exception as e: print(e) elif rule_os == "osx": print("OS matched OSX...sending to the OSX vagrant") for action in purple_actions: print("Running: {}".format(action)) timenow = datetime.datetime.utcnow() date = timenow.strftime('%Y-%m-%d') hourminsec = timenow.strftime('%H:%M:%S') time_to_log = date+" "+hourminsec try: vagrant = runcmd_nodb_osx.delay(action, rule_name, rule_uuid, osx) data = json.dumps({'time': time_to_log, 'rule_name': rule_name, 'action': action, 'mitre_attack_phase': mitre_phase, 'mitre_attack_technique': mitre_tech, 'host': osx}) logging.info(data) write_row(time_to_log, rule_name, action, mitre_phase, mitre_tech, osx) ''' # if you want to post to slack uncomment this and set the slack hook above json = {'text': "Automated Purple Team --> Simulation: {} | Action: {} | Host: {} | Execution Time: {} UTC".format(rule_name,action,osx,datetime.datetime.utcnow())} post_to_slack(hook,json) ''' time.sleep(randint(2, 30)) except Exception as e: print(e) elif rule_os == "linux": print("OS matched Linux...sending to the Linux vagrant") for action in purple_actions: print("Running: {}".format(action)) timenow = datetime.datetime.utcnow() date = timenow.strftime('%Y-%m-%d') hourminsec = timenow.strftime('%H:%M:%S') time_to_log = date+" "+hourminsec try: vagrant = runcmd_nodb_linux.delay(action, rule_name, rule_uuid, linux) data = json.dumps({'time': time_to_log, 'rule_name': rule_name, 'action': action, 'mitre_attack_phase': mitre_phase, 'mitre_attack_technique': mitre_tech, 'host': linux}) logging.info(data) write_row(time_to_log, rule_name, action, mitre_phase, mitre_tech, linux) ''' # if you want to post to slack uncomment this and set the slack hook above json = {'text': "Automated Purple Team --> Simulation: {} | Action: {} | Host: {} | Execution Time: {} UTC".format(rule_name,action,osx,datetime.datetime.utcnow())} post_to_slack(hook,json) ''' time.sleep(randint(2, 30)) except Exception as e: print(e) elif rule_os == "kali": print("OS matched Kali...sending to the Kali Linux vagrant") for action in purple_actions: print("Running: {}".format(action)) timenow = datetime.datetime.utcnow() date = timenow.strftime('%Y-%m-%d') hourminsec = timenow.strftime('%H:%M:%S') time_to_log = date+" "+hourminsec try: vagrant = runcmd_nodb_kali.delay(action, rule_name, rule_uuid, kali) data = json.dumps({'time': time_to_log, 'rule_name': rule_name, 'action': action, 'mitre_attack_phase': mitre_phase, 'mitre_attack_technique': mitre_tech, 'host': kali}) logging.info(data) write_row(time_to_log, rule_name, action, mitre_phase, mitre_tech, kali) ''' #if you want to post to slack uncomment this and set the slack hook above #json = {'text': "Automated Purple Team --> Simulation: {} | Action: {} | Host: {} | Execution Time: {} UTC".format(rule_name,action,osx,datetime.datetime.utcnow())} #post_to_slack(hook,json) ''' time.sleep(randint(2, 30)) except Exception as e: print(e) else: print("I received an unknown OS") except KeyboardInterrupt: print("CTRL-C received, exiting...") except Exception as e: print(e) def parse_yaml(ioc_filename): print(banner2) print("YAML FILE: {}".format(ioc_filename)) try: raw_iocs = yaml.load_all(open(ioc_filename, 'r').read()) start_log("Adversarial Simulation", "1.0") for raw_ioc in raw_iocs: scenario = raw_ioc.get('meta').get('scenario') purple = raw_ioc.get('meta').get('purple_actions') # if we cant find the scenario tag, default to run_uuid if not scenario: run_uuid(ioc_filename) # if the scenario field is found and if it's true run the run_scenario function if scenario is True: run_scenario(ioc_filename) close_log() except KeyboardInterrupt: print("CTRL-C received, exiting...") except Exception as e: print(e) def main(): parser = ArgumentParser(description="adversarial-simulation ") parser.add_argument("-f", "--simfile", action="store", default=None, required=True, dest="simfile", help="Path to simulation file you want to run") args = parser.parse_args() config = ConfigParser.RawConfigParser() try: config.read('config.ini') except Exception as e: print(e) sys.exit(0) global windows windows = config.get('vms', 'windows') global osx osx = config.get('vms', 'osx') global linux linux = config.get('vms', 'linux') global kali kali = config.get('vms', 'kali') global console_output console_log_output = config.get('console_log_output', 'enabled') # logging function to log json to a file logging.basicConfig(level=logging.DEBUG, format='%(message)s', filename='simulation.log', filemode='w') if console_log_output == 'True' or console_log_output == 'true': # logging function to give info to the console console = logging.StreamHandler() console.setLevel(logging.INFO) # set a format which is simpler for console use formatter = logging.Formatter('%(levelname)-4s : %(message)s') # tell the handler to use this format console.setFormatter(formatter) # add the handler to the root logger logging.getLogger('').addHandler(console) else: '' parse_yaml(args.simfile) if __name__ == '__main__': main()

39.686411

196

0.54504

0e4099b5054e5f542b9bd9e68b972db57d955253

1,212

py

Python

store/migrations/0015_order_coupon.py

Jerome-Celle/Blitz-API

7dfb7b837ed47b11afcfaa5f5aee831c1aa4e5e0

[ "MIT" ]

3

2019-10-22T00:16:49.000Z

2021-07-15T07:44:43.000Z

store/migrations/0015_order_coupon.py

Jerome-Celle/Blitz-API

7dfb7b837ed47b11afcfaa5f5aee831c1aa4e5e0

[ "MIT" ]

1,183

2018-04-19T18:40:30.000Z

2022-03-31T21:05:05.000Z

store/migrations/0015_order_coupon.py

Jerome-Celle/Blitz-API

7dfb7b837ed47b11afcfaa5f5aee831c1aa4e5e0

[ "MIT" ]

12

2018-04-17T19:16:42.000Z

2022-01-27T00:19:59.000Z

# Generated by Django 2.0.8 on 2019-01-08 18:35 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('store', '0014_optional_applicable_anything'), ] operations = [ migrations.AddField( model_name='historicalorder', name='coupon', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='store.Coupon'), ), migrations.AddField( model_name='order', name='coupon', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='orders', to='store.Coupon', verbose_name='Applied coupon'), ), migrations.AlterField( model_name='coupon', name='value', field=models.DecimalField(decimal_places=2, max_digits=6, verbose_name='Value'), ), migrations.AlterField( model_name='historicalcoupon', name='value', field=models.DecimalField(decimal_places=2, max_digits=6, verbose_name='Value'), ), ]

34.628571

165

0.628713

cb089350f29a336069b96da619a104cfbf6753c3

4,657

py

Python

ditto/flickr/fetch/filesfetchers.py

garrettc/django-ditto

fcf15beb8f9b4d61634efd4a88064df12ee16a6f

[ "MIT" ]

54

2016-08-15T17:32:41.000Z

2022-02-27T03:32:05.000Z

ditto/flickr/fetch/filesfetchers.py

garrettc/django-ditto

fcf15beb8f9b4d61634efd4a88064df12ee16a6f

[ "MIT" ]

229

2015-07-23T12:50:47.000Z

2022-03-24T10:33:20.000Z

ditto/flickr/fetch/filesfetchers.py

garrettc/django-ditto

fcf15beb8f9b4d61634efd4a88064df12ee16a6f

[ "MIT" ]

8

2015-09-10T17:10:35.000Z

2022-03-25T13:05:01.000Z

import os from django.core.files import File from . import FetchError from ..models import Photo from ...core.utils.downloader import DownloadException, filedownloader # A single class that fetches original photo/video files for existing # Photo objects. Doesn't use the API. class OriginalFilesFetcher(object): """ Fetch the original photo files for a single Account. Not based off FlickrFetcher because we don't use the API so it's quite different. But still has a similar external appearance. Use something like: results = OriginalFilesFetcher(account=account_object).fetch() results is a dict that will have: 'success': Boolean. 'account': String. Indicating the Account (eg, its User's username). 'fetched': Integer. If success, the number of files fetched, if any. 'messages': List of strings. If no success, the failure message(s). """ def __init__(self, account): self.account = None self.results = [] self.results_count = 0 self.return_value = {"fetched": 0} if account.user: self.return_value["account"] = account.user.username else: self.return_value["success"] = False self.return_value["messages"] = ["This account has no Flickr User"] self.account = account def fetch(self, fetch_all=False): """ Download and save original photos and videos for all Photo objects (or just those that don't already have them). self.account must be an Account object first. fetch_all -- Boolean. Fetch ALL photos/videos, even if we've already got them? """ # Might already have success=False from __init__(): if "success" not in self.return_value: self._fetch_files(fetch_all) self.return_value["fetched"] = self.results_count return self.return_value def _fetch_files(self, fetch_all): """ Download and save original photos and videos for all Photo objects (or just those that don't already have them). fetch_all -- Boolean. Fetch ALL photos/videos, even if we've already got them? """ photos = Photo.objects.filter(user=self.account.user) if not fetch_all: photos = photos.filter(original_file="") error_messages = [] for photo in photos: try: self._fetch_and_save_file(photo=photo, media_type="photo") self.results_count += 1 except FetchError as e: error_messages.append(str(e)) if photo.media == "video": try: self._fetch_and_save_file(photo=photo, media_type="video") self.results_count += 1 except FetchError as e: error_messages.append(str(e)) if len(error_messages) > 0: self.return_value["success"] = False self.return_value["messages"] = error_messages else: self.return_value["success"] = True def _fetch_and_save_file(self, photo, media_type): """ Downloads a video or photo file and saves it to the Photo object. Expects: photo -- A Photo object. media_type -- String, either 'photo' or 'video'. Raises FetchError if something goes wrong. """ if media_type == "video": url = photo.remote_video_original_url # Accepted video formats: # https://help.yahoo.com/kb/flickr/sln15628.html # BUT, they all seem to be sent as video/mp4. acceptable_content_types = [ "video/mp4", ] else: url = photo.remote_original_url acceptable_content_types = [ "image/jpeg", "image/jpg", "image/png", "image/gif", ] filepath = False try: # Saves the file to /tmp/: filepath = filedownloader.download(url, acceptable_content_types) except DownloadException as e: raise FetchError(e) if filepath: # Reopen file and save to the Photo: reopened_file = open(filepath, "rb") django_file = File(reopened_file) if media_type == "video": photo.video_original_file.save(os.path.basename(filepath), django_file) else: photo.original_file.save(os.path.basename(filepath), django_file)

31.466216

87

0.587932

9cb136c60f59a551fd2956a0e41b20f978350ef2

3,994

py

Python

analysis/plotting/scripts/2021415_plot_classification.py

zieglerad/damselfly

d0ee9b13aa5981a91f62765cba6a263b584e7f25

[ "MIT" ]

null

analysis/plotting/scripts/2021415_plot_classification.py

zieglerad/damselfly

d0ee9b13aa5981a91f62765cba6a263b584e7f25

[ "MIT" ]

null

analysis/plotting/scripts/2021415_plot_classification.py

zieglerad/damselfly

d0ee9b13aa5981a91f62765cba6a263b584e7f25

[ "MIT" ]

null

import damselfly as df import numpy as np import pickle as pkl import os import matplotlib.pyplot as plt temp = 10.0 result_date = '210607' result_train_dset = '210607_df1_multiclass_ch3' result_test_dset = '210607_df2_multiclass_test_ch3' result_model = 'df_conv6_fc2_multiclass_3ch' result_domain = 'freq' result_epoch = 54 plot_date = '210607' plot_font_size = 18 #saved_networks = '/home/az396/project/deepfiltering/training/checkpoints' #datasets = '/home/az396/project/deepfiltering/data/datasets' #dataset = f'{checkpoint_domain}/{checkpoint_dset}_temp{temp}.pkl' #checkpoint = f'date{checkpoint_date}_dset_name{checkpoint_dset}_temp{temp}_model{checkpoint_model}_domain_{checkpoint_domain}/epoch{epoch}.pth' confusion_matrix_result_name = f'{result_date}_confusion_matrix_train_dset_{result_train_dset}_test_dset_{result_test_dset}_model_{result_model}_domain_{result_domain}_epoch{result_epoch}.pkl' results = '/home/az396/project/damselfly/analysis/results' plots = '/home/az396/project/damselfly/analysis/plotting/plots' # load the matrices, with open(os.path.join(results, confusion_matrix_result_name), 'rb') as infile: matrices = pkl.load(infile) for s in ['train', 'test']: plot = df.plot.ConfusionMatrix(matrices[s], font_size=plot_font_size) plot_name = f'{plot_date}_{s}_confusion_matrix_train_dset_{result_train_dset}_test_dset_{result_test_dset}_model_{result_model}_domain_{result_domain}_epoch_{result_epoch}.png' plot[1].set_title(f'Confusion Matrix, Set = {s}', size=24) plt.savefig(os.path.join(plots, plot_name)) #tpr_list = [] #fpr_list = [] #eff_mats_train = [] #eff_mats_test = [] #for temp in compare_temps: # model_path = date + '_temp' + str(temp) + model + epoch_str # train_path = os.path.join(top, model_path, 'train_mat.pkl') # test_path = os.path.join(top, model_path, 'test_mat.pkl') # tpr_path = os.path.join(top, model_path, 'tpr.pkl') # fpr_path = os.path.join(top, model_path, 'fpr.pkl') # with open(train_path, 'rb') as infile: # train_matrices.append(pkl.load(infile)) # with open(test_path, 'rb') as infile: # test_matrices.append(pkl.load(infile)) # with open(tpr_path, 'rb') as infile: # tpr_list.append(pkl.load(infile)) # with open(fpr_path, 'rb') as infile: # fpr_list.append(pkl.load(infile)) #### # do the same for the efficiency temps #for temp in efficiency_temps: # train_path = os.path.join(top, date + '_temp' + str(temp) + model + epoch_str, 'train_mat.pkl') # test_path = os.path.join(top, date + '_temp' + str(temp) + model + epoch_str, 'test_mat.pkl') # with open(train_path, 'rb') as infile: # eff_mats_train.append(pkl.load(infile)) # with open(test_path, 'rb') as infile: # eff_mats_test.append(pkl.load(infile)) #### ## plot selected classification matrices #train_name = date + '_compare_train' + model + '.png' #test_name = date + '_compare_test' + model + '.png' #df.plot.ClassificationMatrix(train_matrices, compare_temps, save_path, train_name) #df.plot.ClassificationMatrix(test_matrices, compare_temps, save_path, test_name) #### ## plot true positive rate, false alarms ## #det_eff_name = date + '_det_eff' + model + '.png' #fa_name = date + '_false_alarms' + model + '.png' #df.plot.ClassMatrixObservable(eff_mats_train, eff_mats_test, efficiency_temps, [0, 0], save_path, det_eff_name) #df.plot.ClassMatrixObservable(eff_mats_train, eff_mats_test, efficiency_temps, [1, 0], save_path, fa_name) #### ## ROC curve #roc_name = date + '_roc' + model + '.png' #df.plot.ROC(tpr_list, fpr_list, compare_temps, save_path, roc_name) #info_list = [] #name = date + '_compare_10epoch' + model + '.png' #for temp in compare_temps: # print(temp) # info_path = os.path.join(top, date + '_temp' + str(temp) + model, 'info.pkl') # with open(info_path, 'rb') as infile: # info_list.append(pkl.load(infile)) #save_path = '/home/az396/project/deepfiltering/analysis/plot/training_loss/compare_loss' #df.plot.CompareTrainLoss(info_list, compare_temps, save_path, name)

33.283333

192

0.744367

b5a978eabb5441b009825f47e557887543fed6e0

31,667

py

Python

nodes/test_scripts/test_cuda_inference.py

mrfmap/mrfmap_ros

8c1e108860ff297f39591d97f8f8ce2937b29a51

[ "BSD-3-Clause" ]

6

2020-07-15T21:00:49.000Z

2021-05-12T07:16:38.000Z

nodes/test_scripts/test_cuda_inference.py

mrfmap/mrfmap_ros

8c1e108860ff297f39591d97f8f8ce2937b29a51

[ "BSD-3-Clause" ]

2

2020-08-14T16:16:21.000Z

2020-11-12T07:43:22.000Z

nodes/test_scripts/test_cuda_inference.py

mrfmap/mrfmap_ros

8c1e108860ff297f39591d97f8f8ce2937b29a51

[ "BSD-3-Clause" ]

2

2020-08-16T15:53:14.000Z

2021-05-12T07:16:41.000Z

#!/usr/bin/env python import numpy as np from mrfmap_ros.MRFMapGenerator import MRFMapGenerator from mrfmap_ros.GVDBPyModules import GVDBInference, GVDBImage, gvdb_params, PangolinViewer, KeyframeSelector import pdb from geometry import se3, SE3 import cv2 import glob import yaml dtype=np.float32 from time import time _tstart_stack = [] def tic(): _tstart_stack.append(time()) def toc(fmt="Elapsed: %s s"): print fmt % (time() - _tstart_stack.pop()) show_map = False show_pango_viewer = True vis_likelihoods = True plot_1d = False plot_2d = False # Note: This script is only useful when MRFMap is compiled with DEBUG enabled! if show_map: from mayavi import mlab if __name__ == "__main__": main_dir = '/home/icoderaven/bagfiles/' datasets = ['coffin_world_640'] dataset = datasets[0] config_dir = '../../config/' params_config_file = config_dir+dataset+'.yaml' use_multiplicative_noise = False noise_val = 0.1 noise_str = str(noise_val).replace('.', '_') # hfov = 0.9799151 img_dims = np.array([640, 480]) # img_dims = np.array([320, 240]) use_realsense_noise = False if use_realsense_noise: # Load the polynomials with open(config_dir+'realsense_bias_poly5x.yml') as f: bias_poly = np.array(yaml.load(f)).reshape( img_dims[1]/20, img_dims[0]/20, 3) # Now just repeat to img dims for easy noisy image generation bias_poly = np.repeat(np.repeat(bias_poly, 20, axis=0), 20, axis=1) with open(config_dir+'realsense_stddev_poly5x.yml') as f: stddev_poly = np.array(yaml.load(f)).reshape( img_dims[1]/20, img_dims[0]/20, 3) # Now just repeat to img dims for easy noisy image generation stddev_poly = np.repeat( np.repeat(stddev_poly, 20, axis=0), 20, axis=1) downsample_image = False downsample_times = 1 # path_str = 'data/res_640x480/' # path_str = 'data/res_640_more/' # path_str = 'data/res_640x480_fixed/' # path_str = 'data/airsim_factory/' path_str = main_dir + dataset + '/' # Determine the number of cameras inside this directory if use_multiplicative_noise: file_list = glob.glob(path_str + 'depth_noise_' + noise_str + '/0_depth_*.npy') else: file_list = glob.glob(path_str + 'depth_*.npy') num_images = len(file_list) # num_images = 1 print 'Detected {0} images'.format(num_images) cam_id = 0 map_indices = [] tic() print 'Creating params...' if downsample_image: img_dims = img_dims/(2**downsample_times) if glob.glob(params_config_file) == []: print 'No params_config_file!, bailing' exit mrfmap = MRFMapGenerator( params_config_file, main_dir+dataset+'/generated/', 'mrfmap') mrfmap.inference = GVDBInference(True, False) if show_pango_viewer: pango_viewer = PangolinViewer("MyViewer", mrfmap.inference) print 'Done!' poses = [] depth_imgs = [] gt_depth_imgs = [] image_ptrs = [] # list_of_images = [0,1,2,11,10] list_of_images = range(num_images) num_images = 0 mrfmap.inference.set_selected_x_y(338, 204) for image_index in list_of_images: pose = np.load(path_str+'pose_' + str(image_index) + '.npy') cam_in_body = np.eye(4) cam_in_body[:3, :3] = np.array( [[0, 0, 1], [-1, 0, 0], [0, -1, 0]]) pose_cpp = np.dot(pose, cam_in_body).astype(dtype) origin = pose_cpp[:3, 3] camera_normal = pose_cpp[:3, 2] if use_multiplicative_noise: depth_img = np.load(path_str + 'depth_noise_' + noise_str + '/0_depth_' + str(image_index) + '.npy') else: depth_img = np.load(path_str + 'depth_' + str(image_index) + '.npy') depth_img_cpp = np.array(depth_img) if use_realsense_noise: gt_depth_imgs.append(depth_img_cpp) # Yay! First apply the bias biased_depth_img = depth_img_cpp + \ bias_poly[:, :, 2] * depth_img_cpp**2 + \ bias_poly[:, :, 1]*depth_img_cpp + bias_poly[:, :, 0] # Now get stddev values stddev_depth_img = stddev_poly[:, :, 2] * depth_img_cpp**2 + \ stddev_poly[:, :, 1]*depth_img_cpp + stddev_poly[:, :, 0] # Now sample from stddev values and add to biased depth image generated_img = biased_depth_img + stddev_depth_img * \ np.random.randn(img_dims[1], img_dims[0]) # pdb.set_trace() # Assign this to the depth_img_cpp depth_img_cpp = generated_img.astype(dtype) if downsample_image: for i in range(downsample_times): # Downsample the image # depth_img_cpp = cv2.pyrDown(depth_img_cpp) depth_img_cpp = cv2.resize( depth_img_cpp, (depth_img_cpp.shape[1]/2, depth_img_cpp.shape[0]/2), interpolation=cv2.INTER_NEAREST) if mrfmap.keyframe_selector.is_keyframe(pose_cpp.astype(dtype)): poses.append(pose_cpp) depth_imgs.append(depth_img_cpp) image_ptrs.append(GVDBImage(depth_img_cpp)) mrfmap.add_data(pose_cpp.astype(dtype), image_ptrs[-1]) if show_pango_viewer: pango_viewer.add_keyframe( pose_cpp.astype(dtype), depth_img_cpp) num_images += 1 else: print 'oof, not a keyframe' if show_pango_viewer: pango_viewer.add_frame(pose_cpp.astype(dtype), depth_img_cpp) toc() # mrfmap.save_map() if show_map: mrfmap.show_map() if vis_likelihoods: # VISUALIZE LIKELIHOODS import matplotlib.pyplot as plt from matplotlib.patches import Rectangle from matplotlib.widgets import CheckButtons plt.style.use('seaborn-muted') image = np.zeros(depth_imgs[0].shape, dtype=dtype) mrfmap.inference.get_likelihood_image(cam_id, image) expected_depth_image = np.zeros(depth_imgs[0].shape, dtype=dtype) mrfmap.inference.get_expected_depth_image(cam_id, expected_depth_image) # fig2 = plt.figure(2) # ax_1 = plt.subplot2grid((1, 2), (0, 0), colspan=1) # ax_2 = plt.subplot2grid((1, 2), (0, 1), colspan=1) # ax_1.imshow(depth_imgs[cam_id]) # ax_2.imshow(expected_depth_image) # plt.show() img = np.exp(image) fig = plt.figure(0) ax_like_img = plt.subplot2grid((2, 2), (0, 0), colspan=1) ax_depth_img = plt.subplot2grid((2, 2), (0, 1), colspan=1) ax_like_imshow_handle = ax_like_img.imshow(img, picker=True) ax_depth_imshow_handle = ax_depth_img.imshow( depth_imgs[cam_id], picker=True) ax_data = plt.subplot2grid((2, 2), (1, 0), colspan=2, picker=True) rax = plt.axes([0.85, 0.6, 0.2, 0.4]) labels_right = [ ['ray_mu_d_i', False], ['last_outgoing', False], ['pos_log_msg_sum', False], ['neg_norm', True], ['pos_msg', True], ['length', False], ['pos_iso_msg', False], ['prod_mu_o_0s', False] ] right_check = CheckButtons(rax, tuple( [label[0] for label in labels_right]), tuple([label[1] for label in labels_right])) ray_idx = [None] voxel_ids = [None] node_ids = [None] selected_node = None brick_ids = [None] z_distances = [None] click_mode = None def onclick(event): global voxel_ids global z_distances global node_ids global brick_ids global selected_node global atlas_ids global click_mode indx_x = (event.xdata + 0.5).astype(np.int) indx_y = (event.ydata + 0.5).astype(np.int) print indx_x, indx_y # print b_is, z_given_b_is if event.inaxes is ax_data: # We're in the data plot, clicking should open up the window that # pushes out messages being sent by the rays data = ax_data.get_lines()[0].get_data() ind = np.searchsorted(data[0], event.xdata) if click_mode == 1: vox_id = voxel_ids[ind] elif click_mode == 3: vox_id = atlas_ids[ind] print 'Index is '+str(ind) + ' voxel id is ' + \ str(vox_id) + ' dist is '+str(data[0][ind]) mrfmap.inference.set_selected_voxel(vox_id) selected_node = [node_ids[ind], brick_ids[:, ind], vox_id] # Perform mrfmap.inference again to print output? mrfmap.inference.perform_inference_dryrun() # Also show the line for selected voxel ax_data.vlines(z_distances[ind], 0, 1.0, linestyles='dotted', alpha=0.8) elif event.inaxes is ax_like_img: # Set the x and y mrfmap.inference.set_selected_x_y(indx_x, indx_y) # We should also reset selected node mrfmap.inference.set_selected_voxel(0) # Call the diagnostic method diag_image = np.zeros( depth_imgs[0].shape, dtype=dtype).flatten() if event.button == 1: click_mode = 1 mrfmap.inference.get_diagnostic_image( cam_id, diag_image, 0) # Extract data from image # Get num_voxels from last entry stride = 10 num_voxels = diag_image[-1].astype(np.int) z_distances = diag_image[:stride*num_voxels:stride] alphas = diag_image[1:stride*num_voxels:stride] w_is = diag_image[4:stride*num_voxels:stride] vis_is = diag_image[5:stride*num_voxels:stride] voxel_ids = np.frombuffer(diag_image, dtype=np.uint64)[ 1:num_voxels*(stride/2):(stride/2)] node_ids = np.frombuffer(diag_image, dtype=np.uint64)[ 3:num_voxels*(stride/2):(stride/2)] brick_ids_x = np.frombuffer(diag_image, dtype=np.uint8)[ 8*4:stride*4*num_voxels:stride*4] brick_ids_y = np.frombuffer(diag_image, dtype=np.uint8)[ 8*4+1:stride*4*num_voxels:stride*4] brick_ids_z = np.frombuffer(diag_image, dtype=np.uint8)[ 8*4+2:stride*4*num_voxels:stride*4] brick_ids = np.vstack( (brick_ids_x, brick_ids_y, brick_ids_z)) # Plot corresponding data ax_data.clear() ax_data.plot(z_distances, alphas, label='alphas', marker='.') ax_data.plot(z_distances, w_is, label='w_is', marker='.') ax_data.plot(z_distances, vis_is, label='vis_is', marker='.') print 'Sum of w_is is '+str(np.sum(w_is)) elif event.button == 3: click_mode = 3 mrfmap.inference.get_diagnostic_image( cam_id, diag_image, 1) # Extract data from image # Get num_voxels from last entry stride = 12 num_voxels = diag_image[-1].astype(np.int) z_distances = diag_image[:stride*num_voxels:stride] ray_mu_d_i = diag_image[1:stride*num_voxels:stride] last_outgoing = diag_image[2:stride*num_voxels:stride] pos_log_msg_sum = diag_image[3:stride*num_voxels:stride] neg_norm = diag_image[4:stride*num_voxels:stride] pos_msg = diag_image[5:stride*num_voxels:stride] length = diag_image[6:stride*num_voxels:stride] pos_iso_msg = diag_image[7:stride*num_voxels:stride] atlas_ids = np.frombuffer(diag_image, dtype=np.uint64)[ 4:num_voxels*(stride/2):(stride/2)] prod_mu_o_0s = diag_image[10:stride*num_voxels:stride] dict_labels = {'ray_mu_d_i': ray_mu_d_i, 'last_outgoing': last_outgoing, 'pos_log_msg_sum': pos_log_msg_sum, 'neg_norm': neg_norm, 'pos_msg': pos_msg, 'length': length, 'pos_iso_msg': pos_iso_msg, 'prod_mu_o_0s': prod_mu_o_0s} # Plot corresponding data ax_data.clear() for label in labels_right: if label[1]: ax_data.plot(z_distances, dict_labels[label[0]], label=label[0], marker='.') # Draw circle on image for obj in ax_like_img.findobj(match=type(plt.Circle((1, 1)))): obj.remove() circle = plt.Circle( (indx_x, indx_y), 1, color='red', fill=False) ax_like_img.add_artist(circle) d_meas = depth_imgs[cam_id][indx_y, indx_x] print 'Expected depth here is ' + \ str(expected_depth_image[indx_y, indx_x] ) + ' actual is '+str(d_meas) if np.isnan(d_meas): d_meas = 10.0 xlims = ax_data.get_xlim() ylims = ax_data.get_ylim() ax_data.vlines(d_meas, 0, 1.0, 'blue', linestyles='dashed', alpha=0.8) ax_data.annotate(s='$d_{meas}$', xy=(( (d_meas-xlims[0])/(xlims[1]-xlims[0])), 1.01), xycoords='axes fraction', verticalalignment='right', horizontalalignment='right bottom', rotation=0) if use_realsense_noise: gt_depth = gt_depth_imgs[cam_id][indx_y, indx_x] gt_plus_bias = d_meas + \ np.polynomial.polynomial.polyval( gt_depth, bias_poly[indx_y, indx_x]) ax_data.vlines(gt_plus_bias, 0, 1.0, linestyles='-.', alpha=0.8) ax_data.annotate(s='$d_{bias}$', # = '+('%.2f' % d_meas) +'$', xy=(( (gt_plus_bias-xlims[0])/(xlims[1]-xlims[0])), 1.01), xycoords='axes fraction', verticalalignment='right', horizontalalignment='right bottom', rotation=0) ax_data.vlines(gt_depth, 0, 1.0, 'red', linestyles=':', alpha=0.8) ax_data.annotate(s='$d_{gt}$', # = '+('%.2f' % d_meas) +'$', xy=(( (gt_depth-xlims[0])/(xlims[1]-xlims[0])), 1.01), xycoords='axes fraction', verticalalignment='right', horizontalalignment='right bottom', rotation=0) # Get the corresponding sigma delta = np.polynomial.polynomial.polyval( gt_depth, stddev_poly[indx_y, indx_x]) ax_data.add_patch( Rectangle((gt_depth - delta, 0.0), 2*delta, ylims[1], alpha=0.2, ec='black', ls=':')) ax_data.grid(b=True, which='major', color='k', linestyle=':', alpha=0.8) ax_data.grid(b=True, which='minor', axis='x', color='k', linestyle=':', alpha=0.2) ax_data.minorticks_on() extraticks = [d_meas] ax_data.set_xticks(list(ax_data.get_xticks())[ 1:-1] + extraticks) ax_data.set_xlabel('Cell Depth $||d_i||$') ax_data.legend() # Should also get the messages being sent along this ray fig.canvas.draw() def onpick(event): line = event.artist if not type(line) == type(plt.imshow(np.zeros((1, 1)))): xdata, ydata = line.get_data() ind = event.ind print 'Index is '+str(ind) # Get nearest voxel id if ind is not None: # Get corresponding voxel id ray_indices = VecUInt([]) mrfmap.inference.get_indices_along_ray( 0, ray_idx[0], ray_indices) vox_idx = np.array(ray_indices)[ind[0]] viewer.plot_rays(vox_idx, mrfmap.inference) fig.canvas.draw() def right_check_callback(label): if label in [l[0] for l in labels_right]: idx = [l[0] for l in labels_right].index(label) labels_right[idx][1] = not(labels_right[idx][1]) voxel_cube = [None for i in range(24)] def on_key(event): global cam_id global voxel_cube id_changed = False if event.key == 'n': cam_id = cam_id + 1 if cam_id < num_images - 1 else 0 id_changed = True elif event.key == 'p': cam_id = cam_id - 1 if cam_id > 0 else num_images - 1 id_changed = True elif event.key == 'i': # Perform 1 pass of inference mrfmap.inference.perform_inference() id_changed = True if id_changed: print 'Set cam_id to '+str(cam_id) # Update likelihood image displayed image = np.zeros(depth_imgs[0].shape, dtype=dtype) mrfmap.inference.get_likelihood_image(cam_id, image) img = np.exp(image) ax_like_imshow_handle.set_data(img) ax_depth_imshow_handle.set_data(depth_imgs[cam_id]) if selected_node is not None: # Display this selected voxel? vox_coords = mrfmap.inference.get_voxel_coords( [selected_node[0]]).flatten() brick_id = selected_node[1] # TODO: move these coords by brick index render_choices = ['brick', 'voxel'] edges = [ # Back (0, 1), (1, 2), (2, 3), (3, 0), # Front (5, 4), (4, 7), (7, 6), (6, 5), # Front-to-back (0, 4), (1, 5), (2, 6), (3, 7)] i = 0 for render_choice in render_choices: if render_choice is 'brick': vmin = vox_coords[:3] side = vox_coords[3] - vox_coords[0] elif render_choice is 'voxel': side = 1 vmin = vox_coords[:3] + brick_id vertices = np.array([ vmin + [0, side, 0], vmin + [side, side, 0], vmin + [side, 0, 0], vmin + [0, 0, 0], vmin + [0, side, side], vmin + [side, side, side], vmin + [side, 0, side], vmin + [0, 0, side]]) centered_vertices = vertices - \ np.array([[width], [breadth], [height]] ).transpose()/(2.0*resolution) scaled_vertices = centered_vertices * resolution homogenized_vertices = np.hstack( (scaled_vertices, np.ones((8, 1)))) transformed_vertices = np.dot(np.linalg.inv( poses[cam_id]), homogenized_vertices.transpose()) pts = np.dot(K, transformed_vertices[:3, :]) pts = pts[:]/pts[2, :] pts = pts.transpose() for e in edges: if voxel_cube[i] is not None: ax_depth_img.lines.remove(voxel_cube[i]) if render_choice is 'brick': style = 'r-' line_width = 0.4 else: style = 'r-' line_width = 0.9 voxel_cube[i], = ax_depth_img.plot( (pts[e[0]][0], pts[e[1]][0]), (pts[e[0]][1], pts[e[1]][1]), style, linewidth=line_width) i += 1 # Change the event clicked location to the mean of the projected vertices # mean = pts.mean(axis=0)[:2] # Find the projected center of the voxel mean = (pts[3] + pts[5])/2.0 print 'mean of points is '+str(mean) event.xdata = mean[0] event.ydata = mean[1] event.button = 3 event.inaxes = ax_like_img if 0 <= event.xdata < depth_img.shape[1] and 0 <= event.ydata < depth_img.shape[0]: onclick(event) fig.canvas.draw() cid = fig.canvas.mpl_connect('button_press_event', onclick) rbid = right_check.on_clicked(right_check_callback) kid = fig.canvas.mpl_connect('key_press_event', on_key) plt.show() if plot_1d: # PLOT 1D LIKELIHOOD PLOTS # Now test likelihoods of perturbed camera poses import matplotlib.pyplot as plt num_poses = 11 deltas = np.linspace(-0.1, 0.1, num_poses) labels = ['rx', 'ry', 'rz', 'x', 'y', 'z'] np.set_printoptions(precision=3, suppress=True) axes_to_plot = range(6) for i in [cam_id]: # range(num_images): pose_cpp = poses[i] depth_img_cpp = depth_imgs[i] sampled_poses = [] ray_likelihoods = [[]]*6 for j in axes_to_plot: print '!!Evaluating for axis '+labels[j] likes = [] for k in range(num_poses): print "Axis::{0} Pose::{1}".format(j, k) delta_vector = np.zeros(6) delta_vector[j] = deltas[k] T = SE3.group_from_algebra( se3.algebra_from_vector(delta_vector)) pose = np.dot(pose_cpp, T) sampled_poses.append(pose) # print 'pose is '+str(pose) mrfmap.inference.set_pose(i, pose.astype(dtype)) # pdb.set_trace() sum = mrfmap.inference.compute_likelihood(i) likes.append(sum) ray_likelihoods[j] = np.array(likes) # viewer.visualize_poses(sampled_poses, resolution, scaled_dims) # viewer.show() f, ax = plt.subplots(6, 1) handles = [] for j in axes_to_plot: handle = ax[j].plot(deltas, ray_likelihoods[j]) handles.append(handle[0]) f.legend(handles, labels, 'upper right') plt.show() # Compute likelihood of 2D locations with known yaw for a particular camera # create a square grid around ground truth location # import matplotlib.pyplot as plt # num_images = 2 # f, ax = plt.subplots(1, num_images) # grid_dim = 5 # eval_dim = 3 # cell_res = 0.05 # grid = np.mgrid[0:grid_dim:1, 0:grid_dim:1].reshape(2, -1).T # # grid = zip(range(grid_dim), range(grid_dim)) # for cam_id in range(num_images): # grid_likes = np.zeros((grid_dim*eval_dim, grid_dim*eval_dim)) # for item in grid: # start = cell_res * \ # np.array([item[0] - grid_dim/2, item[1] - grid_dim/2]) # end = start + cell_res # xys = np.mgrid[start[0]:end[0]: (end[0] - start[0])/eval_dim, # start[1]:end[1]: (end[1] - start[1])/eval_dim].reshape(2, -1).T # vectors = np.hstack((xys, np.zeros((xys.shape[0], 4)))) # pose_cpp = poses[cam_id] # depth_img_cpp = depth_imgs[cam_id] # for delta_vector in vectors: # T = SE3.group_from_algebra(se3.algebra_from_vector(delta_vector)) # pose = np.dot(pose_cpp, T) # # pdb.set_trace() # mrfmap.inference.add_likelihood_camera(pose.astype(dtype), hfov, # depth_img_cpp.astype(dtype)) # likes = VecFloat([]) # mrfmap.inference.compute_ray_likelihoods(likes) # grid_likes[item[0]*eval_dim:(item[0]+1)*eval_dim, # item[1]*eval_dim:(item[1]+1)*eval_dim] = np.array(likes).reshape(eval_dim, eval_dim) # ax[cam_id].imshow(grid_likes, interpolation='none', # extent=[-(grid_dim/2 - 0.5 / eval_dim)*cell_res, (grid_dim/2+0.5 / eval_dim)*cell_res, # (grid_dim/2+0.5 / eval_dim)*cell_res, -(grid_dim/2 - 0.5 / eval_dim)*cell_res]) # plt.show() if plot_2d: # Compute likelihood of 2D locations with known yaw for a particular camera # create a square grid around ground truth location # import matplotlib.pyplot as plt f, ax = plt.subplots(1, 2) axes_to_compare = [3, 5] # first axis on x axis, second on y grid_dim = 4 eval_dim = 5 grid_res = 0.1 cell_res = grid_res/eval_dim grid = np.mgrid[0:grid_dim:1, 0:grid_dim:1].reshape(2, -1).T grid_likes = np.zeros((grid_dim*eval_dim, grid_dim*eval_dim)) eval_poses = [[None for _ in range(grid_dim*eval_dim)] for _ in range(grid_dim*eval_dim)] for item in grid: print item start = grid_res * \ np.array([item[0] - grid_dim/2, item[1] - grid_dim/2]) end = start + grid_res xys = np.mgrid[start[0]:end[0]: (end[0] - start[0])/eval_dim, start[1]:end[1]: (end[1] - start[1])/eval_dim].reshape(2, -1).T vectors = np.zeros((xys.shape[0], 6)) vectors[:, axes_to_compare[0]] = xys[:, 1] vectors[:, axes_to_compare[1]] = xys[:, 0] pose_cpp = poses[cam_id] depth_img_cpp = depth_imgs[cam_id] likes = [] for i, delta_vector in enumerate(vectors): T = SE3.group_from_algebra( se3.algebra_from_vector(delta_vector)) pose = np.dot(pose_cpp, T) # pdb.set_trace() eval_poses[item[0]*eval_dim + int(i/eval_dim)][item[1]*eval_dim + i % eval_dim] = pose mrfmap.inference.set_pose(cam_id, pose.astype(dtype)) # pdb.set_trace() sum = mrfmap.inference.compute_likelihood(cam_id) likes.append(sum) grid_likes[item[0]*eval_dim:(item[0]+1)*eval_dim, item[1]*eval_dim:(item[1]+1)*eval_dim] = np.array(likes).reshape(eval_dim, eval_dim) ax[0].imshow(grid_likes, interpolation='none', picker=True) nx = grid_likes.shape[1] ny = grid_likes.shape[0] no_labels = grid_dim+1 # how many labels to see on axis x step_x = int(nx / (no_labels - 1)) # step between consecutive labels step_y = int(ny / (no_labels - 1)) # step between consecutive labels # pixel count at label position x_positions = np.arange(0, nx+1, step_x) # pixel count at label position y_positions = np.arange(0, ny+1, step_y) x_labels = np.linspace(-nx*cell_res/2.0, nx*cell_res/2.0, nx+1)[::step_x] # labels you want to see y_labels = np.linspace(-ny*cell_res/2.0, ny*cell_res/2.0, ny+1)[::step_y] # labels you want to see ax[0].set_xticks(x_positions) ax[0].set_xticklabels(x_labels.tolist()) ax[0].set_yticks(y_positions) ax[0].set_yticklabels(y_labels.tolist()) ax[0].set_xlabel('Displacement in '+labels[axes_to_compare[0]]) ax[0].set_ylabel('Displacement in '+labels[axes_to_compare[1]]) rect = None imshow_img = None def onclick_eval(event): global rect global imshow_img global cam_id print event.xdata, event.ydata x = (event.xdata + 0.5).astype(np.int) y = (event.ydata + 0.5).astype(np.int) print x, y if event.inaxes is ax[1]: # We're in the image plot, don't do anything pass else: # Show the likelihood image corresponding to this pixel coord mrfmap.inference.set_pose(cam_id, eval_poses[y][x]) print 'Pose is ' print eval_poses[y][x] img = np.zeros(depth_imgs[0].shape, dtype=dtype) mrfmap.inference.get_likelihood_image(cam_id, img) img = np.exp(img) if imshow_img is None: imshow_img = ax[1].imshow( img, picker=True, cmap='brg') plt.colorbar(imshow_img) else: imshow_img.set_data(img) # Maybe also highlight the specific pixel? if rect is not None: rect.remove() rect = plt.Rectangle((x-0.5, y-0.5), 1, 1, edgecolor='red', facecolor='none') ax[0].add_artist(rect) # Should also get the messages being sent along this ray f.canvas.draw() cid = f.canvas.mpl_connect('button_press_event', onclick_eval) plt.show() # from mayavi import mlab # plot_surf = mlab.surf(grid_likes, warp_scale='auto') # mlab.outline(plot_surf) # mlab.axes(plot_surf, xlabel='x', ylabel='y') # mlab.show()

43.379452

121

0.504437

54045de1587eb5b5b1867e9bc46b52c0dd0c9875

1,166

py

Python

igibson/objects/cube.py

suresh-guttikonda/iGibson

a69e623058180146466cd52d4bb3c00d1facdacf

[ "MIT" ]

360

2020-04-02T11:12:09.000Z

2022-03-24T21:46:58.000Z

igibson/objects/cube.py

suresh-guttikonda/iGibson

a69e623058180146466cd52d4bb3c00d1facdacf

[ "MIT" ]

169

2020-04-07T21:01:05.000Z

2022-03-31T10:07:39.000Z

igibson/objects/cube.py

suresh-guttikonda/iGibson

a69e623058180146466cd52d4bb3c00d1facdacf

[ "MIT" ]

94

2020-04-09T23:22:17.000Z

2022-03-17T21:49:03.000Z

import pybullet as p from igibson.objects.stateful_object import StatefulObject class Cube(StatefulObject): """ Cube shape primitive """ def __init__(self, pos=[1, 2, 3], dim=[1, 2, 3], visual_only=False, mass=1000, color=[1, 1, 1, 1]): super(Cube, self).__init__() self.basePos = pos self.dimension = dim self.visual_only = visual_only self.mass = mass self.color = color def _load(self): """ Load the object into pybullet """ baseOrientation = [0, 0, 0, 1] colBoxId = p.createCollisionShape(p.GEOM_BOX, halfExtents=self.dimension) visualShapeId = p.createVisualShape(p.GEOM_BOX, halfExtents=self.dimension, rgbaColor=self.color) if self.visual_only: body_id = p.createMultiBody(baseCollisionShapeIndex=-1, baseVisualShapeIndex=visualShapeId) else: body_id = p.createMultiBody( baseMass=self.mass, baseCollisionShapeIndex=colBoxId, baseVisualShapeIndex=visualShapeId ) p.resetBasePositionAndOrientation(body_id, self.basePos, baseOrientation) return body_id

32.388889

105

0.64837

fc9774b93a9b40cd18b682819d2669ab743b8e89

732,002

py

Python

template_container_mouse/labels/slice_135.py

lkondratova/Brainplot

3c8a88c1995dedeaa5cbd88ee71499c7cf9c571d

[ "MIT" ]

null

template_container_mouse/labels/slice_135.py

lkondratova/Brainplot

3c8a88c1995dedeaa5cbd88ee71499c7cf9c571d

[ "MIT" ]

null

template_container_mouse/labels/slice_135.py

lkondratova/Brainplot

3c8a88c1995dedeaa5cbd88ee71499c7cf9c571d

[ "MIT" ]

null

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854.144691

865

0.496094

9f4d7f91aa95c8b9f2d98da668794b631b28bace

2,024

py

Python

examples/dfp/v201511/base_rate_service/create_product_template_base_rates.py

wbrp/googleads-python-lib

c0f8ce6c4acfe88ce8f913a4f0e0e92b548e1022

[ "Apache-2.0" ]

1

2020-05-23T11:32:32.000Z

examples/dfp/v201511/base_rate_service/create_product_template_base_rates.py

wbrp/googleads-python-lib

c0f8ce6c4acfe88ce8f913a4f0e0e92b548e1022

[ "Apache-2.0" ]

null

examples/dfp/v201511/base_rate_service/create_product_template_base_rates.py

wbrp/googleads-python-lib

c0f8ce6c4acfe88ce8f913a4f0e0e92b548e1022

[ "Apache-2.0" ]

2

2018-04-20T02:16:33.000Z

2020-11-12T20:58:54.000Z

#!/usr/bin/python # # Copyright 2015 Google Inc. 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. """This example creates a product template base rate. To determine which base rates exist, run get_all_base_rates.py. """ # Import appropriate modules from the client library. from googleads import dfp PRODUCT_TEMPLATE_ID = 'INSERT_PRODUCT_TEMPLATE_ID_HERE' RATE_CARD_ID = 'INSERT_RATE_CARD_ID_HERE' def main(client, product_template_id, rate_card_id): # Initialize appropriate service. base_rate_service = client.GetService( 'BaseRateService', version='v201511') # Create a product template base rate. product_template_base_rate = { 'xsi_type': 'ProductTemplateBaseRate', 'rateCardId': rate_card_id, 'productTemplateId': product_template_id, # Set the rate to be $2. 'rate': { 'currencyCode': 'USD', 'microAmount': 2000000 } } # Create base rates on the server. base_rates = base_rate_service.createBaseRates( [product_template_base_rate]) if base_rates: for base_rate in base_rates: print ('A product template base rate with ID \'%s\' and rate \'%.2f\' %s' ' was created.' % (base_rate['id'], base_rate['rate']['microAmount'], base_rate['rate']['currencyCode'])) if __name__ == '__main__': # Initialize client object. dfp_client = dfp.DfpClient.LoadFromStorage() main(dfp_client, PRODUCT_TEMPLATE_ID, RATE_CARD_ID)

31.625

79

0.698123

d24694ac380d147d06f6628311905a84a58f547e

548

py

Python

plotly/validators/sankey/stream/_maxpoints.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/sankey/stream/_maxpoints.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/sankey/stream/_maxpoints.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

import _plotly_utils.basevalidators class MaxpointsValidator(_plotly_utils.basevalidators.NumberValidator): def __init__( self, plotly_name='maxpoints', parent_name='sankey.stream', **kwargs ): super(MaxpointsValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop('edit_type', 'calc'), max=kwargs.pop('max', 10000), min=kwargs.pop('min', 0), role=kwargs.pop('role', 'info'), **kwargs )

30.444444

76

0.611314

b654e636bfbf7fe9134a3251cd1c758d08272587

4,241

py

Python

irqtest.py

danicampora/Micropython-scheduler

1252b057a8fa6396e0b1ed9e5610c80a4dfa02dd

[ "MIT" ]

1

2022-03-30T16:13:13.000Z

irqtest.py

danicampora/Micropython-scheduler

1252b057a8fa6396e0b1ed9e5610c80a4dfa02dd

[ "MIT" ]

null

irqtest.py

danicampora/Micropython-scheduler

1252b057a8fa6396e0b1ed9e5610c80a4dfa02dd

[ "MIT" ]

2

2020-07-21T16:01:16.000Z

2020-08-14T10:18:04.000Z

# irqtest.py Demonstrates, with minimal hardware, the use of the scheduler in responding to pin interrupts # Author: Peter Hinch # V1.02 6th Sep 2014 # Also demonstrates the debounced switch library (which is threaded) # A thread sets up initial conditions and blocks pending an interrupt. When it occurs the following things happen # 1. The scheduler's default callback function runs recording the fact that the event has happened. # 2. The user's callback runs. This is still in the interrupt handler's context: Micropython rules apply # 3. (Later) the thread wakes up. It receives a count of the number of interrupts which have occurred while it # was blocked. # This demo runs one thread as an oscillator pulsing (via a link) an input pin. The callback function toggles # an lED and pulses an output pin. The blocking thread toggles another LED and prints a message. # The optional pushbuttons print a message when operated. import pyb from usched import Sched, Poller, Timeout, Pinblock, wait from switch import Switch # Library supporting debounced switches # HARDWARE # MicroPython board with pin X7 linked to pin X8 # Optionally provide two pushbuttons wired to be capable of grounding X5 and X6 respectively # The interrupt handler pulses pin Y10 to enable timings to be measured, notably minimum pulse duration # and latency # THREADS: def stop(fTim, objSch): # Stop the scheduler after fTim seconds yield from wait(fTim) objSch.stop() def oscillator(freq_hz = 1): # Toggles X7 forever. outpin = pyb.Pin(pyb.Pin.board.X7, pyb.Pin.OUT_PP) # Push pull output pin on X7 wf = Timeout(1/(2*freq_hz)) while True: outpin.low() yield wf() # Duration will be imprecise owing to contention outpin.high() yield wf() class Irq_handler(object): # Using an object to demonstrate communication between def __init__(self, lstLed, testpin): # the interrupt handler and its thread self.lstLed = lstLed self.testpin = testpin def callback(self, irqno): # BEWARE: runs in interrupt's context. MicroPython rules apply self.testpin.high() # along with normal concurrency caveats self.testpin.low() # Pulse of 6.8uS on Y10 self.lstLed[1].toggle() def irqtest_thread(): # Thread blocks on an interrupt. lstLeds = [pyb.LED(x) for x in range(1,5)] # Initialise all four on board LED's testpin = pyb.Pin(pyb.Pin.board.Y10, pyb.Pin.OUT_PP) # Pin Y10 pulsed when handler runs mypin = pyb.Pin.board.X8 # X8 used for interrupt request han = Irq_handler(lstLeds, testpin) wf = Pinblock(mypin, pyb.ExtInt.IRQ_FALLING, pyb.Pin.PULL_NONE, han.callback) # Blocking interrupt handler count = 0 while True: result = (yield wf()) # Wait for the interrupt lstLeds[0].toggle() # Toggle LED print("Interrupt recieved ", result) def x5print(*args): print("X5 released " +args[0]) # Demo of argument passing def x6print(*args): print("X6 pressed " + args[0]) # USER TEST PROGRAM # Runs forever unless you pass a number of seconds def test(duration = 0): # Runs oscillator, counts interrupts, responds to switches if duration: print("Test interrupt on pin X8 for {:3d} seconds".format(duration)) objSched = Sched() # Requires jumper between pins X7 and X8 objSched.add_thread(oscillator(1)) # 1Hz objSched.add_thread(irqtest_thread()) Switch(objSched, 'X5', open_func = x5print, open_func_args = ("Red",)) # X5 triggers on open Switch(objSched, 'X6', x6print, ("Yellow",)) # X6 triggers on close if duration: objSched.add_thread(stop(duration, objSched)) objSched.run() test(30)

49.313953

122

0.621316

940a751dc4cdad41bab1f51e8fb550e200b3520d

2,858

py

Python

photoblog_root/settings.py

markableidinger/one-page-photoblog

5fa0b37a7e6b42fdf356f1d23b262be1fe3bc729

[ "MIT" ]

null

photoblog_root/settings.py

markableidinger/one-page-photoblog

5fa0b37a7e6b42fdf356f1d23b262be1fe3bc729

[ "MIT" ]

null

photoblog_root/settings.py

markableidinger/one-page-photoblog

5fa0b37a7e6b42fdf356f1d23b262be1fe3bc729

[ "MIT" ]

null

"""Settings file for single page photoblog.""" import os import json BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) CREDENTIALS = json.load(open(os.path.join(BASE_DIR, 'secrets.json'), 'r')) SECRET_KEY = CREDENTIALS['secret_key'] # Necessary stuff for django storages with s3 AWS_ACCESS_KEY_ID = CREDENTIALS['aws_access_key'] AWS_SECRET_ACCESS_KEY = CREDENTIALS['aws_secret_key'] AWS_STORAGE_BUCKET_NAME = CREDENTIALS['s3_bucket_name'] DEFAULT_FILE_STORAGE = 'storages.backends.s3boto.S3BotoStorage' DEBUG = True ALLOWED_HOSTS = [] INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'storages', 'rest_framework', 'entries', 'corsheaders', ] MIDDLEWARE_CLASSES = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'photoblog_root.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'photoblog_root.wsgi.application' DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': CREDENTIALS['db_name'], 'USER': CREDENTIALS['db_user'], 'PASSWORD': CREDENTIALS['db_password'], 'HOST': CREDENTIALS['db_host'], 'PORT': CREDENTIALS['db_port'], } } AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True STATIC_URL = '/static/' STATIC_ROOT = 'staticfiles' CORS_ORIGIN_ALLOW_ALL = True

26.462963

91

0.686494

4f95db10a5d228539842f58445a4f3fb08ac10d0

3,764

py

Python

qualitycc.py

rootstrap/NWPU-Crowd-Sample-Code

fe8e986d23e88f7b9defabbd0b3f6f655803152d

[ "MIT" ]

132

2020-01-11T06:04:43.000Z

2022-03-31T02:51:28.000Z

qualitycc.py

rootstrap/NWPU-Crowd-Sample-Code

fe8e986d23e88f7b9defabbd0b3f6f655803152d

[ "MIT" ]

25

2020-01-14T07:32:48.000Z

2021-11-16T07:18:48.000Z

qualitycc.py

rootstrap/NWPU-Crowd-Sample-Code

fe8e986d23e88f7b9defabbd0b3f6f655803152d

[ "MIT" ]

34

2020-01-13T02:16:23.000Z

2022-03-25T15:08:27.000Z

# -*- coding: utf-8 -*- # import torch # import torch.nn.functional as F # from torch.autograd import Variable # import numpy as np # from math import exp # import math # def gaussian(window_size, sigma): # gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)]) # return gauss/gauss.sum() # def create_window(window_size, channel): # _1D_window = gaussian(window_size, 1.5).unsqueeze(1) # _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) # window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) # return window # def _ssim(img1, img2, window, window_size, channel, L=1): # mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel) # mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel) # mu1_sq = mu1.pow(2) # mu2_sq = mu2.pow(2) # mu1_mu2 = mu1*mu2 # sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq # sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq # sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2 # C1 = 0.01 ** 2 # C2 = 0.03 ** 2 # ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2)) # return ssim_map.mean() # def calc_ssim(gtmap, pdmap): # window = create_window(11, 1) # if gtmap.is_cuda: # window = window.cuda(gtmap.get_device()) # window = window.type_as(gtmap) # return _ssim(gtmap, pdmap, window, 11, 1) # def calc_psnr(gtmap, pdmap): # #print(gtmap.sum(), pdmap.sum()) # gtmax = gtmap.max() # mse = torch.sqrt(torch.mean((gtmap - pdmap) ** 2)) # if gtmax == 0 or mse == 0: # return "NaN" # psnr = 20 * torch.log10(gtmax / mse) # print(mse.item(), gtmax.item(), psnr.item()) # return psnr import numpy from scipy.ndimage import gaussian_filter from numpy.lib.stride_tricks import as_strided as ast import numpy as np import math """ Hat tip: http://stackoverflow.com/a/5078155/1828289 """ def block_view(A, block=(3, 3)): """Provide a 2D block view to 2D array. No error checking made. Therefore meaningful (as implemented) only for blocks strictly compatible with the shape of A.""" # simple shape and strides computations may seem at first strange # unless one is able to recognize the 'tuple additions' involved ;-) shape = (A.shape[0] // block[0], A.shape[1] // block[1])+ block strides = (block[0]* A.strides[0], block[1]* A.strides[1])+ A.strides return ast(A, shape= shape, strides= strides) def calc_ssim(img1, img2, C1=0.01**2, C2=0.03**2): bimg1 = block_view(img1, (11,11)) bimg2 = block_view(img2, (11,11)) s1 = numpy.sum(bimg1, (-1, -2)) s2 = numpy.sum(bimg2, (-1, -2)) ss = numpy.sum(bimg1*bimg1, (-1, -2)) + numpy.sum(bimg2*bimg2, (-1, -2)) s12 = numpy.sum(bimg1*bimg2, (-1, -2)) vari = ss - s1*s1 - s2*s2 covar = s12 - s1*s2 ssim_map = (2*s1*s2 + C1) * (2*covar + C2) / ((s1*s1 + s2*s2 + C1) * (vari + C2)) return numpy.mean(ssim_map) # def psnr(img1, img2): # mse = numpy.mean( (img1 - img2) ** 2 ) # if mse == 0: # return 100 # PIXEL_MAX = 255.0 # return 20 * math.log10(PIXEL_MAX / math.sqrt(mse)) def calc_psnr(img1, img2): ''' if np.max(img1) != 0: img1 = img1/np.max(img1)*255 if np.max(img2) != 0: img2 = img2/np.max(img2)*255 ''' mse = np.mean( (img1 - img2) ** 2 ) img1max = np.max(img1) if mse == 0 or img1max == 0: return "NaN" return 20 * math.log10(np.max(img1) / math.sqrt(mse))

33.017544

106

0.615569

fcd1003e70dd30ec908c5366c16bd9f20d4b2aea

189

py

Python

src/accounts/admin.py

RobetSlovev39/AutoGraph

c8bdb358b95143ab0d8c6f7c475a6c21f7a76b95

[ "MIT" ]

null

src/accounts/admin.py

RobetSlovev39/AutoGraph

c8bdb358b95143ab0d8c6f7c475a6c21f7a76b95

[ "MIT" ]

null

src/accounts/admin.py

RobetSlovev39/AutoGraph

c8bdb358b95143ab0d8c6f7c475a6c21f7a76b95

[ "MIT" ]

null

from .models import User from django.contrib import admin from django.contrib.auth.admin import UserAdmin as BaseUserAdmin @admin.register(User) class UserAdmin(BaseUserAdmin): pass

18.9

64

0.804233

b1720a84d6267182a63fbe5bfb9729da808d4c25

867

py

Python

src/pretalx/submission/migrations/0040_submission_created_data.py

xhub/pretalx

33bd07ec98ddeb5b7ff35fe7e30c4d38bef57d7e

[ "Apache-2.0" ]

null

src/pretalx/submission/migrations/0040_submission_created_data.py

xhub/pretalx

33bd07ec98ddeb5b7ff35fe7e30c4d38bef57d7e

[ "Apache-2.0" ]

1

2019-07-05T20:03:42.000Z

src/pretalx/submission/migrations/0040_submission_created_data.py

xhub/pretalx

33bd07ec98ddeb5b7ff35fe7e30c4d38bef57d7e

[ "Apache-2.0" ]

null

# Generated by Django 2.2.1 on 2019-05-01 20:36 from django.db import migrations from django_scopes import scopes_disabled def fill_created(apps, schema_editor): with scopes_disabled(): Submission = apps.get_model('submission', 'Submission') for submission_id in Submission.objects.all().values_list('pk', flat=True): submission = Submission.objects.get(pk=submission_id) submission.created = getattr( submission.logged_actions().order_by('timestamp').first(), 'timestamp', None ) if submission.created: submission.save(update_fields=['created']) class Migration(migrations.Migration): dependencies = [ ('submission', '0039_submission_created'), ] operations = [ migrations.RunPython(fill_created, migrations.RunPython.noop), ]

30.964286

92

0.66436